An Integrative Bioinformatic Approach for Studying Escape Mutations in Human Immunodeficiency Virus Type 1<i>gag</i>in the Pumwani Sex Worker Cohort
Bibliographic record
Abstract
Human immunodeficiency virus type 1 (HIV-1) is able to evade the host cytotoxic T-lymphocyte (CTL) response through a variety of escape avenues. Epitopes that are presented to CTLs are first processed in the presenting cell in several steps, including proteasomal cleavage, transport to the endoplasmic reticulum, binding by the HLA molecule, and finally presentation to the T-cell receptor. An understanding of the potential of the virus to escape CTL responses can aid in designing an effective vaccine. To investigate such a potential, we analyzed HIV-1 gag from 468 HIV-1-positive Kenyan women by using several bioinformatic approaches that allowed the identification of positively selected amino acids in the HIV-1 gag region and study of the effects that these mutations could have on the various stages of antigen processing. Correlations between positively selected residues and mean CD4 counts also allowed study of the effect of mutation on HIV disease progression. A number of mutations that could create or destroy proteasomal cleavage sites or reduce binding affinity of the transport antigen processing protein, effectively hindering epitope presentation, were identified. Many mutations correlated with the presence of specific HLA alleles and with lower or higher CD4 counts. For instance, the mutation V190I in subtype A1-infected individuals is associated with HLA-B*5802 (P = 4.73 x 10(-4)), a rapid-progression allele according to other studies, and also to a decreased mean CD4 count (P = 0.019). Thus, V190I is a possible HLA escape mutant. This method classifies many positively selected mutations across the entire gag region according to their potential for immune escape and their effect on disease progression.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".