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Record W4297100571 · doi:10.1101/2022.09.24.508386

Cortical miR-709 links glutamatergic signaling to NREM sleep EEG slow waves in an activity-dependent manner

2022· preprint· en· W4297100571 on OpenAlexafffund
Konstantinos Kompotis, Géraldine M. Mang, Jeffrey Hubbard, Sònia Jiménez, Yann Emmenegger, Christos Polysopoulos, Charlotte N. Hor, Leonore Wigger, Sébastien S. Hébert, Valérie Mongrain, Paul Franken

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de MontréalCanadian Sleep & Circadian NetworkUniversité LavalCentre hospitalier de l'Université Laval
FundersCanadian Institutes of Health ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsNeuroscienceGlutamatergicForebrainBiologyNon-rapid eye movement sleepWakefulnessElectroencephalographyContext (archaeology)Conditional gene knockoutTranscriptomemicroRNAGene expressionGeneReceptorPhenotypeGlutamate receptorGeneticsCentral nervous system

Abstract

fetched live from OpenAlex

Abstract MicroRNAs (miRNAs) are key post-transcriptional regulators of gene expression that have been implicated in a plethora of neuronal processes. Nevertheless, their role in regulating brain activity in the context of sleep has so far received little attention. To test their involvement, we deleted mature miRNAs in post-mitotic neurons at two developmental ages, i.e., in early adulthood using conditional Dicer knockout (cKO) mice and in adult mice using an inducible conditional Dicer cKO (icKO) line. In both models, electroencephalographic (EEG) activity was affected and the response to sleep deprivation (SD), altered; while rapid-eye-movement sleep (REMS) rebound was compromised in both, EEG delta (1-4 Hz) power during non-REM sleep (NREMS) was reduced in cKO mice and increased in icKO mice. We subsequently investigated the effects of SD on the miRNA transcriptome and found that the expression of 48 forebrain miRNAs was affected, in particular, the activity-dependent miRNA miR-709. In vivo inhibition of miR-709 in the brain increased EEG power during NREMS in the slow-delta (0.75-1.75 Hz) range, particularly after periods of prolonged wakefulness. Transcriptome analysis of primary cortical neurons in vitro revealed that miR-709 regulates endosomal trafficking and glutamatergic receptor activity. A subset of the genes involved in glutamatergic transmission was affected also in the cortices of sleep-deprived, miR-709-inhibited mice. Our data implicate miRNAs in the regulation of EEG activity and indicate that miR-709 links neuronal excitability during wakefulness to brain synchrony during sleep, likely through the regulation of endosomal trafficking and glutamatergic signaling. Significance Statement MicroRNAs (miRNAs) are key regulators of gene expression playing vital roles both in postnatal brain development and its functioning in adult organisms. Here, we highlight a fundamental role for miRNAs in shaping EEG slow waves, which reflect synchronous neuronal firing, characteristic of NREM sleep (NREMS) in the adult murine cortex. Disruption of the miRNA-biogenesis machinery affected brain synchrony differently, depending on when it occurred during development. Moreover, sleep deprivation altered the expression of several miRNAs in a brain-region specific manner. Among those, we identified miR-709 to affect the expression of genes involved in endosomal-trafficking and glutamatergic-transmission, thereby linking neuronal activity during wakefulness to slow EEG waves during subsequent sleep. The current study causally implicates this specific miRNA and the molecular pathways it targets in modifying the generation of NREMS EEG slow waves, which are important in synaptic plasticity and brain functioning.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.241
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2022
Admission routes2
Has abstractyes

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