Effects of photo-biomodulation on Gut Microbiota: A Meta-analysis Based on 16S rRNA Sequencing Data

Effects of photo-biomodulation on Gut Microbiota: A Meta-analysis Based on 16S rRNA Sequencing Data | 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 Effects of photo-biomodulation on Gut Microbiota: A Meta-analysis Based on 16S rRNA Sequencing Data Letian Chen, Shijing Wang, Yueying Lu, Jingjing Cui, Yuming Fu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4279563/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Photo-biomodulation (PBM) can affect the gut microbiome, which may be one of the important mechanisms by which such therapy exerts its effects. However, with the burgeoning interest in the gut microbiome, differences in research methods and conditions among independent studies make it a challenge to fully understand the impact of the gut microbiome on the health of organisms. Therefore, we conducted a meta-analysis of 16S rRNA gene datasets generated by 8 published studies about the effects of PBM on the gut microbiome to investigate how the gut microbiome is altered across wave bands (infrared [IR], ultraviolet [UV] and visible light [VIS]), species (human, rat and mouse), and physique (healthy and unhealthy). Our results show that PBM can significantly affect the community diversity, species composition, and overall expression of metabolic pathways of the gut microbiome. VIS and UV can enrich key probiotics, such as Akkermansia muciniphila , in the gut microbiome while IR leads to a decrease of probiotics and an increase in harmful bacteria. In summary, we systematically analyzed the changes in the gut microbiome under different light conditions, providing a theoretical basis for the clinical application of PBM to regulate human health. 16S rRNA sequencing photo-biomodulation gut microbiome meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Photo-biomodulation (PBM) refers to the use of visible, infrared, and ultraviolet light to treat immune system dysfunction, alleviate inflammation, combat osteoporosis and address various other health problems ( 1 ). Although this technique has been in use since the early 20th century ( 2 ), PBM in the context of modern medical practice was redefined by Mester et al. ( 3 ). As a treatment widely used in clinical practice, PBM is a major subject of research interest. Several studies have demonstrated an inextricable connection between PBM and the gut microbiome. Deaver et al. ( 4 ) found that light influences the gut microbial community via daily circadian rhythms. Moreover, light has also been shown to exert indirect effects on microbial communities through pathways such as vitamin D secretion, which plays a role in immune function and gut microbiome stability ( 5 – 8 ). The association between the gut microbiome and various diseases has garnered widespread attention, particularly the role of the gut microbiome in conditions ranging from cardiovascular to neurological diseases ( 9 – 12 ). With the important links between the gut microbiome and disease in mind, we explored how PBM interacts with the gut microbiome. Importantly, combining microbiota inoculation with PBM has been shown to effectively enhance therapeutic outcomes. Tang et al. ( 13 ) found that inoculation of Saccharomyces boulardii during PBM significantly accelerated the regression rate of jaundice in newborns. Thus, understanding how PBM can influence the physiology of organisms via the gut microbiome has considerable health implications. The relationship between PBM and the gut microbiome has been explored ( 14 – 16 ). However, the majority of studies have concentrated on the processes through which PBM and the gut microbiome collaboratively influence an organism’s vital functions; evidence on how PBM impacts the structure and function of the gut microbiome remains limited. In addition, the substantial variability in research subjects and analytical methods precludes a unified understanding and consensus across studies. Furthermore, research incorporating the gut microbiome within the scope of PBM—considering variables such as different wavelengths—is scant. The relatively homogeneous sample structures in existing studies also contribute to an incomplete understanding of the impact of PBM on the gut microbiome. In the present study, we systematically investigated the relationship between PBM and the gut microbiome, in the form of meta-analysis, by querying published reports and existing databases for the impact of PBM on the gut microbiome, integrating relevant studies to answer more general scientific questions about how PBM affects changes in the gut microbiome. This approach will enable the development and optimization of PBM with the gut microbiome as the vector and target, which may then form the basis for effective clinical treatments for osteoporosis, colitis, and other diseases related to the gut microbiome. Materials and Methods Data set selection and filter We queried publicly available databases, including NCBI and Web of Science (WOS), for studies related to the keywords “photo-biomodulation” and “Gut Microbiome” using medical subject headings (MeSH). Each study was manually evaluated to ascertain if it satisfied the inclusion criteria. Criteria for study selection based on pre-defined inclusion and exclusion standards. Studies were included if they: (i) used PBM or alternative light exposure on samples; (ii) used 16s rRNA sequencing for the characterization of the microbiome; (iii) concentrated exclusively on the gut microbiome within living organisms; and/or (iv) offered accessible raw data for analysis. Studies were excluded if they were: (i) reviews, systematic reviews, or meta-analyses; (ii) studies subject to duplicate publication; (iii) studies that utilize ionizing radiation as an intervention measure; and/or (iv) studies that lack critical information necessary, such as primer sequences or essential metadata. The metadata were further summarized from studies that fulfilled the criteria for meta-analysis. We used the wave bands of PBM as the principal factor modulated and controlled for, and other variables (i.e., species, authors, physique, etc.) as minor factors. Some studies included PBM with full-spectrum irradiation. These were grouped with VIS because the exposure was concentrated within the visible light spectrum. In addition, physique was simplified to logical data according to diseased or not (i.e., healthy and unhealthy) because of the multiple levels. Sequence Preprocessing and Taxonomic Profiling Sequencing data was downloaded, using Aspera ( https://www.ibm.com/products/aspera ), directly from the NCBI Sequence Read Archive (SRA) through the accessions listed in Table S1 , except for the study of Chen et al. ( 17 ), whose data we obtained from the corresponding author. After organizing the sequencing data and summarizing metadata from the mapping files (the mapping file from Bosman et al. ( 16 ) was obtained through email), Vsearch v2.14.2 ( 18 ) and Usearch v11.0.667 ( 19 ) were applied for sequence preprocessing. Vsearch was first used to merge the sequencing data with paired reads ( 15 , 16 , 20 , 21 ), and remove the barcodes, primers and redundancy, while non-cluster denoising was performed using Usearch. A high-quality filtered sequence with more than 97% consistency was clustered to obtain amplicon sequence variants (ASVs). After removing chimeras and creating a features table, the ASVs were annotated based on the SILVA-138-99 taxonomy database ( 22 , 23 ) using Vsearch with a confidence cutoff of 0.8. Diversity analysis Prior to further analysis, the feature table obtained from annotation was standardized for rarefaction with the R package, vegan v2.6.4 ( 24 ), with the minimum sample data amount. For α diversity, Chao1 and Shannon metrics were generated with Usearch v10.0.240 ( 25 ). We performed four sets of α diversity analysis: 1) differences across the four wave bands as a major factor and fixed effect (IR, UV, VIS, Control), and 2) differences across species (humans, rats, mice), 3) physique (healthy and unhealthy), and 4) authors, as minor factors. Significance was evaluated with a t-test for physique and ANOVA with Tukey’s HSD correction for the others. Generalized linear mixed models (GLMM) were fitted with the R package, lme4 v1.1.33 ( 26 ). Authors and species were used as random effects in the model. For β diversity, weighted-Unifrac distance metrics were generated with Usearch v10.0.240 and principal coordinate analysis (PCoA) was carried out with the vegan v2.6.4 package. To identify significant differences among groups, a permutational multivariate analysis of variance (PERMANOVA) was used with adonis2. Group dispersion was calculated using betadisper before testing for significance. Network Analysis To explore the co-association across wave bands, Spearman correlation coefficients for abundant ASVs (above 0.05% total relative abundance and present in at least five samples) were computed using Hmisc v5.0.1 in R ( 27 ). Based on this, gut microbiome interaction networks under different wave bands was constructed using thresholds of 0.7 for the correlation coefficient and 0.05 for the P-value. Network metrics, including average, network diameter, graph density, modularity, average clustering coefficient and average path length, were calculated ( 28 ). Network visualization was performed with Gephi v0.10 ( 29 ) using the Fruchterman Reingold layout. Differential Abundance and Random Forest Classification Differential abundance was computed using LEfSe ( 30 ), using the default, recommended settings at an adjusted P-value of 0.05 for significant taxa and an LDA effect size of at least 4 for every significant taxon. GLMM were fitted with the R package, glmmTMB v1.1.7 ( 31 ). Authors and species were used as a random effect in the model. The random forest model was trained with a five-fold cross-validation using the R package, randomForest ( 32 ) to identify key genera as biomarker taxa correlated with wave bands. 