Toll-like Interleukin 1 Receptor Regulator Is an Important Modulator of Inflammation Responsive Genes
Bibliographic record
Abstract
TILRR (Toll-like interleukin-1 receptor regulator), a transcript variant of FREM1, is a novel regulatory component, which stimulates innate immune responses through binding to IL-1R1 (Interleukin-1 receptor, type 1) and TLR (Toll-like receptor) complex. However, it is not known whether TILRR expression influences other genes in the NFκB signal transduction and pro-inflammatory responses. Our previous study identified FREM1 as a novel candidate gene in HIV-1 resistance/susceptibility in the Pumwani Sex worker cohort. In this study, we investigated the effect of TILRR overexpression on expression of genes in the NFκB signaling pathway in vitro. The effect of TILRR on mRNA expression of 84 genes related to NFκB signal transduction pathway was investigated by qRT-PCR. Overexpression of TILRR on pro-inflammatory cytokine/chemokine(s) secretion in cell culture supernatants was analyzed using Bioplex multiplex bead assay. We found that TILRR overexpression significantly influenced expression of many genes in HeLa and VK2/E6E7 cells. Several cytokine/chemokine(s), including IL-6, IL-8 (CXCL8), IP-10, MCP-1, MIP-1β, and RANTES (CCL5) were significantly increased in the cell culture supernatants following TILRR overexpression. Although how TILRR influences the expression of these genes needs to be further studied, we are the first to show the influence of TILRR on many genes in the NFκB inflammatory pathways. The NFκB inflammatory response pathways are extremely important in microbial infection and pathogenesis, including HIV-1 transmission. Further study of the role of TILRR may identify the novel intervention targets and strategies against HIV infection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".