Bibliographic record
Abstract
MicroRNAs (miRNAs) have been shown to play important roles in physiological as well as multiple malignant processes including acute myeloid leukemia (AML). In an effort to gain further insight into the role of miRNAs in AML, we have applied the Illumina massively parallel sequencing platform to carry out an in depth analysis of the miRNA transcriptome in a murine leukemia progression model, based on the engineered over-expression of the nucleoporin 98(NUP98)-homeobox HOXD13 fusion gene (ND13), followed by conversion into AML inducing cells upon transduction with the oncogenic collaborator Meis1. Of the over 307 identified miRNA/miRNA* species in both libraries, sequence counts varied between 2 and 136,558, indicating a remarkable expression range. Our finding of extensive sequence variations (isomiRs) for almost all miRNA and miRNA* species adds additional complexity to the miRNA transcriptome. A stringent target prediction analysis coupled with in-vitro target validation revealed the potential for miRNA-mediated release of oncogenes that facilitates leukemic progression from the preleukemic to leukemia inducing state. Besides over 50 putative novel miRNAs, we found a high abundance of miRNA* species, implying a functional role for these. To further elucidate the function of miRNA*s, we took advantage of 9 deep sequencing libraries from a variety of cell lines to determine the most abundant complementary strand of know miRNAs. Comparing miRNA/miRNA* ratios across the miRNA sequence libraries revealed that most ratios remain constant across tissues and species, allowing a novel classification of miRNAs into α-duplexes, miRNAs duplexes with a dominant strand and β-duplexes with both strands being abundant. However, certain ratios were highly variable across the libraries examined as exemplified for the ratio of miR-223/miR-223*. Bioinformatics as well as functional analysis revealed a possible supporting function of miR-223* to the differentiating role of miR-223 in normal normal bone marrow as well as AML. Taken together, by using deep sequencing we provided deep insight into the changes of the miRNA transcriptome in the development of AML. Furthermore, we propose a new classification for miRNA duplexes and provide evidence for a possible role a miRNA* in the development of acute myeloid leukemia.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".