Defective IL-17- and IL-22-dependent mucosal host response to Candida albicans determines susceptibility to oral candidiasis in mice expressing the HIV-1 transgene
Bibliographic record
Abstract
BACKGROUND: The tissue-signaling cytokines IL-17 and IL-22 are critical to host defense against oral Candida albicans infection, by their induction of oral antimicrobial peptide expression and recruitment of neutrophils. Mucosal Th17 cells which produce these cytokines are preferentially depleted in HIV-infected patients. Here, we tested the hypothesis that defective IL-17- and IL-22-dependent host responses to C. albicans determine the phenotype of susceptibility to oropharyngeal candidiasis (OPC) in transgenic (Tg) mice expressing HIV-1. RESULTS: Naïve CD4+ T-cells and the differentiated Th1, Th2, Th17, Th1Th17 and Treg lineages were all profoundly depleted in cervical lymph nodes (CLNs) of these Tg mice. However, naive CD4+ cells from Tg mice maintained the capacity to differentiate into these lineages in response to polarizing cytokines in vitro. Expression of Il17, Il22, S100a8 and Ccl20 was enhanced in oral mucosal tissue of non-Tg, but not of Tg mice, after oral infection with C. albicans. Treatment of infected Tg mice with the combination of IL-17 and IL-22, but not IL-17 or Il-22 alone, significantly reduced oral burdens of C. albicans and abundance of Candida hyphae in the epithelium of tongues of infected Tg mice, and restored the ability of the Tg mice to up-regulate expression of S100a8 and Ccl20 in response to C. albicans infection. CONCLUSIONS: These findings demonstrate that defective IL-17- and IL-22-dependent induction of innate mucosal immunity to C. albicans is central to the phenotype of susceptibility to OPC in these HIV transgenic mice.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".