scholarly journals Network diffusion on multiple-layers: current approaches and integrative analysis of Rheumatoid Arthritis data.

2017 ◽  
Author(s):  
Noemi Di Nanni ◽  
Matteo Gnocchi ◽  
Marco Moscatelli ◽  
Luciano Milanesi ◽  
Ettore Mosca
Keyword(s):  
Rheumatoid Arthritis ◽  
Protein Interactions ◽  
Single Layer ◽  
Joint Analysis ◽  
Protein Protein Interactions ◽  
Functional Protein ◽  
Detection Function ◽  
Module Detection ◽  
Rheumatoid Arthritis Data ◽  
Network Diffusion

Network Diffusion has been proposed in several applications, thanks to its ability of amplifying biological signals and prioritizing genes that may be associated with a disease. Not surprising, the success of Network Diffusion on a “single layer” led to the first approaches for the joint analysis of multi-omics data. Here, we review integrative methods based on Network Diffusion that have been proposed with several aims (e.g. patient stratification, module detection, function prediction). We used Network Diffusion to analyse, in the context of physical and functional protein-protein interactions, genetic variation, DNA methylation and gene expression data from a study on Rheumatoid Arthritis. We identified functionally related genes with multiple alterations.

scholarly journals Network diffusion on multiple-layers: current approaches and integrative analysis of Rheumatoid Arthritis data.

2017 ◽  
Author(s):  
Noemi Di Nanni ◽  
Matteo Gnocchi ◽  
Marco Moscatelli ◽  
Luciano Milanesi ◽  
Ettore Mosca
Keyword(s):  
Rheumatoid Arthritis ◽  
Protein Interactions ◽  
Single Layer ◽  
Joint Analysis ◽  
Protein Protein Interactions ◽  
Functional Protein ◽  
Detection Function ◽  
Module Detection ◽  
Rheumatoid Arthritis Data ◽  
Network Diffusion

Network Diffusion has been proposed in several applications, thanks to its ability of amplifying biological signals and prioritizing genes that may be associated with a disease. Not surprising, the success of Network Diffusion on a “single layer” led to the first approaches for the joint analysis of multi-omics data. Here, we review integrative methods based on Network Diffusion that have been proposed with several aims (e.g. patient stratification, module detection, function prediction). We used Network Diffusion to analyse, in the context of physical and functional protein-protein interactions, genetic variation, DNA methylation and gene expression data from a study on Rheumatoid Arthritis. We identified functionally related genes with multiple alterations.

scholarly journals Network-Based Gene Expression Biomarkers for Cold and Heat Patterns of Rheumatoid Arthritis in Traditional Chinese Medicine

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Cheng Lu ◽  
Xuyan Niu ◽  
Cheng Xiao ◽  
Gao Chen ◽  
Qinglin Zha ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Cell Proliferation ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Protein Interactions ◽  
Acid Metabolism ◽  
Protein Protein Interactions ◽  
Significant Gene ◽  
Heat Pattern

In Traditional Chinese Medicine (TCM), patients with Rheumatoid Arthritis (RA) can be classified into two main patterns: cold-pattern and heat-pattern. This paper identified the network-based gene expression biomarkers for both cold- and heat-patterns of RA. Gene expression profilings of CD4+ T cells from cold-pattern RA patients, heat-pattern RA patients, and healthy volunteers were obtained using microarray. The differentially expressed genes and related networks were explored using DAVID, GeneSpring software, and the protein-protein interactions (PPI) method. EIF4A2, CCNT1, and IL7R, which were related to the up-regulation of cell proliferation and the Jak-STAT cascade, were significant gene biomarkers of the TCM cold pattern of RA. PRKAA1, HSPA8, and LSM6, which were related to fatty acid metabolism and the I-κB kinase/NF-κB cascade, were significant biomarkers of the TCM heat-pattern of RA. The network-based gene expression biomarkers for the TCM cold- and heat-patterns may be helpful for the further stratification of RA patients when deciding on interventions or clinical trials.

scholarly journals Molecular Dynamics-Guided Design of a Functional Protein–ATRP Conjugate That Eliminates Protein–Protein Interactions

2021 ◽  
Vol 32 (4) ◽  
pp. 821-832
Author(s):  
Bibifatima Kaupbayeva ◽  
Susanne Boye ◽  
Aravinda Munasinghe ◽  
Hironobu Murata ◽  
Krzysztof Matyjaszewski ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Functional Protein

scholarly journals PPI-MASS: An Interactive Web Server to Identify Protein-Protein Interactions From Mass Spectrometry-Based Proteomics Data

2021 ◽  
Vol 8 ◽  
Author(s):  
Mariela González-Avendaño ◽  
Simón Zúñiga-Almonacid ◽  
Ian Silva ◽  
Boris Lavanderos ◽  
Felipe Robinson ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Web Server ◽  
Target Protein ◽  
Protein Protein Interactions ◽  
Proteomics Data ◽  
Functional Protein ◽  
Web Platform ◽  
The Web

Mass spectrometry-based proteomics methods are widely used to identify and quantify protein complexes involved in diverse biological processes. Specifically, tandem mass spectrometry methods represent an accurate and sensitive strategy for identifying protein-protein interactions. However, most of these approaches provide only lists of peptide fragments associated with a target protein, without performing further analyses to discriminate physical or functional protein-protein interactions. Here, we present the PPI-MASS web server, which provides an interactive analytics platform to identify protein-protein interactions with pharmacological potential by filtering a large protein set according to different biological features. Starting from a list of proteins detected by MS-based methods, PPI-MASS integrates an automatized pipeline to obtain information of each protein from freely accessible databases. The collected data include protein sequence, functional and structural properties, associated pathologies and drugs, as well as location and expression in human tissues. Based on this information, users can manipulate different filters in the web platform to identify candidate proteins to establish physical contacts with a target protein. Thus, our server offers a simple but powerful tool to detect novel protein-protein interactions, avoiding tedious and time-consuming data postprocessing. To test the web server, we employed the interactome of the TRPM4 and TMPRSS11a proteins as a use case. From these data, protein-protein interactions were identified, which have been validated through biochemical and bioinformatic studies. Accordingly, our web platform provides a comprehensive and complementary tool for identifying protein-protein complexes assisting the future design of associated therapies.

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