Biology subjects

Huttenhower, C.

Publications and source records attributed to Huttenhower, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Microbial-associated acylated putrescines as immunomodulatory molecules in inflammatory bowel diseases

The gut microbiota regulates intestinal immunity through metabolite production, yet most disease-associated metabolites remain functionally uncharacterized. In inflammatory bowel diseases (IBD), where the microbial metabolome is profoundly altered, we identify N-acyl putrescines as microbiome-associated metabolites enriched across two independent cohorts. N-oleoylputrescine (NOP) emerges as the primary immunomodulatory candidate, inducing robust transcriptional responses in dendritic cells and colonic organoids. Enterocloster species harboring nonribosomal peptide synthetase gene clusters synthesize NOP, confirmed by isotope-tracing in vitro and germ-free mouse colonization in vivo. NOP suppresses core IBD inflammatory pathways in mouse dendritic cells and human monocytes, reducing signatures of histologic inflammation and therapy non-response. NOP dampens inflammation in four colitis models, decreases myeloid cell NF-{kappa}B activation, and suppresses type 1 immune responses through a T cell-intrinsic mechanism. That NOP accumulates in IBD despite its anti-inflammatory properties reveals a holobiont defense strategy: the gut microbiota deploys immunomodulatory metabolites as a compensatory response to restore homeostasis.

immunology↗

bioRxiv2026

Enhanced multi-omic viral profiling from microbial community sequencing with BAQLaVa

Viruses are crucial components of microbial communities, both phage that infect bacterial community members as well as pathogenic and other eukaryotic viruses. However, they remain unobserved by most current technologies, due to combinations of experimental and analytical factors. To address the latter, we developed the BAQLaVa algorithm for high-resolution profiling of >120,000 viral species (viral genome bins, VGBs) via reference-based metagenome (MGX) or metatranscriptome (MTX) alignment to complementary nucleotide markers and proteome sets. In comprehensive benchmarking, BAQLaVa substantially outperformed alternatives, achieving species-level recall and precision regularly over 90%. We applied BAQLaVa to MGX and MTX samples from the HMP2 IBDMDB cohort to identify previously undescribed viral perturbations in inflammatory bowel diseases. Most notably, virome diversity was reduced in tandem with bacterial diversity during inflammation, in contrast to previous findings based on a narrower range of viral detection. A subset of viruses were enriched during IBD, associated with carriage of abortive infection anti-defense systems such as AbiL and PD-{lambda}-2, as well as genes involved in the regulation of lysogeny. Leveraging the corresponding viral profiles, we also inferred phage-host relationships using scalable co-occurrence and covariation signals, even in the absence of host references or genome annotations. By enabling high sensitivity and specificity viral profiling from metagenomes or metatranscriptomes, BAQLaVa provides a scalable framework for virome epidemiology and systematic analysis of virus-host interactions.

bioinformatics↗

bioRxiv2025

Comparative Assessment of Large Language Models for Microbial Phenotype Annotation

Large language models (LLMs) are increasingly used to extract knowledge from text, yet their coverage and reliability in biology remain unclear. Microbial phenotypes are especially important to assess, as comprehensive data remain sparse except for well-studied organisms and they underpin our understanding of microbial characteristics, functional roles, and applications. Here, we systematically assessed the biological knowledge encoded in publicly available LLMs for structured phenotype annotation of microbial species. We evaluated the performance of over 50 LLMs, including state-of-the-art models such as Claude Sonnet 4 and the GPT-5 family of models. Across phenotypes, LLMs reached accurate assignments for many species, but performance varied widely by model and trait, and no single model dominated. Model self-reported confidence is informative, with higher confidence aligning with higher accuracy, and can be used to prioritize phenotype assignment, effectively distinguishing between high-and low-confidence inferences. Overall, our study outlines the utility and limitations of text-based LLMs for phenotype characterization in microbiology.

bioinformatics↗

bioRxiv2025

Pan-microbiome analysis along the human respiratory axis reveals an ecological continuum in health and collapse in disease

