Structural and dynamical analysis of biological networks

Biological networks are currently being studied with approaches derived from the mathematical and physical sciences. Their structural analysis enables to highlight nodes with special properties that have sometimes been correlated with the biological importance of a gene or a protein. However, biological networks are dynamic both on the evolutionary time-scale, and on the much shorter time-scale of physiological processes. There is therefore no unique network for a given cellular process, but potentially many realizations, each with different properties as a consequence of regulatory mechanisms. Such realizations provide snapshots of a same network in different conditions, enabling the study of condition-dependent structural properties. True dynamical analysis can be obtained through detailed mathematical modeling techniques that are not easily scalable to full network models.

Clustering algorithms in biomedical research: a review

Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting. Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important. This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications.

Enabling dynamic network analysis through visualization in TVNViewer

Background

Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis.

Results

In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets.

Conclusions

TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder

Most pathway and gene-set enrichment methods prioritize genes by their main effect and do not account for variation due to interactions in the pathway. A portion of the presumed missing heritability in genome-wide association studies (GWAS) may be accounted for through gene-gene interactions and additive genetic variability. In this study, we prioritize genes for pathway enrichment in GWAS of bipolar disorder (BD) by aggregating gene-gene interaction information with main effect associations through a machine learning (evaporative cooling) feature selection and epistasis network centrality analysis. We validate this approach in a two-stage (discovery/replication) pathway analysis of GWAS of BD. The discovery cohort comes from the Wellcome Trust Case Control Consortium (WTCCC) GWAS of BD, and the replication cohort comes from the National Institute of Mental Health (NIMH) GWAS of BD in European Ancestry individuals. Epistasis network centrality yields replicated enrichment of Cadherin signaling pathway, whose genes have been hypothesized to have an important role in BD pathophysiology but have not demonstrated enrichment in previous analysis. Other enriched pathways include Wnt signaling, circadian rhythm pathway, axon guidance and neuroactive ligand-receptor interaction. In addition to pathway enrichment, the collective network approach elevates the importance of ANK3, DGKH and ODZ4 for BD susceptibility in the WTCCC GWAS, despite their weak single-locus effect in the data. These results provide evidence that numerous small interactions among common alleles may contribute to the diathesis for BD and demonstrate the importance of including information from the network of gene-gene interactions as well as main effects when prioritizing genes for pathway analysis.

Comparative expression profiling identifies differential roles for Myogenin and p38α MAPK signaling in myogenesis

Skeletal muscle differentiation is mediated by a complex gene expression program requiring both the muscle-specific transcription factor Myogenin (Myog) and p38α MAPK (p38α) signaling. However, the relative contribution of Myog and p38α to the formation of mature myotubes remains unknown. Here, we have uncoupled the activity of Myog from that of p38α to gain insight into the individual roles of these proteins in myogenesis. Comparative expression profiling confirmed that Myog activates the expression of genes involved in muscle function. Furthermore, we found that in the absence of p38α signaling, Myog expression leads to the down-regulation of genes involved in cell cycle progression. Consistent with this, the expression of Myog is sufficient to induce cell cycle exit. Interestingly, p38α-defective, Myog-expressing myoblasts fail to form multinucleated myotubes, suggesting an important role for p38α in cell fusion. Through the analysis of p38α up-regulated genes, the tetraspanin CD53 was identified as a candidate fusion protein, a role confirmed both ex vivo in primary myoblasts, and in vivo during myofiber regeneration in mice. Thus, our study has revealed an unexpected role for Myog in mediating cell cycle exit and has identified an essential role for p38α in cell fusion through the up-regulation of CD53.

Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes

BACKGROUND

To understand the roles they play in complex diseases, genes need to be investigated in the networks they are involved in. Integration of gene expression and network data is a promising approach to prioritize disease-associated genes. Some methods have been developed in this field, but the problem is still far from being solved.

RESULTS

In this paper, we developed a method, Networked Gene Prioritizer (NGP), to prioritize cancer-associated genes. Applications on several breast cancer and lung cancer datasets demonstrated that NGP performs better than the existing methods. It provides stable top ranking genes between independent datasets. The top-ranked genes by NGP are enriched in the cancer-associated pathways. The top-ranked genes by NGP-PLK1, MCM2, MCM3, MCM7, MCM10 and SKP2 might coordinate to promote cell cycle related processes in cancer but not normal cells.

CONCLUSIONS

In this paper, we have developed a method named NGP, to prioritize cancer-associated genes. Our results demonstrated that NGP performs better than the existing methods.

A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data

Background

Several biclustering algorithms have been proposed to identify biclusters, in which genes share similar expression patterns across a number of conditions. However, different algorithms would yield different biclusters and further lead to distinct conclusions. Therefore, some testing and comparisons between these algorithms are strongly required.

Methods

In this study, five biclustering algorithms (i.e. BIMAX, FABIA, ISA, QUBIC and SAMBA) were compared with each other in the cases where they were used to handle two expression datasets (GDS1620 and pathway) with different dimensions in Arabidopsis thaliana (A. thaliana)

GO (gene ontology) annotation and PPI (protein-protein interaction) network were used to verify the corresponding biological significance of biclusters from the five algorithms. To compare the algorithms’ performance and evaluate quality of identified biclusters, two scoring methods, namely weighted enrichment (WE) scoring and PPI scoring, were proposed in our study. For each dataset, after combining the scores of all biclusters into one unified ranking, we could evaluate the performance and behavior of the five biclustering algorithms in a better way.

