Metabolite-Tfh axis as a key modulator of protective humoral immunity to the historical SA14-14-2 Japanese encephalitis vaccine 3685
Bibliographic record
Abstract
Abstract Description Japanese encephalitis (JE) is the leading cause of viral encephalitis globally, with approximately 100,000 cases annually, a high fatality rate of 30%, and neurological sequelae in 50% of survivors. The live attenuated SA14-14-2 vaccine is widely used and regarded as the gold standard for JE vaccine development. However, its durability and immunological effectiveness remain uncertain. This study assessed the magnitude and longevity of humoral and T-cell immunity in a longitudinal cohort of 87 young adults vaccinated with SA14-14-2. Samples were collected at baseline and at five time points over three years post-vaccination. Among seronegative individuals, only 36% seroconverted, while 18% remained non-seroconverters. Of the vaccinees, 41 achieved seroprotection (FRNT50 ≥ 10), but these levels declined drastically within two years. Interestingly, we observed that non-seroconverters exhibited significantly lower frequencies of JEV-specific T follicular helper (Tfh) cells and impaired T-B cell interactions, leading to poor class-switching and a likely failure to seroconvert. Metabolomics analyses identified unique metabolites enriched in non-seroconverters. Further studies revealed that a metabolite suppressed Tfh responses, disrupting GC-activity and abrogating vaccine-induced humoral immunity. These findings highlight a novel mechanism of metabolite-mediated modulation of vaccine efficacy and emphasize the need for optimizing JE and related flavivirus vaccination strategies. Funding Sources Supported by Department of Biotechnology Grants - BT/PR30223/MED/2018; BT/PR25335/NER/2017 Topic Categories Vaccines and Immunotherapy (VAC)
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".