3L, three-Lactobacilli on recovering of microbiome and immune-damage by cyclophosphamide chemotherapy

3L, three-Lactobacilli on recovering of microbiome and immune-damage by cyclophosphamide chemotherapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 3L, three-Lactobacilli on recovering of microbiome and immune-damage by cyclophosphamide chemotherapy Shousong Yue, Zhenzhong Zhang, Fei Bian, Yan Zhang, Gao Chen, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2113752/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background We examined the impact of using a probiotic containing three different Lactobacilli (3L) on the gut microbiome of rats following cyclophosphamide (CTX) treatment. CTX corresponded to chemotherapy which is used for human cancer treatment and known to have adversive effects on the immune system. Methods We conducted our experiment with ten rats in five different experimental groups which included control, CTX treated, and then low, medium, and high probiotic treatment with CTX treatment. Of these ten rats in each group, we sequenced the stool of three of them using both ITS and 16S sequencing. We then went on to examine the taxonomic composition of these samples to determine whether probiotic treatment helped the rat’s microbiome return to similar structure as the control rats. Results We used Illumina MiSeq sequencing to generate sequencing data from microbial genomic DNA libraries, which is useful for testing the effects of 3L on bacteria and fungi. Microbiome analysis, phylogenetic and classification reports, and community data have all backed up the experiments and findings that 3L had a significant positive impact on the microbiome. Furthermore, the effect on specific metabolic pathways aids in deriving the study’s conclusion (use of 3L in chemotherapy) to the mode of action, mechanistically by correcting microbiota composition and enhancing specific gut metabolic functions. Conclusions Through experimental results using an in vivo model, we suggested the role of novel natural probiotics 3L, 3 Lactobacilli in the establishment of a strong and sustainable beneficial healthy gut flora, after CTX chemotherapy. We suggested some new adjuvants to chemotherapy as drugs + lactobacillus treament using the rat CTX model (immunosuppression caused by cyclophosphamide). Furthermore, in numerous studies that reported the use of probiotics involving Lactobacillus in post-chemo or post-surgical procedures, we proposed a new probiotic formulation ( L. acidophilus + L. casei + L. plantarum ) to be further studied and explored in the prevention of health condition loss by alteration of the general immune system. Illumina MiSeq sequencing - Gut microflora - Lactobacillus - Immunostimulant - Adjuvant anticancer bioproduct Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Various research challenges are critical for the cancer care continuum and finding treatment at various levels of the most threatening disease to human health [ 1 ]. Following an accumulation of mutational, genetic, and epigenetic alterations, abnormal cells begin to divide without control and soon form a mass of extra-tissue (or tumor) that can spread to the entire body through bloodstream and lymph and becomes deadly [ 2 – 5 ]. Disease-specific mutational events can serve as reliable cancer biomarkers for diagnosis [ 6 – 7 ], which is especially important given that human cells can develop multiple types of primary cancerous tumors [ 8 – 10 ]. However, the high diversity of genetic mutations in mitochondrial DNA, non-coding RNA, microRNA, ubiquitin, RNA editing, spliceosome and/or RNA splicing, and phenotype-specificity of drug sensitivity, which is largely dependent on tumor-stroma interactions, are reasons for cancer treatment difficulties [ 11 – 18 ]. Cancer may even be caused by a change in the mechanisms involved in RNA + peptide mutations, which are required for multifunction in specific cell lines such as pluripotent stem cells, chemosensory cells, or T cell lymphocytes. Cancer may be caused by a malfunction in protein variation and cell multipotency, revealing a new molecular insight and potentially novel clinical application in disease treatment [ 19 – 22 ]. As a result, numerous studies have been conducted to test the effects of nanomedicine, extracellular vesicle bioengineering, gene therapy, immunotherapy, radiotherapy, and thermal ablation on cancer treatment [ 23 – 24 ]. Immunotherapy, or the stimulation of T lymphocytes capacity for antigen-directed toxicity, has received special attention in cancer treatment [ 25 ]. This is also critical, not only because the disease targets the immune system, but also because anticancer strategies such as (chemo)radiotherapy have a negative impact on it [ 26 – 28 ]. Ionizing radiation therapy, which is used in more than half of all cancer cases, causes late inflammatory responses and/or inflammation-associated diseases in the patient’s immune system over time, particularly with high dose rate (chemo)radiotherapy [ 29 – 30 ]. One major source of concern is that (chemo)radiotherapy can result in the formation of a new type of cell (Langherans cells), which are known to impair the immune system’s ability to fight cancer [ 31 ]. Therefore, one of the most difficult challenges for cancer research is to find new ways to continue using (chemo)radiotherapy, which is absolutely necessary to promote T-cell activity in order to cure the disease, while preserving the patient’s natural barriers and immune functions to fight other infections such as pneumonitis [ 32 – 33 ]. The concept of using complementary and/or alternative more natural medicine to treat cancer is not new, treating cancer by injecting bacteria in proximity is well known as microbe mediated tumor therapy, but it may be gaining traction in the purchase of our modern lives [ 34 ]. The microorganisms migrate to the tumor, grow there, and thus activate the patient’s immune system. Bacteria-mediated tumor therapy has been used as a treatment for over a century [ 35 ]. Given the link between the gut and the immune system, gut microbes (or microflora) have also become an important target for boosting T cells and shaping cancer therapy efficacy [ 36 – 37 ]. Using various cancer model studies, it has been demonstrated that a healthy gut flora has a strong influence on the efficacy of anticancer drugs such as cyclophosphamides (CTX) and immune checkpoint inhibitors (ICIs) [ 38 – 41 ]. The current study compares to any of the marketed probiotic formulations, such as L. rhamnosus , which are known to inhibit cancer cell growth in a dose- and time-dependent manner [ 42 ]. It also compares to L. rhamnosus GG, the most studied microbe model in cancer, in terms of potentiating the gut microbiota and protecting against tumor genesis [ 36 – 43 ]. A universal ‘probiotic’ approach is expected to prevent patient selection in view of the different treatments and individualized host responses to gut modulation in this new and challenging research field, primarily focusing on colon cancer [ 44 ]. As a result, we eventually developed a specific 3L Lactobacillus bioproduct (a natural cocktail composed of L. acidophilus , L. casei and L. plantarum ) for future medicinal purposes, which has been shown to have a very beneficial effect not only on physiological but also on biochemical status of hyperlipidemic mice. “3L” has been shown in mice to not only induce beneficial gut flora, but also to significantly reduce cholesterolemia, LDL/HDL ratio, blood lipid concentration and weight gain [ 45 – 46 ]. Human metabolic syndromes and obesity-related pathologies are best studied in rodents [ 47 ]. They are also excellent models for studying human cancer immunology and immunotherapy [ 48 ]. As a result, we used five different groups of rats as experimental models to investigate the effects of 3L on gut flora and CTX chemotherapy. We used Illumina MiSeq sequencing optimized for full complete microbial genome applications to examine the gut microbiome after chemotherapy (the use of cytotoxic drugs such as CTX for cancer treatment). The rats we used were not ill or cancerous; they were healthy rats given cyclophosphamide CTX chemical drug. CTX chemotherapy significantly altered the composition of the microbiome, according to our findings. This relates to a previous study on the same groups of rats, which showed an alteration of the immune system following CTX treatment [ 49 ]. From one week to one month (28 days) after administration, the number of white blood cells in the Lactobacillus preparation-treated animals (H-dose: 5 mg/kg) was higher than that in the group of CTX-treated animals. The CD4+/CD8 + ratio (the ratio of T helper cells to cytotoxic T cells) was also higher in the Lactobacillus preparation-treated animals (L-dose: 1.25, M-dose: 2.50, and H-dose: 5.00 mg/kg). Lactobacillus -treated animals had higher serum levels of interleukin 6 (IL-6) and higher interleukin (IL-6 and IL-2) gene expression but a significant decrease in rat mRNA expression levels of Tumour Necrosis Factor alpha (TNF-alpha) [ 49 ]. The effects on leukocytes, T helper cells, interleukins, and TNF-alpha were dose-dependent [ 49 ], prompting us to test three different doses (L, M, and H) on the microbiome of chemotherapy rat models. Then, we examined the effects of chemotherapy combined with 3L on the gut flora and health status of rats injured by CTX. The overall outcome of CTX + Lactobacillus chemo-treatment was beneficial to an extent not previously reported. Increasing the dose of a Lactobacillus cocktail ( acidophilus , casei , and plantarum ) had a significant positive effect on microbial gut flora. The rats we used were normal, not tumor/cancer model rats, so using a microecosystem like Lactobacillus did not cure cancer. Despite CTX (chemotherapy for tumor treatment), it contributed to improved gut health. Despite CTX chemotherapy, a pharmacological bioproduct high in Lactobacillus strains has been described to significantly contribute to a healthy gut microbiome. Therefore, we propose a new bioactive microbial natural medicinal product to be further tested in rats, then in humans, and possibly used in tumor/cancer treatment or, at the very least, to prevent an altered gut due to CTX chemotherapy. Results Microbiome comparison in rat groups in relation to chemotherapy and Lactobacillus. This descriptive study investigates the fecal microbiota of rats subjected to chemotherapy (cyclophosphamides for cancer) and significant health gut improvement when co-treated with a cocktail of three Lactobacillus spp. in continuation of our work on Lactobacillus / bacillus on cholesterolemia, lipidemia, diarrhea, and scour [ 45 – 46 , 50 ]. Although the sample size is smaller (N = 50), the topic is extremely important in cancer pharmacological treatments because cyclophosphamides (CTX) used against cancerous tumors are frequently found to severely damage the patient’s immune system. We used rats that did not have cancer. They were healthy rats who had been severely harmed by CTX treatments (chemotherapy). The CTX-bioproduct study design includes five different experimental groups: healthy control rats (1), treated rats (CTX chemotherapy) that were either only treated with CTX (2) or treated with CTX and a low (3), middle (4) or high (5) complement dose of Lactobacillus spp. (Fig. 1 ). In a previous study, leukocyte concentration, CD4/CD8, interleukin , and TNF-alpha expression were measured in each group (1–5), showing that CTX has a significant effect on the rat immune system [see 49]. Here, the microbiome was assessed in each group (1–5) using ITS and 16S rRNA gene sequencing on the Illumina MiSeq platform, suggesting that health can be maintained despite CTX when using Lactobacillus (Figs. 2 – 5 & S1-S13 and Tables 1 – 2 & S1-S4). Table 1. Mycobiome (Genus of fungi and yeasts) composition in relation to Lactobacillus and cyclophosphamide treatment in five groups of rats. CK: control healthy conditions; IM: immune-attacked (CTX); L: CTX + 3L-low dose; M: CTX + 3L-middle dose; H: CTX + 3L-high dose. Treatment for Lactobacillus : tritherapy (3L) = L. acidophilus SD65 + L. casei SD07 + L. plantarum SD02 . The group’s dominant genera are highlighted in bold. ° shows fungal genera specifically increased by 3L M-dose in cyclophosphamide conditions. * indicates increased genera in CK and/or H groups. CK IM H M L Acaulium Acremonium Alternaria Aspergillus Candida* Chlamydomyces Coprinellus Cutaneotrichosporon Filobasidium Fusarium* Gibberella Kernia Kodamaea Lecanicillium Mallassezia Meyerozima Microascus Moesziomyces Mortierella Mucor Mycosphaerella Olpidium Papiliotrema Penicillium Periconia Pichia* Phallus Phialocephala Plectosphaerella Pseudogymnoascus Rasamsonia Rhizomucor Rhizophlyctis Rhizopus Rhodotorula Saccharomyces Sarocladium Scytalidium Shizothecium Simplicillium Sodiomyces Talaromyces Tausonia Thermoascus Trichoderma Ustilago Verticillium Wallemia Xerochrysium Xeromyces Acaulium Mucor Olpidium Penicillium Periconia Phallus Xerochrysium Xeromyces Acremonium Aspergillus Chlamydomyces* Fusarium* Mortierella Phallus Rasamsonia Rhizophlyctis Rhizopus° Talaromyces Trichoderma Coprinellus Microascus Mycosphaerella Phialocephala Pseudogymnoascus Rhizomucor Rhizopus° Sarocladium Scytalidium Thermoascus Ustilago Mallassezia Plectosphaerella Rhizopus° Saccharomyces Simplicillium Sodiomyces Tausonia Table 2 Mycobiome (Phylum, Class, Order, Family, and Genus of fungi and yeasts) composition in relation to Lactobacillus and cyclophosphamide treatment in five groups of rats. CK: control healthy conditions; IM: immune-attacked (CTX); L: CTX + 3L-low dose; M: CTX + 3L-middle dose; H: CTX + 3L-high dose. Treatment for Lactobacillus : tritherapy (3L) = L. acidophilus SD65 + L. casei SD07 + L. plantarum SD02 . The group’s dominant genera are highlighted in bold. * displays a significant increase in specific microbes in CK and the three Lactobacillus group doses (L, M and H). ° shows a marked reduction in specific microbes caused by CTX chemotherapy.† shows increased microbes under chemo (see IM), but not under chemotherapy + Lactobacillus conditions (see L, M and H). CK IM H M L Phylum Ascomycota Ascomycota° Ascomycota Ascomycota Ascomycota Basidiomycota* Basidiomycota° Basidiomycota* Basidiomycota* Basidiomycota* Blastocladiomycota Chytridiomycota Glomeromycota Kickxellomycota Kickxellomycota° Kickxellomycota Kickxellomycota Kickxellomycota Mortierellomycota* Mortierellomycota° Mortierellomycota* Mortierellomycota* Mortierellomycota* Mucoromycota Mucoromycota† Mucoromycota Mucoromycota Mucoromycota Olpidiomycota Olpidiomycota† Olpidiomycota Olpidiomycota Olpidiomycota Class Agaricomycetes Agaricostilbomycetes* Agaricostilbomycetes° Agaricostilbomycetes* Agaricostilbomycetes* Agaricostilbomycetes* Blastocladiomycetes Cystobasidiomycetes Dothideomycetes Dothideomycetes° Dothideomycetes Dothideomycetes Dothideomycetes Eurotiomycetes Eurotiomycetes° Eurotiomycetes Eurotiomycetes Eurotiomycetes Exobasidiomycetes Leotiomycetes* Leotiomycetes° Leotiomycetes* Leotiomycetes* Leotiomycetes* Malasseziomycetes Microbotryomycetes Mortierellomycetes Mucoromycetes Mucoromycetes† Mucoromycetes Mucoromycetes Mucoromycetes Olpidiomycetes Pezizomycetes Rhizophlyctidomycetes Saccharomycetes Saccharomycetes° Saccharomycetes Saccharomycetes Saccharomycetes Sordariomycetes Sordariomycetes° Sordariomycetes Sordariomycetes Sordariomycetes Tremellomycetes* Tremellomycetes° Tremellomycetes* Tremellomycetes* Tremellomycetes* Ustilaginomycetes Wallemiomycetes Order Agaricales Capnodiales* Capnodiales° Capnodiales* Capnodiales* Capnodiales* Cystofilobasidiales* Cystofilobasidiales° Cystofilobasidiales* Cystofilobasidiales* Cystofilobasidiales* Eurotiales Eurotiales° Eurotiales Eurotiales Eurotiales Filobasidiales Glomerellales* Glomerellales° Glomerellales* Glomerellales* Glomerellales* Helotiales Hypocreales Hypocreales° Hypocreales Hypocreales Hypocreales Malasseziales Malasseziales Malasseziales* Malasseziales* Malasseziales* Microascales Mortierellales Mucorales Mucorales† Mucorales Mucorales Mucorales Olpidiales Pleosporales Saccharomycetales Saccharomycetales° Saccharomycetales Saccharomycetales Saccharomycetales Sordariales Tremellales Trichosporonales Trichosporonales° Trichosporonales Trichosporonales Trichosporonales Ustilaginales Wallemiales Family Aspergillaceae Aspergillaceae° Aspergillaceae Aspergillaceae Aspergillaceae Cordycipitaceae* Cordycipitaceae° Cordycipitaceae* Cordycipitaceae* Cordycipitaceae* Debaryomycetaceae Didymellaceae Didymellaceae° Didymellaceae Didymellaceae Didymellaceae Hypocreaceae Lichtheimiaceae Malasseziaceae Malasseziaceae* Malasseziaceae* Malasseziaceae* Metschnikowiaceae Microascaceae Mucoraceae Mucoraceae † Mucoraceae Mucoraceae Mucoraceae Mycosphaerellaceae* Mycosphaerellaceae° Mycosphaerellaceae* Mycosphaerellaceae* Mycosphaerellaceae* Nectriaceae Nectriaceae° Nectriaceae Nectriaceae Nectriaceae Plectosphaerellaceae Pleosporaceae Pleosporaceae° Pleosporaceae Pleosporaceae Pleosporaceae Pichiaceae Pichiaceae° Pichiaceae Pichiaceae Pichiaceae Rhizopodaceae* Rhizopodaceae° Rhizopodaceae* Rhizopodaceae* Rhizopodaceae* Thermoascaceae* Thermoascaceae° Thermoascaceae* Thermoascaceae* Thermoascaceae* Trichosporononaceae* Trichosporononaceae° Trichosporononaceae* Trichosporononaceae* Trichosporononaceae* Ustilaginaceae Genus Alternaria Alternaria° Alternaria Alternaria Alternaria Aspergillus Aspergillus° Aspergillus Aspergillus Aspergillus Candida Chlamydomyces* Chlamydomyces° Chlamydomyces* Chlamydomyces* Chlamydomyces* Fusarium Fusarium° Fusarium Fusarium Fusarium Kodamaea Kodamaea° Kodamaea Kodamaea Kodamaea Lecanicillium* Lecanicillium° Lecanicillium* Lecanicillium* Lecanicillium* Malassezia Meyerozima Mucor Mucor † Mucor Mucor Mucor Mycosphaerella Mycosphaerella° Mycosphaerella Mycosphaerella Mycosphaerella Pichia Pichia° Pichia Pichia Pichia Penicillium Rhizomucor* Rhizomucor° Rhizomucor* Rhizomucor* Rhizomucor* Rhizopus* Rhizopus° Rhizopus* Rhizopus* Rhizopus* Sarocladium* Sarocladium° Sarocladium* Sarocladium* Sarocladium* Simplicillium Talaromyces Trichoderma Xerochrysium Xerochrysium † Xerochrysium Xerochrysium Xerochrysium The Venn Diagram depicting the relationships between the five groups in relation to chemotherapy and Lactobacillus suggested a relationship between healthy control and CTX + increasing dose of Lactobacillus spp. (CK and M-H; Figure S1). The position, configuration, and overlap of the circles indicating the relationships between the groups showed a gradual increase in gut flora and overall health depending on the Lactobacillus spp. complement dose (L-to-H; Figure S1). The IM group (CTX alone) was further along the bottom representing a higher number of total unique OTUs (17.24%), similarly to L dose (number of total unique OTUs: 15.82%). The Venn Diagram showed that the core microbiomes in the H and control groups were related when we looked at the number of total unique OTUs (10.52–10.82%), whereas M complement doses of Lactobacillus spp. remained too closely related to CTX alone conditions (15.07%; Fig. 2 A). The middle complement dose (×2, two-fold) fell between the control (CK) and immune-damaged (IM) groups (Figure S1). The H-dose group was opposite the IM group, with no overlap with the control group (CK). As a result, increasing the Lactobacillus concentration (more than five-fold) appeared to be required to produce an even more significant beneficial effect on the microbiome of rats treated by CTX (Figure S1). The five groups appear to have relatively similar levels of similarity on Venn diagrams (Figure S1). All samples (three per group) and OTU numbers were consistent across the groups CK-H. In each group, approximately the same number of ITS and 16S sequences were obtained in different biological replicates (Table S1). The five different groups obtained roughly the same sequence quantity (Table S1). There were no differences in the number of OTUs found at each taxonomic level (phylum, class, order, family, genus, and species; Tables S2 & S3). Although all of the OTUs could be classified, the classification of OTUs at different taxonomic levels in five rat groups related to chemotherapy and Lactobacillus treatment revealed no discernible differences in OTU counting (Tables S2 & S3). This was seen in both grouped (CK, IM, L, M and H) and ungrouped (C101, C103, C105, IM015, IM021, IM024, L102, L103, L104, M201, M202, M203, H105, H204 and H205) samples (Tables S2 & S3). As a result, the sample sizes were similar, particularly for Family and Genus (Figure S2). Using the OTU table for sample diversity in PCA, rank abundance curve, NMDS, principle coordinate analysis (PcoA), Bray-Curtis distance plot (default semimetric), binary Jaccard distance matrix (metric), and UPGMA, however, H was clustered with CK. The PCA based on OTU composition revealed significant differences in the five groups (Fig. 2 AB). At the community level, PCA revealed similarities between the chemotherapy + high lactobacillus spp. and control groups, with Fusarium , Talaromyces , Sarocladium , Aspergillus , and Mucor falling outside a common spectrum of microbe genera (Fig. 2 A). Orthogonal Projections to Latent Structures and Discriminant Analysis (OPLS-DA) showed Mucor and Talaromyces to be associated with ill conditions (chemotherapy alone), but a wide range of microbes to be associated with control and chemotherapy + high Lactobacillus conditions (Fig. 2 B). The OPLS-DA analysis showed that the distances between the CK and H groups were very small, while the L, M, and IM groups were clearly separated. Fusarium , Sarocladium , Kodamaea , Verticillium , Candida , and Chlamydomyces were among the fungi with overlapping distributions in the two groups, CK and H. Trichoderma , Acremonium , and Malassezia were more closely associated with the Lactobacillus groups H, M, and L. Pichia and Aspergillus were separated from this group but mixed with the CK control group. Microbial fungi such as Mucor and Talaromyces , as well as Xeromyces , Xerochrysium and Penicillium , were associated with CTX and the immune-damage group (Fig. 2AB). In addition to PCA, CK-samples tended to cluster with H-, M- and L-samples in NMDS (Fig. 3 ). The IM group differed significantly from the other groups, with clear mean differences between microbiomes from CTX-treated samples and those from controls (no treatment) and rats treated with Lactobacillus in addition to CTX (Bray-Curtis; Fig. 3 A). The NMDS graph using the Jaccard index collapsed very clear information: CK grouped with H-samples, showing mean similarities between H-microbiomes and controls (Jaccard; Fig. 3 B). Using PcoA as a principle analysis, the same grouping was observed, lending support to PCA and NMDS (Fig. 4 ). Both the Bray-Curtis (abundance) and Jaccard (0/1 data) indices showed a pair of communities with comparable species richness (H and CK; Fig. 4 ). The similarity between the CK and H samples was confirmed by unweighted and non-metric MDS analysis (UPGMA). H branches clustered with CK with a low distance value (0.005 − 0.0028) on the UPGMA tree (Figure S3A). There were also unweighted pairs found between M and L groups (distance value 0.031–0.111, Figure S3A; 0.003, Figure S2B). The branches representing the immune-damaged group (IM) clustered at the bottom of the tree, indicating the mean distance (or difference) of IM compared to CK, H, M, and L (Figure S3). Therefore, many of our results (PCA, PcoA, NMDS, and UPGMA) do show Lactobacillus -control clustering, but even in these plots, it is difficult to see how much closer the high Lactobacillus group is to controls when compared to IM because of the large outlier within the IM grouping (Figs. 2 – 5 & S3). We couldn’t conclude that the control and Lactobacillus groups had more microbial diversity, but in our rank abundance curve (i.e. species richness and species evenness), the IM group (CTX alone) was further along the x axis, representing a higher number of total unique OTUs (Figure S4). We used Specaccum (number of species vs number of samples) to show how species richness increased as sample size increased. The curve rapidly reached a plateau. There were no significant differences in species richness when increasing the number of samples (up to 12) lumped into a single analysis, which was not done on a per group basis (Figure S5). In grouped and ungrouped samples, the Chao1, Simpson, Shannon, Pielou_e, observed species and Goods_coverage indices (alpha-diversity) were calculated (Figure S6 & Table S4). These indices (Chao1, Pielou_e and observed_species) indicated that the CK and H groups had similar community richness and species evenness (Figure S6 & Table S4). The goods_coverage index showed significant differences between H and IM. Goods_coverage metrics at OTU levels (sample completeness, p = 0.76) showed a high level of microbial diversity in H (Figure S6 & Table S4). Mycobiome of five different groups of rats profiled in relation to chemotherapy and Lactobacillus . Examining of individual taxon abundance using heatmaps was useful to analyze fungal taxa clustering based on the abundance of each fungus in the five rat groups (Figs. 5 & S7). A heatmap analysis of fungi revealed the relative abundance of each taxon in CK, IM, H, M, and L. In this descriptive study, Table 1 lists the various fungal taxa found in the CK, CTX, and 3L groups. From the Acaulium (syn. Scopulariopsis ) genus to Xeromyces , several broad types of microbes were identified in the CK group (Fig. 5 & Table 1 ). CK (control, healthy condition) had high levels of Candida , Cutaneotrichosporon , Filobasidum , Fusarium , Kernia , Kodamaea , Lecanicillium , Meyerozima , Papiliotrema ( Cryptococcus ), Pichia , Rhodoturula , Verticillium , and Wallemia , whereas IM (immune-attacked) had high levels of Acaulium , Mucor , Olpidium , Penicillium , Periconia , Phallus , Xerochrysium , and Xeromyces (Fig. 5 & Table 1 ). However, treating rats with Lactobacillus in addition to Cyclophosphamide increased relative fecal abundance of many different fungal taxa, including Acremonium , Aspergillus , Chlamydomyces , Fusarium , Mortierella , Phallus , Rasamsonia , Rhizophlyctis , Rhizopus , Talaromyces , and Trichoderma (in H group), Coprinellus , Microascus , Mycosphaerella , Phialocephala , Pseudogymnoascus , Rhizomucor , Rhizophlyctis , Rhizopus , Sarocladium , Scytalidium , Thermoascus , and Ustilago (in M group), and Mallassezia , Plectosphaerella , Rhizophlyctis , Rhizopus , Saccharomyces , Simplicillium , Sodiomyces , and Tausonia (in L group; Fig. 5 & Table 1 ). As a result, among the regulated fungi are species that are not known as animal commensals or pathogens. Lecanicillium fungi are classified as generalist entomopathogenic fungi [ 51 ]. Ustilago is a Poaceae plant pathogen [ 52 ]. Phallus mushrooms are big saprotrophic mushrooms [ 53 ]. However, the presence of these fungi in the rat microbiota is not necessarily suspect and may merit further investigation. The breeding history of rats takes place in the Institute of Medicine’s Class II animal facility in SAMS (Specific Free Pathogen/SPF facilities and acute hospital care settings that are designed to keep organisms in sterile environments). Saprophytic basidiomycetes are well-known wood-decaying fungi, but Phallus sequences have been found in animal penis and urethra, where they play a role in male fertility [ 54 ]. In fact, little is known about the fungal flora of rodent’s digestive and reproductory tracts. Lecanicillium species are pathogens that parasitize not only insects but also worms and many other fungi, which could explain their presence in gut fungi associated with rats. Lecanicillium strains have been found in gut fungi associated with marmots [ 55 ]. A large variety of ‘forgotten’ odd fungi, including Ustilaginales and Ustilago sp., are found in the human digestive tract [ 56 – 59 ], as seen in rodents (Fig. 5 & Table 1 ). So it is not surprising that fungal sequences like Lecanicillium , Phallus , and Ustilago have been found in the fecal DNA of CTX-rat models. It has been described in a variety of other animal species, including humans. What is more unusual or surprising is the discovery that these fungi are differentially regulated by CTX and/or 3L conditions, which is a critical key point in addressing their prevalence in the gut microbial system (Fig. 5 & Table 1 ). Phallus was found in cyclophophasmide-treated rats and rats treated with CTX + high 3L lactobacilli in the heatmap (Fig. 5 ). Despite CTX treatment, medium and low doses of 3L were able to eradicate Phallus fungi, as shown by triplicate samples (Figure S7 & Table 1 ), even though Phallus infection was not prevalent in all IM samples (Figure S7). More interestingly, the heatmap analysis highlighted two taxa in particular, Fusarium and Pichia , which were found in high abundance in feces from control and high lactobacillus -treated groups and samples or were repeatedly found in control samples but significantly altered by chemotherapy (Figs. 5 & S7). Furthermore, there was a correlation between Rhizopus and Lactobacillus treatment. Chemotherapy signifcantly reduced Rhizopus -levels, but increased when Lactobacillus was added to phosphamide. Rhizopus was found in abundance in H, M, and L groups (Figs. 5 & S7). The Rhizopus microbe was more abundant in M samples (CTX + middle dose/2.5 ml/kg bodyweight of Lactobacillus ), indicating that a specific dose of bioproduct should be chosen for effective regulation of specific microbes (Figs. 5 , S7 & Table 1 ). We found that high-, medium-, and low-dose 3L cocktail gavages were effective in lowering Acaulium , Mucor , Olpidium , Penicillium , Periconia , Xerochrysium , and Xeromyces levels (Figs. 5 , S7 & Table 1 ). The microbial composition distribution histograms of each sample were displayed at the phylum, order, class, family, and genus levels (Fig. 6 & Table 2 ) in our descriptive analysis of rats in relation to CTX and Lactobacillus (N = 50; analysis of groups and individual samples, the same chemotherapy session, one drug, five shots, addition of Lactobacilli , 3L-test, three different doses, comparison with controls, beneficial effects analysis). The dominant microbial phyla were similar in control and 3L therapy conditions (Fig. 6 A & Table 2 ). CTX, on the other hand, caused a significant decrease in Ascomycota-levels, which was not seen with high-doses of 3L during chemotherapy (Fig. 6 A). Many other microbial fungal phyla, including Basidiomycota, Kickxellomycota, and Mortierellomycota benefited from 3L gavage (Fig. 6 A & Table 2 ). Mucoromycota and Olpidiomycota levels in rat fecal microbiomes increased during CTX chemotherapy but remained low when H, M, or L doses of Lactobacillus were added to CTX (Fig. 6 A & Table 2 ). Similarly, analysis of the distribution of microbial fungal classes, orders, families, and genera in the five rat groups showed specific beneficial effects of Lactobacillus treatment in addition to cancer chemotherapy (Fig. 6 B-E & Table 2 ). Dothideomycetes, Eurotiomycetes, Saccharomycetes, Sordariomycetes, and Tremellomycetes were the most abundant microbial fungal classes in healthy control rats without any other treatment than normal saline injection. CTX chemotherapy had a significant impact on all five classes. Chemotherapy also increased Mucoromycetes levels in the fecal microbiome. With high dose injections of 3L, Dothideomycetes, Eurotiomycetes, and Sordariomycetes were kept at normal levels. Mucoromycetes were kept at normal levels in all three Lactobacilli -treated samples. Agaricostilbocytes, Leotiomycetes, and Tremellomycetes, were also recovered at normal levels after probiotic treatments (Fig. 6 B & Table 2 ). Eurotiales, Hypocreales, Saccharomycetales, and Trichosporonales were the most abundant microbes on an order level not only in CK, but also in H group. The IM group had a different microbial order profiling, with significantly altered levels of Eurotiales, Hypocreales, Saccharomycetales, and Trichosporonales, as well as significantly increased levels of Mucorales. Lactobacillus treatment restored normal levels of Capnodiales, Cystofilobasidiales and Glomerellales that had been affected by chemotherapy. Lactobacillus H, M, and L doses were effective in controlling Mucorales levels. A low dose of Lactobacillus was also particularly effective in stimulating Mallasseziales levels, emphasing the importance of controlling Lactobacillus dose to target specific microbial orders (Fig. 6 C & Table 2 ). Four major microbial families were identified in rat fecal samples related to CTX and 3L therapy: Aspergillaceae , Didymellaceae , Nectriaceae , and Pleosporaceae . Surprizingly, these microbial families were vulnerable to chemotherapy alone, but were kept alive by combining 3L with CTX-chemo treatment (Fig. 6 D). To maintain the levels of Aspergillaceae , a gradual increase of 3L seemed to be required (Fig. 6 D). CTX also reduced the levels of Cordycipitaceae, Mycosphaerellaceae, Rhizopodaceae, Thermoascaceae , and Trichosporonaceae , but these levels were maintained when CTX was combined with Lactobacillus gavage. This was not true for all of the microbial families found in rat feces. Pichiaceae was one of the microbial families that were down-regulated after CTX treatment, which could not be reversed by adding Lactobacillus during chemo-treatment (Fig. 6 D). However, Lactobacillus at high, medium, and low doses had a clear beneficial effect on Mucoraceae control (Fig. 6 D). Mucoraceae -levels in the IM group were extremely high, which could be reversed by adding H, M, or L doses of Lactobacillus (Fig. 6 D). A low dose of the bioproduct was found to induce especially high levels of Mallasseziaceae (Fig. 6 D & Table 2 ). Alternaria , Aspergillus , Fusarium , and Mycosphaerella were the main microbial genera characteristic of the CK and H groups, respectively, while Mucor was a diagnosis of immune-damage caused by CTX treatment. The addition of Lactobacillus to chemotherapy effectively controlled Mucor . Mucor -levels were found to be extremely low in the H, M, and L groups of rats related to Chemo + Lacto treatment. On Xerochrysium , similar effects were observed. Xerochrysium -levels rose during chemotherapy, but were kept under control by using Lactobacillus at low, medium, and high doses. Other microbial genera such as Chlamydomyces , Lecanicillium , Rhizomucor , and Sarocladium were maintained by Lactobacillus at low, medium, or high doses. Only Pichia was not maintained by Lactobacillus treatment, regardless of the dose of 3L bioproduct (Fig. 6 E & Table 2 ). When all triplicates were compared (Figure S8), Ascomycota levels were found to be remarkably high in CK and H triplicates (Figure S8A). In contrast, Ascomycota levels were particularly low in IM021, L103, and M201. Mucoromycota and Olpidiomycota were abundant in IM015 samples (Figure S8A). Mucoromycota and Olpidiomycota were significantly lower in Lactobacillus samples, particularly H (H105, H204, and H205). Mortierellomycota were missing in IM triplicates (IM015, IM021, and IM024), but present in C101, C103, L104, M202, M203, H105, H204, and H205 (Figure S8A). High levels of Sordariomycetes, Saccharomycetes, and Tremellomycetes were found in control triplicates (C101, C103, and C105) in ungrouped samples. Sordariomycetes and Tremellomycetes levels remained high in L102, L104, M202, M203, H105, H204, and H205. Eurotiomycetes were found in very low concentrations in IM021. Mucoromycetes were found in abundance in IM015 sample. Eurotiomycetes and Mucoromycetes were kept to normal conditions in all H samples (Figure S8B). On the order level, IM015 was distinguished by a high Mucorales/low Saccharomycetales ratio (Figure S8C). Despite the fact that Saccharomycetales remained low in all H, M, and L Lactobacillus -treated samples, Mucorales levels in Lactobacillus samples were comparable to controls (Figure S8C). Furthermore, Capnodiales levels in medium and high Lactobacillus samples M202-H205 were comparable to those found in C101, C103, and C105. Glomerellales levels were high in both the control (C101 and C103) and Lactobacillus (L104) samples (Figure S8C). Microbial family profiling was diverse in all samples, but particularly in the CK and H groups. C101, C103, C105, H105, H204, and H205 all showed high levels of Nectriaceae and Trichosporonaceae , as well as a variety of other families ranging from Aspergilaceae to Microascaceae . Notably, none of the Lactobacillus samples had the high levels of Mucoraceae found in IM015 (Figure S8D). In the genus taxa summary from ungrouped samples, IM015 had high levels of Mucor , whereas C101, C102, C105, L102, L104, M202, M203, H105, H204, and H205 had high levels of Fusarium but no Mucor to the extent seen in IM105 (Figure S8E). As a result, ungrouped samples of CTX-related rat fecal microbiomes and the effects of adding specific bioproducts also argued for Lactobacillus rather beneficial role in maintaining host health microbiome during chemotherapy. The analysis of metagenome sequence data (CK versus M) revealed a pattern that overlapped with enriched core microbes in the order Trichosporonales and the phylum Basidiomycota (Figure S9). The relative abundance of Fungi, Ascomycota, Sordariomycetes, Hypocreales, Nectriaceae , and Fusarium in CK and H class samples was very high (above 60000–140000). In the IM, L, and M classes of samples, the relative abundance of Fusarium fungi was less than 50000 (Figure S10A). Fusarium was identified as a key biomarker (i.e, a key community member) of the CK group by LEfSe (LDA, Krustal-Wallis and Wilcoxon; Figure S10B). Comparative metagenomics and network analysis at the phylum level showed a high degree of similarity between control and Lactobacillus -treated rat fecal samples, as well as the dominance of Ascomycota in this network (Figure S11). This CK- Lactobacillus group is not