International Journal of Genomics

International Journal of Genomics / 2014 / Article

Research Article | Open Access

Volume 2014 |Article ID 837365 | 18 pages | https://doi.org/10.1155/2014/837365

Comparative Analysis of Glycogene Expression in Different Mouse Tissues Using RNA-Seq Data

Academic Editor: Giulia Piaggio
Received13 May 2014
Accepted10 Jun 2014
Published09 Jul 2014

Abstract

Glycogenes regulate a wide array of biological processes in the development of organisms as well as different diseases such as cancer, primary open-angle glaucoma, and renal dysfunction. The objective of this study was to explore the role of differentially expressed glycogenes (DEGGs) in three major tissues such as brain, muscle, and liver using mouse RNA-seq data, and we identified 579, 501, and 442 DEGGs for brain versus liver (BvL579), brain versus muscle (BvM501), and liver versus muscle (LvM442) groups. DAVID functional analysis suggested inflammatory response, glycosaminoglycan metabolic process, and protein maturation as the enriched biological processes in BvL579, BvM501, and LvM442, respectively. These DEGGs were then used to construct three interaction networks by using GeneMANIA, from which we detected potential hub genes such as PEMT and HPXN (BvL579), IGF2 and NID2 (BvM501), and STAT6 and FLT1 (LvM442), having the highest degree. Additionally, our community analysis results suggest that the significance of immune system related processes in liver, glycosphingolipid metabolic processes in the development of brain, and the processes such as cell proliferation, adhesion, and growth are important for muscle development. Further studies are required to confirm the role of predicted hub genes as well as the significance of biological processes.

1. Introduction

Cell function within an organism has huge variation due to gene expression pattern although all cells in an individual mammal have almost identical DNA [1]. It is important to know how cells and tissues differ in gene expression which is regulated during developmental changes in different tissues, consequently affecting their biological functions. The pattern of glycogene expression is one of important factors that provide clues about many biological functions, developmental changes, and diseases in human, mouse, and tissues of other organisms [24]. Glycogene is a gene that is responsible for the glycosylation of proteins, lipids, and proteoglycans and includes genes associated with the synthesis of glycans such as glycosyltransferase, sugar-nucleotide transporters, and sulfotransferases [5, 6]. Similarly, the term glycogenome involves all genes that play a role in glycosylation and represents more than 600 genes in the mouse genome [7]. Glycosylation is one of the most common posttranslational modification reactions [8]. The glycogene expression is relatively weak as compared to other molecules; however, different glycogenes are upregulated by specific tissues depending on the local conditions [5].

In order to understand the pathology of various diseases, recently, many studies have targeted and explored the role of glycogenes expression in various diseases [912]. Alteration in the glycogene expression is also associated with drug resistance of various types of cancers including pancreatic cancer epithelial-mesenchymal transition [13], breast cancer [14], and hepatocarcinoma [15]. The change in the glycogene expression is also associated with conjunctival epithelium [2] and primary open-angle glaucoma (POAG) [16]. Moreover, the defects in the normal expression of glycogenes in tonsillar B lymphocytes are also correlated with the proteinuria and renal dysfunction in IgA nephropathy [17, 18].

Several high-throughput microarray studies have been applied to investigate the functional roles of various glycogenes [11, 19, 20]. However, to the best of our knowledge, no study till date has utilized RNA-Seq data in the existing databases to specifically explore the role of glycogenes. Previously, we identified the role of glycogenes in skeletal muscle development in MYOGkd cells by using RNA-Seq data. Therefore, in this work, we intend to investigate the role of glycogenes in various biological processes that are involved in the development of brain, muscle, and liver tissues using the precomputed expression values from mouse RNA-seq data. Functional and pathway analysis of differentially expressed glycogenes (DEGGs) between BvL579, BvM501, and LvM442 were enriched with gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, respectively. Additionally, the interaction networks of DEGGs were constructed to identify the hub genes as well as to detect the functional modules in these networks by using community analysis. We expect that the findings of our study may shed new lights on the roles of glycogenes in the development of these tissues and their pathogenesis.

2. Materials and Methods

2.1. Datasets

The precomputed expression values with no spike for the brain, liver, and muscles C57BL6 mouse tissues were downloaded from WoldLab (http://woldlab.caltech.edu/~alim/RNAseq/) for our computational analysis. These precomputed expression values represent the RNA-seq analysis initially performed by Mortazavi et al., using Illumina Genome Analyzer and expression values computed by using ERANGE package [21]. To remove low expressed genes the dataset was filtered by removing genes with RPKM value <1 in all the three tissues.

2.2. Fold Change Analysis

Fold change is often used in analysis of gene expression data of RNAseq experiments, for measuring change in the expression level of a gene and comparing the expression of genes between two sets of arrays, for example, case and control sets. Fold change analysis of genes commonly expressed between two different samples was done to compare the gene expression in glycogenes [22, 23]. Fold change was calculated as the ratio of brain versus liver, brain versus muscle, and liver versus muscle groups. Additionally, to further investigate the role of glycogenes in different tissues, the differentially expressed genes were manually verified in the UniProt database [24] to check whether they represent a glycogene or not. A gene was considered as a glycogene if it was annotated as a “glycoprotein” in the “Keywords” section of the UniProt database.

2.3. Functional Analysis

DAVID (http://david.abcc.ncifcrf.gov/home.jsp) functional annotation cluster analysis was performed on the list of differentially expressed glycogenes (DEGGs) with a fold change of ≥2. Only those terms that reported a value of ≤0.05 and count number ≥5 genes were selected for analysis. The gene ontology (GO) term biological process (BP) in DAVID was used to categorize enriched biological themes in the list of DEGGs. Pathway mapping was performed using the KEGG Automatic Annotation Server (KAAS) (http://www.genome.jp/kegg/kaas/) [25]. The amino acid sequences of these DEGGs were uploaded to the KAAS web server as an input using single-directional best hit (SBH) method to assign orthologs. KAAS offers functional annotation of genes in a genome via a BLAST similarity searches against a manually curated set of ortholog groups in the KEGG GENES database. KAAS assigned a KEGG orthology (KO) number to genes in the data sets, which were mapped to one of KEGG’s reference pathways.

2.4. Network Analysis

The functional interactions between DEGGs were analyzed by GeneMANIA webserver [26]. The GO term biological process was used to create the interaction network between the DEGGs and 50 additional genes by using mouse as a source species. The relationship between the genes in the network includes coexpression, physical and genetic interactions, pathways, colocalization, protein domain similarity, and predicted interactions. The network was filtered by removing all the interactions where weights <0.1.

2.5. Identification of Hub Genes

Generally, the biological networks exhibit the scale-free property [27] where only a few nodes in the network have many connections that represent hubs in the network. Hub genes were identified by calculating the node degree distribution [28] by using the NetworkAnalyzer plugin of Cytoscape. A glycogene with the highest degree distribution was considered as a hub in the current network.

2.6. Community Analysis

The functional modules within the network were further detected by using the greedy community-structure detection algorithm, implemented in the Cytoscape plugin GLay (http://brainarray.mbni.med.umich.edu/sugang/glay/) [29]. GLay offers Cytoscape users a diverse group of community structure algorithms and graph layout functions for network clustering and structured visualization [29]. To identify the overrepresented biological functions within each cluster, the detected clusters were subjected to functional enrichment analyses by DAVID functional analysis tool. For enrichment analysis, only those communities were analyzed which have at least 10 nodes.

3. Results

3.1. Identification of Differentially Expressed Glycogenes (DEGGs)

We downloaded the precomputed RPKM values for mouse transcriptome that was mapped and quantified previously by RNA-Seq analysis [21]. This RNA-Seq data consists of RNA from adult mouse brain, liver, and skeletal muscle tissues and represents 33598 genes in each tissue sample. The data was further filtered by excluding the genes with RPKM values <1 from the analysis. As a result, 15,237, 11,920, and 12,202 genes were identified from brain, liver, and muscle samples, respectively (Table 1(a)). Then three groups (brain versus liver, brain versus muscle, and liver versus muscle) were created and the number of genes that were common in each group were identified and summarized in Table 1(b). From the table, it can be observed that the number of common genes between brain versus liver is 10653, brain versus muscle is 10820, and liver versus muscle is 9833, respectively. These shared genes in each group were then used to calculate the fold change, which was defined as the ratio of RPKM values of brain versus liver, brain versus muscle, and liver versus muscle groups. In this study, the total fold change of ≥2 was considered to classify the differentially expressed genes (DEGs). Based on this definition, there are 5228 DEGs between brain versus liver, 5283 between brain versus muscle, and 3840 between liver versus muscle. The list of DEGs in each group was further filtered by retaining only those genes that were annotated as a “glycoprotein” in the UniProt database. Therefore, 579, 501, and 442 differentially expressed glycogenes (DEGGs) for brain versus liver (BvL579), brain versus muscle (BvM501), and liver versus muscle (LvM442) groups were selected for the final analysis (Table 1(b)). The complete workflow of the analysis is shown in Figure 1.

