Development of a molecular barcoding tool for Plasmodium malaria

由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。找出快速和准确地识别引起人类疾病的疟疾种类(例如恶性疟原虫、间日疟原虫、疟疾、卵形疟原虫、诺氏疟原虫)和感染的地理来源的遗传标记，将是确定输入到消除疟疾国家的暴发源的宝贵工具。这项研究项目将需要对原虫线粒体(Mt)和顶体(Ap)序列进行DNA分析，以创建可靠的遗传条形码，用于识别物种和跟踪疟疾的地理移动，可能用于业务背景。先前在恶性疟原虫(n=711)中的工作发现证据表明，细胞器基因组是非重组和共遗传的，高度连锁产生了一组相对较少的地理信息标记(Preston等人，2014)。我们的项目将以这一认识为基础，重点关注所有导致人类疾病的疟原虫，并涉及：(1)从含有疟疾流行地区疟原虫分离株DNA的干血滤纸中对mt和ap基因组进行测序；(2)确定mt/ap中所有基因组变异的特征，并进行群体遗传分析以了解多样性；(3)确定遗传条形码的特征，以识别不同的疟原虫物种和地理区域；(4)利用TwistDx RPA技术开发可在该领域应用的快速条形码工具。目标(1)将涉及对现场收集的滤纸样本(n=500，来自疟疾中心合作者Cally Roper博士和Colin Sutherland博士)应用选择性全基因组扩增方法(Leichty AR等，2014)，然后利用MiSeq技术(LSHTM)进行测序。由此产生的原始测序数据将得到已发表研究(n=2,500，15个群体，www.marariagen.net)的补充。测序数据的分析将在具有已建立管道的LSHTM计算集群上进行(见Campino等人，2016)，并涉及序列的比对和组装。对于(2)，基因组变异(如SNPs)将从比对中确定特征，并用于构建系统发育树，按基因和种群计算遗传多样性，并确定所选择的遗传区域。对于(3)，将使用基于系统发育的(例如，内部节点SNP特征)、种群遗传(例如，种群分化Fst)和统计(例如，随机森林)方法来建立分子条形码多态。MT/AP条形码标记的验证将通过分析新公布的数据和额外的测序进行。对于(4)，条形码和任何其他已知的耐药性标记将被整合到TwistDx的RPA技术中。最终的原型将在对照和疟疾样本上进行测试。一旦得到验证，我们将尝试在参与消除疟疾活动的合作者的网站上前瞻性地试验该工具(例如，肯尼亚北部-Harold Ocholla博士；亚马逊巴西-Simone Santos博士)。所有目标都将导致科学出版物的出版。这项工作结合了生物信息学、分子生物学、流行病学和翻译活动，包括诊断开发和测试。

Malaria caused by Plasmodium parasites kills ~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. Identifying genetic markers that quickly and accurately identifies the malaria species that cause human disease (e.g. P.falciparum, P.vivax, P.malariae, P.ovale, P.knowlesi) and the geographic origin of infections would be a valuable tool for locating the source of outbreaks imported to malaria elimination countries. This research project will entail DNA analysis of plasmodial mitochondria (Mt) and the apicoplast (Ap) sequences to create reliable genetic barcodes for identifying species and tracking the geographical movement of malaria, potentially for an operational context. Previous work in P.falciparum (n=711) found evidence that the organellar genomes are non-recombining and co-inherited, and the high degree of linkage produces a panel of relatively few markers that is geographically informative (Preston et al, 2014). Our project will build on this insight, focusing on all Plasmodia that cause human disease, and involve the: (1) Sequencing of Mt and Ap genomes from dry blood filter papers containing Plasmodium isolate DNA, sourced from malaria endemic areas;(2) Characterisation of all genomic variation in the Mt/Ap, and a population genetic analysis to understand diversity;(3) Characterisation of a genetic barcode to identify different Plasmodium species and geographical regions; (4) Development of a rapid barcoding tool using TwistDx RPA technology that could be applied in the field. Objective (1) will involve applying the selective whole genome amplification method (Leichty AR et al, 2014) on field-collected filter paper samples (n=500, sourced from Malaria Centre collaborators - Drs. Cally Roper and Colin Sutherland), followed by sequencing on the MiSeq technology (LSHTM). The resulting raw sequencing data will be complemented by those from published studies (n=2,500, 15 populations, www.malariagen.net). Analysis of the sequencing data will be performed on the LSHTM computing cluster with established pipelines (see Campino et al, 2016), and involve both alignment and assembly of sequences. For (2), genomic variants (e.g. SNPs) will be characterized from the alignments, and used to construct phylogenetic trees, calculate genetic diversity by gene and population, and identify genetic regions under selection. For (3), molecular barcoding polymorphisms will be established using phylogenetic-based (e.g. internal node SNP characterization), population genetic (e.g. population differentiation Fst) and statistical (e.g. random forest) methods. Validation of the Mt/Ap barcoding markers will be performed by analysis of newly published data and additional sequencing. For (4), the barcode and any additional known drug resistance markers will be incorporated into the RPA technology at TwistDx. The resulting prototype will be tested on control and malaria samples. Once validated, we will attempt to prospectively trial the tool in sites of collaborators that are involved in malaria elimination activities (e.g. Northern Kenya - Dr. Harold Ocholla; Amazon Brazil - Dr. Simone Santos). All objectives will lead to scientific publications. The work combines bioinformatics, molecular biology, epidemiology and translational activities, including diagnostic development and testing.