80% of the samples were allocated as the training set, using species classification data that was distilled to the genus level as the input predictive variables. Functional Prediction Profiles We used PICRUSt ( 33 ) to infer the functional potential of 16S rRNA gene data between the UV, IR, VIS, and control. The outputs with database Kyoto encyclopedia of genes and genomes (KEGG) ( 34 ) orthologs were used for analysis of variance. The Kruskal-Wallis and LSD tests were used to analyze the differences in functional and metabolic pathways across wave bands; the Wilcoxon test was used to analyze the differences between the control and the other groups. Since low-abundance pathways contribute negligibly to the gut microbiome, we excluded pathways manifesting relative abundances under 1%. Results Summary of Studies of the Gut Microbiome under PBM Searching in NCBI and WOS yielded 330 articles, with 282 articles obtained after eliminating duplications (Fig. 1 ). Of these, 33 met our criteria for meta-analysis during pre-selection, while 16 lacked clear information regarding a specific, public location of sequencing data; 9 were missing metadata for analysis. Finally, datasets were acquired from 8 gut microbiome studies, two of which provided sequencing data and metadata after emailing the corresponding authors. Of the 442 samples included and their corresponding sequencing data, 213 samples lacked the corresponding metadata, so 229 valid samples were finally included, with 126 human samples and 103 animal samples. The relevant descriptions and metadata are listed in Table S1 . After a series of preprocessing, including denoising the sequencing data, OTUs which were not assigned at the genus level were discarded. Collapsing the data to the genus level reduced the sensitivity to fine-scale differences in species or strain abundances across case and control groups, but we were able to effectively attenuate batch effects affecting cross-study comparisons in meta-analysis. Nonetheless, one study ( 21 ) did not pass quality filtering and was removed from the analysis. Community Diversity and Structure Significantly Change under PBM For α diversity, in addition to wave bands (Fig. 2 A-B), there were significant differences across species and authors (Fig S1 ). The same results were obtained from the analysis of β diversity distance (Fig S2). Therefore, we used GLMM to analyze α diversity to strip the role of minor factors in the influence of PBM on gut microbiome, and then explored the influence of different wave bands on the gut microbiome. As expected, authors and species had a strong effect on both Chao1 richness and Shannon diversity as random effects (Fig S 3). After stripping away the random effects, we found that infrared irradiation in the model’s prediction leads to a significant decrease in the Chao1 index (P < 0.01), while visible light and ultraviolet radiation reduce and increase Chao1 richness, respectively, but neither was significant (Fig. 2 C). For the Shannon diversity generalized linear hybrid model, the influence trend of the illumination band is basically the same as that of the Chao1 richness model, but the statistical results show that only ultraviolet radiation in the model’s prediction leads to a significant increase in the Shannon index (P < 0.01, Fig. 2 D). Among these indices, Chao1 richness mainly characterizes the number of community species by the number of highly abundant species and the estimated number of rare species, while Shannon diversity superimposes the proportion of species on the basis of the number of species to characterize the uniformity of species distribution. Based on this, it appears that infrared and visible light irradiation mainly affect the total number of species of intestinal flora, resulting in a decrease in the α diversity of the gut microbiota. While ultraviolet irradiation has a certain impact on the total number of species, it also greatly improves the uniformity of species, thereby improving the α diversity of gut microbiota. An interaction network can reflect the relationships of cooperation and competition between microorganisms, which drives the dynamic changes in the community composition and function of the gut microbiome, ultimately affecting the influence of the gut microbiome on the host. We constructed an interaction network at the genus level to analyze the changes in the gut microbiome under different wave bands of illumination, from the perspective of the community network. In four interacting networks, 40, 45, 60 and 29 nodes are connected by 76, 167, 183 and 64 edges, respectively (Fig. 3 A). The network nodes are mainly Firmicutes and Bacteroidetes , and a change in the number of nodes of the two phyla was observed between the different groups, with the phyla Verrucomicrobia and Saccharibacteria increasing in the IR and UV groups (Fig. 3 A). In addition, the weighted, proximity, and mediation centrality of the interaction network in the IR group were significantly greater than those in control group (Fig. 3 B), indicating that the integrity of the gut microbiota and the interspecies information transfer efficiency after infrared irradiation were significantly improved. Analysis of the robustness also revealed that the average and natural connectivity of the IR group were markedly greater than those of the other groups (Fig. 3 C). As for negative and positive cohesion, the proportion of positive cohesion in the IR group was significantly reduced (Fig. 3 D), although relatively high in each group. The Composition of the Gut Microbiota Exhibits an Opposite Response across Wave Bands Preliminary observation of the stacked histogram of the phylum composition in different health states and wave bands (Fig. 4 A) showed that the relative abundance of Verrucomicrobia and Saccharibacteria show similar trends to those in the interaction network. We first used GLMM model to explore the influence of light band on the change in relative abundance of the two phyla, using the beta distribution as a function distribution family of relative abundance between 0 and 1. For the GLMM model of Verrucomicrobia , we found that infrared irradiation led to a significant decrease in the relative abundance in the model (P < 0.05, Fig. 4 B), while visible and ultraviolet irradiation increased the relative abundance, with the effect of visible light being particularly significant (P < 0.001, Fig. 4 B). For the GLMM model of Saccharibacteria , we found that infrared irradiation led to a significant decrease in the genus’ relative abundance in the model (P < 0.001, Fig. 4 C), while visible and ultraviolet radiation reduced and increased the relative abundance, respectively, but neither was significant. For the random-effects aspect of the two models, we found that author had a limited effect on the relative abundance of Verrucomicrobia (Fig S 4C), while the effect on Saccharibacteria was negligible (Fig S4A). In contrast, the effect of species was the inverse of the study authors (Fig S4B, D). We next used LDA effective size (LEfSe) to determine the changes in bacterial abundance in the gut microbiome related to PBM, especially Verrucomicrobia . As shown in the species taxonomic clade (Fig. 4 D), the Verrucomicrobia phylum was significantly enriched in the PBM and UV groups, which is basically consistent with the prediction of the generalized linear mixing model. Notably, at the genus taxonomic level, the visible light-irradiated PBM group was enriched with Akkermansia muciniphila of the Verrucomicrobia phylum, based on a generalized linear mixed model to simulate the relative abundance changes of only this genus; the same significant results were obtained (P < 0.001, Fig S6), while infrared irradiation of PBM significantly reduced the abundance of this genus (P < 0.05, Fig S6). This genus of probiotics was first identified in the early 21st century. Its deficiency or decrease has been associated with a variety of diseases, such as obesity, diabetes, hepatic steatosis, inflammation, and malignancy( 22 ). In order to explore the changes in the gut microbiome, especially probiotics, under different wave bands more generally, we further constructed a random forest classifier of five-fold cross-validation using species classification data down to the genus level as input, and identified key bacterial genera as biomarker taxa associated with different wavelength light. The cross-validated error curve tended to stabilize when 20 genera were used (Fig S5), so these 20 genera were used as biomarker taxa. According to the model prediction results (Fig. 4 E), the 20 marker genera were distributed in the four phyla of Firmicutes , Proteobacteria , Bacteroidetes and Actinobacteria , which predominate the gut microbiome. Among these, the proportion of markers from the phylum Firmicutes was up to 12, and four of the five most abundant genera