Getting connected: analysis and principles of biological networks

Modular transcriptional repertoire and MicroRNA target analyses characterize genomic dysregulation in the thymus of Down syndrome infants

Trisomy 21-driven transcriptional alterations in human thymus were characterized through gene coexpression network (GCN) and miRNA-target analyses. We used whole thymic tissue--obtained at heart surgery from Down syndrome (DS) and karyotipically normal subjects (CT)--and a network-based approach for GCN analysis that allows the identification of modular transcriptional repertoires (communities) and the interactions between all the system's constituents through community detection. Changes in the degree of connections observed for hierarchically important hubs/genes in CT and DS networks corresponded to community changes. Distinct communities of highly interconnected genes were topologically identified in these networks. The role of miRNAs in modulating the expression of highly connected genes in CT and DS was revealed through miRNA-target analysis. Trisomy 21 gene dysregulation in thymus may be depicted as the breakdown and altered reorganization of transcriptional modules. Leading networks acting in normal or disease states were identified. CT networks would depict the "canonical" way of thymus functioning. Conversely, DS networks represent a "non-canonical" way, i.e., thymic tissue adaptation under trisomy 21 genomic dysregulation. This adaptation is probably driven by epigenetic mechanisms acting at chromatin level and through the miRNA control of transcriptional programs involving the networks' high-hierarchy genes.

Practical aspects of gene regulatory inference via conditional inference forests from expression data

Gene regulatory network (GRN) inference is an active area of research that facilitates understanding the complex interplays between biological molecules. We propose a novel framework to create such GRNs, based on Conditional Inference Forests (CIFs) as proposed by Strobl et al. Our framework consists of using ensembles of Conditional Inference Trees (CITs) and selecting an appropriate aggregation scheme for variant selection prior to network construction. We show on synthetic microarray data that taking the original implementation of CIFs with conditional permutation scheme (CIF_cond ) may lead to improved performance compared to Breiman's implementation of Random Forests (RF). Among all newly introduced CIF-based methods and five network scenarios obtained from the DREAM4 challenge, CIF_cond performed best. Networks derived from well-tuned CIFs, obtained by simply averaging P-values over tree ensembles (CIF_mean ) are particularly attractive, because they combine adequate performance with computational efficiency. Moreover, thresholds for variable selection are based on significance levels for P-values and, hence, do not need to be tuned. From a practical point of view, our extensive simulations show the potential advantages of CIF_mean -based methods. Although more work is needed to improve on speed, especially when fully exploiting the advantages of CITs in the context of heterogeneous and correlated data, we have shown that CIF methodology can be flexibly inserted in a framework to infer biological interactions. Notably, we confirmed biologically relevant interaction between IL2RA and FOXP1, linked to the IL-2 signaling pathway and to type 1 diabetes.

From Proteomic Analysis to Potential Therapeutic Targets: Functional Profile of Two Lung Cancer Cell Lines, A549 and SW900, Widely Studied in Pre-Clinical Research

Lung cancer is a serious health problem and the leading cause of cancer death worldwide. The standard use of cell lines as in vitro pre-clinical models to study the molecular mechanisms that drive tumorigenesis and access drug sensitivity/effectiveness is of undisputable importance. Label-free mass spectrometry and bioinformatics were employed to study the proteomic profiles of two representative lung cancer cell lines and to unravel the specific biological processes. Adenocarcinoma A549 cells were enriched in proteins related to cellular respiration, ubiquitination, apoptosis and response to drug/hypoxia/oxidative stress. In turn, squamous carcinoma SW900 cells were enriched in proteins related to translation, apoptosis, response to inorganic/organic substances and cytoskeleton organization. Several proteins with differential expression were related to cancer transformation, tumor resistance, proliferation, migration, invasion and metastasis. Combined analysis of proteome and interactome data highlighted key proteins and suggested that adenocarcinoma might be more prone to PI3K/Akt/mTOR and topoisomerase IIα inhibitors, and squamous carcinoma to Ck2 inhibitors. Moreover, ILF3 overexpression in adenocarcinoma, and PCNA and NEDD8 in squamous carcinoma shows them as promising candidates for therapeutic purposes. This study highlights the functional proteomic differences of two main subtypes of lung cancer models and hints several targeted therapies that might assist in this type of cancer.

A novel topological centrality measure capturing biologically important proteins

Topological centrality in protein interaction networks and its biological implications have widely been investigated in the past. In the present study, a novel metric of centrality-weighted sum of loads eigenvector centrality (WSL-EC)-based on graph spectra is defined and its performance in identifying topologically and biologically important nodes is comparatively investigated with common metrics of centrality in a human protein-protein interaction network. The metric can capture nodes from peripherals of the network differently from conventional eigenvector centrality. Different metrics were found to selectively identify hub sets that are significantly associated with different biological processes. The widely accepted metrics degree centrality, betweenness centrality, subgraph centrality and eigenvector centrality are subject to a bias towards super-hubs, whereas WSL-EC is not affected by the presence of super-hubs. WSL-EC outperforms other metrics of centrality in detecting biologically central nodes such as pathogen-interacting, cancer, ageing, HIV-1 or disease-related proteins and proteins involved in immune system processes and autoimmune diseases in the human interactome.

Correlation-Based Network Generation, Visualization, and Analysis as a Powerful Tool in Biological Studies: A Case Study in Cancer Cell Metabolism

In the last decade vast data sets are being generated in biological and medical studies. The challenge lies in their summary, complexity reduction, and interpretation. Correlation-based networks and graph-theory based properties of this type of networks can be successfully used during this process. However, the procedure has its pitfalls and requires specific knowledge that often lays beyond classical biology and includes many computational tools and software. Here we introduce one of a series of methods for correlation-based network generation and analysis using freely available software. The pipeline allows the user to control each step of the network generation and provides flexibility in selection of correlation methods and thresholds. The pipeline was implemented on published metabolomics data of a population of human breast carcinoma cell lines MDA-MB-231 under two conditions: normal and hypoxia. The analysis revealed significant differences between the metabolic networks in response to the tested conditions. The network under hypoxia had 1.7 times more significant correlations between metabolites, compared to normal conditions. Unique metabolic interactions were identified which could lead to the identification of improved markers or aid in elucidating the mechanism of regulation between distantly related metabolites induced by the cancer growth.

In silico frameworks for systematic pre-clinical screening of potential anti-leukemia therapeutics

INTRODUCTION

Leukemia is a collection of highly heterogeneous cancers that arise from neoplastic transformation and clonal expansion of immature hematopoietic cells. Post-treatment recurrence is high, especially among elderly patients, thus necessitating more effective treatment modalities. Development of novel anti-leukemic compounds relies heavily on traditional in vitro screens which require extensive resources and time. Therefore, integration of in silico screens prior to experimental validation can improve the efficiency of pre-clinical drug development. Areas covered: This article reviews different methods and frameworks used to computationally screen for anti-leukemic agents. In particular, three approaches are discussed including molecular docking, transcriptomic integration, and network analysis. Expert opinion: Today's data deluge presents novel opportunities to develop computational tools and pipelines to screen for likely therapeutic candidates in the treatment of leukemia. Formal integration of these methodologies can accelerate and improve the efficiency of modern day anti-leukemic drug discovery and ease the economic and healthcare burden associated with it.