The human respiratory tract (RT) harbors complex microbial communities whose functions are critical to health and disease. Yet, current insights remain fragmented across anatomical sites, populations, and clinical states, limiting the fields ability to define common patterns in health and disease. Here, we present the first global respiratory pan-microbiome atlas, a resource integrating over 4,000 metagenomes across upper, intermediate, and lower RT from diverse cohorts encompassing health, pneumonia, COVID-19, and cystic fibrosis. Standardized taxonomic profiling reveals marked biogeographic structure: in health, lower RT communities largely represent filtered subsets of upper RT microbiota. Respiratory disease disrupts this organization, with reproducible depletion of core taxa at specific locations such as Rothia mucilaginosa and Fusobacterium pseudoperiodonticum, the latter being present in 88% of healthy sputum samples. Source-tracking analyses further support the collapse of inter-compartmental connectivity in disease and show the emergence of invasive taxa of unclear origin. Finally, prevalence-based models outperform abundance-based models in detecting disease-associated disruptions, providing greater sensitivity to shifts in community stability. Altogether, this atlas defines the healthy RT microbiome as a spatially structured ecosystem and provides a foundational reference for advancing personalized care and systems-level models of respiratory disease.

bioinformatics↗

bioRxiv2025

Item recognition is associated with gut microbiota composition in healthy humans

Murine studies show that the gut microbiota - the collection of the microbes residing in the large intestine - affects memory performance in the host. However, whether commensal gut bacteria are linked to human episodic memory remains unknown. Here, we investigated whether individual differences in episodic memory performance were associated with differences in the indigenous gut microbiota composition between individuals. We show that greater gut microbiota alpha diversity was associated with better item recognition and that gut microbiota dissimilarity index (beta diversity) between participants was associated with differences in their performance. Finally, our results suggest that Prevotella copri might play a role in the relationship between gut microbiota and human item recognition in healthy individuals. In a sample size larger than previous human studies and examining unmanipulated gut microbiota, we provide evidence that episodic memory in healthy humans is linked to their gut microbiota composition. SIGNIFICANCE STATEMENTUnderstanding the relationship between the gut microbiota - the collective of microbes in our gut - and human cognition represents a pivotal frontier in neuroscience. Our study dives into this quest by exploring the connection between the indigenous gut microbiota and episodic memory in healthy individuals. While prior research has illuminated this relationship in murine models, our study extends these findings to humans, providing compelling evidence that gut microbiota diversity and structural composition are associated with differences in episodic memory performance. By shifting the focus from probiotic-based interventions to the study of our commensal gut microbes, our findings shed light on how the gut microbiota, in its natural state, could be linked to interindividual differences in human cognition.

animal behavior and cognition↗

bioRxiv2025

Commonly used compositional data analysis implementations are not advantageous in microbial differential abundance analyses benchmarked against biological ground truth

Previous benchmarking of differential abundance (DA) analysis methods in microbiome studies have employed synthetic data, simulations, and "real data" examples, but to the best of our knowledge, none have yet employed experimental data with known "ground truth" differential abundance. A key debate in the field centers on whether compositional methods are necessary for DA analysis, which is challenging to answer due to the lack of ground truth data. To address this gap, we created the Bioconductor data package MicrobiomeBenchmarkData, featuring three microbiome datasets with established biological ground truths: 1) diverse oral microbiomes from supragingival and subgingival plaques, expected to favor aerobic and anaerobic bacteria, respectively, 2) low-diversity microbiomes from healthy vaginas and bacterial vaginosis, conditions that have been well-characterized through cell culture and microscopy, and 3) a spike-in dataset with constant, known absolute abundances of three bacteria. We benchmarked 17 DA approaches and demonstrated that compositional DA methods are not beneficial but rather lack sensitivity, show increased variability in constant-abundance spike-ins, and, most surprisingly, more frequently produce paradoxical results with DA in the wrong direction for the low-diversity microbiome. Conversely, commonly used methods in microbiome literature, such as LEfSe, the Wilcoxon test, and RNA-seq-derived methods, performed best. We conclude that researchers continue using widely adopted non-parametric or RNA-seq DA methods and that further development of compositional methods includes benchmarking against datasets with known biological ground truth.

bioinformatics↗

bioRxiv2025

Inflammatory disease microbiomes share a functional pathogenicity predicted by C-reactive protein

We examine disease-specific and cross-disease functions of the human gut microbiome by colonizing germ-free mice, at risk for inflammatory arthritis, colitis, or neuroinflammation, with over 100 human fecal microbiomes from subjects with rheumatoid arthritis, ankylosing spondylitis, multiple sclerosis, ulcerative colitis, Crohns disease, or colorectal cancer. We find common inflammatory phenotypes driven by microbiomes from individuals with intestinal inflammation or inflammatory arthritis, as well as distinct functions specific to microbiomes from multiple sclerosis patients. Inflammatory disease in mice colonized with human microbiomes correlated with systemic inflammation, measured by C-reactive protein, in the human donors. These cross-disease patterns of human microbiome pathogenicity mirror features of the inflammatory diseases, including therapeutic targets and the presence or absence of systemic inflammation, suggesting shared and disease-specific mechanisms by which the microbiome is shaped and drives pathogenic inflammatory responses.

