Gut microbiota Signatures in Schistosoma japonicum Infection-Induced Liver Cirrhosis Patients

Gut microbiota Signatures in Schistosoma japonicum Infection-Induced Liver Cirrhosis Patients | 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 Gut microbiota Signatures in Schistosoma japonicum Infection-Induced Liver Cirrhosis Patients Qifeng Gui, Feng Zhu, Chi Xiao, Haifeng Lu, Qin Zhang, Jia Xu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-71566/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2021 Read the published version in Infectious Diseases of Poverty → Version 1 posted 10 You are reading this latest preprint version Abstract Background One of the serious complications induced by Schistosoma japonicum infection is the development of liver cirrhosis. Several studies have assessed the role of gut microbiota in cirrhosis of different etiologies, but none has done so in the context of S. japonicum infection in humans. Here, to explore the possible role of gut microbiota in S. japonicum infection-induced liver cirrhosis, we conducted an observational clinical study. Methods Twenty-four patients with S. japonicum infection-induced liver cirrhosis and 25 age- and gender-matched controls from Zhejiang Province, China, were enrolled in the study. Fecal samples were collected and used for 16S rRNA gene hypervariable V4 region Illumina MiSeq sequencing. Results Eight hundred seven operational taxonomic units (OTUs) were detected, of which 491 were common in the two groups, while 123 and 193 were unique for the control and cirrhosis groups, respectively. Overall, no significant differences in both alpha and beta diversities and overall microbiota structure were found in the two groups. However, Bacilli (Class) and Lactobacillales (Order) were significantly higher in patients with S. japonicum infection-induced liver cirrhosis than in healthy individuals. Conclusions Altogether, our results suggest that the gut microbiota of S. japonicum infection-induced liver cirrhosis patients is similar to that of healthy individuals. Infectious Diseases Gut microbiota 16S Rrna Schistosoma japonicum Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Schistosomiasis is one of the most prevalent parasitic diseases in the world [ 1 ]. An estimated 240 million people are affected by this disease, together with 800 million more at risk of infection [ 2 ]. Worryingly, schistosomiasis alone is associated with a high disease burden of as much as 3.3 million disability-adjusted life years (DALYs), accounting for more than 10% of the global burden of Neglected Tropical Diseases (NTDs) [ 3 , 4 ]. There are 5 known etiologic agents of schistosomiasis, all of which are trematode parasites of the Schistosoma genus: S. japonicum , S. mansoni , S. haematobium , S. intercalatum , and S. mekongi [ 5 ]. Only S. japonicum exists in China, and it is predominant in 12 provinces south of the Yangtze River [ 6 ]. In the 1950s, an estimated 11.6 million people were infected, while more than 100 million more were at risk of infection [ 7 ]. From the mid-1950s, the number of infected cases has reduced by over 99%; however, schistosomiasis is still endemic in 140 counties in China [ 1 ]. Liver cirrhosis is a common (end-stage) consequence of several liver diseases and a major cause of mortality worldwide [ 8 ]. Previous studies have reported an association between gut microbiota alterations and chronic liver disease [ 9 , 10 ], including cirrhosis secondary to hepatitis B or C virus infections [ 11 , 12 ], alcohol over-consumption [ 13 , 14 ], nonalcoholic fatty liver disease [ 13 , 15 , 16 ], and even primary biliary cirrhosis [ 17 ]. Furthermore, cirrhosis complications, including hepatic encephalopathy, spontaneous bacterial peritonitis, and sepsis, were linked to gut microbiota alterations [ 18 ]. Some studies on the gut-liver axis have even demonstrated that the gut microbiota (and bacterial translocation) plays an important role in the pathogenesis of cirrhosis and its complications [ 13 , 19 ]. Recent studies using not only animal models but also human clinical samples have demonstrated a clear relationship between gut microbiota alterations and Schistosoma spp. infection [ 20 , 21 , 22 , 23 , 24 , 25 ]. However, no study has explored the relationship between gut microbiota and S. japonicum infection-induced liver cirrhosis; the potential role of gut microbiota alterations in disease onset and progression is still unclear. Here, to explore the possible association between gut microbiota and S. japonicum infection-induced liver cirrhosis, we conducted an observational clinical study. Fecal samples collected from 24 patients and 25 matched healthy individuals from the Zhejiang Province, China, were used for 16S rRNA Illumina MiSeq sequencing. The main objectives of this study were: (1) to characterize the relevant gut microbiota alterations associated with S. japonicum infection-induced liver cirrhosis; and (2) to identify potential bacterial taxa to be used as non-invasive biomarkers for the early diagnosis of liver cirrhosis secondary to schistosomiasis. Methods Ethics Statement This study was conducted according to the principles expressed in the Declaration of Helsinki. All experiments were approved by the First Affiliated Hospital Clinical Research Ethics Committee, School of Medicine, Zhejiang University (ref: 2017411-1), and the Jiaxing Wangdian People's Hospital Ethics Committee (ref: 2017002). All participants provided written informed consent. Exclusion criteria and subject enrolment Eligibility was defined by the application of the following exclusion criteria: (I) history of digestive disorders, such as other causes of liver disease, irritable bowel syndrome, inflammatory bowel disease, and chronic diarrhea; (II) history of previous gastrointestinal surgery; (III) history of probiotics, prebiotics, synbiotics, antibiotics, acid suppressors, metformin, and gastrointestinal motility targeting-drugs use, less than eight weeks before enrollment; (IV) history of bowel preparation, less than four weeks before enrollment; (V) diagnosis of major diseases such as malignant tumors, cardiovascular disease, respiratory, autoimmune and allergic diseases, neurological disorders, renal insufficiency, diabetes, uncontrolled hypertension (blood pressure ≥ 150 / 90 mmHg), dyslipidemia, depression, mania, and bipolar affective disorder; and (VI) hepatitis B virus, hepatitis C virus and HIV infection. From December 2017 to November 2019, 24 patients with S. japonicum infection-induced liver cirrhosis and 26 age- and gender-matched healthy individuals were enrolled from Hangzhou and Jiaxing, northern cities of the Zhejiang Province, China, an S. japonicum infection endemic area. Since the stool specimen from one healthy individual was unsatisfactory, the healthy controls group was ultimately composed of 25 individuals. All participant demographics information, laboratory tests, computed tomography (CT), and B ultrasonic scans were collected from the hospital electronic medical records system. Additionally, questionnaires were filled by all subjects. Schistosomiasis diagnosis was performed using the Kato-Katz method to detect S. japonicum eggs in stool samples. Liver cirrhosis was diagnosed according to clinical and biochemical data and imaging exams (CT or B ultrasonic scans) [ 26 , 27 ]. Liver function was evaluated with the Child-Pugh classification system [ 28 ]. Fecal sample collection The participants were trained by a researcher to obtain a complete stool collection method. Fresh stool samples (self-collected from patients and controls) were collected into anaerobic bags (Mitsubishi Gas Chemical). Samples were divided into five 200 mg aliquots, in the first 30 min after collection, and immediately stored at − 80 °C for posterior analysis. Genomic DNA extraction Total genomic DNA was extracted per sample using the MagPure Stool DNA KF kit B (Magen,Guangzhou, China) according to the manufacturer's instructions. DNAs were quantified in the Qubit Fluorometer (Invitrogen, Qubit 2.0) using the Qubit® dsDNA BR Assay kit (Invitrogen, CA, USA). DNA quality was assessed using gel electrophoresis (1% agarose gels). Negative controls containing the elution buffer only were included during DNA extraction and quantification to monitor possible contamination. Library construction and sequencing Bacterial 16S rRNA gene hypervariable region V4 was amplified using degenerate polymerase chain reaction (PCR) and the primers 515Fw (5’-GTGCCAGCMGCCGCGGTAA-3’) and 806Rv (5’-GGACTACHVGGGTWTCTAAT-3’), which contained the Illumina adapter sequence, a pad, and a linker. Fifty microliters reaction volumes, containing 30 ng of DNA templates together with the primers (10 µM) and the PCR master mix, were used. Amplification was performed under the following conditions: 95 °C for 3 minutes; 30 cycles of 95 °C for 45 seconds, 56 °C for 45 seconds, and 72 °C for 45 seconds; and a final extension step at 72 °C for 10 minutes. PCR products were purified using Agencourt AMPure XP beads (Beckman Coulter, CA, USA) according to the manufacturer’s instructions. Library quality was evaluated using the 2100 Bioanalyzer (HT DNA 1K Labchip 760517) instrument (Agilent Technologies, CA, USA). Sequencing was performed using the Illumina HiSeq 2500 platform (BGI, Shenzhen, China) following the Illumina standard pipelines to get 2 × 250 bp paired-end reads. Bioinformatics analysis Raw reads were filtered to remove adaptors and low-quality and/or ambiguous bases, and paired-end reads were merged using the Fast Length Adjustment of Short reads (FLASH, v1.2.11) software [ 29 ]. Sequences were clustered into operational taxonomic units (OTUs) with a cutoff value of 97% using the UPARSE software (v7 .0.1090) [ 30 ]. Chimeric sequences were detected (compared with the Gold database) using the UCHIME software (v4.2.40) [ 31 ] and removed from the analysis. Sequences of representative OTUs were then taxonomically classified using the Ribosomal Database Project Classifier (v.2.2) with a minimum confidence threshold of 0.6 and trained on the Greengenes database (v201305) using the QIIME pipeline (v1.8.0) [ 32 ]. The USEARCH_global command [ 33 ] was used to quantify OTUs abundance in each sample. Bacterial diversity analysis Alpha and beta diversities were estimated using the MOTHUR software (v1.31.2) [ 34 ] and the QIIME pipeline (v1.8.0) [ 32 ], respectively. Sample clustering was conducted using the QIIME pipeline (v1.8.0) [ 32 ] and the unweighted pairwise grouping method with averaging (UPGMA). Barplots and heatmaps of different classification levels were plotted with the R package (v3.4.1) and the R package “gplots” tool, respectively. Venn diagrams and accumulation curves were obtained using the R package “VennDiagram” (v 3.1.1). A principal component analysis (PCA) was performed using the R package “ade4” tool. Taxonomic characterization was performed using a Linear Discriminant Analysis (LDA) with the calculation