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Record W7018472409

Deciphering messenger ribonucleoprotein composition using single molecule resolution microscopy

2019· dissertation· en· W7018472409 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2019
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill University
Fundersnot available
KeywordsMessenger RNPRibonucleoproteinMessenger RNABiogenesisRNARNA-binding proteinRibosome
DOInot available

Abstract

fetched live from OpenAlex

Messenger RNA (mRNA) molecules expressed in the nuclei of eukaryotic organisms associate with many proteins to facilitate processing and maturation of mRNA, as well as packaging into messenger ribonucleoprotein particle (mRNP) complexes to facilitate nuclear export of transcripts.Existing methods used to study mRNPs have succeeded in identifying a number of proteins involved in mRNP biogenesis, but have failed to provide information with regards to mRNP structure or the heterogeneous pool of mRNPs existing in the cell.Here we describe steps taken towards the development of a novel technique aimed at quantifying protein abundance, in terms of stoichiometry, of mRNP proteins on specifically purified mRNA transcripts from cellular extracts by affinity purification and single-molecule microscopy.In developing this method, we aim to investigate the influence of mRNA length on mRNP protein stoichiometry, and to determine how mRNAs of different lengths may use the pool of mRNP biogenesis factors differently to achieve effective nuclear export. Résumé:Les molécules d'ARN messager (ARNm) exprimées dans les noyaux des organismes eucaryotes s'associent à certaines protéines pour faciliter le traitement et la maturation de l'ARNm, ainsi que pour former des complexes de particules ribonucléoprotéines messagères (RNPm) pour faciliter l'exportation hors du noyau.Les méthodes existantes utilisées pour étudier les RNPm ont réussi à identifier un certain nombre de protéines impliquées dans la biogenèse de la RNPm, mais ne parviennent pas à fournir des informations sur la structure de la RNPm ou l'hétérogénéité des RNPm qui existent dans la cellule.Nous décrivons ici les mesures prises pour le développement d'une nouvelle technique visant à quantifier l'abondance des protéines, en termes de stœchiométrie, des RNPm sur des ARNm spécifiquement purifiés à partir d'extraits cellulaires par purification d'affinité et microscopie à molécule unique.En développant cette méthode, nous cherchons à étudier l'influence de la longueur de l'ARNm sur la stœchiométrie des RNPm, afin de voir comment les ARNm de différentes longueurs peuvent utiliser différemment les facteurs de biogenèse de la RNPm pour faciliter l'exportation hors du noyau.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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 designBench or experimental
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

Citations0
Published2019
Admission routes1
Has abstractyes

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