Results

Both WE and PPI scoring methods has been proved effective to validate biological significance of the biclusters, and a significantly positive correlation between the two sets of scores has been tested to demonstrate the consistence of these two methods.

A comparative study of the above five algorithms has revealed that: (1) ISA is the most effective one among the five algorithms on the dataset of GDS1620 and BIMAX outperforms the other algorithms on the dataset of pathway. (2) Both ISA and BIMAX are data-dependent. The former one does not work well on the datasets with few genes, while the latter one holds well for the datasets with more conditions. (3) FABIA and QUBIC perform poorly in this study and they may be suitable to large datasets with more genes and more conditions. (4) SAMBA is also data-independent as it performs well on two given datasets. The comparison results provide useful information for researchers to choose a suitable algorithm for each given dataset.

INDI: a computational framework for inferring drug interactions and their associated recommendations

INDI is a similarity-based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice.

INDI is a similarity-based drug–drug interaction prediction method, capable of handling both pharmacokinetic and pharmacodynamic interactions.

INDI predicts the severity of the interaction and the Cytochrome P450 isozyme involved in pharmacokinetic interactions.

We show the prevalence of known and predicted drug interactions in drug adverse reports and in chronic medications taken by hospitalized patients.

Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve)⩾0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/∼bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.

The preservation of bidirectional promoter architecture in eukaryotes: what is the driving force?

Background

The bidirectional gene architecture has been studied in many organisms, and the conservation of bidirectional arrangement has received considerable attention. However, the explanation for the evolutionary conservation about this genomic structure is still insufficient. In this study the large scale identification and pathway enrichment analysis for bidirectional genes were performed in several eukaryotes and the comparative analysis of this arrangement between human and mouse were dissected for the purpose of discovering the driving force of the preservation of this genomic structure.

Results

We identified the bidirectional gene pairs in eight different species and found this structure to be prevalent in eukaryotes. The pathway enrichment analysis indicated the bidirectional genes at the genome level are conserved in certain pathways, such as the DNA repair and some other fundamental cellular pathways. The comparative analysis about the gene expression, function, between human and mouse bidirectional genes were also performed and we observed that the selective force of this architecture doesn't derive from the co-regulation between paired genes, but the functional bias of bidirectional genes at whole genome level is observed strengthened during evolution.

Conclusions

Our result validated the coexpression of bidirectional genes; however failed to support their functional relevance. The conservation of bidirectional promoters seems not the result of functional connection between paired genes, but the functional bias at whole genome level, which imply that the genome-wide functional constraint is important for the conservation of bidirectional structure.

Current understanding of the formation and adaptation of metabolic systems based on network theory

Formation and adaptation of metabolic networks has been a long-standing question in biology. With recent developments in biotechnology and bioinformatics, the understanding of metabolism is progressively becoming clearer from a network perspective. This review introduces the comprehensive metabolic world that has been revealed by a wide range of data analyses and theoretical studies; in particular, it illustrates the role of evolutionary events, such as gene duplication and horizontal gene transfer, and environmental factors, such as nutrient availability and growth conditions, in evolution of the metabolic network. Furthermore, the mathematical models for the formation and adaptation of metabolic networks have also been described, according to the current understanding from a perspective of metabolic networks. These recent findings are helpful in not only understanding the formation of metabolic networks and their adaptation, but also metabolic engineering.

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies

The current paradigm of human genetics research is to analyze variation of a single data type (i.e., DNA sequence or RNA levels) to detect genes and pathways that underlie complex traits such as disease state or drug response. While these studies have detected thousands of variations that associate with hundreds of complex phenotypes, much of the estimated heritability, or trait variability due to genetic factors, remain unexplained. We may be able to account for a portion of the missing heritability if we incorporate a systems biology approach into these analyses. Rapid technological advances will make it possible for scientists to explore this hypothesis via the generation of high-throughput omics data - transcriptomic, proteomic and methylomic to name a few. Analyzing this 'meta-dimensional' data will require clever statistical techniques that allow for the integration of qualitative and quantitative predictor variables. For this article, we examine two major categories of approaches for integrated data analysis, give examples of their use in experimental and in silico datasets, and assess the limitations of each method.

Network biology methods integrating biological data for translational science

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.

Semantically linking molecular entities in literature through entity relationships

BACKGROUND

Text mining tools have gained popularity to process the vast amount of available research articles in the biomedical literature. It is crucial that such tools extract information with a sufficient level of detail to be applicable in real life scenarios. Studies of mining non-causal molecular relations attribute to this goal by formally identifying the relations between genes, promoters, complexes and various other molecular entities found in text. More importantly, these studies help to enhance integration of text mining results with database facts.

