Mutability and hypermutation antagonize immunoglobulin codon optimality
Bibliographic record
Abstract
Abstract The efficacy of polyclonal antibody responses is inherently linked to paratope diversity, as generated through V(D)J recombination and somatic hypermutation (SHM). These processes arose in early jawed vertebrates; however, little is known about how immunoglobulin diversity, mutability, and hypermutation have evolved in tandem with another more ubiquitous feature of protein-coding DNA – codon optimality. Here, we explore these relationships through analysis of germline IG genes, natural V(D)J repertoires, serum VH usage, and monoclonal antibody (mAb) expression, each through the lens of multiple optimality metrics. Strikingly, proteomic serum IgG sequencing showed that germline IGHV codon optimality positively correlated with VH representation after influenza vaccination, and in vitro , codon deoptimization of mAbs with synonymous amino acid sequences caused consistent expression loss. Germline V genes exhibit a range of codon optimality that is maintained by functionality, and inversely related to mutability. SHM caused a load-dependent deoptimization of IGH VDJ repertoires within human tonsils, bone marrow, and lymph nodes (including SARS-CoV-2-specific clones from mRNA vaccinees), influenza-infected mice, and zebrafish. Comparison of natural mutation profiles to true random suggests the presence of selective pressures that constrain deoptimization. These findings shed light on immunoglobulin evolution, providing unanticipated insights into the antagonistic relationship between variable region diversification, codon optimality, and antibody secretion; ultimately, the need for diversity takes precedence over that for the most efficient expression of the antibody repertoire.
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 | 0.001 |
| 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 | 0.001 | 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".