iRefScape. A Cytoscape plug-in for visualization and data mining of protein interaction data from iRefIndex

Functional Genomics Approach Identifies Novel Signaling Regulators of TGFα Ectodomain Shedding

Ectodomain shedding of cell-surface precursor proteins by metalloproteases generates important cellular signaling molecules. Of importance for disease is the release of ligands that activate the EGFR, such as TGFα, which is mostly carried out by ADAM17 [a member of the A-disintegrin and metalloprotease (ADAM) domain family]. EGFR ligand shedding has been linked to many diseases, in particular cancer development, growth and metastasis, as well as resistance to cancer therapeutics. Excessive EGFR ligand release can outcompete therapeutic EGFR inhibition or the inhibition of other growth factor pathways by providing bypass signaling via EGFR activation. Drugging metalloproteases directly have failed clinically because it indiscriminately affected shedding of numerous substrates. It is therefore essential to identify regulators for EGFR ligand cleavage. Here, integration of a functional shRNA genomic screen, computational network analysis, and dedicated validation tests succeeded in identifying several key signaling pathways as novel regulators of TGFα shedding in cancer cells. Most notably, a cluster of genes with NFκB pathway regulatory functions was found to strongly influence TGFα release, albeit independent of their NFκB regulatory functions. Inflammatory regulators thus also govern cancer cell growth-promoting ectodomain cleavage, lending mechanistic understanding to the well-known connection between inflammation and cancer.Implications: Using genomic screens and network analysis, this study defines targets that regulate ectodomain shedding and suggests new treatment opportunities for EGFR-driven cancers. Mol Cancer Res; 16(1); 147-61. ©2017 AACR.

Overcoming the divide between ataxias and spastic paraplegias: Shared phenotypes, genes, and pathways

Autosomal-dominant spinocerebellar ataxias, autosomal-recessive spinocerebellar ataxias, and hereditary spastic paraplegias have traditionally been designated in separate clinicogenetic disease classifications. This classification system still largely frames clinical thinking and genetic workup in clinical practice. Yet, with the advent of next-generation sequencing, phenotypically unbiased studies have revealed the limitations of this classification system. Various genes (eg, SPG7, SYNE1, PNPLA6) traditionally rooted in either the ataxia or hereditary spastic paraplegia classification system have now been shown to cause ataxia on the one end of the disease continuum and hereditary spastic paraplegia on the other. Other genes such as GBA2 and KIF1C were almost simultaneously published as both a hereditary spastic paraplegia and an ataxia gene. The variability and fluidity of observed phenotypes along the ataxia-spasticity spectrum warrants a rethinking of the traditional classification system. We propose to replace this divisive diagnosis-driven ataxia and hereditary spastic paraplegia classification system by a descriptive, unbiased approach of modular phenotyping. This approach is also open to expansion of the phenotype beyond ataxia and spasticity, which often occur as part of broader multisystem neuronal dysfunction. The concept of a continuous ataxia-spasticity disease spectrum is further supported by ataxias and hereditary spastic paraplegias sharing not only overlapping phenotypes and underlying genes, but also common cellular pathways and disease mechanisms. This suggests a shared vulnerability of cerebellar and corticospinal neurons for common pathophysiological processes. It might be this mechanistic overlap that drives their clinical overlap. A mechanistically inspired classification system will help to pave the way for mechanism-based strategies for drug development. © 2017 International Parkinson and Movement Disorder Society.

Protein-protein interaction analysis for functional characterization of helicases

Helicases are enzymes involved in nucleic acid metabolism, playing major roles in replication, transcription, and repair. Defining helicases oligomerization state and transient and persistent protein interactions is essential for understanding of their function. In this article we review current methods for the protein-protein interaction analysis, and discuss examples of its application to the study of helicases: Pif1 and DDX3. Proteomics methods are our main focus - affinity pull-downs and chemical cross-linking followed by mass spectrometry. We review advantages and limitations of these methods and provide general guidelines for their implementation in the functional analysis of helicases.

