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Ley, R. E.

Publications and source records attributed to Ley, R. E..

At least 19 recordsLinked to original sources
bioRxiv2026

Micro-Cm: restrictase-free microbiome-wide chromosome conformation profiling

Mobile genetic elements like plasmids, viruses and transposons can considerably augment the genomic repertoire of individual bacterial members of a complex multi-species microbiome and influence community dynamics. As linking a mobile element to its bacterial host based on metagenome sequencing alone proves challenging, such assays have been augmented with high-throughput chromosome conformation capture (Hi-C). However, the efficacy of Hi-C metagenomics is constrained by the protocol limitations and a lack of ground-truth reference datasets. In order to overcome these limitations, we present Micro-C metagenomics (Micro-Cm) - an adaptation of a superior, restrictase-free Micro-C technique for processing microbiome samples and mapping plasmid-host associations. We validated the developed experimental protocol and bioinformatic workflow on a simulated, defined consortium of diverse gut bacterial species and applied them to a long-read human gut microbiome sample. The proportion of valid reads in the synthetic community was an order of magnitude higher than that observed in multiple Hi-C metagenomic studies. For both samples, we obtained high-quality contact maps, which in the case of the synthetic community revealed fine-scale chromosome interactions. Moreover, successful recovery of plasmid-host interactions in the simulated community validated the method, which we then applied to the real stool sample. Our plasmid-host association analysis in a complex bacterial community successfully identified bacterial hosts for most of the identified complete plasmids. Our results show that Micro-Cm method improves profiling of complex microbiomes, exploration of mobile genetic element dynamics and community-wide, detailed investigation of chromosomal conformation patterns.

microbiology↗
bioRxiv2026

Co-targeting an AMPK--MAPK axis reprograms CAFs and suppresses PDAC

Pancreatic ductal adenocarcinoma (PDAC) is a fatal cancer characterized by limited therapeutic options and a highly treatment-resistant tumor microenvironment. Beyond tumor-intrinsic genetic alterations, growing evidence indicates that host-microbiome interactions influence cancer progression through microbial metabolites. However, how microbiome-derived metabolites influence oncogenic signaling in PDAC remains unclear. Here, integrated profiling revealed a consistent reduction of the microbial metabolite acetic acid in fecal samples from treatment-naive patients with PDAC and in a genetically defined Drosophila model recapitulating key PDAC driver alterations. Acetic acid activates AMP-activated protein kinase, and pharmacological activation of this pathway together with inhibition of mitogen-activated protein kinase signaling suppressed tumor growth in fly and mouse models. Combined pathway targeting restored AMPK activity and suppressed cancer-associated fibroblast activation. These findings identify a microbiome-associated metabolic vulnerability in PDAC and suggest that coordinated targeting of metabolic and oncogenic signaling may restrain tumor progression and improve therapeutic strategies.

cancer biology↗
bioRxiv2026

StrainVis: interactive visual strain-level analysis of microbiome data

BackgroundMicrobiomes contain multiple conspecific strains whose genomic differences arise from both single nucleotide variants (SNVs) and structural variation (insertions, deletions, recombination). Recently, computational tools to assess strain-level differences became available, based either on average nucleotide identity (ANI) or on the average pairwise synteny (APSS) of strains, which are sensitive, respectively, to either SNVs or to structural variation. However, strain-level analyses remain technically challenging and fragmented across approaches and combining these complementary signals typically requires substantial bioinformatic expertise. ResultsHere we present StrainVis, a web-based analysis and visualization platform that integrates outputs from both ANI- and APSS-based strain tracking tools to enable unified, interactive exploration of within-species diversity. StrainVis allows users to perform per-species and multi-species comparisons, incorporate metadata and gene annotations, and generate statistical summaries and publication-ready figures without programming. ConclusionsBy lowering technical barriers and enabling joint interpretation of sequence and structural variation, StrainVis makes advanced strain-level microbiome analysis accessible to a broader community and facilitates discovery of evolutionary patterns that would be missed by single-method approaches alone.