associated with IM samples (in blue; Figure S11A). Mucoromycota (in orange) dominated in CTX- immune-attacked ill rat feces (Figure S11B). CTX and CTX + Lactobacillus therapy effects on bacteriome and metabolic pathways. The relative abundance of each functional category (biosynthesis, degradation/utilization/assimilation, generation of precursor metabolite and energy, glycan pathways and metabolic clusters) was calculated using pathway abundance and read count abundance (MetaCyc; Figure S12). Differential abundance was found primarily for respiration, fermentation, fatty acid/lipid/carbohydrate degradation, and biosynthetic pathways (Figure S12A). Similarly, in MetaCyc, raw counts for metabolic pathways and enzymes, metabolites, and reaction orthologs revealed a strong statistical significance of differential abundance, primarily for cofactor, prosthetic group, electron carrier, vitamin, fatty acid, and lipid biosynthesis (Figure S12B). Some metabolic pathways in the MetaCyc database can be labeled with a low-level bacterial taxon [ 60 ]. As a result, we used MetaCyc to find metabolic pathways and/or bacterial taxa that are specifically related to the five groups of rats for chemotherapy (Figs. 7 & S13). A specific pathway (PWY-7839), 6-hydroxymethyl-dihydropterin diphosphate biosynthesis I, which converts GTP into pterin precursors (methanopterin and sarcinapterin) for the biosynthesis of several cofactors in specific bacterial strains, was found to be particularly highly expressed in CK and Lactobacillus -treated samples due to an increase in S24-7 Muribaculaceae , Prevotella , Clostridiales , Bacteroides , and CF231 Paraprevotellaceae (Fig. 7 A). Treatments with M- and H-doses were clearly effective in increasing the levels of Bacteroides and Prevotella , both of which are essential in the pyridoxine pathway required for vitamin B6 synthesis (PYRIDOXSYN-PWY, pyridoxal 5’-phosphate (PLP) biosynthesis I; Fig. 7 B). Furthermore, Lactobacillus treatment restored the abundance of helicobacterial taxa required for the TCA cycle (tricarboxylic acid cycle) or the Krebs cycle. Despite chemotherapy, with Lactobacillus treatment, not only Helicobacter -levels, but also “ Flexispira ” (in purple), Rothia , and Halomonas levels, were maintained (Fig. 7 C). Lactobacillus L-, M-, or H-doses, had no effects on Bacillales. To control Halomonas , a high dose of 3L (5.0 ml/kg) was strictly required (Fig. 7 C). In IM samples, a formaldehyde oxydation peak was observed. This was linked to the emergence of Enterococcus bacteria in immune compromised conditions (Fig. 7 D). Many Enterococcus species are known to be commensals and are not actively causing infection. In our case (Fig. 7 D), our results show a peak of Enterococcus linked to chemotherapy (CTX alone), implying that Enterococcus is an active infection. The addition of Lactobacillus to chemotherapy completely eliminated it; no Enterococcus peak was observed in CK, H and M groups (Fig. 7 D). TCA-GLYOX-BYPASS, the superpathway or bypass that integrates the common prokaryotic Krebs cycle (TCA) with the glyoxylate shunt, benefited from Lactobacillus treatment during chemotherapy (+ CTX). In both CK and H-dose conditions, a high diversity of bacterial taxa was observed. Enrichment of Enterobacteriaceae , Rothia , Cupriavidus , Halomonas , and Devosia was detected in controls and persisted during CTX chemotherapy when an additive probiotic treatment with high doses of 3L was used. Treatment with Lactobacillus was ineffective on Bacillales at 1.25-5 ml/kg doses (Fig. 7 E). Lactobacillus at a medium-dose (2.5 ml/kg) was particularly effective in stimulating Enterobacteriaceae (Fig. 7 E). Lactobacillus at a high-dose (5 ml/kg) was particularly effective in stimulating Devosia (Fig. 7 E). Similarly, the additive Lactobacillus treatment positively regulated Bacteroidales , Bacteroides , Enterobacteriaceae , Halomonas , and Devosia responsible for (prokaryotic) TCA cycle I (Fig. 7 F). CTX chemotherapy and/or treatment with 3L bioproduct had a significant impact on tRNA charging and microbiome. We observed the main stimulatory effects of 3L on S24-7 , Prevotella , Bacteroides , Ruminococcus , CF231 , and Oscillospira using high doses of Lactobacillus (Fig. 7 G). Finally, Illumina and MetaCyc analyses revealed that a middle dose of Lactobacillus had a strong effect on Enterobacteriaceae , which mediate the bacterial superpathway of coenzyme Q ubiquinol-8 biosynthesis (UBISYN-PWY, Fig. 7 H). When other types of metabolic pathways were examined (MetaCyc), the effects of Lactobacillus in addition to CTX were less obvious (Figure S13). No particular bacteria were found for the MetaCyc L-methionine salvage cycle III (PWY-7527, Figure S13A). Lactobacillus doses (M and H) primarily stimulated the anaerobic pathway for oleate biosynthesis IV (PWY-7664), however, the IM group had one sample that was much higher in the abundance of this pathway than all the H Lactobacillus group. The medium group appeared to have higher overall levels than the high group, possibly indicating an effect of 3L on this pathway ( Prevotella and Bacteroides ) but making any dose response relationship difficult to determine (Figure S13B). M- and H-doses of the bioproduct apparently had similar beneficial effects on mycolate biosynthesis (PWYG-321), with high levels of Bacteroides accumulating in M-treated samples (Figure S13C). Analysis of bacterial strains involved in the pathway teichoic acid (poly-glycerol) biosynthesis, which is part of cell wall biogenesis, seemed to have a positive effect of 3L bioproduct (M and/or H) as an additive to chemotherapy. Lactobacillus contributed to the low levels of Clostridiales , Mogibacteriaceae , Ruminococcaceae , and Gemella , while strains such as Jeotgalicoccus were stimulated (Figure S13D). Except for enterobacter in some low-dose Lactobacillus samples, no specific bacterial strains were identified for the superpathway of L-threonine metabolism (Figure S13E). Chemotherapy (immune-attacked; IM) reduced the levels of Clostridiales , Ruminococcaceae , Ruminococcus , and Oscillospira in the pathway UDP-N-acetyl-D-glucosamine biosynthesis I (UDPNAGSYN-PWY), which could be avoided by combining CTX with a high dose (5 ml/kg) of Lactobacillus (Figure S13F). Discussion Cancer, a cell disease caused by DNA changes, is a major burden of threat to human health worldwide. When chemotherapy is envisioned as the primary or perhaps only way to prevent cancer development, the burden of threat to human health increases. Using one or more anti-cancer chemical drugs, such as cyclophosphamide (cytophosphane, CTX), kills lymphoma or any cancer cells, but it also kills or seriously alters the patient immune system, potentially limiting life and health expectancies, just like the disease. This is shown in a previous study from Zhang et al. in rats where CTX was shown to alter several immune marker indicators like the number of white blood cells, CD4+/CD8 + ratio, the serum levels of interleukin 6 (IL-6) and interleukin gene expression [see 49]. In this previous study, which used the same chemotherapy rat model (see Fig. 1 ), the CTX-increased expression of TNF-alpha cytokine, an endocrine mediator of inflammatory and immune functions, known to regulate cell growth, cell signaling, but with many side effects of cytotoxicity in transformed cells, was another indication of immune attacks in CTX conditions [ 49 ]. This is also described in our study of chemo-damaged rats when a strong beneficial healthy gut flora is suppressed by CTX treatment. We examined the microbiome of five groups of rats in relation to chemotherapy, revealing that rats treated with CTX had a completely altered microbiome. Importantly, we show that Lactobacillus treatments are particularly effective at maintaining healthy gut flora in the rat intestine, allowing us to establish strong healthy conditions in rats despite chemo (see Figs. 2 – 4 & S1-S6). Though there have been previous reports on the use of Lactobacillus in gut flora and gastrointestinal tract protection [ 61 – 64 ], we will always seek the best solution, and we are still looking for a probiotic remedy that has a strong and significant impact on the adversive effects developed by CTX therapy, is active on benefical microbes, improves microbial balance, activates nutrients and stimulates gut-powered immune systems. The use of probiotics in chemotherapy is still rather uncommon, not applicable to all ages and populations, and an emerging field with many contradictory clinical results when it comes to interaction with the host or patient consumption [ 65 – 66 ]. Manipulation of microbiota by natural probiotics vs. chemical drugs is a constant challenge no only in human but also in veterinary medicine, especially for genetic diseases like cancer. The safety and stability of chemotherapeutic drugs such as CTX in cancer clinical trials are questionable [ 67 – 68 ]. We always look into clinical trials for anticancer methods that are highly efficiency, have a low resistance capacity, and have no impact on the patient’s quality of life or health conditions. Some chemicals can cause the organism to develop resistance. The repeated use of the same class of chemicals to control a disease, such as cancer, can have a variety of negative consequences. When the organism becomes ill and resistant, the chemical (CTX) is used more frequently, and the adjuvant must eventually be added as the CTX level rises. Despite this, no comprehensive microbiological medical study of the impact of Lactobacillus during chemotherapy has been conducted. The rat response to a new bioproduct (3L: L. acidophilus + L. casei + L. plantarum ) added to chemo/cyclophosphamide CTX shots was studied as a preliminary set for more extensive biomedicine cancer research. We found that combining acidophilus , casei , and plantarum is especially beneficial for maintaining the gut flora and thus the immune system during chemotherapy. We show that combining three Lactobacilli strains has significant beneficial effects on rat gut microbiota during chemotherapy, prompting us to test the formulation for human health (see Figs. 2 – 7 & S1-S13). We present MiSeq data for rat feces microbiome variations under five different conditions: normal and healthy, CTX chemotherapy and immune-attack, low, medium, and high doses of Lactobacillus (3L), with special consideration for fungi phylum, class, order, family, genus, and species (see Figs. 5 – 6 & S7-S11). We show in this study, that rats given CTX plus high doses of a new natural microbial biomedical product, the bioproduct 3L, have preserved microbiota that are critical for maintaining a strong immune system and a healthy condition. Such findings are especially significant because it has been established that microbial dysbiosis is associated with carcinogeneis in cancers ranging from colon to liver to pancreas. The growth of nocive fungi ( Malassezia ) in the gut microbiome, in particular, can promote oncogenesis via activation of mannose-binding lectins [ 69 ], urging medication to control the microbiome and MBL. Chemotherapy has been shown to alter immune, metabolic, and physiological functions, as well as potentially stimulate invasive fungal infection in patients [ 70 – 71 ]. So it is not surprizing that CTX alters gut flora and increases mucor or xerochrysium infection in groups of rat models (see Figs. 5 – 6 , S7-S11 & Tables 1 – 2 ). The most striking finding of our study in five groups of rats in relation to chemotherapy is perhaps the beneficial regulation of gut flora after Lactobacillus (3L) supplementation at various doses. High doses of 3L have been shown to maintain beneficial healthy normal gut flora in CTX-treated rats (see Figs. 2 – 7 , S1-S12 & Tables 1 – 2 ), urging the method or lactobacillus treatment additive to CTX to be tested on cancer rat models. We show that high doses of L. acidophilus + L. casei + L. plantarum are especially important for regulating Ascomycota and Capnodiales levels. Because ascomycetes are known to be used in medicine with the antibiotics penicillin and cephalosporin [ 72 ], and endophytic sooty mold fungi (Dothideomycetes) can be important for tissue health, environmental adaptation, and stress tolerance [ 73 ], this could be a significant discovery for cancer treatment. Interestingly, high doses of 3L (H treatment) have been shown to stimulate the levels of many different fungi families, including Aspergilaceae, Microascaceae , Nectriaceae , and Trichosporonaceae . Despite the fact that many of these fungi are plant and human pathogens, many of them are also biodegraders and biocontrol agents that could be used in medical applications [ 74 ]. For example, given that these ascomycete fungi can help degrade residual CTX and control cyclophosphamide toxicity [ 75 ], this could be used in chemotherapy. Another important aspect of using chemo + lactobacillus is that 3L (high dose, 5.0 ml/kg) has been shown to keep pathogens like mucormycetes (Mucormycota, Mucoraceae) and chytridiomycetes (Olpidiomycota, Olpidiaceae) at bay (see Figs. 6 , S8 & Tables 1 – 2 ). High mucorales typically invade the blood vessels and are linked to emerging infectious diseases such as mucormycosis in addition to other zygomycoses [ 76 ]. Chytrid fungi cause chytridiomycosis, an emerging disease in amphibians, and subcutaneous phycomycosis in humans [ 77 ]. As a result, this is a comprehensive pattern of many different fungal infections that could be regulated by Lactobacillus bioproduct 3L. Furthermore, 3L has the potential to influence the magnitude of beneficial fungal components of the intestinal microbiota, known as the gut mycobiome. Some fungi from the diet or the environment play an important role in mediating interaction in gut bacterial communities and regulating metabolic homeostasis. This is true for Eurotiales, Hypocreales, Saccharomycetales, and Trichosporonales, all of which are important components of the healthy mycobiome in both control and 3L-treated rats (see Figs. 6 , S7-S11 & Tables 1 – 2 ). The structure of the human gut microbial community is determined by genetics and environmental factors, but the fungi that mediate changes in this structure and cause disease are relatively common in humans and rodents [ 78 – 79 ]. Although it is commonly assumed that probiotics do not colonize the digestive tract or other parts of the human body, there is evidence of transient probiotic or foodborne strain colonization of the human gut via various mucosadhesion-related proteins on the probiotic cell surface [ 80 – 81 ]. There is also the option of encapsulating Lactobacilli [ 82 ]. As a result, it may be critical to use 3L during cancer chemotherapy to maintain the mycobiome balance, microbial community interactions, bacterial-fungal interactions, fungal-fungal interactions, and host-fungal interactions [ 83 ]. CTX injections, like diabetis [ 84 ], caused significant changes in Mortierellomycota-levels, which could be reversed by adding 3L (see Figure S8A). Therefore, using a 3L mix could be extremely useful in targeting specific components of the mycobiome, which plays a key role in the development of diseases ranging from cancer to diabetes. It is probably important to note that the effect of 3L tested in rats can be dose-dependent in this prospect for curing or optimizing health. To treat the rats for Mallasseziomycetes, a low dose of 3L Lactobacillus cocktail (1.25 ml/kg) was required (see Fig. 6 C & Table 2 ), which could be important in medications for specific pathologies such as colorectal cancer. Mallasseziomycetes fungi are associated with late-stage colorectal cancer [ 85 ]. We also found that 3L bioproduct doses have a different effect on the mycobiome during the stages of chemotherapy treatment in rats. Lactobacillus strains must be present in sufficient quantities for 3L to be effective. Lactobacillus ingredients must eventually be gradually increased in order to regulate a specific fungal group, such as Aspergillaceae , Cordycipitaceae, Mycosphaerellaceae, Rhizopodaceae, Thermoascaceae , and Trichosporonaceae (see Fig. 6 D & Tables 1 – 2 ). During chemotherapy, it is critical to regulate the fungal mycobiome. The gut mycobiome is involved in microbiome assembly and immune functionality, prompting the modulation of specific fungi to regulate both the gut microbiome and the immune system during chemotherapy [ 86 ]. While 3L is effective in modulating yeasts in the order Saccharomycetales (Ascomycota; see Fig. 6 & Table 2 ), Pichia is one of the few microbial genera that is affected by CTX chemotherapy but does not respond to Lactobacillus treatment (see Fig. 6 E & Tables 1 – 2 ). One possible explanation is that 3L controls gut fungi but not oral fungi or genera like Pichia and Candida [ 87 ]. Perhaps the formulation of 3L can still be improved to treat both oral and gut mycobiomes. Adding one or more Lactobacillus strains, such as L. reuteri , to 3L may be very effective in controlling the entire mycobiome on oral and gut tissues (see Jørgensen et al., 2017 [ 88 ] & our descriptive study on 3L in chemo). The plethora of metabolic functions maintained by 3L treatment in five groups of rats in relation to CTX chemotherapy was one of our study’s most striking findings about Lactobacillus and chemo (see Fig. 7 & S12-S13). Not only cancer, but also chemotherapy, has a significant impact on cell metabolism [ 89 – 92 ]. Therefore, targeting of chemotherapy (and cancer) metabolism as a complementary strategy is a promising approach not only for disease intervention, but also for preserving physiological functions in the patient immune system. We show here that a preparation of L. acidophilus ( SD65 ), L. casei ( SD07 ), and L. plantarum ( SD02 ) is extremely effective in maintaining and/or stimulating many different metabolic pathways via a beneficial effect on gut flora (see Fig. 7 & S13). After treatment with our three Lactobacilli cocktail (3L), the gut flora of chemotherapy-treated rats is rich in Firmicutes-Clostridia-Clostridiales- S24-7 , as found in healthy control conditions (see Figs. 7 A & 7 G). Overall, the detailed composition of the bacteriome using MetaCyc data suggests shared patterns of microbial strains and metabolic activities in healthy and 3L-treated groups for a wide range of systems. Bacteroidales- Muribaculaceae - S24-7 is an important component of the microbiome for carbohydrate metabolism, while Deferribacteraceae upregulates genes for amino acid and vitamin metabolism [ 93 ]. In the gastrointestinal tract, Bacteroidales- Prevotellaceae - Prevotella - Paraprevotella plays a key role for glucose (central carbon) metabolism, polysaccharide breakdown, glycogen storage, sulfate assimilation, and the production of propionate, which has anti-cancer and anti-inflammatory properties [ 94 – 97 ]. Campylobacterales- Helicobacteraceae - Helicobacter ( pylori ) is not always infectious or linked to metabolic syndromes like atherosclerosis. In many cases, it can be considered very beneficial to prevent the development of autoimmune diseases in humans. H. pylori can aid in the regulation of fatty acid metabolism. H. pylori is also known to contribute to the metabolic fate of pyruvate to lactate-acetate, nucleotide biosynthesis, oxidative metabolism, and thus cellular respiration [ 98 – 100 ]. This dual aspect (“beneficial pathogen”) highlights the significance of our finding with 3L controlling Helicobacter levels during chemotherapy (see Fig. 7 C). This includes the ability of 3L to control Helicobacters of the genus Flexispira , a urease-producing microorganism from the mid-colon and jejunum in humans that is typically associated with diarrhea symptoms as well as complete febrile illness such as malaise, arthralgias, pain, leg swelling and polyserositis [ 101 – 102 ]. It also includes a beneficial control of 3L for Actinomycetales- Micrococcaceae - Rothia and Oceanospirillales- Halomonadaceae - Halomonas (see Fig. 7 C), which may be important because Rothia and Halomonas are both prevalent in oral (salivae), oropharynx, respiratory tract and intestinal (gut) microbiota where they contribute to maintain healthy mucosal surfaces (iron scavenged from food, breakdown of proline and glutamine-rich proteins, glutamate and central carbohydrate metabolism) [ 103 – 104 ]. The beneficial effects of high doses of 3L (in addition to CTX in rats) on the levels of Burkholderiales- Burkholderiaceae - Cupriavidus and Rhizobiales- Hyphomicrobiaceae - Devosia further suggest the stimulatory effects of 3L-pharmacological agents on central carbon metabolism and energy production (see Figs. 7 E & 7 F). Cupriavidus bacteria have a diverse metabolic range that can be used to produce sulfur-biofuels as well as energy sources from hydrogen and CO 2 [ 105 ]. Similarly, Rhizobium and Devosia are known to express a wide range of metabolic activities, including carbohydrate, cysteine, methionine, branch-chain amino acid, and phosphorus compound metabolism, all of which are ideal for sustaining cellular and genetic component synthesis, energy transfer, and/or mycotoxin degradation [ 106 – 107 ]. Furthermore, the 3L mix may increase the levels of Bacillales- Staphylococcaceae - Jeotgalicoccus (see Figure S13D), which is important for biotin/vitamin H or vitamin B7 metabolism, cofactor in carboxylase activities in the gut, and its relationship with health [ 108 ]. The use of Lactobacillus (3L) to potentially stimulate amino acid, biotin, carbohydrate, glucose, iron, nitrogen, oxygen, phosphorus, protein, pyruvate, sulfide, and vitamin metabolism as a complement to chemo has been highlighted (see Figs. 7 & S13). The most notable effect of Lactobacillus treatment on the microbiome is perhaps found for Enterococcus (see Fig. 7 D). Chemotherapy (CTX) significantly increases the risk of Enterococcus peak (see Fig. 7 D), which in humans becomes a risk of ulcerative colitis or even colorectal cancer [ 109 ]. Probiotics such as 3L ( 3-Lactobacilli ) have been found to be very effective in raising bacteroides and clostridiales and keeping Enterococcaceae at bay in order to restore the gut flora to healthy conditions during chemotherapy (see Figs. 7 & S13). As a result, our 3L cocktail appears to be capable of controlling not only the mycobiome, but also the entire bacterial microbial metabolism in a strain-dependent manner. Different Lactobacilli formulations have been shown to modify gut flora and thus general metabolism and behavior not only in humans, but also in fishes and rodents [ 110 – 111 ]. We suggest that Lactobacilli , like other general natural bacterial probiotics, can be used as an adjuvant treatment during chemotherapy to maintain gut flora and stimulate the patient’s immune system. Rats outperformed mice as pre-clinical models models for human microbiota engraftment. Rats’ microbial communities are more similar to those of humans. More human microbial species were captured by rats than by mice [ 112 ]. As a result, rats are frequently used in cross-species microbiome analysis to study a wide range of human pathologies [ 113 – 114 ]. The beneficial pharmacological effects of a new probiotic formula on the gut microbiota of rats after one-month treatment with three bio, three natural Lactobacilli species in CTX-injury are reported here. The comprehensive and comparative analysis is divided into three parts: (1) healthy condition and chemo, (2) CTX + Lactobabillus , and (3) different Lactobacillus doses. We describe the microbial profiles associated with chemotherapy, as well as the various Lactobacillus doses required to counteract specific aversive or inhibitory effects of CTX in rodents. A systematic analysis of the gut microbiota in groups of rats exposed to cyclophosphamide shots + bioproduct suggests not only the infectious microbial pathogens induced by the CTX chemical treatment, but also the key beneficial microbial families induced by 3L to help maintain the immune system. L. casei supports the growth of L. acidophilus , which produces carbohydrate-digesting enzymes, whereas L. plantarum , a more adaptable and versatile strain, produces a slew of antimicrobial substances that aid in their survival in the gastrointestinal tract under any conditions. Our first results in CTX chemo-rats could pave the way for future human chemotherapy attempts. As a main result of “chemo”, fragile health conditions and microbial infections are frequently associated with a weakened immune system. Hence, complementary and/or alternative clinical medicine for cancer prevention and/or treatment is required. One major finding in our study was that a tritherapy of Lactobacilli could save the body from chemo in rodents by having many beneficial effects on gut flora (mycobiome and bacteriome) and cell energy metabolism. The immune system, which relies on energy to reduce the risk of chronic diseases, is strongly linked to body composition (gut flora and metabolism). Importantly, because 3L has beneficial effects on many different bacteria and metabolic systems (see Fig. 8 ), it is very likely that it could work as an additive to chemical drugs not only for cancer, but also for many different metabolic diseases. It eliminates specific invading agents such as fungi ( mucor ) and infectious bacteria ( enterococcus ). Furthermore, the diversity of bacteria upregulated by 3L treatment (see Fig. 8 ) is such that our probiotic formula ( L. acidophilus SD65 , L. casei SD07 , and L. plantarum SD02 ) can be easily modified to target specific metabolic systems in humans. One advantage of our initial work in rats is that different doses of Lactobacillus have different effects on gut flora (see Figs. 7 & 8 ). Therefore, more research should be conducted to test the effects of different SD65-SD07-SD02 ratios in 3L mixtures or in combination with other Lactobacilli or beneficial bacteria mixtures (see Fig. 8 ). Here, we offer a research feasibility suggestion for immunization or improved immune systems in chemo. Our 3L bioproduct could serve as the significant and strong basis for the development of a large family of medicinal microbial bioproducts that would be used on cancer patients rather than chemo-animal models. Conclusion The formulated 3L probiotic has a relevant action on the rat gut microbiome in chemotherapy conditions. While CTX and anticancer drugs have a number of potential side effects, as documented here, including 3L in chemotherapy has been shown to benefit gut flora and thus health conditions. Interactions between fungi and bacteria regulate health and disease. 3L appears to keep fungi and bacteria that are beneficial to health. The results on bacteria are limited to specific metabolic pathways, which appears to be how 3L approaches chemotherapy. The results in rats show that the efficiency, frequency, and dependability of high-dose retain attention over a month of treatment (five CTX-chemotherapy sessions or ‘shots’). High dose of 3L appears to restore gut microbiota to normal levels, strengthen the host’s overall immune defense, and strongly preserve health conditions. Although much remains to be learned from studying 3L, this is not insignificant in our ongoing search for tools to improve the quality of life of cancer patients undergoing chemotherapy. Materials And Methods Lactobacillus preparation for medical-industrial use. We previously developped a three-strain lactobacillus probiotic formula (3L) to combat cholesterolemia and hyperlipidemia [ 45 ]. Our Industrial Laboratory platform for natural medicine in Jinan (Shandong Province, P.R. China) produced the same probiotic formula used in this study against immune damages in cyclophosphamide chemotherapy ( L. acidophilus SD65 , L. casei SD07 , and L. plantarum SD02 ). Following Yue et al. (2014) [ 45 ], pure cultures of the three bacterial strains were grown in de Man, Rogosa, and Sharpe (MRS) agar liquid medium and placed in an anaerobic workstation held at 37°C (industrial platform). Each strain’s bacterial cells were harvested for 3L preparation by centrifugation at 2000 × g for 20 min (4°C). Each strain’s cell pellet was resuspended in sterile saline water solution at a concentration of 10 9 CFU/ml and stored at 4°C. The tripartite L probiotic solution (3L) was freshly prepared by mixing equal volumes of cold suspensions of SD65, SD07, and SD02 and stored in cold conditions (4 ~ 10°C) for later use. Rats were given a daily dose (0.3 ml) of bioproducts administered intra-gastrically via a stainless-steel needle, along with chemotherapy (cyclophosphamide, CTX; Fig. 1 ). Preparation of five groups in a CTX-induced immunosuppression model in rats. Jinan Pengyue Laboratory Animal Company supplied the rats (Product license SCXK (LU) 20140007). In our laboratory, the rats were bred in the Class II clean animal facility of the Institute of Medicine (Shandong Academy of Medical Sciences) with the setting temperature of 20–26°C (relative humidity: 40–70%, ventilation rate ≥ 15 times/hour). They all passed the quarantine inspection (Laboratory animal use license No.: STXK(LU)20170003, issued by Shandong Provincial Department of Science and Technology). The feed and drinking bottle were replaced every two-three days or if necessary. Fifty healthy Specific Pathogen Free (SPF) Sprague-Dawley (SD) male rats were divided into five experimental groups (N = 50 males, young adults, 8–9 weeks old; body weight: ~260–316 g): 1) Control healthy (CK), 2) Immune attacked (IM), 3) Immune attacked and treated with low Lactobacillus dosage of 1.25 ml/kg bodyweight (L), 4) Immune attacked and treated with middle Lactobacillus dosage of 2.5 ml/kg bodyweight (M), and 5) Immune attacked and treated with high Lactobacillus dosage of 5.0 ml/kg bodyweight (H). The total number of rats studied was 50, with 10 rats in each group (Fig. 1 ). They were fed in two five-rat cages (feeding density: ≤ 5/cage). Four rats in each cage were labeled with neutral red on the head, neck, back, and tail. The fifth rat was unmarked. Before the experiment, each group was given an equal volume of animal drinking water, and 5.0 ml/kg of animal drinking water was supplemented before gavage). The control rats (CK group) were given a continuous gastric perfusion of normal saline (NS). The immune system of rats in groups 2–5 was attacked by an intraperitoneal injection of cyclophosphamide (CTX, 10 mg/ml). Another study looked at the effects of CTX on the immune system of rats [ 49 ]. A companion study highlighted the negative effects of CTX cyclophosphamide chemotherapy on the rat immune system as wells as the beneficial effects of Lactobacillus preparation on cyclophosphamide-induced immunosuppression [ 49 ]. Following the findings from Zhang et al. (2020) on CTX chemotherapy and immunodepression in rats [ 49 ], the current experiment on chemotherapy and microbiome was followed for approximately 28 days (D28). CTX was only administered to “nude” rats on day 1, 5, 8, 15, and 22 (IM group; Fig. 1 ). On each of the five shots, CTX was injected intraperitoneally at a dosage volume of 4 ml/kg. The control group received saline shots of the same volume. The 3L preparation was administered via gavage, which is a tube that runs from the mouth to the stomach. “Covered” rats received a 3L continuous gastric perfusion in NS (1.25 ml/kg, low dose, L group; 2.50 ml/kg, middle dose, M group; 5.0 ml/kg, high dose, H group; Fig. 1 ). L, M, and H, like IM, corresponded to five CTX injections chemotherapy (Fig. 1 ). In each rat group, fresh fecal samples were collected for analysis on D28. Feces were collected with sterile disposable plastic spoon (SteriPlast sample spoon) and placed in a 1.5 ml Eppendorf tube that had been sterilized. Fecal samples from the control CK, IM, L, M and H groups were stored at -80°C until DNA extraction, Illumina MiSeq Sequencing, and microbiome profiling comparisons between the five groups (Fig. 1 ). Preparation of microbial genomic DNA samples for Illumina MiSeq. DNA was extracted from the five groups of rats (CK, IM, L, M, and H) in the chemotherapy model using the method previously selected for mice and piglet fecal microbiome analysis [ 45 , 50 ]. This method was dependable for fecal DNA sample testing, quantity, purity, and quality control, as well as Illumina sequencing [ 50 ]. As described in Yue et al. (2020) [ 50 ], 2.0 g of fecal samples from Group CK-H rats were processed for microbial genomic DNA extraction using QIAamp Fast DNA Stool MiniKit (Qiagen GmbH, Hilden, Germany) and used as template (10 ng) in PCR reactions employing universal primers. For 16S samples (V3-V4, 480 bp, Miseq-PE250), the primers 338F 5’-ACTCCTCGGGAGGCAGCA-3’ and 806R 5’-GGACTACHVGGGTWTCTAAT − 3’ (Personal Biotechnology Co, Ltd, Shanghai, China) were used. The primers ITS5F 5’-GGAAGTAAAAGTCGTAACAAGG-3’ and ITS1R 5’-GCTGCGTTCTTCATCGATGC-3’ (Personal Biotechnology Co, Ltd, Shanghai, China) were used in PCR reactions for ITS (ITS1, 250 bp, Miseq-PE250). Each Illumina sequencing sample corresponded to three rats from the same group. Consequently, fifteen different samples (CK: C101, C103, C105; IM: IM015, IM021, IM024; L: L102, L103, L104; M: M201, M202, M203; H: H105, H204, H205) were subjected to Illumina MiSeq (NCBI SubmissionID: SUB9725559; BioProject ID: PRJNA754332; BioSamples: SAMN20769197-SAMN20769206; Accession Numbers: SRX11740945-SRX11740959). Three biological samples were tested in each group of rats during microbiome analysis in relation to CTX chemotherapy, so we conducted individuals, replicates, and comparison groups. Prior to sequencing, ITS and 16S rDNA products (TransGen Biotech, Beijing, China) were amplified in a Takara Master Thermal Cycler Dice (Takara, Dalian, China) programmed for an initial denaturation of 95°C for 3 min, followed by 30 cycles of 94°C for 30 s, 50°C for 30 s, 72°C for 1 min, and a final extension of 72°C for 7 min. Q5® high-fidelity DNA polymerase (New England BioLabs Inc., Ipswich, Massachussets, USA) was used for PCR amplification. Each group’s PCR products or amplicons were purified using a 2 percent agarose gel electrophoresis (Bio-Rad Beijing, China) and a gel recovery kit (Axygen®, AxyPrep DNA gel extraction kit, New York, USA). The PCR product concentration was determined in a microplate reader (BioTek™, FLx800™) using a fluorescence reagent-based method (Quant-iT PicoGreen dsDNA Assay Kit, Fisher Scientific™, Loughborough, UK). Illumina MiSeq Sequencing. In the five groups of rats (CK-H), MiSeq sequencing by Illumina was used to generate sequencing data from microbial genomic DNA libraries for chemotherapy + bioproduct research. We used the same Illumina TruSeq Nano DNA LT Library Prep Kit used for human genome and gut microbiota sequencing (Human Genome Assembly: The Genome Sequencing Consortium, 2001) [ 115 ]. MiSeq’s goal was to add adapter sequences to the ends of microbial DNA fragments in order to generate indexed libraries for single- and paired-end reads [ 116 ]. To begin, rat fecal microbial genomic DNA amplicons were subjected to terminal end repair. End Repair module (Mix2) excised the 5’-end of DNA and replaced it with a phosphate group. Meanwhile, the 3’-end’s missing base was filled. To prevent self-ligation, an adenosine base was added to the 3’-end of each microbial DNA sequence. This also ensured that the sequencing linker was properly linked to each DNA target sequence. To immobilize DNA in flow cells, a sequencer corresponding to a library-specific tag (Index Sequence) was added to the 5’-end of the PCR amplicons. To purify the microbial library system, self-ligated fragments were removed using BECKMAN AMPure XP Beads (Beckman Coulter™, Illkirch, France). PCR amplicons were used as a template in a second-PCR run to enrich the libraries as much as possible for the DNA of interest. The PCR conditions were the same as described in the section on preparing microbial genomic DNA samples for Illumina sequencing. Before high-throughput sequencing, PCR amplicons were purified using the Beckman magnetic beads screening method and analyzed by 2 percent agarose gel electrophoresis. Prior to high-throughput sequencing, the quality of each rat fecal microbial genomic DNA library was checked on an Agilent Bioanalyzer with an Agilent High Sensitivity DNA kit (Agilent Technologies Inc., Waldbronn, Germany). On Agilent check, each DNA library produced only a single peak and no joints. The sequence librairies were then quantified using the Quant-iT PicoGreen dsDNA assay kit Promega on the Quantifluor fluorescence quantitative system (Promega Corporation, Madison, USA). The concentration of the library was greater than 2 nM. DNA samples were mixed after serial dilutions, denatured with NaOH, and sequenced. Illumina MiSeq Reagent kit v3 was used on a 600 cycles MiSeq Sequencer (Illumina Next Generation) to perform two-end sequencing with 2 x 300 bp reading length. The target DNA fragment’s optimal sequencing length was between 200 and 450 bp. Based on primers and barcode information, reading sequences were identified and assigned to the corresponding samples. USEARCH (v5.2.236, http://www.drive5.com/usearch ) was used to remove all chimeric sequences (or artifacts formed by incorrectly joined sequences) [ 117 ]. QIIME (Quantitative Insights Into Microbial Ecology, v1.8.0, http://qiime.org ) was used to identify unreliable sequences (replication errors, nucleotide base substitutions, insert deletions, and so on) for microbial genomic DNA [ 118 ]. Sequences with more than one base mismatch and/or more than eight consecutive identical bases were discarded [ 117 – 119 ]. Other sequences were classified into Operational Taxonomic Units (OTUs), which were then used for microbial DNA taxonomic identification and phylogenetic analysis [ 120 ]. The diversity level of each sample was assessed using OTU values, and the depth of sequencing (enhanced microbial community analysis) was demonstrated using rarefaction curve analysis [ 121 ]. The composition