Adhesion Molecule–dependent Mechanisms Regulate the Rate of Macrophage Clearance During the Resolution of Peritoneal Inflammation

Macrophage clearance is essential for the resolution of inflammation. Much is known about how monocytes enter the inflammatory site but little is known about how resultant macro-phages are cleared. We have previously demonstrated that macrophage clearance from resolving peritonitis occurs by emigration into draining lymphatics rather than local apoptosis. We now examine mechanisms for this process, in particular by evaluating the hypothesis that modulation of adhesion interactions between macrophages and cells lining the lymphatics regulates the rate of macrophage clearance. We demonstrate in vivo that macrophages adhere specifically to mesothelium overlying draining lymphatics and that their emigration rate is regulated by the state of macrophage activation. We observed that macrophage–mesothelial adhesion is Arg-Gly-Asp (RGD) sensitive and partially mediated by very late antigen (VLA)-4 and VLA-5 but not αv or β2 integrins. Moreover, macrophage clearance into lymphatics can be blocked in vivo by RGD peptides and VLA-4 and VLA-5 but not β2 blocking antibodies. This is the first evidence that macrophage emigration from the inflamed site is controlled and demonstrates that this is exerted through specific adhesion molecule regulation of macrophage–mesothelial interactions. It highlights the importance of adhesion molecules governing entry of cells into the lymphatic circulation, thus opening a new avenue for manipulating the resolution of inflammation

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.peer-reviewe