Identification of Global miRNA Targetomes within Human Hematopoietic Progenitors and Acute Myeloid Leukemia Uncovers Unique Molecular Networks Centered on Mitochondria Function in Leukemic Stem Cells
Bibliographic record
Abstract
Previous studies comparing transcriptome and proteome profiles of the cells that constitute both the normal and malignant hematopoietic hierarchies have revealed substantial uncoupling of transcript and protein expression levels. Despite the widely established role of miRNAs in regulating many cellular functions, the precise contribution of miRNA-mediated post-transcriptional regulation linking gene expression and proteome signatures of hematopoietic stem cells (HSC) and leukemic stem cells (LSC) remains largely unknown. Previous efforts to characterize miRNA targets have largely relied on computational prediction algorithms, which suffer from high rates of false-positive and negative target predictions and ultimately preclude discovery of non-canonical miRNA-target interactions. In this study, we perform Argonaute 2 (AGO2) crosslinking and immunoprecipitation (CLIP)-seq to experimentally map endogenous miRNA-target interactions in human CD34+ hematopoietic stem and progenitor cells (HSPC) from cord blood and six primary AML patient samples in an unbiased, transcriptome-wide manner (Manakov et al. 2022). Using this approach, we identify hundreds of miRNAs directly ligated to thousands of unique targets from miRNA-target chimeric sequencing reads. We find limited overlap (~30%) between miRNA targets captured by CLIP-seq and computationally predicted targets, underscoring the importance of functional validation of miRNA targets in specific cell types. Surprisingly, we find remarkable similarity between abundant miRNAs from HSPC and AML samples, including miR-142 and miRNAs encoded by the miR-17-92 cluster. However, the targets and the gene regulatory networks controlled by these highly abundant miRNAs are vastly different between HSPC and AML. In HSPC, we find that targets of abundant miRNAs are enriched in cell cycle regulation, histone modification and megakaryocyte/myeloid differentiation. In contrast, targets of the same miRNAs are enriched in protein localization/translation, neutrophil function, regulation of apoptosis and electron transport chain (ETC) pathways in AML. Collectively, we find that the composition of the abundant miRNAs is similar in normal HSPC and AML tumors; however they control divergent gene regulatory networks. To identify miRNA-target networks that play functional roles in LSC, we performed small RNA sequencing in 23 AML patient samples where each sorted fraction was assessed for LSC content by xenotransplantation. We derived an LSC-specific miRNA signature consisting of 5 miRNAs (LSC miR-5 score) predictive of overall survival and capable of stratifying AML patients independent of mRNA expression and other established indicators of outcome. Comparison of CLIP-seq targets of the LSC-specific miRNAs from HSPC and AML patient blasts revealed the existence of AML-specific gene regulatory networks centred on oxidative phosphorylation, ETC and cell-matrix adhesion. We further interrogated the functional significance of one miRNA from the LSC miR-5 score, miR-let-7c. We find that miR-let-7c is highly expressed in LSC-enriched populations of two AML in vitro models that recapitulate the leukemic hierarchy. Enforced expression of miR-let-7c in OCI-AML22 cells resulted in expansion of the LSC-enriched CD34+CD38- population and reduction in the CD34- terminally differentiated blast population. Comparison of miR-let-7c targets from HSPC and AML patient samples revealed the existence of distinct gene regulatory networks. In HSPC, miR-let-7c targets are enriched in cell cycle and regulation of transcription, whereas in AML, mitochondrial ETC, oxidative phosphorylation, and cell-matrix adhesion were the most enriched pathways. Moreover, analysis of miR-let-7c targets identified a high prevalence of transcripts encoded by the mitochondrial (mt) DNA (MT-ATP6, MT-ATP8, MT-ND1, -2, -4, -5, MT-CO1), pointing towards a largely uncharacterized function of miRNAs in regulating transcripts encoded by mtDNA in AML. Collectively, miRNA sequencing in LSC-enriched AML samples and identification of miRNA targetomes in HSPC and AML patient samples have revealed unexpected mechanisms of post-transcriptional regulation in normal and malignant hematopoietic stem cells.
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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 | 0.000 |
| 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".