Metagenomic Shotgun Sequencing Data Research Articles

Gardnerella diversity and ecology in pregnancy and preterm birth.

The vaginal microbiome has been linked to negative health outcomes including preterm birth. Specific taxa, including Gardnerella spp., have been identified as risk factors for these conditions. Historically, microbiome analysis methods have treated all Gardnerella spp. as one species, but the broad diversity of Gardnerella has become more apparent. We explore the diversity of Gardnerella clades and genomic species in the vaginal microbiome of pregnant women and their associations with microbiome composition and preterm birth. Relative abundance of Gardnerella clades and genomic species and other taxa was quantified in shotgun metagenomic sequencing data from three distinct cohorts of pregnant women. We also assessed the diversity and abundance of Gardnerella variants in 16S rRNA gene amplicon sequencing data from seven previously conducted studies in differing populations. Individual microbiomes often contained multiple Gardnerella variants, and the number of clades was associated with increased microbial load, or the ratio of non-human reads to human reads. Taxon co-occurrence patterns were largely consistent across Gardnerella clades and among cohorts. Some variants previously described as rare were prevalent in other cohorts, highlighting the importance of surveying a diverse set of populations to fully capture the diversity of Gardnerella. The diversity of Gardnerella both across populations and within individual vaginal microbiomes has long been unappreciated, as has been the intra-species diversity of many other members of the vaginal microbiome. The broad genomic diversity of Gardnerella has led to its reclassification as multiple species; here we demonstrate the diversity of Gardnerella found within and between vaginal microbiomes.IMPORTANCEThe present study shows that single microbiomes can contain all currently known species of Gardnerella and that multiple similar species can exist within the same environment. Furthermore, surveys of demographically distinct populations suggest that some species appear more commonly in certain populations. Further studies in broad and diverse populations will be necessary to fully understand the ecological roles of each Gardnerella sp., how they can co-exist, and their distinct impacts on microbial communities, preterm birth, and other health outcomes.

Statistical and computational methods for integrating microbiome, host genomics, and metabolomics data.

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed.

Altered gut mycobiome in patients with end-stage renal disease and its correlations with serum and fecal metabolomes

BackgroundThe relationship between the gut mycobiome and end-stage renal disease (ESRD) remains largely unexplored.MethodsIn this study, we compared the gut fungal populations of 223 ESRD patients and 69 healthy controls (HCs) based on shotgun metagenomic sequencing data, and analyzed their associations with host serum and fecal metabolites.ResultsOur findings revealed that ESRD patients had a higher diversity in the gut mycobiome compared to HCs. Dysbiosis of the gut mycobiome in ESRD patients was characterized by a decrease of Saccharomyces cerevisiae and an increase in various opportunistic pathogens, such as Aspergillus fumigatus, Cladophialophora immunda, Exophiala spinifera, Hortaea werneckii, Trichophyton rubrum, and others. Through multi-omics analysis, we observed a substantial contribution of the gut mycobiome to host serum and fecal metabolomes. The opportunistic pathogens enriched in ESRD patients were frequently and positively correlated with the levels of creatinine, homocysteine, and phenylacetylglycine in the serum. The populations of Saccharomyces, including the HC-enriched Saccharomyces cerevisiae, were frequently and negatively correlated with the levels of various toxic metabolites in the feces.ConclusionsOur results provided a comprehensive understanding of the associations between the gut mycobiome and the development of ESRD, which had important implications for guiding future therapeutic studies in this field.

Whole community shotgun metagenomes of two biological soil crust types from the Mojave Desert.

We present six whole community shotgun metagenomic sequencing data sets of two types of biological soil crusts sampled at the ecotone of the Mojave Desert and Colorado Desert in California. These data will help us understand the diversity and function of biocrust microbial communities, which are essential for desert ecosystems.

A comparative genomics study of the microbiome and freshwater resistome in Southern Pantanal.

