Biology subjects

Almagro-Garcia, J.

Publications and source records attributed to Almagro-Garcia, J..

3 recordsLinked to original sources

bioRxiv2018

The origins and relatedness structure of mixed infections vary with local prevalence of P. falciparum malaria

Individual malaria infections can carry multiple strains of Plasmodium falciparum with varying levels of relatedness. Yet, how local epidemiology affects the properties of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from genome sequencing data, which estimates the number of strains, their proportions, identity-by-descent (IBD) profiles and individual haplotypes. Applying it to the Pf3k data set, we find that the rate of mixed infection varies from 29% to 63% across countries and that 51% of mixed infections involve more than two strains. Further-more, we estimate that 47% of symptomatic dual infections contain sibling strains likely to have been co-transmitted from a single mosquito, and find evidence of mixed infections propagated over successive infection cycles. Finally, leveraging data from the Malaria Atlas Project, we find that prevalence correlates within Africa, but not Asia, with both the rate of mixed infection and the level of IBD.

epidemiology↗

bioRxiv2017

Origins of the current outbreak of multidrug resistant malaria in Southeast Asia: a retrospective genetic study

BackgroundAntimalarial failure is rapidly spreading across parts of Southeast Asia where dihydroartemisinin-piperaquine (DHA-PPQ) is used as first line treatment. The first published reports came from western Cambodia in 2013. Here we analyse genetic changes in the Plasmodium falciparum population of western Cambodia in the six years prior to that.\n\nMethodsWe analysed genome sequence data on 1492 P. falciparum samples from Southeast Asia, including 464 collected in western Cambodia between 2007 and 2013. Different epidemiological origins of resistance were identified by haplotypic analysis of the kelch13 artemisinin resistance locus and the plasmepsin 2-3 piperaquine resistance locus.\n\nFindingsWe identified over 30 independent origins of artemisinin resistance, of which the O_SCPCAPKELC_SCPCAP1 lineage accounted for 91% of DHA-PPQ-resistant parasites. In 2008, O_SCPCAPKELC_SCPCAP1 combined with O_SCPCAPPLAC_SCPCAP1, the major lineage associated with piperaquine resistance. By 2012, the O_SCPCAPKELC_SCPCAP1/O_SCPCAPPLAC_SCPCAP1 co-lineage had reached over 60% frequency in western Cambodia and had spread to northern Cambodia.\n\nInterpretationThe O_SCPCAPKELC_SCPCAP1/O_SCPCAPPLAC_SCPCAP1 co-lineage emerged in the same year that DHA-PPQ became the first line antimalarial drug in western Cambodia and spread aggressively thereafter, displacing other artemisinin-resistant parasite lineages. These findings have significant implications for management of the global health risk associated with the current outbreak.\n\nFundingWellcome Trust, Bill & Melinda Gates Foundation, Medical Research Council, UK Department for International Development, and Intramural Research Program of the US National Institute of Allergy and Infectious Diseases, National Institutes of Health.

evolutionary biology↗

bioRxiv2017

Deconvoluting multiple infections in Plasmodium falciparum from high throughput sequencing data

MotivationThe presence of multiple infecting strains of the malarial parasite Plasmodium falciparum affects key phenotypic traits, including drug resistance and risk of severe disease. Advances in protocols and sequencing technology have made it possible to obtain high-coverage genome-wide sequencing data from blood samples and blood spots taken in the field. However, analysing and interpreting such data is challenging because of the high rate of multiple infections present.\n\nResultsWe have developed a statistical method and implementation for deconvolving multiple genome sequences present in an individual with mixed infections. The software package DEploid uses haplotype structure within a reference panel of clonal isolates as a prior for haplotypes present in a given sample. It estimates the number of strains, their relative proportions and the haplotypes presented in a sample, allowing researchers to study multiple infection in malaria with an unprecedented level of detail.\n\nAvailability and implementationThe open source implementation DEploid is freely available at https://github.com/mcveanlab/DEploid under the conditions of the GPLv3 license. An R version is available at https://github.com/mcveanlab/DEploid-r.\n\nContactjoe.zhu@well.ox.ac.uk or mcvean@well.ox.ac.uk

bioinformatics↗