Biology subjects

Zeller, G.

Publications and source records attributed to Zeller, G..

At least 19 recordsLinked to original sources

bioRxiv2026

The Ocean Microbiomics Database reveals thermal niches and projected pressure on marine microbes in a warming ocean

Temperature is a key driver of ocean microbial community structure, yet its role in shaping global distributions and their response to warming remains unclear due to fragmented studies, inconsistent methods, and limited environmental context. We present the Ocean Microbiomics Database (OMDB) containing 274,282 genomes forming 31,058 species-level clusters, searchable at https://omdb.microbiomics.io. We show surface temperature is predicted from community composition, driven by thermal niche partitioning among congeneric taxa. Using these thermal niches to model present-day distributions globally, we find that projected warming results in microbial richness declining faster than relative abundance, with losses concentrated at high latitudes and among poorly characterized lineages. Resolving 5,891 species into four thermal clusters reveals ecological structure correlated with genome streamlining, protein thermostability, and marker-gene physicochemistry, enabling prediction of a microorganism's abundance-maximizing temperature directly from its coding sequence. This positions OMDB as a platform spanning molecular traits to global ecology, offering a genome-resolved foundation for understanding how temperature, shown to be a primary driver of ocean microbial life, will continue to shape it as the ocean warms.

microbiology↗

bioRxiv2026

Microbial Invasion and Immunosuppression Drives Adenoma Progression in Early Colorectal Cancer Development

Several inherited predispositions to colorectal cancer exist, most notably familial adenomatous polyposis (FAP), which typically involves a germline loss-of-function mutation in one APC allele. A second hit in APC or related loci leads to aberrant Wnt signalling, triggering adenoma formation with near-complete penetrance. Yet, substantial variability in disease onset and severity, even among siblings with identical APC germline mutations, implicates environmental modifiers. Emerging evidence points to the gut microbiome as a critical regulator of adenoma initiation and progression, particularly in early-onset CRC. Here, we show that bacterial invasion is associated with neutrophil immunosuppression, T-cell exclusion and adenoma progression in a porcine model of FAP. Longitudinal mapping of progressing and regressing polyps using single-cell RNA sequencing, spatial transcriptomics and integrated microbiome profiling resolved the cellular and microbial architecture underlying these divergent lesion states. Single-cell RNA-seq identified 35 cell subpopulations and showed that regressing polyps are enriched for central-memory CD4+ T cells, cytotoxic CD8+ T cells and inflammatory neutrophils, whereas progressing polyps contain immunosuppressive/PD-L1-associated neutrophils, regulatory and dysfunctional T cells. Spatial transcriptomics with a custom host panel and targeted bacterial probes linked bacterial-high adenomatous regions to immunosuppressive neutrophil niches and T cell exclusion, while regressing lesions showed predominantly lumen-adjacent bacterial localization and spatially organized immune surveillance. These results provide a spatially resolved map of cellular and microbial organisation in early colorectal polyp progression and regression and reveal a microbiome-driven, neutrophil-mediated mechanism of early tumour progression with implications for CRC interception. Grant support: This study was funded by the German Research Foundation (DFG) through SFB1371 "Microbiome signature" (Project no. 395357507; TF, DS, FE, DH) and by the German Ministry for Research and Education (BMBF) through Mi-EOCRC consortium project (Project no. 01KD2102D; KF and DH).

cancer biology↗

bioRxiv2026

Distinct colorectal cancer genotypes shape microbial ecosystems and reveal stage-specific microbiota dependencies

