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.

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.

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.

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.

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.

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.

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.

Tailored graph ensembles as proxies or null models for real networks I: tools for quantifying structure

We study the tailoring of structured random graph ensembles to real networks, with the objective of generating precise and practical mathematical tools for quantifying and comparing network topologies macroscopically, beyond the level of degree statistics. Our family of ensembles can produce graphs with any prescribed degree distribution and any degree-degree correlation function, its control parameters can be calculated fully analytically, and as a result we can calculate (asymptotically) formulae for entropies and complexities, and for information-theoretic distances between networks, expressed directly and explicitly in terms of their measured degree distribution and degree correlations.

PDbase: a database of Parkinson's disease-related genes and genetic variation using substantia nigra ESTs

Background

Parkinson's disease (PD) is one of the most common neurodegenerative disorders, clinically characterized by impaired motor function. Since the etiology of PD is diverse and complex, many researchers have created PD-related research resources. However, resources for brain and PD studies are still lacking. Therefore, we have constructed a database of PD-related gene and genetic variations using the substantia nigra (SN) in PD and normal tissues. In addition, we integrated PD-related information from several resources.

Results

We collected the 6,130 SN expressed sequenced tags (ESTs) from brain SN normal tissues and PD patients SN tissues using full-cDNA library and normalized cDNA library construction methods from our previous study. The SN ESTs were clustered in 2,951 unigene clusters and assigned in 2,678 genes. We then found up-regulated 57 genes and down-regulated 48 genes by comparing normal and PD SN ESTs frequencies with over 0.9 cut-off probability of differential expression based on the Audic and Claverie method. In addition, we integrated disease-related information from public resources. To examine the characteristics of these PD-related genes, we analyzed alternative splicing events, single nucleotide polymorphism (SNP) markers located in the gene regions, repeat elements, gene regulation elements, and pathways and protein-protein interaction networks.

Conclusion

We constructed the PDbase database to capture the PD-related gene, genetic variation, and functional elements. This database contains 2,698 PD-related genes through ESTs discovered from human normal and PD patients SN tissues, and through integrating several public resources. PDbase provides the mitochondrion proteins, microRNA gene regulation elements, single nucleotide polymorphisms (SNPs) markers within PD-related gene structures, repeat elements, and pathways and networks with protein-protein interaction information. The PDbase information can aid in understanding the causation of PD. It is available at http://bioportal.kobic.re.kr/PDbase/. Supplementary data is available at http://bioportal.kobic.re.kr/PDbase/suppl.jsp

Human liver rate-limiting enzymes influence metabolic flux via branch points and inhibitors

Background

Rate-limiting enzymes, because of their relatively low velocity, are believed to influence metabolic flux in pathways. To investigate their regulatory role in metabolic networks, we look at the global organization and interactions between rate-limiting enzymes and compounds such as branch point metabolites and enzyme inhibitors in human liver.

Results

Based on 96 rate-limiting enzymes and 132 branch point compounds from human liver, we found that rate-limiting enzymes surrounded 76.5% of branch points. In a compound conversion network from human liver, the 128 branch points involved showed a dramatically higher average degree, betweenness centrality and closeness centrality as a whole. Nearly half of the in vivo inhibitors were products of rate-limiting enzymes, and covered 75.34% of the inhibited targets in metabolic inhibitory networks.

Conclusion

From global topological organization, rate-limiting enzymes as a whole surround most of the branch points; so they can influence the flux through branch points. Since nearly half of the in vivo enzyme inhibitors are produced by rate-limiting enzymes in human liver, these enzymes can initiate inhibitory regulation and then influence metabolic flux through their natural products.

MicroRNAs: potential regulators involved in human anencephaly

MicroRNAs (miRNAs) are posttranscriptional regulators of messenger RNA activity. Neural tube defects (NTDs) are severe congenital anomalies that substantially impact an infant's morbidity and mortality. The miRNAs are known to be dynamically regulated during neurodevelopment; their role in human NTDs, however, is still unknown. In this study, we show the presence of a specific miRNA expression profile from tissues of fetuses with anencephaly, one of the most severe forms of NTDs. Furthermore, we map the target genes of these miRNAs in the human genome. In comparison to healthy human fetal brain tissues, tissues from fetuses with anencephaly exhibited 97 down-regulated and 116 up-regulated miRNAs. The microarray findings were extended using real-time qRT-PCR for nine miRNAs. Specifically, of these validated miRNAs, miR-126, miR-198, and miR-451 were up-regulated, while miR-9, miR-212, miR-124, miR-138, and miR-103/107 were down-regulated in the tissues of fetuses with anencephaly. A bioinformatic analysis showed 881 potential target genes that are regulated by the validated miRNAs. Seventy-nine of these potential genes are involved in a protein interaction network. There were 6 co-occurrence annotations within the GOSlim process and 7 co-occurrence annotations within the GOSlim function found by GeneCodis 2.0. Our results suggest that miRNA dysregulation is possibly involved in the pathogenesis of anencephaly.

