Daily Sepsis Research Analysis

Sepsis triggers impaired macrophage bacterial phagocytosis, rendering the host more vulnerable to secondary infections, a manifestation termed sepsis-associated immunosuppression. Glutamine (Gln) is a vital nutrient in critical illness that not only supports energy production and biomass synthesis but also potentially exerts immunomodulatory effects. The aim of the present study was to investigate whether supplementation of Gln modulates macrophage phagocytosis and mitigates sepsis-induced immunosuppression. Using a murine model of polymicrobial sepsis, we evaluated the effects of Gln supplementation on bacterial load, cytokine production, and survival. In multiple in vitro assays, we employed molecular and pharmacological approaches to dissect Gln-dependent signaling pathways in recovering the immunosuppressive macrophages. We found that Gln deficiency impaired macrophage phagocytosis and exacerbated sepsis-induced immunosuppression. In contrast, exogenous Gln supplementation restored macrophage function and improved survival in septic mice-effects that were abolished upon macrophage depletion. Mechanistically, Gln promoted glutamine-fructose-6-phosphate transaminase (GFAT)-dependent protein O-GlcNAcylation, leading to dynamin-related protein 1 (DRP1) oligomerization. Concurrently, Gln activated a GFAT-mediated, cyclin-dependent kinase 1-dependent pathway that induced DRP1 phosphorylation at Ser-616 irrelevant of O-GlcNAcylation. These effects enhanced DRP1-mediated mitochondrial fission, increased mitochondrial calcium efflux, and sustained cytosolic calcium levels essential for phagocytosis. In conclusion, our study demonstrates that Gln strengthens macrophage phagocytosis and alleviates immunosuppression in sepsis through a dual GFAT-DRP1 mechanism co-ordinating mitochondrial dynamics and calcium signaling, highlighting the GFAT-DRP1-calcium axis as a potential therapeutic target for treating sepsis-induced immunosuppression.

IMPORTANCE: Sepsis is a leading cause of death in children. Early recognition and treatment improve outcomes, but predictive models have not to date improved early diagnosis. OBJECTIVE: To develop machine learning models to estimate the probability of developing sepsis in the subsequent 48 hours. DESIGN, SETTING, AND PARTICIPANTS: This was a multisite registry for model derivation and validation using electronic health record (EHR) data from January 2016 through February 2020 and temporal validation from January 2021 through December 2022. The performance of machine learning algorithms was compared to predict development of sepsis and septic shock via logistic regression, specifically ridge regression and gradient tree boosting. Five health systems contributing to the Pediatric Emergency Care Applied Research Network were included. Emergency department (ED) visits for children aged 2 months or older to less than 18 years of age excluding patients with ED disposition of death or transfer, trauma diagnosis, or sepsis present during predictive features window. The TRIPOD-AI reporting guideline was followed, and data analysis was conducted from September 2023 to July 2025. EXPOSURES: Patient and physiologic characteristics within the first 4 hours of ED care. MAIN OUTCOMES AND MEASURES: Sepsis, defined as suspected infection with a Phoenix Sepsis Criteria (PSC) score of 2 or more or death within 48 hours of ED arrival. RESULTS: The dataset included 1 604 422 eligible visits in the training cohort and 719 298 visits in the test cohort. Performance characteristics for the PSC sepsis prediction models were AUROC of 0.92 (95% CI, 0.92-0.93) for logistic regression and 0.94 (95% CI, 0.93-0.94) for gradient tree boosting. AUROCs for PSC shock models were 0.92 or greater. The gradient tree boosting models had positive likelihood ratios ranging from 4.67 (95% CI, 4.61-4.74) to 6.18 (95% CI, 6.08-6.28) for sepsis and from 4.16 (95% CI, 4.07-4.24) to 5.83 (95% CI, 5.67-5.99) for septic shock. Predictive features included emergency severity index, age-adjusted vital signs, and medical complexity. Assessment of model performance fairness was similar for all demographic characteristics except payor; AUROC for patients with Medicaid insurance was better than for those with commercial payers. CONCLUSIONS AND RELEVANCE: Using a large multicenter population, models were developed and validated with high AUROC to predict the future development of sepsis based on EHR data collected in the ED. The models achieved positive likelihood ratios to predict sepsis and septic shock. The results highlight the opportunity for future studies that combine EHR-based models with clinical judgment to improve prediction.

Septic shock is one of the leading causes of morbidity and mortality in hospital patients. The present study aimed to investigate the potential of transforming growth factor β induced (TGFBI) in macrophage functions and progression of septic shock. Mice were treated with caecal ligation and puncture (CLP) to establish a septic shock model in vivo. Histopathologic analysis was performed using haematoxylin and eosin (HE) staining. Gene expression was detected using reverse transcription-quantitative PCR and Western blot. Cytokine release was detected using an enzyme-linked immunosorbent assay. The enrichment of TGFBI was detected using chromatin immunoprecipitation assay. Cellular functions were detected using Cell Counting Kit-8, lactate dehydrogenase, flow cytometry, PI staining, and terminal deoxynucleotidyl transferase-mediated dUTP nick end labelling staining assays. TGFBI was downregulated in septic shock patients and models. TGFBI overexpression suppressed the pyroptosis of macrophages by inhibiting the non-canonical inflammasome, promoting the bacterial killing ability of macrophages. Wedelolactone-mediated inhibition of pyroptosis alleviated sepsis-induced multiple organ failure. Moreover, TGFBI inhibited M1 macrophage polarisation. Suppressor of variegation 3-9 homologue 2 (SUV39H2)-mediated histone methylation of TGFBI, resulting in the downregulation of TGFBI. Signal transducer and activator of transcription 1 (Stat1) was identified as a new co-activator of SUV39H2 to inhibit the transcription of TGFBI. However, inhibition of SUV39H2 and Stat1 upregulated TGFBI. Furthermore, Stat1 deficiency inhibited the pyroptosis of macrophages and alleviated sepsis-induced multiple organ failure. In summary, our findings establish an immune checkpoint, whereby TGFBI, via inhibiting macrophage pyroptosis and M1 polarisation, suppresses the progression of septic shock.