RESULTS

We describe, compare and evaluate two frameworks developed for the prediction of non-causal or 'entity' relations (REL) between gene symbols and domain terms. For the corresponding REL challenge of the BioNLP Shared Task of 2011, these systems ranked first (57.7% F-score) and second (41.6% F-score). In this paper, we investigate the performance discrepancy of 16 percentage points by benchmarking on a related and more extensive dataset, analysing the contribution of both the term detection and relation extraction modules. We further construct a hybrid system combining the two frameworks and experiment with intersection and union combinations, achieving respectively high-precision and high-recall results. Finally, we highlight extremely high-performance results (F-score > 90%) obtained for the specific subclass of embedded entity relations that are essential for integrating text mining predictions with database facts.

CONCLUSIONS

The results from this study will enable us in the near future to annotate semantic relations between molecular entities in the entire scientific literature available through PubMed. The recent release of the EVEX dataset, containing biomolecular event predictions for millions of PubMed articles, is an interesting and exciting opportunity to overlay these entity relations with event predictions on a literature-wide scale.

Long noncoding RNA-mediated maintenance of DNA methylation and transcriptional gene silencing

Establishment of silencing by noncoding RNAs (ncRNAs) via targeting of chromatin remodelers is relatively well investigated; however, their role in the maintenance of silencing is poorly understood. Here, we explored the functional role of the long ncRNA Kcnq1ot1 in the maintenance of transcriptional gene silencing in the one mega-base Kcnq1 imprinted domain in a transgenic mouse model. By conditionally deleting the Kcnq1ot1 ncRNA at different stages of mouse development, we suggest that Kcnq1ot1 ncRNA is required for the maintenance of the silencing of ubiquitously imprinted genes (UIGs) at all developmental stages. In addition, Kcnq1ot1 ncRNA is also involved in guiding and maintaining the CpG methylation at somatic differentially methylated regions flanking the UIGs, which is a hitherto unknown role for a long ncRNA. On the other hand, silencing of some of the placental-specific imprinted genes (PIGs) is maintained independently of Kcnq1ot1 ncRNA. Interestingly, the non-imprinted genes (NIGs) that escape RNA-mediated silencing are enriched with enhancer-specific modifications. Taken together, this study illustrates the gene-specific maintenance mechanisms operational at the Kcnq1 locus for tissue-specific transcriptional gene silencing and activation.

Nonparametric simulation of signal transduction networks with semi-synchronized update

Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process.

Cross-Genome Clustering of Human and C. elegans G-Protein Coupled Receptors

G-protein coupled receptors (GPCRs) are one of the largest groups of membrane proteins and are popular drug targets. The work reported here attempts to perform cross-genome phylogeny on GPCRs from two widely different taxa, human versus C. elegans genomes and to address the issues on evolutionary plasticity, to identify functionally related genes, orthologous relationship, and ligand binding properties through effective bioinformatic approaches. Through RPS blast around 1106 nematode GPCRs were given chance to associate with previously established 8 types of human GPCR profiles at varying E-value thresholds and resulted 32 clusters were illustrating co-clustering and class-specific retainsionship. In the significant thresholds, 81% of the C. elegans GPCRs were associated with 32 clusters and 27 C. elegans GPCRs (2%) inferred for orthology. 177 hypothetical proteins were observed in cluster association and could be reliably associated with one of 32 clusters. Several nematode-specific GPCR clades were observed suggesting lineage-specific functional recruitment in response to environment.

The use of a DNA biobank linked to electronic medical records to characterize pharmacogenomic predictors of tacrolimus dose requirement in kidney transplant recipients

OBJECTIVE

Tacrolimus, an immunosuppressive drug widely prescribed in kidney transplantation, requires therapeutic drug monitoring due to its marked interindividual pharmacokinetic variability and narrow therapeutic index. Previous studies have established that CYP3A5 rs776746 is associated with tacrolimus clearance, blood concentration, and dose requirement. The importance of other drug absorption, distribution, metabolism, and elimination (ADME) gene variants has not been well characterized.

METHODS

We used novel DNA biobank and electronic medical record resources to identify ADME variants associated with tacrolimus dose requirement. Broad ADME genotyping was performed on 446 kidney transplant recipients, who had been dosed to a steady state with tacrolimus. The cohort was obtained from Vanderbilt's DNA biobank, BioVU, which contains linked deidentified electronic medical record data. Genotyping included Affymetrix drug-metabolizing enzymes and transporters Plus (1936 polymorphisms), custom Sequenom Massarray iPLEX Gold assay (95 polymorphisms), and ancestry-informative markers. The primary outcome was tacrolimus dose requirement defined as blood concentration to dose ratio.

RESULTS

In analyses, which adjusted for race and other clinical factors, we replicated the association of tacrolimus blood concentration to dose ratio with CYP3A5 rs776746 (P=7.15×10), and identified associations with nine variants in linkage disequilibrium with rs776746, including eight CYP3A4 variants. No NR1I2 variants were significantly associated. Age, weight, and hemoglobin were also significantly associated with the outcome. In final models, rs776746 explained 39% of variability in dose requirement and 46% was explained by the model containing clinical covariates.

CONCLUSION

This study highlights the utility of DNA biobanks and electronic medical records for tacrolimus pharmacogenomic research.

