Leucine-rich repeats and calponin homology containing 4 (Lrch4) regulates the innate immune response
Bibliographic record
Abstract
Toll-like receptors (TLRs) are pathogen-recognition receptors that trigger the innate immune response. Recent reports have identified accessory proteins that provide essential support to TLR function through ligand delivery and receptor trafficking. Herein, we introduce leucine-rich repeats (LRRs) and calponin homology containing 4 (Lrch4) as a novel TLR accessory protein. Lrch4 is a membrane protein with nine LRRs in its predicted ectodomain. It is widely expressed across murine tissues and has two expression variants that are both regulated by lipopolysaccharide (LPS). Predictive modeling indicates that Lrch4 LRRs conform to the horseshoe-shaped structure typical of LRRs in pathogen-recognition receptors and that the best structural match in the protein database is to the variable lymphocyte receptor of the jawless vertebrate hagfish. Silencing Lrch4 attenuates cytokine induction by LPS and multiple other TLR ligands and dampens the in vivo innate immune response. Lrch4 promotes proper docking of LPS in lipid raft membrane microdomains. We provide evidence that this is through regulation of lipid rafts as Lrch4 silencing reduces cell surface gangliosides, a metric of raft abundance, as well as expression and surface display of CD14, a raft-resident LPS co-receptor. Taken together, we identify Lrch4 as a broad-spanning regulator of the innate immune response and a potential molecular target in inflammatory disease.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".