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.

Loss of post-translational modification sites in disease

Understanding and predicting molecular cause of disease is one of the major challenges for biology and medicine. One particular area of interest continues to be computational analyses of disease-associated amino acid substitutions. To this end, various studies have been performed to identify molecular functions disrupted by disease-causing mutations. Here, we investigate the influence of disease-associated mutations on post-translational modifications. In particular, we study the loss of modification target sites as a consequence of disease mutation. We find that about 5% of disease-associated mutations may affect known modification sites, either partially (4%) of fully (1%), compared to about 2% of putatively neutral polymorphisms. Most of the fifteen post-translational modification types analyzed were found to be disrupted at levels higher than expected by chance. Molecular functions and physiochemical properties at sites of disease mutation were also compared to those of neutral polymorphisms involved in the process of post-translational modification site disruption. Disease-associated mutations in the neighborhood of post-translationally modified sites were found to be enriched in mutations that change polarity, charge, and hydrophobicity of the wild-type amino acids. Overall, these results further suggest that disruption of modification sites is an important but not the major cause of human genetic disease.

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.

Prognostic value of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and TRAIL receptors in renal cell cancer

PURPOSE

The death ligand tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) and its receptors (TRAIL-R) are involved in immune surveillance and tumor development. Here, we studied a possible association between the expression of TRAIL/TRAIL-Rs and the prognosis in patients with renal cell carcinomas (RCC).

EXPERIMENTAL DESIGN

A tissue microarray containing RCC tumor tissue samples and corresponding normal tissue samples from 838 patients was generated. Expression of TRAIL and TRAIL-Rs was examined by immunohistochemistry and the effect of TRAIL and TRAIL-R expression on disease-specific survival was assessed.

RESULTS

High TRAIL-R2 expression levels were associated with high-grade RCCs (P < 0.001) and correlated negatively with disease-specific survival (P = 0.01). Similarly, high TRAIL expression was associated with a shorter disease-specific survival (P = 0.01). In contrast, low TRAIL-R4 expression was associated with high-stage RCCs (P < 0.001) as well as with the incidence of distant metastasis (P = 0.03) and correlated negatively with disease-specific survival (P = 0.02). In patients without distant metastasis, multivariate Cox regression analyses revealed that TRAIL-R2 and TRAIL are independent prognostic factors for cancer-specific survival (in addition to tumor extent, regional lymph node metastasis, grade of malignancy, and type of surgery).

CONCLUSION

High TRAIL-R2, high TRAIL, and low TRAIL-R4 expression levels are associated with a worse disease-specific survival in patients with RCCs. Therefore, the assessment of TRAIL/TRAIL-R expression offers valuable prognostic information that could be used to select patients for adjuvant therapy studies. Moreover, our findings are of relevance for a potential experimental therapeutic administration of TRAIL-R agonists in patients with RCCs.

Identification of c-Src tyrosine kinase substrates in platelet-derived growth factor receptor signaling

c-Src non-receptor tyrosine kinase is an important component of the platelet-derived growth factor (PDGF) receptor signaling pathway. c-Src has been shown to mediate the mitogenic response to PDGF in fibroblasts. However, the exact components of PDGF receptor signaling pathway mediated by c-Src remain unclear. Here, we used stable isotope labeling with amino acids in cell culture (SILAC) coupled with mass spectrometry to identify Src-family kinase substrates involved in PDGF signaling. Using SILAC, we were able to detect changes in tyrosine phosphorylation patterns of 43 potential c-Src kinase substrates in PDGF receptor signaling. This included 23 known c-Src kinase substrates, of which 16 proteins have known roles in PDGF signaling while the remaining 7 proteins have not previously been implicated in PDGF receptor signaling. Importantly, our analysis also led to identification of 20 novel Src-family kinase substrates, of which 5 proteins were previously reported as PDGF receptor signaling pathway intermediates while the remaining 15 proteins represent novel signaling intermediates in PDGF receptor signaling. In validation experiments, we demonstrated that PDGF indeed induced the phosphorylation of a subset of candidate Src-family kinase substrates - Calpain 2, Eps15 and Trim28 - in a c-Src-dependent fashion.

DASMI: exchanging, annotating and assessing molecular interaction data

MOTIVATION

Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet.

RESULTS

We introduce the DASMI system for the dynamic exchange, annotation and assessment of molecular interaction data. DASMI is based on the widely used Distributed Annotation System (DAS) and consists of a data exchange specification, web servers for providing the interaction data and clients for data integration and visualization. The decentralized architecture of DASMI affords the online retrieval of the most recent data from distributed sources and databases. DASMI can also be extended easily by adding new data sources and clients. We describe all DASMI components and demonstrate their use for protein and domain interactions.

