Secreted Protein Acidic and Rich in Cysteine (Sparc) KO Leads to an Accelerated Ageing Phenotype Which Is Improved by Exercise Whereas SPARC Overexpression Mimics Exercise Effects in Mice
Bibliographic record
Abstract
Secreted protein acidic and rich in cysteine (SPARC) is a matricellular glycoprotein implicated in various functions, including metabolism, tissue regeneration, and functional homeostasis. SPARC/Sparc declines with ageing but increases with exercise. We aim to verify two hypotheses: (1) SPARC deficiency leads to an ageing-like phenotype (metabolic decline, muscle loss, etc.), and (2) SPARC overexpression would mimic exercise, counteract ageing, and improve age-related changes. Our mice experiments are divided into two parts. First, we explore the consequences of Sparc knockout (KO) and compare them to the ageing effects. We also observe the effects of exercise. In the second part, we study the effects of SPARC overexpression and compare them to the exercise benefits. At the end, we make an analysis of the results to point out the analogies between Sparc KO and the ageing-like phenotype on the one hand and make comparisons between SPARC overexpression and exercise in the context of exercise counteracting ageing. The measurements were mainly related to tissue weights, adiposity, metabolism, and muscle strength. The main findings are that Sparc KO reduced glucose tolerance, muscle glucose transporter expression, and abdominal adipose tissue weight but increased glycogen content in the muscle. SPARC overexpression increased muscle strength, muscle mass, and expressions of the muscle glucose transporter and mitochondrial oxidative phosphorylation but lowered the glycemia and the adiposity, especially in males. Collectively, these findings, and the data we have previously reported, show that Sparc KO mice manifest an ageing-like phenotype, whereas SPARC overexpression and exercise generate similar benefits. The benefits are towards counteracting both the SPARC deficiency-induced ageing-like phenotype as well as reversing the age-related changes. The potential applications of these findings are to build/optimize Sparc KO-based animal models of various health conditions and, on the other hand, to develop therapies based on introducing SPARC or targeting SPARC-related pathways to mimic exercise against age-related and metabolic disorders.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".