Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington\u2019s disease mice

Lucile Mégret,Christian Neri,Julia Dancourt,Jeff Aaronson,Satish Sasidharan Nair,Jim Rosinski

doi:10.1186/s12859-020-3418-9

Abstract

BackgroundMicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature selection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data from a knock-in mouse model (Hdh mice) of Huntington’s disease (HD), a disease caused by CAG repeat expansion in huntingtin (htt). This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh mice.ResultsRemarkably, compared to previous analyzes of this multidimensional dataset, the miRAMINT approach retained only 31 explanatory striatal miRNA-mRNA pairs that are precisely associated with the shape of CAG repeat dependence over time, among which 5 pairs with a strong change of target expression levels. Several of these pairs were previously associated with neuronal homeostasis or HD pathogenesis, or both. Such miRNA-mRNA pairs were not detected in cortex.ConclusionsThese data suggest that miRNA regulation has a limited global role in HD while providing accurately-selected miRNA-target pairs to study how the brain may compute molecular responses to HD over time. These data also provide a methodological framework for researchers to explore how shape analysis can enhance multidimensional data analytics in biology and disease.

Highlights

MicroRNA regulation is associated with several diseases, including neurodegenerative diseases
Multimodal selection of miRNA targets To understand how the dynamics of miRNA regulation may work on a system level in the brain of Hdh mice, we applied miRNA regulation analysis via multimodal integration, a pipeline in which novelty is to combine shape analysis with random forest analysis (Fig. 1)
MiRAMINT analysis (Fig. 1) comprises weighted gene correlation network analysis (WGCNA) analysis for reducing data complexity, followed by (i) RF analysis for selecting explanatory variables, in which a p-value is computed for each predictor variable and in which RF analysis is iterated until the number of hypotheses is stable across consecutive iterations, (ii) shape analysis for matching the miRNA and mRNA expression profiles across conditions, (iii) evidence for binding sites and (iv) bona fide comparison of the gene targets retained into the model to protein expression profiles

Summary

Introduction

MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. We addressed this question by integrating shape analysis and feature selection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data from a knock-in mouse model (Hdh mice) of Huntington’s disease (HD), a disease caused by CAG repeat expansion in huntingtin (htt) This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh mice. More sophisticated algorithms in this category of methods include deep learning methods such as for example DeepMirTar [17] This category comprises combinatorial ensemble approaches for improving the coverage and robustness of miRNA target prediction [18]

Methods

Results

Discussion

Conclusion