Improved microarray-based decision support with graph encoded interactome data

In the past, microarray studies have been criticized due to noise and the limited overlap between gene signatures. Prior biological knowledge should therefore be incorporated as side information in models based on gene expression data to improve the accuracy of diagnosis and prognosis in cancer. As prior knowledge, we investigated interaction and pathway information from the human interactome on different aspects of biological systems. By exploiting the properties of kernel methods, relations between genes with similar functions but active in alternative pathways could be incorporated in a support vector machine classifier based on spectral graph theory. Using 10 microarray data sets, we first reduced the number of data sources relevant for multiple cancer types and outcomes. Three sources on metabolic pathway information (KEGG), protein-protein interactions (OPHID) and miRNA-gene targeting (microRNA.org) outperformed the other sources with regard to the considered class of models. Both fixed and adaptive approaches were subsequently considered to combine the three corresponding classifiers. Averaging the predictions of these classifiers performed best and was significantly better than the model based on microarray data only. These results were confirmed on 6 validation microarray sets, with a significantly improved performance in 4 of them. Integrating interactome data thus improves classification of cancer outcome for the investigated microarray technologies and cancer types. Moreover, this strategy can be incorporated in any kernel method or non-linear version of a non-kernel method.

Restoration of gamma-sarcoglycan localization and mechanical signal transduction are independent in murine skeletal muscle

Limb girdle muscular dystrophy 2C is caused by mutations in the gamma-sarcoglycan gene (gsg) that results in loss of this protein, and disruption of the sarcoglycan (SG) complex. Signal transduction after mechanical perturbation is mediated, in part, through the SG complex and leads to phosphorylation of tyrosines on the intracellular portions of the sarcoglycans. This study tested if the Tyr(6) in the intracellular region of gamma-sarcoglycan protein (gamma-SG) was necessary for proper localization of the protein in skeletal muscle membranes or for the normal pattern of ERK1/2 phosphorylation after eccentric contractions. Viral mediated gene transfer of wild type gsg (WTgsg) and mutant gsg lacking Tyr(6) (Y6Agsg) was performed into the muscles of gsg(-/-) mice. Muscles were examined for production and stability of the gamma-SG, as well as the level of ERK1/2 phosphorylation before and after eccentric contraction. Sarcolemmal localization of gamma-SG was achieved regardless of which construct was expressed. However, only expression of WTgsg corrected the aberrant ERK1/2 phosphorylation associated with the absence of gamma-SG, whereas Y6Agsg failed to have any effect. This study shows that localization of gamma-SG does not require Tyr(6), but localization alone is insufficient for restoration of normal signal transduction patterns after mechanical perturbation.

Mutation@A Glance: an integrative web application for analysing mutations from human genetic diseases

Although mutation analysis serves as a key part in making a definitive diagnosis about a genetic disease, it still remains a time-consuming step to interpret their biological implications through integration of various lines of archived information about genes in question. To expedite this evaluation step of disease-causing genetic variations, here we developed Mutation@A Glance (http://rapid.rcai.riken.jp/mutation/), a highly integrated web-based analysis tool for analysing human disease mutations; it implements a user-friendly graphical interface to visualize about 40 000 known disease-associated mutations and genetic polymorphisms from more than 2600 protein-coding human disease-causing genes. Mutation@A Glance locates already known genetic variation data individually on the nucleotide and the amino acid sequences and makes it possible to cross-reference them with tertiary and/or quaternary protein structures and various functional features associated with specific amino acid residues in the proteins. We showed that the disease-associated missense mutations had a stronger tendency to reside in positions relevant to the structure/function of proteins than neutral genetic variations. From a practical viewpoint, Mutation@A Glance could certainly function as a ‘one-stop’ analysis platform for newly determined DNA sequences, which enables us to readily identify and evaluate new genetic variations by integrating multiple lines of information about the disease-causing candidate genes.

