期刊
MICROBIOLOGY AND MOLECULAR BIOLOGY REVIEWS
卷 82, 期 2, 页码 -出版社
AMER SOC MICROBIOLOGY
DOI: 10.1128/MMBR.00071-17
关键词
RNA sequencing; transcriptomics; apicomplexan parasites; host-parasite relationship; host-pathogen interactions; immune response; malaria; pathogenesis
类别
资金
- United Kingdom Medical Research Council (MRC)
- United Kingdom Department for International Development (DFID) under the MRC/DFID Concordat agreement
- European Union [MR/L006529/1]
- MRC [MR/L006529/1] Funding Source: UKRI
Transcriptomics, the analysis of genome-wide RNA expression, is a common approach to investigate host and pathogen processes in infectious diseases. Technical and bioinformatic advances have permitted increasingly thorough analyses of the association of RNA expression with fundamental biology, immunity, patho-genesis, diagnosis, and prognosis. Transcriptomic approaches can now be used to realize a previously unattainable goal, the simultaneous study of RNA expression in host and pathogen, in order to better understand their interactions. This exciting prospect is not without challenges, especially as focus moves from interactions in vitro under tightly controlled conditions to tissue- and systems-level interactions in animal models and natural and experimental infections in humans. Here we review the contribution of transcriptomic studies to the understanding of malaria, a parasitic disease which has exerted a major influence on human evolution and continues to cause a huge global burden of disease. We consider malaria a paradigm for the transcriptomic assessment of systemic host-pathogen interactions in humans, because much of the direct host-pathogen interaction occurs within the blood, a readily sampled compartment of the body. We illustrate lessons learned from transcriptomic studies of malaria and how these lessons may guide studies of host-pathogen interactions in other infectious diseases. We propose that the potential of transcriptomic studies to improve the understanding of malaria as a disease remains partly untapped because of limitations in study design rather than as a consequence of technological constraints. Further advances will require the integration of transcriptomic data with analytical approaches from other scientific disciplines, including epidemiology and mathematical modeling.
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