Metagenomic sequencing enables the in-depth study of microbes and their functions in humans, animals, and the environment. While sequencing data is deposited in public databases, the associated contextual data is often not complete and needs to be retrieved from primary publications. This lack of access to sample-level metadata like clinical data or in situ observations impedes cross-study comparisons and meta-analyses. We therefore created the Metalog database, a repository of manually curated metadata for metagenomics samples across the globe. It contains 80 423 samples from humans (including 66 527 of the gut microbiome), 10 744 animal samples, 5547 ocean water samples, and 23 455 samples from other environmental habitats such as soil, sediment, or fresh water. Samples have been consistently annotated for a set of habitat-specific core features, such as demographics, disease status, and medication for humans; host species and captivity status for animals; and filter sizes and salinity for marine samples. Additionally, all original metadata is provided in tabular form, simplifying focused studies e.g. into nutrient concentrations. Pre-computed taxonomic profiles facilitate rapid data exploration, while links to the SPIRE database enable genome-based analyses. The database is freely available for browsing and download at https://metalog.embl.de/.

The microbiota in individual habitats differ in both relative composition and absolute abundance. While sequencing approaches determine the relative abundances of taxa and genes, they do not provide information on their absolute abundances. Here, we developed a machine-learning approach to predict fecal microbial loads (microbial cells per gram) solely from relative abundance data. Applying our prediction model to a large-scale metagenomic dataset (n = 34,539), we demonstrated that microbial load is the major determinant of gut microbiome variation and is associated with numerous host factors, including age, diet, and medication. We further found that for several diseases, changes in microbial load, rather than the disease condition itself, more strongly explained alterations in patients' gut microbiome. Adjusting for this effect substantially reduced the statistical significance of the majority of disease-associated species. Our analysis reveals that the fecal microbial load is a major confounder in microbiome studies, highlighting its importance for understanding microbiome variation in health and disease.

Small open reading frames (smORFs) shorter than 100 codons are widespread and perform essential roles in microorganisms, where they encode proteins active in several cell functions, including signal pathways, stress response, and antibacterial activities. However, the ecology, distribution and role of small proteins in the global microbiome remain unknown. Here, we construct a global microbial smORFs catalog (GMSC) derived from 63,410 publicly available metagenomes across 75 distinct habitats and 87,920 high-quality isolate genomes. GMSC contains 965 million non-redundant smORFs with comprehensive annotations. We find that archaea harbor more smORFs proportionally than bacteria. We moreover provide a tool called GMSC-mapper to identify and annotate small proteins from microbial (meta)genomes. Overall, this publicly-available resource demonstrates the immense and underexplored diversity of small proteins.

Patients with cirrhosis are susceptible to develop bacterial infections that trigger acute decompensation (AD) and acute-on-chronic liver failure (ACLF). Infections with multidrug-resistant organisms (MDRO) are associated with deleterious outcome. MDRO colonisation frequently proceeds MDRO infections and antibiotic therapy has been associated with MDRO colonisation.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental and host-associated habitats to create the AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, few of which match existing databases. AMPSphere provides insights into the evolutionary origins of peptides, including by duplication or gene truncation of longer sequences, and we observed that AMP production varies by habitat. To validate our predictions, we synthesized and tested 100 AMPs against clinically relevant drug-resistant pathogens and human gut commensals both in vitro and in vivo. A total of 79 peptides were active, with 63 targeting pathogens. These active AMPs exhibited antibacterial activity by disrupting bacterial membranes. In conclusion, our approach identified nearly one million prokaryotic AMP sequences, an open-access resource for antibiotic discovery.

Meta'omic data on microbial diversity and function accrue exponentially in public repositories, but derived information is often siloed according to data type, study or sampled microbial environment. Here we present SPIRE, a Searchable Planetary-scale mIcrobiome REsource that integrates various consistently processed metagenome-derived microbial data modalities across habitats, geography and phylogeny. SPIRE encompasses 99 146 metagenomic samples from 739 studies covering a wide array of microbial environments and augmented with manually-curated contextual data. Across a total metagenomic assembly of 16 Tbp, SPIRE comprises 35 billion predicted protein sequences and 1.16 million newly constructed metagenome-assembled genomes (MAGs) of medium or high quality. Beyond mapping to the high-quality genome reference provided by proGenomes3 (http://progenomes.embl.de), these novel MAGs form 92 134 novel species-level clusters, the majority of which are unclassified at species level using current tools. SPIRE enables taxonomic profiling of these species clusters via an updated, custom mOTUs database (https://motu-tool.org/) and includes several layers of functional annotation, as well as crosslinks to several (micro-)biological databases. The resource is accessible, searchable and browsable via http://spire.embl.de.

The interpretation of genomic, transcriptomic and other microbial 'omics data is highly dependent on the availability of well-annotated genomes. As the number of publicly available microbial genomes continues to increase exponentially, the need for quality control and consistent annotation is becoming critical. We present proGenomes3, a database of 907 388 high-quality genomes containing 4 billion genes that passed stringent criteria and have been consistently annotated using multiple functional and taxonomic databases including mobile genetic elements and biosynthetic gene clusters. proGenomes3 encompasses 41 171 species-level clusters, defined based on universal single copy marker genes, for which pan-genomes and contextual habitat annotations are provided. The database is available at http://progenomes.embl.de/.

Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine learning-based approach to predict prokaryotic antimicrobial peptides (AMPs) by leveraging a vast dataset of 63,410 metagenomes and 87,920 microbial genomes. This led to the creation of AMPSphere, a comprehensive catalog comprising 863,498 non-redundant peptides, the majority of which were previously unknown. We observed that AMP production varies by habitat, with animal-associated samples displaying the highest proportion of AMPs compared to other habitats. Furthermore, within different human-associated microbiota, strain-level differences were evident. To validate our predictions, we synthesized and experimentally tested 50 AMPs, demonstrating their efficacy against clinically relevant drug-resistant pathogens both in vitro and in vivo. These AMPs exhibited antibacterial activity by targeting the bacterial membrane. Additionally, AMPSphere provides valuable insights into the evolutionary origins of peptides. In conclusion, our approach identified AMP sequences within prokaryotic microbiomes, opening up new avenues for the discovery of antibiotics.

Recent evidence suggests a role for the microbiome in pancreatic ductal adenocarcinoma (PDAC) aetiology and progression.

Fecal microbiota transplantation (FMT) is a therapeutic intervention for inflammatory diseases of the gastrointestinal tract, but its clinical mode of action and subsequent microbiome dynamics remain poorly understood. Here we analyzed metagenomes from 316 FMTs, sampled pre and post intervention, for the treatment of ten different disease indications. We quantified strain-level dynamics of 1,089 microbial species, complemented by 47,548 newly constructed metagenome-assembled genomes. Donor strain colonization and recipient strain resilience were mostly independent of clinical outcomes, but accurately predictable using LASSO-regularized regression models that accounted for host, microbiome and procedural variables. Recipient factors and donor-recipient complementarity, encompassing entire microbial communities to individual strains, were the main determinants of strain population dynamics, providing insights into the underlying processes that shape the post-FMT gut microbiome. Applying an ecology-based framework to our findings indicated parameters that may inform the development of more effective, targeted microbiome therapies in the future, and suggested how patient stratification can be used to enhance donor microbiota colonization or the displacement of recipient microbes in clinical practice.

Genomes are critical units in microbiology, yet ascertaining quality in prokaryotic genome assemblies remains a formidable challenge. We present GUNC (the Genome UNClutterer), a tool that accurately detects and quantifies genome chimerism based on the lineage homogeneity of individual contigs using a genome's full complement of genes. GUNC complements existing approaches by targeting previously underdetected types of contamination: we conservatively estimate that 5.7% of genomes in GenBank, 5.2% in RefSeq, and 15-30% of pre-filtered "high-quality" metagenome-assembled genomes in recent studies are undetected chimeras. GUNC provides a fast and robust tool to substantially improve prokaryotic genome quality.

Gut viruses are important, yet often neglected, players in the complex human gut microbial ecosystem. Recently, the number of human gut virome studies has been increasing; however, we are still only scratching the surface of the immense viral diversity. In this study, 254 virus-enriched fecal metagenomes from 204 Danish subjects were used to generate the anish nteric irme atalog (DEVoC) containing 12,986 nonredundant viral scaffolds, of which the majority was previously undescribed, encoding 190,029 viral genes. The DEVoC was used to compare 91 healthy DEVoC gut viromes from children, adolescents, and adults that were used to create the DEVoC. Gut viromes of healthy Danish subjects were dominated by phages. While most phage genomes (PGs) only occurred in a single subject, indicating large virome individuality, 39 PGs were present in more than 10 healthy subjects. Among these 39 PGs, the prevalences of three PGs were associated with age. To further study the prevalence of these 39 prevalent PGs, 1,880 gut virome data sets of 27 studies from across the world were screened, revealing several age-, geography-, and disease-related prevalence patterns. Two PGs also showed a remarkably high prevalence worldwide-a crAss-like phage (20.6% prevalence), belonging to the tentative subfamily, and a previously undescribed circular temperate phage infecting Bacteroides dorei (14.4% prevalence), called LoVEphage because it encodes ots f iral lements. Due to the LoVEphage's high prevalence and novelty, public data sets in which the LoVEphage was detected were assembled, resulting in an additional 18 circular LoVEphage-like genomes (67.9 to 72.4 kb). Through generation of the DEVoC, we added numerous previously uncharacterized viral genomes and genes to the ever-increasing worldwide pool of human gut viromes. The DEVoC, the largest human gut virome catalog generated from consistently processed fecal samples, facilitated the analysis of the 91 healthy Danish gut viromes. Characterizing the biggest cohort of healthy gut viromes from children, adolescents, and adults to date confirmed the previously established high interindividual variation in human gut viromes and demonstrated that the effect of age on the gut virome composition was limited to the prevalence of specific phage (groups). The identification of a previously undescribed prevalent phage illustrates the usefulness of developing virome catalogs, and we foresee that the DEVoC will benefit future analysis of the roles of gut viruses in human health and disease.