Extending Signaling Pathways with Protein–Interaction Networks. Application to Apoptosis

The knowledge of signaling pathways is central to understanding the biological mechanisms of organisms since it has been identified that in eukaryotic organisms, the number of signaling pathways determines the number of ways the organism will react to external stimuli. Signaling pathways are studied using protein interaction networks constructed from protein-protein interaction data obtained from high-throughput experiments. However, these high-throughput methods are known to produce very high rates of false positive and negative interactions. To construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed.A weighted interaction graph of Saccharomyces Cerevisiae was constructed. The weights were obtained using a Bayesian probabilistic network to estimate the posterior probability of interaction between two proteins given the gene expression measurement as biological evidence. Only interactions above a threshold were accepted for the network model.We were able to identify some pathway segments, one of which is a segment of the pathway that signals the start of the process of meiosis in S. Cerevisiae.

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A Machine Learning Framework for Predicting Drug-drug Interactions

10.21203/rs.3.rs-503867/v1 ◽

2021 ◽

Author(s):

Suyu Mei ◽

Kun Zhang

Keyword(s):

Drug Interactions ◽

Signaling Pathways ◽

Protein Interaction ◽

Drug Target ◽

Target Genes ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Model Complexity ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks

Abstract Understanding drug-drug interaction is an essential step to reduce the risk of adverse drug events before clinical drug co-prescription. Existing methods commonly integrate multiple heterogeneous data sources to increase model performance but result in a high model complexity. To elucidate the molecular mechanisms behind drug-drug interactions and reserve rational biological interpretability is a major concern in computational modeling. In this study, we propose a simple representation of drug target profiles to depict drug pairs, based on which an l2-regularized logistic regression model is built to predict drug-drug interactions. In addition, we develop several statistical metrics to measure the communication intensity, interaction efficacy and action range between two drugs in the context of human protein-protein interaction networks and signaling pathways. Cross validation and independent test show that the simple feature representation via drug target profiles is effective to predict drug-drug interactions and outperforms the existing data integration methods. Statistical results show that two drugs easily interact when they target common genes, or their target genes communicate with each other via short paths in protein-protein interaction networks or through cross-talks between signaling pathways. The unravelled mechanisms provide biological insights into potential pharmacological risks of known drug-drug interactions and drug target genes.

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Nodes with high centrality in protein interaction networks are responsible for driving signaling pathways in diabetic nephropathy

PeerJ ◽

10.7717/peerj.1284 ◽

2015 ◽

Vol 3 ◽

pp. e1284 ◽

Cited By ~ 14

Author(s):

Maryam Abedi ◽

Yousof Gheisari

Keyword(s):

Gene Expression ◽

Diabetic Nephropathy ◽

Signaling Pathways ◽

Protein Interaction ◽

Enrichment Analysis ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Pathway Enrichment Analysis ◽

Complex Disorders ◽

Pathway Enrichment

In spite of huge efforts, chronic diseases remain an unresolved problem in medicine. Systems biology could assist to develop more efficient therapies through providing quantitative holistic sights to these complex disorders. In this study, we have re-analyzed a microarray dataset to identify critical signaling pathways related to diabetic nephropathy. GSE1009 dataset was downloaded from Gene Expression Omnibus database and the gene expression profile of glomeruli from diabetic nephropathy patients and those from healthy individuals were compared. The protein-protein interaction network for differentially expressed genes was constructed and enriched. In addition, topology of the network was analyzed to identify the genes with high centrality parameters and then pathway enrichment analysis was performed. We found 49 genes to be variably expressed between the two groups. The network of these genes had few interactions so it was enriched and a network with 137 nodes was constructed. Based on different parameters, 34 nodes were considered to have high centrality in this network. Pathway enrichment analysis with these central genes identified 62 inter-connected signaling pathways related to diabetic nephropathy. Interestingly, the central nodes were more informative for pathway enrichment analysis compared to all network nodes and also 49 differentially expressed genes. In conclusion, we here show that central nodes in protein interaction networks tend to be present in pathways that co-occur in a biological state. Also, this study suggests a computational method for inferring underlying mechanisms of complex disorders from raw high-throughput data.

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Elucidation of Signaling Pathways from Large-Scale Phosphoproteomic Data Using Protein Interaction Networks

Cell Systems ◽

10.1016/j.cels.2016.11.005 ◽

2016 ◽

Vol 3 (6) ◽

pp. 585-593.e3 ◽

Cited By ~ 32

Author(s):

Jan Daniel Rudolph ◽

Marjo de Graauw ◽

Bob van de Water ◽

Tamar Geiger ◽

Roded Sharan

Keyword(s):

Signaling Pathways ◽

Protein Interaction ◽

Large Scale ◽

Protein Interaction Networks ◽

Interaction Networks

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PADPIN: protein-protein interaction networks of angiogenesis, arteriogenesis, and inflammation in peripheral arterial disease

Physiological Genomics ◽

10.1152/physiolgenomics.00125.2014 ◽

2015 ◽

Vol 47 (8) ◽

pp. 331-343 ◽

Cited By ~ 8

Author(s):

Liang-Hui Chu ◽

Chaitanya G. Vijay ◽

Brian H. Annex ◽

Joel S. Bader ◽

Aleksander S. Popel

Keyword(s):

Peripheral Arterial Disease ◽

Signaling Pathways ◽

Protein Interaction ◽

Arterial Disease ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Mouse Strains ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks ◽

Peripheral Arterial

Peripheral arterial disease (PAD) results from an obstruction of blood flow in the arteries other than the heart, most commonly the arteries that supply the legs. The complexity of the known signaling pathways involved in PAD, including various growth factor pathways and their cross talks, suggests that analyses of high-throughput experimental data could lead to a new level of understanding of the disease as well as novel and heretofore unanticipated potential targets. Such bioinformatic analyses have not been systematically performed for PAD. We constructed global protein-protein interaction networks of angiogenesis (Angiome), immune response (Immunome), and arteriogenesis (Arteriome) using our previously developed algorithm GeneHits. The term “PADPIN” refers to the angiome, immunome, and arteriome in PAD. Here we analyze four microarray gene expression datasets from ischemic and nonischemic gastrocnemius muscles at day 3 posthindlimb ischemia (HLI) in two genetically different C57BL/6 and BALB/c mouse strains that display differential susceptibility to HLI to identify potential targets and signaling pathways in angiogenesis, immune, and arteriogenesis networks. We hypothesize that identification of the differentially expressed genes in ischemic and nonischemic muscles between the strains that recovers better (C57BL/6) vs. the strain that recovers more poorly (BALB/c) will help for the prediction of target genes in PAD. Our bioinformatics analysis identified several genes that are differentially expressed between the two mouse strains with known functions in PAD including TLR4, THBS1, and PRKAA2 and several genes with unknown functions in PAD including EphA4, TSPAN7, SLC22A4, and EIF2a.

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