Effect of Nutrient Metabolism on Cartilaginous Tissue Formation
Bibliographic record
Abstract
Despite the potential of tissue engineering approaches for cartilage repair, a major shortcoming is the low biosynthetic response of chondrocytes. While different strategies have been investigated to upregulate tissue formation, a novel approach may be to control nutrient metabolism. Although known for their anaerobic metabolism of glucose, chondrocytes are more synthetically active when cultured under conditions that elicit mixed aerobic-anaerobic metabolism. Here, we postulate this metabolic switch induces hypoxia inducible factor 1α (HIF-1α) signaling leading to improved tissue growth. Transition to different metabolic states can result in the pooling of intracellular metabolites, several of which can stabilize HIF-1α by interfering with proline-hydroxylase-2 (PHD2). Chondrocytes cultured under increased media availability accelerated tissue deposition (2.2 to 3.5-fold) with the greatest effect occurring at intermediate volumes (2 mL/106 cells). Under higher media volumes, metabolism switched from anaerobic to mixed aerobic-anaerobic. At and beyond this transition, maximal changes in PHD2 activity (- 45%), HIF-1α protein expression (8-fold increase), and HIF-1 gene target expression were observed (2.0 to 2.7-fold increase). Loss-of-function studies using YC-1 (to degrade HIF-1α) confirmed the involvement of HIF-1 signaling under these conditions. Lastly, targeted metabolomic studies of glucose metabolites (14 in total) revealed that both intracellular lactate and succinate correlated with PHD2 activity. Although both metabolites can inhibit PHD2, this effect can most likely be attributed to lactate as succinate was only present in trace amounts. However, addition work (e.g., 13C flux analyses) are required to confirm this assertion. Nevertheless, by harnessing this newly identified metabolic switch, functional engineered cartilage implants may be developed without the need for sophisticated methods which would allow for improved translation into the clinical realm.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".