belonged to this phylum. The rho heat map reveals that nearly half of the marker genera showed high relative abundance in the IR group, that is, as microbial markers of infrared irradiation, while three of the top five genera showed high relative abundance in the IR group (Fig. 4 F). Infrared Exerts Negative Effects on Functional Pathways of the Gut Microbiome We examined the functional Pathways associated with gut microbiome compositions using the Tax4Fun software. Among the pathways with relative abundance above 1% (Fig. 5 A), metabolism-related pathways accounted for more than 50%, with the Kruskal-Wallis test of KEGG level 2 pathways showing that there were significant differences in xenobiotics biodegradation and metabolism, and metabolism of various nutrients (P < 0.05). In terms of genetic and environmental information processing, although genetic translation had no significant differences, gene replication, repair, and folding and sorting and degradation pathways were significantly different (P < 0.05); the associated signal transduction and membrane transport pathways also displayed more significant changes (P < 0.01). In addition, prokaryotic cellular processes exhibited significant changes (P < 0.01, Fig. 5 S). In general, there was trend that infrared light decreased pathway expression while ultraviolet increased it. For pathways with unique changes in one group, infrared significantly changed the expression of more pathways, which manifested as a decrease in cellular processes of prokaryotes and an increase in the pathways of genetic information. In the other two groups, UV increased glycan biosynthesis and metabolism, and reduced the expression of membrane transport pathways. In contrast, the PBM group exhibited a weaker effect on function and metabolic pathways than the other two groups, which may be attributed to the relatively weak penetration of visible light. Of note, the gut microbiome in the IR group produced a significant reduction in antimicrobial resistance (P < 0.05), though there was no difference in the intergroup comparison. We also analyzed those KEGG level 3 pathways that belonged to the KEGG level 2 pathways which had unique changes in each group (Fig. 5 B-C). For the IR group, three pathways related to biofilm formation were significantly reduced under infrared irradiation (P < 0.05); drug resistance to cationic antimicrobial peptide (CAMP) and beta − lactam also decreased. In terms of genetic information processing, the expression of sulfur relay system and protein export was upregulated (P < 0.01), while the expression of gene repair pathways, such as base excision and mismatch repair, as well as DNA replication pathways closely related to microbial reproduction, were significantly increased (P < 0.05). For the UV group, we found that the expression of five pathways related to glycosphingolipid biosynthesis and degradation was significantly increased (P < 0.05), and the expression of N-glycan biosynthesis and some of the glycan degradation pathways was also significantly increased (P < 0.001). The group UV generally showed a marked upward regulation trend in the expression of each pathway. Discussion Based on 229 sample data from 8 articles, we performed a meta-analysis of clinical studies and experimental models. Although the sample size was relatively limited, the data still contain relatively broad and important factors worthy of examination, such as wave bands and species. After preprocessing the experimental data, we found that the analytical methods used in this project drew the same conclusions as the original research trends, and reducing the accuracy of data analysis to the genus level did not affect the accuracy of the gut microbiome analysis, but also effectively reduced the batch effect between different studies ( 24 ), which facilitates better aggregation and integration of sequencing data from different studies. Based on our analysis of α and β diversity, the impact of different light wavelengths on gut microbiota diversity is most pronounced with UV and IR, which is closely related to the higher penetrability of these two non-visible light types compared to VIS ( 25 )(Fig. 3 ). Akkermansia muciniphila is a key probiotic found in the gut ( 27 ). Its reduced presence can lead to thinning of the mucosal layer and weakening of the intestinal barrier function, making it easier for toxins to enter the body ( 28 ). The significant reduction in the abundance of this genus due to infrared means that when using this wavelength for patient treatment, additional supplementation of Akkermansia muciniphila should be considered to avoid side effects related to its reduction. Conversely, Akkermansia muciniphila plays an important role in the process of tumor rehabilitation and obesity improvement ( 30 ). Another study has found that centenarians have higher levels of Akkermansia muciniphila in their gut ( 31 ). The significant increase in the abundance of this species in the gut microbiome in the VIS group may lead to a an approach to promote the colonization and enrichment of supplemental or native Akkermansia muciniphila in clinical treatment. Saccharibacteria is widely distributed in the oral cavity, stomach, skin, and gut of healthy individuals; this phylum may be associated with the occurrence and recovery of various types of inflammation ( 32 ). It has been suggested that Saccharibacteria may reduce serum creatinine concentration, increase glomerular filtration rate, and improve kidney function ( 33 ). In a study of Clanis bilineata tsingtauica , Qian et al. ( 34 ) found that TM7 ( Saccharibacteria ) can regulate the secretion of various important metabolites through amino acid and carbohydrate metabolic pathways. The abundance of this phylum is also strongly negatively affected by infrared, which introduces a potential risk during PBM treatment. Furthermore, random forest prediction results also indicated that VIS and UV irradiation enrich probiotic genera within the gut microbiota ( 35 – 37 ), while infrared leads to the enrichment of more harmful bacteria in the gut microbiota ( 38 , 39 ). These enriched bacteria can affect the overall gut microbiota by consuming intestinal substances, secreting metabolites, and affecting other microbiota, ultimately leading to changes in human health. The above phenomenon may be caused by changes in the overall secretion capacity of short-chain fatty acids ( 40 ) as well as alterations in the physiological and biochemical functions related to drug resistance ( 41 ) in the gut microbiome under the influence of PBM. Specifically, genetically related pathways are upregulated in the gut microbiome after infrared irradiation. However, there is significant downregulation in pathways related to the formation of biofilms and antibiotic resistance, leading to a decrease in important physiological and biochemical functions, such as resistance to external environmental disturbances, cell division, and communication ( 40 – 42 ), which, in turn, leads to the reduction or even disappearance of some species in the gut microbiota. This may underlie why GLMM predicts a decrease in α-diversity and a reduction in the relative abundance of Saccharibacteria and Akkermansia muciniphila after infrared irradiation. However, infrared has been used to treat diseases, such as Parkinson and Alzheimer disease, which suggests that the therapeutic effect of infrared may not be achieved through the effect of specific bacteria. Further analysis of the gut microbiota interaction network revealed that the proportion of positive and negative correlations remains relatively balanced, indicating more complex network attributes. Positive correlations between nodes have been shown to represent cooperative interspecies relationships, such as mutualistic symbiosis, while negative correlations represent competitive and antagonistic interspecies relationships, such as predation and competition ( 26 ), indicating that infrared will enhance the competitive nature among various species within the gut microbiota, leading to a balance between species competition and cooperation. Consequently, the overall integrity of the gut microbiota and the efficiency of interspecies information exchange are significantly improved, ultimately promoting the stability of the microbial community and enhancing its resistance to disturbances (Fig. 3 D). Therefore, the gut microbiome plays a role in the process by which infrared radiation affects the vital activities of the organism, which means that the macroscopic structure and metabolic changes in the microbiome can be causal to disease improvement. The gut microbiota after UV irradiation generally exhibited upregulation in pathways involved in N-glycan and glycosphingolipid synthesis. N-glycan, covalently linked to proteins via N-glycosidic bonds at asparagine residues, plays a crucial role in protein synthesis processes ( 43 ). Glycosphingolipids are widely present in human nerve cells and play important roles in the transmission of nerve impulses. In the gut microbiome, glycosphingolipids are located on the cell membrane and participate in intermicrobial communication ( 44 ). Studies have shown that Akkermansia muciniphila is able to utilize N-glycan and glycosphingolipids as important carbon sources ( 53 ), and the abundance of Akkermansia muciniphila decreases significantly under conditions of N-glycan deficiency ( 54 ). Moreover, N-glycan is an essential nutrient