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 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) | 0.000 | 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 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".