Protein-protein Interaction Subnetworks Research Articles

Functional analysis of gene expression profiling-based prediction in bladder cancer.

The present study aimed to analyze the modification of gene expression in bladder cancer (BC) by identifying significant differentially expressed genes (DEGs) and functionally assess them using bioinformatics analysis. To achieve this, two microarray datasets, GSE24152 (which included 10 fresh tumor tissue samples from urothelial bladder carcinoma patients and 7 benign mucosa samples from the bladder), and GSE42089 (which included 10 tissues samples from urothelial cell carcinoma patients and 8 tissues samples from the normal bladder), were downloaded from the Gene Expression Omnibus database for further analysis. Differentially expressed genes (DEGs) were screened between benign the mucosa and control groups in GSE24152 and GSE42089 datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on overlapping DEGs identified in GSE24152 and GSE42089. Protein-protein interaction (PPI) networks and sub-networks were then constructed to identify key genes and main pathways. GO terms analysis was also performed for the selected clusters. In total, 1,325 DEGs in GSE24152 and 647 DEGs in GSE42089 were screened, in which 619 common DEGs were identified. The DEGs were mainly enriched in pathways and GO terms associated with mitotic and chromosome assembly, including nucleosome assembly, spindle checkpoint and DNA replication. In the interaction network, progesterone receptor (PGR), MAF bZIP transcription factor G (MAFG), cell division cycle 6 (CDC6) and members of the minichromosome maintenance family (MCMs) were identified as key genes. Histones were also considered to be significant factors in BC. Nucleosome assembly and sequence-specific DNA binding were the most significant clustered GO terms. In conclusion, the DEGs, including PGR, MAFG, CDC6 and MCMs, and those encoding the core histone family were closely associated with the development of BC via pathways associated with mitotic and chromosome assembly.

Integrated Modules Analysis to Explore the Molecular Mechanisms of Phlegm-Stasis Cementation Syndrome with Ischemic Heart Disease.

Background: Ischemic heart disease (IHD) has been the leading cause of death for several decades globally, IHD patients usually hold the symptoms of phlegm-stasis cementation syndrome (PSCS) as significant complications. However, the underlying molecular mechanisms of PSCS complicated with IHD have not yet been fully elucidated.Materials and Methods: Network medicine methods were utilized to elucidate the underlying molecular mechanisms of IHD phenotypes. Firstly, high-quality IHD-associated genes from both human curated disease-gene association database and biomedical literatures were integrated. Secondly, the IHD disease modules were obtained by dissecting the protein-protein interaction (PPI) topological modules in the String V9.1 database and the mapping of IHD-associated genes to the PPI topological modules. After that, molecular functional analyses (e.g., Gene Ontology and pathway enrichment analyses) for these IHD disease modules were conducted. Finally, the PSCS syndrome modules were identified by mapping the PSCS related symptom-genes to the IHD disease modules, which were further validated by both pharmacological and physiological evidences derived from published literatures.Results: The total of 1,056 high-quality IHD-associated genes were integrated and evaluated. In addition, eight IHD disease modules (the PPI sub-networks significantly relevant to IHD) were identified, in which two disease modules were relevant to PSCS syndrome (i.e., two PSCS syndrome modules). These two modules had enriched pathways on Toll-like receptor signaling pathway (hsa04620) and Renin-angiotensin system (hsa04614), with the molecular functions of angiotensin maturation (GO:0002003) and response to bacterium (GO:0009617), which had been validated by classical Chinese herbal formulas-related targets, IHD-related drug targets, and the phenotype features derived from human phenotype ontology (HPO) and published biomedical literatures.Conclusion: A network medicine-based approach was proposed to identify the underlying molecular modules of PSCS complicated with IHD, which could be used for interpreting the pharmacological mechanisms of well-established Chinese herbal formulas (e.g., Tao Hong Si Wu Tang, Dan Shen Yin, Hunag Lian Wen Dan Tang and Gua Lou Xie Bai Ban Xia Tang). In addition, these results delivered novel understandings of the molecular network mechanisms of IHD phenotype subtypes with PSCS complications, which would be both insightful for IHD precision medicine and the integration of disease and TCM syndrome diagnoses.

Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders

BackgroundIntegrating rare variation from trio family and case–control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DDs), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through the study of rare variation, only two risk genes have been identified.MethodsWe used hierarchical Bayesian modeling of rare-variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence data for SCZ (1,077 trios, 6,699 cases, and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls).ResultsFor SCZ, we estimate there are 1,551 risk genes. There are more risk genes and they have weaker effects than for NDDs. We provide power analyses to predict the number of risk-gene discoveries as more data become available. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs (ρ>0.55), but low between SCZ and the NDDs (ρ<0.3). An in-depth analysis of 288 NDD genes shows there is highly significant protein–protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichment, single-cell RNA-seq cell types, and multi-region developmental brain RNA-seq.ConclusionsWe have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs (https://github.com/hoangtn/extTADA). We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.

Molecular mechanisms of breast cancer metastasis by gene expression profile analysis.

Metastasis is the main cause of breast cancer-related mortalities. The present study aimed to uncover the relevant molecular mechanisms of breast cancer metastasis and to explore potential biomarkers that may be used for prognosis. Expression profile microarray data GSE8977, which contained 22 stroma samples (15 were from normal breast and 7 were from invasive ductal carcinoma tumor samples), were obtained from the Gene Expression Omnibus database. Following data preprocessing, differentially expressed genes (DEGs) were selected based on analyses conducted using the linear models for microarray analysis package from R and Bioconductor software. The resulting data were used in subsequent function and pathway enrichment analyses, as well as protein-protein interaction (PPI) network and subnetwork analyses. Transcription factors (TFs) and tumor-associated genes were also identified among the DEGs. A total of 234 DEGs were identified, which were enriched in immune response, cell differentiation and cell adhesion-related functions and pathways. Downregulated DEGs included TFs, such as the proto-oncogene SPI1, pre-B-cell leukemia homeobox 3 (PBX3) and lymphoid enhancer-binding factor 1 (LEF1), as well as tumor suppressors (TSs), such as capping actin protein, gelsolin like (CAPG) and tumor protein p53-inducible nuclear protein 1 (TP53INP1). Upregulated DEGs also included TFs and tumor suppressors, consisting of transcription factor 7-like 2 (TCF7L2) and pleiomorphic adenoma gene-like 1 (PLAGL1). DEGs that were identified at the hub nodes in the PPI network and the subnetwork were epidermal growth factor receptor (EGFR) and spleen-associated tyrosine kinase (SYK), respectively. Several genes crucial in the metastasis of breast cancer were identified, which may serve as potential biomarkers, many of which were associated with cell adhesion, proliferation or immune response, and may influence breast cancer metastasis by regulating these function or pathways.

Integrated clinicopathological features and gene microarray analysis of pancreatic neuroendocrine tumors

Pancreatic neuroendocrine tumors are relatively rare pancreatic neoplasms over the world. Investigations about molecular biology of PNETs are insufficient for nowadays. We aimed to explore the expression of messenger RNA and regulatory processes underlying pancreatic neuroendocrine tumors from different views. The expression profile of GSE73338 were downloaded, including samples with pancreatic neuroendocrine tumors. First, the Limma package was utilized to distinguish the differentially expressed messenger RNA. Gene Ontology classification and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to explore the functions and pathways of target genes. In addition, we constructed a protein-protein interaction network. NEK2, UBE2C, TOP2A and PPP1R1A were revealed with continuous genomic alterations in higher tumor stage. 91 up-regulated and 36 down-regulated genes were identified to be differentially expressed in malignant PNETs. Locomotory behavior was significantly enriched for biological processes of metastasis PNETs. GCGR and GNAS were identified as the hub of proteins in the protein-protein interaction sub-network of malignant PNETs. We showed the gene expression differences in PNETs according to different clinicopathological aspects. NEK2, UBE2C, TOP2A are positively associated with high tumor grade, and PPP1R1A negatively. GCGR and GNAS are regarded as the hub of the PPI sub-network. CXCR4 may affect the progression of PNETs via the CXCR4-CXCL12-CXCR7 chemokine receptor axis. However, more studies are required.

Integrated analysis of differentially expressed genes and pathways in triple‑negative breast cancer.

