Background: MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative\ndiseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited\nfor analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature\nselection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data\nfrom a knock-in mouse model (Hdh mice) of Huntingtonâ??s disease (HD), a disease caused by CAG repeat expansion\nin huntingtin (htt). This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh\nmice.\nResults: Remarkably, compared to previous analyzes of this multidimensional dataset, the miRAMINT approach\nretained only 31 explanatory striatal miRNA-mRNA pairs that are precisely associated with the shape of CAG repeat\ndependence over time, among which 5 pairs with a strong change of target expression levels. Several of these\npairs were previously associated with neuronal homeostasis or HD pathogenesis, or both. Such miRNA-mRNA pairs\nwere not detected in cortex.\nConclusions: These data suggest that miRNA regulation has a limited global role in HD while providing accuratelyselected\nmiRNA-target pairs to study how the brain may compute molecular responses to HD over time. These data\nalso provide a methodological framework for researchers to explore how shape analysis can enhance\nmultidimensional data analytics in biology and disease.
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