immunology↗

bioRxiv2025

Quantifying Metagenomic Strain Associations fromMicrobiomes with Anpan

Genetic and genomic variation among microbial strains can dramatically influence their phenotypes and environmental impact, including on human health. However, inferential methods for quantifying these differences have been lacking. Strain-level metagenomic profiling data has several features that make traditional statistical methods challenging to use, including high dimensionality, extreme variation among samples, and complex phylogenetic relatedness. We present Anpan, a set of quantitative methods addressing three key challenges in microbiome strain epidemiology. First, adaptive filtering designed to interrogate microbial strain gene carriage is combined with linear models to identify strain-specific genetic elements associated with host health outcomes and other phenotypes. Second, phylogenetic generalized linear mixed models are used to characterize the association of sub-species lineages with such phenotypes. Finally, random effects models are used to identify pathways more likely to be retained or lost by outcome-associated strains. We validated our methods by simulation, showing that we achieve more accurate effect size estimation and a lower false positive rate compared to alternative methodologies. We then applied our methods to a dataset of 1,262 colorectal cancer patients, identifying functionally adaptive genes and strong phylogenetic effects associated with CRC status, sometimes complementing and sometimes extending known species-level microbiome CRC biomarkers. Anpans methods have been implemented as a publicly available R library to support microbial community strain and genetic epidemiology in a variety of contexts, environments, and phenotypes.

bioinformatics↗

bioRxiv2024

MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery

A key question in microbial community analysis is determining which microbial features are associated with community properties such as environmental or health phenotypes. This statistical task is impeded by characteristics of typical microbial community profiling technologies, including sparsity (which can be either technical or biological) and the compositionality imposed by most nucleotide sequencing approaches. Many models have been proposed that focus on how the relative abundance of a feature (e.g. taxon or pathway) relates to one or more covariates. Few of these, however, simultaneously control false discovery rates, achieve reasonable power, incorporate complex modeling terms such as random effects, and also permit assessment of prevalence (presence/absence) associations and absolute abundance associations (when appropriate measurements are available, e.g. qPCR or spike-ins). Here, we introduce MaAsLin 3 (Microbiome Multivariable Associations with Linear Models), a modeling framework that simultaneously identifies both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 also newly accounts for compositionality with experimental (spike-ins and total microbial load estimation) or computational techniques, and it expands the space of biological hypotheses that can be tested with inference for new covariate types. On a variety of synthetic and real datasets, MaAsLin 3 outperformed current state-of-the-art differential abundance methods in testing and inferring associations from compositional data. When applied to the Inflammatory Bowel Disease Multi-omics Database, MaAsLin 3 corroborated many previously reported microbial associations with the inflammatory bowel diseases, but notably 77% of associations were with feature prevalence rather than abundance. In summary, MaAsLin 3 enables researchers to identify microbiome associations with higher accuracy and more specific association types, especially in complex datasets with multiple covariates and repeated measures.

microbiology↗

bioRxiv2024

Evaluating metagenomic analyses for undercharacterized environments: what's needed to light up the microbial dark matter?

Non-human-associated microbial communities play important biological roles, but they remain less understood than human-associated communities. Here, we assess the impact of key environmental sample properties on a variety of state-of-the-art metagenomic analysis methods. In simulated datasets, all methods performed similarly at high taxonomic ranks, but newer marker-based methods incorporating metagenomic assembled genomes outperformed others at lower taxonomic levels. In real environmental data, taxonomic profiles assigned to the same sample by different methods showed little agreement at lower taxonomic levels, but the methods agreed better on community diversity estimates and estimates of the relationships between environmental parameters and microbial profiles.

microbiology↗

bioRxiv2024

Early life microbial succession in the gut follows common patterns in humans across the globe

Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution ({+/-}3 months) for the first 1.5 years of life. Using 3,154 samples from 1,827 infants across 12 countries, we trained a random forest model, achieving a root mean square error of 2.61 months. We identified key taxonomic predictors of age, including declines in Bifidobacterium spp. and increases in Faecalibacterium prausnitzii and Lachnospiraceae. Microbial succession patterns are conserved across infants from diverse human populations, suggesting universal developmental trajectories. Functional analysis confirmed trends in key microbial genes involved in feeding transitions and dietary exposures. This model provides a normative benchmark of "microbiome age" for assessing early gut maturation that can be used alongside other measures of child development.

