Human OTULIN mutations can cause distinct inflammatory disease phenotypes depending on the protein domain that is mutated
Bibliographic record
Abstract
Abstract Linear ubiquitination is a post-translational modification that controls many immune signaling pathways. Linear ubiquitin chains are added to targets by LUBAC and removed by OTULIN, thus the opposing actions of LUBAC and OTULIN are critical for immune homeostasis. To understand mechanisms of how ubiquitination controls inflammation, we leveraged the use of biospecimens from patients with a novel bi-allelic OTULIN mutation. These patients have pyoderma gangrenosum (PG), an extremely rare inflammatory skin disease. OTULIN mutations have been previously described to cause a systemic autoinflammatory disease called ORAS, yet these patients define a new monogenic disease. We sought to understand this discrepancy through functional analyses of OTULIN mutations that culminate in PG vs ORAS. ORAS-causing OTULIN mutations impair the catalytic domain. However, the mutation in PG patients affects the domain responsible for binding LUBAC. Further, OTULIN’s ability to downregulate linear ubiquitin or NF-kB activity was unaffected. Patients’ transcriptional signatures showed elevated expression of genes involved in neutrophil activation and antigen presentation, and reduced expression of genes involved in Th17 differentiation, TLR signaling, and NOD-like receptor signaling. Deep immunophenotyping by CyTOF revealed that PG patients have higher frequencies of T cells and subtle defects of B cells and myeloid cells. These patients’ unique phenotype suggests an unrecognized function for OTULIN in skin inflammation. This discovery adds to the emerging spectrum of human immune-mediated diseases caused by defects in the ubiquitin pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".