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. 2022 Jul 19:13:888661.
doi: 10.3389/fimmu.2022.888661. eCollection 2022.

Pyroptotic Patterns in Blood Leukocytes Predict Disease Severity and Outcome in COVID-19 Patients

Yingkui Tang  1 , Peidong Zhang  1 , Qiuyu Liu  2 , Luyang Cao  3 , Jingsong Xu  1   3
Affiliations

Pyroptotic Patterns in Blood Leukocytes Predict Disease Severity and Outcome in COVID-19 Patients

Yingkui Tang et al. Front Immunol. .

Abstract

The global coronavirus disease 2019 (COVID-19) pandemic has lasted for over 2 years now and has already caused millions of deaths. In COVID-19, leukocyte pyroptosis has been previously associated with both beneficial and detrimental effects, so its role in the development of this disease remains controversial. Using transcriptomic data (GSE157103) of blood leukocytes from 126 acute respiratory distress syndrome patients (ARDS) with or without COVID-19, we found that COVID-19 patients present with enhanced leukocyte pyroptosis. Based on unsupervised clustering, we divided 100 COVID-19 patients into two clusters (PYRcluster1 and PYRcluster2) according to the expression of 35 pyroptosis-related genes. The results revealed distinct pyroptotic patterns associated with different leukocytes in these PYRclusters. PYRcluster1 patients were in a hyperinflammatory state and had a worse prognosis than PYRcluster2 patients. The hyperinflammation of PYRcluster1 was validated by the results of gene set enrichment analysis (GSEA) of proteomic data (MSV000085703). These differences in pyroptosis between the two PYRclusters were confirmed by the PYRscore. To improve the clinical treatment of COVID-19 patients, we used least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model based on differentially expressed genes between PYRclusters (PYRsafescore), which can be applied as an effective prognosis tool. Lastly, we explored the upstream transcription factors of different pyroptotic patterns, thereby identifying 112 compounds with potential therapeutic value in public databases.

Keywords: COVID-19; leukocytes; prognosis model; pyroptosis; transcription factors.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
(A) Boxplot of 35 pyroptosis-related genes’ relative expression between different types of patients. C: COVID-19 patients; NC: none-COVID-19 patients. (B) The Pearson’s correlation between 35 pyroptosis-related genes in COVID -19 patients, R value represents the Pearson’s correlation coefficient. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Figure 2
Figure 2
(A) Gene set variation analysis (GVSA) analysis shows COVID-19 patients’ leukocytes may have been significantly damaged during viral infection and are undergoing damage repair. C: COVID-19 patients; NC: none-COVID-19 patients. (B) The abundance of leukocytes between the different types of patients. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, no significance.
Figure 3
Figure 3
(A) Consensus clustering matrix for k = 2. (B) The heatmap of 35 pyroptosis-related genes between the two PYRclusters. Red represents high expression; blue represents low expression. (C) Boxplot of significant pyroptosis-related genes’ relative expression between two PYRclusters. (D–F) The HFD45, ventilator-free days, D-dimer levels between the two PYRclusters. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, no significance.
Figure 4
Figure 4
(A) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of 570 DEGs Between two PYRclusters, “up” means these pathways of PYRcluster2 were upregulated when compared to PYRcluster1; “down” means these pathways were downregulated. (B) Leukocytes with significantly different expression levels among PYRclusters. (C) ImmuneScore calculated by “estimate” package between two PYRclusters. (D) Pearson’s correlation between expressions of 35 pyroptosis-related genes and abundance of leukocytes, R value represents the Pearson’s correlation coefficient. Annotated bars above and to the left indicate in which PYRcluster each pyroptosis-related gene or leukocyte is highly expressed.
Figure 5
Figure 5
(A) Heatmap of the DEGs between the gene clusters, different clinical data was shown in the annotation. (B) Pyrscore between two PYRclusters. (C, D) Pearson’s correlations between pyrscore and ventilator-free days(C), HFD45 (D), R value represents the Pearson’s correlation coefficient; grey area represents the 95% confidence interval for the linear fit. The maximum value of ventilator-free days is 28 since this 28-day time frame was initially chosen because most subjects with ARDS will have died or been extubated by Day 28. (E) Mean-squared error (MSE) of different numbers of variables revealed by the LASSO regression model. The red dots represent the MSE values; the grey lines represent the standard error (SE); the two vertical dotted lines on the left and right, respectively, represent optimal values by minimum criteria and 1-SE criteria. “Lambda” is the tuning parameter. (F) AUC of patients in the training group and test group. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, no significance.
Figure 6
Figure 6
(A, B, C) Pearson’s correlations between PYRsafescore and HFD45 (A), ventilator-free days (B), APACHE-II (C), R value represents the Pearson’s correlation coefficient; grey area represents the 95% confidence interval for the linear fit. (D) Heatmap of signature genes of PYRsafescore; expression of these genes was highly correlated with HFD45 and PYRsafescore. (E) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of signature genes of PYRsafescore. (F) Transcription factor enrichment of 570 DEGs between PYRclusters using “clusterProfiler” package based on MSigDB gene set: TFT (transcription factor targets) gene set.
Figure 7
Figure 7
(A) Transcription factors regulatory network of PYRcluster1. “Degree” means the number of edges connected to the node. (B) Pearson’s correlation of differentially expressed pyroptosis-related genes and transcription factors in PYRclusters; R value represents the Pearson’s correlation coefficient. Annotation on the left represents in which PYRcluster each pyroptosis-related gene is significantly highly expressed. (C) Pearson’s correlation between different clinical data and transcription factors; R value represents the Pearson’s correlation coefficient.
Figure 8
Figure 8
Different patterns of pyroptosis of blood leukocytes in patients with COVID-19.
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