Gene Ontology and Biological Pathway-Based Analysis

2019 ◽  
pp. 121-134
Author(s):  
Ju Han Kim
Keyword(s):  
Gene Ontology ◽  
Biological Pathway

The gene ontology and electro localization of ovine skin derived EST-SSR markers

2011 ◽  
Vol 33 (7) ◽  
pp. 731-737 ◽  
Author(s):  
Zun-Bao WANG ◽  
Zong-Sheng ZHAO ◽  
Peng YU ◽  
Hong-Bin WU ◽  
Qian BAN ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Ssr Markers

A Novel Prediction of Quaternary Structural Type of Proteins with Gene Ontology

2020 ◽  
Vol 27 (4) ◽  
pp. 313-320 ◽  
Author(s):  
Xuan Xiao ◽  
Wei-Jie Chen ◽  
Wang-Ren Qiu
Keyword(s):  
Gene Ontology ◽  
Feature Extraction ◽  
Extraction Method ◽  
Quaternary Structure ◽  
Structural Type ◽  
Sequence Information ◽  
Prediction System ◽  
Data Set ◽  
Feature Extraction Method ◽  
Prediction Rate

Background: The information of quaternary structure attributes of proteins is very important because it is closely related to the biological functions of proteins. With the rapid development of new generation sequencing technology, we are facing a challenge: how to automatically identify the four-level attributes of new polypeptide chains according to their sequence information (i.e., whether they are formed as just as a monomer, or as a hetero-oligomer, or a homo-oligomer). Objective: In this article, our goal is to find a new way to represent protein sequences, thereby improving the prediction rate of protein quaternary structure. Methods: In this article, we developed a prediction system for protein quaternary structural type in which a protein sequence was expressed by combining the Pfam functional-domain and gene ontology. turn protein features into digital sequences, and complete the prediction of quaternary structure through specific machine learning algorithms and verification algorithm. Results: Our data set contains 5495 protein samples. Through the method provided in this paper, we classify proteins into monomer, or as a hetero-oligomer, or a homo-oligomer, and the prediction rate is 74.38%, which is 3.24% higher than that of previous studies. Through this new feature extraction method, we can further classify the four-level structure of proteins, and the results are also correspondingly improved. Conclusion: After the applying the new prediction system, compared with the previous results, we have successfully improved the prediction rate. We have reason to believe that the feature extraction method in this paper has better practicability and can be used as a reference for other protein classification problems.

Gene Ontology-Facilitated Genome Analysis

2003 ◽  
Vol 4 (7) ◽  
pp. 569-574
Author(s):  
Hanqing Xie
Keyword(s):  
Gene Ontology ◽  
Genome Analysis

Overexpression of miR-340-5p Inhibits Skin Fibroblast Proliferation by Targeting Kruppel-like Factor 2

2019 ◽  
Vol 20 (13) ◽  
pp. 1147-1154 ◽  
Author(s):  
Ling Chen ◽  
Qian Li ◽  
Xun Lu ◽  
Xiaohua Dong ◽  
Jingyun Li
Keyword(s):  
Gene Ontology ◽  
Skin Fibroblast ◽  
Kegg Pathway ◽  
Normal Skin ◽  
Skin Fibroblasts ◽  
Differentially Expressed ◽  
Luciferase Reporter ◽  
Fibroblast Proliferation ◽  
Pathway Analyses ◽  
Normal Skin Fibroblast

<P>Objective: MicroRNA (miR)-340-5p has been identified to play a key role in several cancers. However, the function of miR-340-5p in skin fibroblasts remains largely unknown. </P><P> Methods: Gain of function experiments were performed by infecting normal skin fibroblast cells with a lentivirus carrying 22-bp miR-340-5p. Cell proliferation was detected by Cell Counting Kit-8 (CCK-8) assay. To uncover the mechanisms, mRNA-seq was used. Differentially expressed mRNAs were further determined by Gene Ontology and KEGG pathway analyses. The protein levels were analysed by Western blotting. A dual-luciferase reporter assay was used to detect the direct binding of miR-340-5p with the 3&#039;UTR of Kruppel-like factor 2 (KLF2). </P><P> Results: MiR-340-5p lentivirus infection suppressed normal skin fibroblast proliferation. The mRNAseq data revealed that 41 mRNAs were differentially expressed, including 22 upregulated and 19 downregulated transcripts in the miR-340-5p overexpression group compared with those in the control group. Gene Ontology and KEGG pathway analyses revealed that miR-340-5p overexpression correlated with the macromolecule biosynthetic process, cellular macromolecule biosynthetic process, membrane, and MAPK signalling pathway. Bioinformatics analysis and luciferase reporter assays showed that miR-340-5p binds to the 3&#039;UTR of KLF2. Forced expression of miR-340-5p decreased the expression of KLF2 in normal skin fibroblasts. Overexpression of KLF2 restored skin fibroblast proliferation in the miR-340-5p overexpression group. </P><P> Conclusion: This study demonstrates that miR-340-5p may suppress skin fibroblast proliferation, possibly through targeting KLF2. These findings could help us understand the function of miR-340-5p in skin fibroblasts. miR-340-5p could be a therapeutic target for preventing scarring.</P>

Network Pharmacology Approach uncovering Pathways involved in targeting Hsp90 through Curcumin and Epigallocatechin to control Inflammation.