(a) Total number of genes in each sample before and after filtering. Each tissue sample consists of 33598 genes that were filtered by removing all those genes that showed RPKM value < 1

Data filtering
TissueTotal number of genesTotal number of genes after filtering

Brain3359815,237
Liver3359811,920
Muscle3359812,202

(b) Total number of differentially expressed genes between different tissues

Genes commonly expressed between different tissues
TissueNumber of expressed genesNumber of differentially expressed genes (DEGs) 
(≥2 fold change)
Number of differentially expressed glycogenes (DEGGs) 
(≥2 fold change)

Brain versus liver10,6535228579
Brain versus muscle10,8205283501
Liver versus muscle9,8333840442

3.2. Functional Annotation and Pathway Analysis

To classify biological processes that are enriched in the BvL579, BvM5, Annotation Cluster (FAC) tool available in the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (http://david.abcc.ncifcrf.gov/home.jsp). The GO term “Biological Process” was used for annotations, and top 10 GO terms having statistically significant values from the resulting functional analysis for each group are listed in Table 2. From this table, it can be seen that the GO terms that are enriched in BvL579 glycogenes represent functions necessary for response to wounding, inflammatory response, and blood coagulation (Table 2(a)). The enriched GO terms in BvM501 include functions related to various metabolic (glycoprotein, glycosaminoglycan, sphingolipid, etc.) processes (Table 2(b)). Similarly, functions related to protein maturation, inflammatory response, and complement activation are enriched in LvM442 samples (Table 2(c)).

(a) BvL579

GO TermTotal number of genesP value

GO:0009611~response to wounding532.98E − 23
GO:0002526~acute inflammatory response234.83E − 16
GO:0006954~inflammatory response351.43E − 15
GO:0051604~protein maturation232.51E − 14
GO:0007596~blood coagulation205.40E − 14
GO:0050817~coagulation205.40E − 14
GO:0007599~hemostasis207.17E − 14
GO:0006952~defense response457.57E − 13
GO:0050878~regulation of body fluid levels205.84E − 12
GO:0016485~protein processing207.21E − 12

(b) BvM501

GO TermTotal number of genesP value

GO:0009100~glycoprotein metabolic process301.76E − 18
GO:0009101~glycoprotein biosynthetic process238.1E − 14
GO:0070085~glycosylation176.09E − 10
GO:0006486~protein amino acid glycosylation176.09E − 10
GO:0043413~biopolymer glycosylation176.09E − 10
GO:0030203~glycosaminoglycan metabolic process113.1E − 08
GO:0006665~sphingolipid metabolic process122.79E − 07
GO:0006643~membrane lipid metabolic process123.9E − 07
GO:0006022~aminoglycan metabolic process114.45E − 07
GO:0051604~protein maturation146.1E − 07

(c) LvM442

GO TermTotal number of genesP value

GO:0051604~protein maturation203.17E − 13
GO:0016485~protein processing191.12E − 12
GO:0009100~glycoprotein metabolic process231.63E − 12
GO:0006954~inflammatory response276.69E − 12
GO:0009611~response to wounding331.07E − 11
GO:0002526~acute inflammatory response172.53E − 11
GO:0009101~glycoprotein biosynthetic process191.14E − 10
GO:0051605~protein maturation by peptide bond cleavage151.33E − 10
GO:0006956~complement activation114.25E − 09
GO:0002541~activation of plasma proteins involved in acute inflammatory response114.25E − 09

In addition to DAVID functional analysis, we also identified the biological pathways of BvL579, BvM501, and LvM442 DEGGs annotated in the present study. FASTA formatted amino acid sequences of DEGGs in these three sets were fed into the KAAS for prediction of distinct pathways. A total of 210 pathways were predicted for BvL579, whereas 193 and 198 pathways were predicted for BvM501 and LvM442, respectively. The top 10 KEGG pathways for each of the three categories are shown in Table 3 and a complete list of all pathways is provided in Supplementary Table S1 (Supplementary Material is available online at http://dx.doi.org/10.1155/2014/837365). From these tables, it can be observed that the majority of the DEGGs were found to be associated with important biological processes, many being classified in signaling (e.g., PI3K-Akt and Rap1 signaling) pathways, cytokine-cytokine receptor interaction, or ECM-receptor interaction or being involved in adhesion related functions.

(a) BvL579

Pathway nameNumber of mapped Genes

04151 PI3K-Akt signaling pathway34
04610 Complement and coagulation cascades34
04142 Lysosome31
04060 Cytokine-cytokine receptor interaction23
05200 Pathways in cancer21
04015 Rap1 signaling pathway18
04510 Focal adhesion18
05205 Proteoglycans in cancer18
04514 Cell adhesion molecules CAMs16
04976 Bile secretion16

(b) BvM501

Pathway nameNumber of mapped Genes

04142 Lysosome29
04151 PI3K-Akt signaling pathway24
04060 Cytokine-cytokine receptor interaction19
04514 Cell adhesion molecules CAMs19
05200 Pathways in cancer17
04141 Protein processing in endoplasmic reticulum15
04510 Focal adhesion15
05166 HTLV-I infection13
04512 ECM-receptor interaction12
04610 Complement and coagulation cascades12

(c) LvM442

Pathway nameNumber of mapped Genes

04142 Lysosome33
04151 PI3K-Akt signaling pathway26
04060 Cytokine-cytokine receptor interaction23
04141 Protein processing in endoplasmic reticulum17
05205 Proteoglycans in cancer17
04610 Complement and coagulation cascades15
04514 Cell adhesion molecules CAMs14
04510 Focal adhesion14
04512 ECM-receptor interaction13
04145 Phagosome13

3.3. Interaction Network Construction and Identification of Hub Genes

DEGGs were mapped to the GeneMANIA to investigate how these genes interact with each other and additional genes that are related to a set of query genes by using a very large set of functional interaction data [30]. The genes showing significant interactions with weights higher than 0.1 were selected only for the network analysis. By integrating these relationships, a network between DEGGs and additional related genes was constructed for all the three groups, namely, BvL579, BvM501, and LvM442 (Figure 2). A GeneMANIA network analysis for interactions among the glycogene products suggested enrichment of GO terms related to positive regulation of locomotion, cell motility, and migration for BvL579 (Table 4(a)), as well as LvM442 samples (Table 4(c)). On the contrary, the processes related to extracellular matrix were overrepresented in BvM501 samples (Table 4(b)).

(a) BvL579

GO idDescription -value

GO:0040017positive regulation of locomotion1.83E − 23
GO:2000147positive regulation of cell motility1.83E − 23
GO:0030335positive regulation of cell migration3.40E − 23
GO:0051272positive regulation of cellular component movement3.40E − 23
GO:0050817coagulation1.94E − 20
GO:0010876lipid localization3.80E − 20
GO:0007596blood coagulation4.54E − 20
GO:0007599hemostasis7.56E − 20
GO:0009897external side of plasma membrane2.15E − 18
GO:0000323lytic vacuole1.05E − 17

(b) BvM501

GO idDescription -value

GO:0031012extracellular matrix2.43E − 23
GO:0005578proteinaceous extracellular matrix1.37E − 17
GO:0009100glycoprotein metabolic process1.99E − 16
GO:0000323lytic vacuole2.87E − 16
GO:0005764lysosome2.87E − 16
GO:0044420extracellular matrix part3.18E − 15
GO:0005604basement membrane1.20E − 14
GO:0005773vacuole4.56E − 14
GO:0030335positive regulation of cell migration5.17E − 12
GO:0005178integrin binding5.17E − 12

(c) LvM442

GO idDescription -value

GO:0000323lytic vacuole1.09E − 20
GO:0005764lysosome1.09E − 20
GO:0051272positive regulation of cellular component movement8.78E − 20
GO:0030335positive regulation of cell migration1.04E − 19
GO:2000147positive regulation of cell motility1.79E − 19
GO:0040017positive regulation of locomotion6.43E − 19
GO:0005773vacuole1.70E − 18
GO:0019838growth factor binding2.80E − 18
GO:0009100glycoprotein metabolic process2.11E − 15
GO:0031012extracellular matrix1.48E − 14

The three interaction networks created from GeneMANIA were then exported to Cytoscape 2.8.2, a bioinformatics package for biological network visualization and data integration [31]. The initial network for BvL579 consists of 395 nodes and 1149 edges which were further filtered to 395 nodes and 1109 edges by removing duplicate edges (Figure 2(a)). In case of BvM501, the initial network consists of 352 nodes and 794 edges, whereas the filtered network has 352 nodes and 748 edges (Figure 2(b)). Similarly, the initial network for LvM442 network has 307 nodes and 695 edges, while the final network is represented by 307 nodes and 653 edges (Figure 2(c)). All the genes in the network are represented by circles and the interactions between them are represented as edges. Additionally, each query gene is shown in red whereas the additional related genes predicted by GeneMANIA are shown in cyan (Figure 2).

