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Updated: Feb 14, 2026

A Simple Composite Phenotype Scoring System for Evaluating Mouse Models of Cerebellar Ataxia
Published on: May 21, 2010
Genotypic and phenotypic characterization of the Sdccag8Tn(sb-Tyr)2161B.CA1C2Ove mouse model
Katie Weihbrecht1,2,3, Wesley A Goar1,2,3, Calvin S Carter1,2,3
1Department of Pediatrics, University of Iowa, Iowa City, lowa, United States of America.
Abstract:
Nephronophthisis-related ciliopathies (NPHP-RC) are a group of disorders that present with end-stage renal failure in childhood/adolescence, kidney cysts, retinal degeneration, and cerebellar hypoplasia. One disorder that shares clinical features with NPHP-RC is Bardet-Biedl Syndrome (BBS). Serologically defined colon cancer antigen 8 (SDCCAG8; also known as NPHP10 and BBS16) is an NPHP gene that is also associated with BBS. To better understand the patho-mechanisms of NPHP and BBS caused by loss of SDCCAG8 function, we characterized an SDCCAG8 mouse model (Sdccag8Tn(sb-Tyr)2161B.CA1C2Ove) generated by Sleeping Beauty Transposon (SBT)-mediated insertion mutagenesis. Consistent with the previously reported, independent SDCCAG8 mouse models, our mutant mice display pre-axial polydactyly in their hind limbs. In addition, we report patterning defects in the secondary palate, brain abnormalities, as well as neonatal lethality associated with developmental defects in the lung in our mouse model. The neonatal lethality phenotype is genetic background dependent and rescued by introducing 129S6/SvEvTac background. Genetic modifier(s) responsible for this effect were mapped to a region between SNPs rs3714172 and rs3141832 on chromosome 11. While determining the precise genetic lesion in our mouse model, we found that SBT insertion resulted in a deletion of multiple exons from both Sdccag8 and its neighboring gene Akt3. We ascribe the patterning defects in the limb and the secondary palate as well as lung abnormalities to loss of SDCCAG8, while the developmental defects in the brain are likely due to the loss of AKT3. This mouse model may be useful to study features not observed in other SDCCAG8 models but cautions are needed in interpreting data.
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