Biology subjects

Franzosa, E. A.

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

5 recordsLinked to original sources

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↗