Whole-exome sequencing in obsessive-compulsive disorder identifies rare mutations in immunological and neurodevelopmental pathways

Studies of rare genetic variation have identified molecular pathways conferring risk for developmental neuropsychiatric disorders. To date, no published whole-exome sequencing studies have been reported in obsessive-compulsive disorder (OCD). We sequenced all the genome coding regions in 20 sporadic OCD cases and their unaffected parents to identify rare de novo (DN) single-nucleotide variants (SNVs). The primary aim of this pilot study was to determine whether DN variation contributes to OCD risk. To this aim, we evaluated whether there is an elevated rate of DN mutations in OCD, which would justify this approach toward gene discovery in larger studies of the disorder. Furthermore, to explore functional molecular correlations among genes with nonsynonymous DN SNVs in OCD probands, a protein-protein interaction (PPI) network was generated based on databases of direct molecular interactions. We applied Degree-Aware Disease Gene Prioritization (DADA) to rank the PPI network genes based on their relatedness to a set of OCD candidate genes from two OCD genome-wide association studies (Stewart et al., 2013; Mattheisen et al., 2014). In addition, we performed a pathway analysis with genes from the PPI network. The rate of DN SNVs in OCD was 2.51 × 10(-8) per base per generation, significantly higher than a previous estimated rate in unaffected subjects using the same sequencing platform and analytic pipeline. Several genes harboring DN SNVs in OCD were highly interconnected in the PPI network and ranked high in the DADA analysis. Nearly all the DN SNVs in this study are in genes expressed in the human brain, and a pathway analysis revealed enrichment in immunological and central nervous system functioning and development. The results of this pilot study indicate that further investigation of DN variation in larger OCD cohorts is warranted to identify specific risk genes and to confirm our preliminary finding with regard to PPI network enrichment for particular biological pathways and functions.

Characterization of protein complexes and subcomplexes in protein-protein interaction databases

The identification and characterization of protein complexes implicated in protein-protein interaction data are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. This paper provides a novel characterization of subcomplexes in protein interaction databases, stressing definition and representation issues, quantification, biological validation, network metrics, motifs, modularity, and gene ontology (GO) terms. The paper introduces the concept of "nested group" as a way to represent subcomplexes and estimates that around 15% of those nested group with the higher Jaccard index may be a result of data artifacts in protein interaction databases, while a number of them can be found in biologically important modular structures or dynamic structures. We also found that network centralities, enrichment in essential proteins, GO terms related to regulation, imperfect 5-clique motifs, and higher GO homogeneity can be used to identify proteins in nested complexes.

Integrating proteomics profiling data sets: a network perspective

Understanding disease mechanisms often requires complex and accurate integration of cellular pathways and molecular networks. Systems biology offers the possibility to provide a comprehensive map of the cell's intricate wiring network, which can ultimately lead to decipher the disease phenotype. Here, we describe what biological pathways are, how they function in normal and abnormal cellular systems, limitations faced by databases for integrating data, and highlight how network models are emerging as a powerful integrative framework to understand and interpret the roles of proteins and peptides in diseases.

Comparative Genomics and Evolutionary Modularity of Prokaryotes

The soaring number of high-quality genomic sequences has ushered in the era of post-genomic research where our understanding of organisms has dramatically shifted towards defining the function of genes within their larger biological contexts. As a result, novel high-throughput experimental technologies are being increasingly employed to uncover physical and functional associations of genes and proteins in complex biological processes. Through the construction and analysis of physical, genetic and metabolic networks generated for the model organisms, such as Escherichia coli, organizational principles of the genome have been deduced, such as modularity, which has important implications toward understanding prokaryotic evolution and adaptation to novel lifestyles.

Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development

Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic-based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.

