Characterizing Scales of Genetic Recombination and Antibiotic Resistance in Pathogenic Bacteria Using Topological Data Analysis

TL;DR

This paper introduces topological data analysis tools to quantify and understand the scale of lateral gene transfer and antibiotic resistance spread in pathogenic bacteria, with a focus on Staphylococcus aureus and the human microbiome.

Contribution

It applies topological data analysis to characterize gene transfer scales and resistance spread, providing novel insights into bacterial evolution and resistance dynamics.

Findings

Topological methods reveal the frequency of gene transfer events.

Analysis shows the microbiome as a potential reservoir for resistance genes.

Case study on Staphylococcus aureus demonstrates the approach's effectiveness.

Abstract

Pathogenic bacteria present a large disease burden on human health. Control of these pathogens is hampered by rampant lateral gene transfer, whereby pathogenic strains may acquire genes conferring resistance to common antibiotics. Here we introduce tools from topological data analysis to characterize the frequency and scale of lateral gene transfer in bacteria, focusing on a set of pathogens of significant public health relevance. As a case study, we examine the spread of antibiotic resistance in Staphylococcus aureus. Finally, we consider the possible role of the human microbiome as a reservoir for antibiotic resistance genes.