Latitudinal Landscape Genomics and Ecology of Anopheles Darlingi

项目摘要 尽管2005-2015年拉丁美洲在减少疟疾方面取得了重大进展，但疟疾病例再次增加。 在过去的几年里增加到近100万，其中75%的病例报告在委内瑞拉和巴西。 我们强调，生态变量（如生态交错区和土地利用类），影响主要 新热带病媒达氏按蚊（在巴西和委内瑞拉）和区域病媒安氏按蚊。白跗关节（相等） 对安的重要性。委内瑞拉的darlingi）在很大程度上是无法量化或无法识别的，构成了一个主要的 信息鸿沟拟议研究的前提是，人类改造的景观类型（河流，采矿， 在可衡量的传播决定因素方面， （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，以促进当地有针对性的干预措施， 有效减少传播。将这些措施与景观基因组学相结合，我们建议确定 并以空间上明确的方式量化影响疟疾病媒微观演变的景观特征。这将 为异质景观中的局部矢量适应提供了新的见解，并将有助于确定为什么 在某些地理位置，新热带疟疾热点有时持续多年。我们 假设，丰富的生态驱动力，增殖和生存在一个。darlingi和An.白跗 S.L.由于每个物种独特的生态特征（生态位），它们之间的差异很大。我们预计， 研究结果将提供新的信息，差异栖息地的适宜性，可以适用于整个 广泛和大部分重叠的分布。darlingi和An.白跗关节多态性发现 通过全基因组重测序，结合昆虫学和社会经济因素的测量， 加强人与病媒接触的研究，将揭示促进病媒因子进化的遗传基础 病媒种群之间的扩散和联系，从而能够更有效地监测病媒。一个关键 我们对安的作用的理解中的遗留问题。Darlingi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森林砍伐/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采用景观基因组学， 测试是否一个。达林吉保持遗传连接性，或因交错区的抗性而被隔离 被高森林覆盖所打断。而基于RADseq或个体基因的群体遗传调查 可以检测整体种群结构，全基因组测序研究表明，这些有限 调查可能会错过重要的祖先模式、基因流动和适应证据（冈比亚按蚊 1000 Genomes Consortium 2017）。在这里，我们提出了一个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， 一个. darlingi和An.白跗关节在自然条件下采集。

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.