Global meta-analysis reveals the drivers of gut microbiome variation across vertebrates

Global meta-analysis reveals the drivers of gut microbiome variation across vertebrates | 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 Global meta-analysis reveals the drivers of gut microbiome variation across vertebrates Yong Xie, Songsong Xu, Yufei Xi, Zixin Li, Erwei Zuo, Kai Xing, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3909606/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Shifts in their gut microbial composition and diversity are a known mechanism vertebrates use to adapt to environmental conditions. However, the relative contribution of individual environmental factors to gut microbiota composition and diversity remains poorly understood. To understand the broad influence of different environmental factors on gut microbiome of vertebrates, we collected 6508 16S rRNA gene sequencing samples of gut bacterial communities from 113 host species, spanning seven different classes as well as different types of feeding behaviors and host habitats. Furthermore, we identified the common antibiotic resistomes and their potential mobility between terrestrial vertebrate gut microbiomes (n = 489) and their sympatric soil environment samples (n = 203) using metagenomic sequencing analysis. Results We demonstrate that host diet patterns have a significant impact on changes in the gut microbiome. We reveal the phylum Fusobacteria is enriched in the gut of carnivorous vertebrates, while in the gut of herbivorous vertebrates there was a larger representation of Verrucomicrobia. Climate factors are also strongly associated with gut microbiome variation among vertebrates. We show that the abundance of Bacteroidetes increases gradually from high- to low-latitude zones, while Proteobacteria show a decreasing trend. In particular, we found that bacA and its flanking sequences are highly homologous among the genomes of mammals, avian gut communities, and sympatric soil biomes, suggesting that the bacA resistance gene may undergo horizontal transfer between vertebrates and sympatric environments. Conclusions Our findings show diet patterns and climatic factors play key roles in promoting specific taxa in vertebrate gut microbiota. In addition, we comprehensively decipher the common antibiotic resistance groups of wild vertebrates and their sympatric soil biological environment samples, and provide evidence of potential horizontal transfers of the bacA gene. These results significantly advance our knowledge of the diversity and structure of gut microbiomes in vertebrates and their association with environmental factors, and provide crucial insights to better manage the soil ARG pool. Microbial diversity Gut microbiome Diet Climate variation Antibiotic resistomes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Adaptation is one of the most striking features of biological organisms and an essential capability for survival in diverse environments [ 1 ]. Together with other sources such as host standing variation and mutations, the trillions of microorganisms inhabiting animal guts may drive adaptive benefits [ 2 ]. The gut microbiota could facilitate the diversification of host dietary niches altering future host adaptive trajectories [ 3 ]. For instance, the gut microbiota of koalas, specialized in eating Eucalyptus, harbor bacterial functional pathways associated with the digestion of plant secondary metabolites in a higher measure than the microbiome of wombats, a sister species [ 4 , 5 ]. Shifts in the gut microbiota may also affect host physiological and metabolic function, such as body mass index [ 6 ], visceral fat [ 7 ] and metabolic syndrome [ 8 ] in humans, as well as feed efficiency [ 9 ], methane emissions, rumen and blood metabolites, and milk production efficiency in cattle [ 10 ]. It has been hypothesized the existence of selective pressures that could result in the reshaping of the microbiota to benefit host fitness [ 3 ]. For example, Toll-like receptor deficiency in mice exacerbates intestinal dysbacteriosis, which negatively contributes to the metabolism and ability of host to harvest energy [ 11 ]. Yet a comprehensive understanding of the contribution of gut microbiome to adaptive evolution in vertebrates has not been achieved, as most recent studies have been limited to one species, a small number of samples, or confined geographic distributions. Both in humans and other vertebrates the composition of the gut microbiota is affected by a number of factors, such as host dietary patterns, phylogeny [ 12 , 13 , 14 , 15 ], social interactions [ 16 , 17 ], and climate [ 18 ]. Variation in climate in particular is a key factor that influences the physiology and immunity of the host, as well as its selection from surrounding microbes [ 19 , 20 ]. In recent years, several studies have focused on the diet and phylogeny of hosts, which are known factors leading to changes in animal intestinal microbiota [ 21 , 22 , 23 ], while environmental factors and responses to different local climates have rarely been investigated. Although there are several reports regarding the effect of climate variation on gut microbial communities [ 24 , 25 , 26 ], few studies have reported on the relationship between climate factors and vertebrate gut microbiota at global landscape. While host diet and climate variation are essential factors in shaping the gut microbiota, other factors, such as antibiotic exposure and gastrointestinal dysfunction, also contribute to alterations in the gut microbiota of vertebrates. Notably, antibiotic-resistance genes (ARGs) can be shared between animal, soil, and human bacteria via horizontal gene transfer [ 27 , 28 , 29 ], and the increasing environmental contamination with ARGs may therefore be contributing to the growing antibiotic resistance and multi-resistance observed globally. Investigating the presence of ARGs in vertebrate gut microbiomes is vital and will help guide future antibiotic use and conservation efforts. In the present study, we collected 6508 samples of 16S rRNA gene sequence from all seven classes of vertebrates ( Mammalia , Aves , Reptil es, Amphib ia, Actinopteryg ii, Chondrichthyes and Cyclostomata ). We further collected data for metagenomic sequences including 489 terrestrial vertebrate gut microbiomes and 203 sympatric soil biological environment samples. The aims of the study are: (1) assess the effects of environmental factors on vertebrate gut microbiome diversity; (2) characterize the patterns of vertebrate gut microbiome composition and function, both in terms of host diet pattern and habitats; (3) identify potential horizontal transfer of ARGs between vertebrate gut microbiomes and sympatric soil samples. Methods Study cohorts and data retrieval We systematically searched existing literature (Google Scholar, Web of Science) for the following keywords: “fecal/gut microbiome”, “vertebrate” and “16S V4 /V3-V4 sequencing”. We identified additional articles [ 30 ] through references cited in the retrieved results. We obtained 12,640 samples based on 16S rRNA gene sequencing from 99 published articles up to September 2022. We then retained for further analyses only samples with known hosts and confirmed origins. We then performed quality control for experimental animal, sample type, amplification primer and sequencing method as follows: 1) Samples from all vertebrates except livestock and poultry; 2) Samples of distal gut microbiota or fresh feces were retained; 3) Collected samples were not treated (such as antibiotic therapy) in a way that affected the composition of gut microbiome; 4) Samples were amplified using the V3-V4 or V4 hypervariable regions of the 16S rRNA gene; 5) Samples were sequenced with amplicon sequencing on Illumina platform; 6) Number of samples at each location was greater than 8. This resulted in a dataset of 6508 samples spread across seven continents (Fig. 1 ). Species name, species category (class and order), diet information (carnivorous, herbivorous and omnivorous), habitat type (terrestrial, aquatic and amphibia), captivity status (wild or captive), threatened status (as indicated by the IUCN Red List for July 2022, least concerned: LC; near threatened: NT; vulnerable: VU; endangered: EN; critically endangered: CR; Extinct in the Wild: EW), sampling location, sampling climate zone, average earth surface temperature at the sampling point, and sampling year are included in Additional file 1 : Table S1 . For the collection of metagenomes, we first collected a large number of soil metagenomes from around the world based on the work of Ji M et al. [ 31 ], Bahram et al. [ 32 ], and Delgado-Baquerizo et al. [ 33 ]. Based on the region where soil metagenomic samples were collected, we can accurately search for terrestrial vertebrate intestinal metagenome samples in the same region on Google Scholar and Web of Science websites. As of December 2022, we have obtained a total of 489 vertebrate and 203 soil environmental metagenomic sequencing samples (Fig. 1 ). Sampling information is included in Additional file 1: Table S2 . 16S rRNA sequencing analysis Paired-end sequencing reads were merged using the VSEARCH software [ 34 ] based on the UCHIME algorithm. We then filtered the sequences for quality, and we used Cutadapt 4.1 [ 35 ] to remove barcodes and primer sequences from V3-V4 or V4 regions according to corresponding primers. The V3 region in the V3-V4 region was removed, and the V4 region was retained [ 36 ]. Operational taxonomic units (OTUs) were identified by clustering 16S rRNA gene sequence data at a 97% nucleotide similarity cutoff in the UPARSE program (version 7.1) [ 37 ]. We filtered out chimeras from the datasets using UCHIME [ 38 ]. Each 16S rRNA gene sequence was assigned a taxonomic identity with the Ribosomal Database Project (RDP) classifier and compared against the Silva 16S rRNA gene database (version SSU128) using a confidence threshold of 70% [ 39 ]. Community diversity analysis was performed in QIIME2 (Version 2021.02). Alpha diversity calculations were done using Shannon index (quantitative community richness) and Observed OTUs (qualitative community richness). Beta diversity analysis was performed to assess dissimilarities in microbial communities between groups using Jaccard distance (qualitative community dissimilarity) and Bray-Curtis distance (quantitative community dissimilarity). Metagenomic sequencing analysis Before assembly all metagenomic sequencing analysis reads were assessed with FastQC (version 0.11.8) for quality control and quality trimmed using Trimmomatic (version 0.39) with the parameters “SLIDINGWINDOW:4:20 MINLEN:50”. The high-quality clean reads were then assembled into contigs using the MEGAHIT software (version 1.2.9) [ 40 ] with the default settings. Emboss [ 41 ] was used to convert nucleic acid sequences to protein sequences. Representative sequences were aligned against the NCBI non-redundant database to enable taxonomic identification with Diamond (version 0.9.21) using a cutoff e-value of 1 × 10 − 10 , a minimum query coverage of 80%, and a minimum sequence identity of 80%. Species composition and abundance information at the genus level were obtained from the annotated results [ 42 ]. Antibiotic resistance genes were annotated with the Structured Antibiotic Resistance Genes (SARG) database using the ARGs-OAP v2.0 pipeline [ 43 ]. The resfams antibiotic resistance protein family data were subjected to functional metagenomic selection using the hmmscan function of the HMMER software package (v3.3.2) using the following parameters: --cut_ga and --tblout [ 44 ]. To assess the potential mechanism of ARG migration, amino acid sequences of flanking genes containing ARG contigs were compared to the SARG database using BLASTP (e-value ≤ 1e − 5) and assigned to genes by the highest-scoring annotated hit with > 80% similarity that covered > 70% of the length of the query protein. Flanking gene sequences containing ARG contigs were annotated in the UniProt database ( https://www.uniprot.org/ ) using a 60% minimum sequence identity comparison threshold and 50% minimum coverage. Multiple Regressions on Matrices (MRMs) We used MRMs to study host Phylogeny, habitat type (aquatic, amphibious and terrestrial), diet (carnivorous, omnivorous and herbivorous), threatened status (LC, NT, VU, EN, CR), captivity status (wild or captive), climate (annual average temperature), and geographical location (latitude and longitude). In the order listed above, the habitat type from 0 to 2, the diet was coded from 0 to 2, the threatened status from 5 to 1, the binary variables for captive status were set as 0 (wild) and 1 (captive). Gower distances among communities were separately calculated for all independent variables, while Euclidean distances were calculated for Shannon index differences, and Bray-Curtis distances were calculated to estimate beta diversity among gut communities. For MRMs using the EcoDist R package [ 45 ], the effect size and significance were obtained by comparing actual data to a random arrangement (n = 1000 for all analyses). Co-occurrence network The co-occurrence of OTUs in microbial communities across the four habitat climate types was analyzed. To reduce network complexity and facilitate the identification of the core gut microbial community, Only OTU with mean relative abundances > 0.1% and presence in > 50% of samples of a given group were used for the above analyses, as described by [ 46 ]. Only co-occurrences associated with correlation (Spearman ρ) > 