source for Akkermansia muciniphila in vitro. Therefore, UV irradiation may enhance the abundance of Akkermansia muciniphila by enhancing the overall synthesis of N-glycan and glycosphingolipids in the gut microbiome, a view supported by the GLMM predictions (Fig. 3 D). Conclusion Here, we conducted a systematic review and meta-analysis to elucidate the association between PBM and the gut microbiome. We explored the effects of PBM light wavelengths as environmental factors on the composition, structure, and functionality of the gut microbiome. Our outcomes reveal that UV and VIS exposure lead to a significant enrichment of probiotics within the gut microbiota, while infrared exposure had the opposite effect. These observations indicate that PBM can be strategically utilized as a potential adjunct in modulating the gut microbiome. Nonetheless, it is imperative to recognize the adverse effects that PBM may exert on the gut microbiome, which warrants consideration and provides valuable insights for subsequent empirical investigation into the mechanisms by which PBM affects organism health and disease through its interaction with the gut microbiome. Furthermore, by integrating the effects of infrared irradiation on various aspects of the microbiome, we found that infrared PBM elicits significant differences in the mechanisms of action on vital activities compared to other wave bands. This represents an important direction worthy of attention in subsequent research. Declarations Acknowledgement This work was financially supported by the National Natural Science Foundation of China (82271921, 32261133528) and the Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team (JCTD-2020-04). Author Contributions LC performed the analysis and wrote the manuscript. YL, JC and YF helped with writing the manuscript. SW helped sort metadata file. LC, YF and HL conceived of this study and assisted with the interpretation of results. All authors approved the final manuscript. Data Availability Sequence data available on NCBI are listed in Supplementary Table 1. The datasets and R code generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare no competing interests. References Lipko, N. B. (2022) Photobiomodulation: Evolution and Adaptation. Photobiomodul Photomed Laser Surg 40 , 213–233. https://doi.org/10.1089/photob.2021.0145. Grzybowski, A. and Pietrzak, K. (2012) From patient to discoverer--Niels Ryberg Finsen (1860–1904) --the founder of phototherapy in dermatology. Clin Dermatol 30 , 451–455. https://doi.org/10.1016/j.clindermatol.2011.11.019. E. Mester, G. Ludany, M. Selyei, and B. Szende, “The stimulating effect of low power laser rays on biological systems.,” Medical Univ., Budapest, 1968. 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(2004) Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int J Syst Evol Microbiol 54 , 1469–1476. https://doi.org/10.1099/ijs.0.02873-0. Naito, Y., Uchiyama, K. and Takagi, T. (2018) A next-generation beneficial microbe: Akkermansia muciniphila. J Clin Biochem Nutr 63 , 33–35. https://doi.org/10.3164/jcbn.18-57. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.revised.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. 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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-4279563","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293572982,"identity":"1c1b0d0f-f3e8-4fc0-b738-9b0954d43efd","order_by":0,"name":"Letian Chen","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Letian","middleName":"","lastName":"Chen","suffix":""},{"id":293572985,"identity":"4fcc7d1b-7521-4abd-b175-94a055a0aee3","order_by":1,"name":"Shijing Wang","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Shijing","middleName":"","lastName":"Wang","suffix":""},{"id":293572988,"identity":"921a90b6-ac85-46c6-8cf0-901141a41664","order_by":2,"name":"Yueying Lu","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Yueying","middleName":"","lastName":"Lu","suffix":""},{"id":293572991,"identity":"8eb44350-0bd0-4025-8fb2-57e880bda59a","order_by":3,"name":"Jingjing Cui","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Cui","suffix":""},{"id":293572994,"identity":"c614e43c-b867-491b-b7e7-649de82c8ac8","order_by":4,"name":"Yuming Fu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBAC9gYQacDAwC8BEWBsIKSF5wBUi+QM0rSAdN0gWotEjpnUjYI7dptv9xh++MFgI7vhAPOzB4S0SOcYPEvedueMsWQPQ5rxhgNs5gb4tNhDtBxONruRYyDBw3A4ccMBHjYJImw5nGw8I8f45x+G/8RrsTMAMXgYDhChhedZsTVQS4LEnWNl1jIGycYzD7OZ4dfCnrzxds6fw/b8s5s333xTYSfbd7z5GV4tDAwc4OBJbABzQGxm/OqBgP0BiLQnqG4UjIJRMApGLgAAyThGmLyBTG4AAAAASUVORK5CYII=","orcid":"","institution":"Beihang University","correspondingAuthor":true,"prefix":"","firstName":"Yuming","middleName":"","lastName":"Fu","suffix":""},{"id":293572996,"identity":"27c77d00-2c2b-4b52-af8d-3329ffe7ca38","order_by":5,"name":"Hong Liu","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-04-17 05:59:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4279563/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4279563/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55094293,"identity":"ca5e4c50-b9ce-4f9f-895c-52e305824c70","added_by":"auto","created_at":"2024-04-22 13:41:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":532069,"visible":true,"origin":"","legend":"\u003cp\u003eSelection strategy for studies included in the meta-analysis\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4279563/v1/6058b554c6ee262a69acd815.png"},{"id":55093880,"identity":"7aca7493-8383-45e5-8cc2-6b3aa619f230","added_by":"auto","created_at":"2024-04-22 13:33:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":553905,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of alpha (Chao1 and Shannon) diversity among wave bands. A, B Preliminary comparison of alpha diversity. Different letters indicate significant differences (P \u0026lt; 0.05). C, D Comparison of the alpha diversity among wave bands with GLMM. The control group is shown as the baseline (*P \u0026lt; 0.05, **P \u0026lt; 0.01). IR: Infrared; VIS: visible light; UV: Ultraviolet.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4279563/v1/a731dd9b5130417a6b1a8d97.png"},{"id":55093881,"identity":"accacee3-7fa9-4060-8ffb-15fec4b20574","added_by":"auto","created_at":"2024-04-22 13:33:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2139140,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial network and relevant indicators across wave bands. A The Nodes represent genera; node size represents degree; colors represent different phylum levels; edge color represents correlation among nodes. B Comparison of betweenness centrality, closeness centrality, and weighted degree among the networks. C Robustness analysis after random removal of 5-40% nodes based on average degree and natural connectivity. D Cohesion index, which reflects the dynamic changes in microbial community structure through the proportion of positive (cooperative) and negative (competitive) correlations. IR: Infrared; VIS: visible light; UV: Ultraviolet.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4279563/v1/f07d7ac572b255ac95a32886.png"},{"id":55093886,"identity":"58426a30-dd86-4ed2-b72c-a0046bb1f736","added_by":"auto","created_at":"2024-04-22 13:33:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2440189,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic composition based on GLMM and the differential genera (biomarkers) associated with wave bands. A Relative abundance of the 7 most abundant phyla. B, C Comparison of the effects on \u003cem\u003eSaccharibacteria\u003c/em\u003e and \u003cem\u003eVerrucomicrobia\u003c/em\u003e among wave bands based on GLMM, with the control group as the baseline\u003cem\u003e \u003c/em\u003e(*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001). D Cladogram of the microbial species with significant differences. The colors indicate the different wave bands, with the species classification at the level of phylum, class, order, family, and genus. E A random forest approach was used to identify 20 genera, ranked in order of contribution from largest to smallest. F Heatmap showing the relative abundance of the 20 biomarkers. IR: Infrared; VIS: visible light; UV: Ultraviolet.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4279563/v1/59a7a47fc51a470bf08352a6.png"},{"id":55093882,"identity":"603dfd38-85c4-4267-891e-7a5e2642cda1","added_by":"auto","created_at":"2024-04-22 13:33:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2113652,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive functional profiling of microbial communities by PICRUSt analysis. Multiple comparisons were performed on A KEGG 2 pathways between wave bands (Kruskal-Wallis test). Comparisons were performed on KEGG 3 pathways of B the IR group and C the UV group compared to the control group (Wilcoxon test). *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001; IR: Infrared; VIS: visible light; UV: Ultraviolet.