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Title: The resistome and genomic reconnaissance in the age of malaria elimination
Authors: Krittikorn Kümpornsin
Theerarat Kochakarn
Thanat Chookajorn
Mahidol University
Wellcome Sanger Institute
Keywords: Biochemistry, Genetics and Molecular Biology;Immunology and Microbiology;Medicine
Issue Date: 1-Jan-2019
Citation: DMM Disease Models and Mechanisms. Vol.12, No.12 (2019)
Abstract: © 2019. Published by The Company of Biologists Ltd. Malaria is an infectious disease caused by parasitic protozoa in the Plasmodium genus. A complete understanding of the biology of these parasites is challenging in view of their need to switch between the vertebrate and insect hosts. The parasites are also capable of becoming highly motile and of remaining dormant for decades, depending on the stage of their life cycle. Malaria elimination efforts have been implemented in several endemic countries, but the parasites have proven to be resilient. One of the major obstacles for malaria elimination is the development of antimalarial drug resistance. Ineffective treatment regimens will fail to remove the circulating parasites and to prevent the local transmission of the disease. Genomic epidemiology of malaria parasites has become a powerful tool to track emerging drug-resistant parasite populations almost in real time. Population-scale genomic data are instrumental in tracking the hidden pockets of Plasmodium in nationwide elimination efforts. However, genomic surveillance data can be useful in determining the threat only when combined with a thorough understanding of the malarial resistome - the genetic repertoires responsible for causing and potentiating drug resistance evolution. Even though long-term selection has been a standard method for drug target identification in laboratories, its implementation in largescale exploration of the druggable space in Plasmodium falciparum, along with genome-editing technologies, have enabled mapping of the genetic repertoires that drive drug resistance. This Review presents examples of practical use and describes the latest technology to show the power of real-time genomic epidemiology in achieving malaria elimination.
ISSN: 17548411
Appears in Collections:Scopus 2019

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