This study explores the resistome and bacterial diversity of two small lakes in the Southern Pantanal, one in Aquidauana sub-region, close to a farm, and one in Abobral sub-region, an environmentally preserved area. Shotgun metagenomic sequencing data from water column samples collected near and far from the floating macrophyte Eichhornia crassipes were used. The Abobral small lake exhibited the highest diversity and abundance of antibiotic resistance genes (ARGs), antibiotic resistance classes (ARGCs), phylum, and genus. RPOB2 and its resistance class, multidrug resistance, were the most abundant ARG and ARGC, respectively. Pseudomonadota was the dominant phylum across all sites, and Streptomyces was the most abundant genus considering all sites.

From Whole Genome Shotgun Sequencing to Viral Community Profiling: The ViromeScan Tool.

ViromeScan is an innovative metagenomic analysis tool that allows characterizing the taxonomy of viral communities from raw data of metagenomics sequencing, efficiently denoising samples from reads of other microorganisms. This means that users can use the same shotgun metagenomic sequencing data to fully characterize complex microbial ecosystems, including bacteria and viruses. Here we describe the analysis procedure with some examples, illustrating the processes computed by ViromeScan from raw data to the final output.

Commensal Oral Microbiota, Disease Severity, and Mortality in Fibrotic Lung Disease.

Rationale: Oral microbiota associate with diseases of the mouth and serve as a source of lung microbiota. However, the role of oral microbiota in lung disease is unknown. Objectives: To determine associations between oral microbiota and disease severity and death in idiopathic pulmonary fibrosis (IPF). Methods: We analyzed 16S rRNA gene and shotgun metagenomic sequencing data of buccal swabs from 511 patients with IPF in the multicenter CleanUP-IPF (Study of Clinical Efficacy of Antimicrobial Therapy Strategy Using Pragmatic Design in IPF) trial. Buccal swabs were collected from usual care and antimicrobial cohorts. Microbiome data were correlated with measures of disease severity using principal component analysis and linear regression models. Associations between the buccal microbiome and mortality were determined using Cox additive models, Kaplan-Meier analysis, and Cox proportional hazards models. Measurements and Main Results: Greater buccal microbial diversity associated with lower FVC at baseline (mean difference, -3.60; 95% confidence interval [CI], -5.92 to -1.29% predicted FVC per 1-unit increment). The buccal proportion of Streptococcus correlated positively with FVC (mean difference, 0.80; 95% CI, 0.16 to 1.43% predicted per 10% increase) (n = 490). Greater microbial diversity was associated with an increased risk of death (hazard ratio, 1.73; 95% CI, 1.03-2.90), whereas a greater proportion of Streptococcus was associated with a reduced risk of death (HR, 0.85; 95% CI, 0.73 to 0.99). The Streptococcus genus was mainly composed of Streptococcus mitis species. Conclusions: Increasing buccal microbial diversity predicts disease severity and death in IPF. The oral commensal S. mitis spp associates with preserved lung function and improved survival.

Identification and functional analysis of novel protein-encoding sequences related to stress-resistance.

Currently, industrial bioproducts are less competitive than chemically produced goods due to the shortcomings of conventional microbial hosts. Thus, is essential developing robust bacteria for improved cell tolerance to process-specific parameters. In this context, metagenomic approaches from extreme environments can provide useful biological parts to improve bacterial robustness. Here, in order to build genetic constructs that increase bacterial resistance to diverse stress conditions, we recovered novel protein-encoding sequences related to stress-resistance from metagenomic databases using an in silico approach based on Hidden-Markov-Model profiles. For this purpose, we used metagenomic shotgun sequencing data from microbial communities of extreme environments to identify genes encoding chaperones and other proteins that confer resistance to stress conditions. We identified and characterized 10 novel protein-encoding sequences related to the DNA-binding protein HU, the ATP-dependent protease ClpP, and the chaperone protein DnaJ. By expressing these genes in Escherichia coli under several stress conditions (including high temperature, acidity, oxidative and osmotic stress, and UV radiation), we identified five genes conferring resistance to at least two stress conditions when expressed in E. coli. Moreover, one of the identified HU coding-genes which was retrieved from an acidic soil metagenome increased E. coli tolerance to four different stress conditions, implying its suitability for the construction of a synthetic circuit directed to expand broad bacterial resistance.

