Tafazzin knockdown in murine mesenchymal stem cells enhances the tafazzin knockdown mediated elevation in interleukin-10 secretion from murine B lymphocytes
Bibliographic record
Abstract
Abstract Barth Syndrome is a rare X-linked genetic disorder caused by mutations in the TAFAZZIN gene. We recently demonstrated that tafazzin (Taz) protein deficiency in murine mesenchymal stems (MSCs) reduces immune function of activated wild type (WT) B lymphocytes. Interleukin-10 (IL-10) is a key anti-inflammatory cytokine capable of exerting immunosuppressive effects on myeloid cells. Here we examined if Taz deficiency in murine MSCs altered proliferation and IL-10 production in Taz deficient lipopolysaccharide (LPS)-activated murine B lymphocytes. Bone marrow MSCs and splenic B lymphocytes were isolated from WT or Taz knockdown (TazKD) mice. WT or Taz deficient MSCs were co-cultured with either LPS-activated WT or LPS-activated Taz deficient B lymphocytes for 24 h and B cell proliferation and IL-10 production determined. Taz deficient MSCs exhibited increased phosphatidylinositol-3-kinase (PI3K) mRNA expression compared to WT MSCs indicative of enhanced immunosuppression. Co-culture of Taz deficient MSCs with Taz deficient LPS-activated B cells resulted in a greater reduction in proliferation of B cells compared to Taz deficient MSCs co-cultured with LPS-activated WT B cells. In addition, co-culture of Taz deficient MSCs with Taz deficient LPS-activated B cells resulted in an enhanced production of IL-10 compared to Taz deficient MSCs co-cultured with LPS-activated WT B cells. Thus, Taz deficiency in murine MSCs potentiates the Taz knockdown-mediated elevation in IL-10 secretion from LPS-activated Taz knockdown B lymphocytes. These data suggest that Taz deficient MSCs may modulate the activity of other Taz deficient immune cells potentially promoting an enhanced immunosuppressive state.
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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 | 0.001 |
| 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".