Biology subjects

Minot, S. S.

Publications and source records attributed to Minot, S. S..

8 recordsLinked to original sources

bioRxiv2025

The type VI secretion system governs strain maintenance in a wild mammalian gut microbiome

Bacteria inhabiting the mammalian gut coexist in dense communities where contact-dependent antagonism mechanisms are widespread. The type VI secretion system (T6SS) is an interbacterial toxin delivery pathway prevalent among gut Bacteroidales, yet its function in naturally evolved microbiomes remains poorly defined. Here, we examine the role of the T6SS in Bacteroides within a physiologically relevant gut community derived from wild mice (the WildR microbiome). Using newly developed genetic tools and a strategy for functional replacement of strains within the WildR community, we demonstrate that the WildR isolate B. acidifaciens employs a T6SS to antagonize co-resident Bacteroidales. We also show that loss of T6SS function compromises the long-term maintenance of B. acidifaciens in the community but not its initial colonization, establishing the system as a determinant of strain persistence. The T6SS we identified resides on an integrative and conjugative element (ICE). ICE-seq, a targeted sequencing approach, reveals that the T6SS-ICE is distributed among select Bacteroidales and Muribaculaceae species in the WildR microbiome, between which it appears to be recently exchanged. We also show that transfer of the T6SS-ICE to WildR isolate Phocaeicola vulgatus confers transient colonization benefits in mice, but is linked to eventual population decline. Our findings demonstrate that the T6SS can stabilize the presence of specific strains within a complex, co-evolved gut microbiome, yet its value is context dependent and constrained by the ecological and physiological landscape of the host community.

microbiology↗

bioRxiv2025

Community assembly modeling of microbial evolution within Barrett's esophagus and esophageal adenocarcinoma

Mathematical modeling of somatic evolution, a process impacting both host cells and microbial communities in the human body, can capture important dynamics driving carcinogenesis. Here we considered models for esophageal adenocarcinoma (EAC), a cancer that has dramatically increased in incidence over the past few decades in Western populations, with high case fatality rates due to late-stage diagnoses. Despite advancements in genomic analyses of the precursor Barretts esophagus (BE), prevention of late-stage EAC remains a significant clinical challenge. Previous microbiome studies in BE and EAC have focused on quantifying static microbial abundance differences rather than evolutionary dynamics. Using whole genome sequencing data from esophageal tissues, we first applied a robust bioinformatics pipeline to extract non-host DNA reads, mapped these putative reads to microbial taxa, and retained those taxa with high genomic coverage. When applying mathematical models of microbial evolution to sequential stages of progression to EAC, we observed evidence of neutral dynamics in community assembly within normal esophageal tissue and BE, but not EAC. In a case-control study of BE patients who progressed to EAC cancer outcomes (CO) versus those who had non-cancer outcomes (NCO) during follow-up (mean=10.5 years), we found that Helicobacter pylori deviated significantly from the neutral expectation in BE NCO, suggesting that factors related to H. pylori or H. pylori infection itself may influence EAC risk. Additionally, simulations incorporating selection recapitulated non-neutral behaviors observed in the datasets. Formally modeling dynamics during progression holds promise in clinical applications by offering a deeper understanding of microbial involvement in cancer development.

cancer biology↗

bioRxiv2023

Fusobacterium sphaericum sp. nov., isolated from a human colon tumor, is prevalent in various human body sites and induces IL-8 secretion from colorectal cancer cells

Cancerous tissue is a largely unexplored microbial niche that provides a unique environment for the colonization and growth of specific bacterial communities, and with it, the opportunity to identify novel bacterial species. Here, we report distinct features of a novel Fusobacterium species, F. sphaericum sp. nov. (Fs), isolated from primary colon adenocarcinoma tissue. We acquire the complete closed genome and associated methylome of this organism and phylogenetically confirm its classification into the Fusobacterium genus, with F. perfoetens as its closest neighbor. Fs is phenotypically and genetically distinct, with morphological analysis revealing its coccoid shape, that while similar to F. perfoetens is rare for most Fusobacterium members. Fs displays a metabolic profile and antibiotic resistance repertoire consistent with other Fusobacterium species. In vitro, Fs has adherent and immunomodulatory capabilities, as it intimately associates with human colon cancer epithelial cells and promotes IL-8 secretion. Analysis of the prevalence and abundance of Fs in >20,000 human metagenomic samples shows that it is a low-prevalence member within human stool with variable relative abundance, found in both healthy controls and patients with colorectal cancer (CRC). Our study sheds light on a novel bacterial species isolated directly from the human CRC tumor niche, and given its interaction with cancer epithelial cells suggests that its role in human health and disease warrants further investigation.

microbiology↗

bioRxiv2022

Robust Harmonization of Microbiome Studies by Phylogenetic Scaffolding with MaLiAmPi

