Elevated GLUT4 Levels in Human Skeletal Muscle Microtissues is Accompanied by Functional Insulin Dependence
Bibliographic record
Abstract
Abstract Insulin resistance in skeletal muscle is a hallmark of type 2 diabetes mellitus (T2D). While two-dimensional myotube cultures offer a controlled environment for studying T2D-related metabolic dysfunction, insulin-dependent glucose transporter type 4 (GLUT4) levels are limited and insulin-independent glucose transporter type 1 (GLUT1) expression dominates; reducing physiological relevance. Three-dimensional skeletal muscle microtissue cultures offer a promising alternative, and unlike 2D myotubes, are amenable to repeated contractile stimulation. However, microtissue GLUT1 and GLUT4 glucose transporter profiles remain under-characterized, particularly under physiological glucose and insulin conditions, which is evaluated herein. We report that GLUT1 levels trended ∼3.0-fold lower in microtissues compared with myotubes in 2D culture, although not statistically significant ( p = 0.072), while GLUT4 levels were ∼12-fold higher ( p < 0.0001), leading to a ∼60-fold increase in the GLUT4:GLUT1 ratio ( p = 0.023). Notably, the microtissue GLUT4:GLUT1 profile approached, but did not match that of native human muscle. Microtissues required supraphysiological insulin conditions for the development of maximal contractility, while physiological glucose levels were sufficient. Insulin withdrawal restored insulin responsiveness but impaired microtissue contractile strength ( p < 0.0001) and fatigue resistance ( p = 0.015). Our findings indicate that the glucose transporter profile of microtissues offers improved physiological relevance. However, their reliance on insulin to maintain contractile function limits their suitability for modeling T2D. The implementation of a robust, insulin-free differentiation protocol would facilitate the development of a microtissue-based T2D model which can be applied to study contraction-mediated increases in insulin sensitivity as a therapeutic approach.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".