The node degree was then calculated for all the nodes in each network by using Network Analyzer plugin of Cytoscape. The higher the node degree, the more important the gene was, and the gene was denoted as a hub gene. The genes with highest node degree include PEMT (BvL579) and IGf2 (BvM501), having node degree of 29 and 14, respectively. Both PEMT and IGF2 were predicted by GeneMANIA as related genes in their respective networks and do not represent the glycogenes. However, the genes with second highest node degrees of 23 and 13 are represented by glycogenes HPXN and NID2 in BvL579 and BvM501 networks (Figures 3(a) and 3(b)). Additionally, NID2 was also predicted by GeneMANIA as a related gene in BvM501 network because it was removed from the query list as it showed a fold change of less than 2 in our analysis. In LvM442 network, the top node degree genes are STAT6, and the glycogene FLT1 (Figure 3(c)), both having the node degree of 13.

3.4. Community Analysis and Functional Annotation of the Detected Modules

In order to identify the biologically related genes in the three networks, we employed Fast Greedy community-structure identification algorithm, implemented in the Cytoscape plugin GLay (http://brainarray.mbni.med.umich.edu/sugang/glay/) [29] to identify functional modules, 31 (Figure 4(a)), 40 (Figure 4(b)), and 31 (Figure 4(c)) clusters were detected for BvL579, BvM501, and LvM442 networks, respectively. However, only those communities were selected for enrichment analyses which have at least 10 nodes. Therefore, based on this criterion, 8, 9, and 8 communities for BvL579, BvM501, and LvM442 networks were finally analyzed for overrepresentation of GO terms.

To biologically categorize these clusters, DAVID functional analysis tool was used to classify the genes in each module and observed the enrichment of GO term “Biological Process” in all the selected modules. The enrichment analysis for the modules for three networks is described as follows.

BvL579. The top 10 statistically significant enriched GO terms for DEGGs in all 8 clusters for BvL579 community analysis are summarized in Table 5(a). The most statistically significant GO terms that were enriched in cluster 1 were related to immune response, complement activation, and protein maturation. Clusters 2, 3, and 7 show enrichment for GO terms response to wounding, blood coagulation, and hemostasis. Clusters 2 and 3 also show overrepresentation of terms such as blood vessel development and vasculature development. The GO terms of cluster 4 were mostly related to hair cycle, development of epidermis, and ectoderm. The GO functions of cluster 5 were most related to localization, motility and migration of cells, and regulation of cell proliferation. Cluster 6 was significantly enriched with functions related to homeostasis, transport, and metabolism of cholesterol, and lipid as well as sterol. Cluster 8 showed overrepresentation of functional terms related to the metabolism of glycosphingolipid, glycolipid, sphingolipid, and ganglioside.

(a) BvL579

Cluster numberTermP value

1GO:0002541~activation of plasma proteins involved in acute inflammatory response5.02E − 22
GO:0006956~complement activation5.02E − 22
GO:0002526~acute inflammatory response2.26E − 20
GO:0006959~humoral immune response2.25E − 19
GO:0051604~protein maturation3.43E − 19
GO:0051605~protein maturation by peptide bond cleavage3.19E − 18
GO:0002253~activation of immune response3.20E − 18
GO:0016485~protein processing6.27E − 18
GO:0006957~complement activation, alternative pathway8.91E − 18
GO:0006954~inflammatory response1.44E − 17

2GO:0051094~positive regulation of developmental process5.33E − 05
GO:0042592~homeostatic process4.25E − 04
GO:0009611~response to wounding7.29E − 04
GO:0001568~blood vessel development9.88E − 04
GO:0001944~vasculature development0.001101764
GO:0050817~coagulation0.001394123
GO:0007596~blood coagulation0.001394123
GO:0007599~hemostasis0.001452434
GO:0050878~regulation of body fluid levels0.002774109
GO:0007169~transmembrane receptor protein tyrosine kinase signaling pathway0.003100357

3GO:0050817~coagulation2.74E − 11
GO:0007596~blood coagulation2.74E − 11
GO:0007599~hemostasis3.08E − 11
GO:0050878~regulation of body fluid levels1.97E − 10
GO:0042060~wound healing1.26E − 09
GO:0009611~response to wounding5.75E − 08
GO:0007167~enzyme linked receptor protein signaling pathway9.33E − 08
GO:0001568~blood vessel development5.52E − 07
GO:0001944~vasculature development6.64E − 07
GO:0001525~angiogenesis4.71E − 05

4GO:0022404~molting cycle process2.16E − 04
GO:0022405~hair cycle process2.16E − 04
GO:0001942~hair follicle development2.16E − 04
GO:0042633~hair cycle2.42E − 04
GO:0042303~molting cycle2.42E − 04
GO:0008544~epidermis development0.003102493
GO:0007398~ectoderm development0.00369522
GO:0050678~regulation of epithelial cell proliferation0.009595347
GO:0048732~gland development0.01092817
GO:0007173~epidermal growth factor receptor signaling pathway0.025611634

5GO:0007166~cell surface receptor linked signal transduction1.34E − 05
GO:0016477~cell migration2.69E − 05
GO:0042127~regulation of cell proliferation3.44E − 05
GO:0051674~localization of cell4.88E − 05
GO:0048870~cell motility4.88E − 05
GO:0006928~cell motion6.35E − 05
GO:0008284~positive regulation of cell proliferation2.52E − 04
GO:0007229~integrin-mediated signaling pathway4.87E − 04
GO:0040007~growth6.74E − 04
GO:0007507~heart development0.001229375

6GO:0055088~lipid homeostasis2.16E − 16
GO:0042632~cholesterol homeostasis2.64E − 15
GO:0055092~sterol homeostasis2.64E − 15
GO:0030301~cholesterol transport3.97E − 13
GO:0015918~sterol transport3.97E − 13
GO:0033344~cholesterol efflux7.23E − 13
GO:0006869~lipid transport3.89E − 12
GO:0008203~cholesterol metabolic process4.74E − 12
GO:0010876~lipid localization8.01E − 12
GO:0016125~sterol metabolic process1.02E − 11

7GO:0009611~response to wounding3.88E − 07
GO:0007596~blood coagulation2.94E − 06
GO:0050817~coagulation2.94E − 06
GO:0007599~hemostasis3.11E − 06
GO:0050878~regulation of body fluid levels7.69E − 06
GO:0042060~wound healing1.91E − 05
GO:0051818~disruption of cells of other organism during symbiotic interaction3.08E − 05
GO:0051851~modification by host of symbiont morphology or physiology3.08E − 05
GO:0051883~killing of cells in other organism during symbiotic interaction3.08E − 05
GO:0031640~killing of cells of another organism4.30E − 05

8GO:0006689~ganglioside catabolic process5.11E − 06
GO:0046479~glycosphingolipid catabolic process1.79E − 05
GO:0019377~glycolipid catabolic process1.79E − 05
GO:0001573~ganglioside metabolic process2.38E − 05
GO:0046466~membrane lipid catabolic process1.15E − 04
GO:0030149~sphingolipid catabolic process1.15E − 04
GO:0019915~lipid storage1.29E − 04
GO:0006687~glycosphingolipid metabolic process2.73E − 04
GO:0006664~glycolipid metabolic process3.90E − 04
GO:0050885~neuromuscular process controlling balance4.98E − 04