0.6 and statistical significance ( P value) < 0.01 were considered for further analysis [ 47 ], and visualization of the co-occurrence network was conducted using the Fruchtermann-Reingold layout of the interactive platform Gephi version 0.9.7. Nodes indicated individual microbial taxa (OTUs) in the microbiome network [ 48 ]. Network edges represented the pairwise correlations between nodes, suggesting a biologically or biochemically meaningful interactions [ 49 ]. The centrality reveals the role of nodes as Bridges between network components, while the degree reveals the role of nodes directly linked to other OTUs in the community. Centrality and degree are often chosen as critical criteria to identify keystone species in the symbiotic network [ 50 ], possible keystone node were those that demonstrated high betweenness centrality values. Statistical analyses We compared gut bacterial community characteristics with Kruskal–Wallis (multiple group comparison) or two-tailed Wilcoxon rank-sum (pairwise comparison) tests. A false discovery rate (FDR)-corrected P < 0.05 was considered statistically significant for these tests. The R package “ggplot2” [ 51 ] was used to visualize box plots of data distributions. Variations in the bacterial community compositions and functions of different samples were evaluated by Non-metric MultiDimensional Scaling (NMDS) based on the Bray–Curtis distance. We used a dated host phylogeny for all species from http://timetree.org . We conducted geographic analysis using the ArcGIS 10.2 software (Environmental Systems Research Institute Inc., Redlands, USA). The LEfSe analysis was performed with the following parameters: the alpha value for factorial Kruskal–Wallis test among classes was < 0.05, and the threshold on the logarithmic LDA score for discriminative features was 6.0. Functional profiles were inferred with PICRUst (version 2.5.1). The inferred genes and their functions were aligned with the Kyoto Encyclopedia of Genes and Genomes. PICRUSt-predicted functional profiles were analyzed by STAMP (version 2.1.3), with significance assessed by Welch’s t-test with Storey’s FDR (reported * P 0.5). Results Overview of data related to the vertebrate gut microbiome To comprehensively depict the gut microbiomes of vertebrates, we analyzed the diversity and composition of the gut microbial communities in 113 species of vertebrates, including Mammals (n = 4,668 from 60 species), Aves (n = 792 from 25 species), Reptiles (n = 347 from 10 species), Amphibia (n = 104 from 5 species), Actinopterygii (n = 520 from 9 species), Chondrichthyes (n = 33 from 2 species) and Cyclostomata (n = 44 from 3 species). The samples were collected from vertebrates inhabiting seven continents and with different diet, habitat types, captive status, threatened status and climate zones (Additional file 1: Table S1 ). Overall we obtained 39,613 operational taxonomic units (OTUs) from 1,290 genera within 47 phyla. The phylum-level relative abundances were compared among vertebrate lineages, revealing that the dominance of Proteobacteria in the gut microbiota of Actinopterygii (Fig. 2 A; Additional file 2: Fig. S1 ). Notably, previous studies have shown that Proteobacteria are involved in various biogeochemical processes in aquatic ecosystems [ 52 , 53 ] and are commonly found in the intestines of aquatic organisms [ 54 , 55 ]. We also noted that Fusobacteria is common in carnivorous animals such as California condor , Gentoo Penguin , Northern fur seal and Leopard , while Verrucomicrobia is prevalent in the guts of herbivores (Fig. 2 B; Additional file 2: Fig. S1 ). In particular, the genus Verrucomicrobiales of the phylum Verrucomicrobia had been identified as a core taxon in the fecal microbiome of herbivores, suggesting it may play a key role in fiber digestion [ 56 , 57 ]. The gut microbiome of different animal taxa may be shaped by environmental (i.e., habitat type, geography, and climate) and genetic factors [ 58 , 22 ]. The extent to which these factors may contribute to the diversity and composition of vertebrate gut microbiomes is yet not fully understood. We first calculated the alpha diversity (i.e. a measure of microbiome diversity) of gut microbial communities using the Shannon index. The gut microbiota of mammals showed the highest level of alpha diversity (i.e. Shannon index = 5.26), while the gut microbiota of birds scored the lowest (i.e. Shannon index = 3.14) (Fig. 2 C). Furthermore, herbivores had the highest gut microbial diversity, while carnivores had the lowest ( P < 0.0001; Fig. 2 D). Wild vertebrates showed higher diversity than captive cohorts ( P < 0.01). In addition, the microbial communities of terrestrial vertebrates were more diverse than those of aquatic and amphibian species ( P 0.05). We also observed lower alpha diversity levels in threatened species (i.e., Ailuropoda melanoleuca ) compared to unthreatened species (i.e., Ochotona curzoniae ) (Additional file 3: Fig. S2 A), which is consistent with what was reported by a previous study [ 30 ]. We then performed MRM (Matrix Multiple Regression) analysis to evaluate the importance of various factors affecting gut microbiome of vertebrates. Each of our four MRM models (one per diversity metric) had a significant overall fit. Host diet and habitat climate were the only significant explanatory variables (BH-corrected P < 0.05; Fig. 2 E, F; Additional file 3: Fig. S2 B). Host diet explained a substantial amount of alpha- and betadiversity variation (~ 8–24%) and was significant for all diversity metrics tested (i.e., Shannon index, Bray-Curtis, Observed Feature, and Jaccard distance). Habitat climate also explained a substantial amount of the variation in ɑ and β diversity (~ 11–16%). These results further demonstrated that gut microbiome variations in vertebrates are primarily driven by diet and climate factors. In order to show the significant impact of host diet and habitat climate factors on microbial diversity, we first examined the intestinal microbial diversity of vertebrates of the same class or order with different dietary habits, and obtained the same results as Fig. 2 D(Additional file 3: Fig. S2 C). Following this, we noticed that the greatest microbial diversity was observed at intermediate latitudes (Fig. 2 G; R2 = 0.03, p < 2e-16). This was consistent with the two other major types of ecosystems on Earth, that is, ocean [ 59 ] and air [ 60 ]. At the same time, we can also notice that richness increases with increasing temperature. There is good evidence that the main drivers of latitudinal diversity patterns are pH and soil temperature [ 32 , 61 ], and the salinity and temperature of water [ 62 ]. In addition, the microbial Shannon index increases year by year with the increase in sample collection year (Additional file 3: Fig. S2 D), while global surface temperature is increasing year by year [ 63 ]. Therefore, temperature can be considered an important factor driving microbial diversity in the gut of vertebrates. Changes in vertebrate gut microbial composition and function in response to diet and climate factors To assess the effects of dietary patterns on composition and function of vertebrate gut microbiome, we performed Non-metric MultiDimensional Scaling (NMDS) based on genus-level Bray − Curtis distance. In NMDS analysis, the plots of carnivores and herbivores clustered separately, while there was no clear separation between herbivores and omnivores (Fig. 3 A). We selected the gut microbiota with relative abundance higher than 0.1% in at least 50% of the individuals at genus level to generate a heatmap of species relative abundance. We observed remarkable variations in both the composition of microbial communities depending on host diet (Fig. 3 B-D). For example, the genera including Acinetobacter , Cetobacterium , Serratia , Aeromonas , Rhizobium , Enterococcus , Catellicoccus , Clostridium_XI and Lactococcus were enriched in carnivores, whereas the relative abundance of genera including Escherichia / Shigella and Streptococcus was higher in herbivores. In addition, genera including Clostrdium_XIVa , Clostrdium_IV and Methanomassiliicoccus were more abundant in omnivores. The dominant KEGG (Kyoto Encyclopedia of Genes and Genomes) functional categories (level 2) found in gut microbiome of herbivores and carnivores included metabolism, genetic information processing, environmental information processing and cellular processes (Fig. 3 E). Notably, herbivore gut microbiomes were enriched in metabolically relevant pathways (e.g., Glycan biosynthesis and metabolism, Replication and repair, Amino acid metabolism, Nucleotide metabolism), while carnivore gut microbiomes were characterized by protein-related pathways (e.g., Protein families: signaling and cellular processes, Membrane transport, Cellular community-prokaryotes, Signal transduction) (Fig. 3 E). Our findings therefore confirm that diet is a critical factor shaping the composition and function of vertebrate gut microbiomes. Climate factors are also strong factors in the determination of gut microbiomes [ 64 , 26 ]. Importantly, our analysis revealed that the microbiome diversity (i.e., Shannon index) increased gradually from high- to low-latitude zones (Fig. 4 A; Additional file 4: Fig. S3 A, B). We also detected differences in beta diversity (i.e. a measure of the similarity or dissimilarity of two communities) within the four climate regional samples. As shown in the NMDS plot, the polar samples clustered closely together, whereas temperate and tropical samples clustered less closely on NMDS1 (Fig. 4 B), indicating more diverse gut microbial compositions in the low-latitude region. Seven major microbial phyla dominated across the four climate zones: Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacteria, Tenericutes and Euryarchaeota (Fig. 4 C), which together constituted up to 96.4% OTUs. Notably, the richness of Bacteroidetes phylum (class Bacteroidia, order Bacteroidales, family Prevotellaceae and genus Prevotella ) in gut microbiota of vertebrates increased from high- to low-latitudes where the hosts reside (Fig. 4 C; Additional file 5: Fig. S4 ). In contrast, the abundance of members of Proteobacteria phylum (class Gemmaproteobacteria and genus Photobacterium ) decreased from high- to low-latitude zones (Fig. 4 C; Additional file 5: Fig. S4 ). In addition, we controlled for batch and individual study effects in the sequencing data to further illustrate the changing trends of Bacteroidetes and Proteobacteria in vertebrate gut microbiota in different climate regions (Fig .4D,E). We also constructed co-occurrence microbial networks to identify robust microbial association patterns in vertebrate gut for four climate zones considered(Fig. 5 ; Additional file 6: Fig. S5 ). Interestingly, the co-occurrence network of tropical zone included the largest amount of significantly co-occurring OTUs, while the network of polar zone contained the least OTUs. Compared with the network in low latitudes, the structural features, nodes and edges of the co-occurrence network in high latitudes were lower than those in other regions, suggesting that gut microbial communities at high latitudes are more susceptible to perturbations by climatic conditions. In addition, the gradually increasing coexisting clusters of high-betweenness centrality Bacteroidetes and the gradually decreasing high-betweenness centrality Proteobacteria from high to low latitudes may be related to climate change and make adaptive changes. These results further confirm the known associations between climate factors and gut microbiome of vertebrates. Gene mobility potentials of common antibiotic resistomes in the gut microbiome of vertebrates and their sympatric soil biological environment Soil biological samples have a large gene pool of antibiotic resistant bacteria [ 65 ]. ARGs threaten vertebrates health worldwide, but the common resistome and ARGs mobility between vertebrate and their sympatric soil biological environment remain unclear. In order to fully decipher the common resistome and potential mobility of ARGs, we collected and analyzed 489 vertebrate gut microbial samples and 203 sympatric soil environment samples using metagenomic sequencing (Fig. 1 ; Additional file 1: Table S2 ). We found 89.4% ARGs belonged to the top types (i.e., multidrug, tetracycline, bacitracin, rifamycin, macrolide, novobiocin, vancomycin, beta_lactam, polymyxin, quinolone, and aminoglycoside) Moreover, these types accounted for 94.7% of the total abundance of ARGs (Fig. 6 A). The 70 subtypes of ARGs, mainly comprising multidrug ( MexF ), tetracycline ( tetA(48) ), bacitracin ( bacA ), rifamycin ( RbpA ), macrolide ( macB ), novobiocin ( novA ), vancomycin ( vanXI ), beta_lactam ( TEM-116 ), polymyxin ( rosB ), quinolone ( mfpA ), and aminoglycoside ( amrB ) resistance genes, were shared between the vertebrate gut microbiomes and their sympatric soil biological environmental samples (Fig. 6 B). More specifically, a large number of overlapping ARGs (13.97%) were shared among mammals, aves and soil biological environmental samples (Fig. 6 B). We also observed the bacA (also known as UppP, undecaprenyl-diphosphate or -pyrophosphate phosphatase) gene, which confers resistance to bacitracin, was dominant in mammals, aves and soil biological environmental samples (Fig. 6 C). We further investigated the exchange potential of ARGs between vertebrate gut microbiomes and the soil in their environment with metagenomic sequencing analysis. We firstly found that E. coli was enriched in the