Recent approaches to the prioritization of candidate disease genes

Many efforts are still devoted to the discovery of genes involved with specific phenotypes, in particular, diseases. High-throughput techniques are thus applied frequently to detect dozens or even hundreds of candidate genes. However, the experimental validation of many candidates is often an expensive and time-consuming task. Therefore, a great variety of computational approaches has been developed to support the identification of the most promising candidates for follow-up studies. The biomedical knowledge already available about the disease of interest and related genes is commonly exploited to find new gene-disease associations and to prioritize candidates. In this review, we highlight recent methodological advances in this research field of candidate gene prioritization. We focus on approaches that use network information and integrate heterogeneous data sources. Furthermore, we discuss current benchmarking procedures for evaluating and comparing different prioritization methods.

Microparticle conferred microRNA profiles--implications in the transfer and dominance of cancer traits

BACKGROUND

Microparticles (MPs) are membrane vesicles which are released from normal and malignant cells following a process of budding and detachment from donor cells. MPs contain surface antigens, proteins and genetic material and serve as vectors of intercellular communication. MPs comprise the major source of systemic RNA including microRNA (miRNA), the aberrant expression of which appears to be associated with stage, progression and spread of many cancers. Our previous study showed that MPs carry both transcripts and miRNAs associated with the acquisition of multidrug resistance in cancer.

RESULTS

Herein, we expand on our previous finding and demonstrate that MPs carry the transcripts of the membrane vesiculation machinery (floppase and scramblase) as well as nucleic acids encoding the enzymes essential for microRNA biogenesis (Drosha, Dicer and Argonaute). We also demonstrate using microarray miRNA profiling analysis, the selective packaging of miRNAs (miR-1228*, miR-1246, miR-1308, miR-149*, miR-455-3p, miR-638 and miR-923) within the MP cargo upon release from the donor cells.

CONCLUSIONS

These miRNAs are present in both haematological and non-haematological cancer cells and are involved in pathways implicated in cancer pathogenesis, membrane vesiculation and cascades regulated by ABC transporters. Our recent findings reinforce our earlier reports that MP transfer 're-templates' recipient cells so as to reflect donor cell traits. We now demonstrate that this process is likely to occur via a process of selective packaging of nucleic acid species, including regulatory nucleic acids upon MP vesiculation. These findings have significant implications in understanding the cellular basis governing the intercellular acquisition and dominance of deleterious traits in cancers.

miR-376a suppresses proliferation and induces apoptosis in hepatocellular carcinoma

MicroRNAs are known to be involved in the pathogenesis of hepatocellular carcinoma (HCC). This study aims to explore the potential biological function of miR-376a, which was found to be inhibited after partial hepatectomy, in HCC. We discovered that miR-376a was frequently down-regulated in HCC cell lines and HCC tissues, while higher relative level of miR-376a was significantly associated with high serum AFP level. Over-expression of miR-376a not only inhibited proliferation but induced apoptosis in Huh7 cells. Additionally, p85α (PIK3R1) was identified as a direct and functional target of miR-376a in Huh7 cells. Moreover, we confirmed that p85α and miR-376a were inversely correlated in HCC. These findings suggest that down-regulation of miR-376a may contribute to the development of HCC by targeting p85α.

Traditional chinese medicine zheng in the era of evidence-based medicine: a literature analysis

Zheng, which is also called a syndrome or pattern, is the basic unit and a key concept of traditional Chinese medicine (TCM) theory. Zheng can be considered a further stratification of patients when it is integrated with biomedical diagnoses in clinical practice to achieve higher efficacies. In an era of evidence-based medicine, confronted with the vast and increasing volume of TCM data, there is an urgent need to explore these resources effectively using techniques of knowledge discovery in databases. The application of effective data mining in the analysis of multiple extensively integrated databases can supply new information about TCM Zheng research. In this paper, we screened the published literature on TCM Zheng-related studies in the SinoMed and PubMed databases with a novel data mining approach to obtain an overview of the Zheng research landscape in the hope of contributing to a better understanding of TCM Zheng in the era of evidence-based medicine. In our results, contrast was found in Zheng in different studies, and several determinants of Zheng were identified. The data described in this paper can be used to assess Zheng research studies based on the title and certain characteristics of the abstract. These findings will benefit modern TCM Zheng-related studies and guide future Zheng study efforts.

Human gene correlation analysis (HGCA): a tool for the identification of transcriptionally co-expressed genes

Background

Bioinformatics and high-throughput technologies such as microarray studies allow the measure of the expression levels of large numbers of genes simultaneously, thus helping us to understand the molecular mechanisms of various biological processes in a cell.

Findings

We calculate the Pearson Correlation Coefficient (r-value) between probe set signal values from Affymetrix Human Genome Microarray samples and cluster the human genes according to the r-value correlation matrix using the Neighbour Joining (NJ) clustering method. A hyper-geometric distribution is applied on the text annotations of the probe sets to quantify the term overrepresentations. The aim of the tool is the identification of closely correlated genes for a given gene of interest and/or the prediction of its biological function, which is based on the annotations of the respective gene cluster.

Conclusion

Human Gene Correlation Analysis (HGCA) is a tool to classify human genes according to their coexpression levels and to identify overrepresented annotation terms in correlated gene groups. It is available at: http://biobank-informatics.bioacademy.gr/coexpression/.

