Gene-gene Interaction Network Research Articles

Abstract 4490: Comparisons of genetic alterations of breast cancer between East and West: Special emphases on young patients with ER+/HER2- tumors

Abstract A rapid surge of female breast cancer has been observed in young East Asians. As a first step toward understanding this phenomenon, we compared the genetic alterations between breast cancer form Taiwan (East) and Western countries (West). Under the same array platforms, the differences in gene copy number alterations (n = 120 in East and 1,948 in West) and gene expressions (n = 327 in East and 423 in West) were analyzed as further stratified by age and estrogen receptor (ER)/ HER2 status. Gene-gene interaction networks were explored by analyzing the overlapping gene with ingenuity pathway analysis. The ingenuity pathway analysis revealed the common differences between East and West in NFkB, PI3K, Akt, MAPK, ERK, IFNα, and Jnk networks. Among the patients <50 years with ER+/ HER2- tumors, we identified a uniquely higher frequency of the APOA1/C3/A4/A5 (the lipid metabolizing genes) gene cluster loss (29% vs. 12%, P = .005) and low expressions of these four genes (East/ West ratios ranged from .07 to .39) in East than that in West. We conclude that NFkB, PI3K, Akt, MAPK, ERK, IFNα, and Jnk networks are common differences between East and West, while APOA1/C3/A4/A5 loss and low expressions are unique genetic alterations for young Asian patients with ER+/ HER2- tumors. Citation Format: Ching-Hung Lin, Tzu-Pin Lu, Ruby Yun-Ju Huang, Yen-Shen Lu, Ko-Yun Lo, Kuan-Ting Kuo, Eric Y. Chuang, Pei-Fang Wu, I-Chun Chen, Jean Paul Thiery, Chiun-Sheng Huang, Ann-Lii Cheng. Comparisons of genetic alterations of breast cancer between East and West: Special emphases on young patients with ER+/HER2- tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4490.

Bayesian state space models for dynamic genetic network construction across multiple tissues.

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Downregulation of GSTK1 Is a Common Mechanism Underlying Hypertrophic Cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is characterized by left ventricular hypertrophy and is associated with a number of potential outcomes, including impaired diastolic function, heart failure, and sudden cardiac death. Various etiologies have been described for HCM, including pressure overload and mutations in sarcomeric and non-sarcomeric genes. However, the molecular pathogenesis of HCM remains incompletely understood. In this study, we performed comparative transcriptome analysis to identify dysregulated genes common to five mouse HCM models of differing etiology: (i) mutation of myosin heavy chain 6, (ii) mutation of tropomyosin 1, (iii) expressing human phospholamban on a null background, (iv) knockout of frataxin, and (v) transverse aortic constriction. Gene-by-gene comparison identified five genes dysregulated in all five HCM models. Glutathione S-transferase kappa 1 (Gstk1) was significantly downregulated in the five models, whereas myosin heavy chain 7 (Myh7), connective tissue growth factor (Ctgf), periostin (Postn), and reticulon 4 (Rtn4) were significantly upregulated. Gene ontology comparison revealed that 51 cellular processes were significantly enriched in genes dysregulated in each transcriptome dataset. Among them, six processes (oxidative stress, aging, contraction, developmental process, cell differentiation, and cell proliferation) were related to four of the five genes dysregulated in all HCM models. GSTK1 was related to oxidative stress only, whereas the other four genes were related to all six cell processes except MYH7 for oxidative stress. Gene–gene functional interaction network analysis suggested correlative expression of GSTK1, MYH7, and actin alpha 2 (ACTA2). To investigate the implications of Gstk1 downregulation for cardiac function, we knocked out gstk1 in zebrafish using the clustered regularly interspaced short palindromic repeats/Cas9 system. We found that expression of the zebrafish homologs of MYH7, ACTA2, and actin alpha 1 were increased in the gstk1-knockout zebrafish. In vivo imaging of zebrafish expressing a fluorescent protein in cardiomyocytes showed that gstk1 deletion significantly decreased the end diastolic volume and, to a lesser extent, end systolic volume. These results suggest that downregulation of GSTK1 may be a common mechanism underlying HCM of various etiologies, possibly through increasing oxidative stress and the expression of sarcomere genes.

