New reference genomes to distinguish the sympatric malaria parasites, Plasmodium ovale curtisi and Plasmodium ovale wallikeri.

Despite Plasmodium ovale curtisi (Poc) and wallikeri (Pow) being important human-infecting malaria parasites that are widespread across Africa and Asia, little is known about their genome diversity. Morphologically identical, Poc and Pow are indistinguishable and commonly misidentified. Recent rises in the incidence of Poc/Pow infections have renewed efforts to address fundamental knowledge gaps in their biology, and to develop diagnostic tools to understand their epidemiological dynamics and malaria burden. A major roadblock has been the incompleteness of available reference assemblies (PocGH01, PowCR01; ~ 33.5 Mbp). Here, we applied multiple sequencing platforms and advanced bioinformatics tools to generate new reference genomes, Poc221 (South Sudan; 36.0 Mbp) and Pow222 (Nigeria; 34.3 Mbp), with improved nuclear genome contiguity (> 4.2 Mbp), annotation and completeness (> 99% Plasmodium spp., single copy orthologs). Subsequent sequencing of 6 Poc and 15 Pow isolates from Africa revealed a total of 22,517 and 43,855 high-quality core genome SNPs, respectively. Genome-wide levels of nucleotide diversity were determined to be 2.98 × 10-4 (Poc) and 3.43 × 10-4 (Pow), comparable to estimates for other Plasmodium species. Overall, the new reference genomes provide a robust foundation for dissecting the biology of Poc/Pow, their population structure and evolution, and will contribute to uncovering the recombination barrier separating these species.

A computational method for predicting the most likely evolutionary trajectories in the stepwise accumulation of resistance mutations.

Pathogen evolution of drug resistance often occurs in a stepwise manner via the accumulation of multiple mutations that in combination have a non-additive impact on fitness, a phenomenon known as epistasis. The evolution of resistance via the accumulation of point mutations in the DHFR genes of Plasmodium falciparum (Pf) and Plasmodium vivax (Pv) has been studied extensively and multiple studies have shown epistatic interactions between these mutations determine the accessible evolutionary trajectories to highly resistant multiple mutations. Here, we simulated these evolutionary trajectories using a model of molecular evolution, parameterised using Rosetta Flex ddG predictions, where selection acts to reduce the target-drug binding affinity. We observe strong agreement with pathways determined using experimentally measured IC50 values of pyrimethamine binding, which suggests binding affinity is strongly predictive of resistance and epistasis in binding affinity strongly influences the order of fixation of resistance mutations. We also infer pathways directly from the frequency of mutations found in isolate data, and observe remarkable agreement with the most likely pathways predicted by our mechanistic model, as well as those determined experimentally. This suggests mutation frequency data can be used to intuitively infer evolutionary pathways, provided sufficient sampling of the population.

Rapid profiling of Plasmodium parasites from genome sequences to assist malaria control.

BACKGROUND: Malaria continues to be a major threat to global public health. Whole genome sequencing (WGS) of the underlying Plasmodium parasites has provided insights into the genomic epidemiology of malaria. Genome sequencing is rapidly gaining traction as a diagnostic and surveillance tool for clinical settings, where the profiling of co-infections, identification of imported malaria parasites, and detection of drug resistance are crucial for infection control and disease elimination. To support this informatically, we have developed the Malaria-Profiler tool, which rapidly (within minutes) predicts Plasmodium species, geographical source, and resistance to antimalarial drugs directly from WGS data. RESULTS: The online and command line versions of Malaria-Profiler detect ~ 250 markers from genome sequences covering Plasmodium speciation, likely geographical source, and resistance to chloroquine, sulfadoxine-pyrimethamine (SP), and other anti-malarial drugs for P. falciparum, but also providing mutations for orthologous resistance genes in other species. The predictive performance of the mutation library was assessed using 9321 clinical isolates with WGS and geographical data, with most being single-species infections (P. falciparum 7152/7462, P. vivax 1502/1661, P. knowlesi 143/151, P. malariae 18/18, P. ovale ssp. 5/5), but co-infections were identified (456/9321; 4.8%). The accuracy of the predicted geographical profiles was high to both continental (96.1%) and regional levels (94.6%). For P. falciparum, markers were identified for resistance to chloroquine (49.2%; regional range: 24.5% to 100%), sulfadoxine (83.3%; 35.4- 90.5%), pyrimethamine (85.4%; 80.0-100%) and combined SP (77.4%). Markers associated with the partial resistance of artemisinin were found in WGS from isolates sourced from Southeast Asia (30.6%). CONCLUSIONS: Malaria-Profiler is a user-friendly tool that can rapidly and accurately predict the geographical regional source and anti-malarial drug resistance profiles across large numbers of samples with WGS data. The software is flexible with modifiable bioinformatic pipelines. For example, it is possible to select the sequencing platform, display specific variants, and customise the format of outputs. With the increasing application of next-generation sequencing platforms on Plasmodium DNA, Malaria-Profiler has the potential to be integrated into point-of-care and surveillance settings, thereby assisting malaria control. Malaria-Profiler is available online (bioinformatics.lshtm.ac.uk/malaria-profiler) and as standalone software ( https://github.com/jodyphelan/malaria-profiler ).

