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

doi:10.21203/rs.3.rs-3909606/v1

Download PDF

Research Article

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

https://doi.org/10.21203/rs.3.rs-3909606/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

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

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, Reptiles, Amphibia, Actinopterygii, 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.

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 < 0.05, effect size > 0.5).

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. 2A; 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. 2B; 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. 2C). Furthermore, herbivores had the highest gut microbial diversity, while carnivores had the lowest (P < 0.0001; Fig. 2D). 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 < 0.0001), with the difference between aquatic and amphibian vertebrates not being statistically significant (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. S2A), 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. 2E, F; Additional file 3: Fig. S2B). 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. 2D(Additional file 3: Fig. S2C). Following this, we noticed that the greatest microbial diversity was observed at intermediate latitudes (Fig. 2G; 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. S2D), 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. 3A). 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. 3B-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. 3E). 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. 3E). 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. 4A; Additional file 4: Fig. S3A, 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. 4B), 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. 4C), 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. 4C; 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. 4C; 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. 6A). 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. 6B). More specifically, a large number of overlapping ARGs (13.97%) were shared among mammals, aves and soil biological environmental samples (Fig. 6B). 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. 6C).

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. 7A), 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. 7A), 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. 7B). 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.

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.

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.

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

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

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. Roles of the gut microbiota in the adaptive evolution of mammalian species[J]. Philosophical Transactions of the Royal Society B, 2020, 375(1808): 20190597.
Brice K L, Trivedi P, Jeffries T C, et al. The Koala (Phascolarctos cinereus) faecal microbiome differs with diet in a wild population[J]. PeerJ, 2019, 7: e6534.
Blyton M D J, Soo R M, Whisson D, et al. Faecal inoculations alter the gastrointestinal microbiome and allow dietary expansion in a wild specialist herbivore, the koala[J]. Animal Microbiome, 2019, 1(1): 1-18.
Goodrich J K, Waters J L, Poole A C, et al. Human genetics shape the gut microbiome[J]. Cell, 2014, 159(4): 789-799.
Beaumont M, Goodrich J K, Jackson M A, et al. Heritable components of the human fecal microbiome are associated with visceral fat[J]. Genome biology, 2016, 17(1): 1-19.
Lim M Y, You H J, Yoon H S, et al. The effect of heritability and host genetics on the gut microbiota and metabolic syndrome[J]. Gut, 2017, 66(6): 1031-1038.
Li F, Li C, Chen Y, et al. Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle[J]. Microbiome, 2019, 7: 1-17.
Wallace R J, Sasson G, Garnsworthy P C, et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions[J]. Science advances, 2019, 5(7): eaav8391.
Vijay-Kumar M, Aitken J D, Carvalho F A, et al. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5[J]. Science, 2010, 328(5975): 228-231.
Ley R E, Hamady M, Lozupone C, et al. Evolution of mammals and their gut microbes[J]. science, 2008, 320(5883): 1647-1651.