Identification of donor Bacteroides vulgatus genes encoding proteins that correlate with early colonization following fecal transplant of patients with recurrent Clostridium difficile

Due to suppressive antibiotics, patients with recurrent Clostridium difficile have gut microbial communities that are devoid of most commensal microbes. Studies have shown that most of the failures using fecal microbe transplantation (FMT) for recurrent C. difficile occur during the first 4 weeks following transplantation. To identify features of donor Bacteroides vulgatus that lead to early colonization, we used two data sets that collected fecal samples from recipients at early times points post FMT. The first analysis used the shotgun metagenomic DNA sequencing data set from Aggarwala et al. consisting of 7 FMT donors and 13 patients with recurrent C. difficile with fecal samples taken as early as 24 h post FMT. We identified 2 FMT donors in which colonization of recipients by donor B. vulgatus was detected as early as 24 h post FMT. We examined a second data set from Hourigan et al. that collected fecal samples from C. difficile infected children and identified 1 of 3 FMT that also had early colonization of the donor B. vulgatus. We found 19 genes out of 4911 encoding proteins were unique to the 3 donors that had early colonization. A gene encoding a putative chitobiase was identified that was in a gene complex that had been previously identified to enhance colonization in mice. A gene encoding a unique fimbrillin (i.e., pili) family protein and 17 genes encoding hypothetical proteins were also specific for early colonizing donors. Most of the genes encoding hypothetical proteins had neighboring genes that encoded proteins involved in mobilization or transposition. Finally, analysis of 42 paired fecal samples from the human microbiome project (HMP) found no individuals had all 19 genes while 2 individuals had none of the 19 genes. Based on the results from our study, consideration should be given to the screening of FMT donors for these B. vulgatus genes found to enhance early colonization that would be of benefit to promote colonization following FMT.

Genetic strategies for sex-biased persistence of gut microbes across human life

Although compositional variation in the gut microbiome during human development has been extensively investigated, strain-resolved dynamic changes remain to be fully uncovered. In the current study, shotgun metagenomic sequencing data of 12,415 fecal microbiomes from healthy individuals are employed for strain-level tracking of gut microbiota members to elucidate its evolving biodiversity across the human life span. This detailed longitudinal meta-analysis reveals host sex-related persistence of strains belonging to common, maternally-inherited species, such as Bifidobacterium bifidum and Bifidobacterium longum subsp. longum. Comparative genome analyses, coupled with experiments including intimate interaction between microbes and human intestinal cells, show that specific bacterial glycosyl hydrolases related to host-glycan metabolism may contribute to more efficient colonization in females compared to males. These findings point to an intriguing ancient sex-specific host-microbe coevolution driving the selective persistence in women of key microbial taxa that may be vertically passed on to the next generation.

CRISPR dynamics during the interaction between bacteria and phage in the first year of life.

Gut microbiomes in infancy have a profound impact on health in adulthood. CRISPRs play an essential role in the interaction between bacteria and phages. However, the dynamics of CRISPRs in gut microbiomes during early life are poorly understood. In this study, using shotgun metagenomic sequencing data from 82 Swedish infants' gut microbiomes, 1882 candidate CRISPRs were identified, and their dynamics were analysed. We found large-scale turnover of CRISPRs and their spacers during the first year of life. As well as changes in relative abundance of the bacteria containing CRISPR, acquisition, loss and mutation of spacers were observed within the same CRISPR array sampled over time. Accordingly, the inferred interaction network of bacteria and phage was distinct at different times. This research underpins CRISPR dynamics and their potential role in the interaction between bacteria and phage in early life.

Reconstruction of the personal information from human genome reads in gut metagenome sequencing data

Human DNA present in faecal samples can result in a small number of human reads in gut shotgun metagenomic sequencing data. However, it is presently unclear how much personal information can be reconstructed from such reads, and this has not been quantitatively evaluated. Such a quantitative evaluation is necessary to clarify the ethical concerns related to data sharing and to enable efficient use of human genetic information in stool samples, such as for research and forensics. Here we used genomic approaches to reconstruct personal information from the faecal metagenomes of 343 Japanese individuals with associated human genotype data. Genetic sex could be accurately predicted based on the sequencing depth of sex chromosomes for 97.3% of the samples. Individuals could be re-identified from the matched genotype data based on human reads recovered from the faecal metagenomic data with 93.3% sensitivity using a likelihood score-based method. This method also enabled us to predict the ancestries of 98.3% of the samples. Finally, we performed ultra-deep shotgun metagenomic sequencing of five faecal samples as well as whole-genome sequencing of blood samples. Using genotype-calling approaches, we demonstrated that the genotypes of both common and rare variants could be reconstructed from faecal samples. This included clinically relevant variants. Our approach can be used to quantify personal information contained within gut metagenome data.

