Vascular endothelial-derived SPARCL1 exacerbates viral pneumonia through pro-inflammatory macrophage activation.

Inflammation induced by lung infection is a double-edged sword, moderating both anti-viral and immune pathogenesis effects; the mechanism of the latter is not fully understood. Previous studies suggest the vasculature is involved in tissue injury. Here, we report that expression of Sparcl1, a secreted matricellular protein, is upregulated in pulmonary capillary endothelial cells (EC) during influenza-induced lung injury. Endothelial overexpression of SPARCL1 promotes detrimental lung inflammation, with SPARCL1 inducing 'M1-like' macrophages and related pro-inflammatory cytokines, while SPARCL1 deletion alleviates these effects. Mechanistically, SPARCL1 functions through TLR4 on macrophages in vitro, while TLR4 inhibition in vivo ameliorates excessive inflammation caused by endothelial Sparcl1 overexpression. Finally, SPARCL1 expression is increased in lung ECs from COVID-19 patients when compared with healthy donors, while fatal COVID-19 correlates with higher circulating SPARCL1 protein levels in the plasma. Our results thus implicate SPARCL1 as a potential prognosis biomarker for deadly COVID-19 pneumonia and as a therapeutic target for taming hyperinflammation in pneumonia.

Vascular Endothelial-derived SPARCL1 Exacerbates Viral Pneumonia Through Pro-Inflammatory Macrophage Activation.

Inflammation upon infectious lung injury is a double-edged sword: while tissue-infiltrating immune cells and cytokines are necessary to control infection, these same factors often aggravate injury. Full appreciation of both the sources and targets of inflammatory mediators is required to facilitate strategies to maintain antimicrobial effects while minimizing off-target epithelial and endothelial damage. Recognizing that the vasculature is centrally involved in tissue responses to injury and infection, we observed that pulmonary capillary endothelial cells (ECs) exhibit dramatic transcriptomic changes upon influenza injury punctuated by profound upregulation of Sparcl1 . Endothelial deletion and overexpression of SPARCL1 implicated this secreted matricellular protein in driving key pathophysiologic symptoms of pneumonia, which we demonstrate result from its effects on macrophage polarization. SPARCL1 induces a shift to a pro-inflammatory "M1-like" phenotype (CD86 + CD206 - ), thereby increasing associated cytokine levels. Mechanistically, SPARCL1 acts directly on macrophages in vitro to induce the pro-inflammatory phenotype via activation of TLR4, and TLR4 inhibition in vivo ameliorates inflammatory exacerbations caused by endothelial Sparcl1 overexpression. Finally, we confirmed significant elevation of SPARCL1 in COVID-19 lung ECs in comparison with those from healthy donors. Survival analysis demonstrated that patients with fatal COVID-19 had higher levels of circulating SPARCL1 protein compared to those who recovered, indicating the potential of SPARCL1 as a biomarker for prognosis of pneumonia and suggesting that personalized medicine approaches might be harnessed to block SPARCL1 and improve outcomes in high-expressing patients.

Validating a Proteomic Signature of Severe COVID-19.

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19. DESIGN: Prospective observational cohort study. SETTING: Two hospitals in the United States. PATIENTS: One hundred sixty-seven hospitalized adults with COVID-19. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006). CONCLUSIONS: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Association of vancomycin plus piperacillin-tazobactam with early changes in creatinine versus cystatin C in critically ill adults: a prospective cohort study.

