International Union of Basic and Clinical Pharmacology. LXXXV: calcium-activated chloride channels

Calcium-activated chloride channels (CaCCs) are widely expressed in various tissues and implicated in physiological processes such as sensory transduction, epithelial secretion, and smooth muscle contraction. Transmembrane proteins with unknown function 16 (TMEM16A) has recently been identified as a major component of CaCCs. Detailed molecular analysis of TMEM16A will be needed to understand its structure-function relationships. The role this channel plays in physiological systems remains to be established and is currently a subject of intense investigation.

Time-course network analysis reveals TNF-α can promote G1/S transition of cell cycle in vascular endothelial cells

MOTIVATION

Tumor necrosis factor-alpha (TNF-α), a major inflammatory cytokine, is closely related to several cardiovascular pathological processes. However, its effects on the cell cycle of vascular endothelial cells (VECs) have been the subject of some controversy. To investigate the molecular mechanism underlying this process, we constructed time-course protein-protein interaction (PPI) networks of TNF-α induced regulation of cell cycle in VECs using microarray datasets and genome-wide PPI datasets. Then, we analyzed the topological properties of the responsive PPI networks and calculated the node degree and node betweenness centralization of each gene in the networks. We found that p21, p27 and cyclinD1, key genes of the G1/S checkpoint, are in the center of responsive PPI networks and their roles in PPI networks are significantly altered with induction of TNF-α. According to the following biological experiments, we proved that TNF-α can promote G(1)/S transition of cell cycle in VECs and facilitate the cell cycle activation induced by vascular endothelial growth factor.

CONTACT

shaoli@mail.tsinghua.edu.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Modeling metabolic networks for mammalian cell systems: general considerations, modeling strategies, and available tools

Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks. Analysis and simulations are used to test our knowledge by refuting or validating new hypotheses regarding a complex system, which can result in predictive capabilities that lead to better experimental design. Different types of models can be used for this purpose, depending on the type and amount of information available for the specific system. Stoichiometric models are based on the metabolic structure of the system and allow explorations of steady state distributions in the network. Detailed kinetic models provide a description of the dynamics of the system, they involve a large number of reactions with varied kinetic characteristics and require a large number of parameters. Models based on statistical information provide a description of the system without information regarding structure and interactions of the networks involved. The development of detailed models for mammalian cell metabolism has only recently started to grow more strongly, due to the intrinsic complexities of mammalian systems, and the limited availability of experimental information and adequate modeling tools. In this work we review the strategies, tools, current advances, and recent models of mammalian cells, focusing mainly on metabolism, but discussing the methodology applied to other types of networks as well.

Visual data mining of coexpression data to set research priorities in cardiac development research

Over the past decade, an immense amount of biomedical data have become available in the public domain due to the development of ever-more efficient screening tools such as expression microarrays. To fully leverage this important new resource, it has become imperative to develop new methodologies for mining and visualizing data to make inferences beyond the scope of the original experiments. This need motivated the development of a new freely available web-based application called StarNet ( http://vanburenlab.medicine.tamhsc.edu/starnet2.html ). Here we describe the use of StarNet, which functions primarily as a query tool that draws correlation networks centered about a gene of interest. To support inferences and the development of new hypotheses using the resulting correlation network, StarNet queries all genes in the correlation network against a database of known interactions and displays the results in a second graph and provides a statistical test of Gene Ontology term enrichment (keyword enrichment) to provide tentative summary functional annotations for the correlation network. Finally, StarNet provides additional tools for comparing networks drawn from two different selected data sets, thus providing methods for making inferences and developing new hypotheses about differential wiring for different regulatory domains.

Minimotif Miner 3.0: database expansion and significantly improved reduction of false-positive predictions from consensus sequences

Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300,000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.

Transcriptomic Profiling of Medial Temporal Lobe Epilepsy

Epilepsy is one of the most prevalent neurological disorders affecting ~1% of the population. Medial temporal lobe epilepsy (MTLE) is the most frequent type of epilepsy observed in adults who do not respond to pharmacological treatment. The reason for intractability in these patients has not been systematically studied. Further, no markers are available that can predict the subset of patients who will not respond to pharmacotherapy. To identify potential biomarkers of epileptogenicity, we compared the mRNA profiles of surgically resected tissue from seizure zones with non-seizure zones from cases of intractable MTLE. We identified 413 genes that exhibited ≥2-fold change that were statistically significant across these two groups. Several of these differentially expressed genes have not been previously described in the context of MTLE including claudin 11 (CLDN11) and bone morphogenetic protein receptor, type IB (BMPR1B). In addition, we found significant downregulation of a subset of gamma-aminobutyric acid (GABA) associated genes. We also identified molecules such as BACH2 and ADAMTS15, which are already known to be associated with epilepsy. We validated one upregulated molecule, serine/threonine kinase 31 (STK31) and one downregulated molecule, SMARCA4, by immunohistochemical labeling of tissue sections. These molecules need to be further confirmed in large-scale studies to determine their potential use as diagnostic as well as prognostic markers in intractable MTLE.