In Silico Analysis of Changes in Predicted Metabolic Capabilities of Intestinal Microbiota after Fecal Microbial Transplantation for Treatment of Recurrent Clostridioides difficile Infection.

Although highly effective in treating recurrent Clostridioides difficile infection (RCDI), the mechanisms of action of fecal microbial transplantation (FMT) are not fully understood. The aim of this study was to explore microbially derived products or pathways that could contribute to the therapeutic efficacy of FMT. Stool shotgun metagenomic sequencing data from 18 FMT-treated RCDI patients at 4 points in time were used for the taxonomic and functional profiling of their gut microbiome. The abundance of the KEGG orthology (KO) groups was subjected to univariate linear mixed models to assess the significance of the observed differences between 0 (pre-FMT), 1, 4, and 12 weeks after FMT. Of the 59,987 KO groups identified by shotgun metagenomic sequencing, 27 demonstrated a statistically significant change after FMT. These KO groups are involved in many cellular processes, including iron homeostasis, glycerol metabolism, and arginine regulation, all of which have been implicated to play important roles in bacterial growth and virulence in addition to modulating the intestinal microbial composition. Our findings suggest potential changes in key KO groups post-FMT, which may contribute to FMT efficacy beyond the restored microbial composition/diversity and metabolism of bile acids and short-chain fatty acids. Future larger studies that include a fecal metabolomics analysis combined with animal model validation work are required to further elucidate the molecular mechanisms.

Facilitative interaction networks in experimental microbial community dynamics.

Facilitative interactions between microbial species are ubiquitous in various types of ecosystems on the Earth. Therefore, inferring how entangled webs of interspecific interactions shift through time in microbial ecosystems is an essential step for understanding ecological processes driving microbiome dynamics. By compiling shotgun metagenomic sequencing data of an experimental microbial community, we examined how the architectural features of facilitative interaction networks could change through time. A metabolic modeling approach for estimating dependence between microbial genomes (species) allowed us to infer the network structure of potential facilitative interactions at 13 time points through the 110-day monitoring of experimental microbiomes. We then found that positive feedback loops, which were theoretically predicted to promote cascade breakdown of ecological communities, existed within the inferred networks of metabolic interactions prior to the drastic community-compositional shift observed in the microbiome time-series. We further applied "directed-graph" analyses to pinpoint potential keystone species located at the "upper stream" positions of such feedback loops. These analyses on facilitative interactions will help us understand key mechanisms causing catastrophic shifts in microbial community structure.

The Lung Immune Prognostic Index (LIPI) stratifies prognostic groups and correlates with gut microbiota (GM) in patients with advanced renal cell carcinoma (RCC).