(b) BvM501

Cluster numberTermP value

1GO:0040008~regulation of growth3.73E − 08
GO:0051604~protein maturation7.27E − 08
GO:0001558~regulation of cell growth1.29E − 06
GO:0016485~protein processing2.32E − 05
GO:0016044~membrane organization7.38E − 05
GO:0016064~immunoglobulin mediated immune response9.61E − 05
GO:0051605~protein maturation by peptide bond cleavage1.09E − 04
GO:0019724~B cell mediated immunity1.09E − 04
GO:0002684~positive regulation of immune system process1.29E − 04
GO:0006958~complement activation, classical pathway2.00E − 04

2GO:0009100~glycoprotein metabolic process2.16E − 06
GO:0050654~chondroitin sulfate proteoglycan metabolic process9.34E − 06
GO:0006029~proteoglycan metabolic process8.82E − 05
GO:0048754~branching morphogenesis of a tube1.02E − 04
GO:0019800~peptide cross-linking via chondroitin 4-sulfate glycosaminoglycan1.89E − 04
GO:0001763~morphogenesis of a branching structure3.20E − 04
GO:0001568~blood vessel development4.29E − 04
GO:0001944~vasculature development4.79E − 04
GO:0030204~chondroitin sulfate metabolic process6.09E − 04
GO:0006813~potassium ion transport8.13E − 04

3GO:0001501~skeletal system development1.07E − 16
GO:0030509~BMP signaling pathway6.34E − 14
GO:0001503~ossification4.02E − 12
GO:0060348~bone development7.43E − 12
GO:0030500~regulation of bone mineralization6.08E − 11
GO:0007178~transmembrane receptor protein serine/threonine kinase signaling pathway7.75E − 11
GO:0070167~regulation of biomineral formation9.25E − 11
GO:0007167~enzyme linked receptor protein signaling pathway1.84E − 10
GO:0007369~gastrulation2.22E − 10
GO:0051216~cartilage development3.31E − 10

4GO:0006928~cell motion9.88E − 05
GO:0016055~Wnt receptor signaling pathway2.59E − 04
GO:0016477~cell migration4.37E − 04
GO:0048870~cell motility7.09E − 04
GO:0051674~localization of cell7.09E − 04
GO:0007160~cell-matrix adhesion0.001189285
GO:0009100~glycoprotein metabolic process0.001250564
GO:0031589~cell-substrate adhesion0.001570006
GO:0007155~cell adhesion0.005368157
GO:0022610~biological adhesion0.005405426

5GO:0006689~ganglioside catabolic process1.66E − 05
GO:0019377~glycolipid catabolic process5.78E − 05
GO:0046479~glycosphingolipid catabolic process5.78E − 05
GO:0001573~ganglioside metabolic process7.69E − 05
GO:0009611~response to wounding2.01E − 04
GO:0046466~membrane lipid catabolic process3.70E − 04
GO:0030149~sphingolipid catabolic process3.70E − 04
GO:0019915~lipid storage4.16E − 04
GO:0006687~glycosphingolipid metabolic process8.77E − 04
GO:0009311~oligosaccharide metabolic process0.001247784

6GO:0042592~homeostatic process6.03E − 06
GO:0043270~positive regulation of ion transport0.001725638
GO:0051270~regulation of cell motion0.002725255
GO:0048771~tissue remodeling0.003048492
GO:0001894~tissue homeostasis0.003842174
GO:0051050~positive regulation of transport0.004095371
GO:0007169~transmembrane receptor protein tyrosine kinase signaling pathway0.004147057
GO:0032869~cellular response to insulin stimulus0.004461779
GO:0042127~regulation of cell proliferation0.004903945
GO:0010765~positive regulation of sodium ion transport0.005901212

7GO:0001666~response to hypoxia0.006023043
GO:0070482~response to oxygen levels0.006648281
GO:0016042~lipid catabolic process0.009864913
GO:0006644~phospholipid metabolic process0.011805904
GO:0019637~organophosphate metabolic process0.013020542
GO:0006916~anti-apoptosis0.013774773
GO:0043066~negative regulation of apoptosis0.037899056
GO:0043069~negative regulation of programmed cell death0.038883095
GO:0060548~negative regulation of cell death0.03908111

8NANA

9GO:0007160~cell-matrix adhesion0.001495186
GO:0031589~cell-substrate adhesion0.001809131
GO:0051085~chaperone mediated protein folding requiring cofactor0.006635187
GO:0051084~“de novo” posttranslational protein folding0.008618097
GO:0007155~cell adhesion0.00916406
GO:0022610~biological adhesion0.009200307
GO:0050982~detection of mechanical stimulus0.010597488
GO:0006458~“de novo” protein folding0.010597488
GO:0007259~JAK-STAT cascade0.025656487
GO:0030001~metal ion transport0.036163834
GO:0030001~metal ion transport0.036163834

(c) LvM442

Cluster numberTermP value

1GO:0001568~blood vessel development1.06E − 18
GO:0001944~vasculature development1.64E − 18
GO:0048514~blood vessel morphogenesis8.45E − 16
GO:0001525~angiogenesis3.48E − 12
GO:0048754~branching morphogenesis of a tube9.17E − 11
GO:0007167~enzyme linked receptor protein signaling pathway5.50E − 10
GO:0035239~tube morphogenesis1.14E − 09
GO:0001763~morphogenesis of a branching structure1.32E − 09
GO:0035295~tube development5.53E − 09
GO:0001569~patterning of blood vessels3.29E − 08

2GO:0040008~regulation of growth4.27E − 07
GO:0009611~response to wounding4.18E − 06
GO:0006954~inflammatory response3.42E − 05
GO:0032101~regulation of response to external stimulus1.89E − 04
GO:0048585~negative regulation of response to stimulus7.82E − 04
GO:0006952~defense response0.001399518
GO:0001558~regulation of cell growth0.002134607
GO:0050867~positive regulation of cell activation0.002784166
GO:0032102~negative regulation of response to external stimulus0.004518678
GO:0008283~cell proliferation0.004842417

3GO:0007160~cell-matrix adhesion7.84E − 05
GO:0031589~cell-substrate adhesion1.14E − 04
GO:0044242~cellular lipid catabolic process9.80E − 04
GO:0016042~lipid catabolic process9.93E − 04
GO:0051346~negative regulation of hydrolase activity0.006807864
GO:0008218~bioluminescence0.007742186
GO:0018401~peptidyl-proline hydroxylation to 4-hydroxy-L-proline0.007742186
GO:0019471~4-hydroxyproline metabolic process0.007742186
GO:0009101~glycoprotein biosynthetic process0.007780584
GO:0007155~cell adhesion0.00843311

4GO:0001501~skeletal system development7.15E − 04
GO:0006955~immune response0.011579
GO:0051085~chaperone mediated protein folding requiring cofactor0.013230724
GO:0051084~“de novo” posttranslational protein folding0.01716759
GO:0045453~bone resorption0.019783921
GO:0006458~“de novo” protein folding0.021089619
GO:0046849~bone remodeling0.027593503
GO:0006952~defense response0.045862312

5GO:0001916~positive regulation of T cell mediated cytotoxicity4.08E − 05
GO:0001914~regulation of T cell mediated cytotoxicity6.22E − 05
GO:0002711~positive regulation of T cell mediated immunity8.81E − 05
GO:0002474~antigen processing and presentation of peptide antigen via MHC class I1.53E − 04
GO:0002709~regulation of T cell mediated immunity1.72E − 04
GO:0031343~positive regulation of cell killing2.84E − 04
GO:0001912~positive regulation of leukocyte mediated cytotoxicity2.84E − 04
GO:0001910~regulation of leukocyte mediated cytotoxicity4.86E − 04
GO:0031341~regulation of cell killing4.86E − 04
GO:0002821~positive regulation of adaptive immune response6.25E − 04

6GO:0002526~acute inflammatory response4.67E − 14
GO:0045087~innate immune response4.65E − 13
GO:0002684~positive regulation of immune system process1.53E − 12
GO:0050778~positive regulation of immune response3.29E − 12
GO:0006954~inflammatory response3.41E − 12
GO:0002541~activation of plasma proteins involved in acute inflammatory response4.05E − 12
GO:0006956~complement activation4.05E − 12
GO:0048584~positive regulation of response to stimulus4.13E − 11
GO:0006952~defense response4.75E − 11
GO:0006959~humoral immune response5.29E − 11