Analysis of diverse regulatory networks in a hierarchical context shows consistent tendencies for collaboration in the middle levels

Gene regulatory networks have been shown to share some common aspects with commonplace social governance structures. Thus, we can get some intuition into their organization by arranging them into well-known hierarchical layouts. These hierarchies, in turn, can be placed between the extremes of autocracies, with well-defined levels and clear chains of command, and democracies, without such defined levels and with more co-regulatory partnerships between regulators. In general, the presence of partnerships decreases the variation in information flow amongst nodes within a level, more evenly distributing stress. Here we study various regulatory networks (transcriptional, modification, and phosphorylation) for five diverse species, Escherichia coli to human. We specify three levels of regulators--top, middle, and bottom--which collectively govern the non-regulator targets lying in the lowest fourth level. We define quantities for nodes, levels, and entire networks that measure their degree of collaboration and autocratic vs. democratic character. We show individual regulators have a range of partnership tendencies: Some regulate their targets in combination with other regulators in local instantiations of democratic structure, whereas others regulate mostly in isolation, in more autocratic fashion. Overall, we show that in all networks studied the middle level has the highest collaborative propensity and coregulatory partnerships occur most frequently amongst midlevel regulators, an observation that has parallels in corporate settings where middle managers must interact most to ensure organizational effectiveness. There is, however, one notable difference between networks in different species: The amount of collaborative regulation and democratic character increases markedly with overall genomic complexity.

AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

Background

Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry.

Results

We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four.

Conclusions

By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at http://jimcooperlab.mcdb.ucsb.edu/autosome.

Increasing alternative promoter repertories is positively associated with differential expression and disease susceptibility

Background

Alternative Promoter (AP) usages have been shown to enable diversified transcriptional regulation of individual gene in a context-specific (e.g., pathway, cell lineage, tissue type, and development stage et. ac.) way. Aberrant uses of APs have been directly linked to mechanism of certain human diseases. However, whether or not there exists a general link between a gene's AP repertoire and its expression diversity is currently unknown. The general relation between a gene's AP repertoire and its disease susceptibility also remains largely unexplored.

Methodology/Principal Findings

Based on the differential expression ratio inferred from all human microarray data in NCBI GEO and the list of disease genes curated in public repositories, we systemically analyzed the general relation of AP repertoire with expression diversity and disease susceptibility. We found that genes with APs are more likely to be differentially expressed and/or disease associated than those with Single Promoter (SP), and genes with more APs are more likely differentially expressed and disease susceptible than those with less APs. Further analysis showed that genes with increased number of APs tend to have increased length in all aspects of gene structure including 3′ UTR, be associated with increased duplicability, and have increased connectivity in protein-protein interaction network.

Conclusions

Our genome-wide analysis provided evidences that increasing alternative promoter repertories is positively associated with differential expression and disease susceptibility.

Overexpression of periostin and lumican in esophageal squamous cell carcinoma

To identify biomarkers for early detection for esophageal squamous cell carcinoma (ESCC), we previously carried out a genome-wide gene expression profiling study using an oligonucleotide microarray platform. This analysis led to identification of several transcripts that were significantly upregulated in ESCC compared to the adjacent normal epithelium. In the current study, we performed immunohistochemical analyses of protein products for two candidates genes identified from the DNA microarray analysis, periostin (POSTN) and lumican (LUM), using tissue microarrays. Increased expression of both periostin and lumican was observed in 100% of 137 different ESCC samples arrayed on tissue microarrays. Increased expression of periostin and lumican was observed in carcinoma as well as in stromal cell in the large majority of cases. These findings suggest that these candidates can be investigated in the sera of ESCC patients using ELISA or multiple reaction monitoring (MRM) type assays to further explore their utility as biomarkers.

