MicroRNA Expression Profiling in Sorted AML Subpopulations: A Possible Role for miR-155/BIC in Stem Cell Maintenance and Leukemogenesis.
Bibliographic record
Abstract
Abstract MicroRNAs (miRNAs) are a new class of non-coding small RNAs that negatively regulate the expression of protein-encoding genes. Mature miRNAs are excised sequentially from primary miRNA (pri-miRNA) foldback precursor transcripts, and regulate gene expression at the post-transcriptional level. miRNAs functionally suppress gene expression by either inhibition of protein synthesis or by direct cleavage of the target mRNA. miRNA expression is tissue and developmental stage restricted, suggesting important roles in tissue specification and/or cell lineage determination. miRNAs are implicated in the regulation of diverse processes including cell growth control, apoptosis, fat metabolism and insulin secretion, and may be involved in the maintenance of the embryonic stem cell state. Several recent lines of evidence suggest a role for miRNAs in hematological malignancies. Many characterized miRNAs are located at fragile sites, minimal loss of heterozygosity regions, minimal regions of amplification or common breakpoint regions in human cancers. For example, chromosomal translocation t(8;17) in an aggressive B-cell leukemia results in fusion of miR-142 precursor and a truncated MYC gene. Furthermore, both miR-15 and miR-16 are located within a 30 kb deletion in CLL, and in most cases of this cancer both genes are deleted or underexpressed. In addition, mice transplanted with hematopoietic stem cells (HSC) overexpressing both c-Myc and the miR-17–92 polycistron developed cancers earlier with a more aggressive nature when compared to lymphomas generated by c-myc alone. To address the role of miRNAs in the regulation and maintenance of the hematopoietic stem cell state and leukemogenesis, we sorted 6 primary AML patient samples into 4 populations based on the expression of CD34/CD38 and performed miRNA array analysis. We identified a subset of miRNAs whose expression profile could discriminate the CD34+/CD38- fractions from more mature populations. In particular, BIC/miR-155 was found to be over-expressed in leukemic stem cells (LSC). Validation by qRT-PCR revealed this expression pattern in 5 of the 6 sorted AML samples. Furthermore, within umbilical cord blood (CB) cells, BIC/miR-155 is more highly expressed in the primitive CD34+38- fraction as compared to mature sub-fractions as assessed by Affymetrix microarray. miRNA array analysis also revealed elevated levels of miR-155 in bulk primary AMLs as compared to normal BM. Intriguingly, BIC/miR-155 was first identified as a common retroviral insertion site in avian leucosis virus induced B cell lymphomas, and BIC/miR-155 overexpression has been observed in all subtypes of Hodgkin’s lymphoma. To test the hypothesis that miR-155 is important in LSC/HSC function, we designed lentiviral vectors for RNAi mediated knockdown of BIC/miR-155. Knockdown of BIC/miR-155 within a novel CD34+ leukemic cell line resulted in a loss of CD34 expression and reduced proliferative potential. Additionally, knockdown within CB led to alterations in colony forming capacity. Additionally, we have recently generated lentiviral vectors for the enforced overexpression of BIC/miR-155. In vivo studies to investigate the effects of BIC/miR-155 over-expression and knockdown are ongoing and will be discussed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".