TAOK3 limits age-associated inflammation by negatively modulating macrophage differentiation and their production of TNFα
Bibliographic record
Abstract
Abstract Background Human aging is characterized by a state of chronic inflammation, termed inflammaging, for which the causes are incompletely understood. It is known, however, that macrophages play a driving role in establishing inflammaging by promoting pro-inflammatory rather than anti-inflammatory responses. Numerous genetic and environmental risk factors have been implicated with inflammaging, most of which are directly linked to pro-inflammatory mediators IL-6, IL1Ra, and TNFα. Genes involved in the signaling and production of those molecules have also been highlighted as essential contributors. TAOK3 is a serine/threonine kinase of the STE-20 kinase family that has been associated with an increased risk of developing auto-immune conditions in several genome-wide association studies (GWAS). Yet, the functional role of TAOK3 in inflammation has remained unexplored. Results We found that mice deficient in the serine/Threonine kinase Taok3 developed severe inflammatory disorders with age, which was more pronounced in female animals. Further analyses revealed a drastic shift from lymphoid to myeloid cells in the spleens of those aged mice. This shift was accompanied by hematopoietic progenitor cells skewing in Taok3−/− mice that favored myeloid lineage commitment. Finally, we identified that the kinase activity of the enzyme plays a vital role in limiting the establishment of proinflammatory responses in macrophages. Conclusions Essentially, Taok3 deficiency promotes the accumulation of monocytes in the periphery and their adoption of a pro-inflammatory phenotype. These findings illustrate the role of Taok3 in age-related inflammation and highlight the importance of genetic risk factors in this condition.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".