gut microbiomes of the mammal and aves cohorts (Fig. 7 A), which is consistent with the existing literature [ 30 ]. The genome of E. coli harbors a number of different ARGs such as PBP transpeptidase domain, Bleomycin resistance protein, MarR , mfp and CblA (Fig. 7 A), indicating the potential for multi-drug resistance in the gut microbiome of mammals and aves. We then evaluated the exchange potential of mobile genetic elements in the flanking genetic sequences in assembled contigs. Notably we identified four contigs containing bacA with high sequence similarity to E. coli O25b-ST131 across mammals, aves and their sympatric soil biological environmental samples (Fig. 7 B). The flanking sequence of bacA also contains genes encoding transferases that catalyze the transfer of ARGs between vertebrates and members of sympatric soil bacterial communities. These results suggested that ARGs and ARG-containing bacteria might be transferred between soil biological environments and vertebrates. Discussion Here we collected 6508 fecal samples from 113 vertebrate species spanning seven classes with different feeding behaviors and habitats, by using 16S rRNA sequencing to investigate the ecological and biological drivers of gut microbial diversity and composition. We firstly evaluated the relative contribution of these drivers to the diversity of gut bacterial communities in vertebrate guts, and demonstrated that diet and climate factors have the strongest impact on gut microbial diversity. We also confirmed the effects of diet and climate on gut microbial composition and function. We then identified common antibiotic resistance and potential horizontal transfer of ARGs between terrestrial vertebrate gut microbiomes and their sympatric soil biological environmental samples using metagenomic datasets analysis. In conclusion, our study indicates both diet and climate factors play a critical role in driving the diversity and composition of gut microbiomes in vertebrates and reveals the potential threat that soil ARGs represent for animal health. The abundant and diverse gut microbial communities live in the guts of both humans and animals are essential for the host physiology, ecology, and evolution [ 66 , 67 , 68 , 69 ]. Gut microbiota are densely populated microbial communities that include many different types of bacteria that play essential and critical roles in regulation and adaptation of vertebrates to diverse lifestyles [ 70 ]. Here we firstly evaluated a range of host and environmental factors that may influence the gut microbial diversity in vertebrates. Among all factors considered, diet patterns were the strongest predictor of microbial community diversity, followed by climate factors. Accumulating evidence has been pointing toward long-term dietary patterns having profound effects on the diversity and structure of the trillions of microorganisms residing in animal guts [ 71 ]. As a specific example, two co-evolution studies of mammals and their gut microbiota have found that both gut microbiota composition and functions are adapted to the animal diet (herbivorous, carnivorous and omnivorous) [ 12 , 72 ]. In another more recent study, the convergently evolved composition of the gut microbiomes in rodent species, despite their phylogenetic diversity, strongly suggests that diet is the major force shaping microbiota [ 73 ]. Here we observed that gut microbial diversity increased from carnivores to herbivores, consistently with previous studies [ 12 , 74 ]. Herbivores typically have a diet based on plant polysaccharides, while the diet of carnivores is rich in high-fat and high-protein products. Carnivores can absorb proteins and fats thanks solely to their own enzymes [ 75 , 76 ], while herbivores have more complex gastrointestinal tracts with prolonged chyme retention and diverse mutualistic microbial communities that facilitate the digestion of fibers [ 77 , 78 ]. Our results also show that gut microbiota functions in herbivores are enriched in biometabolically competent microbiota, while the gut microbiota of carnivores is enriched for protein transport. In particular, the genus Fusobacterium was significantly enriched in the gut of carnivores, consistently with previous reports that Fusobacterium is commonly found in higher concentrations in healthy carnivore hosts and is associated with protein-rich diets [ 12 , 79 ]. We also found that the family Rhizobiaceae and genus Rhizobium were abundant in the gut of herbivorous vertebrates. Rhizobium is a kind of bacterium symbiotic with plant roots, which can form symbiotic nodules with the roots of leguminous plants and fix nitrogen in the nodules, thus providing a nitrogen source for plants [ 80 ]. The enriched Rhizobium by eating behaviors maybe improve the performance of fiber digestion and nutrition absorption of herbivores [ 81 ]. Gut microbiota plasticity in response to environmental cues may allow hosts to rapidly adapt to ecological change [ 82 , 64 ]. Recent evidence has suggested that gut microbiota are affected by both warming environment and changes to host ecology driven by climate variation [ 24 , 83 , 84 ]. We found the diversity and structure of gut microbiota among vertebrates are associated with climatic factors. Specifically, we noted the taxa of phylum Bacteroides and Proteobacteria are significantly correlated with climate factors. A previous study showed that the phylum Bacteroides and Proteobacteria, both gram-negative bacteria, are strongly affected by temperature changes [ 85 ]. Interestingly, the phylum Proteobacteria could adapt to tolerate dryness and low temperatures by forming durable spores to protect itself under extreme conditions [ 86 ]. For instance, the abundance of Proteobacteria in freshwater lakes was enriched in winter, probably due to the strong adaptability of it to low temperature extremes [ 87 ]. In contrast, the phylum Bacteroides can adapt to relatively high temperature environments. For example,the abundance of Bacteroidetes in grasslands showed an increased trend with a rise in temperature [ 88 ]. We detected a gradually increasing co-occurring clusters of taxa belonging to phylum Bacteroidetes were detected from high- to low-latitudes. Climate factors should be considered to have a critical contribution to the diversity of gut microbiota in vertebrates. The development and spread of antibiotic resistance in bacteria is a growing global health threat to humans and animals [ 89 , 90 ]. Soil is one of earth’s largest reservoirs of ARGs (i.e., the soil antibiotic resistome), and is the habitat of many pathogens associated with clinical infections and animal disease outbreaks [ 46 ]. One of the most serious health concerns is the transfer of ARGs from soil to anthropogenic, animal, and plant settings, which would pose a severe threat to human and animal health [ 91 ]. However, there are still major unknowns associated with the transfer of ARGs and ARG-containing bacteria between soil biological environments and vertebrates. In this study, we detected a relatively high abundance of ARGs encoding resistance genes to bacitracin (8.2%) and multidrug (6.7%) in wild vertebrates and in the sympatric environmental samples. In particular we identified a potential horizontal transfer of the bacA gene between the gut microbiome of wild vertebrate (i.e., Melursus ursinus , Casuarius bennetti and Torgos tracheliotos ) and forest soil bioenvironment samples. The bacA gene is associated with bacitracin resistance, suggesting a widespread use of this antibiotic. It has already been reported that the bacitracin resistance gene can be transferred to humans through close contact with ruminants [ 92 ]. The use of bacitracin as a growth promoter in the veterinary practice has significantly contributed to animal health, welfare and performance, as well as to the overall productivity of the industry. However, bacitracin is banned as a feed additive in livestock farming by most countries. Based on our findings we recommend that the relevant government authorities increase the surveillance on how this drug is being procured. Our findings highlight the health threats of soil bioenvironments harboring ARGs. We identified forested areas where the control of soil antibiotic resistance needs to be prioritized. Conclusion In conclusion, this is the gut microbiome research effort with the largest number of individuals collected at the global scale across vertebrates. Our results can help to identify the major modulators of the diversity and structure of gut microbiomes. Our findings confirm that diet patterns and climate factors play key roles in promoting specific taxa in vertebrate gut microbiota. In addition, we comprehensively deciphered the common antibiotic resistomes of wild vertebrates and their sympatric soil biological environment samples, and found evidence of potential horizontal transfers of the bacA gene. These results significantly advance our knowledge of the diversity and structure of gut microbiomes in vertebrates and their association with environmental factors, and provide crucial insights to better manage the soil ARG pool. Abbreviations ARG Antibiotic resistant gene 16S rRNA 16S ribosomal ribonucleic acid rRNA OTU Operational taxonomic units MRM Multiple regressions on matrice MAG metagenome-assembled genome IUCN International Union for Conservation of Nature QIIME Quantitative insights into microbial ecology LEfSe Linear discriminant analysis Effect Size LDA Linear discriminant analysis FDR False discovery rate PICRUSt2 Phylogenetic investigation of communities by reconstruction of unobserved states Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. Competing Interests The authors declare that they have no competing interests. Funding This work was supported by grants from the National Natural Science Foundation of China (31972539, 32102511). Science Technology Innovation and Industrial Development of Shenzhen Dapeng New District (Grant NO.PT202101-05). The China Scholarship Council (Grant No.202003250046). Authors' contributions LB and KX: Conceptualization, Methodology, Investigation, Writing-review & editing, Supervision. YX and SX: Methodology, Software, Investigation, Data curation, Formal analysis, Writingc-original draft. YX, ZL, EZ and KL: Investigation, Data curation. All authors read and approved the manuscript. Acknowledgements We thank Dr. Martien A. M. Groenen and Ole. Madsen for the discussion, critical reading, and revision of the manuscript. Authors' information Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, China Yong Xie, Zixin Li, Erwei Zuo, Lijing Bai & Kui Li College of Animal Science and Technology, China Agricultural University, Beijing 100193, China Songsong Xu Animal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China Yufei Xi & Kai Xing Animal Breeding and Genomics, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands Lijing Bai Corresponding authors Correspondence to Kai Xing and Lijing Bai References Zitnik M, Sosič R, Feldman M W, et al. Evolution of resilience in protein interactomes across the tree of life[J]. Proceedings of the National Academy of Sciences, 2019, 116(10): 4426-4433. Mallott E K, Amato K R. Host specificity of the gut microbiome[J]. Nature Reviews Microbiology, 2021, 19(10): 639-653. Moeller A H, Sanders J G. 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Larsson D G J, Flach C F. Antibiotic resistance in the environment[J]. Nature Reviews Microbiology, 2022, 20(5): 257-269. Zheng D, Yin G, Liu M, et al. Global biogeography and projection of soil antibiotic resistance genes[J]. Science Advances, 2022, 8(46): eabq8015. Jing R, Yan Y. Metagenomic analysis reveals antibiotic resistance genes in the bovine rumen[J]. Microbial Pathogenesis, 2020, 149: 104350. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1Tables12.xls Additional file 1: Table S1. Host samples and corresponding information for gut bacterial community 16S rRNA gene sequencing samples recovered from global vertebrates. Table S2. Corresponding information for metagenomically sequenced samples of vertebrate gut bacterial communities worldwide and soil metagenomically sequenced samples. Additionalfile2FigureS1.pdf Additional file 2: Fig. S1. Phylum-level composition of gut community diversity shown by host phylogeny and with other relevant host metadata. The time-calibrated host phylogenetic tree was obtained from http://timetree.org and the colors of the branches represent the host class (red = Cyclostomata ; dark blue = Chondrichthyes ; light blue = Actinopterygii ; dark yellow = Amphibia ; light yellow = Reptilia ; purple = Aves ; green = Mammalia ). The data mapped onto the tree (from the inner to outer circles) show the threatened status (obtained from the IUCN Red List in July 2022), host diet, and the relative abundances of bacterial phyla in each host. Relative abundances are averages estimated by subsampling OTUs from all samples of each host species (subsampling to 10,000 for each host species). Additionalfile3FigureS2.pdf Additional file 3: Fig. S2. A Alpha diversity levels in gut communities grouped by host vertebrate captivity status, habitat type and threatened status. FDR-corrected Wilcoxon rank sum tests were used to determine significance. ***: P < 0.001, **: P < 0.01, *: P < 0.05. B The plots show the BH-adjusted p values (Adj. p value) and partial regression coefficients (Coef.) for multiple regression on matrix (MRM) tests used to determine how much microbial diversity variance was explained by host diet, captivity status, geographic location, habitat, phylogeny, climate and threatened status. Asterisk denotes significance (Adj. p 1.5 IQR, respectively. CDiversity of gut microbiomes in vertebrates with different diets (same class or order). D Microbial diversity of vertebrate gut microbial samples collected in different years is shown. Additionalfile4FigureS3.pdf Additional file 4: Fig. S3. A Diversity of gut microbiomes in vertebrates from different climate regions (same order or class). B Diversity of gut microbiomes in vertebrates from different climate regions (same diet habits). Additionalfile5FigureS4.pdf Additional file 5: Fig. S4. Relative abundance of intestinal flora analyzed at the level of class (A), order (B), family (C), and genus (D). Additionalfile6FigureS5.pdf Additional file 6: Fig. S5. Co-occurrence network constructed by correlation of intestinal communities at the level of class (A), order (B), family (C), and genus (D). 