Identifying disease genes from PPI networks weighted by gene expression under different conditions

2016 ◽  
Author(s):  
Ping Luo ◽  
Li-Ping Tian ◽  
Jishou Ruan ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Disease Genes ◽  
Ppi Networks

scholarly journals Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 437 ◽  
Author(s):  
Giulia Fiscon ◽  
Federica Conte ◽  
Lorenzo Farina ◽  
Paola Paci
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Detection Algorithm ◽  
Cell Phenotype ◽  
Disease Genes ◽  
Network Medicine ◽  
Topological Properties ◽  
Systematic Analysis ◽  
Ppi Networks ◽  
Non Coding Rnas

Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.

On the limits of active module identification

2021 ◽  
Author(s):  
Olga Lazareva ◽  
Jan Baumbach ◽  
Markus List ◽  
David B Blumenthal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Diameter ◽  
Extensive Study ◽  
Biological Knowledge ◽  
Expression Data ◽  
Module Identification ◽  
Ppi Networks ◽  
Novel Algorithms ◽  
Context Specific

Abstract In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

scholarly journals NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W234-W241 ◽  
Author(s):  
Guangyan Zhou ◽  
Othman Soufan ◽  
Jessica Ewald ◽  
Robert E W Hancock ◽  
Niladri Basu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Visual Analytics ◽  
Regulatory Networks ◽  
Meta Analysis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Perform Gene Expression ◽  
And Performance

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

scholarly journals Functional Annotation and Identification of Candidate Disease Genes by Computational Analysis of Normal Tissue Gene Expression Data

PLoS ONE ◽  
2008 ◽  
Vol 3 (6) ◽  
pp. e2439 ◽  
Author(s):  
Laura Miozzi ◽  
Rosario Michael Piro ◽  
Fabio Rosa ◽  
Ugo Ala ◽  
Lorenzo Silengo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Functional Annotation ◽  
Normal Tissue ◽  
Computational Analysis ◽  
Disease Genes ◽  
Expression Data ◽  
Tissue Gene Expression

scholarly journals Comprehensive Analysis of Photoreceptor Gene Expression and the Identification of Candidate Retinal Disease Genes

Cell ◽  
2001 ◽  
Vol 107 (5) ◽  
pp. 579-589 ◽  
Author(s):  
Seth Blackshaw ◽  
Rebecca E. Fraioli ◽  
Takahisa Furukawa ◽  
Constance L. Cepko
Keyword(s):  
Gene Expression ◽  
Retinal Disease ◽  
Comprehensive Analysis ◽  
Disease Genes

scholarly journals Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Jialiang Yang ◽  
◽  
Tao Huang ◽  
Francesca Petralia ◽  
Quan Long ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Disease ◽  
Principal Component ◽  
Tissue Expression ◽  
Whole Body ◽  
Disease Genes ◽  
Related Gene ◽  
Tissue Specific ◽  
Age Related ◽  
Holistic Understanding

Abstract Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed and some essential tissues (e.g., heart and lung) show much stronger “co-aging” than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases.

scholarly journals Integration of eQTL and Parkinson’s disease GWAS data implicates 11 disease genes

2019 ◽  
Author(s):  
Demis A. Kia ◽  
David Zhang ◽  
Sebastian Guelfi ◽  
Claudia Manzoni ◽  
Leon Hubbard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Candidate Genes ◽  
Gwas Data ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Protein Ubiquitination ◽  
Ppi Networks ◽  
Core Proteins

AbstractSubstantial genome-wide association study (GWAS) work in Parkinson’s disease (PD) has led to an increasing number of loci shown reliably and robustly to be associated with the increased risk of the disease. Prioritising causative genes and pathways from these studies has proven problematic. Here, we present a comprehensive analysis of PD GWAS data with expression and methylation quantitative trait loci (eQTL/mQTL) using Colocalisation analysis (Coloc) and transcriptome-wide association analysis (TWAS) to uncover putative gene expression and splicing mechanisms driving PD GWAS signals. Candidate genes were further characterised by determining cell-type specificity, weighted gene co-expression (WGNCA) and protein-protein interaction (PPI) networks.Gene-level analysis of expression revealed 5 genes (WDR6, CD38, GPNMB, RAB29, TMEM163) that replicated using both Coloc and TWAS analyses in both GTEx and Braineac expression datasets. A further 6 genes (ZRANB3, PCGF3, NEK1, NUPL2, GALC, CTSB) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell-types compared to neurons. The WGNCA analysis showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes related with protein ubiquitination (protein ubiquitination (p=7.47e-10) and ubiquitin-dependent protein catabolic process (p = 2.57e-17) in nucleus accumbens, caudate and putamen, while TMEM163 and ZRANB3 were both important in modules indicating regulation of signalling (p=1.33e-65] and cell communication (p=7.55e-35) in the frontal cortex and caudate respectively. PPI analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known Mendelian PD and parkinsonism proteins than would be expected by chance. The proteins core proteins this network were enriched for regulation of the ERBB receptor tyrosine protein kinase signalling pathways.Together, these results point to a number of candidate genes and pathways that are driving the associations observed in PD GWAS studies.

