Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice.
Animals
Brain
/ metabolism
Disease Models, Animal
Gene Expression Regulation
Gene Knock-In Techniques
Humans
Huntingtin Protein
/ genetics
Huntington Disease
/ genetics
Machine Learning
Mice
MicroRNAs
/ metabolism
Neurons
/ metabolism
Proteomics
RNA, Messenger
/ metabolism
RNA-Seq
Trinucleotide Repeats
Biological precision
Machine learning
Multidimensional data
Predictive accuracy
Shape analysis
miRNA regulation
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
24 Feb 2020
24 Feb 2020
Historique:
received:
29
01
2020
accepted:
17
02
2020
entrez:
26
2
2020
pubmed:
26
2
2020
medline:
25
4
2020
Statut:
epublish
Résumé
MicroRNA (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. Remarkably, 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. These 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.
Sections du résumé
BACKGROUND
BACKGROUND
MicroRNA (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.
RESULTS
RESULTS
Remarkably, 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.
CONCLUSIONS
CONCLUSIONS
These 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.
Identifiants
pubmed: 32093602
doi: 10.1186/s12859-020-3418-9
pii: 10.1186/s12859-020-3418-9
pmc: PMC7041117
doi:
Substances chimiques
Huntingtin Protein
0
MicroRNAs
0
RNA, Messenger
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
75Subventions
Organisme : CHDI Foundation
ID : A-12273
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