MicroRNAs in liver disease

MicroRNAs are small noncoding RNA molecules that regulate gene expression posttranscriptionally through complementary base pairing with thousands of messenger RNAs. They regulate diverse physiological, developmental, and pathophysiological processes. Recent studies have uncovered the contribution of microRNAs to the pathogenesis of many human diseases, including liver diseases. Moreover, microRNAs have been identified as biomarkers that can often be detected in the systemic circulation. We review the role of microRNAs in liver physiology and pathophysiology, focusing on viral hepatitis, liver fibrosis, and cancer. We also discuss microRNAs as diagnostic and prognostic markers and microRNA-based therapeutic approaches for liver disease.

An integrative approach of linking traditional Chinese medicine pattern classification and biomedicine diagnosis

Traditional Chinese medicine (TCM) is a medical system with over 3000 years of continuous practice experience and refinement through treatment observations. The TCM pattern classification (also defined as Syndrome or Zheng differentiation) and treatment of ill health is the basis and the key concept of the TCM theory. All diagnostic and therapeutic methods in TCM are based on the differentiation of TCM pattern. TCM pattern can be considered as the TCM theoretical interpretation of the symptom profiles. Pattern classification is often used as a guideline in disease classification in TCM practice and has been recently incorporated with biomedical diagnosis, resulting in the increasing research interest of TCM pattern among various disciplines of integrative medicine. This paper describes the historical evolution on the integration of the TCM pattern classification and disease diagnosis in biomedicine, the methodology of pattern classification for diseases, efficacy of TCM practice with integration of TCM pattern classification and biomedical disease diagnosis, and the biological basis of TCM pattern. TCM pattern classification, which may lead to new findings in biological sciences, was also discussed.

Genome-wide differential genetic profiling characterizes colorectal cancers with genetic instability and specific routes to HLA class I loss and immune escape

AIM

We compared the expression of genes related to inflammatory and cytotoxic functions between MSI and MSS (HLA-class I-negative and HLA-class I-positive) colorectal cancers (CRCs), seeking evidence of differences in inflammatory mediators and cytotoxic T-cell responses. Twenty-two CRCs were divided into three study groups as a function of HLA class I expression and MSI phenotype: 8 MSI tumours, 6 MSS/HLA- tumours and 6 MSS/HLA+ tumours (controls).

FINDINGS

A first comparison between eight MSI and six MSS/HLA-positive (control) cancers, based on microarray analysis on an Affymetrix(®) HG-U133-Plus-PM plate, identified 1974 differentially expressed genes (P < 0.05). We grouped genes in Gene Ontology functional categories: apoptotic programme (72 genes, P = 5.5·10(-3)), leucocyte activation (43 genes, P = 1.8·10(-5)), T-cell activation (24 genes, P = 6.3·10(-4)), inflammatory response (40 genes, 2.3·10(-2)) and cytokine production (10 genes, P = 1.9·10(-2)). Real-time PCR and immunohistochemical evaluation were used to validate the data, finding that increased mRNA levels of pro-inflammatory cytokines and cytotoxic mediators were associated with greater infiltration by CD8+T lymphocytes in the MSI group (P < 0.001). Finally, HLA-class I-negative tumours were not grouped together but rather in accordance with features of the gene expression profile of MSI or MSS tumours. As expected, genes associated with antigen processing machinery and MHC class I molecules (TAP2, B2m) were downregulated in MSS/HLA-class I-negative CRCs (n = 6) in comparison to controls.

CONCLUSIONS

In conclusion, microarray and immunohistochemical data may be useful to comprehensively assess tumour-host interactions and differentiate MSI from MSS cancers. The two types of tumour, MSI/HLA-class I-negative and MSS/HLA-class I-negative, showed marked differences in the composition and intensity of infiltrating leucocytes, suggesting that their immune escape strategies involve distinct pathways.

PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools

Background

Gas chromatography–mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines.

Results

PyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS).

Conclusions

PyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. We demonstrate data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Automated sample processing and quantitation with PyMS can provide substantial time savings compared to more traditional interactive software systems that tightly integrate data processing with the graphical user interface.

Global tests of P-values for multifactor dimensionality reduction models in selection of optimal number of target genes

Background

Multifactor Dimensionality Reduction (MDR) is a popular and successful data mining method developed to characterize and detect nonlinear complex gene-gene interactions (epistasis) that are associated with disease susceptibility. Because MDR uses a combinatorial search strategy to detect interaction, several filtration techniques have been developed to remove genes (SNPs) that have no interactive effects prior to analysis. However, the cutoff values implemented for these filtration methods are arbitrary, therefore different choices of cutoff values will lead to different selections of genes (SNPs).

Methods

We suggest incorporating a global test of p-values to filtration procedures to identify the optimal number of genes/SNPs for further MDR analysis and demonstrate this approach using a ReliefF filter technique. We compare the performance of different global testing procedures in this context, including the Kolmogorov-Smirnov test, the inverse chi-square test, the inverse normal test, the logit test, the Wilcoxon test and Tippett’s test. Additionally we demonstrate the approach on a real data application with a candidate gene study of drug response in Juvenile Idiopathic Arthritis.

Results

Extensive simulation of correlated p-values show that the inverse chi-square test is the most appropriate approach to be incorporated with the screening approach to determine the optimal number of SNPs for the final MDR analysis. The Kolmogorov-Smirnov test has high inflation of Type I errors when p-values are highly correlated or when p-values peak near the center of histogram. Tippett’s test has very low power when the effect size of GxG interactions is small.