of the LDA effect size (LEfSe). Statistical analysis Due to the lack of data on effect size, a power calculation might be difficult. However, a recent similar study on S. japonicum infection in which each group consisted of 24 animal subjects might have helped with power calculations [ 23 ]. Continuous variables are presented as mean ± standard deviation (SD), and categorical variables are presented as percentages (%). The student’s t -test or Wilcoxon test and the χ2 test were used to evaluate differences between the two groups for continuous and categorical variables, respectively. Whenever applicable, the false discovery rate (FDR) was used to correct the calculated p-values. Statistical analyses were performed using the SPSS software (v 22.0; SPSS, IL, USA). Statistical significance was given by p < 0.05, FDR 2. Results Participants’ characterization The study population included a total of 24 patients with S. japonicum infection-induced liver cirrhosis (12 males and 12 females), and 25 age- and gender-matched healthy individuals (12 males and 13 females). The average ages of cirrhosis and control groups were comparable: 82.7 ± 7.3 years old and 82.3 ± 7.1 years old, respectively (Table 1). Furthermore, no significant differences were observed concerning gender, body mass index (BMI), or smoking and drinking habits between the two groups. In addition, the platelet counts and the serum levels of total bilirubin (TB), direct bilirubin (DB), and alanine aminotransferase (ALT) were comparable between the groups. On the other hand, while the serum levels of aspartate aminotransferase (AST) and glutamyl transpeptidase (GGT), together with the prothrombin time (PT) were significantly increased in the S. japonicum infection-induced liver cirrhosis group, the total protein, and globulin serum levels were significantly decreased in the same group, always in comparison with the control group (Table 1; at least p < 0.05). Regarding disease severity stratification, performed only for the cirrhosis group, 13 patients (54.2%) were classified as Child-Pugh A, 7 (29.2%) as Child-Pugh B, and 4 (16.7%) as Child-Pugh C (Table 1). Gut microbial diversity is comparable in individuals with S. japonicum infection-induced liver cirrhosis and healthy controls To characterize the gut microbiota in the context of S. japonicum infection-induced liver cirrhosis, we sequenced bacterial 16S rRNAs extracted from fecal samples and employed an OTU-based bioinformatics analysis to estimate alpha and beta diversities. Importantly, the obtained accumulation curve reached a plateau, indicating that most of the gut microbial populations have been detected at the used sequencing depth (Fig. 1 ). A total of 807 OTUs were obtained, of which 491 were equally detected in samples from cirrhosis individuals and healthy controls (Fig. 2 ). Notably, as represented in the Venn diagram, 123 and 193 OTUs were uniquely detected in healthy individuals and patients with S. japonicum infection-induced liver cirrhosis, respectively (Fig. 2 ). Concerning alpha diversity, we used 6 different metrics for the analysis: the Observed species, Chao, ACE, Shannon, Simpson, and Good’s coverage indexes were calculated. Overall, we did not detect any significant changes when healthy individuals and patients with S. japonicum infection-induced liver cirrhosis were compared. However, a slight tendency towards a lower bacterial diversity was observed in the cirrhosis group (particularly looking at Observed species, Chao, ACE, and Shannon indexes) (Fig. 3 ). In addition, considering the small number of cases showing the disease severity in the cirrhosis group (13 Child-Pugh A, 7 Child-Pugh B, and 4 Child-Pugh C cases), we did not discuss the variance in microbiota data further by disease severity. We further analyzed beta diversity (based on weighted UniFrac distances), to evaluate differences between samples. Overall, and once more, no major differences were observed when healthy individuals and patients with S. japonicum infection-induced liver cirrhosis were compared (Fig. 4 A). PCA data (Fig. 4 B) confirmed this similarity. Samples from each group showed heterogeneous distributions that were not species-specific, indicating that the structure of gut bacterial communities is similar in healthy individuals and patients with S. japonicum infection-induced liver cirrhosis (Fig. 4 B). Fecal microbial communities of healthy individuals and patients with S. japonicum infection-induced liver cirrhosis are slightly different Next, we employed a qualitative approach to perform a more in-depth characterization of the intestinal bacterial communities present in the stool samples collected from patients and controls. Briefly, we evaluated bacterial relative abundance at both phylum and genus levels (Fig. 5 A and B, respectively). The analysis of the most abundant bacterial phyla in the fecal samples collected from the two different groups of individuals revealed a similar picture: Bacteroidetes , Firmicutes , and Proteobacteria were the three most relevant phyla in both groups, accounting for 97.1% and 94.4% frequencies in healthy controls and cirrhosis patients, respectively (Fig. 5 A). On the other hand, some different patterns were observed at the genus level, being Bacteroides , Prevotella , and Faecalibacterium , the three most prevalent genera in the gut of healthy individuals, while in cirrhosis patients, the third most prevalent genus was Escherichia (Fig. 5 B). Additionally, to explore statistical relevance, we performed a LEfSe analysis: p 2 were considered statistically significant (Figs. 6 and 7 ). Of note, we also considered FDR values in this analysis to minimize the number of false-positive results. Overall, we have found that only the Bacilli Class and the Lactobacillales Order were significantly increased in cirrhosis patients compared to healthy individuals (Fig. 8 ). Discussion One of the serious complications induced by S. japonicum infection is the development of liver cirrhosis [ 35 ]. Most egg-laying adult Schistosoma trematodes have liver tropism, and the local (hepatic) host immune responses to the deposited eggs result in granuloma formation, which eventually evolves to progressive liver fibrosis and portal hypertension [ 36 ]. Both in vivo and clinical studies focusing on S. japonicum infections revealed that hepatic stellate cells (HSCs) are the key drivers of hepatic fibrosis [ 35 , 37 ], via collagen production and deposition. Importantly, liver fibrosis presentations have the potential to evolve into liver cirrhosis or liver cancer in the future [ 38 ]. Several studies have assessed the role of gut microbiota in cirrhosis of different etiologies [ 11 , 16 , 39 , 40 , 41 ]. Microbiota dysbiosis was often reported in cirrhosis patients: (I) autochthonous/non-autochthonous taxa ratio reduction; (II) Firmicutes / Bacteroidetes ratio inversion; or (III) increased prevalence of potentially pathogenic bacteria [ 42 , 43 , 44 ]. Interestingly, the liver-gut axis was involved in the physiopathology of cirrhosis: overgrown intestinal bacteria have the potential to translocate across the leaky gut barrier and reach the liver, leading to cirrhosis development or progression [ 45 ]. However, few studies explored the gut microbiota in the context of S. japonicum infection-induced liver cirrhosis. In fact, to our knowledge, this is the first study exploring the possible association between gut microbiota and S. japonicum infection-induced liver cirrhosis in humans. Surprisingly, here, we found that the microbiota of S. japonicum infection-induced liver cirrhosis patients was as rich and diverse as that of matched healthy individuals. Overall, these results are not consistent with findings reported for in vivo studies. For instance, in a mouse model of S. japonicum Ova-induced granulomas, a significantly higher beta diversity was detected versus controls [ 23 ]. Similarly, an overall reduction in alpha and a significant increase in beta diversities were found in the gut of S. mansoni infected mice compared to uninfected controls [ 21 ]. However, some studies align with our findings. For instance, Schneeberger et al. suggested that S. mansoni infection (in children) does not affect the gut microbial composition [ 25 ]. Furthermore, the taxonomic composition at the phylum level was similar in cirrhosis patients and healthy individuals (prevalence of Bacteroidetes , Firmicutes , and Proteobacteria ), aligning with S. japonicum in vivo infections’ data [ 23 ]. Additionally, even at the genus level, no major changes were detected, indicating that in our context, there is no gut dysbiosis in S. japonicum infection-induced liver cirrhosis patients. This result is somehow unexpected and different from other studies that have reported gut dysbiosis in the context of human schistosomiasis (e.g., those caused by S. mansoni and S. haematobium ) [ 20 , 21 , 22 , 23 , 24 ]. Additionally, in vivo studies have reported Schistosoma infection-related alterations in gut microbial composition. For instance, S. japonicum infection in mice was associated with significant changes in the gut prevalence of Firmicutes (relative decrease), Bacteroidetes , and Proteobacteria (relative increase) [ 23 ]. However, we did find some composition alterations in the gut microbiota of S. japonicum infection-induced liver cirrhosis patients. Bacilli (at the class level) and Lactobacillales (at the order level) were significantly increased in patients with S. japonicum infection-induced liver cirrhosis compared to healthy individuals. Importantly, our results are consistent with those of a previous study, which may suggest that the increased prevalence of Lactobacillales in gut microbiota is a hallmark of human liver cirrhosis [ 46 ]. Additionally, a different study showed that Lactobacillales and Bacilli were the key contributors to gut dysbiosis in primary liver cancer and proposed them as potential diagnostic markers [ 47 ]. Altogether, these findings suggest a strong association between gut Bacilli and Lactobacillales and liver disease that deserves to be further explored in different pathological contexts. Overall, our data do not support the idea that S. japonicum infection-induced cirrhosis is associated with significant alterations of gut microbiota profiles, opposing hypotheses proposed by others concerning schistosomiasis. Some reasons may justify our opposing report. Different studies focused on different Schistosoma spp. that have different organ tropisms: e.g., S. mansoni and S. japonicum target the gut and liver, while S. haematobium targets the bladder and the urogenital system [ 20 , 21 , 22 , 24 ]. Importantly, the notion that S. haematobium infection was equally associated with human gut dysbiosis suggests that potential alterations of intestinal microbial communities in the context of schistosomiasis are probably a consequence of infection-induced systemic immune responses (and not of local events). In fact, some