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Ghansah, A.

Publications and source records attributed to Ghansah, A..

4 recordsLinked to original sources

Hookworm genomic diversity and population structure from accessible sample types: A validated approach to generate genome-wide polymorphism datasets from individual third-stage larvae

Hookworm infection is a neglected tropical disease affecting hundreds of thousands of people annually in the tropics and sub-tropics. Population genomic approaches have the potential to improve our understanding of hookworm infection dynamics and control efficacy. Here, we validate an approach to generate genome-wide polymorphism datasets from accessible sample types in the zoonotic hookworm (Ancylostoma ceylanicum) then apply the validated approach in the human hookworm (Necator americanus) to compare laboratory- and field-derived samples. We first present an optimized method for purifying nucleic acid from individual third-stage hookworm larvae (L3s). We then measure the accuracy of variant call datasets generated through whole genome amplification (WGA) and next-generation sequencing (NGS). We demonstrate that WGA via multiple displacement amplification (MDA) introduces predictable biases that are exacerbated by low inputs and poor sample preservation but show that with sufficient input mass ([≥]0.1ng) we are still able to produce highly accurate variant call datasets from nucleic acid concentrations that reflect those of individual L3s. Using our validated approach, we infer laboratory- and field-collected samples of N. americanus as distinct populations, with higher levels of heterozygosity and nucleotide diversity identified in field-collected samples, suggesting signatures of inbreeding and/or drift are detectable in laboratory specimens within several years of initiation of infection. We also show that, despite expected reductions in heterozygosity, laboratory samples still possess numerous heterozygous sites, and we demonstrate that a reference genome generated from an adult worm from an early laboratory passage performs well for variant calling in both laboratory- and field-derived samples. Moving forward, our optimized method for nucleic acid purification can be broadly applied to generate input for any amplification-based approach where sequencing individual hookworm L3s, rather than a pool of specimens, is preferred. Our validated population genomics workflow can be used to characterize structure and connectivity of hookworm populations in endemic communities, with the goal of leveraging these insights to improve our approach to hookworm treatment and control. AUTHOR SUMMARYInfection with the human hookworm, Necator americanus, is a significant cause of morbidity in the global south. Population genomic techniques have the potential to improve our understanding of hookworm infection and control. However, third-stage larvae, or L3, which are the hookworm life-cycle stage we routinely have access to when screening infected people, are less than a millimeter long, and this small size makes generating genomic datasets from individual worms difficult. Here, we introduce an optimized approach for purifying DNA from individual L3s and validate its use with whole genome amplification (WGA) for population genomics. We found that WGA from L3s produces sequencing datasets that are biased in their breadth and depth of coverage across the hookworm genome. But by ensuring a minimum threshold for the mass of DNA input to WGA and setting strict criteria for variant filtration, these biases can be overcome to produce highly accurate variant calls genome-wide. We then use this approach to demonstrate reduced genetic diversity in a recently established laboratory hookworm strain as compared to field-collected samples. Our study outlines a specimen-through-analysis workflow that can be used with accessible sample types to measure population structure and diversity of hookworms in endemic communities.

genomics↗

A complex Plasmodium falciparum cryptotype circulating at low frequency across the African continent

The population structure of the malaria parasite Plasmodium falciparum can reveal underlying demographic and adaptive evolutionary processes. Here, we analyse population structure in 4,376 P. falciparum genomes from 21 countries across Africa. We identified a strongly differentiated cluster of parasites, comprising [~]1.2% of samples analysed, geographically distributed over 13 countries across the continent. Members of this cluster, named AF1, carry a genetic background consisting of a large number of highly differentiated variants, rarely observed outside this cluster, at a multitude of genomic loci distributed across most chromosomes. At these loci, the AF1 haplotypes appear to have common ancestry, irrespective of the sampling location; outside the shared loci, however, AF1 members are genetically similar to their sympatric parasites. AF1 parasites sharing up to 23 genomic co-inherited regions were found in all major regions of Africa, at locations over 7,000 km apart. We coined the term cryptotype to describe a complex common background which is geographically widespread, but concealed by genomic regions of local origin. Most AF1 differentiated variants are functionally related, comprising structural variations and single nucleotide polymorphisms in components of the MSP1 complex and several other genes involved in interactions with red blood cells, including invasion and erythrocyte antigen export. We propose that AF1 parasites have adapted to some as yet unidentified evolutionary niche, by acquiring a complex compendium of interacting variants that rarely circulate separately in Africa. As the cryptotype spread across the continent, it appears to have been maintained mostly intact in spite of recombination events, suggesting a selective advantage. It is possible that other cryptotypes circulate in Africa, and new analysis methods may be needed to identify them.

