Despite the high burden of Plasmodium vivax malaria in South Asian countries, the genetic diversity of circulating parasite populations is not well described. Determinants of antimalarial drug susceptibility for P. vivax in the region have not been characterised. Our genomic analysis of global P. vivax (n = 558) establishes South Asian isolates (n = 92) as a distinct subpopulation, which shares ancestry with some East African and South East Asian parasites. Signals of positive selection are linked to drug resistance-associated loci including pvkelch10, pvmrp1, pvdhfr and pvdhps, and two loci linked to P. vivax invasion of reticulocytes, pvrbp1a and pvrbp1b. Significant identity-by-descent was found in extended chromosome regions common to P. vivax from India and Ethiopia, including the pvdbp gene associated with Duffy blood group binding. Our investigation provides new understanding of global P. vivax population structure and genomic diversity, and genetic evidence of recent directional selection in this important human pathogen.
Background Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. Methods A deep learning-based approach (called “DeepSweep”) was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep. Results Using simulated genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60–75% overlap of hits at P < 0.0001). DeepSweep hits included regions proximal to known drug resistance loci for both P. falciparum (e.g. pfcrt, pfdhps and pfmdr1) and P. vivax (e.g. pvmrp1). Conclusion The deep learning approach can detect positive selection signatures in malaria parasite WGS data. Further, as the approach is generalizable, it may be trained to detect other types of selection. With the ability to rapidly generate WGS data at low cost, machine learning approaches (e.g. DeepSweep) have the potential to assist parasite genome-based surveillance and inform malaria control decision-making.
Plasmodium falciparum parasites resistant to antimalarial treatments have hindered malaria disease control. Sulfadoxine-pyrimethamine (SP) was used globally as a first-line treatment for malaria after wide-spread resistance to chloroquine emerged and, although replaced by artemisinin combinations, is currently used as intermittent preventive treatment of malaria in pregnancy and in young children as part of seasonal malaria chemoprophylaxis in sub-Saharan Africa. The emergence of SP-resistant parasites has been predominantly driven by cumulative build-up of mutations in the dihydrofolate reductase (pfdhfr) and dihydropteroate synthetase (pfdhps) genes, but additional amplifications in the folate pathway rate-limiting pfgch1 gene and promoter, have recently been described. However, the genetic make-up and prevalence of those amplifications is not fully understood. We analyse the whole genome sequence data of 4,134 P. falciparum isolates across 29 malaria endemic countries, and reveal that the pfgch1 gene and promoter amplifications have at least ten different forms, occurring collectively in 23% and 34% in Southeast Asian and African isolates, respectively. Amplifications are more likely to be present in isolates with a greater accumulation of pfdhfr and pfdhps substitutions (median of 1 additional mutations; P<0.00001), and there was evidence that the frequency of pfgch1 variants may be increasing in some African populations, presumably under the pressure of SP for chemoprophylaxis and anti-folate containing antibiotics used for the treatment of bacterial infections. The selection of P. falciparum with pfgch1 amplifications may enhance the fitness of parasites with pfdhfr and pfdhps substitutions, potentially threatening the efficacy of this regimen for prevention of malaria in vulnerable groups. Our work describes new pfgch1 amplifications that can be used to inform the surveillance of SP drug resistance, its prophylactic use, and future experimental work to understand functional mechanisms.
Characterising the genomic variation and population dynamics of Plasmodium falciparum parasites in high transmission regions of Sub-Saharan Africa is crucial to the long-term efficacy of regional malaria elimination campaigns and eradication. Whole-genome sequencing (WGS) technologies can contribute towards understanding the epidemiology and structural variation landscape of P. falciparum populations, including those within the Lake Victoria basin, a region of intense transmission. Here we provide a baseline assessment of the genomic diversity of P. falciparum isolates in the Lake region of Kenya, which has sparse genetic data. Lake region isolates are placed within the context of African-wide populations using Illumina WGS data and population genomic analyses. Our analysis revealed that P. falciparum isolates from Lake Victoria form a cluster within the East African parasite population. These isolates also appear to have distinct ancestral origins, containing genome-wide signatures from both Central and East African lineages. Known drug resistance biomarkers were observed at similar frequencies to those of East African parasite populations, including the S160N/T mutation in the pfap2mu gene, which has been associated with delayed clearance by artemisinin-based combination therapy. Overall, our work provides a first assessment of P. falciparum genetic diversity within the Lake Victoria basin, a region targeting malaria elimination.
Pathogen evolution of drug resistance often occurs in a stepwise manner via accumulation of multiple mutations, which in combination have a non-additive impact on fitness, a phenomenon known as epistasis. Epistasis complicates the sequence-structure-function relationship and undermines our ability to predict evolution. We present a computational method to predict evolutionary trajectories that accounts for epistasis, using the Rosetta Flex ddG protocol to estimate drug binding free energy changes upon mutation and an evolutionary model based in thermodynamics and statistical mechanics. We apply this method to predict evolutionary trajectories to known multiple mutations associated with resistant phenotypes in malaria. Resistance to the combination drug sulfadoxine-pyrimethamine (SP) in malaria-causing species Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has arisen via the accumulation of multiple point mutations in the DHFR and DHPS genes. Four known PfDHFR pyrimethamine resistance mutations are highly prevalent in field-isolates and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to the highly resistant quadruple mutation N51I,C59R,S108N,I164L. We simulated the possible evolutionary trajectories to this quadruple PfDHFR mutation as well as the homologous PvDHFR mutations. In both cases, our most probable pathways agreed well with those determined experimentally. We also applied this method to predict the most likely evolutionary pathways to observed multiple mutations associated with sulfadoxine resistance in PfDHPS and PvDHPS. This novel method can be applied to any drug-target system where the drug acts by binding to the target.
The World Health Organization (WHO) recommends surveillance of molecular markers of resistance to anti-malarial drugs. This is particularly important in the case of mass drug administration (MDA), which is endorsed by the WHO in some settings to combat malaria. Dihydroartemisinin-piperaquine (DHA-PPQ) is an artemisinin-based combination therapy which has been used in MDA. This review analyses the impact of MDA with DHA-PPQ on the evolution of molecular markers of drug resistance. The review is split into two parts. Section I reviews the current evidence for different molecular markers of resistance to DHA-PPQ. This includes an overview of the prevalence of these molecular markers in Plasmodium falciparum Whole Genome Sequence data from the MalariaGEN Pf3k project. Section II is a systematic literature review of the impact that MDA with DHA-PPQ has had on the evolution of molecular markers of resistance. This systematic review followed PRISMA guidelines. This review found that despite being a recognised surveillance tool by the WHO, the surveillance of molecular markers of resistance following MDA with DHA-PPQ was not commonly performed. Of the total 96 papers screened for eligibility in this review, only 20 analysed molecular markers of drug resistance. The molecular markers published were also not standardized. Overall, this warrants greater reporting of molecular marker prevalence following MDA implementation. This should include putative pfcrt mutations which have been found to convey resistance to DHA-PPQ in vitro.
Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities.
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