Proteomic analysis of tyrosine phosphorylation during human liver transplantation.
Bibliographic record
Abstract
BACKGROUND: Ischemia-reperfusion (I/R) causes a dramatic reprogramming of cell metabolism during liver transplantation and can be linked to an alteration of the phosphorylation level of several cellular proteins. Over the past two decades, it became clear that tyrosine phosphorylation plays a pivotal role in a variety of important signalling pathways and was linked to a wide spectrum of diseases. Functional profiling of the tyrosine phosphoproteome during liver transplantation is therefore of great biological significance and is likely to lead to the identification of novel targets for drug discovery and provide a basis for novel therapeutic strategies. RESULTS: Using liver biopsies collected during the early phases of organ procurement and transplantation, we aimed at characterizing the global patterns of tyrosine phosphorylation during hepatic I/R. A proteomic approach, based on the purification of tyrosine phosphorylated proteins followed by their identification using mass spectrometry, allowed us to identify Nck-1, a SH2/SH3 adaptor, as a potential regulator of I/R injury. Using immunoblot, cell fractionation and immunohistochemistry, we demonstrate that Nck-1 phosphorylation, expression and localization were affected in liver tissue upon I/R. In addition, mass spectrometry identification of Nck-1 binding partners during the course of the transplantation also suggested a dynamic interaction between Nck-1 and actin during I/R. CONCLUSION: Taken together, our data suggest that Nck-1 may play a role in I/R-induced actin reorganization, which was previously reported to be detrimental for the hepatocytes of the transplanted graft. Nck-1 could therefore represent a target of choice for the design of new organ preservation strategies, which could consequently help to reduce post-reperfusion liver damages and improve transplantation outcomes.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".