A comparative evaluation of data-merging and meta-analysis methods for reconstructing gene-gene interactions.

BackgroundWe address the problem of integratively analyzing multiple gene expression, microarray datasets in order to reconstruct gene-gene interaction networks. Integrating multiple datasets is generally believed to provide increased statistical power and to lead to a better characterization of the system under study. However, the presence of systematic variation across different studies makes network reverse-engineering tasks particularly challenging. We contrast two approaches that have been frequently used in the literature for addressing systematic biases: meta-analysis methods, which first calculate opportune statistics on single datasets and successively summarize them, and data-merging methods, which directly analyze the pooled data after removing eventual biases. This comparative evaluation is performed on both synthetic and real data, the latter consisting of two manually curated microarray compendia comprising several E. coli and Yeast studies, respectively. Furthermore, the reconstruction of the regulatory network of the transcription factor Ikaros in human Peripheral Blood Mononuclear Cells (PBMCs) is presented as a case-study.ResultsThe meta-analysis and data-merging methods included in our experimentations provided comparable performances on both synthetic and real data. Furthermore, both approaches outperformed (a) the naïve solution of merging data together ignoring possible biases, and (b) the results that are expected when only one dataset out of the available ones is analyzed in isolation. Using correlation statistics proved to be more effective than using p-values for correctly ranking candidate interactions. The results from the PBMC case-study indicate that the findings of the present study generalize to different types of network reconstruction algorithms.ConclusionsIgnoring the systematic variations that differentiate heterogeneous studies can produce results that are statistically indistinguishable from random guessing. Meta-analysis and data merging methods have proved equally effective in addressing this issue, and thus researchers may safely select the approach that best suit their specific application.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1038-1) contains supplementary material, which is available to authorized users.

Exploring mechanisms of Panax notoginseng saponins in treating coronary heart disease by integrating gene interaction network and functional enrichment analysis.

To investigate the mechanisms of Panax notoginseng saponins (PNS) in treating coronary heart disease (CHD) by integrating gene interaction network and functional enrichment analysis. Text mining was used to get CHD and PNS associated genes. Gene-gene interaction networks of CHD and PNS were built by the GeneMANIA Cytoscape plugin. Advanced Network Merge Cytoscape plugin was used to analyze the two networks. Their functions were analyzed by gene functional enrichment analysis via DAVID Bioinformatics. Joint subnetwork of CHD network and PNS network was identifified by network analysis. The 11 genes of the joint subnetwork were the direct targets of PNS in CHD network and enriched in cytokine-cytokine receptor interaction pathway. PNS could affect other 85 genes by the gene-gene interaction of joint subnetwork and these genes were enriched in other 7 pathways. The direct mechanisms of PNS in treating CHD by targeting cytokines to relieve the inflflammation and the indirect mechanisms of PNS in treating CHD by affecting other 7 pathways through the interaction of joint subnetwork of PNS and CHD network. The genes in the 7 pathways could be potential targets for the immunologic adjuvant, anticoagulant, hypolipidemic, anti-platelet and anti-hypertrophic activities of PNS. The key mechanisms of PNS in treating CHD could be anticoagulant and hypolipidemic which are indicated by analyzing biological functions of hubs in the merged network.

Revealing new candidate genes for reproductive traits in pigs: combining Bayesian GWAS and functional pathways.

