Researchers Identify Druggable Genomic Targets in Malaria Parasite
News Jan 11, 2018 | by Laura Elizabeth Mason, Science Writer, Technology Networks
Using whole-genome analysis and chemogenomics, scientists have discovered novel antimalarial drug targets and drug-resistance genes. The researchers analyzed >250 Plasmodium falciparum cell lines, which were resistant to 37 different anti-malarial compounds.
Senior author Elizabeth Winzeler, PhD, UC San Diego School of Medicine, explained the implications of this research in a recent press release: "This exploration of the P. falciparum resistome — the collection of antibiotic resistance genes — and its druggable genome will help guide new drug discovery efforts and advance our understanding of how the malaria parasite evolves to fight back."
The unicellular protozoan, P. falciparum, is responsible for ~50% of all malaria cases and can be transmitted to humans via the bite of an infected female Anopheles mosquito.
"A single human infection can result in a person containing upwards of a trillion asexual blood stage parasites," explained Winzeler. "Even with a relatively slow random mutation rate, these numbers confer extraordinary adaptability. In just a few cycles of replication, the P. falciparum genome can acquire a random genetic change that may render at least one parasite resistant to the activity of a drug or human-encoded antibody."
As well as confirming previously known drug-resistance genes, this study highlights novel targets, which will likely prove important for both structural biology and drug discovery efforts, and advance our general understanding of the parasite’s resistance mechanisms. Drug-inhibitor pairs identified in the study include; thymidylate synthase and a benzoquinazolinone, farnesyltransferase and a pyrimidinedione, and a dipeptidylpeptidase and an arylurea. The study was published in Science.
"Our findings showed and underscored the challenging complexity of evolved drug resistance in P. falciparum," said Winzeler, "but [the research] also identified new drug targets or resistance genes for every compound for which resistant parasites were generated. It revealed the complicated chemogenetic landscape of P. falciparum, but also provided a potential guide for designing new small-molecule inhibitors to fight this pathogen."
Machine learning – a field of artificial intelligence that uses statistical techniques to enable computer systems to ‘learn’ from data – can be used to analyse electronic health records and predict the risk of emergency hospital admissions, a new study from The George Institute for Global Health at the University of Oxford has found.
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