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As the only known organ that can completely regenerate in mammals, deer antler is of real significance in the field of regenerative medicine. Recent studies have shown that the regenerative capacity of the antlers comes from the pedicle periosteum and the cells resident in the periosteum possess the attributes of stem cells. Currently, the molecular mechanism of antler regeneration remains unclear. In the present study, we compared the potentiated and dormant antler stem cells using isobaric tags for the relative and absolute quantification (iTRAQ) labeling of the peptides, coupled with two-dimensional liquid chromatography-tandem mass spectrometry (LC-MS/MS) to compare the proteome profiles. Proteins were identified by searching against the NCBI nr database and our own Cervine transcriptome database, and bioinformatics analysis was conducted to identify the differentially expressed proteins. Based on this searching strategy, we identified 169 differentially expressed proteins in total, consisting of 70 up- and 99 down-regulated in the potentiated vs. dormant antler stem cells. Reliability of the iTRAQ was confirmed via quantitative real-time polymerase chain reaction (qRT-PCR) to measure the expression of selected genes. We identified transduction pathways through the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, such as HIF-1 and PI3K-AKT signaling pathways that play important roles in regulating the regeneration of antlers. In summary, the initiation stage of antler regeneration, a process from dormant to potentiated states in antler stem cells, is regulated by multiple proteins and complicated signal networks.
Pubmed ID: 27792145
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A Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes in a functionally grouped network. It can be used in combination with GOlorize. The identifiers can be uploaded from a text file or interactively from a network of Cytoscape. The type of identifiers supported can be easily extended by the user. ClueGO performs single cluster analysis and comparison of clusters. From the ontology sources used, the terms are selected by different filter criteria. The related terms which share similar associated genes can be combined to reduce redundancy. The ClueGO network is created with kappa statistics and reflects the relationships between the terms based on the similarity of their associated genes. On the network, the node colour can be switched between functional groups and clusters distribution. ClueGO charts are underlying the specificity and the common aspects of the biological role. The significance of the terms and groups is automatically calculated. ClueGO is easy updatable with the newest files from Gene Ontology and KEGG. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsCommercial instrument and chemical vendor. Developer and manufacturer of specialized technological products for life science research and clinical diagnostics markets.
View all literature mentionsSoftware platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.
View all literature mentionsDatabase of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)
View all literature mentionsSoftware tool for protein identification and relative protein expression analysis. Used in protein research to identify proteins and search large numbers of post translational modifications. Compatible with all proteomics MS/MS systems.
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