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. 2021 Nov 30:12:774776.
doi: 10.3389/fimmu.2021.774776. eCollection 2021.

m6A Regulator-Mediated Methylation Modification Patterns and Characteristics of Immunity in Blood Leukocytes of COVID-19 Patients

Xiangmin Qiu  1 , Xiaoliang Hua  2   3 , Qianyin Li  1 , Qin Zhou  1 , Juan Chen  1
Affiliations

m6A Regulator-Mediated Methylation Modification Patterns and Characteristics of Immunity in Blood Leukocytes of COVID-19 Patients

Xiangmin Qiu et al. Front Immunol. .

Abstract

Both RNA N6-methyladenosine (m6A) modification of SARS-CoV-2 and immune characteristics of the human body have been reported to play an important role in COVID-19, but how the m6A methylation modification of leukocytes responds to the virus infection remains unknown. Based on the RNA-seq of 126 samples from the GEO database, we disclosed that there is a remarkably higher m6A modification level of blood leukocytes in patients with COVID-19 compared to patients without COVID-19, and this difference was related to CD4+ T cells. Two clusters were identified by unsupervised clustering, m6A cluster A characterized by T cell activation had a higher prognosis than m6A cluster B. Elevated metabolism level, blockage of the immune checkpoint, and lower level of m6A score were observed in m6A cluster B. A protective model was constructed based on nine selected genes and it exhibited an excellent predictive value in COVID-19. Further analysis revealed that the protective score was positively correlated to HFD45 and ventilator-free days, while negatively correlated to SOFA score, APACHE-II score, and crp. Our works systematically depicted a complicated correlation between m6A methylation modification and host lymphocytes in patients infected with SARS-CoV-2 and provided a well-performing model to predict the patients' outcomes.

Keywords: COVID-19; immune characteristics; leukocytes; m6A methylation modification; protective model.

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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
The diagram and workflow of the project. (A) The overview of m6A RNA methylation modification in blood lymphocytes of patients infected with SARS-CoV-2, including ‘writers’, ‘readers’, and ‘erasers’. (B) The study flow chart.
Figure 2
Figure 2
COVID-19 patients were characterized by upregulated m6A genes and activation of the lymphocytes. (A) The expression of 20 m6A genes of blood leukocytes between patients with or without COVID-19. (B) Correlation plot of 20 m6A genes. The positive correlation was marked with blue, and negative correlation was marked with red. The size of circle represents the absolute value of correlation coefficients. (C) GSVA enrichment analysis showing activated interferon pathways in COVID-19 patients. Red represents high expression, blue represents low expression. (D) The abundance of leukocytes in patients with or without COVID-19. (E) The significant leukocytes fractions in patients with or without COVID-19. (F) The heatmap of correlation between leukocytes and m6A genes. The positive correlation was marked with blue, and negative correlation was marked with red. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, no significance.
Figure 3
Figure 3
Biological progression between the two m6A clusters. (A) Consensus clustering matrix for k = 2. (B) The heatmap of m6A genes between the two m6A clusters. Red represents high expression, blue represents low expression. (C) Expression levels of significant m6A genes between the two m6A clusters. (D) The HFD45 between the two m6A clusters. (E) The innate immune pathways-related genes between the m6A clusters. (F, G) GSVA analysis showing the activation of classical pathways and distinct biological processes in metabolism and immune response. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, no significance.
Figure 4
Figure 4
Immune characteristics between the two m6A clusters. (A) The abundance of leukocytes between the m6A clusters. (B) The immunoscore between the two m6A clusters. (C) Expression levels of immune checkpoint genes between the m6A clusters. (D) The KEGG enrichment analysis based on DEGs of the two clusters. The color bar represents the p values of the pathways. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns, no significance.
Figure 5
Figure 5
Clinical manifestations and m6A modification levels between the two m6A clusters. (A) Heatmap of the DEGs between the gene clusters. m6A cluster and clinical feature annotation was used. (B) ICU, age, and diabetes proportions between the m6A clusters. (C) m6A score between the m6A clusters.
Figure 6
Figure 6
Construction of a protective model to predict patients with COVID-19. (A) Venn plot between DEGs of COVID-19 and DEGs of clusters. (B, C) Construction of a protective model based on intersecting DEGs. (D, E) The HFD45 of patients in the training set and testing set ranked by protective score. (F, G) AUC of patients in the training set and testing set. (H, I) The heatmap of the model genes in the training set and testing set.
Figure 7
Figure 7
The enrichment of model genes and correlations between protective score and clinical information. (A) The biological process of the model-related genes. (B–F) The correlations between protective score and HFD45 (B), SOFA score (C), APACHE-II score (D), crp (E), and ventilator-free days (F).
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