7GO:0009611~response to wounding2.97E − 06
GO:0006958~complement activation, classical pathway7.07E − 06
GO:0002526~acute inflammatory response7.33E − 06
GO:0002455~humoral immune response mediated by circulating immunoglobulin8.69E − 06
GO:0006956~complement activation2.20E − 05
GO:0002541~activation of plasma proteins involved in acute inflammatory response2.36E − 05
GO:0016064~immunoglobulin mediated immune response4.70E − 05
GO:0019724~B cell mediated immunity5.25E − 05
GO:0002449~lymphocyte mediated immunity1.02E − 04
GO:0002250~adaptive immune response1.36E − 04

8GO:0051450~myoblast proliferation0.002206368
GO:0048012~hepatocyte growth factor receptor signaling pathway0.002206368
GO:0060665~regulation of branching involved in salivary gland morphogenesis by mesenchymal-epithelial signaling0.003674845
GO:0060638~mesenchymal-epithelial cell signaling0.004408354
GO:0060693~regulation of branching involved in salivary gland morphogenesis0.006605966
GO:0060688~regulation of morphogenesis of a branching structure0.018252999
GO:0001889~liver development0.031208893
GO:0000187~activation of MAPK activity0.036205594
GO:0043406~positive regulation of MAP kinase activity0.042595839

BvM501. The top 10 statistically significant enriched GO terms for DEGGs in all 9 clusters for BvM501 community analysis are summarized in (Table 5(b)). Cluster 1 of this network shows overrepresentation of functions related to regulation of cell growth, protein maturation, and immune response. Cluster 2 is enriched in GO terms related to proteoglycan metabolic process as well as the development of blood vessel and vasculature. Cluster 3 is enriched in the functions related to the development of bone, cartilage, and skeletal system. The GO terms that are overrepresented in cluster 4 show enrichment for functions related to adhesion, such as biological adhesion, cell adhesion, cell-substrate adhesion, and cell-matrix adhesion. The processes related to localization, motility, and migration of cells are also overrepresented in cluster 4. Cluster 5 showed overrepresentation of functional terms related to the metabolism of glycosphingolipid, glycolipid, sphingolipid, and ganglioside. Additionally, the functions related to the metabolism of lipid and oligosaccharides are also enriched in this group. In cluster 6, functions related to homeostasis, remodelling, and regulation of transport are overrepresented. Response to hypoxia and oxygen levels, apoptosis, and metabolic processes such as phospholipid and organophosphate metabolic process are enriched in cluster 7. Similar to cluster 4, the GO terms that are overrepresented in Cluster 9 show enrichment for functions related to biological adhesion. Protein folding and metal ion transport are the other important enriched functions present in this cluster. Cluster 8 did not yield the enrichment of any statistically significant GO category.

LvM442. Table 5(c) summarizes the top 10 statistically significant enriched GO terms for DEGGs in all 8 clusters for LvM442 community analysis. The GO terms related to development and morphogenesis of blood vessels, vasculature, and tube are enriched in cluster 1. Cluster 2 shows enrichment of functions related to various types of responses, such as response to wounding, external stimulus, and defense as well as inflammatory response. Other enriched functional groups in this cluster are regulation of cell growth, proliferation, and cell activation. One of the overrepresented functional groups in cluster 3 shows enrichment for functions related to biological adhesion, lipid catabolic process, and 4-hydroxyproline metabolic process. Cluster 4 represents the enrichment of functions related to protein folding and bone remodeling and resorption. Cluster 5 shows overrepresentation of GO terms such as regulation of T cell mediated cytotoxicity or immunity, regulation of leukocyte mediated cytotoxicity, and regulation of cell killing. Clusters 6 and 7 are enriched in functions related to immune or inflammatory response and complement activation. Finally, cluster 8 exhibited overrepresentation of signaling pathways such as hepatocyte growth factor receptor signaling and mesenchymal-epithelial cell signaling, in addition to processes concerning MAP kinase activity.

4. Discussion

The current study offers the first thorough insight into the glycogene analysis of brain, muscle, and liver tissues from mouse RNA-Seq data. Understanding the structure and function of these glycogenes is essential for studying the development of various tissues as well as their functional roles. Most of the serum glycoproteins originates from liver suggesting that liver diseases associated with aberrant glycosylation can be reflected by the changes in serum glycoproteins [32]. Recently, the significance of glycomics of central nervous system (CNS) to identify potential glycobiomarkers in neurological diseases [33] and alterations in brain glycoproteins resulting from the aging process was shown [34]. Recent studies in the glycomics field also offered insights into the biological significance of the glycome in the pathogenesis of diseases in humans [32]. Additionally, many studies have highlighted the significance of glycoconjugates during skeletal muscle development [7, 23, 35].

With the evolution of bioinformatics during the past decades, molecular target discovery and targeted therapeutics have become a critical remedial treatment for diseases [36]. In this work, we screened DEGGs between brain, muscle, and liver tissues using RNA-Seq data and performed functional and pathway enrichment analysis using DAVID and KEGG tools. The most significant enriched GO terms for BvL579 are processes related to response to wounding or inflammatory response. These processes are represented by genes that play a prominent roles in innate immunity such as Mannan-binding lectin serine protease 1 (MASP1), Mannan-binding lectin serine protease 2 (MASP2), Mannose-binding protein A (MBL1), and Mannose-binding protein C (MBL2) [37, 38]. BvM501 is enriched in functions related to glycoprotein metabolic processes and includes genes that code for various transferases such as N-acetylglucosamine-1-phosphotransferase subunit gamma (GNPTG) [39], Beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1) [40], and Beta-1,3-galactosyltransferase 6 (B3GALT6) [41]. Similarly, the top statistically significant enriched processes in LvM442 are related to protein maturation or processing and are represented by genes such as Battenin (CLN3) and Methionine aminopeptidase 2 (METAP2). The CLN3 protein is involved in the late endosomal/lysosomal membrane transport [42], whereas METAP2 protein catalyzes the hydrolytic cleavage of the N-terminal methionine from newly synthesized polypeptides [43].

Additionally, three interaction networks of DEGGs were constructed for BvL579, BvM501, and LvM442 samples, and node degrees of genes were calculated. HPXN, NID2, and FLT1 were the glycogenes with the highest degree in BvL579, BvM501, and LvM442 networks, respectively. However, these genes represent the second highest node degree in their respective networks as genes (PEMT, IGF2, and STAT6) with top node degree that do not code for glycoproteins.

HPXN (~60 kDa glycoprotein) is mainly synthesized by liver cells [44], secreted to the plasma where it binds free heme or hemin and inhibits its role in free radical reactions [45]. HPXN is also reported to be expressed by the cells of immune systems, ganglionic and photoreceptor cells of the retina, cells of the peripheral nervous system, and the mesangial cells of kidney [4648]. Recently, it was shown that HPX protein is also present in various regions of mouse brain [49]. NID2 is homologous to another member of the nidogen family, NID1, and both are found in all basement membranes (BMs) [50]. Both nidogens have a similar distribution in various organs during development; however, in adult tissues nidogen-2 distribution becomes more confined [5153]. Additionally, these nidogens show a broad range of interacting partners including other BM proteins such as laminin, collagen IV, and perlecan [51, 5355]. They are involved in various functions including the regulation of cell attachment [56], neutrophil chemotaxis [57], trophoblast outgrowth [58], angiogenesis [59], osteoblast, and myogenic differentiation [60, 61]. Vascular endothelial growth factors (VEGFs) constitute a family of six polypeptides (VEGF-A, -B, -C, -D, -E, and PlGF) that regulate blood and lymphatic vessel development [62]. VEGF signaling occurs by binding to various cellular receptors such as VEGFR1 (FLT1), VEGFR2 (FLK1), and VEGFR3 (FLT4) [63, 64] and neuropilin-1 [65] and -2 (NRP1 and NRP2) [66] and heparan sulfate proteoglycans (HSPG) [67]. FLT1 and FLK1 are closely related receptor tyrosine kinases and both share common and specific ligands [68]. FLT1 has weaker kinase activity than FLK1 [68]; however, FLT1 is essential for normal development and angiogenesis as reported in previous FLT1 null mutant mice studies [6971].