bioinformatics↗
bioRxiv2025

MMonitor: Software for Real-Time Monitoring of Microbial Communities Using Long Reads

Real-time monitoring of microbial communities offers valuable insights into microbial dynamics across diverse environments. However, many existing metagenome analysis tools require advanced computational expertise and are not designed for monitoring. We present MMonitor, an open source software platform for the real-time analysis and visualization of metagenomic Oxford Nanopore Technologies (ONT) sequencing data. MMonitor includes two components: a desktop application for running bioinformatics pipelines through a graphical user interface (GUI) or command-line interface (CLI), and a web-based dashboard for interactive result inspection. The dashboard provides taxonomic composition over time, quality scores, diversity indices, and taxonomy-metadata correlations. Integrated pipelines enable automated de-novo assembly and reconstruction of metagenome-assembled genomes (MAGs). To validate MMonitor, we tracked human gut microbial populations in three bioreactors using 16S rRNA gene sequencing, and applied it to whole-genome sequencing (WGS) data to generate high-quality annotated MAGs and reveal functional insights. We also compare MMonitor to other software for real-time metagenomic analysis, highlighting the strengths and limitations of each tool for this use case. By performing automated time-series analyzes, sample management, and updating of reference databases, MMonitor addresses current limitations and supports dynamic microbiome research in various fields. IMPORTANCEMetagenome monitoring is essential for understanding and managing microbial communities in dynamic environments. Rapid and accurate analysis of these communities allows for timely interventions in biologically active environments, for example, in industrial biotechnology, environmental surveillance, and medical diagnostics. Metagenome monitoring tools should have real-time capabilities, intuitive interfaces, convenient setup and distribution, and suitable visual representations for effective monitoring. MMonitor addresses these challenges by offering a platform that combines real-time analysis of nanopore sequencing data with customizable pipelines and advanced visualization tools. It is designed to be accessible to researchers of all levels of computational skill. It supports efficient taxonomic and functional analyses based on popular metagenome software and automates taxonomic profiling, genome assembly, binning, and annotation. The dashboard helps to interpret data and present results. MMonitor is designed to track metagenomes and we hope that it can impact microbiome research and applications in health, biotechnology, agricultural, and environmental sciences.

bioinformatics↗
bioRxiv2025

Human gut flagellome profiling using FlaPro reveals TLR5-related phenotype-specific alterations in IBD

BackgroundFlagellin is the protein monomer of the bacterial flagellum, which confers motility, allowing bacteria to reach their favored niches. Flagellin is highly conserved across bacterial species and thus the target of the innate immune receptor Toll-like receptor 5 (TLR5). In the gut, bacterial flagellin agonizes human TLR5, triggering a pro-inflammatory response. However, the ability to bind and activate TLR5 varies considerably between different flagellins, suggesting that the composition of an individuals flagellin repertoire - the flagellome - may mediate the inflammatory response to the microbiome, with relevance to inflammatory bowel diseases. However, to date, methods to assess the inflammatory potential of a flagellome are lacking. MethodsWe constructed a curated database of human gut microbiome-derived flagellins. To predict the inflammatory potential of the flagellome by sorting flagellins into either "stimulatory" (strong TLR5 agonists) or "silent" (weak TLR5 agonists), we trained a machine learning model on experimentally characterized flagellins with known binding and stimulatory activities. The FlaPro pipeline was implemented using the Snakemake workflow engine for high-throughput analysis and is available at https://github.com/leylabmpi/FlaPro. A publicly available multi-omics dataset from an inflammatory bowel disease (IBD) cohort was used to explore associations between flagellome features and clinical status. FindingsFlaPro enables robust profiling of the human gut flagellome from metagenomic and metatranscriptomic data. Analysis of the IBD datasets revealed a depletion of flagellome diversity and a reduced silent-to-stimulatory flagellin abundance ratio in Crohns disease and ulcerative colitis, observed at both the genomic and transcriptional levels. Multiple condition-specific alterations were identified at the level of individual flagellin clusters. InterpretationThese findings indicate that IBD is associated with distinct alterations in the gut flagellome, particularly in relation to TLR5 recognition. Flagellome features represent a functionally interpretable class of microbiome-derived markers with potential utility in microbiome-wide association studies in the context of human health and disease. FundingThis work was supported by the Max Planck Society and the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme Grant agreement ID: 101142834 (ERC Advanced Grant SilentFlame).

