Latitudinal Landscape Genomics and Ecology of Anopheles Darlingi

项目摘要 尽管2005-2015年间拉丁美洲在减少疟疾方面取得了重大进展，但疟疾病例再次 在过去几年中增加到近100万例，其中75%报告在委内瑞拉和巴西。 我们强调，影响初级生态系统的生态变量(如生态交错带和土地利用类别) 新热带媒介按蚊(在巴西和委内瑞拉)和区域媒介按蚊。白化病S.L.(等于 对一个人来说很重要。委内瑞拉的Darlingi)在很大程度上是未量化或未识别的，构成了主要的 信息鸿沟。拟议研究的前提是人为改变的景观类型(河流、采矿、 农业)在持续疟疾热点地区在可测量的传播决定因素上存在显著差异 (生态学、昆虫学、社会经济学)。这些决定因素在景观类型中的量化 委内瑞拉和巴西对疟疾监测至关重要，以促进对大多数人的当地有针对性的干预 有效减少传播。将这些措施与景观基因组学结合起来，我们建议识别 并以空间明确的方式量化影响疟疾病媒微进化的景观特征。这将是 提供了对异质景观中局部媒介适应的新见解，并将有助于确定原因 新热带疟疾热点在某些地理位置持续存在，有时持续多年。我们 假设一个物种丰度、繁殖和生存的生态驱动力。Darlingi和An.白化病 S.L.由于每个物种独特的生态特征(生态位)，将有很大的不同。我们预计我们的 研究结果将提供有关不同生境适宜性的新信息，这些信息可应用于整个 广泛且在很大程度上重叠的分布。Darlingi和An.白化病S.L.多态发现 通过全基因组重测序，结合昆虫学和社会经济因素的测量 这加强了人与媒介的接触，将揭示促进媒介的因素的进化遗传基础 病媒种群之间的扩散和连通性，从而能够更有效地监测病媒。一把钥匙 在我们对AN的作用的理解中仍然存在问题。达令吉景观基因组学在疟疾传播中的作用 基因流动是否有助于整个砍伐/退化生境的快速适应和定居 亚马逊盆地。我们将在巴西和委内瑞拉的区域范围内应用景观基因组学来 测试是否存在。Darlingi维持遗传连通性或因跨交错带的抗性而被隔离 其间有高大的森林覆盖率。而基于RADSEQ或个体基因的群体遗传学调查 能够检测总体种群结构的全基因组测序研究表明，这些都是有限的 调查可能会遗漏重要的祖先模式、基因流动和适应证据(冈比亚按蚊 1000基因组联盟2017)。在这里，我们提出了一种综合的方法来识别和量化 疟疾传播的生态、昆虫学和社会经济驱动因素，基于对 一种。Darlingi和An.白化病S.L.在自然野外条件下采集。

Project Summary Despite major progress in malaria reduction in Latin America from 2005-2015, malaria cases have again increased to nearly one million in the last few years, with 75% of these cases reported in Venezuela and Brazil. We emphasize that ecological variables (such as ecotones and land use classes) that impact the primary neotropical vector Anopheles darlingi (in Brazil and Venezuela) and the regional vector An. albitarsis s.l. (equal in importance to An. darlingi in Venezuela) are largely unquantified or unidentified, constituting a major information gap. The premise of the proposed study is that human-modified landscape types (riverine, mining, agricultural) in persistent malaria hotspots differ significantly in measurable determinants of transmission (ecological, entomological, socioeconomic). Quantification of these determinants in landscape types in Venezuela and Brazil is essential for malaria surveillance to facilitate local targeted interventions to most effectively reduce transmission. Coupling these measures with landscape genomics, we propose to identify and quantify, in a spatially explicit way, landscape features affecting microevolution of malaria vectors. This will provide new insights into local vector adaptation in heterogeneous landscapes, and will help determine why neotropical malaria hotspots persist, at times over multiple years, in certain geographic locations. We hypothesize that the ecological drivers of abundance, proliferation and survival in An. darlingi and An. albitarsis s.l. will differ significantly due to unique ecological signatures (niches) of each species. We anticipate that our findings will provide new information about differential habitat suitability that can be applied throughout the broad and largely overlapping distributions of An. darlingi and An. albitarsis s.l. Polymorphism discovery through whole genome resequencing, integrated with measures of entomological and socioeconomic factors that intensify human-vector contact, will reveal the evolutionary genetic basis of factors promoting vector proliferation and connectivity among vector populations, allowing for more effective vector surveillance. A key remaining issue in our understanding of the role of An. darlingi landscape genomics in malaria transmission is whether gene flow contributes to rapid adaptation and colonization of deforested/degraded habitats throughout the Amazon Basin. We will employ landscape genomics at a regional scale in both Brazil and Venezuela to test whether An. darlingi maintains genetic connectivity or is isolated by resistance across ecotones punctuated by high forest cover. Whereas population genetic surveys based on RADseq or individual genes can detect overall population structure, whole genome sequencing studies have shown that these limited surveys may miss important patterns of ancestry, gene flow and evidence of adaptation (Anopheles gambiae 1000 Genomes Consortium 2017). Herein we present an integrated approach to identify and quantify ecological, entomological and socioeconomic drivers of malaria transmission, based on multi-scale analysis of An. darlingi and An. albitarsis s.l. collected under natural field conditions.