Reactivity profiles of broadly neutralizing anti-HIV-1 antibodies are distinct from those of pathogenic autoantibodies
Bibliographic record
Abstract
OBJECTIVE: Broadly neutralizing antibodies (bNt Abs) against HIV-1 are rarely produced during natural infection, and efforts to induce such Abs by vaccination have been unsuccessful. Thus, elucidating the nature and cellular origins of bNt Abs is a high priority for vaccine research. As the bNt monoclonal Abs (MAbs) 2F5, 4E10 and 2G12 have been reported to bind select autoantigens, we investigated whether these MAbs display a broader range of autoreactivity and how their autoreactivity compares with that of pathogenic autoAbs. METHODS: An autoantigen microarray comprising 106 connective tissue disease-related autoantigens and control antigens was developed and used, in combination with ELISAs, to compare the reactivity profiles of MAbs 4E10, 2F5 and 2G12 to those of four pathogenic autoAbs derived from patients with antiphospholipid-syndrome (APS), and to serum from a patient with systemic lupus erythematosus (SLE). RESULTS: The APS MAbs and SLE serum reacted strongly with multiple autoantigens on the microarray, whereas anti-HIV-1 MAb reactivity was limited mainly to HIV-1-related antigens. The APS autoAbs reacted strongly with CL, yet only 4E10 bound CL at high concentrations; both 2F5 and 4E10 bound their HIV-1 epitopes with a 2-3-log higher apparent affinity than CL. Moreover, the polyreactivity of 4E10, but not CL15, could be blocked with dried milk. CONCLUSION: The reactivity profiles of bNt anti-HIV-1 MAbs are fundamentally distinct from those of pathogenic autoAbs that arise from dysregulated tolerance mechanisms. This suggests that the limited polyreactivity observed for the bNt MAbs, and for HIV-1-Nt Abs in general, may arise through alternative mechanisms, such as extensive somatic mutation due to persistent antigen selection during chronic infection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".