From proteome lists to biological impact--tools and strategies for the analysis of large MS data sets

MS has become a method-of-choice for proteome analysis, generating large data sets, which reflect proteome-scale protein-protein interaction and PTM networks. However, while a rapid growth in large-scale proteomics data can be observed, the sound biological interpretation of these results clearly lags behind. Therefore, combined efforts of bioinformaticians and biologists have been made to develop strategies and applications to help experimentalists perform this crucial task. This review presents an overview of currently available analytical strategies and tools to extract biologically relevant information from large protein lists. Moreover, we also present current research publications making use of these tools as examples of how the presented strategies may be incorporated into proteomic workflows. Emphasis is placed on the analysis of Gene Ontology terms, interaction networks, biological pathways and PTMs. In addition, topics including domain analysis and text mining are reviewed in the context of computational analysis of proteomic results. We expect that these types of analyses will significantly contribute to a deeper understanding of the role of individual proteins, protein networks and pathways in complex systems.

BioNet: an R-Package for the functional analysis of biological networks

MOTIVATION

Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules.

RESULTS

The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization.

AVAILABILITY

The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de.

The power of protein interaction networks for associating genes with diseases

Motivation: Understanding the association between genetic diseases and their causal genes is an important problem concerning human health. With the recent influx of high-throughput data describing interactions between gene products, scientists have been provided a new avenue through which these associations can be inferred. Despite the recent interest in this problem, however, there is little understanding of the relative benefits and drawbacks underlying the proposed techniques.

Results: We assessed the utility of physical protein interactions for determining gene–disease associations by examining the performance of seven recently developed computational methods (plus several of their variants). We found that random-walk approaches individually outperform clustering and neighborhood approaches, although most methods make predictions not made by any other method. We show how combining these methods into a consensus method yields Pareto optimal performance. We also quantified how a diffuse topological distribution of disease-related proteins negatively affects prediction quality and are thus able to identify diseases especially amenable to network-based predictions and others for which additional information sources are absolutely required.

Availability: The predictions made by each algorithm considered are available online at http://www.cbcb.umd.edu/DiseaseNet

Contact:carlk@cs.umd.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Identification and preliminary characterization of novel small molecules that inhibit growth of human lung adenocarcinoma cells

Drug treatment for human lung cancers remains unsatisfactory, despite the identification of many potential therapeutic targets (such as mutant KRAS protein) and the approval of agents that inhibit the tyrosine kinase activity of mutant epidermal growth factor receptor (EGFR). To seek new therapeutic strategies against lung tumors, the authors have screened 189,290 small molecules for their ability to retard growth of human lung adenocarcinoma cell lines, which harbor mutations in EGFR or KRAS. Four candidates that are structurally different from common tyrosine kinase inhibitors were selected for further study. The authors describe one small molecule (designated lung cancer screen-1 [LCS-1]) in detail here. Identification of the targets of LCS-1 and other growth inhibitors found in this screen may help to develop new agents for the treatment of lung adenocarcinomas, including those driven by mutant EGFR and KRAS.

Construction and analysis of protein-protein interaction networks

Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.

Exploring the caves: cavins, caveolins and caveolae

Caveolae are ampullate (flask-shaped) invaginations that are abundant in the plasma membrane of many mammalian cell types. Although caveolae are implicated in a wide range of processes including endothelial transcytosis, lipid homeostasis and cellular signalling, a detailed molecular picture of many aspects of their function has been elusive. Until recently, the only extensively characterised protein components of caveolae were the caveolins. Recently, data from several laboratories have demonstrated that a family of four related proteins, termed cavins 1-4, plays key roles in caveolar biogenesis and function. Salient properties of the cavin family include their propensity to form complexes with each other and their different but overlapping tissue distribution. This review summarises recent data on the cavins, and sets them in the context of open questions on the construction and function of caveolae. The discovery of cavins implies that caveolae might have unexpectedly diverse structural properties, in accord with the wide range of functions attributed to these 'little caves'.

Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state "signature". These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.

Extracting consistent knowledge from highly inconsistent cancer gene data sources

BACKGROUND

Hundreds of genes that are causally implicated in oncogenesis have been found and collected in various databases. For efficient application of these abundant but diverse data sources, it is of fundamental importance to evaluate their consistency.

RESULTS

First, we showed that the lists of cancer genes from some major data sources were highly inconsistent in terms of overlapping genes. In particular, most cancer genes accumulated in previous small-scale studies could not be rediscovered in current high-throughput genome screening studies. Then, based on a metric proposed in this study, we showed that most cancer gene lists from different data sources were highly functionally consistent. Finally, we extracted functionally consistent cancer genes from various data sources and collected them in our database F-Census.