A novel survival-based tissue microarray of pancreatic cancer validates MUC1 and mesothelin as biomarkers

BACKGROUND

One-fifth of patients with seemingly 'curable' pancreatic ductal adenocarcinoma (PDA) experience an early recurrence and death, receiving no definable benefit from a major operation. Some patients with advanced stage tumors are deemed 'unresectable' by conventional staging criteria (e.g. liver metastasis), yet progress slowly. Effective biomarkers that stratify PDA based on biologic behavior are needed. To help researchers sort through the maze of biomarker data, a compendium of ∼2500 published candidate biomarkers in PDA was compiled (PLoS Med, 2009. 6(4) p. e1000046).

METHODS AND FINDINGS

Building on this compendium, we constructed a survival tissue microarray (termed s-TMA) comprised of short-term (cancer-specific death <12 months, n = 58) and long-term survivors (>30 months, n = 79) who underwent resection for PDA (total, n = 137). The s-TMA functions as a biological filter to identify bona fide prognostic markers associated with survival group extremes (at least 18 months separate survival groups). Based on a stringent selection process, 13 putative PDA biomarkers were identified from the public biomarker repository. Candidates were tested against the s-TMA by immunohistochemistry to identify the best markers of tumor biology. In a multivariate model, MUC1 (odds ratio, OR = 28.95, 3+ vs. negative expression, p = 0.004) and MSLN (OR = 12.47, 3+ vs. negative expression, p = 0.01) were highly predictive of early cancer-specific death. By comparison, pathologic factors (size, lymph node metastases, resection margin status, and grade) had ORs below three, and none reached statistical significance. ROC curves were used to compare the four pathologic prognostic features (ROC area = 0.70) to three univariate molecular predictors (MUC1, MSLN, MUC2) of survival group (ROC area = 0.80, p = 0.07).

CONCLUSIONS

MUC1 and MSLN were superior to pathologic features and other putative biomarkers as predicting survival group. Molecular assays comparing cancers from short and long survivors are an effective strategy to screen biomarkers and prioritize candidate cancer genes for diagnostic and therapeutic studies.

Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets

Signaling and transcription are tightly integrated processes that underlie many cellular responses to the environment. A network of signaling events, often mediated by post-translational modification on proteins, can lead to long-term changes in cellular behavior by altering the activity of specific transcriptional regulators and consequently the expression level of their downstream targets. As many high-throughput, "-omics" methods are now available that can simultaneously measure changes in hundreds of proteins and thousands of transcripts, it should be possible to systematically reconstruct cellular responses to perturbations in order to discover previously unrecognized signaling pathways. This chapter describes a computational method for discovering such pathways that aims to compensate for the varying levels of noise present in these diverse data sources. Based on the concept of constraint optimization on networks, the method seeks to achieve two conflicting aims: (1) to link together many of the signaling proteins and differentially expressed transcripts identified in the experiments "constraints" using previously reported protein-protein and protein-DNA interactions, while (2) keeping the resulting network small and ensuring it is composed of the highest confidence interactions "optimization". A further distinctive feature of this approach is the use of transcriptional data as evidence of upstream signaling events that drive changes in gene expression, rather than as proxies for downstream changes in the levels of the encoded proteins. We recently demonstrated that by applying this method to phosphoproteomic and transcriptional data from the pheromone response in yeast, we were able to recover functionally coherent pathways and to reveal many components of the cellular response that are not readily apparent in the original data. Here, we provide a more detailed description of the method, explore the robustness of the solution to the noise level of input data and discuss the effect of parameter values.

Bayesian Joint Modeling of Multiple Gene Networks and Diverse Genomic Data to Identify Target Genes of a Transcription Factor

We consider integrative modeling of multiple gene networks and diverse genomic data, including protein-DNA binding, gene expression and DNA sequence data, to accurately identify the regulatory target genes of a transcription factor (TF). Rather than treating all the genes equally and independently a priori in existing joint modeling approaches, we incorporate the biological prior knowledge that neighboring genes on a gene network tend to be (or not to be) regulated together by a TF. A key contribution of our work is that, to maximize the use of all existing biological knowledge, we allow incorporation of multiple gene networks into joint modeling of genomic data by introducing a mixture model based on the use of multiple Markov random fields (MRFs). Another important contribution of our work is to allow different genomic data to be correlated and to examine the validity and effect of the independence assumption as adopted in existing methods. Due to a fully Bayesian approach, inference about model parameters can be carried out based on MCMC samples. Application to an E. coli data set, together with simulation studies, demonstrates the utility and statistical efficiency gains with the proposed joint model.