BackgroundReproductive traits such as number of stillborn piglets (SB) and number of teats (NT) have been evaluated in many genome-wide association studies (GWAS). Most of these GWAS were performed under the assumption that these traits were normally distributed. However, both SB and NT are discrete (e.g. count) variables. Therefore, it is necessary to test for better fit of other appropriate statistical models based on discrete distributions. In addition, although many GWAS have been performed, the biological meaning of the identified candidate genes, as well as their functional relationships still need to be better understood. Here, we performed and tested a Bayesian treatment of a GWAS model assuming a Poisson distribution for SB and NT in a commercial pig line. To explore the biological role of the genes that underlie SB and NT and identify the most likely candidate genes, we used the most significant single nucleotide polymorphisms (SNPs), to collect related genes and generated gene-transcription factor (TF) networks.ResultsComparisons of the Poisson and Gaussian distributions showed that the Poisson model was appropriate for SB, while the Gaussian was appropriate for NT. The fitted GWAS models indicated 18 and 65 significant SNPs with one and nine quantitative trait locus (QTL) regions within which 18 and 57 related genes were identified for SB and NT, respectively. Based on the related TF, we selected the most representative TF for each trait and constructed a gene-TF network of gene-gene interactions and identified new candidate genes.ConclusionsOur comparative analyses showed that the Poisson model presented the best fit for SB. Thus, to increase the accuracy of GWAS, counting models should be considered for this kind of trait. We identified multiple candidate genes (e.g. PTP4A2, NPHP1, and CYP24A1 for SB and YLPM1, SYNDIG1L, TGFB3, and VRTN for NT) and TF (e.g. NF-κB and KLF4 for SB and SOX9 and ELF5 for NT), which were consistent with known newborn survival traits (e.g. congenital heart disease in fetuses and kidney diseases and diabetes in the mother) and mammary gland biology (e.g. mammary gland development and body length).Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0189-x) contains supplementary material, which is available to authorized users.

Literature Mining and Ontology based Analysis of Host-Brucella Gene-Gene Interaction Network.

Brucella is an intracellular bacterium that causes chronic brucellosis in humans and various mammals. The identification of host-Brucella interaction is crucial to understand host immunity against Brucella infection and Brucella pathogenesis against host immune responses. Most of the information about the inter-species interactions between host and Brucella genes is only available in the text of the scientific publications. Many text-mining systems for extracting gene and protein interactions have been proposed. However, only a few of them have been designed by considering the peculiarities of host–pathogen interactions. In this paper, we used a text mining approach for extracting host-Brucella gene–gene interactions from the abstracts of articles in PubMed. The gene–gene interactions here represent the interactions between genes and/or gene products (e.g., proteins). The SciMiner tool, originally designed for detecting mammalian gene/protein names in text, was extended to identify host and Brucella gene/protein names in the abstracts. Next, sentence-level and abstract-level co-occurrence based approaches, as well as sentence-level machine learning based methods, originally designed for extracting intra-species gene interactions, were utilized to extract the interactions among the identified host and Brucella genes. The extracted interactions were manually evaluated. A total of 46 host-Brucella gene interactions were identified and represented as an interaction network. Twenty four of these interactions were identified from sentence-level processing. Twenty two additional interactions were identified when abstract-level processing was performed. The Interaction Network Ontology (INO) was used to represent the identified interaction types at a hierarchical ontology structure. Ontological modeling of specific gene–gene interactions demonstrates that host–pathogen gene–gene interactions occur at experimental conditions which can be ontologically represented. Our results show that the introduced literature mining and ontology-based modeling approach are effective in retrieving and analyzing host–pathogen gene–gene interaction networks.

Deciphering the association between gene function and spatial gene-gene interactions in 3D human genome conformation

BackgroundA number of factors have been investigated in the context of gene function prediction and analysis, such as sequence identity, gene expressions, and gene co-evolution. However, three-dimensional (3D) conformation of the genome has not been tapped to analyse gene function, probably largely due to lack of genome conformation data until recently.MethodsWe construct the genome-wide spatial gene-gene interaction networks for three different human B-cells or cell lines from their chromosomal contact data generated by the Hi-C chromosome conformation capturing technique. The G-SESAME and Fast-SemSim are used to calculate function similarity between interacted / non-interacted genes. The Gene Ontology statistics computed from the gene-gene interaction networks is used for gene function prediction.ResultsWe compare the function similarity of gene pairs that do not spatially interact and that have interactions. We find that genes that have strong spatial interactions tend to have highly similar function in terms of biological process, molecular function and cellular component of the Gene Ontology. And even though the level of gene-gene interactions generally have no or weak correlation with either sequential genomic distance or sequence identity between genes, the interacted genes with high function similarity tend to have stronger interactions, somewhat shorter genomic distance and significantly higher sequence identity. And combining genomic distance or sequence identity with spatial gene-gene interaction information informs gene-gene function similarity much better than using either one of them alone, suggesting gene-gene interaction information is largely complementary with genomic distance and sequence identity in the context of gene function analysis. We develop and evaluate a new gene function prediction method based on gene-gene interacting networks, which can predict gene function well for a large number of human genes.ConclusionsIn this work, we demonstrate that the spatial conformation of the human genome is relevant to gene function similarity and is useful for gene function prediction.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2093-0) contains supplementary material, which is available to authorized users.

