Daubenberger, C. | bioRxiv Science

bioRxiv2021

Analysis of nucleic acids extracted from rapid diagnostic tests reveals a significant proportion of false positive test results associated with recent malaria treatment

Surveillance programs often use malaria rapid diagnostic tests (RDTs) to determine the proportion of the population carrying parasites in their peripheral blood to assess the malaria transmission intensity. Despite an increasing number of reports on false-negative and false-positive RDT results, there is a lack of systematic quality control activities for RDTs deployed in malaria surveillance programs. Our study provides a larger scale comparative evaluation of RDTs used in the 2018 Malaria Indicator Survey (MIS) conducted on Bioko Island, Equatorial Guinea. We conducted a molecular analysis by extraction of nucleic acids from 1,800 negative and 1,065 positive RDTs followed by qPCR analysis. These results were combined with a dataset collected in a comprehensive questionnaire from each MIS participant. Of the 2,865 RDTs that were collected in 2018 on Bioko Island and analysed in our study, 4.7% had a false-negative result. These false-negative RDT results were associated with low parasite density infections. In a substantial proportion of samples, we identified masked pfhrp2 and pfhrp3 gene deletions in which at least one P. falciparum strain carried a gene deletion. Among all positive RDTs analysed, 28.4% were tested negative by qPCR and therefore considered to be false-positive. Analysing the questionnaire data collected from the participants, this high proportion of false-positive RDT results could be explained by PfHRP2 antigen persistence after recent malaria treatment. We conclude that malaria surveillance depending solely on RDTs needs well-integrated quality control procedures assessing the extend and impact of reduced sensitivity and specificity of RDTs on malaria control programs.

microbiology↗

bioRxiv2019

Re-annotation of the Theileria parva genome refines 53% of the proteome and uncovers essential components of N-glycosylation, a conserved pathway in many organisms

BackgroundThe apicomplexan parasite Theileria parva causes a livestock disease called East coast fever (ECF), with millions of animals are at risk in sub-Saharan East and Southern Africa, the geographic distribution of T. parva. Over a million bovines die each year of ECF, with a tremendous economic burden to pastoralists in endemic countries. Comprehensive, accurate parasite genome annotation can facilitate the discovery of novel chemotherapeutic targets for disease treatment, as well as elucidate the biology of the parasite. However, genome annotation remains a significant challenge because of limitations in the quality and quantity of the data being used to inform the location and function of protein-coding genes and, when RNA data are used, the underlying biological complexity of the processes involved in gene expression. Here, we apply our recently published RNAseq dataset derived from the schizont life-cycle stage of T. parva to update structural and functional gene annotations across the entire nuclear genome. ResultsThe re-annotation effort lead to evidence-supported updates in over half of all protein-coding sequence (CDS) predictions, including exon changes, gene merges and gene splitting, an increase in average CDS length of approximately 50 base pairs, and the identification of 128 new genes. Among the new genes identified were those involved in N-glycosylation, a process previously thought not to exist in this organism and a potentially new chemotherapeutic target pathway for treating ECF. Alternatively-spliced genes were identified, and antisense and multi-gene family transcription were extensively characterized. ConclusionsThe process of re-annotation led to novel insights into the organization and expression profiles of protein-coding sequences in this parasite, and uncovered a minimal N-glycosylation pathway that changes our current understanding of the evolution of this post-translation modification in apicomplexan parasites.

genomics↗