Dual inhibition of phosphoinositide 3-kinases delta and gamma reduces chronic B cell activation and autoantibody production in a mouse model of lupus
Bibliographic record
Abstract
Phosphoinositide 3-kinase delta (PI3Kδ) plays key roles in normal B cell activation and is chronically activated in malignant B cells. Targeting of PI3Kδ using FDA-approved drugs Idelalisib or Umbralisib has shown efficacy in treatment of multiple B cell malignancies. Duvelisib, an inhibitor targeting both PI3Kδ and PI3Kγ (PI3Kδγi) has also been used for treatment of several leukemias and lymphomas and was suggested to offer potential additional benefits in supressing T cell and inflammatory responses. Transcriptomics analyses indicated that while most B cell subsets predominantly express PI3Kδ, plasma cells upregulate PI3Kγ. We thus assessed whether PI3Kδγi treatment can impact chronic B cell activation in the context of an autoantibody-mediated disease. Using the TAPP1R218LxTAPP2R211L (TAPP KI) mouse model of lupus-like disease driven by dysregulated PI3K pathway activity, we performed 4 week PI3Kδγi treatments and found significant reduction in CD86+ B cells, germinal center B cells, follicular helper T cells and plasma cells in multiple tissues. This treatment also significantly attenuated the abnormally elevated serum levels of IgG isotypes observed in this model. The profile of autoantibodies generated was markedly altered by PI3Kδγi treatment, with significant reductions in IgM and IgG targeting nuclear antigens, matrix proteins and other autoantigens. Kidney pathology was also impacted, with reduced IgG deposition and glomerulonephritis. These results indicate that dual inhibition of PI3Kδ and PI3Kγ can target autoreactive B cells and may have therapeutic benefits in autoantibody-mediated disease.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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".