Renal repair and regeneration: Study of a candidate gene HCaRG
Bibliographic record
Abstract
Introduction An important process in renal regeneration after injury is the conversion of tubular cells to a migratory phenotype; surviving cells migrate to denuded areas, proliferate and differentiate in order to restore nephron structure and function. Beside its effect on renal cell proliferation and differentiation, our previous studies support a role for HCaRG (hypertension‐related calcium‐regulated gene) in the motile behavior increment of kidney cells. Results To better understand the evolvement of HCaRG in this phenomenon, we used the Yeast 2–hybrid technique to screen human kidney proteins that interact with HCaRG. Screening using HCaRG as bait revealed its interaction with β‐actin, Na + /K + ATPase and NKCC. This direct protein interaction was confirmed by several techniques, and their co‐localization at the leading edge of migrating cells was found by immunocytochemistry. This stimulatory effect of HCaRG on migration could be due to actin reorganisation coupled to ions transport activation. We produced two stable kidney cell lines (HEK293 and MDCK‐C7) overexpressing HCaRG to help us identify its function. We used oligonucleotide microarray analysis to determine changes in gene expression between HCaRG over expressing cells and their controls. Microarray results for selected genes were confirmed by quantitative RT‐PCR. HCaRG affects the expression of several genes implicated in cell motility. Conclusion A better comprehension of the mechanisms and molecular pathways involved in the functions of HCaRG will enable us to conceive better means of stimulating the regeneration of renal function after a lesion. Supported by CIHR.
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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) | 0.003 | 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".