2019 ◽  
Vol 16 ◽  
Author(s):  
Umme Hani ◽  
Shivananda Kandagalla ◽  
B.S. Sharath ◽  
K Jyothsna. ◽  
H Manjunatha.
Keyword(s):  
Gene Ontology ◽  
Molecular Chaperones ◽  
Curcuma Longa ◽  
Interaction Network ◽  
Network Pharmacology ◽  
Heme Oxygenase 1 ◽  
Nrf2 Pathway ◽  
Functional Behavior ◽  
Inflammatory Proteins ◽  
Prime Target

: Hsp90 are molecular chaperones of chronic inflammatory proteins and have emerged as prime target for treatment of inflammation. Principal components from Curcuma longa and Camellia sinensis, Curcumin and EGC respectively possesses anti-inflammatory properties inhibiting cytokines responsible for inflammation. Both act on common pathways in upregulation of heme oxygenase 1 through Pkcδ-Nrf2 pathway and downregulation of Tlr4, which in turn suppress expression of Hsp90. Curcumin and EGC were also found to bind -N and -C terminal domain of Hsp90 respectively. Based on this, work was designed with network pharmacological approach. Hsp90 associated gene targets of Curcumin and EGC were collected from databases, and gene ontology studies were done. PPI were obtained from string database for specific genes involved in Pkcδ-Nrf2 and Tlr4 pathway. Protein interaction network was constructed by cytoscape, and networks of Hsp90, Curcumin and EGC were merged to get common genes involved in Pkcδ-Nrf2 and Tlr4 pathway. Cluego analysis was done for obtained common genes to identify functional behavior in human diseases. Main proteins involved were identified as key regulators in Pkcδ-Nrf2 and Tlr4 pathway for controlling expression of Hsp90 from Curcumin and EGC in inflammation. Docking was performed on main proteins, Hsp90, Pkcδ and Tlr4 with Curcumin and EGC, significant binding energy was obtained for docked complexes. Combinatorial effects of Curcumin and EGC were observed in Pkcδ-Nrf2 and Tlr4pathway. Present study is an attempt to unravel common pathways mediated in intervention of Curcumin and EGC for suppression of Hsp90 associated with inflammation.

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

Discovering Therapeutic Protein Targets for Bladder Cancer Using Proteomic Data Analysis

2020 ◽  
Vol 13 (2) ◽  
pp. 150-172 ◽  
Author(s):  
Samira Bahrami ◽  
Bahram Kazemi ◽  
Hakimeh Zali ◽  
Peter C. Black ◽  
Abbas Basiri ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Gene Ontology ◽  
Monoclonal Antibodies ◽  
Membrane Proteins ◽  
Growth Factor Receptor ◽  
Subcellular Location ◽  
Cancer Tissue ◽  
Heat Shock Protein 60 ◽  
Absolute Quantitation ◽  
Muscle Invasive

Background: Bladder cancer accounts for almost 54% of urinary system cancer and is the second most frequent cause of death in genitourinary malignancies after prostate cancer. About 70% of bladder tumors are non-muscle-invasive, and the rest are muscle-invasive. Recurrence of the tumor is the common feature of bladder cancer. Chemotherapy is a conventional treatment for MIBC, but it cannot improve the survival rate of these patients sufficiently. Therefore, researchers must develop new therapies. Antibody-based therapy is one of the most important strategies for the treatment of solid tumors. Selecting a suitable target is the most critical step for this strategy. Objective: The aim of this study is to detect therapeutic cell surface antigen targets in bladder cancer using data obtained by proteomic studies. Methods: Isobaric tag for relative and absolute quantitation (iTRAQ) analysis had identified 131 overexpressed proteins in baldder cancer tissue and reverse-phase proteomic array (RPPA) analysis had been done for 343 tumor tissues and 208 antibodies. All identified proteins from two studies (131+208 proteins) were collected and duplicates were removed (331 unique proteins). Gene ontology study was performed using gene ontology (GO) and protein analysis through evolutionary relationships (PANTHER) databases. The Human Protein Atlas database was used to search the protein class and subcellular location of membrane proteins obtained from the PANTHER analysis. Results: Membrane proteins that could be suitable therapeutic targets for bladder cancer were selected. These included: Epidermal growth factor receptor (EGFR), Her2, Kinase insert domain receptor (KDR), Heat shock protein 60 (HSP60), HSP90, Transferrin receptor (TFRC), Activin A Receptor Like Type 1 (ACVRL1), and cadherin 2 (CDH2). Monoclonal antibodies against these proteins or their inhibitors were used for the treatment of different cancers in preclinical and clinical trials. Conclusion: These monoclonal antibodies and inhibitor molecules and also their combination can be used for the treatment of bladder cancer.

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