Triple-negative breast cancer (TNBC) is a heterogeneous disease characterized by an aggressive phenotype and reduced survival. The aim of the present study was to investigate the molecular mechanisms involved in the carcinogenesis of TNBC and to identify novel target molecules for therapy. The differentially expressed genes (DEGs) in TNBC and normal adjacent tissue were assessed by analyzing the GSE41970 microarray data using Qlucore Omics Explorer, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes. Pathway enrichment analyses for DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery online resource. A protein-protein interaction (PPI) network was constructed using Search Tool for the Retrieval of Interacting Genes, and subnetworks were analyzed by ClusterONE. The PPI network and subnetworks were visualized using Cytoscape software. A total of 121 DEGs were obtained, of which 101 were upregulated and 20 were downregulated. The upregulated DEGs were significantly enriched in 14 pathways and 83 GO biological processes, while the downregulated DEGs were significantly enriched in 18 GO biological processes. The PPI network with 118 nodes and 1,264 edges was constructed and three subnetworks were extracted from the entire network. The significant hub DEGs with high degrees were identified, including TP53, glyceraldehyde-3-phosphate dehydrogenase, cyclin D1, HRAS and proliferating cell nuclear antigen, which were predominantly enriched in the cell cycle pathway and pathways in cancer. A number of critical genes and pathways were revealed to be associated with TNBC. The present study may provide an improved understanding of the pathogenesis of TNBC and contribute to the development of therapeutic targets for TNBC.

Identifying DCN and HSPD1 as Potential Biomarkers in Colon Cancer Using 2D-LC-MS/MS Combined with iTRAQ Technology.

Colon cancer is one of the most common types of gastrointestinal cancers and the fourth cause of cancer death worldwide. To discover novel diagnostic biomarkers for colon cancer and investigate potential mechanisms of oncogenesis, quantitative proteomic approach using iTRAQ-tagging and 2D-LC-MS/MS was performed to characterize proteins alterations in colon cancer and non-neoplastic colonic mucosa (NNCM) using laser capture microdissection-harvested from the two types of tissues, respectively. As a result, 188 DEPs were identified, and the differential expression of two DEPs (DCN and HSPD1) was further verified by Western blotting and immunohistochemistry. KEGG pathway analysis disclosed that the DEPs were related to signaling pathways associated with cancer; furthermore, DCN and HSPD1 are in the relative central hub position among protein-protein interaction subnetwork of the DEPs. The results not only shed light on the mechanism by the DEPs contributed to colonic carcinogenesis, but also showed that DCN and HSPD1 are novel potential biomarkers for the diagnosis of colon cancer.

BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes.

Considering the roles of protein complexes in many biological processes in the cell, detection of protein complexes from available protein-protein interaction (PPI) networks is a key challenge in the post genome era. Despite high dynamicity of cellular systems and dynamic interaction between proteins in a cell, most computational methods have focused on static networks which cannot represent the inherent dynamicity of protein interactions. Recently, some researchers try to exploit the dynamicity of PPI networks by constructing a set of dynamic PPI subnetworks correspondent to each time-point (column) in a gene expression data. However, many genes can participate in multiple biological processes and cellular processes are not necessarily related to every sample, but they might be relevant only for a subset of samples. So, it is more interesting to explore each subnetwork based on a subset of genes and conditions (i.e., biclusters) in a gene expression data. Here, we present a new method, called BiCAMWI to employ dynamicity in detecting protein complexes. The preprocessing phase of the proposed method is based on a novel genetic algorithm that extracts some sets of genes that are co-regulated under some conditions from input gene expression data. Each extracted gene set is called bicluster. In the detection phase of the proposed method, then, based on the biclusters, some dynamic PPI subnetworks are extracted from input static PPI network. Protein complexes are identified by applying a detection method on each dynamic PPI subnetwork and aggregating the results. Experimental results confirm that BiCAMWI effectively models the dynamicity inherent in static PPI networks and achieves significantly better results than state-of-the-art methods. So, we suggest BiCAMWI as a more reliable method for protein complex detection.

Network-Based Comparative Analysis of Arabidopsis Immune Responses to Golovinomyces orontii and Botrytis cinerea Infections.