719 Background: The LIPI score has been reported as an independent prognostic factor in RCC patients treated with immune checkpoint inhibitors (ICI) or tyrosine kinase inhibitors (TKI). Here, we aimed to correlate LIPI score and GM composition in patients with RCC. Methods: We prospectively collected fecal samples of all comers RCC patients who started a 1st or beyond line therapy (standard or clinical trial) in the NCT0457446 at Gustave Roussy. Neutrophil to lymphocyte ratio (dNLR) and lactate dehydrogenase (LDH) were obtained from routine blood tests. Shot gun metagenomic sequencing (MGS) data were analyzed by multivariate and pair-wise/fold ratio . Clinical benefit ratio (CBR, complete response + partial response + stable disease, RECIST1.1) and overall survival (OS, from treatment start) were evaluated. Multivariate analysis (MVA) for OS included age, gender, therapy line, IMDC, histology, hypoalbuminemia. Results: From February 2016 to July 2021, 160 patients were screened and 102 were included. Median age was 61 years (22-89), patients were mostly males (75%) and clear cell histology (90%). Patients were treated in 1st (15%), 2nd (53%) and beyond (32%) lines. Treated with ICI monotherapy (65%), followed by TKI or mTOR (20%) and ICI combination (19%). IMDC was 32% good (G), 54% intermediate (I) and 14% poor (P) and LIPI was 69% G, 25% I and 6% P. Median OS was 42.9, 17.7 and 8.3 months in patients with LIPI G, I and P, respectively (p<0.0001) . Among IMDC risk groups, IMDC G + LIPI G had better OS compared to other subgroups (p=0.017), and those with IMDC P + LIPI P had the worse OS. Overall, LIPI G had higher rates of CBR than I+P LIPI (74% vs 50%; p=0.0158). At MVA, LIPI was independently associated with OS (HR 6.25, 2.02-24.34; p=0.0187). Overall, Parabacteroides merdae and Veillonella parvula were enriched in LIPI I+P, while LIPI G harbored Faecalibacterium prausnitzii. In patients treated with ICI monotherapy, LIPI I+P were enriched with Bacteroides spp ( P. merdae, Phocaeicola vulgatus), and LIPI G had an overrepresentation of Ruminococcaceae unclass bacterium. Conclusions: We report the first MGS study correlating LIPI score and GM composition in RCC patients. LIPI score correlates with clinical outcomes (OS and CBR) and helped to better-stratified IMDC risk groups. Patients LIPI G harbor health-related commensals, while I and P groups are associated with harmful ones. LIPI score could represent a clinically relevant score to stratify mRCC patients. Clinical trial information: NCT04567446 .

Phylogeny analysis of whole protein-coding genes in metagenomic data detected an environmental gradient for the microbiota.

Environmental factors affect the growth of microorganisms and therefore alter the composition of microbiota. Correlative analysis of the relationship between metagenomic composition and the environmental gradient can help elucidate key environmental factors and establishment principles for microbial communities. However, a reasonable method to quantitatively compare whole metagenomic data and identify the primary environmental factors for the establishment of microbiota has not been reported so far. In this study, we developed a method to compare whole proteomes deduced from metagenomic shotgun sequencing data, and quantitatively display their phylogenetic relationships as metagenomic trees. We called this method Metagenomic Phylogeny by Average Sequence Similarity (MPASS). We also compared one of the metagenomic trees with dendrograms of environmental factors using a comparison tool for phylogenetic trees. The MPASS method correctly constructed metagenomic trees of simulated metagenomes and soil and water samples. The topology of the metagenomic tree of samples from the Kirishima hot springs area in Japan was highly similarity to that of the dendrograms based on previously reported environmental factors for this area. The topology of the metagenomic tree also reflected the dynamics of microbiota at the taxonomic and functional levels. Our results strongly suggest that MPASS can successfully classify metagenomic shotgun sequencing data based on the similarity of whole protein-coding sequences, and will be useful for the identification of principal environmental factors for the establishment of microbial communities. Custom Perl script for the MPASS pipeline is available at https://github.com/s0sat/MPASS.

Site Specialization of Human Oral Veillonella Species.

Veillonella species are abundant members of the human oral microbiome with multiple interspecies commensal relationships. Examining the distribution patterns of Veillonella species across the oral cavity is fundamental to understanding their oral ecology. In this study, we used a combination of pangenomic analysis and oral metagenomic information to clarify Veillonella taxonomy and to test the site specialist hypothesis for the Veillonella genus, which contends that most oral bacterial species are adapted to live at specific oral sites. Using isolate genome sequences combined with shotgun metagenomic sequence data, we showed that Veillonella species have clear, differential site specificity: Veillonella parvula showed strong preference for supra- and subgingival plaque, while closely related V. dispar, as well as more distantly related V. atypica, preferred the tongue dorsum, tonsils, throat, and hard palate. In addition, the provisionally named Veillonella sp. Human Microbial Taxon 780 showed strong site specificity for keratinized gingiva. Using comparative genomic analysis, we identified genes associated with thiamine biosynthesis and the reductive pentose phosphate cycle that may enable Veillonella species to occupy their respective habitats. IMPORTANCE Understanding the microbial ecology of the mouth is fundamental for understanding human physiology. In this study, metapangenomics demonstrated that different Veillonella species have clear ecological preferences in the oral cavity of healthy humans, validating the site specialist hypothesis. Furthermore, the gene pool of different Veillonella species was found to be reflective of their ecology, illuminating the potential role of vitamins and carbohydrates in determining Veillonella distribution patterns and interspecies interactions.

