Cell & Developmental Biology

Cell & Developmental Biology
Open Access

ISSN: 2168-9296

+44 1478 350008

Research Article - (2013) Volume 2, Issue 2

Glycomic Signature of Mouse Embryonic Stem Cells During Differentiation

Rania Harfouche1,2*, Somak Ray3, Melinda Sanchez, Ushashi Dadwal1, Steven R Head4, Aaron Goldman1,2 and Shiladitya Sengupta1,2,5,6*
1BWH-HST Center for Biomedical Engineering, Harvard Medical School, Cambridge, USA
2Harvard-MIT Division of Health Sciences and Technology, Harvard Medical School, Cambridge, USA
3The Barnett Institute, Boston, USA
4DNA Array Core Facility, The Scripps Research Institute, La Jolla, USA
5Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Cambridge, USA
6Dana Farber Cancer Institute, Boston, USA
*Corresponding Author(s): Rania Harfouche, BWH-HST Center for Biomedical Engineering, Harvard Medical School, 65 Landsdowne Street, Cambridge MA 02139, USA Email:
Shiladitya Sengupta, BWH-HST Center for Biomedical Engineering, Harvard Medical School, 65 Landsdowne Street Cambridge MA 02139, USA, Tel: (617) 768-8994 Email:

Abstract

Background: The glycome has emerged as a key regulator of cell fate, partly through its ability to potentiate the action of numerous signaling pathways. We recently demonstrated that a sulfated component of the glycome plays a critical role in promoting the differentiation of embryonic stem cell (ESC)-derived embryoid bodies by modulating downstream growth factors, such as the insulin-like growth factor (IGF) signaling axis. However, the exact components of the glycome which promote ESC differentiation versus stemness remain uncharacterized, due to the lack of a rapid, simple and easily quantifiable methodology. As a proof-of-concept in this study, we utilized a custom-made glycoarray in combination with bioinformatics and molecular biology tools in order to uncover novel glyco-signatures underlying ESC differention in an embryoid body model. A better elucidation of the glycomic transcriptomal signature underlying ESC differentiation would allow us to better manipulate these cells towards a desired lineage.

Method: We used a custom-designed Affymetrix microarray, the Glycogene-chip, to screen the transcriptome of differentiating embryoid bodies versus that of undifferentiated ESC. In conjunction with gene ontology, pathway analyses, real-time PCR and immunoblotting, we validated the involvement of the IGF family, and furthermore, uncovered novel differentially regulated genes belonging to the glycoprotein (Angiopoietin-1 and Angiopoietin-like members), sulfotransferase, sulfatase and glycosyltransferase families.

Conclusion: These results suggest that the Glycogene-chip, in conjunction with the embryoid body model, provides a fast and reliable tool to uncover novel glycomic signatures that are critical to maitain ESC stemness versus differentiation. In turn, this will allow us to understand the mechanisms governing ESC fate, bringing us one step closer towards finding a new paradigm for the regenerative medicine field.

<

Keywords: Embryonic stem cells, Differentiation, Glycome, Microarray, Angiopoietin

Abbreviations

ABIN: A20 Binding Inhibitor of NF-kappaB; AN: Analyze Networks; ANGPT: Angiopoieitin; ANGPTL: Angiopoieitinlike; AP: Activator Protein; BRB: Biometric Research Branch; CBF: Core-Binding Factor; CFG: Consortium for Functional Glycomics; CHST: Carbohydrate-Specific Sulfotransferase; DCN: Decorin; ESC: Embryonic Stem Cells; EXTL: Exostoses (multiple)-like; FGF: Fibroblast Growth Factor; FKHR: Forkhead Transcription Factor; GO: Gene Ontology; GOBP: Gene Ontology Biological Process; IGF: Insulin-like Growth Factor; IGFBP: IGF-binding protein; ITGA: Integrin Alpha; GALNS: Galactosamine (N-acetyl)-6-sulfate Sulfatase; HS3ST: Heparan Sulfate (glucosamine) 3-O-sulfotransferase; LIF: Leukemia Inhibitory Factor; PDGF: Plated-Derived Growth Factor; LUM: Lumican; PECAM: Platelet Endothelial Cell Adhesion Molecule; SP: Specificity Protein; ST3GAL: Beta-galactoside alpha-2,3-sialyltransferase; STAT: Signal Transducer And Activator Of Transcription; TGF: Transforming Growth Factor; NDST: N-deacetylase/N-sulfotransferase; TIE: Tyrosine Kinase with Immunoglobulin-like and EGF-like Domains; SULF: Sulfatase.VCAM: Vascular Cell Adhesion Molecule

Background

Within the past decade, glycomics has emerged as a key modulator of cellular function and homeostasis. As such, glycosylation of protein and lipids constitutes one of the most common posttranslational modifications in eukaryotes, resulting in modulation of key developmental functions, including innate immunity, signal transduction and cell differentiation [1,2]. These pleitropic effects of the glycome are mainly mediated by the presence of a wide range of glyco-enzymes and their respective isoforms, as well as by the fact that a single glyco-conjugate can simultaneously modulate the activity of numerous growth factors/receptor complexes due to the complexation of various factors onto one conjugate [3,4]. Glyco-conjugates are therefore more potent determinants of cell fate as opposed to the action of a single growth factor/receptor pair.

Despite these predominant roles of the glycome in governing cell fate, its underlying mechanisms of action remain largely uncharacterized, especially in the stem cell field. Effectively, the mechanisms which act as a switch between stem cell proliferation versus differentiation remain the subject of extensive studies, and harnessing these mechanisms would have immense potential in the field of regenerative medicine. Most studies thus far have focused on the roles of various growth factors to induce stem cell renewal or to promote their differentiation into selective lineages [5,6]. The few studies which have investigated glycome-related changes governing stem cell fate have used either lectin or antibody arrays which monitor specific glycoprotein-receptor interactions [7,8]. These studies have all encountered similar limitations, including false negatives and analyses artefacts (as many lectins have low affinity for their ligands and glyco-antibodies are scarce), growth factor/cytokine redundancy and irreproducibility.

We were one of the first groups to report that the glycome is a critical modulator of embryonic stem cell (ESC) fate [9]. Specifically, using an embryoid body model, we demonstrated that a sulfated subset of the glycome (the heparan sulfate glycosaminoglycans) directed ESC differentiation towards the mesodermal lineage, partly through modulation of the insulin-like growth factor (IGF) pathway [9,10]. Similarly, recent studies imply that the glycome is dynamically regulated during (embryonic or precursor) stem cell differentiation [11,12]. The exact players involved in ESC differentiation versus stemness, however, remain uncharacterized. A main reason for this lacuna remains the lack of a rapid, simple and easily quantifiable screening methodology to probe the ESC glycome.

In this study, we took advantage of a custom-designed oligonucleotide array developed and annotated by the Consortium for Functional Glycomics (CFG), which specifically probes for glycomic transcripts, in order to screen for the glyco-specific transcriptome underlying ESC differentiation [13-16]. As a proofof- concept, we employed a well-established embryoid body model of ESC differentiation, which recapitulates embryonic development in vivo, and compared its transcriptomal profile over time to that of undifferentiated ESC using bioinformatic analyses. We uncovered novel genes implicated in ESC differentiation versus stemness and validated these findings by real-time PCR and immunoblotting. Some of the genes of interest included glycoproteins, namely the Angiopoietin (ANGPT) and Angiopoietin-like (ANGPTL) families, as well as sulfotransferases, sulfatases and glycosyltransferases.

Materials and Methods

Materials

The RNeasy kit was obtained from Qiagen (Valencia, CA), whereas the iScript cDNA synthesis kit, iTaq SYBR Green Supermix and MyIQ PCR cycler were all from Bio-Rad Laboratories (Hercules, CA). All primers were obtained from Integrated DNA Technologies (Coralville, IA). Tie-2 receptor antibodies (sc-324, dilution 1:250) were purchased from Santa Cruz Biotechnology (Santa Cruz, CA).

Stem cell culture

A murine embryonic stem cell (ES) line derived from a 129 substrain (9TR#1) was purchased from ATCC (Rockville, MD). ES were maintained undifferentiated on gelatin-coated plates using a growth medium consisting of L-glutamine-containing DMEM, 0.1 mM non-essential amino acids, 0.1 mM sodium pyruvate, 0.1 mM 2-mercaptoethanol, all from Invitrogen (Carlsbad, CA), as well as 1000 U/ml LIF (Chemicon Inc., Temecula, CA) and 15% fetal bovine serum (HyClone, Logan, UT). Differentiation into embryoid bodies was induced by removing LIF from the medium and seeding the cells on ultra-low attachment 100mm dishes (Corning, Lowell, MA). Cells were imaged in real-time using bright field microscopy (Nikon Eclipse TE-2000 U).

RNA preparation and quality control

ES were differentiated on ultra-low attachment dishes to induce embryoid body formation. Quadruplet dishes were harvested for RNA extraction and the same samples subjected to real-time PCR and Glycogene-chip analyses. Undifferentiated ES were considered as Day 0 control. At indicated time-points, cells were harvested and total RNA extracted using the Qiagen RNeasy Mini Kit, following the manufacturer’s instructions. All samples were treated with DNAseI to prevent genomic DNA contamination. Quantification and purity of total RNA was assessed by A260/A280 absorption using a microvolume plate reader (Biotek, Winooski, VT). RNA quality was further investigated by the CFG core E facility using the Agilent Technologies 2100 Bioanalyzer using the RNA 6000 Nano LabChip (Santa Clara, CA) and was deemed to be excellent.

