Specific Protein-protein Interaction Networks Research Articles

Abstract 1226: A genome wide pharmacological perturbation platform

Abstract Background: Large scale genetic perturbations screens have been instrumental for many key discoveries. In the last decade sequence specific genetic perturbations created RNAi and CRISPR (PMID: 25446316). Recently, specific genome targeting approaches using single guide RNAs (sgRNAs) for the CRISPR-Cas9 system have been repurposed to induce sequence-specific repression or activation of gene expression at a genome-wide scale. Different perturbation approaches have different effects and encompass a trade-off between specificity and duration in establishing a measurable perturbation which can result in different biological readouts. However, bench-side strategies are time consuming and expensive. The method described here aims to build a systems-based computational platform to assess pharmacological and biological perturbations at genome-wide network level.Methods: Our network tool NetDecoder (Nucleic Acids Res. 2016; 44(10):e100) that models genome-wide information flow will be used to build context specific protein-protein interaction networks. These networks will be subsequently subjected to in silico perturbations and their impacts on network properties will be assessed. AVANA and Demeter datasets will be used during method validation procedures. Results: Using publicly available expression datasets we generated context specific Breast Cancer networks and individually manipulated nodes and edges of context-specific networks associated with pharmacological FDA approved targets. Our perturbation networks captured cellular rewiring by engulfing new gene players, alterations of the network size and its connectivity. Conclusions: Our in-silico genome wide perturbation platform provides a flexible approach to fine-tune single and tandem manipulations, allowing for inexpensively monitoring a range of pharmacological interventions using large omics data. Citation Format: Cristina Correia, Choong Yong Ung, Cheng Zhang, Scott H. Kaufmann, Hu Li. A genome wide pharmacological perturbation platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1226.

Network Analyses of the Differential Expression of Heat Shock Proteins in Glioma.

Heat shock protein (HSP) is a family of highly conserved protein, which exists widely in various organisms and has a variety of important physiological functions. Currently, there is no systematic analysis of HSPs in human glioma. The aim of this study was to investigate the characteristics of HSPs through constructing protein-protein interaction network (PPIN) considering the expression level of HSPs in glioma. After the identification of the differentially expressed HSPs in glioma tissues, a specific PPIN was constructed and found that there were many interactions between the differentially expressed HSPs in glioma. Subcellular localization analysis shows that HSPs and their interacting proteins distribute from the cell membrane to the nucleus in a multilayer structure. By functional enrichment analysis, gene ontology analysis, and Kyoto Encyclopedia of Genes and Genomes pathway analysis, the potential function of HSPs and two meaningful enrichment pathways was revealed. In addition, nine HSPs (DNAJA4, DNAJC6, DNAJC12, HSPA6, HSP90B1, DNAJB1, DNAJB6, DNAJC10, and SERPINH1) are prognostic markers for human brain glioma. These analyses provide a full view of HSPs about their expression, biological process, as well as clinical significance in glioma.

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

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.

INBIA: a boosting methodology for proteomic network inference

BackgroundThe analysis of tissue-specific protein interaction networks and their functional enrichment in pathological and normal tissues provides insights on the etiology of diseases. The Pan-cancer proteomic project, in The Cancer Genome Atlas, collects protein expressions in human cancers and it is a reference resource for the functional study of cancers. However, established protocols to infer interaction networks from protein expressions are still missing.ResultsWe have developed a methodology called Inference Network Based on iRefIndex Analysis (INBIA) to accurately correlate proteomic inferred relations to protein-protein interaction (PPI) networks. INBIA makes use of 14 network inference methods on protein expressions related to 16 cancer types. It uses as reference model the iRefIndex human PPI network. Predictions are validated through non-interacting and tissue specific PPI networks resources. The first, Negatome, takes into account likely non-interacting proteins by combining both structure properties and literature mining. The latter, TissueNet and GIANT, report experimentally verified PPIs in more than 50 human tissues. The reliability of the proposed methodology is assessed by comparing INBIA with PERA, a tool which infers protein interaction networks from Pathway Commons, by both functional and topological analysis.ConclusionResults show that INBIA is a valuable approach to predict proteomic interactions in pathological conditions starting from the current knowledge of human protein interactions.

The protein-protein interaction network and clinical significance of heat-shock proteins in esophageal squamous cell carcinoma.

Heat-shock proteins (HSPs), one of the evolutionarily conserved protein families, are widely found in various organisms, and play important physiological functions. Nevertheless, HSPs have not been systematically analyzed in esophageal squamous cell carcinoma (ESCC). In this study, we applied the protein-protein interaction (PPI) network methodology to explore the characteristics of HSPs, and integrate their expression in ESCC. First, differentially expressed HSPs in ESCC were identified from our previous RNA-seq data. By constructing a specific PPI network, we found differentially expressed HSPs interacted with hundreds of neighboring proteins. Subcellular localization analyses demonstrated that HSPs and their interacting proteins distributed in multiple layers, from membrane to nucleus. Functional enrichment annotation analyses revealed known and potential functions for HSPs. KEGG pathway analyses identified four significant enrichment pathways. Moreover, three HSPs (DNAJC5B, HSPA1B, and HSPH1) could serve as promising targets for prognostic prediction in ESCC, suggesting these HSPs might play a significant role in the development of ESCC. These multiple bioinformatics analyses have provided a comprehensive view of the roles of heat-shock proteins in esophageal squamous cell carcinoma.