CONCLUSIONS

Although they have very low gene overlapping, most cancer gene data sources are highly consistent at the functional level, which indicates that they can separately capture partial genes in a few key pathways associated with cancer. Our results suggest that the sample sizes currently used for cancer studies might be inadequate for consistently capturing individual cancer genes, but could be sufficient for finding a number of cancer genes that could represent functionally most cancer genes. The F-Census database provides biologists with a useful tool for browsing and extracting functionally consistent cancer genes from various data sources.

Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

MOTIVATION

Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values.

RESULTS

The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes.

CONTACT

dargenio@bmsr.usc.edu

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Role for protein-protein interaction databases in human genetics

Proteomics and the study of protein-protein interactions are becoming increasingly important in our effort to understand human diseases on a system-wide level. Thanks to the development and curation of protein-interaction databases, up-to-date information on these interaction networks is accessible and publicly available to the scientific community. As our knowledge of protein-protein interactions increases, it is important to give thought to the different ways that these resources can impact biomedical research. In this article, we highlight the importance of protein-protein interactions in human genetics and genetic epidemiology. Since protein-protein interactions demonstrate one of the strongest functional relationships between genes, combining genomic data with available proteomic data may provide us with a more in-depth understanding of common human diseases. In this review, we will discuss some of the fundamentals of protein interactions, the databases that are publicly available and how information from these databases can be used to facilitate genome-wide genetic studies.

Biana: a software framework for compiling biological interactions and analyzing networks

Background

The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.

Results

We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php.

Conclusions

BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

Detection of characteristic sub pathway network for angiogenesis based on the comprehensive pathway network

Background

Pathways in biological system often cooperate with each other to function. Changes of interactions among pathways tightly associate with alterations in the properties and functions of the cell and hence alterations in the phenotype. So, the pathway interactions and especially their changes over time corresponding to specific phenotype are critical to understanding cell functions and phenotypic plasticity.

Methods

With prior-defined pathways and incorporated protein-protein interaction (PPI) data, we counted PPIs between corresponding gene sets of each pair of distinct pathways to construct a comprehensive pathway network. Then we proposed a novel concept, characteristic sub pathway network (CSPN), to realize the phenotype-specific pathway interactions. By adding gene expression data regarding a given phenotype, angiogenesis, active PPIs corresponding to stimulation of interleukin-1 (IL-1) and tumor necrosis factor α (TNF-α) on human umbilical vein endothelial cells (HUVECs) respectively were derived. Two kinds of CSPN, namely the static or the dynamic CSPN, were detected by counting active PPIs.

Results

A comprehensive pathway network containing 37 signalling pathways as nodes and 263 pathway interactions were obtained. Two phenotype-specific CSPNs for angiogenesis, corresponding to stimulation of IL-1 and TNF-α on HUVEC respectively, were addressed. From phenotype-specific CSPNs, a static CSPN involving interactions among B cell receptor, T cell receptor, Toll-like receptor, MAPK, VEGF, and ErbB signalling pathways, and a dynamic CSPN involving interactions among TGF-β, Wnt, p53 signalling pathways and cell cycle pathway, were detected for angiogenesis on HUVEC after stimulation of IL-1 and TNF-α respectively. We inferred that, in certain case, the static CSPN maintains related basic functions of the cells, whereas the dynamic CSPN manifests the cells' plastic responses to stimulus and therefore reflects the cells' phenotypic plasticity.

Conclusion

The comprehensive pathway network helps us realize the cooperative behaviours among pathways. Moreover, two kinds of potential CSPNs found in this work, the static CSPN and the dynamic CSPN, are helpful to deeply understand the specific function of HUVEC and its phenotypic plasticity in regard to angiogenesis.

An ontology-based search engine for protein-protein interactions

BACKGROUND

Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database.

RESULTS

We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions.

CONCLUSION

Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.

