Literature DB >> 29530606

Achieving a Predictive Understanding of Antimicrobial Stress Physiology through Systems Biology.

Sean G Mack1, Randi L Turner2, Daniel J Dwyer3.   

Abstract

The dramatic spread and diversity of antibiotic-resistant pathogens has significantly reduced the efficacy of essentially all antibiotic classes, bringing us ever closer to a postantibiotic era. Exacerbating this issue, our understanding of the multiscale physiological impact of antimicrobial challenge on bacterial pathogens remains incomplete. Concerns over resistance and the need for new antibiotics have motivated the collection of omics measurements to provide systems-level insights into antimicrobial stress responses for nearly 20 years. Although technological advances have markedly improved the types and resolution of such measurements, continued development of mathematical frameworks aimed at providing a predictive understanding of complex antimicrobial-associated phenotypes is critical to maximize the utility of multiscale data. Here we highlight recent efforts utilizing systems biology to enhance our knowledge of antimicrobial stress physiology. We provide a brief historical perspective of antibiotic-focused omics measurements, highlight new measurement discoveries and trends, discuss examples and opportunities for integrating measurements with mathematical models, and describe future challenges for the field.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Keywords:  antibiotics; antimicrobials; metabolic modeling; microbial physiology; resistance; systems biology

Mesh:

Substances:

Year:  2018        PMID: 29530606     DOI: 10.1016/j.tim.2018.02.004

Source DB:  PubMed          Journal:  Trends Microbiol        ISSN: 0966-842X            Impact factor:   17.079


  5 in total

Review 1.  Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review.

Authors:  Wan Yean Chung; Yan Zhu; Mohd Hafidz Mahamad Maifiah; Naveen Kumar Hawala Shivashekaregowda; Eng Hwa Wong; Nusaibah Abdul Rahim
Journal:  J Antibiot (Tokyo)       Date:  2020-09-08       Impact factor: 2.649

2.  A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Authors:  Jason H Yang; Sarah N Wright; Meagan Hamblin; Douglas McCloskey; Miguel A Alcantar; Lars Schrübbers; Allison J Lopatkin; Sangeeta Satish; Amir Nili; Bernhard O Palsson; Graham C Walker; James J Collins
Journal:  Cell       Date:  2019-05-09       Impact factor: 41.582

Review 3.  A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.

Authors:  Xinxing Li; Ziyi Zhang; Buwen Liang; Fei Ye; Weiwei Gong
Journal:  J Antibiot (Tokyo)       Date:  2021-09-14       Impact factor: 2.649

4.  Culture-enriched human gut microbiomes reveal core and accessory resistance genes.

Authors:  Frédéric Raymond; Maurice Boissinot; Amin Ahmed Ouameur; Maxime Déraspe; Pier-Luc Plante; Sewagnouin Rogia Kpanou; Ève Bérubé; Ann Huletsky; Paul H Roy; Marc Ouellette; Michel G Bergeron; Jacques Corbeil
Journal:  Microbiome       Date:  2019-04-05       Impact factor: 14.650

5.  Different Pathways Mediate Amphotericin-Lactoferrin Drug Synergy in Cryptococcus and Saccharomyces.

Authors:  Yu-Wen Lai; Chi Nam Ignatius Pang; Leona T Campbell; Sharon C A Chen; Marc R Wilkins; Dee A Carter
Journal:  Front Microbiol       Date:  2019-10-01       Impact factor: 5.640

  5 in total

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