Sonic hedgehog is a polarized signal for motor neuron regeneration in adult zebrafish

In contrast to mammals, the spinal cord of adult zebrafish has the capacity to reinitiate generation of motor neurons after a lesion. Here we show that genes involved in motor neuron development, i.e., the ventral morphogen sonic hedgehog a (shha), as well as the transcription factors nkx6.1 and pax6, together with a Tg(olig2:egfp) transgene, are expressed in the unlesioned spinal cord of adult zebrafish. Expression is found in ependymoradial glial cells lining the central canal in ventrodorsal positions that match expression domains of these genes in the developing neural tube. Specifically, Tg(olig2:egfp)(+) ependymoradial glial cells, the adult motor neuron progenitors (pMNs), coexpress Nkx6.1 and Pax6, thus defining an adult pMN-like zone. shha is expressed in distinct ventral ependymoradial glial cells. After a lesion, expression of all these genes is strongly increased, while relative spatial expression domains are maintained. In addition, expression of the hedgehog (hh) receptors patched1 and smoothened becomes detectable in ependymoradial glial cells including those of the pMN-like zone. Cyclopamine-induced knock down of hh signaling significantly reduces ventricular proliferation and motor neuron regeneration. Expression of indicator genes for the FGF and retinoic acid signaling pathways was also increased in the lesioned spinal cord. This suggests that a subclass of ependymoradial glial cells retain their identity as motor neuron progenitors into adulthood and are capable of reacting to a sonic hedgehog signal and potentially other developmental signals with motor neuron regeneration after a spinal lesion.

Functionally guided alignment of protein interaction networks for module detection

MOTIVATION

Functional module detection within protein interaction networks is a challenging problem due to the sparsity of data and presence of errors. Computational techniques for this task range from purely graph theoretical approaches involving single networks to alignment of multiple networks from several species. Current network alignment methods all rely on protein sequence similarity to map proteins across species.

RESULTS

Here we carry out network alignment using a protein functional similarity measure. We show that using functional similarity to map proteins across species improves network alignment in terms of functional coherence and overlap with experimentally verified protein complexes. Moreover, the results from functional similarity-based network alignment display little overlap (<15%) with sequence similarity-based alignment. Our combined approach integrating sequence and function-based network alignment alongside graph clustering properties offers a 200% increase in coverage of experimental datasets and comparable accuracy to current network alignment methods.

AVAILABILITY

Program binaries and source code is freely available at http://www.stats.ox.ac.uk/research/bioinfo/resources.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Network properties of complex human disease genes identified through genome-wide association studies

Background

Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias.

Principal findings

We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process.

Conclusions

This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

ELM: the status of the 2010 eukaryotic linear motif resource

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a 'Bar Code' format, which also displays known instances from homologous proteins through a novel 'Instance Mapper' protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation.

Algorithms for effective querying of compound graph-based pathway databases

BACKGROUND

Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools.

RESULTS

Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool PATIKAweb (Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases.

CONCLUSION

The PATIKA Project Web site is http://www.patika.org. PATIKAweb version 2.1 is available at http://web.patika.org.

The platelet proteome

PURPOSE OF REVIEW

The proteome is the pool of proteins expressed at a given time and circumstance. The word 'proteomics' summarizes several technologies for visualization, quantitation and identification of these proteins. Recent advances in these techniques are helping to elucidate platelet processes which are relevant to bleeding and clotting disorders, transfusion medicine and regulation of angiogenesis.

RECENT FINDINGS

Over 1100 platelet proteins have been identified using proteomic techniques. Various subproteomes have been characterized, including platelet releasates (the 'secretome'), alpha and dense granules, membrane and cytoskeletal proteins, platelet-derived microparticles, and the platelet 'phosphoproteome'. Proteomic data about platelets have become increasingly available in integrated databases.

