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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.
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Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Related Experiment Video

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Demonstrating a Multi-drug Resistant Mycobacterium tuberculosis Amplification Microarray
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Microarray and network-based identification of functional modules and pathways of active tuberculosis.

Zhong-Rui Bian1, Juan Yin2, Wen Sun2

  • 1Department of Cardiology, The Second Hospital of Shandong University, Jinan 250033, Shandong Province, China.

Microbial Pathogenesis
|February 13, 2017
PubMed
Summary
This summary is machine-generated.

Diagnosing active tuberculosis (TB) and monitoring treatment response are challenging. This study identified key gene modules and pathways in active TB using integrated network analysis, offering potential biomarkers for diagnosis and treatment.

Keywords:
Active tuberculosisDifferential expression networkDifferential modulesEgo genesReactome

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Diagnosing active tuberculosis (TB) and monitoring treatment response present significant clinical challenges.
  • Identifying reliable biomarkers for active TB is crucial for effective patient management.

Purpose of the Study:

  • To identify a diagnostic module set and key biological pathways associated with active tuberculosis (TB).
  • To leverage integrated analysis of microarray and network-based methods for improved TB diagnostics.

Main Methods:

  • Construction of a protein-protein interaction (PPI) network integrated with gene expression data.
  • Identification of differential expression networks (DENs) and extraction of ego genes.
  • Module discovery using the EgoNet algorithm and pathway enrichment analysis via Reactome database.

Main Results:

  • Identification of 47 ego genes and 47 candidate modules from the DEN.
  • Extraction of 7 statistically significant ego modules (Module 4, 7, 9, 19, 25, 38, 43) with high classification accuracy.
  • Enrichment analysis revealed pathways including 'formation of a pool of free 40S subunits', 'eukaryotic translation termination', and 'nonsense mediated decay' in active TB.

Conclusions:

  • The identified differential modules and pathways may serve as potential biomarkers for active TB diagnosis and treatment monitoring.
  • This study provides valuable insights into the molecular mechanisms underlying active TB.