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Record W2003372369 · doi:10.1016/j.jalz.2013.05.684

P2–042: Characterizing upregulated miRNAs during preclinical prion disease: Possible role in neuroprotection

2013· article· en· W2003372369 on OpenAlexaff
Anna Majer, Yulian Niu, Stephanie A. Booth

Bibliographic record

VenueAlzheimer s & Dementia · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPrion Diseases and Protein Misfolding
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsNeurodegenerationmicroRNANeuroprotectionBiologyDownregulation and upregulationGeneMicroarray analysis techniquesGene expressionTranscription factorRegulation of gene expressionCell biologyNeuroscienceDiseaseComputational biologyGeneticsMedicinePathology

Abstract

fetched live from OpenAlex

Similar to Alzheimer's disease, prion diseases are another type of neurodegenerative disorders where the buildup of normal host protein leads to neuronal toxicity. More specifically, the infectious prion protein (PrPSc), which has a different conformation than the normal cellular prion protein (PrPC) accumulates in the brain resulting in degeneration. Both Alzheimer's and prion diseases exhibit pre-clinical pathological features involving synaptic dysfunction and dendrite degeneration. Using transcriptional gene expression profiling we recently found that neurons initiate a protective response during pre-clinical prion infection. This protective effect decreased with disease progression and was completely abolished during clinical disease. To enhance our understanding of this protective process we further investigated temporal changes in RNA regulatory molecules called microRNAs (miRNAs). MiRNAs are a major class of post-transcription gene regulators that function in two main capacities: as rheostats that fine tune a few gene targets whose levels are critically important for function or as molecular switches by regulating vital transcription factors which govern entire pathways. Further understanding of the involvement of miRNAs will illuminate these neuroprotective pathways. Interestingly, we found the upregulation of 7 miRNAs in prion-induced neurodegeneration of which 3 (miR-29a-5p, miR-124a-5p, miR-132–3p) have known neurprotective effects. We are currently characterizing neuronal-specific function in the remaining 4 deregulated miRNAs. Bioinformatic prediction programs were employed to curate a list of potential miRNA targets which was further refined using mRNA microarray data. Luciferase assays were employed to validate these predictions. Functional screens of candidate miRNAs were initiated on mouse post-mitotic primary hippocampal cultures to test effects of these miRNAs on neuron morphology (i.e. dendrite complexity and spine morphology). The function of 4 deregulated miRNAs has been implicated in dendrite formation and neuronal survival. Luciferase reporter assays are underway to confirm these predictions. The effect of these miRNAs on neuronal morphology is being tested. We are currently investigating potential neuroprotective roles of 4 miRNAs that were upregulated during pre-clinical disease. This study will enhance our repertoire of known neuroprotective miRNAs which could be useful avenues for therapy (i.e. manipulating the concentration of these miRNAs in prion infected animals may affect disease progression).

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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