How to use and integrate bioinformatics tools to compare proteomic data from distinct conditions? A tutorial using the pathological similarities between Aortic Valve Stenosis and Coronary Artery Disease as a case-study.
Nowadays we are surrounded by a plethora of bioinformatics tools, powerful enough to deal with the large amounts of data arising from proteomic studies, but whose application is sometimes hard to find. Therefore, we used a specific clinical problem - to discriminate pathophysiology and potential biomarkers between two similar cardiovascular diseases, aortic valve stenosis (AVS) and coronary artery disease (CAD) - to make a step-by-step guide through four bioinformatics tools: STRING, DisGeNET, Cytoscape and ClueGO. Proteome data was collected from articles available on PubMed centered on proteomic studies enrolling subjects with AVS or CAD. Through the analysis of gene ontology provided by STRING and ClueGO we could find specific biological phenomena associated with AVS, such as down-regulation of elastic fiber assembly, and with CAD, such as up-regulation of plasminogen activation. Moreover, through Cytoscape and DisGeNET we could pinpoint surrogate markers either for AVS (e.g. popeye domain containing protein 2 and 28S ribosomal protein S36, mitochondrial) or for CAD (e.g. ankyrin repeat and SOCS box protein 7) which deserve future validation. Data recycling and integration as well as research orientation are among the main advantages of resorting to bioinformatics analysis, hence these tutorials can be of great convenience for proteomics investigators.



As we saw for aortic valve stenosis and coronary artery disease, it can be of great relevance to perform preliminary bioinformatics analysis with already published proteomics data. It not only saves us time in the lab (avoiding work duplication) as it points out new hypothesis to explain the phenotypical presentation of the diseases as well as new surrogate markers with clinical relevance, deserving future scrutiny. These essential steps can be easily overcome if one follows the steps proposed in our tutorial for STRING, DisGeNET, Cytoscape and ClueGO utilization.
Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal; Unidade de Investigação Cardiovascular, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto, Portugal. Electronic address: fabiotrindade@ua.pt.
Fábio
F
Trindade
QOPNA, Mass Spectrometry Center, Department of Chemistry, University of Aveiro, Aveiro, Portugal.
Rita
R
Ferreira
Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal.
Beatriz
B
Magalhães
Unidade de Investigação Cardiovascular, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.
Adelino
A
Leite-Moreira
Unidade de Investigação Cardiovascular, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.
Inês
I
Falcão-Pires
Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal; Unidade de Investigação Cardiovascular, Departamento de Cirurgia e Fisiologia, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.
Rui
R
Vitorino
D001024:Aortic Valve Stenosis / Q000209:etiology / Q000378:metabolism* / Q000473:pathology; D019295:Computational Biology / Q000379:methods*; D003324:Coronary Artery Disease / Q000209:etiology / Q000378:metabolism* / Q000473:pathology; D030562:Databases, Protein; D063990:Gene Ontology; D005982:Glutathione Transferase / Q000378:metabolism; D006801:Humans; D020543:Proteome / Q000032:analysis*; D016212:Transforming Growth Factor beta / Q000378:metabolism
D020543:Proteome; D016212:Transforming Growth Factor beta; D005982:Glutathione Transferase
Bioinformatics; ClueGO; Cytoscape; DisGeNET; Proteomics; STRING