A DNA Methylation Signature from Buccal Swabs to Identify Tuberculosis Infection.

DNA methylation Tuberculosis biosignature buccal swabs classifier

Journal

The Journal of infectious diseases
ISSN: 1537-6613
Titre abrégé: J Infect Dis
Pays: United States
ID NLM: 0413675

Informations de publication

Date de publication:
04 Jul 2024
Historique:
received: 10 01 2024
revised: 29 05 2024
accepted: 24 06 2024
medline: 4 7 2024
pubmed: 4 7 2024
entrez: 4 7 2024
Statut: aheadofprint

Résumé

Tuberculosis (TB) is amongst the largest infectious causes of death worldwide and there is a need for a time- and resource-effective diagnostic method. 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. Buccal swabs were collected from pulmonary TB patients (n= 7), TB exposed (n= 7), and controls (n= 9) in Sweden. Using Illumina MethylationEPIC array the DNAm status was determined. 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. Lastly, we used machine learning to identify a panel of seven CpG sites discriminative for TB and developed a TB classifier. In the validation cohort the classifier performed with an AUC of 0.94, sensitivity of 0.92, and specificity of 1. In summary, the result from this study shows clinical implications of using DNAm signatures from buccal swabs to explore new diagnostic strategies for TB.

Sections du résumé

BACKGROUND BACKGROUND
Tuberculosis (TB) is amongst the largest infectious causes of death worldwide and there is a need for a time- and resource-effective diagnostic method. 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 METHODS
Buccal swabs were collected from pulmonary TB patients (n= 7), TB exposed (n= 7), and controls (n= 9) in Sweden. Using Illumina MethylationEPIC array the DNAm status was determined.
RESULTS 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. Lastly, we used machine learning to identify a panel of seven CpG sites discriminative for TB and developed a TB classifier. In the validation cohort the classifier performed with an AUC of 0.94, sensitivity of 0.92, and specificity of 1.
CONCLUSION 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.

Identifiants

pubmed: 38962817
pii: 7705862
doi: 10.1093/infdis/jiae333
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.

Auteurs

Lovisa Karlsson (L)

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

Isabelle Öhrnberg (I)

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

Shumaila Sayyab (S)

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

David Martínez-Enguita (D)

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

Mika Gustafsson (M)

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

Patricia Espinoza (P)

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

Melissa Méndez-Aranda (M)

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

Cesar Ugarte-Gil (C)

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

Lameck Diero (L)

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

Ronald Tonui (R)

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

Jakob Paues (J)

Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.
Department of Infectious Diseases, Linköping University Hospital, Linköping, Sweden.

Maria Lerm (M)

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

Classifications MeSH