Recognizing the structure and function of biological networks is indispensable for the investigation of biological processes. In this work, we identified 8, 9, and 8 functional modules or communities for BvL579, BvM501, and LvM442 networks using fast greedy algorithm implemented as GLAY [29], plugin for cytoscape. Furthermore, the function of each module in all the three networks was explored by using functional annotation tool DAVID. Our analysis shows that the modules that are common in BvL579 and LvM442 networks show enrichment for processes related to immune and inflammatory response and response to wounding. Liver is known to play an important role in innate immunity, an important primary line of defense against infection [72]. Additionally, Kupffer cells in the liver are one of the earliest to be affected by bacterial or sterile insults and add to the inflammatory response [73]. This sterile inflammation can be responsible for liver injury; it may also play a role in liver repair [72]. In case of the modules of BvM501 and LvM442 networks, the enrichment of processes related to cell adhesion, proliferation, and regulation of cell growth is observed. The expression of myogenic regulatory factors (MRFs) describes the different stages of skeletal muscle development that includes myoblast proliferation, cell-cycle exit, cell fusion, and the maturation of myotubes to form myofibers [23]. Cell adhesion molecules such as cadherins are glycoproteins that mediate homotypic cell-cell adhesion through their extracellular domain [74], and this cadherin-dependent adhesion is necessary for diverse types of cellular functions [75]. Finally, glycosphingolipid or glycolipid metabolic processes show overrepresentation in modules of BvL579 and BvM501 networks. Glycosphingolipids are universally expressed in all vertebrate cells as well as body fluids, but they are richer in the nervous system [76]. Previous studies have established the role of glycosphingolipids in the development of the brain among various species [77, 78].

5. Conclusion

Overall, in this work we have used a systems biology approach to identify the DEGGs, their functional enrichment, and identified potential hub genes by constructing three glycogene interaction networks. We also identified functional modules in these networks and indicate the significance of immune system related processes in liver, and glycosphingolipid metabolic processes in the development of brain. Similarly, we observe that the processes such as cell proliferation, adhesion, and growth are important for muscle development. The findings deduced from the current work are in consistence with the previous studies. Experimental validation will be required to confirm the predictions made in this study that will establish the role of predicted hubs as well as enriched functional processes in these tissues.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

Authors would like to acknowledge the support from the Biomedical Informatics Center of ICMR, PGIMER (Chandigarh, India); School of Chemistry and Biochemistry, Thapar University, (Patiala, India); and School of Biotechnology, Yeungnam University (Gyeongsan, South Korea) for providing lab facilities to conduct this research.

Supplementary Materials

Supplementary Table S1: List of all KEGG pathways reported for different tissue groups. Each sheet contains the list of KEGG pathways and the number of mapped genes in brain versus liver (BvL579), brain versus muscle (BvM501), and liver versus muscle (LvM442) groups.