Glycogene-chip oligonucleotide array hybridization

GLYCOv4, referred to herein as Glycogene-chip, is a custom-made Affymetrix oligonucleotide array (Affymetrix, Santa Clara, CA) designed for the Consortium for Functional Glycomics (GFC) at the Scripps Research Institute (La Jolla, CA) [13]. Information is available online at: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL11098. This array includes probes for~1246 murine probe-ids related to mouse glycome and developmental genes, where each gene is represented by 11 probe pairs. Unlike commercial oligonucleotide microarrays, it includes genes not currently represented, including glycosyltransferases, carbohydrate-binding proteins and proteoglycans. RNA (0.5 μg) from quadruplate samples was amplified using the Ambion Message Amp II Biotin Enhanced Single Round aRNA Amplification Kit (Agilent Technologies) and hybridized overnight to the GLYCOv4 array using a standard Affymetrix protocol, as previously described [17]. Glycogenechips were scanned using the Affymetrix GeneChip Scanner 3000 7G with default settings and a target intensity of 250 for scaling.

Glycogene-chip data analysis

The dendogram was generated by unsupervised hierarchical clustering using Biometric Research Branch (BRB) ArrayTools, as previously described [13]. Acquired probe level data was normalized using the RMA Express 1.0 with quantile normalization, median polish and background adjustment. Differential expression between transcripts was generated using the Limma package in the R software [18]. The fold changes and standard errors were estimated by fitting a linear model for each gene and empirical Bayes smoothing was applied to the standard errors. Results are presented as fold induction, and were calculated using the moderated t-statistic, the p-value, and the adjusted p-value, the latter being generated using the Benjamini and Hochberg’s method. The transcripts identified as differentially expressed were those with adjusted p-value<0.1 and fold change >1.4. These anaylsis were performed by the GFC. The Gene Ontology Biological Process (GOBP) terms were retrieved using the Bioconductor [19] packages ‘biomaRt’ and ‘GO.db’ and GeneGo Metacore (www.genego.com) biological pathway analysis tool [20].

Biological chart and pathway analyses

The Cytoscape (http://cytoscape.org/) plugin ClueGO [21] was used to generate functionally-grouped molecular function network charts (26.03.2012, cutoff of Q = 0.000 01 level of significance). Significance for enrichment and depletion of groups and terms was calculated by a two-sided hypergeometric test with a Bonferroni correction for multiple testing. The differentially expressed genes were subjected to biological pathway analysis from the GeneGo Metacore software (www.genego.com) using Analyze Networks (AN) algorithm with default settings [20]. Enrichment analysis was performed by mapping gene IDs of the dataset onto gene IDs in entities of built-in functional ontologies represented in MetaCore by pathway maps. Gene networks were then segregated into their respective cellular compartments. The gene content of the uploaded files is used as the input list for generation of biological networks using Analyze Networks (AN) algorithm with default settings. This is a variant of the shortest paths algorithm with main parameters of relative enrichment with the uploaded data, and relative saturation of networks with canonical pathways. These networks are built on the fly and are unique for the uploaded data. In this workflow the networks are prioritized based on the number of fragments of canonical pathways on the network.

Real-time PCR

RNA was extracted from cells using the RNeasy kit and reversetranscribed with the High-Capacity cDNA Reverse Transcription kit, according to the manufacturer’s instructions. The resulting cDNA was subjected to SYBR Green real-time PCR using primers designed to amplify ANGPT-1, ANGPTL-2, -4, -6, CHST-1, EXTL-1, GALNS, HS3ST-1, NANOG and SULF-1. GADPH was used as the endogenous control [22]. Primers were designed to span exon-exon boundaries using the Universal Probe Library Assay Design Center (Roche Applied Science) and. Normalized reporter (Rn) values were calculated using the threshold cycle value (CT) for each gene, as follows:

Rn=2 X-delatadelta CT, where deltadelta CT=average CTtarget-average CTexperimental control.

Results were expressed as mean ± SEM of quadruplate samples and repeated at least twice. Statisticalcomparisons were obtained using one-way ANOVA, followed by the New man-Keuls test. Probability (p) values less than 0.05 were considered significant.

Immunoblotting

Cells were washed twice with PBS and directly lysed in 3X loading buffer containing 12% sodium dodecyl sulfate, 15% 2-mercaptoethanol, 1 mM sodium orthovandate and protease inhibitor cocktail tablets from Roche Applied Science. Cells were further homogenized by passing the lysates 3 times through an insulin needle. Samples were then heated for 5 min at 100°C and equal amounts loaded onto tris-glycine SDSpolyacrylamide gels. Proteins were electrophoretically transferred onto polyvinylidene difluoride membranes, blocked for 1 h with 5% non-fat dry milk, and subsequently incubated overnight at 4°C with primary antibodies directed against phosphor-Tie-2 receptor. Proteins were detected using horseradish peroxidase-conjugated anti-rabbit secondary antibodies and Lumi-LightPLUS Western Blotting Substrate (Roche Applied Science). The blots were developed using GeneSnap and optical densities off the protein bands quantified using GeneTools (both from SynGene, Frederick, MD). Predetermined molecular weight standards were used as markers. Proteins were normalized against total Tie-2. Statistical comparisons of 3 immunoblots were obtained using one-way ANOVA, followed by the Newman-Keuls test. Probability (p) values less than 0.05 were considered significant.

Results and Discussion

A glycome-selective microarray shows specific clustering during ESC differentiation

We used a standard model of ESC differentiation into embryoid bodies, which spontaneously recapitulates the three stages of in vivo embryonic development, namely mesoderm, endoderm and ectoderm [9,23,24]. These cells were maintained in suspension for 5 to 15 days on ultra-low adherent wells and in the absence of leukemia inhibitory factor (LIF) (Figure 1A). In contrast, ESC maintained in the adherent state and in the presence of LIF, represented as Day 0, served as the undifferentiated control, before gastrulation has occurred. Cells maintained for 5 days differentiate into cystic, symmetrical embryoid bodies, representing the beginning of the gastrulation stage [9,24,25]. Day 7 represent the organogenesis stage, beginning with the development of the cardiovascular system, whereas Day 10 and therafter are characterized by a high proportion of endodermallyderived cell types and neurogenesis [9,24]. This model hence allows us to accurately monitor glycome-selective changes occurring during all stages of development.

cell-developmental-embryonic-stem-cells

Figure 1: Dendogram and Venn diagram of glycogene expression in differentiating embryonic stem cells (ESC). (A) Representative model of ESC differentiation relative to Day 0 taken in real-time by light microscopy. (B) The dendrogram was constructed using centered correlation and average linkage. The four biological replicates are shown for each differentiation time-point (days 5, 7, 10 and 15), with Day 0 representing undifferentiated ESC. (C) Venn diagram reveals 260, 308 and 373 regulated transcripts between undifferentiated ESC (Day 0) and ESC differentiated for 7, 10 and 15 days, respectively, whereas 180 transcripts overlapped between all time-points (7, 10 and 15) relative to Day 0. The comparisons used thresholds of adjusted p-value < 0.10 and magnitude fold change >1.4.

RNA was extracted and labeled from these ESC, with each timepoint represented by quadruplate experimental groups. Following microarray hybridization, gene expression patterns were analyzed using unsupervised hierarchical cluster analysis in order to assess experimental reproducibility and uncover general clustering patterns. The resulting dendogram (Figure 1B) demonstrates excellent sample reproducibility among each quadruplates, as shown by the short branches between them, which was further corroborated by quality assessment parameters (results not shown). In addition, days 0 and 5 clustered close together, as did days 10 and 15. There was a clear separation between days 0 and 5 from the rest of the experimental time-points, reflecting clear differences in the glycogene expression profiles as the ESC differentiated, as expected from the embryoid body model [9,23,24].

A more detailed investigation of the differential glycomic profile during ESC differentiation was investigated using the Venn diagram, which allowed us to focus on two groups for subsequent analyses. Figure 1C shows that 180 overlapping transcripts changed significantly between all time-points (omitting Day 5), indicating developmentallyconserved roles of the glycome throughout differentiation. We focused on these 180 genes for the first investigation. The number of genes that were unique to distinct time-points, with respect to Day 0, were as follows: 111 genes for Day 15, 18 genes for Day 10, 28 genes for Day 7 and 5 genes for Day 5. Hence, the Day 15 time-point would allow us to uncover the most glycome-related changes underlying ESC differentiation. Since, based on the dendogram of Figure 1A, days 15 and 10 clustered close together, we decided to also focus on the subset of genes (70) overlapping in only these two time-points, for a total of 181 genes for the second investigation. Since there were the less differences between Day 5 versus Day 0 using this array, we did not use this time-point for subsequent analyses. This is not to imply a lack of transcriptomal changes between both time-points, but since we’re using a focused microarray that doesn’t include most of the genome, subtle differences might be masked, as indicated by the dendogram of Figure 1B.

ESC exhibit a distinct glycomic transcriptomal signature during differentiation

Firstly, we focused on the upregulated (top 75, Table 1) and downregulated (69, Table 2) genes, omitting housekeeping genes, which significantly overlapped between all time-points.Transcripts identified as differentially expressed were those with adjusted p-value<0.1 and fold change >1.4. Results were annotated using gene ontology (GO) biological processes with fold changes tabulated using Day 0 as the denominator.The complete results from the list of the 180 transcripts are available upon request.

