A major genetic locus controlling natural Plasmodium falciparum infection is shared by East and West African Anopheles gambiae

Background: Genetic linkage mapping identified a region of chromosome 2L in the Anopheles gambiae genome that exerts major control over natural infection by Plasmodium falciparum. This 2L Plasmodium-resistance interval was mapped in mosquitoes from a natural population in Mali, West Africa, and controls the numbers of P. falciparum oocysts that develop on the vector midgut. An important question is whether genetic variation with respect to Plasmodium-resistance exists across Africa, and if so whether the same or multiple geographically distinct resistance mechanisms are responsible for the trait. Methods: To identify P falciparum resistance loci in pedigrees generated and infected in Kenya, East Africa, 28 microsatellite loci were typed across the mosquito genome. Genetic linkage mapping was used to detect significant linkage between genotype and numbers of midgut oocysts surviving to 7–8 days post-infection. Results: A major malaria-control locus was identified on chromosome 2L in East African mosquitoes, in the same apparent position originally identified from the West African population. Presence of this resistance locus explains 75% of parasite free mosquitoes. The Kenyan resistance locus is named EA_Pfin1 (East Africa_ Plasmodium falciparum Infection Intensity). Conclusion: Detection of a malaria-control locus at the same chromosomal location in both East and West African mosquitoes indicates that, to the level of genetic resolution of the analysis, the same mechanism of Plasmodium-resistance, or a mechanism controlled by the same genomic region, is found across Africa, and thus probably operates in A. gambiae throughout its entire range

Malaria in Africa: Vector Species' Niche Models and Relative Risk Maps

A central theoretical goal of epidemiology is the construction of spatial models of disease prevalence and risk, including maps for the potential spread of infectious disease. We provide three continent-wide maps representing the relative risk of malaria in Africa based on ecological niche models of vector species and risk analysis at a spatial resolution of 1 arc-minute (9 185 275 cells of approximately 4 sq km). Using a maximum entropy method we construct niche models for 10 malaria vector species based on species occurrence records since 1980, 19 climatic variables, altitude, and land cover data (in 14 classes). For seven vectors (Anopheles coustani, A. funestus, A. melas, A. merus, A. moucheti, A. nili, and A. paludis) these are the first published niche models. We predict that Central Africa has poor habitat for both A. arabiensis and A. gambiae, and that A. quadriannulatus and A. arabiensis have restricted habitats in Southern Africa as claimed by field experts in criticism of previous models. The results of the niche models are incorporated into three relative risk models which assume different ecological interactions between vector species. The “additive” model assumes no interaction; the “minimax” model assumes maximum relative risk due to any vector in a cell; and the “competitive exclusion” model assumes the relative risk that arises from the most suitable vector for a cell. All models include variable anthrophilicity of vectors and spatial variation in human population density. Relative risk maps are produced from these models. All models predict that human population density is the critical factor determining malaria risk. Our method of constructing relative risk maps is equally general. We discuss the limits of the relative risk maps reported here, and the additional data that are required for their improvement. The protocol developed here can be used for any other vector-borne disease

Adaptation to Aridity in the Malaria Mosquito Anopheles gambiae: Chromosomal Inversion Polymorphism and Body Size Influence Resistance to Desiccation

Chromosomal inversions are thought to confer a selective advantage in alternative habitats by protecting co-adapted alleles from recombination. The frequencies of two inversions (2La and 2Rb) of the afro-tropical malaria mosquito Anopheles gambiae change gradually along geographical clines, increasing in frequency with degree of aridity. Such clines can result from gene flow and local selection acting upon alternative karyotypes along the cline, suggesting that these inversions may be associated with tolerance to xeric conditions. Since water loss represents a major challenge in xeric habitats, it can be supposed that genes inside these inversions are involved in water homeostasis. To test this hypothesis, we compared the desiccation resistance of alternative karyotypes from a colonised 2Rb/2La polymorphic population of A. gambiae from Cameroon. The strain included only the molecular form S, one of the genetic units marking incipient speciation in this taxon. Day-old mosquitoes of both sexes were assayed individually for time to death in a dry environment and the karyotype of each was determined post-mortem using molecular diagnostic assays for each inversion. In agreement with expectations based on their eco-geographical distribution, we found that 2La homokaryotypes survived significantly longer (1.3 hours) than the other karyotypes. However, there was weak support for the effect of 2Rb on desiccation resistance. Larger mosquitoes survived longer than smaller ones. Median survival of females was greater than males, but the effect of sex on desiccation resistance was weakly supported, indicating that differential survival was correlated to differences between sexes in average size. We found weak evidence for a heterotic effect of 2La karyotype on size in females. These results support the notion that genes located inside the 2La inversion are involved in water balance, contributing towards local adaptation of A. gambiae to xeric habitats, beyond the adaptive value conferred by a larger body size

Inferring selection in the Anopheles gambiae species complex: an example from immune-related serine protease inhibitors