New genes drive the evolution of gene interaction networks in the human and mouse genomes.

BackgroundThe origin of new genes with novel functions creates genetic and phenotypic diversity in organisms. To acquire functional roles, new genes must integrate into ancestral gene-gene interaction (GGI) networks. The mechanisms by which new genes are integrated into ancestral networks, and their evolutionary significance, are yet to be characterized. Herein, we present a study investigating the rates and patterns of new gene-driven evolution of GGI networks in the human and mouse genomes.ResultsWe examine the network topological and functional evolution of new genes that originated at various stages in the human and mouse lineages by constructing and analyzing three different GGI datasets. We find a large number of new genes integrated into GGI networks throughout vertebrate evolution. These genes experienced a gradual integration process into GGI networks, starting on the network periphery and gradually becoming highly connected hubs, and acquiring pleiotropic and essential functions. We identify a few human lineage-specific hub genes that have evolved brain development-related functions. Finally, we explore the possible underlying mechanisms driving the GGI network evolution and the observed patterns of new gene integration process.ConclusionsOur results unveil a remarkable network topological integration process of new genes: over 5000 new genes were integrated into the ancestral GGI networks of human and mouse; new genes gradually acquire increasing number of gene partners; some human-specific genes evolved into hub structure with critical phenotypic effects. Our data cast new conceptual insights into the evolution of genetic networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0772-4) contains supplementary material, which is available to authorized users.

High-Throughput Tag-Sequencing Analysis of Early Events Induced by Ochratoxin A in HepG-2 Cells.

Ochratoxin A (OTA) is produced by fungi of the species Aspergillus and Penicillium. OTA has displayed hepatotoxicity in mammals. Although recent studies have indicated that OTA influences liver function, little is known regarding its impact on differential early liver toxicity. In this study, we report high-throughput tag-sequencing (Tag-seq) analysis of the transcriptome using Solexa Analyzer platform after 4 h of OTA treatment on HepG-2 cells. The analyses of differentially expressed genes revealed the substantial changes. A total of 21,449 genes were identified and quantified, with 2726 displaying significantly altered expression levels. Expression level data were then integrated with a network of gene-gene interactions, and biological pathways to obtain a systems-level view of changes in the transcriptome that occur with OTA resistance. Our data suggest that OTA exposure leads to an imbalance in zinc finger expression and shed light on splicing factor and mitochondrial-based mechanisms.

Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

Identification of similarities and differences between myeloid and lymphoid acute leukemias using a gene-gene interaction network

Acute myeloid leukemia (AML) is a cancer that affects the myeloid line of blood cells. Acute lymphoid leukemia (ALL) is a type of leukemia that targets the lymphoid line of blood cells. As the comparison of these two types facilitates in the understanding of their molecular pathology, exploring the similarities and differences in the mechanisms of them is worthwhile. We identified 28 novel AML- and ALL-related genes shared in both of them using a short path algorithm. By integrating gene ontology (GO) and KEGG pathway annotations, we revealed the underlying molecular features of AML and ALL. We finally obtained 160 optimal GO terms that could satisfactorily distinguish two types. Further analysis revealed that the results agree well with previous knowledge. Determining the common and different features between AML and ALL facilitates the classification of leukemia and is thus clinically relevant for exploring the molecular markers.

The role of visualization and 3-D printing in biological data mining.

BackgroundBiological data mining is a powerful tool that can provide a wealth of information about patterns of genetic and genomic biomarkers of health and disease. A potential disadvantage of data mining is volume and complexity of the results that can often be overwhelming. It is our working hypothesis that visualization methods can greatly enhance our ability to make sense of data mining results. More specifically, we propose that 3-D printing has an important role to play as a visualization technology in biological data mining. We provide here a brief review of 3-D printing along with a case study to illustrate how it might be used in a research setting.ResultsWe present as a case study a genetic interaction network associated with grey matter density, an endophenotype for late onset Alzheimer’s disease, as a physical model constructed with a 3-D printer. The synergy or interaction effects of multiple genetic variants were represented through a color gradient of the physical connections between nodes. The digital gene-gene interaction network was then 3-D printed to generate a physical network model.ConclusionsThe physical 3-D gene-gene interaction network provided an easily manipulated, intuitive and creative way to visualize the synergistic relationships between the genetic variants and grey matter density in patients with late onset Alzheimer’s disease. We discuss the advantages and disadvantages of this novel method of biological data mining visualization.

