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: 10473084
• 负责人: JEFFREY A. BAILEY
• 金额: $ 8.03万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-08 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10473084
• 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; Structural Models; 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; 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; Structural Models; 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 疟疾在撒哈拉以南非洲仍然是内在的，这在很大程度上是由于持续进化和毒品传播 阻力可以实现正在进行的大规模控制和消除工作。我们将利用 使用最先进的分子反转问题（MIPS）的寄生虫的高通量基因分型 跨越数千种感染的有针对性测序 整个刚果民主共和国（DRC）和边境国家的精选地区。这将为我们提供一张地图 具有前所未有的量表和解决方案，以定义抗疟药的演变和传播 突变。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 在此和其他环境中研究所有已知的耐药性区域和一般寄生虫种群结构。 将其应用于来自刚果民主共和国和边境国家的良好注释样本，我们将定义 耐药性突变的流行率并根据其遗传单倍型定义它们 独特的指纹。然后，我们将绘制这些耐药性单倍型并研究它们的传播和空间 协会和进一步检查其流行病学关联和相互作用。在这个详细的空间上 地图，我们将检查感兴趣的特定地区是否有临时变化。刚果民主共和国中的这些焦点区域 代表潜水员的生态和人口特征，可能会影响抵抗力传播和进化 包括拥有最小医疗保健基础设施和持续冲突地区的地区。我们的最后工作将是 将更复杂的模型应用于丰富的序列数据，以估算寄生虫的流动和 DRC内部和之间的电阻突变。这包括开发新的人口结构 模型（MALECOT）融合了具有多种菌株的寄生虫感染，可以更好 在多克隆感染中，对完整数据集的理解通常是非洲流行国家的多数。 该模型应广泛适用于其他疟疾或混合菌株感染的研究。我们将 还利用空间显式模型以相对和绝对项来定义一般的寄生虫流 比较和对比与抗性寄生虫的流动。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 该项目增强了我们对疟疾景观遗传学的理解以及 抗性抗性，它将提供改善公共卫生干预措施的工具和分析框架 这将直接告知刚果民主共和国国家疟疾控制计划。