The genetic interaction between HIV and the antibody repertoire
Bibliographic record
Abstract
Abstract The interaction between human immunodeficiency virus (HIV) and the antibody repertoire (AbR) during chronic infection can provide important information for HIV vaccine research, yet has not been well-characterized on a systems level. We deeply sequenced the HIV population and the AbR of ten HIV-infected, antiretroviral (ART)-naïve individuals, each with 10-20 longitudinal samples spanning 4-14 years. Our unbiased sequencing approach identified partitions of AbRs showing evidence of interaction with autologous HIV populations. We show that these HIV-associated partitions are enriched for the V gene segments of known HIV broadly neutralizing antibodies (bnAbs), indicating that the HIV-responding component of the AbR can be identified via time-series genetic data. Despite this evidence for larger-scale AbR/HIV interactions at the sub-population level, we found little to no evidence for antagonistic coevolution (i.e. an arms race). This suggests that antagonistic coevolution is either rare, or hard to detect, which has important vaccine design implications.
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 | 0.001 | 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".