Extremely Complex Populations of Small RNAs in the Mouse Retina and RPE/Choroid
Bibliographic record
Abstract
PURPOSE: MicroRNAs (miRNAs) are small noncoding RNAs of approximately 18 to 22 nucleotides in length that regulate gene expression. They are widely expressed in the retina, being both required for its normal development and perturbed in disease. The aim of this study was to apply new high-throughput sequencing techniques to more fully characterize the miRNAs and other small RNAs expressed in the retina and retinal pigment epithelium (RPE)/choroid of the mouse. METHODS: Retina and RPE/choroid were dissected from eyes of 3-month-old C57BL/6J mice. Small RNA libraries were prepared and deep sequencing performed on a genome analyzer. Reads were annotated by alignment to miRBase, other noncoding RNA databases, and the mouse genome. RESULTS: Annotation of 9 million reads to 320 miRNAs in retina and 340 in RPE/choroid provides the most comprehensive profiling of miRNAs to date. Two novel miRNAs were identified in retina. Members of the sensory organ-specific miR-183, -182, -96 cluster were among the most highly expressed, retina-enriched miRNAs. Remarkably, miRNA "isomiRs," which vary slightly in length and are differentially detected by Taqman RT-qPCR assays, existed for all the microRNAs identified in both tissues. More variation occurred at the 3' ends, including nontemplated additions of T and A. Drosha-independent mirtron miRNAs and other small RNAs derived from snoRNAs were also detected. CONCLUSIONS: Deep sequencing has revealed the complexity of small RNA expression in the mouse retina and RPE/choroid. This knowledge will improve the design and interpretation of future functional studies of the role of miRNAs and other small RNAs in retinal 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 | 0.001 | 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) | 0.000 | 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".