Network of Cancer Genes (NCG 3.0): integration and analysis of genetic and network properties of cancer genes

The identification of a constantly increasing number of genes whose mutations are causally implicated in tumor initiation and progression (cancer genes) requires the development of tools to store and analyze them. The Network of Cancer Genes (NCG 3.0) collects information on 1494 cancer genes that have been found mutated in 16 different cancer types. These genes were collected from the Cancer Gene Census as well as from 18 whole exome and 11 whole-genome screenings of cancer samples. For each cancer gene, NCG 3.0 provides a summary of the gene features and the cross-reference to other databases. In addition, it describes duplicability, evolutionary origin, orthology, network properties, interaction partners, microRNA regulation and functional roles of cancer genes and of all genes that are related to them. This integrated network of information can be used to better characterize cancer genes in the context of the system in which they act. The data can also be used to identify novel candidates that share the same properties of known cancer genes and may therefore play a similar role in cancer. NCG 3.0 is freely available at http://bio.ifom-ieo-campus.it/ncg.

Invasive markers identified by gene expression profiling in pancreatic cancer

BACKGROUND

Molecular profiling has proven utility as a diagnostic and predictive tool in clinical oncology. However, a clinically relevant gene expression profile in pancreatic cancer remains elusive.

METHODS

Primary and metastatic pancreatic cancer cell lines (BxPC-3 and AsPC-1), were stimulated with phorbol-12-myristate 13-acetate (PMA), a known inducer of cell invasion. Affymetrix gene expression microarray analysis was performed, comparing gene expression to unstimulated controls. Differential expression was identified using ArrayAssist, and confirmed using quantitative real-time PCR. Bioinformatic analysis was performed using Pathway Studio and GOstat. The derived gene expression was further validated in fresh frozen pancreatic tumour samples. The ability of the derived 3 gene expression markersto differentiate between pancreatic adenocarcinoma (PDAC) and other neoplasms, and its association with clinicopathological variables was examined.

RESULTS

PMA-induced significant changes in cell line gene expression, from which distinctive 3 potential invasive markers were derived. Expression of these genes, uPA, MMP-1 and IL1-R1 was confirmed in human pancreatic tumours, and was found to differentiate PDAC from other pancreatic neoplasms. The expression of IL1-R1 in PDAC is a novel finding. We found that the expression of MMP-1 was associated with high-grade PDAC (p = 0.035, Wilcoxon rank sum).

CONCLUSION

We have identified three potential invasive markers, uPA, MMP-1 and IL1-R1, whose gene expression may differentiate PDAC from other pancreatic neoplasms, and potentially reflect a more invasive phenotype.

Hypotensive effect of atorvastatin in hypertensive patients: the association among flow-mediated dilation, oxidative stress and endothelial dysfunction

INTRODUCTION

To investigate the hypothesis that atorvastatin decreases blood pressure (BP) values and improves endothelial function assessed by flow-mediated dilation (FMD) in normolipidaemic hypertensive patients.

MATERIAL AND METHODS

Fifty-six hypertensive patients were randomized in a 2 : 1 proportion to atorvastatin (80 mg/day/3 months; group A; n = 39) or previous standard anti-hypertensive therapy (group B), which means the patients were treated with angiotensin-converting enzyme inhibitors, diuretics, β-blockers, calcium antagonists and angiotensin receptor blockers. The study had a crossover design: after 3 months, both groups were changed (group A* stopped and group B* started atorvastatin treatment). Nitric oxide (NO), total antioxidant status (TAS), endothelin-1 (ET-1), and peroxide concentrations as well as FMD were measured before, after 3 and after 6 months of treatment. Atorvastatin added to existing treatment decreased BP in both groups.

RESULTS

Flow-mediated dilation improved in both statin-treated groups, but only significantly in group B* (from 11.9 ±8.3% to 22.1 ±9.0%; p < 0.05). In patients with FMD improvement, there was a greater BP reduction. After treatment discontinuation, FMD significantly decreased (from 19.6 ±12.6% to 13.0 ±10.5%; p < 0.05), which was consistent with BP increase. Changes in FMD were not significantly related to the increase in NO and TAS concentrations and decrease in ET-1 and peroxides measurements.

CONCLUSIONS

The hypotensive effect of atorvastatin is associated with FMD improvement in normolipidaemic, hypertensive patients. Although this could be related to changes in oxidative stress and endothelial function, this was not demonstrated in this study and warrants further investigation.

