Rapid profiling of Plasmodium parasites from genome sequences to assist malaria control

Abstract

Abstract Background 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. Results 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%). Conclusions 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 ).

Keywords

• Drug resistance
• Malaria
• Plasmodium parasites
• Genomics
• Diagnostics
• Whole genome sequencing