studies suggest this [ 20 , 21 , 22 ]. For instance, mesenteric lymph node cells isolated from mice infected with S. mansoni (dysbiotic) responded differently compared to the ones isolated from controls: higher interleukin (IL)-4, IL-10, and IL-17 secretion, together with lower interferon-γ production [ 22 ]. Relevantly, aging is accompanied by a deterioration of immune responses, recently named as immunosenescence or immune-aging [ 48 ]. In our study, the enrolled patients were quite old (average of 82.7 ± 7.3 years old), which may justify the absence of phenotype: their potentially weaker immune responses to S. japonicum infection were not strong enough to impact their gut microbial communities. Nevertheless, our geographic target-region conditioned the age range of our study population, which is a limitation of our study. Since 1995, due to the application of effective Schistosomiasis control measures, there are almost no newly reported cases in Zhejiang [ 1 ]. Therefore, patients with S. japonicum infection-induced liver cirrhosis are not many and tend to be of advanced ages [ 27 ]. Therefore, we cannot exclude a possible age selection bias that, however, reflects the reality of the population studied: among the cirrhosis patients, 17 and 7 individuals were 80 or 90 years of age or older, respectively, and none of the others was less than 60 years old. We can, however, also hypothesize that our population has a survival bias. Living patients may be the ones with better outcomes due to stable gut microbiota profiles, while the fatalities may have been a consequence of gut microbiota disorders. This is, however, only a speculative exercise. Therefore, further studies focusing on much larger and heterogeneous populations (different geographies, with a wider age range) are needed to completely understand if S. japonicum infection-induced liver cirrhosis is accompanied by gut microbiota alterations. In addition, the effective Schistosomiasis control in Zhejiang and advanced age of participants might affect our conclusion because it was generally almost impossible to include a cohort of age-matched acute S. japonicum infected patients without liver cirrhosis. An additional factor that should be considered in future studies is the timing of infection, particularly if the study is designed to find biomarkers for cirrhosis diagnosis and/or prognosis. Indeed, the infection status may also condition the results. Human studies have mainly targeted adolescents [ 20 ] or children [ 24 ], and consequently, the early phase of infection. Additionally, the most commonly used mouse model of schistosomiasis in microbiome studies evaluated acute infection – less than 8 weeks post-challenge according to the model characterization [ 49 , 50 , 51 ]. The in vivo published data refers to stool samples collected at 28 days [ 21 ], 42 days [ 23 ], and 8 weeks [ 22 ] after infection. We cannot exclude that our different findings are just a consequence of our study context. Cirrhosis is the end-stage of chronic schistosomiasis, quite far time-wise from the early phase of infection. Both systemic immune responses and Schistosoma infection were associated with gut microbiota alterations [ 20 ]. However, in S. japonicum infection-induced liver cirrhosis, both are expected to decrease in magnitude, which may partially explain our findings. Conclusion In conclusion, overall, we showed that gut microbial taxonomic profiles and diversity in S. japonicum infection-induced liver cirrhosis patients appear to be similar to the ones from age-matched healthy people. Therefore, in our context, patients with S. japonicum infection-induced liver cirrhosis present a “healthy” gut structure, which may partially explain the better prognosis of cirrhosis secondary to this infection, compared to cirrhosis of different etiology in China [ 26 , 27 ]. Future studies with much larger sample sizes across wider age ranges are, however, needed to further explore the relationship between S. japonicum infection-induced liver cirrhosis and gut microbiota and potentially disclose the possible mechanisms by which gut microbiota alterations influence cirrhosis and/or vice versa . Importantly, these studies should also evaluate immunity and inflammation to finally address if the gut-liver axis is implicated in S. japonicum infection-induced liver cirrhosis development and progression. Such studies should be further complemented with in vivo works with transplantation of human gut microbiota samples to ultimately establish (or exclude) a causal relationship between dysbiosis and cirrhosis secondary to schistosomiasis. Abbreviations OTUs: operational taxonomic units; DALYs: disability-adjusted life years; NTDs: Neglected Tropical Diseases; CT: computed tomography; PCA: principal component analysis; LDA: Linear Discriminant Analysis; LEfSe: LDA effect size; SD: standard deviation; FDR: false discovery rate; ALT: alanine aminotransferase; AST: spartate aminotransferase; BMI: body mass index; DB: direct bilirubin; GGT: glutamyl transpeptidase; PT: prothrombin time; SLC: S. japonicum infection-induced liver cirrhosis; TB: total bilirubin; HSCs: hepatic stellate cells; IL: interleukin. Declarations Acknowledgements We would like to thank Editage (www.editage.cn) for English language editing, and BGI-Shenzhen for genome-sequencing services. Authors ’ contributions Conceptualization: Y.M.Y. and H.L.J. Data curation: Q.F.G. and F.Z. Formal analysis: Q.F.G., C.X. and J.X. Funding acquisition: Q.F.G., C.X. and Q.Z. Investigation: F.Z. Methodology: H.F.L. Supervision: Q.Z. Writing - original draft: Q.F.G. Writing - review & editing: Y.M.Y. and H.L.J. All authors read and approved the final manuscript. Funding This study was supported by the Key Disciplines Construction Plan of Zhejiang Province Traditional Chinese Medicine (2017-XK-A31), the Chinese Medicine Research Program of Zhejiang Province (2020ZB148), and the National Key Research and Development Program of China (2018YFC2000301, 2018YFC2000500). Availability of data and materials The data supporting the conclusions of this article are included within the article. The datasets analysed during the present study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate All participants were approved by the First Affiliated Hospital Clinical Research Ethics Committee, School of Medicine, Zhejiang University (ref: 2017411-1), and the Jiaxing Wangdian People's Hospital Ethics Committee (ref: 2017002). All participants provided written informed consent in Chinese. 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PLoS Negl Trop Dis. 2015;9:e0003861. Schneeberger PHH, Coulibaly JT, Panic G, Daubenberger C, Gueuning M, Frey JE, et al. Investigations on the interplays between Schistosoma mansoni, praziquantel and the gut microbiome. Parasit Vectors. 2018;11:168. Wu W, Feng A, Huang Y. Research and control of advanced schistosomiasis japonica in China. Parasitol Res. 2015;114:17-27. Song LG, Wu XY, Sacko M, Wu ZD. History of schistosomiasis epidemiology, current status, and challenges in China: on the road to schistosomiasis elimination. Parasitol Res. 2016;115:4071-81. Cholongitas E, Papatheodoridis GV, Vangeli M, Terreni N, Patch D, Burroughs AK. Systematic review: The model for end-stage liver disease--should it replace Child-Pugh's classification for assessing prognosis in cirrhosis? Aliment Pharmacol Ther. 2005;22:1079-89. Magoc T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27:2957-63. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996-8. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194-200. 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. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537-41. Carson JP, Ramm GA, Robinson MW, McManus DP, Gobert GN. Schistosome-Induced Fibrotic Disease: The Role of Hepatic Stellate Cells. Trends Parasitol. 2018;34:524-40. Anthony B, Allen JT, Li YS, McManus DP. Hepatic stellate cells and parasite-induced liver fibrosis. Parasit Vectors. 2010;3:60. Seki E, Brenner DA. Recent advancement of molecular mechanisms of liver fibrosis. J Hepatobiliary Pancreat Sci. 2015;22:512-8. Kamdem SD, Moyou-Somo R, Brombacher F, Nono JK. Host Regulators of Liver Fibrosis During Human Schistosomiasis. Front Immunol. 2018;9:2781. Wei X, Jiang S, Chen Y, Zhao X, Li H, Lin W, et al. Cirrhosis related functionality characteristic of the fecal microbiota as revealed by a metaproteomic approach. BMC Gastroenterol. 2016;16:121. Schwimmer JB, Johnson JS, Angeles JE, Behling C, Belt PH, Borecki I, et al. Microbiome Signatures Associated With Steatohepatitis and Moderate to Severe Fibrosis in Children With Nonalcoholic Fatty Liver Disease. Gastroenterology. 2019;157:1109-22. Bajaj JS, Thacker LR, Fagan A, White MB, Gavis EA, Hylemon PB, et al. Gut microbial RNA and DNA analysis predicts hospitalizations in cirrhosis. JCI Insight. 2018;3: Bajaj JS, Heuman DM, Hylemon PB, Sanyal AJ, White MB, Monteith P, et al. Altered profile of human gut microbiome is associated with cirrhosis and its complications. J Hepatol. 2014;60:940-7. Bajaj JS, Betrapally NS, Hylemon PB, Heuman DM, Daita K, White MB, et al. Salivary microbiota reflects changes in gut microbiota in cirrhosis with hepatic encephalopathy. Hepatology. 2015;62:1260-71. Ling Z, Liu X, Cheng Y, Jiang X, Jiang H, Wang Y, et al. Decreased Diversity of the Oral Microbiota of Patients with Hepatitis B Virus-Induced Chronic Liver Disease: A Pilot Project. Sci Rep. 2015;5:17098. Oikonomou T, Papatheodoridis GV, Samarkos M, Goulis I, Cholongitas E. Clinical impact of microbiome in patients with decompensated cirrhosis. World J Gastroenterol. 2018;24:3813-20. Bajaj JS, Ahluwalia V, Steinberg JL, Hobgood S, Boling PA, Godschalk M, et al. Elderly patients have an altered gut-brain axis regardless of the presence of cirrhosis. Sci Rep. 2016;6:38481. Zhang L, Wu YN, Chen T, Ren CH, Li X, Liu GX. Relationship between intestinal microbial dysbiosis and primary liver cancer. Hepatobiliary Pancreat Dis Int. 2019;18:149-57. Walford RL. THE IMMUNOLOGIC THEORY OF AGING. Gerontologist. 1964;4:195-7. Ferrari TC, Moreira PR. Neuroschistosomiasis: clinical symptoms and pathogenesis. Lancet Neurol. 2011;10:853-64. Seki T, Kumagai T, Kwansa-Bentum B, Furushima-Shimogawara R, Anyan WK, Miyazawa Y, et al. Interleukin-4 (IL-4) and IL-13 suppress excessive neutrophil infiltration and hepatocyte damage during acute murine schistosomiasis japonica. Infect Immun. 