Differential response to prolonged amoxicillin treatment: long-term resilience of the microbiome versus long-lasting perturbations in the gut resistome

ABSTRACT The collateral impact of antibiotics on the microbiome has attained increasing attention. However, the ecological consequences of long-term antibiotic exposure on the gut microbiome, including antibiotic resistance, are still limited. Here, we investigated long-term exposure effects to amoxicillin on the human gut microbiome and resistome. Fecal samples were collected from 20 patients receiving 3-months of amoxicillin or placebo treatment as part of a Norwegian multicenter clinical trial on chronic low back pain (AIM study). Samples were collected at baseline, last day of treatment, and 9 months after antibiotic cessation. The abundance and diversity of microbial and resistome composition were characterized using whole shotgun and functional metagenomic sequencing data. While the microbiome profiles of placebo subjects were stable over time, discernible changes in diversity and overall microbiome composition were observed after amoxicillin treatment. In particular, health-associated short-chain fatty acid producing species significantly decreased in proportion. However, these changes were short-lived as the microbiome showed overall recovery 9 months post-treatment. On the other hand, exposure to long-term amoxicillin was associated with an increase in total antimicrobial resistance gene load and diversity of antimicrobial resistance genes, with persistent changes even at 9 months post-treatment. Additionally, beta-lactam resistance was the most affected antibiotic class, suggesting a targeted response to amoxicillin, although changes at the gene level varied across individuals. Overall, our results suggest that the impact of prolonged amoxicillin exposure was more explicit and long-lasting in the fecal resistome than in microbiome composition. Such information is relevant for designing rational administration guidelines for antibiotic therapies.

Phage-Host Prediction Using a Computational Tool Coupled with 16S rRNA Gene Amplicon Sequencing.

Metagenomics studies have revealed tremendous viral diversity in aquatic environments. Yet, while the genomic data they have provided is extensive, it is unannotated. For example, most phage sequences lack accurate information about their bacterial host, which prevents reliable phage identification and the investigation of phage-host interactions. This study aimed to take this knowledge further, using a viral metagenomic framework to decipher the composition and diversity of phage communities and to predict their bacterial hosts. To this end, we used water and sediment samples collected from seven sites with varying contamination levels in the Ebrié Lagoon in Abidjan, Ivory Coast. The bacterial communities were characterized using the 16S rRNA metabarcoding approach, and a framework was developed to investigate the virome datasets that: (1) identified phage contigs with VirSorter and VIBRANT; (2) classified these contigs with MetaPhinder using the phage database (taxonomic annotation); and (3) predicted the phages' bacterial hosts with a machine learning-based tool: the Prokaryotic Virus-Host Predictor. The findings showed that the taxonomic profiles of phages and bacteria were specific to sediment or water samples. Phage sequences assigned to the Microviridae family were widespread in sediment samples, whereas phage sequences assigned to the Siphoviridae, Myoviridae and Podoviridae families were predominant in water samples. In terms of bacterial communities, the phyla Latescibacteria, Zixibacteria, Bacteroidetes, Acidobacteria, Calditrichaeota, Gemmatimonadetes, Cyanobacteria and Patescibacteria were most widespread in sediment samples, while the phyla Epsilonbacteraeota, Tenericutes, Margulisbacteria, Proteobacteria, Actinobacteria, Planctomycetes and Marinimicrobia were most prevalent in water samples. Significantly, the relative abundance of bacterial communities (at major phylum level) estimated by 16S rRNA metabarcoding and phage-host prediction were significantly similar. These results demonstrate the reliability of this novel approach for predicting the bacterial hosts of phages from shotgun metagenomic sequencing data.

Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth’s environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.