Stretch-induced network reconfiguration of collagen fibres in the human facet capsular ligament

Biomaterials can display complex spatial patterns of cellular responses to external forces. Revealing and predicting the role of these patterns in material failure require an understanding of the statistical dependencies between spatially distributed changes in a cell's local biomechanical environment, including altered collagen fibre kinematics in the extracellular matrix. Here, we develop and apply a novel extension of network science methods to investigate how excessive tensile stretch of the human cervical facet capsular ligament (FCL), a common source of chronic neck pain, affects the local reorganization of collagen fibres. We define collagen alignment networks based on similarity in fibre alignment angles measured by quantitative polarized light imaging. We quantify the reorganization of these networks following macroscopic loading by describing the dynamic reconfiguration of network communities, regions of the material that display similar fibre alignment angles. Alterations in community structure occur smoothly over time, indicating coordinated adaptation of fibres to loading. Moreover, flexibility, a measure of network reconfiguration, tracks the loss of FCL's mechanical integrity at the onset of anomalous realignment (AR) and regions of AR display altered community structure. These findings use novel network-based techniques to explain abnormal collagen fibre reorganization, a dynamic and coordinated multivariate process underlying tissue failure.

Identification of large disjoint motifs in biological networks

BACKGROUND

Biological networks provide great potential to understand how cells function. Network motifs, frequent topological patterns, are key structures through which biological networks operate. Finding motifs in biological networks remains to be computationally challenging task as the size of the motif and the underlying network grow. Often, different copies of a given motif topology in a network share nodes or edges. Counting such overlapping copies introduces significant problems in motif identification.

RESULTS

In this paper, we develop a scalable algorithm for finding network motifs. Unlike most of the existing studies, our algorithm counts independent copies of each motif topology. We introduce a set of small patterns and prove that we can construct any larger pattern by joining those patterns iteratively. By iteratively joining already identified motifs with those patterns, our algorithm avoids (i) constructing topologies which do not exist in the target network (ii) repeatedly counting the frequency of the motifs generated in subsequent iterations. Our experiments on real and synthetic networks demonstrate that our method is significantly faster and more accurate than the existing methods including SUBDUE and FSG.

CONCLUSIONS

We conclude that our method for finding network motifs is scalable and computationally feasible for large motif sizes and a broad range of networks with different sizes and densities. We proved that any motif with four or more edges can be constructed as a join of the small patterns.

Multiplatform serum metabolic phenotyping combined with pathway mapping to identify biochemical differences in smokers

Aim: Determining perturbed biochemical functions associated with tobacco smoking should be helpful for establishing causal relationships between exposure and adverse events. Results: A multiplatform comparison of serum of smokers (n = 55) and never-smokers (n = 57) using nuclear magnetic resonance spectroscopy, UPLC–MS and statistical modeling revealed clustering of the classes, distinguished by metabolic biomarkers. The identified metabolites were subjected to metabolic pathway enrichment, modeling adverse biological events using available databases. Perturbation of metabolites involved in chronic obstructive pulmonary disease, cardiovascular diseases and cancer were identified and discussed. Conclusion: Combining multiplatform metabolic phenotyping with knowledge-based mapping gives mechanistic insights into disease development, which can be applied to next-generation tobacco and nicotine products for comparative risk assessment.

Identification, analysis, and interpretation of a human serum metabolomics causal network in an observational study

Untargeted metabolomics, measurement of large numbers of metabolites irrespective of their chemical or biologic characteristics, has proven useful for identifying novel biomarkers of health and disease. Of particular importance is the analysis of networks of metabolites, as opposed to the level of an individual metabolite. The aim of this study is to achieve causal inference among serum metabolites in an observational setting. A metabolomics causal network is identified using the genome granularity directed acyclic graph (GDAG) algorithm where information across the genome in a deeper level of granularity is extracted to create strong instrumental variables and identify causal relationships among metabolites in an upper level of granularity. Information from 1,034,945 genetic variants distributed across the genome was used to identify a metabolomics causal network among 122 serum metabolites. We introduce individual properties within the network, such as strength of a metabolite. Based on these properties, hypothesized targets for intervention and prediction are identified. Four nodes corresponding to the metabolites leucine, arichidonoyl-glycerophosphocholine, N-acyelyalanine, and glutarylcarnitine had high impact on the entire network by virtue of having multiple arrows pointing out, which propagated long distances. Five modules, largely corresponding to functional metabolite categories (e.g. amino acids), were identified over the network and module boundaries were determined using directionality and causal effect sizes. Two families, each consists of a triangular motif identified in the network had essential roles in the network by virtue of influencing a large number of other nodes. We discuss causal effect measurement while confounders and mediators are identified graphically.

Integrative analysis of human omics data using biomolecular networks

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.

Maintaining Genome Stability in Defiance of Mitotic DNA Damage

The implementation of decisions affecting cell viability and proliferation is based on prompt detection of the issue to be addressed, formulation and transmission of a correct set of instructions and fidelity in the execution of orders. While the first and the last are purely mechanical processes relying on the faithful functioning of single proteins or macromolecular complexes (sensors and effectors), information is the real cue, with signal amplitude, duration, and frequency ultimately determining the type of response. The cellular response to DNA damage is no exception to the rule. In this review article we focus on DNA damage responses in G2 and Mitosis. First, we set the stage describing mitosis and the machineries in charge of assembling the apparatus responsible for chromosome alignment and segregation as well as the inputs that control its function (checkpoints). Next, we examine the type of issues that a cell approaching mitosis might face, presenting the impact of post-translational modifications (PTMs) on the correct and timely functioning of pathways correcting errors or damage before chromosome segregation. We conclude this essay with a perspective on the current status of mitotic signaling pathway inhibitors and their potential use in cancer therapy.

VisualGraphX: interactive graph visualization within Galaxy

MOTIVATION

We developed VisualGraphX, a web-based, interactive visualization tool for large-scale graphs. Current graph visualization tools that follow the rich-internet paradigm lack an interactive and scalable visualization of graph-based data. VisualGraphX aims to provide a universal graph visualization tool that empowers the users to efficiently explore the data for themselves at a large scale. It is available as a visualization plugin for the Galaxy platform, such that VisualGraphX can be integrated into custom analysis pipelines.

AVAILABILITY AND IMPLEMENTATION

VisualGraphX has been released as a visualization plugin for the Galaxy platform under AFL 3.0 and is available with instructions and application data at http://gitlab.com/comptrans/VisualGraphX/ CONTACT: bjoern.voss@ibvt.uni-stuttgart.de.

