Sex-Biased Aging Effects on Ig Somatic Hypermutation Targeting
Bibliographic record
Abstract
Aged individuals, particularly males, display an impaired level of Ab response compared with their younger counterparts, yet the molecular mechanisms responsible for the discrepancy are not well understood. We hypothesize that some of this difference may be linked to B cell somatic hypermutation (SHM) targeting, including error-prone DNA repair activities that are crucial to Ab diversification. To examine the effects of aging on SHM targeting, we analyzed B cell Ig repertoire sequences from 27 healthy male and female human subjects aged 20-89. By studying mutation patterns based on 985,069 mutations obtained from 123,415 sequences, we found that the SHM mutability hierarchies on microsequence motifs (i.e., SHM hot/cold spots) are mostly consistent between different age and sex groups. However, we observed a lower frequency in mutations involving Phase II SHM DNA repair activities in older males, but not in females. We also observed, from a separate study, a decreased expression level of DNA mismatch repair genes involved in SHM in older individuals compared with younger individuals, with larger fold changes in males than in females. Finally, we showed that the balance between Phase I versus Phase II SHM activities impacts the resulting Ig phenotypes. Our results showed that the SHM process is altered in some older individuals, providing insights into observed clinical differences in immunologic responses between different age and sex groups.
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) | 0.000 | 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".