Rapid profiling of Plasmodium parasites from genome sequences to assist malaria control.
Rapid profiling of Plasmodium parasites from genome sequences to assist malaria control.
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DOI：
10.1186/s13073-023-01247-7
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发表时间：
2023-11-10
期刊：
影响因子：
12.3
通讯作者：
Clark TG
中科院分区：
文献类型：
--
作者：
Phelan JE;Turkiewicz A;Manko E;Thorpe J;Vanheer LN;van de Vegte-Bolmer M;Ngoc NTH;Binh NTH;Thieu NQ;Gitaka J;Nolder D;Beshir KB;Dombrowski JG;Di Santi SM;Bousema T;Sutherland CJ;Campino S;Clark TG
疟疾仍然是全球公共卫生的主要威胁。对潜在疟原虫寄生虫的全基因组测序（WGS）为了解疟疾的基因组流行病学提供了见解。基因组测序作为临床环境的诊断和监测工具正在迅速获得关注，其中共感染的分析、输入性疟原虫的识别以及耐药性的检测对于感染控制和消除疾病至关重要。为了在信息上支持这一点，我们开发了 Malaria-Profiler 工具，该工具可以直接根据 WGS 数据快速（在几分钟内）预测疟原虫种类、地理来源和抗疟药物耐药性。 Malaria-Profiler 的在线和命令行版本可从基因组序列中检测到 250 个标记，涵盖疟原虫物种形成、可能的地理来源以及对氯喹、磺胺多辛-乙胺嘧啶 (SP) 和恶性疟原虫其他抗疟疾药物的耐药性，同时还提供其他物种的直系同源耐药基因的突变。使用全基因组测序和地理数据对 9321 个临床分离株评估了突变库的预测性能，其中大多数是单种感染（恶性疟原虫 7152/7462、间日疟原虫 1502/1661、诺氏疟原虫 143/151、三日疟原虫 18/18、卵形疟原虫亚种 5/5），但也发现了混合感染（456/9321；4.8%）。预测地理剖面的准确度达到大陆级（96.1%）和区域级（94.6%）。对于恶性疟原虫，鉴定出对氯喹（49.2％；区域范围：24.5％至100％）、磺胺多辛（83.3％；35.4-90.5％）、乙胺嘧啶（85.4％；80.0-100％）和组合SP（77.4％）的抗性标记。在来自东南亚的分离株（30.6%）的全基因组测序中发现了与青蒿素部分耐药相关的标志物。 Malaria-Profiler 是一款用户友好的工具，可以利用 WGS 数据快速准确地预测大量样本的地理区域来源和抗疟疾耐药性概况。该软件非常灵活，具有可修改的生物信息学管道。例如，可以选择测序平台、显示特定变体以及自定义输出格式。随着新一代疟原虫 DNA 测序平台的应用不断增加，Malaria-Profiler 有可能集成到护理点和监测环境中，从而协助疟疾控制。 Malaria-Profiler 可在线获取 (bioinformatics.lshtm.ac.uk/malaria-profiler) 和独立软件 (https://github.com/jodyphelan/malaria-profiler)。在线版本包含可在 10.1186/s13073-023-01247-7 获取的补充材料。
Malaria continues to be a major threat to global public health. Whole genome sequencing (WGS) of the underlying Plasmodium parasites has provided insights into the genomic epidemiology of malaria. Genome sequencing is rapidly gaining traction as a diagnostic and surveillance tool for clinical settings, where the profiling of co-infections, identification of imported malaria parasites, and detection of drug resistance are crucial for infection control and disease elimination. To support this informatically, we have developed the Malaria-Profiler tool, which rapidly (within minutes) predicts Plasmodium species, geographical source, and resistance to antimalarial drugs directly from WGS data. The online and command line versions of Malaria-Profiler detect ~ 250 markers from genome sequences covering Plasmodium speciation, likely geographical source, and resistance to chloroquine, sulfadoxine-pyrimethamine (SP), and other anti-malarial drugs for P. falciparum, but also providing mutations for orthologous resistance genes in other species. The predictive performance of the mutation library was assessed using 9321 clinical isolates with WGS and geographical data, with most being single-species infections (P. falciparum 7152/7462, P. vivax 1502/1661, P. knowlesi 143/151, P. malariae 18/18, P. ovale ssp. 5/5), but co-infections were identified (456/9321; 4.8%). The accuracy of the predicted geographical profiles was high to both continental (96.1%) and regional levels (94.6%). For P. falciparum, markers were identified for resistance to chloroquine (49.2%; regional range: 24.5% to 100%), sulfadoxine (83.3%; 35.4– 90.5%), pyrimethamine (85.4%; 80.0–100%) and combined SP (77.4%). Markers associated with the partial resistance of artemisinin were found in WGS from isolates sourced from Southeast Asia (30.6%). Malaria-Profiler is a user-friendly tool that can rapidly and accurately predict the geographical regional source and anti-malarial drug resistance profiles across large numbers of samples with WGS data. The software is flexible with modifiable bioinformatic pipelines. For example, it is possible to select the sequencing platform, display specific variants, and customise the format of outputs. With the increasing application of next-generation sequencing platforms on Plasmodium DNA, Malaria-Profiler has the potential to be integrated into point-of-care and surveillance settings, thereby assisting malaria control. Malaria-Profiler is available online (bioinformatics.lshtm.ac.uk/malaria-profiler) and as standalone software (https://github.com/jodyphelan/malaria-profiler). The online version contains supplementary material available at 10.1186/s13073-023-01247-7.
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影响因子：
10.7
作者：
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通讯作者：
Castresana, J
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