microbiology↗

bioRxiv2024

Compositional Differential Abundance Testing: Defining and Finding a New Type of Health-Microbiome Associations

A major task of microbiome epidemiology is association analysis, where the goal is to identify microbial features related to host health. This is commonly performed by differential abundance (DA) analysis, which, by design, examines each microbe as isolated from the rest of the microbiome. This does not properly account for the microbiomes compositional nature or microbe-microbe ecological interactions, and can lead to confounded findings, i.e., microbes that only appear to associate with health through their confounding association with health-related, biologically informative microbes. To remedy these issues, we present Compositional Differential Abundance (CompDA) analysis, a novel approach for health-microbiome association. CompDA provides a novel approach to identify health-related microbes by examining the microbiome holistically, which a) accounts for the datas compositionality and ecological interactions, and b) has clear interpretations corresponding to host health as affected by microbiome-based interventions. CompDA prioritizes health-related microbes and controls false discoveries by implementing recent advances from high-dimensional statistics, and can be flexibly adapted to many common tasks in modern microbiome epidemiology, including enhancing microbiome-based machine learning by providing rigorous p-values to prioritize important features. We validate the performance of CompDA, and compare against canonical microbiome association methods including DA with extensive, real-data-informed simulation studies. Lastly, we report novel and consistent findings of CompDA in application, based on re-examination of recently reported microbial signatures of colorectal cancer in a meta-analysis.

genomics↗

bioRxiv2024

Discovering and exploring the hidden diversity of human gut viruses using highly enriched virome samples

Viruses are an abundant and crucial component of the human microbiome, but accurately discovering them via metagenomics is still challenging. Currently, the available viral reference genomes poorly represent the diversity in microbiome samples, and expanding such a set of viral references is difficult. As a result, many viruses are still undetectable through metagenomics even when considering the power of de novo metagenomic assembly and binning, as viruses lack universal markers. Here, we describe a novel approach to catalog new viral members of the human gut microbiome and show how the resulting resource improves metagenomic analyses. We retrieved >3,000 viral-like particles (VLP) enriched metagenomic samples (viromes), evaluated the efficiency of the enrichment in each sample to leverage the viromes of highest purity, and applied multiple analysis steps involving assembly and comparison with hundreds of thousands of metagenome-assembled genomes to discover new viral genomes. We reported over 162,000 viral sequences passing quality control from thousands of gut metagenomes and viromes. The great majority of the retrieved viral sequences ([~]94.4%) were of unknown origin, most had a CRISPR spacer matching host bacteria, and four of them could be detected in >50% of a set of 18,756 gut metagenomes we surveyed. We included the obtained collection of sequences in a new MetaPhlAn 4.1 release, which can quantify reads within a metagenome matching the known and newly uncovered viral diversity. Additionally, we released the viral database for further virome and metagenomic studies of the human microbiome.

microbiology↗

bioRxiv2023

Identification and targeting of microbial putrescine acetylation in bloodstream infections

The growth of antimicrobial resistance (AMR) has highlighted an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe bacterial infections profoundly alter host metabolism, prior studies have largely ignored alterations in microbial metabolism in this context. Performing metabolomics on patient and mouse plasma samples, we identify elevated levels of bacterially-derived N- acetylputrescine during gram-negative bloodstream infections (BSI), with higher levels associated with worse clinical outcomes. We discover that SpeG is the bacterial enzyme responsible for acetylating putrescine and show that blocking its activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity enhances bacterial membrane permeability and results in increased intracellular accumulation of antibiotics, allowing us to overcome AMR of clinical isolates both in culture and in vivo. This study highlights how studying pathogen metabolism in the natural context of infection can reveal new therapeutic strategies for addressing challenging infections.

