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Record W3023782267 · doi:10.7554/elife.53999

Intracellular calcium leak lowers glucose storage in human muscle, promoting hyperglycemia and diabetes

2020· article· en· W3023782267 on OpenAlexaff
Eshwar R. Tammineni, Natalia Kraeva, Lourdes Figueroa, Carlo Manno, Carlos A Ibarra, Amira Klip, Sheila Riazi, Eduardo Rı́os

Bibliographic record

VenueeLife · 2020
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsHospital for Sick ChildrenToronto General HospitalUniversity of TorontoUniversity Health Network
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Center for Research ResourcesNational Institute of General Medical Sciences
KeywordsDiabetes mellitusIntracellularCalcium in biologyCalciumEndocrinologyInternal medicineLeakCell biologyChemistryMedicineBiology

Abstract

fetched live from OpenAlex

Most glucose is processed in muscle, for energy or glycogen stores. Malignant Hyperthermia Susceptibility (MHS) exemplifies muscle conditions that increase [Ca 2+ ] cytosol . 42% of MHS patients have hyperglycemia. We show that phosphorylated glycogen phosphorylase (GP a ), glycogen synthase (GS a ) – respectively activated and inactivated by phosphorylation – and their Ca 2+ -dependent kinase (PhK), are elevated in microsomal extracts from MHS patients’ muscle. Glycogen and glucose transporter GLUT4 are decreased. [Ca 2+ ] cytosol , increased to MHS levels, promoted GP phosphorylation. Imaging at ~100 nm resolution located GP a at sarcoplasmic reticulum (SR) junctional cisternae, and apo -GP at Z disk. MHS muscle therefore has a wide-ranging alteration in glucose metabolism: high [Ca 2+ ] cytosol activates PhK, which inhibits GS, activates GP and moves it toward the SR, favoring glycogenolysis. The alterations probably cause these patients’ hyperglycemia. For basic studies, MHS emerges as a variable stressor, which forces glucose pathways from the normal to the diseased range, thereby exposing novel metabolic links.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.366
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.023
GPT teacher head0.265
Teacher spread0.242 · 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 teacher head, 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

Citations35
Published2020
Admission routes1
Has abstractyes

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