Tracking the flow of malaria parasites and drug resistance within the DRC and across its borders

追踪刚果民主共和国境内和跨境的疟原虫流动和耐药性

• 批准号: 10631299
• 负责人: JEFFREY A. BAILEY
• 金额: $ 8.1万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-21 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10631299
• 关键词: Adult; Africa; Africa South of the Sahara; African; Amodiaquine; Anti-malarial drug resistance; Antimalarials; Area; Artemisinins; Child; Computer software; Conflict (Psychology); Congo; Country; Data; Data Set; Democratic Republic of the Congo; Development; Drug resistance; Epidemiology; Evolution; Falciparum Malaria; Fingerprint; Frequencies; Genes; Genetic; Genetic Markers; Genetic Variation; Genotype; Geographic Locations; Geography; Grant; Haplotypes; Healthcare; Individual; Infection; Infrastructure; Intervention; Laboratories; Link; Locales; London; Malaria; Maps; Metadata; Modeling; Molecular; Parasite resistance; Parasites; Parasitic infection; Pattern; Plasmodium falciparum; Population; Population Genetics; Prevalence; Province; Public Health; Research Personnel; Resistance; Resolution; Risk Factors; Sampling; Site; Spatial Distribution; Structure; Tanzania; Uganda; Work; Zambia; base; biobank; college; drug-sensitive; genetic evolution; genomic locus; improved; in vivo; interest; malaria infection; migration; programs; public health intervention; repository; resistance allele; resistance gene; resistance mutation; resistant Plasmodium falciparum; targeted sequencing; tool; whole genome

ABSTRACT Malaria​ ​remains​ ​endemic​ ​in​ ​sub-Saharan​ ​Africa​ ​in​ ​large​ ​part​ ​due​ ​to​ ​continued​ ​evolution​ ​and​ ​spread​ ​of​ ​drug resistance​ ​which​ ​undermines​ ​ongoing​ ​large-scale​ ​control​ ​and​ ​elimination​ ​efforts.​ ​We​ ​will​ ​leverage high-throughput​ ​genotyping​ ​of​ ​parasites​ ​using​ ​a​ ​state-of-the-art​ ​panel​ ​of​ ​molecular​ ​inversion​ ​probes​ ​(MIPs)​ ​for targeted​ ​sequencing​ ​of​ ​thousands​ ​of​ ​resistance​ ​and​ ​neutral​ ​loci​ ​across​ ​thousands​ ​of​ ​infections​ ​covering​ ​the entire​ ​Democratic​ ​Republic​ ​of​ ​Congo​ ​(DRC)​ ​and​ ​select​ ​areas​ ​in​ ​bordering​ ​countries.​ ​This​ ​will​ ​provide​ ​us​ ​a​ ​map with​ ​an​ ​unprecedented​ ​scale​ ​and​ ​resolution​ ​to​ ​define​ ​the​ ​evolution​ ​and​ ​spread​ ​of​ ​antimalarial​ ​resistance mutations.​ ​In​ ​this​ ​project,​ ​we​ ​will​ ​first​ ​develop​ ​our​ ​genotyping​ ​panel​ ​which​ ​should​ ​provide​ ​a​ ​high-resolution​ ​tool for​ ​studying​ ​all​ ​known​ ​drug​ ​resistance​ ​loci​ ​and​ ​general​ ​parasite​ ​population​ ​structure​ ​in​ ​this​ ​and​ ​other​ ​settings. Applying​ ​this​ ​to​ ​well​ ​annotated​ ​samples​ ​from​ ​across​ ​the​ ​DRC​ ​and​ ​bordering​ ​countries,​ ​we​ ​will​ ​define​ ​the prevalence​ ​of​ ​drug​ ​resistance​ ​mutations​ ​and​ ​define​ ​them​ ​based​ ​on​ ​their​ ​genetic​ ​haplotypes​ ​which​ ​act​ ​as​ ​a unique​ ​fingerprints.​ ​We​ ​will​ ​then​ ​map​ ​these​ ​drug​ ​resistance​ ​haplotypes​ ​and​ ​study​ ​their​ ​spread​ ​and​ ​spatial associations​ ​and​ ​further​ ​examine​ ​their​ ​epidemiologic​ ​associations​ ​and​ ​interactions.​ ​Onto​ ​this​ ​detailed​ ​spatial map,​ ​we​ ​will​ ​​ ​examine​ ​specific​ ​locales​ ​of​ ​interest​ ​for​ ​temporal​ ​changes.​ ​​ ​These​ ​focal​ ​regions​ ​in​ ​the​ ​DRC represent​ ​diverse​ ​ecologic​ ​and​ ​demographic​ ​features​ ​that​ ​likely​ ​impact​ ​resistance​ ​spread​ ​and​ ​evolution including​ ​areas​ ​with​ ​minimal​ ​health​ ​care​ ​infrastructure​ ​and​ ​ongoing​ ​regions​ ​of​ ​conflict.​ ​Our​ ​final​ ​work​ ​will​ ​be​ ​to apply​ ​more​ ​sophisticated​ ​models​ ​to​ ​the​ ​rich​ ​sequence​ ​data​ ​in​ ​order​ ​to​ ​estimate​ ​the​ ​flow​ ​of​ ​parasites​ ​and resistance​ ​mutations​ ​within​ ​and​ ​between​ ​the​ ​DRC.​ ​This​ ​includes​ ​the​ ​development​ ​of​ ​new​ ​​ ​population​ ​structure models​ ​(MALECOT)​ ​incorporating​ ​parasite​ ​infections​ ​that​ ​have​ ​multiple​ ​strains​ ​allowing​ ​for​ ​a​ ​better understanding​ ​of​ ​the​ ​full​ ​dataset​ ​as​ ​in​ ​polyclonal​ ​infections​ ​are​ ​often​ ​the​ ​majority​ ​in​ ​endemic​ ​African​ ​countries. This​ ​model​ ​should​ ​be​ ​broadly​ ​applicable​ ​to​ ​other​ ​studies​ ​of​ ​malaria​ ​or​ ​infections​ ​with​ ​mixed​ ​strains.​ ​We​ ​will also​ ​leverage​ ​​ ​spatially​ ​explicit​ ​models​ ​to​ ​define​ ​general​ ​parasite​ ​flow​ ​in​ ​both​ ​relative​ ​and​ ​absolute​ ​terms comparing​ ​and​ ​contrasting​ ​to​ ​the​ ​flow​ ​of​ ​resistant​ ​parasites.​ ​This​ ​work​ ​will​ ​provide​ ​the​ ​patterns​ ​of​ ​general​ ​gene flow​ ​and​ ​barriers​ ​on​ ​the​ ​spread​ ​of​ ​specific​ ​drug​ ​resistance​ ​alleles​ ​and​ ​haplotypes.​ ​In​ ​summary,​ ​not​ ​only​ ​will this​ ​project​ ​enhance​ ​our​ ​understanding​ ​of​ ​malaria​ ​landscape​ ​genetics​ ​and​ ​the​ ​evolution​ ​and​ ​spread​ ​of antimalarial​ ​resistance,​ ​it​ ​will​ ​provide​ ​tools​ ​and​ ​analysis​ ​framework​ ​for​ ​improving​ ​public​ ​health​ ​interventions that​ ​will​ ​directly​ ​inform​ ​the​ ​DRC​ ​National​ ​Malaria​ ​Control​ ​Program.

