Biology subjects

Quinn-Bohmann, N.

Publications and source records attributed to Quinn-Bohmann, N..

3 recordsLinked to original sources

bioRxiv2025

A data-driven modeling framework for mapping genotypes to synthetic microbial community functions

Microbial communities play a central role in transforming environments across Earth, driving both physical and chemical changes. By harnessing these capabilities, synthetic microbial communities, assembled from the bottom up, offer valuable insights into the mechanisms that govern community functions. These communities can also be tailored to produce desired outcomes, such as the synthesis of health-related metabolites or nitrogen fixation to improve plant productivity. Widely used computational models predict synthetic community functions using species abundances as inputs, making it impossible to predict the effects of species not included in the training data. We bridge this gap using a data-driven community genotype function (dCGF) model. By lifting the representation of each species to a high-dimensional genetic feature space, dCGF learns a mapping from community genetic feature matrices to community functions. We demonstrate that dCGF can accurately predict communities in a fixed environmental context that are composed in part or entirely from new species with known genetic features. In addition, dCGF facilitates the identification of species roles for a community function and hypotheses about how specific genetic features influence community functions. In sum, dCGF provides a new data-driven avenue for modeling synthetic microbial communities using genetic information, which could empower model-driven design of microbial communities.

systems biology↗

bioRxiv2024

Understanding disease-associated metabolic changes in human colon epithelial cells using iColonEpithelium metabolic reconstruction

The colon epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colon epithelium, our present study focuses on the generation of the first cell-type specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. The iColonEpithelium is also capable of performing metabolic tasks specific to the cell type. A unique transport reaction compartment has been included to allow simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We integrated single-cell RNA sequencing data from Crohns Diseases (CD) and ulcerative colitis (UC) samples with the iColonEpithelium metabolic network to predict metabolic signatures of colonocytes between CD and UC compared to healthy samples. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and our network has the potential to be integrated with gut microbiome models to explore the metabolic interactions between host and gut microbiota under various conditions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/619644v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@77e2deorg.highwire.dtl.DTLVardef@cf7de2org.highwire.dtl.DTLVardef@51c178org.highwire.dtl.DTLVardef@174e2c9_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease.

The human upper respiratory tract (URT) microbiome, like the gut microbiome, varies across individuals and between health and disease states. However, study-to-study heterogeneity in reported case-control results has made the identification of consistent and generalizable URT-disease associations difficult. In order to address this issue, we assembled 26 independent 16S amplicon sequencing data sets from case-control URT studies, with approximately 2-3 studies per respiratory condition and ten distinct conditions covering common chronic and acute respiratory diseases. We leveraged the healthy control data across studies to investigate URT associations with age, sex and geographic location, in order to isolate these associations from health and disease states. We found several robust genus-level associations, across multiple independent studies, with either health or disease status. We identified disease associations specific to a particular respiratory condition and associations general to all conditions. Ultimately, we reveal robust associations between the URT microbiome, health, and disease, which hold across multiple studies and can help guide follow-up work on potential URT microbiome diagnostics and therapeutics.

microbiology↗