Total Brain Volume Increase and Selective White Matter Loss in the Hgsnat(-/-) and Sgsh (-/-) Mouse Models Related to Sanfillipo Syndrome

Background:   Sanfillippo syndrome (SFS) is a rare autosomal recessive lysosomal storage disease, also known as mucopolysaccharidosis (MPS) III.  There are four different types, A-D, all of which are indistinguishable in the clinic. HGSNAT (heparan-α-glucosaminide N-acetyltransferase) and its mutations are associated with SFS type C and the SGSH(N-sulfoglucosamine sulfohydrolase) and its mutations are associated with SFS type A.  In a recent report (Rumsey et al. 2014) it was determined that 13 of 21 children diagnosed with SFS type A also met the criteria for autism diagnosis.

Objectives:   To provide a first screen of two novel homozygote knockout mouse lines – C57BL/6N-Hgsnat^em4Tcp and C57BL/6N-Sgsh^em3Tcp.

Methods:  

Mouse lines were generated using Cas9, to introduce frameshift indels in the coding regions of their respective genes. In total, 39 fixed mouse brains were examined.  16 of which were WT (C57BL6/N), 11 Hgsnat(-/-) mice, and 13 Sgsh(-/-) mice.  The mice were P60 ± 2 days and equally distributed between both sexes. We used whole brain MRI to provide an indication if – and where – mutations in these genes affect the brain.

MRI Acquisition – A multi-channel 7.0 Tesla MRI scanner was used to acquire anatomical images of the brain. A T2-weighted, 3-D fast spin-echo sequence was used (restricting sampling to a circular region in the two phase encode dimensions). This sequence yielded an image with 40 μm isotropic voxels (3D pixel) in ~14 h.

Data Analysis – To visualize and compare any differences the images are registered together. The goal of the registration is to model how the deformation fields relate to genotype (Lerch et al., 2008). Volume differences are then calculated either in individual voxels or for 159 different segmented brain regions in each groups (Dorr et al. 2008, Ullmann et al. 2013, and Steadman et al. 2014). Multiple comparisons were controlled for using the False Discovery Rate (FDR) (Genovese et al., 2002).

Results:  

For both the Hgsnat(-/-) and Sgsh(-/-) mice the total brain volumes were significantly larger than the WT mice (450 mm³ ± 7 for Hgsnat, and 450 ± 11 for Sgsh versus 432 ± 14 for the WT, FDRs <1%); therefore, relative volumes were examined to account for these differences. 14 regions were found to be significantly different in the Hgsnat(-/-) brains, and 27 regions were found to be different in the Sgsh(-/-) brains.  Since mutations in both genes are implicated in SFS, it is not surprising that the same 14 regions affected in Hgsnat null mice were also affected in Sgsh null mice albeit to a greater extent.  Analysis of the images voxelwise shows a similar pattern of differences in both models, but with the Sgsh(-/-) mice showing a stronger phenotype (Figure 1). Interestingly, despite a total brain volume increase, the white matter is decreased in both models, with the anterior commissure, cingulum bundle, corpus callosum, internal capsule all found to be smaller.

Conclusions:   These two SFS models show very similar characteristics with decreased volume in several white matter tracts despite an overall increase in total brain volume.