Simulation and visualization of multiple KEGG pathways using BioNSi

Yeheskel, Adva; Reiter, Adam; Pasmanik‐Chor, Metsada; Rubinstein, Amir

doi:10.12688/f1000research.13254.1

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…In organisms, proteins function through coordinated interactions with different proteins in order to accomplish a series of biochemical functions [27][28][29]. Thus, pathway analysis is the most direct and essential method to systematically and comprehensively understand the cellular biological processes involved in the pathogenesis of diseases, and the mechanism of function of medicines [30].…”

Section: Discussionmentioning

confidence: 99%

iTRAQ-based proteomics analysis on insomnia rats treated with Mongolian medical warm acupuncture

Man

et al. 2020

Bioscience Reports

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Objective: To explore the proteomic changes in the hypothalamus of rats treated with Mongolian medical warm acupuncture for insomnia therapy based proteomics. Method: We used an iTRAQ-based quantitative proteomic approach to identify proteins that potential molecular mechanisms involved in the treatment of insomnia by Mongolian medical warm acupuncture. Result: In total, 7477 proteins were identified, of which 36 proteins showed increased levels and 45 proteins showed decreased levels in insomnia model group (M) compared with healthy control group (C), 72 proteins showed increased levels and 44 proteins showed decreased levels from the warm acupuncture treated insomnia group (W) compared with healthy controls (C), 28 proteins showed increased levels and 17 proteins showed decreased levels from the warm acupuncture-treated insomnia group (W) compared with insomnia model group (M). Compared with healthy control groups, warm acupuncture-treated insomnia group showed obvious recovered. Bioinformatics analysis indicated that up-regulation of neuroactive ligand–receptor interaction and oxytocin signaling was the most significantly elevated regulate process of Mongolian medical warm acupuncture treatment for insomnia. Proteins showed that increased/decreased expression in the warm acupuncture-treated insomnia group included Prolargin (PRELP), NMDA receptor synaptonuclear-signaling and neuronal migration factor (NSMF), Transmembrane protein 41B (TMEM41B) and Microtubule-associated protein 1B (MAP1B) to adjust insomnia. Conclusion: A combination of findings in the present study suggest that warm acupuncture treatment is efficacious in improving sleep by regulating the protein expression process in an experimental rat model and may be of potential benefit in treating insomnia patients with the added advantage with no adverse effects.

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Section: Discussionmentioning

confidence: 99%

iTRAQ-based proteomics analysis on insomnia rats treated with Mongolian medical warm acupuncture

Man

et al. 2020

Bioscience Reports

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“…The BioNSi global network that includes all of these nine pathways contains 385 nodes (genes and small molecules) that are connected by 631 edges, in addition to gene self-inhibition edges that represent biological degradation ( 30 ). KEGG pathway information includes activation and inhibition edges.…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, BioNSi enables the upload of expression values from high-throughput experiments and simulate the gene or protein interactions acquired from KEGG accordingly. Data generated may facilitate the investigation of the entire dynamic network under different biological conditions ( 30 , 31 ). Given the vital roles of the INSR and IGF1R signaling pathways in breast cancer, and to gain new insight into potential commonalities and divergences in expression patterns between both receptors and their downstream mediators, we employed the BioNSi bioinformatics tool for network simulation.…”

Section: Introductionmentioning

confidence: 99%

Systems Analysis of Insulin and IGF1 Receptors Networks in Breast Cancer Cells Identifies Commonalities and Divergences in Expression Patterns