TMAinspiration: Decode Interdependencies in Multifactorial Tissue Microarray Data

There are no satisfying tools in tissue microarray (TMA) data analysis up to now to analyze the cooperative behavior of all measured markers in a multifactorial TMA approach. The developed tool TMAinspiration is not only offering an analysis option to close this gap but also offering an ecosystem consisting of quality control concepts and supporting scripts to make this approach a platform for informed practice and further research. The TMAinspiration method is specifically focusing on the demands of the TMA analysis by controlling errors and noise by a generalized regression scheme while at the same time avoiding to introduce a priori too many constraints into the analysis of the data. So, we are testing partitions of a proximity table to find an optimal support for a ranking scheme of molecular dependencies. The idea of combining several partitions to one ensemble, which is balancing the optimization process, is based on the main assumption that all these perspectives on the cellular network need to be self-consistent. Several application examples in breast cancer and one in squamous cell carcinoma demonstrate that this procedure is nicely confirming a priori knowledge on the expression characteristics of protein markers, while also integrating many new results discovered in the treasury of a bigger TMA experiment. The code and software are now freely available at: http://complex-systems.uni-muenster.de/tma_inspiration.html.

Comparison of large networks with sub-sampling strategies

Networks are routinely used to represent large data sets, making the comparison of networks a tantalizing research question in many areas. Techniques for such analysis vary from simply comparing network summary statistics to sophisticated but computationally expensive alignment-based approaches. Most existing methods either do not generalize well to different types of networks or do not provide a quantitative similarity score between networks. In contrast, alignment-free topology based network similarity scores empower us to analyse large sets of networks containing different types and sizes of data. Netdis is such a score that defines network similarity through the counts of small sub-graphs in the local neighbourhood of all nodes. Here, we introduce a sub-sampling procedure based on neighbourhoods which links naturally with the framework of network comparisons through local neighbourhood comparisons. Our theoretical arguments justify basing the Netdis statistic on a sample of similar-sized neighbourhoods. Our tests on empirical and synthetic datasets indicate that often only 10% of the neighbourhoods of a network suffice for optimal performance, leading to a drastic reduction in computational requirements. The sampling procedure is applicable even when only a small sample of the network is known, and thus provides a novel tool for network comparison of very large and potentially incomplete datasets.

Integrative analysis of cancer genes in a functional interactome

The post-genomic era has resulted in the accumulation of high-throughput cancer data from a vast array of genomic technologies including next-generation sequencing and microarray. As such, the large amounts of germline variant and somatic mutation data that have been generated from GWAS and sequencing projects, respectively, show great promise in providing a systems-level view of these genetic aberrations. In this study, we analyze publicly available GWAS, somatic mutation, and drug target data derived from large databanks using a network-based approach that incorporates directed edge information under a randomized network hypothesis testing procedure. We show that these three classes of disease-associated nodes exhibit non-random topological characteristics in the context of a functional interactome. Specifically, we show that drug targets tend to lie upstream of somatic mutations and disease susceptibility germline variants. In addition, we introduce a new approach to measuring hierarchy between drug targets, somatic mutants, and disease susceptibility genes by utilizing directionality and path length information. Overall, our results provide new insight into the intrinsic relationships between these node classes that broaden our understanding of cancer. In addition, our results align with current knowledge on the therapeutic actionability of GWAS and somatic mutant nodes, while demonstrating relationships between node classes from a global network perspective.

Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach

Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/.

Control of complex networks requires both structure and dynamics

The study of network structure has uncovered signatures of the organization of complex systems. However, there is also a need to understand how to control them; for example, identifying strategies to revert a diseased cell to a healthy state, or a mature cell to a pluripotent state. Two recent methodologies suggest that the controllability of complex systems can be predicted solely from the graph of interactions between variables, without considering their dynamics: structural controllability and minimum dominating sets. We demonstrate that such structure-only methods fail to characterize controllability when dynamics are introduced. We study Boolean network ensembles of network motifs as well as three models of biochemical regulation: the segment polarity network in Drosophila melanogaster, the cell cycle of budding yeast Saccharomyces cerevisiae, and the floral organ arrangement in Arabidopsis thaliana. We demonstrate that structure-only methods both undershoot and overshoot the number and which sets of critical variables best control the dynamics of these models, highlighting the importance of the actual system dynamics in determining control. Our analysis further shows that the logic of automata transition functions, namely how canalizing they are, plays an important role in the extent to which structure predicts dynamics.

Unwinding the Novel Genes Involved in the Differentiation of Embryonic Stem Cells into Insulin-Producing Cells: A Network-Based Approach

Diabetes is one of the main causes of death in the world. Diabetes is marked by high blood glucose levels and develops when the body doesn't produce enough insulin or is not able to use insulin effectively, or both. Type I diabetes is a chronic sickness caused by lack of insulin due to the autoimmune destruction of pancreatic insulin-producing beta cells. Research on permanent cure for diabetes is in progress with several remarkable findings in the past few decades among which stem cell therapy has turned out to be a promising way to cure diabetes. Stem cells have the remarkable potential to differentiate into glucose-responsive beta cells through controlled differentiation protocols. Discovering novel targets that could potentially influence the differentiation to specific cell type will help in disease therapy. The present work focuses on finding novel genes or transcription factors involved in the human embryonic stem cell differentiation into insulin-producing beta cells using network biology approach. The interactome of 321 genes and their associated molecules involved in human embryonic stem cell differentiation into beta cells was constructed, which includes 1937 nodes and 8105 edges with a scale-free topology. Pathway analysis for the hubs obtained through MCODE revealed that four highly interactive hubs were relevant to embryonic stem cell differentiation into insulin-producing cells. Their role in different pathways and stem cell differentiation was studied. Centrality parameters were applied to identify the potential controllers of the differentiation processes: BMP4, SALL4, ZIC1, NTS, RNF2, FOXO1, AKT1 and GATA4. This type of approach gives an insight to identify potential genes/transcription factors which may play influential role in many complex biological processes.

Computational methods to identify metabolic sub-networks based on metabolomic profiles

Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.

A Systems Biology Approach to Reveal Putative Host-Derived Biomarkers of Periodontitis by Network Topology Characterization of MMP-REDOX/NO and Apoptosis Integrated Pathways

Periodontitis, a formidable global health burden, is a common chronic disease that destroys tooth-supporting tissues. Biomarkers of the early phase of this progressive disease are of utmost importance for global health. In this context, saliva represents a non-invasive biosample. By using systems biology tools, we aimed to (1) identify an integrated interactome between matrix metalloproteinase (MMP)-REDOX/nitric oxide (NO) and apoptosis upstream pathways of periodontal inflammation, and (2) characterize the attendant topological network properties to uncover putative biomarkers to be tested in saliva from patients with periodontitis. Hence, we first generated a protein-protein network model of interactions ("BIOMARK" interactome) by using the STRING 10 database, a search tool for the retrieval of interacting genes/proteins, with "Experiments" and "Databases" as input options and a confidence score of 0.400. Second, we determined the centrality values (closeness, stress, degree or connectivity, and betweenness) for the "BIOMARK" members by using the Cytoscape software. We found Ubiquitin C (UBC), Jun proto-oncogene (JUN), and matrix metalloproteinase-14 (MMP14) as the most central hub- and non-hub-bottlenecks among the 211 genes/proteins of the whole interactome. We conclude that UBC, JUN, and MMP14 are likely an optimal candidate group of host-derived biomarkers, in combination with oral pathogenic bacteria-derived proteins, for detecting periodontitis at its early phase by using salivary samples from patients. These findings therefore have broader relevance for systems medicine in global health as well.

Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity

BACKGROUND

Recent findings have reemphasized the importance of epistasis, or gene-gene interactions, as a contributing factor to the unexplained heritability of obesity. Network-based methods such as statistical epistasis networks (SEN), present an intuitive framework to address the computational challenge of studying pairwise interactions between thousands of genetic variants. In this study, we aimed to analyze pairwise interactions that are associated with Body Mass Index (BMI) between SNPs from twelve genes robustly associated with obesity (BDNF, ETV5, FAIM2, FTO, GNPDA2, KCTD15, MC4R, MTCH2, NEGR1, SEC16B, SH2B1, and TMEM18).

METHODS

We used information gain measures to identify all SNP-SNP interactions among and between these genes that were related to obesity (BMI > 30 kg/m(2)) within the Framingham Heart Study Cohort; interactions exceeding a certain threshold were used to build an SEN. We also quantified whether interactions tend to occur more between SNPs from the same gene (dyadicity) or between SNPs from different genes (heterophilicity).

RESULTS

We identified a highly connected SEN of 709 SNPs and 1241 SNP-SNP interactions. Combining the SEN framework with dyadicity and heterophilicity analyses, we found 1 dyadic gene (TMEM18, P-value = 0.047) and 3 heterophilic genes (KCTD15, P-value = 0.045; SH2B1, P-value = 0.003; and TMEM18, P-value = 0.001). We also identified a lncRNA SNP (rs4358154) as a key node within the SEN using multiple network measures.

CONCLUSION

This study presents an analytical framework to characterize the global landscape of genetic interactions from genome-wide arrays and also to discover nodes of potential biological significance within the identified network.

A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels

Background

A deeper understanding of differences and similarities in transcriptional regulation between species can uncover important information about gene functions and the role of genes in disease. Deciphering such patterns between mice and humans is especially important since mice play an essential role in biomedical research.

Results

Here, in order to characterize evolutionary changes between humans and mice, we compared gene co-expression maps to evaluate the conservation of co-expression. We show that the conservation of co-expression connectivity of homologous genes is negatively correlated with molecular evolution rates, as expected. Then we investigated evolutionary aspects of gene sets related to functions, tissues, pathways and diseases. Genes expressed in the testis, eye and skin, and those associated with regulation of transcription, olfaction, PI3K signalling, response to virus and bacteria were more divergent between mice and humans in terms of co-expression connectivity. Surprisingly, a deeper investigation of the PI3K signalling cascade revealed that its divergence is caused by the most crucial genes of this pathway, such as mTOR and AKT2. On the other hand, our analysis revealed that genes expressed in the brain and in the bone, and those associated with cell adhesion, cell cycle, DNA replication and DNA repair are most strongly conserved in terms of co-expression network connectivity as well as having a lower rate of duplication events. Genes involved in lipid metabolism and genes specific to blood showed a signature of increased co-expression connectivity in the mouse. In terms of diseases, co-expression connectivity of genes related to metabolic disorders is the most strongly conserved between mice and humans and tumor-related genes the most divergent.

Conclusions

This work contributes to discerning evolutionary patterns between mice and humans in terms of gene interactions. Conservation of co-expression is a powerful approach to identify gene targets and processes with potential similarity and divergence between mice and humans, which has implications for drug testing and other studies employing the mouse as a model organism.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-015-0534-7) contains supplementary material, which is available to authorized users.

Metabolic and Physiological Responses of Shiraz and Cabernet Sauvignon (Vitis vinifera L.) to Near Optimal Temperatures of 25 and 35 °C

Shiraz and Cabernet Sauvignon (Cs) grapevines were grown at near optimal temperatures (25 or 35 °C). Gas exchange, fluorescence, metabolic profiling and correlation based network analysis were used to characterize leaf physiology. When grown at 25 °C, the growth rate and photosynthesis of both cultivars were similar. At 35 °C Shiraz showed increased respiration, non-photochemical quenching and reductions of photosynthesis and growth. In contrast, Cs maintained relatively stable photosynthetic activity and growth regardless of the condition. In both cultivars, growth at 35 °C resulted in accumulations of secondary sugars (raffinose, fucose and ribulose) and reduction of primary sugars concentration (glucose, fructose and sucrose), more noticeably in Shiraz than Cs. In spite of similar patterns of metabolic changes in response to growth at 35 °C, significant differences in important leaf antioxidants and antioxidant precursors (DHA/ascorbate, quinates, cathechins) characterized the cultivar response. Correlation analysis reinforced Shiraz sensitivity to the 35 °C, showing higher number of newly formed edges at 35 °C and higher modularity in Shiraz as compared to Cs. The results suggest that the optimal growth temperatures of grapevines are cultivar dependent, and allow a first insight into the variability of the metabolic responses of grapevines under varied temperatures.

The players may change but the game remains: network analyses of ruminal microbiomes suggest taxonomic differences mask functional similarity

By mapping translated metagenomic reads to a microbial metabolic network, we show that ruminal ecosystems that are rather dissimilar in their taxonomy can be considerably more similar at the metabolic network level. Using a new network bi-partition approach for linking the microbial network to a bovine metabolic network, we observe that these ruminal metabolic networks exhibit properties consistent with distinct metabolic communities producing similar outputs from common inputs. For instance, the closer in network space that a microbial reaction is to a reaction found in the host, the lower will be the variability of its enzyme copy number across hosts. Similarly, these microbial enzymes that are nearby to host nodes are also higher in copy number than are more distant enzymes. Collectively, these results demonstrate a widely expected pattern that, to our knowledge, has not been explicitly demonstrated in microbial communities: namely that there can exist different community metabolic networks that have the same metabolic inputs and outputs but differ in their internal structure.

A significance test for graph-constrained estimation

Graph-constrained estimation methods encourage similarities among neighboring covariates presented as nodes of a graph, and can result in more accurate estimates, especially in high-dimensional settings. Variable selection approaches can then be utilized to select a subset of variables that are associated with the response. However, existing procedures do not provide measures of uncertainty of estimates. Further, the vast majority of existing approaches assume that available graph accurately captures the association among covariates; violations to this assumption could severely hurt the reliability of the resulting estimates. In this article, we present a new inference framework, called the Grace test, which produces coefficient estimates and corresponding p-values by incorporating the external graph information. We show, both theoretically and via numerical studies, that the proposed method asymptotically controls the type-I error rate regardless of the choice of the graph. We also show that when the underlying graph is informative, the Grace test is asymptotically more powerful than similar tests that ignore the external information. We study the power properties of the proposed test when the graph is not fully informative and develop a more powerful Grace-ridge test for such settings. Our numerical studies show that as long as the graph is reasonably informative, the proposed inference procedures deliver improved statistical power over existing methods that ignore external information.

