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

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

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

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

Funding: A.T. was funded by a Newton Institutional Links Grant (British Council, no. 261868591). S.C. was funded by BloomsburySET and Medical Research Council UK grants (MR/M01360X/1, MR/R025576/1, MR/R020973/1, and MR/X005895/1). T.G.C. was funded by the Medical Research Council UK (Grant nos. MR/M01360X/1, MR/N010469/1, MR/R025576/1, MR/R020973/1, and MR/X005895/1).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 (DBPβ, NBPXα and NBPXβ), 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 DBPβ and DBPγ 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.Publisher PDFPeer reviewe

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

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

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

Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions

We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe