Background: Severe septic syndromes deeply impair innate and adaptive immunity. While neutrophils represent the first line of defense against infection, little is known about their phenotype and functions during sepsis-induced immunosuppression. The objective of this study was thus to perform for the first time a global evaluation of neutrophil alterations in immunosuppressed septic patients based on phenotypic, functional and transcriptomic studies. In addition, the potential association of these parameters and deleterious outcomes was assessed.Methods: Peripheral blood was collected from 9 septic shock patients at D3-4 and D6-8 presenting with features of sepsis-induced immunosuppression and compared to 8 healthy controls.Results: In order to get on overview of potential neutrophil alterations after sepsis, we performed transcriptomic analyses on purified neutrophils from 9 septic shock patients and 8 healthy volunteers. For each time point, comparisons were made between patients and controls. Venn diagrams indicated that 364 up-regulated genes and 328 down-regulated genes were common between the two analyses (Supplementary Tables 1 and 2). Interestingly, most of the differentially expressed genes are involved in cell maturation (CD177), apoptosis (STK4, Caspase 8), cell recruitment and chemotaxis (CD44, TPST, MMP9, CREB1), and antimicrobial properties (ARG1, STOM, ADAM9, CD63, YKL40) of neutrophils.Conclusions: The aim of the current study was to perform an extensive investigation of neutrophil alterations during sepsis-induced immunosuppression through phenotypic, functional and transcriptomic studies. Notably, transcriptomic study on purified neutrophils revealed differentially expressed genes between septic patients and healthy volunteers.

9 septic shock patients at D3-4 and D6-8 presenting with features of sepsis-induced immunosuppression and compared to 8 healthy controls

Septic shock (2021 ICD-10-CM code* = R65.21)

Normalization processing and quality controls were performed using R/Bioconductor (R version 3.0.0 (2013-04-03)) (R Development Core Team – 2009). We performed quality controls at both probe and probeset levels). Raw expression profiles were normalized using the GC-RMA algorithm (R/Bioconductor). To reduce the number of probeset, we applied a filtering process based on the MAS5.0 p-values.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64457