Selective MicroRNA-Offset RNA Expression in Human Embryonic Stem Cells

Small RNA molecules, including microRNAs (miRNAs), play critical roles in regulating pluri-potency, proliferation and differentiation of embryonic stem cells. miRNA-offset RNAs (moRNAs) are similar in length to miRNAs, align to miRNA precursor (pre-miRNA) loci and are therefore believed to derive from processing of the pre-miRNA hairpin sequence. Recent next generation sequencing (NGS) studies have reported the presence of moRNAs in human neurons and cancer cells and in several tissues in mouse, including pluripotent stem cells. In order to gain additional knowledge about human moRNAs and their putative development-related expression, we applied NGS of small RNAs in human embryonic stem cells (hESCs) and fibroblasts. We found that certain moRNA isoforms are notably expressed in hESCs from loci coding for stem cell-selective or cancer-related miRNA clusters. In contrast, we observed only sparse moRNAs in fibroblasts. Consistent with earlier findings, most of the observed moRNAs derived from conserved loci and their expression did not appear to correlate with the expression of the adjacent miRNAs. We provide here the first report of moRNAs in hESCs, and their expression profile in comparison to fibroblasts. Moreover, we expand the repertoire of hESC miRNAs. These findings provide an expansion on the known repertoire of small non-coding RNA contents in hESCs.Peer reviewe

Large-scale production of megakaryocytes from human pluripotent stem cells by chemically defined forward programming.

The production of megakaryocytes (MKs)--the precursors of blood platelets--from human pluripotent stem cells (hPSCs) offers exciting clinical opportunities for transfusion medicine. Here we describe an original approach for the large-scale generation of MKs in chemically defined conditions using a forward programming strategy relying on the concurrent exogenous expression of three transcription factors: GATA1, FLI1 and TAL1. The forward programmed MKs proliferate and differentiate in culture for several months with MK purity over 90% reaching up to 2 × 10(5) mature MKs per input hPSC. Functional platelets are generated throughout the culture allowing the prospective collection of several transfusion units from as few as 1 million starting hPSCs. The high cell purity and yield achieved by MK forward programming, combined with efficient cryopreservation and good manufacturing practice (GMP)-compatible culture, make this approach eminently suitable to both in vitro production of platelets for transfusion and basic research in MK and platelet biology.This work was supported by the Leukemia and Lymphoma Society grant, the UK Medical Research Council (Roger Pedersen), the National Institute for Health Research (NIHR; RP-PG-0310-1002; Willem Ouwehand and Cedric Ghevaert) and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust – Medical Research Council Cambridge Stem Cell Institute. Cedric Ghevaert is supported by the British Heart Foundation (FS/09/039); Marloes Tijssen is supported by the European Hematology Association (Research fellowship) and the British Heart Foundation (PG/13/77/30375). Catherine Hobbs was supported by the National Health Service Blood and Transplant. Matthew Trotter was supported by a Medical Research Council Centre grant (MRC Centre for Stem Cell Biology and Regenerative Medicine); since participation in the work described, Matthew Trotter has become an employee of Celgene Research SLU, part of Celgene Corporation. Nicole Soranzo's research and Sanger Institute affiliates are supported by the Wellcome Trust (WT098051 and WT091310), the EU FP7 (Epigenesys 257082 and Blueprint HEAL TH-F5-2011-282510). The Cambridge Biomedical Centre (BRC) hIPSCs core facility is funded by the NIHR.This is the final version of the article. It first appeared from Nature Publishing Group via https://doi.org/10.1038/ncomms1120

Examples of predicted binding sites for moR-103a-2-3p.

<p>a) perfect moR-103a-2-3p ‘seed’ matching sites, b) predicted minimum free energy sites for moR-103a-2-3p, c) common predicted site for miR-103a-3p and moR-103a-2-3p, d) predicted minimum free energy site for moR-103a-2-3p.</p

The ten most significantly down-regulated genes in miR-103a-3p transfection study.

<p>Fold changes of the genes in both transfection studies, and the number of perfect 7mer seed matches found in their 3’ UTRs for both miR and moR are shown. (ns = expression of the gene is not significantly changed).</p><p>The ten most significantly down-regulated genes in miR-103a-3p transfection study.</p

Read counts and percentages compared to raw data in total of seven small RNA-seq libraries during bioinformatics workflow from raw reads to miRNA and moRNA sequences.

<p>Reads were aligned to reference sequences by allowing two mismatches.</p><p>Read counts and percentages compared to raw data in total of seven small RNA-seq libraries during bioinformatics workflow from raw reads to miRNA and moRNA sequences.</p

Analysis of pre-miR-103a-2 small RNA derivatives.

<p>a) Expression of unique isomiR and isomoR reads from the extended precursor of hsa-mir-103a-2. The first line indicates the predicted precursor based on moRNA reads. The second line <b>bolded</b> shows the miRBase hairpin precursor. The colors indicate mature products: red = moR-5p, blue = miR-5p, orange = miR-3p, green = moR-3p. Values on the right side indicate the raw counts of each read found from different libraries, order is HS401—HS181—HFF-1. All of the observed moR-103a-2 isomoRs are shown, but only the most abundant isomiR reads are shown. b) The minimal free energy structure of the extended hairpin sequence of hsa-mir-103a-2 (mfe = -37.50 kcal/mol). c) qRT-PCR of pre-miR-103a-2 hairpin derived small RNAs. Bars indicate logarithmic fold change relative to HFF-1 fibroblasts with Standard Deviation (SD; number of replicates n = 3 for HS401, HS181, FES29, HEK293; n = 2 for iPSC p5, iPSC p8).</p

List of 15 most overexpressed moRNAs in hESC lines when compared to HFF-1 foreskin fibroblast by statistical computing using DEseq algorithm.

<p>Normalized read count (RPM), P-value and the name of surrounding miRNA cluster (location) are shown for each moRNA. Relative expression analysis was made using reads mapping to the genome allowing 2 mismatches.</p><p>List of 15 most overexpressed moRNAs in hESC lines when compared to HFF-1 foreskin fibroblast by statistical computing using DEseq algorithm.</p

Analysis of miRNA and moRNA correlation, and moRNA lenght distribution.

<p>Scatter plots of expression levels (reads per million; RPM) of miRNAs vs. moRNAs derived from common extended hairpin precursor arms are shown in a) hESCs and b) HFF-1. Spearman correlation coefficient for hESC data is 0.219 (p-value = 0.000231) and for HFF-1 0.381 (p-value = 4.43e-11). Only reads where the RPM value of both miR and moR is greater than 0.5 are shown. c) moR-5p and, d) moR-3p length distributions from hESC lines HS401, HS181, H9 (Morin et al., 2008) and H1 (Bar et al., 2008) are shown in bar graphs. *moR-3p reads were not detected in H1 data.</p

Number of predicted miR-103a-3p and moR-103a-2-3p targets in the sets of genes down-regulated by both mimics, down-regulated by only miR-103a-3p and down-regulated by moR-103a-2-3p.

<p>The analysis was made only for genes with an annotated 3’ UTR in GRCh37/hg19.</p><p>Number of predicted miR-103a-3p and moR-103a-2-3p targets in the sets of genes down-regulated by both mimics, down-regulated by only miR-103a-3p and down-regulated by moR-103a-2-3p.</p