A neutrophil - B-cell axis impacts tissue damage control during sepsis via Cxcr4

Sepsis is a life-threatening condition characterized by uncontrolled systemic inflammation and coagulation, leading to multi-organ failure. Therapeutic options to prevent sepsis-associated immunopathology remain scarce. Here, we established a mouse model of long-lasting disease tolerance during severe sepsis, manifested by diminished immunothrombosis and organ damage in spite of a high pathogen burden. We found that, both neutrophils and B cells emerged as key regulators of tissue integrity. Enduring changes in the transcriptional profile of neutrophils, included upregulated Cxcr4 expression in protected, tolerant hosts. Neutrophil Cxcr4 upregulation required the presence of B cells, suggesting that B cells promoted disease tolerance by improving tissue damage control via the suppression of neutrophils' tissue damaging properties. Finally, therapeutic administration of a Cxcr4 agonist successfully promoted tissue damage control and prevented liver damage during sepsis. Our findings highlight the importance of a critical B-cell/neutrophil interaction during sepsis and establish neutrophil Cxcr4 activation as a potential means to promote disease tolerance during sepsis. Overall design: 200 neutrophils (single/live/CD45+/CD3-/CD19-/Ly6G+/Ly-6Cint+) were sorted from mouse bone marrow or blood single cell suspensions into cell lysis buffer (0.2% Triton X-100 and 2 U/µl RNase Inhibitor) using a FACSAria Fusion cytometer. Cell lysates were stored at -80°C. Library preparation was performed according to Smart-Seq2, followed by sequencing of pooled libraries on the Illumina HiSeq 2000/2500 (50 bp single-read setup) at the Biomedical Sequencing Facility of the Medical University of Vienna and CeMM. For analysis, reads were adapter-trimmed (Trimmomatic) and aligned to the mm10 reference genome (STAR aligner). Counting of reads mapping to genes was performed using the summarizeOverlaps function (Bioconductor R package GenomicAlignments). Differentially expressed genes were identified using DESeq2, whereby separate models per organ and condition and formulated for all pairwise comparisons. Filtering was performed by independent hypothesis weighting (ihw R package).