Artificial Metagenomic Approach Reveals Genetic Elements Stoking Antibiotic Resistance
In a groundbreaking development, researchers at the University of Oregon's Department of Bioengineering, based at the Phil and Penny Knight Campus for Accelerating Scientific Impact, have developed a technology that could revolutionise the way we combat disease resistance.
The study, titled "Exploring antibiotic resistance in diverse homologs of the dihydrofolate reductase protein family through broad mutational scanning", was published in Science Advances. The researchers' technology aims to address the gap in understanding resistance mechanisms across the diverse protein family of DHFR, a key antibiotic target.
Karl Romanowicz, a postdoctoral scholar in the Plesa lab, spearheaded the study. The team constructed a phylogenetically diverse library of 1,536 DHFR homologs, primarily derived from host-associated metagenomes. This library represents 759 bacterial species, including many clinically relevant pathogens.
The study involved using a synthetic metagenomics approach and DropSynth, a scalable gene synthesis platform. Researchers used DropSynth to generate approximately 1,500 genes related to DHFR homologs. The focus was on identifying genetic factors driving antimicrobial resistance across various microbes, with a particular emphasis on sensitive regions of the DHFR protein.
Broad mutational scanning of DHFR homologs and 100,000 mutants revealed key insights into fitness and resistance. This provides the most comprehensive analysis of complementation and inhibitor tolerance to date. The study's findings extend beyond antimicrobial resistance, potentially impacting various fields including cancer research, viral evolution, and protein design.
Moreover, the technology developed by the researchers could play a significant role in the use of AI for drug development and combating disease resistance. It addresses a critical bottleneck in using AI for drug development by generating vast biological datasets for training powerful machine learning systems.
Karl Romanowicz stated that this technology could transform research in areas such as cancer genes, viral evolution, and protein design. The research, published in GEN, indicates that DropSynth was able to achieve this at a fraction of traditional costs.
The study's focus on DHFR and its resistance mechanisms is significant as it is a key antibiotic target. Understanding the resistance mechanisms could pave the way for the development of new antibiotics and strategies to combat antibiotic resistance. The implications of this research are far-reaching and could potentially save countless lives in the future.