  1. Supplementary Materials

References

  1. J. Salzman, H. Jiang, and W. H. Wong, “Statistical modeling of RNA-Seq data,” Statistical Science, vol. 26, no. 1, pp. 62–83, 2011. View at: Publisher Site | Google Scholar | MathSciNet
  2. F. Mantelli, L. Schaffer, R. Dana, S. R. Head, and P. Argüeso, “Glycogene expression in conjunctiva of patients with dry eye: downregulation of Notch signaling,” PLoS ONE, vol. 50, no. 6, pp. 2666–2672, 2010. View at: Google Scholar
  3. A. Ishii, S. Hitoshi, I. Fujimoto et al., “Developmental changes in the expression of glycogenes and the content of N-glycans in the mouse cerebral cortex,” Glycobiology, vol. 17, no. 3, pp. 261–276, 2007. View at: Publisher Site | Google Scholar
  4. Y. Nakayama, N. Nakamura, D. Tsuji, K. Itoh, and A. Kurosaka, “Genetic diseases associated with protein glycosylation disorders in mammals,” in Genetic Disorders, InTechOpen, 2013. View at: Publisher Site | Google Scholar
  5. http://www.stelic.com/cn27/pg122.html.
  6. N. Taniguchi, A. Suzuki, Y. Ito et al., Experimental Glycoscience: Glycobiology, Springer, New York, NY, USA, 2009.
  7. M. Janot, A. Audfray, C. Loriol, A. Germot, A. Maftah, and F. Dupuy, “Glycogenome expression dynamics during mouse C2C12 myoblast differentiation suggests a sequential reorganization of membrane glycoconjugates,” BMC Genomics, vol. 10, no. 483, 2009. View at: Publisher Site | Google Scholar
  8. Z. Zhang, J. Sun, L. Hao, C. Liu, H. Ma, and L. Jia, “Modification of glycosylation mediates the invasive properties of murine hepatocarcinoma cell lines to lymph nodes,” PLoS ONE, vol. 20, no. 8, Article ID e65218, 2013. View at: Publisher Site | Google Scholar
  9. S. Ngamukote, M. Yanagisawa, T. Ariga, S. Ando, and R. K. Yu, “Developmental changes of glycosphingolipids and expression of glycogenes in mouse brains,” Journal of Neurochemistry, vol. 103, no. 6, pp. 2327–2341, 2007. View at: Publisher Site | Google Scholar
  10. A. Urita, T. Matsuhashi, T. Onodera et al., “Alterations of high-mannose type N-glycosylation in human and mouse osteoarthritis cartilage,” Arthritis and Rheumatism, vol. 63, no. 11, pp. 3428–3438, 2011. View at: Publisher Site | Google Scholar
  11. C. Saravanan, Z. Cao, S. R. Head, and N. Panjwani, “Analysis of differential expression of glycosyltransferases in healing corneas by glycogene microarrays,” Glycobiology, vol. 20, no. 1, pp. 13–23, 2010. View at: Publisher Site | Google Scholar
  12. C. Saravanan, Z. Cao, S. R. Head, and N. Panjwani, “Detection of differentially expressed wound-healing- related glycogenes in galectin-3-deficient mice,” Investigative Ophthalmology and Visual Science, vol. 50, no. 12, pp. 5690–5696, 2009. View at: Publisher Site | Google Scholar
  13. K. A. Maupin, A. Sinha, E. Eugster et al., “Glycogene expression alterations associated with pancreatic cancer Epithelial-mesenchymal transition in complementary model systems,” PLoS ONE, vol. 5, no. 9, Article ID e13002, 2010. View at: Publisher Site | Google Scholar
  14. H. Ma, X. Miao, Q. Ma, W. Zheng, H. Zhou, and L. Jia, “Functional roles of glycogene and N-glycan in multidrug resistance of human breast cancer cells,” IUBMB Life, vol. 65, no. 5, pp. 409–422, 2013. View at: Publisher Site | Google Scholar
  15. R. Guo, L. Cheng, Y. Zhao et al., “Glycogenes mediate the invasive properties and chemosensitivity of human hepatocarcinoma cells,” The International Journal of Biochemistry and Cell Biology, vol. 45, no. 2, pp. 347–358, 2013. View at: Publisher Site | Google Scholar
  16. S. Diskin, J. Kumar, Z. Cao et al., “Detection of differentially expressed glycogenes in trabecular meshwork of eyes with primary open-angle glaucoma,” Investigative Ophthalmology and Visual Science, vol. 47, no. 4, pp. 1491–1499, 2006. View at: Publisher Site | Google Scholar
  17. T. Inoue, H. Sugiyama, M. Kitagawa et al., “Abnormalities of glycogenes in tonsillar lymphocytes in IgA nephropathy,” Advances in Otorhinolaryngology, vol. 72, pp. 71–74, 2011. View at: Google Scholar
  18. T. Inoue, H. Sugiyama, Y. Hiki et al., “Differential expression of glycogenes in tonsillar B lymphocytes in association with proteinuria and renal dysfunction in IgA nephropathy,” Clinical Immunology, vol. 136, no. 3, pp. 447–455, 2010. View at: Publisher Site | Google Scholar
  19. R. A. Kroes, G. Dawson, and J. R. Moskal, “Focused microarray analysis of glyco-gene expression in human glioblastomas.,” Journal of neurochemistry, vol. 103, supplement s1, pp. 14–24, 2007. View at: Publisher Site | Google Scholar
  20. Z. Tan, W. Lu, X. Li et al., “Altered N-glycan expression profile in epithelial-to-mesenchymal transition of NMuMG cells revealed by an integrated strategy using mass spectrometry and glycogene and lectin microarray analysis,” Journal of Proteome Research, vol. 13, no. 6, pp. 2783–2795, 2014. View at: Publisher Site | Google Scholar
  21. A. Mortazavi, B. A. Williams, K. McCue, L. Schaeffer, and B. Wold, “Mapping and quantifying mammalian transcriptomes by RNA-Seq,” Nature Methods, vol. 5, no. 7, pp. 621–628, 2008. View at: Publisher Site | Google Scholar
  22. Y. Guo, Q. Sheng, J. Li, F. Ye, D. C. Samuels, and Y. Shyr, “Large scale comparison of gene expression levels by microarrays and RNAseq using TCGA data,” PLoS ONE, vol. 8, no. 8, Article ID e71462, 2013. View at: Publisher Site | Google Scholar
  23. E. J. Lee, A. Malik, S. Pokharel et al., “Identification of genes differentially expressed in myogenin knock-down bovine muscle satellite cells during differentiation through RNA sequencing analysis,” PLoS ONE, vol. 9, no. 3, Article ID e92447, 2014. View at: Google Scholar
  24. http://www.uniprot.org/.
  25. Y. Moriya, M. Itoh, S. Okuda, A. C. Yoshizawa, and M. Kanehisa, “KAAS: an automatic genome annotation and pathway reconstruction server,” Nucleic Acids Research, vol. 35, no. 2, supplement, pp. W182–W185, 2007. View at: Publisher Site | Google Scholar
  26. J. Montojo, K. Zuberi, H. Rodriguez et al., “GeneMANIA cytoscape plugin: fast gene function predictions on the desktop,” Bioinformatics, vol. 26, no. 22, Article ID btq562, pp. 2927–2928, 2010. View at: Publisher Site | Google Scholar
  27. R. Albert, “Scale-free networks in cell biology,” Journal of Cell Science, vol. 118, no. 21, pp. 4947–4957, 2005. View at: Publisher Site | Google Scholar
  28. X. R. Wu, Y. Zhu, and Y. Li, “Analyzing protein interaction networks via random graph model,” International Journal of Information Technology, vol. 11, pp. 125–132, 2005. View at: Google Scholar
  29. G. Su, A. Kuchinsky, J. H. Morris, D. J. States, and F. Meng, “GLay: community structure analysis of biological networks,” Bioinformatics, vol. 26, no. 24, pp. 3135–3137, 2010. View at: Publisher Site | Google Scholar
  30. D. W. Farley, S. L. Donaldson, O. Comes et al., “The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function,” Nucleic Acids Research, vol. 38, no. 2, pp. W214–W220, 2010. View at: Publisher Site | Google Scholar
  31. M. E. Smoot, K. Ono, J. Ruscheinski, P. Wang, and T. Ideker, “Cytoscape 2.8: new features for data integration and network visualization,” Bioinformatics, vol. 27, no. 3, pp. 431–432, 2011. View at: Publisher Site | Google Scholar
  32. B. Adamczyk, T. Tharmalingam, and P. M. Rudd, “Glycans as cancer biomarkers,” Biochimica et Biophysica Acta—General Subjects, vol. 1820, no. 9, pp. 1347–1353, 2012. View at: Publisher Site | Google Scholar
  33. R. Barone, L. Sturiale, A. Palmigiano, M. Zappia, and D. Garozzo, “Glycomics of pediatric and adulthood diseases of the central nervous system,” Journal of Proteomics, vol. 75, no. 17, pp. 5123–5139, 2012. View at: Publisher Site | Google Scholar
  34. Y. Sato and T. Endo, “Alteration of brain glycoproteins during aging,” Geriatrics and Gerontology International, vol. 10, pp. S32–S40, 2010. View at: Publisher Site | Google Scholar
  35. D. D. W. Cornelison, S. A. Wilcox-Adelman, P. F. Goetinck, H. Rauvala, A. C. Rapraeger, and B. B. Olwin, “Essential and separable roles for Syndecan-3 and Syndecan-4 in skeletal muscle development and regeneration,” Genes and Development, vol. 18, no. 18, pp. 2231–2236, 2004. View at: Publisher Site | Google Scholar
  36. P. Dinh, C. Sotiriou, and M. J. Piccart, “The evolution of treatment strategies: aiming at the target,” Breast, vol. 16, supplement 2, pp. 10–16, 2007. View at: Publisher Site | Google Scholar
  37. A. J. Nauta, N. Raashou-Jensen, A. Roos et al., “Mannose-binding lectin engagement with late apoptotic and necrotic cells,” European Journal of Immunology, vol. 33, no. 10, pp. 2853–2863, 2003. View at: Publisher Site | Google Scholar
  38. M. Møller-Kristensen, S. Thiel, A. Sjöholm, M. Matsushita, and J. C. Jensenius, “Cooperation between MASP-1 and MASP-2 in the generation of C3 convertase through the MBL pathway,” International Immunology, vol. 19, no. 2, pp. 141–149, 2007. View at: Publisher Site | Google Scholar
  39. Y. Qian, I. Lee, W. Lee et al., “Functions of the α, β, and γsubunits of UDP-GlcNAc:Lysosomal enzyme N-acetylglucosamine-1-phosphotransferase,” The Journal of Biological Chemistry, vol. 285, no. 5, pp. 3360–3370, 2010. View at: Publisher Site | Google Scholar
  40. B. Kuhn, J. Benz, M. Greif, A. M. Engel, H. Sobek, and M. G. Rudolph, “The structure of human α-2, 6-sialyltransferase reveals the binding mode of complex glycans,” Acta Crystallographica D, vol. 69, pp. 1826–1838, 2013. View at: Google Scholar
  41. X. Bai, D. Zhou, J. R. Brown, B. E. Crawford, T. Hennet, and J. D. Esko, “Biosynthesis of the linkage region of glycosaminoglycans: cloning and activity of galactosyltransferase II, the sixth member of the β1,3-galactosyltransferase family (β3GalT6),” Journal of Biological Chemistry, vol. 276, no. 51, pp. 48189–48195, 2001. View at: Google Scholar
