Hypochlorous Acid: A Natural Adjuvant That Facilitates Antigen Processing, Cross-Priming, and the Induction of Adaptive Immunity
Bibliographic record
Abstract
The production of hypochlorous acid (HOCl) is a characteristic of granulocyte activation, a hallmark of the early phase of innate immune responses. In this study, we show that, in addition to its well-established role as a microbicide, HOCl can act as a natural adjuvant of adaptive immunity. HOCl enhances the T cell responses to the model Ag OVA, facilitating the processing and presentation of this protein via the class II MHC pathway. HOCl modification also enhances cross-presentation of the tumor Ag tyrosinase-related protein 2 via class I MHC. The adjuvant effects of HOCl are independent of TLR signaling. The enhanced presentation of HOCl-modified OVA is mediated via modification of the N-linked carbohydrate side chain rather than formation of protein aldehydes or chloramines. HOCl-modified OVA is taken up more efficiently by APCs and is degraded more efficiently by proteinases. Atomic force microscopy demonstrated that enhanced uptake is mediated via specific receptor binding, one candidate for which is the scavenger receptor lectin-like oxidized low-density lipoprotein receptor, which shows enhanced binding to chlorinated OVA. A function of HOCl is therefore to target glycoprotein Ags to scavenger receptors on the APC surface. This additional mechanism linking innate and adaptive immunity suggests novel strategies to enhance immunity to vaccines.
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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 | 0.001 | 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".