Scholarlink

<p>Post Covid-19, tuberculosis (TB) was on the rise at an alarming rate due to the immunosuppressive treatment given to COVID-infected individuals. An estimated 10.8 million people contracted TB in 2023 and 1.25&#xa0;million people died from TB (WHO 2024 Global TB Report). This drives for more collective efforts towards controlling the spread and eradication of tuberculosis by designing more effective therapies against modulating TB bacteria. Protein–protein interaction (PPI) networks help us to understand genes associated with disease phenotype and to identify associated genes with similar functionality and therapeutic targets. Current study emphasizes on building a protein–protein interaction (PPI) network based on the validated protein targets for TB drugs, which were used to identify functionally associated proteins from the “Search Tool for the Retrieval of Interacting Genes” (STRING) database. An integrated protein–protein interaction (i-PPI) map was constructed and subjected to clustering analysis to identify hub proteins. The hub proteins were ranked and datamined across Knockout Gene studies, to identify highly potential drug targets for TB. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were carried out to verify the function, process, and pathways enriched by the identified proteins. Further, based on protein-chemical interactions, an effort was made to reposition new chemical compounds against the identified protein targets. Based on the protein–protein framework, 13 clusters were identified. Proteins namely RplA, RplB, RplE, RplM, RplR, RpsD, RpsI, RpsK, FtsQ, FtsW, and PbpB, showing high involvement in rRNA binding function of the protein translation process were identified as hub proteins from the said clusters. The functional and pathway enrichment analysis of the hub proteins demonstrated that they were mainly abundant in the protein synthesis pathway. The study demonstrates the use of an integrated protein–protein framework approach to identify hub proteins as a prioritized list of druggable targets. This provides a new course for the development of molecular targets for the treatment of tuberculosis and helps in identifying unexploited drug targets, and repositioning chemical compounds against them.</p> Graphical abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Network-based drug repositioning approach for tuberculosis

  • 期刊论文

  • 发表日期：

    2026年1月13日

查看全文
  • Abhijeet Dhulap,
  • Paromita Banerjee

摘要

Post Covid-19, tuberculosis (TB) was on the rise at an alarming rate due to the immunosuppressive treatment given to COVID-infected individuals. An estimated 10.8 million people contracted TB in 2023 and 1.25 million people died from TB (WHO 2024 Global TB Report). This drives for more collective efforts towards controlling the spread and eradication of tuberculosis by designing more effective therapies against modulating TB bacteria. Protein–protein interaction (PPI) networks help us to understand genes associated with disease phenotype and to identify associated genes with similar functionality and therapeutic targets. Current study emphasizes on building a protein–protein interaction (PPI) network based on the validated protein targets for TB drugs, which were used to identify functionally associated proteins from the “Search Tool for the Retrieval of Interacting Genes” (STRING) database. An integrated protein–protein interaction (i-PPI) map was constructed and subjected to clustering analysis to identify hub proteins. The hub proteins were ranked and datamined across Knockout Gene studies, to identify highly potential drug targets for TB. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were carried out to verify the function, process, and pathways enriched by the identified proteins. Further, based on protein-chemical interactions, an effort was made to reposition new chemical compounds against the identified protein targets. Based on the protein–protein framework, 13 clusters were identified. Proteins namely RplA, RplB, RplE, RplM, RplR, RpsD, RpsI, RpsK, FtsQ, FtsW, and PbpB, showing high involvement in rRNA binding function of the protein translation process were identified as hub proteins from the said clusters. The functional and pathway enrichment analysis of the hub proteins demonstrated that they were mainly abundant in the protein synthesis pathway. The study demonstrates the use of an integrated protein–protein framework approach to identify hub proteins as a prioritized list of druggable targets. This provides a new course for the development of molecular targets for the treatment of tuberculosis and helps in identifying unexploited drug targets, and repositioning chemical compounds against them.

Graphical abstract