A comprehensive exploration of common and specific plant responses to biotrophs and necrotrophs is necessary for a better understanding of plant immunity. Here, we compared the Arabidopsis defense responses evoked by the biotrophic fungus Golovinomyces orontii and the necrotrophic fungus Botrytis cinerea through integrative network analysis. Two time-course transcriptional datasets were integrated with an Arabidopsis protein-protein interaction (PPI) network to construct a G. orontii conditional PPI sub-network (gCPIN) and a B. cinerea conditional PPI sub-network (bCPIN). We found that hubs in gCPIN and bCPIN played important roles in disease resistance. Hubs in bCPIN evolved faster than hubs in gCPIN, indicating the different selection pressures imposed on plants by different pathogens. By analyzing the common network from gCPIN and bCPIN, we identified two network components in which the genes were heavily involved in defense and development, respectively. The co-expression relationships between interacting proteins connecting the two components were different under G. orontii and B. cinerea infection conditions. Closer inspection revealed that auxin-related genes were overrepresented in the interactions connecting these two components, suggesting a critical role of auxin signaling in regulating the different co-expression relationships. Our work may provide new insights into plant defense responses against pathogens with different lifestyles.

Crosstalk of dynamic functional modules in lung development of rhesus macaques.

Lung development follows a complex series of dynamic histogenic events that depend on the fluctuation of gene expression, and the disruption of gene regulation could lead to devastating consequences, such as diseases in adulthood. In order to investigate the mechanism of lung development, we performed RNA sequencing by Illumina HiSeq™ 2000 to measure mRNA expression in lung tissues of nine rhesus macaques spanning from foetuses at gestation of 45 days to postnatal at 7 days. This development period was divided into three developmental stages, including the early stage (45-100 gestational days), the middle stage (137-163 gestational days) and the late stage (after birth at 4-7 days). Firstly, we identified stage-specific genes, based on which we found that the principle biological processes of the early stage were mainly associated with internal growth signalling, while the middle and late stage-specific genes controlled the external stress signalling. Then, we constructed a stage-specific protein-protein interaction (PPI) subnetwork, extracted dynamic modules, and identified crosstalk between modules. Moreover, we found four active pathways that could mediate the crosstalk, including the Notch signalling pathway, cell cycle, NOD-like receptor signalling pathway, and Toll-like receptor signalling pathway. These pathways not only played crucial roles in lung development, but also were implicated in lung diseases. Finally, some important bridgers, such as PSEN2, HSP90AA1 and CASP8, were discovered to explain the potential mechanism of crosstalk. Therefore, our study presents the landscape of gene expression of lung development of rhesus macaques, and provides an extended insight into the lung development mechanism.

Identification of genes and signaling pathways associated with squamous cell carcinoma by bioinformatics analysis.

The present study aimed to investigate the genes and signaling pathways associated with squamous cell carcinoma (SCC) by bioinformatics analysis. For this purpose, the GSE2503 was downloaded from the Gene Expression Omnibus database, and the differentially expressed genes (DEGs) between 6 normal skin and 5 SCC samples were analyzed using the Linear Models for Microarray Data package. Gene Ontology (GO) and pathway enrichment analysis of DEGs were performed, followed by functional annotation and construction of a protein-protein interaction (PPI) network. Subnetwork modules were subsequently identified and analyzed. A total of 181 DEGs, including 95 upregulated and 86 downregulated DEGs, were identified, in addition to 20 GO biological processes terms enriched by upregulated DEGs and 14 enriched by downregulated DEGs. The upregulated DEGs were enriched in 18 pathways, and the downregulated DEGs were enriched in 7 pathways. Following functional annotation, three upregulated transcription factors (TFs), including hypoxia inducible factor 1, alpha subunit (HIF1A), and six downregulated TFs were identified. In the PPI network and subnetwork, matrix metallopeptidase 1 (MMP1), also known as interstitial collagenase, and interleukin 8 (IL8) were the hub genes with the highest degree of connectivity (degree =8). Integrin alpha (ITGA)6 and 2 were enriched in several pathways, including focal adhesion and extracellular matrix-receptor interaction. DEGs of SCC were primarily enriched in pathways associated with cancer and cell adhesion. Therefore, DEGs such as IL8, MMP1, HIF1A, ITGA6 and ITGA2 may be potential targets for the diagnosis and treatment of SCC.

Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses.

