Biology subjects

Noecker, C.

Publications and source records attributed to Noecker, C..

12 recordsLinked to original sources

bioRxiv2026

Eggerthella lenta evades bacteriophage through reversible megabase-scale inversions of capsular polysaccharide gene clusters

Bacteriophages are a promising tool for microbiome editing, yet their development has been constrained by limited insights into bacteriophage-host interactions within their shared mammalian body habitat. We isolated a lytic phage {Phi}KL11 that efficiently targets a disease-associated member of the human gut microbiota, Eggerthella lenta, during in vitro growth. However, {Phi}KL11 selects for a pre-existing and reversible bacteriophage-resistant sub-population in mice. Long-read sequencing revealed a massive genomic inversion event, representing >50% of the E. lenta genome, enriched in response to bacteriophage infection. Transcriptomics linked this inversion to the altered expression of three capsular polysaccharide synthesis (CPS) gene clusters and transmission electron microscopy confirmed differential capsule production. Finally, we show that {Phi}KL11 has a broad host range attributable to CPS and other strain-variable genes. These findings suggest a previously unrecognized strategy for phage evasion in the gut, involving megabase-scale genomic inversions and reversible capsule variation driving phage resistance.

microbiology↗

bioRxiv2026

A widespread gut bacterial lineage distinguished by redox metabolism and phage defense

Genomic variation within gut microbial species can have consequences for host health and disease. However, for low abundance species, these variations can be difficult to capture by both culture-dependent and -independent approaches. Here, we focus on the prevalent but low abundance gut Actinomycetota Eggerthella lenta. We developed a selective media for sensitive and specific isolation of E. lenta from human stool. Genomes from 87 new E. lenta isolates were combined with prior high-quality assemblies, shedding light on within-species functional diversity. Phylogenetic analysis revealed a broadly distributed subclade, which we refer to as E. lenta Group B. This lineage was differentiated by its metabolic potential and bacteriophage defense, though mobile elements were shared broadly across the species. Notably, Group B was positively associated with intestinal inflammation in subjects with inflammatory bowel disease. Overall, these results emphasize the importance of bacterial population structure in host-microbiome interactions and provide a framework to study low-abundance gut taxa. HIGHLIGHTSO_LISelective media enables E. lenta isolation and reveals high prevalence in humans C_LIO_LIDiscovery of a distinctive lineage within E. lenta undergoing genome reduction C_LIO_LIE. lenta Group B has altered metabolism, phage defense, and disease associations C_LIO_LIA widespread conjugative plasmid could enable improved genetics C_LI

microbiology↗

bioRxiv2026

Female-enriched Eggerthella lenta drives neuroinflammation and IFN-γ via host receptor TLR2

Women are at increased risk of autoimmune diseases, including multiple sclerosis (MS); however, the degree to which sex differences in the gut microbiota impact autoimmunity remains largely unexplored. Our 27-cohort meta-analysis revealed 60 sex-associated gut bacterial species. Leveraging an independent clinical cohort, we demonstrate that female-enriched species significantly associate with MS status and clinical disability (EDSS). Top female-enriched species Eggerthella lenta drove disease in the experimental autoimmune encephalomyelitis (EAE) MS model, consistent with brain and gut lamina propria T cell infiltration and MS-associated T helper (Th) signatures. E. lenta induced intestinal Th1 and Th17 in healthy mice, independent of bacterial viability. Mechanistically, we demonstrate that TLR2 directly drives E. lenta-induced IFN-{gamma} production in Th cells and is necessary for exacerbation of EAE. Together, we identify a causal host-microbe axis contributing to sex differences in autoimmunity and provide a framework for evaluating sex as a biological variable in human microbiome research. HIGHLIGHTSO_LI27-cohort meta-analysis identifies a robust sex-signature in human gut microbiota. C_LIO_LIFemale-enriched species are associated with MS risk and severity. C_LIO_LIFemale-enriched Eggerthella lenta exacerbates the EAE model. C_LIO_LIE. lenta impacts neuroinflammation via toll-like receptor 2. C_LI