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3909606","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276563926,"identity":"23d3b285-eacd-4b67-836e-e872f96e9297","order_by":0,"name":"Yong Xie","email":"","orcid":"","institution":"Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Xie","suffix":""},{"id":276563927,"identity":"51d2881d-7e10-4401-b0c2-9772075ed4b6","order_by":1,"name":"Songsong Xu","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Songsong","middleName":"","lastName":"Xu","suffix":""},{"id":276563928,"identity":"b2718a03-5d8e-41f4-b6a2-cede483d7bd1","order_by":2,"name":"Yufei Xi","email":"","orcid":"","institution":"Beijing University of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Yufei","middleName":"","lastName":"Xi","suffix":""},{"id":276563929,"identity":"ef690736-b531-4cd5-802c-67afc0cbc080","order_by":3,"name":"Zixin Li","email":"","orcid":"","institution":"Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zixin","middleName":"","lastName":"Li","suffix":""},{"id":276563930,"identity":"c33aaa46-5e7a-4808-8529-738e7ead7915","order_by":4,"name":"Erwei Zuo","email":"","orcid":"","institution":"Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Erwei","middleName":"","lastName":"Zuo","suffix":""},{"id":276563931,"identity":"a179f359-ce31-4c92-9063-96f61e3d1259","order_by":5,"name":"Kai Xing","email":"","orcid":"","institution":"China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Xing","suffix":""},{"id":276563932,"identity":"924ded78-2d06-4a85-8b15-22afac6f47cf","order_by":6,"name":"Lijing Bai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYBACAwbGxgMJPBIMDOwNxGtpgGjhOUC0FgYGiFqJBCK1mLMfbjjwQMYiTz7y8cMPP/4wyJsT0mLZkwh2WLHh7TRjyd42BsOdDYQcdgCiJXHj7BwGCd4GhgSDA4S0nH8I1TLzDPPPP3+I0XIDast8CR42aR42IrRYzoDasoEnzcxatk3CcAMhLeb86Q8f/uypS5zffvjxzTd/bOQJ2gIGjD2gcAAzJYhRDwI/GBjkG4hVPApGwSgYBSMOAABz+0WLigAE8gAAAABJRU5ErkJggg==","orcid":"","institution":"Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Lijing","middleName":"","lastName":"Bai","suffix":""},{"id":276563933,"identity":"2ea3df4b-562c-4801-bc3e-5aabb9e191b2","order_by":7,"name":"Kui Li","email":"","orcid":"","institution":"Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Kui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-01-30 03:44:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3909606/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3909606/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52102368,"identity":"06fbc5db-65b0-47d0-b1a5-9c41eaafb8a2","added_by":"auto","created_at":"2024-03-06 19:15:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73466,"visible":true,"origin":"","legend":"\u003cp\u003eLocations where 16s samples and metagenomic data were collected across the globe.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/39ba9b11fc100783ccf2198e.png"},{"id":52102373,"identity":"00a78433-562c-4533-8e66-0b27245ea054","added_by":"auto","created_at":"2024-03-06 19:15:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of the effects of different factors on the diversity of vertebrate gut microbial communities.\u003c/strong\u003e Stacked bar plot of microbiome distribution at the phylum taxonomic level showing the top 10 categories within each grouping, with the remaining categories shown in gray, grouped by host vertebrate class (\u003cstrong\u003eA\u003c/strong\u003e) and diet (\u003cstrong\u003eB\u003c/strong\u003e). Alpha diversity levels in gut communities grouped by host vertebrate class (\u003cstrong\u003eC\u003c/strong\u003e) and diet (\u003cstrong\u003eD\u003c/strong\u003e). FDR-corrected Wilcoxon rank sum tests were used to determine significance. ***: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **:\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.01, *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. The plots show the BH-adjusted\u003cem\u003e p \u003c/em\u003evalues (Adj. \u003cem\u003ep\u003c/em\u003e value) and partial regression coefficients (Coef.) for multiple regression on matrix (MRM) tests used to determine how much microbial diversity variance was explained by host diet, captivity status, geographic location, habitat, phylogeny, climate and threatened status. The boxplots show the distributions of coefficients and adjusted \u003cem\u003eP\u003c/em\u003e values obtained from the MRM tests for each of 100 random data subset (each subsample including only one sample per species). \u003cstrong\u003eE\u003c/strong\u003eand \u003cstrong\u003eF\u003c/strong\u003e respectively use the Bray-Curtis index and Jaccard distance. Asterisk denotes significance (Adj. \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for ≥ 95% of dataset subsets). Box centerlines, edges, whiskers, and points signify the median, interquartile range (IQR), 1.5×IQR, and \u0026gt; 1.5×IQR, respectively. \u003cstrong\u003eG\u003c/strong\u003eLatitudinal distribution of vertebrate gut diversity. The best polynomial fit was determined on the basis of the corrected Akaike information criterion (AIC) for the given datasets in this study. The line shows the second-order polynomial fit based on ordinary least-squares regression (R2 = 0.03,\u003cem\u003e p\u003c/em\u003e\u0026lt; 2.2e-16). The color gradient represents the temperature at the collection location corresponding to each sample. Shapes of symbols denote whether a sample originated from the Northern Hemisphere (square) or the Southern Hemisphere (triangle).\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/aa8535cdfa1ba417411a5e5f.png"},{"id":52102374,"identity":"edca31f2-ebe9-4515-a4b7-0cfa23e4a69e","added_by":"auto","created_at":"2024-03-06 19:15:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":270364,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of diet on the composition of vertebrate gut microbiota. A \u003c/strong\u003eBray–Curtis-based nonmetric multidimensional scaling (NMDS) plot which shows that each diet is associated with different gut bacterial communities in vertebrates. The Bray–Curtis distance was calculated to represents dissimilarities in the composition of bacterial communities. \u003cstrong\u003eB \u003c/strong\u003eImpact of diet on genus-level taxonomic composition: distributions 47 genera with relative abundances \u0026gt;0.1% and present in ≥50% samples within gut communities of vertebrates with different diets. Genera highlighted in red have higher abundance, those highlighted in blue lower abundance. \u003cstrong\u003eC\u003c/strong\u003e and \u003cstrong\u003eD\u003c/strong\u003e Results of LEfSe analysis. \u003cstrong\u003eC\u003c/strong\u003e: The histogram displays species whose LAD Score exceeds the default value (6). The length of the histogram bars represent the impact of the different diets. \u003cstrong\u003eD\u003c/strong\u003e: Species that displayed no significant differences are marked in yellow. The colors represented by vertebrates with different diets have the same color as the different dietary states in the evolutionary cladogram. \u003cstrong\u003eE \u003c/strong\u003ePICURSt2 functions prediction of microbiota in carnivores and herbivores. Functional differences between the two groups.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/965f575b52801ed320990ec1.png"},{"id":52102370,"identity":"d401581c-d5f4-46f1-a9d1-60252be2c4b8","added_by":"auto","created_at":"2024-03-06 19:15:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffects of different climate regions on vertebrate gut microbial communities. \u003c/strong\u003eAlpha- (\u003cstrong\u003eA\u003c/strong\u003e) and beta-diversity (\u003cstrong\u003eB\u003c/strong\u003e) levels of gut microbial communities in vertebrates from different climatic regions. \u003cstrong\u003eC\u003c/strong\u003e Stacked bar plot of microbiome distribution at the phylum taxonomic level showing the top 7 categories within each grouping, with the remaining categories shown in gray, grouped by the climate zone in which the hosts are located. \u003cstrong\u003eD\u003c/strong\u003e The relative abundance trends of Proteobacteria and Bacteroidetes in the intestinal tracts of vertebrates across different climate zones within the same class or order. \u003cstrong\u003eE\u003c/strong\u003e The relative abundance trends of Proteobacteria and Bacteroidetes in the intestinal tracts of vertebrates across different climate zones within the same diet.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/ff74714fa02c45630413144c.png"},{"id":52102375,"identity":"587c29ff-9779-4128-9a1e-51777b32053e","added_by":"auto","created_at":"2024-03-06 19:15:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-occurrence network constructed by correlating vertebrate intestinal communities in polar, temperate, subtropical and tropical regions.\u003c/strong\u003e The nodes in network are colored by phylum. The connections stands for a strong (spearman’s ρ \u0026gt; 0.6) and significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) correlations. The size of each node is proportional to the Centrality betweenness, and the thickness of each edge is proportional to the value of the Spearman correlation coefficient. “Centrality betweenness” is a measure of how often the shortest path between two nodes transverses through the focal node.\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/43b02f2b8cbf1e6fe1ef9e77.png"},{"id":52102378,"identity":"0eb73a56-e3e9-4554-ad39-29162e77cd17","added_by":"auto","created_at":"2024-03-06 19:15:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":150123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAntibiotic resistance genes in the gut microbiota of vertebrates and in soil\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e Stacked bar plots showing the relative abundance of the top 10 antibiotic resistance gene types detected in each grouping, with the remaining types shown in gray. \u003cstrong\u003eB \u003c/strong\u003eResistance genes overlap between environments. Nodes of different colors represent different habitat environments, and the size of the nodes is proportional to the number of resistance genes in the environment. The width of the edge is proportional to the number of ARGs shared between two domains. Only overlaps of ≥ 5 ARGs are shown. \u003cstrong\u003eC\u003c/strong\u003e Heat map showing the relative abundance of ARG subtypes covering 10 different resistance types in 4 vertebrate categories and 5 soil categories. The top 50 relative abundance ARG subtypes are shown. Red colors indicate the log10 relative abundances of ARG subtypes based on the scale in the lower left.\u003c/p\u003e","description":"","filename":"Binder16.png","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/2b78b807914000ef3d48ba6d.png"},{"id":52102380,"identity":"3fe05ff5-e32f-439b-9bc3-4bc3e916f572","added_by":"auto","created_at":"2024-03-06 19:15:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of high-risk ARGs in transmission chains (vertebrates-soil). A\u003c/strong\u003eBinary heatmap of resistance gene resistance (gray,resistance observed; white, no resistance) assessed for the 8 most abundant metagenome assembled genomes (MAGs) recovered from metagenomic data. \u003cstrong\u003eB\u003c/strong\u003e Comparison of ARGs containing highly similar DNA fragments obtained from the macrogenomes of different host populations.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/afff96aec6fa4656801c8bc3.png"},{"id":52635234,"identity":"4f79d479-ee02-4dd2-8db2-73f67800bf23","added_by":"auto","created_at":"2024-03-13 22:16:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2823057,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/9b2b4fb4-48e2-425c-8cd8-7ea932be6c29.pdf"},{"id":52102371,"identity":"fd06d238-3a11-4616-87f3-1fac50d8826b","added_by":"auto","created_at":"2024-03-06 19:15:21","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2497024,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S1. \u003c/strong\u003eHost samples and corresponding information for gut bacterial community 16S rRNA gene sequencing samples recovered from global vertebrates. \u003cstrong\u003eTable S2\u003c/strong\u003e. Corresponding information for metagenomically sequenced samples of vertebrate gut bacterial communities worldwide and soil metagenomically sequenced samples.