The gut microbiota has emerged as an important modifier of colorectal cancer (CRC), yet how tumor genotype influences host-microbiota interactions and whether microbial signals are required throughout tumor progression remain unclear. Here, we combined genetically engineered mouse models, microbial multi-omics and a germ-free-compatible orthotopic transplantation system to define the functional contribution of the microbiota across distinct stages of CRC evolution. Across multiple CRC genotypes, we identified tumor-associated microbial ecosystem states characterized by distinct taxonomic, functional and metabolic configurations. To directly test their contribution to tumor progression, we established the first orthotopic CRC transplantation platform compatible with long-term experimentation in germ-free mice, enabling side-by-side comparison of genetically identical tumors in the presence or absence of microbiota. Using organoids spanning low-grade adenoma, high-grade adenoma and adenocarcinoma states, we found that the dependence on the presence of microbiota progressively decreases during malignant evolution. Whereas adenoma-derived organoids exhibited profound dependence on microbial exposure and failed or were markedly impaired in establishing tumors under germ-free conditions, adenocarcinoma organoids engrafted and metastasized in both germ-free and specific pathogen-free (SPF) hosts. Unexpectedly, comprehensive histological, immunological and transcriptomic analyses revealed highly similar tumor ecosystem states in advanced tumors arising under both microbial conditions, arguing against broad immune or epithelial defects as a primary explanation for the observed phenotype. Together, our findings demonstrate that distinct oncogenic drivers establish specific microbial ecosystem states and reveal a stage-dependent role of the microbiota during colorectal tumorigenesis. Whereas microbial signals are critical during early stages of tumor progression and may promote malignant transformation, advanced tumors progressively acquire microbiota-independent growth programs and increasingly impose genotype-specific ecological signatures on the surrounding microbial ecosystem. More broadly, we establish a versatile framework for the causal dissection of tumor-microbiota interactions in cancer.

cancer biology↗

bioRxiv2026

Investigating Enzyme Function by Geometric Matching of Catalytic Motifs

The rapidly growing universe of predicted protein structures offers opportunities for data driven exploration but requires computationally scalable and interpretable tools(1-3). We developed a method to detect catalytic features in protein structures, providing insights into enzyme function and mechanism. A library of 6780 3D coordinate sets describing enzyme catalytic sites, referred to as templates, has been collected from manually curated examples of 762 enzyme catalytic mechanisms described in the Mechanism and Catalytic Site Atlas(4-6). For template searching we optimised the geometric-matching algorithm Jess(7). We implemented RMSD and residue orientation filters to differentiate catalytically informative matches from spurious ones. We validated this approach on a non-redundant set of high quality experimental (n=3751, <40% amino acid identity) enzyme structures with well annotated catalytic sites as well as predicted structures of the human proteome. We show matching catalytic templates solely on structure is more sensitive than sequence- and 3D-structure-based approaches in identifying homology between distantly related enzymes. Since geometric matching does not depend on conserved sequence motifs or even common evolutionary history, we are able to identify examples of structural active site similarity in highly divergent and possibly convergent enzymes(8). Such examples make interesting case studies into the evolution of enzyme function. Though not intended for characterizing substrate-specific binding pockets, the speed and knowledge-driven interpretability of our method make it well suited for expanding enzyme active-site annotation across large predicted proteomes. We provide the method and template library as a Python module, Enzyme Motif Miner, at https://github.com/rayhackett/enzymm.

bioinformatics↗

bioRxiv2025

A toolkit for transposon libraries and functional genomics in intestinal Bacteroidales

Members of the order Bacteroidales include some of the most prevalent and abundant bacterial species in the healthy human gut microbiota. Yet, most of the functions encoded in their genomes remain poorly characterized, limiting our understanding of the different roles they play in the human gut microbiome. Towards addressing this gap, we developed tools and methods for genome-wide transposon mutagenesis in Bacteroidales, including broad-range transposon vectors with several antibiotic selection markers, a dual conjugation-cloning donor strain, and protocols for convenient library generation in liquid media. We then created saturated, barcoded, insertion mutant libraries in the type strains of three key representatives of the main genera within Bacteroidales: Bacteroides uniformis (ATCC 8492), Phocaeicola vulgatus (ATCC 8482) and Parabacteroides merdae (ATCC 43184). Based on the dense transposon insertion profiles and a workflow for comparing essentialomes across species, we identified 275 core essential genes shared across the three species, and 163 species-specific essential genes, some of which could be explained by functional redundancy and alternative metabolic pathways. We further identified essential non-protein coding elements and essential protein domains with known and unknown functions. Finally, using insertion directionality bias, we could map potential toxic modalities in the three genomes, including toxin-antitoxin pairs, mobile elements encoding toxic products and enzymes leading to toxic metabolic intermediates. Overall, the tools, workflows and genome-wide resources reported here expand the experimental repertoire for characterizing genes in key bacteria of the human gut microbiome, and pave the way for the establishment of similar genetic toolkits for other gut bacteria.

microbiology↗

bioRxiv2025

Machine learning inference of natural product chemistry across biosynthetic gene cluster types