Simulation Studies Informed by RNA‐seq Data Suggest the Utility of a Multi‐network Bayesian Graphical Model Algorithm for the Study of Hypertension in the Dahl S Rat

In the past, we used static Bayesian graphical models to investigate the role of individual genes in the development of hypertension. Here, we present a simulation study for an experimental design that will allow us to directly compare the networks of gene-gene interactions in the Dahl S (SS) rat and in SS rats with GWAS genes associated with hypertension knocked out (KO). RNA-seq was performed on 8 tissues from an SS rat and two KO rats, which had been fed either a 0.4% or 4% salt diet for 7 days. The list of transcripts whose expression values met a fold change cutoff in the SS but not the KO rats underwent ontology analysis using DAVID, which found pathways that were overrepresented in the list and the number of differentially regulated genes in those pathways; this number was used to modify networks from our previous graphical network study. For each pathway, three networks were created: one which mirrored the previous pathway and two in which five or ten connections were changed. Data was then simulated from these networks and analyzed using a Bayesian network inference algorithm. The algorithm was sufficiently powered by our simulated data set to accurately describe differences between simulated pathways and to elucidate the structure of those pathways, implying that our experimental design has the potential to yield valuable insights into the role of GWAS genes in the onset of hypertension. (HL082798)

Retraction note: Gene-gene interaction network analysis of ovarian cancer using TCGA data.

The Publisher and Editor regretfully retract this article (Ying H, Lv J, Ying T, Jin S, Shao J, Wang L, et al. Gene-gene interaction network analysis of ovarian cancer using TCGA data. J Ovarian Res. 2013;6:88) because the peer-review process was inappropriately influenced and compromised. As a result, the scientific integrity of the article cannot be guaranteed. A systematic and detailed investigation suggests that a third party was involved in supplying fabricated details of potential peer reviewers for a large number of manuscripts submitted to different journals. In accordance with recommendations from COPE we have retracted all affected published articles, including this one. It was not possible to determine beyond doubt that the authors of this particular article were aware of any third party attempts to manipulate peer review of their manuscript.

CD4 T-cell transcriptome analysis reveals aberrant regulation of STAT3 and Wnt signaling pathways in rheumatoid arthritis: evidence from a case-control study.

IntroductionRheumatoid arthritis (RA) is a systemic autoimmune disease in which T cells play a pivotal role in the pathogenesis. Knowledge in terms of the CD4 T-cell transcriptome in RA is limited. The aim of this study was to examine the whole-genome transcription profile of CD4 T cells in RA by comparing patients with RA to healthy controls.MethodsPeripheral blood CD4 T cells were isolated from 53 RA patients with active disease and 45 healthy individuals; 13 cases and 10 controls were enrolled in microarray analysis. The remaining 40 cases and 35 controls were recruited as an independent cohort for the validation study. Bioinformatics was performed on Gene Ontology (GO), gene-gene interaction networks, and pathway analysis. The gene modules, by combining the results from GO, gene networks, and pathway analysis, were selected for further validation.ResultsThe CD4 T cells showed 1,496 differentially expressed (DE) genes in RA patients relative to healthy individuals. GO analysis revealed that the DE genes were enriched in immune response, T-cell response, apoptosis process, and Wnt receptor signaling. Pathway analysis also identified that ‘Wnt signaling pathway’ was differentially regulated between two groups (P = 2.78 × 10−10). By gene-gene network analysis, we found that the DE genes were enriched in T-cell receptor (TCR), JAK-STAT signaling, and Wnt signaling pathway. By gene module analysis, we found that a number of DE genes overlapped in the three different analyses. In total, 23 genes were selected for further validation, and nine genes were confirmed. Of these, four genes (SOCS3, CBL, IFNAR1, and PIK3CA) were involved in STAT3 (signal transducer and activator of transcription 3) signaling, and three genes (CBL, KLF9, and CSNK2A1) were involved in the Wnt signaling pathway. Additionally, several zinc finger transcription factors (ZEB1, ZNF292, and ZNF644) were confirmed.ConclusionsWe report here the first case–control study of the CD4 T-cell transcriptome profile in RA. Our data provide evidence that CD4 T cells from patients with RA have abnormal functional networks in STAT3 signaling and Wnt signaling. Our results also suggest that the aberrant expression of several zinc finger transcription factors (ZEB1, ZNF292, and ZNF644) may be potential pathogenic factors for RA.Electronic supplementary materialThe online version of this article (doi:10.1186/s13075-015-0590-9) contains supplementary material, which is available to authorized users.