\u003c/p\u003e","description":"","filename":"Additionalfile1Tables12.xls","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/ec2f0453e9e0d6ebecd17fb8.xls"},{"id":52102377,"identity":"53c273d9-636e-44f5-b10a-f72a582382de","added_by":"auto","created_at":"2024-03-06 19:15:22","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":374898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2: Fig. S1\u003c/strong\u003e. Phylum-level composition of gut community diversity shown by host phylogeny and with other relevant host metadata. The time-calibrated host phylogenetic tree was obtained from http://timetree.org and the colors of the branches represent the host class (red = \u003cem\u003eCyclostomata\u003c/em\u003e; dark blue = \u003cem\u003eChondrichthyes\u003c/em\u003e; light blue = \u003cem\u003eActinopterygii\u003c/em\u003e; dark yellow = \u003cem\u003eAmphibia\u003c/em\u003e; light yellow = \u003cem\u003eReptilia\u003c/em\u003e; purple = \u003cem\u003eAves\u003c/em\u003e; green =\u003cem\u003e Mammalia\u003c/em\u003e). The data mapped onto the tree (from the inner to outer circles) show the threatened status (obtained from the IUCN Red List in July 2022), host diet, and the relative abundances of bacterial phyla in each host. Relative abundances are averages estimated by subsampling OTUs from all samples of each host species (subsampling to 10,000 for each host species).\u003c/p\u003e","description":"","filename":"Additionalfile2FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/79f9423812692bdc152a1dcf.pdf"},{"id":52104147,"identity":"81f11123-695d-4f0e-9ccc-be683ae5046e","added_by":"auto","created_at":"2024-03-06 19:23:22","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":234600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 3: Fig. S2\u003c/strong\u003e. \u003cstrong\u003eA\u003c/strong\u003e Alpha diversity levels in gut communities grouped by host vertebrate captivity status, habitat type and threatened status. FDR-corrected Wilcoxon rank sum tests were used to determine significance. ***: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **:\u003cem\u003e P\u003c/em\u003e \u0026lt; 0.01, *: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05. \u003cstrong\u003eB\u003c/strong\u003e The plots show the BH-adjusted \u003cem\u003ep\u003c/em\u003e values (Adj. \u003cem\u003ep\u003c/em\u003e value) and partial regression coefficients (Coef.) for multiple regression on matrix (MRM) tests used to determine how much microbial diversity variance was explained by host diet, captivity status, geographic location, habitat, phylogeny, climate and threatened status. Asterisk denotes significance (Adj. \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 for ≥ 95% of dataset subsets). Box centerlines, edges, whiskers, and points signify the median, interquartile range (IQR), 1.5×IQR, and \u0026gt;1.5 IQR, respectively. \u003cstrong\u003eC\u003c/strong\u003eDiversity of gut microbiomes in vertebrates with different diets (same class or order). \u003cstrong\u003eD \u003c/strong\u003eMicrobial diversity of vertebrate gut microbial samples collected in different years is shown.\u003c/p\u003e","description":"","filename":"Additionalfile3FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/99c326eee2cfb720d5be0620.pdf"},{"id":52102381,"identity":"d1b4c0a2-eed8-4f3c-bf76-971243f79aab","added_by":"auto","created_at":"2024-03-06 19:15:22","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":167926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 4: Fig. S3.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e Diversity of gut microbiomes in vertebrates from different climate regions (same order or class). \u003cstrong\u003eB\u003c/strong\u003e Diversity of gut microbiomes in vertebrates from different climate regions (same diet habits).\u003c/p\u003e","description":"","filename":"Additionalfile4FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/f4ac3f2160c0f3269ebd62cd.pdf"},{"id":52102376,"identity":"9ca396a9-679c-4f22-95d6-14a41ce40403","added_by":"auto","created_at":"2024-03-06 19:15:21","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":167731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 5: Fig. S4\u003c/strong\u003e. Relative abundance of intestinal flora analyzed at the level of class (\u003cstrong\u003eA\u003c/strong\u003e), order (\u003cstrong\u003eB\u003c/strong\u003e), family (\u003cstrong\u003eC\u003c/strong\u003e), and genus (\u003cstrong\u003eD\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Additionalfile5FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/24092bdeb240161c67176543.pdf"},{"id":52102382,"identity":"97a38d30-26f9-42b6-9506-466925ffe216","added_by":"auto","created_at":"2024-03-06 19:15:22","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":6008278,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 6: Fig. S5\u003c/strong\u003e. Co-occurrence network constructed by correlation of intestinal communities at the level of class (\u003cstrong\u003eA\u003c/strong\u003e), order (\u003cstrong\u003eB\u003c/strong\u003e), family (\u003cstrong\u003eC\u003c/strong\u003e), and genus (\u003cstrong\u003eD\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Additionalfile6FigureS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3909606/v1/0d4780d2d23716cbb9f52543.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global meta-analysis reveals the drivers of gut microbiome variation across vertebrates","fulltext":[{"header":"Background","content":"\u003cp\u003eAdaptation is one of the most striking features of biological organisms and an essential capability for survival in diverse environments [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Together with other sources such as host standing variation and mutations, the trillions of microorganisms inhabiting animal guts may drive adaptive benefits [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The gut microbiota could facilitate the diversification of host dietary niches altering future host adaptive trajectories [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For instance, the gut microbiota of koalas, specialized in eating Eucalyptus, harbor bacterial functional pathways associated with the digestion of plant secondary metabolites in a higher measure than the microbiome of wombats, a sister species [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eShifts in the gut microbiota may also affect host physiological and metabolic function, such as body mass index [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], visceral fat [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and metabolic syndrome [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] in humans, as well as feed efficiency [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], methane emissions, rumen and blood metabolites, and milk production efficiency in cattle [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It has been hypothesized the existence of selective pressures that could result in the reshaping of the microbiota to benefit host fitness [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. For example, Toll-like receptor deficiency in mice exacerbates intestinal dysbacteriosis, which negatively contributes to the metabolism and ability of host to harvest energy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Yet a comprehensive understanding of the contribution of gut microbiome to adaptive evolution in vertebrates has not been achieved, as most recent studies have been limited to one species, a small number of samples, or confined geographic distributions.\u003c/p\u003e \u003cp\u003eBoth in humans and other vertebrates the composition of the gut microbiota is affected by a number of factors, such as host dietary patterns, phylogeny [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], social interactions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and climate [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Variation in climate in particular is a key factor that influences the physiology and immunity of the host, as well as its selection from surrounding microbes [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In recent years, several studies have focused on the diet and phylogeny of hosts, which are known factors leading to changes in animal intestinal microbiota [\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], while environmental factors and responses to different local climates have rarely been investigated. Although there are several reports regarding the effect of climate variation on gut microbial communities [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], few studies have reported on the relationship between climate factors and vertebrate gut microbiota at global landscape.\u003c/p\u003e \u003cp\u003eWhile host diet and climate variation are essential factors in shaping the gut microbiota, other factors, such as antibiotic exposure and gastrointestinal dysfunction, also contribute to alterations in the gut microbiota of vertebrates. Notably, antibiotic-resistance genes (ARGs) can be shared between animal, soil, and human bacteria via horizontal gene transfer [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and the increasing environmental contamination with ARGs may therefore be contributing to the growing antibiotic resistance and multi-resistance observed globally. Investigating the presence of ARGs in vertebrate gut microbiomes is vital and will help guide future antibiotic use and conservation efforts.\u003c/p\u003e \u003cp\u003eIn the present study, we collected 6508 samples of 16S rRNA gene sequence from all seven classes of vertebrates (\u003cem\u003eMammalia\u003c/em\u003e, \u003cem\u003eAves\u003c/em\u003e, \u003cem\u003eReptil\u003c/em\u003ees, \u003cem\u003eAmphib\u003c/em\u003eia, \u003cem\u003eActinopteryg\u003c/em\u003eii, \u003cem\u003eChondrichthyes\u003c/em\u003e and \u003cem\u003eCyclostomata\u003c/em\u003e). We further collected data for metagenomic sequences including 489 terrestrial vertebrate gut microbiomes and 203 sympatric soil biological environment samples. The aims of the study are: (1) assess the effects of environmental factors on vertebrate gut microbiome diversity; (2) characterize the patterns of vertebrate gut microbiome composition and function, both in terms of host diet pattern and habitats; (3) identify potential horizontal transfer of ARGs between vertebrate gut microbiomes and sympatric soil samples.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy cohorts and data retrieval\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe systematically searched existing literature (Google Scholar, Web of Science) for the following keywords: \u0026ldquo;fecal/gut microbiome\u0026rdquo;, \u0026ldquo;vertebrate\u0026rdquo; and \u0026ldquo;16S V4 /V3-V4 sequencing\u0026rdquo;. We identified additional articles [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] through references cited in the retrieved results. We obtained 12,640 samples based on 16S rRNA gene sequencing from 99 published articles up to September 2022. We then retained for further analyses only samples with known hosts and confirmed origins.\u003c/p\u003e \u003cp\u003eWe then performed quality control for experimental animal, sample type, amplification primer and sequencing method as follows: 1) Samples from all vertebrates except livestock and poultry; 2) Samples of distal gut microbiota or fresh feces were retained; 3) Collected samples were not treated (such as antibiotic therapy) in a way that affected the composition of gut microbiome; 4) Samples were amplified using the V3-V4 or V4 hypervariable regions of the 16S rRNA gene; 5) Samples were sequenced with amplicon sequencing on Illumina platform; 6) Number of samples at each location was greater than 8. This resulted in a dataset of 6508 samples spread across seven continents (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Species name, species category (class and order), diet information (carnivorous, herbivorous and omnivorous), habitat type (terrestrial, aquatic and amphibia), captivity status (wild or captive), threatened status (as indicated by the IUCN Red List for July 2022, least concerned: LC; near threatened: NT; vulnerable: VU; endangered: EN; critically endangered: CR; Extinct in the Wild: EW), sampling location, sampling climate zone, average earth surface temperature at the sampling point, and sampling year are included in Additional file 1 : Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the collection of metagenomes, we first collected a large number of soil metagenomes from around the world based on the work of Ji M et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], Bahram et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], and Delgado-Baquerizo et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Based on the region where soil metagenomic samples were collected, we can accurately search for terrestrial vertebrate intestinal metagenome samples in the same region on Google Scholar and Web of Science websites. As of December 2022, we have obtained a total of 489 vertebrate and 203 soil environmental metagenomic sequencing samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sampling information is included in Additional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003e16S rRNA sequencing analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePaired-end sequencing reads were merged using the VSEARCH software [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] based on the UCHIME algorithm. We then filtered the sequences for quality, and we used Cutadapt 4.1 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] to remove barcodes and primer sequences from V3-V4 or V4 regions according to corresponding primers. The V3 region in the V3-V4 region was removed, and the V4 region was retained [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOperational taxonomic units (OTUs) were identified by clustering 16S rRNA gene sequence data at a 97% nucleotide similarity cutoff in the UPARSE program (version 7.1) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. We filtered out chimeras from the datasets using UCHIME [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Each 16S rRNA gene sequence was assigned a taxonomic identity with the Ribosomal Database Project (RDP) classifier and compared against the Silva 16S rRNA gene database (version SSU128) using a confidence threshold of 70% [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Community diversity analysis was performed in QIIME2 (Version 2021.02). Alpha diversity calculations were done using Shannon index (quantitative community richness) and Observed OTUs (qualitative community richness). Beta diversity analysis was performed to assess dissimilarities in microbial communities between groups using Jaccard distance (qualitative community dissimilarity) and Bray-Curtis distance (quantitative community dissimilarity).