Gene Profiling of a 3D Psoriatic Skin Model Enriched in T Cells: Downregulation of PTPRM Promotes Keratinocyte Proliferation through Excessive ERK1/2 Signaling
Bibliographic record
Abstract
Psoriasis is a complex, immune-mediated skin disease involving a wide range of epithelial and immune cells. The underlying mechanisms that govern the epidermal defects and immunological dysfunction observed in this condition remain largely unknown. In recent years, the emergence of new, more sophisticated models has allowed the evolution of our knowledge of the pathogenesis of psoriasis. The development of psoriatic skin biomaterials that more closely mimic native psoriatic skin provides advanced preclinical models that will prove relevant in predicting clinical outcomes. In this study, we used a tissue-engineered, two-layered (dermis and epidermis) human skin substitute enriched in T cells as a biomaterial to study both the cellular and molecular mechanisms involved in psoriasis’ pathogenesis. Gene profiling on microarrays revealed significant changes in the profile of genes expressed by the psoriatic skin substitutes compared with the healthy ones. Two genes, namely, PTPRM and NELL2, whose products influence the ERK1/2 signaling pathway have been identified as being deregulated in psoriatic substitutes. Deregulation of these genes supports excessive activation of the ERK1/2 pathway in psoriatic skin substitutes. Most importantly, electrophoresis mobility shift assays provided evidence that the DNA-binding properties of two downstream nuclear targets of ERK1/2, both the NF-κB and Sp1 transcription factors, are increased under psoriatic conditions. Moreover, the results obtained with the inhibition of RSK, a downstream effector of ERK1/2, supported the therapeutic potential of inhibiting this signaling pathway for psoriasis treatment. In conclusion, this two-layered human psoriatic skin substitute enriched in T cells may prove particularly useful in deciphering the mechanistic details of psoriatic pathogenesis and provide a relevant biomaterial for the study of potential therapeutic targets.
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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) | 0.000 | 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 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".