Network analyses elucidate the role of SMYD3 in esophageal squamous cell carcinoma.

SMYD3 is a member of the SET and myeloid‐Nervy‐DEAF‐1 (MYND) domain‐containing protein family of methyltransferases, which are known to play critical roles in carcinogenesis. Expression of SMYD3 is elevated in various cancers, including esophageal squamous cell carcinoma (ESCC), and is correlated with the survival time of patients with ESCC. Here, we dissect gene expression data, from a previously described KYSE150 ESCC cell line in which SMYD3 had been knocked down, by integration with the protein–protein interaction (PPI) network, to find the new potential biological roles of SMYD3 and subsequent target genes. By construction of a specific PPI network, differentially expressed genes (DEGs), following SMYD3 knockdown, were identified as interacting with thousands of neighboring proteins. Enrichment analyses from the DAVID Functional Annotation Chart found significant Gene Ontology (GO) terms associated with transcription activities, which were closely related to SMYD3 function. For example, YAP1 and GATA3 might be a target gene for SMYD3 to regulate transcription. Enrichment annotation of the total DEG PPI network by GO ‘Biological Process’ generated a connected functional map and found 532 significant terms, including known and potential biological roles of SMYD3 protein, such as expression regulation, signal transduction, cell cycle, cell metastasis, and invasion. Subcellular localization analyses found that DEGs and their interacting proteins were distributed in multiple layers, which might reflect the intricate biological processes at the spatial level. Our analysis of the PPI network has provided important clues for future detection of the biological roles and mechanisms, as well as the target genes of SMYD3.

A 80-gene set potentially predicts the relapse in laryngeal carcinoma optimized by support vector machine.

The present study was performed to identify a gene set for predicting the relapse in laryngeal carcinoma using large data analysis methods. Two gene expression profile data of laryngeal carcinoma (GSE27020 and GSE25727) were downloaded from public database. Genes associated with tumor relapse, namely informative genes, were identified by Cox regression analysis. Then the protein-protein interaction (PPI) network consisting of informative genes was constructed. Afterwards, the optimized support vector machine (SVM) classifier was constructed to classify the relapsed laryngeal carcinoma samples based on genes in specific PPI network. Furthermore, the efficiency of the SVM classifier was verified by other two independent datasets. A total of 331 informative genes were obtained from GSE27020 and GSE25757 datasets. A PPI network specific to laryngeal carcinoma relapse was constructed which contained informative genes and critical non-informative genes. The top 10 genes in specific PPI network were APP, NTRK1, TP53, PTEN, FN1, ELAVL1, HSP90AA1, XPO1, LDHA and CDK2 ranked by BC (betweenness centrality) value. The optimized SVM classifier including top 80 genes showed accuracy of 100% to classify the relapsed cases from laryngeal carcinoma samples. Next, the efficiency of the SVM classifier to predict relapse samples was verified in another independent datasets, which showed accuracy of 97.47%. The informative genes in the optimized SVM classifier were enriched in several pathways associated with tumor progression. A 80-gene set was identified as biomarker to predict the relapse of laryngeal carcinoma, which would be potentially applied in decision of different treatments for patients with different relapse risks.

Functional analyses of specific protein-protein interaction networks using affinity clamping technology

Functional analyses of specific protein-protein interaction networks using affinity clamping technology

Insights into the pathogenesis of axial spondyloarthropathy from network and pathway analysis

BackgroundComplex chronic diseases are usually not caused by changes in a single causal gene but by an unbalanced regulating network resulting from the dysfunctions of multiple genes or their products. Therefore, network based systems approach can be helpful for the identification of candidate genes related to complex diseases and their relationships. Axial spondyloarthropathy (SpA) is a group of chronic inflammatory joint diseases that mainly affect the spine and the sacroiliac joints. The pathogenesis of SpA remains largely unknown.ResultsIn this paper, we conducted a network study of the pathogenesis of SpA. We integrated data related to SpA, from the OMIM database, proteomics and microarray experiments of SpA, to prioritize SpA candidate disease genes in the context of human protein interactome. Based on the top ranked SpA related genes, we constructed a SpA specific PPI network, identified potential pathways associated with SpA, and finally sketched an overview of biological processes involved in the development of SpA.ConclusionsThe protein-protein interaction (PPI) network and pathways reflect the link between the two pathological processes of SpA, i.e., immune mediated inflammation, as well as imbalanced bone modelling caused new boneformation and bone loss. We found that some known disease causative genes, such as TNFand ILs, play pivotal roles in this interaction.