De Novo Assembly of Plasmodium knowlesi Genomes From Clinical Samples Explains the Counterintuitive Intrachromosomal Organization of Variant SICAvar and kir Multiple Gene Family Members.

Plasmodium knowlesi, a malaria parasite of Old World macaque monkeys, is used extensively to model Plasmodium biology. Recently, P. knowlesi was found in the human population of Southeast Asia, particularly Malaysia. P. knowlesi causes uncomplicated to severe and fatal malaria in the human host with features in common with the more prevalent and virulent malaria caused by Plasmodium falciparum. As such, P. knowlesi presents a unique opportunity to develop experimental translational model systems for malaria pathophysiology informed by clinical data from same-species human infections. Experimental lines of P. knowlesi represent well-characterized genetically stable parasites, and to maximize their utility as a backdrop for understanding malaria pathophysiology, genetically diverse contemporary clinical isolates, essentially wild-type, require comparable characterization. The Oxford Nanopore PCR-free long-read sequencing platform was used to sequence and de novo assemble P. knowlesi genomes from frozen clinical samples. The sequencing platform and assembly pipelines were designed to facilitate capturing data and describing, for the first time, P. knowlesi schizont-infected cell agglutination (SICA) var and Knowlesi-Interspersed Repeats (kir) multiple gene families in parasites acquired from nature. The SICAvar gene family members code for antigenically variant proteins analogous to the virulence-associated P. falciparum erythrocyte membrane protein (PfEMP1) multiple var gene family. Evidence presented here suggests that the SICAvar family members have arisen through a process of gene duplication, selection pressure, and variation. Highly evolving genes including PfEMP1family members tend to be restricted to relatively unstable sub-telomeric regions that drive change with core genes protected in genetically stable intrachromosomal locations. The comparable SICAvar and kir gene family members are counter-intuitively located across chromosomes. Here, we demonstrate that, in contrast to conserved core genes, SICAvar and kir genes occupy otherwise gene-sparse chromosomal locations that accommodate rapid evolution and change. The novel methods presented here offer the malaria research community not only new tools to generate comprehensive genome sequence data from small clinical samples but also new insight into the complexity of clinically important real-world parasites

Plasmodium knowlesi – clinical isolate genome sequencing to inform translational same-species model system for severe malaria

DRO is supported by the Wellcome Trust ISSF award 204821/Z/16/Z. Bioinformatics and computational biology analyses were supported by the University of St Andrews Bioinformatics Unit (AMD3BIOINF), funded by Wellcome Trust ISSF award 105621/Z/14/Z. The sample BioBank was compiled with informed consent (Medial Research Council, www.mrc.ac.uk, grant G0801971). Genome sequencing was supported by Tenovus Scotland (T16/03).Malaria is responsible for unacceptably high morbidity and mortality, especially in Sub-Saharan African Nations. Malaria is caused by member species’ of the genus Plasmodium and despite concerted and at times valiant efforts, the underlying pathophysiological processes leading to severe disease are poorly understood. Here we describe zoonotic malaria caused by Plasmodium knowlesi and the utility of this parasite as a model system for severe malaria. We present a method to generate long-read third-generation Plasmodium genome sequence data from archived clinical samples using the MinION platform. The method and technology are accessible, affordable and data is generated in real-time. We propose that by widely adopting this methodology important information on clinically relevant parasite diversity, including multiple gene family members, from geographically distinct study sites will emerge. Our goal, over time, is to exploit the duality of P. knowlesi as a well-used laboratory model and human pathogen to develop a representative translational model system for severe malaria that is informed by clinically relevant parasite diversity.Publisher PDFPeer reviewe

Getting close to nature – Plasmodium knowlesi reference genome sequences from contemporary clinical isolates

Supporting data for the attached publication. De Novo assembled genomes of Plasmodium knowlesi generated using Nanopore long reads. Supporting statistical results are included. Variant call files from structural variant calls are attached for each genome. Additionally, statistical results from BUSCO, QUAST, Pomoxis and AGAT are included. Annotation files in GFF3 format including further analyses of these annotation files are attached. Scripts for post analysis are attached with scripts for data generation presented on Github repository: "Pknowlesi_denovo_genome_assembly

De Novo Assembly of Plasmodium knowlesi Genomes From Clinical Samples Explains the Counterintuitive Intrachromosomal Organization of Variant SICAvar and kir Multiple Gene Family Members

Plasmodium knowlesi, a malaria parasite of Old World macaque monkeys, is used extensively to model Plasmodium biology. Recently, P. knowlesi was found in the human population of Southeast Asia, particularly Malaysia. P. knowlesi causes uncomplicated to severe and fatal malaria in the human host with features in common with the more prevalent and virulent malaria caused by Plasmodium falciparum. As such, P. knowlesi presents a unique opportunity to develop experimental translational model systems for malaria pathophysiology informed by clinical data from same-species human infections. Experimental lines of P. knowlesi represent well-characterized genetically stable parasites, and to maximize their utility as a backdrop for understanding malaria pathophysiology, genetically diverse contemporary clinical isolates, essentially wild-type, require comparable characterization. The Oxford Nanopore PCR-free long-read sequencing platform was used to sequence and de novo assemble P. knowlesi genomes from frozen clinical samples. The sequencing platform and assembly pipelines were designed to facilitate capturing data and describing, for the first time, P. knowlesi schizont-infected cell agglutination (SICA) var and Knowlesi-Interspersed Repeats (kir) multiple gene families in parasites acquired from nature. The SICAvar gene family members code for antigenically variant proteins analogous to the virulence-associated P. falciparum erythrocyte membrane protein (PfEMP1) multiple var gene family. Evidence presented here suggests that the SICAvar family members have arisen through a process of gene duplication, selection pressure, and variation. Highly evolving genes including PfEMP1family members tend to be restricted to relatively unstable sub-telomeric regions that drive change with core genes protected in genetically stable intrachromosomal locations. The comparable SICAvar and kir gene family members are counter-intuitively located across chromosomes. Here, we demonstrate that, in contrast to conserved core genes, SICAvar and kir genes occupy otherwise gene-sparse chromosomal locations that accommodate rapid evolution and change. The novel methods presented here offer the malaria research community not only new tools to generate comprehensive genome sequence data from small clinical samples but also new insight into the complexity of clinically important real-world parasites

De Novo Assembly of Plasmodium knowlesi Genomes From Clinical Samples Explains the Counterintuitive Intrachromosomal Organization of Variant SICAvar and kir Multiple Gene Family Members

Published DOI: https://doi.org/10.3389/fgene.2022.855052 Supporting data for the attached publication. De Novo assembled genomes of Plasmodium knowlesi generated using Nanopore long reads. Supporting statistical results are included. Variant call files from structural variant calls are attached for each genome. Additionally, statistical results from BUSCO, QUAST, Pomoxis and AGAT are included. Annotation files in GFF3 format including further analyses of these annotation files are attached. Scripts for post analysis are attached with scripts for data generation presented on Github repository: "Pknowlesi_denovo_genome_assembly