The Mechanism Research of Qishen Yiqi Formula by Module-Network Analysis

Qishen Yiqi formula (QSYQ) has the effect of tonifying Qi and promoting blood circulation, which is widely used to treat the cardiovascular diseases with Qi deficiency and blood stasis syndrome. However, the mechanism of QSYQ to tonify Qi and promote blood circulation is rarely reported at molecular or systems level. This study aimed to elucidate the mechanism of QSYQ based on the protein interaction network (PIN) analysis. The targets' information of the active components was obtained from ChEMBL and STITCH databases and was further used to search against protein-protein interactions by String database. Next, the PINs of QSYQ were constructed by Cytoscape and were analyzed by gene ontology enrichment analysis based on Markov Cluster algorithm. Finally, based on the topological parameters, the properties of scale-free, small world, and modularity of the QSYQ's PINs were analyzed. And based on function modules, the mechanism of QSYQ was elucidated. The results indicated that Qi-tonifying efficacy of QSYQ may be partly attributed to the regulation of amino acid metabolism, carbohydrate metabolism, lipid metabolism, and cAMP metabolism, while QSYQ improves the blood stasis through the regulation of blood coagulation and cardiac muscle contraction. Meanwhile, the “synergy” of formula compatibility was also illuminated.

MicroRNA-195-5p, a new regulator of Fra-1, suppresses the migration and invasion of prostate cancer cells

Background

An increasing number of studies have demonstrated that deregulation of microRNAs (miRNAs) was a common event in tumor tissues and miRNAs would be treated as ideal tumor biomarkers or therapeutic targets. miR-195-5p (termed as miR-195 for briefly in the following part) was suggested to function as a tumor suppressor in cancer development and progression. However, the roles of miR-195 in human prostate cancer are still elusive. Thus, this study was performed to investigate the biological functions and its molecular mechanisms of miR-195 in human prostate cancer cell lines, discussing whether it has a potential to be a therapeutic way of prostate cancer.

Methods

Two human prostate cancer cell lines were analyzed for the expression of miR-195 by quantitative real-time reverse transcription–polymerase chain reaction (RT–PCR). A gain-of-function study of miR-195 was conducted by transfecting mimics into DU145 and PC3 cells and cell motility and invasion ability were evaluated by wound healing assay and transwell assay. Tissue microarray, and immunohistochemistry with antibodies against Fra-1 was performed using the peroxidase and DAB methods. The target gene of miR-195 was determined by luciferase assay, quantitative RT–PCR and western blot. The regulation of motility by miR-195 was analyzed by western blot.

Results

miR-195 was frequently down-regulated in both prostate cancer cell lines, DU145 and PC3. Overexpression of miR-195 significantly repressed the capability of migration and invasion of prostate cancer cells. In addition, we identified Fra-1, a cell motility regulator, as a novel target of miR-195. Fra-1 was up-regulated in prostate cancer tissues. We also observed that inhibition of miR-195 or restoration of Fra-1 in miR-195-over-expressed prostate cancer cells partially reversed the suppressive effects of miR-195. Furthermore, we demonstrated miR-195 could inhibit prostate cancer cell motility by regulated the expression of c-Met, MMP1, MMP9.

Conclusions

miR-195 can repress the migration and invasion of prostate cancer cells via regulating Fra-1. Our results indicate that miR-195 could be a tumor suppressor and may have a potential to be a diagnostics or therapeutic target in prostate cancer.

Electronic supplementary material

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Network Topologies Decoding Cervical Cancer

According to the GLOBOCAN statistics, cervical cancer is one of the leading causes of death among women worldwide. It is found to be gradually increasing in the younger population, specifically in the developing countries. We analyzed the protein-protein interaction networks of the uterine cervix cells for the normal and disease states. It was found that the disease network was less random than the normal one, providing an insight into the change in complexity of the underlying network in disease state. The study also portrayed that, the disease state has faster signal processing as the diameter of the underlying network was very close to its corresponding random control. This may be a reason for the normal cells to change into malignant state. Further, the analysis revealed VEGFA and IL-6 proteins as the distinctly high degree nodes in the disease network, which are known to manifest a major contribution in promoting cervical cancer. Our analysis, being time proficient and cost effective, provides a direction for developing novel drugs, therapeutic targets and biomarkers by identifying specific interaction patterns, that have structural importance.

A Note on Using the Adjusted Rand Index for Link Prediction in Networks

As network data gains popularity for research in various fields, the need for methods to predict future links or find missing links in the data increases. One subset of the methodology used to solve this problem involves creating a similarity measure between each pair of nodes in the network; unfortunately, these methods can be shown to have arbitrary cutoffs and poor performance. To address these shortcomings, we use the adjusted Rand index to create a similarity measure between nodes that has a natural threshold of zero. The effectiveness of this method is then compared to a number of other similarity measures and assessed on a variety of simulated data sets with block model structure and three real network data sets. Under this particular formulation of the adjusted Rand index, information is also provided on dissimilarity. As such, we then go on to test its use for detecting incorrect links in network data, highlighting the dual use of the approach.

A systematic analysis of human lipocalin family and its expression in esophageal carcinoma

The lipocalin proteins (lipocalins) are a large family of small proteins characterized by low sequence similarity and highly conserved crystal structures. Lipocalins have been found to play important roles in many human diseases. For this reason, a systemic analysis of the molecular properties of human lipocalins is essential. In this study, human lipocalins were found to contain four structurally conserved regions (SCRs) and could be divided into two subgroups. A human lipocalin protein-protein interaction network (PPIN) was constructed and integrated with their expression data in esophageal carcinoma. Many lipocalins showed obvious co-expression patterns in esophageal carcinoma. Their subcellular distributions also suggested these lipocalins may transfer signals from the extracellular space to the nucleus using the pathway-like paths. These analyses also expanded our knowledge about this human ancient protein family in the background of esophageal carcinoma.

Community structure analysis of transcriptional networks reveals distinct molecular pathways for early- and late-onset temporal lobe epilepsy with childhood febrile seizures

Age at epilepsy onset has a broad impact on brain plasticity and epilepsy pathomechanisms. Prolonged febrile seizures in early childhood (FS) constitute an initial precipitating insult (IPI) commonly associated with mesial temporal lobe epilepsy (MTLE). FS-MTLE patients may have early disease onset, i.e. just after the IPI, in early childhood, or late-onset, ranging from mid-adolescence to early adult life. The mechanisms governing early (E) or late (L) disease onset are largely unknown. In order to unveil the molecular pathways underlying E and L subtypes of FS-MTLE we investigated global gene expression in hippocampal CA3 explants of FS-MTLE patients submitted to hippocampectomy. Gene coexpression networks (GCNs) were obtained for the E and L patient groups. A network-based approach for GCN analysis was employed allowing: i) the visualization and analysis of differentially expressed (DE) and complete (CO) - all valid GO annotated transcripts - GCNs for the E and L groups; ii) the study of interactions between all the system's constituents based on community detection and coarse-grained community structure methods. We found that the E-DE communities with strongest connection weights harbor highly connected genes mainly related to neural excitability and febrile seizures, whereas in L-DE communities these genes are not only involved in network excitability but also playing roles in other epilepsy-related processes. Inversely, in E-CO the strongly connected communities are related to compensatory pathways (seizure inhibition, neuronal survival and responses to stress conditions) while in L-CO these communities harbor several genes related to pro-epileptic effects, seizure-related mechanisms and vulnerability to epilepsy. These results fit the concept, based on fMRI and behavioral studies, that early onset epilepsies, although impacting more severely the hippocampus, are associated to compensatory mechanisms, while in late MTLE development the brain is less able to generate adaptive mechanisms, what has implications for epilepsy management and drug discovery.

