Senataxin is required for optimal class switch recombination to the immunoglobulin A isotype
Bibliographic record
Abstract
Senataxin (SETX) is an RNA/DNA helicase that plays a pivotal role in transcription, R-loop resolution, and the DNA damage response (DDR). Dysfunction of SETX, however, has been implicated in neurodegenerative diseases, including amyotrophic lateral sclerosis and ataxia with oculomotor apraxia type 2. Importantly, both R-loop resolution and the DDR are essential for class switch recombination (CSR) in B cells, a critical step in generating antibody diversity. Here we demonstrate that ex vivo polyclonal stimulation of SETX +/+ and SETX -/- mouse splenocytes results in equivalent amounts of IgM- and IgG-secreting B cells but significantly fewer IgA-producing B cells in SETX -/- mice. Additionally, SETX -/- mice generate significantly reduced antigen specific IgA titers following infection with influenza A virus. Sequencing of the IgA-secreting B cell repertoire revealed reduced clonal diversity in SETX -/- mice. SETX -/- mice also had increased R-loop formation within the IgA locus. Collectively, these data highlight an important role for SETX in mediating CSR to IgA. As such, a further understanding of SETX’s role in the immune response is important for expanding our knowledge of both the general immunobiology relating to CSR as well as the immunological phenotypes associated with neurodegenerative diseases associate with mutations in SETX.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".