PROJECT SUMMARY Melioidosis, a tropical infection commonly manifesting as acute pneumonia and sepsis in humans, is caused by the Gram-negative Tier 1 select agent Burkholderia pseudomallei and represents a global public health threat with an estimated 165,000 cases and 89,000 deaths annually worldwide (an overall mortality rate of 54%). Unfortunately numerous barriers exist to decreasing the burden of melioidosis including lack of an effective vaccine, difficulty diagnosing and identifying high-risk patients, and challenges in treatment due to extensive antimicrobial resistance of B. pseudomallei and lack of targeted immunotherapies. While work to date spotlights the promise of the application of advanced technologies to melioidosis and identifies several targets for further study, an incomplete understanding of the pathogenic mechanisms underlying host susceptibility and outcome impedes efforts to prevent, diagnose, risk-stratify and treat this infection. The overall hypothesis of this project is that by generating and integrating a rich compendium of multidimensional data – transcriptomic, proteomic, and metabolomic – from circulating immune cells and blood of patients with melioidosis and selected controls, it is possible to identify fundamental biological pathways and processes activated during melioidosis. Such comprehensive data, complemented by targeted in vitro experiments, are desperately needed to inform the design of vaccines, diagnostics, prognostics and therapeutics to combat this infectious threat. This hypothesis will be tested in the following aims: 1) Identify biological pathways that distinguish melioidosis from other causes of sepsis; 2) Define biological processes and develop prognostic signatures that predict death in melioidosis; and 3) Validate the function of key genes and pathways in human cells infected with B. pseudomallei in vitro. This application leverages the investigators’ scientific and clinical expertise in melioidosis, cutting edge bioinformatics capacity in transcriptomics, proteomics, and metabolomics both as independent domains and integrated together, and a rich clinical and biological dataset of over 5,000 septic patients due to melioidosis or to other infections. The successful completion of this project will yield an unprecedented granular overview of mechanisms leading to melioidosis and the molecular signatures associated with clinical outcomes, while also providing a unique resource to the scientific community to guide further research into this important but neglected disease.