bioinformatics↗
bioRxiv2025

Flagellin in the human gut microbiome is a diet-adjustable adjuvant for vaccination

The intestinal microbiota is thought to modulate immune responsiveness to vaccines. Human studies on this topic, however, have yielded inconsistent results1,2. We hypothesized that the microbiome would influence innate immune responses, and thus vaccine reactogenicity, more directly than vaccine immunogenicity. To test this, we established the {micro}HEAT (Microbial-Human Ecology And Temperature) study, which longitudinally profiled the fecal microbiota, oral body temperature and serum antibody responses of 171 healthy adults (18-40 years old) before and after vaccination for SARS-CoV-2. Increased temperature after vaccination ({Delta}T) was associated with habitual diet and with baseline metabolic and immune markers. The microbiomes of {Delta}T-high ({Delta}Thi) participants were characterized by high expression of flagellin and an overabundance of the flagellated bacterium Waltera. Fecal samples from {Delta}Thi participants induced more inflammation in human cells and stronger post-vaccine temperature responses in mice compared to {Delta}Tlo samples, suggesting a causal role for the microbiome. Moreover, Waltera flagellin replicated the inflammatory phenotypes in vitro and was modulable via a dietary additive. Overall, these data suggest that flagellin from the gut microbiome stimulates innate immunity and vaccine reactogenicity, and that this axis can be manipulated via diet. These findings have implications for improving human vaccine tolerance and immunogenicity.

microbiology↗
bioRxiv2025

Infection with gut parasites correlates with gut microbiome diversity across human populations in Africa

Soil-transmitted helminths (STH) are common in (sub)tropical regions and primarily affect impoverished populations. STHs reside in the gut, interacting both with the gut microbiota and host immunity. Clinical STH detection is laborious and often not performed within the context of gut microbiome studies. Here, we assessed whether fecal metagenome data could be used to assess STH infection, and to relate STH infection to microbiome diversity. We generated 310 gut metagenomes for mother-child pairs from two different locations in Gabon: one rural and one semi-urban. The presence and abundance of four STH parasites (Ascaris lumbricoides, Strongyloides stercoralis, Trichuris trichiura, and Necator americanus) were assessed using both microscopy and qPCR. Sequence data were used to characterize the microbiomes and to detect the presence of these four STH parasites. We found that metagenome data could accurately detect the presence of all four STHs, as reflected by high sensitivity and specificity compared to microscopy or qPCR detection. Furthermore, the number of STH species present in stool was associated with microbiome diversity and with the abundances of specific taxa, most notably in young children. We applied this approach to microbiome data from five other populations in Africa and corroborated our findings. Our results demonstrate that human intestinal parasites can be accurately detected via metagenomic sequencing, and highlight how infection with multiple STH parasites is linked to consistent features in the gut microbiome in populations in Africa.

microbiology↗
bioRxiv2025

Selection and transmission of the gut microbiome alone shifts mammalian behavior

Natural selection acts on phenotypic variation, which is influenced by genetic and environmental factors including the microbiome. Whether microbiome-mediated host phenotypes can be selected and transmitted remains untested in vertebrates. Here, we first identified locomotor activity as a trait transmissible through the gut microbiome in mice. We then performed a selection experiment, where we serially transferred microbiomes from low-activity mice to independently bred germ-free mice. Over four transfer rounds, microbiome transfer significantly reduced locomotor activity. Reduced locomotion was associated with increased Lactobacilli and their metabolite indolelactate. In a test of causality, independent administration to the mouse gut of Lactobacillus johnsonii or indolelactate reduced locomotion. These findings demonstrate that selection and transmission of microbes can modulate host traits independently of selection on the mammalian genome. One Sentence SummarySerial transfer of the gut microbiome of low-activity mice to germfree recipients reproducibly transferred activity levels, revealing the microbiomes potential to impact the hosts response to selection without host genomic changes.

