Haplotype of non-synonymous mutations within IL-23R is associated with susceptibility to severe malaria anemia in a P. falciparum holoendemic transmission area of Kenya

Abstract Background Improved understanding of the molecular mechanisms involved in pediatric severe malarial anemia (SMA) pathogenesis is a crucial step in the design of novel therapeutics. Identification of host genetic susceptibility factors in immune regulatory genes offers an important tool for deciphering malaria pathogenesis. The IL-23/IL-17 immune pathway is important for both immunity and erythropoiesis via its effects through IL-23 receptors (IL-23R). However, the impact of IL-23R variants on SMA has not been fully elucidated. Methods Since variation within the coding region of IL-23R may influence the pathogenesis of SMA, the association between IL-23R rs1884444 (G/T), rs7530511 (C/T), and SMA (Hb < 6.0 g/dL) was examined in children (n = 369, aged 6–36 months) with P. falciparum malaria in a holoendemic P. falciparum transmission area. Results Logistic regression analysis, controlling for confounding factor of anemia, revealed that individual genotypes of IL-23R rs1884444 (G/T) [GT; OR = 1.34, 95% CI = 0.78–2.31, P = 0.304 and TT; OR = 2.02, 95% CI = 0.53–7.74, P = 0.286] and IL-23R rs7530511 (C/T) [CT; OR = 2.6, 95% CI = 0.59–11.86, P = 0.202 and TT; OR = 1.66, 95% CI = 0.84–3.27, P = 0.142] were not associated with susceptibility to SMA. However, carriage of IL-23R rs1884444T/rs7530511T (TT) haplotype, consisting of both mutant alleles, was associated with increased susceptibility to SMA (OR = 1.12, 95% CI = 1.07–4.19, P = 0.030). Conclusion Results presented here demonstrate that a haplotype of non-synonymous IL-23R variants increase susceptibility to SMA in children of a holoendemic P. falciparum transmission area

Phylogeography and population structure of the tsetse fly Glossina pallidipes in Kenya and the Serengeti ecosystem.

Glossina pallidipes is the main vector of animal African trypanosomiasis and a potential vector of human African trypanosomiasis in eastern Africa where it poses a large economic burden and public health threat. Vector control efforts have succeeded in reducing infection rates, but recent resurgence in tsetse fly population density raises concerns that vector control programs require improved strategic planning over larger geographic and temporal scales. Detailed knowledge of population structure and dispersal patterns can provide the required information to improve planning. To this end, we investigated the phylogeography and population structure of G. pallidipes over a large spatial scale in Kenya and northern Tanzania using 11 microsatellite loci genotyped in 600 individuals. Our results indicate distinct genetic clusters east and west of the Great Rift Valley, and less distinct clustering of the northwest separate from the southwest (Serengeti ecosystem). Estimates of genetic differentiation and first-generation migration indicated high genetic connectivity within genetic clusters even across large geographic distances of more than 300 km in the east, but only occasional migration among clusters. Patterns of connectivity suggest isolation by distance across genetic breaks but not within genetic clusters, and imply a major role for river basins in facilitating gene flow in G. pallidipes. Effective population size (Ne) estimates and results from Approximate Bayesian Computation further support that there has been recent G. pallidipes population size fluctuations in the Serengeti ecosystem and the northwest during the last century, but also suggest that the full extent of differences in genetic diversity and population dynamics between the east and the west was established over evolutionary time periods (tentatively on the order of millions of years). Findings provide further support that the Serengeti ecosystem and northwestern Kenya represent independent tsetse populations. Additionally, we present evidence that three previously recognized populations (the Mbeere-Meru, Central Kenya and Coastal "fly belts") act as a single population and should be considered as a single unit in vector control

A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning

Vector control is an effective strategy for reducing vector-borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub-Saharan Africa. We used random forest regression to (i) build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania and (ii) provide novel vector control recommendations. Inputs for the models included field survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data and exhibited high accuracy based on cross-validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and was used to inform our vector control recommendations. Post hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g., suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria Basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impacts of climate change in this and other vector systems

Data from: Temporal genetic differentiation in Glossina pallidipes tsetse fly populations in Kenya