David L A, Maurice C F, Carmody R N, et al. Diet rapidly and reproducibly alters the human gut microbiome[J]. Nature, 2014, 505(7484): 559-563.
Nelson T M, Rogers T L, Carlini A R, et al. Diet and phylogeny shape the gut microbiota of A ntarctic seals: a comparison of wild and captive animals[J]. Environmental microbiology, 2013, 15(4): 1132-1145.
Gao F, Lv Y W, Long J, et al. Butyrate improves the metabolic disorder and gut microbiome dysbiosis in mice induced by a high-fat diet[J]. Frontiers in pharmacology, 2019, 10: 1040.
Li H, Qu J, Li T, et al. Pika population density is associated with the composition and diversity of gut microbiota[J]. Frontiers in Microbiology, 2016, 7: 758.
Grieneisen L E, Livermore J, Alberts S, et al. Group living and male dispersal predict the core gut microbiome in wild baboons[J]. Integrative and Comparative Biology, 2017, 57(4): 770-785.
Rothschild D, Weissbrod O, Barkan E, et al. Environment dominates over host genetics in shaping human gut microbiota[J]. Nature, 2018, 555(7695): 210-215.
Li K, Dan Z, Gesang L, et al. Comparative analysis of gut microbiota of native Tibetan and Han populations living at different altitudes[J]. PloS one, 2016, 11(5): e0155863.
Wall C B, Ricci C A, Foulds G E, et al. The effects of environmental history and thermal stress on coral physiology and immunity[J]. Marine Biology, 2018, 165: 1-15.
Youngblut N D, Reischer G H, Walters W, et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades[J]. Nature communications, 2019, 10(1): 1-15.
Kim P S, Shin N R, Lee J B, et al. Host habitat is the major determinant of the gut microbiome of fish[J]. Microbiome, 2021, 9(1): 166.
Murillo T, Schneider D, Fichtel C, et al. Dietary shifts and social interactions drive temporal fluctuations of the gut microbiome from wild redfronted lemurs[J]. ISME Communications, 2022, 2(1): 3.
Watson S E, Hauffe H C, Bull M J, et al. Global change-driven use of onshore habitat impacts polar bear faecal microbiota[J]. The ISME journal, 2019, 13(12): 2916-2926.
Woodhams D C, Bletz M C, Becker C G, et al. Host-associated microbiomes are predicted by immune system complexity and climate[J]. Genome biology, 2020, 21: 1-20.
Chen S, Holyoak M, Liu H, et al. Global warming responses of gut microbiota in moose (Alces alces) populations with different dispersal patterns[J]. Journal of Zoology, 2022, 318(1): 63-73.
Campbell T P, Sun X, Patel V H, et al. The microbiome and resistome of chimpanzees, gorillas, and humans across host lifestyle and geography[J]. The ISME journal, 2020, 14(6): 1584-1599.
Ma L, Xia Y, Li B, et al. Metagenomic assembly reveals hosts of antibiotic resistance genes and the shared resistome in pig, chicken, and human feces[J]. Environmental science & technology, 2016, 50(1): 420-427.
McKinney C W, Dungan R S, Moore A, et al. Occurrence and abundance of antibiotic resistance genes in agricultural soil receiving dairy manure[J]. FEMS Microbiology Ecology, 2018, 94(3): fiy010.
Huang G, Qu Q, Wang M, et al. Global landscape of gut microbiome diversity and antibiotic resistomes across vertebrates[J]. Science of The Total Environment, 2022, 838: 156178.
Ji M, Kong W, Jia H, et al. Polar soils exhibit distinct patterns in microbial diversity and dominant phylotypes[J]. Soil Biology and Biochemistry, 2022, 166: 108550.
Bahram M, Hildebrand F, Forslund S K, et al. Structure and function of the global topsoil microbiome[J]. Nature, 2018, 560(7717): 233-237.
Delgado-Baquerizo M, Oliverio A M, Brewer T E, et al. A global atlas of the dominant bacteria found in soil[J]. Science, 2018, 359(6373): 320-325.
Rognes T, Flouri T, Nichols B, et al. VSEARCH: a versatile open source tool for metagenomics[J]. PeerJ, 2016, 4: e2584.
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads[J]. EMBnet. journal, 2011, 17(1): 10-12.
Liu P Y, Wu W K, Chen C C, et al. Evaluation of compatibility of 16S rRNA V3V4 and V4 amplicon libraries for clinical microbiome profiling[J]. BioRxiv, 2020: 2020.08. 18.256818.
Edgar R C. UPARSE: highly accurate OTU sequences from microbial amplicon reads[J]. Nature methods, 2013, 10(10): 996-998.
Edgar R C, Haas B J, Clemente J C, et al. UCHIME improves sensitivity and speed of chimera detection[J]. Bioinformatics, 2011, 27(16): 2194-2200.
Cole J R, Tiedje J M. History and impact of RDP: a legacy from Carl Woese to microbiology[J]. RNA biology, 2014, 11(3): 239-243.