  42. K. Uusi-Rauva, A. Kyttälä, R. Van Der Kant et al., “Neuronal ceroid lipofuscinosis protein CLN3 interacts with motor proteins and modifies location of late endosomal compartments,” Cellular and Molecular Life Sciences, vol. 69, no. 12, pp. 2075–2089, 2012. View at: Publisher Site | Google Scholar
  43. Q. Xiao, F. Zhang, B. A. Nacev, J. O. Liu, and D. Pei, “Protein N-terminal processing: substrate specificity of Escherichia coli and human methionine aminopeptidases,” Biochemistry, vol. 49, no. 26, pp. 5588–5599, 2010. View at: Publisher Site | Google Scholar
  44. J. Chen-Roetling, W. Liu, and R. F. Regan, “Hemopexin decreases hemin accumulation and catabolism by neural cells,” Neurochemistry International, vol. 60, no. 5, pp. 488–494, 2012. View at: Publisher Site | Google Scholar
  45. E. Tolosano, S. Fagoonee, N. Morello, F. Vinchi, and V. Fiorito, “Heme scavenging and the other facets of hemopexin,” Antioxidants & Redox Signaling, vol. 12, no. 2, pp. 305–320, 2010. View at: Publisher Site | Google Scholar
  46. L. Camborieux, N. Bertrand, and J. P. Swerts, “Changes in expression and localization of hemopexin and its transcripts in injured nervous system: a comparison of central and peripheral tissues,” Neuroscience, vol. 82, no. 4, pp. 1039–1052, 1997. View at: Publisher Site | Google Scholar
  47. R. C. Hunt, D. M. Hunt, N. Gaur, and A. Smith, “Hemopexin in the human retina: protection of the retina against heme-mediated toxicity,” Journal of Cellular Physiology, vol. 168, no. 1, pp. 71–80, 1996. View at: Google Scholar
  48. J. J. Kapojos, A. van Den Berg, H. van Goor et al., “Production of hemopexin by TNF-α stimulated human mesangial cells,” Kidney International, vol. 63, no. 5, pp. 1681–1686, 2003. View at: Publisher Site | Google Scholar
  49. R.-C. Li, S. Saleem, G. Zhen et al., “Hemehemopexin complex attenuates neuronal cell death and stroke damage,” Journal of Cerebral Blood Flow & Metabolism, vol. 29, no. 5, pp. 953–964, 2009. View at: Publisher Site | Google Scholar
  50. M. Bechtel, M. V. Keller, W. Bloch et al., “Different domains in nidogen-1 and nidogen-2 drive basement membrane formation in skin organotypic cocultures,” The FASEB Journal, vol. 26, no. 9, pp. 3637–3648, 2012. View at: Publisher Site | Google Scholar
  51. E. Kohfeldt, T. Sasaki, W. Göhring, and R. Timpl, “Nidogen-2: a new basement membrane protein with diverse binding properties,” Journal of Molecular Biology, vol. 282, no. 1, pp. 99–109, 1998. View at: Publisher Site | Google Scholar
  52. M. Murshed, N. Smyth, N. Miosge et al., “The absence of nidogen 1 does not affect murine basement membrane formation,” Molecular and Cellular Biology, vol. 20, no. 18, pp. 7007–7012, 2000. View at: Publisher Site | Google Scholar
  53. K. Salmivirta, J. F. Talts, M. Olsson, T. Sasaki, R. Timpl, and P. Ekblom, “Binding of mouse nidogen-2 to basement membrane components and cells and its expression in embryonic and adult tissues suggest complementary functions of the two nidogens,” Experimental Cell Research, vol. 279, no. 2, pp. 188–201, 2002. View at: Publisher Site | Google Scholar
  54. J. W. Fox, U. Mayer, R. Nischt et al., “Recombinant nidogen consists of three globular domains and mediates binding of laminin to collagen type IV,” EMBO Journal, vol. 10, no. 11, pp. 3137–3146, 1991. View at: Google Scholar
  55. M. S. P. Ho, K. Böse, S. Mokkapati, R. Nischt, and N. Smyth, “Nidogens-extracellular matrix linker molecules,” Microscopy Research and Technique, vol. 71, no. 5, pp. 387–395, 2008. View at: Publisher Site | Google Scholar
  56. S. Chakravarti, M. F. Tam, and A. E. Chung, “The basement membrane glycoprotein entactin promotes cell attachment and binds calcium ions,” The Journal of Biological Chemistry, vol. 265, no. 18, pp. 10597–10603, 1990. View at: Google Scholar
  57. R. M. Senior, H. D. Gresham, G. L. Griffin, E. J. Brown, and A. E. Chung, “Entactin stimulates neutrophil adhesion and chemotaxis through interactions between its Arg-Gly-Asp (RGD) domain and the leukocyte response integrin,” Journal of Clinical Investigation, vol. 90, no. 6, pp. 2251–2257, 1992. View at: Publisher Site | Google Scholar
  58. F. D. Yelian, N. A. Edgeworth, L.-J. Dong, A. E. Chung, and D. R. Armant, “Recombinant entactin promotes mouse primary trophoblast cell adhesion and migration through the Arg-Gly-Asp (RGD) recognition sequence,” Journal of Cell Biology, vol. 121, no. 4, pp. 923–929, 1993. View at: Publisher Site | Google Scholar
  59. R. F. Nicosia, S. V. Nicosia, and M. Smith, “Vascular endothelial growth factor, platelet-derived growth factor, and insulin-like growth factor-1 promote rat aortic angiogenesis in vitro,” The American Journal of Pathology, vol. 145, no. 5, pp. 1023–1029, 1994. View at: Google Scholar
  60. N. Kimura, T. Toyoshima, T. Kojima, and M. Shimane, “Entactin-2: a new member of basement membrane protein with high homology to entactin/nidogen,” Experimental Cell Research, vol. 241, no. 1, pp. 36–45, 1998. View at: Publisher Site | Google Scholar
  61. R. Neu, S. Adams, and B. Munz, “Differential expression of entactin-1/nidogen-1 and entactin-2/nidogen-2 in myogenic differentiation,” Differentiation, vol. 74, no. 9-10, pp. 573–582, 2006. View at: Publisher Site | Google Scholar
  62. F. S. Grünewald, A. E. Prota, A. Giese, and K. Ballmer-Hofer, “Structure-function analysis of VEGF receptor activation and the role of coreceptors in angiogenic signaling,” Biochimica et Biophysica Acta: Proteins and Proteomics, vol. 1804, no. 3, pp. 567–580, 2010. View at: Publisher Site | Google Scholar
  63. B. I. Terman, M. E. Carrion, E. Kovacs, B. A. Rasmussen, R. L. Eddy, and T. B. Shows, “Identification of a new endothelial cell growth factor receptor tyrosine kinase,” Oncogene, vol. 6, no. 9, pp. 1677–1683, 1991. View at: Google Scholar
  64. M. Shibuya, S. Yamaguchi, A. Yamane et al., “Nucleotide sequence and expression of a novel human receptor-type tyrosine kinase gene (flt) closely relatd to the fms family,” Oncogene, vol. 5, no. 4, pp. 519–524, 1990. View at: Google Scholar
  65. K. Pajusola, O. Aprelikova, J. Korhonen et al., “FLT4 receptor tyrosine kinase contains seven immunoglobulin-like loops and is expressed in multiple human tissues and cell lines,” Cancer Research, vol. 52, no. 20, pp. 5738–5743, 1992. View at: Google Scholar
  66. G. Neufeld, O. Kessler, and Y. Herzog, “The interaction of neuropilin-1 and neurropilin-2 with tyrosine-kinase receptors for VEGF,” Advances in Experimental Medicine and Biology, vol. 5, no. 15, pp. 81–90, 2002. View at: Publisher Site | Google Scholar
  67. S. Tessler, P. Rockwell, D. Hicklin et al., “Heparin modulates the interaction of VEGF165 with soluble and cell associated flk-1 receptors,” Journal of Biological Chemistry, vol. 269, no. 17, pp. 12456–12461, 1994. View at: Google Scholar
  68. J. Nishi, T. Minamino, H. Miyauchi et al., “Vascular endothelial growth factor receptor-1 regulates postnatal angiogenesis through inhibition of the excessive activation of Akt,” Circulation Research, vol. 103, no. 3, pp. 261–268, 2008. View at: Publisher Site | Google Scholar
  69. G.-H. Fong, J. Rossant, M. Gertsenstein, and M. L. Breitman, “Role of the Flt-1 receptor tyrosine kinase in regulating the assembly of vascular endothelium,” Nature, vol. 376, no. 6535, pp. 66–70, 1995. View at: Publisher Site | Google Scholar
  70. G. Fong, L. Zhang, D. Bryce, and J. Peng, “Increased hemangioblast commitment, not vascular disorganization, is the primary defect in flt-1 knock-out mice,” Development, vol. 126, no. 13, pp. 3015–3025, 1999. View at: Google Scholar
  71. J. B. Kearney, C. A. Ambler, K.-A. Monaco, N. Johnson, R. G. Rapoport, and V. L. Bautch, “Vascular endothelial growth factor receptor Flt-1 negatively regulates developmental blood vessel formation by modulating endothelial cell division,” Blood, vol. 99, no. 7, pp. 2397–2407, 2002. View at: Publisher Site | Google Scholar
  72. B. Gao, W. Jeong, and Z. Tian, “Liver: an organ with predominant innate immunity,” Hepatology, vol. 47, no. 2, pp. 729–736, 2008. View at: Publisher Site | Google Scholar
  73. V. Bieghs and C. Trautwein, “The innate immune response during liver inflammation and metabolic disease,” Trends in Immunology, vol. 34, no. 9, pp. 446–452, 2013. View at: Publisher Site | Google Scholar
  74. S. Charrasse, M. Causeret, F. Comunale, A. Bonet-Kerrache, and C. Gauthier-Rouvière, “Rho GTPases and cadherin-based cell adhesion in skeletal muscle development,” Journal of Muscle Research and Cell Motility, vol. 24, no. 4–6, pp. 309–313, 2003. View at: Google Scholar
  75. K. A. Knudsen, L. Myers, and S. A. McElwee, “A role for the Ca2+-dependent adhesion molecule, N-Cadherin, in myoblast interaction during myogenesis,” Experimental Cell Research, vol. 188, no. 2, pp. 175–184, 1990. View at: Publisher Site | Google Scholar
  76. R. K. Yu, Y. Nakatani, and M. Yanagisawa, “The role of glycosphingolipid metabolism in the developing brain,” Journal of Lipid Research, vol. 50, pp. S440–S445, 2009. View at: Publisher Site | Google Scholar
  77. L. Svennerholm, K. Bostrom, P. Fredman, J. E. Månsson, B. Rosengren, and B. M. Rynmark, “Human brain gangliosides: developmental changes from early fetal stage to advanced age,” Biochimica et Biophysica Acta, vol. 1005, no. 2, pp. 109–117, 1989. View at: Publisher Site | Google Scholar
  78. R. K. Yu, L. J. Macala, T. Taki, H. M. Weinfeld, and F. S. Yu, “Developmental changes in ganglioside composition and synthesis in embryonic rat brain,” Journal of Neurochemistry, vol. 50, no. 6, pp. 1825–1829, 1988. View at: Publisher Site | Google Scholar

Copyright © 2014 Ahmad Firoz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

2011 Views | 689 Downloads | 6 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19.