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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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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. |
format | Online Article Text |
id | pubmed-7677515 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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