Li D, Luo R, Liu C M, et al. MEGAHIT v1. 0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices[J]. Methods, 2016, 102: 3-11.
Rice P, Longden I, Bleasby A. EMBOSS: the European molecular biology open software suite[J]. Trends in genetics, 2000, 16(6): 276-277.
Buchfink B, Xie C, Huson D H. Fast and sensitive protein alignment using DIAMOND[J]. Nature methods, 2015, 12(1): 59-60.
Yin X, Jiang X T, Chai B, et al. ARGs-OAP v2. 0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes[J]. Bioinformatics, 2018, 34(13): 2263-2270.
Gibson M K, Forsberg K J, Dantas G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology[J]. The ISME journal, 2015, 9(1): 207-216.
Goslee S C, Urban D L. The ecodist package for dissimilarity-based analysis of ecological data[J]. Journal of Statistical Software, 2007, 22: 1-19.
Delgado-Baquerizo M, Hu H W, Maestre F T, et al. The global distribution and environmental drivers of the soil antibiotic resistome[J]. Microbiome, 2022, 10(1): 1-14.
Steinhauser D, Krall L, Müssig C, et al. Correlation networks[J]. Analysis of biological networks, 2008, 305: 333.
Layeghifard M, Hwang D M, Guttman D S. Disentangling interactions in the microbiome: a network perspective[J]. Trends in microbiology, 2017, 25(3): 217-228.
Weiss S, Van Treuren W, Lozupone C, et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision[J]. The ISME journal, 2016, 10(7): 1669-1681.
Banerjee S, Schlaeppi K, van der Heijden M G A. Keystone taxa as drivers of microbiome structure and functioning[J]. Nature Reviews Microbiology, 2018, 16(9): 567-576.
Wickham H, Wickham H. Programming with ggplot2[J]. ggplot2: Elegant graphics for data analysis, 2016: 241-253.
Damashek J, Francis C A. Microbial nitrogen cycling in estuaries: from genes to ecosystem processes[J]. Estuaries and Coasts, 2018, 41(3): 626-660.
Zhang J, Yang Y, Zhao L, et al. Distribution of sediment bacterial and archaeal communities in plateau freshwater lakes[J]. Applied Microbiology and Biotechnology, 2015, 99: 3291-3302.
Egerton S, Culloty S, Whooley J, et al. The gut microbiota of marine fish[J]. Frontiers in microbiology, 2018, 9: 873.
Wang A R, Ran C, Ringø E, et al. Progress in fish gastrointestinal microbiota research[J]. Reviews in Aquaculture, 2018, 10(3): 626-640.
O’Donnell M M, Harris H M B, Ross R P, et al. Core fecal microbiota of domesticated herbivorous ruminant, hindgut fermenters, and monogastric animals[J]. Microbiologyopen, 2017, 6(5): e00509.
Sullam K E, Essinger S D, Lozupone C A, et al. Environmental and ecological factors that shape the gut bacterial communities of fish: a meta‐analysis[J]. Molecular ecology, 2012, 21(13): 3363-3378.
Cahana I, Iraqi F A. Impact of host genetics on gut microbiome: Take‐home lessons from human and mouse studies[J]. Animal models and experimental medicine, 2020, 3(3): 229-236.
Sunagawa S, Coelho L P, Chaffron S, et al. Structure and function of the global ocean microbiome[J]. Science, 2015, 348(6237): 1261359.
Zhao J, Jin L, Wu D, et al. Global airborne bacterial community—interactions with Earth’s microbiomes and anthropogenic activities[J]. Proceedings of the National Academy of Sciences, 2022, 119(42): e2204465119.
Fierer N, Jackson R B. The diversity and biogeography of soil bacterial communities[J]. Proceedings of the National Academy of Sciences, 2006, 103(3): 626-631.
Herlemann D P R, Labrenz M, Jürgens K, et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea[J]. The ISME journal, 2011, 5(10): 1571-1579.
Arias P, Bellouin N, Coppola E, et al. Climate Change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; technical summary[J]. 2021.
Bestion E, Jacob S, Zinger L, et al. Climate warming reduces gut microbiota diversity in a vertebrate ectotherm[J]. Nature ecology & evolution, 2017, 1(6): 0161.
Delgado-Baquerizo M, Oliverio A M, Brewer T E, et al. A global atlas of the dominant bacteria found in soil[J]. Science, 2018, 359(6373): 320-325.
Turnbaugh P J, Ley R E, Mahowald M A, et al. An obesity-associated gut microbiome with increased capacity for energy harvest[J]. nature, 2006, 444(7122): 1027-1031.
Stanley D, Denman S E, Hughes R J, et al. Intestinal microbiota associated with differential feed conversion efficiency in chickens[J]. Applied microbiology and biotechnology, 2012, 96: 1361-1369.
