λ Light Chain Bias Associated With Enhanced Binding and Function of Anti-HIV Env Glycoprotein Antibodies
Bibliographic record
Abstract
The humoral response to human immunodeficiency virus (HIV) remains incompletely understood. In this report, we describe biased λ light chain use during the HIV Env glycoprotein (Env) response in HIV infection and vaccination. We examined HIV Env binding (and neutralization) in the context of light chain use in subjects with acute HIV infection, chronic HIV infection, and among HIV vaccinees. In all populations tested, there was a λ chain bias for HIV Env binding antibodies, compared with other HIV antigens (such as p24) or tetanus toxoid. In subjects with chronic HIV infection, a λ bias was noted for neutralization, with λ antibodies accounting for up to 90% of all neutralization activity observed. This is the first report of antibody function in a human infection being tied to light chain use. In HIV infection, antibodies expressing λ light chains tended to have longer CDRL3s, increased light chain contact with HIV Env, and less hypermutation in the heavy chain, compared with antibodies using the κ light chain. These data also support an evolutionary model for the understanding the various κ to λ light chain ratios observed across species and suggest that the λ light chain bias against HIV provides the host an advantage in developing a more efficient humoral response.
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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".