scholarly journals Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

2021 ◽  
Author(s):  
Rebeca Queiroz Figueiredo ◽  
Tamara Raschka ◽  
Alpha Tom Kodamullil ◽  
Martin Hofmann-Apitius ◽  
Sarah Mubeen ◽  
...  
Keyword(s):  
Molecular Interactions ◽  
Protein Interactions ◽  
Systematic Approach ◽  
Joint Analysis ◽  
Protein Protein Interactions ◽  
Case Scenario ◽  
Human Interactome ◽  
Disease Mechanisms ◽  
Interactome Network ◽  
Different Levels

AbstractIn this work, we attempt to address a key question in the joint analysis of transcriptomic data: can we correlate the patterns we observe in transcriptomic datasets to known molecular interactions and pathway knowledge to broaden our understanding of disease pathophysiology? We present a systematic approach that sheds light on the patterns observed in hundreds of transcriptomic datasets from over sixty indications by using pathways and molecular interactions as a template. Our analysis employs transcriptomic datasets to construct dozens of disease specific co-expression networks, alongside a human interactome network of protein-protein interactions described in the literature. Leveraging the interoperability between these two network templates, we explore patterns both common and particular to these diseases on three different levels. Firstly, at the node-level, we identify the most and least common proteins in these diseases and evaluate their consistency against the interactome as a proxy for their prevalence in the scientific literature. Secondly, we overlay both network templates to analyze common correlations and interactions across diseases at the edge-level. Thirdly, we explore the similarity between patterns observed at the disease level and pathway knowledge to identify pathway signatures associated with specific diseases and indication areas. Finally, we present a case scenario in the context of schizophrenia, where we show how our approach can be used to investigate disease pathophysiology.

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.

Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins

2013 ◽  
pp. 222-238
Author(s):  
Young-Rae Cho ◽  
Aidong Zhang
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Functional Characterization ◽  
Flow Simulation ◽  
Protein Protein Interactions ◽  
Systematic Analysis ◽  
Graph Theoretic ◽  
Interactome Network ◽  
Protein Interactome ◽  
Hub Proteins

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

scholarly journals Developing drugs for the ‘undruggable’

BioTechniques ◽  
2020 ◽  
Vol 69 (4) ◽  
pp. 239-241
Author(s):  
Abigail Sawyer
Keyword(s):  
Protein Interactions ◽  
Therapeutic Targets ◽  
Protein Protein Interactions ◽  
Human Interactome ◽  
Novel Therapeutic

There are up to 650,000 ‘undruggable’ protein-protein interactions (PPIs) in the human interactome that can be potentially considered as novel therapeutic targets. How does the ‘undruggable’ become ‘druggable’?

scholarly journals PathwayMatcher: proteoform-centric network construction enables fine-granularity multiomics pathway mapping

GigaScience ◽  
2019 ◽  
Vol 8 (8) ◽  
Author(s):  
Luis Francisco Hernández Sánchez ◽  
Bram Burger ◽  
Carlos Horro ◽  
Antonio Fabregat ◽  
Stefan Johansson ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Knowledge Bases ◽  
Protein Isoforms ◽  
Biomedical Data ◽  
Protein Protein Interactions ◽  
Gene Sets ◽  
Functional Knowledge ◽  
Pathway Analyses ◽  
Different Levels

Abstract Background Mapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers (e.g., gene sets) in pathway knowledge bases. However, the isoform and posttranslational modification states of proteins are lost when converting input and pathways into gene-centric lists. Findings Based on the Reactome knowledge base, we built a network of protein-protein interactions accounting for the documented isoform and modification statuses of proteins. We then implemented a command line application called PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher) to query this network. PathwayMatcher supports multiple types of omics data as input and outputs the possibly affected biochemical reactions, subnetworks, and pathways. Conclusions PathwayMatcher enables refining the network representation of pathways by including proteoforms defined as protein isoforms with posttranslational modifications. The specificity of pathway analyses is hence adapted to different levels of granularity, and it becomes possible to distinguish interactions between different forms of the same protein.

scholarly journals NMR Spectroscopy of supramolecular chemistry on protein surfaces

2020 ◽  
Vol 16 ◽  
pp. 2505-2522
Author(s):  
Peter Bayer ◽  
Anja Matena ◽  
Christine Beuck
Keyword(s):  
Nmr Spectroscopy ◽  
Supramolecular Chemistry ◽  
Molecular Interactions ◽  
Protein Interactions ◽  
Atomic Resolution ◽  
Protein Protein Interactions ◽  
Protein Surfaces ◽  
Interaction Partners ◽  
Binding Pockets ◽  
Nmr Methods

As one of the few analytical methods that offer atomic resolution, NMR spectroscopy is a valuable tool to study the interaction of proteins with their interaction partners, both biomolecules and synthetic ligands. In recent years, the focus in chemistry has kept expanding from targeting small binding pockets in proteins to recognizing patches on protein surfaces, mostly via supramolecular chemistry, with the goal to modulate protein–protein interactions. Here we present NMR methods that have been applied to characterize these molecular interactions and discuss the challenges of this endeavor.

scholarly journals PathwayMatcher: proteoform-centric network construction enables fine-granularity multi-omics pathway mapping

2018 ◽  
Author(s):  
Luis Francisco Hernández Sánchez ◽  
Bram Burger ◽  
Carlos Horro ◽  
Antonio Fabregat ◽  
Stefan Johansson ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Biomedical Data ◽  
Protein Protein Interactions ◽  
Post Translational Modification ◽  
Post Translational Modifications ◽  
Network Construction ◽  
Gene Sets ◽  
Functional Knowledge ◽  
Pathway Analyses ◽  
Different Levels

AbstractBackgroundMapping biomedical data to functional knowledge is an essential task in bioinformatics and can be achieved by querying identifiers, e.g. gene sets, in pathway knowledgebases. However, the isoform and post-translational modification states of proteins are lost when converting input and pathways into gene-centric lists.FindingsBased on the Reactome knowledgebase, we built a network of protein-protein interactions accounting for the documented isoform and modification statuses of proteins. We then implemented a command line application called PathwayMatcher (github.com/PathwayAnalysisPlatform/PathwayMatcher) to query this network. PathwayMatcher supports multiple types of omics data as input, and outputs the possibly affected biochemical reactions, subnetworks, and pathways.ConclusionsPathwayMatcher enables refining the network-representation of pathways by including isoform and post-translational modifications. The specificity of pathway analyses is hence adapted to different levels of granularity and it becomes possible to distinguish interactions between different forms of the same protein.

scholarly journals Dynamic rewiring of the human interactome by interferon signalling

2019 ◽  
Author(s):  
Craig H. Kerr ◽  
Michael A. Skinnider ◽  
Angel M. Madero ◽  
Daniel D.T. Andrews ◽  
R. Greg Stacey ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Interaction Network ◽  
Cell Types ◽  
Antiviral Response ◽  
Type I ◽  
Protein Protein Interactions ◽  
Human Interactome ◽  
Viral Pathogens ◽  
Protein Protein Interaction ◽  
Dependent Protein

ABSTRACTBackgroundThe type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes (ISGs). Transcriptomic and biochemical approaches have established comprehensive catalogues of ISGs across species and cell types, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to delineate the effects of IFN signalling on the human proteome, culminating in the use of protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.ResultsWe identified >27,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer ISG protein synthesis.ConclusionsOur map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing ISGs in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

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