microbiology↗

bioRxiv2023

Profiling novel lateral gene transfer events in the human microbiome

Lateral gene transfer (LGT) is an important mechanism for genome diversification in microbial populations, including the human microbiome. While prior work has surveyed LGT events in human-associated microbial isolate genomes, the scope and dynamics of novel LGT events arising in personal microbiomes are not well understood, as there are no widely adopted computational methods to detect, quantify, and characterize LGT from complex microbial communities. We addressed this by developing, benchmarking, and experimentally validating a computational method (WAAFLE) to profile novel LGT events from assembled metagenomes. Applying WAAFLE to >2K human metagenomes from diverse body sites, we identified >100K putative high-confidence but previously uncharacterized LGT events ([~]2 per assembled microbial genome-equivalent). These events were enriched for mobile elements (as expected), as well as restriction-modification and transport functions typically associated with the destruction of foreign DNA. LGT frequency was quantifiably influenced by biogeography, the phylogenetic similarity of the involved taxa, and the ecological abundance of the donor taxon. These forces manifest as LGT networks in which hub species abundant in a community type donate unequally with their close phylogenetic neighbors. Our findings suggest that LGT may be a more ubiquitous process in the human microbiome than previously described. The open-source WAAFLE implementation, documentation, and data from this work are available at http://huttenhower.sph.harvard.edu/waafle.

bioinformatics↗

bioRxiv2022

Extending and improving metagenomic taxonomic profiling with uncharacterized species with MetaPhlAn 4

Metagenomic assembly enables novel organism discovery from microbial communities, but from most metagenomes it can only capture few abundant organisms. Here, we present a method - MetaPhlAn 4 - to integrate information from both metagenome assemblies and microbial isolate genomes for improved and more comprehensive metagenomic taxonomic profiling. From a curated collection of 1.01M prokaryotic reference and metagenome-assembled genomes, we defined unique marker genes for 26,970 species-level genome bins, 4,992 of them taxonomically unidentified at the species level. MetaPhlAn 4 explains [~]20% more reads in most international human gut microbiomes and >40% in less-characterized environments such as the rumen microbiome, and proved more accurate than available alternatives on synthetic evaluations while also reliably quantifying organisms with no cultured isolates. Application of the method to >24,500 metagenomes highlighted previously undetected species to be strong biomarkers for host conditions and lifestyles in human and mice microbiomes, and showed that even previously uncharacterized species can be genetically profiled at the resolution of single microbial strains. MetaPhlAn 4 thus integrates the novelty of metagenomic assemblies with the sensitivity and fidelity of reference-based analyses, providing efficient metagenomic profiling of uncharacterized species and enabling deeper and more comprehensive microbiome biomarker detection.

bioinformatics↗

bioRxiv2022

Human threat learning is associated with gut microbiota composition

Rodent studies have shown that the gut microbiota can influence threat and safety learning, which has been linked to anxiety phenotypes. In humans, it has been demonstrated that microbiota composition varies with anxiety disorders, but evidence showing an association with threat learning is lacking. Here, we tested whether individual variability in threat and safety learning was related to gut microbiota composition in healthy adults. We found that threat, but not safety learning varies with individuals microbiome composition. Our results provide evidence that the gut microbiota is associated with excitatory threat learning across species. Significance StatementLearning from threats and safety is a core mechanism of anxiety disorders, and studies in rodent models have shown that the gut microbiota can modulate such behaviors. Although previous literature on humans shows a relationship between emotional circuits and gut microbiota, the evidence linking learning and microbiota is lacking. In a Pavlovian threat conditioning paradigm, we show that patterns of gut microbiota composition in healthy humans relate to their patterns of threat learning, but not safety learning. Our findings suggest one mechanism by which the human gut microbiota is associated with anxiety-related behaviors.

animal behavior and cognition↗

bioRxiv2021

High-sensitivity pattern discovery in large, paired multi-omic datasets

Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features is essential. Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets. HAllA efficiently integrates hierarchical hypothesis testing with false discovery rate correction to reveal significant linear and non-linear block-wise relationships among continuous and/or categorical data. We optimized and evaluated HAllA using heterogeneous synthetic datasets of known association structure, where HAllA outperformed all-against-all and other block testing approaches across a range of common similarity measures. We then applied HAllA to a series of real-world multi-omics datasets, revealing new associations between gene expression and host immune activity, the microbiome and host transcriptome, metabolomic profiling, and human health phenotypes. An open-source implementation of HAllA is freely available at http://huttenhower.sph.harvard.edu/halla along with documentation, demo datasets, and a user group. Author SummaryModern scientific datasets increasingly include multiple measurements of many complementary data types. Here, we present HAllA, a method and implementation that overcomes the statistical challenges presented by data of this type by using feature similarity within each dataset to find statistically significant groups of features between them. We applied HAllA to simulated and real datasets, showing that HAllA outperformed existing procedures and identified compelling biological relationships. HAllA is widely applicable to diverse data structures and presents the user with grouped results that are easier to interpret than traditional methods.

bioinformatics↗