The impact of climate change on the geographical distribution of two vectors of Chagas disease: Implications for the force of infection

Chagas disease, caused by the parasite Trypanosoma cruzi, is the most important vector-borne disease in Latin America. The vectors are insects belonging to the Triatominae (Hemiptera, Reduviidae), and are widely distributed in the Americas. Here, we assess the implications of climatic projections for 2050 on the geographical footprint of two of the main Chagas disease vectors: Rhodnius prolixus (tropical species) and Triatoma infestans (temperate species).We estimated the epidemiological implications of current to future transitions in the climatic niche in terms of changes in the force of infection (FOI) on the rural population of two countries: Venezuela (tropical) and Argentina (temperate). The climatic projections for 2050 showed heterogeneous impact on the climatic niches of both vector species, with a decreasing trend of suitability of areas that are currently at high-to-moderate transmission risk. Consequently, climatic projections affected differently the FOI for Chagas disease in Venezuela and Argentina. Despite the heterogeneous results, our main conclusions point out a decreasing trend in the number of new cases of Tr. cruzi human infections per year between current and future conditions using a climatic niche approach.Centro de Estudios Parasitológicos y de VectoresFacultad de Ciencias Naturales y Muse

A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology

Chikungunya is a viral disease transmitted to humans primarily via the bites of infected Aedes mosquitoes. The virus caused a major epidemic in the Indian Ocean in 2004, affecting millions of inhabitants, while cases have also been observed in Europe since 2007. We developed a stochastic spatiotemporal model of Aedes albopictus-borne chikungunya transmission based on our recently developed environmentally-driven vector population dynamics model. We designed an integrated modelling framework incorporating large-scale gridded climate datasets to investigate disease outbreaks on Reunion Island and in Italy. We performed Bayesian parameter inference on the surveillance data, and investigated the validity and applicability of the underlying biological assumptions. The model successfully represents the outbreak and measures of containment in Italy, suggesting wider applicability in Europe. In its current configuration, the model implies two different viral strains, thus two different outbreaks, for the two-stage Reunion Island epidemic. Characterisation of the posterior distributions indicates a possible relationship between the second larger outbreak on Reunion Island and the Italian outbreak. The model suggests that vector control measures, with different modes of operation, are most effective when applied in combination: adult vector intervention has a high impact but is short-lived, larval intervention has a low impact but is long-lasting, and quarantining infected territories, if applied strictly, is effective in preventing large epidemics. We present a novel approach in analysing chikungunya outbreaks globally using a single environmentally-driven mathematical model. Our study represents a significant step towards developing a globally applicable Ae. albopictus-borne chikungunya transmission model, and introduces a guideline for extending such models to other vector-borne diseases

The impact of climate change on the geographical distribution of two vectors of Chagas disease: Implications for the force of infection

Chagas disease, caused by the parasite Trypanosoma cruzi, is the most important vector-borne disease in Latin America. The vectors are insects belonging to the Triatominae (Hemiptera, Reduviidae), and are widely distributed in the Americas. Here, we assess the implications of climatic projections for 2050 on the geographical footprint of two of the main Chagas disease vectors: Rhodnius prolixus (tropical species) and Triatoma infestans (temperate species).We estimated the epidemiological implications of current to future transitions in the climatic niche in terms of changes in the force of infection (FOI) on the rural population of two countries: Venezuela (tropical) and Argentina (temperate). The climatic projections for 2050 showed heterogeneous impact on the climatic niches of both vector species, with a decreasing trend of suitability of areas that are currently at high-to-moderate transmission risk. Consequently, climatic projections affected differently the FOI for Chagas disease in Venezuela and Argentina. Despite the heterogeneous results, our main conclusions point out a decreasing trend in the number of new cases of Tr. cruzi human infections per year between current and future conditions using a climatic niche approach.Centro de Estudios Parasitológicos y de VectoresFacultad de Ciencias Naturales y Muse

Climate, Environmental and Socio-Economic Change: Weighing Up the Balance in Vector-Borne Disease Transmission

Arguably one of the most important effects of climate change is the potential impact on human health. While this is likely to take many forms, the implications for future transmission of vector-borne diseases (VBDs), given their ongoing contribution to global disease burden, are both extremely important and highly uncertain. In part, this is owing not only to data limitations and methodological challenges when integrating climate-driven VBD models and climate change projections, but also, perhaps most crucially, to the multitude of epidemiological, ecological and socio-economic factors that drive VBD transmission, and this complexity has generated considerable debate over the past 10-15 years. In this review, we seek to elucidate current knowledge around this topic, identify key themes and uncertainties, evaluate ongoing challenges and open research questions and, crucially, offer some solutions for the field. Although many of these challenges are ubiquitous across multiple VBDs, more specific issues also arise in different vector-pathogen systems

