Biology subjects

Franzosa, E. A.

Publications and source records attributed to Franzosa, E. A..

15 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

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

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

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↗

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↗

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↗

bioRxiv2021

A Statistical Model for Describing and Simulating Microbial Community Profiles

Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, nonindependence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSAs model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbemicrobe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa") or between features and "phenotypes" to be simulated for method benchmarking. Here, we demonstrate SparseDOSSAs performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSAs utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.

bioinformatics↗

bioRxiv2021

{-}Multivariable Association Discovery in Population-scale Meta-omics Studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses general linear models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g. counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2s linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel disease (IBD) across multiple time points and omics profiles.

microbiology↗

bioRxiv2021

Human gut bacteria produce TH17-modulating bile acid metabolites

The microbiota plays a pivotal role in gut immune homeostasis. Bacteria influence the development and function of host immune cells, including T helper cells expressing interleukin-17a (TH17 cells). We previously reported that the bile acid metabolite 3-oxolithocholic acid (3-oxoLCA) inhibits TH17 cell differentiation1. While it was suggested that gut-residing bacteria produce 3-oxoLCA, the identity of such bacteria was unknown. Furthermore, it was not clear whether 3-oxoLCA and other immunomodulatory bile acids are associated with gut inflammatory pathologies in humans. Using a high-throughput screen, we identified human gut bacteria and corresponding enzymes that convert the secondary bile acid lithocholic acid into 3-oxoLCA as well as the abundant gut metabolite isolithocholic acid (isoLCA). Like 3-oxoLCA, isoLCA suppressed TH17 differentiation by inhibiting ROR{gamma}t (retinoic acid receptor-related orphan nuclear receptor {gamma}t), a key TH17 cell-promoting transcription factor. Levels of both 3-oxoLCA and isoLCA and the 3-hydroxysteroid dehydrogenase (3-HSDH) genes required for their biosynthesis were significantly reduced in patients with inflammatory bowel diseases (IBD). Moreover, levels of these bile acids were inversely correlated with expression of TH17 cell-associated genes. Overall, our data suggest that bacterially produced TH17 cell-inhibitory bile acids may reduce the risk of autoimmune and inflammatory disorders such as IBD.

immunology↗

bioRxiv2021

A Bacterial Bile Acid Metabolite Modulates Treg Activity through the Nuclear Hormone Receptor NR4A1

Bile acids act as signaling molecules that regulate immune homeostasis, including the differentiation of CD4+ T cells into distinct T cell subsets. The bile acid metabolite isoallolithocholic acid (isoalloLCA) enhances the differentiation of anti-inflammatory regulatory T cells (Treg cells) by facilitating the formation of a permissive chromatin structure in the promoter region of the transcription factor forkhead box P3 (Foxp3). Here, we identify gut bacteria that synthesize isoalloLCA from 3-oxolithocholic acid and uncover a gene cluster responsible for the conversion in members of the abundant human gut bacterial phylum Bacteroidetes. We also show that the nuclear hormone receptor NR4A1 is required for the effect of isoalloLCA on Treg cells. Moreover, the levels of isoalloLCA and its biosynthetic genes are significantly reduced in patients with inflammatory bowel diseases, suggesting that isoalloLCA and its bacterial producers may play a critical role in maintaining immune homeostasis in humans.

immunology↗

bioRxiv2020

Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3

Culture-independent analyses of microbial communities have advanced dramatically in the last decade, particularly due to advances in methods for biological profiling via shotgun metagenomics. Opportunities for improvement continue to accelerate, with greater access to multi-omics, microbial reference genomes, and strain-level diversity. To leverage these, we present bioBakery 3, a set of integrated, improved methods for taxonomic, strain-level, functional, and phylogenetic profiling of metagenomes newly developed to build on the largest set of reference sequences now available. Compared to current alternatives, MetaPhlAn 3 increases the accuracy of taxonomic profiling, and HUMAnN 3 improves that of functional potential and activity. These methods detected novel disease-microbiome links in applications to CRC (1,262 metagenomes) and IBD (1,635 metagenomes and 817 metatranscriptomes). Strain-level profiling of an additional 4,077 metagenomes with StrainPhlAn 3 and PanPhlAn 3 unraveled the phylogenetic and functional structure of the common gut microbe Ruminococcus bromii, previously described by only 15 isolate genomes. With open-source implementations and cloud-deployable reproducible workflows, the bioBakery 3 platform can help researchers deepen the resolution, scale, and accuracy of multi-omic profiling for microbial community studies.

microbiology↗