Accurate Detection of the microRNA Transcriptome in a Leukemia Progression Model.
Bibliographic record
Abstract
Abstract So far, only little is known about microRNAs (miRNAs) and their role in the development of acute myeloid leukemia (AML). As a result of the heterogeneity of AML, current miRNA expression profiling approaches, comparing AML subgroups with normal bone marrow, only allow a crude picture of the expression dynamics of miRNAs within AML development. In order to refine the role of miRNAs in the stepwise pathogenesis of AML, we extensively characterized the miRNA transcriptome in the murine “preleukemic” NUP98-HOXD13 (ND13) and leukemic ND13+Meis1 AML cell line model, simulating the conversion of a non-leukemic myeloid progenitor into a highly aggressive from of AML-inducing cells upon transduction with the Hox co-factor Meis1 (Pineault et al Leukemia19:636, 2005). To obtain a comprehensive and quantitative picture of the miRNA transcriptome in the ND13/ND13+Meis1 leukemia progression model we used a combination of platforms including a deep sequencing approach based on the Solexa™ platform, miRNA microarrays and real time PCR. Within one run on the Solexa™ deep sequencing platform, 9.22E+07 (ND13) and 7.08E+07 (ND13+Meis1) bases were sequenced, reflecting 3.41E+06 and 2.62E+06 27 nucleotides reads, respectively. Besides snoRNAs, snRNAs, tRNAs and other small RNAs, bioinformatic analysis revealed in both samples more than 60% microRNAs. From this dataset 266 miRNA species in the ND13, and 270 miRNA species in the ND13+Meis1 cells were identified, including miRNAs as well as miRNAs*. Interestingly, in both libraries, miRNAs frequently exhibited variations in their mature sequence. Absolute miRNA expression levels varied between 1 and 130,228 tags, indicating a remarkable expression range between the miRNAs. Strikingly, considering only miRNA sequences with ≥100 tags and ≥1.5 fold change, 23 (∼8.6%) miRNAs were upregulated and 52 (∼19.5%) miRNAs downregulated between the preleukemic and leukemic lines, including differential expression of miRNAs located in the Hox-cluster like miR-10b (7.3 fold upregulation) and miR-196b (4.4 fold upregulation). In addition, by determining the RNA secondary structure and structure similarities, 108 putative new miRNAs species were identified. Surprisingly, no correlations were seen between the Solexa™ platform and absolute miRNA expression levels detected with three commercially available array platforms (Ambion, Invitrogen, Exiquon), depending on the method of comparison producing r-values between -0.8 and 0.5. In contrast, a better correlation was calculated comparing the fold changes of miRNAs in ND13 and ND13+Meis1 samples, producing r-values up to 0.645. These results demonstrate that assessment of miRNA expression levels is very variable depending on the method used and that more than one approach should be favored. In conclusion, the conversion of a myeloid “preleukemic” cell line into a leukemia inducing cell line revealed specific changes of the miRNA transcriptome involving up- and downregulation of small and apparently defined groups of the detected miRNAs. These findings as well as the detection of over a hundred novel miRNAs point to well-defined roles for miRNAs in the development of AML.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".