A comprehensive joint analysis of the long and short RNA transcriptomes of human erythrocytes
Bibliographic record
Abstract
BACKGROUND: Human erythrocytes are terminally differentiated, anucleate cells long thought to lack RNAs. However, previous studies have shown the persistence of many small-sized RNAs in erythrocytes. To comprehensively define the erythrocyte transcriptome, we used high-throughput sequencing to identify both short (18-24 nt) and long (>200 nt) RNAs in mature erythrocytes. RESULTS: Analysis of the short RNA transcriptome with miRDeep identified 287 known and 72 putative novel microRNAs. Unexpectedly, we also uncover an extensive repertoire of long erythrocyte RNAs that encode many proteins critical for erythrocyte differentiation and function. Additionally, the erythrocyte long RNA transcriptome is significantly enriched in the erythroid progenitor transcriptome. Joint analysis of both short and long RNAs identified several loci with co-expression of both microRNAs and long RNAs spanning microRNA precursor regions. Within the miR-144/451 locus previously implicated in erythroid development, we observed unique co-expression of several primate-specific noncoding RNAs, including a lncRNA, and miR-4732-5p/-3p. We show that miR-4732-3p targets both SMAD2 and SMAD4, two critical components of the TGF-β pathway implicated in erythropoiesis. Furthermore, miR-4732-3p represses SMAD2/4-dependent TGF-β signaling, thereby promoting cell proliferation during erythroid differentiation. CONCLUSIONS: Our study presents the most extensive profiling of erythrocyte RNAs to date, and describes primate-specific interactions between the key modulator miR-4732-3p and TGF-β signaling during human erythropoiesis.
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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 | 0.001 | 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) | 0.001 | 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".