Immunoinhibitory DNA Vaccine Protects Against Autoimmune Diabetes Through cDNA Encoding a Selective CTLA-4 (CD152) Ligand
Bibliographic record
Abstract
Cytotoxic T lymphocyte antigen 4 (CTLA-4 or CD152) is a strong negative regulator of T cell activity. Like CD28 (a positive regulator) it binds to B7-1 and B7-2, and there is no known natural selective ligand. Monoclonal antibodies to CTLA-4 generally have a masking effect, enhancing rather than suppressing responses. However, a single amino acid substitution in B7-1 (W88 > A; denoted B7-1wa) abrogates binding to CD28 but not to CTLA-4. We constructed plasmids encoding B7-1 or B7-1wa, as cell-surface or Ig fusion proteins. In a bound state, B7-1-Ig enhanced CD3-mediated T cell activation, but B7-1wa-Ig was inhibitory, as expected of a CTLA-4 ligand. To alter immunity in vivo, we inoculated mice intramuscularly (i.m.) with a carcinoembryonic antigen (CEA) plasmid. Gene transfer was amplified by electroporation. Co-injection of a B7-1wa (membrane-bound form) plasmid blocked induction of anti-CEA immunity, whereas a B7-1 plasmid was stimulatory. We studied this DNA covaccination method in nonobese diabetic (NOD) mice with autoimmune diabetes. Delivery of either preproinsulin I (PPIns) or B7-1wa cDNA alone did not suppress the autoimmune anti-insulin response of spleen cells. However, co-delivery of B7-1wa and PPIns cDNA abrogated reactivity to insulin and ameliorated disease. Interferon-gamma and interleukin-4 were both depressed, arguing against a Th2 bias. Reactivity to glutamic acid decarboxylase 65, another major islet autoantigen, was not altered and suppressor cells were not identified, suggesting induction of tolerance to insulin by either T cell anergy or deletion. Selective engagement of CTLA-4 through gene transfer represents a novel and powerful way to block autoimmunity specifically.
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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 | 0.001 |
| 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".