This study aimed to explore the underlying genes and pathways associated with pancreatic ductal adenocarcinoma (PDAC) by bioinformatics analyses. Gene expression profile GSE43795 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between six PDAC and five non-neoplastic pancreatic tissue samples were analyzed using the limma package. Gene ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by functional annotation and protein-protein interaction (PPI) network construction. Finally, the sub-network was identified and pathway enrichment analysis was performed on the contained DEGs. A total of 374 downregulated and 559 upregulated DEGs were identified. The downregulated DEGs were enriched in GO terms associated with digestion and transport and pathways related to metabolism, while the upregulated DEGs were enriched in GO terms associated with the cell cycle and mitosis and pathways associated with the occurrence of cancer including the cell cycle pathway. Following functional annotation, the oncogene pituitary tumor-transforming 1 (PTTG1) was upregulated. In the PPI network and sub-network, cell division cycle 20 (CDC20) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) were hub genes with high connectivity degrees. Additionally, DEGs in the sub-network including cyclin B1 (CCNB1) were mainly enriched in the cell cycle and p53 signaling pathways. In conclusion, the cell cycle and p53 signaling pathways may play significant roles in PDAC, and DEGs including CDC20, BUB1B, CCNB1 and PTTG1 may be potential targets for PDAC diagnosis and treatment.

Role of NSC319726 in ovarian cancer based on the bioinformatics analyses.

AimThis study aimed to explore the molecular mechanisms of NSC319726 in ovarian cancer by bioinformatics analyses.MethodsGene expression profile GSE35972 was downloaded from the Gene Expression Omnibus database. The data set contains six samples, including three samples of TOV112D cells untreated and three samples of TOV112D cells treated with NSC319726. The differentially expressed genes (DEGs) between untreated and treated samples were analyzed using the limma package. Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed using the signaling pathway impact analysis package, followed by the functional annotation of DEGs and protein–protein interaction network construction. Finally, the subnetwork was identified, and Gene Ontology functional enrichment analysis was performed on the DEGs enriched in the subnetwork.ResultsA total of 120 upregulated and 126 downregulated DEGs were identified. Six Kyoto Encyclopedia of Genes and Genomes pathways were significantly perturbed by DEGs, and the pathway of oocyte meiosis was identified as the most perturbed one. Oocyte meiosis was enriched by eight downregulated DEGs, such as ribosomal protein S6 kinase, 90 kDa, and polypeptide 6 (RPS6KA6). After functional annotation, eight transcription factors were upregulated (such as B-cell CLL/lymphoma 6 [BCL6]), and three transcription factors were downregulated. Seven tumor suppressor genes, such as forkhead box O3 (FOXO3), were upregulated. Additionally, in the protein–protein interaction network and subnetwork, cyclin B1 (CCNB1) and cell division cycle 20 (CDC20) were hub genes, which were also involved in the functions related to mitotic cell cycle.ConclusionNSC319726 may play an efficient role against ovarian cancer via targeting genes, such as RPS6KA6, BCL6, FOXO3, CCNB1, and CDC20, which are involved in oocyte meiosis pathway.

Tissue-Specific Molecular Biomarker Signatures of Type 2 Diabetes: An Integrative Analysis of Transcriptomics and Protein–Protein Interaction Data

Type 2 diabetes mellitus is a major global public health burden. A complex metabolic disease, type 2 diabetes affects multiple different tissues, demanding a "systems medicine" approach to biomarker and novel diagnostic discovery, not to mention data integration across omics-es. In the present study, transcriptomics data from different tissues including beta-cells, pancreatic islets, arterial tissue, peripheral blood mononuclear cells, liver, and skeletal muscle of 228 samples were integrated with protein-protein interaction data and genome scale metabolic models to unravel the molecular and tissue-specific biomarker signatures of type 2 diabetes mellitus. Classifying differentially expressed genes, reconstruction and topological analysis of active protein-protein interaction subnetworks indicated that genomic reprogramming depends on the type of tissue, whereas there are common signatures at different levels. Among all tissue and cell types, Mannosidase Alpha Class 1A Member 2 was the common signature at genome level, and activation-ppara reaction, which stimulates a nuclear receptor protein, was found out as the mutual reporter at metabolic level. Moreover, miR-335 and miR-16-5p came into prominence in regulation of transcription at different tissues. On the other hand, distinct signatures were observed for different tissues at the metabolome level. Various coenzyme-A derivatives were significantly enriched metabolites in pancreatic islets, whereas skeletal muscle was enriched for cholesterol, malate, L-carnitine, and several amino acids. Results have showed utmost importance concerning relations between T2D and cancer, blood coagulation, neurodegenerative diseases, and specific metabolic and signaling pathways.

