Brain network-based determination of genes that clear mutant huntingtin fibrils

Huntington disease (HD) is caused by a CAG trinucleotide repeat expansion within the huntingtin (HTT) gene which produces a mutant HTT (mHTT) protein. This mHTT protein aggregates within neurons, forming fibrils that interfere with cellular functions and result in neurodegeneration. The spatiotemporal dynamics of HD pathogenesis is a complex interplay between mHTT production, spread, and likely clearance: fibrils spread from cell-to-cell in prion-like and concentration-dependent manners, and initial seeding of fibrils is preferentially seen in certain cell types (striatal medium spiny neurons). Furthermore, evidence from other disorders (Parkinson's) indicate the presence of neuroprotective genes (e.g. GBA) that clear pathogenic proteins. In HD, clearance genes remain an area of active investigation.

Neurodegeneration can be observed through magnetic resonance imaging (MRI) as tissue atrophy, and its progression can be captured through statistical modeling applied to atrophy data. I hypothesize that by combining spatial gene expression data with network modeling of atrophy, and specifically searching for genes expressed in areas in which atrophy is expected but instead protected, I will identify candidate clearance genes.

I have previously applied deformation-based morphometry (DBM) to the Cambridge HD mouse MRI dataset (n=276) to sensitively detect both broad and localized patterns of

(previously unidentified) atrophy across sex, strain, and CAG number as part of my undergraduate thesis. To detect clearance genes, I will apply the Subtype and Stage Inference (SuStaIn) algorithm to the atrophy outputs of DBM. SuStaIn is a machine-learning technique used to uncover data-driven disease subtypes with distinct temporal progression patterns by taking cross-sectional data as an input. SuStaIn has been validated for use with neurodegenerative disorders, and in parallel, is used to model pathogenic spread of (alpha-synuclein) fibrils in Parkinson’s from mouse MRI. This step will provide network-based models of pathogenic spread from which I can determine brain areas that are particularly susceptible or resilient to atrophy.

To determine clearance genes, I will turn to the imaging-transcriptomics framework that is widely used in neuroimaging. In this framework, genes are identified based on their preferential expression within brain regions implicated in neuroimaging findings. I will use the Allen Mouse Brain Atlas (which contains genome-wide, whole-brain normative spatial gene expression data) and identify genes preferentially expressed in brain regions identified as resilient from the previous step, thus providing a list of candidate clearance genes that can be tested in further studies. These genes will be further subject to enrichment analyses to identify biological processes and pathways underlying resilience to neurodegeneration.

MRI of HD mouse models provides an unbiased, whole-brain view of atrophy under controlled conditions (e.g. CAG repeat number) thus limiting the influence of non-disease-related factors. The application of SuStaIn to HD mouse MRI is a novel approach that, in combination with imaging transcriptomics, will identify candidate protective genes. Research on targeted therapies for HD, including gene therapy, would benefit from the identification of such genes. Within the lab, it will be the basis for projects in which we will further confirm the protective functions of these genes.