Scholarlink

Biofilms are structured microbial communities using a self-synthetic extracellular polymeric substance (EPS) matrix, providing structural support and shielding microbes from unfavorable external environments. These biofilm-producing microbial communities play a significant role in healthcare-associated infections in medical facilities and industrial device contamination and biofouling. The systems biology strategy of metabolomics enables powerful biomolecule assessment within biological complexes and has emerged as a powerful tool in biofilm research. Also, it facilitates the discovery of metabolic pathways essential for biofilm development, quorum sensing, and secondary metabolite production. Advances in analytical techniques, such as mass spectrometry (MS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS) have enabled the profiling of biofilm-specific metabolites. The primary use of metabolomics in biofilm research lies in inhibitor identification of biofilm development through compounds from natural and synthetic origins and assessing their metabolic impact. The discovery of new pharmaceuticals depends on identifying weaknesses in metabolic pathways, including lipid, nucleotide, amino acid, and carbohydrate metabolism, on disrupting biofilm integrity. Key metabolic bottlenecks that can serve as potential drug targets could be identified using metabolic profiling. Additionally, artificial intelligence and machine learning (AI/ML) applications in metabolomics facilitate robust data processing, predictive modeling, and pattern recognition for biofilm control strategies. Challenges in biofilm metabolomics include the complexity of biofilm matrices, heterogeneity in metabolic signatures, and the necessity for robust data analysis pipelines. In the future, precision-targeted biofilm inhibitors, biofilm-specific pathway interventions, and customized anti-biofilm therapies based on metabolic profiles will be developed. This integrative metabolomics-driven approach is significant for advancing biofilm research, improving infection management, and moderating biofilm-related concerns in healthcare and industrial setups.

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

Metabolomics Approaches in Understanding the Biofilm Formation and Its Inhibitors

  • 章节

  • 发表日期：

    2025年

查看全文
  • Barath Gajendran,
  • Hemavathy Nagarajan,
  • Busi Siddhartha,
  • Sharada Ramasubramanyan,
  • Dinakara Rao Ampasala,
  • Ramya Chandar Charles Mariasoosai,
  • Sampathkumar Ranganathan

摘要

Biofilms are structured microbial communities using a self-synthetic extracellular polymeric substance (EPS) matrix, providing structural support and shielding microbes from unfavorable external environments. These biofilm-producing microbial communities play a significant role in healthcare-associated infections in medical facilities and industrial device contamination and biofouling. The systems biology strategy of metabolomics enables powerful biomolecule assessment within biological complexes and has emerged as a powerful tool in biofilm research. Also, it facilitates the discovery of metabolic pathways essential for biofilm development, quorum sensing, and secondary metabolite production. Advances in analytical techniques, such as mass spectrometry (MS), nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS) have enabled the profiling of biofilm-specific metabolites. The primary use of metabolomics in biofilm research lies in inhibitor identification of biofilm development through compounds from natural and synthetic origins and assessing their metabolic impact. The discovery of new pharmaceuticals depends on identifying weaknesses in metabolic pathways, including lipid, nucleotide, amino acid, and carbohydrate metabolism, on disrupting biofilm integrity. Key metabolic bottlenecks that can serve as potential drug targets could be identified using metabolic profiling. Additionally, artificial intelligence and machine learning (AI/ML) applications in metabolomics facilitate robust data processing, predictive modeling, and pattern recognition for biofilm control strategies. Challenges in biofilm metabolomics include the complexity of biofilm matrices, heterogeneity in metabolic signatures, and the necessity for robust data analysis pipelines. In the future, precision-targeted biofilm inhibitors, biofilm-specific pathway interventions, and customized anti-biofilm therapies based on metabolic profiles will be developed. This integrative metabolomics-driven approach is significant for advancing biofilm research, improving infection management, and moderating biofilm-related concerns in healthcare and industrial setups.