Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance

Frontiers in Genetics

Debmalya Barh¹  Preetam Ghosh²  Ranjith Kumavath³  Madangchanok Imchen³  Jamseel Moopantakath³  Sandeep Tiwari⁴  Vasco Azevedo⁴ 

[1] Centre for Genomics and Applied Gene Technology, Institute of Integrative Omics and Applied Biotechnology, Purba Medinipur, India;Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States;Department of Genomic Science, School of Biological Sciences, Central University of Kerala, Kasaragod, India;Laboratório de Genética Celular e Molecular, Departamento de Biologia Geral, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil;

关键词: antibiotic resistance;    multidrug resistance;    whole genome sequence;    metagenomics;    next generation sequencing;    nanoparticles;   

DOI  :  10.3389/fgene.2020.563975

来源: DOAJ

【 摘 要 】

A multitude of factors, such as drug misuse, lack of strong regulatory measures, improper sewage disposal, and low-quality medicine and medications, have been attributed to the emergence of drug resistant microbes. The emergence and outbreaks of multidrug resistance to last-line antibiotics has become quite common. This is further fueled by the slow rate of drug development and the lack of effective resistome surveillance systems. In this review, we provide insights into the recent advances made in computational approaches for the surveillance of antibiotic resistomes, as well as experimental formulation of combinatorial drugs. We explore the multiple roles of antibiotics in nature and the current status of combinatorial and adjuvant-based antibiotic treatments with nanoparticles, phytochemical, and other non-antibiotics based on synergetic effects. Furthermore, advancements in machine learning algorithms could also be applied to combat the spread of antibiotic resistance. Development of resistance to new antibiotics is quite rapid. Hence, we review the recent literature on discoveries of novel antibiotic resistant genes though shotgun and expression-based metagenomics. To decelerate the spread of antibiotic resistant genes, surveillance of the resistome is of utmost importance. Therefore, we discuss integrative applications of whole-genome sequencing and metagenomics together with machine learning models as a means for state-of-the-art surveillance of the antibiotic resistome. We further explore the interactions and negative effects between antibiotics and microbiomes upon drug administration.

【 授权许可】

Unknown