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Related Concept Videos

Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

134
Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
134

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

Lovisa Karlsson1, Isabelle Öhrnberg1, Shumaila Sayyab1

  • 1Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences.

The Journal of Infectious Diseases
|July 4, 2024
PubMed
Summary

DNA methylation signatures in buccal swabs show promise for diagnosing tuberculosis (TB). This novel approach uses DNA methylation (DNAm) from cheek cells to identify TB, offering a potentially faster and more cost-effective diagnostic strategy.

Keywords:
DNA methylationbiosignaturebuccal swabsclassifiertuberculosis

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Area of Science:

  • Epigenetics
  • Molecular Diagnostics
  • Infectious Disease Research

Background:

  • Tuberculosis (TB) remains a leading global infectious cause of mortality.
  • There is a critical need for efficient and cost-effective diagnostic methods for TB.
  • Current diagnostic approaches can be time-consuming and resource-intensive.

Purpose of the Study:

  • To explore the potential of DNA methylation (DNAm) signatures in buccal swabs as a diagnostic tool for TB.
  • To investigate if DNAm patterns can differentiate between TB patients, exposed individuals, and healthy controls.
  • To develop a machine learning-based classifier for TB diagnosis using DNAm data.

Main Methods:

  • Buccal swabs were collected from TB patients, TB-exposed individuals, and controls in Sweden.
  • DNA methylation status was analyzed using the Illumina MethylationEPIC array.
  • A validation cohort from Kenya and Peru was used to confirm findings.
  • Machine learning algorithms were employed to identify discriminative CpG sites and build a TB classifier.

Main Results:

  • 5644 differentially methylated CpG sites were identified between TB patients and controls.
  • A TB consensus disease module enriched in TB-associated genes was found.
  • A classifier based on 7 CpG sites demonstrated high performance in the validation cohort (AUC=0.94, sensitivity=0.92, specificity=1).

Conclusions:

  • DNA methylation signatures in buccal swabs hold significant clinical potential for TB diagnosis.
  • This approach offers a novel strategy for developing more accessible and efficient TB diagnostic tools.
  • Further research can explore the broader application of DNAm in infectious disease diagnostics.