MotifMap: integrative genome-wide maps of regulatory motif sites for model species

Background

A central challenge of biology is to map and understand gene regulation on a genome-wide scale. For any given genome, only a small fraction of the regulatory elements embedded in the DNA sequence have been characterized, and there is great interest in developing computational methods to systematically map all these elements and understand their relationships. Such computational efforts, however, are significantly hindered by the overwhelming size of non-coding regions and the statistical variability and complex spatial organizations of regulatory elements and interactions. Genome-wide catalogs of regulatory elements for all model species simply do not yet exist.

Results

The MotifMap system uses databases of transcription factor binding motifs, refined genome alignments, and a comparative genomic statistical approach to provide comprehensive maps of candidate regulatory elements encoded in the genomes of model species. The system is used to derive new genome-wide maps for yeast, fly, worm, mouse, and human. The human map contains 519,108 sites for 570 matrices with a False Discovery Rate of 0.1 or less. The new maps are assessed in several ways, for instance using high-throughput experimental ChIP-seq data and AUC statistics, providing strong evidence for their accuracy and coverage. The maps can be usefully integrated with many other kinds of omic data and are available at http://motifmap.igb.uci.edu/.

Conclusions

MotifMap and its integration with other data provide a foundation for analyzing gene regulation on a genome-wide scale, and for automatically generating regulatory pathways and hypotheses. The power of this approach is demonstrated and discussed using the P53 apoptotic pathway and the Gli hedgehog pathways as examples.

Recovering protein-protein and domain-domain interactions from aggregation of IP-MS proteomics of coregulator complexes

Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/.

Biological sequence motif discovery using motif-x

The Web-based motif-x program provides a simple interface to extract statistically significant motifs from large data sets, such as MS/MS post-translational modification data and groups of proteins that share a common biological function. Users upload data files and download results using common Web browsers on essentially any Web-compatible computer. Once submitted, data analyses are performed rapidly on an associated high-speed computer cluster and they produce both syntactic and image-based motif results and statistics. The protocols presented demonstrate the use of motif-x in three common user scenarios.

Evolution of biological interaction networks: from models to real data

We are beginning to uncover common mechanisms leading to the evolution of biological networks. The driving force behind these advances is the increasing availability of comparative data in several species.

Integration of breast cancer gene signatures based on graph centrality

BACKGROUND

Various gene-expression signatures for breast cancer are available for the prediction of clinical outcome. However due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene expression and clinical outcome.

RESULTS

In this paper, we propose a method to integrate different breast cancer gene signatures by using graph centrality in a context-constrained protein interaction network (PIN). The context-constrained PIN for breast cancer is built by integrating complete PIN and various gene signatures reported in literatures. Then, we use graph centralities to quantify the importance of genes to breast cancer. Finally, we get reliable gene signatures that are consisted by the genes with high graph centrality. The genes which are well-known breast cancer genes, such as TP53 and BRCA1, are ranked extremely high in our results. Compared with previous results by functional enrichment analysis, graph centralities, especially the eigenvector centrality and subgraph centrality, based gene signatures are more tightly related to breast cancer. We validate these signatures on genome-wide microarray dataset and found strong association between the expression of these signature genes and pathologic parameters.

CONCLUSIONS

In summary, graph centralities provide a novel way to connect different cancer signatures and to understand the mechanism of relationship between gene expression and clinical outcome of breast cancer. Moreover, this method is not only can be used on breast cancer, but also can be used on other gene expression related diseases and drug studies.

Which clustering algorithm is better for predicting protein complexes?

Background

Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.

Results

In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.

Conclusions

While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm

The reconstruction and analysis of tissue specific human metabolic networks

Human tissues have distinct biological functions. Many proteins/enzymes are known to be expressed only in specific tissues and therefore the metabolic networks in various tissues are different. Though high quality global human metabolic networks and metabolic networks for certain tissues such as liver have already been studied, a systematic study of tissue specific metabolic networks for all main tissues is still missing. In this work, we reconstruct the tissue specific metabolic networks for 15 main tissues in human based on the previously reconstructed Edinburgh Human Metabolic Network (EHMN). The tissue information is firstly obtained for enzymes from Human Protein Reference Database (HPRD) and UniprotKB databases and transfers to reactions through the enzyme-reaction relationships in EHMN. As our knowledge of tissue distribution of proteins is still very limited, we replenish the tissue information of the metabolic network based on network connectivity analysis and thorough examination of the literature. Finally, about 80% of proteins and reactions in EHMN are determined to be in at least one of the 15 tissues. To validate the quality of the tissue specific network, the brain specific metabolic network is taken as an example for functional module analysis and the results reveal that the function of the brain metabolic network is closely related with its function as the centre of the human nervous system. The tissue specific human metabolic networks are available at .