Lung cancer cell lines: Useless artifacts or invaluable tools for medical science?

Multiple cell lines (estimated at 300-400) have been established from human small cell (SCLC) and non-small cell lung cancers (NSCLC). These cell lines have been widely dispersed to and used by the scientific community worldwide, with over 8000 citations resulting from their study. However, there remains considerable skepticism on the part of the scientific community as to the validity of research resulting from their use. These questions center around the genomic instability of cultured cells, lack of differentiation of cultured cells and absence of stromal-vascular-inflammatory cell compartments. In this report we discuss the advantages and disadvantages of the use of cell lines, address the issues of instability and lack of differentiation. Perhaps the most important finding is that every important, recurrent genetic and epigenetic change including gene mutations, deletions, amplifications, translocations and methylation-induced gene silencing found in tumors has been identified in cell lines and vice versa. These "driver mutations" represented in cell lines offer opportunities for biological characterization and application to translational research. Another potential shortcoming of cell lines is the difficulty of studying multistage pathogenesis in vitro. To overcome this problem, we have developed cultures from central and peripheral airways that serve as models for the multistage pathogenesis of tumors arising in these two very different compartments. Finally the issue of cell line contamination must be addressed and safeguarded against. A full understanding of the advantages and shortcomings of cell lines is required for the investigator to derive the maximum benefit from their use.

NetPath: a public resource of curated signal transduction pathways

NetPath, a novel community resource of curated human signaling pathways is presented and its utility demonstrated using immune signaling data.

We have developed NetPath as a resource of curated human signaling pathways. As an initial step, NetPath provides detailed maps of a number of immune signaling pathways, which include approximately 1,600 reactions annotated from the literature and more than 2,800 instances of transcriptionally regulated genes - all linked to over 5,500 published articles. We anticipate NetPath to become a consolidated resource for human signaling pathways that should enable systems biology approaches.

Understanding protein phosphorylation on a systems level

Protein kinase phosphorylation is central to the regulation and control of protein and cellular function. Over the past decade, the development of many high-throughput approaches has revolutionized the understanding of protein phosphorylation and allowed rapid and unbiased surveys of phosphoproteins and phosphorylation events. In addition to this technological advancement, there have also been computational improvements; recent studies on network models of protein phosphorylation have provided many insights into the cellular processes and pathways regulated by phosphorylation. This article gives an overview of experimental and computational techniques for identifying and analyzing protein phosphorylation on a systems level.

Inferring the Sign of Kinase-Substrate Interactions by Combining Quantitative Phosphoproteomics with a Literature-Based Mammalian Kinome Network

Protein phosphorylation is a reversible post-translational modification commonly used by cell signaling networks to transmit information about the extracellular environment into intracellular organelles for the regulation of the activity and sorting of proteins within the cell. For this study we reconstructed a literature-based mammalian kinase-substrate network from several online resources. The interactions within this directed graph network connect kinases to their substrates, through specific phosphosites including kinasekinase regulatory interactions. However, the "signs" of links, activation or inhibition of the substrate upon phosphorylation, within this network are mostly unknown. Here we show how we can infer the "signs" indirectly using data from quantitative phosphoproteomics experiments applied to mammalian cells combined with the literature-based kinase-substrate network. Our inference method was able to predict the sign for 321 links and 153 phosphosites on 120 kinases, resulting in signed and directed subnetwork of mammalian kinase-kinase interactions. Such an approach can rapidly advance the reconstruction of cell signaling pathways and networks regulating mammalian cells.