of the five samples corresponding to the five rat groups (CK-H) was examined at different taxonomic levels: phylum, order, class, family, genus, and species (i.e. complete microbiome; Fig. 1 ). Tables, box plots and histograms were used to display the microbiome results (OTU, 100%; R software). Rare OTUs (with an abundance proportion of less than 0.001%) were excluded from microbiome analysis [ 122 ]. Venn diagrams in R (Treat*/2.5.1_Venn) were used to display shared and unique OTUs within each group (as calculated by R). Rarefaction curves were drawn to reflect the microbial diversity among samples using an OTU abundance matrix to study complete microbial structure in each group. Specifically, we compared the number of OTUs in five different groups of rats (CK-H) at the same sequencing depth and justify the level of diversity in each sample (QIIME2, alpha rarefaction curve). The length of the curve reflects the number of sample sequencing depths; the longer the curve, the greater the sequencing depth, which increases the likelihood of observing increased microbial diversity. The slope of the curve reflects the effect of sequencing depth on the sample’s microbial diversity. A rarefaction curve with a flat slope indicates that the sequencing results are sufficient to reflect microbial diversity and that increasing sequencing depth will not detect more novel OTUs. A bump rarefaction curve (high slope) indicates that the diversity has not been exhausted, and that increasing sequencing depth could aid in the detection of more OTUs (Treat*/2.3.2_arare). We also measured “Specaccum” (species accumulation curve) in five groups of chemotherapy-treated rats and 3L. Specaccum, like the rarefaction curve, indicates the extent of increase in microbial community richness with increasing sample size [ 123 ]. We estimated whether the sample size was sufficient to reflect the different underlying bacterial communities of the different groups or samples using R’s specaccum function. Using R in vegan (S3 method; Treat*/2.3.3_specaccum), the specaccum species accumulation curve was plotted for the total number of OTUs in each sample from the OTU abundance matrix [ 124 ]. Furthermore, the rank abundance curve (RAC) was used to determine the number of highly abundant versus rare OTUs in each community [ 125 ]. For RAC, OTU values were sorted and transformed into Log2 data in R (Treat*/2.3.4_rabund). Other multiple indices were used to assess microbial alpha diversity in different rat groups. Using QIIME software (QIIME 2) in R, we included the Chao1 index and ACE index to reflect community richness, and Shannon-Simpson indices to reflect both evenness and richness of the bacterial community in each of the five groups of rats related to cancer chemotherapy and lactobacillus treatment (CK-H) [ 119 – 125 ]. Multiple statistical analysis tools (Metastats) in Mothur software ( http://metastats.cbcb.umd.edu ) were used to analyze the differences in gut flora structure and related microbial species between the groups, providing the sequence difference (or absolute abundance) of two samples/groups based on P and Q values [ 126 ]. Composition analysis included OTU number analysis of each taxonomic level. According to OTU classification results, we analyzed the OTU number of every sample at each taxonomic level (Kingdom, Phyllum, Class, Order, Family, Genus, Species). The results were plotted as a histogram by R language. OTU number of groups and individual samples was shown at each taxonomic level. The ordinate showed the relative abundance of each taxon, the longer the bar means the higher the relative abundance of the corresponding taxon in the sample. To show the differences in the composition of taxa between samples (groups and individual samples), the abundance difference between five groups and fifteen samples was compared one by one, and whether the difference was significant was determined by statistical test (Metastats analysis, Mothur Software) [ 127 ]. A heatmap [ 128 ] was used in this descriptive study to show fungal microbial taxa clustering based on the abundance of each taxon in five groups and fifteen individual samples related to chemotherapy and Lactobacillus treatment in rats. The relative abundance of each taxon in each sample was used to create a microbial species heatmap (Treat*/2.2.1_taxa). The relative abundance values were all log transformed to reduce the degree of difference. If the taxon’s relative abundance is 0, half of the minimum abundance value will be substituted for it. As a result, heatmaps were created using the R sofware package “gplots” of sofware R, and the distance algorithm is “euclidian”, and the clustering method is “complete”, as used by Yue et al. (2020) on curing piglets [ 50 , 128 ]. LEfSe was used to calculate the difference in community composition between groups based on linear discriminant analysis (LDA) effect size. LDA is combined with Krustal-Wallis and Wilcoxon rank sum tests in LEfSe analysis to identify key biomarkers (i.e, key community members) [ 129 ]. The Galaxy Online Analysis Platform was used for sample group comparison and visual analysis results for LefSe analysis ( http://huttenhower.sph.harvard.edu/galaxy ; Treat*/2.5.5_LEfSe). Sequence denoising or clustering. The primary tasks carried out the DADA2 method are priming, quality filtering, denoise, Mosaic, and chimera removal [ 130 ]. Instead of clustering based on similarity, it now only uses dereplication, or 100% similarity clustering. The Amplicon Sequence Variants (ASVs) or feature sequences (corresponding to the OTU representative sequences) are referred to each decontamination sequence produced by the use of DADA2 quality control, and the feature schedule is the frequency of these sequences in the sample or group of samples (corresponding to the OTU Options). The current mainstream analysis platforms (QIIME2 and VSEARCH) promote the denoising and feature sequence generation method represented by DADA2 [ 131 ]. “Operational Taxonomic Units” (“OTUs”), which is Esperanto for “suboptimal, imprecise rubbish”, are described as “the features produced by clustering methods” in QIIME2. It is believed that the clustering analysis method was established on the basis of OTUs, which is not ideal or accurate ( https://docs.qiime2.org/2019.7/tutorials/overview ). DADA2 is therefore picked for analysis by default. However, the OTU clustering based VSEARCH method from Rognes et al. (2016) [ 131 ] is still an option because the aforementioned methods have not yet been optimized for all amplicon types. Priming, splicing, quality filtering, weight removal, chimera removal, clustering, and so forth are all the main components of the VSEARCH method [ 131 ]. VSEARCH is a 64-bit open source free analysis program specifically for USEARCH. The accuracy of the software’s clustering and chimera removal is superior to that of USEARCH’s UPARSE algorithm [see 119, 131]. Therefore, for functional gene analysis, the VSEARCH approach was automatically chosen. Classification and phylogenetic analysis. OTU representative sequences were used as taxa in FastTree tool to construct phylogenetic trees (Newick) [ 132 ]. Using MEGAN [ 133 ], the abundance and taxonomic composition of OTUs in each sample were projected to the microbiological classification tree from NCBI ( https://www.ncbi.nlm.nih.gov/taxonomy ). At each taxonomic level, hierarchical trees (GraPhlAn) were constructed using the entire sample population. Taxonomic units were distinguished by different colors in GraPhlan, and their abundance distribution was reflected by the node size, i.e. the average relative abundance of the taxonomic unit [ 134 ]. KronaTools 2.4 software was used to deliver interactive presentations of community taxonomy to groups of chemotherapy-related rats [ 135 ]. The primary goal of the ß-diversity analysis was to compare the similarity of community structure between the various groups. Principal component analysis (PCA), multidimensional scaling (MDS), and clustering analysis (CA) were used to observe differences between groups using the natural decomposition of the community data structure and sample ordination. PCA compares the similarity of samples using Euclidean distance, regardless of the original variables’s possible interrelationship (R software). Before classifying the group or sample distances, nonmetric MDS only considers the size of the relationship between samples (UniFrac distance matrices of Unweighted and Weighted in R software). CA methods such as Unweighted Pair-Group Method with Arithmetic Means (UPGMA), single-linkage clustering, and complete-linkage clustering, like Nonmetric MDS analysis, use any distance to evaluate sample similarity (QIIME-R). The T-test was used to test the Weighted and Unweighted UniFrac distance between or within groups using QIIME. 1000 Monte Carlo permutations were used to test the statistical significance. This fully describes the differences in flora structure size between or within groups of chemotherapy-related rat fecal samples. Finally, the massive amount of community data generated by high-throughput DNA sequencing in five groups of rats (CK-H) necessitated the use of more statistical analyses such as Constrained Ordination and Supervised Learning. The commonly used Constrained Ordering and Supervized Learning methods, including redundancy analysis (RDA), canonical analysis (CNA), and partial least squares discriminant analysis (PLS-DA, Variable Importance in Projection or VIP value in R), extracted the pattern characteristics associated with the original microbial community in accordance with a known sample correlation (a sample distribution or grouping information) or a sample test indicator (continuous variable). The greater the VIP value, the greater the species’s contribution to group differences. Furthermore, using QIIME software, Adonis/PERMANOVA (permutational multivariate analysis of variance) analysis was performed to determine whether the differences between groups were statistically significant. In terms of P value, the smaller the P value, the greater the difference between chemotherapy groups of rats. Identification of microbiomes and pathway abundances. Most of the current population diversity studies using 16S rRNA genes use 97% sequence similarity as the OTU threshold, which is roughly equivalent to the sequence similarity used for species identification in taxonomy in our descriptive study using chemotherapy rat models. The UCLUST sequence alignment tool from Edgar (2010) [ 119 ] was used with the QIIME sofware to merge bacterial sequences with the similarity value of 97% into specific OTUs and select the highest abundance in each OTU as the representative bacterial sequence, as described for ITS and fungal sequences. The matrix file (i.e. OTU table) was then constructed based on OTU abundance in each sample, and this matrix file (i.e. OTU table) was transformed to BIOM (Biological Observation Matrix) file format, which was then transferred to other analysis tools. Using QIIME software with the default paramaters, the taxonomy information for each OTU was obtained by comparing the representative sequence of OTU to the template sequence of the corresponding database, as described for analysis of ITS sequences. Different types of sequences (e.g. 16S rRNA sequences of bacteria or ITS sequences of fungi) were classified based on the corresponding database: a) 16S rRNA gene database for bacteria (Greengenes database; Release 13.8, http://greengenes.secondgenome.com ) [ 136 ], and b) ITS sequence database for fungi (UNITE database; Release 8.0, https://unite.ut.ee ) [ 137 ]. In theory, all microbial sequences should be able to be classified into specific species and strains. However, due to the wide variety of micro-organisms, it is difficult to include complete information on all species in the current commonly used database. Furthermore, sequencing read length limits classification ability. As a result, not all OTU representative sequences can obtain genus or species taxonomic information as found for ITS. The microbiome analysis was two-steps: 1) Classify-sklearn algorithm with QIIME2 ( https://github.com/QIIME2/q2-feature-classifier ) was used for UNITE database (release 8.0; ITS) and Greengenes database (Release 13.8; 16S). Species annotation was performed in QIIME2 software using a pre-trained Naive Bayes classifier with default parameters for each ASV/OTU representative sequence. 2) The BROCC algorithm was used for the NT database ( https://github.com/kylebittinger/q2-brocc#the-brocc-algorithm ) [ 138 ]. We used blastn to align the OTU sequence with the NT database (or a specific sequence filtered from it). The brocc.py script is then called to get the comment information based on the recommended parameters. Using this procedure, for fungal ITS sequences, in the UNITE database, Candida (CUG-Ser1 clade, Debaryomycetaceae) and Pichia (Pichiaceae) were identified as two different genera ( Candida : C. africana / albicans sp. ; Pichia : P. aff. Alni/barkeri/bovicola sp. ). The same was done for Penicillium (formal name) and Talaromyces fungi. The International Code of Nomenclature for Algae, Fungi, and Plants (ICN) mandated single name nomenclature for fungi [ 139 ]. The abandonment of dual nomenclature resulted in significant changes in Penicillium and Talaromyces taxonomy and nomenclature. Houbraken and Samson (2011) demonstrated that species formely classified in the Penicillium subgenus Biverticullium are resolved in a monophyletic clade with the former teleomorph genus Talaromyces , but the remaining Penicillium species are associated with the younger teleomorph genus name Eupenicillium based on four phylogenies [ 140 ]. The general scientific community working on this fungi accepted this classification fairly well [see 141]. Identification of Fusarium (= Gibberella ) was assisted by use of www.fusarium.org . (monophyletic node F3) [ 142 ]. We used the localization of the nt (2019.8 download, ftp:/ftp.ncbi.nih.gov/blast/db ) database for functional genes and other requirements such as pathways identification in MetaCyc. The largest metabolic reference database in the life sciences with experimental data is called MetaCyc (metacyc.org). At the moment, it has 2,722 pathways from 3,009 various organisms. Information on different primary and secondary metabolic pathways, as well as associated metabolites, biochemical processes, enzymes, and genes, can be found in MetaCyc. By storing representative, experimentally verified metabolic pathways, it seeks to categorize all life’s metabolic processes [ 60 , 142 – 145 ]. We can attempt to identify bacterial metabolic pathways with notable differences between groups after obtaining the abundance data for those pathways. Here, we apply the metagenomeSeq method (normalized pathway abundance). FitFeatureModel is used to fit the distribution of each ASV/OTU sequence with a zero-selective log-normal model, and used to assess the significance of the difference. These are the analysis’s findings for differences in MetaCyc metabolic pathways between groups: Group A refers to the A group before the folder name, and group B is up-regulated relative to Group A when the value of logFC(log2(fold change)) on the horizontal axis is positive and down-regulated when it is negative. The label for the each different MetaCyc metabolic pathways is in the ordinate. Different colors indicate the level of significance. Finally, the species composition of the various metabolic pathways was examined using the stratified sample metabolic pathway abundance table (PATH_abun_strat.TSV), which was based on the significantly different metabolic pathways. To specify the specific metabolic pathways examined, we used the “-f $ pathway” option when calling humann2_barplot_py. The ordinate value was set as the relative abundance, the abscissa was arranged according to the sample group, and the samples within the group were arranged according to similarity. The outcomes are presented as Figs. 7 & S13. By default, we conducted species composition analysis for MetaCyc metabolic pathways with differences. Declarations Ethics approval and consent to participate The study is reported in accordance with ARRIVE guidelines ( https://arriveguidelines.org ). We confirm that all methods were carried out in accordance with relevant guidelines and regulations. We confirm that all experimental protocols were approved by a named institutional and/or licensing committee/s We confirm that the use of live animals (rats) in this study was approved by the Shandong Academy of Medical Sciences Ethics Committee and was licensed by Shandong Province (Governmental license SYXK (LU) 20170003). Consent for publication Not applicable Availability of Data and Material (ADM) Our 16S and ITS sequencing data in CTX-rat model are updated at NCBI (SubmissionID: SUB9725559; BioProject ID: PRJNA754332), e.g. by the linkage of one or more BioSamples (SAMN20769197-SAMN20769206; Accession Numbers: SRX11740945-SRX11740959). The locus_tag prefixes for each linked BioSample are included in the locustagprefix.txt file that can accessed from BioProject ID PRJNA754332 in the submission portal: https://submit.ncbi.nlm.nih.gov/subs/bioproject/SUB9725559/overview https://submit.ncbi.nlm.nih.gov/subs/bioproject (released January the first, 2022) Competing interests The authors declare no competing interests. Funding The plan to develop a new bio-product for immunomodulation during chemotherapy was supported by grant supports from Shandong Province Overseas High-Level Talents Program (Taishan scholar, #tshw20091015), Key Research and Development of Shandong Province (#2016GGH3111), and Agricultural Science and Technology Innovation Engineering Program of Shandong Academy of Agricultural Sciences (CXGC2017A01-1). Authors’ contributions S.Y., J.L., and J.F.P. designed concept and research, S.Y., Z.Z., F.B., Y.Z., G.C., Y.F.Z., J.L., and J.F.P. carried out research, Z.Z. and J.L. (supervision) reared rats in Specific Free Pathogen facilities/acute hospital care settings (Class II animal facility) and set up chemotherapy assay, S.Y., F.B., Y.Z., G.C., Y.F.Z., and J.F.P. produced and injected Lactobacillus, S.Y., Z.Z., F.B., Y.Z., G.C., and J.L. collected feces in SFP facilities and prepared genomic DNA samples, S.Y., F.B., Y.Z., G.C., Y.F.Z., and J.F.P. performed molecular biology and prepared 16S and ITS samples for sequencing, BGI Co. Ltd (Beijing) ran 16S and ITS Illumina sequencing, S.Y., J.L., and J.F.P. validated methods, S.Y., J.L., and J.F.P. analyzed and interpreted data, Y.F.Z. helped the classification (bacteria and fungi), S.Y., J.L., and J.F.P. prepared all figures and tables, J.F.P. wrote the first draft of the manuscript. Acknowledgment We acknowledge Beijing Genomics Institute (BGI Co., Ltd) for Illumina MiSeq sequencing (ITS and 16S). References Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74. Salk JJ, Fox EJ, Loeb LA. Mutational heterogeneity in human cancers: origins and consequences. Annu Rev Pathol. 2010;5:51–75. Chakravarthi BVSK, Nepal S, Varambally S. Genomic and epigenomic alterations in cancer. J Am Pathol. 2016;186:1724–35. Mroz EA, Rocco JW. The challenges of tumor genetic diversity. Cancer. 2017;123:917–27. Takeshima H, Ushijima T. Accumulation of genetic and epigenetic alterations in normal cells and cancer risk. NPJ Precis Oncol. 2019;3:7. Herceg Z, Hainaut P. Genetic and epigenetics as biomarkers for cancer detection, diagnosis and prognosis. Mol Oncol. 2007;1:26–41. Berg M, Søreide K. Genetic and epigenetic traits as biomarkers in colorectal cancer. Int J Mol Sci. 2011;12:9426–39. Loeb LA, Loeb KR, Anderson JP. Multiple mutations and cancer. Proc Natl Acad Sci USA. 2003;100:776–81. Vogt A, Schmid S, Heinimann K, Frick H, Herrmann C, Cerny T, et al. Multiple primary tumours: challenges and approaches, a review. ESMO Open. 2017;2:e000172. Wang L, Shilatifard A. UTX mutations in human cancer. Cancer Cell. 2019;35:168–76. Chatterjee A, Mambo E, Sidransky D. Mitochondrial DNA mutations in human cancer. Oncogene. 2006;25:4663–74. Anastasiadou E, Jacob LS, Slack FJ. Non-coding RNA networks in cancer. Nat Rev Cancer. 2013;18:5–18. Lin S, Gregory RI. MicroRNA biogenesis pathways in cancer. Nat Rev Cancer. 2016;15:321–33. Gallo LH, Ko J, Donoghue DJ. The importance of regulatory ubiquitination in cancer and metastasis. Cell Cycle. 2017;16:634–48. Ben-Aroya S, Levanon EY. A-to-I. RNA editing: an overlooked source of cancer mutations. Cancer Cell. 2018;33:P789–90. Shuai S, Suzuki H, Diaz-Navarro A, Nadeu F, Kumar SA, Gutierrez-Fernandez A, et al. The U1 spliceosomal RNA is recurrently mutated in multiple cancers. Nature. 2019;574:712–6. Wang E, Aifantis I. RNA splicing and cancer. Trends Cancer. 2020;6:P631–44. Wilkinson S, Harmon SA, Terrigino NT, Karzai F, Pinto PA, Madan RA, et al. A case report of multiple primary prostate tumors with differential drug sensitivity. Nat Commun. 2020;11:837. Vaklavas C, Blume SW, Grizzle WE. Translational dysregulation in cancer: molecular insights and potential clinical applications in biomarker development. Front Oncol. 2017;7:158. Picimbon JF. A new view of genetic mutations. Australas Med J. 2017;10:715–8. Picimbon JF. Evolution of protein physical structures in insect chemosensory systems. Olfactory Concepts of Insect Control-Alternative to Insecticides, Vol. 2, Springer Nature Switzerland AG, 2019. pp. 231–63. Yue S, Picimbon JF. Lactobacillus for ribosome peptide editing cancer. Clin Transl Oncol. 2022; submitted. Charmsaz S, Prencipe M, Kiely M, Pidgeon GP, Collins DM. Innovative technologies changing cancer treatment. Cancers (Basel). 2018;10:208. Pucci C, Martinelli C, Ciofani G. Innovative approaches for cancer treatment: current perspectives and new challenges. Ecancermedicalscience. 2019;13:961. Waldman AD, Fritz JM, Lenardo MJ. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat Rev Immunol. 2020;20:651–68. Gonzales H, Hagerling C, Werb Z. Roles of the immune system in cancer: from tumor initiation to metastatic progression. Genes Dev. 2018;32:1267–84. Kaur P, Asea A. Radiation-induced effects and the immune system in cancer. Front Oncol. 2012. http://doi.org/10.3389/fonc.2012.00191 . Carvalho HA, Villar RC. Radiotherapy and immune response: the systemic effects of a local treatment. Clinics. 2018;73:e557s. Alfonso JCL, Papaxenopoulou LA, Mascheroni P, Meyer-Hermann M, Hatzikirou H. On the immunological consequences of conventionally fractionated radiotherapy. Science. 2020;23:100897. Lumniczky K, Candélias SM, Gaipl US, Frey B. Editorial: radiation and the immune system: current knowledge and future perspectives. Front Immunol. 2018. https://doi.org/10.3389/fimmu.2017.01933 . Price JG, Idoyaga J, Salmon H, Hogstad B, Bigarella CL, Ghaffari S, et al. CDKN1A regulates Langerhans cell survival and promotes Treg cell generation upon exposure to ionizing irradiation. Nat Immunol. 2015;16:1060–8. Scheithauer K, Belka C, Lauber K, Gaipl US. Immunological aspects of radiotherapy. Radiat Oncol. 2014;9:185. Najafi M, Shirazi A, Motevaseli E, Geraily Gh, Norouzi F, Heidari M, et al. The melatonin immunomodulatory actions in radiotherapy. Biophys Rev. 2017;9:139–48. Eastham LL, Howard CM, Balachadran P, Pasco DS, Claudio PP. Eating green: shining light on the use of dietary phytochemicals as a modern approach in the prevention and treatment of head and neck cancers. Curr Top Med Chem. 2018;18:182–91. Akolpoglu MB, Alapan Y, Dogan NO, Baltaci SF, Yasa O, Tural GA, et al. Magnetically steerable bacterial microrobots moving in 3D biological matrices for stimuli-responsive cargo delivery. Sci Adv. 2022;8:eabo6163. Vivarelli S, Salemi R, Candido S, Falzone L, Santagati M, Stefania S, et al. Gut microbiota and cancer: from pathogenesis to therapy. Cancers (basel). 2019;11:38. Cheng WY, Wu CY, Yu J. The role of gut microbiota in cancer treatment: friend or foe? Gut 2020; 69: 1897 – 1876. Davis LS. Flora-ishing guts assist cancer immunotherapies. Sci Immunol. 2018;3:eaat0813. Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT, Daillère R, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 2018;359:91–7. Li Y, Tinoco R, Elmén L, Segota I, Xian Y, Fujita Y, et al. Gut microbiota dependent anti-tumor immunity restricts melanoma growth in Rnf5 -/- mice. Nat Commun. 2019;10:1492. Ma W, Mao Q, Xia W, Dong G, Yu C, Jiang F. Gut microbiota shapes the efficiency of cancer therapy. Front Microbiol. 2019. https://doi.org/10.3389/fmicb.2019.01050 . Dehghani N, Tafvizi F, Jafari P. Cell cycle arrest and anti-cancer potential of probiotic Lactobacillus rhamnosus against HT-29 cancer cells. Bioimpacts. 2021;11:245–52. Chen L, Li H, Chen Y, Yang Y. Lactobacillus rhamnosus GG reduced mortality of septic mice by modulating gut microbiota composition and metabolic profiles. Nutrition. 2020;78:110863. Fong W, Li Q, Yu J. Gut microbiota modulation: a novel strategy for prevention and treatment of colorectal cancer. Oncogene. 2020;39:4925–43. Yue S, Chinnapandi B, Ge H, Zou X, Chen X, Wang C, et al. A Lactobacillus cocktail changes gut flora and reduces cholesterolemia and weight gain in hyperlipidemia mice. SOJ Microbiol Infect Dis. 2014;2:1–16. Yue S, Liu X, Fu J. Lactobacillus tritherapy for cholesterol and heart diseases. RR J Microbiol Biotechnol. 2016;5:23–6. Tian H, Lyu Y, Yang YG, Hu Z. Humanized rodent models for cancer research. Front Oncol. 2020; https://doi.org/10,3389/fonc.2020.01696. Czpirer C. Cancer research in rat models. Rat Genomics. Methods in Molecular Biology (Methods and Protocols), Vol. 597, Humana Press, 2010. pp. 445–58. Zhang Z, Yue S, Chen L, Teng S, Sun P, Wang Y, et al. Effect of Lactobacillus preparation on immunosuppression induced by cyclophosphamide in rats. Food Drug. 2020;22:103–7. Yue S, Li Z, Hu F, Picimbon JF. Curing piglets from diarrhea and preparation of a healthy microbiome with Bacillus treatment for industrial animal breeding. Sci Rep. 2020;10:19476. Su L, Zhu H, Guo Y, Du X, Guo J, Zhang L, et al. Lecanicillium coprophilum (Cordypitaceae, Hypocreales), a new species of fungus from the feces of Marmota monax in China. Phytotaxa. 2019;387:55–62. Jermy A. Ustilago gives plants a Pep talk. Nat Rev Microbiol. 2012;10:444–5. Verma RK, Pandro V, Raj D, Patel D, Asaiya AJK. Diversity of macro-fungi in Central India-XX: Phallus atrovolvatus and Phallus merulinus . Van Sangyan. 2019;6:1–8. Rota A, Calicchio E, Nardoni S, Fratini F, Ebani VV, Sgorbini M, et al. Presence and distribution of fungi and bacteria in the reproductive tract of healthy stallions. Theriogenology. 2011;76:464–70. Su L, et al. Coprophilum (Cordypitaceae, Hypocreales), a new species of fungus from the feces of Marmota monax in China. Phytotaxa. 2019;387:55–62. McGhie TA, Huber TW, Kassis CE, Jinadatha C. Ustilago species as a cause of central line-related blood stream infection. Am J Med Sci. 2013;345:254–5. Huseyin CE, O’Toole PW, Cotter PD, Scanlan PD. Forgotten fungi-the gut mycobiome in human health and disease. FEMS Microbiol Rev. 2017;41:479–511. Sam QH, Chang MW, Chai LY. The fungal mycobiome and its interaction with gut bacteria in the host. Int J Mol Sci. 2017;18:330. Li J, Chen D, Yu B, He J, Zheng P, Mao X, et al. Fungi in gastrointestinal tracts of human and mice: from community to functions. Microb Ecol. 2018;75:821–9. Caspi R, Billington R, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 2018;46:D633–9. Hove H, Norgaard H, Mortensen PB. Lactic acid bacteria and the human gastrointestinal tract. Eur J Clin Nutr. 1999;53:339–50. Fooks LJ, Gibson GR. Probiotics as modulators of the gut flora. Br J Nutr. 2002;88:39–49. Walter J. Ecological role of Lactobacilli in the gastrointestinal tract: implications for fundamental and biomedical research. Appl Environ Microbiol. 2008;74:4985–96. Hemarajata P, Versalovic J. Effects of probiotics on gut microbiota: mechanisms of intestinal immunomodulation and neuromodulation. Th Adv Gastroenterol. 2013;26:39–51. Panebianco C, Latiano T, Pazienza V. Microbiota manipulation by probiotics administration as emerging tool in cancer prevention and therapy. Front Oncol. 2020. https://doi.org/10.3389/fonc.2020.00679 . Lu K, Dong S, Wu X, Jin R, Chen H. Probiotics in cancer. Front Oncol. 2021. https://doi.org/10.3389/fonc.2021.638148 . Yu AQ, Li L. The potential role of probiotics in cancer prevention and treatment. Nutr Cancer. 2016;68:535–44. Legesse Bedada TL, Feto TK, Awoke KS, Garedew AD, Yifat FT, Birri DJ, et al. Probiotics for cancer alternative prevention and treatment. Biomed Pharmacother. 2020;129:110409. Aykut B, Pushalkar S, Chen R, Li Q, Abengozar R, Kim JI, et al. The fungal mycobiome promotes pancreatic oncogenesis via activation of MBL. Nature. 2019;574:267–7. Korula A, Abraham A, Abubacker FN, Viswabandya A, Lakshmi KM, Abraham OC, et al. Invasive fungal infection following chemotherapy for acute myeloid leukaemia-Experience from a developing country. Mycoses. 2017;60:686–91. Iida N, Mizukoshi E, Yamashita T, Terashima T, Arai K, Seishima J, et al. Overuse of antianaerobic drug is associated with poor postchemotherapy prognosis of patients with hepatocellular carcinoma. Int J Cancer 2019; 145: 2701–2711 (2019). Wainwright M. Some highlights in the history of fungi in medicine - a personal journey. Fungal Biol Rev. 2008;22:97–102. Nishad JH, Singh A, Gautam VS, Kumar D, Kumar J, Kharwar RN, et al. Endophytic fungi: a cryptic fountainhead for biodiversity, functional metabolites, host stress tolerance, and myco-mediated nanoparticles (Nps) synthesis. Endophytes and Secondary Metabolites, References Series in Phytochemistry, Springer Cham, 2018. pp. 1–29. Lombard L, van der Merwe NA, Groenewald JZ, Crous PW. Generic concepts in Nectriaceae. Stud Mycol. 2015;80:189–245. Fraiser LH, Kanekal S, Kehrer JP. Cyclophosphamide toxicity. Characterising and avoiding the problem. Drugs. 1991;42:781–95. Nicolás FE, Murcia L, Navarro E, Navarro-Mendoza MI, Pérez-Arques C, Garre V. Mucorales species and macrophages. J Fungi (Basel). 2020;6:94. Rosenblum EB, Voyles J, Poorten TJ, Stajich JE. The deadly chytrid fungus: a story of an emerging pathogen. PLoS Pathog. 2010;6:e1000550. Wos-Oxley ML, Bleich A, Oxley APA, Kahl S, Janus LM, Smoczek A, et al. Comparative evaluation of establishing a human gut microbial community within rodent models. Gut Microbes. 2012;3:234–49. American Society for Microbiology. One health: fungal pathogens of humans, animals, and plants: Report on an American Academy of Microbiology Colloqium held in Washington (DC). 2019 (PMID: 31769941). Roselli M, Natella F, Zinno P, Guantario B, Canali R, Schifano E, et al. Colonization ability and impact on human gut microbiota of foodborne microbes from traditional or probiotic-added fermented foods: a systemic review. Front Nutr. 2021;8:689084. Han S, Lu Y, Xie J, Fei Y, Zheng G, Wang Z, et al. Probiotic gastrointestinal transit and colonization after oral administration: a long journey. Front Cell Infect Microbiol. 2021;11:609722. Magro Reque P, Brandelli A. Encapsulation of probiotics and nutraceuticals: applications in functional food industry. Trends Food Sci Technol. 2021;114:1–10. Witherden EA, Shoale S, Hall RA, Moyes DL. The human mucosal mycobiome and fungal community interactions. J Fungi. 2017;3:56. Jayasudha R, Das T, Kalyana Chakravarthy S, Sai Prashanthi G, Bhargava A, Tyagi M, et al. Gut mycobiomes are altered in people with type 2 Diabetes Mellitus and Diabetic Retinopathy. PLoS ONE. 2020;15:e0243077. Coker OO, Nakatsu G, Dai RZ, Wu WKK, Wong SH, Ng SC, et al. Enteric fungal microbiota dysbiosis and ecological alterations in colorectal cancer. Gut. 2019;68:654–62. van Tilburg Bernardes E, Pettersen VK, Gutierrez MW, Laforest-Lapointe I, Jendzjowsky NG, Cavin JB, et al. Intestinal fungi are causally implicated in microbiome assembly and immune development in mice. Nat Commun. 2020;11:2577. Ghannoum MA, Jurevic RJ, Mukherjee PK, Cui F, Sikaroodi M, Naqvi A, et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 2010;6:e1000713. Jørgensen MR, Kragelund C, Jensen P, Keller MK, Twetman S. Probiotic Lactobacillus reuteri has antifungal effects on oral Candida species in vitro . J Oral Microbiol. 2017;9:1274582. Locasale JW, Cantley LC. Altered metabolism in cancer. BMC Biol. 2010;8:88. Heske CM. Beyond energy metabolism: exploiting the additional roles of NAMPT for cancer therapy. Front Oncol. 2020;9:1514. Desbats MA, Giacomini I, Prayer-Galetti T, Montopoli M. Metabolic plasticity in chemotherapy resistance. Front Oncol. 2020;10:281. Van Soom T, El Bakkali S, Gebruers N, Verbelen H, Tjalma W, van Breda E. The effects of chemotherapy on energy metabolic aspects in cancer patients: a systematic review. Clin Nutr. 2020;39:1863–77. Chung YW, Gwak HJ, Moon S, Rho M, Ryu JH. Functional dynamics of bacterial species in the mouse gut microbiome revealed by metagenomic and metatranscriptomic analyses. PLoS ONE. 2020;15:e0227886. Kovatcheva-Datchary P, Nilsson A, Akrami R, Lee YS, De Vadder F, Arora T, et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella . Cell Metabol. 2015;22:971–82. Polansky O, Sekelova Z, Faldynova M, Sebkova A, Sisak F, Rychlik I. Important metabolic pathways and biological processes expressed by chicken cecal microbiota. Appl Environ Microbiol. 2016;82:1569–76. Franke T, Deppenmeier U. Physiology and central carbon metabolism of the gut bacterium Prevotella copri . Mol Microbiol. 2018;109:528–40. Precup G, Vodnar DC. Gut Prevotella as a possible biomarker of diet and its eubiotic versus dysbiotic roles: a comprehensive literature review. Brit J Nutr. 2019;122:131–40. Owyang SY, Luther J, Kao JY. Helicobacter pylori : beneficial for most? Expert Rev Gastroenterol Hepatol. 2011;5:649–51. Abadi ATB. Helicobacter pylori : a beneficial gastric pathogen? Front Med (Lausanne). 2014;1:26. Marais M, Mendz GL, Hazell SL, Mégraud F. Metabolism and genetics of Helicobacter pylori : the genome era. Microbiol Mol Biol Rev. 1999;63:642–74. Weir S, Cuccherini B, Whitney AM, Ray ML, MacGregor JP, Steigerwalt A, et al. Recurrent bacteremia caused by a “ Flexispira ”-like organism in a patient with X-linked (Bruton’s) aggammaglobulinemia. J Clin Microbiol. 1999;37:2439–45. Iten A, Graf S, Egger M, Tauber M, Graf J. Helicobacter sp. flexispira bacteremia in an immunocompetent young adult. J Clin Microbiol. 2001;39:1716–20. Wei G, Tian N, Siezen R, Schuppan D, Helmerhorst EJ. Identification of food-grade substilisins as gluten-degrading enzymes to treat celiac disease. Am J Physiol Gastrointest Liver Physiol. 2016;311:G571–80. Moore RE, Townsend SD. Temporal development of the infant gut microbiome. Open Biol. 2019;9:190128. Lü C, Xia Y, Liu D, Zhao R, Gao R, Liu H, et al. Cupriavidus necator H16 uses flavocytochrome c sulfide dehydrogenase to oxidize self-produced and added sulfide. Appl Environ Microbiol. 2017;83:e01610-17. Sosa OA. Phosphorus redox reactions as pinch hitters in microbial metabolism. Proc Natl Acad Sci USA. 2018;115:7–8. Talwar C, Nagar S, Kumar R, Scaria J, Lal R, Negi RK. Defining the environmental adaptations of genus Devosia : insights into its expansive short peptide transport system and positively selected genes. Sci Rep. 2020;10:1151. McMahon RJ. Biotin in metabolism and molecular biology. Annu Rev Nutr. 2002;22:221–39. Vieira de Almeida C, Taddei A, Amedei A. The controversial role of Enterococcus faecalis in colorectal cancer . Th Adv Gastroenterol. 2018;11:1756284818783606. Falcinelli S, Rodiles A, Unniappan S, Picchietti S, Gioacchini G, Merrifield DL, et al. Probiotic treatment reduces appetite and glucose level in the zebrafish model. Sci Rep. 2016;6:18061. Wang M, Chen Y, Wang Y, Li Y, Zhang X, Zheng H, et al. Beneficial changes of gut microbiota and metabolism in weaned rats with Lactobacillus acidophilus NCFM and Bifidobacterium lactis Bi-07 supplementation. J Funct Foods. 2018;48:252–65. Lleal M, Sarrabayrouse G, Willamil J, Santiago A, Pozuelo M, Manichanh C. A single faecal microbiota transplantation modulates the microbiome and improves clinical manifestations in a rat model of colitis. EBioMedicine. 2019;48:630–41. Zhang SL, Bai L, Goel N, Bailey A, Jang CJ, Bushman FD, et al. Human and rat gut microbiome composition is maintained following sleep restrictions. Proc Natl Acad Sci USA. 2017;114:E1564–71. Coklo M, Maslov DR, Kraljevic Pavelic S. Modulation of gut microbiota in healthy rats after exposure to nutritional supplements. Gut Microbes. 2020;12:1–28. Human Genome Assembly. The Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921. Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59. Ravi RK, Walton K, Khosroheidari M. MiSeq: A next generation sequencing platform for genomic analysis. Disease Gene Identification, Methods in Molecular Biology, Vol. 1706, Humana Press, 2018. pp. 223–32. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7. Blaxter M, Mann J, Chapman T, Thomas F, Whitton C, Floyd R, et al. Defining operational taxonomic units using DNA barcode data. Philos Trans R Soc Lond B Biol Sci. 2005;360:1935–43. Cameron ES, Schmidt PJ, Tremblay BJ, Emelko MB, Müller KM. To rarefy or not to rarefy: enhancing microbial community analysis through next-generation sequencing. BioRxiv. 2020. DOI: 10.1101/2020.09.09.290049 . Gotelli NJ, Colwell RK. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett. 2001;4:379–91. Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens M, Oksanen M, et al. The vegan package. Community Ecol package. 2007;10:631–7. Izsák J, Pavoine S. Links between the species abundance distribution and the shape of the corresponding rank abndance curve. Ecol Indic. 2012;14:1–6. White JR, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol. 2009;5:e1000352. Barter LL, Yu B. Superheat. An R package for creating beautiful and extendable heatmaps for visualizing complex data. J Comput Graph Stat. 2018;27:910–22. Warnes GR, Bolker B, Bonebakker L, Gentleman R, Liaw WHA, Lumley T, et al. Gplots: various R programming tools for plotting data. 2015; R package version 2.17.0. