Computational Genomics Approaches Against Antibacterial Drug Resistance

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Background

Antimicrobial resistance is a natural evolutionary process in response to antimicrobial exposure; however, the indiscriminate use of antimicrobials is accelerating such resistance. Resistance develops when microorganisms (e.g., bacteria, fungi, viruses, and parasites) evolve mechanisms to evade damage (e.g., drug inactivation/alteration, efflux pumps, porin loss, biofilm formation, reduced intracellular drug accumulation, modification of drug binding sites) caused by the contact with antimicrobial drugs, such as antibiotics, antifungals, antivirals, antimalarials, and anthelmintic, which involves genetic changes. In fact, 50% of prescribed antimicrobials are considered unnecessary and often medication is given without professional oversight (self-medication, e.g., colds, flu, headaches, minor infections). Moreover, this practice affects 62% of the currently used antibiotics while the rest may also have a role (direct or indirect). As a result, the medicine/treatment becomes ineffective and infections persist in the body, increasing the risk of spread to others, prolonged illness, disability, and death. Furthermore, major medical procedures (e.g., organ transplantation, cancer chemotherapy, diabetes management and surgery) also gets compromised. Infections with resistant pathogens also prompt a higher health care cost (estimated budget of $20 billion annually in the United States) compared to non-resistant infections due to longer duration of illness/hospitalization, additional tests, and use of more expensive drugs.

The search for alternative treatment (e.g., antimicrobial compounds, bacteriophages and phage-associated enzymes, alternative drug targets) presents a viable option to replace antimicrobials as the main source of treatment. Targeted drug development retains major challenges from candidate selection to in vitro and in vivo experiments and clinical trials. Hence, the advances in scientific knowledge (i.e., diseases and pathogens), advent of “-omics” approaches (e.g., genomics, transcriptomics and proteomics), and bioinformatics breakthroughs can usher a ‘big-data era’ that may lead to improved identification of putative targets via the application of in silico tools that can shorten the drug development timeline in a cost-efficient manner.

This Research Topic welcomes, but is not limited to, the following sub-topics:
• Advanced computational approaches to combat antibiotic resistance.
• Omics approaches to combat antibiotic resistance.
• Post-genomics era and comparative genomics approaches to combat antibiotic resistance.
• Computational and experimental approaches to combat antibiotic resistance.
• Reverse vaccinology and immunoinformatics
• Software and databases

Keywords: Antibacterial drug resistance, Next generation Sequencing, Machine Learning, Pan-genomics

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