Insights from the protein-protein interaction network analysis of Mycobacterium tuberculosis toxinantitoxin systems

Thakur, Zoozeal; Dharra, Renu; Saini, Vandana; Kumar, Ajit; Mehta, Promod K

doi:10.6026/97320630013380

Cited by 16 publications

(11 citation statements)

References 29 publications

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“…The network was analyzed using the cytoHubba plugin of Cytoscape ( http://apps.cytoscape.org/apps/cytohubba ), which includes 12 algorithms for filtering hub genes from PPI modules. The stress and betweenness (Chin et al, 2014 ; Thakur et al, 2017 ) statistical measures were used to evaluate the importance of nodes in the PPI network. The top 15 overlapping DEGs in the integrated results obtained with the two algorithms were considered significant genes.…”

Section: Methodsmentioning

confidence: 99%

Periostin Contributes to Immunoglobulin a Nephropathy by Promoting the Proliferation of Mesangial Cells: A Weighted Gene Correlation Network Analysis

Lin

et al. 2021

Front. Genet.

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Immunoglobulin A nephropathy (IgAN) is a known cause of end-stage kidney disease, but the pathogenesis and factors affecting prognosis are not fully understood. In the present study, we carried out weighted gene correlation network analysis (WGCNA) to identify hub genes related to the occurrence of IgAN and validated candidate genes in experiments using mouse mesangial cells (MMCs) and clinical specimens (kidney tissue from IgAN patients and healthy controls). We screened the GSE37460 and GSE104948 differentially expressed genes common to both datasets and identified periostin (POSTN) as one of the five key genes using the cytoHubba plugin of Cytoscape software and by receiver-operating characteristic curve analysis. The top 25% of genes in the GSE93798 dataset showing variable expression between IgAN and healthy tissue were assessed by WGCNA. The royalblue module in WGCNA was closely related to creatinine and estimated glomerular filtration rate (eGFR) in IgAN patients. POSTN had very high module membership and gene significance values for creatinine (0.82 and 0.66, respectively) and eGFR (0.82 and −0.67, respectively), indicating that it is a co-hub gene. In MMCs, POSTN was upregulated by transforming growth factor β1, and stimulation of MMCs with recombinant POSTN protein resulted in an increase in the level of proliferating cell nuclear antigen (PCNA) and a decrease in that of B cell lymphoma-associated X protein, which were accompanied by enhanced MMC proliferation. POSTN gene knockdown had the opposite effects. Immunohistochemical analysis of kidney tissue specimens showed that POSTN and PCNA levels were elevated, whereas the rate of apoptosis was reduced in IgAN patients relative to healthy controls. POSTN level in the kidney tissue of IgAN patients was positively correlated with creatinine level and negatively correlated with eGFR. Thus, POSTN promotes the proliferation of MCs to promote renal dysfunction in IgAN.

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Section: Methodsmentioning

confidence: 99%

Periostin Contributes to Immunoglobulin a Nephropathy by Promoting the Proliferation of Mesangial Cells: A Weighted Gene Correlation Network Analysis

Lin

et al. 2021

Front. Genet.

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“…The high resolution ddgmonomer (HRDM) [35] protocol in Rosetta is used to predict point mutations, and the comparative modeling protocol is used to predict multi-point mutations [36]. Quality assessment of predicted structures is performed by Verify3D [37], and Q-mean [38].…”

Section: Proposed Methodologymentioning

confidence: 99%

Personalized drug-response prediction model for lung cancer patients using machine learning

Qureshi¹

2020

Preprint

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Lung cancer caused by mutations in the epidermal growth factor receptor (EGFR) is a major cause of cancer deaths worldwide. EGFR Tyrosine kinase inhibitors (TKIs) have been developed, and have shown increased survival rates and quality of life in clinical studies. However, drug resistance is a major issue, and treatment efficacy is lost after about an year. Therefore, predicting the response to targeted therapies for lung cancer patients is a significant research problem. In this work, we address this issue and propose a personalized model to predict the drug-response of lung cancer patients. This model uses clinical information, geometrical properties of the drug binding site, and the binding free energy of the drug-protein complex. The proposed model achieves state of the art performance with 97.5% accuracy, 100% recall, 95% precision, and 96.3% F1-score with a random forest classifier. This model can also be tested on other types of cancer and diseases, and we believe that it may help in taking optimal clinical decisions for treating patients with targeted therapies

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“…The approaches using protein sequence and genomic data contain a study of the absence or presence of genes in associated species, gene fusion events, preservation of gene neighborhood, interconnected mutations on surfaces of protein, the resemblance of phylogenetic trees, co-occurrence of sequence domains, functional and co-expression features [17]. Sometimes, integration of these features is used to predict new interactions or to approximate the validity of PPIs, which are evaluated experimentally [18]. Some features such as likeness in the Gene Ontology (GO) term annotation, co-expression, sequence and the existence of possibly interacting domains of the protein pair under many conditions or numerous tissues have been revealed to be applicable predictors of protein-protein interactions [19].…”

Section: Computational Methods To Detect Ppimentioning

confidence: 99%

Computer aided analysis of disease linked protein networks

Sabetian

Shamsir

2019

Bioinformation

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Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of complex biological systems including network pharmacology and disease network has also been discussed in this review.

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Insights from the protein-protein interaction network analysis of Mycobacterium tuberculosis toxinantitoxin systems

Cited by 16 publications

References 29 publications

Periostin Contributes to Immunoglobulin a Nephropathy by Promoting the Proliferation of Mesangial Cells: A Weighted Gene Correlation Network Analysis

Periostin Contributes to Immunoglobulin a Nephropathy by Promoting the Proliferation of Mesangial Cells: A Weighted Gene Correlation Network Analysis

Personalized drug-response prediction model for lung cancer patients using machine learning

Computer aided analysis of disease linked protein networks

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