bioRxivMicrobiology
Probabilistic data integration identifies reliable gametocyte-specific proteins and transcripts in malaria parasites
L. Meerstein-Kessel, R. van der Lee, W. Stone, K. Lanke, D. A. Baker, P. Alano, F. Silvestrini, C. J. Janse, S. M. Khan, M. van de Vegte-Bolmer, W. Graumans, R. Siebelink-Stoter, T. W. Kooij, M. Marti, C. Drakeley, J. J. Campo, T. J. van Dam, R. Sauerwein, T. Bousema, M. A. Huynen
Abstract
Plasmodium gametocytes are the sexual forms of the malaria parasite essential for transmission to mosquitoes. To better understand how gametocytes differ from asexual blood-stage parasites, we performed a systematic analysis of available omics data for P. falciparum and other Plasmodium species. 18 transcriptomic and proteomic data sets were evaluated for the presence of curated \"gold standards\" of 41 gametocyte-specific versus 46 non-gametocyte genes and integrated using Bayesian probabilities, resulting in gametocyte-specificity scores for all P. falciparum genes.\n\nTo illustrate the utility of the gametocyte score, we explored newly predicted gametocyte-specific genes as potential biomarkers of gametocyte carriage and exposure. We analyzed the humoral immune response in field samples against 30 novel gametocyte-specific antigens and found five antigens to be differentially recognized by gametocyte carriers as compared to malaria-infected individuals without detectable gametocytes. We also validated the gametocyte-specificity of 15 identified gametocyte transcripts on culture material and samples from naturally infected individuals, resulting in eight transcripts that were >1000-fold higher expressed in gametocytes compared to asexual parasites and whose transcript abundance allowed gametocyte detection in naturally infected individuals. Our integrated genome-wide gametocyte-specificity scores provide a comprehensive resource to identify targets and monitor P. falciparum gametocytemia.
- organism: human
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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