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum-evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26:1641–50. Huson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 2011;21:1552–60. Asnicar F, Weingart G, Tickle TL, Huttenhower C, Segata N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. Peer J. 2015;3:e1029. Ondov BD, Bergman NH, Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Inf. 2011;12:385. De Santis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72. Kõljalg U, Nilsson R, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, et al. Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol. 2013;22:5271–7. Nilsson RH, Ryberg M, Kristiansson E, Abarenkov K, Larsson KH, Köljalg U. Taxonomic reliability of DNA sequences in public sequence databases: a fungal perspective. PLoS ONE. 2006;1:e59. McNeill J, Turland NJ. Major changes to the Code of Nomenclature-Melbourne, July 2011. Taxon. 2011;60:1495–7. Houbraken J, Samson RA. Phylogeny of Penicillium and the segregation of Trichocomaceae into three families. Stud Mycol. 2011;70:1–51. Visagie CM, Hirooka Y, Tanney JB, Whitfield E, Mwange K, Meijer M, et al. Aspergillus , Penicillium and Talaromyces isolated from house dust samples collected around the world. Stud Mycol. 2014; 78: 63–139. Crous PW, Lombard L, Sandoval-Denis M, Seifert KA, Schroers HJ, Chaverri P, et al. Fusarium : more than a node or a foot-shaped basal cell. Stud Mycol. 2021;98:100116. Karp PD, Caspi R. A survey of metabolic diseases emphasizing the MetaCyc family. Arch Toxicol. 2011;85:1015–33. Caspi R, Dreher K, Karp PD. The challenge of constructing, classifying, and representing metabolic pathways. FEMS Microbiol Lett. 2013;345:85–93. Caspi R, Altman R, Billington R, Dreher K, Foerster H, Fulcher CA, et al. The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2014;42:D459–71. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2113752","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":141628450,"identity":"c1530358-fe92-47fd-a216-0fdbba3f3720","order_by":0,"name":"Shousong Yue","email":"","orcid":"","institution":"Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shousong","middleName":"","lastName":"Yue","suffix":""},{"id":141628451,"identity":"ea2796ab-ad59-4767-8e8e-a5d5925f2da4","order_by":1,"name":"Zhenzhong Zhang","email":"","orcid":"","institution":"Shandong Academy of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhenzhong","middleName":"","lastName":"Zhang","suffix":""},{"id":141628452,"identity":"6d9a4868-8ce0-4586-9aae-4621c448b49b","order_by":2,"name":"Fei Bian","email":"","orcid":"","institution":"Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Bian","suffix":""},{"id":141628454,"identity":"85874d1d-c15d-477c-a16b-6880d951177a","order_by":3,"name":"Yan Zhang","email":"","orcid":"","institution":"Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":141628455,"identity":"81c71775-701c-4e36-9e39-82bef21f1a0f","order_by":4,"name":"Gao Chen","email":"","orcid":"","institution":"Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gao","middleName":"","lastName":"Chen","suffix":""},{"id":141628457,"identity":"c5d55e03-993e-4fec-936b-d25c6500396d","order_by":5,"name":"Youfeng Zhu","email":"","orcid":"","institution":"Shandong Academy of Agricultural Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Youfeng","middleName":"","lastName":"Zhu","suffix":""},{"id":141628459,"identity":"3b7d3b5f-37d2-4455-a12b-fa4ade0d2d35","order_by":6,"name":"Jun Li","email":"","orcid":"","institution":"Shandong Academy of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Li","suffix":""},{"id":141628461,"identity":"275b9916-7088-4df4-b091-37be5a92b904","order_by":7,"name":"Jean-François Picimbon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACxuYDDAcYDBgSGBiYDwD5EjKEtbQlwLSwJYC08BC2BqyQAaSFxwDEIKyFuY334IEfBdvy+KV7Pr+6UWPBw8B++OgG/A7jSzjYY3C7WHLO2W3WOceADuNJS7uBV8v8HoPDDAa3EzfcyN1mnMMG1CLBY4ZfSxsPTEvOM+OcfyRqYX6c20aUFphfZqSZMef2SfCwEfKLYRvv4Q8//tzO45dIfvw551udHD/74WP4tTQgIoJNAkziUw4C8khxx/yBkOpRMApGwSgYmQAA17NLEw/vw1IAAAAASUVORK5CYII=","orcid":"","institution":"Shandong Academy of Agricultural Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jean-François","middleName":"","lastName":"Picimbon","suffix":""}],"badges":[],"createdAt":"2022-09-28 17:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2113752/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2113752/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27568185,"identity":"2e38cbf4-1550-4551-aa43-946ff59df723","added_by":"auto","created_at":"2022-10-10 16:49:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":373890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLactobacillus\u003c/em\u003etritherapy experimental model on rats undergoing CTX chemotherapy conditions. After twenty-eight days of chemotherapy (cyclophosphamide CTX, 10 mg/ml), the red dot represents the immune-attacked group with depleted gut flora. CTX was administered to rats on D1, D5, D8, D15, and D22, in that order. Continuous perfusion of normal saline (NS) was used. Groups L, M, and H, rats were given a probiotic medication (one dose) daily. \u003cem\u003eLactobacillus\u003c/em\u003e(3L: \u003cem\u003eL. acidophilus\u003c/em\u003e \u003cem\u003eSD65\u003c/em\u003e, \u003cem\u003eL. casei SD07 \u003c/em\u003eand\u003cem\u003e L. plantarum SD02\u003c/em\u003e). L: low dose (1.25 mg/kg), M: middle dose (2.50 mg/kg), H: high dose (5.0 mg/kg). The blue dots represent the groups that have kept their gut flora close to control healthy conditions (\u003cem\u003eLactobacillus\u003c/em\u003e-treated groups). The dark blue dot represents the group (high 3L) with gut flora (microbiome) that is highly similar to control healthy conditions (CK). Chemotherapy begins in the +CTX groups at time 0. After twenty-eight days of treatment (D28), fecal samples were collected in each group. N= total number of rats in experiment, n= number of rats per group.\u003c/p\u003e","description":"","filename":"Yueetal.BMCMicrobiol2022Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/d92aa2ba71dff05da229048a.jpg"},{"id":27568187,"identity":"2b4a5278-4689-4438-854d-367a12944793","added_by":"auto","created_at":"2022-10-10 16:49:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78695,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis and fungal species matrix in five groups of rats related to chemo and \u003cem\u003eLactobacillus\u003c/em\u003e. A) Partial Least Squares Discriminant Analysis (PLS-DA) in CK-H groups. Fungal species matrix (\u003cem\u003eleft\u003c/em\u003e) and sample grouping data (\u003cem\u003eright\u003c/em\u003e) based on each species’ Variable Importance in Projection (VIP). VIP\u0026gt;1, the greater the value, the greater the species’ contribution to the differences between groups. \u003cem\u003eMucor \u003c/em\u003econtributes significantly to the differences between the IM (immune-attacked, ill, CTX, chemo) and other groups (CK: control healthy conditions and L-H: \u003cem\u003eLactobacillus\u003c/em\u003e-treated). \u003cem\u003eLactobacillus\u003c/em\u003e tritherapy: \u003cem\u003eL. acidophilus\u003c/em\u003e \u003cem\u003eSD65\u003c/em\u003e, \u003cem\u003eL. casei SD07, L. plantarum SD02\u003c/em\u003e (3L). L: 3L-low dose (1.25 mg/kg), M: 3L-middle dose (2.50 mg/kg), H: 3L-high dose (5.0 mg/kg). Each point or dot represents a sample; dots of the same color belong to the same group; and the same groups (three points or dots) are denoted by ovals (\u003cem\u003eright\u003c/em\u003e). The closer the distance between similar groups and the greater the distance beween different groups, the better the classification model. H-dose and CK groups are very close on PLS-DA classification model for rats related to chemotherapy and \u003cem\u003eLactobacillus\u003c/em\u003e, with IM as the far point (R(v3.1.1), BGI Co., Ltd).\u003cstrong\u003e \u003c/strong\u003eB) Discriminant Analysis using Orthogonal Projection to Latent Structures (OPLS-DA). \u003cem\u003eFusarium\u003c/em\u003e and \u003cem\u003esarocladium\u003c/em\u003e play a significant role in the differences between CK-H groups and IM (see *). \u003cem\u003eTalaromyces\u003c/em\u003e is associated with IM conditions (see circle in purple). \u003cem\u003eMucor \u003c/em\u003eis associated with CTX + low-dose of 3L bioproduct (see circle in orange). The high 3L probiotic treatment samples (H, in pink) cluster with the non-treated controls (CK, in green; \u003cem\u003eSarocladium\u003c/em\u003e, \u003cem\u003eKodamaea\u003c/em\u003e, \u003cem\u003eVerticillium\u003c/em\u003e, \u003cem\u003eCandida\u003c/em\u003e, \u003cem\u003eChlamydomyces\u003c/em\u003e, and \u003cem\u003eFusarium\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/2d2581c6d186f2720b03e2a2.jpg"},{"id":27567916,"identity":"05e7051f-9f49-4bca-b106-c78c62a22a25","added_by":"auto","created_at":"2022-10-10 16:44:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78036,"visible":true,"origin":"","legend":"\u003cp\u003eUnweighted UniFrac Nonmetric MultiDimensional Scaling analysis of CTX + 3L rat groups. Each point represents a different sample, and different colored points represent different groups (CK: control healthy conditions; IM: immune-attacked, ill, CTX treatment; L: 3L-low dose; M: 3L-middle dose; H: 3L-high dose). The shorter the distance between the two points, the greater the similarity, between the two samples in terms of microbial community structure. A) Bray-Curtis index (common, ellipse, and hull). B) Jaccard index (common, ellipse, and hull). In A \u0026amp; B, the distance between the two samples or groups is very close, and the similarity of the microbial community structure between the two samples or groups is very high (in A: CK and 3L; in B: CK and H; R(v3.1.1), BGI Co., Ltd).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/6bc1c4d94e44719b9be4dad9.jpg"},{"id":27568539,"identity":"31fb91d4-8c52-4dae-b5ec-d0a2fa071ec0","added_by":"auto","created_at":"2022-10-10 16:54:44","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":90922,"visible":true,"origin":"","legend":"\u003cp\u003eBeta-diversity analysis of five rat groups in relation to CTX chemotherapy and \u003cem\u003eLactobacillus\u003c/em\u003e. Each point represents a different sample, and different colored points represent different groups (five groups/ CK: control healthy conditions; IM: immune-attacked, ill, CTX-treated; L: 3L-low dose; M: 3L-middle dose; H: 3L-high dose). The shorter the distance between the two points or dots, the greater the similarity of the microbial community structure between the two samples. The percentage in parentheses in the coordinates (X-axis, 1\u003csup\u003est\u003c/sup\u003e principal component, Axis 1; Y-axis, 2\u003csup\u003end\u003c/sup\u003e principal component, Axis 2) represents the proportion of the differences in the original data that can be explained by the corresponding principal component. A) Principal Coordinates Analysis (PCoA) derived from unweighted and weighted UniFrac and Bray-Curtis index (common, ellipse, and hull). B) Principal Coordinates Analysis (PCoA) derived from unweighted and weighted UniFrac and Jaccard index (common, ellipse, and hull). A-B) The distance between the two groups is very close, as is the similarity of the microbial community structure between the two groups (in A: CK and 3L; in B: CK and H; R(v3.1.1), BGI Co., Ltd).\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/89748eede25f3689ba8b32de.jpg"},{"id":27569042,"identity":"0c6fcea5-e40b-4e45-a2b4-646dedcf240f","added_by":"auto","created_at":"2022-10-10 17:04:44","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":542395,"visible":true,"origin":"","legend":"\u003cp\u003eClustering of\u003cstrong\u003e \u003c/strong\u003efungal species based on abundance species (heatmap) in rat samples in relation to CTX chemotherapy, + 3L-treatment and improved gut microbiome strength. Fungal genus level log-scaled percentage heatmap in rat groups. Different colors represent various groups: 1) normal (control healthy, CK), 2) ill (immune-damaged, IM), 3) chemotherapy + 3L-low dose (L), 4) chemotherapy + 3L-middle dose (M) and 5) chemotherapy + 3L-high dose (H). The similarity of all fungal species is indicated by longitudinal clustering between the five rat groups. Horizontal clustering denotes the resemblance of specific fungal genera among the five rat groups (CK-H). The closer the distance and the shorter the branch length, the more similar the genus composition is between the groups and samples. A) Hierarchical clustering based on both groups and taxa (variables: columns and rows). The relative abundance of specific fungal genera is used to link groups (map both clustered). B) Hierarchical clustering on taxa (variable: rows). The ordering of groups or samples is unaffected by the relative abundance of fungi (map taxa clustered). In the genus-heatmap of rat groups in relation to chemo/3L, fifty differentially regulated fungi are shown. * shows lack of \u003cem\u003ePichia\u003c/em\u003e in \u003cem\u003eLactobacillus\u003c/em\u003e groups and abundance of \u003cem\u003eFusarium\u003c/em\u003e in both CK and H. Color gradients represent the amounts of individual fungal genera. The color gradation from red firebrick to navy-blue represents a decrease in the abundance of fungal genus (R(v3.1.1), BGI Co., Ltd).\u003c/p\u003e","description":"","filename":"Yueetal.BMCMicrobiol2022Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/667eb9e9a4b817eb82c840be.jpg"},{"id":27568183,"identity":"bdbe8095-0c04-4478-8dc3-0d35c4d13b37","added_by":"auto","created_at":"2022-10-10 16:49:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":189411,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of fungal taxa in rat groups in relation to CTX chemotherapy, 3L, and gut microbiome at the phylum, order, class, family, and genus level. A) Phylum-level. B) Class-level, C) Order-level, D) Family-level, and E) Genus-level. Each fungal taxon’s ratio or relative abundance in a specific group is directly displayed (color code). The histogram was drawn at the phylum-to-genus level using the top 20 most abundant fungal taxa in rat fecal samples. The curve shows that the rat gut flora is down-regulated after chemotherapy, but the microbiome is up-regulated during high-dose \u003cem\u003eLactobacillus\u003c/em\u003e (A-E, R(v3.1.1), BGI Co., Ltd).\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/b2ebf7ed8bc18b51287eca6d.jpg"},{"id":27567903,"identity":"367c3687-ddf1-409b-af4d-9d71f05d0c18","added_by":"auto","created_at":"2022-10-10 16:44:44","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":433549,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies distribution map of distinct MetaCyc metabolic pathways in rat groups in relation to chemotherapy and addition of 3L bioproduct to gut, and immune strengthening. Each group had three samples tested. CK: normal (control healthy), IM: immune-damaged (ill), L: chemotherapy + 3L-low dose, M: chemotherapy + 3L-middle dose, H: chemotherapy + 3L-high dose. A) 6-hydroxymethyl-dihydropterin diphosphate biosynthesis III, B) Pyridoxal 5’-phosphate biosynthesis I, C) TCA cycle VIII, D) Formaldehyde oxidation I, E) Superpathway of glyoxylate bypass and TCA, F) TCA cycle I, G) tRNA charging, and H) superpathway of ubiquinol-8 biosynthesis. The abscissa is composed of various sample labels that are grouped according to various colors. The groups’ samples are arranged in accordance with how closely the data match. The relative abundance of metabolic pathways is represented by the ordinate. At the same taxonomic level (genus level is used by default), the contributions of various taxon species to the metabolic pathway are dispplayed in various colors. With control CK and healthy conditions for both metabolism and gut flora, a high dose of \u003cem\u003eLactobacillus\u003c/em\u003e(H group) is ordered.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/8e82a225ebf8317044fb938e.jpg"},{"id":27568190,"identity":"1dbaff43-c567-460e-97f3-97b7a0c9a82e","added_by":"auto","created_at":"2022-10-10 16:49:45","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":338932,"visible":true,"origin":"","legend":"\u003cp\u003eRegulation of gut microbiome by adding 3L to cyclophosphamide (CTX) chemotherapy in rats. The addition of 3L-\u003cem\u003eLactobacillus\u003c/em\u003etritherapy to CTX treatment reduces \u003cem\u003eMucor, Xerochrysium, \u003c/em\u003eand \u003cem\u003eEnterocococcacea\u003c/em\u003e. 3L promotes the growth of numerous beneficial bacterial families (\u003cem\u003eBurkholderiaceae\u003c/em\u003e, \u003cem\u003eDefferibacteraceae\u003c/em\u003e, \u003cem\u003eHalomonadaceae\u003c/em\u003e, \u003cem\u003eHelicobacteraceae\u003c/em\u003e, \u003cem\u003eHyphomicrobiaceae\u003c/em\u003e, \u003cem\u003eMuribaculaceae\u003c/em\u003e, \u003cem\u003ePrevotellaceae \u003c/em\u003eand \u003cem\u003eStaphylococcaceae\u003c/em\u003e) that correspond to various metabolic pathways (see Fig. 7).\u003c/p\u003e\n\u003cp\u003e3L (\u003cem\u003eLactobacillus\u003c/em\u003e bioproduct): \u003cem\u003eL. acidophilus\u003c/em\u003e \u003cem\u003eSD65\u003c/em\u003e, \u003cem\u003eL. casei SD07, L. plantarum SD02\u003c/em\u003e. L: 3L-low dose (1.25 mg/kg), M: 3L-middle dose (2.50 mg/kg), H: 3L-high dose (5.0 mg/kg).\u003c/p\u003e","description":"","filename":"Yueetal.BMCMicrobiol2022Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/b1aa4f17ef932f3eee1bdab4.jpg"},{"id":28937552,"identity":"e18d1628-946f-4b10-8f2f-8c4ce641c6b3","added_by":"auto","created_at":"2022-11-11 07:44:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1291115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/5209defc-d30c-4490-bce5-6c3a61f575b5.pdf"},{"id":27567908,"identity":"243a13ef-6356-4eaa-89f8-0d9b8e7c98d4","added_by":"auto","created_at":"2022-10-10 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16:54:44","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":16237,"visible":true,"origin":"","legend":"","description":"","filename":"Yueetal.BMCMicrobiol2022TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/7f1140dd8fe04b3cc5ba3269.docx"},{"id":27567914,"identity":"0cf90742-daa5-4e69-8305-a9379d3a9675","added_by":"auto","created_at":"2022-10-10 16:44:45","extension":"docx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":16180,"visible":true,"origin":"","legend":"","description":"","filename":"Yueetal.BMCMicrobiol2022TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/db6478da37ae94241ad8de2e.docx"},{"id":27568892,"identity":"6dbe2ab5-4671-4c6b-b67a-f72abfc35db6","added_by":"auto","created_at":"2022-10-10 16:59:45","extension":"docx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":16545,"visible":true,"origin":"","legend":"","description":"","filename":"Yueetal.BMCMicrobiol2022TableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/ccbab1abe12c4647ed18cbb2.docx"},{"id":27568888,"identity":"efcd635d-cfae-4462-ae1e-9e4bd179db75","added_by":"auto","created_at":"2022-10-10 16:59:44","extension":"docx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":16398,"visible":true,"origin":"","legend":"","description":"","filename":"Yueetal.BMCMicrobiol2022TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-2113752/v1/9c788df05695dd8e6c4e41dd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"3L, three-Lactobacilli on recovering of microbiome and immune-damage by cyclophosphamide chemotherapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVarious research challenges are critical for the cancer care continuum and finding treatment at various levels of the most threatening disease to human health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Following an accumulation of mutational, genetic, and epigenetic alterations, abnormal cells begin to divide without control and soon form a mass of extra-tissue (or tumor) that can spread to the entire body through bloodstream and lymph and becomes deadly [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Disease-specific mutational events can serve as reliable cancer biomarkers for diagnosis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which is especially important given that human cells can develop multiple types of primary cancerous tumors [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the high diversity of genetic mutations in mitochondrial DNA, non-coding RNA, microRNA, ubiquitin, RNA editing, spliceosome and/or RNA splicing, and phenotype-specificity of drug sensitivity, which is largely dependent on tumor-stroma interactions, are reasons for cancer treatment difficulties [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Cancer may even be caused by a change in the mechanisms involved in RNA\u0026thinsp;+\u0026thinsp;peptide mutations, which are required for multifunction in specific cell lines such as pluripotent stem cells, chemosensory cells, or T cell lymphocytes. Cancer may be caused by a malfunction in protein variation and cell multipotency, revealing a new molecular insight and potentially novel clinical application in disease treatment [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a result, numerous studies have been conducted to test the effects of nanomedicine, extracellular vesicle bioengineering, gene therapy, immunotherapy, radiotherapy, and thermal ablation on cancer treatment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Immunotherapy, or the stimulation of T lymphocytes capacity for antigen-directed toxicity, has received special attention in cancer treatment [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This is also critical, not only because the disease targets the immune system, but also because anticancer strategies such as (chemo)radiotherapy have a negative impact on it [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Ionizing radiation therapy, which is used in more than half of all cancer cases, causes late inflammatory responses and/or inflammation-associated diseases in the patient\u0026rsquo;s immune system over time, particularly with high dose rate (chemo)radiotherapy [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. One major source of concern is that (chemo)radiotherapy can result in the formation of a new type of cell (Langherans cells), which are known to impair the immune system\u0026rsquo;s ability to fight cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, one of the most difficult challenges for cancer research is to find new ways to continue using (chemo)radiotherapy, which is absolutely necessary to promote T-cell activity in order to cure the disease, while preserving the patient\u0026rsquo;s natural barriers and immune functions to fight other infections such as pneumonitis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The concept of using complementary and/or alternative more natural medicine to treat cancer is not new, treating cancer by injecting bacteria in proximity is well known as microbe mediated tumor therapy, but it may be gaining traction in the purchase of our modern lives [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The microorganisms migrate to the tumor, grow there, and thus activate the patient\u0026rsquo;s immune system. Bacteria-mediated tumor therapy has been used as a treatment for over a century [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Given the link between the gut and the immune system, gut microbes (or microflora) have also become an important target for boosting T cells and shaping cancer therapy efficacy [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Using various cancer model studies, it has been demonstrated that a healthy gut flora has a strong influence on the efficacy of anticancer drugs such as cyclophosphamides (CTX) and immune checkpoint inhibitors (ICIs) [\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The current study compares to any of the marketed probiotic formulations, such as \u003cem\u003eL. rhamnosus\u003c/em\u003e, which are known to inhibit cancer cell growth in a dose- and time-dependent manner [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. It also compares to \u003cem\u003eL. rhamnosus\u003c/em\u003e GG, the most studied microbe model in cancer, in terms of potentiating the gut microbiota and protecting against tumor genesis [\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40 CR41 CR42\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. A universal \u0026lsquo;probiotic\u0026rsquo; approach is expected to prevent patient selection in view of the different treatments and individualized host responses to gut modulation in this new and challenging research field, primarily focusing on colon cancer [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. As a result, we eventually developed a specific 3L \u003cem\u003eLactobacillus\u003c/em\u003e bioproduct (a natural cocktail composed of \u003cem\u003eL. acidophilus\u003c/em\u003e, \u003cem\u003eL. casei\u003c/em\u003e and \u003cem\u003eL. plantarum\u003c/em\u003e) for future medicinal purposes, which has been shown to have a very beneficial effect not only on physiological but also on biochemical status of hyperlipidemic mice. \u0026ldquo;3L\u0026rdquo; has been shown in mice to not only induce beneficial gut flora, but also to significantly reduce cholesterolemia, LDL/HDL ratio, blood lipid concentration and weight gain [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Human metabolic syndromes and obesity-related pathologies are best studied in rodents [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. They are also excellent models for studying human cancer immunology and immunotherapy [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. As a result, we used five different groups of rats as experimental models to investigate the effects of 3L on gut flora and CTX chemotherapy. We used Illumina MiSeq sequencing optimized for full complete microbial genome applications to examine the gut microbiome after chemotherapy (the use of cytotoxic drugs such as CTX for cancer treatment). The rats we used were not ill or cancerous; they were healthy rats given cyclophosphamide CTX chemical drug. CTX chemotherapy significantly altered the composition of the microbiome, according to our findings. This relates to a previous study on the same groups of rats, which showed an alteration of the immune system following CTX treatment [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. From one week to one month (28 days) after administration, the number of white blood cells in the \u003cem\u003eLactobacillus\u003c/em\u003e preparation-treated animals (H-dose: 5 mg/kg) was higher than that in the group of CTX-treated animals. The CD4+/CD8\u0026thinsp;+\u0026thinsp;ratio (the ratio of T helper cells to cytotoxic T cells) was also higher in the \u003cem\u003eLactobacillus\u003c/em\u003e preparation-treated animals (L-dose: 1.25, M-dose: 2.50, and H-dose: 5.00 mg/kg). \u003cem\u003eLactobacillus\u003c/em\u003e-treated animals had higher serum levels of interleukin 6 (IL-6) and higher interleukin (IL-6 and IL-2) gene expression but a significant decrease in rat mRNA expression levels of Tumour Necrosis Factor alpha (TNF-alpha) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The effects on leukocytes, T helper cells, interleukins, and TNF-alpha were dose-dependent [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], prompting us to test three different doses (L, M, and H) on the microbiome of chemotherapy rat models. Then, we examined the effects of chemotherapy combined with 3L on the gut flora and health status of rats injured by CTX. The overall outcome of CTX\u0026thinsp;+\u0026thinsp;\u003cem\u003eLactobacillus\u003c/em\u003e chemo-treatment was beneficial to an extent not previously reported. Increasing the dose of a \u003cem\u003eLactobacillus\u003c/em\u003e cocktail (\u003cem\u003eacidophilus\u003c/em\u003e, \u003cem\u003ecasei\u003c/em\u003e, and \u003cem\u003eplantarum\u003c/em\u003e) had a significant positive effect on microbial gut flora. The rats we used were normal, not tumor/cancer model rats, so using a microecosystem like \u003cem\u003eLactobacillus\u003c/em\u003e did not cure cancer. Despite CTX (chemotherapy for tumor treatment), it contributed to improved gut health. Despite CTX chemotherapy, a pharmacological bioproduct high in \u003cem\u003eLactobacillus\u003c/em\u003e strains has been described to significantly contribute to a healthy gut microbiome. Therefore, we propose a new bioactive microbial natural medicinal product to be further tested in rats, then in humans, and possibly used in tumor/cancer treatment or, at the very least, to prevent an altered gut due to CTX chemotherapy.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eMicrobiome comparison in rat groups in relation to chemotherapy and\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLactobacillus.\u003c/span\u003e This descriptive study investigates the fecal microbiota of rats subjected to chemotherapy (cyclophosphamides for cancer) and significant health gut improvement when co-treated with a cocktail of three \u003cem\u003eLactobacillus\u003c/em\u003e spp. in continuation of our work on \u003cem\u003eLactobacillus\u003c/em\u003e/\u003cem\u003ebacillus\u003c/em\u003e on cholesterolemia, lipidemia, diarrhea, and scour [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Although the sample size is smaller (N\u0026thinsp;=\u0026thinsp;50), the topic is extremely important in cancer pharmacological treatments because cyclophosphamides (CTX) used against cancerous tumors are frequently found to severely damage the patient\u0026rsquo;s immune system. We used rats that did not have cancer. They were healthy rats who had been severely harmed by CTX treatments (chemotherapy). The CTX-bioproduct study design includes five different experimental groups: healthy control rats (1), treated rats (CTX chemotherapy) that were either only treated with CTX (2) or treated with CTX and a low (3), middle (4) or high (5) complement dose of \u003cem\u003eLactobacillus\u003c/em\u003e spp. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In a previous study, leukocyte concentration, CD4/CD8, \u003cem\u003einterleukin\u003c/em\u003e, and \u003cem\u003eTNF-alpha\u003c/em\u003e expression were measured in each group (1\u0026ndash;5), showing that CTX has a significant effect on the rat immune system [see 49]. Here, the microbiome was assessed in each group (1\u0026ndash;5) using ITS and 16S rRNA gene sequencing on the Illumina MiSeq platform, suggesting that health can be maintained despite CTX when using \u003cem\u003eLactobacillus\u003c/em\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; S1-S13 and Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; S1-S4).\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Mycobiome (Genus of fungi and yeasts) composition in relation to \u003cem\u003eLactobacillus\u003c/em\u003e and cyclophosphamide treatment in five groups of rats. CK: control healthy conditions; IM: immune-attacked (CTX); L: CTX + 3L-low dose; M: CTX + 3L-middle dose; H: CTX + 3L-high dose. Treatment for \u003cem\u003eLactobacillus\u003c/em\u003e: tritherapy (3L) = \u003cem\u003eL. acidophilus\u003c/em\u003e \u003cem\u003eSD65\u003c/em\u003e + \u003cem\u003eL. casei SD07 + L. plantarum SD02\u003c/em\u003e. The group\u0026rsquo;s dominant genera are highlighted in bold. \u0026deg; shows fungal genera specifically increased by 3L M-dose in cyclophosphamide conditions. * indicates increased genera in CK and/or H groups.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"561\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.678571428571427%\"\u003eCK\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.964285714285715%\"\u003eIM\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.178571428571427%\"\u003eH\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.428571428571427%\"\u003eM\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.75%\"\u003eL\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"22.678571428571427%\"\u003e\u003cem\u003eAcaulium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eAcremonium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eAlternaria\u003c/em\u003e\u003cbr\u003e\u003cem\u003eAspergillus\u003c/em\u003e\u003cbr\u003e\u003cem\u003eCandida*\u003c/em\u003e\u003cbr\u003e\u003cem\u003eChlamydomyces\u003c/em\u003e\u003cbr\u003e\u003cem\u003eCoprinellus\u003c/em\u003e\u003cbr\u003e\u003cem\u003eCutaneotrichosporon\u003c/em\u003e\u003cbr\u003e\u003cem\u003eFilobasidium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eFusarium*\u003c/em\u003e\u003cbr\u003e\u003cem\u003eGibberella\u003c/em\u003e\u003cbr\u003e\u003cem\u003eKernia\u003c/em\u003e\u003cbr\u003e\u003cem\u003eKodamaea\u003c/em\u003e\u003cbr\u003e\u003cem\u003eLecanicillium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eMallassezia\u003c/em\u003e\u003cbr\u003e\u003cem\u003eMeyerozima\u003c/em\u003e\u003cbr\u003e\u003cem\u003eMicroascus\u003c/em\u003e\u003cbr\u003e\u003cem\u003eMoesziomyces\u003c/em\u003e\u003cbr\u003e\u003cem\u003eMortierella\u003c/em\u003e\u003cbr\u003e\u003cem\u003eMucor\u003c/em\u003e\u003cbr\u003e\u003cem\u003eMycosphaerella\u003c/em\u003e\u003cbr\u003e\u003cem\u003eOlpidium\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePapiliotrema\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePenicillium\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePericonia\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePichia*\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePhallus\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePhialocephala\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePlectosphaerella\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePseudogymnoascus\u003c/em\u003e\u003cbr\u003e\u003cem\u003eRasamsonia\u003c/em\u003e\u003cbr\u003e\u003cem\u003eRhizomucor\u003c/em\u003e\u003cbr\u003e\u003cem\u003eRhizophlyctis\u003c/em\u003e\u003cbr\u003e\u003cem\u003eRhizopus\u003c/em\u003e\u003cbr\u003e\u003cem\u003eRhodotorula\u003c/em\u003e\u003cbr\u003e\u003cem\u003eSaccharomyces\u003c/em\u003e\u003cbr\u003e\u003cem\u003eSarocladium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eScytalidium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eShizothecium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eSimplicillium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eSodiomyces\u003c/em\u003e\u003cbr\u003e\u003cem\u003eTalaromyces\u003c/em\u003e\u003cbr\u003e\u003cem\u003eTausonia\u003c/em\u003e\u003cbr\u003e\u003cem\u003eThermoascus\u003c/em\u003e\u003cbr\u003e\u003cem\u003eTrichoderma\u003c/em\u003e\u003cbr\u003e\u003cem\u003eUstilago\u003c/em\u003e\u003cbr\u003e\u003cem\u003eVerticillium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eWallemia\u003c/em\u003e\u003cbr\u003e\u003cem\u003eXerochrysium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eXeromyces\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n 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width=\"16.964285714285715%\"\u003e\u003cem\u003eAcaulium\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; 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\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eMucor\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eOlpidium\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003ePenicillium\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePericonia\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003ePhallus\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003eXerochrysium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eXeromyces\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n 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width=\"20.178571428571427%\"\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003eAcremonium\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eAspergillus\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eChlamydomyces*\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003eFusarium*\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eMortierella\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003ePhallus\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eRasamsonia\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eRhizophlyctis\u003c/em\u003e\u003cbr\u003eRhizopus\u0026deg;\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eTalaromyces\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eTrichoderma\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n 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width=\"21.428571428571427%\"\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eCoprinellus\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eMicroascus\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eMycosphaerella\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003ePhialocephala\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003ePseudogymnoascus\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eRhizomucor\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eRhizopus\u0026deg;\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003eSarocladium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eScytalidium\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cem\u003eThermoascus\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eUstilago\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n 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width=\"18.75%\"\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eMallassezia\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003ePlectosphaerella\u003c/em\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003eRhizopus\u0026deg;\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eSaccharomyces\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eSimplicillium\u003c/em\u003e\u003cbr\u003e\u003cem\u003eSodiomyces\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003eTausonia\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMycobiome (Phylum, Class, Order, Family, and Genus of fungi and yeasts) composition in relation to \u003cem\u003eLactobacillus\u003c/em\u003e and cyclophosphamide treatment in five groups of rats. CK: control healthy conditions; IM: immune-attacked (CTX); L: CTX\u0026thinsp;+\u0026thinsp;3L-low dose; M: CTX\u0026thinsp;+\u0026thinsp;3L-middle dose; H: CTX\u0026thinsp;+\u0026thinsp;3L-high dose. Treatment for \u003cem\u003eLactobacillus\u003c/em\u003e: tritherapy (3L)\u0026thinsp;=\u0026thinsp;\u003cem\u003eL. acidophilus SD65\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eL. casei SD07\u0026thinsp;+\u0026thinsp;L. plantarum SD02\u003c/em\u003e. The group\u0026rsquo;s dominant genera are highlighted in bold. * displays a significant increase in specific microbes in CK and the three \u003cem\u003eLactobacillus\u003c/em\u003e group doses (L, M and H). \u0026deg; shows a marked reduction in specific microbes caused by CTX chemotherapy.