Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001

TL;DR

This paper presents the first genome-scale metabolic model of Yersinia pestis, enabling better understanding of its physiology, cryptic genes, and potential targets for therapeutics, crucial for public health and bioterrorism defense.

Contribution

The study develops a comprehensive metabolic model of Y. pestis and introduces CryptFind, a systematic method to identify cryptic genes, advancing pathogen analysis and drug target discovery.

Findings

Model accurately reflects Y. pestis metabolic needs.

CryptFind successfully identifies cryptic genes, including betB.

Potential metabolic weaknesses for therapeutics identified.

Abstract

The gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one the deadliest pathogens known to man. Despite its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost importance. Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome, and physiological and biochemical data from literature. Our model demonstrates excellent agreement with Y. pestis known metabolic needs and capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene. Despite all of our medical advances, we still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology of this deadly pathogen. Using our theoretical model, we can study the cells phenotypic behavior under different circumstances and identify metabolic weaknesses which may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the usage of genomic data for pharmaceutical purposes.