A DNA Methylation Signature From Buccal Swabs to Identify Tuberculosis Infection

Author:

Karlsson Lovisa1ORCID,Öhrnberg Isabelle1,Sayyab Shumaila1ORCID,Martínez-Enguita David2ORCID,Gustafsson Mika2,Espinoza Patricia3,Méndez-Aranda Melissa4,Ugarte-Gil Cesar56,Diero Lameck78,Tonui Ronald79,Paues Jakob110,Lerm Maria1ORCID

Affiliation:

1. Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University , Linköping , Sweden

2. Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University , Linköping , Sweden

3. Facultad de Medicina, Universidad Peruana Cayetano Heredia , Lima, Peru

4. Laboratorios de Investigación y Desarrollo, Facultad de Ciencias e Ingeniería, Universidad Peruana Cayetano Heredia , Lima, Peru

5. Facultad de Medicina

6. Instituto de Medicina Tropical Alexander Von Humboldt, Universidad Peruana Cayetano Heredia , Lima , Peru

7. AMPATH Kenya, Moi University , Eldoret , Kenya

8. Department of Medicine, Moi University , Eldoret , Kenya

9. Department of Pathology, Moi University , Eldoret , Kenya

10. Department of Infectious Diseases, Linköping University Hospital , Linköping , Sweden

Abstract

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.

Funder

Heart and Lung Foundation

Swedish Research Council

Publisher

Oxford University Press (OUP)

Reference49 articles.

1. Immunological mechanisms of human resistance to persistent Mycobacterium tuberculosis infection;Simmons;Nat Rev Immunol,2018

2. Gamma interferon release assays for detection of Mycobacterium tuberculosis infection;Pai;Clin Microbiol Rev,2014

3. False-positive tuberculin reactions due to non-tuberculous mycobacterial infections;Cobelens;Int J Tuberc Lung Dis,2007

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