Hazzard, B. | bioRxiv Science

bioRxiv2026

Comprehensive characterization of Plasmodium vivax antigens using a high-density peptide array

Plasmodium vivax is the second most prevalent Plasmodium species, with 2.5 billion people at risk of infection worldwide and around 10 million cases of clinical vivax malaria every year. Despite the clinical importance of this pathogen, very little is known about the P. vivax proteins recognized by the host immune system, which hinders our ability to select vaccine candidates or develop efficient serological markers. To comprehensively characterize immunogenic P. vivax proteins, we designed a high-density peptide array containing 4.2 million peptides covering the entire protein sequence of all P. vivax genes and analyzed antibody responses of infected and malaria-naive individuals. We identified a total of 283 proteins that are commonly immunogenic in symptomatic individuals. These proteins included most proteins known to be involved in erythrocyte invasion, a putative new invasion protein, several nucleoporins, and many uncharacterized proteins that should be further investigated for their roles during blood-stage infections. These analyses also revealed a unique pattern of antibody response against PIR proteins in asymptomatic individuals, that could be associated with protection against clinical vivax malaria. Overall, these data provide an agnostic and comprehensive perspective on immunogenic P. vivax proteins and constitute an important resource for the malaria community to develop new tools for better detecting and eliminating this important human pathogen.

microbiology↗

bioRxiv2022

Long read single cell RNA sequencing reveals the isoform diversity of Plasmodium vivax transcripts

Plasmodium infections often consist of heterogenous populations of parasites at different developmental stages and with distinct transcriptional profiles, which complicates gene expression analyses. The advent of single cell RNA sequencing (scRNA-seq) enabled disentangling this complexity and has provided robust and stage-specific characterization of Plasmodium gene expression. However, scRNA-seq information is typically derived from the end of each mRNA molecule (usually the 3-end) and therefore fails to capture the diversity in transcript isoforms documented in bulk RNA-seq data. Here, we describe the sequencing of scRNA-seq libraries using Pacific Biosciences (PacBio) chemistry to characterize full-length Plasmodium vivax transcripts from single cell parasites. Our results show that many P. vivax genes are transcribed into multiple isoforms, primarily through variations in untranslated region (UTR) length or splicing, and that the expression of these isoforms is often developmentally regulated. Our findings demonstrate that long read sequencing can be used to characterize mRNA molecules at the single cell level and provides an additional resource to better understand the regulation of gene expression throughout the Plasmodium life cycle. Author SummarySingle cell RNA-sequencing is a valuable tool for identifying cell specific differences in heterogenous populations. However, scRNA-seq has limitations in assigning reads to genes of organisims with poorly annotated UTRs, due to the poly-A caputre utilized by some scRNA-seq technologies, this technical limitation also makes identifying transcript specific differences, like alternative splicing, difficult. Despite its importance in human disease the P. vivax genome annotation is still relatively sparce, especially in the UTRs, and very little is known about transcript differece in the different life stages of the parasite life cycle. Here, we utilize a modified version of 10X scRNA-seq technology to capture full length transcripts via PacBio sequencing from both sporozoite and blood stages of P. vivax. This allowed us to predict full length stage specific transcripts for P. vivax as well as identify important variation in the previously poorly annotated UTRs. These findings will aide in futhering our understanding of P. vivax transcript regulation across the life cycle stages.

genomics↗