MétaCan
Menu
Back to cohort

Differential Expression and Subcellular Localization of MK5, ERK3 and ERK4 in Cardiac Fibroblasts and Myocytes

2016· article· en· W4389007994 on OpenAlexaffabout
Pramod Sahadevan, Sherin A. Nawaito, Fatiha Sahmi, Louis Villeneuve, Matthias Gaestel, Bruce G. Allen

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMyocyteCell biologyProtein kinase AKinaseBiologyMedicineChemistry

Abstract

fetched live from OpenAlex

Background and objectives MAP kinase‐activated protein kinase‐5 (MK5, PRAK) was originally discovered as target of p38 MAPK. Subsequent studies suggested that MK5 activity is regulated by atypical MAPKs ERK3 and 4 (Extracellular Signal‐regulated Kinase 3 and 4). The physiological role of MK5, in addition to the mechanisms regulating its activity and subcellular localization, remain controversial. MK5 mRNA is highly expressed in heart. Both ERK3 and MK5 haploinsufficient mice show reduced cardiac collagen expression following pressure overload induced by constriction of the transverse aorta. Furthermore, scar rupture was more frequent in MK5 haploinsufficient mice following myocardial ischemia induced by ligation of the left anterior descending coronary artery. Thus, ERK3‐MK5 signalling may play a role in fibrosis. The present study was to determine the expression and subcellular localization of ERK3, ERK4, and MK5 in cardiac fibroblasts and myocytes. Methods Cardiac fibroblasts were isolated from MK5 +/+ , MK5 +/− and MK5 −/− mice. Subconfluent cultures of fibroblasts from passages 0, 1, 2 and 3 were used. Proteins and RNA were also prepared from freshly isolated adult mouse cardiac myocytes. Protein and mRNA levels were determined by immunoblot and quantitative real‐time PCR, respectively. The subcellular localization of ERK3 and MK5 was determined by immunocytofluorescence and confocal microscopy. Results MK5 and ERK3 immunoreactivity was detected in fibroblasts but negligible in myocytes. In contrast, ERK4 immunoreactivity was detected in myocytes. The abundance of MK5 mRNA in fibroblasts and myocytes was similar. In contrast, ERK3 and ERK4 mRNA was more abundant in myocytes. Confocal microscopy revealed that, in fibroblasts, MK5 and ERK4 immunoreactivity localized to the nucleus whereas ERK3 immunoreactivity localized to the cytoplasm. A similar pattern of subcellular distribution was observed in passage numbers 0–3. ERK3 immunoreactivity and subcellular localization was similar in fibroblasts from MK5 +/+ , MK5 +/− and MK5 −/− mice. In addition ERK3 immunoreactivity was unaffected by the acute knockdown of MK5 using siRNA. In contrast, ERK4 immunoreactivity was reduced in fibroblasts from MK5 −/− mice. Conclusion In actively dividing cardiac fibroblasts, MK5 and ERK4 were observed in the nucleus whereas ERK3 was cytoplasmic. Furthermore, in contrast to other cell systems, in cardiac fibroblasts ERK3 was not destabilized by the absence of MK5, suggesting the possible presence of an unidentified binding partner in these cells. Reduced levels of ERK4 immunoreactivity in MK5 −/− fibroblasts suggests a role for MK5 in determining the expression or stability of ERK4 in these cells. Interestingly, in spite of having comparable amounts of MK5 mRNA and a 2‐fold greater abundance of ERK3 mRNA, MK5 and ERK3 immunoreactivity was negligible in myocytes. In contrast, ERK4 immunoreactivity was greater in myocytes. These observations suggest ERK3, ERK4, and MK5 play cell specific roles in the heart. Support or Funding Information This study was supported by a grant from the Heart and Stroke Foundation of Canada.

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 score0.003

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 head0.006
GPT teacher head0.195
Teacher spread0.189 · 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
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicMelanoma and MAPK PathwaysFrench-language works237,207