<i>rd1 </i>Mouse Retina Shows Imbalance in Cellular Distribution and Levels of TIMP-1/MMP-9, TIMP-2/MMP-2 and Sulfated Glycosaminoglycans
Bibliographic record
Abstract
BACKGROUND: The rd1 mouse retina displays fast degeneration of photoreceptors resulting in a depletion of almost all rod photoreceptors by postnatal day 21 (PN21). To evaluate the role of proteinases in the pathophysiology of this animal model of retinitis pigmentosa, C3H rd1 and congenic wild-type (wt) mice retinas were analyzed. MATERIAL AND METHODS: The cellular localization and levels of proteins, matrix metalloproteinases (MMPs), their endogenous inhibitors (TIMPs), total sulfated glycosaminoglycans (sGAG) and nature of saccharides in rd1 and wt retinal extracts were compared. RESULTS: MMP-2/TIMP-2 and MMP-9/TIMP-1 were predominantly localized in the interphotoreceptor matrix (IPM) of both genotypes, but MMP-2/TIMP-2 also appeared in the Muller cell fibers of rd1 retina. In rd1 retinal extracts the levels of total proteins were lower and those of active MMP-9, MMP-2, TIMP-1 and total sGAG were higher than those of wt extracts. Despite an increase in TIMP-1, active MMP-9/MMP-2 were disproportionately elevated in rd1 compared to wt retina. With increasing age, MMPs in wt retinas were decreased but were increased in rd1. The sialylation of proteoglycans in PN2 and PN7 rd1 retinas was lower, and galactosylation was higher than that in wt retinas. CONCLUSIONS: MMP-9/MMP-2 and TIMP-1/TIMP-2 are associated with IPM, possibly after secretion by retinal pigmented epithelial cells. In degenerating rd1 retina, MMP-2/TIMP-2 are associated with the Muller cell fibers, which apparently play a central role in modifying the balance between MMPs and TIMPs. Elevated sGAG and proteolysis due to an imbalance in the levels of TIMPs and active MMP-9/MMP-2 in rd1 retina possibly contribute to retinal degeneration in the rd1 mouse.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".