Hendry, J. A. | Search bioRxiv

bioRxiv2023

Flexible and cost-effective genomic surveillance of P. falciparum malaria with targeted nanopore sequencing

Genomic surveillance of Plasmodium falciparum malaria can provide policy-relevant information about antimalarial drug resistance, rapid diagnostic test failure, and the evolution of vaccine targets. Yet the large and low complexity genome of P. falciparum limits the scope of genomic surveillance, as whole-genome sequencing approaches are costly and targeted approaches are challenging to develop. Moreover, the majority of the morbidity and mortality caused by P. falciparum occurs in sub-Saharan Africa, where resource constraints can make implementing genomic surveillance difficult. Here, we demonstrate a flexible and cost-effective approach for targeted nanopore sequencing of P. falciparum to enable genomic surveillance of malaria in low-resource settings. We release open-source software that facilitates rapid and flexible design of amplicon sequencing panels for P. falciparum, coupled with a simple and cost-effective protocol that uses dried blood spots as input. We use this software to design two amplicon panels. The first, called NOMADS8, targets seven major antimalarial drug-resistance associated genes as well as the highly polymorphic gene msp2. The second, NOMAD16, incorporates an additional eight genes including the vaccine target csp and genes coding for the antigens detected in rapid diagnostic tests, hrp2 and hrp3. The panels generate reads between 3 to 4kbp that span the entire coding sequence of most target genes. We validate the panels and protocol on mock and field samples, demonstrating robust sequencing coverage across targets, high single-nucleotide polymorphism calling accuracy within coding sequences, and the ability to explore the within-sample diversity of mixed P. falciparum infections.

genomics↗

bioRxiv2020

Elucidating relationships between P.falciparum prevalence and measures of genetic diversity with a combined genetic-epidemiological model of malaria

There is an abundance of malaria genetic data being collected from the field, yet using this data to understand features of regional epidemiology remains a challenge. A key issue is the lack of models that relate parasite genetic diversity to epidemiological parameters. Classical models in population genetics characterize changes in genetic diversity in relation to demographic parameters, but fail to account for the unique features of the malaria life cycle. In contrast, epidemiological models, such as the Ross-Macdonald model, capture malaria transmission dynamics but do not consider genetics. Here, we have developed an integrated model encompassing both parasite evolution and regional epidemiology. We achieve this by combining the Ross-Macdonald model with an intra-host continuous-time Moran model, thus explicitly representing the evolution of individual parasite genomes in a traditional epidemiological framework. Implemented as a stochastic simulation, we use the model to explore relationships between measures of parasite genetic diversity and parasite prevalence, a widely-used metric of transmission intensity. First, we explore how varying parasite prevalence influences genetic diversity at equilibrium. We find that multiple genetic diversity statistics are correlated with prevalence, but the strength of the relationships depends on whether variation in prevalence is driven by host- or vector-related factors. Next, we assess the responsiveness of a variety of statistics to malaria control interventions, finding that those related to mixed infections respond quickly (~ months) whereas other statistics, such as nucleotide diversity, may take decades to respond. These findings provide insights into the opportunities and challenges associated with using genetic data to monitor malaria epidemiology. Author summaryKnowledge of how the prevalence of P.falciparum malaria varies, either between regions or through time, is critical to the operation of malaria control programs. Yet obtaining this information through traditional methods is fraught with challenges. Parasite genetic data is increasingly accessible, and may provide an alternative means to estimate P.falciparum prevalence in the field. However, our understanding of how the genetic diversity of parasite populations relates to prevalence is limited, and suitable models to guide our understanding are largely lacking. Here, we merge two classical models - the Ross-Macondald and the Moran - to produce a framework in which the relationships between parasite genetic diversity and prevalence can be explored. We find that several genetic diversity statistics are correlated with prevalence, although to differing degrees, and over different time scales. Overall, statistics related to mixed infection are robustly and rapidly responsive to changes in prevalence, suggesting they may be a useful focal point for the development of malaria surveillance methods that harness genetic data.

genomics↗

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↗