Dataset Information

Network Protein Interaction in the Link between Stroke and Periodontitis Interplay: A Pilot Bioinformatic Analysis.

ABSTRACT: The clinical interaction between stroke and periodontitis has been consistently studied and confirmed. Hence, exploring potentially new protein interactions in this association using bioinformatic strategies presents potential interest. In this exploratory study, we conducted a protein-protein network interaction (PPI) search with documented encoded proteins for both stroke and periodontitis. Genes of interest were collected via GWAS database. The STRING database was used to predict the PPI networks, first in a sensitivity purpose (confidence cut-off of 0.7), and then with a highest confidence cut-off (0.9). Genes over-representation was inspected in the final network. As a result, we foresee a prospective protein network of interaction between stroke and periodontitis. Inflammation, pro-coagulant/pro-thrombotic state and, ultimately, atheroma plaque rupture is the main biological mechanism derived from the network. These pilot results may pave the way to future molecular and therapeutic studies to further comprehend the mechanisms between these two conditions.

SUBMITTER: Leira Y

PROVIDER: S-EPMC8160956 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Network Protein Interaction in the Link between Stroke and Periodontitis Interplay: A Pilot Bioinformatic Analysis.

Leira Yago Y Mascarenhas Paulo P Blanco Juan J Sobrino Tomás T Mendes José João JJ Machado Vanessa V Botelho João J

Genes 20210520 5

The clinical interaction between stroke and periodontitis has been consistently studied and confirmed. Hence, exploring potentially new protein interactions in this association using bioinformatic strategies presents potential interest. In this exploratory study, we conducted a protein-protein network interaction (PPI) search with documented encoded proteins for both stroke and periodontitis. Genes of interest were collected via GWAS database. The STRING database was used to predict the PPI netw ...[more]

PMID: 34065604

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Project description:BackgroundThe investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates for future experimental validations. Reformulating the problem in the field of network theory, the set of proteins can be represented as the nodes of a network, while the interactions between them as the edges. The resulting protein-protein interaction (PPI) network enables the use of link prediction techniques in order to discover new probable connections. Therefore, here we aimed to offer a novel approach to the link prediction task in PPI networks, utilizing a generative machine learning model.ResultsWe created a tool that consists of two modules, the data processing framework and the machine learning model. As data processing, we used a modified breadth-first search algorithm to traverse the network and extract induced subgraphs, which served as image-like input data for our model. As machine learning, an image-to-image translation inspired conditional generative adversarial network (cGAN) model utilizing Wasserstein distance-based loss improved with gradient penalty was used, taking the combined representation from the data processing as input, and training the generator to predict the probable unknown edges in the provided induced subgraphs. Our link prediction tool was evaluated on the protein-protein interaction networks of five different species from the STRING database by calculating the area under the receiver operating characteristic, the precision-recall curves and the normalized discounted cumulative gain (AUROC, AUPRC, NDCG, respectively). Test runs yielded the averaged results of AUROC = 0.915, AUPRC = 0.176 and NDCG = 0.763 on all investigated species.ConclusionWe developed a software for the purpose of link prediction in PPI networks utilizing machine learning. The evaluation of our software serves as the first demonstration that a cGAN model, conditioned on raw topological features of the PPI network, is an applicable solution for the PPI prediction problem without requiring often unavailable molecular node attributes. The corresponding scripts are available at https://github.com/semmelweis-pharmacology/ppi_pred .

Project description:The interaction between proteoglycan and link protein extracted from bovine articular cartilage (15-18-month-old animals) was investigated in 0.5 M-guanidinium chloride. The proteoglycans, radiolabelled as the aggregate (A1 fraction), were fractionated by two 'dissociative' density-gradient centrifugations (A1D1D1) followed by a rate-zonal centrifugation (S1) to yield an A1D1D1S1 preparation. At least 65% of these proteoglycans were able to bind to hyaluronate, but only 52% were able to bind to link protein as assessed by chromatography on Sepharose CL-2B. Over 80% of the [3H]link-protein preparation, radiolabelled as the aggregate, was able to interact with proteoglycan as assessed by chromatography on Sepharose CL-4B. Equilibrium-boundary-centrifugation studies performed at low link-protein concentrations (2.42 x 10(-9) M-5.93 x 10(-8) M) were analysed by Scatchard-type plots and indicated a Kd of 1.5 x 10(-8) M and a stoichiometry, n = 0.56, i.e. approx. 56% of those proteoglycans capable of binding to link protein had a strong site for link protein if a 1:1 stoichiometry were assumed. However, experiments performed at higher link-protein concentrations (3.5 x 10(-7) M and 8 x 10(-7) M) yielded stoichiometry values which were link-protein-concentration-dependent. Non-equilibrium binding studies using chromatography on Sepharose CL-2B and rate-zonal centrifugation yielded apparent stoichiometries between 0.6 and 7.5 link-protein molecules per proteoglycan monomer as a function of increasing link-protein concentration. It was concluded that a proportion of the proteoglycan molecules had a strong site for binding a single link protein (Kd 1.5 x 10(-8) M) and that at high link-protein concentrations a weaker, open-ended, process of link-protein self-association nucleated upon the strong link-protein-proteoglycan complex occurred. Hyaluronate oligosaccharides appeared to abolish a proportion of this self-association (as observed by Bonnet, Dunham & Hardingham [(1985) Biochem. J. 228, 77-85] in a study of link-protein-hyaluronate-oligosaccharide interactions) so as to leave a link protein:proteoglycan stoichiometry of 2. It is not clear whether this second link-protein molecule binds directly to the proteoglycan or to the first link protein.

Project description:Background2019 Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 pandemic has already had a serious influence on human existence, causing a huge public health concern for countries all around the world. Because SARS-CoV-2 infection can be spread by contact with the oral cavity, the link between oral illness and COVID-19 is gaining traction. Through bioinformatics approaches, we explored the possible molecular mechanisms linking the COVID-19 and periodontitis to provide the basis and direction for future research.MethodsTranscriptomic data from blood samples of patients with COVID-19 and periodontitis was downloaded from the Gene Expression Omnibus database. The shared differentially expressed genes were identified. The analysis of Gene Ontology, Kyoto Encyclopedia of Genesand Genomes pathway, and protein-protein interaction network was conducted for the shared differentially expressed genes. Top 5 hub genes were selected through Maximal Clique Centrality algorithm. Then mRNA-miRNA network of the hub genes was established based on miRDB database, miRTarbase database and Targetscan database. The Least absolute shrinkage and selection operator regression analysis was used to discover possible biomarkers, which were then investigated in relation to immune-related genes.ResultsFifty-six shared genes were identified through differential expression analysis in COVID-19 and periodontitis. The function of these genes was enriched in regulation of hormone secretion, regulation of secretion by cell. Myozenin 2 was identified through Least absolute shrinkage and selection operator regression Analysis, which was down-regulated in both COVID-19 and periodontitis. There was a positive correlation between Myozenin 2 and the biomarker of activated B cell, memory B cell, effector memory CD4 T cell, Type 17 helper cell, T follicular helper cell and Type 2 helper cell.ConclusionBy bioinformatics analysis, Myozenin 2 is predicted to correlate to the pathogenesis and immune infiltrating of COVID-19 and periodontitis. However, more clinical and experimental researches are needed to validate the function of Myozenin 2.

Dataset Information

Network Protein Interaction in the Link between Stroke and Periodontitis Interplay: A Pilot Bioinformatic Analysis.

Publications

Network Protein Interaction in the Link between Stroke and Periodontitis Interplay: A Pilot Bioinformatic Analysis.

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