Biology subjects

Ross, J. N.

Publications and source records attributed to Ross, J. N..

2 recordsLinked to original sources

bioRxiv2019

Discovery of genes encoding a Streptolysin S-like toxin biosynthetic cluster in a select highly pathogenic methicillin resistant Staphylococcus aureus JKD6159 strain

BackgroundStaphylococcus aureus (S. aureus) is a major human pathogen owing to its arsenal of virulence factors, as well as its acquisition of multi-antibiotic resistance. Here we report the identification of a Streptolysin S (SLS) like biosynthetic gene cluster in a highly virulent community-acquired methicillin resistant S. aureus (MRSA) isolate, JKD6159. Examination of the SLS-like gene cluster in JKD6159 shows significant homology and gene organization to the SLS-associated biosynthetic gene (sag) cluster responsible for the production of the major hemolysin SLS in Group A Streptococcus.\n\nResultsWe took a comprehensive approach to elucidating the putative role of the sag gene cluster in JKD6159 by constructing a mutant in which one of the biosynthesis genes (sagB homologue) was deleted in the parent JKD6159 strain. Assays to evaluate bacterial gene regulation, biofilm formation, antimicrobial activity, as well as complete host cell response profile and comparative in vivo infections in Balb/Cj mice were conducted.\n\nConclusionsAlthough no significant phenotypic changes were observed in our assays, we postulate that the SLS-like toxin produced by this strain of S. aureus may be a highly specialized virulence factor utilized in specific environments for selective advantage; studies to better understand the role of this newly discovered virulence factor in S. aureus warrant further investigation.

microbiology↗

bioRxiv2018

Novel antimicrobial peptide discovery using machine learning and biophysical selection of minimal bacteriocin domains

Bacteriocins are ribosomally produced antimicrobial peptides that represent an untapped source of promising antibiotic alternatives. However, inherent challenges in isolation and identification of natural bacteriocins in substantial yield have limited their potential use as viable antimicrobial compounds. In this study, we have developed an overall pipeline for bacteriocin-derived compound design and testing that combines sequence-free prediction of bacteriocins using a machine-learning algorithm and a simple biophysical trait filter to generate minimal 20 amino acid peptide candidates that can be readily synthesized and evaluated for activity. We generated 28,895 total 20-mer peptides and scored them for charge, -helicity, and hydrophobic moment, allowing us to identify putative peptide sequences with the highest potential for interaction and activity against bacterial membranes. Of those, we selected sixteen sequences for synthesis and further study, and evaluated their antimicrobial, cytotoxicity, and hemolytic activities. We show that bacteriocin-based peptides with the overall highest scores for our biophysical parameters exhibited significant antimicrobial activity against E. coli and P. aeruginosa. Our combined method incorporates machine learning and biophysical-based minimal region determination, to create an original approach to rapidly discover novel bacteriocin candidates amenable to rapid synthesis and evaluation for therapeutic use.

microbiology↗