Evidence map›Paper›PMID 32093602›Full record

ArticleBMC bioinformatics2020

Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice.

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

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 44% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Lucile MégretSorbonne Université, CNRS UMR8256, INSERM ERL U1164, Brain-C Lab, Paris, France. lucile.megret@sorbonne-universite.fr.
Satish Sasidharan NairSorbonne Université, CNRS UMR8256, INSERM ERL U1164, Brain-C Lab, Paris, France.
Julia DancourtSorbonne Université, CNRS UMR8256, INSERM ERL U1164, Brain-C Lab, Paris, France.
Jeff AaronsonCHDI Foundation, Princeton, NJ, USA.
Jim RosinskiCHDI Foundation, Princeton, NJ, USA.
Christian NeriSorbonne Université, CNRS UMR8256, INSERM ERL U1164, Brain-C Lab, Paris, France. christian.neri@inserm.fr.ORCID http://orcid.org/0000-0002-3790-2978
Centre National de la Recherche Scientifique · FRCHDI Foundation · US

Funding

CHDI Foundation A-12273
6 · The paper itself

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.

Indexed as

Machine LearningAnimalsBrainDisease Models, AnimalGene Expression RegulationGene Knock-In TechniquesHumansHuntingtin ProteinHuntington DiseaseMiceMicroRNAsNeuronsProteomicsRNA, MessengerRNA-SeqTrinucleotide RepeatsHuntingtin ProteinMicroRNAsRNA, MessengerBiological precisionMachine learningmiRNA regulationMultidimensional dataPredictive accuracyShape analysis

Identifiers

PMID32093602
PMCPMC7041117
OpenAlexW3006981867

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.