\u003c/p\u003e \u003cp\u003e \u003cb\u003eMetagenomic sequencing analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBefore assembly all metagenomic sequencing analysis reads were assessed with FastQC (version 0.11.8) for quality control and quality trimmed using Trimmomatic (version 0.39) with the parameters \u0026ldquo;SLIDINGWINDOW:4:20 MINLEN:50\u0026rdquo;. The high-quality clean reads were then assembled into contigs using the MEGAHIT software (version 1.2.9) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] with the default settings. Emboss [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] was used to convert nucleic acid sequences to protein sequences. Representative sequences were aligned against the NCBI non-redundant database to enable taxonomic identification with Diamond (version 0.9.21) using a cutoff e-value of 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e, a minimum query coverage of 80%, and a minimum sequence identity of 80%. Species composition and abundance information at the genus level were obtained from the annotated results [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAntibiotic resistance genes were annotated with the Structured Antibiotic Resistance Genes (SARG) database using the ARGs-OAP v2.0 pipeline [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The resfams antibiotic resistance protein family data were subjected to functional metagenomic selection using the hmmscan function of the HMMER software package (v3.3.2) using the following parameters: --cut_ga and --tblout [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. To assess the potential mechanism of ARG migration, amino acid sequences of flanking genes containing ARG contigs were compared to the SARG database using BLASTP (e-value\u0026thinsp;\u0026le;\u0026thinsp;1e\u0026thinsp;\u0026minus;\u0026thinsp;5) and assigned to genes by the highest-scoring annotated hit with \u0026gt;\u0026thinsp;80% similarity that covered\u0026thinsp;\u0026gt;\u0026thinsp;70% of the length of the query protein. Flanking gene sequences containing ARG contigs were annotated in the UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using a 60% minimum sequence identity comparison threshold and 50% minimum coverage.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMultiple Regressions on Matrices (MRMs)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe used MRMs to study host Phylogeny, habitat type (aquatic, amphibious and terrestrial), diet (carnivorous, omnivorous and herbivorous), threatened status (LC, NT, VU, EN, CR), captivity status (wild or captive), climate (annual average temperature), and geographical location (latitude and longitude). In the order listed above, the habitat type from 0 to 2, the diet was coded from 0 to 2, the threatened status from 5 to 1, the binary variables for captive status were set as 0 (wild) and 1 (captive). Gower distances among communities were separately calculated for all independent variables, while Euclidean distances were calculated for Shannon index differences, and Bray-Curtis distances were calculated to estimate beta diversity among gut communities. For MRMs using the EcoDist R package [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], the effect size and significance were obtained by comparing actual data to a random arrangement (n\u0026thinsp;=\u0026thinsp;1000 for all analyses).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCo-occurrence network\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe co-occurrence of OTUs in microbial communities across the four habitat climate types was analyzed. To reduce network complexity and facilitate the identification of the core gut microbial community, Only OTU with mean relative abundances\u0026thinsp;\u0026gt;\u0026thinsp;0.1% and presence in \u0026gt;\u0026thinsp;50% of samples of a given group were used for the above analyses, as described by [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Only co-occurrences associated with correlation (Spearman ρ)\u0026thinsp;\u0026gt;\u0026thinsp;0.6 and statistical significance (\u003cem\u003eP\u003c/em\u003e value)\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were considered for further analysis [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and visualization of the co-occurrence network was conducted using the Fruchtermann-Reingold layout of the interactive platform Gephi version 0.9.7. Nodes indicated individual microbial taxa (OTUs) in the microbiome network [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Network edges represented the pairwise correlations between nodes, suggesting a biologically or biochemically meaningful interactions [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The centrality reveals the role of nodes as Bridges between network components, while the degree reveals the role of nodes directly linked to other OTUs in the community. Centrality and degree are often chosen as critical criteria to identify keystone species in the symbiotic network [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], possible keystone node were those that demonstrated high betweenness centrality values.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analyses\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe compared gut bacterial community characteristics with Kruskal\u0026ndash;Wallis (multiple group comparison) or two-tailed Wilcoxon rank-sum (pairwise comparison) tests. A false discovery rate (FDR)-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for these tests. The R package \u0026ldquo;ggplot2\u0026rdquo; [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] was used to visualize box plots of data distributions. Variations in the bacterial community compositions and functions of different samples were evaluated by Non-metric MultiDimensional Scaling (NMDS) based on the Bray\u0026ndash;Curtis distance. We used a dated host phylogeny for all species from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timetree.org\u003c/span\u003e\u003cspan address=\"http://timetree.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. We conducted geographic analysis using the ArcGIS 10.2 software (Environmental Systems Research Institute Inc., Redlands, USA). The LEfSe analysis was performed with the following parameters: the alpha value for factorial Kruskal\u0026ndash;Wallis test among classes was \u0026lt;\u0026thinsp;0.05, and the threshold on the logarithmic LDA score for discriminative features was 6.0. Functional profiles were inferred with PICRUst (version 2.5.1). The inferred genes and their functions were aligned with the Kyoto Encyclopedia of Genes and Genomes. PICRUSt-predicted functional profiles were analyzed by STAMP (version 2.1.3), with significance assessed by Welch\u0026rsquo;s t-test with Storey\u0026rsquo;s FDR (reported *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, effect size\u0026thinsp;\u0026gt;\u0026thinsp;0.5).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eOverview of data related to the vertebrate gut microbiome\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo comprehensively depict the gut microbiomes of vertebrates, we analyzed the diversity and composition of the gut microbial communities in 113 species of vertebrates, including \u003cem\u003eMammals\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;4,668 from 60 species), \u003cem\u003eAves\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;792 from 25 species), \u003cem\u003eReptiles\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;347 from 10 species), \u003cem\u003eAmphibia\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;104 from 5 species), \u003cem\u003eActinopterygii\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;520 from 9 species), \u003cem\u003eChondrichthyes\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;33 from 2 species) and \u003cem\u003eCyclostomata\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;44 from 3 species). The samples were collected from vertebrates inhabiting seven continents and with different diet, habitat types, captive status, threatened status and climate zones (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall we obtained 39,613 operational taxonomic units (OTUs) from 1,290 genera within 47 phyla. The phylum-level relative abundances were compared among vertebrate lineages, revealing that the dominance of Proteobacteria in the gut microbiota of Actinopterygii (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Additional file 2: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Notably, previous studies have shown that Proteobacteria are involved in various biogeochemical processes in aquatic ecosystems [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and are commonly found in the intestines of aquatic organisms [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. We also noted that Fusobacteria is common in carnivorous animals such as \u003cem\u003eCalifornia condor\u003c/em\u003e, \u003cem\u003eGentoo Penguin\u003c/em\u003e, \u003cem\u003eNorthern fur seal\u003c/em\u003e and \u003cem\u003eLeopard\u003c/em\u003e, while Verrucomicrobia is prevalent in the guts of herbivores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB; Additional file 2: Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In particular, the genus Verrucomicrobiales of the phylum Verrucomicrobia had been identified as a core taxon in the fecal microbiome of herbivores, suggesting it may play a key role in fiber digestion [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe gut microbiome of different animal taxa may be shaped by environmental (i.e., habitat type, geography, and climate) and genetic factors [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The extent to which these factors may contribute to the diversity and composition of vertebrate gut microbiomes is yet not fully understood. We first calculated the alpha diversity (i.e. a measure of microbiome diversity) of gut microbial communities using the Shannon index. The gut microbiota of mammals showed the highest level of alpha diversity (i.e. Shannon index\u0026thinsp;=\u0026thinsp;5.26), while the gut microbiota of birds scored the lowest (i.e. Shannon index\u0026thinsp;=\u0026thinsp;3.14) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Furthermore, herbivores had the highest gut microbial diversity, while carnivores had the lowest (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Wild vertebrates showed higher diversity than captive cohorts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In addition, the microbial communities of terrestrial vertebrates were more diverse than those of aquatic and amphibian species (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), with the difference between aquatic and amphibian vertebrates not being statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). We also observed lower alpha diversity levels in threatened species (i.e., \u003cem\u003eAiluropoda melanoleuca\u003c/em\u003e) compared to unthreatened species (i.e., \u003cem\u003eOchotona curzoniae\u003c/em\u003e) (Additional file 3: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA), which is consistent with what was reported by a previous study [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe then performed MRM (Matrix Multiple Regression) analysis to evaluate the importance of various factors affecting gut microbiome of vertebrates. Each of our four MRM models (one per diversity metric) had a significant overall fit. Host diet and habitat climate were the only significant explanatory variables (BH-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, F; Additional file 3: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). Host diet explained a substantial amount of alpha- and betadiversity variation (~\u0026thinsp;8\u0026ndash;24%) and was significant for all diversity metrics tested (i.e., Shannon index, Bray-Curtis, Observed Feature, and Jaccard distance). Habitat climate also explained a substantial amount of the variation in ɑ and β diversity (~\u0026thinsp;11\u0026ndash;16%). These results further demonstrated that gut microbiome variations in vertebrates are primarily driven by diet and climate factors.