microbiology↗

bioRxiv2025

Shifts in the human gut microbiome during cancer chemotherapy are diet-dependent

Numerous studies have implicated both dietary intake and the human gut microbiome in colorectal cancer (CRC) treatment outcomes. However, little is known about how patients adjust their dietary intake during cancer chemotherapy or if these dietary changes contribute to treatment-associated alterations in the gut microbiome. We performed paired longitudinal diet and microbiome analysis during CRC treatment with oral fluoropyrimidines (NCT04054908) and validated key associations using cell culture assays. At each timepoint, diet was measured by averaging up to 3 consecutive days of 24-hour dietary records (NCI ASA24; 35 patients; 2.5{+/-}1.1 timepoints per patient), while microbiome composition was measured from stool (16S rRNA gene sequencing, metagenomics, qPCR; 40 patients; 5.6{+/-}1.4 timepoints per patient). Diet quality significantly decreased during chemotherapy. Carbohydrate and refined grain intake increased, accompanied by decreased consumption of fats, nuts and seeds, and fat-soluble micronutrients. Multiple individual dietary components were strongly linked to the gut microbiome. Decreases in theobromine intake correlated with decreases in overall microbial diversity and more gastrointestinal toxicities. Diet shifts partly explained changes in bacterial abundance during chemotherapy, including more severe depletion of Faecalibacterium prausnitzii in patients with decreased vitamin K1 intake. Changes in diet were correlated with multiple bacterial gene families involved in micronutrient metabolism and drug sensitivity. Increased copper intake was linked to decreased Fusobacterium nucleatum in patients and inhibited F. nucleatum in cell assays. Together, these data suggest that changes in diet during chemotherapy contribute to changes in gut bacterial diversity, taxonomic composition, and gene abundance. Our approach may generalize to other cancer therapies and emphasizes the need for collecting more robust dietary data in clinical microbiome studies.

microbiology↗

bioRxiv2024

Coordinated variation in the immune systems and microbiomes of healthy humans is linked to tonic interferon states

Human immune systems are highly variable, with most variation attributable to non-genetic sources. The gut microbiome crucially shapes the immune system; however, its relationship with the baseline immune states of healthy humans remains incompletely understood. Therefore, we performed multi-omic profiling of 110 healthy participants through the ImmunoMicrobiome study. A factor-based integrative approach identified coordinated variation, revealing that the tonic interferon response was amongst the most variable immune features in healthy participants. Microbiome composition, pathways, and stool metabolites varied concomitantly with interferon response pathways. Distinct transcriptional programs involving inflammation and TGF-{beta} in SIGLEC-1high monocytes and CD69high activated MAIT and NK cells were representative of these programs. Our study provides extensive data to examine the relationship between the immune states and microbiomes of healthy individuals at steady state, which paves the way for delineating inter-individual differences relevant for disease susceptibility and responses to therapy.

immunology↗

bioRxiv2024

Expansion of a bacterial operon during cancer treatment ameliorates drug toxicity

Dose-limiting toxicities remain a major barrier to drug development and therapy, revealing the limited predictive power of human genetics. Herein, we demonstrate the utility of a more comprehensive approach to studying drug toxicity through longitudinal study of the human gut microbiome during colorectal cancer (CRC) treatment (NCT04054908) coupled to cell culture and mouse experiments. 16S rRNA gene sequencing revealed significant shifts in gut microbial community structure during oral fluoropyrimidine treatment across multiple patient cohorts, in mouse small and large intestinal contents, and in patient-derived ex vivo communities. Metagenomic sequencing revealed marked shifts in pyrimidine-related gene abundance during oral fluoropyrimidine treatment, including enrichment of the preTA operon, which is sufficient for the inactivation of active metabolite 5-fluorouracil (5-FU). preTA+ bacteria depleted 5-FU in gut microbiota grown ex vivo and the mouse distal gut. Germ-free and antibiotic-treated mice experienced increased fluoropyrimidine toxicity, which was rescued by colonization with the mouse gut microbiota, preTA+ E. coli, or preTA-high CRC patient stool. Finally, preTA abundance was negatively associated with fluoropyrimidine toxicity in patients. Together, these data support a causal, clinically relevant interaction between a human gut bacterial operon and the dose-limiting side effects of cancer treatment. Our approach is generalizable to other drugs, including cancer immunotherapies, and provides valuable insights into host-microbiome interactions in the context of disease. One Sentence Summary: Gut microbial enzymes can be used to predict and prevent anticancer drug toxicity.

systems biology↗

bioRxiv2023

Microbiome single cell atlases generated with a commercial instrument

Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters genome by sequence similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.