Proteome-based systems biology analysis of the diabetic mouse aorta reveals major changes in fatty acid biosynthesis as potential hallmark in diabetes mellitus-associated vascular disease

BACKGROUND

Macrovascular complications of diabetes mellitus are a major risk factor for cardiovascular morbidity and mortality. Currently, studies only partially described the molecular pathophysiology of diabetes mellitus-associated effects on vasculature. However, better understanding of systemic effects is essential in unraveling key molecular events in the vascular tissue responsible for disease onset and progression.

METHODS AND RESULTS

Our overall aim was to get an all-encompassing view of diabetes mellitus-induced key molecular changes in the vasculature. An integrative proteomic and bioinformatics analysis of data from aortic vessels in the low-dose streptozotocin-induced diabetic mouse model (10 animals) was performed. We observed pronounced dysregulation of molecules involved in myogenesis, vascularization, hypertension, hypertrophy (associated with thickening of the aortic wall), and a substantial reduction of fatty acid storage. A novel finding is the pronounced downregulation of glycogen synthase kinase-3β (Gsk3β) and upregulation of molecules linked to the tricarboxylic acid cycle (eg, aspartate aminotransferase [Got2] and hydroxyacid-oxoacid transhydrogenase [Adhfe1]). In addition, pathways involving primary alcohols and amino acid breakdown are altered, potentially leading to ketone-body production. A number of these findings were validated immunohistochemically. Collectively, the data support the hypothesis that in this diabetic model, there is an overproduction of ketone-bodies within the vessels using an alternative tricarboxylic acid cycle-associated pathway, ultimately leading to the development of atherosclerosis.

CONCLUSIONS

Streptozotocin-induced diabetes mellitus in animals leads to a reduction of fatty acid biosynthesis and an upregulation of an alternative ketone-body formation pathway. This working hypothesis could form the basis for the development of novel therapeutic intervention and disease management approaches.

Integrating in silico resources to map a signaling network

The abundance of publicly available life science databases offers a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and we discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol for building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature.

The protein interaction landscape of the human CMGC kinase group

Cellular information processing via reversible protein phosphorylation requires tight control of the localization, activity, and substrate specificity of protein kinases, which to a large extent is accomplished by complex formation with other proteins. Despite their critical role in cellular regulation and pathogenesis, protein interaction information is available for only a subset of the 518 human protein kinases. Here we present a global proteomic analysis of complexes of the human CMGC kinase group. In addition to subgroup-specific functional enrichment and modularity, the identified 652 high-confidence kinase-protein interactions provide a specific biochemical context for many poorly studied CMGC kinases. Furthermore, the analysis revealed a kinase-kinase subnetwork and candidate substrates for CMGC kinases. Finally, the presented interaction proteome uncovered a large set of interactions with proteins genetically linked to a range of human diseases, including cancer, suggesting additional routes for analyzing the role of CMGC kinases in controlling human disease pathways.

A survey of protein interaction data and multigenic inherited disorders

Background

Multigenic diseases are often associated with protein complexes or interactions involved in the same pathway. We wanted to estimate to what extent this is true given a consolidated protein interaction data set. The study stresses data integration and data representation issues.

Results

We constructed 497 multigenic disease groups from OMIM and tested for overlaps with interaction and pathway data. A total of 159 disease groups had significant overlaps with protein interaction data consolidated by iRefIndex. A further 68 disease overlaps were found only in the KEGG pathway database. No single database contained all significant overlaps thus stressing the importance of data integration. We also found that disease groups overlapped with all three interaction data types: n-ary, spoke-represented complexes and binary data – thus stressing the importance of considering each of these data types separately.

Conclusions

Almost half of our multigenic disease groups could potentially be explained by protein complexes and pathways. However, the fact that no database or data type was able to cover all disease groups suggests that no single database has systematically covered all disease groups for potential related complex and pathway data. This survey provides a basis for further curation efforts to confirm and search for overlaps between diseases and interaction data. The accompanying R script can be used to reproduce the work and track progress in this area as databases change. Disease group overlaps can be further explored using the iRefscape plugin for Cytoscape.

Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction

Background

Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction.

Results

Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins – this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein’s membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn.

Conclusions

These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.