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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».