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4279563/v1/0a655f6bd4eb689330e5788b.png"},{"id":56731822,"identity":"30eed13c-ab50-4ed4-8235-ea2f60f081fa","added_by":"auto","created_at":"2024-05-19 13:01:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6219547,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4279563/v1/a912c7f6-05f9-4638-b617-019df8295c61.pdf"},{"id":55094294,"identity":"c09ddf64-4e60-4210-8eb4-dcf423645a5f","added_by":"auto","created_at":"2024-04-22 13:41:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1458906,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.revised.docx","url":"https://assets-eu.researchsquare.com/files/rs-4279563/v1/b82535ed94e1dc3202de83dc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of photo-biomodulation on Gut Microbiota: A Meta-analysis Based on 16S rRNA Sequencing Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhoto-biomodulation (PBM) refers to the use of visible, infrared, and ultraviolet light to treat immune system dysfunction, alleviate inflammation, combat osteoporosis and address various other health problems (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although this technique has been in use since the early 20th century (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), PBM in the context of modern medical practice was redefined by Mester et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). As a treatment widely used in clinical practice, PBM is a major subject of research interest.\u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated an inextricable connection between PBM and the gut microbiome. Deaver et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) found that light influences the gut microbial community via daily circadian rhythms. Moreover, light has also been shown to exert indirect effects on microbial communities through pathways such as vitamin D secretion, which plays a role in immune function and gut microbiome stability (\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The association between the gut microbiome and various diseases has garnered widespread attention, particularly the role of the gut microbiome in conditions ranging from cardiovascular to neurological diseases (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). With the important links between the gut microbiome and disease in mind, we explored how PBM interacts with the gut microbiome. Importantly, combining microbiota inoculation with PBM has been shown to effectively enhance therapeutic outcomes. Tang et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) found that inoculation of \u003cem\u003eSaccharomyces boulardii\u003c/em\u003e during PBM significantly accelerated the regression rate of jaundice in newborns. Thus, understanding how PBM can influence the physiology of organisms via the gut microbiome has considerable health implications.\u003c/p\u003e \u003cp\u003eThe relationship between PBM and the gut microbiome has been explored (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, the majority of studies have concentrated on the processes through which PBM and the gut microbiome collaboratively influence an organism\u0026rsquo;s vital functions; evidence on how PBM impacts the structure and function of the gut microbiome remains limited. In addition, the substantial variability in research subjects and analytical methods precludes a unified understanding and consensus across studies. Furthermore, research incorporating the gut microbiome within the scope of PBM\u0026mdash;considering variables such as different wavelengths\u0026mdash;is scant. The relatively homogeneous sample structures in existing studies also contribute to an incomplete understanding of the impact of PBM on the gut microbiome.\u003c/p\u003e \u003cp\u003eIn the present study, we systematically investigated the relationship between PBM and the gut microbiome, in the form of meta-analysis, by querying published reports and existing databases for the impact of PBM on the gut microbiome, integrating relevant studies to answer more general scientific questions about how PBM affects changes in the gut microbiome. This approach will enable the development and optimization of PBM with the gut microbiome as the vector and target, which may then form the basis for effective clinical treatments for osteoporosis, colitis, and other diseases related to the gut microbiome.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData set selection and filter\u003c/h2\u003e \u003cp\u003eWe queried publicly available databases, including NCBI and Web of Science (WOS), for studies related to the keywords \u0026ldquo;photo-biomodulation\u0026rdquo; and \u0026ldquo;Gut Microbiome\u0026rdquo; using medical subject headings (MeSH). Each study was manually evaluated to ascertain if it satisfied the inclusion criteria.\u003c/p\u003e \u003cp\u003eCriteria for study selection based on pre-defined inclusion and exclusion standards. Studies were included if they: (i) used PBM or alternative light exposure on samples; (ii) used 16s rRNA sequencing for the characterization of the microbiome; (iii) concentrated exclusively on the gut microbiome within living organisms; and/or (iv) offered accessible raw data for analysis. Studies were excluded if they were: (i) reviews, systematic reviews, or meta-analyses; (ii) studies subject to duplicate publication; (iii) studies that utilize ionizing radiation as an intervention measure; and/or (iv) studies that lack critical information necessary, such as primer sequences or essential metadata.\u003c/p\u003e \u003cp\u003eThe metadata were further summarized from studies that fulfilled the criteria for meta-analysis. We used the wave bands of PBM as the principal factor modulated and controlled for, and other variables (i.e., species, authors, physique, etc.) as minor factors. Some studies included PBM with full-spectrum irradiation. These were grouped with VIS because the exposure was concentrated within the visible light spectrum. In addition, physique was simplified to logical data according to diseased or not (i.e., healthy and unhealthy) because of the multiple levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSequence Preprocessing and Taxonomic Profiling\u003c/h2\u003e \u003cp\u003eSequencing data was downloaded, using Aspera (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibm.com/products/aspera\u003c/span\u003e\u003cspan address=\"https://www.ibm.com/products/aspera\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), directly from the NCBI Sequence Read Archive (SRA) through the accessions listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, except for the study of Chen et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), whose data we obtained from the corresponding author. After organizing the sequencing data and summarizing metadata from the mapping files (the mapping file from Bosman et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) was obtained through email), Vsearch v2.14.2 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and Usearch v11.0.667 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) were applied for sequence preprocessing. Vsearch was first used to merge the sequencing data with paired reads (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and remove the barcodes, primers and redundancy, while non-cluster denoising was performed using Usearch. A high-quality filtered sequence with more than 97% consistency was clustered to obtain amplicon sequence variants (ASVs). After removing chimeras and creating a features table, the ASVs were annotated based on the SILVA-138-99 taxonomy database (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) using Vsearch with a confidence cutoff of 0.8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDiversity analysis\u003c/h2\u003e \u003cp\u003ePrior to further analysis, the feature table obtained from annotation was standardized for rarefaction with the R package, vegan v2.6.4 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), with the minimum sample data amount. For α diversity, Chao1 and Shannon metrics were generated with Usearch v10.0.240 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We performed four sets of α diversity analysis: 1) differences across the four wave bands as a major factor and fixed effect (IR, UV, VIS, Control), and 2) differences across species (humans, rats, mice), 3) physique (healthy and unhealthy), and 4) authors, as minor factors. Significance was evaluated with a t-test for physique and ANOVA with Tukey\u0026rsquo;s HSD correction for the others. Generalized linear mixed models (GLMM) were fitted with the R package, lme4 v1.1.33 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Authors and species were used as random effects in the model. For β diversity, weighted-Unifrac distance metrics were generated with Usearch v10.0.240 and principal coordinate analysis (PCoA) was carried out with the vegan v2.6.4 package. To identify significant differences among groups, a permutational multivariate analysis of variance (PERMANOVA) was used with adonis2. Group dispersion was calculated using betadisper before testing for significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Analysis\u003c/h2\u003e \u003cp\u003eTo explore the co-association across wave bands, Spearman correlation coefficients for abundant ASVs (above 0.05% total relative abundance and present in at least five samples) were computed using Hmisc v5.0.1 in R (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Based on this, gut microbiome interaction networks under different wave bands was constructed using thresholds of 0.7 for the correlation coefficient and 0.05 for the P-value. Network metrics, including average, network diameter, graph density, modularity, average clustering coefficient and average path length, were calculated (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Network visualization was performed with Gephi v0.10 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) using the Fruchterman Reingold layout.