Y‐RNA subtype ratios in plasma extracellular vesicles are cell type‐ specific and are candidate biomarkers for inflammatory diseases
Bibliographic record
Abstract
Major efforts are made to characterize the presence of microRNA (miRNA) and messenger RNA in blood plasma to discover novel disease-associated biomarkers. MiRNAs in plasma are associated to several types of macromolecular structures, including extracellular vesicles (EV), lipoprotein particles (LPP) and ribonucleoprotein particles (RNP). RNAs in these complexes are recovered at variable efficiency by commonly used EV- and RNA isolation methods, which causes biases and inconsistencies in miRNA quantitation. Besides miRNAs, various other non-coding RNA species are contained in EV and present within the pool of plasma extracellular RNA. Members of the Y-RNA family have been detected in EV from various cell types and are among the most abundant non-coding RNA types in plasma. We previously showed that shuttling of full-length Y-RNA into EV released by immune cells is modulated by microbial stimulation. This indicated that Y-RNAs could contribute to the functional properties of EV in immune cell communication and that EV-associated Y-RNAs could have biomarker potential in immune-related diseases. Here, we investigated which macromolecular structures in plasma contain full length Y-RNA and whether the levels of three Y-RNA subtypes in plasma (Y1, Y3 and Y4) change during systemic inflammation. Our data indicate that the majority of full length Y-RNA in plasma is stably associated to EV. Moreover, we discovered that EV from different blood-related cell types contain cell-type-specific Y-RNA subtype ratios. Using a human model for systemic inflammation, we show that the neutrophil-specific Y4/Y3 ratios and PBMC-specific Y3/Y1 ratios were significantly altered after induction of inflammation. The plasma Y-RNA ratios strongly correlated with the number and type of immune cells during systemic inflammation. Cell-type-specific "Y-RNA signatures" in plasma EV can be determined without prior enrichment for EV, and may be further explored as simple and fast test for diagnosis of inflammatory responses or other immune-related diseases.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".