Group IIA secreted phospholipase A2 is associated with the pathobiology leading to COVID-19 mortality

Abstract

There is an urgent need to identify the cellular and molecular mechanisms responsible for severe COVID-19 that results in death. We initially performed both untargeted and targeted lipidomics as well as focused biochemical analyses of 127 plasma samples and found elevated metabolites associated with secreted phospholipase A2 (sPLA2) activity and mitochondrial dysfunction in patients with severe COVID-19. Deceased COVID-19 patients had higher levels of circulating, catalytically active sPLA2 group IIA (sPLA2-IIA), with a median value that was 9.6-fold higher than that for patients with mild disease and 5.0-fold higher than the median value for survivors of severe COVID-19. Elevated sPLA2-IIA levels paralleled several indices of COVID-19 disease severity (e.g., kidney dysfunction, hypoxia, multiple organ dysfunction). A decision tree generated by machine learning identified sPLA2-IIA levels as a central node in the stratification of patients who died from COVID-19. Random forest analysis and least absolute shrinkage and selection operator-based (LASSO-based) regression analysis additionally identified sPLA2-IIA and blood urea nitrogen (BUN) as the key variables among 80 clinical indices in predicting COVID-19 mortality. The combined PLA-BUN index performed significantly better than did either one alone. An independent cohort (n = 154) confirmed higher plasma sPLA2-IIA levels in deceased patients compared with levels in plasma from patients with severe or mild COVID-19, with the PLA-BUN index-based decision tree satisfactorily stratifying patients with mild, severe, or fatal COVID-19. With clinically tested inhibitors available, this study identifies sPLA2-IIA as a therapeutic target to reduce COVID-19 mortality.

Keywords: COVID-19; Cellular immune response; Inflammation; Molecular pathology.

Figure 1. Untargeted lipidomics analysis and COVID-19 status.

Plasma samples from non–COVID-19 patients, those with mild COVID-19, those with severe COVID-19, and deceased COVID-19 patients were subjected to untargeted metabolomics analyses. Lipidome data were extracted from the metabolomics data set and analyzed. (A) Volcano plots show significant alterations in the lipidome of the deceased COVID-19 patients compared with that of the non–COVID-19 patients, patients with mild COVID-19, and patients with severe COVID-19. Colored areas highlight compounds with a FC of greater than 1.5 and a FDR of less than 0.1. (B) Heatmap of the top 20 metabolites whose abundances varied markedly across non–COVID-19 patients (Non–COVID-19), patients with mild COVID-19 (Mild), patients with severe COVID-19 (Severe), and deceased COVID-19 patients (Deceased). (C) Abundances of 2 lyso-PLs, 2 FFAs, and 2 short-chain acyl carnitines extracted from the untargeted lipid data. C16:0e lyso-PC in the upper right panel is an example of a PC-containing lysolipid that did not meet the FC and FDR criteria in A and is not a primary substrate of sPLA₂-IIA. The other 5 compounds were selected from the colored regions in A (FDR <0.1) and may have resulted from the action of sPLA₂-IIA. The levels in each panel were further compared using a 1-sided Wilcoxon test with Holm’s correction for multiple testing. For the box plots, the upper and lower bounds indicate the 75th (Q3) and 25th (Q1) percentiles, respectively; the line within the box indicates the median value; whiskers extend to values within 1.5 IQR (IQR, Q3–Q1) of the upper or lower bound; outlying values are shown between 1.5 and 3 IQR beyond the upper or lower bound. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. (D) Model of PLA₂ reaction showing how PLA₂ hydrolyzes the sn-2 position of the glycerol backbone of phospholipids to form lyso-PL and FFA products.

Figure 2. Association between sPLA₂-IIA and COVID-19 status.

