Downregulation of cystathionine<i>β</i>-synthase and cystathionine<i>γ</i>-lyase expression stimulates inflammation in kidney ischemia-reperfusion injury
Bibliographic record
Abstract
Inflammation plays a critical role in kidney ischemia-reperfusion injury but mechanisms of increased proinflammatory cytokine expression are not completely understood. Kidney has a high expression of cystathionine-β-synthase (CBS) and cystathionine-γ-lyase (CSE) that can synthesize hydrogen sulfide. CBE and CSE are also responsible for the synthesis of cysteine, an essential precursor for glutathione, an antioxidant. Reduced hydrogen sulfide and glutathione production is associated with multiple organ injury. Although pro- and anti-inflammatory effects of hydrogen sulfide have been reported, its role in ischemia-reperfusion-induced inflammation in the kidney has not been well addressed. The aim of this study was to investigate the effect of CBS and CSE-mediated hydrogen sulfide and glutathione production on kidney inflammatory response and the mechanism involved. The left kidney of Sprague-Dawley rat was subjected to 45-min ischemia followed by reperfusion for 24 h. Ischemia-reperfusion caused a significant decrease in CBS and CSE mRNA and protein levels with a concomitant reduction of glutathione and hydrogen sulfide production in the kidney while the expression of proinflammatory cytokine expression (MCP-1, IL-6) was elevated. Hypoxia-reoxygenation of proximal tubular cells led to a decrease in CBS and CSE expression and an increase in proinflammatory cytokine expression. Supplementation of glutathione or hydrogen sulfide donor (NaHS) effectively attenuated cytokine expression in tubular cells. These results suggested that ischemia-reperfusion impaired CBS and CSE-mediated glutathione and hydrogen sulfide production in the kidney, which augmented the expression of proinflammatory cytokines. Regulation of CBS and CSE expression may be therapeutically relevant in alleviating ischemia-reperfusion-induced inflammation and improving kidney function.
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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) | 0.001 | 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 | 0.001 |
| 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".