Genome-wide genetic variation and molecular surveillance of drug resistance in Plasmodium falciparum isolates from asymptomatic individuals in Ouélessébougou, Mali.

Sequence analysis of Plasmodium falciparum parasites is informative in ensuring sustained success of malaria control programmes. Whole-genome sequencing technologies provide insights into the epidemiology and genome-wide variation of P. falciparum populations and can characterise geographical as well as temporal changes. This is particularly important to monitor the emergence and spread of drug resistant P. falciparum parasites which is threatening malaria control programmes world-wide. Here, we provide a detailed characterisation of genome-wide genetic variation and drug resistance profiles in asymptomatic individuals in South-Western Mali, where malaria transmission is intense and seasonal, and case numbers have recently increased. Samples collected from Ouélessébougou, Mali (2019-2020; n = 87) were sequenced and placed in the context of older Malian (2007-2017; n = 876) and African-wide (n = 711) P. falciparum isolates. Our analysis revealed high multiclonality and low relatedness between isolates, in addition to increased frequencies of molecular markers for sulfadoxine-pyrimethamine and lumefantrine resistance, compared to older Malian isolates. Furthermore, 21 genes under selective pressure were identified, including a transmission-blocking vaccine candidate (pfCelTOS) and an erythrocyte invasion locus (pfdblmsp2). Overall, our work provides the most recent assessment of P. falciparum genetic diversity in Mali, a country with the second highest burden of malaria in West Africa, thereby informing malaria control activities.

Population dynamics and drug resistance mutations in Plasmodium falciparum on the Bijagós Archipelago, Guinea-Bissau.

Following integrated malaria control interventions, malaria burden on the Bijagós Archipelago has significantly decreased. Understanding the genomic diversity of circulating Plasmodium falciparum malaria parasites can assist infection control, through identifying drug resistance mutations and characterising the complexity of population structure. This study presents the first whole genome sequence data for P. falciparum isolates from the Bijagós Archipelago. Amplified DNA from P. falciparum isolates sourced from dried blood spot samples of 15 asymptomatic malaria cases were sequenced. Using 1.3 million SNPs characterised across 795 African P. falciparum isolates, population structure analyses revealed that isolates from the archipelago cluster with samples from mainland West Africa and appear closely related to mainland populations; without forming a separate phylogenetic cluster. This study characterises SNPs associated with antimalarial drug resistance on the archipelago. We observed fixation of the PfDHFR mutations N51I and S108N, associated with resistance to sulphadoxine-pyrimethamine, and the continued presence of PfCRT K76T, associated with chloroquine resistance. These data have relevance for infection control and drug resistance surveillance; particularly considering expected increases in antimalarial drug use following updated WHO recommendations, and the recent implementation of seasonal malaria chemoprevention and mass drug administration in the region.

Population genetic analysis of Plasmodium knowlesi reveals differential selection and exchange events between Borneo and Peninsular sub-populations.