With ever-increasing volumes of sequencing data for biosynthetic gene clusters (BGCs), computational methods for the prediction of resulting secondary metabolites are critically needed. Here, we present CHAMOIS, a machine learning tool inferring metabolite properties from protein domains in BGCs. Out of 539 relevant chemical properties from the ChemOnt ontology, CHAMOIS predicts 120 with an AUPRC > 0.5. Although entirely data-driven, CHAMOIS infers many protein-metabolite links that are consistent with the scientific literature and suggests interesting novel biosynthetic functions of uncharacterized proteins. Finally, to guide experimental BGC characterisation, CHAMOIS can pinpoint which BGC within a given genome produces a pre-specified metabolite.

bioinformatics↗

bioRxiv2025

PyOrthoANI, PyFastANI, and Pyskani: a suite of Python libraries for computation of average nucleotide identity

SummaryThe average nucleotide identity (ANI) metric has become the gold standard for prokaryotic species delineation in the genomics era. The most popular ANI algorithms are available as command-line tools and/or web applications, making it inconvenient or impossible to incorporate them into bioinformatic workflows, which utilize the popular Python programming language. Here, we present PyOrthoANI, PyFastANI, and Pyskani, Python libraries for three popular ANI computation methods. ANI values produced by PyOrthoANI, PyFastANI, and Pyskani are virtually identical to those produced by OrthoANI, FastANI, and skani, respectively. All three libraries integrate seamlessly with BioPython, making it easy and convenient to use, compare, and benchmark popular ANI algorithms within Python-based workflows. Availability and ImplementationSource code is open-source and available via GitHub (PyOrthoANI, https://github.com/althonos/orthoani; PyFastANI, https://github.com/althonos/pyfastani; Pyskani, https://github.com/althonos/pyskani). Supplementary InformationSupplementary data are available on bioRxiv.

bioinformatics↗

bioRxiv2024

Long-term fasting remodels gut microbial metabolism and host metabolism

Long-term fasting has become a promising research subject for its potential of treating and preventing metabolic diseases. However, little is known about its impact on the functional capacity of the gut microbiome and the combined effect on the serum metabolome. Here, we demonstrate extensive remodelling of the gut microbial ecosystem in humans (n=92) after an average of 9.8 days of fasting ([~]250 kcal / day). Fasting transiently affected the relative abundance of the majority of bacterial species (306 decreased and 210 increased out of 772). Species changes could largely be explained by their genomic repertoire of carbohydrate-active enzymes (CAZymes), which were investigated here for the first time. Fasting induced extensive abundance changes in CAZyme families, depleting families with dietary fibre substrates and increasing families with host-derived glycan substrates. Likewise, we observed extensive changes in the serum metabolome, with 382 out of 721 metabolites significantly affected (246 increased and 136 decreased). In-depth metagenome-metabolome co-variation analysis suggested Oscillibacter species to be key producers of indole-3-propionic acid, a crucial metabolite for cardiometabolic health. Together, our results provide an unprecedented view on the impact of long-term fasting on gut microbiome composition and function.

microbiology↗

bioRxiv2024

Unraveling interindividual differences and functional consequences of gut microbial metabolism of immunosuppressants

A major challenge in kidney transplantation (KT) is the large interpatient variability in the pharmacokinetics of immunosuppressive drugs. Here, we explored the role of the gut microbiome in interindividual variation in immunosuppressive drug metabolism. Analysis of 38 fecal communities, including 10 from KT recipients, and 45 bacterial species against 25 drugs, revealed significant interindividual and drug-specific differences in metabolism. Notably, 15 of 16 immunosuppressants tested were metabolized by at least one microbial community, and we found specific bacterial species, such as Bacteroides uniformis, to be potent metabolizers. We identified 18 different metabolites for 16 drugs, including two previously undescribed metabolites for sirolimus and everolimus. Our study reveals the functional impact of microbial metabolism on key immunosuppressants, including inactivation of tacrolimus, activation and potential increase in toxicity of mycophenolate mofetil (MMF), and shows that the microbial metabolite of methylprednisolone exhibits a 2.6-fold increase in epithelial permeability compared to the parent drug. Through a gain-of-function genetic screen we identified the B. uniformis enzyme BACUNI_RS05305 to be responsible for MMF activation. Using machine learning to model microbial community drug metabolism, abundance features of prevalent species predicted the biotransformation of some drugs well, while for others, a priori experimental information on bacterial genes and enzyme protein structures led to improved predictions. Our research highlights the potential of gut microbiome features to explain interindividual variability in immunosuppressive therapies and sets the stage for clinical trials to identify microbiome-encoded signatures predictive of drug metabolism in KT patients. One Sentence SummaryThis study reveals interindividual variability in gut microbial metabolism of immunosuppressive drugs mediated by specific bacterial species and enzymes.