Conclusions

The proposed global tests can serve as a screening approach prior to individual tests to prevent false discovery. Strong power in small sample sizes and well controlled Type I error in absence of GxG interactions make global tests highly recommended in epistasis studies.

High-throughput analysis of epistasis in genome-wide association studies with BiForce

MOTIVATION

Gene-gene interactions (epistasis) are thought to be important in shaping complex traits, but they have been under-explored in genome-wide association studies (GWAS) due to the computational challenge of enumerating billions of single nucleotide polymorphism (SNP) combinations. Fast screening tools are needed to make epistasis analysis routinely available in GWAS.

RESULTS

We present BiForce to support high-throughput analysis of epistasis in GWAS for either quantitative or binary disease (case-control) traits. BiForce achieves great computational efficiency by using memory efficient data structures, Boolean bitwise operations and multithreaded parallelization. It performs a full pair-wise genome scan to detect interactions involving SNPs with or without significant marginal effects using appropriate Bonferroni-corrected significance thresholds. We show that BiForce is more powerful and significantly faster than published tools for both binary and quantitative traits in a series of performance tests on simulated and real datasets. We demonstrate BiForce in analysing eight metabolic traits in a GWAS cohort (323 697 SNPs, >4500 individuals) and two disease traits in another (>340 000 SNPs, >1750 cases and 1500 controls) on a 32-node computing cluster. BiForce completed analyses of the eight metabolic traits within 1 day, identified nine epistatic pairs of SNPs in five metabolic traits and 18 SNP pairs in two disease traits. BiForce can make the analysis of epistasis a routine exercise in GWAS and thus improve our understanding of the role of epistasis in the genetic regulation of complex traits.

AVAILABILITY AND IMPLEMENTATION

The software is free and can be downloaded from http://bioinfo.utu.fi/BiForce/.

CONTACT

wenhua.wei@igmm.ed.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

iBIRA – integrated bioinformatics information resource access

Purpose

Bioinformatics is an emerging discipline where the interdisciplinary research holds great promise for the advancement of research and development in many complex areas. The research output generates a huge amount of data and information. Because of the heterogeneous nature of bioinformatics resources, difficulty in accessing pertinent information is the biggest challenge for the bioinformatics community. The integration of bioinformatics resources in a comprehensive manner is advocated by the bioinformatics user community as well as by information scientists serving this community. There are have already been some efforts made for integration of bioinformatics resources by the discrete bioinformatics community, but these are based on the requirement of their own area and arena. This paper aims to discuss the design and development of a tool for the integration of various heterogeneous bioinformatics information resources available over the internet.

Design/methodology/approach

The authors have developed a tool with the acronym “iBIRA” (Integrated Bioinformatics Information Resource Access) that associates the bioinformatics community with the bioinformatics “resourceome” (the term suggested for the “full set of bioinformatics resources” by Cannata et al.). Available over the internet. iBIRA (www.ibiranet.in) integrates bioinformatics resources in a way such that it is possible to locate, connect and communicate different categories of resources in a cohesive manner. A software engineering and database‐driven approach was used for the integration and organization of bioinformatics resources. Computational programming such as Hypertext Preprocessor (PHP), a server‐side dynamic web programming language, and MySQL as a database management system have been used. Dublin Core Metadata Standards have been used for the design of metadata for bioinformatics resources..

Findings

The term “resource” in the area of bioinformatics covers various entities such as journals, molecular biology databases, online annotation tools, patents, published documents (articles, books, etc), protocols, software tools, and web servers. It has been found that bioinformatics resources are heterogeneous in nature and available over the internet in different forms and formats. The fact that bioinformatics resources are scattered over the internet makes resource discovery difficult for the bioinformatics community, and there is need for a system that reorganizes these resources. The integration of all the resources of bioinformatics at a single platform (called “iBIRA”) provides significant “value added” to the bioinformatics community, those serving this population.

Originality/value

The iBIRA tool is a meta‐server developed to provide information service about the availability of various bioinformatics resources to the bioinformatics community. This will provide a value‐added benefit to the population in helping them to locate relevant resources for their education, research and training.

Functional genome annotation of Drosophila seminal fluid proteins using transcriptional genetic networks

Predicting functional gene annotations remains a significant challenge, even in well-annotated genomes such as yeast and Drosophila. One promising, high-throughput method for gene annotation is to use correlated gene expression patterns to annotate target genes based on the known function of focal genes. The Drosophila melanogaster transcriptome varies genetically among wild-derived inbred lines, with strong genetic correlations among the transcripts. Here, we leveraged the genetic correlations in gene expression among known seminal fluid protein (SFP) genes and the rest of the genetically varying transcriptome to identify 176 novel candidate SFPs (cSFPs). We independently validated the correlation in gene expression between seven of the cSFPs and a known SFP gene, as well as expression in male reproductive tissues. We argue that this method can be extended to other systems for which information on genetic variation in gene expression is available.

Pattern-driven neighborhood search for biclustering of microarray data

BACKGROUND

Biclustering aims at finding subgroups of genes that show highly correlated behaviors across a subgroup of conditions. Biclustering is a very useful tool for mining microarray data and has various practical applications. From a computational point of view, biclustering is a highly combinatorial search problem and can be solved with optimization methods.

