Integration-independent Transgenic Huntington Disease Fragment Mouse Models Reveal Distinct Phenotypes and Life Span in Vivo.

The cascade of events that lead to cognitive decline, motor deficits, and psychiatric symptoms in patients with Huntington disease (HD) is triggered by a polyglutamine expansion in the N-terminal region of the huntingtin (HTT) protein. A significant mechanism in HD is the generation of mutant HTT fragments, which are generally more toxic than the full-length HTT. The protein fragments observed in human HD tissue and mouse models of HD are formed by proteolysis or aberrant splicing of HTT. To systematically investigate the relative contribution of the various HTT protein proteolysis events observed in vivo, we generated transgenic mouse models of HD representing five distinct proteolysis fragments ending at amino acids 171, 463, 536, 552, and 586 with a polyglutamine length of 148. All lines contain a single integration at the ROSA26 locus, with expression of the fragments driven by the chicken ß-actin promoter at nearly identical levels. The transgenic mice N171-Q148 and N552-Q148 display significantly accelerated phenotypes and a shortened life span when compared with N463-Q148, N536-Q148, and N586-Q148 transgenic mice. We hypothesized that the accelerated phenotype was due to altered HTT protein interactions/complexes that accumulate with age. We found evidence for altered HTT complexes in caspase-2 fragment transgenic mice (N552-Q148) and a stronger interaction with the endogenous HTT protein. These findings correlate with an altered HTT molecular complex and distinct proteins in the HTT interactome set identified by mass spectrometry. In particular, we identified HSP90AA1 (HSP86) as a potential modulator of the distinct neurotoxicity of the caspase-2 fragment mice (N552-Q148) when compared with the caspase-6 transgenic mice (N586-Q148).

Benefits of global mutant huntingtin lowering diminish over time in a Huntington's disease mouse model.

We have developed an inducible Huntington's disease (HD) mouse model that allows temporal control of whole-body allele-specific mutant huntingtin (mHtt) expression. We asked whether moderate global lowering of mHtt (~50%) was sufficient for long-term amelioration of HD-related deficits and, if so, whether early mHtt lowering (before measurable deficits) was required. Both early and late mHtt lowering delayed behavioral dysfunction and mHTT protein aggregation, as measured biochemically. However, long-term follow-up revealed that the benefits, in all mHtt-lowering groups, attenuated by 12 months of age. While early mHtt lowering attenuated cortical and striatal transcriptional dysregulation evaluated at 6 months of age, the benefits diminished by 12 months of age, and late mHtt lowering did not ameliorate striatal transcriptional dysregulation at 12 months of age. Only early mHtt lowering delayed the elevation in cerebrospinal fluid neurofilament light chain that we observed in our model starting at 9 months of age. As small-molecule HTT-lowering therapeutics progress to the clinic, our findings suggest that moderate mHtt lowering allows disease progression to continue, albeit at a slower rate, and could be relevant to the degree of mHTT lowering required to sustain long-term benefits in humans.

Genetic Contributors to Intergenerational CAG Repeat Instability in Huntington's Disease Knock-In Mice.

Huntington's disease (HD) is a neurodegenerative disorder caused by the expansion of a CAG trinucleotide repeat in exon 1 of the HTT gene. Longer repeat sizes are associated with increased disease penetrance and earlier ages of onset. Intergenerationally unstable transmissions are common in HD families, partly underlying the genetic anticipation seen in this disorder. HD CAG knock-in mouse models also exhibit a propensity for intergenerational repeat size changes. In this work, we examine intergenerational instability of the CAG repeat in over 20,000 transmissions in the largest HD knock-in mouse model breeding datasets reported to date. We confirmed previous observations that parental sex drives the relative ratio of expansions and contractions. The large datasets further allowed us to distinguish effects of paternal CAG repeat length on the magnitude and frequency of expansions and contractions, as well as the identification of large repeat size jumps in the knock-in models. Distinct degrees of intergenerational instability were observed between knock-in mice of six background strains, indicating the occurrence of trans-acting genetic modifiers. We also found that lines harboring a neomycin resistance cassette upstream of Htt showed reduced expansion frequency, indicative of a contributing role for sequences in cis, with the expanded repeat as modifiers of intergenerational instability. These results provide a basis for further understanding of the mechanisms underlying intergenerational repeat instability.

Large-scale phenome analysis defines a behavioral signature for Huntington's disease genotype in mice.

Rapid technological advances for the frequent monitoring of health parameters have raised the intriguing possibility that an individual's genotype could be predicted from phenotypic data alone. Here we used a machine learning approach to analyze the phenotypic effects of polymorphic mutations in a mouse model of Huntington's disease that determine disease presentation and age of onset. The resulting model correlated variation across 3,086 behavioral traits with seven different CAG-repeat lengths in the huntingtin gene (Htt). We selected behavioral signatures for age and CAG-repeat length that most robustly distinguished between mouse lines and validated the model by correctly predicting the repeat length of a blinded mouse line. Sufficient discriminatory power to accurately predict genotype required combined analysis of >200 phenotypic features. Our results suggest that autosomal dominant disease-causing mutations could be predicted through the use of subtle behavioral signatures that emerge in large-scale, combinatorial analyses. Our work provides an open data platform that we now share with the research community to aid efforts focused on understanding the pathways that link behavioral consequences to genetic variation in Huntington's disease.