MicroRNAs in Normal Human Terminal Granulocytic Differentiation

• Published in: Blood 2009-11, Vol.114 (22), p.1463-1463
• Main Authors: Sun, Su Ming, Dijkstra, Menno K, Bijkerk, André C, Brooijmans, Rik, Valk, Peter J, Erkeland, Stefan J, Löwenberg, Bob, Jongen-Lavrencic, Mojca
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood.V114.22.1463.1463

Abstract 1463 Poster Board I-486 Normal human myelopoiesis is a complex biological process, where the balance between cell proliferation, differentiation and apoptosis is tightly regulated by a transcriptional program that results in the production of appropriate numbers of circulating mature myeloid cells. MicroRNAs (miRNAs) are small non-coding RNAs of 18∼25 nt that can affect cellular protein levels. Several studies show specific miRNA expression patterns in different subtypes of myeloid malignancies, however only limited data is available on miRNA expression patterns during normal myeloid differentiation of primary human cells. We set out to characterize miRNA expression patterns in the different stages of granulocytic differentiation in two models. First myeloblast, promyelocytes, metamyelocytes and granulocytes from normal human bone marrow were cell-sorted with flow cytometry using the markers CD10, CD11, CD34, CD36, CD45 and CD117. Second, CD34+ cells from primary human fetal livers were differentiated in vitro towards neutrophils. MiRNA expression levels were determined at different time points (day 0, 3 and 10), representing different stages of granulocytic differentiation. MiRNA expression was measured using the qPCR platform, containing 365 miRNAs, from Applied Biosystems. To identify potential miRNA target genes, we performed mRNA expression profiling in the latter in vitro differentiation. The negative correlations between miRNA and mRNA expression were identified and integrated with a target prediction database (Targetscan). The miRNA profiling showed that approximately 70% of the 365 miRNAs analyzed, were expressed during granulocytic differentiation and that the miRNA expression pattern during this process change significantly in both models. Principal component analysis showed clear separation of the different subsets of granulopoiesis based on the miRNA expression. We determined the differentially expressed miRNAs between the various subsets using ANOVA with a P value