Network analyses reveal novel aspects of ALS pathogenesis

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by selective loss of motor neurons, muscle atrophy and paralysis. Mutations in the human VAMP-associated protein B (hVAPB) cause a heterogeneous group of motor neuron diseases including ALS8. Despite extensive research, the molecular mechanisms underlying ALS pathogenesis remain largely unknown. Genetic screens for key interactors of hVAPB activity in the intact nervous system, however, represent a fundamental approach towards understanding the in vivo function of hVAPB and its role in ALS pathogenesis. Targeted expression of the disease-causing allele leads to neurodegeneration and progressive decline in motor performance when expressed in the adult Drosophila, eye or in its entire nervous system, respectively. By using these two phenotypic readouts, we carried out a systematic survey of the Drosophila genome to identify modifiers of hVAPB-induced neurotoxicity. Modifiers cluster in a diverse array of biological functions including processes and genes that have been previously linked to hVAPB function, such as proteolysis and vesicular trafficking. In addition to established mechanisms, the screen identified endocytic trafficking and genes controlling proliferation and apoptosis as potent modifiers of ALS8-mediated defects. Surprisingly, the list of modifiers was mostly enriched for proteins linked to lipid droplet biogenesis and dynamics. Computational analysis reveals that most modifiers can be linked into a complex network of interacting genes, and that the human genes homologous to the Drosophila modifiers can be assembled into an interacting network largely overlapping with that in flies. Identity markers of the endocytic process were also found to abnormally accumulate in ALS patients, further supporting the relevance of the fly data for human biology. Collectively, these results not only lead to a better understanding of hVAPB function but also point to potentially relevant targets for therapeutic intervention.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease causing loss of motor neurons and consequently a progressive deterioration of motor functions. ALS is uniformly fatal with death occurring 5 years after onset of symptoms. There is currently no effective treatment for ALS. Several mutations in a gene called hVAPB have shown that this gene is causative of a type of ALS known as ALS8. In this study we sought to identify genes and cellular processes that are involved in the toxicity conferred by the defective ALS8 allele. By using the power of Drosophila genetics, we performed a large scale genomic screen and identified a number of genes that can affect hVAPB-mediated toxicity. These modifiers cluster into functional pathways known to be involved in ALS as well as novel ones. The relevance of these modifiers and mechanisms for the human disease was confirmed by showing that the human homologues of the fly modifiers can be organized into a network that closely resembles that of the Drosophila genes. Identifying cellular processes and proteins that modulate hVAPB pathological activity can facilitate the discovery of an effective treatment for ALS.

Module-based association analysis for omics data with network structure

Module-based analysis (MBA) aims to evaluate the effect of a group of biological elements sharing common features, such as SNPs in the same gene or metabolites in the same pathways, and has become an attractive alternative to traditional single bio-element approaches. Because bio-elements regulate and interact with each other as part of network, incorporating network structure information can more precisely model the biological effects, enhance the ability to detect true associations, and facilitate our understanding of the underlying biological mechanisms. However, most MBA methods ignore the network structure information, which depicts the interaction and regulation relationship among basic functional units in biology system. We construct the connectivity kernel and the topology kernel to capture the relationship among bio-elements in a module, and use a kernel machine framework to evaluate the joint effect of bio-elements. Our proposed kernel machine approach directly incorporates network structure so to enhance the study efficiency; it can assess interactions among modules, account covariates, and is computational efficient. Through simulation studies and real data application, we demonstrate that the proposed network-based methods can have markedly better power than the approaches ignoring network information under a range of scenarios.

Prognostic models in coronary artery disease: Cox and network approaches

Predictive assessment of the risk of developing cardiovascular diseases is usually provided by computational approaches centred on Cox models. The complex interdependence structure underlying clinical data patterns can limit the performance of Cox analysis and complicate the interpretation of results, thus calling for complementary and integrative methods. Prognostic models are proposed for studying the risk associated with patients with known or suspected coronary artery disease (CAD) undergoing vasodilator stress echocardiography, an established technique for CAD detection and prognostication. In order to complement standard Cox models, network inference is considered a possible solution to quantify the complex relationships between heterogeneous data categories. In particular, a mutual information network is designed to explore the paths linking patient-associated variables to endpoint events, to reveal prognostic factors and to identify the best possible predictors of death. Data from a prospective, multicentre, observational study are available from a previous study, based on 4313 patients (2532 men; 64±11 years) with known (n=1547) or suspected (n=2766) CAD, who underwent high-dose dipyridamole (0.84 mg kg(-1) over 6 min) stress echocardiography with coronary flow reserve (CFR) evaluation of left anterior descending (LAD) artery by Doppler. The overall mortality was the only endpoint analysed by Cox models. The estimated connectivity between clinical variables assigns a complementary value to the proposed network approach in relation to the established Cox model, for instance revealing connectivity paths. Depending on the use of multiple metrics, the constraints of regression analysis in measuring the association strength among clinical variables can be relaxed, and identification of communities and prognostic paths can be provided. On the basis of evidence from various model comparisons, we show in this CAD study that there may be characteristic factors involved in prognostic stratification whose complexity suggests an exploration beyond the analysis provided by the still fundamental Cox approach.

Properties of networks with partially structured and partially random connectivity

Networks studied in many disciplines, including neuroscience and mathematical biology, have connectivity that may be stochastic about some underlying mean connectivity represented by a non-normal matrix. Furthermore, the stochasticity may not be independent and identically distributed (iid) across elements of the connectivity matrix. More generally, the problem of understanding the behavior of stochastic matrices with nontrivial mean structure and correlations arises in many settings. We address this by characterizing large random N×N matrices of the form A=M+LJR, where M,L, and R are arbitrary deterministic matrices and J is a random matrix of zero-mean iid elements. M can be non-normal, and L and R allow correlations that have separable dependence on row and column indices. We first provide a general formula for the eigenvalue density of A. For A non-normal, the eigenvalues do not suffice to specify the dynamics induced by A, so we also provide general formulas for the transient evolution of the magnitude of activity and frequency power spectrum in an N-dimensional linear dynamical system with a coupling matrix given by A. These quantities can also be thought of as characterizing the stability and the magnitude of the linear response of a nonlinear network to small perturbations about a fixed point. We derive these formulas and work them out analytically for some examples of M,L, and R motivated by neurobiological models. We also argue that the persistence as N→∞ of a finite number of randomly distributed outlying eigenvalues outside the support of the eigenvalue density of A, as previously observed, arises in regions of the complex plane Ω where there are nonzero singular values of L(-1)(z1-M)R(-1) (for z∈Ω) that vanish as N→∞. When such singular values do not exist and L and R are equal to the identity, there is a correspondence in the normalized Frobenius norm (but not in the operator norm) between the support of the spectrum of A for J of norm σ and the σ pseudospectrum of M.