Identification of the key genes connected with plasma cells of multiple myeloma using expression profiles.

ObjectiveTo uncover the potential regulatory mechanisms of the relevant genes that contribute to the prognosis and prevention of multiple myeloma (MM).MethodsMicroarray data (GSE13591) were downloaded, including five plasma cell samples from normal donors and 133 plasma cell samples from MM patients. Differentially expressed genes (DEGs) were identified by Student’s t-test. Functional enrichment analysis was performed for DEGs using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Transcription factors and tumor-associated genes were also explored by mapping genes in the TRANSFAC, the tumor suppressor gene (TSGene), and tumor-associated gene (TAG) databases. A protein–protein interaction (PPI) network and PPI subnetworks were constructed by Cytoscape software using the Search Tool for the Retrieval of Interacting Genes (STRING) database.ResultsA total of 63 DEGs (42 downregulated, 21 upregulated) were identified. Functional enrichment analysis showed that HLA-DRB1 and VCAM1 might be involved in the positive regulation of immune system processes, and HLA-DRB1 might be related to the intestinal immune network for IgA production pathway. The genes CEBPD, JUND, and ATF3 were identified as transcription factors. The top ten nodal genes in the PPI network were revealed including HLA-DRB1, VCAM1, and TFRC. In addition, genes in the PPI subnetwork, such as HLA-DRB1 and VCAM1, were enriched in the cell adhesion molecules pathway, whereas CD4 and TFRC were both enriched in the hematopoietic cell pathway.ConclusionSeveral crucial genes correlated to MM were identified, including CD4, HLA-DRB1, TFRC, and VCAM1, which might exert their roles in MM progression via immune-mediated pathways. There might be certain regulatory correlations between HLA-DRB1, CD4, and TFRC.

MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring.

Network-based analyses are commonly used as powerful tools to interpret the findings of genome-wide association studies (GWAS) in a functional context. In particular, identification of disease-associated functional modules, i.e., highly connected protein-protein interaction (PPI) subnetworks with high aggregate disease association, are shown to be promising in uncovering the functional relationships among genes and proteins associated with diseases. An important issue in this regard is the scoring of subnetworks by integrating two quantities: disease association of individual gene products and network connectivity among proteins. Current scoring schemes either disregard the level of connectivity and focus on the aggregate disease association of connected proteins or use a linear combination of these two quantities. However, such scoring schemes may produce arbitrarily large subnetworks which are often not statistically significant or require tuning of parameters that are used to weigh the contributions of network connectivity and disease association.Here, we propose a parameter-free scoring scheme that aims to score subnetworks by assessing the disease association of interactions between pairs of gene products. We also incorporate the statistical significance of network connectivity and disease association into the scoring function. We test the proposed scoring scheme on a GWAS dataset for two complex diseases type II diabetes (T2D) and psoriasis (PS). Our results suggest that subnetworks identified by commonly used methods may fail tests of statistical significance after correction for multiple hypothesis testing. In contrast, the proposed scoring scheme yields highly significant subnetworks, which contain biologically relevant proteins that cannot be identified by analysis of genome-wide association data alone. We also show that the proposed scoring scheme identifies subnetworks that are reproducible across different cohorts, and it can robustly recover relevant subnetworks at lower sampling rates.

Molecular mechanisms for alcoholic hepatitis based on analysis of gene expression profile.

Background:Alcoholic hepatitis (AH) is an acute manifestation of alcoholic liver disease with high mortality rates.Objectives:Our aim was to study the molecular mechanisms of AH.Materials and Methods:The differentially expressed genes (DEGs) in liver between AH and control cases were identified by analyzing the GSE28619 microarray data using t-test. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) enrichment analyses were performed using DAVID online tool. The protein-protein interaction (PPI) network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and the subnetwork was identified by BioNet. Both PPI network and subnetwork were visualized using the Cytoscape software.Results:Total 908 DEGs (551 up- and 357 down-regulated DEGs) were obtained. The up-regulated DEGs were significantly enriched in 15 pathways and 112 GO biological processes. The down-regulated DEGs were significantly enriched in 22 pathways and 84 GO biological processes. The PPI network with 608 nodes and 2878 interactions was constructed and the subnetwork with 53 nodes and 131 interactions was also identified. The hub DEGs (TSPO, PPIB, NME1 and NME2) were extracted in this subnetwork.Conclusions:TSPO might contribute to the liver damage and AH progression induced by mitochondrial dysfunction through oxidative stress of liver. TSPO interacted with PPIB might play important roles in liver damage in AH. The interaction between NME1 and NME2 might contribute to the transformation from AH to hepatocellular carcinoma.

