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Record W3196755709 · doi:10.3389/fgene.2021.755076

Editorial: Epigenetic Mechanisms and Their Involvement in Rare Diseases

2021· editorial· en· W3196755709 on OpenAlexafffund
Mojgan Rastegar, Dag H. Yasui

Bibliographic record

VenueFrontiers in Genetics · 2021
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health ResearchChildren's Hospital Research Institute of ManitobaNational Institutes of HealthNatural Sciences and Engineering Research Council of CanadaOntario Rett Syndrome Association
KeywordsEpigeneticsBiologyMedicineComputational biologyGeneticsBioinformaticsGene

Abstract

fetched live from OpenAlex

Keywords: epigenetics and rare diseases, MeCP2 isoforms and rett syndrome (RTT), DNA methylation and histone modifications, ATRX and gene regulatory mechanisms, activity dependent neuroprotective protein (ADNP) and chromatin remodeling, Beckwith-Wiedemann Syndrome (BWS) and Prader-Willi Syndrome (PWS), O-linked-D-Nacetylglucosamine (O-GlcNAc), MYCN-related epigenetic factors and non-coding regulatory RNAs Editorial on the Research Topic Epigenetic Mechanisms and Their Involvement in Rare DiseasesEpigenetic mechanisms are diverse modes of gene regulation, acting independent of genetic sequences.Epigenetics involves an array of "readers, " "writers, " and "erasers, " with key roles in development, health, and disease.One important aspect of epigenetics is involvement in rare diseases.This special topic covers a series of original research and review articles that further our knowledge about epigenetic mechanisms in rare diseases.One well-studied example of rare diseases caused by genetic mutations is Rett Syndrome (RTT).RTT is due to de novo mutations in the X-linked Methyl CpG Binding Protein 2 (MECP2) gene.The multi-functional "MeCP2" protein plays important roles in neuronal maturation and brain development.Focusing on RTT, Sharifi and Yasui, provide an overview about MeCP2 protein biology and its functional relevance to RTT.The authors explain how MECP2 mutations contribute to disease mechanisms, describing lessons learnt from RTT mice and model systems.They describe how MECP2 expression is distributed among different organs, using helpful schematics.They further discuss MeCP2 DNA binding activities, and its association not only with CpG dinucleotide methylation, but also with CpH methylation in the context of CpA, CpC, or CpT.The authors elaborate on MeCP2 function as a dual transcriptional regulator, as an activator or effective suppressor of gene transcription.The authors also discuss MeCP2 splice variants; MeCP2E1 and MeCP2E2, MeCP2 role in liquid phase separation, and potential therapeutic strategies for RTT.Complementing the first paper, Good et al., offer a timely review entitled "MeCP2: the genetic driver of Rett Syndrome epigenetics."The authors discuss how RTT-associated MECP2 gene mutations can modify its DNA binding activities, and chromatin bundling capabilities, while altering MeCP2 protein stability.They further discuss the role of MeCP2 in alternative splicing and micro-RNA processing.Other aspects of MeCP2 function, diverse protein domains, and different mutations are also well-discussed.Interestingly, the authors explain the impact of proteasomal degradation through MeCP2 PEST sequences, and circadian-dependent dynamics of MeCP2 isoforms.Finally, the authors highlight the complexity of RTT pathology with differential relevance of MeCP2 isoforms.The third paper on RTT is an original research article by Pejhan et al.The authors studied MeCP2 homeostasis regulatory network in the frontal cerebrum, hippocampus, amygdala, and cerebellum of post-mortem brain tissues from RTT patients and non-RTT controls.The authors

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.014
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0030.002
Research integrity0.0140.017
Insufficient payload (model declined to judge)0.0100.007

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 head0.005
GPT teacher head0.228
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations11
Published2021
Admission routes2
Has abstractyes

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