evolutionary biology↗
bioRxiv2024

Kinetic and structure-based comparisons of silent and stimulatory flagellin interactions with TLR5

The bacterial protein flagellin is the sole ligand of the innate immune receptor Toll- like receptor 5 (TLR5). Flagellins with strong agonism bind TLR5 at their D1 and D0 domains, while poor agonist "silent" flagellins bind with D1 only. However, D0-TLR5 interactions insufficiently explain the silent phenotype. Here, we characterize the D1 domain binding kinetics of the silent flagellin RhFlaB compared to the strong canonical agonist StFliC. Using Surface Plasmon Resonance, we show that the RhFlaB-D1 binds more strongly than StFliC-D1, but forms shorter-lived complexes. Cryo-EM analysis of RhFlaB showed its D1 primary interface (PI) contains two distinct hydrophobic pockets, which should facilitate rapid association, while its D1s secondary interface (SI) is dominated by negatively charged residues, which likely impede residue-residue interactions. Comparisons with an existing StFliC-D1 structure suggest that strong agonism requires strong binding at both the PI and SI of the D1, and silent flagellins evade detection partly through weaker SI interactions. These findings offer a foundation for designing targeted interventions that can modulate TLR5 activity to influence immunity, inflammation, or even gut microbiome composition.

biochemistry↗
bioRxiv2022

ResMiCo: increasing the quality of metagenome-assembled genomes with deep learning

The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging to detect for taxonomically novel genomic data. Assembly errors can affect all downstream analyses of the assemblies. Accuracy for the state of the art in reference-free misassembly prediction does not exceed an AUPRC of 0.57, and it is not clear how well these models generalize to real-world data. Here, we present the Residual neural network for Misassembled Contig identification (ResMiCo), a deep learning approach for reference-free identification of misassembled contigs. To develop ResMiCo, we first generated a training dataset of unprecedented size and complexity that can be used for further benchmarking and developments in the field. Through rigorous validation, we show that ResMiCo is substantially more accurate than the state of the art, and the model is robust to novel taxonomic diversity and varying assembly methods. ResMiCo estimated 4.7% misassembled contigs per metagenome across multiple real-world datasets. We demonstrate how ResMiCo can be used to optimize metagenome assembly hyperparameters to improve accuracy, instead of optimizing solely for contiguity. The accuracy, robustness, and ease-of-use of ResMiCo make the tool suitable for general quality control of metagenome assemblies and assembly methodology optimization. Author summaryMetagenome assembly quality is fundamental to all downstream analyses of such data. The number of metagenome assemblies, especially metagenome-assembled genomes (MAGs), is rapidly increasing, but tools to assess the quality of these assemblies lack the accuracy needed for robust quality control. Moreover, existing models have been trained on datasets lacking complexity and realism, which may limit their generalization to novel data. Due to the limitations of existing models, most studies forgo such approaches and instead rely on CheckM to assess assembly quality, an approach that only utilizes a small portion of all genomic information and does not identify specific misassemblies. We harnessed existing large genomic datasets and high-performance computing to produce a training dataset of unprecedented size and complexity and thereby trained a deep learning model for predicting misassemblies that can robustly generalize to novel taxonomy and varying assembly methodologies.

bioinformatics↗
bioRxiv2022

Longitudinal comparison of the developing gut virome in infants and their mothers

The virome of the human gut and its development in early life are poorly understood. Here we performed viral metagenomic sequencing on stool samples from a multiethnic, socioeconomically diverse cohort of 53 infants collected longitudinally over their first 3 years of life and their mothers to investigate and compare their viromes. The asymptomatic infant virome consisted of bacteriophages, dietary/environmental viruses, and human pathogenic viruses, in contrast to the material virome, in which sequence reads from human pathogenic viruses were absent or present at extremely low levels. Picornaviruses and phages in the family Microviridae (microviruses) dominated the infant virome, while microviruses and tomato mosaic virus dominated the maternal virome. As the infants aged, the human pathogenic and dietary/environmental virus components remained distinct from the materal virome, while the phage component evolved to become more similar. However, the composition of the evolving infant virome was not determined by the mother and was still maturing to the adult virome at three years of age. ImportanceThe development of the human gut virome in early childhood is poorly understood. Here we use viral metagenomic sequencing in a cohort of 53 infants to the characterize their gut viromes and compare them to their mothers.. This study finds that the infant virome consists of phages and human pathogenic viruses in asymptomatic individuals and is still maturing into the adult virome at three years of age.

