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Record W4377233071 · doi:10.1101/2023.05.19.541292

Genetic excision of the regulatory cardiac troponin I extension in high heart rate mammal clades

2023· preprint· en· W4377233071 on OpenAlexafffund
William Joyce, Kai He, Mengdie Zhang, Samuel Ogunsola, Xini Wu, Kelvin T. Joseph, David Bogomolny, Wenhua Yu, Mark S. Springer, Jiuyong Xie, Anthony V. Signore, Kevin L. Campbell

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaNovo Nordisk FondenNovo Nordisk
KeywordsTerminal (telecommunication)Extension (predicate logic)Internal medicineComputer scienceMedicineComputer networkProgramming language

Abstract

fetched live from OpenAlex

Abstract Mammalian cardiac troponin I (cTnI) contains a highly conserved N-terminal extension harboring protein kinase A targets (Ser 23/24 ) which are phosphorylated during ß-adrenergic stimulation to increase cardiomyocyte relaxation rate. Here, we show that the Ser 23/24 encoding exon 3 of TNNI3 was pseudoexonized multiple times in shrews and moles to mimic Ser 23/24 phosphorylation without adrenergic stimulation, facilitating the evolution of exceptionally high resting heart rates (∼1000 beats min -1 ). We further reveal alternative exon 3 splicing in distantly related bat families and that both exon 3 - and exon 3 + cTnI isoforms are incorporated into cardiac myofibrils. Finally, exon 3 of human TNNI3 is shown to exhibit a relatively low splice strength score, offering an evolutionarily informed strategy to excise this exon to improve diastolic function during heart failure.

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 score0.002
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.214
Teacher spread0.202 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicRNA and protein synthesis mechanisms→French-language works237,207→