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Abundance and Random Forest Classification\u003c/h2\u003e \u003cp\u003eDifferential abundance was computed using LEfSe (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), using the default, recommended settings at an adjusted P-value of 0.05 for significant taxa and an LDA effect size of at least 4 for every significant taxon. GLMM were fitted with the R package, glmmTMB v1.1.7 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Authors and species were used as a random effect in the model.\u003c/p\u003e \u003cp\u003eThe random forest model was trained with a five-fold cross-validation using the R package, randomForest (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) to identify key genera as biomarker taxa correlated with wave bands. 80% of the samples were allocated as the training set, using species classification data that was distilled to the genus level as the input predictive variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Prediction Profiles\u003c/h2\u003e \u003cp\u003eWe used PICRUSt (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) to infer the functional potential of 16S rRNA gene data between the UV, IR, VIS, and control. The outputs with database Kyoto encyclopedia of genes and genomes (KEGG) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) orthologs were used for analysis of variance. The Kruskal-Wallis and LSD tests were used to analyze the differences in functional and metabolic pathways across wave bands; the Wilcoxon test was used to analyze the differences between the control and the other groups. Since low-abundance pathways contribute negligibly to the gut microbiome, we excluded pathways manifesting relative abundances under 1%.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Studies of the Gut Microbiome under PBM\u003c/h2\u003e \u003cp\u003eSearching in NCBI and WOS yielded 330 articles, with 282 articles obtained after eliminating duplications (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these, 33 met our criteria for meta-analysis during pre-selection, while 16 lacked clear information regarding a specific, public location of sequencing data; 9 were missing metadata for analysis. Finally, datasets were acquired from 8 gut microbiome studies, two of which provided sequencing data and metadata after emailing the corresponding authors. Of the 442 samples included and their corresponding sequencing data, 213 samples lacked the corresponding metadata, so 229 valid samples were finally included, with 126 human samples and 103 animal samples. The relevant descriptions and metadata are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAfter a series of preprocessing, including denoising the sequencing data, OTUs which were not assigned at the genus level were discarded. Collapsing the data to the genus level reduced the sensitivity to fine-scale differences in species or strain abundances across case and control groups, but we were able to effectively attenuate batch effects affecting cross-study comparisons in meta-analysis. Nonetheless, one study (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) did not pass quality filtering and was removed from the analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCommunity Diversity and Structure Significantly Change under PBM\u003c/h2\u003e \u003cp\u003eFor α diversity, in addition to wave bands (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B), there were significant differences across species and authors (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The same results were obtained from the analysis of β diversity distance (Fig S2). Therefore, we used GLMM to analyze α diversity to strip the role of minor factors in the influence of PBM on gut microbiome, and then explored the influence of different wave bands on the gut microbiome.\u003c/p\u003e \u003cp\u003eAs expected, authors and species had a strong effect on both Chao1 richness and Shannon diversity as random effects (Fig S 3). After stripping away the random effects, we found that infrared irradiation in the model\u0026rsquo;s prediction leads to a significant decrease in the Chao1 index (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while visible light and ultraviolet radiation reduce and increase Chao1 richness, respectively, but neither was significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). For the Shannon diversity generalized linear hybrid model, the influence trend of the illumination band is basically the same as that of the Chao1 richness model, but the statistical results show that only ultraviolet radiation in the model\u0026rsquo;s prediction leads to a significant increase in the Shannon index (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Among these indices, Chao1 richness mainly characterizes the number of community species by the number of highly abundant species and the estimated number of rare species, while Shannon diversity superimposes the proportion of species on the basis of the number of species to characterize the uniformity of species distribution. Based on this, it appears that infrared and visible light irradiation mainly affect the total number of species of intestinal flora, resulting in a decrease in the α diversity of the gut microbiota. While ultraviolet irradiation has a certain impact on the total number of species, it also greatly improves the uniformity of species, thereby improving the α diversity of gut microbiota.\u003c/p\u003e \u003cp\u003eAn interaction network can reflect the relationships of cooperation and competition between microorganisms, which drives the dynamic changes in the community composition and function of the gut microbiome, ultimately affecting the influence of the gut microbiome on the host. We constructed an interaction network at the genus level to analyze the changes in the gut microbiome under different wave bands of illumination, from the perspective of the community network. In four interacting networks, 40, 45, 60 and 29 nodes are connected by 76, 167, 183 and 64 edges, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The network nodes are mainly \u003cem\u003eFirmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e, and a change in the number of nodes of the two phyla was observed between the different groups, with the phyla \u003cem\u003eVerrucomicrobia\u003c/em\u003e and \u003cem\u003eSaccharibacteria\u003c/em\u003e increasing in the IR and UV groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In addition, the weighted, proximity, and mediation centrality of the interaction network in the IR group were significantly greater than those in control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), indicating that the integrity of the gut microbiota and the interspecies information transfer efficiency after infrared irradiation were significantly improved. Analysis of the robustness also revealed that the average and natural connectivity of the IR group were markedly greater than those of the other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). As for negative and positive cohesion, the proportion of positive cohesion in the IR group was significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), although relatively high in each group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe Composition of the Gut Microbiota Exhibits an Opposite Response across Wave Bands\u003c/h2\u003e \u003cp\u003ePreliminary observation of the stacked histogram of the phylum composition in different health states and wave bands (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) showed that the relative abundance of \u003cem\u003eVerrucomicrobia\u003c/em\u003e and \u003cem\u003eSaccharibacteria\u003c/em\u003e show similar trends to those in the interaction network. We first used GLMM model to explore the influence of light band on the change in relative abundance of the two phyla, using the beta distribution as a function distribution family of relative abundance between 0 and 1. For the GLMM model of \u003cem\u003eVerrucomicrobia\u003c/em\u003e, we found that infrared irradiation led to a significant decrease in the relative abundance in the model (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), while visible and ultraviolet irradiation increased the relative abundance, with the effect of visible light being particularly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). For the GLMM model of \u003cem\u003eSaccharibacteria\u003c/em\u003e, we found that infrared irradiation led to a significant decrease in the genus\u0026rsquo; relative abundance in the model (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), while visible and ultraviolet radiation reduced and increased the relative abundance, respectively, but neither was significant. For the random-effects aspect of the two models, we found that author had a limited effect on the relative abundance of \u003cem\u003eVerrucomicrobia\u003c/em\u003e (Fig S 4C), while the effect on \u003cem\u003eSaccharibacteria\u003c/em\u003e was negligible (Fig S4A). In contrast, the effect of species was the inverse of the study authors (Fig S4B, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next used LDA effective size (LEfSe) to determine the changes in bacterial abundance in the gut microbiome related to PBM, especially \u003cem\u003eVerrucomicrobia\u003c/em\u003e. As shown in the species taxonomic clade (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), the \u003cem\u003eVerrucomicrobia\u003c/em\u003e phylum was significantly enriched in the PBM and UV groups, which is basically consistent with the prediction of the generalized linear mixing model. Notably, at the genus taxonomic level, the visible light-irradiated PBM group was enriched with \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e of the \u003cem\u003eVerrucomicrobia\u003c/em\u003e phylum, based on a generalized linear mixed model to simulate the relative abundance changes of only this genus; the same significant results were obtained (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig S6), while infrared irradiation of PBM significantly reduced the abundance of this genus (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig S6). This genus of probiotics was first identified in the early 21st century. Its deficiency or decrease has been associated with a variety of diseases, such as obesity, diabetes, hepatic steatosis, inflammation, and malignancy(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to explore the changes in the gut microbiome, especially probiotics, under different wave bands more generally, we further constructed a random forest classifier of five-fold cross-validation using species classification data down to the genus level as input, and identified key bacterial genera as biomarker taxa associated with different wavelength light. The cross-validated error curve tended to stabilize when 20 genera were used (Fig S5), so these 20 genera were used as biomarker taxa. According to the model prediction results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), the 20 marker genera were distributed in the four phyla of \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e, which predominate the gut microbiome. Among these, the proportion of markers from the phylum \u003cem\u003eFirmicutes\u003c/em\u003e was up to 12, and four of the five most abundant genera belonged to this phylum. The rho heat map reveals that nearly half of the marker genera showed high relative abundance in the IR group, that is, as microbial markers of infrared irradiation, while three of the top five genera showed high relative abundance in the IR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInfrared Exerts Negative Effects on Functional Pathways of the Gut Microbiome\u003c/h2\u003e \u003cp\u003eWe examined the functional Pathways associated with gut microbiome compositions using the Tax4Fun software. Among the pathways with relative abundance above 1% (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), metabolism-related pathways accounted for more than 50%, with the Kruskal-Wallis test of KEGG level 2 pathways showing that there were significant differences in xenobiotics biodegradation and metabolism, and metabolism of various nutrients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In terms of genetic and environmental information processing, although genetic translation had no significant differences, gene replication, repair, and folding and sorting and degradation pathways were significantly different (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); the associated signal transduction and membrane transport pathways also displayed more significant changes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, prokaryotic cellular processes exhibited significant changes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eS). In general, there was trend that infrared light decreased pathway expression while ultraviolet increased it. For pathways with unique changes in one group, infrared significantly changed the expression of more pathways, which manifested as a decrease in cellular processes of prokaryotes and an increase in the pathways of genetic information. In the other two groups, UV increased glycan biosynthesis and metabolism, and reduced the expression of membrane transport pathways. In contrast, the PBM group exhibited a weaker effect on function and metabolic pathways than the other two groups, which may be attributed to the relatively weak penetration of visible light. Of note, the gut microbiome in the IR group produced a significant reduction in antimicrobial resistance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), though there was no difference in the intergroup comparison.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also analyzed those KEGG level 3 pathways that belonged to the KEGG level 2 pathways which had unique changes in each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-C). For the IR group, three pathways related to biofilm formation were significantly reduced under infrared irradiation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05); drug resistance to cationic antimicrobial peptide (CAMP) and beta\u0026thinsp;\u0026minus;\u0026thinsp;lactam also decreased. In terms of genetic information processing, the expression of sulfur relay system and protein export was upregulated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the expression of gene repair pathways, such as base excision and mismatch repair, as well as DNA replication pathways closely related to microbial reproduction, were significantly increased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFor the UV group, we found that the expression of five pathways related to glycosphingolipid biosynthesis and degradation was significantly increased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the expression of N-glycan biosynthesis and some of the glycan degradation pathways was also significantly increased (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The group UV generally showed a marked upward regulation trend in the expression of each pathway.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on 229 sample data from 8 articles, we performed a meta-analysis of clinical studies and experimental models. Although the sample size was relatively limited, the data still contain relatively broad and important factors worthy of examination, such as wave bands and species. After preprocessing the experimental data, we found that the analytical methods used in this project drew the same conclusions as the original research trends, and reducing the accuracy of data analysis to the genus level did not affect the accuracy of the gut microbiome analysis, but also effectively reduced the batch effect between different studies (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), which facilitates better aggregation and integration of sequencing data from different studies.\u003c/p\u003e \u003cp\u003eBased on our analysis of α and β diversity, the impact of different light wavelengths on gut microbiota diversity is most pronounced with UV and IR, which is closely related to the higher penetrability of these two non-visible light types compared to VIS (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e is a key probiotic found in the gut (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Its reduced presence can lead to thinning of the mucosal layer and weakening of the intestinal barrier function, making it easier for toxins to enter the body (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The significant reduction in the abundance of this genus due to infrared means that when using this wavelength for patient treatment, additional supplementation of \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e should be considered to avoid side effects related to its reduction. Conversely, \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e plays an important role in the process of tumor rehabilitation and obesity improvement (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Another study has found that centenarians have higher levels of \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e in their gut (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The significant increase in the abundance of this species in the gut microbiome in the VIS group may lead to a an approach to promote the colonization and enrichment of supplemental or native \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e in clinical treatment.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSaccharibacteria\u003c/em\u003e is widely distributed in the oral cavity, stomach, skin, and gut of healthy individuals; this phylum may be associated with the occurrence and recovery of various types of inflammation (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). It has been suggested that \u003cem\u003eSaccharibacteria\u003c/em\u003e may reduce serum creatinine concentration, increase glomerular filtration rate, and improve kidney function (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In a study of \u003cem\u003eClanis bilineata tsingtauica\u003c/em\u003e, Qian et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) found that TM7 (\u003cem\u003eSaccharibacteria\u003c/em\u003e) can regulate the secretion of various important metabolites through amino acid and carbohydrate metabolic pathways. The abundance of this phylum is also strongly negatively affected by infrared, which introduces a potential risk during PBM treatment. Furthermore, random forest prediction results also indicated that VIS and UV irradiation enrich probiotic genera within the gut microbiota (\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), while infrared leads to the enrichment of more harmful bacteria in the gut microbiota (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). These enriched bacteria can affect the overall gut microbiota by consuming intestinal substances, secreting metabolites, and affecting other microbiota, ultimately leading to changes in human health.