Geographic genetic profiling of human Plasmodium malaria

由恶性疟原虫引起的疟疾每年造成约60万人死亡，国际航空旅行增加的人口流动性带来了将寄生虫重新引入消除地区和将抗药性寄生虫分散到新地区的进一步风险。一个简单的遗传标记，可以快速准确地识别感染的地理来源，这将是一个有价值的工具，用于定位疫情的源头，并发现抗药性寄生虫从亚洲传播到非洲。事实证明，遗传标记在追踪和根除脊髓灰质炎等疾病方面极其宝贵。然而，以前的疟疾基因条形码候选人依赖于识别寄生虫细胞核中发现的DNA标记，这表明个体寄生虫之间存在太多的遗传变异，无法准确使用。现在，在细胞核外被称为线粒体和顶质体的细胞器中发现的DNA序列已经被分析。这些基因只能通过母系遗传，因此比核DNA序列更稳定。该方法提案中概述的研究将创建计算工具，这将有助于利用线粒体和顶质体序列创建可靠的遗传条形码，用于在业务范围内跟踪疟疾的地理移动。人类疟疾可以由6种不同的疟原虫引起。我们将根据线粒体和顶质体序列为6个物种中的每一个开发遗传条形码。我们将开发新的分析方法，即使在混合感染中也可以区分不同的物种。我们还将完善鉴定条形码方法，用于区分来自同一物种地理上不同人群的感染。最重要的是，我们将开发分析软件，可以从复杂的混合感染中推断出条形码，这在世界许多地方的疟疾患者中很常见。我们将创建一个在线资源，以促进条形码的广泛使用。它将对全世界的疟疾控制机构和研究小组有实际用途。

Malaria caused by Plasmodium falciparum kills about 600,000 people per year, and increased population mobility through international air travel carries further risks of re-introducing parasites to elimination areas and dispersing drug resistant parasites to new regions. A simple genetic marker that quickly and accurately identifies the geographic origin of infections would be a valuable tool for locating the source of outbreaks, and spotting the spread of drug resistant parasites from Asia into Africa. Genetic markers have proved extremely valuable in tracking and eradicating diseases, such as Polio. However, the previous candidates for malaria genetic barcodes have relied on identifying DNA markers found in the parasite nucleus, which shows too much genetic variation between individual parasites to be used accurately. Now, DNA sequences found outside the nucleus in organelles called the mitochondria and the apicoplast have been analysed. These are only inherited through maternal lines and therefore much more stable over generations than nuclear DNA sequences. The research outlined in this methodology proposal will create computational tools which will help to exploit use of mitochondria and the apicoplast sequences to create reliable genetic barcodes for tracking the geographical movement of malaria in an operational context. Human malaria can be caused by one of 6 different Plasmodium species. We will develop genetic barcodes based on mitochondria and apicoplast sequences for each of the 6 species. We will develop new analytical approaches which can discriminate the different species even in mixed infections. We will also refine the exisiting barcoding methodology for discrimninating between infections originating from geographically distinct populations of the same species. Most crucially we will develop analytical software which can infer barcodes from complex mixed infections which are commonly found in malaria patients in many parts of the world.We will create a publically available online resource to facilitate the widespread use of barcoding. It will be of practical use to malaria control agencies and research groups worldwide.