microbiology↗

bioRxiv2024

Large-scale computational analyses of gut microbial CAZyme repertoires enabled by Cayman

Carbohydrate-active enzymes (CAZymes) are crucial for digesting glycans, but bioinformatics tools for CAZyme profiling and interpretation of substrate preferences in microbial community data are lacking. To address this, we developed a CAZyme profiler (Cayman) and a hierarchical substrate annotation scheme. Leveraging these, we genomically survey CAZymes in human gut microbes (n=107,683 genomes), which suggests novel mucin-foraging species. In a subsequent meta-analysis of CAZyme repertoires in Western versus non-Western gut metagenomes (n=4,281) we find that non-Western metagenomes are richer in fibre-degrading CAZymes despite lower overall CAZyme richness. We additionally pinpoint the taxonomic drivers underlying these CAZyme community shifts. A second meta-analysis comparing colorectal cancer patients (CRC) to controls (n=1,998) shows that CRC metagenomes are deprived of dietary fibre-targeting, but enriched in glycosaminoglycan-targeting CAZymes. A genomic analysis of co-localizing CAZyme domains predicts novel substrates for CRC-enriched CAZymes. Cayman is broadly applicable across microbial communities and freely available from https://github.com/zellerlab/cayman.

microbiology↗

bioRxiv2023

Machine learning-based meta-analysis reveals gut microbiome alterations associated with Parkinson's disease

There is strong interest in exploring the potential of the gut microbiome for Parkinsons disease (PD) diagnosis and treatment. However, a consensus on the microbiome features associated with PD and a multi-study assessment of their diagnostic value is lacking. Here, we present a machine learning meta-analysis of PD microbiome studies of unprecedented scale (including 4,490 samples). Within most studies, microbiome-based machine learning models could accurately classify PD patients. However, models were study-specific and did not generalise well across other studies. By training models on multiple datasets, we could improve their general applicability and disease specificity as assessed against microbiomes from other neurodegenerative diseases. Meta-analysis of shotgun metagenomes moreover delineated PD-associated microbial pathways potentially contributing to the deterioration of gut health and favouring the translocation of pathogenic molecules along the gut-brain axis. Strikingly, diverse microbial pathways for the biotransformation of solvents and pesticides were enriched in PD. These results align with the epidemiological evidence that exposure to these molecules increases PD risk and raise the question of whether gut microbial metabolism modulates their toxicity. Taken together, we offer the most comprehensive overview to date about the PD gut microbiome and provide future reference for its diagnostic and functional potential.

microbiology↗

bioRxiv2023

ATF6 activation alters colonic lipid metabolism causing tumor-associated microbial adaptation