SUMMARY

Proteomic experiments in resting and activated platelets have identified novel signaling pathways and secreted proteins which may represent therapeutic targets, as well as potential cancer biomarkers.

HLungDB: an integrated database of human lung cancer research

The human lung cancer database (HLungDB) is a database with the integration of the lung cancer-related genes, proteins and miRNAs together with the corresponding clinical information. The main purpose of this platform is to establish a network of lung cancer-related molecules and to facilitate the mechanistic study of lung carcinogenesis. The entries describing the relationships between molecules and human lung cancer in the current release were extracted manually from literatures. Currently, we have collected 2585 genes and 212 miRNA with the experimental evidences involved in the different stages of lung carcinogenesis through text mining. Furthermore, we have incorporated the results from analysis of transcription factor-binding motifs, the promoters and the SNP sites for each gene. Since epigenetic alterations also play an important role in lung carcinogenesis, genes with epigenetic regulation were also included. We hope HLungDB will enrich our knowledge about lung cancer biology and eventually lead to the development of novel therapeutic strategies. HLungDB can be freely accessed at http://www.megabionet.org/bio/hlung.

PathBuilder--open source software for annotating and developing pathway resources

SUMMARY

We have developed PathBuilder, an open-source web application to annotate biological information pertaining to signaling pathways and to create web-based pathway resources. PathBuilder enables annotation of molecular events including protein-protein interactions, enzyme-substrate relationships and protein translocation events either manually or through automated importing of data from other databases. Salient features of PathBuilder include automatic validation of data formats, built-in modules for visualization of pathways, automated import of data from other pathway resources, export of data in several standard data exchange formats and an application programming interface for retrieving existing pathway datasets.

AVAILABILITY

PathBuilder is freely available for download at http://pathbuilder.sourceforge.net/ under the terms of GNU lesser general public license (LGPL: http://www.gnu.org/copyleft/lesser.html). The software is platform independent and has been tested on Windows and Linux platforms.

CONTACT

pandey@jhmi.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An integrative approach to reveal driver gene fusions from paired-end sequencing data in cancer

Cancer genomes contain many aberrant gene fusions-a few that drive disease and many more that are nonspecific passengers. We developed an algorithm (the concept signature or 'ConSig' score) that nominates biologically important fusions from high-throughput data by assessing their association with 'molecular concepts' characteristic of cancer genes, including molecular interactions, pathways and functional annotations. Copy number data supported candidate fusions and suggested a breakpoint principle for intragenic copy number aberrations in fusion partners. By analyzing lung cancer transcriptome sequencing and genomic data, we identified a novel R3HDM2-NFE2 fusion in the H1792 cell line. Lung tissue microarrays revealed 2 of 76 lung cancer patients with genomic rearrangement at the NFE2 locus, suggesting recurrence. Knockdown of NFE2 decreased proliferation and invasion of H1792 cells. Together, these results present a systematic analysis of gene fusions in cancer and describe key characteristics that assist in new fusion discovery.

Proteomic and phospho-proteomic profile of human platelets in basal, resting state: insights into integrin signaling

During atherogenesis and vascular inflammation quiescent platelets are activated to increase the surface expression and ligand affinity of the integrin αIIbβ3 via inside-out signaling. Diverse signals such as thrombin, ADP and epinephrine transduce signals through their respective GPCRs to activate protein kinases that ultimately lead to the phosphorylation of the cytoplasmic tail of the integrin αIIbβ3 and augment its function. The signaling pathways that transmit signals from the GPCR to the cytosolic domain of the integrin are not well defined. In an effort to better understand these pathways, we employed a combination of proteomic profiling and computational analyses of isolated human platelets. We analyzed ten independent human samples and identified a total of 1507 unique proteins in platelets. This is the most comprehensive platelet proteome assembled to date and includes 190 membrane-associated and 262 phosphorylated proteins, which were identified via independent proteomic and phospho-proteomic profiling. We used this proteomic dataset to create a platelet protein-protein interaction (PPI) network and applied novel contextual information about the phosphorylation step to introduce limited directionality in the PPI graph. This newly developed contextual PPI network computationally recapitulated an integrin signaling pathway. Most importantly, our approach not only provided insights into the mechanism of integrin αIIbβ3 activation in resting platelets but also provides an improved model for analysis and discovery of PPI dynamics and signaling pathways in the future.