Computational modeling of drug response with applications to neuroscience

The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.

Nitrogen-driven stem elongation in poplar is linked with wood modification and gene clusters for stress, photosynthesis and cell wall formation

BACKGROUND

Nitrogen is an important nutrient, often limiting plant productivity and yield. In poplars, woody crops used as feedstock for renewable resources and bioenergy, nitrogen fertilization accelerates growth of the young, expanding stem internodes. The underlying molecular mechanisms of nitrogen use for extension growth in poplars are not well understood. The aim of this study was to dissect the nitrogen-responsive transcriptional network in the elongation zone of Populus trichocarpa in relation to extension growth and cell wall properties.

RESULTS

Transcriptome analyses in the first two internodes of P. trichocarpa stems grown without or with nitrogen fertilization (5 mM NH4NO3) revealed 1037 more than 2-fold differentially expressed genes (DEGs). Co-expression analysis extracted a network containing about one-third of the DEGs with three main complexes of strongly clustered genes. These complexes represented three main processes that were responsive to N-driven growth: Complex 1 integrated growth processes and stress suggesting that genes with established functions in abiotic and biotic stress are also recruited to coordinate growth. Complex 2 was enriched in genes with decreased transcript abundance and functionally annotated as photosynthetic hub. Complex 3 was a hub for secondary cell wall formation connecting well-known transcription factors that control secondary cell walls with genes for the formation of cellulose, hemicelluloses, and lignin. Anatomical and biochemical analysis supported that N-driven growth resulted in early secondary cell wall formation in the elongation zone with thicker cell walls and increased lignin. These alterations contrasted the N influence on the secondary xylem, where thinner cell walls with lower lignin contents than in unfertilized trees were formed.

CONCLUSION

This study uncovered that nitrogen-responsive elongation growth of poplar internodes is linked with abiotic stress, suppression of photosynthetic genes and stimulation of genes for cell wall formation. Anatomical and biochemical analysis supported increased accumulation of cell walls and secondary metabolites in the elongation zone. The finding of a nitrogen-responsive cell wall hub may have wider implications for the improvement of tree nitrogen use efficiency and opens new perspectives on the enhancement of wood composition as a feedstock for biofuels.

Systems genomics evaluation of the SH-SY5Y neuroblastoma cell line as a model for Parkinson's disease

Background

The human neuroblastoma cell line, SH-SY5Y, is a commonly used cell line in studies related to neurotoxicity, oxidative stress, and neurodegenerative diseases. Although this cell line is often used as a cellular model for Parkinson’s disease, the relevance of this cellular model in the context of Parkinson’s disease (PD) and other neurodegenerative diseases has not yet been systematically evaluated.

Results

We have used a systems genomics approach to characterize the SH-SY5Y cell line using whole-genome sequencing to determine the genetic content of the cell line and used transcriptomics and proteomics data to determine molecular correlations. Further, we integrated genomic variants using a network analysis approach to evaluate the suitability of the SH-SY5Y cell line for perturbation experiments in the context of neurodegenerative diseases, including PD.

Conclusions

The systems genomics approach showed consistency across different biological levels (DNA, RNA and protein concentrations). Most of the genes belonging to the major Parkinson’s disease pathways and modules were intact in the SH-SY5Y genome. Specifically, each analysed gene related to PD has at least one intact copy in SH-SY5Y. The disease-specific network analysis approach ranked the genetic integrity of SH-SY5Y as higher for PD than for Alzheimer’s disease but lower than for Huntington’s disease and Amyotrophic Lateral Sclerosis for loss of function perturbation experiments.

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Inference of the oxidative stress network in Anopheles stephensi upon Plasmodium infection

Ookinete invasion of Anopheles midgut is a critical step for malaria transmission; the parasite numbers drop drastically and practically reach a minimum during the parasite's whole life cycle. At this stage, the parasite as well as the vector undergoes immense oxidative stress. Thereafter, the vector undergoes oxidative stress at different time points as the parasite invades its tissues during the parasite development. The present study was undertaken to reconstruct the network of differentially expressed genes involved in oxidative stress in Anopheles stephensi during Plasmodium development and maturation in the midgut. Using high throughput next generation sequencing methods, we generated the transcriptome of the An. stephensi midgut during Plasmodium vinckei petteri oocyst invasion of the midgut epithelium. Further, we utilized large datasets available on public domain on Anopheles during Plasmodium ookinete invasion and Drosophila datasets and arrived upon clusters of genes that may play a role in oxidative stress. Finally, we used support vector machines for the functional prediction of the un-annotated genes of An. stephensi. Integrating the results from all the different data analyses, we identified a total of 516 genes that were involved in oxidative stress in An. stephensi during Plasmodium development. The significantly regulated genes were further extracted from this gene cluster and used to infer an oxidative stress network of An. stephensi. Using system biology approaches, we have been able to ascertain the role of several putative genes in An. stephensi with respect to oxidative stress. Further experimental validations of these genes are underway.

E-cadherin interactome complexity and robustness resolved by quantitative proteomics

E-cadherin-mediated cell-cell adhesion and signaling plays an essential role in development and maintenance of healthy epithelial tissues. Adhesiveness mediated by E-cadherin is conferred by its extracellular cadherin domains and is regulated by an assembly of intracellular adaptors and enzymes associated with its cytoplasmic tail. We used proximity biotinylation and quantitative proteomics to identify 561 proteins in the vicinity of the cytoplasmic tail of E-cadherin. In addition, we used proteomics to identify proteins associated with E-cadherin-containing adhesion plaques from a cell-glass interface, which enabled the assignment of cellular localization to putative E-cadherin-interacting proteins. Moreover, by tagging identified proteins with GFP (green fluorescent protein), we determined the subcellular localization of 83 putative E-cadherin-proximal proteins and identified 24 proteins that were previously uncharacterized as part of adherens junctions. We constructed and characterized a comprehensive E-cadherin interaction network of 79 published and 394 previously uncharacterized proteins using a structure-informed database of protein-protein interactions. Finally, we found that calcium chelation, which disrupts the interaction of the extracellular E-cadherin domains, did not disrupt most intracellular protein interactions with E-cadherin, suggesting that the E-cadherin intracellular interactome is predominantly independent of cell-cell adhesion.

