Strain-resolved transcriptomics: exploring functional heterogeneity of the gut microbiota in health and disease
Name
burgos robles-eburgos-sm-csb-2025-thesis.pdf
Description
Thesis PDF
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1.21 MB
Format
Adobe PDF
Checksum (MD5)
c12c3063a0562713cf0dbc0b9d0eefd8
Author(s)
Burgos Robles, Emanuel Felipe
Advisor(s)
Smillie, Chris S.
Alm, Eric
Date Issued
September 2025
Publisher
Massachusetts Institute of Technology
Abstract
The gut microbiome plays a critical role in inflammatory bowel diseases (IBDs), yet current analyses treat bacterial species as functionally uniform, ignoring extensive strain-level diversity that may drive disease mechanisms. Here, we developed a strain-resolved metatranscriptomics framework to investigate how transcriptional activity varies across bacterial lineages and relates to IBD pathogenesis. Using paired metagenomics and metatranscriptomics data from 1,067 fecal samples (103 IBD and 335 non-IBD patients), we first constructed phylogenetic trees for over 250 bacterial species using the single nucleotide variants within essential housekeeping genes, enabling the identification of bacterial strains. Next, we devised a statistical approach to assign mRNA reads to these strains, leveraging the natural genetic variation that is present across them. My analysis revealed that closely related bacterial strains exhibit dramatically different transcriptional programs, with some strains enriched in IBD patients showing upregulation of genes involved in stress response, sugar metabolism pathways, and antimicrobial resistance. Notably, we identified transcriptionally active but genomically low-abundance taxa, highlighting the importance of measuring the transcriptional activities of strains beyond species composition. Lineage-aware differential expression analysis uncovered strain-specific adaptations to inflammatory environments. This strain-resolved approach provides a powerful framework for understanding microbial functional heterogeneity and identifying specific bacterial lineages that could potentially contribute to disease pathogenesis, potentially guiding more targeted microbiome-based therapeutic interventions.
MIT Department
Massachusetts Institute of Technology. Computational and Systems Biology Program
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