\u0026dagger; shows increased microbes under chemo (see IM), but not under chemotherapy\u0026thinsp;+\u0026thinsp;\u003cem\u003eLactobacillus\u003c/em\u003e conditions (see L, M and H).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhylum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAscomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAscomycota\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAscomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAscomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasidiomycota*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasidiomycota\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBasidiomycota*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBasidiomycota*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBasidiomycota*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlastocladiomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChytridiomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomeromycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKickxellomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKickxellomycota\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eKickxellomycota\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKickxellomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKickxellomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortierellomycota*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMortierellomycota\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortierellomycota*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMortierellomycota*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMortierellomycota*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucoromycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMucoromycota\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMucoromycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMucoromycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMucoromycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlpidiomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOlpidiomycota\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOlpidiomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlpidiomycota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOlpidiomycota\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgaricomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgaricostilbomycetes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgaricostilbomycetes\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgaricostilbomycetes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgaricostilbomycetes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgaricostilbomycetes*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlastocladiomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystobasidiomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDothideomycetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDothideomycetes\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eDothideomycetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDothideomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDothideomycetes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEurotiomycetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurotiomycetes\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEurotiomycetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEurotiomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEurotiomycetes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExobasidiomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeotiomycetes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeotiomycetes\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeotiomycetes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLeotiomycetes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLeotiomycetes*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalasseziomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrobotryomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortierellomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucoromycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMucoromycetes\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMucoromycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMucoromycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMucoromycetes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlpidiomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePezizomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRhizophlyctidomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSaccharomycetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaccharomycetes\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaccharomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaccharomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSaccharomycetes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSordariomycetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSordariomycetes\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSordariomycetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSordariomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSordariomycetes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTremellomycetes*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTremellomycetes\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTremellomycetes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTremellomycetes*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTremellomycetes*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUstilaginomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWallemiomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgaricales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapnodiales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCapnodiales\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCapnodiales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCapnodiales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCapnodiales*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystofilobasidiales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCystofilobasidiales\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCystofilobasidiales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCystofilobasidiales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCystofilobasidiales*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEurotiales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEurotiales\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEurotiales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEurotiales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEurotiales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFilobasidiales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerellales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlomerellales\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGlomerellales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlomerellales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGlomerellales*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHelotiales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypocreales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypocreales\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHypocreales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHypocreales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHypocreales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalasseziales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalasseziales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalasseziales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalasseziales*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMalasseziales*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroascales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortierellales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucorales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMucorales\u0026dagger;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMucorales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMucorales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMucorales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlpidiales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleosporales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSaccharomycetales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaccharomycetales\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaccharomycetales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaccharomycetales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSaccharomycetales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSordariales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTremellales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrichosporonales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrichosporonales\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTrichosporonales\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrichosporonales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrichosporonales\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUstilaginales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWallemiales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAspergillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAspergillaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAspergillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAspergillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAspergillaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCordycipitaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCordycipitaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCordycipitaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCordycipitaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCordycipitaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDebaryomycetaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDidymellaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDidymellaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDidymellaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDidymellaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eDidymellaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHypocreaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLichtheimiaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMalasseziaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMalasseziaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMalasseziaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMalasseziaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMetschnikowiaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMicroascaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMucoraceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMucoraceae\u003c/em\u003e\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMucoraceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMucoraceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMucoraceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerellaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerellaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerellaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerellaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerellaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNectriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNectriaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNectriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eNectriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNectriaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlectosphaerellaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePleosporaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePleosporaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePleosporaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePleosporaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePleosporaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePichiaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePichiaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePichiaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePichiaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePichiaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRhizopodaceae*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRhizopodaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRhizopodaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRhizopodaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRhizopodaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eThermoascaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eThermoascaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eThermoascaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eThermoascaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eThermoascaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrichosporononaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTrichosporononaceae\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTrichosporononaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTrichosporononaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTrichosporononaceae*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUstilaginaceae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAlternaria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAlternaria\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAlternaria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAlternaria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAlternaria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAspergillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAspergillus\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAspergillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eAspergillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAspergillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCandida\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChlamydomyces*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eChlamydomyces\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChlamydomyces*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eChlamydomyces*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eChlamydomyces*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFusarium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFusarium\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFusarium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFusarium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFusarium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKodamaea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eKodamaea\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eKodamaea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eKodamaea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eKodamaea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLecanicillium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLecanicillium\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLecanicillium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLecanicillium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eLecanicillium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMalassezia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMeyerozima\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMucor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMucor\u003c/em\u003e\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMucor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMucor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMucor\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerella\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMycosphaerella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePichia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePichia\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePichia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePichia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePichia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePenicillium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRhizomucor*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRhizomucor\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRhizomucor*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRhizomucor*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRhizomucor*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRhizopus*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRhizopus\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRhizopus*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRhizopus*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRhizopus*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSarocladium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSarocladium\u0026deg;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSarocladium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSarocladium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSarocladium*\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSimplicillium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTalaromyces\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTrichoderma\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eXerochrysium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eXerochrysium\u003c/em\u003e\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eXerochrysium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eXerochrysium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eXerochrysium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Venn Diagram depicting the relationships between the five groups in relation to chemotherapy and \u003cem\u003eLactobacillus\u003c/em\u003e suggested a relationship between healthy control and CTX\u0026thinsp;+\u0026thinsp;increasing dose of \u003cem\u003eLactobacillus\u003c/em\u003e spp. (CK and M-H; Figure S1). The position, configuration, and overlap of the circles indicating the relationships between the groups showed a gradual increase in gut flora and overall health depending on the \u003cem\u003eLactobacillus\u003c/em\u003e spp. complement dose (L-to-H; Figure S1). The IM group (CTX alone) was further along the bottom representing a higher number of total unique OTUs (17.24%), similarly to L dose (number of total unique OTUs: 15.82%). The Venn Diagram showed that the core microbiomes in the H and control groups were related when we looked at the number of total unique OTUs (10.52\u0026ndash;10.82%), whereas M complement doses of \u003cem\u003eLactobacillus\u003c/em\u003e spp. remained too closely related to CTX alone conditions (15.07%; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The middle complement dose (\u0026times;2, two-fold) fell between the control (CK) and immune-damaged (IM) groups (Figure S1). The H-dose group was opposite the IM group, with no overlap with the control group (CK). As a result, increasing the \u003cem\u003eLactobacillus\u003c/em\u003e concentration (more than five-fold) appeared to be required to produce an even more significant beneficial effect on the microbiome of rats treated by CTX (Figure S1).\u003c/p\u003e \u003cp\u003eThe five groups appear to have relatively similar levels of similarity on Venn diagrams (Figure S1). All samples (three per group) and OTU numbers were consistent across the groups CK-H. In each group, approximately the same number of ITS and 16S sequences were obtained in different biological replicates (Table S1). The five different groups obtained roughly the same sequence quantity (Table S1). There were no differences in the number of OTUs found at each taxonomic level (phylum, class, order, family, genus, and species; Tables S2 \u0026amp; S3). Although all of the OTUs could be classified, the classification of OTUs at different taxonomic levels in five rat groups related to chemotherapy and \u003cem\u003eLactobacillus\u003c/em\u003e treatment revealed no discernible differences in OTU counting (Tables S2 \u0026amp; S3). This was seen in both grouped (CK, IM, L, M and H) and ungrouped (C101, C103, C105, IM015, IM021, IM024, L102, L103, L104, M201, M202, M203, H105, H204 and H205) samples (Tables S2 \u0026amp; S3). As a result, the sample sizes were similar, particularly for Family and Genus (Figure S2). Using the OTU table for sample diversity in PCA, rank abundance curve, NMDS, principle coordinate analysis (PcoA), Bray-Curtis distance plot (default semimetric), binary Jaccard distance matrix (metric), and UPGMA, however, H was clustered with CK.\u003c/p\u003e \u003cp\u003eThe PCA based on OTU composition revealed significant differences in the five groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eAB). At the community level, PCA revealed similarities between the chemotherapy\u0026thinsp;+\u0026thinsp;high \u003cem\u003elactobacillus\u003c/em\u003e spp. and control groups, with \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eTalaromyces\u003c/em\u003e, \u003cem\u003eSarocladium\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e, and \u003cem\u003eMucor\u003c/em\u003e falling outside a common spectrum of microbe genera (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eOrthogonal Projections to Latent Structures and Discriminant Analysis (OPLS-DA) showed \u003cem\u003eMucor\u003c/em\u003e and \u003cem\u003eTalaromyces\u003c/em\u003e to be associated with ill conditions (chemotherapy alone), but a wide range of microbes to be associated with control and chemotherapy\u0026thinsp;+\u0026thinsp;high \u003cem\u003eLactobacillus\u003c/em\u003e conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The OPLS-DA analysis showed that the distances between the CK and H groups were very small, while the L, M, and IM groups were clearly separated. \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eSarocladium\u003c/em\u003e, \u003cem\u003eKodamaea\u003c/em\u003e, \u003cem\u003eVerticillium\u003c/em\u003e, \u003cem\u003eCandida\u003c/em\u003e, and \u003cem\u003eChlamydomyces\u003c/em\u003e were among the fungi with overlapping distributions in the two groups, CK and H. \u003cem\u003eTrichoderma\u003c/em\u003e, \u003cem\u003eAcremonium\u003c/em\u003e, and \u003cem\u003eMalassezia\u003c/em\u003e were more closely associated with the \u003cem\u003eLactobacillus\u003c/em\u003e groups H, M, and L. \u003cem\u003ePichia\u003c/em\u003e and \u003cem\u003eAspergillus\u003c/em\u003e were separated from this group but mixed with the CK control group. Microbial fungi such as \u003cem\u003eMucor\u003c/em\u003e and \u003cem\u003eTalaromyces\u003c/em\u003e, as well as \u003cem\u003eXeromyces\u003c/em\u003e, \u003cem\u003eXerochrysium\u003c/em\u003e and \u003cem\u003ePenicillium\u003c/em\u003e, were associated with CTX and the immune-damage group (Fig.\u0026nbsp;2AB).\u003c/p\u003e \u003cp\u003eIn addition to PCA, CK-samples tended to cluster with H-, M- and L-samples in NMDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The IM group differed significantly from the other groups, with clear mean differences between microbiomes from CTX-treated samples and those from controls (no treatment) and rats treated with \u003cem\u003eLactobacillus\u003c/em\u003e in addition to CTX (Bray-Curtis; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The NMDS graph using the Jaccard index collapsed very clear information: CK grouped with H-samples, showing mean similarities between H-microbiomes and controls (Jaccard; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Using PcoA as a principle analysis, the same grouping was observed, lending support to PCA and NMDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Both the Bray-Curtis (abundance) and Jaccard (0/1 data) indices showed a pair of communities with comparable species richness (H and CK; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The similarity between the CK and H samples was confirmed by unweighted and non-metric MDS analysis (UPGMA). H branches clustered with CK with a low distance value (0.005\u0026thinsp;\u0026minus;\u0026thinsp;0.0028) on the UPGMA tree (Figure S3A). There were also unweighted pairs found between M and L groups (distance value 0.031\u0026ndash;0.111, Figure S3A; 0.003, Figure S2B). The branches representing the immune-damaged group (IM) clustered at the bottom of the tree, indicating the mean distance (or difference) of IM compared to CK, H, M, and L (Figure S3). Therefore, many of our results (PCA, PcoA, NMDS, and UPGMA) do show \u003cem\u003eLactobacillus\u003c/em\u003e-control clustering, but even in these plots, it is difficult to see how much closer the high Lactobacillus group is to controls when compared to IM because of the large outlier within the IM grouping (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; S3). We couldn\u0026rsquo;t conclude that the control and Lactobacillus groups had more microbial diversity, but in our rank abundance curve (i.e. species richness and species evenness), the IM group (CTX alone) was further along the x axis, representing a higher number of total unique OTUs (Figure S4). We used Specaccum (number of species vs number of samples) to show how species richness increased as sample size increased. The curve rapidly reached a plateau. There were no significant differences in species richness when increasing the number of samples (up to 12) lumped into a single analysis, which was not done on a per group basis (Figure S5). In grouped and ungrouped samples, the Chao1, Simpson, Shannon, Pielou_e, observed species and Goods_coverage indices (alpha-diversity) were calculated (Figure S6 \u0026amp; Table S4). These indices (Chao1, Pielou_e and observed_species) indicated that the CK and H groups had similar community richness and species evenness (Figure S6 \u0026amp; Table S4). The goods_coverage index showed significant differences between H and IM. Goods_coverage metrics at OTU levels (sample completeness, p\u0026thinsp;=\u0026thinsp;0.76) showed a high level of microbial diversity in H (Figure S6 \u0026amp; Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eMycobiome of five different groups of rats profiled in relation to chemotherapy and\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLactobacillus\u003c/span\u003e. Examining of individual taxon abundance using heatmaps was useful to analyze fungal taxa clustering based on the abundance of each fungus in the five rat groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; S7). A heatmap analysis of fungi revealed the relative abundance of each taxon in CK, IM, H, M, and L. In this descriptive study, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the various fungal taxa found in the CK, CTX, and 3L groups. From the \u003cem\u003eAcaulium\u003c/em\u003e (syn. \u003cem\u003eScopulariopsis\u003c/em\u003e) genus to \u003cem\u003eXeromyces\u003c/em\u003e, several broad types of microbes were identified in the CK group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). CK (control, healthy condition) had high levels of \u003cem\u003eCandida\u003c/em\u003e, \u003cem\u003eCutaneotrichosporon\u003c/em\u003e, \u003cem\u003eFilobasidum\u003c/em\u003e, \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eKernia\u003c/em\u003e, \u003cem\u003eKodamaea\u003c/em\u003e, \u003cem\u003eLecanicillium\u003c/em\u003e, \u003cem\u003eMeyerozima\u003c/em\u003e, \u003cem\u003ePapiliotrema\u003c/em\u003e (\u003cem\u003eCryptococcus\u003c/em\u003e), \u003cem\u003ePichia\u003c/em\u003e, \u003cem\u003eRhodoturula\u003c/em\u003e, \u003cem\u003eVerticillium\u003c/em\u003e, and \u003cem\u003eWallemia\u003c/em\u003e, whereas IM (immune-attacked) had high levels of \u003cem\u003eAcaulium\u003c/em\u003e, \u003cem\u003eMucor\u003c/em\u003e, \u003cem\u003eOlpidium\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, \u003cem\u003ePericonia\u003c/em\u003e, \u003cem\u003ePhallus\u003c/em\u003e, \u003cem\u003eXerochrysium\u003c/em\u003e, and \u003cem\u003eXeromyces\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, treating rats with \u003cem\u003eLactobacillus\u003c/em\u003e in addition to Cyclophosphamide increased relative fecal abundance of many different fungal taxa, including \u003cem\u003eAcremonium\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003eChlamydomyces\u003c/em\u003e, \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eMortierella\u003c/em\u003e, \u003cem\u003ePhallus\u003c/em\u003e, \u003cem\u003eRasamsonia\u003c/em\u003e, \u003cem\u003eRhizophlyctis\u003c/em\u003e, \u003cem\u003eRhizopus\u003c/em\u003e, \u003cem\u003eTalaromyces\u003c/em\u003e, and \u003cem\u003eTrichoderma\u003c/em\u003e (in H group), \u003cem\u003eCoprinellus\u003c/em\u003e, \u003cem\u003eMicroascus\u003c/em\u003e, \u003cem\u003eMycosphaerella\u003c/em\u003e, \u003cem\u003ePhialocephala\u003c/em\u003e, \u003cem\u003ePseudogymnoascus\u003c/em\u003e, \u003cem\u003eRhizomucor\u003c/em\u003e, \u003cem\u003eRhizophlyctis\u003c/em\u003e, \u003cem\u003eRhizopus\u003c/em\u003e, \u003cem\u003eSarocladium\u003c/em\u003e, \u003cem\u003eScytalidium\u003c/em\u003e, \u003cem\u003eThermoascus\u003c/em\u003e, and \u003cem\u003eUstilago\u003c/em\u003e (in M group), and \u003cem\u003eMallassezia\u003c/em\u003e, \u003cem\u003ePlectosphaerella\u003c/em\u003e, \u003cem\u003eRhizophlyctis\u003c/em\u003e, \u003cem\u003eRhizopus\u003c/em\u003e, \u003cem\u003eSaccharomyces\u003c/em\u003e, \u003cem\u003eSimplicillium\u003c/em\u003e, \u003cem\u003eSodiomyces\u003c/em\u003e, and \u003cem\u003eTausonia\u003c/em\u003e (in L group; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As a result, among the regulated fungi are species that are not known as animal commensals or pathogens. \u003cem\u003eLecanicillium\u003c/em\u003e fungi are classified as generalist entomopathogenic fungi [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. \u003cem\u003eUstilago\u003c/em\u003e is a Poaceae plant pathogen [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. \u003cem\u003ePhallus\u003c/em\u003e mushrooms are big saprotrophic mushrooms [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. However, the presence of these fungi in the rat microbiota is not necessarily suspect and may merit further investigation. The breeding history of rats takes place in the Institute of Medicine\u0026rsquo;s Class II animal facility in SAMS (Specific Free Pathogen/SPF facilities and acute hospital care settings that are designed to keep organisms in sterile environments). Saprophytic basidiomycetes are well-known wood-decaying fungi, but \u003cem\u003ePhallus\u003c/em\u003e sequences have been found in animal penis and urethra, where they play a role in male fertility [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In fact, little is known about the fungal flora of rodent\u0026rsquo;s digestive and reproductory tracts. \u003cem\u003eLecanicillium\u003c/em\u003e species are pathogens that parasitize not only insects but also worms and many other fungi, which could explain their presence in gut fungi associated with rats. \u003cem\u003eLecanicillium\u003c/em\u003e strains have been found in gut fungi associated with marmots [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. A large variety of \u0026lsquo;forgotten\u0026rsquo; odd fungi, including Ustilaginales and \u003cem\u003eUstilago\u003c/em\u003e sp., are found in the human digestive tract [\u003cspan additionalcitationids=\"CR57 CR58\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], as seen in rodents (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). So it is not surprising that fungal sequences like \u003cem\u003eLecanicillium\u003c/em\u003e, \u003cem\u003ePhallus\u003c/em\u003e, and \u003cem\u003eUstilago\u003c/em\u003e have been found in the fecal DNA of CTX-rat models. It has been described in a variety of other animal species, including humans. What is more unusual or surprising is the discovery that these fungi are differentially regulated by CTX and/or 3L conditions, which is a critical key point in addressing their prevalence in the gut microbial system (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePhallus\u003c/em\u003e was found in cyclophophasmide-treated rats and rats treated with CTX\u0026thinsp;+\u0026thinsp;high 3L lactobacilli in the heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Despite CTX treatment, medium and low doses of 3L were able to eradicate \u003cem\u003ePhallus\u003c/em\u003e fungi, as shown by triplicate samples (Figure S7 \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), even though \u003cem\u003ePhallus\u003c/em\u003e infection was not prevalent in all IM samples (Figure S7). More interestingly, the heatmap analysis highlighted two taxa in particular, \u003cem\u003eFusarium\u003c/em\u003e and \u003cem\u003ePichia\u003c/em\u003e, which were found in high abundance in feces from control and high \u003cem\u003elactobacillus\u003c/em\u003e-treated groups and samples or were repeatedly found in control samples but significantly altered by chemotherapy (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; S7). Furthermore, there was a correlation between \u003cem\u003eRhizopus\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e treatment. Chemotherapy signifcantly reduced \u003cem\u003eRhizopus\u003c/em\u003e-levels, but increased when \u003cem\u003eLactobacillus\u003c/em\u003e was added to phosphamide. \u003cem\u003eRhizopus\u003c/em\u003e was found in abundance in H, M, and L groups (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; S7). The \u003cem\u003eRhizopus\u003c/em\u003e microbe was more abundant in M samples (CTX\u0026thinsp;+\u0026thinsp;middle dose/2.5 ml/kg bodyweight of \u003cem\u003eLactobacillus\u003c/em\u003e), indicating that a specific dose of bioproduct should be chosen for effective regulation of specific microbes (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, S7 \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We found that high-, medium-, and low-dose 3L cocktail gavages were effective in lowering \u003cem\u003eAcaulium\u003c/em\u003e, \u003cem\u003eMucor\u003c/em\u003e, \u003cem\u003eOlpidium\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e, \u003cem\u003ePericonia\u003c/em\u003e, \u003cem\u003eXerochrysium\u003c/em\u003e, and \u003cem\u003eXeromyces\u003c/em\u003e levels (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e, S7 \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe microbial composition distribution histograms of each sample were displayed at the phylum, order, class, family, and genus levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in our descriptive analysis of rats in relation to CTX and \u003cem\u003eLactobacillus\u003c/em\u003e (N\u0026thinsp;=\u0026thinsp;50; analysis of groups and individual samples, the same chemotherapy session, one drug, five shots, addition of \u003cem\u003eLactobacilli\u003c/em\u003e, 3L-test, three different doses, comparison with controls, beneficial effects analysis). The dominant microbial phyla were similar in control and 3L therapy conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). CTX, on the other hand, caused a significant decrease in Ascomycota-levels, which was not seen with high-doses of 3L during chemotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Many other microbial fungal phyla, including Basidiomycota, Kickxellomycota, and Mortierellomycota benefited from 3L gavage (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Mucoromycota and Olpidiomycota levels in rat fecal microbiomes increased during CTX chemotherapy but remained low when H, M, or L doses of \u003cem\u003eLactobacillus\u003c/em\u003e were added to CTX (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, analysis of the distribution of microbial fungal classes, orders, families, and genera in the five rat groups showed specific beneficial effects of \u003cem\u003eLactobacillus\u003c/em\u003e treatment in addition to cancer chemotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-E \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Dothideomycetes, Eurotiomycetes, Saccharomycetes, Sordariomycetes, and Tremellomycetes were the most abundant microbial fungal classes in healthy control rats without any other treatment than normal saline injection. CTX chemotherapy had a significant impact on all five classes. Chemotherapy also increased Mucoromycetes levels in the fecal microbiome. With high dose injections of 3L, Dothideomycetes, Eurotiomycetes, and Sordariomycetes were kept at normal levels. Mucoromycetes were kept at normal levels in all three \u003cem\u003eLactobacilli\u003c/em\u003e-treated samples. Agaricostilbocytes, Leotiomycetes, and