Mycobacterium tuberculosis glyceraldehyde-3-phosphate dehydrogenase plays a dual role-As an adhesin and as a receptor for plasmin(ogen)

The spread of infection is directly determined by the ability of a pathogen to invade and infect host tissues. The process involves adherence due to host-pathogen interactions and traversal into deeper tissues. Mycobacterium tuberculosis (Mtb) primarily infects the lung but is unique in its ability to infect almost any other organ of the human host including immune privileged sites such as the central nervous system (CNS). The extreme invasiveness of this bacterium is not fully understood. In the current study, we report that cell surface Mtb glyceraldehyde-3-phosphate dehydrogenase (GAPDH) functions as a virulence factor by multiple mechanisms. Firstly, it serves as a dual receptor for both plasminogen (Plg) and plasmin (Plm). CRISPRi-mediated silencing of this essential enzyme confirmed its role in the recruitment of Plg/Plm. Our studies further demonstrate that soluble GAPDH can re-associate on Mtb bacilli to promote plasmin(ogen) recruitment. The direct association of plasmin(ogen) via cell surface GAPDH or by the re-association of soluble GAPDH enhanced bacterial adherence to and traversal across lung epithelial cells. Furthermore, the association of GAPDH with host extracellular matrix (ECM) proteins coupled with its ability to recruit plasmin(ogen) may endow cells with the ability of directed proteolytic activity vital for tissue invasion.

Biological Machine Learning Combined with Campylobacter Population Genomics Reveals Virulence Gene Allelic Variants Cause Disease

Highly dimensional data generated from bacterial whole-genome sequencing is providing an unprecedented scale of information that requires an appropriate statistical analysis framework to infer biological function from populations of genomes. The application of genome-wide association study (GWAS) methods is an appropriate framework for bacterial population genome analysis that yields a list of candidate genes associated with a phenotype, but it provides an unranked measure of importance. Here, we validated a novel framework to define infection mechanism using the combination of GWAS, machine learning, and bacterial population genomics that ranked allelic variants that accurately identified disease. This approach parsed a dataset of 1.2 million single nucleotide polymorphisms (SNPs) and indels that resulted in an importance ranked list of associated alleles of porA in Campylobacter jejuni using spatiotemporal analysis over 30 years. We validated this approach using previously proven laboratory experimental alleles from an in vivo guinea pig abortion model. This framework, termed µPathML, defined intestinal and extraintestinal groups that have differential allelic porA variants that cause abortion. Divergent variants containing indels that defeated automated annotation were rescued using biological context and knowledge that resulted in defining rare, divergent variants that were maintained in the population over two continents and 30 years. This study defines the capability of machine learning coupled with GWAS and population genomics to simultaneously identify and rank alleles to define their role in infectious disease mechanisms.