The zoonotic Plasmodium knowlesi parasite is a growing public health concern in Southeast Asia, especially in Malaysia, where elimination of P. falciparum and P. vivax malaria has been the focus of control efforts. Understanding of the genetic diversity of P. knowlesi parasites can provide insights into its evolution, population structure, diagnostics, transmission dynamics, and the emergence of drug resistance. Previous work has revealed that P. knowlesi fall into three main sub-populations distinguished by a combination of geographical location and macaque host (Macaca fascicularis and M. nemestrina). It has been shown that Malaysian Borneo groups display profound heterogeneity with long regions of high or low divergence resulting in mosaic patterns between sub-populations, with some evidence of chromosomal-segment exchanges. However, the genetic structure of non-Borneo sub-populations is less clear. By gathering one of the largest collections of P. knowlesi whole-genome sequencing data, we studied structural genomic changes across sub-populations, with the analysis revealing differences in Borneo clusters linked to mosquito-related stages of the parasite cycle, in contrast to differences in host-related stages for the Peninsular group. Our work identifies new genetic exchange events, including introgressions between Malaysian Peninsular and M. nemestrina-associated clusters on various chromosomes, including in parasite invasion genes (DBP[Formula: see text], NBPX[Formula: see text] and NBPX[Formula: see text]), and important proteins expressed in the vertebrate parasite stages. Recombination events appear to have occurred between the Peninsular and M. fascicularis-associated groups, including in the DBP[Formula: see text] and DBP[Formula: see text] invasion associated genes. Overall, our work finds that genetic exchange events have occurred among the recognised contemporary groups of P. knowlesi parasites during their evolutionary history, leading to apparent mosaicism between these sub-populations. These findings generate new hypotheses relevant to parasite evolutionary biology and P. knowlesi epidemiology, which can inform malaria control approaches to containing the impact of zoonotic malaria on human communities.

Population-based genomic study of Plasmodium vivax malaria in seven Brazilian states and across South America.

Background: Brazil is a unique and understudied setting for malaria, with complex foci of transmission associated with human and environmental conditions. An understanding of the population genomic diversity of P. vivax parasites across Brazil can support malaria control strategies. Methods: Through whole genome sequencing of P. vivax isolates across 7 Brazilian states, we use population genomic approaches to compare genetic diversity within country (n = 123), continent (6 countries, n = 315) and globally (26 countries, n = 885). Findings: We confirm that South American isolates are distinct, have more ancestral populations than the other global regions, with differentiating mutations in genes under selective pressure linked to antimalarial drugs (pvmdr1, pvdhfr-ts) and mosquito vectors (pvcrmp3, pvP45/48, pvP47). We demonstrate Brazil as a distinct parasite population, with signals of selection including ABC transporter (PvABCI3) and PHIST exported proteins. Interpretation: Brazil has a complex population structure, with evidence of P. simium infections and Amazonian parasites separating into multiple clusters. Overall, our work provides the first Brazil-wide analysis of P. vivax population structure and identifies important mutations, which can inform future research and control measures. Funding: AI is funded by an MRC LiD PhD studentship. TGC is funded by the Medical Research Council (Grant no. MR/M01360X/1, MR/N010469/1, MR/R025576/1, MR/R020973/1 and MR/X005895/1). SC is funded by Medical Research Council UK grants (MR/M01360X/1, MR/R025576/1, MR/R020973/1 and MR/X005895/1) and Bloomsbury SET (ref. CCF17-7779). FN is funded by The Shloklo Malaria Research Unit - part of the Mahidol Oxford Research Unit, supported by the Wellcome Trust (Grant no. 220211). ARSB is funded by São Paulo Research Foundation - FAPESP (Grant no. 2002/09546-1). RLDM is funded by Brazilian National Council for Scientific and Technological Development - CNPq (Grant no. 302353/2003-8 and 471605/2011-5); CRFM is funded by FAPESP (Grant no. 2020/06747-4) and CNPq (Grant no. 302917/2019-5 and 408636/2018-1); JGD is funded by FAPESP fellowships (2016/13465-0 and 2019/12068-5) and CNPq (Grant no. 409216/2018-6).

Geographical classification of malaria parasites through applying machine learning to whole genome sequence data.

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.

How has mass drug administration with dihydroartemisinin-piperaquine impacted molecular markers of drug resistance? A systematic review.

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.

Characterizing the genomic variation and population dynamics of Plasmodium falciparum malaria parasites in and around Lake Victoria, Kenya.

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.

Using deep learning to identify recent positive selection in malaria parasite sequence data.

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.

Distinctive genetic structure and selection patterns in Plasmodium vivax from South Asia and East Africa.

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.

Genetic diversity of the Plasmodium falciparum GTP-cyclohydrolase 1, dihydrofolate reductase and dihydropteroate synthetase genes reveals new insights into sulfadoxine-pyrimethamine antimalarial drug resistance.

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.