A Microbiome-Driven Mechanistic Framework for Antimicrobial Resistance Emergence

Thesis Abstract

Microbiomes function as dynamic reservoirs and interaction milieus that influence the emergence and dissemination of antimicrobial resistance (AMR) through complex ecological and genetic exchanges, yet a coherent mechanistic framework integrating microbial community dynamics with resistance evolution remains lacking. This study aims to develop a microbiome-driven mechanistic framework for AMR emergence, integrating ecological, genetic, and functional processes to predict how microbial community structure and function modulate resistance trajectories under antimicrobial exposure. Specific objectives are (1) to characterize baseline gut and environmental microbiome compositions and resistomes in representative hosts and settings; (2) to quantify the impact of antimicrobial exposure on microbial diversity, horizontal gene transfer (HGT) potential, and resistance gene expression; (3) to identify key microbial taxa, ecological interactions, and metabolic pathways that mediate resistance emergence; (4) to develop and validate a mechanistic framework that integrates community ecology, network interactions, and gene-level dynamics to predict AMR emergence; and (5) to propose targeted intervention points to mitigate AMR evolution. The study adopts a mixed-methods design combining longitudinal observational components with laboratory-based experiments. The population includes 300 human participants at risk of AMR acquisition across hospital, community, and agricultural interfaces, complemented by environmental samples (soil, wastewater, and animal-associated microbiomes) totaling 180 samples collected over 12 months. Data collection employs multi-omics instruments 16S rRNA gene sequencing for taxonomic profiling; shotgun metagenomics and resistome sequencing for cataloguing resistance determinants; metatranscriptomics to measure active expression of resistance genes; and metabolomics to characterize functional outputs of microbial communities. In parallel, in vitro microcosm experiments simulate antimicrobial exposure across defined synthetic consortia to test causal links between community composition, HGT rates (assessed via conjugation assays and plasmid sequencing), and resistance gene activation. Clinical metadata and antibiotic usage records are gathered to correlate exposure histories with microbiome and resistome dynamics. Data analysis integrates statistical, network-based, and mechanistic modeling approaches. Descriptive statistics summarize baseline diversity and resistome metrics; longitudinal mixed-effects models quantify temporal changes in diversity, gene abundance, and expression in relation to antimicrobial exposure, adjusting for confounders. Regression analyses identify predictors of AMR emergence at the gene, mobile element, and organism levels. Co-occurrence and antibiotic resistance gene networks elucidate interaction structures that facilitate HGT and resistance proliferation, with centrality measures identifying keystone taxa and elements. Structural equation modeling tests a hypothesized pathway from ecological disturbance and metabolic shifts to increased HGT potential and resistance activation. A mechanistic framework is then formalized using a systems-d dynamics approach, integrating ecological, evolutionary, and molecular scales, and validated against independent cohorts and environmental datasets. Sensitivity analyses explore robustness to sampling bias and parameter uncertainty. Key expected findings include (i) delineation of microbiome configurations that predispose or resist AMR emergence under specific antimicrobial regimens; (ii) identification of keystone taxa and plasmid types driving HGT and resistance gene dissemination; (iii) demonstration of metabolic states and ecological interactions that modulate resistance gene expression; and (iv) a validated framework capable of predicting AMR emergence risk under varying antibiotic pressures and ecological contexts. The study anticipates that certain community states, such as high-diversity, metabolically redundant microbiomes with limited HGT drivers, will show reduced AMR emergence, whereas disturbed or low-diversity communities will exhibit amplified resistance development. Contribution to knowledge includes (a) a unified mechanistic framework bridging microbiome ecology, gene transfer dynamics, and AMR evolution; (b) methodological integration of multi-omics with ecological modeling to forecast resistance trajectories; and (c) evidence-based intervention targets—such as stewardship practices and microbiome-modulating strategies—that can mitigate AMR emergence. The main conclusion is that AMR emergence is an emergent property of coupled ecological and molecular processes within microbiomes, modulated by antimicrobial pressures and network topology; recommendations emphasize tailored antibiotic stewardship, microbiome-preserving practices, and environmental controls to disrupt critical pathways of resistance evolution.