Network wiring of pleiotropic kinases yields insight into protective role of diabetes on aneurysm.

Recent studies suggest a protective role of diabetes in the development of aneurysm, but the biological mechanisms behind this are still unknown. This type of association is not present in the case of diabetes and atherosclerosis despite similar risk factors for aneurysm and atherosclerosis. We postulate the existence of genes that disrupt the pathways needed for the onset of aneurysm in the presence of diabetes. Motivated by the significance of genetic interactions in understanding disease-disease associations, we tackle this problem by integrating protein-protein interaction and genetic interaction data, i.e., we examine the biological pathways related to the three diseases that contain genes involved in the following genetic interactions: one gene in a genetic interaction is part of a diabetes pathway, the other gene is part of an aneurysm, or an atherosclerosis pathway. We create a protein-protein interaction sub-network that contains disease pathways described above. We then use a "brokerage" measure - a topological measure that identifies proteins in this sub-network whose removal severely affects the interconnectedness of their neighbourhood, enabling such proteins to disrupt the pathway they are in. We identify a set of proteins with high brokerage values and find this set to be enriched in biological functions, including cell-matrix adhesion, which facilitates mechanisms that have already been suggested as possible causes of diabetes-aneurysm association. We further narrow down our set to 16 proteins that are involved in an aneurysm or an atherosclerosis pathway and are encoded by genes participating in genetic interactions with a gene in a diabetes pathway. This set is enriched in kinases and phosphorylation processes, with two pleiotropic kinases that are involved in both aneurysm and atherosclerosis pathways. Kinases can turn on or off proteins, explaining how functional changes of such proteins could result in the disruption of pathways. So if in an aneurysm-related pathway a gene is turned off, the onset of the disease could be prevented. However, mutations of pleiotropic genes could have effects only on one of the traits, which explains why pleiotropic kinases that are involved in both aneurysm and atherosclerosis pathways could disrupt aneurysm pathways explaining the reduced risk of aneurysm in diabetes patients, but not affect the atherosclerosis pathways.

Protein-protein Interaction Network Analyses for Elucidating the Roles of LOXL2-delta72 in Esophageal Squamous Cell Carcinoma

Lysyl oxidase-like 2 (LOXL2), a member of the lysyl oxidase (LOX) family, is a copper-dependent enzyme that catalyzes oxidative deamination of lysine residues on protein substrates. LOXL2 was found to be overexpressed in esophageal squamous cell carcinoma (ESCC) in our previous research. We later identified a LOXL2 splicing variant LOXL2-delta72 and we overexpressed LOXL2-delta72 and its wild type counterpart in ESCC cells following microarray analyses. First, the differentially expressed genes (DEGs) of LOXL2 and LOXL2-delta72 compared to empty plasmid were applied to generate protein-protein interaction (PPI) sub-networks. Comparison of these two sub-networks showed hundreds of different proteins. To reveal the potential specific roles of LOXL2- delta72 compared to its wild type, the DEGs of LOXL2-delta72 vs LOXL2 were also applied to construct a PPI sub-network which was annotated by Gene Ontology. The functional annotation map indicated the third PPI sub-network involved hundreds of GO terms, such as "cell cycle arrest", "G1/S transition of mitotic cell cycle", "interphase", "cell-matrix adhesion" and "cell-substrate adhesion", as well as significant "immunity" related terms, such as "innate immune response", "regulation of defense response" and "Toll signaling pathway". These results provide important clues for experimental identification of the specific biological roles and molecular mechanisms of LOXL2-delta72. This study also provided a work flow to test the different roles of a splicing variant with high-throughput data.

Identification of highly synchronized subnetworks from gene expression data

BackgroundThere has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously.MethodsHere we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search.ResultsApplication of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization.ConclusionsInclusion of interaction dynamics is important to the identification of relevant gene networks.