2012;80:159-68. Chen L, Chen Q, Hou W, He L. High-throughput dynamic analysis of differentially expressed genes in splenic dendritic cells from mice infected with Schistosoma japonicum. Immunol Lett. 2017;184:15-22. Tables Table 1. Demographic and clinical characteristics of the study population per group. SLC group Control group P-value n 24 25 Gender (male/female) 12/12 12/13 0.889 Age (years) 82.7 ± 7.3 82.3 ± 7.1 0.852 Active smoker (%) 0% 12% 0.248 Active drinker (%) 0% 20% 0.066 BMI (Kg/m 2 ) 22.4 ± 1.8 23.3 ± 2.3 0.915 Platelets (10 9 /L) 152.3 ± 108.3 179.6 ± 59.5 0.275 PT (s) 19.4 ± 8.1 15.3 ± 1.6 0.003 Total protein (g/L) 59.5 ± 8.3 74.0 ± 4.1 < 0.001 Albumin (g/L) 31.4 ± 5.1 29.6 ± 4.4 0.186 Globulin (g/L) 27.7 ± 6.0 44.5 ± 3.8 < 0.001 TB (µmol/L) 19.8 ± 19.3 15.3 ± 5.1 0.711 DB (µmol/L) 7.9 ± 9.9 4.2 ± 1.5 0.509 AST (U/L) 32.2 ± 17.5 17.6 ± 6.6 < 0.001 ALT (U/L) 22.0 ± 16.0 22.2 ± 5.4 0.114 GGT (U/L) 61.0 ± 51.6 33.6 ± 21.5 0.024 Child-Pugh A 13 / NA B 7 / NA C 4 / NA Note: The continuous and categorical variables are listed as mean ± SD and percentage (%), respectively. Statistical differences were determined using the Student's t-test (parametric data) or the Wilcoxon test (non-parametric data) and the χ2 test for continuous and categorical variables, respectively. Abbreviations: ALT - alanine aminotransferase; AST - aspartate aminotransferase; BMI - body mass index; DB - direct bilirubin; GGT - glutamyl transpeptidase; PT - prothrombin time; SLC - S. japonicum infection-induced liver cirrhosis; and TB - total bilirubin. Cite Share Download PDF Status: Published Journal Publication published 26 Mar, 2021 Read the published version in Infectious Diseases of Poverty → Version 1 posted Review # 2 received at journal 12 Oct, 2020 Editorial decision: Major revision 12 Oct, 2020 Review # 1 received at journal 07 Oct, 2020 Reviewer # 2 agreed at journal 28 Sep, 2020 Reviewer # 1 agreed at journal 22 Sep, 2020 Editor assigned by journal 16 Sep, 2020 Reviewers invited by journal 16 Sep, 2020 Editor invited by journal 15 Sep, 2020 Submission checks completed at journal 12 Sep, 2020 First submitted to journal 10 Sep, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-71566","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":2287740,"identity":"cbd6b100-5aa5-498e-847f-4b3ccbb4e44e","order_by":0,"name":"Qifeng Gui","email":"","orcid":"","institution":"Zhejiang University School of Medicine First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qifeng","middleName":"","lastName":"Gui","suffix":""},{"id":2287741,"identity":"f643f1b7-7063-4510-ab45-c611d1b28be0","order_by":1,"name":"Feng Zhu","email":"","orcid":"","institution":"Zhejiang University School of Medicine First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zhu","suffix":""},{"id":2287742,"identity":"7cc3b079-2957-463e-8c28-7249723dec04","order_by":2,"name":"Chi Xiao","email":"","orcid":"","institution":"Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chi","middleName":"","lastName":"Xiao","suffix":""},{"id":2287743,"identity":"c3f7d8ed-99a4-4781-83bf-c2b22551a797","order_by":3,"name":"Haifeng Lu","email":"","orcid":"","institution":"Zhejiang University School of Medicine First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Lu","suffix":""},{"id":2287744,"identity":"02afe3b9-708e-41e8-b6b9-11f6f1d4badc","order_by":4,"name":"Qin Zhang","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Zhang","suffix":""},{"id":2287745,"identity":"d3d6e21b-7f0f-4f51-adcf-0ba088c44184","order_by":5,"name":"Jia Xu","email":"","orcid":"","institution":"Zhejiang University School of Medicine First Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Xu","suffix":""},{"id":2287746,"identity":"5601dd39-568c-424e-b3d8-f71f216424ca","order_by":6,"name":"Huilin Jin","email":"","orcid":"","institution":"Wangdian People’s Hospital, Jiaxing, Zhejiang","correspondingAuthor":false,"prefix":"","firstName":"Huilin","middleName":"","lastName":"Jin","suffix":""},{"id":2287747,"identity":"666f78a2-0481-4e49-9b33-dff2b5a84b06","order_by":7,"name":"Yunmei Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3RsYoCMRCA4ZGV2Ixum2r3FWbZ4gp9mNi4lSAI1hHBSrANXOEziGAdGbBarC0s7hG0OxBFVwvLxE4wfxeYjwkMQCj0gYkostS9ygTAVk8P0moINTiKTu5PkhjpZESvq59LvT6G2QqRi8W4JDiOGOJf7SJimKPk/liXVDM7BnmwLhKtcyTuT6CkqDllIKlcBH7OqLgQFbn4kTplxvYUVqTmR4Sik+5kBraDzWxXoNw7SDrn+ym1TFPDy7//UTuJjYO8kvZxTPSdvxfrN4ZDoVDoq7oBnR0/HaIKtP4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-9086-6101","institution":"The First Affiliated Hospital, School of Medicine, Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Yunmei","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2020-09-03 11:51:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-71566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-71566/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40249-021-00821-8","type":"published","date":"2021-03-26T15:00:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":2416448,"identity":"04b0ff20-f6a5-4338-9056-6eed1ff7e23f","added_by":"auto","created_at":"2020-09-15 15:30:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":878697,"visible":true,"origin":"","legend":"Accumulation curves attest to the sequencing depth adequacy. Accumulation curve is given by cumulative data of OTUs per sample. Each box and whiskers’ plot represents OTUs quantified for the respective number of samples sequenced (e.g., the first plot refers to one sample, while the second plot represents the cumulative data of two samples, and so forth).","description":"","filename":"OnlineFig1.Png","url":"https://assets-eu.researchsquare.com/files/rs-71566/v1/OnlineFig1.Png"},{"id":2416449,"identity":"e2f8ec25-6fda-45aa-bf1e-3ea048a26b94","added_by":"auto","created_at":"2020-09-15 15:30:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":450039,"visible":true,"origin":"","legend":"Representation of the OTUs detected per group of samples. Venn diagram depicts the number of OTUs unique for healthy individuals (blue circle) and patients with S. japonicum infection-induced liver cirrhosis (SLC; purple circle), as well as the ones shared by the two groups (circles’ intersection). ","description":"","filename":"OnlineFig2.Png","url":"https://assets-eu.researchsquare.com/files/rs-71566/v1/OnlineFig2.Png"},{"id":2416450,"identity":"5000be1c-4bcc-496e-b430-77391c069822","added_by":"auto","created_at":"2020-09-15 15:30:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":805614,"visible":true,"origin":"","legend":"Alpha-diversity estimations are similar for control and cirrhosis groups. Intra-sample bacterial species diversity and richness were evaluated through the calculation of 6 different metrics. Results are presented as box and whiskers’ plots per group (control and S. japonicum infection-induced liver cirrhosis – SLC - plots in red and blue, respectively) and per metric: (A) Observed species; (B) Chao index; (C) ACE index; (D) Shannon index; (E) Simpson index; and (F) Good-coverage index. No significant differences were detected between groups. ","description":"","filename":"OnlineFig3.Png","url":"https://assets-eu.researchsquare.com/files/rs-71566/v1/OnlineFig3.Png"},{"id":2416451,"identity":"8ed8b1c7-a476-40f7-8451-0b44540700d4","added_by":"auto","created_at":"2020-09-15 15:30:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":278535,"visible":true,"origin":"","legend":"Gut microbiota diversity is not affected in patients with S. japonicum infection-induced liver cirrhosis (SLC). Beta diversity was calculated based on weighted UniFrac distances and is represented per group as box and whiskers’ plots (A). A principal component analysis (PCA) was also performed and is represented (per sample, and group) (B). Yellow plot/symbols and blue or red plot/symbols refer to SLC and healthy control samples, respectively. No statistical differences were detected between the two groups.","description":"","filename":"OnlineFig4.Png","url":"https://assets-eu.researchsquare.com/files/rs-71566/v1/OnlineFig4.Png"},{"id":2416452,"identity":"5fff9019-f7e3-4c7a-aa34-be19712cc7bd","added_by":"auto","created_at":"2020-09-15 15:30:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":930907,"visible":true,"origin":"","legend":"Gut bacterial composition is affected in S. japonicum infection-induced liver cirrhosis (SLC) patients. The relative abundance of bacterial phyla (A) and genera (B) detected in fecal samples was determined and is represented per group of samples. At the genus level, relative abundances of less than 0.5% were grouped and are represented as “others.” Phyla and genera are color-coded.","description":"","filename":"OnlineFig5.Png","url":"https://assets-eu.researchsquare.com/files/rs-71566/v1/OnlineFig5.Png"},{"id":2416453,"identity":"0d5155fa-b023-4cdf-99a1-b5164d76d43f","added_by":"auto","created_at":"2020-09-15 15:30:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1550647,"visible":true,"origin":"","legend":"Taxonomic differences in gut microbiotas of S. japonicum infection-induced liver cirrhosis (SLC) patients. LDA with the calculation of LDA effect size (LEfSe) was performed to identify the differentially abundant taxa in the two groups of samples: represented in red and green for healthy controls and SLC patients, respectively. LDA scores \u003e2 were considered statistically significant. ","description":"","filename":"OnlineFig6.Png","url":"https://assets-eu.researchsquare.com/files/rs-71566/v1/OnlineFig6.Png"},{"id":2416454,"identity":"2b50c4d5-fdad-4cd1-8e8b-3c1988cad104","added_by":"auto","created_at":"2020-09-15 15:30:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2515551,"visible":true,"origin":"","legend":"Gut microbiota community differences between healthy individuals and S. japonicum infection-induced liver cirrhosis (SLC) patients. Linear discriminant analysis (LDA) was translated into a cladogram that is represented. The red and green nodes represent specific microorganisms relevant in healthy controls and cirrhosis patients, respectively. The diameter of each node is proportional to the taxon’s abundance. Significant bacterial taxonomic groups are identified: genus – g; family – f; species – s; order – o).","description":"","filename":"OnlineFig7.Png","url":"https://assets-eu.researchsquare.com/files/rs-71566/v1/OnlineFig7.Png"},{"id":2416455,"identity":"a7f36fdd-1daf-4f7d-98be-2ae6d1a80121","added_by":"auto","created_at":"2020-09-15 15:30:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":226261,"visible":true,"origin":"","legend":"Bacilli Class and Lactobacillales Order are increased in S. japonicum infection-induced liver cirrhosis (SLC) patients. The relative abundance of relevant taxa in the gut of SLC patients at the Class (A) and Order (B) levels was calculated and is represented per group: red and blue columns refer to healthy individuals and SLC patients. Statistical significance was calculated and is represented: * FDR \u003c 0.05","description":"","filename":"OnlineFig8.Png","url":"https://assets-eu.researchsquare.com/files/rs-71566/v1/OnlineFig8.Png"},{"id":15668998,"identity":"226d47f7-a754-4baa-809b-28348e1c2820","added_by":"auto","created_at":"2021-11-18 13:51:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1924354,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-71566/v1/569d6892-d4e4-44c2-99bf-48ff0bcdb183.