Endoplasmic reticulum unfolded protein responses (UPRER) contribute to cancer development and the activating transcription factor 6 (ATF6) is involved in microbiota-dependent tumorigenesis. Here, we substantiate the clinical relevance of ATF6 in early-onset and late colorectal cancer patient cohorts. Transcriptional analysis in intestinal epithelial cells (IEC) of ATF6 transgenic mice (nATF6IEC) identified bacteria-specific changes in cellular metabolism enriched for fatty acid biosynthesis. Untargeted metabolomics and isotype-labeling confirmed ATF6-related enrichment of long chain fatty acids in colonic tissue of patients, mice and organoid cultures. FASN inhibition and microbiota transfer in germ-free nATF6IEC mice confirmed the causal involvement of ATF6-induced lipid alterations in tumorigenesis. The selective expansion of tumor-relevant microbial taxa was mechanistically linked to long chain fatty acid exposure, using bioorthogonal non-canonical amino acid tagging (BONCAT) and growth analysis of Desulfovibrio isolates. We postulate chronic ATF6 signaling in the epithelium to select for tumor-promoting microbiota by altering lipid metabolism. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/565267v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@be3756org.highwire.dtl.DTLVardef@231da4org.highwire.dtl.DTLVardef@16f3aaeorg.highwire.dtl.DTLVardef@1758fe3_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Chronic ATF6 signaling in the colonic epithelium alters lipid metabolism to select a tumor-promoting microbiota C_FIG O_LIBiallelic expression of activated ATF6 (p50 nuclear fragment) in intestinal epithelial cells (nATF6IEC) induces spontaneous colon tumors in SPF but not GF mice C_LIO_LIMechanistically, biallelic SPF nATF6IEC mice alter colonic lipid metabolism, including the upregulation of LCFAs and Fasn C_LIO_LIInhibition of FASN prevents colon tumor formation in mice, and reduces the tumor-promoting potential of the intestinal microbiota (FMT) C_LIO_LIExposure of fl/fl control mouse microbiota to LCFAs ex vivo translationally activates tumor-associated bacteria, including Desulfovibrio fairfieldensis C_LIO_LIHuman CRC patients show ATF6 upregulation, FASN co-occurrence and increased LCFAs in T tissue C_LIO_LIATF6 activity links with CRC-associated microbiota in patients, including Desulfovibrio C_LI Created with BioRender.com nATF6: activated activating transcription factor 6; LCFA: long-chain fatty acids; SAFA: saturated fatty acids; Fasn: fatty acid synthase; C75 i.p.: intraperitoneal injection of the Fasn inhibitor C75.

cancer biology↗

bioRxiv2022

High-throughput profiling of drug interactions in Gram-positive bacteria

Drug combinations present a powerful strategy to tackle antimicrobial resistance, but have not been systematically tested in many bacterial species. Here, we used an automated high-throughput setup to profile [~] 8000 combinations between 65 antibacterial drugs in three Gram-positive species: the model species, Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulate previously known drug interactions, but also identify ten times more interactions than previously reported in the pathogen S. aureus, including two synergies that were also effective in multi-drug resistant clinical S. aureus isolates in vitro and in vivo. Interactions were largely species-specific and mostly synergistic for drugs targeting the same cellular process, as observed also for Gram-negative species1. Yet, the dominating synergies are clearly distinct between Gram-negative and Gram-positive species, and are driven by different bottlenecks in drug uptake and vulnerabilities of their cell surface structures. To further explore interactions of commonly prescribed non-antibiotic drugs with antibiotics, we tested 2728 of such combinations in S. aureus, detecting a plethora of unexpected antagonisms that could compromise the efficacy of antimicrobial treatments in the age of polypharmacy. We uncovered even more synergies than antagonisms, some of which we could demonstrate as effective combinations in vivo against multi-drug resistant clinical isolates. Among them, we showed that the antiaggregant ticagrelor interferes with purine metabolism and changes the surface charge of S. aureus, leading to strong synergies with cationic antibiotics. Overall, this exemplifies the untapped potential of approved non-antibacterial drugs to be repurposed as antibiotic adjuvants. All data can be browsed through an interactive interface (https://apps.embl.de/combact/).

microbiology↗

bioRxiv2022

Evaluation of microbiome association models under realistic and confounded conditions

BackgroundIn microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance (DA) testing is lacking, and it remains unexplored how these cope with confounding. Previous DA benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations. ResultsHere we develop a simulation framework which implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole-metagenome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies to a much greater extent than in previous benchmarks. With extensively parametrized simulations we benchmark the performance of eighteen DA methods and further evaluate the best ones on confounded simulations. Only linear models, limma, fastANCOM, and the Wilcoxon test properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that post hoc adjustment can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications. ConclusionsFor microbiome association studies tight error control is critical. The unsatisfactory performance of many DA methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