RESULTS

We describe a stochastic pattern-driven neighborhood search algorithm for the biclustering problem. Starting from an initial bicluster, the proposed method improves progressively the quality of the bicluster by adjusting some genes and conditions. The adjustments are based on the quality of each gene and condition with respect to the bicluster and the initial data matrix. The performance of the method was evaluated on two well-known microarray datasets (Yeast cell cycle and Saccharomyces cerevisiae), showing that it is able to obtain statistically and biologically significant biclusters. The proposed method was also compared with six reference methods from the literature.

CONCLUSIONS

The proposed method is computationally fast and can be applied to discover significant biclusters. It can also used to effectively improve the quality of existing biclusters provided by other biclustering methods.

Empirical evidence of the applicability of functional clustering through gene expression classification

The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene interactions allows us to simplify the analysis of gene expression data to make it more robust, compact, and interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning. Using 10 benchmark data sets, we demonstrate that functional clustering significantly outperforms random clustering without biological relevance. We also show that functional clustering performs comparably to gene expression clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of functional clustering as a feature extraction technique is evaluated and discussed.

Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

Background

Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.

Methods

To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.

Results

Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between AKT3 rs2125230-PRKCQ rs571715 and disease aggressiveness using SEN-guided MDR (p = 0.011).

Conclusions

In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between AKT3-PRKCQ and aggressive PCA requires further validation using independent observational studies.

Functional organization and its implication in evolution of the human protein-protein interaction network

BACKGROUND

Based on the distinguishing properties of protein-protein interaction networks such as power-law degree distribution and modularity structure, several stochastic models for the evolution of these networks have been purposed, motivated by the idea that a validated model should reproduce similar topological properties of the empirical network. However, being able to capture topological properties does not necessarily mean it correctly reproduces how networks emerge and evolve. More importantly, there is already evidence suggesting functional organization and significance of these networks. The current stochastic models of evolution, however, grow the network without consideration for biological function and natural selection.

RESULTS

To test whether protein interaction networks are functionally organized and their impacts on the evolution of these networks, we analyzed their evolution at both the topological and functional level. We find that the human network is shown to be functionally organized, and its function evolves with the topological properties of the network. Our analysis suggests that function most likely affects local modularity of the network. Consistently, we further found that the topological unit is also the functional unit of the network.

CONCLUSION

We have demonstrated functional organization of a protein interaction network. Given our observations, we suggest that its significance should not be overlooked when studying network evolution.

Hsp90 inhibitors and drug resistance in cancer: the potential benefits of combination therapies of Hsp90 inhibitors and other anti-cancer drugs

Hsp90 is a chaperone protein that interacts with client proteins that are known to be in the cell cycle, signaling and chromatin-remodeling pathways. Hsp90 inhibitors act additively or synergistically with many other drugs in the treatment of both solid tumors and leukemias in murine tumor models and humans. Hsp90 inhibitors potentiate the actions of anti-cancer drugs that target Hsp90 client proteins, including trastuzumab (Herceptin™) which targets Her2/Erb2B, as Hsp90 inhibition elicits the drug effects in cancer cell lines that are otherwise resistant to the drug. A phase II study of the Hsp90 inhibitor 17-AAG and trastuzumab showed that this combination therapy has anticancer activity in patients with HER2-positive metastatic breast cancer progressing on trastuzumab. In this review, we discuss the results of Hsp90 inhibitors in combination with trastuzumab and other cancer drugs. We also discuss recent results from yeast focused on the genetics of drug resistance when Hsp90 is inhibited and the implications that this might have in understanding the effects of genetic variation in treating cancer in humans.

Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations

It is well established that autism spectrum disorders (ASD) have a strong genetic component; however, for at least 70% of cases, the underlying genetic cause is unknown. Under the hypothesis that de novo mutations underlie a substantial fraction of the risk for developing ASD in families with no previous history of ASD or related phenotypes--so-called sporadic or simplex families--we sequenced all coding regions of the genome (the exome) for parent-child trios exhibiting sporadic ASD, including 189 new trios and 20 that were previously reported. Additionally, we also sequenced the exomes of 50 unaffected siblings corresponding to these new (n = 31) and previously reported trios (n = 19), for a total of 677 individual exomes from 209 families. Here we show that de novo point mutations are overwhelmingly paternal in origin (4:1 bias) and positively correlated with paternal age, consistent with the modest increased risk for children of older fathers to develop ASD. Moreover, 39% (49 of 126) of the most severe or disruptive de novo mutations map to a highly interconnected β-catenin/chromatin remodelling protein network ranked significantly for autism candidate genes. In proband exomes, recurrent protein-altering mutations were observed in two genes: CHD8 and NTNG1. Mutation screening of six candidate genes in 1,703 ASD probands identified additional de novo, protein-altering mutations in GRIN2B, LAMC3 and SCN1A. Combined with copy number variant (CNV) data, these results indicate extreme locus heterogeneity but also provide a target for future discovery, diagnostics and therapeutics.