\u003c/p\u003e \u003cp\u003eIn order to show the significant impact of host diet and habitat climate factors on microbial diversity, we first examined the intestinal microbial diversity of vertebrates of the same class or order with different dietary habits, and obtained the same results as Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD(Additional file 3: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). Following this, we noticed that the greatest microbial diversity was observed at intermediate latitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG; R2\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2e-16). This was consistent with the two other major types of ecosystems on Earth, that is, ocean [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and air [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. At the same time, we can also notice that richness increases with increasing temperature. There is good evidence that the main drivers of latitudinal diversity patterns are pH and soil temperature [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and the salinity and temperature of water [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In addition, the microbial Shannon index increases year by year with the increase in sample collection year (Additional file 3: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eD), while global surface temperature is increasing year by year [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Therefore, temperature can be considered an important factor driving microbial diversity in the gut of vertebrates.\u003c/p\u003e \u003cp\u003e \u003cb\u003eChanges in vertebrate gut microbial composition and function in response to diet and climate factors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess the effects of dietary patterns on composition and function of vertebrate gut microbiome, we performed Non-metric MultiDimensional Scaling (NMDS) based on genus-level Bray\u0026thinsp;\u0026minus;\u0026thinsp;Curtis distance. In NMDS analysis, the plots of carnivores and herbivores clustered separately, while there was no clear separation between herbivores and omnivores (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We selected the gut microbiota with relative abundance higher than 0.1% in at least 50% of the individuals at genus level to generate a heatmap of species relative abundance. We observed remarkable variations in both the composition of microbial communities depending on host diet (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D). For example, the genera including \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eCetobacterium\u003c/em\u003e, \u003cem\u003eSerratia\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eCatellicoccus\u003c/em\u003e, \u003cem\u003eClostridium_XI\u003c/em\u003e and \u003cem\u003eLactococcus\u003c/em\u003e were enriched in carnivores, whereas the relative abundance of genera including \u003cem\u003eEscherichia\u003c/em\u003e/\u003cem\u003eShigella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e was higher in herbivores. In addition, genera including \u003cem\u003eClostrdium_XIVa\u003c/em\u003e, \u003cem\u003eClostrdium_IV\u003c/em\u003e and \u003cem\u003eMethanomassiliicoccus\u003c/em\u003e were more abundant in omnivores. The dominant KEGG (Kyoto Encyclopedia of Genes and Genomes) functional categories (level 2) found in gut microbiome of herbivores and carnivores included metabolism, genetic information processing, environmental information processing and cellular processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Notably, herbivore gut microbiomes were enriched in metabolically relevant pathways (e.g., Glycan biosynthesis and metabolism, Replication and repair, Amino acid metabolism, Nucleotide metabolism), while carnivore gut microbiomes were characterized by protein-related pathways (e.g., Protein families: signaling and cellular processes, Membrane transport, Cellular community-prokaryotes, Signal transduction) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Our findings therefore confirm that diet is a critical factor shaping the composition and function of vertebrate gut microbiomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClimate factors are also strong factors in the determination of gut microbiomes [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Importantly, our analysis revealed that the microbiome diversity (i.e., Shannon index) increased gradually from high- to low-latitude zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Additional file 4: Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA, B). We also detected differences in beta diversity (i.e. a measure of the similarity or dissimilarity of two communities) within the four climate regional samples. As shown in the NMDS plot, the polar samples clustered closely together, whereas temperate and tropical samples clustered less closely on NMDS1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), indicating more diverse gut microbial compositions in the low-latitude region. Seven major microbial phyla dominated across the four climate zones: Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Fusobacteria, Tenericutes and Euryarchaeota (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), which together constituted up to 96.4% OTUs. Notably, the richness of Bacteroidetes phylum (class Bacteroidia, order Bacteroidales, family Prevotellaceae and genus \u003cem\u003ePrevotella\u003c/em\u003e) in gut microbiota of vertebrates increased from high- to low-latitudes where the hosts reside (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC; Additional file 5: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). In contrast, the abundance of members of Proteobacteria phylum (class Gemmaproteobacteria and genus \u003cem\u003ePhotobacterium\u003c/em\u003e) decreased from high- to low-latitude zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC; Additional file 5: Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). In addition, we controlled for batch and individual study effects in the sequencing data to further illustrate the changing trends of Bacteroidetes and Proteobacteria in vertebrate gut microbiota in different climate regions (Fig .4D,E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe also constructed co-occurrence microbial networks to identify robust microbial association patterns in vertebrate gut for four climate zones considered(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Additional file 6: Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Interestingly, the co-occurrence network of tropical zone included the largest amount of significantly co-occurring OTUs, while the network of polar zone contained the least OTUs. Compared with the network in low latitudes, the structural features, nodes and edges of the co-occurrence network in high latitudes were lower than those in other regions, suggesting that gut microbial communities at high latitudes are more susceptible to perturbations by climatic conditions. In addition, the gradually increasing coexisting clusters of high-betweenness centrality Bacteroidetes and the gradually decreasing high-betweenness centrality Proteobacteria from high to low latitudes may be related to climate change and make adaptive changes. These results further confirm the known associations between climate factors and gut microbiome of vertebrates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGene mobility potentials of common antibiotic resistomes in the gut microbiome of vertebrates and their sympatric soil biological environment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSoil biological samples have a large gene pool of antibiotic resistant bacteria [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. ARGs threaten vertebrates health worldwide, but the common resistome and ARGs mobility between vertebrate and their sympatric soil biological environment remain unclear. In order to fully decipher the common resistome and potential mobility of ARGs, we collected and analyzed 489 vertebrate gut microbial samples and 203 sympatric soil environment samples using metagenomic sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Additional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). We found 89.4% ARGs belonged to the top types (i.e., multidrug, tetracycline, bacitracin, rifamycin, macrolide, novobiocin, vancomycin, beta_lactam, polymyxin, quinolone, and aminoglycoside) Moreover, these types accounted for 94.7% of the total abundance of ARGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The 70 subtypes of ARGs, mainly comprising multidrug (\u003cem\u003eMexF\u003c/em\u003e), tetracycline (\u003cem\u003etetA(48)\u003c/em\u003e), bacitracin (\u003cem\u003ebacA\u003c/em\u003e), rifamycin (\u003cem\u003eRbpA\u003c/em\u003e), macrolide (\u003cem\u003emacB\u003c/em\u003e), novobiocin (\u003cem\u003enovA\u003c/em\u003e), vancomycin (\u003cem\u003evanXI\u003c/em\u003e), beta_lactam (\u003cem\u003eTEM-116\u003c/em\u003e), polymyxin (\u003cem\u003erosB\u003c/em\u003e), quinolone (\u003cem\u003emfpA\u003c/em\u003e), and aminoglycoside (\u003cem\u003eamrB\u003c/em\u003e) resistance genes, were shared between the vertebrate gut microbiomes and their sympatric soil biological environmental samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). More specifically, a large number of overlapping ARGs (13.97%) were shared among mammals, aves and soil biological environmental samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). We also observed the \u003cem\u003ebacA\u003c/em\u003e (also known as UppP, undecaprenyl-diphosphate or -pyrophosphate phosphatase) gene, which confers resistance to bacitracin, was dominant in mammals, aves and soil biological environmental samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further investigated the exchange potential of ARGs between vertebrate gut microbiomes and the soil in their environment with metagenomic sequencing analysis. We firstly found that \u003cem\u003eE. coli\u003c/em\u003e was enriched in the gut microbiomes of the mammal and aves cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), which is consistent with the existing literature [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The genome of \u003cem\u003eE. coli\u003c/em\u003e harbors a number of different ARGs such as PBP transpeptidase domain, Bleomycin resistance protein, \u003cem\u003eMarR\u003c/em\u003e, \u003cem\u003emfp\u003c/em\u003e and \u003cem\u003eCblA\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), indicating the potential for multi-drug resistance in the gut microbiome of mammals and aves. We then evaluated the exchange potential of mobile genetic elements in the flanking genetic sequences in assembled contigs. Notably we identified four contigs containing \u003cem\u003ebacA\u003c/em\u003e with high sequence similarity to \u003cem\u003eE. coli O25b-ST131\u003c/em\u003e across mammals, aves and their sympatric soil biological environmental samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The flanking sequence of \u003cem\u003ebacA\u003c/em\u003e also contains genes encoding transferases that catalyze the transfer of ARGs between vertebrates and members of sympatric soil bacterial communities. These results suggested that ARGs and ARG-containing bacteria might be transferred between soil biological environments and vertebrates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere we collected 6508 fecal samples from 113 vertebrate species spanning seven classes with different feeding behaviors and habitats, by using 16S rRNA sequencing to investigate the ecological and biological drivers of gut microbial diversity and composition. We firstly evaluated the relative contribution of these drivers to the diversity of gut bacterial communities in vertebrate guts, and demonstrated that diet and climate factors have the strongest impact on gut microbial diversity. We also confirmed the effects of diet and climate on gut microbial composition and function. We then identified common antibiotic resistance and potential horizontal transfer of ARGs between terrestrial vertebrate gut microbiomes and their sympatric soil biological environmental samples using metagenomic datasets analysis. In conclusion, our study indicates both diet and climate factors play a critical role in driving the diversity and composition of gut microbiomes in vertebrates and reveals the potential threat that soil ARGs represent for animal health.\u003c/p\u003e \u003cp\u003eThe abundant and diverse gut microbial communities live in the guts of both humans and animals are essential for the host physiology, ecology, and evolution [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Gut microbiota are densely populated microbial communities that include many different types of bacteria that play essential and critical roles in regulation and adaptation of vertebrates to diverse lifestyles [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Here we firstly evaluated a range of host and environmental factors that may influence the gut microbial diversity in vertebrates. Among all factors considered, diet patterns were the strongest predictor of microbial community diversity, followed by climate factors.