Predicting meiotic pathways in human fetal oogenesis

Gene function prediction has proven valuable in formulating testable hypotheses. It is particularly useful for exploring biological processes that are experimentally intractable, such as meiotic initiation and progression in the human fetal ovary. In this study, we developed the first functional gene network for the human fetal ovary, HFOnet, by probabilistically integrating multiple genomic features using a naïve Bayesian model. We demonstrated that this network could accurately recapture known functional connections between genes, as well as predict new connections. Our findings suggest that known meiosis-specific genes (i.e., with functions only in meiotic processes in the germ cells) make either no or a few functional connections but are highly clustered with neighbor genes. In contrast, known nonspecific meiotic genes (i.e., with functions in both meiotic and nonmeiotic processes in the germ cells and somatic cells) exhibit numerous connections but low clustering coefficients, indicating their role as central modulators of diverse pathways, including those in meiosis. We also predicted novel genes that may be involved in meiotic initiation and DNA repair. This global functional network provides a much-needed framework for exploring gene functions and pathway components in early human female meiosis that are difficult to tackle by traditional in vivo mammalian genetics.

Identification of functionally related genes using data mining and data integration: a breast cancer case study

Background

The identification of the organisation and dynamics of molecular pathways is crucial for the understanding of cell function. In order to reconstruct the molecular pathways in which a gene of interest is involved in regulating a cell, it is important to identify the set of genes to which it interacts with to determine cell function. In this context, the mining and the integration of a large amount of publicly available data, regarding the transcriptome and the proteome states of a cell, are a useful resource to complement biological research.

Results

We describe an approach for the identification of genes that interact with each other to regulate cell function. The strategy relies on the analysis of gene expression profile similarity, considering large datasets of expression data. During the similarity evaluation, the methodology determines the most significant subset of samples in which the evaluated genes are highly correlated. Hence, the strategy enables the exclusion of samples that are not relevant for each gene pair analysed. This feature is important when considering a large set of samples characterised by heterogeneous experimental conditions where different pools of biological processes can be active across the samples. The putative partners of the studied gene are then further characterised, analysing the distribution of the Gene Ontology terms and integrating the protein-protein interaction (PPI) data. The strategy was applied for the analysis of the functional relationships of a gene of known function, Pyruvate Kinase, and for the prediction of functional partners of the human transcription factor TBX3. In both cases the analysis was done on a dataset composed by breast primary tumour expression data derived from the literature. Integration and analysis of PPI data confirmed the prediction of the methodology, since the genes identified to be functionally related were associated to proteins close in the PPI network. Two genes among the predicted putative partners of TBX3 (GLI3 and GATA3) were confirmed by in vivo binding assays (crosslinking immunoprecipitation, X-ChIP) in which the putative DNA enhancer sequence sites of GATA3 and GLI3 were found to be bound by the Tbx3 protein.

Conclusion

The presented strategy is demonstrated to be an effective approach to identify genes that establish functional relationships. The methodology identifies and characterises genes with a similar expression profile, through data mining and integrating data from publicly available resources, to contribute to a better understanding of gene regulation and cell function. The prediction of the TBX3 target genes GLI3 and GATA3 was experimentally confirmed.

Empirical Bayes analysis of quantitative proteomics experiments

Background

Advances in mass spectrometry-based proteomics have enabled the incorporation of proteomic data into systems approaches to biology. However, development of analytical methods has lagged behind. Here we describe an empirical Bayes framework for quantitative proteomics data analysis. The method provides a statistical description of each experiment, including the number of proteins that differ in abundance between 2 samples, the experiment's statistical power to detect them, and the false-positive probability of each protein.

Methodology/Principal Findings

We analyzed 2 types of mass spectrometric experiments. First, we showed that the method identified the protein targets of small-molecules in affinity purification experiments with high precision. Second, we re-analyzed a mass spectrometric data set designed to identify proteins regulated by microRNAs. Our results were supported by sequence analysis of the 3′ UTR regions of predicted target genes, and we found that the previously reported conclusion that a large fraction of the proteome is regulated by microRNAs was not supported by our statistical analysis of the data.

Conclusions/Significance

Our results highlight the importance of rigorous statistical analysis of proteomic data, and the method described here provides a statistical framework to robustly and reliably interpret such data.

Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

Oral magnesium supplementation reduces ambulatory blood pressure in patients with mild hypertension

BACKGROUND

Accumulating evidence implicates a role of Mg(2+) in the pathophysiology of essential hypertension. Previous studies evaluating the antihypertensive efficacy of Mg(2+) supplementation gave contradictory results. This study aimed to investigate the effect of oral Mg(2+) supplementation on 24-h blood pressure (BP) and intracellular ion status in patients with mild hypertension.

METHODS

A total of 48 patients with mild uncomplicated hypertension participated in the study. Among them, 24 subjects were assigned to 600 mg of pidolate Mg(2+) daily in addition to lifestyle recommendations for a 12-week period and another 24 age- and sex-matched controls were only given lifestyle recommendations. At baseline and study-end (12 weeks) ambulatory BP monitoring, determination of serum and intracellular ion levels, and 24-h urinary collections for determination of urinary Mg(2+) were performed in all study subjects.

RESULTS

In the Mg(2+) supplementation group, small but significant reductions in mean 24-h systolic and diastolic BP levels were observed, in contrast to control group (-5.6 +/- 2.7 vs. -1.3 +/- 2.4 mm Hg, P < 0.001 and -2.8 +/- 1.8 vs. -1 +/- 1.2 mm Hg, P = 0.002, respectively). These effects of Mg(2+) supplementation were consistent in both daytime and night-time periods. Serum Mg(2+) levels and urinary Mg(2+) excretion were significantly increased in the intervention group. Intracellular Mg(2+) and K(+) levels were also increased, while intracellular Ca(2+) and Na(+) levels were decreased in the intervention group. None of the intracellular ions were significantly changed in the control group.

CONCLUSION

This study suggests that oral Mg(2+) supplementation is associated with small but consistent ambulatory BP reduction in patients with mild hypertension.

The role of glutathione S- transferase M1 and T1 gene polymorphisms and oxidative stress-related parameters in Egyptian patients with essential hypertension

BACKGROUND

Essential hypertension is a complex, multifactorial, polygenic disease in which the underlying genetic components remain unknown. Glutathione S-transferase (GST) enzyme is involved in detoxification of reactive oxygen species. This study aimed to investigate GSTM1 and GSTT1 gene polymorphisms in Egyptian essential hypertensive patients and their relationship with oxidative stress-related parameters.

METHODS

The study included 40 newly-diagnosed, untreated, essential hypertensive patients and 40 normotensive subjects. Plasma levels of malondialdehyde (MDA), and nitrate/nitrite and erythrocyte reduced glutathione (GSH), activities of catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and glutathione S-transferase (GST) were measured. Genotyping for GSTM1 and GSTT1 was performed.

RESULTS

The frequency of GSTM1+ve/GSTT1+ve in hypertensives (5%) was lower than in normotensives (37.5%).The frequency of GSTM1-ve/GSTT1-ve was elevated in hypertensives (35%) as compared to normotensives (7.5%). Plasma MDA was higher and nitrate/nitrite was lower in hypertensives than in normotensives. Erythrocyte GSH, activities of CAT, SOD, GSH-Px, and GST of hypertensives were lower than normotensives. Moreover, GST activity was lower in subjects with GSTM1-ve/GSTT1-ve than in those with GSTM1+ve/GSTT1+ve. In hypertensives, both systolic and diastolic blood pressures were negatively correlated with activities of CAT, GSH-Px, and GST.

CONCLUSIONS

GSTM1-ve/GSTT1-ve is a potential genetic factor to predict development of essential hypertension and permit early therapeutic intervention. The significant association between blood pressure and oxidative stress-related parameters indicates the pathogenic role of oxidative stress in hypertension. Antioxidants could be useful in the management of essential hypertension to prevent progressive deterioration and target organ damage however, further studies involving long-term clinical trials may help to assess the efficacy of these therapeutic agents.

