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PLoS Comput Biol. 2013;9(12):e1003392. doi: 10.1371/journal.pcbi.1003392. Epub 2013 Dec 12.

Inferring developmental stage composition from gene expression in human malaria.

Author information

1
Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, United States of America.
2
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
3
Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi ; Liverpool School of Tropical Medicine, Liverpool, United Kingdom.
4
The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.
5
College of Osteopathic Medicine, Michigan State University, East Lansing, Michigan, United States of America ; Blantyre Malaria Project, University of Malawi College of Medicine, Blantyre, Malawi.
6
Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, United States of America ; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America.
7
Department of Biology, Loyola University Chicago, Chicago, Illinois, United States of America.
8
Faculty of Medicine and Pharmacy, Cheikh Anta Diop University, Dakar, Senegal.
9
Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, United States of America ; Department of Medicine, Albert Einstein College of Medicine, Bronx, New York, New York, United States of America.
10
Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, United States of America ; The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.
11
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America ; The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.

Abstract

In the current era of malaria eradication, reducing transmission is critical. Assessment of transmissibility requires tools that can accurately identify the various developmental stages of the malaria parasite, particularly those required for transmission (sexual stages). Here, we present a method for estimating relative amounts of Plasmodium falciparum asexual and sexual stages from gene expression measurements. These are modeled using constrained linear regression to characterize stage-specific expression profiles within mixed-stage populations. The resulting profiles were analyzed functionally by gene set enrichment analysis (GSEA), confirming differentially active pathways such as increased mitochondrial activity and lipid metabolism during sexual development. We validated model predictions both from microarrays and from quantitative RT-PCR (qRT-PCR) measurements, based on the expression of a small set of key transcriptional markers. This sufficient marker set was identified by backward selection from the whole genome as available from expression arrays, targeting one sentinel marker per stage. The model as learned can be applied to any new microarray or qRT-PCR transcriptional measurement. We illustrate its use in vitro in inferring changes in stage distribution following stress and drug treatment and in vivo in identifying immature and mature sexual stage carriers within patient cohorts. We believe this approach will be a valuable resource for staging lab and field samples alike and will have wide applicability in epidemiological studies of malaria transmission.

PMID:
24348235
PMCID:
PMC3861035
DOI:
10.1371/journal.pcbi.1003392
[Indexed for MEDLINE]
Free PMC Article

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