Clinical outcomes in non-small-cell lung cancer patients with EGFR mutations: pooled analysis

Non-small-cell lung cancer (NSCLC) with mutations in the epidermal growth factor receptor (EGFR) is a distinct subgroup of NSCLCs that is particularly responsive to EGFR tyrosine-kinase inhibitors (TKIs). A weighted pooled analysis of available studies was performed to evaluate clinical outcome in patients with EGFR-mutated NSCLC who were treated with chemotherapy or EGFR TKIs. Median progression-free survival (PFS) times were pooled from prospective or retrospective studies that evaluated chemotherapy or single-agent EGFR TKIs (erlotinib or gefitinib) in patients with NSCLC and EGFR mutations. Among the studies identified for inclusion in the analysis, 12 evaluated erlotinib (365 patients), 39 evaluated gefitinib (1069 patients) and 9 evaluated chemotherapy (375 patients). Across all studies, the most common EGFR mutations were deletions in exon 19 and the L858R substitution in exon 21. In the weighted pooled analysis, the overall median PFS was 13.2 months with erlotinib, 9.8 months with gefitinib and 5.9 months with chemotherapy. Using a two-sided permutation, erlotinib and gefitinib produced a longer median PFS versus chemotherapy, both individually (P= 0.000 and P= 0.002, respectively) and as a combined group (EGFR TKI versus chemotherapy, P= 0.000). EGFR TKIs appear to be the most effective treatment for patients with advanced EGFR-mutant NSCLC. Ongoing prospective trials comparing the efficacy of first-line chemotherapy and EGFR TKIs in EGFR-mutant disease should provide further insight into the most appropriate way to treat this specific group of patients.

Cancer systems biology

Cancer is a complex and heterogeneous disease, not only at a genetic and biochemical level, but also at a tissue, organism, and population level. Multiple data streams, from reductionist biochemistry in vitro to high-throughput "-omics" from clinical material, have been generated with the hope that they encode useful information about phenotype and, ultimately, tumour behaviour in response to drugs. While these data stand alone in terms of the biology they represent, there is the enticing prospect that if incorporated into systems biology models, they can help understand complex systems behaviour and provide a predictive framework as an additional tool in understanding how tumours change and respond to treatment over time. Since these biological data are heterogeneous and frequently qualitative rather than quantitative, at the present time a single systems biology approach is unlikely to be effective; instead, different computational and mathematical approaches should be tailored to different types of data, and to each other, in order to test and re-test hypotheses. In time, these models might converge and result in usable tractable models which accurately represent human cancer. Likewise, biologists and clinicians need to understand what the requirements of systems biology are so that compatible data are produced for computational modelling. In this review, we describe some theoretical approaches (data-driven and process-driven) and experimental methodologies which are being used in cancer research and the clinical context where they might be applied.

Bioinformatics and systems biology of cancers

Molecular databases and bioinformatics methods and tools are essential for modern cancer research. Multilevel analyses of all the protein-coding genes, thousands of proteins, and hundreds of metabolites require integration in terms of signaling and metabolic pathways and networks. This chapter provides background and examples of genomic, gene expression, epigenomic, proteomic, and metabolomic investigations of cancer progression and emergence of invasive and metastatic properties of cancers.

Knowledge-based data analysis comes of age

The emergence of high-throughput technologies for measuring biological systems has introduced problems for data interpretation that must be addressed for proper inference. First, analysis techniques need to be matched to the biological system, reflecting in their mathematical structure the underlying behavior being studied. When this is not done, mathematical techniques will generate answers, but the values and reliability estimates may not accurately reflect the biology. Second, analysis approaches must address the vast excess in variables measured (e.g. transcript levels of genes) over the number of samples (e.g. tumors, time points), known as the 'large-p, small-n' problem. In large-p, small-n paradigms, standard statistical techniques generally fail, and computational learning algorithms are prone to overfit the data. Here we review the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models. We show that novel biological insights have been gained using these techniques.

Approaches and applications of quantitative LC-MS for proteomics and activitomics

LC-MS is a powerful technique in biomolecular research. In addition to its uses as a tool for protein and peptide quantization, LC-MS can also be used to quantify the activity of signalling and metabolic pathways in a multiplex and comprehensive manner, i.e. as an 'activitomic' tool. Taking cancer research as an illustrative example of application, this review discusses the concepts of biochemical pathway analysis using LC-MS-based proteomic and activitomic techniques.

