Kwon, M. S. | bioRxiv Science

bioRxiv2024

Pseudomonas aeruginosa essential gene perturbations that confer vulnerability to the mammalian host environment

Multidrug-resistant Pseudomonas aeruginosa causes highly morbid infections that are challenging to treat. While antibiotics reduce bacterial populations during infection, the host environment also plays a key role in inhibiting and eliminating pathogens. Identifying genetic targets that create vulnerabilities to the host environment may uncover strategies to synergize with nutrient limitation or inherent immune processes to clear bacterial infections. Here, we screened a partial knockdown library targeting P. aeruginosa essential and conditionally essential genes in a murine pneumonia model to identify genes with increased vulnerability in the host environment. We found that partial CRISPR interference (CRISPRi) knockdown of 178 genes showed significant fitness defects in mice relative to axenic culture. We validated two important outliers: ispD, encoding a key enzyme in isoprenoid precursor biosynthesis, and pgsA, encoding an enzyme involved in phospholipid synthesis that is strongly upregulated in human infections. Partial knockdown of both genes showed decreased virulence in a mouse survival assay but had little impact on in vitro growth. The use of CRISPRi screening to uncover genetic vulnerabilities represents a promising strategy to prioritize antibacterial targets that interact with the host environment.

microbiology↗

bioRxiv2024

A machine learning framework for extracting information from biological pathway images in the literature

There have been significant advances in literature mining, allowing for the extraction of target information from the literature. However, biological literature often includes biological pathway images that are difficult to extract in an easily editable format. To address this challenge, this study aims to develop a machine learning framework called the "Extraction of Biological Pathway Information" (EBPI). The framework automates the search for relevant publications, extracts biological pathway information from images within the literature, including genes, enzymes, and metabolites, and generates the output in a tabular format. For this, this framework determines the direction of biochemical reactions, and detects and classifies texts within biological pathway images. Performance of EBPI was evaluated by comparing the extracted pathway information with manually curated pathway maps. EBPI will be useful for extracting biological pathway information from the literature in a high-throughput manner, and can be used for pathway studies, including metabolic engineering.

bioinformatics↗