Down Regulated Genes Biological Processes Gen Bank ID Day 7 vs Day 0 Adj. P. Val Day 10 vs Day 0 Adj. P. Val Day 15 vs Day 0 Adj. P. Val
FGF4 chondroblast differentiation NM_010202 0.15 8.68E-20 0.07 4.34E-23 0.07 5.37E-23
MANBA glycoprotein catabolic process NM_27288 0.18 6.65E-19 0.11 5.02E-21 0.15 1.21E-19
PECAM1 positive regulation of tyrosine phosporylation of Star5 protein NM_001032378 0.18 4.07E-17 0.16 7.56E-18 0.23 2.61E-16
LGALS6 induction of programmed cell death NM_010707 0.2 3.02E-16 0.1 1.84E-19 0.09 1.21E-19
ICAM1 response to sulfur dioxide NM_010493 0.21 3.03E-19 0.16 1.33E-20 0.11 2.38E-22
FGF18 endochodral ossification NM_008005 0.24 1.30E-15 0.29 1.24E-14 0.51 1.07E-09
HBEGF positive regulation of keratinocyte migration NM_010415 0.29 2.53E-15 0.4 5.82E-13 0.41 6.17E-13
ANGPTL4 triglyceride homeostasis NM_020581 0.32 3.04E-16 0.21 3.81E-19 0.24 1.89E-18
BMP8 B ossification NM_007559 0.32 3.11E-15 0.29 5.72E-16 0.26 8.53E-17
PDGF A positive regulation of mesenchymal cell proliferation NM_008808 0.33 2.38E-15 0.36 1.17E-14 0.27 6.30E-17
MCAM vascular wound healing NM_023061 0.33 3.95E-16 0.41 1.26E-14 0.42 2.19E-14
MANBA glycoprotein catabolic process uc008rlu 0.34 1.56E-12 0.25 8.20E-15 0.23 2.23E-15
RBPJ positive regulation of transcription of Notch receptor target NM_009035 0.39 4.66E-16 0.4 4.97E-16 0.39 2.23E-16
PTCH2 skin development NM_008958 0.41 4.34E-12 0.35 1.53E-13 0.32 3.04E-14
GLB1 galactose catabolic process uc009rxh 0.41 1.03E-15 0.44 4.54E-15 0.45 4.03E-15
GDF3 somite rostral/caudal axis specification NM_008108 0.42 1.56E-12 0.28 1.06E-15 0.26 1.54E-16
LGALS3 extracellular matrix organization NM_010705 0.42 2.43E-10 0.26 3.32E-14 0.24 8.50E-15
CSF1 positive regulation of macrophage derived from cell differentiation NM_007778 0.45 2.16E-14 0.44 1.02E-14 0.42 2.31E-15
B3GNT7 protein glycosylation NM_145222 0.46 2.68E-09 0.26 4.52E-14 0.21 2.26E-15
GCNT2 metabolic process NM_133219 0.46 9.37E-09 0.38 1.26E-10 0.61 5.88E-06
PMM1 mannose biosynthetic process NM_013872 0.47 2.77E-13 0.25 2.47E-18 0.21 1.53E-19
VEGFC positive regulation of protein autophosphorylation NM_009506 0.47 5.57E-15 0.67 6.69E-10 0.58 2.78E-12
PMM1 mannose biosynthetic process uc007wxu 0.47 2.52E-12 0.26 2.00E-17 0.2 6.45E-19
B3GNT2 sensory perception of smell NM_016888 0.48 1.16E-10 0.36 1.77E-13 0.2 2.14E-15
NGF positive regulation of protein autophosphorylation NM_013609 0.48 2.01E-11 0.48 1.63E-11 0.46 3.13E-12
CAPN1 protein autoprocessing uc008ggk 0.49 4.00E-09 0.49 2.92E-09 0.42 9.50E-11
HSPG2 nuclear-transcribed m-RNA catabolic process, nonsense mediated decay uc008vjf 0.5 6.51E-11 44 1.77E-12 0.4 1.40E-13
CHST6 keratan sulfate biosynthetic process NM_019950 0.5 4.41E-10 0.48 9.39E-11 0.56 5.21E-09
BMP8A ossification NM_007558 0.51 1.41E-11 0.41 3.83E-14 0.39 1.33E-14
CSF1 positive regulation of macrophage derived from cell differentiation NM_007778 0.53 1.07E-11 0.54 1.51E-11 0.52 3.87E-12
IL17B neutrophil chemotaxis NM_019508 0.53 6.83E-11 0.45 8.17E-13 0.42 1.53E-13
CSF1 positive regulation of macrophage derived from cell differentiation uc008qxm 0.53 1.82E-11 0.54 2.22E-11 0.57 1.05E-10
SLC35B2 positive regulation of I-kappaB kinase/NF-kappaB cascade uc008cqw 0.54 4.43E-12 0.43 7.30E-15 0.35 6.71E-17
GDF3 somite rostral/caudal axis specification NM_008108 0.54 6.11E-12 0.4 1.80E-15 0.34 5.81E-17
CAPN1 protein autoprocessing uc008ggj 0.54 4.93E-10 0.47 7.47E-12 0.47 4.85E-12
BMP4 mesenchymal cell differentiation involved in renal system development NM_007554 0.56 4.63E-09 0.54 1.30E-09 0.4 5.22E-13
BDNF negative regulation of neuroblast proliferation NM_007540 0.56 1.84E-11 0.57 2.67E-11 0.6 1.37E-10
GLB1 galactose catabolic process uc009rxj 0.58 2.61E-10 0.54 1.49E-11 0.54 1.62E-11
B3GALT4 protein glycosylation NM_019420 0.59 4.93E-10 0.54 1.93E-11 0.6 5.05E-10
EXTL3 positive regulation of cell growth uc007uiy 0.59 4.42E-09 0.5 1.87E-11 0.48 4.24E-12
TPST2 metabolic process NM_009419 0.6 2.67E-10 0.5 1.09E-12 0.49 4.36E-13
GFPT2 glutamine metabolic process NM_013529 0.6 8.06E-09 0.46 3.75E-12 0.17 3.22E-19
NCSTN membrane protein ectodomain proteolysis NM_021607 0.6 4.46E-06 0.65 3.59E-05 0.66 5.28E-05
SLC35E4 transmembrane transport NM_153142 0.61 3.79E-09 0.6 2.04E-09 0.59 1.03E-09
IGFBP6 regulation of cell growth NM_008344 0.61 1.32E-08 0.7 2.42E-06 0.71 2.64E-06
ACAN collagen fibril organization NM_007424 0.62 3.09E-08 0.45 1.93E-12 0.5 2.09E-11
ASGR2 regulation of protein stability NM_007493 0.62 7.47E-07 0.54 6.83E-09 0.64 1.27E-06
MUC3 biological process uc009abs 0.62 1.99E-09 0.6 2.87E-10 0.61 7.19E-10
CAPN1 protein autoprocessing NM_007600 0.63 1.31E-07 0.56 2.85E-09 0.56 2.83E-09
ALG13 lipid glycolysation uc009umu 0.63 1.21E-06 0.35 4.38E-13 0.21 1.21E-16
CLEC 10A defense response NM_010796 0.63 2.06E-08 0.61 4.11E-09 0.55 1.04E-10
EGFR positive regulation of fibroblast proliferation NM_207655 0.64 3.05E-06 0.48 7.84E-10 0.5 1.90E-09
WNT4 non-canonical Wnt receptor signaling pathway via MAPK cascade NM_009523 0.64 1.35E-07 0.46 5.60E-12 0.6 9.70E-09
GMPPB biosynthetic process NM_177910 0.65 2.85E-09 0.6 9.28E-11 0.67 5.12E-09
GDF15 peripheral nervous system development uc009mmb 0.65 5.10E-09 0.57 2.27E-11 0.58 4.47E-11
CHST 10 long term memory NM_145142 0.65 9.75E-08 0.51 2.27E-11 0.63 1.44E-08
GNPDA1 fructose 6-phosphate metabolic process NM_011937 0.66 1.22E-09 0.57 4.51E-12 0.52 1.32E-13
DHH Leydig cell differentiation NM_007857 0.66 6.74E-06 0.61 4.18E-07 0.63 1.37E-06
SULF2 glial cell-derived neurotrophic factor receptor signalling pathway NM_028072 0.66 1.10E-05 0.6 2.92E-07 0.55 2.01E-08
FGF17 fibroblast growth factor receptor signaling pathway NM_008004 0.67 3.08E-06 0.39 7.09E-13 0.46 2.79E-11
COG1 protein transport NM_013581 0.67 1.54E-08 0.61 3.23E-10 0.57 1.91E-11
SLC35D1 carboxylic acid metabolic process NM_177732 0.67 9.30-E-08 0.63 5.70E-09 0.61 1.23E-09
GCNT2 metabolic process uc007qen 0.68 2.22E-08 0.62 2.88E-10 0.66 2.90E-09
LGALS1 positive regulation of erythrocyte aggregation NM_008495 0.69 1.64E-06 0.67 4.24E-07 0.57 1.58E-09
CHST12 dermatan sulfate biosynthetic process NM_021528 0.69 5.84E-08 0.57 1.64E-11 0.53 1.67E-12
EXTL1 metabolic process NM_019578 0.7 8.88E-06 0.71 1.09E-06 0.64 3.07E-07
DHH Leydig cell differentiation NM_007857 0.7 2.62E-06 0.69 1.13E-06 0.61 1.06E-08
FGFR1 middle ear morphogenesis uc009lgc 0.7 5.35E-03 0.71 5.56E-03 0.47 5.68E-07
RFNJ positive regulation of Notch signaling pathway NM_009053 0.7 2.27E-06 0.65 8.94E-08 0.7 1.38E-06
CHPF biological process NM_001001566 0.71 2.55E-07 0.7 8.78E-08 0.68 2.24E-08

Table 1: Genes that were upregulated between all time-points of differentiating ESC versus Day 0. The top 75 transcripts, excluding housekeeping genes, are shown.