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eGut microbiota Signatures in \u003cem\u003eSchistosoma japonicum\u003c/em\u003e Infection-Induced Liver Cirrhosis Patients\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eSchistosomiasis is one of the most prevalent parasitic diseases in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. An estimated 240\u0026nbsp;million people are affected by this disease, together with 800\u0026nbsp;million more at risk of infection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Worryingly, schistosomiasis alone is associated with a high disease burden of as much as 3.3\u0026nbsp;million disability-adjusted life years (DALYs), accounting for more than 10% of the global burden of Neglected Tropical Diseases (NTDs) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. There are 5 known etiologic agents of schistosomiasis, all of which are trematode parasites of the \u003cem\u003eSchistosoma\u003c/em\u003e genus: \u003cem\u003eS. japonicum\u003c/em\u003e, \u003cem\u003eS. mansoni\u003c/em\u003e, \u003cem\u003eS. haematobium\u003c/em\u003e, \u003cem\u003eS. intercalatum\u003c/em\u003e, and \u003cem\u003eS. mekongi\u003c/em\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Only \u003cem\u003eS. japonicum\u003c/em\u003e exists in China, and it is predominant in 12 provinces south of the Yangtze River [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the 1950s, an estimated 11.6\u0026nbsp;million people were infected, while more than 100\u0026nbsp;million more were at risk of infection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. From the mid-1950s, the number of infected cases has reduced by over 99%; however, schistosomiasis is still endemic in 140 counties in China [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLiver cirrhosis is a common (end-stage) consequence of several liver diseases and a major cause of mortality worldwide [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous studies have reported an association between gut microbiota alterations and chronic liver disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], including cirrhosis secondary to hepatitis B or C virus infections [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], alcohol over-consumption [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], nonalcoholic fatty liver disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and even primary biliary cirrhosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, cirrhosis complications, including hepatic encephalopathy, spontaneous bacterial peritonitis, and sepsis, were linked to gut microbiota alterations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Some studies on the gut-liver axis have even demonstrated that the gut microbiota (and bacterial translocation) plays an important role in the pathogenesis of cirrhosis and its complications [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies using not only animal models but also human clinical samples have demonstrated a clear relationship between gut microbiota alterations and \u003cem\u003eSchistosoma\u003c/em\u003e spp. infection [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, no study has explored the relationship between gut microbiota and \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis; the potential role of gut microbiota alterations in disease onset and progression is still unclear.\u003c/p\u003e \u003cp\u003eHere, to explore the possible association between gut microbiota and \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis, we conducted an observational clinical study. Fecal samples collected from 24 patients and 25 matched healthy individuals from the Zhejiang Province, China, were used for 16S rRNA Illumina MiSeq sequencing. The main objectives of this study were: (1) to characterize the relevant gut microbiota alterations associated with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis; and (2) to identify potential bacterial taxa to be used as non-invasive biomarkers for the early diagnosis of liver cirrhosis secondary to schistosomiasis.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics Statement\u003c/h2\u003e \u003cp\u003eThis study was conducted according to the principles expressed in the Declaration of Helsinki. All experiments were approved by the First Affiliated Hospital Clinical Research Ethics Committee, School of Medicine, Zhejiang University (ref: 2017411-1), and the Jiaxing Wangdian People's Hospital Ethics Committee (ref: 2017002). All participants provided written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExclusion criteria and subject enrolment\u003c/h2\u003e \u003cp\u003eEligibility was defined by the application of the following exclusion criteria: (I) history of digestive disorders, such as other causes of liver disease, irritable bowel syndrome, inflammatory bowel disease, and chronic diarrhea; (II) history of previous gastrointestinal surgery; (III) history of probiotics, prebiotics, synbiotics, antibiotics, acid suppressors, metformin, and gastrointestinal motility targeting-drugs use, less than eight weeks before enrollment; (IV) history of bowel preparation, less than four weeks before enrollment; (V) diagnosis of major diseases such as malignant tumors, cardiovascular disease, respiratory, autoimmune and allergic diseases, neurological disorders, renal insufficiency, diabetes, uncontrolled hypertension (blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;150 / 90\u0026nbsp;mmHg), dyslipidemia, depression, mania, and bipolar affective disorder; and (VI) hepatitis B virus, hepatitis C virus and HIV infection.\u003c/p\u003e \u003cp\u003eFrom December 2017 to November 2019, 24 patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis and 26 age- and gender-matched healthy individuals were enrolled from Hangzhou and Jiaxing, northern cities of the Zhejiang Province, China, an \u003cem\u003eS. japonicum\u003c/em\u003e infection endemic area. Since the stool specimen from one healthy individual was unsatisfactory, the healthy controls group was ultimately composed of 25 individuals. All participant demographics information, laboratory tests, computed tomography (CT), and B ultrasonic scans were collected from the hospital electronic medical records system. Additionally, questionnaires were filled by all subjects. Schistosomiasis diagnosis was performed using the Kato-Katz method to detect \u003cem\u003eS. japonicum\u003c/em\u003e eggs in stool samples. Liver cirrhosis was diagnosed according to clinical and biochemical data and imaging exams (CT or B ultrasonic scans) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Liver function was evaluated with the Child-Pugh classification system [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFecal sample collection\u003c/h2\u003e \u003cp\u003eThe participants were trained by a researcher to obtain a complete stool collection method. Fresh stool samples (self-collected from patients and controls) were collected into anaerobic bags (Mitsubishi Gas Chemical). Samples were divided into five 200\u0026nbsp;mg aliquots, in the first 30\u0026nbsp;min after collection, and immediately stored at \u0026minus;\u0026thinsp;80\u0026nbsp;\u0026deg;C for posterior analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGenomic DNA extraction\u003c/h2\u003e \u003cp\u003eTotal genomic DNA was extracted per sample using the MagPure Stool DNA KF kit B (Magen,Guangzhou, China) according to the manufacturer's instructions. DNAs were quantified in the Qubit Fluorometer (Invitrogen, Qubit 2.0) using the Qubit\u0026reg; dsDNA BR Assay kit (Invitrogen, CA, USA). DNA quality was assessed using gel electrophoresis (1% agarose gels). Negative controls containing the elution buffer only were included during DNA extraction and quantification to monitor possible contamination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eLibrary construction and sequencing\u003c/h2\u003e \u003cp\u003eBacterial 16S rRNA gene hypervariable region V4 was amplified using degenerate polymerase chain reaction (PCR) and the primers 515Fw (5\u0026rsquo;-GTGCCAGCMGCCGCGGTAA-3\u0026rsquo;) and 806Rv (5\u0026rsquo;-GGACTACHVGGGTWTCTAAT-3\u0026rsquo;), which contained the Illumina adapter sequence, a pad, and a linker. Fifty microliters reaction volumes, containing 30\u0026nbsp;ng of DNA templates together with the primers (10\u0026nbsp;\u0026micro;M) and the PCR master mix, were used. Amplification was performed under the following conditions: 95\u0026nbsp;\u0026deg;C for 3 minutes; 30 cycles of 95\u0026nbsp;\u0026deg;C for 45 seconds, 56\u0026nbsp;\u0026deg;C for 45 seconds, and 72\u0026nbsp;\u0026deg;C for 45 seconds; and a final extension step at 72\u0026nbsp;\u0026deg;C for 10 minutes. PCR products were purified using Agencourt AMPure XP beads (Beckman Coulter, CA, USA) according to the manufacturer\u0026rsquo;s instructions. Library quality was evaluated using the 2100 Bioanalyzer (HT DNA 1K Labchip 760517) instrument (Agilent Technologies, CA, USA). Sequencing was performed using the Illumina HiSeq 2500 platform (BGI, Shenzhen, China) following the Illumina standard pipelines to get 2\u0026thinsp;\u0026times;\u0026thinsp;250\u0026nbsp;bp paired-end reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics analysis\u003c/h2\u003e \u003cp\u003eRaw reads were filtered to remove adaptors and low-quality and/or ambiguous bases, and paired-end reads were merged using the Fast Length Adjustment of Short reads (FLASH, v1.2.11) software [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sequences were clustered into operational taxonomic units (OTUs) with a cutoff value of 97% using the UPARSE software (v7 .0.1090) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Chimeric sequences were detected (compared with the Gold database) using the UCHIME software (v4.2.40) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and removed from the analysis. Sequences of representative OTUs were then taxonomically classified using the Ribosomal Database Project Classifier (v.2.2) with a minimum confidence threshold of 0.6 and trained on the Greengenes database (v201305) using the QIIME pipeline (v1.8.0) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The USEARCH_global command [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] was used to quantify OTUs abundance in each sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBacterial diversity analysis\u003c/h2\u003e \u003cp\u003eAlpha and beta diversities were estimated using the MOTHUR software (v1.31.2) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and the QIIME pipeline (v1.8.0) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], respectively. Sample clustering was conducted using the QIIME pipeline (v1.8.0) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and the unweighted pairwise grouping method with averaging (UPGMA). Barplots and heatmaps of different classification levels were plotted with the R package (v3.4.1) and the R package \u0026ldquo;gplots\u0026rdquo; tool, respectively. Venn diagrams and accumulation curves were obtained using the R package \u0026ldquo;VennDiagram\u0026rdquo; (v 3.1.1). A principal component analysis (PCA) was performed using the R package \u0026ldquo;ade4\u0026rdquo; tool. Taxonomic characterization was performed using a Linear Discriminant Analysis (LDA) with the calculation of the LDA effect size (LEfSe).