Unsupervised Classification of the Host Response Identifies Dominant Pathobiological Signatures of Sepsis in Sub-Saharan Africa
Matthew J. Cummings, Julius J. Lutwama, Nicholas Owor, Alin S. Tomoiaga, Jesse Ross, Moses Muwanga, Christopher Nsereko, Irene Nayiga, Stephen Kyebambe, Joseph Shinyale, Thomas Ochar, Moses Kiwubeyi, Rittah Nankwanga, Kai Nie, Hui Xie, Sam Miake-Lye, Bryan Villagomez, Jingjing Qi, Steven J. Reynolds, Martina Cathy Nakibuuka, Xuan Lu, John Kayiwa, Mercy Haumba, Joweria Nakaseegu, Xiaoyu Che, Misaki Wayengera, Sankar Ghosh, Seunghee Kim‐Schulze, W. Ian Lipkin, Barnabas Bakamutumaho, Max R. O’Donnell
Abstract
Abstract Rationale The global burden of sepsis is concentrated in sub-Saharan Africa, where inciting pathogens are diverse and HIV coinfection is a major driver of poor outcomes. Biological heterogeneity inherent to sepsis in this setting is poorly defined. Objectives To identify dominant pathobiological signatures of sepsis in sub-Saharan Africa and their relationship to clinical phenotypes, patient outcomes, and biological classifications of sepsis identified in high-income countries (HICs). Methods We analyzed two prospective cohorts of adults hospitalized with sepsis (severe infection with quick Sepsis-related Organ Failure Assessment score ⩾1) at disparate settings in Uganda (discovery cohort [Entebbe, urban], n = 242; validation cohort [Tororo, rural], n = 253). To identify pathobiological signatures in the discovery cohort, we applied unsupervised clustering to 173 soluble proteins reflecting key domains of the host response to severe infection. A random forest-derived classifier was used to predict signature assignment in the validation cohort. Measurements and Main Results Two signatures (Uganda Sepsis Signature [USS]-1 and USS-2) were identified in the discovery cohort, distinguished by expression of proteins involved in myeloid cell and inflammasome activation, T-cell costimulation and exhaustion, and endothelial barrier dysfunction. A five-protein classifier (area under the receiver operating characteristic curve, 0.97) reproduced two signatures in the validation cohort with similar biological profiles. In both cohorts, USS-2 mapped to a more severe clinical phenotype associated with HIV and related immunosuppression, severe tuberculosis, and increased risk of 30-day mortality. Substantial biological overlap was observed between USS-2 and hyperinflammatory and reactive sepsis phenotypes identified in HICs. Conclusions We identified prognostically enriched pathobiological signatures among patients with sepsis with diverse infections and high HIV prevalence in Uganda. Globally inclusive investigations are needed to define generalizable and context-specific mechanisms of sepsis pathobiology, with the goal of improving access to precision medicine treatment strategies.