AVAILABILITY

The DASMI tools are available at http://www.dasmi.de/ and http://ipfam.sanger.ac.uk/graph. The DAS registry and the DAS 1.53E specification is found at http://www.dasregistry.org/.

DASMIweb: online integration, analysis and assessment of distributed protein interaction data

In recent years, we have witnessed a substantial increase of the amount of available protein interaction data. However, most data are currently not readily accessible to the biologist at a single site, but scattered over multiple online repositories. Therefore, we have developed the DASMIweb server that affords the integration, analysis and qualitative assessment of distributed sources of interaction data in a dynamic fashion. Since DASMIweb allows for querying many different resources of protein and domain interactions simultaneously, it serves as an important starting point for interactome studies and assists the user in finding publicly accessible interaction data with minimal effort. The pool of queried resources is fully configurable and supports the inclusion of own interaction data or confidence scores. In particular, DASMIweb integrates confidence measures like functional similarity scores to assess individual interactions. The retrieved results can be exported in different file formats like MITAB or SIF. DASMIweb is freely available at http://www.dasmiweb.de.

Recent advances in neuroblastoma

PURPOSE OF REVIEW

Although there have been recent advances with multimodal therapy, treatment of neuroblastoma remains a clinical challenge. Despite the identification of several genetic features, there has not been a significant increase in 5-year survival in the last decade. This review will highlight the current operative strategies along with new research developments aimed at improving survival.

RECENT FINDINGS

The goal of surgical intervention in the early stages of neuroblastoma is complete curative resection. In advanced-stage disease, tissue biopsy for staging is the initial goal. In recent years, minimally invasive surgery (MIS) is considered in carefully selected patients. Recent advances in neuroblastoma research have focused on tyrosine kinase inhibition, differentiation, pathway inhibition, and immunotherapy. Several of these targets have shown promising results in vivo and are currently under investigation for potential clinical trials.

SUMMARY

New information on the importance of cell signaling and the targeting of specific genes of interest are providing key insights into neuroblastoma. Only through the discovery of novel treatment strategies made available through the advancement of research will neuroblastoma be survivable for patients with advanced-stage disease.

MedlineRanker: flexible ranking of biomedical literature

The biomedical literature is represented by millions of abstracts available in the Medline database. These abstracts can be queried with the PubMed interface, which provides a keyword-based Boolean search engine. This approach shows limitations in the retrieval of abstracts related to very specific topics, as it is difficult for a non-expert user to find all of the most relevant keywords related to a biomedical topic. Additionally, when searching for more general topics, the same approach may return hundreds of unranked references. To address these issues, text mining tools have been developed to help scientists focus on relevant abstracts. We have implemented the MedlineRanker webserver, which allows a flexible ranking of Medline for a topic of interest without expert knowledge. Given some abstracts related to a topic, the program deduces automatically the most discriminative words in comparison to a random selection. These words are used to score other abstracts, including those from not yet annotated recent publications, which can be then ranked by relevance. We show that our tool can be highly accurate and that it is able to process millions of abstracts in a practical amount of time. MedlineRanker is free for use and is available at http://cbdm.mdc-berlin.de/tools/medlineranker.

Customized treatment in non-small-cell lung cancer based on EGFR mutations and BRCA1 mRNA expression

BACKGROUND

Median survival is 10 months and 2-year survival is 20% in metastatic non-small-cell lung cancer (NSCLC) treated with platinum-based chemotherapy. A small fraction of non-squamous cell lung cancers harbor EGFR mutations, with improved outcome to gefitinib and erlotinib. Experimental evidence suggests that BRCA1 overexpression enhances sensitivity to docetaxel and resistance to cisplatin. RAP80 and Abraxas are interacting proteins that form complexes with BRCA1 and could modulate the effect of BRCA1. In order to further examine the effect of EGFR mutations and BRCA1 mRNA levels on outcome in advanced NSCLC, we performed a prospective non-randomized phase II clinical trial, testing the hypothesis that customized therapy would confer improved outcome over non-customized therapy. In an exploratory analysis, we also examined the effect of RAP80 and Abraxas mRNA levels.