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDue to the lack of data on effect size, a power calculation might be difficult. However, a recent similar study on \u003cem\u003eS. japonicum\u003c/em\u003e infection in which each group consisted of 24 animal subjects might have helped with power calculations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables are presented as percentages (%). The student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Wilcoxon test and the χ2 test were used to evaluate differences between the two groups for continuous and categorical variables, respectively. Whenever applicable, the false discovery rate (FDR) was used to correct the calculated p-values. Statistical analyses were performed using the SPSS software (v 22.0; SPSS, IL, USA). Statistical significance was given by p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, or LDA scores\u0026thinsp;\u0026gt;\u0026thinsp;2.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u0026rsquo; characterization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population included a total of 24 patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis (12 males and 12 females), and 25 age- and gender-matched healthy individuals (12 males and 13 females). The average ages of cirrhosis and control groups were comparable: 82.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u0026nbsp;years old and 82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u0026nbsp;years old, respectively (Table\u0026nbsp;1). Furthermore, no significant differences were observed concerning gender, body mass index (BMI), or smoking and drinking habits between the two groups. In addition, the platelet counts and the serum levels of total bilirubin (TB), direct bilirubin (DB), and alanine aminotransferase (ALT) were comparable between the groups. On the other hand, while the serum levels of aspartate aminotransferase (AST) and glutamyl transpeptidase (GGT), together with the prothrombin time (PT) were significantly increased in the \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis group, the total protein, and globulin serum levels were significantly decreased in the same group, always in comparison with the control group (Table\u0026nbsp;1; at least p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Regarding disease severity stratification, performed only for the cirrhosis group, 13 patients (54.2%) were classified as Child-Pugh A, 7 (29.2%) as Child-Pugh B, and 4 (16.7%) as Child-Pugh C (Table\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGut microbial diversity is comparable in individuals with S. japonicum infection-induced liver cirrhosis and healthy controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the gut microbiota in the context of \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis, we sequenced bacterial 16S rRNAs extracted from fecal samples and employed an OTU-based bioinformatics analysis to estimate alpha and beta diversities. Importantly, the obtained accumulation curve reached a plateau, indicating that most of the gut microbial populations have been detected at the used sequencing depth (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 807 OTUs were obtained, of which 491 were equally detected in samples from cirrhosis individuals and healthy controls (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, as represented in the Venn diagram, 123 and 193 OTUs were uniquely detected in healthy individuals and patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eConcerning alpha diversity, we used 6 different metrics for the analysis: the Observed species, Chao, ACE, Shannon, Simpson, and Good\u0026rsquo;s coverage indexes were calculated. Overall, we did not detect any significant changes when healthy individuals and patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis were compared. However, a slight tendency towards a lower bacterial diversity was observed in the cirrhosis group (particularly looking at Observed species, Chao, ACE, and Shannon indexes) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, considering the small number of cases showing the disease severity in the cirrhosis group (13 Child-Pugh A, 7 Child-Pugh B, and 4 Child-Pugh C cases), we did not discuss the variance in microbiota data further by disease severity.\u003c/p\u003e\n\u003cp\u003eWe further analyzed beta diversity (based on weighted UniFrac distances), to evaluate differences between samples. Overall, and once more, no major differences were observed when healthy individuals and patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis were compared (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). PCA data (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB) confirmed this similarity. Samples from each group showed heterogeneous distributions that were not species-specific, indicating that the structure of gut bacterial communities is similar in healthy individuals and patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFecal microbial communities of healthy individuals and patients with S. japonicum infection-induced liver cirrhosis are slightly different\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we employed a qualitative approach to perform a more in-depth characterization of the intestinal bacterial communities present in the stool samples collected from patients and controls. Briefly, we evaluated bacterial relative abundance at both phylum and genus levels (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA and B, respectively). The analysis of the most abundant bacterial phyla in the fecal samples collected from the two different groups of individuals revealed a similar picture: \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e were the three most relevant phyla in both groups, accounting for 97.1% and 94.4% frequencies in healthy controls and cirrhosis patients, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). On the other hand, some different patterns were observed at the genus level, being \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, and \u003cem\u003eFaecalibacterium\u003c/em\u003e, the three most prevalent genera in the gut of healthy individuals, while in cirrhosis patients, the third most prevalent genus was \u003cem\u003eEscherichia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eAdditionally, to explore statistical relevance, we performed a LEfSe analysis: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Wilcoxon test) and an LDA scores\u0026thinsp;\u0026gt;\u0026thinsp;2 were considered statistically significant (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Of note, we also considered FDR values in this analysis to minimize the number of false-positive results. Overall, we have found that only the \u003cem\u003eBacilli\u003c/em\u003e Class and the \u003cem\u003eLactobacillales\u003c/em\u003e Order were significantly increased in cirrhosis patients compared to healthy individuals (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":" \u003cp\u003eOne of the serious complications induced by \u003cem\u003eS. japonicum\u003c/em\u003e infection is the development of liver cirrhosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Most egg-laying adult \u003cem\u003eSchistosoma\u003c/em\u003e trematodes have liver tropism, and the local (hepatic) host immune responses to the deposited eggs result in granuloma formation, which eventually evolves to progressive liver fibrosis and portal hypertension [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Both \u003cem\u003ein vivo\u003c/em\u003e and clinical studies focusing on \u003cem\u003eS. japonicum\u003c/em\u003e infections revealed that hepatic stellate cells (HSCs) are the key drivers of hepatic fibrosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], via collagen production and deposition. Importantly, liver fibrosis presentations have the potential to evolve into liver cirrhosis or liver cancer in the future [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Several studies have assessed the role of gut microbiota in cirrhosis of different etiologies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Microbiota dysbiosis was often reported in cirrhosis patients: (I) autochthonous/non-autochthonous taxa ratio reduction; (II) \u003cem\u003eFirmicutes\u003c/em\u003e/\u003cem\u003eBacteroidetes\u003c/em\u003e ratio inversion; or (III) increased prevalence of potentially pathogenic bacteria [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Interestingly, the liver-gut axis was involved in the physiopathology of cirrhosis: overgrown intestinal bacteria have the potential to translocate across the leaky gut barrier and reach the liver, leading to cirrhosis development or progression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, few studies explored the gut microbiota in the context of \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis.\u003c/p\u003e \u003cp\u003eIn fact, to our knowledge, this is the first study exploring the possible association between gut microbiota and \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis in humans. Surprisingly, here, we found that the microbiota of \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis patients was as rich and diverse as that of matched healthy individuals. Overall, these results are not consistent with findings reported for \u003cem\u003ein vivo\u003c/em\u003e studies. For instance, in a mouse model of \u003cem\u003eS. japonicum\u003c/em\u003e Ova-induced granulomas, a significantly higher beta diversity was detected versus controls [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, an overall reduction in alpha and a significant increase in beta diversities were found in the gut of \u003cem\u003eS. mansoni\u003c/em\u003e infected mice compared to uninfected controls [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, some studies align with our findings. For instance, Schneeberger \u003cem\u003eet al.