scholarly journals Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia

2019 ◽  
Author(s):  
Robersy Sanchez ◽  
Sally A. Mackenzie
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Cytosine Methylation ◽  
Complex Disease ◽  
Lymphoblastic Leukemia ◽  
Integrated Analysis ◽  
Ras Signaling ◽  
Protein Protein Interaction ◽  
Diagnosis And Prognosis ◽  
Ppi Networks

AbstractGenome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than individual disconnected analyses. Studies of protein-protein interaction (PPI) networks of differentially methylated (DMGs) and expressed genes (DEGs) showed that gene expression and methylation consistently targeted the same gene pathways associated with cancer: Pathways in cancer, Ras signaling pathway, PI3K-Akt signaling pathway, and Rap1 signaling pathway, among others. Detected gene hubs and hub sub-networks are integrated by signature loci associated with cancer that include, for example, NOTCH1, RAC1, PIK3CD, BCL2, and EGFR. Statistical analysis disclosed a stochastic deterministic dependence between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes are probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes, reflecting a coordinated response of methylation and gene-expression regulatory systems. These results support the identification of reliable and stable biomarkers for cancer diagnosis and prognosis.

scholarly journals Gene modules and non-coding RNAs involved in pancreatic tumorigenesis through acinar ductal metaplasia 

2020 ◽  
Author(s):  
Huairong Zhang ◽  
Bingyin Shi ◽  
ZU-HUA GAO ◽  
BO GAO
Keyword(s):  
Gene Expression ◽  
Intraepithelial Neoplasia ◽  
Genetic Alterations ◽  
Ductal Adenocarcinoma ◽  
Nucleosome Assembly ◽  
Go Analysis ◽  
Functional Studies ◽  
Ppi Networks ◽  
Gene Modules ◽  
Non Coding Rnas

Abstract Background: Acinar ductal metaplasia (ADM) is a recently identified precursor lesion that can progress through pancreatic ductal intraepithelial neoplasia (PanIN) to pancreatic ductal adenocarcinoma (PDAC). However, the genetic alterations and the transcriptional regulators at work during the process of ADM-driven PDAC tumorigenesis are largely unknown. We applied a multidimensional integration strategy to unveil the gene modules and non-coding RNAs that drive the ADM-PanIN-PDAC process. Methods: GSE40895 and the microarray datasets were integrated to unmask the regulators linked to ADM, PanIN and PDAC. Based on the differentially expressed genes and protein–protein interaction (PPI) networks for each stage, overlapping and crosstalk gene modules in ADM-PanIN-PDAC were identified using the search tool for the retrieval of interacting genes (STRING) and Cytoscape. The functions of these modules were elucidated by gene ontology (GO) analysis. The expression levels of hub genes and survival analysis were investigated in human PDAC via gene expression profiling interactive analysis (GEPIA). The MiRDB database was used to predict potential non-coding RNAs (ncRNAs) capable of regulating overlap and crosstalk genes.Results: We found several bridging ADM gene modules (e.g. SMARCA1 and H2AFZ), PanIN gene modules (e.g. HDAC11 and SMARCA2) and PDAC gene modules (e.g. OLFR239 and CLIP3). They were enriched in nucleosome assembly, chromatin organization and G-protein coupled receptor signalling pathways by GO analysis. MicroRNAs (e.g. mmu-miR-335-5p and mmu-miR-669n) and lncRNAs (e.g. H19 and Gm14207) took part in this ample crosstalk by regulating the gene expression. Conclusions: SMARCA1, SMARCA2 and CLIP3 were identified as novel crosstalk genes and potential prognostic biomarkers for ADM-driven PDAC carcinogenesis. After validation in clinical and functional studies, transcriptional regulatory non-coding RNAs targeting crosstalk and overlapping genes could represent effective targets for early PDAC intervention.

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