Quantitative Proteomics Reveals the Induction of Mitophagy in Tumor Necrosis Factor-α-activated (TNFα) Macrophages
Bibliographic record
Abstract
Macrophages play an important role in innate and adaptive immunity as professional phagocytes capable of internalizing and degrading pathogens to derive antigens for presentation to T cells. They also produce pro-inflammatory cytokines such as tumor necrosis factor alpha (TNF-α) that mediate local and systemic responses and direct the development of adaptive immunity. The present work describes the use of label-free quantitative proteomics to profile the dynamic changes of proteins from resting and TNF-α-activated mouse macrophages. These analyses revealed that TNF-α activation of macrophages led to the down-regulation of mitochondrial proteins and the differential regulation of several proteins involved in vesicle trafficking and immune response. Importantly, we found that the down-regulation of mitochondria proteins occurred through mitophagy and was specific to TNF-α, as other cytokines such as IL-1β and IFN-γ had no effect on mitochondria degradation. Furthermore, using a novel antigen presentation system, we observed that the induction of mitophagy by TNF-α enabled the processing and presentation of mitochondrial antigens at the cell surface by MHC class I molecules. These findings highlight an unsuspected role of TNF-α in mitophagy and expanded our understanding of the mechanisms responsible for MHC presentation of self-antigens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".