RT Journal Article SR Electronic T1 Multi-omics approach identifies novel pathogen-derived prognostic biomarkers in patients with Pseudomonas aeruginosa bloodstream infection JF bioRxiv FD Cold Spring Harbor Laboratory SP 309898 DO 10.1101/309898 A1 Matthias Willmann A1 Stephan Götting A1 Daniela Bezdan A1 Boris Maček A1 Ana Velic A1 Matthias Marschal A1 Wichard Vogel A1 Ingo Flesch A1 Uwe Markert A1 Annika Schmidt A1 Pierre Kübler A1 Maria Haug A1 Mumina Javed A1 Benedikt Jentzsch A1 Philipp Oberhettinger A1 Monika Schütz A1 Erwin Bohn A1 Michael Sonnabend A1 Kristina Klein A1 Ingo B Autenrieth A1 Stephan Ossowski A1 Sandra Schwarz A1 Silke Peter YR 2018 UL http://biorxiv.org/content/early/2018/04/28/309898.abstract AB Pseudomonas aeruginosa is a human pathogen that causes health-care associated blood stream infections (BSI). Although P. aeruginosa BSI are associated with high mortality rates, the clinical relevance of pathogen-derived prognostic biomarker to identify patients at risk for unfavorable outcome remains largely unexplored. We found novel pathogen-derived prognostic biomarker candidates by applying a multi-omics approach on a multicenter sepsis patient cohort. Multi-level Cox regression was used to investigate the relation between patient characteristics and pathogen features (2298 accessory genes, 1078 core protein levels, 107 parsimony-informative variations in reported virulence factors) with 30-day mortality. Our analysis revealed that presence of the helP gene encoding a putative DEAD-box helicase was independently associated with a fatal outcome (hazard ratio 2.01, p = 0.05). helP is located within a region related to the pathogenicity island PAPI-1 in close proximity to a pil gene cluster, which has been associated with horizontal gene transfer. Besides helP, elevated protein levels of the bacterial flagellum protein FliL (hazard ratio 3.44, p < 0.001) and of a bacterioferritin-like protein (hazard ratio 1.74, p = 0.003) increased the risk of death, while high protein levels of a putative aminotransferase were associated with an improved outcome (hazard ratio 0.12, p < 0.001). The prognostic potential of biomarker candidates and clinical factors was confirmed with different machine learning approaches using training and hold-out datasets. The helP genotype appeared the most attractive biomarker for clinical risk stratification due to its relevant predictive power and ease of detection.