\u003c/p\u003e \u003cp\u003eThe above phenomenon may be caused by changes in the overall secretion capacity of short-chain fatty acids (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) as well as alterations in the physiological and biochemical functions related to drug resistance (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) in the gut microbiome under the influence of PBM. Specifically, genetically related pathways are upregulated in the gut microbiome after infrared irradiation. However, there is significant downregulation in pathways related to the formation of biofilms and antibiotic resistance, leading to a decrease in important physiological and biochemical functions, such as resistance to external environmental disturbances, cell division, and communication (\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), which, in turn, leads to the reduction or even disappearance of some species in the gut microbiota. This may underlie why GLMM predicts a decrease in α-diversity and a reduction in the relative abundance of \u003cem\u003eSaccharibacteria\u003c/em\u003e and \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e after infrared irradiation. However, infrared has been used to treat diseases, such as Parkinson and Alzheimer disease, which suggests that the therapeutic effect of infrared may not be achieved through the effect of specific bacteria. Further analysis of the gut microbiota interaction network revealed that the proportion of positive and negative correlations remains relatively balanced, indicating more complex network attributes. Positive correlations between nodes have been shown to represent cooperative interspecies relationships, such as mutualistic symbiosis, while negative correlations represent competitive and antagonistic interspecies relationships, such as predation and competition (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), indicating that infrared will enhance the competitive nature among various species within the gut microbiota, leading to a balance between species competition and cooperation. Consequently, the overall integrity of the gut microbiota and the efficiency of interspecies information exchange are significantly improved, ultimately promoting the stability of the microbial community and enhancing its resistance to disturbances (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Therefore, the gut microbiome plays a role in the process by which infrared radiation affects the vital activities of the organism, which means that the macroscopic structure and metabolic changes in the microbiome can be causal to disease improvement.\u003c/p\u003e \u003cp\u003eThe gut microbiota after UV irradiation generally exhibited upregulation in pathways involved in N-glycan and glycosphingolipid synthesis. N-glycan, covalently linked to proteins via N-glycosidic bonds at asparagine residues, plays a crucial role in protein synthesis processes (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Glycosphingolipids are widely present in human nerve cells and play important roles in the transmission of nerve impulses. In the gut microbiome, glycosphingolipids are located on the cell membrane and participate in intermicrobial communication (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Studies have shown that \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e is able to utilize N-glycan and glycosphingolipids as important carbon sources (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e), and the abundance of \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e decreases significantly under conditions of N-glycan deficiency (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Moreover, N-glycan is an essential nutrient source for \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e in vitro. Therefore, UV irradiation may enhance the abundance of \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e by enhancing the overall synthesis of N-glycan and glycosphingolipids in the gut microbiome, a view supported by the GLMM predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHere, we conducted a systematic review and meta-analysis to elucidate the association between PBM and the gut microbiome. We explored the effects of PBM light wavelengths as environmental factors on the composition, structure, and functionality of the gut microbiome. Our outcomes reveal that UV and VIS exposure lead to a significant enrichment of probiotics within the gut microbiota, while infrared exposure had the opposite effect. These observations indicate that PBM can be strategically utilized as a potential adjunct in modulating the gut microbiome. Nonetheless, it is imperative to recognize the adverse effects that PBM may exert on the gut microbiome, which warrants consideration and provides valuable insights for subsequent empirical investigation into the mechanisms by which PBM affects organism health and disease through its interaction with the gut microbiome. Furthermore, by integrating the effects of infrared irradiation on various aspects of the microbiome, we found that infrared PBM elicits significant differences in the mechanisms of action on vital activities compared to other wave bands. This represents an important direction worthy of attention in subsequent research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by the National Natural Science Foundation of China (82271921, 32261133528) and the Chinese Academy of Sciences (CAS) Interdisciplinary Innovation Team (JCTD-2020-04).\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eLC performed the analysis and wrote the manuscript. YL, JC and YF helped with writing the manuscript. SW helped sort metadata file. LC, YF and HL conceived of this study and assisted with the interpretation of results. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eSequence data available on NCBI are listed in Supplementary Table 1. The datasets and R code generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLipko, N. B. (2022) Photobiomodulation: Evolution and Adaptation. \u003cem\u003ePhotobiomodul Photomed Laser Surg\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 213\u0026ndash;233. https://doi.org/10.1089/photob.2021.0145.\u003c/li\u003e\n\u003cli\u003eGrzybowski, A. and Pietrzak, K. (2012) From patient to discoverer--Niels Ryberg Finsen (1860\u0026ndash;1904) --the founder of phototherapy in dermatology. \u003cem\u003eClin Dermatol\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 451\u0026ndash;455. https://doi.org/10.1016/j.clindermatol.2011.11.019.\u003c/li\u003e\n\u003cli\u003eE. Mester, G. Ludany, M. Selyei, and B. 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(2018) A next-generation beneficial microbe: Akkermansia muciniphila. \u003cem\u003eJ Clin Biochem Nutr\u003c/em\u003e \u003cstrong\u003e63\u003c/strong\u003e, 33\u0026ndash;35. https://doi.org/10.3164/jcbn.18-57.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"16S rRNA sequencing, photo-biomodulation, gut microbiome, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-4279563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4279563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePhoto-biomodulation (PBM) can affect the gut microbiome, which may be one of the important mechanisms by which such therapy exerts its effects. However, with the burgeoning interest in the gut microbiome, differences in research methods and conditions among independent studies make it a challenge to fully understand the impact of the gut microbiome on the health of organisms. Therefore, we conducted a meta-analysis of 16S rRNA gene datasets generated by 8 published studies about the effects of PBM on the gut microbiome to investigate how the gut microbiome is altered across wave bands (infrared [IR], ultraviolet [UV] and visible light [VIS]), species (human, rat and mouse), and physique (healthy and unhealthy). Our results show that PBM can significantly affect the community diversity, species composition, and overall expression of metabolic pathways of the gut microbiome. VIS and UV can enrich key probiotics, such as \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e, in the gut microbiome while IR leads to a decrease of probiotics and an increase in harmful bacteria. In summary, we systematically analyzed the changes in the gut microbiome under different light conditions, providing a theoretical basis for the clinical application of PBM to regulate human health.\u003c/p\u003e","manuscriptTitle":"Effects of photo-biomodulation on Gut Microbiota: A Meta-analysis Based on 16S rRNA Sequencing Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 13:33:27","doi":"10.21203/rs.3.rs-4279563/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"107caecc-cac9-489e-abbb-386000b69834","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-19T12:53:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-22 13:33:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4279563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4279563","identity":"rs-4279563","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}