<i>Corynebacterium tuberculostearicum</i>, a human skin colonizer, induces the canonical nuclear factor‐κB inflammatory signaling pathway in human skin cells
Bibliographic record
Abstract
Abstract Introduction Corynebacterium tuberculostearicum ( C. t .) is a ubiquitous bacterium that colonizes human skin. In contrast to other members of the genus Corynebacterium , such as toxigenic Corynebacterium diphtheriae or the opportunistic pathogen Corynebacterium jeikeium , several studies suggest that C. t . may play a role in skin health and disease. However, the mechanisms underlying these effects remain poorly understood. Methods To investigate whether C. t . induces inflammatory pathways in primary human epidermal keratinocytes (HEKs) and human cutaneous squamous carcinoma cells (SCCs), cell culture, reverse transcription‐polymerase chain reaction (PCR), enzyme‐linked immunosorbent assay, immunofluorescence microscopy, Western blot, chromatin immunoprecipitation‐PCR, small interfering RNA knockdown and luciferase reporter expression system were used. Results Herein, we demonstrate that C. t . upregulates the messenger RNA (mRNA) and protein levels of inflammatory mediators in two human skin cell lines, HEKs and SCCs. We further show activation of the canonical nuclear factor‐κB (NF‐κB) pathway in response to C. t . infection, including phosphorylation of the inhibitor of κB (IκB), the nuclear translocation of NF‐κB subunit (NF‐κB‐P 65 ) and the recruitment of NF‐κB‐P 65 and RNA polymerase to the NF‐κB response elements at the promoter region of the inflammatory genes. Lastly, the data confirm that C. t .‐induced tumor necrosis factor mRNA expression in HEKs is toll‐like receptor 2 (TLR 2 ) dependent. Conclusion Our results offer a mechanistic model for C. t .‐induced inflammation in human keratinocytes via TLR 2 and activation of IκB kinase and downstream signaling through the canonical NF‐κB pathway. Relevance to chronic inflammatory diseases of the skin and cutaneous oncology is discussed.
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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) | 0.002 | 0.001 |
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".