An overview of human protein databases and their application to functional proteomics in health and disease

Functional proteomics can be defined as a strategy to couple proteomic information with biochemical and physiological analyses with the aim of understanding better the functions of proteins in normal and diseased organs. In recent years, a variety of publicly available bioinformatics databases have been developed to support protein-related information management and biological knowledge discovery. In addition to being used to annotate the proteome, these resources also offer the opportunity to develop global approaches to the study of the functional role of proteins both in health and disease. Here, we present a comprehensive review of the major human protein bioinformatics databases. We conclude this review by discussing a few examples that illustrate the importance of these databases in functional proteomics research.

Novel search method for the discovery of functional relationships

Motivation: Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account.

Results: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.

Contact:mario.albrecht@mpi-inf.mpg.de

Supplementary Information:Supplementary data are available at Bioinformatics online.

Constructing a gene semantic similarity network for the inference of disease genes

Motivation

The inference of genes that are truly associated with inherited human diseases from a set of candidates resulting from genetic linkage studies has been one of the most challenging tasks in human genetics. Although several computational approaches have been proposed to prioritize candidate genes relying on protein-protein interaction (PPI) networks, these methods can usually cover less than half of known human genes.

Results

We propose to rely on the biological process domain of the gene ontology to construct a gene semantic similarity network and then use the network to infer disease genes. We show that the constructed network covers about 50% more genes than a typical PPI network. By analyzing the gene semantic similarity network with the PPI network, we show that gene pairs tend to have higher semantic similarity scores if the corresponding proteins are closer to each other in the PPI network. By analyzing the gene semantic similarity network with a phenotype similarity network, we show that semantic similarity scores of genes associated with similar diseases are significantly different from those of genes selected at random, and that genes with higher semantic similarity scores tend to be associated with diseases with higher phenotype similarity scores. We further use the gene semantic similarity network with a random walk with restart model to infer disease genes. Through a series of large-scale leave-one-out cross-validation experiments, we show that the gene semantic similarity network can achieve not only higher coverage but also higher accuracy than the PPI network in the inference of disease genes.

Contact

ruijiang@tsinghua.edu.cn

Differentially co-expressed interacting protein pairs discriminate samples under distinct stages of HIV type 1 infection

Background

Microarray analyses based on differentially expressed genes (DEGs) have been widely used to distinguish samples across different cellular conditions. However, studies based on DEGs have not been able to clearly determine significant differences between samples of pathophysiologically similar HIV-1 stages, e.g., between acute and chronic progressive (or AIDS) or between uninfected and clinically latent stages. We here suggest a novel approach to allow such discrimination based on stage-specific genetic features of HIV-1 infection. Our approach is based on co-expression changes of genes known to interact. The method can identify a genetic signature for a single sample as contrasted with existing protein-protein-based analyses with correlational designs.

Methods

Our approach distinguishes each sample using differentially co-expressed interacting protein pairs (DEPs) based on co-expression scores of individual interacting pairs within a sample. The co-expression score has positive value if two genes in a sample are simultaneously up-regulated or down-regulated. And the score has higher absolute value if expression-changing ratios are similar between the two genes. We compared characteristics of DEPs with that of DEGs by evaluating their usefulness in separation of HIV-1 stage. And we identified DEP-based network-modules and their gene-ontology enrichment to find out the HIV-1 stage-specific gene signature.

Results

Based on the DEP approach, we observed clear separation among samples from distinct HIV-1 stages using clustering and principal component analyses. Moreover, the discrimination power of DEPs on the samples (70–100% accuracy) was much higher than that of DEGs (35–45%) using several well-known classifiers. DEP-based network analysis also revealed the HIV-1 stage-specific network modules; the main biological processes were related to “translation,” “RNA splicing,” “mRNA, RNA, and nucleic acid transport,” and “DNA metabolism.” Through the HIV-1 stage-related modules, changing stage-specific patterns of protein interactions could be observed.

Conclusions

DEP-based method discriminated the HIV-1 infection stages clearly, and revealed a HIV-1 stage-specific gene signature. The proposed DEP-based method might complement existing DEG-based approaches in various microarray expression analyses.

SigCS base: an integrated genetic information resource for human cerebral stroke

Background

To understand how stroke risk factors mechanistically contribute to stroke, the genetic components regulating each risk factor need to be integrated and evaluated with respect to biological function and through pathway-based algorithms. This resource will provide information to researchers studying the molecular and genetic causes of stroke in terms of genomic variants, genes, and pathways.