bioengineering↗

bioRxiv2022

Mild SARS-CoV-2 infection results in long-lasting microbiota instability

Viruses targeting mammalian cells can indirectly alter the gut microbiota, potentially compounding their phenotypic effects. Multiple studies have observed a disrupted gut microbiota in severe cases of SARS-CoV-2 infection that require hospitalization. Yet, despite demographic shifts in disease severity resulting in a large and continuing burden of non-hospitalized infections, we still know very little about the impact of mild SARS-CoV-2 infection on the gut microbiota in the outpatient setting. To address this knowledge gap, we longitudinally sampled 14 SARS-CoV-2 positive subjects who remained outpatient and 4 household controls. SARS-CoV-2 cases exhibited a significantly less stable gut microbiota relative to controls, as long as 154 days after their positive test. These results were confirmed and extended in the K18-hACE2 mouse model, which is susceptible to SARS-CoV-2 infection. All of the tested SARS-CoV-2 variants significantly disrupted the mouse gut microbiota, including USA-WA1/2020 (the original variant detected in the United States), Delta, and Omicron. Surprisingly, despite the fact that the Omicron variant caused the least severe symptoms in mice, it destabilized the gut microbiota and led to a significant depletion in Akkermansia muciniphila. Furthermore, exposure of wild-type C57BL/6J mice to SARS-CoV-2 disrupted the gut microbiota in the absence of severe lung pathology. IMPORTANCETaken together, our results demonstrate that even mild cases of SARS-CoV-2 can disrupt gut microbial ecology. Our findings in non-hospitalized individuals are consistent with studies of hospitalized patients, in that reproducible shifts in gut microbial taxonomic abundance in response to SARS-CoV-2 have been difficult to identify. Instead, we report a long-lasting instability in the gut microbiota. Surprisingly, our mouse experiments revealed an impact of the Omicron variant, despite producing the least severe symptoms in genetically susceptible mice, suggesting that despite the continued evolution of SARS-CoV-2 it has retained its ability to perturb the intestinal mucosa. These results will hopefully renew efforts to study the mechanisms through which Omicron and future SARS-CoV-2 variants alter gastrointestinal physiology, while also considering the potentially broad consequences of SARS-CoV-2-induced microbiota instability for host health and disease.

microbiology↗

bioRxiv2022

Systems biology illuminates alternative metabolic niches in the human gut microbiome

Human gut bacteria perform diverse metabolic functions with consequences for host health. The prevalent and disease-linked Actinobacterium Eggerthella lenta performs several unusual chemical transformations, but it does not metabolize sugars and its core growth strategy remains unclear. To obtain a comprehensive view of the metabolic network of E. lenta, we generated several complementary resources: defined culture media, metabolomics profiles of strain isolates, and a curated genome-scale metabolic reconstruction. Stable isotope-resolved metabolomics revealed that E. lenta uses acetate as a key carbon source while catabolizing arginine to generate ATP, traits which could be recapitulated in silico by our updated metabolic model. We compared these in vitro findings with metabolite shifts observed in E. lenta-colonized gnotobiotic mice, identifying shared signatures across environments and highlighting catabolism of the host signaling metabolite agmatine as an alternative energy pathway. Together, our results elucidate a distinctive metabolic niche filled by E. lenta in the gut ecosystem.

microbiology↗

bioRxiv2021

MIMOSA2: A metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data

MotivationRecent technological developments have facilitated an expansion of microbiome-metabolome studies, in which a set of microbiome samples are assayed using both genomic and metabolomic technologies to characterize the composition of microbial taxa and the concentrations of various metabolites. A common goal of many of these studies is to identify microbial features (species or genes) that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of such microbe-metabolite links. ResultsWe present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and specific taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and can be customized to incorporate user-defined metabolic pathways. We demonstrate MIMOSA2s ability to identify ground truth microbial mechanisms in simulation datasets, and compare its results with experimentally inferred mechanisms in a dataset describing honeybee gut microbiota. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products. Availability and ImplementationMIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server and R package from www.borensteinlab.com/software_MIMOSA2.html.

microbiology↗

bioRxiv2018

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies

Correlation-based analysis of paired microbiome-metabolome datasets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach have not been comprehensively evaluated. To address this challenge, we introduce a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally use a multi-species metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome datasets. We then compare observed taxon-metabolite correlations in these datasets to calculated ground-truth taxonomic contribution values. We find that in simulations of both a model 10-species community and of complex human gut microbiota, correlation-based analysis poorly identifies key contributors, with extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.\n\nImportanceIdentifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step towards understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples, and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation, and then examining these contributions in simulated datasets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome datasets identifies true contributions with very low predictive value, and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

microbiology↗

bioRxiv2017

BURRITO: An interactive multi-omic tool for visualizing taxa-function relationships in microbiome data

The abundance of both taxonomic groups and gene categories in microbiome samples can now be easily assayed via various sequencing technologies, and visualized using a variety of software tools. However, the assemblage of taxa in the microbiome and its gene content are clearly linked, and tools for visualizing the relationship between these two facets of microbiome composition and for facilitating exploratory analysis of their co-variation are lacking. Here we introduce BURRITO, a web tool for interactive visualization of microbiome multi-omic data with paired taxonomic and functional information. BURRITO simultaneously visualizes the taxonomic and functional compositions of multiple samples and dynamically highlights relationships between taxa and functions to capture the underlying structure of these data. Users can browse for taxa and functions of interest and interactively explore the share of each function attributed to each taxon across samples. BURRITO supports multiple input formats for taxonomic and metagenomic data, allows adjustment of data granularity, and can export generated visualizations as static publication-ready formatted figures. In this paper, we describe the functionality of BURRITO, and provide illustrative examples of its utility for visualizing various trends in the relationship between the composition of taxa and functions in complex microbiomes.

bioinformatics↗