(A) sPLA₂-IIA levels were determined in 127 plasma samples and are shown here sorted within each group. The inset box plot compares the log-transformed data across groups and shows the medians and quartiles. Groups were compared using a 1-sided Wilcoxon test with Holm’s correction for multiple testing. ***P < 0.001 and ****P < 0.0001. Pairwise comparisons were computed from a linear model that included age and sex, and P values were adjusted for multiple comparisons. (B) sPLA₂ enzymatic activity within plasma was assayed in a selected subset of samples. In the box plots in A and B, the upper and lower bounds designate the 75th (Q3) and 25th (Q1) percentiles, respectively; the line within the box indicates the median value; whiskers extend to values within 1.5 IQR (IQR, Q3–Q1) of the upper or lower bound; outlying values are shown between 1.5 and 3 IQR beyond the upper or lower bound. (C) Scatter plot shows plasma sPLA₂-IIA levels versus sPLA₂ activity in the selected subset of samples. Enzyme levels and activity were strongly correlated, indicating that plasma levels of sPLA₂-IIA reflect the levels of active enzyme in the larger sample set. (D) A heatmap showing the significant Spearman correlations (FDR <0.05) between sPLA₂-IIA and other clinical indices of disease severity. Indices that were positively or negatively correlated with sPLA₂-IIA are as indicated. Indices with missing values above 25 were removed, and those with a skewness (absolute value) below 1.0 were log transformed. Index values were mean centered and scaled according to the SD. Blue to red represents low to high index values, with color intensity indicating the value magnitude (see the color scheme). Missing values are shown in gray.

Figure 3. Clinical decision tree predicting COVID-19 severity and mortality.

(A) Clinical decision tree model. Patients were classified on the basis of the indicated clinical indices (shown in orange diamonds) and boundary conditions (above the split arrows). The number of patients following each split is shown in parentheses beneath the split arrow (patients with missing index values were not included in the split). In each node, the percentages of patients in the corresponding categories are shown. The inset graph shows the area under the ROC curve, AUC, of the tree in determining each group designation (e.g., deceased vs. nondeceased patients). (B) Decision surface based on the sPLA₂ and BUN boundary conditions in A. The left and right graphs show the results following application of the sPLA2 and BUN boundary conditions to the subsets of patients in these graphs (split following the 7-category ordinal scale), as indicated in A. (C) PLA-BUN index. The precision, sensitivity/recall, and accuracy in classifying patients with severe COVID-19 and deceased COVID-19 patients (7-category ordinal scale ≥4) by combining both decision boundary conditions of sPLA2 and BUN, as in B (i.e., the PLA-BUN index), are indicated with a red star in each graph, respectively. The corresponding classification results obtained by using the single index of sPLA2 (light blue curve) or BUN (dark blue curve) are shown with varying cutoff values in the corresponding data range (sPLA₂, 3.4–1101.2 ng/mL; BUN, 5–242 mg/dL).

Figure 4. Feature importance ranking of clinical indices.

(A) The relative importance of the 80 clinical indices in separating the deceased patients from patients with severe COVID-19 (n = 30 each) was evaluated in a random forest analysis. In this random forest, an assembly of decision trees (n = 1000) was generated using randomly selected subsets of patients and features (clinical indices) to collectively arrive at the final model prediction (deceased vs. severe). The importance of a feature (i.e., clinical index) was evaluated by the decrease in prediction accuracy, when such a feature was excluded from the model, assessed on the basis of (B) Gini impurity following a node split (MDI) and (C) the permuted values of the feature (MDA). The feature importance was evaluated in 10 repeated random forest analyses. The top 30 features in B and C are shown (the color scheme is proportional to the importance score).

Figure 5. Potential direct and organism-wide pathogenic mechanism of sPLA₂-IIA.

Mechanisms include: (a) hydrolysis of cellular membranes that broadly invoke tissue damage and organ dysfunction; (b) hydrolysis of mitochondrial membranes leading to the release of mtDNA, acetylcarnitine, and DAMPs; (c) internalization of damaged mitochondria by bystander leukocytes to increase inflammatory mediators including lyso-PLs, UFAs, eicosanoids, and cytokines; and (d) hydrolysis of platelet-derived EVs to release eicosanoids, platelet-activating factor, and lyso-PLs.