Lower serum alpha 1 antitrypsin levels in patients with severe COVID-19 compared with patients hospitalized due to non-COVID-19 pneumonia

Understanding COVID-19 outcome: Exploring the prognostic value of soluble biomarkers indicative of endothelial impairment

Host proteins released by the activated endothelial cells during SARS-CoV-2 infection are implicated to be involved in coagulation and endothelial dysfunction. However, the underlying mechanism that governs the vascular dysfunction and disease severity in COVID-19 remains obscure. The study evaluated the serum levels of Bradykinin, Kallikrein, SERPIN A, and IL-18 in COVID-19 (N-42 with 20 moderate and 22 severe) patients compared to healthy controls (HC: N-10) using ELISA at the day of admission (DOA) and day 7 post-admission. The efficacy of the protein levels in predicting disease severity was further determined using machine learning models. The levels of bradykinins and SERPIN A were higher (P ≤ 0.001) in both severe and moderate cases on day 7 post-admission compared to DOA. All the soluble proteins studied were found to elevated (P ≤ 0.01) in severe compared to moderate in day 7 and were positively correlated (P ≤ 0.001) with D-dimer, a marker for coagulation. ROC analysis identified that SERPIN A, IL-18, and bradykinin could predict the clinical condition of COVID-19 with AUC values of 1, 0.979, and 1, respectively. Among the models trained using univariate model analysis, SERPIN A emerged as a strong prognostic biomarker for COVID-19 disease severity. The serum levels of SERPIN A in conjunction with the coagulation marker D-dimer, serve as a predictive indicator for COVID-19 clinical outcomes. However, studies are required to ascertain the role of these markers in disease virulence.

A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning

BACKGROUND

COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients' proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19.

METHODS

A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression.

RESULTS

Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems.

CONCLUSIONS

The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.

The Inhibition of Serine Proteases by Serpins Is Augmented by Negatively Charged Heparin: A Concise Review of Some Clinically Relevant Interactions

Serine proteases are members of a large family of hydrolytic enzymes in which a particular serine residue in the active site performs an essential role as a nucleophile, which is required for their proteolytic cleavage function. The array of functions performed by serine proteases is vast and includes, among others, the following: (i) the ability to fight infections; (ii) the activation of blood coagulation or blood clot lysis systems; (iii) the activation of digestive enzymes; and (iv) reproduction. Serine protease activity is highly regulated by multiple families of protease inhibitors, known collectively as the SERine Protease INhibitor (SERPIN). The serpins use a conformational change mechanism to inhibit proteases in an irreversible way. The unusual conformational change required for serpin function provides an elegant opportunity for allosteric regulation by the binding of cofactors, of which the most well-studied is heparin. The goal of this review is to discuss some of the clinically relevant serine protease-serpin interactions that may be enhanced by heparin or other negatively charged polysaccharides. The paired serine protease-serpin in the framework of heparin that we review includes the following: thrombin-antithrombin III, plasmin-anti-plasmin, C1 esterase/kallikrein-C1 esterase inhibitor, and furin/TMPRSS2 (serine protease Transmembrane Protease 2)-alpha-1-antitrypsin, with the latter in the context of COVID-19 and prostate cancer.

A high α1-antitrypsin/interleukin-10 ratio predicts bacterial pneumonia in adults with community-acquired pneumonia: a prospective cohort study

BACKGROUND

Current microbiological tests fail to identify the causative microorganism in more than half of all pneumonia cases. We explored biomarkers that could be used for differentiating between bacterial and viral pneumonia in patients with community-acquired pneumonia (CAP).

METHODS

In this prospective cohort study conducted in Japan, data obtained from adult patients with bacterial pneumonia, including bacterial and viral coinfections (bacterial pneumonia [BP] group), and purely viral pneumonia (VP group) at diagnosis were analyzed using multivariate logistic regression analysis to identify predictors of bacterial pneumonia. Furthermore, a decision tree was developed using the predictors.

RESULTS

A total of 210 patients were analyzed. The BP and VP groups comprised 108 and 18 patients, respectively. The other 84 patients had no identified causative microorganism. The two groups shared similar characteristics, including disease severity; however, a significant difference (p < 0.05) was observed between the two groups regarding sputum type; sputum volume score; neutrophil counts; and serum levels of interleukin (IL)-8, IL-10, and α1-antitrypsin (AAT). Sputum volume score (p < 0.001), IL-10 (p < 0.001), and AAT (p = 0.008) were ultimately identified as predictors of BP. The area under the curve for these three variables on the receiver operating characteristic (ROC) curve was 0.927 (95% confidence interval [CI]: 0.881-0.974). The ROC curve for sputum volume score and an AAT/IL-10 ratio showed a diagnostic cutoff of 1 + and 65, respectively. Logistic regression analysis using dichotomized variables at the cutoff values showed that the odds ratios for the diagnosis of BP were 10.4 (95% CI: 2.2-50.2) for sputum volume score (absence vs. presence) and 19.8 (95% CI: 4.7-83.2) for AAT/IL-10 ratio (< 65 vs. ≥ 65).

CONCLUSIONS

Considering that obtaining a definitive etiologic diagnosis with the current testing methods is difficult and time consuming, a decision tree with two predictors, namely sputum volume and the AAT/IL-10 ratio, can be useful in predicting BP among patients diagnosed with CAP and facilitating the appropriate use of antibiotics.

TRIAL REGISTRATION

UMIN000034673 registered on November 29, 2018.

Alpha-1-antitrypsin antagonizes COVID-19: a review of the epidemiology, molecular mechanisms, and clinical evidence

Alpha-1-antitrypsin (AAT), a serine protease inhibitor (serpin), is increasingly recognized to inhibit SARS-CoV-2 infection and counter many of the pathogenic mechanisms of COVID-19. Herein, we reviewed the epidemiologic evidence, the molecular mechanisms, and the clinical evidence that support this paradigm. As background to our discussion, we first examined the basic mechanism of SARS-CoV-2 infection and contend that despite the availability of vaccines and anti-viral agents, COVID-19 remains problematic due to viral evolution. We next underscored that measures to prevent severe COVID-19 currently exists but teeters on a balance and that current treatment for severe COVID-19 remains grossly suboptimal. We then reviewed the epidemiologic and clinical evidence that AAT deficiency increases risk of COVID-19 infection and of more severe disease, and the experimental evidence that AAT inhibits cell surface transmembrane protease 2 (TMPRSS2) - a host serine protease required for SARS-CoV-2 entry into cells - and that this inhibition may be augmented by heparin. We also elaborated on the panoply of other activities of AAT (and heparin) that could mitigate severity of COVID-19. Finally, we evaluated the available clinical evidence for AAT treatment of COVID-19.