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Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance

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 beco...

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Autores principales: Imchen, Madangchanok, Moopantakath, Jamseel, Kumavath, Ranjith, Barh, Debmalya, Tiwari, Sandeep, Ghosh, Preetam, Azevedo, Vasco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677515/
https://www.ncbi.nlm.nih.gov/pubmed/33240317
http://dx.doi.org/10.3389/fgene.2020.563975
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author Imchen, Madangchanok
Moopantakath, Jamseel
Kumavath, Ranjith
Barh, Debmalya
Tiwari, Sandeep
Ghosh, Preetam
Azevedo, Vasco
author_facet Imchen, Madangchanok
Moopantakath, Jamseel
Kumavath, Ranjith
Barh, Debmalya
Tiwari, Sandeep
Ghosh, Preetam
Azevedo, Vasco
author_sort Imchen, Madangchanok
collection PubMed
description 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.
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spelling pubmed-76775152020-11-24 Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance Imchen, Madangchanok Moopantakath, Jamseel Kumavath, Ranjith Barh, Debmalya Tiwari, Sandeep Ghosh, Preetam Azevedo, Vasco Front Genet Genetics 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. Frontiers Media S.A. 2020-11-06 /pmc/articles/PMC7677515/ /pubmed/33240317 http://dx.doi.org/10.3389/fgene.2020.563975 Text en Copyright © 2020 Imchen, Moopantakath, Kumavath, Barh, Tiwari, Ghosh and Azevedo. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Imchen, Madangchanok
Moopantakath, Jamseel
Kumavath, Ranjith
Barh, Debmalya
Tiwari, Sandeep
Ghosh, Preetam
Azevedo, Vasco
Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance
title Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance
title_full Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance
title_fullStr Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance
title_full_unstemmed Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance
title_short Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance
title_sort current trends in experimental and computational approaches to combat antimicrobial resistance
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677515/
https://www.ncbi.nlm.nih.gov/pubmed/33240317
http://dx.doi.org/10.3389/fgene.2020.563975
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