TILRR (Toll-like Interleukin-1 Receptor Regulator), an Important Modulator of Inflammatory Responsive Genes, is Circulating in the Blood
Bibliographic record
Abstract
PURPOSE: TILRR (Toll-like interleukin-1 receptor regulator), a variant of FREM1 (Fras-related extracellular matrix 1), is a modulator of many genes in NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) signaling and inflammatory responses. It enhanced the expression of multiple genes in the NF-κB signaling pathway and promoted the production of multiple pro-inflammatory cytokines/chemokines. TILRR is an extracellular matrix protein and expressed in cells and tissues, and has never been considered to exist in the blood. The study aimed to identify circulating TILRR protein in human plasma as a biomarker of systemic inflammation. METHODS AND RESULTS: We developed a multiplex bead array method (Bio-Plex) using 4 monoclonal antibodies targeting different protein domains of FREM1/TILRR to investigate whether TILRR can be detected in blood plasma. The results of the multiplex bead array method were validated by Western blot analysis of affinity-purified TILRR from patient plasma samples. We subsequently analyzed 640 plasma samples from women enrolled in the Pumwani Sex Worker cohort (PSWC) (Nairobi, Kenya). Our study showed that TILRR exists in all patient plasma samples, but its quantities vary greatly among the patients, ranging from 2.38 ng/mL to 5196.79 ng/mL. The plasma TILRR below 2.38 ng/mL can only be detected by affinity purification and Western blot analysis. CONCLUSION: Our in-house developed multiplex bead array method can successfully quantify TILRR protein in plasma samples. Because TILRR is an important modulator of many inflammation-responsive genes, it may be an inflammation biomarker in blood and play a role in modulating systemic inflammation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".