Extracellular matrix mechanical cues (dys)regulate metabolic redox homeostasis due to impaired autophagic flux
Bibliographic record
Abstract
Abstract Extracellular matrix (ECM) stiffness is increasingly recognized as a critical regulator of cellular behavior, governing processes such as proliferation, differentiation, and metabolism. Neurodegenerative diseases are characterized by mitochondrial dysfunction, oxidative stress, impaired autophagy, and progressive softening of the brain tissue, yet research into how mechanical cues influence cellular metabolism in this context remains scarce. In this study, we evaluated the long-term effects of brain-compliant, soft ECM on mitochondrial bioenergetics, redox balance, and autophagic capacity in neuronal cells. Using human neuroblastoma (SH-SY5Y) and mouse hippocampal (HT22) cell lines, as well as primary mouse neurons, we observed that prolonged exposure to soft ECM resulted in mitochondrial bioenergetic dysfunction, redox imbalance, and disrupted autophagic flux. These findings were consistently validated across both human and mouse neuronal cells. Our data indicate a decreased maximal autophagic capacity in cells exposed to long-term soft ECM, potentially due to an imbalance in autophagosome formation and degradation, as demonstrated by decreased LC3 II levels following chloroquine-induced autophagic flux inhibition. This impairment in autophagy was coupled with increased cellular oxidative stress, further indicating metabolic alterations. These findings emphasize the critical role of ECM stiffness in regulating neuronal cell metabolism and suggest that prolonged exposure to soft ECM may mimic key aspects of neurodegenerative disease pathology, thereby enhancing the physiological relevance of in vitro models. This study underscores the necessity for further research into ECM mechanics as a contributing factor in neurodegenerative disease progression and as a potential target for therapeutic strategies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".