Wang, M. A. | Search bioRxiv

bioRxiv2026

Vancomycin promotes key microbiota-pathogen interactions and removes protective bottlenecks to enteric infection

Antibiotic exposure disrupts enteric pathogen colonization resistance, yet how antibiotics reshape pathogen population dynamics, infection bottlenecks, and strain-level heterogeneity in the gut remains poorly understood. Here, we combine high-resolution pathogen barcoding, transcriptomic, and metabolomic analyses to quantify how short-term vancomycin perturbation alters infection ecology in vivo. We use Citrobacter rodentium as a model for human infection by pathogenic Escherichia coli-an antimicrobial resistance priority group-to demonstrate that just two days of vancomycin pre-treatment profoundly reshapes infection trajectories, driving rapid, global gut colonization, a dramatic increase in pathogen founding population size, and preservation of strain diversity across intestinal sites. Notably, vancomycin eliminated the hallmark heterogeneity of C. rodentium infection, resulting in fully reproducible colonization across hosts. Population-level analysis revealed that antibiotic treatment relaxes competitive constraints both with the resident microbiota and among clonal pathogen lineages, allowing early-established founders to persist and expand. Despite accelerated pathogen engraftment and tissue pathology, transcriptomic analysis revealed reduced virulence gene expression. Instead, antibiotic-induced metabolic restructuring of the gut created permissive conditions for pathogen expansion. Interactions with a vancomycin-altered microbiota, dominated by Akkermansia and Bacteroides, further promoted nutrient cross-feeding and influenced epithelial attachment. Together, we illustrate how short-term antibiotic exposure reshapes enteric infection by removing ecological bottlenecks that normally constrain strain diversity and infection outcomes. These findings have implications for antibiotic use, antimicrobial resistance transmission, and therapeutic strategies that rely on competition-driven dynamics, such as strain replacement.

microbiology↗

bioRxiv2024

A Potential Method for Identifying Milk Adulteration and Pb(II) Contamination Scenarios Using Principal Component Analysis from Smartphone Photographs

Heavy metal contaminants and adulteration in cow milk products are major issues affecting milk safety and quality, posing health risks to consumers of all ages. These contaminants are sometimes difficult to detect with the naked eye and can potentially pass sensory tests, particularly in white cow milk. This research explores the detection of lead(II) poisoning in milk post-production and the adulteration of different milk samples using an alternative approach through chemometric techniques based on RGB and Grey Area image analysis. A controlled photography environment was used. We analyzed over 105 samples of control, adulterated, and lead(II)-added milk in this study using image processing software. Each photograph was analyzed to provide triplicate Regions of Interest (ROI), resulting in a total of 315 statistical datasets. We found that Principal Component Analysis (PCA) effectively clustered control white milk and Pb(II)-contaminated milk. Clusters of different adulterants were recognized simply by feeding RGB and Grey Area data into PCA. However, some clusters, such as mixed chocolate milk and white milk with lead(II) contamination, were not well distinguished. In this early-stage method, a comparison study with infrared spectra will be required in future research. This alternative method shows potential promise for deployment in limited settings for real-world food quality surveillance and regulation.

biochemistry↗