Identification of multi-target effects of Huaier aqueous extract via microarray profiling in triple-negative breast cancer cells.

Breast cancer is one of the most common malignant tumors in the world. Long-term maintenance treatment is important for breast cancer. However, effective maintenance treatment is lacking for triple-negative breast cancer (TNBC). Traditional Chinese medicine (TCM) has shown its potential anticancer roles as an effective maintenance treatment for TNBC. However its mechanisms remained unclear. In this study, we detected the differentially expressed genes (DEGs) after treatment with Huaier aqueous extract by using microarray profiling in MDA-MB-231 cells. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and gene-gene interaction network were conducted to confirm the altered biological functions induced by Huaier extract. Screening of DEGs gave 387 genes (226 upregulated and 161 downregulated) in MDA-MB-231 cells which were regulated significantly by Huaier extract. GO and KEGG pathway analysis suggested that a number of functions were affected by Huaier, including proliferation, apoptosis, migration, and angiogenesis. Gene-gene interaction network showed the detailed molecular signal-net. Based on microarray data, we studied several functions of Huaier extract and in return verified the results of microarray profiling. This study had important guidance roles and indicated new research directions.

Systems level analysis of systemic sclerosis shows a network of immune and profibrotic pathways connected with genetic polymorphisms.

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient's subset assignment is stable over 6–12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.

Construction of a gene-gene interaction network with a combined score across multiple approaches.

Recent progress in computational methods for inves-tigating physical and functional gene interactions has provided new insights into the complexity of biological processes. An essential part of these methods is presented visually in the form of gene interaction networks that can be valuable in exploring the mechanisms of disease. Here, a combined network based on gene pairs with an extra layer of re-liability was constructed after converting and combining the gene pair scores using a novel algorithm across multiple approaches. Four groups of kidney cancer data sets from ArrayExpress were downloaded and analyzed to identify differentially expressed genes using a rank prod-ucts analysis tool. Gene co-expression network, protein-protein interac-tion, co-occurrence network and a combined network were constructed using empirical Bayesian meta-analysis approach, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, an odds ratio formula of the cBioPortal for Cancer Genomics and a novel rank algorithm with combined score, respectively. The topological features of these networks were then compared to evaluate their performances. The results indicated that the gene pairs and their relationship rank-ings were not uniform. The values of topological parameters, such as clustering coefficient and the fitting coefficient R(2) of interaction net-work constructed using our ranked based combination score, were much greater than the other networks. The combined network had a classic small world property which transferred information quickly and displayed great resilience to the dysfunction of low-degree hubs with high-clustering and short average path length. It also followed distinct-ly a scale-free network with a higher reliability.

Gene-Gene Interaction Mapping Of Human Cytomegalic Virus through System Biology Approach

Systems biology is concerned with the study of biological systems, by investigating the components of cellular networks and their interactions. The objective of present study is to build gene-gene interaction network of human cytomegalovirus genes with human genes and other influenza causing genes which helps to identify pathways, recognize gene function and find potential drug targets for cytomegalovirus visualized through cytoscape and its plugin. So, genetic interaction is logical interaction between two genes and more than that affects any organism phenotypically. Human cytomegalovirus has many strategies to survive the attack of the host. Human cytomegalovirus infection of host cells induces cellular activation and disturbance of the cell cycle. Further functional analysis was done to know functionally active genes to cause infection and also these genes will be used as targets to prevent infection spread through virus and then ontology analysis was performed to those functionally active genes describes gene products in terms of their associated biological processes, cellular components and molecular functions by using clueGO Plugin.