Tremellomycetes, were also recovered at normal levels after probiotic treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Eurotiales, Hypocreales, Saccharomycetales, and Trichosporonales were the most abundant microbes on an order level not only in CK, but also in H group. The IM group had a different microbial order profiling, with significantly altered levels of Eurotiales, Hypocreales, Saccharomycetales, and Trichosporonales, as well as significantly increased levels of Mucorales. \u003cem\u003eLactobacillus\u003c/em\u003e treatment restored normal levels of Capnodiales, Cystofilobasidiales and Glomerellales that had been affected by chemotherapy. \u003cem\u003eLactobacillus\u003c/em\u003e H, M, and L doses were effective in controlling Mucorales levels. A low dose of \u003cem\u003eLactobacillus\u003c/em\u003e was also particularly effective in stimulating Mallasseziales levels, emphasing the importance of controlling \u003cem\u003eLactobacillus\u003c/em\u003e dose to target specific microbial orders (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Four major microbial families were identified in rat fecal samples related to CTX and 3L therapy: \u003cem\u003eAspergillaceae\u003c/em\u003e, \u003cem\u003eDidymellaceae\u003c/em\u003e, \u003cem\u003eNectriaceae\u003c/em\u003e, and \u003cem\u003ePleosporaceae\u003c/em\u003e. Surprizingly, these microbial families were vulnerable to chemotherapy alone, but were kept alive by combining 3L with CTX-chemo treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). To maintain the levels of \u003cem\u003eAspergillaceae\u003c/em\u003e, a gradual increase of 3L seemed to be required (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). CTX also reduced the levels of \u003cem\u003eCordycipitaceae, Mycosphaerellaceae, Rhizopodaceae, Thermoascaceae\u003c/em\u003e, and \u003cem\u003eTrichosporonaceae\u003c/em\u003e, but these levels were maintained when CTX was combined with \u003cem\u003eLactobacillus\u003c/em\u003e gavage. This was not true for all of the microbial families found in rat feces. \u003cem\u003ePichiaceae\u003c/em\u003e was one of the microbial families that were down-regulated after CTX treatment, which could not be reversed by adding \u003cem\u003eLactobacillus\u003c/em\u003e during chemo-treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). However, \u003cem\u003eLactobacillus\u003c/em\u003e at high, medium, and low doses had a clear beneficial effect on \u003cem\u003eMucoraceae\u003c/em\u003e control (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). \u003cem\u003eMucoraceae\u003c/em\u003e-levels in the IM group were extremely high, which could be reversed by adding H, M, or L doses of \u003cem\u003eLactobacillus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). A low dose of the bioproduct was found to induce especially high levels of \u003cem\u003eMallasseziaceae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eAlternaria\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003eFusarium\u003c/em\u003e, and \u003cem\u003eMycosphaerella\u003c/em\u003e were the main microbial genera characteristic of the CK and H groups, respectively, while \u003cem\u003eMucor\u003c/em\u003e was a diagnosis of immune-damage caused by CTX treatment. The addition of \u003cem\u003eLactobacillus\u003c/em\u003e to chemotherapy effectively controlled \u003cem\u003eMucor\u003c/em\u003e. \u003cem\u003eMucor\u003c/em\u003e-levels were found to be extremely low in the H, M, and L groups of rats related to Chemo\u0026thinsp;+\u0026thinsp;Lacto treatment. On \u003cem\u003eXerochrysium\u003c/em\u003e, similar effects were observed. \u003cem\u003eXerochrysium\u003c/em\u003e-levels rose during chemotherapy, but were kept under control by using \u003cem\u003eLactobacillus\u003c/em\u003e at low, medium, and high doses. Other microbial genera such as \u003cem\u003eChlamydomyces\u003c/em\u003e, \u003cem\u003eLecanicillium\u003c/em\u003e, \u003cem\u003eRhizomucor\u003c/em\u003e, and \u003cem\u003eSarocladium\u003c/em\u003e were maintained by \u003cem\u003eLactobacillus\u003c/em\u003e at low, medium, or high doses. Only \u003cem\u003ePichia\u003c/em\u003e was not maintained by \u003cem\u003eLactobacillus\u003c/em\u003e treatment, regardless of the dose of 3L bioproduct (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen all triplicates were compared (Figure S8), Ascomycota levels were found to be remarkably high in CK and H triplicates (Figure S8A). In contrast, Ascomycota levels were particularly low in IM021, L103, and M201. Mucoromycota and Olpidiomycota were abundant in IM015 samples (Figure S8A). Mucoromycota and Olpidiomycota were significantly lower in \u003cem\u003eLactobacillus\u003c/em\u003e samples, particularly H (H105, H204, and H205). Mortierellomycota were missing in IM triplicates (IM015, IM021, and IM024), but present in C101, C103, L104, M202, M203, H105, H204, and H205 (Figure S8A). High levels of Sordariomycetes, Saccharomycetes, and Tremellomycetes were found in control triplicates (C101, C103, and C105) in ungrouped samples. Sordariomycetes and Tremellomycetes levels remained high in L102, L104, M202, M203, H105, H204, and H205. Eurotiomycetes were found in very low concentrations in IM021. Mucoromycetes were found in abundance in IM015 sample. Eurotiomycetes and Mucoromycetes were kept to normal conditions in all H samples (Figure S8B). On the order level, IM015 was distinguished by a high Mucorales/low Saccharomycetales ratio (Figure S8C). Despite the fact that Saccharomycetales remained low in all H, M, and L \u003cem\u003eLactobacillus\u003c/em\u003e-treated samples, Mucorales levels in \u003cem\u003eLactobacillus\u003c/em\u003e samples were comparable to controls (Figure S8C). Furthermore, Capnodiales levels in medium and high \u003cem\u003eLactobacillus\u003c/em\u003e samples M202-H205 were comparable to those found in C101, C103, and C105. Glomerellales levels were high in both the control (C101 and C103) and \u003cem\u003eLactobacillus\u003c/em\u003e (L104) samples (Figure S8C). Microbial family profiling was diverse in all samples, but particularly in the CK and H groups. C101, C103, C105, H105, H204, and H205 all showed high levels of \u003cem\u003eNectriaceae\u003c/em\u003e and \u003cem\u003eTrichosporonaceae\u003c/em\u003e, as well as a variety of other families ranging from \u003cem\u003eAspergilaceae\u003c/em\u003e to \u003cem\u003eMicroascaceae\u003c/em\u003e. Notably, none of the \u003cem\u003eLactobacillus\u003c/em\u003e samples had the high levels of \u003cem\u003eMucoraceae\u003c/em\u003e found in IM015 (Figure S8D). In the genus taxa summary from ungrouped samples, IM015 had high levels of \u003cem\u003eMucor\u003c/em\u003e, whereas C101, C102, C105, L102, L104, M202, M203, H105, H204, and H205 had high levels of \u003cem\u003eFusarium\u003c/em\u003e but no \u003cem\u003eMucor\u003c/em\u003e to the extent seen in IM105 (Figure S8E). As a result, ungrouped samples of CTX-related rat fecal microbiomes and the effects of adding specific bioproducts also argued for \u003cem\u003eLactobacillus\u003c/em\u003e rather beneficial role in maintaining host health microbiome during chemotherapy.\u003c/p\u003e \u003cp\u003eThe analysis of metagenome sequence data (CK versus M) revealed a pattern that overlapped with enriched core microbes in the order Trichosporonales and the phylum Basidiomycota (Figure S9). The relative abundance of Fungi, Ascomycota, Sordariomycetes, Hypocreales, \u003cem\u003eNectriaceae\u003c/em\u003e, and \u003cem\u003eFusarium\u003c/em\u003e in CK and H class samples was very high (above 60000\u0026ndash;140000). In the IM, L, and M classes of samples, the relative abundance of \u003cem\u003eFusarium\u003c/em\u003e fungi was less than 50000 (Figure S10A). \u003cem\u003eFusarium\u003c/em\u003e was identified as a key biomarker (i.e, a key community member) of the CK group by LEfSe (LDA, Krustal-Wallis and Wilcoxon; Figure S10B). Comparative metagenomics and network analysis at the phylum level showed a high degree of similarity between control and \u003cem\u003eLactobacillus\u003c/em\u003e-treated rat fecal samples, as well as the dominance of Ascomycota in this network (Figure S11). This CK-\u003cem\u003eLactobacillus\u003c/em\u003e group is not associated with IM samples (in blue; Figure S11A). Mucoromycota (in orange) dominated in CTX- immune-attacked ill rat feces (Figure S11B).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCTX and CTX\u0026thinsp;+\u003c/b\u003e\u0026thinsp;\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLactobacillus\u003c/span\u003e \u003cb\u003etherapy effects on bacteriome and metabolic pathways.\u003c/b\u003e The relative abundance of each functional category (biosynthesis, degradation/utilization/assimilation, generation of precursor metabolite and energy, glycan pathways and metabolic clusters) was calculated using pathway abundance and read count abundance (MetaCyc; Figure S12). Differential abundance was found primarily for respiration, fermentation, fatty acid/lipid/carbohydrate degradation, and biosynthetic pathways (Figure S12A). Similarly, in MetaCyc, raw counts for metabolic pathways and enzymes, metabolites, and reaction orthologs revealed a strong statistical significance of differential abundance, primarily for cofactor, prosthetic group, electron carrier, vitamin, fatty acid, and lipid biosynthesis (Figure S12B). Some metabolic pathways in the MetaCyc database can be labeled with a low-level bacterial taxon [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. As a result, we used MetaCyc to find metabolic pathways and/or bacterial taxa that are specifically related to the five groups of rats for chemotherapy (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; S13). A specific pathway (PWY-7839), 6-hydroxymethyl-dihydropterin diphosphate biosynthesis I, which converts GTP into pterin precursors (methanopterin and sarcinapterin) for the biosynthesis of several cofactors in specific bacterial strains, was found to be particularly highly expressed in CK and \u003cem\u003eLactobacillus\u003c/em\u003e-treated samples due to an increase in \u003cem\u003eS24-7 Muribaculaceae\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eClostridiales\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eCF231 Paraprevotellaceae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Treatments with M- and H-doses were clearly effective in increasing the levels of \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e, both of which are essential in the pyridoxine pathway required for vitamin B6 synthesis (PYRIDOXSYN-PWY, pyridoxal 5\u0026rsquo;-phosphate (PLP) biosynthesis I; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Furthermore, \u003cem\u003eLactobacillus\u003c/em\u003e treatment restored the abundance of helicobacterial taxa required for the TCA cycle (tricarboxylic acid cycle) or the Krebs cycle. Despite chemotherapy, with \u003cem\u003eLactobacillus\u003c/em\u003e treatment, not only \u003cem\u003eHelicobacter\u003c/em\u003e-levels, but also \u0026ldquo;\u003cem\u003eFlexispira\u003c/em\u003e\u0026rdquo; (in purple), \u003cem\u003eRothia\u003c/em\u003e, and \u003cem\u003eHalomonas\u003c/em\u003e levels, were maintained (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). \u003cem\u003eLactobacillus\u003c/em\u003e L-, M-, or H-doses, had no effects on Bacillales. To control \u003cem\u003eHalomonas\u003c/em\u003e, a high dose of 3L (5.0 ml/kg) was strictly required (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). In IM samples, a formaldehyde oxydation peak was observed. This was linked to the emergence of \u003cem\u003eEnterococcus\u003c/em\u003e bacteria in immune compromised conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Many \u003cem\u003eEnterococcus\u003c/em\u003e species are known to be commensals and are not actively causing infection. In our case (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), our results show a peak of \u003cem\u003eEnterococcus\u003c/em\u003e linked to chemotherapy (CTX alone), implying that \u003cem\u003eEnterococcus\u003c/em\u003e is an active infection. The addition of \u003cem\u003eLactobacillus\u003c/em\u003e to chemotherapy completely eliminated it; no \u003cem\u003eEnterococcus\u003c/em\u003e peak was observed in CK, H and M groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). TCA-GLYOX-BYPASS, the superpathway or bypass that integrates the common prokaryotic Krebs cycle (TCA) with the glyoxylate shunt, benefited from \u003cem\u003eLactobacillus\u003c/em\u003e treatment during chemotherapy (+\u0026thinsp;CTX). In both CK and H-dose conditions, a high diversity of bacterial taxa was observed. Enrichment of \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, \u003cem\u003eCupriavidus\u003c/em\u003e, \u003cem\u003eHalomonas\u003c/em\u003e, and \u003cem\u003eDevosia\u003c/em\u003e was detected in controls and persisted during CTX chemotherapy when an additive probiotic treatment with high doses of 3L was used. Treatment with \u003cem\u003eLactobacillus\u003c/em\u003e was ineffective on Bacillales at 1.25-5 ml/kg doses (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). \u003cem\u003eLactobacillus\u003c/em\u003e at a medium-dose (2.5 ml/kg) was particularly effective in stimulating \u003cem\u003eEnterobacteriaceae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). \u003cem\u003eLactobacillus\u003c/em\u003e at a high-dose (5 ml/kg) was particularly effective in stimulating \u003cem\u003eDevosia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Similarly, the additive \u003cem\u003eLactobacillus\u003c/em\u003e treatment positively regulated \u003cem\u003eBacteroidales\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, \u003cem\u003eHalomonas\u003c/em\u003e, and \u003cem\u003eDevosia\u003c/em\u003e responsible for (prokaryotic) TCA cycle I (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). CTX chemotherapy and/or treatment with 3L bioproduct had a significant impact on tRNA charging and microbiome. We observed the main stimulatory effects of 3L on \u003cem\u003eS24-7\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eCF231\u003c/em\u003e, and \u003cem\u003eOscillospira\u003c/em\u003e using high doses of \u003cem\u003eLactobacillus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Finally, Illumina and MetaCyc analyses revealed that a middle dose of \u003cem\u003eLactobacillus\u003c/em\u003e had a strong effect on \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, which mediate the bacterial superpathway of coenzyme Q ubiquinol-8 biosynthesis (UBISYN-PWY, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen other types of metabolic pathways were examined (MetaCyc), the effects of \u003cem\u003eLactobacillus\u003c/em\u003e in addition to CTX were less obvious (Figure S13). No particular bacteria were found for the MetaCyc L-methionine salvage cycle III (PWY-7527, Figure S13A). \u003cem\u003eLactobacillus\u003c/em\u003e doses (M and H) primarily stimulated the anaerobic pathway for oleate biosynthesis IV (PWY-7664), however, the IM group had one sample that was much higher in the abundance of this pathway than all the H \u003cem\u003eLactobacillus\u003c/em\u003e group. The medium group appeared to have higher overall levels than the high group, possibly indicating an effect of 3L on this pathway (\u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e) but making any dose response relationship difficult to determine (Figure S13B). M- and H-doses of the bioproduct apparently had similar beneficial effects on mycolate biosynthesis (PWYG-321), with high levels of \u003cem\u003eBacteroides\u003c/em\u003e accumulating in M-treated samples (Figure S13C). Analysis of bacterial strains involved in the pathway teichoic acid (poly-glycerol) biosynthesis, which is part of cell wall biogenesis, seemed to have a positive effect of 3L bioproduct (M and/or H) as an additive to chemotherapy. \u003cem\u003eLactobacillus\u003c/em\u003e contributed to the low levels of \u003cem\u003eClostridiales\u003c/em\u003e, \u003cem\u003eMogibacteriaceae\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, and \u003cem\u003eGemella\u003c/em\u003e, while strains such as \u003cem\u003eJeotgalicoccus\u003c/em\u003e were stimulated (Figure S13D). Except for \u003cem\u003eenterobacter\u003c/em\u003e in some low-dose \u003cem\u003eLactobacillus\u003c/em\u003e samples, no specific bacterial strains were identified for the superpathway of L-threonine metabolism (Figure S13E). Chemotherapy (immune-attacked; IM) reduced the levels of \u003cem\u003eClostridiales\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, and \u003cem\u003eOscillospira\u003c/em\u003e in the pathway UDP-N-acetyl-D-glucosamine biosynthesis I (UDPNAGSYN-PWY), which could be avoided by combining CTX with a high dose (5 ml/kg) of \u003cem\u003eLactobacillus\u003c/em\u003e (Figure S13F).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer, a cell disease caused by DNA changes, is a major burden of threat to human health worldwide. When chemotherapy is envisioned as the primary or perhaps only way to prevent cancer development, the burden of threat to human health increases. Using one or more anti-cancer chemical drugs, such as cyclophosphamide (cytophosphane, CTX), kills lymphoma or any cancer cells, but it also kills or seriously alters the patient immune system, potentially limiting life and health expectancies, just like the disease.\u003c/p\u003e \u003cp\u003eThis is shown in a previous study from Zhang et al. in rats where CTX was shown to alter several immune marker indicators like the number of white blood cells, CD4+/CD8\u0026thinsp;+\u0026thinsp;ratio, the serum levels of interleukin 6 (IL-6) and interleukin gene expression [see 49]. In this previous study, which used the same chemotherapy rat model (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the CTX-increased expression of TNF-alpha cytokine, an endocrine mediator of inflammatory and immune functions, known to regulate cell growth, cell signaling, but with many side effects of cytotoxicity in transformed cells, was another indication of immune attacks in CTX conditions [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This is also described in our study of chemo-damaged rats when a strong beneficial healthy gut flora is suppressed by CTX treatment. We examined the microbiome of five groups of rats in relation to chemotherapy, revealing that rats treated with CTX had a completely altered microbiome. Importantly, we show that \u003cem\u003eLactobacillus\u003c/em\u003e treatments are particularly effective at maintaining healthy gut flora in the rat intestine, allowing us to establish strong healthy conditions in rats despite chemo (see Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026amp; S1-S6). Though there have been previous reports on the use of \u003cem\u003eLactobacillus\u003c/em\u003e in gut flora and gastrointestinal tract protection [\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], we will always seek the best solution, and we are still looking for a probiotic remedy that has a strong and significant impact on the adversive effects developed by CTX therapy, is active on benefical microbes, improves microbial balance, activates nutrients and stimulates gut-powered immune systems.\u003c/p\u003e \u003cp\u003eThe use of probiotics in chemotherapy is still rather uncommon, not applicable to all ages and populations, and an emerging field with many contradictory clinical results when it comes to interaction with the host or patient consumption [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Manipulation of microbiota by natural probiotics vs. chemical drugs is a constant challenge no only in human but also in veterinary medicine, especially for genetic diseases like cancer. The safety and stability of chemotherapeutic drugs such as CTX in cancer clinical trials are questionable [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. We always look into clinical trials for anticancer methods that are highly efficiency, have a low resistance capacity, and have no impact on the patient\u0026rsquo;s quality of life or health conditions. Some chemicals can cause the organism to develop resistance. The repeated use of the same class of chemicals to control a disease, such as cancer, can have a variety of negative consequences. When the organism becomes ill and resistant, the chemical (CTX) is used more frequently, and the adjuvant must eventually be added as the CTX level rises. Despite this, no comprehensive microbiological medical study of the impact of \u003cem\u003eLactobacillus\u003c/em\u003e during chemotherapy has been conducted. The rat response to a new bioproduct (3L: \u003cem\u003eL. acidophilus\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eL. casei\u0026thinsp;+\u0026thinsp;L. plantarum\u003c/em\u003e) added to chemo/cyclophosphamide CTX shots was studied as a preliminary set for more extensive biomedicine cancer research. We found that combining \u003cem\u003eacidophilus\u003c/em\u003e, \u003cem\u003ecasei\u003c/em\u003e, and \u003cem\u003eplantarum\u003c/em\u003e is especially beneficial for maintaining the gut flora and thus the immune system during chemotherapy. We show that combining three \u003cem\u003eLactobacilli\u003c/em\u003e strains has significant beneficial effects on rat gut microbiota during chemotherapy, prompting us to test the formulation for human health (see Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; S1-S13).\u003c/p\u003e \u003cp\u003eWe present MiSeq data for rat feces microbiome variations under five different conditions: normal and healthy, CTX chemotherapy and immune-attack, low, medium, and high doses of \u003cem\u003eLactobacillus\u003c/em\u003e (3L), with special consideration for fungi phylum, class, order, family, genus, and species (see Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026amp; S7-S11). We show in this study, that rats given CTX plus high doses of a new natural microbial biomedical product, the bioproduct 3L, have preserved microbiota that are critical for maintaining a strong immune system and a healthy condition. Such findings are especially significant because it has been established that microbial dysbiosis is associated with carcinogeneis in cancers ranging from colon to liver to pancreas. The growth of nocive fungi (\u003cem\u003eMalassezia\u003c/em\u003e) in the gut microbiome, in particular, can promote oncogenesis via activation of mannose-binding lectins [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], urging medication to control the microbiome and MBL.\u003c/p\u003e \u003cp\u003eChemotherapy has been shown to alter immune, metabolic, and physiological functions, as well as potentially stimulate invasive fungal infection in patients [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. So it is not surprizing that CTX alters gut flora and increases \u003cem\u003emucor\u003c/em\u003e or \u003cem\u003exerochrysium\u003c/em\u003e infection in groups of rat models (see Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, S7-S11 \u0026amp; Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The most striking finding of our study in five groups of rats in relation to chemotherapy is perhaps the beneficial regulation of gut flora after \u003cem\u003eLactobacillus\u003c/em\u003e (3L) supplementation at various doses. High doses of 3L have been shown to maintain beneficial healthy normal gut flora in CTX-treated rats (see Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, S1-S12 \u0026amp; Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), urging the method or \u003cem\u003elactobacillus\u003c/em\u003e treatment additive to CTX to be tested on cancer rat models. We show that high doses of \u003cem\u003eL. acidophilus\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eL. casei\u0026thinsp;+\u0026thinsp;L. plantarum\u003c/em\u003e are especially important for regulating Ascomycota and Capnodiales levels. Because ascomycetes are known to be used in medicine with the antibiotics penicillin and cephalosporin [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], and endophytic sooty mold fungi (Dothideomycetes) can be important for tissue health, environmental adaptation, and stress tolerance [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], this could be a significant discovery for cancer treatment. Interestingly, high doses of 3L (H treatment) have been shown to stimulate the levels of many different fungi families, including \u003cem\u003eAspergilaceae, Microascaceae\u003c/em\u003e, \u003cem\u003eNectriaceae\u003c/em\u003e, and \u003cem\u003eTrichosporonaceae\u003c/em\u003e. Despite the fact that many of these fungi are plant and human pathogens, many of them are also biodegraders and biocontrol agents that could be used in medical applications [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. For example, given that these ascomycete fungi can help degrade residual CTX and control cyclophosphamide toxicity [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], this could be used in chemotherapy. Another important aspect of using chemo\u0026thinsp;+\u0026thinsp;\u003cem\u003elactobacillus\u003c/em\u003e is that 3L (high dose, 5.0 ml/kg) has been shown to keep pathogens like mucormycetes (Mucormycota, Mucoraceae) and chytridiomycetes (Olpidiomycota, Olpidiaceae) at bay (see Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, S8 \u0026amp; Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). High mucorales typically invade the blood vessels and are linked to emerging infectious diseases such as mucormycosis in addition to other zygomycoses [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Chytrid fungi cause chytridiomycosis, an emerging disease in amphibians, and subcutaneous phycomycosis in humans [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. As a result, this is a comprehensive pattern of many different fungal infections that could be regulated by \u003cem\u003eLactobacillus\u003c/em\u003e bioproduct 3L. Furthermore, 3L has the potential to influence the magnitude of beneficial fungal components of the intestinal microbiota, known as the gut mycobiome. Some fungi from the diet or the environment play an important role in mediating interaction in gut bacterial communities and regulating metabolic homeostasis. This is true for Eurotiales, Hypocreales, Saccharomycetales, and Trichosporonales, all of which are important components of the healthy mycobiome in both control and 3L-treated rats (see Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, S7-S11 \u0026amp; Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The structure of the human gut microbial community is determined by genetics and environmental factors, but the fungi that mediate changes in this structure and cause disease are relatively common in humans and rodents [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Although it is commonly assumed that probiotics do not colonize the digestive tract or other parts of the human body, there is evidence of transient probiotic or foodborne strain colonization of the human gut via various mucosadhesion-related proteins on the probiotic cell surface [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. There is also the option of encapsulating \u003cem\u003eLactobacilli\u003c/em\u003e [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. As a result, it may be critical to use 3L during cancer chemotherapy to maintain the mycobiome balance, microbial community interactions, bacterial-fungal interactions, fungal-fungal interactions, and host-fungal interactions [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. CTX injections, like diabetis [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], caused significant changes in Mortierellomycota-levels, which could be reversed by adding 3L (see Figure S8A). Therefore, using a 3L mix could be extremely useful in targeting specific components of the mycobiome, which plays a key role in the development of diseases ranging from cancer to diabetes.\u003c/p\u003e \u003cp\u003eIt is probably important to note that the effect of 3L tested in rats can be dose-dependent in this prospect for curing or optimizing health. To treat the rats for Mallasseziomycetes, a low dose of 3L \u003cem\u003eLactobacillus\u003c/em\u003e cocktail (1.25 ml/kg) was required (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which could be important in medications for specific pathologies such as colorectal cancer. Mallasseziomycetes fungi are associated with late-stage colorectal cancer [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. We also found that 3L bioproduct doses have a different effect on the mycobiome during the stages of chemotherapy treatment in rats. \u003cem\u003eLactobacillus\u003c/em\u003e strains must be present in sufficient quantities for 3L to be effective. \u003cem\u003eLactobacillus\u003c/em\u003e ingredients must eventually be gradually increased in order to regulate a specific fungal group, such as \u003cem\u003eAspergillaceae\u003c/em\u003e, \u003cem\u003eCordycipitaceae, Mycosphaerellaceae, Rhizopodaceae, Thermoascaceae\u003c/em\u003e, and \u003cem\u003eTrichosporonaceae\u003c/em\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD \u0026amp; Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During chemotherapy, it is critical to regulate the fungal mycobiome. The gut mycobiome is involved in microbiome assembly and immune functionality, prompting the modulation of specific fungi to regulate both the gut microbiome and the immune system during chemotherapy [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. While 3L is effective in modulating yeasts in the order Saccharomycetales (Ascomycota; see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), \u003cem\u003ePichia\u003c/em\u003e is one of the few microbial genera that is affected by CTX chemotherapy but does not respond to \u003cem\u003eLactobacillus\u003c/em\u003e treatment (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE \u0026amp; Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). One possible explanation is that 3L controls gut fungi but not oral fungi or genera like \u003cem\u003ePichia\u003c/em\u003e and \u003cem\u003eCandida\u003c/em\u003e [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Perhaps the formulation of 3L can still be improved to treat both oral and gut mycobiomes. Adding one or more \u003cem\u003eLactobacillus\u003c/em\u003e strains, such as \u003cem\u003eL. reuteri\u003c/em\u003e, to 3L may be very effective in controlling the entire mycobiome on oral and gut tissues (see J\u0026oslash;rgensen et al., 2017 [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e] \u0026amp; our descriptive study on 3L in chemo).\u003c/p\u003e \u003cp\u003eThe plethora of metabolic functions maintained by 3L treatment in five groups of rats in relation to CTX chemotherapy was one of our study\u0026rsquo;s most striking findings about \u003cem\u003eLactobacillus\u003c/em\u003e and chemo (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; S12-S13). Not only cancer, but also chemotherapy, has a significant impact on cell metabolism [\u003cspan additionalcitationids=\"CR90 CR91\" citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Therefore, targeting of chemotherapy (and cancer) metabolism as a complementary strategy is a promising approach not only for disease intervention, but also for preserving physiological functions in the patient immune system. We show here that a preparation of \u003cem\u003eL. acidophilus\u003c/em\u003e (\u003cem\u003eSD65\u003c/em\u003e), \u003cem\u003eL. casei\u003c/em\u003e (\u003cem\u003eSD07\u003c/em\u003e), and \u003cem\u003eL. plantarum\u003c/em\u003e (\u003cem\u003eSD02\u003c/em\u003e) is extremely effective in maintaining and/or stimulating many different metabolic pathways via a beneficial effect on gut flora (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; S13). After treatment with our three \u003cem\u003eLactobacilli\u003c/em\u003e cocktail (3L), the gut flora of chemotherapy-treated rats is rich in Firmicutes-Clostridia-Clostridiales-\u003cem\u003eS24-7\u003c/em\u003e, as found in healthy control conditions (see Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA \u0026amp; \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Overall, the detailed composition of the bacteriome using MetaCyc data suggests shared patterns of microbial strains and metabolic activities in healthy and 3L-treated groups for a wide range of systems. Bacteroidales-\u003cem\u003eMuribaculaceae\u003c/em\u003e-\u003cem\u003eS24-7\u003c/em\u003e is an important component of the microbiome for carbohydrate metabolism, while \u003cem\u003eDeferribacteraceae\u003c/em\u003e upregulates genes for amino acid and vitamin metabolism [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. In the gastrointestinal tract, Bacteroidales-\u003cem\u003ePrevotellaceae\u003c/em\u003e-\u003cem\u003ePrevotella\u003c/em\u003e-\u003cem\u003eParaprevotella\u003c/em\u003e plays a key role for glucose (central carbon) metabolism, polysaccharide breakdown, glycogen storage, sulfate assimilation, and the production of propionate, which has anti-cancer and anti-inflammatory properties [\u003cspan additionalcitationids=\"CR95 CR96\" citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Campylobacterales-\u003cem\u003eHelicobacteraceae\u003c/em\u003e-\u003cem\u003eHelicobacter\u003c/em\u003e (\u003cem\u003epylori\u003c/em\u003e) is not always infectious or linked to metabolic syndromes like atherosclerosis. In many cases, it can be considered very beneficial to prevent the development of autoimmune diseases in humans. \u003cem\u003eH. pylori\u003c/em\u003e can aid in the regulation of fatty acid metabolism. \u003cem\u003eH. pylori\u003c/em\u003e is also known to contribute to the metabolic fate of pyruvate to lactate-acetate, nucleotide biosynthesis, oxidative metabolism, and thus cellular respiration [\u003cspan additionalcitationids=\"CR99\" citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. This dual aspect (\u0026ldquo;beneficial pathogen\u0026rdquo;) highlights the significance of our finding with 3L controlling \u003cem\u003eHelicobacter\u003c/em\u003e levels during chemotherapy (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). This includes the ability of 3L to control \u003cem\u003eHelicobacters\u003c/em\u003e of the genus \u003cem\u003eFlexispira\u003c/em\u003e, a urease-producing microorganism from the mid-colon and jejunum in humans that is typically associated with diarrhea symptoms as well as complete febrile illness such as malaise, arthralgias, pain, leg swelling and polyserositis [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. It also includes a beneficial control of 3L for Actinomycetales-\u003cem\u003eMicrococcaceae\u003c/em\u003e-\u003cem\u003eRothia\u003c/em\u003e and Oceanospirillales-\u003cem\u003eHalomonadaceae\u003c/em\u003e-\u003cem\u003eHalomonas\u003c/em\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC), which may be important because \u003cem\u003eRothia\u003c/em\u003e and \u003cem\u003eHalomonas\u003c/em\u003e are both prevalent in oral (salivae), oropharynx, respiratory tract and intestinal (gut) microbiota where they contribute to maintain healthy mucosal surfaces (iron scavenged from food, breakdown of proline and glutamine-rich proteins, glutamate and central carbohydrate metabolism) [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. The beneficial effects of high doses of 3L (in addition to CTX in rats) on the levels of Burkholderiales-\u003cem\u003eBurkholderiaceae\u003c/em\u003e-\u003cem\u003eCupriavidus\u003c/em\u003e and Rhizobiales-\u003cem\u003eHyphomicrobiaceae\u003c/em\u003e-\u003cem\u003eDevosia\u003c/em\u003e further suggest the stimulatory effects of 3L-pharmacological agents on central carbon metabolism and energy production (see Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE \u0026amp; \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). \u003cem\u003eCupriavidus\u003c/em\u003e bacteria have a diverse metabolic range that can be used to produce sulfur-biofuels as well as energy sources from hydrogen and CO\u003csub\u003e2\u003c/sub\u003e [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. Similarly, \u003cem\u003eRhizobium\u003c/em\u003e and \u003cem\u003eDevosia\u003c/em\u003e are known to express a wide range of metabolic activities, including carbohydrate, cysteine, methionine, branch-chain amino acid, and phosphorus compound metabolism, all of which are ideal for sustaining cellular and genetic component synthesis, energy transfer, and/or mycotoxin degradation [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. Furthermore, the 3L mix may increase the levels of Bacillales-\u003cem\u003eStaphylococcaceae\u003c/em\u003e-\u003cem\u003eJeotgalicoccus\u003c/em\u003e (see Figure S13D), which is important for biotin/vitamin H or vitamin B7 metabolism, cofactor in carboxylase activities in the gut, and its relationship with health [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e]. The use of \u003cem\u003eLactobacillus\u003c/em\u003e (3L) to potentially stimulate amino acid, biotin, carbohydrate, glucose, iron, nitrogen, oxygen, phosphorus, protein, pyruvate, sulfide, and vitamin metabolism as a complement to chemo has been highlighted (see Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; S13). The most notable effect of \u003cem\u003eLactobacillus\u003c/em\u003e treatment on the microbiome is perhaps found for \u003cem\u003eEnterococcus\u003c/em\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Chemotherapy (CTX) significantly increases the risk of \u003cem\u003eEnterococcus\u003c/em\u003e peak (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD), which in humans becomes a risk of ulcerative colitis or even colorectal cancer [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Probiotics such as \u003cem\u003e3L\u003c/em\u003e (\u003cem\u003e3-Lactobacilli\u003c/em\u003e) have been found to be very effective in raising bacteroides and clostridiales and keeping \u003cem\u003eEnterococcaceae\u003c/em\u003e at bay in order to restore the gut flora to healthy conditions during chemotherapy (see Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; S13). As a result, our 3L cocktail appears to be capable of controlling not only the mycobiome, but also the entire bacterial microbial metabolism in a strain-dependent manner.