Endoplasmic Protein Nogo-B (RTN4-B) Interacts with GRAMD4 and Regulates TLR9-Mediated Innate Immune Responses
Bibliographic record
Abstract
TLRs are distributed in their characteristic cellular or subcellular compartments to efficiently recognize specific ligands and to initiate intracellular signaling. Whereas TLRs recognizing pathogen-associated lipids or proteins are localized to the cell surface, nucleic acid-sensing TLRs are expressed in endosomes and lysosomes. Several endoplasmic reticulum (ER)-resident proteins are known to regulate the trafficking of TLRs to the specific cellular compartments, thus playing important roles in the initiation of innate immune responses. In this study, we show that an ER-resident protein, Nogo-B (or RTN4-B), is necessary for immune responses triggered by nucleic acid-sensing TLRs, and that a newly identified Nogo-B-binding protein (glucosyltransferases, Rab-like GTPase activators and myotubularins [GRAM] domain containing 4 [GRAMD4]) negatively regulates the responses. Production of inflammatory cytokines in vitro by macrophages stimulated with CpG-B oligonucleotides or polyinosinic:polycytidylic acid was attenuated in the absence of Nogo-B, which was also confirmed in serum samples from Nogo-deficient mice injected with polyinosinic:polycytidylic acid. Although a deficiency of Nogo-B did not change the incorporation or delivery of CpG to endosomes, the localization of TLR9 to endolysosomes was found to be impaired. We identified GRAMD4 as a downmodulator for TLR9 response with a Nogo-B binding ability in ER, because our knockdown and overexpression experiments indicated that GRAMD4 suppresses the TLR9 response and knockdown of Gramd4 strongly enhanced the response in the absence of Nogo-B. Our findings indicate a critical role of Nogo-B and GRAMD4 in trafficking of TLR9.
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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 | 0.000 |
| 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".