DNA methylation profiles in pneumonia patients reflect changes in cell types and pneumonia severity

Abstract

Immune cell-type composition changes with age, potentially weakening the response to infectious diseases. Profiling epigenetics marks of immune cells can help us understand the relationship with disease severity. We therefore leveraged a targeted DNA methylation method to study the differences in a cohort of pneumonia patients (both COVID-19 positive and negative) and unaffected individuals from peripheral blood.This approach allowed us to predict the pneumonia diagnosis with high accuracy (AUC = 0.92), and the PCR positivity to the SARS-CoV-2 viral genome with moderate, albeit lower, accuracy (AUC = 0.77). We were also able to predict the severity of pneumonia (PORT score) with an R² = 0.69. By estimating immune cellular frequency from DNA methylation data, patients under the age of 65 positive to the SARS-CoV-2 genome (as revealed by PCR) showed an increase in T cells, and specifically in CD8+ cells, compared to the negative control group. Conversely, we observed a decreased frequency of neutrophils in the positive compared to the negative group. No significant difference was found in patients over the age of 65. The results suggest that this DNA methylation-based approach can be used as a cost-effective and clinically useful biomarker platform for predicting pneumonias and their severity.

Keywords: DNA methylation; SARS-CoV-2; biomarkers; cell-type deconvolution; pneumonia; targeted bisulfite sequencing.

Figure 1.

a,b,c) ROC curve of the LOOCV penalized logistic regression models (refer to Supplementary Figure 1 for group definition). a) Respiratory Diagnosis (positive: groups I, II, III, and IV vs. negative: group V). b) COVID-19 Diagnosis within the pneumonia group (positive: groups I, II, and III vs. negative: group IV). c) PCR test positivity (positive: groups II, and III vs. negative: groups I, IV, and IV). d) epiPORT model (LOOCV penalized linear regression) based on COVID-19 diagnosis vs. non COVID-19 pneumonia (positive: groups I, II, and III vs. negative: group IV). Individuals with a positive diagnosis are indicated in teal, while negative in red. e) Predictive CpG sites shared among the models (see Supplementary Table 5). The barplot (top) shows the number of shared predictive CpG sites. In the chord diagram (bottom) the shared predictive sites are displayed as ribbons (the thickness is proportional to the number of shared sites) connecting the models (the numbers represent the predictive CpG sites for each model). The overlap was calculated using the VIB/UGent Venn webtool (http://bioinformatics.psb.ugent.be/webtools/Venn/).

Figure 2.

a) Heatmap of the individual samples based on the values of 5^meC-calculated cell types: neutrophils (bs Neutro); B cells (bs B-cells); NK cells (bs NK cells); CD4 T cells (bs CD4+); CD8 T cells (bs CD8+); monocytes (bs Mono). Samples are divided in three groups based on the hierarchical clustering: cluster A (salmon); cluster B (light blue); cluster C (orchid). Technical replicates are labelled in blue. The annotations on the left refers to: age (Age); CT scan classification (CT): atypical, bacterial, non-pneumonia control (CTRL), pneumonia normal (normal), viral; diagnosis classification (Diagnosis): atypical pneumonia (ATYPICALpn), bronchopneumonia (BRONCHOpn), COVID-19 pneumonia (COVID-19), non-pneumonia control (CTRL); PCR test positivity (PCR): negative (PCRneg), positive (PCRpos); ratio between 5^meC-calculated Neutrophils and Lymphocytes (bsNeutroLympho); ratio between Neutrophil and Lymphocyte counts (AbsNeutroLympho); Lymphocyte counts (LymphoAbs); Neutrophil counts (NeutroAbs); Lymphocyte percentage (Lympho %); Neutrophil percentage (Neutro %); white blood cell counts (WBC). Statistical analysis of the distribution of the annotation features among the three clusters: blue for continuous values (Kruskal-Wallis); red for categorical values (Fisher’s exact test): Diagnosis (controls, COVID-19 pneumonia, non-COVID-19 pneumonia); CT (control, viral, non-viral); PCR (positive, negative); statistical significance (p-sig; ns = p-val >0.05; * = p-val <0.05; ** = p-val <0.01; *** = p-val <0.001; **** = p-val <0.0001). The p-value coloured boxes below the p-sig, and above the annotations represents the -log10(p-value). b,c,d) Bar Plot of counts for each category of PCR test positivity (b), CT scan results (c), and Diagnosis (d) in each cluster. (e) Age distribution grouped by PCR test positivity for each cluster. Positive: groups II, and III vs. negative: groups I, IV, and IV (refer to Supplementary Figure 1).

Figure 3.

Violin plots showing the distribution of 5^meC-calculated neutrophils (a), T-cells (b, sum of CD4+, and CD8+), CD4 + T cells (c), CD8 + T cells (d) in three different age groups split by the PCR positivity test. Teal: PCR test negative; red: PCR test positive. Wilcoxon rank sum test was used to compare the means. Top bars are comparing age groups (ns = p-val >0.05; * = p-val <0.05; ** = p-val <0.01; *** = p-val <0.001; **** = p-val <0.0001), while the bottom bars are comparing PCR positive and negative within each group (ns = p-val >0.05; if the p-val <0.05, the value is indicated).