\u003c/p\u003e \u003cp\u003eDifferent \u003cem\u003eLactobacilli\u003c/em\u003e formulations have been shown to modify gut flora and thus general metabolism and behavior not only in humans, but also in fishes and rodents [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. We suggest that \u003cem\u003eLactobacilli\u003c/em\u003e, like other general natural bacterial probiotics, can be used as an adjuvant treatment during chemotherapy to maintain gut flora and stimulate the patient\u0026rsquo;s immune system. Rats outperformed mice as pre-clinical models models for human microbiota engraftment. Rats\u0026rsquo; microbial communities are more similar to those of humans. More human microbial species were captured by rats than by mice [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. As a result, rats are frequently used in cross-species microbiome analysis to study a wide range of human pathologies [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. The beneficial pharmacological effects of a new probiotic formula on the gut microbiota of rats after one-month treatment with three bio, three natural \u003cem\u003eLactobacilli\u003c/em\u003e species in CTX-injury are reported here. The comprehensive and comparative analysis is divided into three parts: (1) healthy condition and chemo, (2) CTX\u0026thinsp;+\u0026thinsp;\u003cem\u003eLactobabillus\u003c/em\u003e, and (3) different \u003cem\u003eLactobacillus\u003c/em\u003e doses. We describe the microbial profiles associated with chemotherapy, as well as the various \u003cem\u003eLactobacillus\u003c/em\u003e doses required to counteract specific aversive or inhibitory effects of CTX in rodents. A systematic analysis of the gut microbiota in groups of rats exposed to cyclophosphamide shots\u0026thinsp;+\u0026thinsp;bioproduct suggests not only the infectious microbial pathogens induced by the CTX chemical treatment, but also the key beneficial microbial families induced by 3L to help maintain the immune system.\u003c/p\u003e \u003cp\u003e \u003cem\u003eL. casei\u003c/em\u003e supports the growth of \u003cem\u003eL. acidophilus\u003c/em\u003e, which produces carbohydrate-digesting enzymes, whereas \u003cem\u003eL. plantarum\u003c/em\u003e, a more adaptable and versatile strain, produces a slew of antimicrobial substances that aid in their survival in the gastrointestinal tract under any conditions. Our first results in CTX chemo-rats could pave the way for future human chemotherapy attempts. As a main result of \u0026ldquo;chemo\u0026rdquo;, fragile health conditions and microbial infections are frequently associated with a weakened immune system. Hence, complementary and/or alternative clinical medicine for cancer prevention and/or treatment is required. One major finding in our study was that a tritherapy of \u003cem\u003eLactobacilli\u003c/em\u003e could save the body from chemo in rodents by having many beneficial effects on gut flora (mycobiome and bacteriome) and cell energy metabolism. The immune system, which relies on energy to reduce the risk of chronic diseases, is strongly linked to body composition (gut flora and metabolism). Importantly, because 3L has beneficial effects on many different bacteria and metabolic systems (see Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), it is very likely that it could work as an additive to chemical drugs not only for cancer, but also for many different metabolic diseases. It eliminates specific invading agents such as fungi (\u003cem\u003emucor\u003c/em\u003e) and infectious bacteria (\u003cem\u003eenterococcus\u003c/em\u003e). Furthermore, the diversity of bacteria upregulated by 3L treatment (see Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) is such that our probiotic formula (\u003cem\u003eL. acidophilus SD65\u003c/em\u003e, \u003cem\u003eL. casei SD07\u003c/em\u003e, and \u003cem\u003eL. plantarum SD02\u003c/em\u003e) can be easily modified to target specific metabolic systems in humans. One advantage of our initial work in rats is that different doses of \u003cem\u003eLactobacillus\u003c/em\u003e have different effects on gut flora (see Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Therefore, more research should be conducted to test the effects of different SD65-SD07-SD02 ratios in 3L mixtures or in combination with other \u003cem\u003eLactobacilli\u003c/em\u003e or beneficial bacteria mixtures (see Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Here, we offer a research feasibility suggestion for immunization or improved immune systems in chemo. Our 3L bioproduct could serve as the significant and strong basis for the development of a large family of medicinal microbial bioproducts that would be used on cancer patients rather than chemo-animal models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe formulated 3L probiotic has a relevant action on the rat gut microbiome in chemotherapy conditions. While CTX and anticancer drugs have a number of potential side effects, as documented here, including 3L in chemotherapy has been shown to benefit gut flora and thus health conditions. Interactions between fungi and bacteria regulate health and disease. 3L appears to keep fungi and bacteria that are beneficial to health. The results on bacteria are limited to specific metabolic pathways, which appears to be how 3L approaches chemotherapy. The results in rats show that the efficiency, frequency, and dependability of high-dose retain attention over a month of treatment (five CTX-chemotherapy sessions or \u0026lsquo;shots\u0026rsquo;). High dose of 3L appears to restore gut microbiota to normal levels, strengthen the host\u0026rsquo;s overall immune defense, and strongly preserve health conditions. Although much remains to be learned from studying 3L, this is not insignificant in our ongoing search for tools to improve the quality of life of cancer patients undergoing chemotherapy.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eLactobacillus\u003c/span\u003e \u003cb\u003epreparation for medical-industrial use.\u003c/b\u003e We previously developped a three-strain \u003cem\u003elactobacillus\u003c/em\u003e probiotic formula (3L) to combat cholesterolemia and hyperlipidemia [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our Industrial Laboratory platform for natural medicine in Jinan (Shandong Province, P.R. China) produced the same probiotic formula used in this study against immune damages in cyclophosphamide chemotherapy (\u003cem\u003eL. acidophilus SD65\u003c/em\u003e, \u003cem\u003eL. casei SD07\u003c/em\u003e, and \u003cem\u003eL. plantarum SD02\u003c/em\u003e). Following Yue et al. (2014) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], pure cultures of the three bacterial strains were grown in de Man, Rogosa, and Sharpe (MRS) agar liquid medium and placed in an anaerobic workstation held at 37\u0026deg;C (industrial platform). Each strain\u0026rsquo;s bacterial cells were harvested for 3L preparation by centrifugation at 2000 \u0026times; g for 20 min (4\u0026deg;C). Each strain\u0026rsquo;s cell pellet was resuspended in sterile saline water solution at a concentration of 10\u003csup\u003e9\u003c/sup\u003e CFU/ml and stored at 4\u0026deg;C. The tripartite L probiotic solution (3L) was freshly prepared by mixing equal volumes of cold suspensions of SD65, SD07, and SD02 and stored in cold conditions (4\u0026thinsp;~\u0026thinsp;10\u0026deg;C) for later use. Rats were given a daily dose (0.3 ml) of bioproducts administered intra-gastrically via a stainless-steel needle, along with chemotherapy (cyclophosphamide, CTX; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePreparation of five groups in a CTX-induced immunosuppression model in rats.\u003c/b\u003e Jinan Pengyue Laboratory Animal Company supplied the rats (Product license SCXK (LU) 20140007). In our laboratory, the rats were bred in the Class II clean animal facility of the Institute of Medicine (Shandong Academy of Medical Sciences) with the setting temperature of 20\u0026ndash;26\u0026deg;C (relative humidity: 40\u0026ndash;70%, ventilation rate\u0026thinsp;\u0026ge;\u0026thinsp;15 times/hour). They all passed the quarantine inspection (Laboratory animal use license No.: STXK(LU)20170003, issued by Shandong Provincial Department of Science and Technology). The feed and drinking bottle were replaced every two-three days or if necessary. Fifty healthy Specific Pathogen Free (SPF) Sprague-Dawley (SD) male rats were divided into five experimental groups (N\u0026thinsp;=\u0026thinsp;50 males, young adults, 8\u0026ndash;9 weeks old; body weight: ~260\u0026ndash;316 g): 1) Control healthy (CK), 2) Immune attacked (IM), 3) Immune attacked and treated with low \u003cem\u003eLactobacillus\u003c/em\u003e dosage of 1.25 ml/kg bodyweight (L), 4) Immune attacked and treated with middle \u003cem\u003eLactobacillus\u003c/em\u003e dosage of 2.5 ml/kg bodyweight (M), and 5) Immune attacked and treated with high \u003cem\u003eLactobacillus\u003c/em\u003e dosage of 5.0 ml/kg bodyweight (H). The total number of rats studied was 50, with 10 rats in each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). They were fed in two five-rat cages (feeding density: \u0026le; 5/cage). Four rats in each cage were labeled with neutral red on the head, neck, back, and tail. The fifth rat was unmarked. Before the experiment, each group was given an equal volume of animal drinking water, and 5.0 ml/kg of animal drinking water was supplemented before gavage). The control rats (CK group) were given a continuous gastric perfusion of normal saline (NS). The immune system of rats in groups 2\u0026ndash;5 was attacked by an intraperitoneal injection of cyclophosphamide (CTX, 10 mg/ml). Another study looked at the effects of CTX on the immune system of rats [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. A companion study highlighted the negative effects of CTX cyclophosphamide chemotherapy on the rat immune system as wells as the beneficial effects of \u003cem\u003eLactobacillus\u003c/em\u003e preparation on cyclophosphamide-induced immunosuppression [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Following the findings from Zhang et al. (2020) on CTX chemotherapy and immunodepression in rats [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], the current experiment on chemotherapy and microbiome was followed for approximately 28 days (D28). CTX was only administered to \u0026ldquo;nude\u0026rdquo; rats on day 1, 5, 8, 15, and 22 (IM group; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). On each of the five shots, CTX was injected intraperitoneally at a dosage volume of 4 ml/kg. The control group received saline shots of the same volume. The 3L preparation was administered via gavage, which is a tube that runs from the mouth to the stomach. \u0026ldquo;Covered\u0026rdquo; rats received a 3L continuous gastric perfusion in NS (1.25 ml/kg, low dose, L group; 2.50 ml/kg, middle dose, M group; 5.0 ml/kg, high dose, H group; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). L, M, and H, like IM, corresponded to five CTX injections chemotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In each rat group, fresh fecal samples were collected for analysis on D28. Feces were collected with sterile disposable plastic spoon (SteriPlast sample spoon) and placed in a 1.5 ml Eppendorf tube that had been sterilized. Fecal samples from the control CK, IM, L, M and H groups were stored at -80\u0026deg;C until DNA extraction, Illumina MiSeq Sequencing, and microbiome profiling comparisons between the five groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePreparation of microbial genomic DNA samples for Illumina MiSeq.\u003c/b\u003e\u0026nbsp;DNA was extracted from the five groups of rats (CK, IM, L, M, and H) in the chemotherapy model using the method previously selected for mice and piglet fecal microbiome analysis [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This method was dependable for fecal DNA sample testing, quantity, purity, and quality control, as well as Illumina sequencing [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. As described in Yue et al. (2020) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], 2.0 g of fecal samples from Group CK-H rats were processed for microbial genomic DNA extraction using QIAamp Fast DNA Stool MiniKit (Qiagen GmbH, Hilden, Germany) and used as template (10 ng) in PCR reactions employing universal primers. For 16S samples (V3-V4, 480 bp, Miseq-PE250), the primers 338F 5\u0026rsquo;-ACTCCTCGGGAGGCAGCA-3\u0026rsquo; and 806R 5\u0026rsquo;-GGACTACHVGGGTWTCTAAT \u0026minus;\u0026thinsp;3\u0026rsquo; (Personal Biotechnology Co, Ltd, Shanghai, China) were used. The primers ITS5F 5\u0026rsquo;-GGAAGTAAAAGTCGTAACAAGG-3\u0026rsquo; and ITS1R 5\u0026rsquo;-GCTGCGTTCTTCATCGATGC-3\u0026rsquo; (Personal Biotechnology Co, Ltd, Shanghai, China) were used in PCR reactions for ITS (ITS1, 250 bp, Miseq-PE250). Each Illumina sequencing sample corresponded to three rats from the same group. Consequently, fifteen different samples (CK: C101, C103, C105; IM: IM015, IM021, IM024; L: L102, L103, L104; M: M201, M202, M203; H: H105, H204, H205) were subjected to Illumina MiSeq (NCBI SubmissionID: SUB9725559; BioProject ID: PRJNA754332; BioSamples: SAMN20769197-SAMN20769206; Accession Numbers: SRX11740945-SRX11740959). Three biological samples were tested in each group of rats during microbiome analysis in relation to CTX chemotherapy, so we conducted individuals, replicates, and comparison groups.\u003c/p\u003e \u003cp\u003ePrior to sequencing, ITS and 16S rDNA products (TransGen Biotech, Beijing, China) were amplified in a Takara Master Thermal Cycler Dice (Takara, Dalian, China) programmed for an initial denaturation of 95\u0026deg;C for 3 min, followed by 30 cycles of 94\u0026deg;C for 30 s, 50\u0026deg;C for 30 s, 72\u0026deg;C for 1 min, and a final extension of 72\u0026deg;C for 7 min. Q5\u0026reg; high-fidelity DNA polymerase (New England BioLabs Inc., Ipswich, Massachussets, USA) was used for PCR amplification. Each group\u0026rsquo;s PCR products or amplicons were purified using a 2 percent agarose gel electrophoresis (Bio-Rad Beijing, China) and a gel recovery kit (Axygen\u0026reg;, AxyPrep DNA gel extraction kit, New York, USA). The PCR product concentration was determined in a microplate reader (BioTek\u0026trade;, FLx800\u0026trade;) using a fluorescence reagent-based method (Quant-iT PicoGreen dsDNA Assay Kit, Fisher Scientific\u0026trade;, Loughborough, UK).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIllumina MiSeq Sequencing.\u003c/b\u003e In the five groups of rats (CK-H), MiSeq sequencing by Illumina was used to generate sequencing data from microbial genomic DNA libraries for chemotherapy\u0026thinsp;+\u0026thinsp;bioproduct research. We used the same Illumina TruSeq Nano DNA LT Library Prep Kit used for human genome and gut microbiota sequencing (Human Genome Assembly: The Genome Sequencing Consortium, 2001) [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. MiSeq\u0026rsquo;s goal was to add adapter sequences to the ends of microbial DNA fragments in order to generate indexed libraries for single- and paired-end reads [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. To begin, rat fecal microbial genomic DNA amplicons were subjected to terminal end repair. End Repair module (Mix2) excised the 5\u0026rsquo;-end of DNA and replaced it with a phosphate group. Meanwhile, the 3\u0026rsquo;-end\u0026rsquo;s missing base was filled. To prevent self-ligation, an adenosine base was added to the 3\u0026rsquo;-end of each microbial DNA sequence. This also ensured that the sequencing linker was properly linked to each DNA target sequence. To immobilize DNA in flow cells, a sequencer corresponding to a library-specific tag (Index Sequence) was added to the 5\u0026rsquo;-end of the PCR amplicons. To purify the microbial library system, self-ligated fragments were removed using BECKMAN AMPure XP Beads (Beckman Coulter\u0026trade;, Illkirch, France). PCR amplicons were used as a template in a second-PCR run to enrich the libraries as much as possible for the DNA of interest. The PCR conditions were the same as described in the section on preparing microbial genomic DNA samples for Illumina sequencing. Before high-throughput sequencing, PCR amplicons were purified using the Beckman magnetic beads screening method and analyzed by 2 percent agarose gel electrophoresis.\u003c/p\u003e \u003cp\u003ePrior to high-throughput sequencing, the quality of each rat fecal microbial genomic DNA library was checked on an Agilent Bioanalyzer with an Agilent High Sensitivity DNA kit (Agilent Technologies Inc., Waldbronn, Germany). On Agilent check, each DNA library produced only a single peak and no joints. The sequence librairies were then quantified using the Quant-iT PicoGreen dsDNA assay kit Promega on the Quantifluor fluorescence quantitative system (Promega Corporation, Madison, USA). The concentration of the library was greater than 2 nM. DNA samples were mixed after serial dilutions, denatured with NaOH, and sequenced. Illumina MiSeq Reagent kit v3 was used on a 600 cycles MiSeq Sequencer (Illumina Next Generation) to perform two-end sequencing with 2 x 300 bp reading length. The target DNA fragment\u0026rsquo;s optimal sequencing length was between 200 and 450 bp.\u003c/p\u003e \u003cp\u003eBased on primers and barcode information, reading sequences were identified and assigned to the corresponding samples. USEARCH (v5.2.236, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.drive5.com/usearch\u003c/span\u003e\u003cspan address=\"http://www.drive5.com/usearch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to remove all chimeric sequences (or artifacts formed by incorrectly joined sequences) [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. QIIME (Quantitative Insights Into Microbial Ecology, v1.8.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://qiime.org\u003c/span\u003e\u003cspan address=\"http://qiime.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to identify unreliable sequences (replication errors, nucleotide base substitutions, insert deletions, and so on) for microbial genomic DNA [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]. Sequences with more than one base mismatch and/or more than eight consecutive identical bases were discarded [\u003cspan additionalcitationids=\"CR118\" citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]. Other sequences were classified into Operational Taxonomic Units (OTUs), which were then used for microbial DNA taxonomic identification and phylogenetic analysis [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. The diversity level of each sample was assessed using OTU values, and the depth of sequencing (enhanced microbial community analysis) was demonstrated using rarefaction curve analysis [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. The composition of the five samples corresponding to the five rat groups (CK-H) was examined at different taxonomic levels: phylum, order, class, family, genus, and species (i.e. complete microbiome; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Tables, box plots and histograms were used to display the microbiome results (OTU, 100%; R software). Rare OTUs (with an abundance proportion of less than 0.001%) were excluded from microbiome analysis [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. Venn diagrams in R (Treat*/2.5.1_Venn) were used to display shared and unique OTUs within each group (as calculated by R). Rarefaction curves were drawn to reflect the microbial diversity among samples using an OTU abundance matrix to study complete microbial structure in each group. Specifically, we compared the number of OTUs in five different groups of rats (CK-H) at the same sequencing depth and justify the level of diversity in each sample (QIIME2, alpha rarefaction curve). The length of the curve reflects the number of sample sequencing depths; the longer the curve, the greater the sequencing depth, which increases the likelihood of observing increased microbial diversity. The slope of the curve reflects the effect of sequencing depth on the sample\u0026rsquo;s microbial diversity. A rarefaction curve with a flat slope indicates that the sequencing results are sufficient to reflect microbial diversity and that increasing sequencing depth will not detect more novel OTUs. A bump rarefaction curve (high slope) indicates that the diversity has not been exhausted, and that increasing sequencing depth could aid in the detection of more OTUs (Treat*/2.3.2_arare). We also measured \u0026ldquo;Specaccum\u0026rdquo; (species accumulation curve) in five groups of chemotherapy-treated rats and 3L. Specaccum, like the rarefaction curve, indicates the extent of increase in microbial community richness with increasing sample size [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e]. We estimated whether the sample size was sufficient to reflect the different underlying bacterial communities of the different groups or samples using R\u0026rsquo;s specaccum function. Using R in vegan (S3 method; Treat*/2.3.3_specaccum), the specaccum species accumulation curve was plotted for the total number of OTUs in each sample from the OTU abundance matrix [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e]. Furthermore, the rank abundance curve (RAC) was used to determine the number of highly abundant versus rare OTUs in each community [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. For RAC, OTU values were sorted and transformed into Log2 data in R (Treat*/2.3.4_rabund). Other multiple indices were used to assess microbial alpha diversity in different rat groups. Using QIIME software (QIIME 2) in R, we included the Chao1 index and ACE index to reflect community richness, and Shannon-Simpson indices to reflect both evenness and richness of the bacterial community in each of the five groups of rats related to cancer chemotherapy and \u003cem\u003elactobacillus\u003c/em\u003e treatment (CK-H) [\u003cspan additionalcitationids=\"CR120 CR121 CR122 CR123 CR124\" citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMultiple statistical analysis tools (Metastats) in Mothur software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metastats.cbcb.umd.edu\u003c/span\u003e\u003cspan address=\"http://metastats.cbcb.umd.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to analyze the differences in gut flora structure and related microbial species between the groups, providing the sequence difference (or absolute abundance) of two samples/groups based on P and Q values [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. Composition analysis included OTU number analysis of each taxonomic level. According to OTU classification results, we analyzed the OTU number of every sample at each taxonomic level (Kingdom, Phyllum, Class, Order, Family, Genus, Species). The results were plotted as a histogram by R language. OTU number of groups and individual samples was shown at each taxonomic level. The ordinate showed the relative abundance of each taxon, the longer the bar means the higher the relative abundance of the corresponding taxon in the sample. To show the differences in the composition of taxa between samples (groups and individual samples), the abundance difference between five groups and fifteen samples was compared one by one, and whether the difference was significant was determined by statistical test (Metastats analysis, Mothur Software) [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e]. A heatmap [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e] was used in this descriptive study to show fungal microbial taxa clustering based on the abundance of each taxon in five groups and fifteen individual samples related to chemotherapy and \u003cem\u003eLactobacillus\u003c/em\u003e treatment in rats. The relative abundance of each taxon in each sample was used to create a microbial species heatmap (Treat*/2.2.1_taxa). The relative abundance values were all log transformed to reduce the degree of difference. If the taxon\u0026rsquo;s relative abundance is 0, half of the minimum abundance value will be substituted for it. As a result, heatmaps were created using the R sofware package \u0026ldquo;gplots\u0026rdquo; of sofware R, and the distance algorithm is \u0026ldquo;euclidian\u0026rdquo;, and the clustering method is \u0026ldquo;complete\u0026rdquo;, as used by Yue et al. (2020) on curing piglets [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLEfSe was used to calculate the difference in community composition between groups based on linear discriminant analysis (LDA) effect size. LDA is combined with Krustal-Wallis and Wilcoxon rank sum tests in LEfSe analysis to identify key biomarkers (i.e, key community members) [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. The Galaxy Online Analysis Platform was used for sample group comparison and visual analysis results for LefSe analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://huttenhower.sph.harvard.edu/galaxy\u003c/span\u003e\u003cspan address=\"http://huttenhower.sph.harvard.edu/galaxy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Treat*/2.5.5_LEfSe).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSequence denoising or clustering.\u003c/b\u003e The primary tasks carried out the DADA2 method are priming, quality filtering, denoise, Mosaic, and chimera removal [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e]. Instead of clustering based on similarity, it now only uses dereplication, or 100% similarity clustering. The Amplicon Sequence Variants (ASVs) or feature sequences (corresponding to the OTU representative sequences) are referred to each decontamination sequence produced by the use of DADA2 quality control, and the feature schedule is the frequency of these sequences in the sample or group of samples (corresponding to the OTU Options). The current mainstream analysis platforms (QIIME2 and VSEARCH) promote the denoising and feature sequence generation method represented by DADA2 [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]. \u0026ldquo;Operational Taxonomic Units\u0026rdquo; (\u0026ldquo;OTUs\u0026rdquo;), which is Esperanto for \u0026ldquo;suboptimal, imprecise rubbish\u0026rdquo;, are described as \u0026ldquo;the features produced by clustering methods\u0026rdquo; in QIIME2. It is believed that the clustering analysis method was established on the basis of OTUs, which is not ideal or accurate (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.qiime2.org/2019.7/tutorials/overview\u003c/span\u003e\u003cspan address=\"https://docs.qiime2.org/2019.7/tutorials/overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). DADA2 is therefore picked for analysis by default. However, the OTU clustering based VSEARCH method from Rognes et al. (2016) [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e] is still an option because the aforementioned methods have not yet been optimized for all amplicon types. Priming, splicing, quality filtering, weight removal, chimera removal, clustering, and so forth are all the main components of the VSEARCH method [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]. VSEARCH is a 64-bit open source free analysis program specifically for USEARCH. The accuracy of the software\u0026rsquo;s clustering and chimera removal is superior to that of USEARCH\u0026rsquo;s UPARSE algorithm [see 119, 131]. Therefore, for functional gene analysis, the VSEARCH approach was automatically chosen.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClassification and phylogenetic analysis.\u003c/b\u003e OTU representative sequences were used as taxa in FastTree tool to construct phylogenetic trees (Newick) [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e]. Using MEGAN [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e], the abundance and taxonomic composition of OTUs in each sample were projected to the microbiological classification tree from NCBI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/taxonomy\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/taxonomy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). At each taxonomic level, hierarchical trees (GraPhlAn) were constructed using the entire sample population. Taxonomic units were distinguished by different colors in GraPhlan, and their abundance distribution was reflected by the node size, i.e. the average relative abundance of the taxonomic unit [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e]. KronaTools 2.4 software was used to deliver interactive presentations of community taxonomy to groups of chemotherapy-related rats [\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e]. The primary goal of the \u0026szlig;-diversity analysis was to compare the similarity of community structure between the various groups. Principal component analysis (PCA), multidimensional scaling (MDS), and clustering analysis (CA) were used to observe differences between groups using the natural decomposition of the community data structure and sample ordination. PCA compares the similarity of samples using Euclidean distance, regardless of the original variables\u0026rsquo;s possible interrelationship (R software). Before classifying the group or sample distances, nonmetric MDS only considers the size of the relationship between samples (UniFrac distance matrices of Unweighted and Weighted in R software). CA methods such as Unweighted Pair-Group Method with Arithmetic Means (UPGMA), single-linkage clustering, and complete-linkage clustering, like Nonmetric MDS analysis, use any distance to evaluate sample similarity (QIIME-R). The T-test was used to test the Weighted and Unweighted UniFrac distance between or within groups using QIIME. 1000 Monte Carlo permutations were used to test the statistical significance. This fully describes the differences in flora structure size between or within groups of chemotherapy-related rat fecal samples.\u003c/p\u003e \u003cp\u003eFinally, the massive amount of community data generated by high-throughput DNA sequencing in five groups of rats (CK-H) necessitated the use of more statistical analyses such as Constrained Ordination and Supervised Learning. The commonly used Constrained Ordering and Supervized Learning methods, including redundancy analysis (RDA), canonical analysis (CNA), and partial least squares discriminant analysis (PLS-DA, Variable Importance in Projection or VIP value in R), extracted the pattern characteristics associated with the original microbial community in accordance with a known sample correlation (a sample distribution or grouping information) or a sample test indicator (continuous variable). The greater the VIP value, the greater the species\u0026rsquo;s contribution to group differences. Furthermore, using QIIME software, Adonis/PERMANOVA (permutational multivariate analysis of variance) analysis was performed to determine whether the differences between groups were statistically significant. In terms of \u003cem\u003eP\u003c/em\u003e value, the smaller the \u003cem\u003eP\u003c/em\u003e value, the greater the difference between chemotherapy groups of rats.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of microbiomes and pathway abundances.\u003c/b\u003e Most of the current population diversity studies using 16S rRNA genes use 97% sequence similarity as the OTU threshold, which is roughly equivalent to the sequence similarity used for species identification in taxonomy in our descriptive study using chemotherapy rat models. The UCLUST sequence alignment tool from Edgar (2010) [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e] was used with the QIIME sofware to merge bacterial sequences with the similarity value of 97% into specific OTUs and select the highest abundance in each OTU as the representative bacterial sequence, as described for ITS and fungal sequences. The matrix file (i.e. OTU table) was then constructed based on OTU abundance in each sample, and this matrix file (i.e. OTU table) was transformed to BIOM (Biological Observation Matrix) file format, which was then transferred to other analysis tools. Using QIIME software with the default paramaters, the taxonomy information for each OTU was obtained by comparing the representative sequence of OTU to the template sequence of the corresponding database, as described for analysis of ITS sequences. Different types of sequences (e.g. 16S rRNA sequences of bacteria or ITS sequences of fungi) were classified based on the corresponding database: a) 16S rRNA gene database for bacteria (Greengenes database; Release 13.8, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://greengenes.secondgenome.com\u003c/span\u003e\u003cspan address=\"http://greengenes.secondgenome.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e], and b) ITS sequence database for fungi (UNITE database; Release 8.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unite.ut.ee\u003c/span\u003e\u003cspan address=\"https://unite.ut.ee\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e]. In theory, all microbial sequences should be able to be classified into specific species and strains. However, due to the wide variety of micro-organisms, it is difficult to include complete information on all species in the current commonly used database. Furthermore, sequencing read length limits classification ability. As a result, not all OTU representative sequences can obtain genus or species taxonomic information as found for ITS. The microbiome analysis was two-steps: 1) Classify-sklearn algorithm with QIIME2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/QIIME2/q2-feature-classifier\u003c/span\u003e\u003cspan address=\"https://github.com/QIIME2/q2-feature-classifier\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used for UNITE database (release 8.0; ITS) and Greengenes database (Release 13.8; 16S). Species annotation was performed in QIIME2 software using a pre-trained Naive Bayes classifier with default parameters for each ASV/OTU representative sequence. 2) The BROCC algorithm was used for the NT database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/kylebittinger/q2-brocc#the-brocc-algorithm\u003c/span\u003e\u003cspan address=\"https://github.com/kylebittinger/q2-brocc#the-brocc-algorithm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e]. We used blastn to align the OTU sequence with the NT database (or a specific sequence filtered from it). The brocc.py script is then called to get the comment information based on the recommended parameters. Using this procedure, for fungal ITS sequences, in the UNITE database, \u003cem\u003eCandida\u003c/em\u003e (CUG-Ser1 clade, Debaryomycetaceae) and \u003cem\u003ePichia\u003c/em\u003e (Pichiaceae) were identified as two different genera (\u003cem\u003eCandida\u003c/em\u003e: \u003cem\u003eC. africana\u003c/em\u003e/\u003cem\u003ealbicans\u003c/em\u003e sp. ; \u003cem\u003ePichia\u003c/em\u003e: \u003cem\u003eP. aff. Alni/barkeri/bovicola\u003c/em\u003e sp. ). The same was done for \u003cem\u003ePenicillium\u003c/em\u003e (formal name) and \u003cem\u003eTalaromyces\u003c/em\u003e fungi. The International Code of Nomenclature for Algae, Fungi, and Plants (ICN) mandated single name nomenclature for fungi [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e]. The abandonment of dual nomenclature resulted in significant changes in Penicillium and Talaromyces taxonomy and nomenclature. Houbraken and Samson (2011) demonstrated that species formely classified in the \u003cem\u003ePenicillium\u003c/em\u003e subgenus \u003cem\u003eBiverticullium\u003c/em\u003e are resolved in a monophyletic clade with the former teleomorph genus \u003cem\u003eTalaromyces\u003c/em\u003e, but the remaining \u003cem\u003ePenicillium\u003c/em\u003e species are associated with the younger teleomorph genus name \u003cem\u003eEupenicillium\u003c/em\u003e based on four phylogenies [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e]. The general scientific community working on this fungi accepted this classification fairly well [see 141]. Identification of \u003cem\u003eFusarium\u003c/em\u003e (=\u0026thinsp;\u003cem\u003eGibberella\u003c/em\u003e) was assisted by use of \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.drive5.com/usearch\" target=\"_blank\"\u003ewww.fusarium.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.fusarium.