Microbiome science is difficult to translate back to patients due to an inability to harmonize 16S rRNA gene-based microbiome data, as differences in the technique will result in different amplicon sequence variants (ASV) from the same microbe. Here we demonstrate that placement of ASV onto a common phylogenetic tree of full-length 16S rRNA alleles can harmonize microbiome studies. Using in silico data approximating 100 healthy human stool microbiomes we demonstrated that phylogenetic placement of ASV can recapitulate the true relationships between communities as compared closed-OTU based approaches (Spearman R 0.8 vs 0.2). Using real data from thousands of human gut and vaginal microbiota, we demonstrate phylogenetic placement, but not closed OTUs, were able to group communities by origin (stool vs vaginal) without being confounded by technique and integrate new data into existing ordination/clustering models for precision medicine. This enables meta-analysis of microbiome studies and the microbiome as a biomarker.

bioinformatics↗

bioRxiv2021

A Co-abundant Gene Analysis for Identification of Gut Microbial Strains Associated with Immune Responses to Oral Cholera Vaccine

The degree of protection conferred after receiving an oral cholera vaccine (OCV) varies based on age, prior exposure to Vibrio cholerae, and unknown factors. Recent evidence suggests that the microbiota may mediate some of the unexplained differences in oral vaccine responses. We used metagenomic sequencing of the microbiota at the time of vaccination, and then related microbial features to immune responses after OCV using a reference-independent gene-level analysis. We found that the presence of sphingolipid-producing bacteria was associated with the development of protective immune responses after OCV. We experimentally tested these results by stimulating human macrophages with Bacteroides xylanisolvens metabolites and found that sphingolipid-containing extracts increased innate immune responses to OCV antigens. Our findings demonstrate a new analytic method for translating metagenomic sequencing data into strain-specific results associated with a biological outcome, and in validating this tool, we identified that microbe-derived sphingolipids impact in vitro immune responses to OCV antigens.

microbiology↗

bioRxiv2020

Gene-level metagenomics identifies genome islands associated with immunotherapy response

Researchers must be able to generate experimentally testable hypotheses from sequencing-based observational microbiome experiments to discover the mechanisms underlying the influence of gut microbes on human health. We describe a novel bioinformatics tool for identifying testable hypotheses based on gene-level metagenomic analysis of WGS microbiome data (geneshot). By applying geneshot to two independent previously published cohorts, we identified microbial genomic islands consistently associated with response to immune checkpoint inhibitor (ICI)-based cancer treatment in culturable type strains. The identified genomic islands are within operons involved in type II secretion, TonB-dependent transport, and bacteriophage growth. These results, as well as the underlying methodology, inform further mechanistic studies and facilitate the development of microbiome-enhanced therapeutics.

bioinformatics↗

bioRxiv2019

Clustering co-abundant genes identifies components of the gut microbiome that are reproducibly associated with colorectal cancer and inflammatory bowel disease

BackgroundWhole-genome \"shotgun\" (WGS) metagenomic sequencing is an increasingly widely used tool for analyzing the metagenomic content of microbiome samples. While WGS data contains gene-level information, it can be challenging to analyze the millions of microbial genes which are typically found in microbiome experiments. To mitigate the ultrahigh dimensionality challenge of gene-level metagenomics, it has been proposed to cluster genes by co-abundance to form Co-Abundant Gene groups (CAGs). However, exhaustive co-abundance clustering of millions of microbial genes across thousands of biological samples has previously been intractable purely due to the computational challenge of performing trillions of pairwise comparisons.\n\nResultsHere we present a novel computational approach to the analysis of WGS datasets in which microbial gene groups are the fundamental unit of analysis. We use the Approximate Nearest Neighbor heuristic for near-exhaustive average linkage clustering to group millions of genes by co-abundance. This results in thousands of high-quality CAGs representing complete and partial microbial genomes. We applied this method to publicly available WGS microbiome surveys and found that the resulting microbial CAGs associated with inflammatory bowel disease (IBD) and colorectal cancer (CRC) were highly reproducible and could be validated independently using multiple independent cohorts.\n\nConclusionsThis powerful approach to gene-level metagenomics provides a powerful path forward for identifying the biological links between the microbiome and human health. By proposing a new computational approach for handling high dimensional metagenomics data, we identified specific microbial gene groups that are associated with disease that can be used to identify strains of interest for further preclinical and mechanistic experimentation.

bioinformatics↗

bioRxiv2018

Functional Analysis of Metagenomes by Likelihood Inference (FAMLI) Successfully Compensates for Multi-Mapping Short Reads from Metagenomic Samples

High-throughput sequencing can establish the functional capacity of a microbial community by cataloging the protein-coding sequences (CDS) present in the metagenome of the community. The relative performance of different computational methods for identifying CDS from whole-genome shotgun sequencing (WGS) is not fully established.\n\nHere we present an automated benchmarking workflow, using synthetic shotgun sequencing reads for which we know the true CDS content of the underlying communities, to determine the relative performance (sensitivity, positive predictive value or PPV, and computational efficiency) of different metagenome analysis tools for extracting the CDS content of a microbial community.\n\nAssembly-based methods are limited by coverage depth, with poor sensitivity for CDS at < 5X depth of sequencing, but have excellent PPV. Mapping-based techniques are more sensitive at low coverage depths, but can struggle with PPV. We additionally describe an expectation maximization based iterative algorithmic approach which we show to successfully improve the PPV of a mapping based technique while retaining improved sensitivity and computational efficiency.

bioinformatics↗