Down regulated genes Biological process Gen Bank Id Day7 vs Day 0 Adj.P.Val Day 10 vs Day 0 Adj.P.Val Day 15 vs Day 0 Adj.P.Val
FGF8 mesodermal cell migration NM_010205 42.3797232 1.01E-22 9.47508413 8.07E-19 5.11704525 2.54E-16
FGF5 signal transduction involved in regulation of gene expression NM_010203 26.6249968 9.02E-20 11.68384194 1.97E-17 2.5661838 1.36E-09
CXCR4 positive regulation of oligodendrocyte differentiation NM_009911 17.5585811 2.83E-21 10.6838942 6.47E-20 5.15100692 6.30E-17
FST hair follicle morphogenesis NM_008046 10.9455272 4.07E-17 4.6532678 1.53E-13 10.1117561 3.87E-17
GPC3 Positive regulation of BMP signalling pathway NM_016697 9.26652576 1.59E-20 14.8298182 1.34E-22 12.9466229 3.72E-22
HAS2 celluar response to platelet-derived growth factor stimulus NM_008216 7.70356913 2.80E-21 17.3878086 2.59E-24 5.40990498 4.71E-20
ST3GAL6 cellular response to interleukin-6 NM_018784 7.16222841 5.10E-16 4.82707743 3.02E-14 8.19634912 7.17E-17
PDGFRA regulation of mesenchyaml stem cell differentiation NM_011058 6.15377193 3.87E-18 6.33145576 2.47E-18 20.8620143 8.22E-23
FGF10 positive regulation of urothelial cell proliferation NM_008002 6.05568444 1.87E-18 3.48723842 2.25E-15 1.9503577 2.65E-10
BMP2 negative regulation of cardiac muscle cell differentiation NM_007553 5.92915174 3.07E-16 9.3431268 2.47E-18 3.68169914 6.55E-14
TGFBR3 organ regenration NM_011578 5.84703718 8.68E-20 7.42068439 4.09E-21 11.6946987 5.37E-23
ST6GAL1 protien glycosylation NM_145933 5.67294357 1.63E-19 4.34391694 3.15E-18 3.37249486 8.53E-17
WNT5A positive regulation of interleukin-8 secretin NM_009524 4.62657159 5.71E-16 5.21939383 1.14E-16 3.48696591 1.71E-14
ATRNL1 G-protien coupled receptor signaling pathway NM_181415 4.37184806 5.20E-17 4.79796121 1.13E-17 4.7087415 1.01E-17
SLC35D3 carbohydrate transport NM_029529 3.68726723 1.51E-13 3.39456334 4.01E-13 1.52523442 2.28E-05
BMP5 male genetalia development NM_007555 3.45441937 4.42E-14 4.40143286 1.26E-15 2.90982973 4.49E-13
SHH positive regulation of mesenchymal cell proliferation involved in urete development NM_009170 2.84535026 2.97E-12 10.2782095 3.96E-19 7.52383004 4.87E-18
SULF1 glial cell derived neurotrophic factor receptor signaling pathway NM_172294 2.8328506 1.61E-15 2.55258764 1.13E-14 1.8043247 6.04E-11
SMO regulation of heart morphogenesis NM_176996 2.70695189 2.48E-14 2.66693471 2.39E-13 2.16278874 1.90E-11
CHST1 keratin sulfate metabolic process NM_023850 2.64294381 1.25E-10 2.91968655 1.51E-11 1.81412014 3.65E-07
IGFBP4 regulation of cell growth NM_010517 2.59971141 2.04E-10 2.99138788 1.16E-11 5.92184608 6.04E-16
FZD1 canonical Wnt receptor signaling pathway involved in osteoblast differentiation NM_021457 2.5929974 5.24E-11 4.22231948 1.47E-14 5.1629406 7.60E-16
PGM1 glucose metabolic process NM_028132 2.54449087 4.62E-15 2.51157005 5.74E-15 3.82588695 2.40E-18
SRGN maintenance of protease location in mast cell secretory granule NM_011157 2.5281043 8.02E-07 9.04627559 1.15E-13 28.6174127 2.33E-17
IGFBP5 negative regulation of smooth muscle cell migration NM_010518 2.52039135 1.33E-11 54.1924939 4.16E-24 49.0119677 1.49E-23
COLEC12 phagocytosis,recognition NM_130449 2.50644878 1.07E-09 2.36517962 2.51E-09 1.76648989 1.85E-06
ACVR2B embryonicforegut morphogenesis NM_007397 2.49946961 1.76E-12 2.22216356 1.82E-11 1.61950818 1.12E-07
IL18 positive regulation of superoxide anion generation NM_008360 2.49609567 5.57E-13 2.01655918 6.05E-11 1.75678725 2.73E-09
GAL3ST4 biosynthetic process NM_001033416 2.45265361 1.64E-11 2.78469868 9.79E-13 1.81364832 1.61E-08
CXCL12 organ regenration NM_001012477 2.43638733 8.05E-14 2.3819499 6.16E-14 4.09876499 5.69E-18
GPC1 heparan sulfate proteoglycan catabolic process NM_016696 2.41965101 8.35E-13 2.43278235 5.58E-13 1.88156515 2.85E-10
GPC6 hepran sulfate proteoglycan binding NM_011821 2.37226306 5.03E-11 3.73767083 1.18E-14 6.05270893 1.96E-17
HES1 negative regulation ofpro-B cell differentiation NM_008235 2.33630154 1.61E-15 1.7039937 1.08E-11 1.64633452 2.96E-11
IGF1R negative regulation of protein kinase B signaling cascade NM_010513 2.32788291 2.37E-12 2.4505507 5.72E-13 2.64244098 9.13E-14
IGFBP4 regulation of cell growth NM_010517 2.30803166 1.75E-09 2.49452869 2.43E-10 4.65968047 8.50E-15
HSPC159 biological process NM_173752 2.28643633 2.50E-14 1.77978842 1.96E-11 1.51118941 6.90E-09
PAPSS1 3'-phosphoadenosine5'-phosphosulfate biosynthetic process NM_011863 2.22345505 7.55E-12 3.00822711 1.20E-14 3.15825684 3.43E-15
GALNT12 carbohydrate metabolic process NM_172693 2.2185426 3.07E-14 1.43576461 5.31E-08 1.56253689 1.13E-09
VEGFA positive regulation of mesenchymal cell proliferation NM_001025257 2.17808801 2.34E-08 4.54797309 8.33E-14 6.94702111 4.27E-16
NAGLU middle ear morphogenesis NM_013792 2.16817882 1.72E-12 3.3499713 2.65E-16 4.71733471 1.44E-18
PIGP GPI anchor biosynthetic process NM_019543 2.16685822 7.38E-14 2.20224463 3.69E-14 2.39139967 3.53E-15
FZD2 hard palate development NM_020510 2.15852417 1.69E-14 3.68226121 4.69E-17 3.26765798 1.85E-16
GALNT10 protien O-linked glycosylaton NM_134189 2.1458266 8.17E-14 2.62941978 7.23E-14 2.18241959 3.27E-12
PIGP GPI anchor biosynthetic process uc008aak 2.1176694 4.20E-14 2.23027137 8.59E-14 2.41404614 9.41E-15
SLC35F1 transport NM_178675 2.09359733 3.71E-09 2.90190844 3.07E-12 5.36672409 2.88E-16
PGM2L1 glucose metabolic process NM_027629 2.07386145 7.84E-10 1.70134356 1.38E-07 1.42281342 4.37E-05
ARSA binding of sperm to zona pellucida NM_009713 2.04579877 5.44E-14 2.11042784 1.72E-13 1.88241796 3.19E-12
IGF2BP3 regulation of translation NM_023670 2.03385031 7.89E-14 2.16576281 1.18E-14 1.75224985 4.19E-12
EXTL2 UDP-N-acetylgalactosamine metabolic process NM_021388 2.02661107 5.57E-13 2.04072741 3.40E-13 1.92157761 1.39E-12
WNT11 adrenal gland development NM_009519 1.99786452 7.49E-13 1.71547546 6.00E-11 2.70778888 2.92E-16
FUT11 protein glycosylation NM_028428 1.99477254 1.94E-11 1.94396838 2.86E-11 1.73087405 8.54E-10
FZD7 substrate adhesion-dependent cell spreading NM_008057 1.97598534 2.60E-12 1.87371495 9.43E-12 1.4459561 9.89E-08
BMP7 positive regulation of hyaluranone cable assembly NM_007557 1.92125066 3.12E-12 2.01653612 6.05E-13 1.66673125 2.03E-10
LARGE muscle cell homeostasis NM_010687 1.91184823 2.29E-11 2.95378278 1.07E-15 2.13292577 6.51E-13
ACVR1 pharyngeal system development NM_007394 1.89679598 1.31E-08 2.27486114 1.07E-10 2.89562597 6.23E-13
IGF2 insulin receptor signaling pathway via phosphatidylinositol3-kinase cascade NM_010514 1.88169069 1.45E-13 6.1570209 8.38E-23 7.57795059 1.76E-23
GALNAC4S-6ST hexose biosynthetic process NM_029935 1.84870816 3.03E-11 2.01067722 1.99E-12 2.51071277 6.78E-15
B4GALNT2 lipid glycosylation NM_008081 1.78603641 4.04E-05 8.15820522 8.20E-15 23.0281618 2.25E-18
FZD8 positive regulation of JUN kinsae activity NM_008058 1.77135361 8.82E-10 1.58880657 2.69E-08 1.64734018 5.78E-09
CHST2 N_acetylglucosamine metabolic process NM_018763 1.73501904 3.47E-09 2.31786732 9.79E-13 4.08678333 3.29E-17
GPC2 neuron differentiation NM_172412 1.71524689 4.26E-07 2.81062501 3.16E-12 2.97005519 8.86E-13
IGF2BP2 regulation of translation NM_183029 1.70770941 4.09E-11 2.16833289 2.70E-14 2.15663684 2.18E-14
MAN2A2 mannose metabolic process uc009iap 1.69900046 4.95E-09 2.06481819 1.21E-11 2.2423753 1.13E-12
GFRA2 nervous system development NM_008115 1.68601544 2.05E-09 2.02091689 5.92E-12 1.4536158 3.43E-07
MDK adrenal gland development NM_010784 1.66964049 1.71E-10 2.06917008 1.63E-13 2.30332421 7.95E-15
ST8SIA4 ganglioside biosynthetic process NM_009183 1.66846652 1.34E-07 2.80887175 3.01E-13 2.80100684 2.46E-13
FREM1 cell-matrixadhesion NM_177863 1.66772539 1.44E-07 3.00801501 9.55E-14 4.51578012 1.38E-16
LMAN2L protien transport NM_001013374 1.65871958 3.15E-09 1.628116 4.41E-09 1.45645991 2.83E-07
ANGPTL2 signal transduction NM_011923 1.64084049 2.31E-08 3.36741256 1.07E-15 3.62870614 1.76E-16
HES6 regulation of transcription from RNA polymerase II promoter NM_019479 1.62914358 3.14E-08 1.55759132 1.26E-07 1.74304583 2.13E-09
POFUT1 fucosylation NM_080463 1.62050504 1.05E-08 1.70715909 1.30E-09 1.76536807 3.63E-10
NAGA glycolipid catabolic process NM_008669 1.61677676 1.64E-09 1.9635424 2.06E-12 2.95211972 1.43E-16
FUT8 integrin-mediated signaling pathway NM_016893 1.58474009 4.42E-08 2.21375951 1.71E-12 3.60985965 1.04E-16
NEU1 metabolic process NM_010893 1.57773264 9.13E-09 2.67651596 3.94E-15 3.26914925 6.71E-17
HES6 regulation of transcription from RNA polymerase II promoter NM_019479 1.55229634 7.83E-07 1.47541392 3.93E-06 1.53019762 8.60E-07

Table 2: Genes that were downregulated between all time-points of differentiating ESC versus Day 0. The top 75 transcripts, excluding housekeeping genes, are shown.