microbiology↗
bioRxiv2022

Silent recognition of flagellins from human gut commensal bacteria by Toll-like receptor 5

Flagellin, the protein unit of the bacterial flagellum, stimulates the innate immune receptor Toll-like receptor (TLR)5 following pattern recognition, or evades TLR5 through lack of recognition. This binary response fails to explain the weak agonism of flagellins from commensal bacteria, raising the question of how TLR5 response is tuned. Here, we describe a novel class of flagellin-TLR5 interaction, termed silent recognition. Silent flagellins are weak agonists despite high affinity binding to TLR5. This dynamic response is tuned by TLR5-flagellin interaction distal to the site of pattern recognition. Silent flagellins are produced primarily by the abundant gut bacteria Lachnospiraceae and are enriched in non-Western populations. These findings provide a mechanism for the innate immune system to tolerate commensal-derived flagellins. One-Sentence SummaryTLR5 sensitively recognizes, but responds weakly to, flagellins from gut commensal bacteria.

microbiology↗
bioRxiv2022

Interpreting tree ensemble machine learning models with endoR

BackgroundTree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only yield limited insights into how microbial taxa or genomic content may be associated. Results: We developed endoR, a method to interpret a fitted tree ensemble model. First, endoR simplifies the fitted model into a decision ensemble from which it then extracts information on the importance of individual features and their pairwise interactions and also visualizes these data as an interpretable network. Both the network and importance scores derived from endoR provide insights into how features, and interactions between them, contribute to the predictive performance of the fitted model. Adjustable regularization and bootstrapping help reduce the complexity and ensure that only essential parts of the model are retained. We assessed the performance of endoR on both simulated and real metagenomic data. We found endoR to infer true associations with more or comparable accuracy than other commonly used approaches while easing and enhancing model interpretation. Using endoR, we also confirmed published results on gut microbiome differences between cirrhotic and healthy individuals. Finally, we utilized endoR to gain insights into components of the microbiome that predict the presence of human gut methanogens, as these hydrogen-consumers are expected to interact with fermenting bacteria in a complex syntrophic network. Specifically, we analyzed a global metagenome dataset of 2203 individuals and confirmed the previously reported association between Methanobacteriaceae and Christensenellales. Additionally, we observed that Methanobacteriaceae are associated with a network of hydrogen-producing bacteria. Conclusion: Our method accurately captures how tree ensembles use features and interactions between them to predict a response. As demonstrated by our applications, the resultant visualizations and summary outputs facilitate model interpretation and enable the generation of novel hypotheses about complex systems. An implementation of endoR is available as an open-source R-package on GitHub (https://github.com/leylabmpi/endoR).

bioinformatics↗
bioRxiv2021

Codiversification of gut microbiota with humans

Some gut microbes have cospeciated with hominids, but whether they further codiversified with human populations is unclear. Here, we identify predominant gut microbial species sharing a parallel evolutionary history with human populations. Patterns of strain transfer between populations are generally consistent with an African origin, and suggest long-term vertical transmission over thousands of generations. We show the same strains also faithfully transmit between mothers and their children. Consistent with the development of intimate symbiosis, species with strongest patterns of codiversification have the smallest genomes. This study reveals long-term fidelity of gut microbiota with human populations through transmission among individuals living in close proximity. Dominance of specific strains in different populations is based in part on vertical transmission and they may provide population-specific health benefits. One-sentence summaryIdentification of gut microbes that codiversified with human populations.