Transcriptome analysis reveals genes commonly induced by Botrytis cinerea infection, cold, drought and oxidative stresses in Arabidopsis

Signaling pathways controlling biotic and abiotic stress responses may interact synergistically or antagonistically. To identify the similarities and differences among responses to diverse stresses, we analyzed previously published microarray data on the transcriptomic responses of Arabidopsis to infection with Botrytis cinerea (a biotic stress), and to cold, drought, and oxidative stresses (abiotic stresses). Our analyses showed that at early stages after B. cinerea inoculation, 1498 genes were up-regulated (B. cinerea up-regulated genes; BUGs) and 1138 genes were down-regulated (B. cinerea down-regulated genes; BDGs). We showed a unique program of gene expression was activated in response each biotic and abiotic stress, but that some genes were similarly induced or repressed by all of the tested stresses. Of the identified BUGs, 25%, 6% and 12% were also induced by cold, drought and oxidative stress, respectively; whereas 33%, 7% and 5.5% of the BDGs were also down-regulated by the same abiotic stresses. Coexpression and protein-protein interaction network analyses revealed a dynamic range in the expression levels of genes encoding regulatory proteins. Analysis of gene expression in response to electrophilic oxylipins suggested that these compounds are involved in mediating responses to B. cinerea infection and abiotic stress through TGA transcription factors. Our results suggest an overlap among genes involved in the responses to biotic and abiotic stresses in Arabidopsis. Changes in the transcript levels of genes encoding components of the cyclopentenone signaling pathway in response to biotic and abiotic stresses suggest that the oxylipin signal transduction pathway plays a role in plant defense. Identifying genes that are commonly expressed in response to environmental stresses, and further analyzing the functions of their encoded products, will increase our understanding of the plant stress response. This information could identify targets for genetic modification to improve plant resistance to multiple stresses.

Identifying feasible metabolic routes in Mycobacterium smegmatis and possible alterations under diverse nutrient conditions

Background

Many studies on M. tuberculosis have emerged from using M. smegmatis MC²155 (Msm), since they share significant similarities and yet Msm is non-pathogenic and faster growing. Although several individual molecules have been studied from Msm, many questions remain open about its metabolism as a whole and its capability to be versatile. Adaptability and versatility are emergent properties of a system, warranting a molecular systems perspective to understand them.

Results

We identify feasible metabolic pathways in Msm in reference condition with transcriptome, phenotypic microarray, along with functional annotation of the genome. Together with transcriptome data, specific genes from a set of alternatives have been mapped onto different pathways. About 257 metabolic pathways can be considered to be feasible in Msm. Next, we probe cellular metabolism with an array of alternative carbon and nitrogen sources and identify those that are utilized and favour growth as well as those that do not support growth. In all, about 135 points in the entire metabolic map are probed. Analyzing growth patterns under these conditions, lead us to hypothesize different pathways that can become active in various conditions and possible alternate routes that may be induced, thus explaining the observed physiological adaptations.

Conclusions

The study provides the first detailed analysis of feasible pathways towards adaptability. We obtain mechanistic insights that explain observed phenotypic behaviour by studying gene-expression profiles and pathways inferred from the genome sequence. Comparison of transcriptome and phenome analysis of Msm and Mtb provides a rationale for understanding commonalities in metabolic adaptability.

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An in silico target identification using Boolean network attractors: Avoiding pathological phenotypes

Target identification aims at identifying biomolecules whose function should be therapeutically altered to cure the considered pathology. An algorithm for in silico target identification using Boolean network attractors is proposed. It assumes that attractors correspond to phenotypes produced by the modeled biological network. It identifies target combinations which allow disturbed networks to avoid attractors associated with pathological phenotypes. The algorithm is tested on a Boolean model of the mammalian cell cycle and its applications are illustrated on a Boolean model of Fanconi anemia. Results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice. Nevertheless, it is expected that the algorithm is of interest for target identification.

Effects of miR-200c on the migration and invasion abilities of human prostate cancer Du145 cells and the corresponding mechanism

microRNAs (miRNAs) have played a key role in human tumorigenesis, tumor progression, and metastasis. On the one hand, miRNAs are aberrantly expressed in many types of human cancer; on the other hand, miRNAs can function as tumor suppressors or oncogenes that target many cancer-related genes. This study aimed to investigate the effects of miRNA-200c (miR-200c) on the biological behavior and mechanism of proliferation, migration, and invasion in the prostate cancer cell line Du145. In this study, Du145 cells were transfected with miR-200c mimics or negative control miR-NC by using an X-tremeGENE siRNA transfection reagent. The relative expression of miR-200c was measured by RT-PCR. The proliferation, migration, and invasion abilities of Du145 cells were detected by CCK8 assays, migration assays and invasion assays, respectively. The expressions of ZEB1, E-cadherin, and vimentin were observed by western blot. Results showed that DU145 cells exhibited a high expression of miR-200c compared with immortalized normal prostate epithelial cell RWPE-1. Du145 cells were then transfected with miR-200c mimics and displayed lower abilities of proliferation, migration, and invasion than those transfected with the negative control. The protein levels of ZEB1 and vimentin were expressed at a low extent in Du145 cells, which were transfected with miR-200c mimics; by contrast, E-cadherin was highly expressed. Hence, miR-200c could significantly inhibit the proliferation of the prostate cancer cell line Du145; likewise, miR-200c could inhibit migration and invasion by epithelial-mesenchymal transition.

Transcriptional network analysis reveals that AT1 and AT2 angiotensin II receptors are both involved in the regulation of genes essential for glioma progression

Gliomas are aggressive primary brain tumors with high infiltrative potential. The expression of Angiotensin II (Ang II) receptors has been associated with poor prognosis in human astrocytomas, the most common type of glioma. In this study, we investigated the role of Angiotensin II in glioma malignancy through transcriptional profiling and network analysis of cultured C6 rat glioma cells exposed to Ang II and to inhibitors of its membrane receptor subtypes. C6 cells were treated with Ang II and specific antagonists of AT1 and AT2 receptors. Total RNA was isolated after three and six hours of Ang II treatment and analyzed by oligonucleotide microarray technology. Gene expression data was evaluated through transcriptional network modeling to identify how differentially expressed (DE) genes are connected to each other. Moreover, other genes co-expressing with the DE genes were considered in these analyses in order to support the identification of enriched functions and pathways. A hub-based network analysis showed that the most connected nodes in Ang II-related networks exert functions associated with cell proliferation, migration and invasion, key aspects for glioma progression. The subsequent functional enrichment analysis of these central genes highlighted their participation in signaling pathways that are frequently deregulated in gliomas such as ErbB, MAPK and p53. Noteworthy, either AT1 or AT2 inhibitions were able to down-regulate different sets of hub genes involved in protumoral functions, suggesting that both Ang II receptors could be therapeutic targets for intervention in glioma. Taken together, our results point out multiple actions of Ang II in glioma pathogenesis and reveal the participation of both Ang II receptors in the regulation of genes relevant for glioma progression. This study is the first one to provide systems-level molecular data for better understanding the protumoral effects of Ang II in the proliferative and infiltrative behavior of gliomas.