Functional Modeling of the Gut Microbiome Reveals Systemic Disease Associated Signatures Related To Extracellular Protea

CSEF · 2026 Computational Science (Senior Division)

Overview

Globally, systemic diseases like cirrhosis, ankylosing spondylitis (AS), and hypertension affect over a billion people, yet their detection depends on invasive procedures or late-stage imaging. Although dysregulated extracellular protease (ECP) activity is linked to tissue inflammation, its potential as a non-invasive biomarker for such diseases remains unexplored. Current research relies on taxonomic composition for assessing host health. Here, I introduce a novel framework for evaluating the gut microbiome based on its ECP capacity, challenging this purely taxonomic approach. I developed a high-confidence functional ECP catalog from 116 bacterial species by screening genomic data for catalytic domains and secretion signals. Using this, I engineered a scoring metric, Community Proteolytic Potential (CPP), weighting CLR-normalized abundances with these protease profiles to quantify a microbial community's functional ECP imbalance. I applied CPP to stool metagenomic data from healthy individuals and three distinct patient cohorts: cirrhosis, AS, and hypertension. My analysis revealed that Serine and Metallo ECP guilds drive consistent shifts across all cohorts (adjusted p-values <0.05). This suggests that these clinically distinct diseases share a common mechanism of abnormally high proteolytic activity. CPP divergence scores produced large disease-control separation with rank-biserial correlations (effect sizes) from 0.41 to 0.62. Furthermore, supervised classifiers trained on CPP features outperformed taxonomic models in 15 cross-validation trials across all three diseases, achieving an average ~10% increase in AUC-ROC scores. These findings suggest functional protease signatures extend beyond the intestine and may inform the development of stool-based diagnostics for tracking systemic disease progression and treatment response.

Competition history

  • CSEF 2026 Computational Science (Senior Division) · Entry S-07-06

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