evolutionary biology↗
bioRxiv2021

SynTracker: a synteny based tool for tracking microbial strains

In the human gut microbiome, specific strains emerge due to within-host evolution and can occasionally be transferred to or from other hosts. Phenotypic variance among such strains can have implications for strain transmission and interaction with the host. Surveilling strains of the same species, within and between individuals, can further our knowledge about the way in which microbial diversity is generated and maintained in host populations. Existing methods to estimate the biological relatedness of similar strains usually rely on either detection of single nucleotide polymorphisms (SNP), which may include sequencing errors, or on the analysis of pangenomes, which can be limited by the requirement for extensive gene databases. To complement existing methods, we developed SynTracker. This strain-comparison tool is based on synteny comparisons between strains, or the comparison of the arrangement of sequence blocks in two homologous genomic regions in pairs of metagenomic assemblies or genomes. Our method is executed in a species-specific manner, has a low sensitivity to SNPs, does not require a pre-existing database, and can correctly resolve strains using complete or draft genomes and metagenomic samples using <5% of the genome length. When applied to metagenomic datasets, we detected person-specific strains with an average sensitivity of 97% and specificity of 99%, and strain-sharing events in mother-infant pairs. SynTracker can be used to study the population structure of specific microbial species between and within environments, to identify evolutionary trajectories in longitudinal datasets, and to further understanding of strain sharing networks.

microbiology↗
bioRxiv2021

Inositol lipid synthesis is widespread in host-associated Bacteroidetes

Ubiquitous in eukaryotes, inositol lipids have finely tuned roles in cellular signaling and membrane homeostasis. In Bacteria, however, inositol lipid production is rare. Recently, the prominent human gut bacterium Bacteroides thetaiotaomicron (BT) was reported to produce inositol lipids, including inositol sphingolipids, but the pathways remain ambiguous and their prevalence unclear. Here, we investigated the gene cluster responsible for inositol lipid synthesis in BT using a novel strain with inducible control of sphingolipid synthesis. We characterized the biosynthetic pathway from myo-inositol-phosphate (MIP) synthesis to phosphoinositol-dihydroceramide, including structural and kinetic studies of the enzyme MIP synthase (MIPS). We determined the crystal structure of recombinant BT MIPS with bound NAD cofactor at 2.0 [A] resolution, and identified the first reported phosphatase for the conversion of bacterially-derived phosphatidylinositol phosphate (PIP) to phosphatidylinositol (PI). Transcriptomic analysis indicated inositol production is nonessential but its loss alters BT capsule expression. Bioinformatic and lipidomic comparisons of Bacteroidetes species revealed a novel second putative pathway for bacterial PI synthesis without a PIP intermediate. Our results indicate that inositol sphingolipid production, via one of the two pathways, is widespread in host-associated Bacteroidetes, and may be implicated in host interactions both indirectly via the capsule and directly through inositol lipid provisioning.

microbiology↗
bioRxiv2021

Struo2: efficient metagenome profiling database construction for ever-expanding microbial genome datasets

Mapping metagenome reads to reference databases is the standard approach for assessing microbial taxonomic and functional diversity from metagenomic data. However, public reference databases often lack recently generated genomic data such as metagenome-assembled genomes (MAGs), which can limit the sensitivity of read-mapping approaches. We previously developed the Struo pipeline in order to provide a straight-forward method for constructing custom databases; however, the pipeline does not scale well with the ever-increasing number of publicly available microbial genomes. Moreover, the pipeline does not allow for efficient database updating as new data are generated. To address these issues, we developed Struo2, which is >3.5-fold faster than Struo at database generation and can also efficiently update existing databases. We also provide custom Kraken2, Bracken, and HUMAnN3 databases that can be easily updated with new genomes and/or individual gene sequences. Struo2 enables feasible database generation for continually increasing large-scale genomic datasets. AvailabilityO_LIStruo2: https://github.com/leylabmpi/Struo2 C_LIO_LIPre-built databases: http://ftp.tue.mpg.de/ebio/projects/struo2/ C_LIO_LIUtility tools: https://github.com/nick-youngblut/gtdb_to_taxdump C_LI

bioinformatics↗