Characterization of Matricellular Protein Expression Signatures in Mechanistically Diverse Mouse Models of Kidney Injury
Bibliographic record
Abstract
Fibrosis is the most common pathophysiological manifestation of Chronic Kidney Disease (CKD). It is defined as excessive deposition of extracellular matrix (ECM) proteins. Embedded within the ECM are a family of proteins called Matricellular Proteins (MCPs), which are typically expressed during chronic pathologies for ECM processing. As such, identifying potential MCPs in the pathological secretome of a damaged kidney could serve as diagnostic/therapeutic targets of fibrosis. Using published RNA-Seq data from two kidney injury mouse models of different etiologies, Folic Acid (FA) and Unilateral Ureteral Obstruction (UUO), we compared and contrasted the expression profile of various members from well-known MCP families during the Acute and Fibrotic injury phases. As a result, we identified common and distinct MCP expression signatures between both injury models. Bioinformatic analysis of their differentially expressed MCP genes revealed similar top annotation clusters from Molecular Function and Biological Process networks, which are those commonly involved in fibrosis. Using kidney lysates from FA- and UUO-injured mice, we selected MCP genes from our candidate list to confirm mRNA expression by Western Blot, which correlated with injury progression. Understanding the expressions of MCPs will provide important insight into the processes of kidney repair, and may validate MCPs as biomarkers and/or therapeutic targets of CKD.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".