<p>Abstract</p> <p>Background</p> <p>Mosquitoes of the <it>Anopheles gambiae </it>species complex are the primary vectors of human malaria in sub-Saharan Africa. Many host genes have been shown to affect <it>Plasmodium </it>development in the mosquito, and so are expected to engage in an evolutionary arms race with the pathogen. However, there is little conclusive evidence that any of these mosquito genes evolve rapidly, or show other signatures of adaptive evolution.</p> <p>Methods</p> <p>Three serine protease inhibitors have previously been identified as candidate immune system genes mediating mosquito-Plasmodium interaction, and serine protease inhibitors have been identified as hot-spots of adaptive evolution in other taxa. Population-genetic tests for selection, including a recent multi-gene extension of the McDonald-Kreitman test, were applied to 16 serine protease inhibitors and 16 other genes sampled from the <it>An. gambiae </it>species complex in both East and West Africa.</p> <p>Results</p> <p>Serine protease inhibitors were found to show a marginally significant trend towards higher levels of amino acid diversity than other genes, and display extensive genetic structuring associated with the 2La chromosomal inversion. However, although serpins are candidate targets for strong parasite-mediated selection, no evidence was found for rapid adaptive evolution in these genes.</p> <p>Conclusion</p> <p>It is well known that phylogenetic and population history in the <it>An. gambiae </it>complex can present special problems for the application of standard population-genetic tests for selection, and this may explain the failure of this study to detect selection acting on serine protease inhibitors. The pitfalls of uncritically applying these tests in this species complex are highlighted, and the future prospects for detecting selection acting on the <it>An. gambiae </it>genome are discussed.</p

The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis

<p>Abstract</p> <p>Background</p> <p>This is the second in a series of three articles documenting the geographical distribution of 41 dominant vector species (DVS) of human malaria. The first paper addressed the DVS of the Americas and the third will consider those of the Asian Pacific Region. Here, the DVS of Africa, Europe and the Middle East are discussed. The continent of Africa experiences the bulk of the global malaria burden due in part to the presence of the <it>An. gambiae </it>complex. <it>Anopheles gambiae </it>is one of four DVS within the <it>An. gambiae </it>complex, the others being <it>An. arabiensis </it>and the coastal <it>An. merus </it>and <it>An. melas</it>. There are a further three, highly anthropophilic DVS in Africa, <it>An. funestus</it>, <it>An. moucheti </it>and <it>An. nili</it>. Conversely, across Europe and the Middle East, malaria transmission is low and frequently absent, despite the presence of six DVS. To help control malaria in Africa and the Middle East, or to identify the risk of its re-emergence in Europe, the contemporary distribution and bionomics of the relevant DVS are needed.</p> <p>Results</p> <p>A contemporary database of occurrence data, compiled from the formal literature and other relevant resources, resulted in the collation of information for seven DVS from 44 countries in Africa containing 4234 geo-referenced, independent sites. In Europe and the Middle East, six DVS were identified from 2784 geo-referenced sites across 49 countries. These occurrence data were combined with expert opinion ranges and a suite of environmental and climatic variables of relevance to anopheline ecology to produce predictive distribution maps using the Boosted Regression Tree (BRT) method.</p> <p>Conclusions</p> <p>The predicted geographic extent for the following DVS (or species/suspected species complex*) is provided for Africa: <it>Anopheles </it>(<it>Cellia</it>) <it>arabiensis</it>, <it>An. </it>(<it>Cel.</it>) <it>funestus*</it>, <it>An. </it>(<it>Cel.</it>) <it>gambiae</it>, <it>An. </it>(<it>Cel.</it>) <it>melas</it>, <it>An. </it>(<it>Cel.</it>) <it>merus</it>, <it>An. </it>(<it>Cel.</it>) <it>moucheti </it>and <it>An. </it>(<it>Cel.</it>) <it>nili*</it>, and in the European and Middle Eastern Region: <it>An. </it>(<it>Anopheles</it>) <it>atroparvus</it>, <it>An. </it>(<it>Ano.</it>) <it>labranchiae</it>, <it>An. </it>(<it>Ano.</it>) <it>messeae</it>, <it>An. </it>(<it>Ano.</it>) <it>sacharovi</it>, <it>An. </it>(<it>Cel.</it>) <it>sergentii </it>and <it>An. </it>(<it>Cel.</it>) <it>superpictus*</it>. These maps are presented alongside a bionomics summary for each species relevant to its control.</p

Chemical graph transformation with stereo-information

Double Pushout graph transformation naturally facilitates the modelling of chemical reactions: labelled undirected graphs model molecules and direct derivations model chemical reactions. However, the most straightforward modelling approach ignores the relative placement of atoms and their neighbours in space. Stereoisomers of chemical compounds thus cannot be distinguished, even though their chemical activity may differ substantially. In this contribution we propose an extended chemical graph transformation system with attributes that encode information about local geometry. The modelling approach is based on the socalled “ordered list method”, where an order is imposed on the set of incident edges of each vertex, and permutation groups determine equivalence classes of orderings that correspond to the same local spatial embedding. This method has previously been used in the context of graph transformation, but we here propose a framework that also allows for partially specified stereoinformation. While there are several stereochemical configurations to be considered, we focus here on the tetrahedral molecular shape, and suggest general principles for how to treat all other chemically relevant local geometries. We illustrate our framework using several chemical examples, including the enumeration of stereoisomers of carbohydrates and the stereospecific reaction for the aconitase enzyme in the citirc acid cycle