AUTHOR=Al Gharaibeh Faris N. , Huang Min , Wynn James L. , Kamaleswaran Rishikesan , Atreya Mihir R. 

TITLE=Transcriptomic mortality signature defines high-risk neonatal sepsis endotype

JOURNAL=Frontiers in Immunology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1601316

DOI=10.3389/fimmu.2025.1601316

ISSN=1664-3224

ABSTRACT=IntroductionNeonatal sepsis remains a leading cause of global childhood mortality, yet treatment options are limited. Clinical and biological heterogeneity hinders the development of targeted therapies. Gene-expression profiling offers a potential strategy to identify neonatal sepsis subtypes and guide targeted intervention.MethodsWe performed secondary analyses of publicly available gene-expression datasets. Differential gene expression analysis and T-distributed Stochastic Neighbor Embedding (t-SNE) identified biologically relevant patient clusters. Mortality and organ dysfunction were compared across clusters to determine clinical relevance.ResultsWe identified three endotypes of neonatal sepsis based on the 100 gene expression mortality signature, distinguishing five non-survivors from 72 survivors across datasets. Compared with other endotypes, Endotype A was associated with high mortality (22% vs. 0%, p=0.003) and cardiac dysfunction (61% vs. 31%, p=0.025). Pathobiology among endotype A patients was primarily driven by neutrophil progenitors.ConclusionsGene-expression profiling can be used to disentangle neonatal sepsis heterogeneity. Dysregulated hyperinflammatory response with emergency granulopoiesis was pathognomonic of high-risk endotype A. Pending further validation, gene-expression-based subclassification may be used to identify at-risk neonates and inform the selection of targeted sepsis therapies.