Background: Glossina pallidipes is a major vector of both Human and Animal African Trypanosomiasis (HAT and AAT) in Kenya. The disease imposes economic burden on endemic regions in Kenya, including south-western Kenya, which has undergone intense but unsuccessful tsetse fly control measures. We genotyped 387 G. pallidipes flies at 13 microsatellite markers to evaluate levels of temporal genetic variation in two regions that have been subjected to intensive eradication campaigns from the 1960s to the 1980s. One of the regions, Nguruman Escarpment, has been subject to habitat alteration due to human activities, while the other, Ruma National Park, has not. In addition, Nguruman Escarpment is impacted by the movement of grazing animals into the area from neighboring regions during the drought season. We collected our samples from three geographically close sampling sites for each of the two regions. Samples were collected between the years 2003 and 2015, spanning ~96 tsetse fly generations. Results: We established that allelic richness averaged 3.49 and 3.63, and temporal Ne estimates averaged 594 in Nguruman Escarpment and 1120 in Ruma National Park. This suggests that genetic diversity is similar to what was found in previous studies of G. pallidipes in Uganda and Kenya, implying that we could not detect a reduction in genetic diversity following the extensive control efforts during the 1960s to the 1980s. However, we did find differences in temporal patterns of genetic variation between the two regions, indicated by clustering analysis, pairwise FST, and Fisher’s exact tests for changes in allele and genotype frequencies. In Nguruman Escarpment, findings indicated differentiation among samples collected in different years, and evidence of local genetic bottlenecks in two locations previous to 2003, and between 2009 and 2015. In contrast, there was no consistent evidence of differentiation among samples collected in different years, and no evidence of local genetic bottlenecks in Ruma National Park. Conclusion: Our findings suggest that, despite extensive control measures especially between the 1960s and the 1980s, tsetse flies in these regions persist with levels of genetic diversity similar to that found in populations that did not experience extensive control measures. Our findings also indicate temporal genetic differentiation in Nguruman Escarpment detected at a scale of > 80 generations, and no similar temporal differentiation in Ruma National Park. The different level of temporal differentiation between the two regions indicates that genetic drift is stronger in Nugruman Escarpment, for as-yet unknown reasons, which may include differences in land management. This suggests land management may have an impact on G. pallidipes population genetics, and reinforces the importance of long term monitoring of vector populations in estimates of parameters needed to model and plan effective species-specific control measures

Data from: Temporal genetic differentiation in Glossina pallidipes tsetse fly populations in Kenya

Background: Glossina pallidipes is a major vector of both Human and Animal African Trypanosomiasis (HAT and AAT) in Kenya. The disease imposes economic burden on endemic regions in Kenya, including south-western Kenya, which has undergone intense but unsuccessful tsetse fly control measures. We genotyped 387 G. pallidipes flies at 13 microsatellite markers to evaluate levels of temporal genetic variation in two regions that have been subjected to intensive eradication campaigns from the 1960s to the 1980s. One of the regions, Nguruman Escarpment, has been subject to habitat alteration due to human activities, while the other, Ruma National Park, has not. In addition, Nguruman Escarpment is impacted by the movement of grazing animals into the area from neighboring regions during the drought season. We collected our samples from three geographically close sampling sites for each of the two regions. Samples were collected between the years 2003 and 2015, spanning ~96 tsetse fly generations. Results: We established that allelic richness averaged 3.49 and 3.63, and temporal Ne estimates averaged 594 in Nguruman Escarpment and 1120 in Ruma National Park. This suggests that genetic diversity is similar to what was found in previous studies of G. pallidipes in Uganda and Kenya, implying that we could not detect a reduction in genetic diversity following the extensive control efforts during the 1960s to the 1980s. However, we did find differences in temporal patterns of genetic variation between the two regions, indicated by clustering analysis, pairwise FST, and Fisher’s exact tests for changes in allele and genotype frequencies. In Nguruman Escarpment, findings indicated differentiation among samples collected in different years, and evidence of local genetic bottlenecks in two locations previous to 2003, and between 2009 and 2015. In contrast, there was no consistent evidence of differentiation among samples collected in different years, and no evidence of local genetic bottlenecks in Ruma National Park. Conclusion: Our findings suggest that, despite extensive control measures especially between the 1960s and the 1980s, tsetse flies in these regions persist with levels of genetic diversity similar to that found in populations that did not experience extensive control measures. Our findings also indicate temporal genetic differentiation in Nguruman Escarpment detected at a scale of > 80 generations, and no similar temporal differentiation in Ruma National Park. The different level of temporal differentiation between the two regions indicates that genetic drift is stronger in Nugruman Escarpment, for as-yet unknown reasons, which may include differences in land management. This suggests land management may have an impact on G. pallidipes population genetics, and reinforces the importance of long term monitoring of vector populations in estimates of parameters needed to model and plan effective species-specific control measures

Microsatellite Data from Nugurman and Ruma

Microsatellite data used in Temporal genetic differentiation in Glossina pallidipes tsetse fly populations in Kenya, STRUCTURE forma