Proteomics analysis of human skeletal muscle reveals novel abnormalities in obesity and type 2 diabetes

OBJECTIVE

Insulin resistance in skeletal muscle is an early phenomenon in the pathogenesis of type 2 diabetes. Studies of insulin resistance usually are highly focused. However, approaches that give a more global picture of abnormalities in insulin resistance are useful in pointing out new directions for research. In previous studies, gene expression analyses show a coordinated pattern of reduction in nuclear-encoded mitochondrial gene expression in insulin resistance. However, changes in mRNA levels may not predict changes in protein abundance. An approach to identify global protein abundance changes involving the use of proteomics was used here.

RESEARCH DESIGN AND METHODS

Muscle biopsies were obtained basally from lean, obese, and type 2 diabetic volunteers (n = 8 each); glucose clamps were used to assess insulin sensitivity. Muscle protein was subjected to mass spectrometry-based quantification using normalized spectral abundance factors.

RESULTS

Of 1,218 proteins assigned, 400 were present in at least half of all subjects. Of these, 92 were altered by a factor of 2 in insulin resistance, and of those, 15 were significantly increased or decreased by ANOVA (P < 0.05). Analysis of protein sets revealed patterns of decreased abundance in mitochondrial proteins and altered abundance of proteins involved with cytoskeletal structure (desmin and alpha actinin-2 both decreased), chaperone function (TCP-1 subunits increased), and proteasome subunits (increased).

CONCLUSIONS

The results confirm the reduction in mitochondrial proteins in insulin-resistant muscle and suggest that changes in muscle structure, protein degradation, and folding also characterize insulin resistance.

GNCPro: navigate human genes and relationships through net-walking

The use of computational applications in biological research is significantly lagging behind other scientific research areas such as physics, mathematics, and geology; more in silico tools are needed. The increasing complexity of biological data makes it more and more difficult for scientists to verify their hypotheses and results against existing discoveries. GNCPro is a free data integration and visualization tool for gaining comprehensive overviews of such complicated biological knowledge. In particular, GNCPro warehouses and encodes biological information as binary relationships. When represented graphically, these binary relationships take on the form of edges that connect the genes and proteins, which are represented by nodes. By using distinguishing features such as colors, shape, and opacity, GNCPro provides a stimulating visual experience in which the user can quickly identify groups of genes by annotations and the types of relationships involved. GNCPro integrates human gene expressions, regulations, gene product modifications, and interactions into one platform while delivering a simple and powerful user interface for systems biology study.

AVAILABILITY

http://GNCPro.sabiosciences.com.

Network of Cancer Genes: a web resource to analyze duplicability, orthology and network properties of cancer genes

The Network of Cancer Genes (NCG) collects and integrates data on 736 human genes that are mutated in various types of cancer. For each gene, NCG provides information on duplicability, orthology, evolutionary appearance and topological properties of the encoded protein in a comprehensive version of the human protein-protein interaction network. NCG also stores information on all primary interactors of cancer proteins, thus providing a complete overview of 5357 proteins that constitute direct and indirect determinants of human cancer. With the constant delivery of results from the mutational screenings of cancer genomes, NCG represents a versatile resource for retrieving detailed information on particular cancer genes, as well as for identifying common properties of precompiled lists of cancer genes. NCG is freely available at: http://bio.ifom-ieo-campus.it/ncg.

Molecular networks for the study of TCM pharmacology

To target complex, multi-factorial diseases more effectively, there has been an emerging trend of multi-target drug development based on network biology, as well as an increasing interest in traditional Chinese medicine (TCM) that applies a more holistic treatment to diseases. Thousands of years' clinic practices in TCM have accumulated a considerable number of formulae that exhibit reliable in vivo efficacy and safety. However, the molecular mechanisms responsible for their therapeutic effectiveness are still unclear. The development of network-based systems biology has provided considerable support for the understanding of the holistic, complementary and synergic essence of TCM in the context of molecular networks. This review introduces available sources and methods that could be utilized for the network-based study of TCM pharmacology, proposes a workflow for network-based TCM pharmacology study, and presents two case studies on applying these sources and methods to understand the mode of action of TCM recipes.