In order to correlate the expression profiles occurring during ESC differentiation into biologically-relevant data, we visualized the GO molecular function related to the 180 overlapping transcripts using ClueGO in Cytoscape (Figure 2). Figure 2A shows the resulting annotation network showing significantGO groups that were upregulated between all time-points, as depicted by 5 major functional themes: (1) growth factor binding; (2) transferase activity-transferring glycosyl groups; (3) carbohydrate binding; (4) receptor binding and (5) growth factor activity. Transcripts identified as differentially expressed were those with adjusted p-value<0.1 and fold change >1.4. Top regulated transcripts (with fold changes >10 across at least one timepoint) included members of the fibroblast growth factor (FGF), platedderived growth factor (PDGF) and transforming growth factor families (TGF) families (Table 1).These transcripts have already been shown to play integral roles in promoting ESC differentiation [24,26,27]. As such, the top regulated transcript, FGF-8, has been implicated in priming mouse ESC towards neurogenesis [28]. Interestingly, several members of the insulin-like growth factor family (IGF) were highly upregulated in differentiating embryoid bodies, a finding previously demonstrated by our group [9,10]. These findings validate the accuracy of combining the Glycogene-chip and embryoid body models and justify their uses in this study. Although the Glycogene-chip focuses on glycomic genes, it also includes a large set of growth/developmental-related transcripts which serve as internal controls, thus explaining the high proportion of regulated growth factor/receptor genes herein obtained. In the realm of using the Glycogene-chip to uncover novel roles of the glycome during ESC differentiation, we focused on several glyco-modifying transcripts that were upregulated to a lesser extent, including sulfatases (SULFs), sulfotransferases (CHST-1 and -2) and the glycoprotein ANGPTL-2.

cell-developmental-Molecular-functional

Figure 2: Classification of the 180 transcripts which significantly overlapped between all time-points of differentiating ESC versus Day 0. Molecular functional annotation network overview chart of the 180 upregulated (A) and downregulated (B) transcripts, which were created using ClueGO in Cytoscape.

The main GO theme that was downregulated was growth factor activity, with the top regulated transcript being fibroblast growth factor-4 (FGF-4, 14.29-fold) (Figure 2B and Table 2). FGF-4 expression was recently shown to positively correlate with the stemness transcription factor Nanog (Figure 3C) [29]. In addition, 5 themes that were not grouped involved catalytic activity, sugar binding, transferase activity, as well as carbohydrate and carbon binding. Altogether, these results indicate that a precise fine-tuning of growth factors is necessary to modulate ESC fate. Table 2 shows the downregulated overlapping transcripts. Besides FGF-4, main downregulated genes included members of the platelet endothelial cell adhesion molecule (PECAM) family, with up to 6.25-fold reduction at Day 10. It’s also interesting that carbohydrate-specific sulfotransferase (CHST) family members (CHST-6, -10 and 12) were downregulated. Along with Table 1, this indicates that the latter family is strongly implicated in all facets of ESC differentiation, and demonstrates the pleitropic functions imparted by various glyco-isoforms.

cell-developmental-PCR-validation

Figure 3: PCR validation of representative 180 transcripts which significantly overlapped between all time-points of differentiating ESC versus Day 0. Representative real-time PCR of some of the top 75 upregulated (ANGPTL-2, SULF-1 and CHST-1) (A) and downregulated (ANGPTL-1 and EXTL-1) (B) genes was performed on RNA extracted from the same ESC differentiation time-course as that used for the Glycogene-chip arrays. GADPH was used as normalization control. (C) Nanog was used as the stemness control. #P<0.05 compared with Day 0 control.

To validate some of the microarray results, 5 genes of interest were analyzed by real-time PCR, using the primer sequences depicted in Table 3. As such, we found that members of the Angiopoietin-like (ANGPTL) family, ANGPTL-2 and 4, were significantly regulated (Figure 3). ANGPTLs comprise a family of glycosylated cytokines (ANGPTL-1 to -6) whose mechanisms of action and functions remain largely uncharacterized [30]. They are mainly located at sites of energy metabolism (liver and adipose tissues), vascular remodeling (uterus and ovaries) and inflammation. Although their roles in ESC per say remain largely unknown, a secondary role has recently emerged for ANGPTL-2 in promoting hematopoietic stem cell expansion [31,32]. PCR validation (Figure 3A) shows that ANGPTL-2 was upregulated by 5.2-fold at Day 10, persisting by Day 15 (6.3 fold). In stark contrast, ANGPTL-4 expression was downregulated immediately following differentiation, to almost null levels (Figure 3B). To our knowledge, this is the first report linking ANGPTL-4 and stem cells. Based on ANGPTL-4’s proposed role as an angiogenic inhibitor, it’s tempting to speculate that the balance between ANGPTL-2 and -4 might act as a switch to mediate stemness versus mesodermal differentiation [33]. Furthermore, recent studies have implicated a synergistic effect of the ANGPTL and IGF axes in promoting hematopoietic stem cell explansion [34]. In order to uncover more novel glycogenes underlying ESC differentiation, we next focused on transcripts regulated to a lesser extent, but still having highly significant adjusted p values . We subjected the same ESC used for Glycogene-chip hybridization to PCR analysis, in order to eliminate any cell variability artefacts. As such, we found that the sulfatase, SULF-1, and the sulfotransferase, CHST-1, were highly upregulated as ESC differentiated, saturating at Day 10, whereas the glycosyltransferase exostoses (multiple)-like-1 (EXTL-1) was significantly downregulated (Figure 3A and 3B). Although the context of these transcripts in mediating ESC fate is unknown, studies using precursor stem cells have implicated a role for SULF-1 as a regulator of neural and hematopoietic differentiation [35,36]. Whereas there are no reports of CHST-1 expression in stem cells, in adult tissues, it is mainly expressed in neural and inflammatory tissues, where it modulates endothelium inflammation thourgh glycosaminoglycan biosynthesis [37]. As for EXTL-1, a promoter of heparin sulfate chain elongation, it’s preponderant role is as a tumor suppressor, which might explain its downregulation as ESC mature from the proliferating to the differentiating stage [38]. In order to confirm the stemness versus differentiation profile of our samples, we also subjected them to PCR analysis against the stemness marker, NANOG. As expected, NANOG transcripts decreased proportionally with time, achieving more than 70% downregulation at Day 7 and almost null expression after 10 and 15 days (87 and 93% decrease, respectively, Figure 3C).

Gene Forward Primer Reverse Primer
ANG-1 CGGATTTCTCTTCCCAGAAAC TCCGACTTCATATTTTCCACAA
ANGPLT-2 CACCGTGAGGCATGTGAA GGTTGGTCTTTATGAACTGAAAGC
ANGPLT-4 GGGACCTTAACTGTGCCAAG GAATGGCTACAGGTACCAAACC
ANGPLT-6 ATCTTCTCTGCTGCCCACA CGTGAGCCTCTGCACAATC
CHST-1 CCATCACAGGACCAGTTGAA GCCTTCCAAGAACATTGCAT
EXTL-1 CAGGAGGTGGCAGGTTCTC GCAGCTTCTCACTGTTCCAGA
GALNS CTCCTGGGAGGAGTTCACAC TGTAACTCCTGAGACGTTCTGC
HS3ST-1 AGTGTGAATTTGCTCCAAAGG GTATCTCCAGTTGCCAATTACTGA
NANOG TTCTTGCTTACAAGGGTCTGC AGAGGAAGGGCGAGGAGA
SULF-1 AAGAGTCACCTTCACCCCTTC GCTH+GAAGTTTGCTATCCACCTC
SDC-4 CCCTTCCCTGAAGTGATTGA AGTTCCTTGGGCTCTGAGG
GADPH CCCCAATGTGTCCGTCGTG GCCTGCTTCACCACCTTCT

Table 3: PCR primers used for real-time PCR experiments. GADPH represents the normalization gene. Nanog represents the stemness internal control gene.

A more detailed mechanistic analysis using the GeneGo Metacore software allowed us to identify possible biological networks modulated by these differentially expressed genes during all stages of ESC differentiation. The data was segregated into cellular compartments for added clarity.This type of analysis provides insight into physical connectivity and functional correlations between proteins. Although it would be interesting to validate these potential correlations in the future, it is not the scope of this study, but rather demonstrates that our methodology can be quickly analyzed in order to uncover not only potential glycome-related ESC signatures, but also underlying signaling mechanisms of action.

