Geographic genetic profiling of human Plasmodium malaria

恶性疟原虫引起的疟疾每年导致约60万人死亡，通过国际航空旅行增加的人口流动性进一步增加了将寄生虫引入消灭地区和将抗药性寄生虫传播到新地区的风险。一种简单的基因标记可以快速准确地识别感染的地理来源，这将是定位疫情来源和发现抗药性寄生虫从亚洲传播到非洲的宝贵工具。事实证明，遗传标记在跟踪和根除脊髓灰质炎等疾病方面非常有价值。然而，之前的疟疾遗传条形码候选者依赖于识别在寄生虫核中发现的DNA标记物，这显示了个体寄生虫之间太多的遗传差异，无法准确使用。现在，在细胞核外被称为线粒体和质外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，因此在世代中比核DNA序列更稳定。该方法提案中概述的研究将创建计算工具，这些工具将有助于利用线粒体和质外体序列来创建可靠的遗传条形码，以便在业务范围内跟踪疟疾的地理移动。人类疟疾可由6种不同的疟原虫中的一种引起。我们将为这6个物种中的每个物种开发基于线粒体和质外体序列的遗传条形码。我们将开发新的分析方法，即使在混合感染中也能区分不同的物种。我们还将完善现有的条形码方法，以区分来自同一物种不同地理种群的感染。最重要的是，我们将开发分析软件，可以从世界许多地区疟疾患者常见的复杂混合感染中推断条形码。我们将创建一个公共可用的在线资源，以促进条形码的广泛使用。它将对世界各地的疟疾控制机构和研究小组具有实际用途。

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.