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The Role of GSK3β in SERCA Dysfunction and the Development of Arrhythmogenic Cardiomyopathy

2021· article· en· W3168911347 on OpenAlexaff
Sophie I. Hamstra, Jessica L. Braun, Stephen P. Chelko, Val A. Fajardo

Bibliographic record

VenueThe FASEB Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsBrock University
Fundersnot available
KeywordsSERCAPhospholambanContractilityCardiomyopathyInternal medicineCardiac function curveEndocrinologyATPaseMedicineHeart failureFibrosisEndoplasmic reticulumChemistryBiologyCell biologyBiochemistryEnzyme

Abstract

fetched live from OpenAlex

The sarco(endo)plasmic reticulum Ca 2+ ‐ATPase (SERCA) pump catalyzes the active transport of Ca 2+ into the sarcoplasmic reticulum and is responsible for over 70% of Ca 2+ turnover after muscle contraction. Phospholamban (PLN) is a well‐known SERCA regulator, and impaired SERCA function due to reductions in SERCA and/or enhanced inhibitory PLN action has been linked to the development of various cardiomyopathies. Arrhythmogenic cardiomyopathy (ACM) is an inherited, non‐ischemic heart disease presenting with ventricular arrhythmias, hypo/dyskinesia, and fibrotic remodeling. Over 60% of ACM cases are caused by pathogenic variants in genes encoding the cardiac desmosome, where Desmoglein‐2 ( DSG2 ) is the second most prevalent desmosomal gene variant in the ACM patient population. Using Dsg2 mutant ( Dsg2 mut/mut ) mice, previous studies showed that the enzyme glycogen synthase kinase‐3β (GSK3β), plays a major role in ACM pathogenesis. Inhibiting GSK3β in these mice improved cardiac contractility and ACM pathological phenotypes. We have shown that GSK3β inhibition in cardiomyocytes can also improve SERCA function in wild‐type (WT) mice; however, the role of GSK3β activity on SERCA function in Dsg2 mut/mut mice is currently unknown. Here, we sought to examine the effects of GSK3β inhibition on SERCA function using a GSK3β‐specific inhibitor, SB216763 (SB2), in Dsg2 mut/mut mice. At 3 weeks of age, both WT and Dsg2 mut/mut mice were treated with SB2 (2.5 mg/kg/day) or equivalent volume/kg/day of vehicle (DMSO) for 13 weeks via intraperitoneal injection. After treatment, mice were sacrificed and hearts were collected and homogenized. Our results show that cardiomyocytes obtained from vehicle treated Dsg2 mut/mut mice had significantly lower SERCA2a expression (‐52%, p <0.01) compared to vehicle treated WT mice, leading to reduced SERCA2a:PLN (‐65%, p =0.04). Maximal SERCA activity was also significantly lower in vehicle treated Dsg2 mut/mut mice (‐65%, p =0.001). However, in myocardium from SB2 treated Dsg2 mut/mut mice, maximal SERCA activity, SERCA2a expression, and SERCA2a:PLN ratio were all restored to near WT levels. We also examined the effects of heterozygous and homozygous GSK3β S9A mutation in the Dsg2 mut/mut mice, which prevents phosphorylation and maintains GSK3β in its constitutively active form. When comparing WT with Dsg2 mut/mut heterozygous and homozygous GSK3β S9A mutants, we saw a significant but non‐progressive decrease in maximal SERCA activity (‐60%, p =0.02 and ‐58%, p =0.03 respectively). In conclusion, these results show that SERCA function is negatively impacted in Dsg2 mut/mut cardiomyocytes. Conversely, GSK3β inhibition with SB2 treatment improved SERCA content and activity to healthy WT levels. These results further illuminate the role of GSK3β in ACM pathogenesis and support the use of GSK3β inhibitors in its treatment, as our new promising results indicate this therapeutic strategy can also improve Ca 2+ handling.

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 integrity0.0000.001
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 head0.212
Teacher spread0.207 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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