IGHV1-69 polymorphism modulates anti-influenza antibody repertoires, correlates with IGHV utilization shifts and varies by ethnicity
Bibliographic record
Abstract
IGHV polymorphism provides a rich source of humoral immune system diversity. One important example is the IGHV1-69 germline gene where the biased use of alleles that encode the critical CDR-H2 Phe54 (F-alleles) to make broadly neutralizing antibodies (HV1-69-sBnAb) to the influenza A hemagglutinin stem domain has been clearly established. However, whether IGHV1-69 polymorphism can also modulate B cell function and Ab repertoire expression through promoter and copy number (CN) variations has not been reported, nor has whether IGHV1-69 allelic distribution is impacted by ethnicity. Here we studied a cohort of NIH H5N1 vaccinees and demonstrate for the first time the influence of IGHV1-69 polymorphism on V-segment usage, somatic hypermutation and B cell expansion that elucidates the dominance of F-alleles in HV1-69-sBnAbs. We provide evidence that Phe54/Leu54 (F/L) polymorphism correlates with shifted repertoire usage of other IGHV germline genes. In addition, we analyzed ethnically diverse individuals within the 1000 genomes project and discovered marked variations in F- and L- genotypes and CN among the various ethnic groups that may impact HV1-69-sBnAb responses. These results have immediate implications for understanding HV1-69-sBnAb responses at the individual and population level and for the design and implementation of "universal" influenza vaccine.
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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) | 0.001 | 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".