A specific amino acid motif of HLA-DRB1 mediates risk and interacts with smoking history in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disease in which genetic risk has been mapped to HLA, but precise allelic associations have been difficult to infer due to limitations in genotyping methodology. Mapping PD risk at highest possible resolution, we performed sequencing of 11 HLA genes in 1,597 PD cases and 1,606 controls. We found that susceptibility to PD can be explained by a specific combination of amino acids at positions 70-74 on the HLA-DRB1 molecule. Previously identified as the primary risk factor in rheumatoid arthritis and referred to as the "shared epitope" (SE), the residues Q/R-K/R-R-A-A at positions 70-74 in combination with valine at position 11 (11-V) is highly protective in PD, while risk is attributable to the identical epitope in the absence of 11-V. Notably, these effects are modified by history of cigarette smoking, with a strong protective effect mediated by a positive history of smoking in combination with the SE and 11-V (P = 10-4; odds ratio, 0.51; 95% confidence interval, 0.36-0.72) and risk attributable to never smoking in combination with the SE without 11-V (P = 0.01; odds ratio, 1.51; 95% confidence interval, 1.08-2.12). The association of specific combinations of amino acids that participate in critical peptide-binding pockets of the HLA class II molecule implicates antigen presentation in PD pathogenesis and provides further support for genetic control of neuroinflammation in disease. The interaction of HLA-DRB1 with smoking history in disease predisposition, along with predicted patterns of peptide binding to HLA, provide a molecular model that explains the unique epidemiology of smoking in PD

Bonobos Maintain Immune System Diversity with Three Functional Types of MHC-B

Fast-evolving MHC class I polymorphism serves to diversify NK cell and CD8 T cell responses in individuals, families, and populations. Because only chimpanzee and bonobo have strict orthologs of all HLA class I, their study gives unique perspectives on the human condition. We defined polymorphism of Papa-B, the bonobo ortholog of HLA-B, for six wild bonobo populations. Sequences for Papa-B exon 2 and 3 were determined from the genomic DNA in 255 fecal samples, minimally representing 110 individuals. Twenty-two Papa-B alleles were defined, each encoding a different Papa-B protein. No Papa-B is identical to any chimpanzee Patr-B, human HLA-B, or gorilla Gogo-B. Phylogenetic analysis identified a Glade of MHC-B, defined by residues 45-74 of the alpha(1) domain, which is broadly conserved among bonobo, chimpanzee, and gorilla. Bonobo populations have 3-14 Papa-B allotypes. Three Papa-B are in all populations, and they are each of a different functional type: allotypes having the Bw4 epitope recognized by killer cell Ig-like receptors of NK cells, allotypes having the Cl epitope also recognized by killer cell Ig-like receptors, and allotypes having neither epitope. For population Malebo, these three Papa-B are the only Papa-B allotypes. Although small in number, their sequence divergence is such that the nucleotide diversity (mean proportional distance) of Papa-B in Malebo is greater than in the other populations and is also greater than expected for random combinations of three Papa-B. Overall, Papa-B has substantially less diversity than Patr-B in chimpanzee subspecies and HLA-B in indigenous human populations, consistent with bonobo having experienced narrower population bottlenecks

Defining KIR and HLA Class I Genotypes at Highest Resolution via High-Throughput Sequencing.

The physiological functions of natural killer (NK) cells in human immunity and reproduction depend upon diverse interactions between killer cell immunoglobulin-like receptors (KIRs) and their HLA class I ligands: HLA-A, HLA-B, and HLA-C. The genomic regions containing the KIR and HLA class I genes are unlinked, structurally complex, and highly polymorphic. They are also strongly associated with a wide spectrum of diseases, including infections, autoimmune disorders, cancers, and pregnancy disorders, as well as the efficacy of transplantation and other immunotherapies. To facilitate study of these extraordinary genes, we developed a method that captures, sequences, and analyzes the 13 KIR genes and HLA-A, HLA-B, and HLA-C from genomic DNA. We also devised a bioinformatics pipeline that attributes sequencing reads to specific KIR genes, determines copy number by read depth, and calls high-resolution genotypes for each KIR gene. We validated this method by using DNA from well-characterized cell lines, comparing it to established methods of HLA and KIR genotyping, and determining KIR genotypes from 1000 Genomes sequence data. This identified 116 previously uncharacterized KIR alleles, which were all demonstrated to be authentic by sequencing from source DNA via standard methods. Analysis of just two KIR genes showed that 22% of the 1000 Genomes individuals have a previously uncharacterized allele or a structural variant. The method we describe is suited to the large-scale analyses that are needed for characterizing human populations and defining the precise HLA and KIR factors associated with disease. The methods are applicable to other highly polymorphic genes.This study was supported by U.S. National Institutes of Health grants U01 AI090905, R01 20 GM109030, R01 AI17892 and U19 AI119350. Authors Steven Norberg and Mostafa Ronaghi are 21 employees of Illumina Inc.This is the author accepted manuscript. It is currently under an indefinite embargo pending publication by Elsevier