Current protocols in the generation of pluripotent stem cells: theoretical, methodological and clinical considerations

Pluripotent stem cells have been derived from various embryonic, fetal and adult sources. Embryonic stem cells (ESCs) and parthenogenic ESCs (pESCs) are derived from the embryo proper while embryonic germ cells (EGCs), embryonal carcinoma cells (ECCs), and germ-line stem cells (GSC) are produced from germ cells. ECCs were the first pluripotent stem cell lines established from adult testicular tumors while EGCs are generated in vitro from primordial germ cells (PGCs) isolated in late embryonic development. More recently, studies have also demonstrated the ability to produce GSCs from adult germ cells, known as spermatogonial stem cells. Unlike ECCs, the source of GSCs are normal, non-cancerous adult tissue. The study of these unique cell lines has provided information that has led to the ability to reprogram somatic cells into an ESC-like state. These cells, called induced pluripotent stem cells (iPSCs), have been derived from a number of human fetal and adult origins. With the promises pluripotent stem cells bring to cell-based therapies there remain several considerations that need to be carefully studied prior to their clinical use. Many of these issues involve understanding key factors regulating their generation, including those which define pluripotency. In this regard, the following article discusses critical aspects of pluripotent stem cell derivation and current issues about their therapeutic potential.

Identification and characterization of endonuclein binding proteins: evidence of modulatory effects on signal transduction and chaperone activity

Background

We have previously identified endonuclein as a cell cycle regulated WD-repeat protein that is up-regulated in adenocarcinoma of the pancreas. Now, we aim to investigate its biomedical functions.

Results

Using the cDNA encoding human endonuclein, we have expressed and purified the recombinant protein from Escherichia coli using metal affinity chromatography. The recombinant protein was immobilized to a column and by affinity chromatography several interacting proteins were purified from several litres of placenta tissue extract. After chromatography the eluted proteins were further separated by two-dimensional gel electrophoresis and identified by tandem mass spectrometry. The interacting proteins were identified as; Tax interaction protein 1 (TIP-1), Aα fibrinogen transcription factor (P16/SSBP1), immunoglobulin heavy chain binding protein (BiP), human ER-associated DNAJ (HEDJ/DNAJB11), endonuclein interaction protein 8 (EIP-8), and pregnancy specific β-1 glycoproteins (PSGs). Surface plasmon resonance analysis and confocal fluorescence microscopy were used to further characterize the interactions.

Conclusions

Our results demonstrate that endonuclein interacts with several proteins indicating a broad function including signal transduction and chaperone activity.

Proteomic, functional and motif-based analysis of C-terminal Src kinase-interacting proteins

C-terminal Src kinase (Csk) that functions as an essential negative regulator of Src family tyrosine kinases (SFKs) interacts with tyrosine-phosphorylated molecules through its Src homology 2 (SH2) domain, allowing it targeting to the sites of SFKs and concomitantly enhancing its kinase activity. Identification of additional Csk-interacting proteins is expected to reveal potential signaling targets and previously undescribed functions of Csk. In this study, using a direct proteomic approach, we identified 151 novel potential Csk-binding partners, which are associated with a wide range of biological functions. Bioinformatics analysis showed that the majority of identified proteins contain one or several Csk-SH2 domain-binding motifs, indicating a potentially direct interaction with Csk. The interactions of Csk with four proteins (partitioning defective 3 (Par3), DDR1, SYK and protein kinase C iota) were confirmed using biochemical approaches and phosphotyrosine 1127 of Par3 C-terminus was proved to directly bind to Csk-SH2 domain, which was consistent with predictions from in silico analysis. Finally, immunofluorescence experiments revealed co-localization of Csk with Par3 in tight junction (TJ) in a tyrosine phosphorylation-dependent manner and overexpression of Csk, but not its SH2-domain mutant lacking binding to phosphotyrosine, promoted the TJ assembly in Madin-Darby canine kidney cells, implying the involvement of Csk-SH2 domain in regulating cellular TJs. In conclusion, the newly identified potential interacting partners of Csk provided new insights into its functional diversity in regulation of numerous cellular events, in addition to controlling the SFK activity.