A top scoring network, using the transcripts from Table 1, revealed genes implicated in 5 main processes: (1) response to wounding (56.0%); (2) response to stress (72.0%); (3) nervous system development (60.0%); (4) neurogenesis (52.0%) and (5) generation of neurons (50.0%) (Figure 4A). Highly connected nodes of interest included ANGPTL-2 and the transcription factor signal transducer and activator of transcription-3 (STAT-3), as well as and IGF-binding protein-2 (IGFBP-2, represented as IBP2 in the pathway) and the membrane glycoprotein integrin alpha-5 (ITGA-5). Altough there is no documented record linking ANGPTL-2 and STAT-3, STAT-3 activation has been implicated in promoting neurogenesis [39], whereas the IGF-2/IBP-2/ITGA-5 axis promotes osteogenesis [40].The CHST sulotransferase family was found to be regulated in the cytoplasm, altough its exact underlying mechanisms remain unclear, transcriptional activation downstream of the core-binding factor-1 (CBF-1) has been proposed to mediate neurogenesis [41].

cell-developmental-Network-validation

Figure 4: Network validation of the 180 transcripts which significantly overlapped between all time-points of differentiating ESC versus Day 0. A top scored biological pathway network of the upregulated (A) and downregulated (B) transcripts are represented, with the gene products seggregated into their respective cellular compartments. These figures were created using the GeneGo Metacore software.The corresponding legend is represented in (C).

A top scoring network, using the transcripts from Table 2, revealed genes implicated in 5 main processes: (1) carbohydrate metabolic process (40.0%); (2) N-acetylglucosamine metabolic process (16.0%); (3) glucosamine metabolic process (16.0%); (4) amino sugar metabolic process (16.0%) and (5) positive regulation of transcription during mitosis (12.0%) (Figure 4B). Highly connected nodes included FGF- 4, PECAM and the CHST family, all with the transcription factor specificity protein-1 (SP-1). Altough the link with CHST has not yet been demonstrated, transcriptional activation of FGF-4 or PECAM downstream of SP-1 has been shown to promote stemness [42] and vasculogenesis [43], repspectively.

Likewise, we performed similar analyses on the 181 genes which were upregulated after 15 days (inclusive of Day 10) of ESC differentiation, in order to uncover glycogenes implicated in the late differentiation stage. We focused on a lower scoring network in order to focus on non-developmentally conserved genes. In addition, the Venn diagram from Figure 1C indicated that this time-point has the largest number of differentially regulated transcripts, thus allowing us to unmask additional glycome-related signatures in differentiating ESC.

The underlying gene ontology analysis revealed 5 significantly grouped functional themes: (1) transferase activity; (2) transferase activity-transfering glycosyl groups; (3) cytokine binding; (4) receptor binding and (5) nucleic acid binding (Figure 5). As in the previous section, the IGF family was highly represented, with the most highly upregulated transcript being IGFBP-1 (191.80-fold, Table 4), underscoring the crucial role of this pathway in mediating all aspects of ESC fate. Interestingly, IGFBP-1 has been implicated in promoting liver regeneration [44]. The second and third most upregulated transcripts were proteoglycan family members, namely lumican (LUM, 45.43-fold) and decorin (DCN, 33.76-fold), which are both involved in promoting neurogenesis [45] and osteogenesis [46]. Investigating genes upregulated at this later differentiation time-point effectively allowed us to unmask novel glycogenes more readily than in the previous section. As such, top 10 regulated transcripts at Day 15 included heparan sulfate 3-O sulfotransferase HS3ST-1 (15.06-fold) and Angiopoietin-1 (ANGPT-1) (4.61-fold) (Table 4). We previously reported that another heparan sulfotransferase, NDST-1, is upregulated during the late stage of embryoid body differentiation, playing a preponderant role during vasculogenesis partly through the modulation of the IGF signaling axis [47]. Whether this is the case for HS3ST-1 remains to be determined, although knockout mice studies mainly suggest a role in clotting [48]. ANGPT-1, a glycosylated cytokine, is a potent promoter of both embryonic and adult angiogenesis, mainly through anti-apoptosic mechanisms downstream of its tyrosine kinase receptor, TIE-2 [49-51]. In stem cell settings, ANGPT-1 appears to promote survival of both endothelial and neural cells [52,53]. Interestingly, recent studies have implicated a synergistic effect between ANGPT and IGF family members during angiogenesis and neurogenesis [54,55].

cell-developmental-transcripts-upregulated

Figure 5: Classification of the 181 transcripts upregulated after 15 days of differentiation. Molecular functional annotation network overview chart of the 181 transcripts which were significantly upregulated after Day 15 of differentiation, inclusive of Day 10, which were created using ClueGO in Cytoscape.

biological process Gen Bank ID Fold Induction Adj.P.Val
Insulin Receptor Signalling pathway NM_008341 191.8 1.79E-19
response to growth factor stimulus NM_008524 45.43 9.29E-17
peptide cross-linking via condroitin4-sulfate glycosaminoglycan NM_007833 33.76 3.22E-19
death NM_025622 17.01 1.24E-17
metaboloic process NM_010474 15.06 5.69E-18
negative regulation of cell -substrate adhesion NM_009262 11.98 5.75E-19
protein glycosylation NM_009177 5.52 3.87E-17
memory gland morphogenesis NM_009371 4.77 4.87E-18
regulation of cell differentiation NM_010544 4.73 2.31E-15
negative regulation of apoptotic process NM_009640 4.61 3.22E-19
trans membrane transport NM_028060 4.4 6.40E-16
cell-cell adhesion NM_016885 3.52 5.65E-16
germ cell migration NM_021704 3.51 2.33E-17
negative regulation of mega karyocyte differentiation NM_019932 3.14 1.58E-09
oligosaccharide metabolic process NM_009181 3.02 2.09E-13
cell adhesion NM_011693 2.97 3.86E-13
cell adhesion NM_181277 2.9 3.00E-13
cell surface receptor linked signal transduction NM_010550 2.84 1.85E-15
negative regulation of androgen receptor signalling pathway NM_010512 2.83 4.62E-14
leukocyte tethering or rolling NM_176973 2.82 3.00E-13
dephosphorylation NM_001081306 2.8 2.87E-09
glial cell migration NM_001081249 2.73 4.35E-14
embryo development NM_173422 2.57 1.53E-13
apoptosis ans survival_Granzyme B signalling NM_001017959 2.53 9.70E-14
cell-cell adhesion NM_010740 2.48 1.47E-02
regulation of ERK1 and ERK2 cascade NM_010207 2.44 2.59E-13
microphage chemotaxis NM_009987 2.43 3.90E-06
glycolipid biosynthetic process NM_011674 2.39 1.14E-10
response to methyl mercury NM_009712 2.36 2.71E-12
cochlea development NM_008010 2.3 8.01E-12
lipid glycosylation NM_026247 2.27 7.74E-12
regualtion of cell growth NM_018741 2.24 4.90E-08
positive regualtiopn of transcription,DNA- dependent NM_009368 2.23 2.37E-09
cell proliferation NM_001037859 2.19 1.41E-09
positive regulation of CD4-positive,alpha-beta T cell differentiation NM_009856 2.14 4.36E-13
positive regulation of cell proliferation NM_010419 2.09 2.50E-09
canonical Wnt receptor signalling pathway NM_001042659 2.06 2.02E-10
Notch signalling pathway NM_007865 2.05 4.19E-09
positive regulation of macrophage activation NM_133775 2.03 1.86E-12
regulation of apoptotic process NM_009370 2.01 2.86E-10
metabolic process NM_015737 2.01 4.61E-10
lipid glycosylation NM_008080 2 8.76E-11
cytokine-mediated signalling pathway NM_008349 1.99 1.61E-11
defense response NM_010739 1.98  4.06-10
positive regulation of chemokine(C-X-C motif) ligand 2 production NM_010545 1.94 2.99E-13
sphingolipid biosynthetic process NM_019737 1.92 3.86E-13
positive regulation of cell proliferation NM_013518 1.9 4.00E-10
regulation of mRNA stability involved in response to stress NM_009951 1.9 1.66E-13
positive regualtion of T cell proliferation NM_031252 1.86 2.27E-09
cell surface receptor signalling pathway NM_019985 1.84 1.94E-09
germ cell programmed cell death NM_021099 1.84 5.15E-10
angiogenesis NM_145154 1.84 2.29E-09
organelle organization NM_007693 1.83 7.02E-12
protein glycosylation NM_009180 1.83 4.52E-10
glucose metabolic process NM_175013 1.8 7.29E-09
decidualization NM_001077184 1.77 3.35E-13
mannose metabolic process NM_025837 1.71 9.91E-11
regulation of developmental process NM_010929 1.71 1.65E-08
protein glycosylation NM_011375 1.71 5.09E-13
neuropeptide signalling pathway NM_010130 1.71 7.66E-04
galactose metabolic process NM_016905 1.69 1.64E-10
inflammatory response NM_145837 1.69 4.82E-12
positive regulation of transcription, DNA-dependent NM_009612 1.68 5.67E-09
nerve maturation NM_010017 1.67 1.17E-11
galactosylceramide catabolic process NM_008079 1.67 2.13E-09
defense response NM_010511 1.66 1.61E-08
skeletal system development NM_007560 1.66 5.82E-08
fucosylation NM_019934 1.66 1.52E-10
galactosylceramide biosynthetic process NM_016922 1.65 1.17E-09
protein transport uc009nqy 1.65 2.61E-11
proteinO-linked glycosylation via thronine NM_139272 1.64 3.45E-10
metabolic process NM_001033441 1.64 3.93E-09
growth NM_145741 1.64 2.37E-10
metabolic process NM_016722 1.64 6.15E-11
trmination of signal transduction NM_011421 1.64 7.33E-10

Table 4: Genes that were downregulated after 15 days of differentiation versus Day 0. The top 75 transcripts, excluding housekeeping genes, are shown.