The expanding proteome of the molecular chaperone HSP90

The molecular chaperone HSP90 maintains the activity and stability of a diverse set of "client" proteins that play key roles in normal and disease biology. Around 20 HSP90 inhibitors that deplete the oncogenic clientele have entered clinical trials for cancer. However, the full extent of the HSP90-dependent proteome, which encompasses not only clients but also proteins modulated by downstream transcriptional responses, is still incompletely characterized and poorly understood. Earlier large-scale efforts to define the HSP90 proteome have been valuable but are incomplete because of limited technical sensitivity. Here we discuss previous large-scale surveys of proteome perturbations induced by HSP90 inhibitors in light of a significant new study using state-of-the-art SILAC technology combined with more sensitive high-resolution mass spectrometry (MS) that extends the catalog of proteomic changes in inhibitor-treated cancer cells. Among wide-ranging changes, major functional responses include downregulation of protein kinase activity and the DNA damage response alongside upregulation of the protein degradation machinery. Despite this improved proteomic coverage, there was surprisingly little overlap with previous studies. This may be due in part to technical issues but is likely also due to the variability of the HSP90 proteome with the inhibitor conditions used, the cancer cell type and the genetic status of client proteins. We suggest future proteomic studies to address these factors, to help distinguish client protein components from indirect transcriptional components and to address other key questions in fundamental and translational HSP90 research. Such studies should also reveal new biomarkers for patient selection and novel targets for therapeutic intervention.

Identification of an Hsp90 mutation that selectively disrupts cAMP/PKA signaling in Saccharomyces cerevisiae

The molecular chaperone Hsp90 cooperates with multiple cochaperone proteins as it promotes the folding and activation of diverse client proteins. Some cochaperones regulate the ATPase activity of Hsp90, while others appear to promote Hsp90 interaction with specific types of client proteins. Through its interaction with the adenylate cyclase Cyr1, the Sgt1 cochaperone modulates the activity of the cAMP pathway in Saccharomyces cerevisiae. A specific mutation in yeast Hsp90, hsc82-W296A, or a mutation in Sgt1, sgt1-K360E, resulted in altered transcription patterns genetically linked to the cAMP pathway. Hsp90 interacted with Cyr1 in vivo and the hsc82-W296A mutation resulted in reduced accumulation of Cyr1. Hsp90-Sgt1 interaction was altered by either the hsc82-W296A or sgt1-K360E mutation, suggesting defective Hsp90-Sgt1 cooperation leads to reduced Cyr1 activity. Microarray analysis of hsc82-W296A cells indicated that over 80 % of all transcriptional changes in this strain may be attributed to altered cAMP signaling. This suggests that a majority of the cellular defects observed in hsc82-W296A cells are due to altered interaction with one specific essential cochaperone, Sgt1 and one essential client, Cyr1. Together our results indicate that specific interaction of Hsp90 and Sgt1 with Cyr1 plays a key role in regulating gene expression, including genes involved in polarized morphogenesis.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

BACKGROUND

Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure.

METHODS

We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape.

RESULTS

The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure.

CONCLUSIONS

By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

3'-Phosphoadenosine 5'-phosphosulfate (PAPS) synthases, naturally fragile enzymes specifically stabilized by nucleotide binding

Activated sulfate in the form of 3′-phosphoadenosine 5′-phosphosulfate (PAPS) is needed for all sulfation reactions in eukaryotes with implications for the build-up of extracellular matrices, retroviral infection, protein modification, and steroid metabolism. In metazoans, PAPS is produced by bifunctional PAPS synthases (PAPSS). A major question in the field is why two human protein isoforms, PAPSS1 and -S2, are required that cannot complement for each other. We provide evidence that these two proteins differ markedly in their stability as observed by unfolding monitored by intrinsic tryptophan fluorescence as well as circular dichroism spectroscopy. At 37 °C, the half-life for unfolding of PAPSS2 is in the range of minutes, whereas PAPSS1 remains structurally intact. In the presence of their natural ligand, the nucleotide adenosine 5′-phosphosulfate (APS), PAPS synthase proteins are stabilized. Invertebrates only possess one PAPS synthase enzyme that we classified as PAPSS2-type by sequence-based machine learning techniques. To test this prediction, we cloned and expressed the PPS-1 protein from the roundworm Caenorhabditis elegans and also subjected this protein to thermal unfolding. With respect to thermal unfolding and the stabilization by APS, PPS-1 behaved like the unstable human PAPSS2 protein suggesting that the less stable protein is evolutionarily older. Finally, APS binding more than doubled the half-life for unfolding of PAPSS2 at physiological temperatures and effectively prevented its aggregation on a time scale of days. We propose that protein stability is a major contributing factor for PAPS availability that has not as yet been considered. Moreover, naturally occurring changes in APS concentrations may be sensed by changes in the conformation of PAPSS2.

Topological analysis and interactive visualization of biological networks and protein structures

Computational analysis and interactive visualization of biological networks and protein structures are common tasks for gaining insight into biological processes. This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks. NetworkAnalyzer has become a standard Cytoscape tool for comprehensive network topology analysis. In addition, RINalyzer provides methods for exploring residue interaction networks derived from protein structures. The first workflow uses NetworkAnalyzer to perform a topological analysis of biological networks. The second workflow applies RINalyzer to study protein structure and function and to compute network centrality measures. The third workflow combines NetworkAnalyzer and RINalyzer to compare residue networks. The full protocol can be completed in ∼2 h.