Methods

Reported genetic variants, gene structure, phenotypes, and literature information regarding stroke were collected and extracted from publicly available databases describing variants, genome, proteome, functional annotation, and disease subtypes. Stroke related candidate pathways and etiologic genes that participate significantly in risk were analyzed in terms of canonical pathways in public biological pathway databases. These efforts resulted in a relational database of genetic signals of cerebral stroke, SigCS base, which implements an effective web retrieval system.

Results

The current version of SigCS base documents 1943 non-redundant genes with 11472 genetic variants and 165 non-redundant pathways. The web retrieval system of SigCS base consists of two principal search flows, including: 1) a gene-based variant search using gene table browsing or a keyword search, and, 2) a pathway-based variant search using pathway table browsing. SigCS base is freely accessible at http://sysbio.kribb.re.kr/sigcs.

Conclusions

SigCS base is an effective tool that can assist researchers in the identification of the genetic factors associated with stroke by utilizing existing literature information, selecting candidate genes and variants for experimental studies, and examining the pathways that contribute to the pathophysiological mechanisms of stroke.

AutismKB: an evidence-based knowledgebase of autism genetics

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder with a prevalence of 0.9–2.6%. Twin studies showed a heritability of 38–90%, indicating strong genetic contributions. Yet it is unclear how many genes have been associated with ASD and how strong the evidence is. A comprehensive review and analysis of literature and data may bring a clearer big picture of autism genetics. We show that as many as 2193 genes, 2806 SNPs/VNTRs, 4544 copy number variations (CNVs) and 158 linkage regions have been associated with ASD by GWAS, genome-wide CNV studies, linkage analyses, low-scale genetic association studies, expression profiling and other low-scale experimental studies. To evaluate the evidence, we collected metadata about each study including clinical and demographic features, experimental design and statistical significance, and used a scoring and ranking approach to select a core data set of 434 high-confidence genes. The genes mapped to pathways including neuroactive ligand–receptor interaction, synapse transmission and axon guidance. To better understand the genes we parsed over 30 databases to retrieve extensive data about expression patterns, protein interactions, animal models and pharmacogenetics. We constructed a MySQL-based online database and share it with the broader autism research community at http://autismkb.cbi.pku.edu.cn, supporting sophisticated browsing and searching functionalities.

Beyond salt: lifestyle modifications and blood pressure

Lifestyle changes have been shown to effect significant blood pressure (BP) reductions. Although there are several proposed neurohormonal links between weight loss and BP, body mass index itself appears to be the most powerful mediator of the weight-BP relationship. There appears to be a mostly linear relationship between weight and BP; as weight is regained, the BP benefit is mostly lost. Physical activity, but more so physical fitness (the physiological benefit obtained from physical activity), has a dose-dependent BP benefit but reaches a plateau at which there is no further benefit. However, even just a modest physical activity can have a meaningful BP effect. A diet rich in fruits and vegetables with low-fat dairy products and low in saturated and total fat (DASH) is independently effective in reducing BP. Of the dietary mineral nutrients, the strongest data exist for increased potassium intake, which reduces BP and stroke risk. Vitamin D is associated with BP benefit, but no causal relationship has been established. Flavonoids such as those found in cocoa and berries may have a modest BP benefit. Neither caffeine nor nicotine has any significant, lasting BP effect. Biofeedback therapies such as those obtained with device-guided breathing have a modest and safe BP benefit; more research is needed before such therapies move beyond those having an adjunctive treatment role. There is a strong, linear relationship between alcohol intake and BP; however, the alcohol effects on BP and coronary heart disease are divergent. The greatest BP benefit seems to be obtained with one drink per day for women and with two per day for men. This benefit is lost or attenuated if the drinking occurs in a binge form or without food. Overall, the greatest and most sustained BP benefit is obtained when multiple lifestyle interventions are incorporated simultaneously.

Proteomic analysis of Col11a1-associated protein complexes

Cartilage plays an essential role during skeletal development within the growth plate and in articular joint function. Interactions between the collagen fibrils and other extracellular matrix molecules maintain structural integrity of cartilage, orchestrate complex dynamic events during embryonic development, and help to regulate fibrillogenesis. To increase our understanding of these events, affinity chromatography and liquid chromatography/tandem mass spectrometry were used to identify proteins that interact with the collagen fibril surface via the amino terminal domain of collagen α1(XI) a protein domain that is displayed at the surface of heterotypic collagen fibrils of cartilage. Proteins extracted from fetal bovine cartilage using homogenization in high ionic strength buffer were selected based on affinity for the amino terminal noncollagenous domain of collagen α1(XI). MS was used to determine the amino acid sequence of tryptic fragments for protein identification. Extracellular matrix molecules and cellular proteins that were identified as interacting with the amino terminal domain of collagen α1(XI) directly or indirectly, included proteoglycans, collagens, and matricellular molecules, some of which also play a role in fibrillogenesis, while others are known to function in the maintenance of tissue integrity. Characterization of these molecular interactions will provide a more thorough understanding of how the extracellular matrix molecules of cartilage interact and what role collagen XI plays in the process of fibrillogenesis and maintenance of tissue integrity. Such information will aid tissue engineering and cartilage regeneration efforts to treat cartilage tissue damage and degeneration.

PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse

PhosphoSitePlus (http://www.phosphosite.org) is an open, comprehensive, manually curated and interactive resource for studying experimentally observed post-translational modifications, primarily of human and mouse proteins. It encompasses 1 30 000 non-redundant modification sites, primarily phosphorylation, ubiquitinylation and acetylation. The interface is designed for clarity and ease of navigation. From the home page, users can launch simple or complex searches and browse high-throughput data sets by disease, tissue or cell line. Searches can be restricted by specific treatments, protein types, domains, cellular components, disease, cell types, cell lines, tissue and sequences or motifs. A few clicks of the mouse will take users to substrate pages or protein pages with sites, sequences, domain diagrams and molecular visualization of side-chains known to be modified; to site pages with information about how the modified site relates to the functions of specific proteins and cellular processes and to curated information pages summarizing the details from one record. PyMOL and Chimera scripts that colorize reactive groups on residues that are modified can be downloaded. Features designed to facilitate proteomic analyses include downloads of modification sites, kinase–substrate data sets, sequence logo generators, a Cytoscape plugin and BioPAX download to enable pathway visualization of the kinase–substrate interactions in PhosphoSitePlus®.

The extended granin family: structure, function, and biomedical implications

The chromogranins (chromogranin A and chromogranin B), secretogranins (secretogranin II and secretogranin III), and additional related proteins (7B2, NESP55, proSAAS, and VGF) that together comprise the granin family subserve essential roles in the regulated secretory pathway that is responsible for controlled delivery of peptides, hormones, neurotransmitters, and growth factors. Here we review the structure and function of granins and granin-derived peptides and expansive new genetic evidence, including recent single-nucleotide polymorphism mapping, genomic sequence comparisons, and analysis of transgenic and knockout mice, which together support an important and evolutionarily conserved role for these proteins in large dense-core vesicle biogenesis and regulated secretion. Recent data further indicate that their processed peptides function prominently in metabolic and glucose homeostasis, emotional behavior, pain pathways, and blood pressure modulation, suggesting future utility of granins and granin-derived peptides as novel disease biomarkers.

Gene3D: a domain-based resource for comparative genomics, functional annotation and protein network analysis

Gene3D http://gene3d.biochem.ucl.ac.uk is a comprehensive database of protein domain assignments for sequences from the major sequence databases. Domains are directly mapped from structures in the CATH database or predicted using a library of representative profile HMMs derived from CATH superfamilies. As previously described, Gene3D integrates many other protein family and function databases. These facilitate complex associations of molecular function, structure and evolution. Gene3D now includes a domain functional family (FunFam) level below the homologous superfamily level assignments. Additions have also been made to the interaction data. More significantly, to help with the visualization and interpretation of multi-genome scale data sets, we have developed a new, revamped website. Searching has been simplified with more sophisticated filtering of results, along with new tools based on Cytoscape Web, for visualizing protein–protein interaction networks, differences in domain composition between genomes and the taxonomic distribution of individual superfamilies.

Prioritizing disease candidate genes by a gene interconnectedness-based approach

Background

Genome-wide disease-gene finding approaches may sometimes provide us with a long list of candidate genes. Since using pure experimental approaches to verify all candidates could be expensive, a number of network-based methods have been developed to prioritize candidates. Such tools usually have a set of parameters pre-trained using available network data. This means that re-training network-based tools may be required when existing biological networks are updated or when networks from different sources are to be tried.

Results

We developed a parameter-free method, interconnectedness (ICN), to rank candidate genes by assessing the closeness of them to known disease genes in a network. ICN was tested using 1,993 known disease-gene associations and achieved a success rate of ~44% using a protein-protein interaction network under a test scenario of simulated linkage analysis. This performance is comparable with those of other well-known methods and ICN outperforms other methods when a candidate disease gene is not directly linked to known disease genes in a network. Interestingly, we show that a combined scoring strategy could enable ICN to achieve an even better performance (~50%) than other methods used alone.

Conclusions

ICN, a user-friendly method, can well complement other network-based methods in the context of prioritizing candidate disease genes.