PCR validation of selected transcripts upregulated at Day 15 is shown in Figure 6, with ANGPT-1 and HS3ST-1 upregulated by 15.27- and 68.54-fold at Day 15, respectively. The importance of the ANGPTL family (Figure 3A and 3B) during ESC differentiation is reiterated here, as ANGPTL-6 transcripts were modestly but significantly upregulated (1.84-fold, Table 4) and PCR-validated (Figure 6). Although ANGPTL-6 mainly has a role as a glycosphingolipidsmodulator, in stem cell settings, it has been associated with hematopoiesis [56]. Interestingly, glycosphingolipids were recently shown to be important regulators of ESC differentiation into the endodermal and ectodermal lineages, partly through the upregulation of the glycosyltransferases beta-galactoside alpha-2,3-sialyltransferase-1 and -5 (ST3GAL-1 and ST3GAL-5) [57], which were upregulated by 5.52- and 1.71-fold in our study (Table 4). Whether there is a mechanistic link between ANGPTLs and ST3GALs during ESC differentiation, however, remains to be investigated. Combined with the other upregulated genes from Table 1, an interesting pattern is emerging. We have previously reported, using the Affymetrix GeneChip human genome array, that ANGPT-1 upregulates transcripts for ANGPTL-4 and CHSTs, while downregulating SULF-1 expression in endothelial cells [47]. It will be interesting to elucidate whether these transcripts are part of a novel pathway mediating vasculogenesis in the context of stem cell biology. In order to unmask an additional glycogene of interest, we looked at another modestly but statistically regulated gene, galactosamine (N-acetyl)-6-sulfate sulfatase (GALNS), which is involved in glycosaminoglycan degradation. GALNS was upregualted by 1.64- fold (Table 4) and PCR validated (Figure 6). GALNS transcripts are upregulated in response to FGF-2 during osteogenesis [58].

cell-developmental-RNA-extracted

Figure 6: PCR validation of representative 181 transcripts which were significantly upregulated after 15 days of differentiation. Representative real-time PCR of some of the 75 upregulated genes, namely HS3ST-1, ANGPT-1, ANGPTL-6 and GALNS, was performed on RNA extracted from the same ESC differentiation time-course as that used for the Glycogene-chip arrays. GADPH was used as endogenous control. #P<0.05 compared with Day 0 control.

Metacore-generated biological pathway of these 181 genes revealed top scoring network implicated in 5 main processes: (1) regulation of sequence-specific DNA binding transcription factor activity (38.8%); (2) positive regulation of sequence-specific DNA binding transcription factor activity (32.7%); (3) positive regulation of cellular process (75.5%); (4) positive regulation of response to stimulus (53.1%) and (5) positive regulation of biological process (77.6%). A highly connected node of interest included ANGPT-1/TIE-2 receptor and A20 binding inhibitor of NF-kappaB-2 (ABIN-2) (Figure 7A). Altough the role of this axis in mediating ESC fate remains unclear, it’s been shown topromote endothelial survival [59]. There were also links between the transcription factors STAT-3 and activator protein-1 (AP-1) with ANGPT-1. Whereas no link is known between AP-1 and ANGPT-1, STAT-3 (which was also correlated with ANGPTL-2 expression, Figure 4) has been implicated in mediating endothelial cell survival through the ANGPT-1/TIE-2 axis [60]. Mechanistic validation of Tie-2 protein activation using immunoblotting showed significant receptor phosphorylation after 7 days of differentiation (Figure 7B). It woul be interesting in the future to investigate downstream mediators of the ANGPT-1/TIE2 aixs in mediating ESC fate using Glycogenechip hybridization in conjunction with Metacore analysis. Another interesting node included the forkhead transcription factor (FKHR) and vascular cell adhesion moelcule-1 (VCAM-1). Our previous work has implicated FKHR as a mediator of vascularization during ESC differentiation [9].

cell-developmental-biological-pathway-network

Figure 7: Network validation of the 181 transcripts upregulated after 15 days of differentiation. (A) A lower scored biological pathway network of the upregulated transcripts are represented, with the gene products seggregated into their respective cellular compartments, as in Figure 4. (B) Immunoblotting of phospho-Tie-2 receptorin Day 7 and undifferentiated (Day 0) ESC. *P<0.05 compared with Day 0 control.

Taken together, the combination of the Glycogene-chip microarray with bioinformatic analyses in the embryoid body model gave us a rapid and reliable method for uncovering potential novel signaling mechanisms which mediate ESC fate. In addition, this methodology allowed us to uncover modestly regulated, but novel, glycogenes which would be masked using other methods.

Conclusion

In conclusion, our study demonstrates that combining the Glycogene-chip with a standard ESC-derived embryoid body model is a powerful screening method to uncover novel glycogenes signatures in differentiatiing versus cycling ESC. Aside from confirming the importance of growth factors/receptors such as the IGF, PDGF, TGF and FGF families, the integration of this method along with bioinformatic analyses and molecular biology tools (real-time PCR and immunoblotting) allowed us to uncover the potential involvement of novel glycogenes belonging to the Angiopoietin (ANGPT) and Angiopetin-like (ANGPTL) families, as well as sulfotransferase, sulfatase and glycosyltransferase families. This study opens the door for future research to elucidate novel glycomic mechanisms which promote stemness versus differentiation. In turn, this would have profound implication for the regenerative medicine field.

Acknowledgement

The glycan analyses were performed by the Protein-Glycan Interaction Core (H) of the CFG, funded by NIGMS (GM62116). The authors wish to thank Mrs Lana Schaffer, Mrs Suzanne Papp and Mr Gilberto Hernandez at the Microarray Core of the CFG for their assistance with the microarray hybridization and ensuing analyses. We also thank Dr Zoltan Szabo and Dr Anne-Laure Papa for their editorial guidance. RH is supported by a CIHR postdoctoral fellowship. SS is supported by a National Institutes of Health Grant R01 (1R01CA135242-01A2) and a DOD grant (W81XWH-07-1-0482).