Tyrosine phosphorylation of nuclear-membrane protein emerin by Src, Abl and other kinases

X-linked recessive Emery-Dreifuss muscular dystrophy (EDMD) is caused by loss of emerin, a nuclear-membrane protein with roles in nuclear architecture, gene regulation and signaling. Phosphoproteomic studies have identified 13 sites of tyrosine phosphorylation in emerin. We validated one study, confirming that emerin is hyper-tyrosine-phosphorylated in Her2-overexpressing cells. We discovered that non-receptor tyrosine kinases Src and Abl each phosphorylate emerin and a related protein, LAP2beta, directly. Src phosphorylated emerin specifically at Y59, Y74 and Y95; the corresponding triple Y-to-F (;FFF') mutation reduced tyrosine phosphorylation by approximately 70% in vitro and in vivo. Substitutions that removed a single hydroxyl moiety either decreased (Y19F, Y34, Y161F) or increased (Y4F) emerin binding to BAF in cells. Y19F, Y34F, Y161F and the FFF mutant also reduced recombinant emerin binding to BAF from HeLa lysates, demonstrating the involvement of both LEM-domain and distal phosphorylatable tyrosines in binding BAF. We conclude that emerin function is regulated by multiple tyrosine kinases, including Her2, Src and Abl, two of which (Her2, Src) regulate striated muscle. These findings suggest roles for emerin as a downstream effector and ;signal integrator' for tyrosine kinase signaling pathway(s) at the nuclear envelope.

Transcriptional network classifiers

Background

Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods.

Results

Our system biology approach is carried out by the Bayesian networks framework. The algorithm consists of two steps: gene filtering by Bayes factor followed by collinearity elimination via network learning. We validate our approach with two clinical data. In the study of lung cancer subtypes discrimination, we obtain a 25-gene classifier from 111 training samples, and the test on 422 independent samples achieves 95% classification accuracy. In the study of thoracic aortic aneurysm (TAA) diagnosis, 61 samples determine a 34-gene classifier, whose diagnosis accuracy on 33 independent samples achieves 82%. The performance comparisons with three other popular methods, PCA/LDA, PAM, and Weighted Voting, confirm that our approach yields superior classification accuracy and a more compact signature.

Conclusions

The system biology approach presented in this paper is able to infer function-dependent transcriptional networks, which in turn can classify biological samples with high accuracy. The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis.

Defining elastic fiber interactions by molecular fishing: an affinity purification and mass spectrometry approach

Deciphering interacting networks of the extracellular matrix is a major challenge. We describe an affinity purification and mass spectrometry strategy that has provided new insights into the molecular interactions of elastic fibers, essential extracellular assemblies that provide elastic recoil in dynamic tissues. Using cell culture models, we defined primary and secondary elastic fiber interaction networks by identifying molecular interactions with the elastic fiber molecules fibrillin-1, MAGP-1, fibulin-5, and lysyl oxidase. The sensitivity and validity of our method was confirmed by identification of known interactions with the bait proteins. Our study revealed novel extracellular protein interactions with elastic fiber molecules and delineated secondary interacting networks with fibronectin and heparan sulfate-associated molecules. This strategy is a novel approach to define the macromolecular interactions that sustain complex extracellular matrix assemblies and to gain insights into how they are integrated into their surrounding matrix.

PIAS proteins: pleiotropic interactors associated with SUMO

The interactions and functions of protein inhibitors of activated STAT (PIAS) proteins are not restricted to the signal transducers and activators of transcription (STATs), but PIAS1, -2, -3 and -4 interact with and regulate a variety of distinct proteins, especially transcription factors. Although the majority of PIAS-interacting proteins are prone to modification by small ubiquitin-related modifier (SUMO) proteins and the PIAS proteins have the capacity to promote the modification as RING-type SUMO ligases, they do not function solely as SUMO E3 ligases. Instead, their effects are often independent of their Siz/PIAS (SP)-RING finger, but dependent on their capability to noncovalently interact with SUMOs or DNA through their SUMO-interacting motif and scaffold attachment factor-A/B, acinus and PIAS domain, respectively. Here, we present an overview of the cellular regulation by PIAS proteins and propose that many of their functions are due to their capability to mediate and facilitate SUMO-linked protein assemblies.

Scaffolds: interaction platforms for cellular signalling circuits

Scaffold proteins influence cellular signalling by binding to multiple signalling enzymes, receptors or ion channels. Although normally devoid of catalytic activity, they have a big impact on controlling the flow of signalling information. By assembling signalling proteins into complexes, they play the part of signal processing hubs. As we learn more about the way signalling components are linked into natural signalling circuits, researchers are becoming interested in building non-natural signalling pathways to test our knowledge and/or to intentionally reprogram cellular behaviour. In this review, we discuss the role of scaffold proteins as efficient tools for assembling intracellular signalling complexes, both natural and artificial.

Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin.