Alternative Splicing Generates a Diacylglycerol Kinase α Transcript That Acts as a Dominant‐Negative Modulator of Superoxide Production in Localized Aggressive Periodontitis
Bibliographic record
Abstract
BACKGROUND: Diacylglycerol (DAG), levels of which are tightly regulated by diacylglycerol kinases (DGKs), is a lipid mediator linked to key biologic functions. Members of the DGK family undergo alternative splicing, generating the protein diversity necessary to control different intracellular DAG pools. DGKα function is altered in polymorphonuclear neutrophils (PMNs) of patients with localized aggressive periodontitis (LAgP), suggesting a genetic basis. Here, the authors assess DGKα spliced transcripts in human LAgP neutrophils. METHODS: In an expression library of a patient with LAgP, PMNs were screened for different DGKα transcripts. Real-time polymerase chain reaction and in vitro expression assays were performed to assess the fate of different transcripts on protein translocation and superoxide production in human leukemia cells (HL-60) and COS-7 cells. RESULTS: A DGKα transcript that lacks exon 10 (DGKαΔ10) and generates a premature stop codon and a truncated protein was identified as being upregulated in LAgP neutrophils. In vitro assays revealed that DGKαΔ10 translocation occurred even in the absence of important regulatory motifs. Transfection of HL-60 neutrophil-like cells with the DGKαΔ10 spliced variant induced an increase in the stimulated production of superoxide anion replicating the phenotype of LAgP PMNs. CONCLUSION: DGKαΔ10 can act as a dominant-negative transcript that can modulate superoxide production and provides an example of genetic regulation of the inflammatory response that may be relevant to human inflammatory diseases such as LAgP.
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 | 0.000 | 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) | 0.001 | 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".