PURPOSE: Although dozens of studies have associated vancomycin + piperacillin-tazobactam with increased acute kidney injury (AKI) risk, it is unclear whether the association represents true injury or a pseudotoxicity characterized by isolated effects on creatinine secretion. We tested this hypothesis by contrasting changes in creatinine concentration after antibiotic initiation with changes in cystatin C concentration, a kidney biomarker unaffected by tubular secretion. METHODS: We included patients enrolled in the Molecular Epidemiology of SepsiS in the ICU (MESSI) prospective cohort who were treated for ≥ 48 h with vancomycin + piperacillin-tazobactam or vancomycin + cefepime. Kidney function biomarkers [creatinine, cystatin C, and blood urea nitrogen (BUN)] were measured before antibiotic treatment and at day two after initiation. Creatinine-defined AKI and dialysis were examined through day-14, and mortality through day-30. Inverse probability of treatment weighting was used to adjust for confounding. Multiple imputation was used to impute missing baseline covariates. RESULTS: The study included 739 patients (vancomycin + piperacillin-tazobactam n = 297, vancomycin + cefepime n = 442), of whom 192 had cystatin C measurements. Vancomycin + piperacillin-tazobactam was associated with a higher percentage increase of creatinine at day-two 8.04% (95% CI 1.21, 15.34) and higher incidence of creatinine-defined AKI: rate ratio (RR) 1.34 (95% CI 1.01, 1.78). In contrast, vancomycin + piperacillin-tazobactam was not associated with change in alternative biomarkers: cystatin C: - 5.63% (95% CI - 18.19, 8.86); BUN: - 4.51% (95% CI - 12.83, 4.59); or clinical outcomes: dialysis: RR 0.63 (95% CI 0.31, 1.29); mortality: RR 1.05 (95%CI 0.79, 1.41). CONCLUSIONS: Vancomycin + piperacillin-tazobactam was associated with creatinine-defined AKI, but not changes in alternative kidney biomarkers, dialysis, or mortality, supporting the hypothesis that vancomycin + piperacillin-tazobactam effects on creatinine represent pseudotoxicity.

Developing well-calibrated illness severity scores for decision support in the critically ill.

Illness severity scores are regularly employed for quality improvement and benchmarking in the intensive care unit, but poor generalization performance, particularly with respect to probability calibration, has limited their use for decision support. These models tend to perform worse in patients at a high risk for mortality. We hypothesized that a sequential modeling approach wherein an initial regression model assigns risk and all patients deemed high risk then have their risk quantified by a second, high-risk-specific, regression model would result in a model with superior calibration across the risk spectrum. We compared this approach to a logistic regression model and a sophisticated machine learning approach, the gradient boosting machine. The sequential approach did not have an effect on the receiver operating characteristic curve or the precision-recall curve but resulted in improved reliability curves. The gradient boosting machine achieved a small improvement in discrimination performance and was similarly calibrated to the sequential models.

Critical Care, Critical Data.

As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.

Heavy-Atom Tunneling Calculations in Thirteen Organic Reactions: Tunneling Contributions are Substantial, and Bell's Formula Closely Approximates Multidimensional Tunneling at ≥250†K.

Multidimensional tunneling calculations are carried out for 13 reactions, to test the scope of heavy-atom tunneling in organic chemistry, and to check the accuracy of one-dimensional tunneling models. The reactions include pericyclic, cycloaromatization, radical cyclization and ring opening, and SN 2. When compared at the temperatures that give the same effective rate constant of 3×10-5  s-1 , tunneling accounts for 25-95 % of the rate in 8 of the 13 reactions. Values of transmission coefficients predicted by Bell's formula, κBell , agree well with multidimensional tunneling (canonical variational transition state theory with small curvature tunneling), κSCT . Mean unsigned deviations of κBell vs. κSCT are 0.08, 0.04, 0.02 at 250, 300 and 400 K. This suggests that κBell is a useful first choice for predicting transmission coefficients in heavy-atom tunnelling.

Computational evidence for heavy-atom tunneling in the bergman cyclization of a 10-membered-ring enediyne.

DFT and CASSCF calculations for the cyclization of (3Z)-cyclodec-3-en-1,5-diyne were carried out to investigate heavy-atom tunneling. At 37 °C, tunneling was computed to enhance the rate by 38-40% over the transition-state theory rate. Intramolecular (12)C/(13)C kinetic isotope effects were predicted to be substantial, with a steep temperature dependence. These results are discussed in relation to recent experimental findings that show heavy-atom tunneling at moderate temperatures. The calculations point to the possibility of a simple computational test for the likelihood of heavy-atom tunneling using standard quantum-chemical information.