Identification of a Protective <i>Leishmania</i> Antigen Dihydrolipoyl Dehydrogenase and Its Responding CD4+ T Cells at Clonal Level
Bibliographic record
Abstract
Abstract There is currently no clinically effective vaccine against cutaneous leishmaniasis because of poor understanding of the Ags that elicit protective CD4+ T cell immunity. In this study, we identified a naturally processed peptide (DLD63–79) that is derived from Leishmania dihydrolipoyl dehydrogenase (DLD) protein. DLD is conserved in all pathogenic Leishmania species, is expressed by both the promastigote and amastigote stages of the parasite, and elicits strong CD4+ T cell responses in mice infected with L. major. We generated I-Ab-DLD63–79 tetramer and identified DLD-specific CD4+ T cells at clonal level. Following L. major infection, DLD63–79–specific CD4+ T cells massively expanded and produced effector cytokines (IFN-γ and TNF). This was followed by a gradual contraction, stable maintenance following lesion resolution, and display of memory (recall) response following secondary challenge. Vaccination with rDLD protein induced strong protection in mice against virulent L. major challenge. Identification of Ags that elicit protective immunity and their responding Ag-specific T cells are critical steps necessary for developing effective vaccines and vaccination strategies against infectious agents, including protozoan parasites.
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 | 0.000 | 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 | 0.000 |
| 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".