\u003c/p\u003e \u003cp\u003eAccumulating evidence has been pointing toward long-term dietary patterns having profound effects on the diversity and structure of the trillions of microorganisms residing in animal guts [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. As a specific example, two co-evolution studies of mammals and their gut microbiota have found that both gut microbiota composition and functions are adapted to the animal diet (herbivorous, carnivorous and omnivorous) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. In another more recent study, the convergently evolved composition of the gut microbiomes in rodent species, despite their phylogenetic diversity, strongly suggests that diet is the major force shaping microbiota [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Here we observed that gut microbial diversity increased from carnivores to herbivores, consistently with previous studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Herbivores typically have a diet based on plant polysaccharides, while the diet of carnivores is rich in high-fat and high-protein products. Carnivores can absorb proteins and fats thanks solely to their own enzymes [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], while herbivores have more complex gastrointestinal tracts with prolonged chyme retention and diverse mutualistic microbial communities that facilitate the digestion of fibers [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Our results also show that gut microbiota functions in herbivores are enriched in biometabolically competent microbiota, while the gut microbiota of carnivores is enriched for protein transport. In particular, the genus \u003cem\u003eFusobacterium\u003c/em\u003e was significantly enriched in the gut of carnivores, consistently with previous reports that Fusobacterium is commonly found in higher concentrations in healthy carnivore hosts and is associated with protein-rich diets [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. We also found that the family Rhizobiaceae and genus \u003cem\u003eRhizobium\u003c/em\u003e were abundant in the gut of herbivorous vertebrates. \u003cem\u003eRhizobium\u003c/em\u003e is a kind of bacterium symbiotic with plant roots, which can form symbiotic nodules with the roots of leguminous plants and fix nitrogen in the nodules, thus providing a nitrogen source for plants [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. The enriched \u003cem\u003eRhizobium\u003c/em\u003e by eating behaviors maybe improve the performance of fiber digestion and nutrition absorption of herbivores [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGut microbiota plasticity in response to environmental cues may allow hosts to rapidly adapt to ecological change [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Recent evidence has suggested that gut microbiota are affected by both warming environment and changes to host ecology driven by climate variation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. We found the diversity and structure of gut microbiota among vertebrates are associated with climatic factors. Specifically, we noted the taxa of phylum Bacteroides and Proteobacteria are significantly correlated with climate factors. A previous study showed that the phylum Bacteroides and Proteobacteria, both gram-negative bacteria, are strongly affected by temperature changes [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Interestingly, the phylum Proteobacteria could adapt to tolerate dryness and low temperatures by forming durable spores to protect itself under extreme conditions [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. For instance, the abundance of Proteobacteria in freshwater lakes was enriched in winter, probably due to the strong adaptability of it to low temperature extremes [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. In contrast, the phylum Bacteroides can adapt to relatively high temperature environments. For example,the abundance of Bacteroidetes in grasslands showed an increased trend with a rise in temperature [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. We detected a gradually increasing co-occurring clusters of taxa belonging to phylum Bacteroidetes were detected from high- to low-latitudes. Climate factors should be considered to have a critical contribution to the diversity of gut microbiota in vertebrates.\u003c/p\u003e \u003cp\u003eThe development and spread of antibiotic resistance in bacteria is a growing global health threat to humans and animals [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Soil is one of earth\u0026rsquo;s largest reservoirs of ARGs (i.e., the soil antibiotic resistome), and is the habitat of many pathogens associated with clinical infections and animal disease outbreaks [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. One of the most serious health concerns is the transfer of ARGs from soil to anthropogenic, animal, and plant settings, which would pose a severe threat to human and animal health [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. However, there are still major unknowns associated with the transfer of ARGs and ARG-containing bacteria between soil biological environments and vertebrates. In this study, we detected a relatively high abundance of ARGs encoding resistance genes to bacitracin (8.2%) and multidrug (6.7%) in wild vertebrates and in the sympatric environmental samples. In particular we identified a potential horizontal transfer of the \u003cem\u003ebacA\u003c/em\u003e gene between the gut microbiome of wild vertebrate (i.e., \u003cem\u003eMelursus ursinus\u003c/em\u003e, \u003cem\u003eCasuarius bennetti\u003c/em\u003e and \u003cem\u003eTorgos tracheliotos\u003c/em\u003e) and forest soil bioenvironment samples. The \u003cem\u003ebacA\u003c/em\u003e gene is associated with bacitracin resistance, suggesting a widespread use of this antibiotic. It has already been reported that the bacitracin resistance gene can be transferred to humans through close contact with ruminants [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. The use of bacitracin as a growth promoter in the veterinary practice has significantly contributed to animal health, welfare and performance, as well as to the overall productivity of the industry. However, bacitracin is banned as a feed additive in livestock farming by most countries. Based on our findings we recommend that the relevant government authorities increase the surveillance on how this drug is being procured. Our findings highlight the health threats of soil bioenvironments harboring ARGs. We identified forested areas where the control of soil antibiotic resistance needs to be prioritized.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this is the gut microbiome research effort with the largest number of individuals collected at the global scale across vertebrates. Our results can help to identify the major modulators of the diversity and structure of gut microbiomes. Our findings confirm that diet patterns and climate factors play key roles in promoting specific taxa in vertebrate gut microbiota. In addition, we comprehensively deciphered the common antibiotic resistomes of wild vertebrates and their sympatric soil biological environment samples, and found evidence of potential horizontal transfers of the \u003cem\u003ebacA\u003c/em\u003e gene. These results significantly advance our knowledge of the diversity and structure of gut microbiomes in vertebrates and their association with environmental factors, and provide crucial insights to better manage the soil ARG pool.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntibiotic resistant gene\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e16S rRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e16S ribosomal ribonucleic acid rRNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOTU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOperational taxonomic units\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple regressions on matrice\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emetagenome-assembled genome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIUCN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Union for Conservation of Nature\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQIIME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative insights into microbial ecology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLEfSe\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinear discriminant analysis Effect Size\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinear discriminant analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse discovery rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePICRUSt2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhylogenetic investigation of communities by reconstruction of unobserved states\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\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\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (31972539, 32102511). Science\u0026nbsp;Technology\u0026nbsp;Innovation and Industrial Development of Shenzhen Dapeng New District (Grant NO.PT202101-05). The China Scholarship Council (Grant No.202003250046).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLB and KX: Conceptualization, Methodology, Investigation, Writing-review \u0026amp; editing, Supervision. YX and SX: Methodology, Software, Investigation, Data curation, Formal analysis, Writingc-original draft. YX, ZL, EZ and KL: Investigation, Data curation. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Martien A. M. Groenen and Ole. Madsen for the discussion, critical reading, and revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, China\u003c/p\u003e\n\u003cp\u003eYong Xie, Zixin Li, Erwei Zuo, Lijing Bai \u0026amp; Kui Li\u003c/p\u003e\n\u003cp\u003eCollege of Animal Science and Technology, China Agricultural University, Beijing 100193, China\u003c/p\u003e\n\u003cp\u003eSongsong Xu\u003c/p\u003e\n\u003cp\u003eAnimal Science and Technology College, Beijing University of Agriculture, Beijing 102206, China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYufei Xi \u0026amp; Kai Xing\u003c/p\u003e\n\u003cp\u003eAnimal Breeding and Genomics, Wageningen University \u0026amp; Research, Wageningen, 6708 PB, The Netherlands\u003c/p\u003e\n\u003cp\u003eLijing Bai\u003c/p\u003e\n\u003cp\u003eCorresponding authors\u003c/p\u003e\n\u003cp\u003eCorrespondence to Kai Xing and Lijing Bai\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZitnik M, Sosič R, Feldman M W, et al. 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Microbial Pathogenesis, 2020, 149: 104350.\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":"Microbial diversity, Gut microbiome, Diet, Climate variation, Antibiotic resistomes","lastPublishedDoi":"10.21203/rs.3.rs-3909606/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3909606/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eShifts in their gut microbial composition and diversity are a known mechanism vertebrates use to adapt to environmental conditions. However, the relative contribution of individual environmental factors to gut microbiota composition and diversity remains poorly understood. To understand the broad influence of different environmental factors on gut microbiome of vertebrates, we collected 6508 16S rRNA gene sequencing samples of gut bacterial communities from 113 host species, spanning seven different classes as well as different types of feeding behaviors and host habitats. Furthermore, we identified the common antibiotic resistomes and their potential mobility between terrestrial vertebrate gut microbiomes (n\u0026thinsp;=\u0026thinsp;489) and their sympatric soil environment samples (n\u0026thinsp;=\u0026thinsp;203) using metagenomic sequencing analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe demonstrate that host diet patterns have a significant impact on changes in the gut microbiome. We reveal the phylum Fusobacteria is enriched in the gut of carnivorous vertebrates, while in the gut of herbivorous vertebrates there was a larger representation of Verrucomicrobia. Climate factors are also strongly associated with gut microbiome variation among vertebrates. We show that the abundance of Bacteroidetes increases gradually from high- to low-latitude zones, while Proteobacteria show a decreasing trend. In particular, we found that \u003cem\u003ebacA\u003c/em\u003e and its flanking sequences are highly homologous among the genomes of mammals, avian gut communities, and sympatric soil biomes, suggesting that the \u003cem\u003ebacA\u003c/em\u003e resistance gene may undergo horizontal transfer between vertebrates and sympatric environments.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur findings show diet patterns and climatic factors play key roles in promoting specific taxa in vertebrate gut microbiota. In addition, we comprehensively decipher the common antibiotic resistance groups of wild vertebrates and their sympatric soil biological environment samples, and provide evidence of potential horizontal transfers of the \u003cem\u003ebacA\u003c/em\u003e gene. These results significantly advance our knowledge of the diversity and structure of gut microbiomes in vertebrates and their association with environmental factors, and provide crucial insights to better manage the soil ARG pool.\u003c/p\u003e","manuscriptTitle":"Global meta-analysis reveals the drivers of gut microbiome variation across vertebrates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 19:15:15","doi":"10.21203/rs.3.rs-3909606/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":"91e9bdac-5b1f-4b21-9668-a4d0014d0dd7","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-27T16:29:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-06 19:15:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3909606","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3909606","identity":"rs-3909606","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}