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Omics and In Silico Approaches in the Surveillance and Monitoring of Antimicrobial Resistance

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Emerging Modalities in Mitigation of Antimicrobial Resistance

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Abstract

Antimicrobial resistance (AMR) is mounting at a distressing rate; diseases that were once curable have now turned out to be frequent public health crisis. This has led to the realization that extensive research must be conducted to mitigate the manifestation and spread of AMR. The mode of action of antimicrobial agents can influence the cellular pathways of microbes that are under genetic control. These pathways involved in antimicrobial resistance can be recognized more efficiently by combining genomics, transcriptomics, proteomics, and metabolomics. The precise and speedy determination of antimicrobial resistance is pivotal in treating an infection and in reducing antibiotic abuse. Today, detection and characterization of AMR have moved from culture techniques and PCR to metagenomics via next-generation sequencing techniques; therefore, suitable tools for examining large-scale data are needed. The increasing access to high-throughput quantitative PCR, microarray, and high-throughput sequencing (HTS) has enabled the identification of many AMR determinants. Extensive comparative studies of organismal and environmental samples have thrown light into the comprehensive spread of antimicrobial resistance genes (ARGs) and the dispersal of multidrug-resistant bacteria, resistance exchange networks, and different habitats and phylogeny that affect the evolutionary dynamics of AMR. Exploring genetic factors contributing to AMR using sequence data poses specific issues that are being tackled by advancements in algorithms and in silico tools that orchestrate genomic data and predict AMR. This chapter focuses on omic approaches, bioinformatic tools, and databases used for studying antimicrobial resistance.

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Authors and Affiliations

  1. Department of Botany and Biotechnology, St. Xavier’s College, Thumba, Thiruvananthapuram, India

    Ajith M. Thomas

  2. Department of Zoology, Mar Ivanios College, Thiruvananthapuram, India

    Lija L. Raju

  3. Nanobiotechnology Laboratory, Department of Biotechnology, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India

    S. Sudheer Khan

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  1. Ajith M. Thomas
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  2. Lija L. Raju
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  3. S. Sudheer Khan
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Correspondence to S. Sudheer Khan .

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Editors and Affiliations

  1. Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada

    Nadeem Akhtar

  2. Institute for Microbiology, Leibniz University, Hannover, Germany

    Kumar Siddharth Singh

  3. Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India

    Prerna

  4. Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Dinesh Goyal

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Thomas, A.M., Raju, L.L., Khan, S.S. (2022). Omics and In Silico Approaches in the Surveillance and Monitoring of Antimicrobial Resistance. In: Akhtar, N., Singh, K.S., Prerna, Goyal, D. (eds) Emerging Modalities in Mitigation of Antimicrobial Resistance. Springer, Cham. https://doi.org/10.1007/978-3-030-84126-3_16

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