genomics↗

Neutral vs. non-neutral genetic footprints of Plasmodium falciparum multiclonal infections

At a time when effective tools for monitoring malaria control and eradication efforts are crucial, the increasing availability of molecular data motivates their application to epidemiology. The multiplicity of infection (MOI), defined as the number of genetically distinct parasite strains co-infecting a host, is one key epidemiological parameter for evaluating malaria interventions. Estimating MOI remains a challenge for high-transmission settings where individuals typically carry multiple co-occurring infections. Several quantitative approaches have been developed to estimate MOI, including two cost-effective ones relying on molecular data: i) THE REAL McCOIL method is based on putatively neutral single nucleotide polymorphism loci, and ii) the varcoding method is a fingerprinting approach that relies on the diversity and limited repertoire overlap of the var multigene family encoding the major Plasmodium falciparum blood-stage antigen PfEMP1 and is therefore under selection. In this study, we assess the robustness of the MOI estimates generated with these two approaches by simulating P. falciparum malaria dynamics under three transmission conditions using an extension of a previously developed stochastic agent-based model. We demonstrate that these approaches are complementary and best considered across distinct transmission intensities. While varcoding can underestimate MOI, it allows robust estimation, especially under high-transmission where repertoire overlap is extremely limited from frequency-dependent selection. In contrast, THE REAL McCOIL often considerably overestimates MOI, but still provides reasonable estimates for low- and moderate-transmission. As many countries pursue malaria elimination targets, defining the most suitable approach to estimate MOI based on sample size and local transmission intensity is highly recommended for monitoring the impact of intervention programs. Author SummaryDespite control and elimination efforts, malaria continues to be a serious public health threat especially in high-transmission regions. Molecular tools for evaluating these efforts include those seeking to estimate multiplicity (or complexity) of infection (MOI), the number of genetically distinct parasite strains co-infecting a host, a key epidemiological parameter. MOI estimation remains challenging in high-transmission regions where hosts typically carry multiple co-infections by Plasmodium falciparum. THE REAL McCOIL and the varcoding are two cost-effective methods relying on distinct parts of the parasite genome, those respectively under neutrality and selection. The more recent varcoding approach relies on the var multigene family encoding for the major blood-stage antigen and contributing to a complex immune evasion strategy of the parasite. We compare the performance of the two methods by simulating disease dynamics under different transmission intensities with a stochastic agent-based model tracking infection by different parasite genomes and immune memory in individual hosts, then sampling resulting infections to estimate MOI. Although THE REAL McCOIL provides reasonable estimates for low- and moderate-transmission, varcoding allows more robust estimates especially under high-transmission. Defining the most suitable approach to estimate MOI based on local transmission intensity is highly recommended for hyper-diverse pathogens such as malaria.

evolutionary biology↗

The Impact of Antimalarial Resistance on the Genetic Structure of Plasmodium falciparum in the DRC

The Democratic Republic of the Congo (DRC) harbors 11% of global malaria cases, yet little is known about the spatial and genetic structure of the parasite population in that country. We sequenced 2537 Plasmodium falciparum infections, including a nationally representative population sample from DRC and samples from surrounding countries, using molecular inversion probes - a novel high-throughput genotyping tool. We identified an east-west divide in haplotypes known to confer resistance to chloroquine and sulfadoxine-pyrimethamine. Furthermore, we identified highly related parasites over large geographic distances, indicative of gene flow and migration. Our results were consistent with a background of isolation by distance combined with the effects of selection for antimalarial drug resistance. This study provides a high-resolution view of parasite genetic structure across a large country in Africa and provides a baseline to study how implementation programs may impact parasite populations.

genomics↗