\u003c/em\u003e suggested that \u003cem\u003eS. mansoni\u003c/em\u003e infection (in children) does not affect the gut microbial composition [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the taxonomic composition at the phylum level was similar in cirrhosis patients and healthy individuals (prevalence of \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e), aligning with \u003cem\u003eS. japonicum in vivo\u003c/em\u003e infections\u0026rsquo; data [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, even at the genus level, no major changes were detected, indicating that in our context, there is no gut dysbiosis in \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis patients. This result is somehow unexpected and different from other studies that have reported gut dysbiosis in the context of human schistosomiasis (e.g., those caused by \u003cem\u003eS. mansoni\u003c/em\u003e and \u003cem\u003eS. haematobium\u003c/em\u003e) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, \u003cem\u003ein vivo\u003c/em\u003e studies have reported \u003cem\u003eSchistosoma\u003c/em\u003e infection-related alterations in gut microbial composition. For instance, \u003cem\u003eS. japonicum\u003c/em\u003e infection in mice was associated with significant changes in the gut prevalence of \u003cem\u003eFirmicutes\u003c/em\u003e (relative decrease), \u003cem\u003eBacteroidetes\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e (relative increase) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, we did find some composition alterations in the gut microbiota of \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis patients. \u003cem\u003eBacilli\u003c/em\u003e (at the class level) and \u003cem\u003eLactobacillales\u003c/em\u003e (at the order level) were significantly increased in patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis compared to healthy individuals. Importantly, our results are consistent with those of a previous study, which may suggest that the increased prevalence of \u003cem\u003eLactobacillales\u003c/em\u003e in gut microbiota is a hallmark of human liver cirrhosis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, a different study showed that \u003cem\u003eLactobacillales\u003c/em\u003e and \u003cem\u003eBacilli\u003c/em\u003e were the key contributors to gut dysbiosis in primary liver cancer and proposed them as potential diagnostic markers [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Altogether, these findings suggest a strong association between gut \u003cem\u003eBacilli\u003c/em\u003e and \u003cem\u003eLactobacillales\u003c/em\u003e and liver disease that deserves to be further explored in different pathological contexts.\u003c/p\u003e \u003cp\u003eOverall, our data do not support the idea that \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced cirrhosis is associated with significant alterations of gut microbiota profiles, opposing hypotheses proposed by others concerning schistosomiasis. Some reasons may justify our opposing report. Different studies focused on different \u003cem\u003eSchistosoma\u003c/em\u003e spp. that have different organ tropisms: e.g., \u003cem\u003eS. mansoni\u003c/em\u003e and \u003cem\u003eS. japonicum\u003c/em\u003e target the gut and liver, while \u003cem\u003eS. haematobium\u003c/em\u003e targets the bladder and the urogenital system [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Importantly, the notion that \u003cem\u003eS. haematobium\u003c/em\u003e infection was equally associated with human gut dysbiosis suggests that potential alterations of intestinal microbial communities in the context of schistosomiasis are probably a consequence of infection-induced systemic immune responses (and not of local events). In fact, some studies suggest this [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For instance, mesenteric lymph node cells isolated from mice infected with \u003cem\u003eS. mansoni\u003c/em\u003e (dysbiotic) responded differently compared to the ones isolated from controls: higher interleukin (IL)-4, IL-10, and IL-17 secretion, together with lower interferon-γ production [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Relevantly, aging is accompanied by a deterioration of immune responses, recently named as immunosenescence or immune-aging [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In our study, the enrolled patients were quite old (average of 82.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u0026nbsp;years old), which may justify the absence of phenotype: their potentially weaker immune responses to \u003cem\u003eS. japonicum\u003c/em\u003e infection were not strong enough to impact their gut microbial communities.\u003c/p\u003e \u003cp\u003eNevertheless, our geographic target-region conditioned the age range of our study population, which is a limitation of our study. Since 1995, due to the application of effective Schistosomiasis control measures, there are almost no newly reported cases in Zhejiang [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Therefore, patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis are not many and tend to be of advanced ages [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, we cannot exclude a possible age selection bias that, however, reflects the reality of the population studied: among the cirrhosis patients, 17 and 7 individuals were 80 or 90\u0026nbsp;years of age or older, respectively, and none of the others was less than 60\u0026nbsp;years old. We can, however, also hypothesize that our population has a survival bias. Living patients may be the ones with better outcomes due to stable gut microbiota profiles, while the fatalities may have been a consequence of gut microbiota disorders. This is, however, only a speculative exercise. Therefore, further studies focusing on much larger and heterogeneous populations (different geographies, with a wider age range) are needed to completely understand if \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis is accompanied by gut microbiota alterations. In addition, the effective Schistosomiasis control in Zhejiang and advanced age of participants might affect our conclusion because it was generally almost impossible to include a cohort of age-matched acute \u003cem\u003eS. japonicum\u003c/em\u003e infected patients without liver cirrhosis.\u003c/p\u003e \u003cp\u003eAn additional factor that should be considered in future studies is the timing of infection, particularly if the study is designed to find biomarkers for cirrhosis diagnosis and/or prognosis. Indeed, the infection status may also condition the results. Human studies have mainly targeted adolescents [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] or children [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and consequently, the early phase of infection. Additionally, the most commonly used mouse model of schistosomiasis in microbiome studies evaluated acute infection \u0026ndash; less than 8 weeks post-challenge according to the model characterization [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The \u003cem\u003ein vivo\u003c/em\u003e published data refers to stool samples collected at 28 days [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], 42 days [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and 8 weeks [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] after infection. We cannot exclude that our different findings are just a consequence of our study context. Cirrhosis is the end-stage of chronic schistosomiasis, quite far time-wise from the early phase of infection. Both systemic immune responses and \u003cem\u003eSchistosoma\u003c/em\u003e infection were associated with gut microbiota alterations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, in \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis, both are expected to decrease in magnitude, which may partially explain our findings.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eIn conclusion, overall, we showed that gut microbial taxonomic profiles and diversity in \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis patients appear to be similar to the ones from age-matched healthy people. Therefore, in our context, patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis present a \u0026ldquo;healthy\u0026rdquo; gut structure, which may partially explain the better prognosis of cirrhosis secondary to this infection, compared to cirrhosis of different etiology in China [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Future studies with much larger sample sizes across wider age ranges are, however, needed to further explore the relationship between \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis and gut microbiota and potentially disclose the possible mechanisms by which gut microbiota alterations influence cirrhosis and/or \u003cem\u003evice versa\u003c/em\u003e. Importantly, these studies should also evaluate immunity and inflammation to finally address if the gut-liver axis is implicated in \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis development and progression. Such studies should be further complemented with \u003cem\u003ein vivo\u003c/em\u003e works with transplantation of human gut microbiota samples to ultimately establish (or exclude) a causal relationship between dysbiosis and cirrhosis secondary to schistosomiasis.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eOTUs: operational taxonomic units; DALYs: disability-adjusted life years; NTDs: Neglected Tropical Diseases; CT: computed tomography; PCA: principal component analysis; LDA: Linear Discriminant Analysis; LEfSe: LDA effect size; SD: standard deviation; FDR: false discovery rate; ALT: alanine aminotransferase; AST: spartate aminotransferase; BMI: body mass index; DB: direct bilirubin; GGT: glutamyl transpeptidase; PT: prothrombin time; SLC: \u003cem\u003eS. \u003c/em\u003e\u003cem\u003ejaponicum\u003c/em\u003e infection-induced liver cirrhosis; TB: total bilirubin; HSCs: hepatic stellate cells; IL: interleukin.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.cn) for English language editing, and BGI-Shenzhen for genome-sequencing services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Y.M.Y. and H.L.J. Data curation: Q.F.G. and F.Z. Formal analysis: Q.F.G., C.X. and J.X. Funding acquisition: Q.F.G., C.X. and Q.Z. Investigation: F.Z. Methodology: H.F.L. Supervision: Q.Z. Writing - original draft: Q.F.G. Writing - review \u0026amp; editing: Y.M.Y. and H.L.J. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Key Disciplines Construction Plan of Zhejiang Province Traditional Chinese Medicine (2017-XK-A31), the Chinese Medicine Research Program of Zhejiang Province (2020ZB148), and the National Key Research and Development Program of China (2018YFC2000301, 2018YFC2000500).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the conclusions of this article are included within the article. The datasets analysed during the present study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were approved by the First Affiliated Hospital Clinical Research Ethics Committee, School of Medicine, Zhejiang University (ref: 2017411-1), and the Jiaxing Wangdian People's Hospital Ethics Committee (ref: 2017002). All participants provided written informed consent in Chinese. Participation was entirely voluntary and only participants with signed informed consent forms were enrolled into the study. and the rights of these subjects were protected.\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\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCao ZG, Zhao YE, Lee Willingham A, Wang TP. Towards the Elimination of Schistosomiasis japonica through Control of the Disease in Domestic Animals in The People's Republic of China: A Tale of over 60Years. Adv Parasitol. 2016;92:269-306.\u003c/li\u003e\n\u003cli\u003eAndrade G, Bertsch DJ, Gazzinelli A, King CH. 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J Hepatol. 2014;60:940-7.\u003c/li\u003e\n\u003cli\u003eBajaj JS, Betrapally NS, Hylemon PB, Heuman DM, Daita K, White MB, et al. Salivary microbiota reflects changes in gut microbiota in cirrhosis with hepatic encephalopathy. Hepatology. 2015;62:1260-71.\u003c/li\u003e\n\u003cli\u003eLing Z, Liu X, Cheng Y, Jiang X, Jiang H, Wang Y, et al. Decreased Diversity of the Oral Microbiota of Patients with Hepatitis B Virus-Induced Chronic Liver Disease: A Pilot Project. Sci Rep. 2015;5:17098.\u003c/li\u003e\n\u003cli\u003eOikonomou T, Papatheodoridis GV, Samarkos M, Goulis I, Cholongitas E. Clinical impact of microbiome in patients with decompensated cirrhosis. World J Gastroenterol. 2018;24:3813-20.\u003c/li\u003e\n\u003cli\u003eBajaj JS, Ahluwalia V, Steinberg JL, Hobgood S, Boling PA, Godschalk M, et al. Elderly patients have an altered gut-brain axis regardless of the presence of cirrhosis. Sci Rep. 2016;6:38481.\u003c/li\u003e\n\u003cli\u003eZhang L, Wu YN, Chen T, Ren CH, Li X, Liu GX. Relationship between intestinal microbial dysbiosis and primary liver cancer. Hepatobiliary Pancreat Dis Int. 2019;18:149-57.\u003c/li\u003e\n\u003cli\u003eWalford RL. THE IMMUNOLOGIC THEORY OF AGING. Gerontologist. 1964;4:195-7.\u003c/li\u003e\n\u003cli\u003eFerrari TC, Moreira PR. Neuroschistosomiasis: clinical symptoms and pathogenesis. Lancet Neurol. 2011;10:853-64.\u003c/li\u003e\n\u003cli\u003eSeki T, Kumagai T, Kwansa-Bentum B, Furushima-Shimogawara R, Anyan WK, Miyazawa Y, et al. Interleukin-4 (IL-4) and IL-13 suppress excessive neutrophil infiltration and hepatocyte damage during acute murine schistosomiasis japonica. Infect Immun. 2012;80:159-68.\u003c/li\u003e\n\u003cli\u003eChen L, Chen Q, Hou W, He L. High-throughput dynamic analysis of differentially expressed genes in splenic dendritic cells from mice infected with Schistosoma japonicum. Immunol Lett. 2017;184:15-22.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Taba\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cp\u003eTable 1. \u003cbr /\u003eDemographic and clinical characteristics of the study population per group.\u003c/p\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eSLC group\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eControl group\u003c/div\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u003cspan class=\"Italic\"\u003eP-value\u003c/span\u003e\u003c/div\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003en\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e24\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e25\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGender (male/female)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12/12\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12/13\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.889\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAge (years)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e82.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.852\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eActive smoker (%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0%\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e12%\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.248\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eActive drinker (%)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0%\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e20%\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.066\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.915\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePlatelets (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e152.3\u0026thinsp;\u0026plusmn;\u0026thinsp;108.3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e179.6\u0026thinsp;\u0026plusmn;\u0026thinsp;59.5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.275\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003ePT (s)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.003\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTotal protein (g/L)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e59.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e74.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAlbumin (g/L)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e31.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e29.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.186\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGlobulin (g/L)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e44.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eTB (\u0026micro;mol/L)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e15.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.711\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eDB (\u0026micro;mol/L)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.509\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eAST (U/L)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e32.2\u0026thinsp;\u0026plusmn;\u0026thinsp;17.5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e17.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e\u0026lt;\u0026thinsp;0.001\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eALT (U/L)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.0\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.114\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eGGT (U/L)\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e61.0\u0026thinsp;\u0026plusmn;\u0026thinsp;51.6\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e33.6\u0026thinsp;\u0026plusmn;\u0026thinsp;21.5\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e0.024\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eChild-Pugh\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eA\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e13\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e/\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eB\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e7\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e/\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eC\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003e/\u003c/div\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cdiv class=\"SimplePara\"\u003eNA\u003c/div\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003eNote: The continuous and categorical variables are listed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and percentage (%), respectively. Statistical differences were determined using the Student's t-test (parametric data) or the Wilcoxon test (non-parametric data) and the \u0026chi;2 test for continuous and categorical variables, respectively. Abbreviations: ALT - alanine aminotransferase; AST - aspartate aminotransferase; BMI - body mass index; DB - direct bilirubin; GGT - glutamyl transpeptidase; PT - prothrombin time; SLC - \u003cspan class=\"Italic\"\u003eS. japonicum\u003c/span\u003e infection-induced liver cirrhosis; and TB - total bilirubin.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiota, 16S Rrna, Schistosoma japonicum","lastPublishedDoi":"10.21203/rs.3.rs-71566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-71566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\u003cp\u003eOne of the serious complications induced by \u003cem\u003eSchistosoma japonicum\u003c/em\u003e infection is the development of liver cirrhosis. Several studies have assessed the role of gut microbiota in cirrhosis of different etiologies, but none has done so in the context of \u003cem\u003eS. japonicum\u003c/em\u003e infection in humans. Here, to explore the possible role of gut microbiota in \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis, we conducted an observational clinical study.\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eTwenty-four patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis and 25 age- and gender-matched controls from Zhejiang Province, China, were enrolled in the study. Fecal samples were collected and used for 16S rRNA gene hypervariable V4 region Illumina MiSeq sequencing.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eEight hundred seven operational taxonomic units (OTUs) were detected, of which 491 were common in the two groups, while 123 and 193 were unique for the control and cirrhosis groups, respectively. Overall, no significant differences in both alpha and beta diversities and overall microbiota structure were found in the two groups. However, \u003cem\u003eBacilli\u003c/em\u003e (Class) and \u003cem\u003eLactobacillales\u003c/em\u003e (Order) were significantly higher in patients with \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis than in healthy individuals.\u003c/p\u003e\u003cp\u003eConclusions\u003c/p\u003e\u003cp\u003eAltogether, our results suggest that the gut microbiota of \u003cem\u003eS. japonicum\u003c/em\u003e infection-induced liver cirrhosis patients is similar to that of healthy individuals.\u003c/p\u003e","manuscriptTitle":"Gut microbiota Signatures in Schistosoma japonicum Infection-Induced Liver Cirrhosis Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-15 15:30:51","doi":"10.21203/rs.3.rs-71566/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2020-10-12T12:00:00+00:00","index":2,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"decision","content":"Major revision","date":"2020-10-12T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-10-07T12:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2020-09-28T12:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-09-22T12:00:00+00:00","index":1,"fulltext":""},{"type":"editorAssigned","content":"","date":"2020-09-16T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-09-16T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-09-15T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-09-12T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-09-10T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65e89c7c-686a-4756-bd1a-602b79451217","owner":[],"postedDate":"September 15th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":510154,"name":"Infectious Diseases"}],"tags":[],"updatedAt":"2021-03-29T15:00:52+00:00","versionOfRecord":{"articleIdentity":"rs-71566","link":"https://doi.org/10.1186/s40249-021-00821-8","journal":{"identity":"infectious-diseases-of-poverty","isVorOnly":false,"title":"Infectious Diseases of Poverty"},"publishedOn":"2021-03-26 15:00:31","publishedOnDateReadable":"March 26th, 2021"},"versionCreatedAt":"2020-09-15 15:30:51","video":"","vorDoi":"10.1186/s40249-021-00821-8","vorDoiUrl":"https://doi.org/10.1186/s40249-021-00821-8","workflowStages":[]},"version":"v1","identity":"rs-71566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-71566","identity":"rs-71566","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}