A DNA Methylation Signature From Buccal Swabs to Identify Tuberculosis Infection

Abstract

Background: Tuberculosis (TB) is among the largest infectious causes of death worldwide, and there is a need for a time- and resource-effective diagnostic methods. In this novel and exploratory study, we show the potential of using buccal swabs to collect human DNA and investigate the DNA methylation (DNAm) signatures as a diagnostic tool for TB.

Methods: Buccal swabs were collected from patients with pulmonary TB (n = 7), TB-exposed persons (n = 7), and controls (n = 9) in Sweden. Using Illumina MethylationEPIC array, the DNAm status was determined.

Results: We identified 5644 significant differentially methylated CpG sites between the patients and controls. Performing the analysis on a validation cohort of samples collected in Kenya and Peru (patients, n = 26; exposed, n = 9; control, n = 10) confirmed the DNAm signature. We identified a TB consensus disease module, significantly enriched in TB-associated genes. Last, we used machine learning to identify a panel of 7 CpG sites discriminative for TB and developed a TB classifier. In the validation cohort, the classifier performed with an area under the curve of 0.94, sensitivity of 0.92, and specificity of 1.

Conclusions: In summary, the result from this study shows clinical implications of using DNAm signatures from buccal swabs to explore new diagnostic strategies for TB.

Keywords: DNA methylation; biosignature; buccal swabs; classifier; tuberculosis.

Graphical Abstract

Figure 1.

DNA methylation patterns in buccal swabs distinguish patients with active tuberculosis (TB) (pink triangles), TB-exposed individuals (green squares), and healthy controls (blue circles). A, Multidimensional scaling plot of the 1000 most variable CpG sites within the dataset. B, Heatmap of beta values of the differently methylated CpG sites (DMCs) identified in pairwise comparison across all groups with stringency criteria of adjusted P < .05 and mean methylation difference >0.2. Dendrogram shows separation based on groups. C, Venn diagram of the DMCs identified in the pairwise comparisons showing the largest amount of DMCs between the TB patients and healthy controls. D, Hierarchical epigenetic dissection of intra-sample heterogeneity of the data showing proportions of different cell types within the mouth swab samples. No significant difference of the cell types between groups was identified (Kruskal-Wallis test, P = .893).

Figure 2.

A validation cohort confirms differential methylation pattern from buccal swabs between patients with tuberculosis (TB) and healthy controls. A, Multidimensional scaling plot of the 1000 most variable CpG sites from buccal swab samples of patients with TB (pink triangles), TB-exposed individuals (green squares), and healthy controls (blue circles) with 85% confidence ellipses. The interferon-gamma release assay (IGRA) status of participants is indicated with black outline. B, Differently methylated CpG sites (DMCs) between the patients and controls from the pilot cohort and validation cohort compared in a Venn analysis showing an overlap of 22 DMCs. C, Multidimensional scaling plot of the 1000 most variable CpG sits in DNA methylomes from buccal swab samples from TB patients (pink), TB-exposed individuals (green), and healthy controls (blue) from Kenya (triangles), Peru (squares), or Sweden (circles).

Figure 3.

Tuberculosis (TB) classifier based on DNA methylation (DNAm) in buccal swab samples accurately classifies patients with TB among healthy controls and exposed individuals. Using machine learning, 20 candidate CpG sites with discriminative features for TB were obtained. A classifier was trained on the pilot cohort (blue) and tested on the validation cohort (orange). A, Sensitivity (y-axis) of the classifier based on DNAm level in 1–20 CpG sites (x-axis). Sensitivity of 0.70–0.94 was reached depending on the number of CpGs included. B, Specificity (y-axis) of the classifier in 1–20 CpG sites (x-axis). Specificity of 0.95–100 was reached depending on the number of CpG sites investigated. C, Receiver operating characteristics (ROCs) of the classifier based on 7 CpG sites (sensitivity of 0.92 and specificity 1) showing the true-positive rate (y-axis) and false-positive rate (x-axis) with an area under the curve (AUC) of 0.94. D, Beta values of the 7 classifier CpG sites for each group. The CpG sites are ordered by importance. All samples from pilot and validation cohort are represented in the plot.

Figure 4.

Disease modules of patients with tuberculosis (TB) from pilot and validation cohort overlap and are enriched in TB-associated genes and pathways. A, Disease modules for the pilot and validation cohort identified based on the differentially methylated CpG sites from each cohort using MODifieR. The disease modules were compared in a Venn analysis showing significant overlap (P < 2.2e-16; odds ratio [OR], 13.56) and a consensus module of 48 genes. B, Network showing the genes in the consensus module and their connections. Hypermethylated CpG sites are shown in red, hypomethylated in blue, mixed methylation pattern in beige, and TB-associated genes with a black outline. There was a significant overlap of TB-associated genes in the interconnected module genes (n = 42) (P = .03; OR, 2.75). The module was explored using KEGG pathway enrichment analysis, and genes enriched in pathways of cell and extracellular matrix (ECM) interactions (light blue area) and genes enriched in immune system pathways (light red area) were identified.