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (monophyletic node F3) [\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe used the localization of the nt (2019.8 download, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp:/ftp.ncbi.nih.gov/blast/db\u003c/span\u003e\u003cspan address=\"http://ftp:/ftp.ncbi.nih.gov/blast/db\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database for functional genes and other requirements such as pathways identification in MetaCyc. The largest metabolic reference database in the life sciences with experimental data is called MetaCyc (metacyc.org). At the moment, it has 2,722 pathways from 3,009 various organisms. Information on different primary and secondary metabolic pathways, as well as associated metabolites, biochemical processes, enzymes, and genes, can be found in MetaCyc. By storing representative, experimentally verified metabolic pathways, it seeks to categorize all life\u0026rsquo;s metabolic processes [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan additionalcitationids=\"CR143 CR144\" citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e]. We can attempt to identify bacterial metabolic pathways with notable differences between groups after obtaining the abundance data for those pathways. Here, we apply the metagenomeSeq method (normalized pathway abundance). FitFeatureModel is used to fit the distribution of each ASV/OTU sequence with a zero-selective log-normal model, and used to assess the significance of the difference. These are the analysis\u0026rsquo;s findings for differences in MetaCyc metabolic pathways between groups: Group A refers to the A group before the folder name, and group B is up-regulated relative to Group A when the value of logFC(log2(fold change)) on the horizontal axis is positive and down-regulated when it is negative. The label for the each different MetaCyc metabolic pathways is in the ordinate. Different colors indicate the level of significance.\u003c/p\u003e \u003cp\u003eFinally, the species composition of the various metabolic pathways was examined using the stratified sample metabolic pathway abundance table (PATH_abun_strat.TSV), which was based on the significantly different metabolic pathways. To specify the specific metabolic pathways examined, we used the \u0026ldquo;-f \u003cspan\u003e$\u003c/span\u003epathway\u0026rdquo; option when calling humann2_barplot_py. The ordinate value was set as the relative abundance, the abscissa was arranged according to the sample group, and the samples within the group were arranged according to similarity. The outcomes are presented as Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u0026amp; S13. By default, we conducted species composition analysis for MetaCyc metabolic pathways with differences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is reported in accordance with ARRIVE guidelines (\u003cu\u003ehttps://arriveguidelines.org\u003c/u\u003e).\u003c/p\u003e\n\u003cp\u003eWe confirm that all methods were carried out in accordance with relevant guidelines and regulations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe confirm that all experimental protocols were approved by a named institutional and/or licensing committee/s\u003c/p\u003e\n\u003cp\u003eWe confirm that the use of live animals (rats) in this study was approved by the Shandong Academy of Medical Sciences Ethics Committee and was licensed by Shandong Province (Governmental license SYXK (LU) 20170003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Material (ADM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur 16S and ITS sequencing data in CTX-rat model are updated at NCBI (SubmissionID: SUB9725559; BioProject ID: PRJNA754332), e.g. by the linkage of one or more BioSamples (SAMN20769197-SAMN20769206; Accession Numbers: SRX11740945-SRX11740959). The locus_tag prefixes for each linked BioSample are included in the locustagprefix.txt file that can accessed from BioProject ID PRJNA754332 in the submission portal: https://submit.ncbi.nlm.nih.gov/subs/bioproject/SUB9725559/overview\u003c/p\u003e\n\u003cp\u003ehttps://submit.ncbi.nlm.nih.gov/subs/bioproject (released January the first, 2022)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plan to develop a new bio-product for immunomodulation during chemotherapy was supported by grant supports from Shandong Province Overseas High-Level Talents Program (Taishan scholar, #tshw20091015), Key Research and Development of Shandong Province (#2016GGH3111), and Agricultural Science and Technology Innovation Engineering Program of Shandong Academy of Agricultural Sciences (CXGC2017A01-1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.Y., J.L., and J.F.P. designed concept and research, S.Y., Z.Z., F.B., Y.Z., G.C., Y.F.Z., J.L., and J.F.P. carried out research, Z.Z. and J.L. (supervision) reared rats in\u0026nbsp;Specific Free Pathogen facilities/acute hospital care settings (Class II animal facility) and set up chemotherapy assay,\u0026nbsp;S.Y., F.B., Y.Z., G.C., Y.F.Z., and J.F.P. produced and injected Lactobacillus, S.Y., Z.Z., F.B., Y.Z., G.C., and J.L. collected feces in SFP facilities and prepared genomic DNA samples, S.Y., F.B., Y.Z., G.C., Y.F.Z., and J.F.P. performed molecular biology and prepared 16S and ITS samples for sequencing, BGI Co. Ltd (Beijing) ran 16S and ITS Illumina sequencing, S.Y., J.L., and J.F.P. validated methods, S.Y., J.L., and J.F.P. analyzed and interpreted data, Y.F.Z. helped the classification (bacteria and fungi), S.Y., J.L., and J.F.P. prepared all figures and tables, J.F.P. wrote the first draft of the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge Beijing Genomics Institute (BGI Co., Ltd) for Illumina MiSeq sequencing (ITS and 16S).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalk JJ, Fox EJ, Loeb LA. Mutational heterogeneity in human cancers: origins and consequences. Annu Rev Pathol. 2010;5:51\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakravarthi BVSK, Nepal S, Varambally S. Genomic and epigenomic alterations in cancer. J Am Pathol. 2016;186:1724\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMroz EA, Rocco JW. The challenges of tumor genetic diversity. Cancer. 2017;123:917\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakeshima H, Ushijima T. Accumulation of genetic and epigenetic alterations in normal cells and cancer risk. NPJ Precis Oncol. 2019;3:7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerceg Z, Hainaut P. Genetic and epigenetics as biomarkers for cancer detection, diagnosis and prognosis. Mol Oncol. 2007;1:26\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerg M, S\u0026oslash;reide K. Genetic and epigenetic traits as biomarkers in colorectal cancer. Int J Mol Sci. 2011;12:9426\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoeb LA, Loeb KR, Anderson JP. Multiple mutations and cancer. Proc Natl Acad Sci USA. 2003;100:776\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVogt A, Schmid S, Heinimann K, Frick H, Herrmann C, Cerny T, et al. Multiple primary tumours: challenges and approaches, a review. ESMO Open. 2017;2:e000172.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Shilatifard A. UTX mutations in human cancer. Cancer Cell. 2019;35:168\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChatterjee A, Mambo E, Sidransky D. Mitochondrial DNA mutations in human cancer. Oncogene. 2006;25:4663\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnastasiadou E, Jacob LS, Slack FJ. Non-coding RNA networks in cancer. Nat Rev Cancer. 2013;18:5\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin S, Gregory RI. MicroRNA biogenesis pathways in cancer. Nat Rev Cancer. 2016;15:321\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallo LH, Ko J, Donoghue DJ. The importance of regulatory ubiquitination in cancer and metastasis. Cell Cycle. 2017;16:634\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBen-Aroya S, Levanon EY. A-to-I. RNA editing: an overlooked source of cancer mutations. Cancer Cell. 2018;33:P789\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShuai S, Suzuki H, Diaz-Navarro A, Nadeu F, Kumar SA, Gutierrez-Fernandez A, et al. The U1 spliceosomal RNA is recurrently mutated in multiple cancers. Nature. 2019;574:712\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang E, Aifantis I. RNA splicing and cancer. Trends Cancer. 2020;6:P631\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilkinson S, Harmon SA, Terrigino NT, Karzai F, Pinto PA, Madan RA, et al. A case report of multiple primary prostate tumors with differential drug sensitivity. Nat Commun. 2020;11:837.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaklavas C, Blume SW, Grizzle WE. Translational dysregulation in cancer: molecular insights and potential clinical applications in biomarker development. Front Oncol. 2017;7:158.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicimbon JF. A new view of genetic mutations. Australas Med J. 2017;10:715\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicimbon JF. Evolution of protein physical structures in insect chemosensory systems. Olfactory Concepts of Insect Control-Alternative to Insecticides, Vol.\u0026nbsp;2, Springer Nature Switzerland AG, 2019. pp.\u0026nbsp;231\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue S, Picimbon JF. \u003cem\u003eLactobacillus\u003c/em\u003e for ribosome peptide editing cancer. Clin Transl Oncol. 2022; submitted.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharmsaz S, Prencipe M, Kiely M, Pidgeon GP, Collins DM. Innovative technologies changing cancer treatment. Cancers (Basel). 2018;10:208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePucci C, Martinelli C, Ciofani G. Innovative approaches for cancer treatment: current perspectives and new challenges. Ecancermedicalscience. 2019;13:961.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaldman AD, Fritz JM, Lenardo MJ. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nat Rev Immunol. 2020;20:651\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonzales H, Hagerling C, Werb Z. Roles of the immune system in cancer: from tumor initiation to metastatic progression. Genes Dev. 2018;32:1267\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur P, Asea A. Radiation-induced effects and the immune system in cancer. Front Oncol. 2012. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.3389/fonc.2012.00191\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2012.00191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho HA, Villar RC. Radiotherapy and immune response: the systemic effects of a local treatment. Clinics. 2018;73:e557s.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlfonso JCL, Papaxenopoulou LA, Mascheroni P, Meyer-Hermann M, Hatzikirou H. On the immunological consequences of conventionally fractionated radiotherapy. Science. 2020;23:100897.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLumniczky K, Cand\u0026eacute;lias SM, Gaipl US, Frey B. Editorial: radiation and the immune system: current knowledge and future perspectives. Front Immunol. 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2017.01933\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2017.01933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrice JG, Idoyaga J, Salmon H, Hogstad B, Bigarella CL, Ghaffari S, et al. CDKN1A regulates Langerhans cell survival and promotes Treg cell generation upon exposure to ionizing irradiation. Nat Immunol. 2015;16:1060\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheithauer K, Belka C, Lauber K, Gaipl US. Immunological aspects of radiotherapy. Radiat Oncol. 2014;9:185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNajafi M, Shirazi A, Motevaseli E, Geraily Gh, Norouzi F, Heidari M, et al. The melatonin immunomodulatory actions in radiotherapy. Biophys Rev. 2017;9:139\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEastham LL, Howard CM, Balachadran P, Pasco DS, Claudio PP. Eating green: shining light on the use of dietary phytochemicals as a modern approach in the prevention and treatment of head and neck cancers. Curr Top Med Chem. 2018;18:182\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkolpoglu MB, Alapan Y, Dogan NO, Baltaci SF, Yasa O, Tural GA, et al. Magnetically steerable bacterial microrobots moving in 3D biological matrices for stimuli-responsive cargo delivery. Sci Adv. 2022;8:eabo6163.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVivarelli S, Salemi R, Candido S, Falzone L, Santagati M, Stefania S, et al. Gut microbiota and cancer: from pathogenesis to therapy. Cancers (basel). 2019;11:38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng WY, Wu CY, Yu J. The role of gut microbiota in cancer treatment: friend or foe? Gut 2020; 69: 1897 \u0026ndash; 1876.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis LS. Flora-ishing guts assist cancer immunotherapies. Sci Immunol. 2018;3:eaat0813.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRouty B, Le Chatelier E, Derosa L, Duong CPM, Alou MT, Daill\u0026egrave;re R, et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 2018;359:91\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Tinoco R, Elm\u0026eacute;n L, Segota I, Xian Y, Fujita Y, et al. Gut microbiota dependent anti-tumor immunity restricts melanoma growth in Rnf5\u003csup\u003e-/-\u003c/sup\u003e mice. Nat Commun. 2019;10:1492.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa W, Mao Q, Xia W, Dong G, Yu C, Jiang F. Gut microbiota shapes the efficiency of cancer therapy. Front Microbiol. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2019.01050\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2019.01050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDehghani N, Tafvizi F, Jafari P. Cell cycle arrest and anti-cancer potential of probiotic \u003cem\u003eLactobacillus rhamnosus\u003c/em\u003e against HT-29 cancer cells. Bioimpacts. 2021;11:245\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen L, Li H, Chen Y, Yang Y. \u003cem\u003eLactobacillus rhamnosus\u003c/em\u003e GG reduced mortality of septic mice by modulating gut microbiota composition and metabolic profiles. Nutrition. 2020;78:110863.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFong W, Li Q, Yu J. Gut microbiota modulation: a novel strategy for prevention and treatment of colorectal cancer. Oncogene. 2020;39:4925\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue S, Chinnapandi B, Ge H, Zou X, Chen X, Wang C, et al. A \u003cem\u003eLactobacillus\u003c/em\u003e cocktail changes gut flora and reduces cholesterolemia and weight gain in hyperlipidemia mice. SOJ Microbiol Infect Dis. 2014;2:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue S, Liu X, Fu J. \u003cem\u003eLactobacillus\u003c/em\u003e tritherapy for cholesterol and heart diseases. RR J Microbiol Biotechnol. 2016;5:23\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian H, Lyu Y, Yang YG, Hu Z. Humanized rodent models for cancer research. Front Oncol. 2020; https://doi.org/10,3389/fonc.2020.01696.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCzpirer C. Cancer research in rat models. Rat Genomics. Methods in Molecular Biology (Methods and Protocols), Vol.\u0026nbsp;597, Humana Press, 2010. pp.\u0026nbsp;445\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Z, Yue S, Chen L, Teng S, Sun P, Wang Y, et al. Effect of \u003cem\u003eLactobacillus\u003c/em\u003e preparation on immunosuppression induced by cyclophosphamide in rats. Food Drug. 2020;22:103\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue S, Li Z, Hu F, Picimbon JF. Curing piglets from diarrhea and preparation of a healthy microbiome with \u003cem\u003eBacillus\u003c/em\u003e treatment for industrial animal breeding. Sci Rep. 2020;10:19476.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu L, Zhu H, Guo Y, Du X, Guo J, Zhang L, et al. \u003cem\u003eLecanicillium coprophilum\u003c/em\u003e (Cordypitaceae, Hypocreales), a new species of fungus from the feces of \u003cem\u003eMarmota monax\u003c/em\u003e in China. Phytotaxa. 2019;387:55\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJermy A. \u003cem\u003eUstilago\u003c/em\u003e gives plants a Pep talk. Nat Rev Microbiol. 2012;10:444\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerma RK, Pandro V, Raj D, Patel D, Asaiya AJK. Diversity of macro-fungi in Central India-XX: \u003cem\u003ePhallus atrovolvatus\u003c/em\u003e and \u003cem\u003ePhallus merulinus\u003c/em\u003e. Van Sangyan. 2019;6:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRota A, Calicchio E, Nardoni S, Fratini F, Ebani VV, Sgorbini M, et al. Presence and distribution of fungi and bacteria in the reproductive tract of healthy stallions. Theriogenology. 2011;76:464\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu L, et al. \u003cem\u003eCoprophilum\u003c/em\u003e (Cordypitaceae, Hypocreales), a new species of fungus from the feces of \u003cem\u003eMarmota monax\u003c/em\u003e in China. Phytotaxa. 2019;387:55\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGhie TA, Huber TW, Kassis CE, Jinadatha C. \u003cem\u003eUstilago\u003c/em\u003e species as a cause of central line-related blood stream infection. Am J Med Sci. 2013;345:254\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuseyin CE, O\u0026rsquo;Toole PW, Cotter PD, Scanlan PD. Forgotten fungi-the gut mycobiome in human health and disease. FEMS Microbiol Rev. 2017;41:479\u0026ndash;511.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSam QH, Chang MW, Chai LY. The fungal mycobiome and its interaction with gut bacteria in the host. Int J Mol Sci. 2017;18:330.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Chen D, Yu B, He J, Zheng P, Mao X, et al. Fungi in gastrointestinal tracts of human and mice: from community to functions. Microb Ecol. 2018;75:821\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaspi R, Billington R, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 2018;46:D633\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHove H, Norgaard H, Mortensen PB. Lactic acid bacteria and the human gastrointestinal tract. Eur J Clin Nutr. 1999;53:339\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFooks LJ, Gibson GR. Probiotics as modulators of the gut flora. Br J Nutr. 2002;88:39\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalter J. Ecological role of \u003cem\u003eLactobacilli\u003c/em\u003e in the gastrointestinal tract: implications for fundamental and biomedical research. Appl Environ Microbiol. 2008;74:4985\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemarajata P, Versalovic J. Effects of probiotics on gut microbiota: mechanisms of intestinal immunomodulation and neuromodulation. Th Adv Gastroenterol. 2013;26:39\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanebianco C, Latiano T, Pazienza V. Microbiota manipulation by probiotics administration as emerging tool in cancer prevention and therapy. Front Oncol. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2020.00679\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2020.00679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu K, Dong S, Wu X, Jin R, Chen H. Probiotics in cancer. Front Oncol. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2021.638148\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2021.638148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu AQ, Li L. The potential role of probiotics in cancer prevention and treatment. Nutr Cancer. 2016;68:535\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLegesse Bedada TL, Feto TK, Awoke KS, Garedew AD, Yifat FT, Birri DJ, et al. Probiotics for cancer alternative prevention and treatment. Biomed Pharmacother. 2020;129:110409.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAykut B, Pushalkar S, Chen R, Li Q, Abengozar R, Kim JI, et al. The fungal mycobiome promotes pancreatic oncogenesis via activation of MBL. Nature. 2019;574:267\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorula A, Abraham A, Abubacker FN, Viswabandya A, Lakshmi KM, Abraham OC, et al. Invasive fungal infection following chemotherapy for acute myeloid leukaemia-Experience from a developing country. Mycoses. 2017;60:686\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIida N, Mizukoshi E, Yamashita T, Terashima T, Arai K, Seishima J, et al. Overuse of antianaerobic drug is associated with poor postchemotherapy prognosis of patients with hepatocellular carcinoma. Int J Cancer 2019; 145: 2701\u0026ndash;2711 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWainwright M. Some highlights in the history of fungi in medicine - a personal journey. Fungal Biol Rev. 2008;22:97\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishad JH, Singh A, Gautam VS, Kumar D, Kumar J, Kharwar RN, et al. Endophytic fungi: a cryptic fountainhead for biodiversity, functional metabolites, host stress tolerance, and myco-mediated nanoparticles (Nps) synthesis. Endophytes and Secondary Metabolites, References Series in Phytochemistry, Springer Cham, 2018. pp.\u0026nbsp;1\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLombard L, van der Merwe NA, Groenewald JZ, Crous PW. Generic concepts in Nectriaceae. Stud Mycol. 2015;80:189\u0026ndash;245.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFraiser LH, Kanekal S, Kehrer JP. Cyclophosphamide toxicity. Characterising and avoiding the problem. Drugs. 1991;42:781\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicol\u0026aacute;s FE, Murcia L, Navarro E, Navarro-Mendoza MI, P\u0026eacute;rez-Arques C, Garre V. Mucorales species and macrophages. J Fungi (Basel). 2020;6:94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenblum EB, Voyles J, Poorten TJ, Stajich JE. The deadly chytrid fungus: a story of an emerging pathogen. PLoS Pathog. 2010;6:e1000550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWos-Oxley ML, Bleich A, Oxley APA, Kahl S, Janus LM, Smoczek A, et al. Comparative evaluation of establishing a human gut microbial community within rodent models. Gut Microbes. 2012;3:234\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Society for Microbiology. One health: fungal pathogens of humans, animals, and plants: Report on an American Academy of Microbiology Colloqium held in Washington (DC). 2019 (PMID: 31769941).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoselli M, Natella F, Zinno P, Guantario B, Canali R, Schifano E, et al. Colonization ability and impact on human gut microbiota of foodborne microbes from traditional or probiotic-added fermented foods: a systemic review. Front Nutr. 2021;8:689084.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan S, Lu Y, Xie J, Fei Y, Zheng G, Wang Z, et al. Probiotic gastrointestinal transit and colonization after oral administration: a long journey. Front Cell Infect Microbiol. 2021;11:609722.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagro Reque P, Brandelli A. Encapsulation of probiotics and nutraceuticals: applications in functional food industry. Trends Food Sci Technol. 2021;114:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitherden EA, Shoale S, Hall RA, Moyes DL. The human mucosal mycobiome and fungal community interactions. J Fungi. 2017;3:56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayasudha R, Das T, Kalyana Chakravarthy S, Sai Prashanthi G, Bhargava A, Tyagi M, et al. Gut mycobiomes are altered in people with type 2 Diabetes Mellitus and Diabetic Retinopathy. PLoS ONE. 2020;15:e0243077.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoker OO, Nakatsu G, Dai RZ, Wu WKK, Wong SH, Ng SC, et al. Enteric fungal microbiota dysbiosis and ecological alterations in colorectal cancer. Gut. 2019;68:654\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Tilburg Bernardes E, Pettersen VK, Gutierrez MW, Laforest-Lapointe I, Jendzjowsky NG, Cavin JB, et al. Intestinal fungi are causally implicated in microbiome assembly and immune development in mice. Nat Commun. 2020;11:2577.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhannoum MA, Jurevic RJ, Mukherjee PK, Cui F, Sikaroodi M, Naqvi A, et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 2010;6:e1000713.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ\u0026oslash;rgensen MR, Kragelund C, Jensen P, Keller MK, Twetman S. Probiotic \u003cem\u003eLactobacillus reuteri\u003c/em\u003e has antifungal effects on oral \u003cem\u003eCandida\u003c/em\u003e species \u003cem\u003ein vitro\u003c/em\u003e. J Oral Microbiol. 2017;9:1274582.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLocasale JW, Cantley LC. Altered metabolism in cancer. BMC Biol. 2010;8:88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeske CM. Beyond energy metabolism: exploiting the additional roles of NAMPT for cancer therapy. Front Oncol. 2020;9:1514.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesbats MA, Giacomini I, Prayer-Galetti T, Montopoli M. Metabolic plasticity in chemotherapy resistance. Front Oncol. 2020;10:281.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Soom T, El Bakkali S, Gebruers N, Verbelen H, Tjalma W, van Breda E. The effects of chemotherapy on energy metabolic aspects in cancer patients: a systematic review. Clin Nutr. 2020;39:1863\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung YW, Gwak HJ, Moon S, Rho M, Ryu JH. Functional dynamics of bacterial species in the mouse gut microbiome revealed by metagenomic and metatranscriptomic analyses. PLoS ONE. 2020;15:e0227886.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovatcheva-Datchary P, Nilsson A, Akrami R, Lee YS, De Vadder F, Arora T, et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of \u003cem\u003ePrevotella\u003c/em\u003e. Cell Metabol. 2015;22:971\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolansky O, Sekelova Z, Faldynova M, Sebkova A, Sisak F, Rychlik I. Important metabolic pathways and biological processes expressed by chicken cecal microbiota. Appl Environ Microbiol. 2016;82:1569\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFranke T, Deppenmeier U. Physiology and central carbon metabolism of the gut bacterium \u003cem\u003ePrevotella copri\u003c/em\u003e. Mol Microbiol. 2018;109:528\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrecup G, Vodnar DC. Gut \u003cem\u003ePrevotella\u003c/em\u003e as a possible biomarker of diet and its eubiotic versus dysbiotic roles: a comprehensive literature review. Brit J Nutr. 2019;122:131\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwyang SY, Luther J, Kao JY. \u003cem\u003eHelicobacter pylori\u003c/em\u003e: beneficial for most? Expert Rev Gastroenterol Hepatol. 2011;5:649\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbadi ATB. \u003cem\u003eHelicobacter pylori\u003c/em\u003e: a beneficial gastric pathogen? Front Med (Lausanne). 2014;1:26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarais M, Mendz GL, Hazell SL, M\u0026eacute;graud F. Metabolism and genetics of \u003cem\u003eHelicobacter pylori\u003c/em\u003e: the genome era. Microbiol Mol Biol Rev. 1999;63:642\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeir S, Cuccherini B, Whitney AM, Ray ML, MacGregor JP, Steigerwalt A, et al. Recurrent bacteremia caused by a \u0026ldquo;\u003cem\u003eFlexispira\u003c/em\u003e\u0026rdquo;-like organism in a patient with X-linked (Bruton\u0026rsquo;s) aggammaglobulinemia. J Clin Microbiol. 1999;37:2439\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIten A, Graf S, Egger M, Tauber M, Graf J. \u003cem\u003eHelicobacter sp. flexispira\u003c/em\u003e bacteremia in an immunocompetent young adult. J Clin Microbiol. 2001;39:1716\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei G, Tian N, Siezen R, Schuppan D, Helmerhorst EJ. Identification of food-grade substilisins as gluten-degrading enzymes to treat celiac disease. Am J Physiol Gastrointest Liver Physiol. 2016;311:G571\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore RE, Townsend SD. Temporal development of the infant gut microbiome. Open Biol. 2019;9:190128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026uuml; C, Xia Y, Liu D, Zhao R, Gao R, Liu H, et al. \u003cem\u003eCupriavidus necator\u003c/em\u003e H16 uses flavocytochrome c sulfide dehydrogenase to oxidize self-produced and added sulfide. Appl Environ Microbiol. 2017;83:e01610-17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSosa OA. Phosphorus redox reactions as pinch hitters in microbial metabolism. Proc Natl Acad Sci USA. 2018;115:7\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalwar C, Nagar S, Kumar R, Scaria J, Lal R, Negi RK. Defining the environmental adaptations of genus \u003cem\u003eDevosia\u003c/em\u003e: insights into its expansive short peptide transport system and positively selected genes. Sci Rep. 2020;10:1151.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcMahon RJ. Biotin in metabolism and molecular biology. Annu Rev Nutr. 2002;22:221\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVieira de Almeida C, Taddei A, Amedei A. The controversial role of \u003cem\u003eEnterococcus faecalis in colorectal cancer\u003c/em\u003e. Th Adv Gastroenterol. 2018;11:1756284818783606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalcinelli S, Rodiles A, Unniappan S, Picchietti S, Gioacchini G, Merrifield DL, et al. Probiotic treatment reduces appetite and glucose level in the zebrafish model. Sci Rep. 2016;6:18061.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Chen Y, Wang Y, Li Y, Zhang X, Zheng H, et al. Beneficial changes of gut microbiota and metabolism in weaned rats with \u003cem\u003eLactobacillus acidophilus\u003c/em\u003e NCFM and \u003cem\u003eBifidobacterium lactis\u003c/em\u003e Bi-07 supplementation. J Funct Foods. 2018;48:252\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLleal M, Sarrabayrouse G, Willamil J, Santiago A, Pozuelo M, Manichanh C. A single faecal microbiota transplantation modulates the microbiome and improves clinical manifestations in a rat model of colitis. EBioMedicine. 2019;48:630\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang SL, Bai L, Goel N, Bailey A, Jang CJ, Bushman FD, et al. Human and rat gut microbiome composition is maintained following sleep restrictions. Proc Natl Acad Sci USA. 2017;114:E1564\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoklo M, Maslov DR, Kraljevic Pavelic S. Modulation of gut microbiota in healthy rats after exposure to nutritional supplements. Gut Microbes. 2020;12:1\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuman Genome Assembly. The Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001;409:860\u0026ndash;921.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRavi RK, Walton K, Khosroheidari M. MiSeq: A next generation sequencing platform for genomic analysis. Disease Gene Identification, Methods in Molecular Biology, Vol.\u0026nbsp;1706, Humana Press, 2018. pp.\u0026nbsp;223\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460\u0026ndash;1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlaxter M, Mann J, Chapman T, Thomas F, Whitton C, Floyd R, et al. Defining operational taxonomic units using DNA barcode data. Philos Trans R Soc Lond B Biol Sci. 2005;360:1935\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCameron ES, Schmidt PJ, Tremblay BJ, Emelko MB, M\u0026uuml;ller KM. To rarefy or not to rarefy: enhancing microbial community analysis through next-generation sequencing. BioRxiv. 2020. DOI:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2020.09.09.290049\u003c/span\u003e\u003cspan address=\"10.1101/2020.09.09.290049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGotelli NJ, Colwell RK. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett. 2001;4:379\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOksanen J, Kindt R, Legendre P, O\u0026rsquo;Hara B, Stevens M, Oksanen M, et al. The vegan package. Community Ecol package. 2007;10:631\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIzs\u0026aacute;k J, Pavoine S. Links between the species abundance distribution and the shape of the corresponding rank abndance curve. Ecol Indic. 2012;14:1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite JR, Nagarajan N, Pop M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol. 2009;5:e1000352.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarter LL, Yu B. Superheat. An R package for creating beautiful and extendable heatmaps for visualizing complex data. J Comput Graph Stat. 2018;27:910\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarnes GR, Bolker B, Bonebakker L, Gentleman R, Liaw WHA, Lumley T, et al. Gplots: various R programming tools for plotting data. 2015; R package version 2.17.0.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRognes T, Flouri T, Nichols B, Quince C, Mah\u0026eacute; F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrice MN, Dehal PS, Arkin AP. FastTree: computing large minimum-evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26:1641\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 2011;21:1552\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsnicar F, Weingart G, Tickle TL, Huttenhower C, Segata N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. Peer J. 2015;3:e1029.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOndov BD, Bergman NH, Phillippy AM. Interactive metagenomic visualization in a Web browser. BMC Inf. 2011;12:385.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Santis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026otilde;ljalg U, Nilsson R, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, et al. Towards a unified paradigm for sequence-based identification of fungi. Mol Ecol. 2013;22:5271\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson RH, Ryberg M, Kristiansson E, Abarenkov K, Larsson KH, K\u0026ouml;ljalg U. Taxonomic reliability of DNA sequences in public sequence databases: a fungal perspective. PLoS ONE. 2006;1:e59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcNeill J, Turland NJ. Major changes to the Code of Nomenclature-Melbourne, July 2011. Taxon. 2011;60:1495\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoubraken J, Samson RA. Phylogeny of \u003cem\u003ePenicillium\u003c/em\u003e and the segregation of \u003cem\u003eTrichocomaceae\u003c/em\u003e into three families. Stud Mycol. 2011;70:1\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVisagie CM, Hirooka Y, Tanney JB, Whitfield E, Mwange K, Meijer M, et al. \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003ePenicillium\u003c/em\u003e and \u003cem\u003eTalaromyces\u003c/em\u003e isolated from house dust samples collected around the world. Stud Mycol. 2014; 78: 63\u0026ndash;139.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrous PW, Lombard L, Sandoval-Denis M, Seifert KA, Schroers HJ, Chaverri P, et al. \u003cem\u003eFusarium\u003c/em\u003e: more than a node or a foot-shaped basal cell. Stud Mycol. 2021;98:100116.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarp PD, Caspi R. A survey of metabolic diseases emphasizing the MetaCyc family. Arch Toxicol. 2011;85:1015\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaspi R, Dreher K, Karp PD. The challenge of constructing, classifying, and representing metabolic pathways. FEMS Microbiol Lett. 2013;345:85\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaspi R, Altman R, Billington R, Dreher K, Foerster H, Fulcher CA, et al. The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2014;42:D459\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Illumina MiSeq sequencing - Gut microflora - Lactobacillus - Immunostimulant - Adjuvant anticancer bioproduct","lastPublishedDoi":"10.21203/rs.3.rs-2113752/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2113752/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eWe examined the impact of using a probiotic containing three different \u003cem\u003eLactobacilli\u003c/em\u003e (3L) on the gut microbiome of rats following cyclophosphamide (CTX) treatment. CTX corresponded to chemotherapy which is used for human cancer treatment and known to have adversive effects on the immune system.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted our experiment with ten rats in five different experimental groups which included control, CTX treated, and then low, medium, and high probiotic treatment with CTX treatment. Of these ten rats in each group, we sequenced the stool of three of them using both ITS and 16S sequencing. We then went on to examine the taxonomic composition of these samples to determine whether probiotic treatment helped the rat\u0026rsquo;s microbiome return to similar structure as the control rats.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe used Illumina MiSeq sequencing to generate sequencing data from microbial genomic DNA libraries, which is useful for testing the effects of 3L on bacteria and fungi. Microbiome analysis, phylogenetic and classification reports, and community data have all backed up the experiments and findings that 3L had a significant positive impact on the microbiome. Furthermore, the effect on specific metabolic pathways aids in deriving the study\u0026rsquo;s conclusion (use of 3L in chemotherapy) to the mode of action, mechanistically by correcting microbiota composition and enhancing specific gut metabolic functions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThrough experimental results using an \u003cem\u003ein vivo\u003c/em\u003e model, we suggested the role of novel natural probiotics 3L, 3 Lactobacilli in the establishment of a strong and sustainable beneficial healthy gut flora, after CTX chemotherapy. We suggested some new adjuvants to chemotherapy as drugs\u0026thinsp;+\u0026thinsp;\u003cem\u003elactobacillus\u003c/em\u003e treament using the rat CTX model (immunosuppression caused by cyclophosphamide). Furthermore, in numerous studies that reported the use of probiotics involving \u003cem\u003eLactobacillus\u003c/em\u003e in post-chemo or post-surgical procedures, we proposed a new probiotic formulation (\u003cem\u003eL. acidophilus\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eL. casei\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eL. plantarum\u003c/em\u003e) to be further studied and explored in the prevention of health condition loss by alteration of the general immune system.\u003c/p\u003e","manuscriptTitle":"3L, three-Lactobacilli on recovering of microbiome and immune-damage by cyclophosphamide chemotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-10 16:44:41","doi":"10.21203/rs.3.rs-2113752/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c48e93d-21c2-4a6e-bc02-f50d6a4c0a86","owner":[],"postedDate":"October 10th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-11-11T07:44:20+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-10 16:44:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2113752","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2113752","identity":"rs-2113752","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}