bioinformatics↗

bioRxiv2022

C. difficile is overdiagnosed in adults and a commensal in infants

Clostridioides difficile is an urgent threat in hospital-acquired infections world-wide, yet the microbial composition associated with C. difficile, in particular in C. difficile infection (CDI) cases, remains poorly characterised. To investigate the gut microbiome composition in CDI patients, we analysed 534 metagenomes from 10 publicly available CDI study populations. We then tracked C. difficile on a global scale, screening 42,900 metagenomes from 253 public studies. Among the CDI cohorts, we detected C. difficile in only 30% of the stool samples from CDI patients. However, we found that multiple other toxigenic species capable of inducing CDI-like symptomatology were prevalent. In addition, the majority of the investigated studies did not adhere to the recommended guidelines for a correct CDI diagnosis. In the global survey, we found that C. difficile prevalence, abundance and biotic context were age-dependent. C. difficile is a rare taxon associated with reduced diversity in healthy adults, but common and associated with increased diversity in infants. We identified a group of species co-occurring with C. difficile exclusively in healthy infants, enriched in obligate anaerobes and in species typical of the healthy adult gut microbiome. C. difficile in healthy infants was therefore associated with multiple indicators of healthy gut microbiome maturation. Our analysis raises concerns about potential CDI overdiagnosis and suggests that C. difficile is an important commensal in infants and that its asymptomatic carriage in adults depends on microbial context.

microbiology↗

bioRxiv2021

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from [~]1,700 novel and known microbial genomes, as well as [~]600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

bioinformatics↗

bioRxiv2021

Accurate de novo identification of biosynthetic gene clusters with GECCO

Biosynthetic gene clusters (BGCs) are enticing targets for (meta)genomic mining efforts, as they may encode novel, specialized metabolites with potential uses in medicine and biotechnology. Here, we describe GECCO (GEne Cluster prediction with COnditional random fields; https://gecco.embl.de), a high-precision, scalable method for identifying novel BGCs in (meta)genomic data using conditional random fields (CRFs). Based on an extensive evaluation of de novo BGC prediction, we found GECCO to be more accurate and over 3x faster than a state-of-the-art deep learning approach. When applied to over 12,000 genomes, GECCO identified nearly twice as many BGCs compared to a rule-based approach, while achieving higher accuracy than other machine learning approaches. Introspection of the GECCO CRF revealed that its predictions rely on protein domains with both known and novel associations to secondary metabolism. The method developed here represents a scalable, interpretable machine learning approach, which can identify BGCs de novo with high precision.

bioinformatics↗

bioRxiv2021

Extended taxonomic profiling of diverse environments with mOTUs 2.6

BackgroundTaxonomic profiling is a fundamental task in microbiome research that aims to detect and quantify the relative abundance of microorganisms in biological samples. Available methods using shotgun metagenomic data generally depend on the availability of sequenced and taxonomically annotated reference genomes. However, the majority of microorganisms have not been cultured yet and lack such reference genomes. Thus, a substantial fraction of microbial community members remains unaccounted for during taxonomic profiling of metagenomes, particularly in samples from underexplored environments. To address this issue, we have developed the mOTU profiler, a tool that enables reference genome-independent species-level profiling of metagenomes. As such, it supports the identification and quantification of both "known" and "unknown" species based on a set of select marker genes. ResultsHere, we present mOTUs3, a command line tool that enables the profiling of metagenomes for >33,000 species-level operational taxonomic units. To achieve this, we leveraged the reconstruction and analysis of >600,000 draft genomes, most of which are metagenome assembled genomes (MAGs), from diverse microbiomes, including soil, freshwater systems, and the gastrointestinal tract of ruminants and other animals, which we found to be greatly underrepresented by reference genomes. Overall, two-thirds of all species-level taxa lacked a reference genome. The cumulative relative abundance of these newly included taxa was low in well-studied microbiomes, such as the human body sites (6-11%). By contrast, they accounted for substantial proportions (ocean, freshwater, soil: 43-63%) or even the vast majority (pig, fish, cattle: 60-80%) of the relative abundance across diverse non-human-associated microbiomes. Using community-developed benchmarks and datasets, we found mOTUs3 to be more accurate than other methods and to be more congruent with 16S rRNA gene-based methods for taxonomic profiling. Furthermore, we demonstrate that mOTUs3 greatly increases the resolution of well-known microbial groups into species-level taxa and helps identify new differentially abundant taxa in comparative metagenomic studies. ConclusionsWe developed mOTUs3 to enable accurate species-level profiling of metagenomes. Compared to other methods, it provides a more comprehensive view of prokaryotic community diversity, in particular for currently underexplored microbiomes. To facilitate comparative analyses by the research community, it is released with >11,000 precomputed profiles for publicly available metagenomes and is freely available at: https://github.com/motu-tool/mOTUs.

bioinformatics↗