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Hoffmann, R. A wiki for the life sciences where authorship matters. Nature Genetics (2008)
 
 
 
 
 

A genome scan using a novel genetic cross identifies new susceptibility loci and traits in a mouse model of rheumatoid arthritis.

Proteoglycan-induced arthritis (PGIA) is a murine model for rheumatoid arthritis (RA) both in terms of its pathology and its genetics. PGIA can only be induced in susceptible mouse strains and their F(2) progeny. Using the F(2) hybrids resulting from an F(1) intercross of a newly identified susceptible (C3H/HeJCr) and an established resistant (C57BL/6) strain of mouse, our goals were to: 1) identify the strain-specific loci that confer PGIA susceptibility, 2) determine whether any pathophysiological parameters could be used as markers that distinguish between nonarthritic and arthritic mice, and 3) analyze the effect of the MHC haplotype on quantitative trait loci (QTL) detection. To identify QTLs, we performed a genome scan on the F(2) hybrids. For pathophysiological analyses, we measured pro- and antiinflammatory cytokines such as IL-1, IL-6, IFN-gamma, IL-4, IL-10, IL-12, Ag-specific T cell proliferation and IL-2 production, serum IgG1 and IgG2 levels of both auto- and heteroantibodies, and soluble CD44. We have identified four new PGIA-linked QTLs (Pgia13 through Pgia16) and confirmed two (Pgia5, Pgia10) from our previous study. All new MHC-independent QTLs were associated with either disease onset or severity. Comprehensive statistical analysis demonstrated that while soluble CD44, IL-6, and IgG1 vs. IgG2 heteroantibody levels differed significantly between the arthritic and nonarthritic groups, only Ab-related parameters colocalized with the QTLs. Importantly, the mixed haplotype (H-2(b) and H-2(k)) of the C3H x C57BL/6 F(2) intercross reduced the detection of several previously identified QTLs to suggestive levels, indicating a masking effect of unmatched MHCs.[1]

References

  1. A genome scan using a novel genetic cross identifies new susceptibility loci and traits in a mouse model of rheumatoid arthritis. Otto, J.M., Chandrasekeran, R., Vermes, C., Mikecz, K., Finnegan, A., Rickert, S.E., Enders, J.T., Glant, T.T. J. Immunol. (2000) [Pubmed]
 
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