抽象的 由于药物的持续进化和传播，疟疾在撒哈拉以南非洲地区仍然是地方性流行病 抵抗破坏了正在进行的大规模控制和消除努力。我们将利用 使用最先进的分子反转探针 (MIP) 面板对寄生虫进行高通量基因分型 对数以千计的耐药基因座和中性基因座进行靶向测序，覆盖了数以千计的感染病例 整个刚果民主共和国（刚果民主共和国）以及毗邻国家的选定区域。这将为我们提供一张地图 以前所未有的规模和分辨率来定义抗疟药耐药性的演变和传播 在这个项目中，我们将首先开发我们的基因分型面板，该面板应提供高分辨率工具 用于研究在此和其他环境中所有已知的耐药位点和一般寄生虫种群结构。 将此应用于来自刚果民主共和国和毗邻国家的注释良好的样本，我们将定义 ​​药物耐药性​​突变的流行​​并根据其遗传​​单倍型来定义它们​​​，单倍型充当​​ 独特的指纹。​​​​然后​我们​将​绘制​这些​抗药​性​单倍型​​并​​研究​它们​的​传播​和​空间 ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​ 地图，​​​​​将检查​感兴趣​的​特定​区域​，​了解​时间​变化。​​​​​刚果民主共和国的这些重点区域​ 代表多样化的生态和人口特征，可能影响抵抗力的传播和进化 包括基础设施最差的地区和冲突持续的地区。我们的最终工作将是 将更复杂的模型应用于丰富的序列数据，以估计寄生虫的流量和 ​​刚果民主共和国内部和之间的抗性突变。​​这包括新​​​​​人口结构的发展​​ 模型（MALECOT）​​包含​​寄生虫​​感染​​，具有​​多种​​菌株​​​​​​​​​​​​​​​ 在非洲流行国家中，了解完整数据集（如多克隆感染）的人通常是大多数人。 这个模型应该广泛适用于疟疾或混合菌株感染的其他研究。我们将 还利用空间显式模型来定义一般寄生虫流的相对和绝对术语 比较和对比抗性寄生虫的流动。​​这项工作将提供​​一般基因的模式​​ ​​特定耐药性等位基因和单倍型传播的流动和障碍。总而言之，不仅会 这个项目​​增强了我们对疟疾​​景观​​遗传学​以及​进化​和​传播​的了解​​ 抗疟药耐药性，它将提供工具和分析框架，以改善公共卫生干预措施 这将直接通知刚果民主共和国国家疟疾控制计划。