References

  1. Turnbull JE, Field RA (2007) Emerging glycomics technologies. Nat Chem Biol 3: 74-77.
  2. Haslam SM, Julien S, Burchell JM, Monk CR, Ceroni A, et al. (2008) Characterizing the glycome of the mammalian immune system. Immunol Cell Biol 86: 564-573.
  3. Rosenberg RD, Shworak NW, Liu J, Schwartz JJ, Zhang L (1997) Heparan sulfate proteoglycans of the cardiovascular system. Specific structures emerge but how is synthesis regulated? J Clin Invest 100: S67-S75.
  4. Rapraeger AC, Krufka A, Olwin BB (1991) Requirement of heparan sulfate for bFGF-mediated fibroblast growth and myoblast differentiation. Science 252: 1705-1708.
  5. Huang Z, Nelson ER, Smith RL, Goodman SB (2007) The sequential expression profiles of growth factors from osteoprogenitors [correction of osteroprogenitors] to osteoblasts in vitro. Tissue Eng 13: 2311-2320.
  6. Sainz J, García-Alcalde F, Blanco A, Concha A (2011) Genome-wide gene expression analysis in mouse embryonic stem cells. Int J Dev Biol 55: 995-1006.
  7. Tao SC, Li Y, Zhou J, Qian J, Schnaar RL, et al. (2008) Lectin microarrays identify cell-specific and functionally significant cell surface glycan markers. Glycobiology 18: 761-769.
  8. Gao J, Liu D, Wang Z (2008) Microarray-based study of carbohydrate-protein binding by gold nanoparticle probes. Anal Chem 80: 8822-8827.
  9. Harfouche R, Hentschel DM, Piecewicz S, Basu S, Print C, et al. (2009) Glycome and transcriptome regulation of vasculogenesis. Circulation 120: 1883-1892.
  10. Piecewicz SM, Pandey A, Roy B, Xiang SH, Zetter BR, et al. (2012) Insulin-like growth factors promote vasculogenesis in embryonic stem cells. PLoS One 7: e32191.
  11. Satomaa T, Heiskanen A, Mikkola M, Olsson C, Blomqvist M, et al. (2009) The N-glycome of human embryonic stem cells. BMC Cell Biol 10: 42.
  12. Heiskanen A, Hirvonen T, Salo H, Impola U, Olonen A, et al. (2009) Glycomics of bone marrow-derived mesenchymal stem cells can be used to evaluate their cellular differentiation stage. Glycoconj J 26: 367-384.
  13. Comelli EM, Head SR, Gilmartin T, Whisenant T, Haslam SM, et al. (2006) A focused microarray approach to functional glycomics: transcriptional regulation of the glycome. Glycobiology 16: 117-131.
  14. Narayan S, Head SR, Gilmartin TJ, Dean B, Thomas EA (2009) Evidence for disruption of sphingolipid metabolism in schizophrenia. J Neurosci Res 87: 278-288.
  15. Brown JR, Fuster MM, Whisenant T, Esko JD (2003) Expression patterns of alpha 2,3-sialyltransferases and alpha 1,3-fucosyltransferases determine the mode of sialyl Lewis X inhibition by disaccharide decoys. J Biol Chem 278: 23352-23359.
  16. Smith FI, Qu Q, Hong SJ, Kim KS, Gilmartin TJ (2005) Head SR: Gene expression profiling of mouse postnatal cerebellar development using oligonucleotide microarrays designed to detect differences in glycoconjugate expression. Gene Expr Patterns 5: 740-749.
  17. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, et al. (1996) Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol 14: 1675-1680.
  18. Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3: Article3.
  19. Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5: R80.
  20. Dezso Z, Nikolsky Y, Sviridov E, Shi W, Serebriyskaya T, et al. (2008) A comprehensive functional analysis of tissue specificity of human gene expression. BMC Biol 6: 49.
  21. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, et al. (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25: 1091-1093.
  22. Willems E, Mateizel I, Kemp C, Cauffman G, Sermon K, et al. (2006) Selection of reference genes in mouse embryos and in differentiating human and mouse ES cells. Int J Dev Biol 50: 627-635.
  23. Vittet D, Prandini MH, Berthier R, Schweitzer A, Martin-Sisteron H, et al. (1996) Embryonic stem cells differentiate in vitro to endothelial cells through successive maturation steps. Blood 88: 3424-3431.
  24. Leahy A, Xiong JW, Kuhnert F, Stuhlmann H (1999) Use of developmental marker genes to define temporal and spatial patterns of differentiation during embryoid body formation. J Exp Zool 284: 67-81.
  25. Lake J, Rathjen J, Remiszewski J, Rathjen PD (2000) Reversible programming of pluripotent cell differentiation. J Cell Sci 113 : 555-566.
  26. Lange S, Heger J, Euler G, Wartenberg M, Piper HM et al. (2009) Platelet-derived growth factor BB stimulates vasculogenesis of embryonic stem cell-derived endothelial cells by calcium-mediated generation of reactive oxygen species. Cardiovasc Res 81: 159-168.
  27. Fei T, Chen YG (2010) Regulation of embryonic stem cell self-renewal and differentiation by TGF-beta family signaling. Sci China Life Sci 53: 497-503.
  28. Wu CY, Whye D, Mason RW, Wang W (2012) Efficient differentiation of mouse embryonic stem cells into motor neurons. J Vis Exp : e3813.
  29. Choi SC, Choi JH, Park CY, Ahn CM, Hong SJ, et al. (2012) Nanog regulates molecules involved in stemness and cell cycle-signaling pathway for maintenance of pluripotency of P19 embryonal carcinoma stem cells. J Cell Physiol 227: 3678-3692.
  30. Kim I, Moon SO, Koh KN, Kim H, Uhm CS, et al. (1999) Molecular cloning, expression, and characterization of angiopoietin-related protein. angiopoietin-related protein induces endothelial cell sprouting. J Biol Chem 274: 26523-26528.
  31. Zhang CC, Kaba M, Ge G, Xie K, Tong W, et al. (2006) Angiopoietin-like proteins stimulate ex vivo expansion of hematopoietic stem cells. Nat Med 12: 240-245.
  32. Broxmeyer HE, Srour EF, Cooper S, Wallace CT, Hangoc G et al. (2012) Angiopoietin-like-2 and -3 act through their coiled-coil domains to enhance survival and replating capacity of human cord blood hematopoietic progenitors. Blood Cells Mol Dis 48: 25-29.
  33. Cazes A, Galaup A, Chomel C, Bignon M, Bréchot N, et al. (2006) Extracellular matrix-bound angiopoietin-like 4 inhibits endothelial cell adhesion, migration, and sprouting and alters actin cytoskeleton. Circ Res 99: 1207-1215.
  34. Zhang CC, Kaba M, Iizuka S, Huynh H, Lodish HF (2008) Angiopoietin-like 5 and IGFBP2 stimulate ex vivo expansion of human cord blood hematopoietic stem cells as assayed by NOD/SCID transplantation. Blood 111: 3415-3423.
  35. Karus M, Denecke B, ffrench-Constant C, Wiese S, Faissner A (2011) The extracellular matrix molecule tenascin C modulates expression levels and territories of key patterning genes during spinal cord astrocyte specification. Development 138: 5321-5331.
  36. Langsdorf A, Do AT, Kusche-Gullberg M, Emerson CP Jr, Ai X (2007) Sulfs are regulators of growth factor signaling for satellite cell differentiation and muscle regeneration. Dev Biol 311: 464-477.
  37. Nishimura M, Naito S (2007) Tissue-specific mRNA expression profiles of human carbohydrate sulfotransferase and tyrosylprotein sulfotransferase. Biol Pharm Bull 30: 821-825.
  38. Kim BT, Kitagawa H, Tamura J, Saito T, Kusche-Gullberg M et al. (2001) Human tumor suppressor EXT gene family members EXTL1 and EXTL3 encode alpha 1,4- N-acetylglucosaminyltransferases that likely are involved in heparan sulfate/ heparin biosynthesis. Proc Natl Acad Sci USA 98: 7176-7181.
  39. Islam O, Loo TX, Heese K (2009) Brain-derived neurotrophic factor (BDNF) has proliferative effects on neural stem cells through the truncated TRK-B receptor, MAP kinase, AKT, and STAT-3 signaling pathways. Curr Neurovasc Res 6: 42-53.
  40. Hamidouche Z, Fromigue O, Ringe J, Haupl T, Marie PJ (2010) Crosstalks between integrin alpha 5 and IGF2/IGFBP2 signalling trigger human bone marrow-derived mesenchymal stromal osteogenic differentiation. BMC Cell Biol 11: 44.
  41. Mizutani K, Yoon K, Dang L, Tokunaga A, Gaiano N (2007) Differential Notch signalling distinguishes neural stem cells from intermediate progenitors. Nature 449: 351-355.
  42. Lamb K, Rosfjord E, Brigman K, Rizzino A (1996) Binding of transcription factors to widely-separated cis-regulatory elements of the murine FGF-4 gene. Mol Reprod Dev 44: 460-471.
  43. Gumina RJ, Kirschbaum NE, Piotrowski K, Newman PJ (1997) Characterization of the human platelet/endothelial cell adhesion molecule-1 promoter: identification of a GATA-2 binding element required for optimal transcriptional activity. Blood 89: 1260-1269.
  44. Arai M, Yokosuka O, Fukai K, Imazeki F, Chiba T, et al. (2004) Gene expression profiles in liver regeneration with oval cell induction. Biochem Biophys Res Commun 317: 370-376.
  45. Dellett M, Hu W, Papadaki V, Ohnuma S (2012) Small leucine rich proteoglycan family regulates multiple signalling pathways in neural development and maintenance. Dev Growth Differ 54: 327-340.
  46. Burns JS, Rasmussen PL, Larsen KH, Schroder HD, Kassem M (2010) Parameters in three-dimensional osteospheroids of telomerized human mesenchymal (stromal) stem cells grown on osteoconductive scaffolds that predict in vivo bone-forming potential. Tissue Eng Part A 16: 2331-2342.
  47. Abdel-Malak NA, Harfouche R, Hussain SN (2007) Transcriptome of angiopoietin 1-activated human umbilical vein endothelial cells. Endothelium 14: 285-302.
  48. Moon AF, Xu Y, Woody SM, Krahn JM, Linhardt RJ, et al. (2012) Dissecting the substrate recognition of 3-O-sulfotransferase for the biosynthesis of anticoagulant heparin. Proc Natl Acad Sci U S A 109: 5265-5270.
  49. Harfouche R, Hasséssian HM, Guo Y, Faivre V, Srikant CB, et al. (2002) Mechanisms which mediate the antiapoptotic effects of angiopoietin-1 on endothelial cells. Microvasc Res 64: 135-147.
  50. Harfouche R, Gratton JP, Yancopoulos GD, Noseda M, Karsan A, et al. (2003) Angiopoietin-1 activates both anti- and proapoptotic mitogen-activated protein kinases. FASEB J 17: 1523-1525.
  51. Jeansson M, Gawlik A, Anderson G, Li C, Kerjaschki D, et al. (2011) Angiopoietin-1 is essential in mouse vasculature during development and in response to injury. J Clin Invest 121: 2278-2289.
  52. Joo HJ, Kim H, Park SW, Cho HJ, Kim HS, et al. (2011) Angiopoietin-1 promotes endothelial differentiation from embryonic stem cells and induced pluripotent stem cells. Blood 118: 2094-2104.
  53. Bai Y, Meng Z, Cui M, Zhang X, Chen F, et al. (2009) An Ang1-Tie2-PI3K axis in neural progenitor cells initiates survival responses against oxygen and glucose deprivation. Neuroscience 160: 371-381.
  54. Nakamura K, Sasajima J, Mizukami Y, Sugiyama Y, Yamazaki M (2010) Hedgehog promotes neovascularization in pancreatic cancers by regulating Ang-1 and IGF-1 expression in bone-marrow derived pro-angiogenic cells. PLoS ONE 5: e8824.
  55. Sivakumar V, Zhang Y, Ling EA, Foulds WS, Kaur C (2008) Insulin-like growth factors, angiopoietin-2, and pigment epithelium-derived growth factor in the hypoxic retina. J Neurosci Res 86: 702-711.
  56. Fatrai S, van Gosliga D, Han L, Daenen SM, Vellenga E, et al. (2011) KRAS(G12V) enhances proliferation and initiates myelomonocytic differentiation in human stem/progenitor cells via intrinsic and extrinsic pathways. J Biol Chem 286: 6061-6070.
  57. Liang YJ, Yang BC, Chen JM, Lin YH, Huang CL, et al. (2011) Changes in glycosphingolipid composition during differentiation of human embryonic stem cells to ectodermal or endodermal lineages. Stem Cells 29: 1995-2004.
  58. Teplyuk NM, Haupt LM, Ling L, Dombrowski C, Mun FK, et al. (2009) The osteogenic transcription factor Runx2 regulates components of the fibroblast growth factor/proteoglycan signaling axis in osteoblasts. J Cell Biochem 107: 144-154.
  59. Tadros A, Hughes DP, Dunmore BJ, Brindle NP (2003) ABIN-2 protects endothelial cells from death and has a role in the antiapoptotic effect of angiopoietin-1. Blood 102: 4407-4409.
  60. Schubert SY, Benarroch A, Monter-Solans J, Edelman ER (2011) Primary monocytes regulate endothelial cell survival through secretion of angiopoietin-1 and activation of endothelial Tie2. Arterioscler Thromb Vasc Biol 31: 870-875.
Citation: Harfouche R, Ray S, Sanchez M, Dadwal U, Head SR, et al. (2013) Glycomic Signature of Mouse Embryonic Stem Cells During Differentiation. Cell Dev Biol 2:120.

Copyright: © 2013 Harfouche R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Top