METHODOLOGY/PRINCIPAL FINDINGS

We treated 123 metastatic non-squamous cell lung carcinoma patients using a customized approach. RNA and DNA were isolated from microdissected specimens from paraffin-embedded tumor tissue. Patients with EGFR mutations received erlotinib, and those without EGFR mutations received chemotherapy with or without cisplatin based on their BRCA1 mRNA levels: low, cisplatin plus gemcitabine; intermediate, cisplatin plus docetaxel; high, docetaxel alone. An exploratory analysis examined RAP80 and Abraxas expression. Median survival exceeded 28 months for 12 patients with EGFR mutations, and was 11 months for 38 patients with low BRCA1, 9 months for 40 patients with intermediate BRCA1, and 11 months for 33 patients with high BRCA1. Two-year survival was 73.3%, 41.2%, 15.6% and 0%, respectively. Median survival was influenced by RAP80 expression in the three BRCA1 groups. For example, for patients with both low BRCA1 and low RAP80, median survival exceeded 26 months. RAP80 was a significant factor for survival in patients treated according to BRCA1 levels (hazard ratio, 1.3 [95% CI, 1-1.7]; P = 0.05).

CONCLUSIONS/SIGNIFICANCE

Chemotherapy customized according to BRCA1 expression levels is associated with excellent median and 2-year survival for some subsets of NSCLC patients , and RAP80 could play a crucial modulating effect on this model of customized chemotherapy.

TRIAL REGISTRATION

(ClinicalTrials.gov) NCT00883480.

Nuclear transport factors in neuronal function

Active nucleocytoplasmic transport of macromolecules requires soluble transport carriers of the importin/karyopherin superfamily. Although the nuclear transport machinery is essential in all eukaryotic cells, neurons must also mobilise importins and associated proteins to overcome unique spatiotemporal challenges. These include switches in importin alpha subtype expression during neuronal differentiation, localized axonal synthesis of importin beta1 to coordinate a retrograde injury signaling complex on axonal dynein, and trafficking of regulatory and signaling molecules from synaptic terminals to cell bodies. Targeting of RNAs encoding critical components of the importins complex and the Ran system to axons allows sophisticated local regulation of the system for mobilization upon need. Finally, a number of importin family members have been associated with mental or neurodegenerative diseases. The extended roles recently discovered for importins in the nervous system might also be relevant in non-neuronal cells, and the localized modes of importin regulation in neurons offer new avenues to interrogate their cytoplasmic functions.

NASCENT: an automatic protein interaction network generation tool for non-model organisms

Large quantity of reliable protein interaction data are available for model organisms in public depositories (e.g., MINT, DIP, HPRD, INTERACT). Most data correspond to experiments with the proteins of Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli and Mus musculus. For other important organisms the data availability is poor or non-existent. Here we present NASCENT, a completely automatic web-based tool and also a downloadable Java program, capable of modeling and generating protein interaction networks even for non-model organisms. The tool performs protein interaction network modeling through gene-name mapping, and outputs the resulting network in graphical form and also in computer-readable graph-forms, directly applicable by popular network modeling software.

POINeT: protein interactome with sub-network analysis and hub prioritization

BACKGROUND

Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools.

RESULTS

We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles.

CONCLUSION

The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at (http://poinet.bioinformatics.tw/).

Advanced proteomic technologies for cancer biomarker discovery

Proteomic technologies have experienced major improvements in recent years. Such advances have facilitated the discovery of potential tumor markers with improved sensitivities and specificities for the diagnosis, prognosis and treatment monitoring of cancer patients. This review will focus on four state-of-the-art proteomic technologies, namely 2D difference gel electrophoresis, MALDI imaging mass spectrometry, electron transfer dissociation mass spectrometry and reverse-phase protein array. The major advancements these techniques have brought about and examples of their applications in cancer biomarker discovery will be presented in this review, so that readers can appreciate the immense progress in proteomic technologies from 1997 to 2008. Finally, a summary will be presented that discusses current hurdles faced by proteomic researchers, such as the wide dynamic range of protein abundance, standardization of protocols and validation of cancer biomarkers, and a 5-year view of potential solutions to such problems will be provided.

MatrixDB, a database focused on extracellular protein-protein and protein-carbohydrate interactions

SUMMARY

MatrixDB (http://matrixdb.ibcp.fr) is a database reporting mammalian protein-protein and protein-carbohydrate interactions involving extracellular molecules. It takes into account the full interaction repertoire of the extracellular matrix involving full-length molecules, fragments and multimers. The current version of MatrixDB contains 1972 interactions corresponding to 4412 experiments and involving 259 extracellular biomolecules.

AVAILABILITY

MatrixDB is freely available at http://matrixdb.ibcp.fr