et al. 2020

Self Cite

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Insulin and insulin-like growth factor-1 (IGF1), acting respectively via the insulin (INSR) and IGF1 (IGF1R) receptors, play key developmental and metabolic roles throughout life. In addition, both signaling pathways fulfill important roles in cancer initiation and progression. The present study was aimed at identifying mechanistic differences between INSR and IGF1R using a recently developed bioinformatics tool, the Biological Network Simulator (BioNSi). This application allows to import and merge multiple pathways and interaction information from the KEGG database into a single network representation. The BioNsi network simulation tool allowed us to exploit the availability of gene expression data derived from breast cancer cell lines with specific disruptions of the INSR or IGF1R genes in order to investigate potential differences in protein expression that might be linked to biological attributes of the specific receptor networks. Modeling-generated information was corroborated by experimental and biological assays. BioNSi analyses revealed that the expression of 75 and 71 genes changed during simulation of IGF1R-KD and INSR-KD, compared to control cells, respectively. Out of 16 proteins that BioNSi analysis was based on, validated by Western blotting, nine were shown to be involved in DNA repair, eight in cell cycle checkpoints, six in proliferation, eight in apoptosis, seven in oxidative stress, six in cell migration, two in energy homeostasis, and three in senescence. Taken together, analyses identified a number of commonalities and, most importantly, dissimilarities between the IGF1R and INSR pathways that might help explain the basis for the biological differences between these networks.

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“…Resulted pathways (Supplementary Table S7) further merged and simulated using Biological Network Simulator (BioNSi) [35] , a cytoscape plug-in. BioNSi provides modeling of biological networks and discrete-time simulation.…”

Section: V) Biological Network Simulationmentioning

confidence: 99%

“…BioNSi [35] , a biological network simulator, was used to simulate the pathways in normals and T2DM patients. The BioNSi is a discrete biological network simulator tool.…”

Section: Vi) Biological Network Simulationmentioning

confidence: 99%

Identification of Perturbed Pathways Rendering Susceptibility to Tuberculosis in Type 2 Diabetes Mellitus Patients Using BioNSi Simulation of Integrated Network of Implicated Human Genes

Rani¹,

Bhargav²,

Datta³

et al. 2021

Preprint

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Adaptive immune response of the Th1 arm is the main defense against tuberculosis (TB). However, in Type 2 Diabetes Mellitus (T2DM) patients, chronic hyperglycemia and inflammation underlie susceptibility to TB and results in poor TB control. The molecular pathways causing susceptibility of diabetics to tuberculosis is not fully understood. Here, an integrative pathway-based approach is used to investigate the perturbed pathways in T2DM patients rendering susceptibility to TB. We obtained 36 genes implicated in the Type 2 diabetes associated tuberculosis (T2DMTB) from literature. Gene expression analysis on T2DM patients’ data (GSE28168) showed that DEFA1 is differentially expressed at Padj < 0.05. The genes CAMP, CD14, CORO1A, LAMP1, TLR4, IL17F and SOCS3 were differentially expressed in T2DM patients at P value < 0.05. 7 microRNAs associated with these T2DMTB genes were obtained from NetworkAnalyst and verified for their literature evidences. The hsa-miR-146a microRNA was differentially expressed at Padj < 0.05. The human host TB susceptibility genes TNFRSF10A, MSRA, GPR148, SLC37A3, PXK, PROK2, REV3L, PGM1, HIST3H2A, PLAC4, LETM2, EMP2 and were also differentially expressed at Padj < 0.05. We included all these genes and added the remaining 28 genes from the T2DMTB set and the rest of differentially expressed genes at Padj < 0.05 in STRING and obtained a well-connected network with high confidence score greater than 0.7. From this network we extracted the KEGG pathways at FDR < 0.05 and retained only Diabetes and TB pathways among the disease pathways. The network was simulated with BioNSi using gene expression data from GSE26168. The Necroptosis pathway showed the maximum perturbations in T2DM patients, followed by NOD-like receptor signaling, Toll-like receptor signaling, NF-kappa-B signaling and MAPK signaling. These pathways likely underlie susceptibility to TB in T2DM patients.

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Simulation and visualization of multiple KEGG pathways using BioNSi

Cited by 9 publications

References 24 publications

iTRAQ-based proteomics analysis on insomnia rats treated with Mongolian medical warm acupuncture

iTRAQ-based proteomics analysis on insomnia rats treated with Mongolian medical warm acupuncture

Systems Analysis of Insulin and IGF1 Receptors Networks in Breast Cancer Cells Identifies Commonalities and Divergences in Expression Patterns

Identification of Perturbed Pathways Rendering Susceptibility to Tuberculosis in Type 2 Diabetes Mellitus Patients Using BioNSi Simulation of Integrated Network of Implicated Human Genes

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