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Related Concept Videos

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Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
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Integrative transcriptome-based drug repurposing in tuberculosis.

Kewalin Samart1,2, Landon Buskirk3,2, Amy Tonielli4,2

  • 1Computational Bioscience Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

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This summary is machine-generated.

This study introduces a computational method to find new tuberculosis treatments by repurposing existing drugs. It identifies 140 potential host-directed therapeutics, offering new hope against drug-resistant tuberculosis.

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Area of Science:

  • Computational biology
  • Immunology
  • Pharmacology

Background:

  • Tuberculosis (TB) is a leading infectious cause of death, with rising antibiotic resistance necessitating novel therapeutic strategies.
  • Host-directed therapeutics (HDTs) that modulate host immunity are crucial, especially by repurposing existing drugs.
  • Transcriptome-based drug discovery, successful in other diseases, faces challenges in bacterial infections like TB due to data heterogeneity and methodological uncertainties.

Purpose of the Study:

  • To develop and apply an integrative computational workflow for systematic identification of FDA-approved drugs as potential TB HDTs.
  • To overcome challenges in transcriptome-based analysis for bacterial infections by integrating multiple scoring methods and consensus signature construction.
  • To discover novel drug candidates and potential therapeutic targets for TB.

Main Methods:

  • Developed an integrative computational workflow combining six connectivity scoring methods.
  • Constructed weighted consensus disease signatures from 21 TB gene expression datasets (microarray and RNA-seq).
  • Applied the framework to systematically identify FDA-approved drugs that reverse TB-associated gene expression patterns.

Main Results:

  • Prioritized 140 high-confidence drug candidates, including known HDTs like statins and vitamin D receptor agonists.
  • Identified novel promising candidates such as niclosamide and tamoxifen, with recent experimental validation.
  • Revealed enrichment for cholesterol metabolism inhibition and immune modulation pathways, and identified 10 key bridging genes as potential druggable targets.

Conclusions:

  • Established transcriptome-based connectivity mapping as a viable approach for systematic HDT discovery in bacterial infections.
  • Provided a robust computational framework applicable to other infectious diseases.
  • Generated immediate opportunities for experimental validation of drug candidates and mechanistic investigation of identified targets in TB.