The dysregulation of metabolic pathways and induction of the pentose phosphate pathway in renal ischaemia–reperfusion injury
Bibliographic record
Abstract
Lipid accumulation is associated with various forms of acute renal injury; however, the causative factors and pathways underpinning this lipid accumulation have not been thoroughly investigated. In this study, we performed lipidomic profiling of renal tissue following ischaemia-reperfusion injury (IRI). We identified a significant accumulation of cholesterol and specific phospholipids and sphingolipids in kidneys 24 h after IRI. In light of these findings, we hypothesised that pathways involved in lipid metabolism may also be altered. Through the analysis of published microarray data, generated from sham and ischaemic kidneys, we identified nephron-specific metabolic pathways affected by IRI and validated these findings in ischaemic renal tissue. In silico analysis revealed the downregulation of several energy and lipid metabolism pathways, including mitochondrial fatty acid beta-oxidation (FAO), peroxisomal lipid metabolism, fatty acid (FA) metabolism, and glycolysis. The pentose phosphate pathway (PPP), which is fuelled by glycolysis, was the only metabolic pathway that was upregulated 24 h following IRI. In this study, we describe the effect of renal IRI on metabolic pathways and how this contributes to lipid accumulation. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".