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- PMID: 21085469
- UKPMCID: 21085469
- DOI: 10.1371/journal.pntd.0000880
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A comprehensive genetic analysis of candidate genes regulating response to Trypanosoma congolense infection in mice.
Goodhead, Ian; Archibald, Alan; Amwayi, Peris; Brass, Andy; Gibson, John; Hall, Neil; Hughes, Margaret A; Limo, Moses; Iraqi, Fuad; Kemp, Stephen J; Noyes, Harry A
PLoS neglected tropical diseases. 2010;4(11):e880.
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Full-text held externally
- PMID: 21085469
- UKPMCID: 21085469
- DOI: 10.1371/journal.pntd.0000880
Abstract
BACKGROUND: African trypanosomes are protozoan parasites that cause "sleeping sickness" in humans and a similar disease in livestock. Trypanosomes also infect laboratory mice and three major quantitative trait loci (QTL) that regulate survival time after infection with T. congolense have been identified in two independent crosses between susceptible A/J and BALB/c mice, and the resistant C57BL/6. These were designated Tir1, Tir2 and Tir3 for Trypanosoma infection response, and range in size from 0.9-12 cM. PRINCIPAL FINDINGS: Mapping loci regulating survival time after T. congolense infection in an additional cross revealed that susceptible C3H/HeJ mice have alleles that reduce survival time after infection at Tir1 and Tir3 QTL, but not at Tir2. Next-generation resequencing of a 6.2 Mbp region of mouse chromosome 17, which includes Tir1, identified 1,632 common single nucleotide polymorphisms (SNP) including a probably damaging non-synonymous SNP in Pram1 (PML-RAR alpha-regulated adaptor molecule 1), which was the most plausible candidate QTL gene in Tir1. Genome-wide comparative genomic hybridisation identified 12 loci with copy number variants (CNV) that correlate with differential gene expression, including Cd244 (natural killer cell receptor 2B4), which lies close to the peak of Tir3c and has gene expression that correlates with CNV and phenotype, making it a strong candidate QTL gene at this locus. CONCLUSIONS: By systematically combining next-generation DNA capture and sequencing, array-based comparative genomic hybridisation (aCGH), gene expression data and SNP annotation we have developed a strategy that can generate a short list of polymorphisms in candidate QTL genes that can be functionally tested.