Semova I, Carten J D, Stombaugh J, et al. Microbiota regulate intestinal absorption and metabolism of fatty acids in the zebrafish[J]. Cell host & microbe, 2012, 12(3): 277-288.
Montoya-Ciriaco N, Gómez-Acata S, Muñoz-Arenas L C, et al. Dietary effects on gut microbiota of the mesquite lizard Sceloporus grammicus (Wiegmann, 1828) across different altitudes[J]. Microbiome, 2020, 8(1): 1-19.
Alberdi A, Aizpurua O, Bohmann K, et al. Do vertebrate gut metagenomes confer rapid ecological adaptation?[J]. Trends in ecology & evolution, 2016, 31(9): 689-699.
Leeming E R, Johnson A J, Spector T D, et al. Effect of diet on the gut microbiota: rethinking intervention duration[J]. Nutrients, 2019, 11(12): 2862.
Muegge B D, Kuczynski J, Knights D, et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans[J]. Science, 2011, 332(6032): 970-974.
Trevelline B K, Kohl K D. The gut microbiome influences host diet selection behavior[J]. Proceedings of the National Academy of Sciences, 2022, 119(17): e2117537119.
Wang A R, Ran C, Ringø E, et al. Progress in fish gastrointestinal microbiota research[J]. Reviews in Aquaculture, 2018, 10(3): 626-640.
Chivers D J, Hladik C M. Morphology of the gastrointestinal tract in primates: comparisons with other mammals in relation to diet[J]. Journal of morphology, 1980, 166(3): 337-386.
Clauss M, Streich W J, Nunn C L, et al. The influence of natural diet composition, food intake level, and body size on ingesta passage in primates[J]. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 2008, 150(3): 274-281.
Russell J B, Rychlik J L. Factors that alter rumen microbial ecology[J]. Science, 2001, 292(5519): 1119-1122.
Stevens C E, Hume I D. Comparative physiology of the vertebrate digestive system[M]. Cambridge University Press, 2004.
Vital M, Gao J, Rizzo M, et al. Diet is a major factor governing the fecal butyrate-producing community structure across Mammalia, Aves and Reptilia[J]. The ISME journal, 2015, 9(4): 832-843.
Dasgupta D, Kumar K, Miglani R, et al. Microbial biofertilizers: Recent trends and future outlook[J]. Recent Advancement in Microbial Biotechnology, 2021: 1-26.
Heath K D, Lau J A. Herbivores alter the fitness benefits of a plant–rhizobium mutualism[J]. Acta Oecologica, 2011, 37(2): 87-92.
Alberdi A, Martin Bideguren G, Aizpurua O. Diversity and compositional changes in the gut microbiota of wild and captive vertebrates: a meta-analysis[J]. Scientific Reports, 2021, 11(1): 22660.
Greenspan S E, Migliorini G H, Lyra M L, et al. Warming drives ecological community changes linked to host-associated microbiome dysbiosis[J]. Nature Climate Change, 2020, 10(11): 1057-1061.
Sepulveda J, Moeller A H. The effects of temperature on animal gut microbiomes[J]. Frontiers in microbiology, 2020, 11: 384.
Magne F, Gotteland M, Gauthier L, et al. The firmicutes/bacteroidetes ratio: a relevant marker of gut dysbiosis in obese patients?[J]. Nutrients, 2020, 12(5): 1474.
Albarracín V H, Dib J R, Ordoñez O F, et al. A harsh life to indigenous proteobacteria at the andean mountains: microbial diversity and resistance mechanisms towards extreme conditions[J]. 2011.
Beall B F N, Twiss M R, Smith D E, et al. Ice cover extent drives phytoplankton and bacterial community structure in a large north‐temperate lake: implications for a warming climate[J]. Environmental microbiology, 2016, 18(6): 1704-1719.
Hayden H L, Mele P M, Bougoure D S, et al. Changes in the microbial community structure of bacteria, archaea and fungi in response to elevated CO2 and warming in an A ustralian native grassland soil[J]. Environmental microbiology, 2012, 14(12): 3081-3096.
Bush K, Courvalin P, Dantas G, et al. Tackling antibiotic resistance[J]. Nature Reviews Microbiology, 2011, 9(12): 894-896.
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.

No competing interests reported.

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 < 0.05 for ≥ 95% of dataset subsets). Box centerlines, edges, whiskers, and points signify the median, interquartile range (IQR), 1.5×IQR, and >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).

Download PDF

Version 1

posted

You are reading this latest preprint version

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

Status:

Version 1

Abstract

Figures

Background

Methods

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1