Construction and analysis of the protein-protein interaction networks for schizophrenia, bipolar disorder, and major depression

BACKGROUND

Schizophrenia, bipolar disorder, and major depression are devastating mental diseases, each with distinctive yet overlapping epidemiologic characteristics. Microarray and proteomics data have revealed genes which expressed abnormally in patients. Several single nucleotide polymorphisms (SNPs) and mutations are associated with one or more of the three diseases. Nevertheless, there are few studies on the interactions among the disease-associated genes and proteins.

RESULTS

This study, for the first time, incorporated microarray and protein-protein interaction (PPI) databases to construct the PPI network of abnormally expressed genes in postmortem brain samples of schizophrenia, bipolar disorder, and major depression patients. The samples were collected from Brodmann area (BA) 10 of the prefrontal cortex. Abnormally expressed disease genes were selected by t-tests comparing the disease and control samples. These genes were involved in housekeeping functions (e.g. translation, transcription, energy conversion, and metabolism), in brain specific functions (e.g. signal transduction, neuron cell differentiation, and cytoskeleton), or in stress responses (e.g. heat shocks and biotic stress).The diseases were interconnected through several "switchboard"-like nodes in the PPI network or shared abnormally expressed genes. A "core" functional module which consisted of a tightly knitted sub-network of clique-5 and -4s was also observed. These cliques were formed by 12 genes highly expressed in both disease and control samples.

CONCLUSIONS

Several previously unidentified disease marker genes and drug targets, such as SBNO2 (schizophrenia), SEC24C (bipolar disorder), and SRRT (major depression), were identified based on statistical and topological analyses of the PPI network. The shared or interconnecting marker genes may explain the shared symptoms of the studied diseases. Furthermore, the "switchboard" genes, such as APP, UBC, and YWHAZ, are proposed as potential targets for developing new treatments due to their functional and topological significance.

A targeted siRNA screen identifies regulators of Cdc42 activity at the natural killer cell immunological synapse

Natural killer (NK) cells kill tumor cells and virally infected cells, and an effective NK cell response requires processes, such as motility, recognition, and directional secretion, that rely on cytoskeletal rearrangement. The Rho guanosine triphosphatase (GTPase) Cdc42 coordinates cytoskeletal reorganization downstream of many receptors. The Rho-related GTPase from plants 1 (ROP1) exhibits oscillatory activation behavior at the apical plasma membrane of growing pollen tubes; however, a similar oscillation in Rho GTPase activity has so far not been demonstrated in mammalian cells. We hypothesized that oscillations in Cdc42 activity might occur within NK cells as they interact with target cells. Through fluorescence lifetime imaging of a Cdc42 biosensor, we observed that in live NK cells forming immunological synapses with target cells, Cdc42 activity oscillated after exhibiting an initial increase. We used protein-protein interaction networks and structural databases to identify candidate proteins that controlled Cdc42 activity, leading to the design of a targeted short interfering RNA screen. The guanine nucleotide exchange factors RhoGEF6 and RhoGEF7 were necessary for Cdc42 activation within the NK cell immunological synapse. In addition, the kinase Akt and the p85α subunit of phosphoinositide 3-kinase (PI3K) were required for Cdc42 activation, the periodicity of the oscillation in Cdc42 activity, and the subsequent polarization of cytotoxic vesicles toward target cells. Given that PI3Ks are targets of tumor therapies, our findings suggest the need to monitor innate immune function during the course of targeted therapy against these enzymes.

Role of VHL gene mutation in human renal cell carcinoma

The Von Hippel-Lindau (VHL) is an inherited neoplasia syndrome caused by the inactivation of VHL tumor suppressor gene, and somatic mutation of this gene has been related to the development of sporadic clear cell renal carcinoma. The affected individuals are at higher risk for the development of tumor in other organs, which include pheochromocytomas, retinal angioma, pancreatic cysts, and CNS hemangioblastomas. The VHL mRNA encodes a protein (pVHL) that contains 213 amino acid residues which migrate with an apparent molecular weight of 24 to 30 kDa. The VHL gene protein has multiple functions that are linked to tumor suppression, but the best recognized and evidently linked to the development of renal cell carcinoma (RCC) is inhibition of hypoxia-inducible factor (HIF), as well as plays a role in targeting HIF for ubiquitin-mediated degradation. Aberrations in VHL's function, either through mutation or promoter hypermethylation, lead to the accumulation of HIF, which will transcriptionally upregulate a sequence of hypoxia responsive genes, including epidermal growth factor, vascular endothelial growth factor, platelet-derived growth factor, and other proangiogenic factors, resulting in upregulated blood vessel growth, one of the prerequisites of a tumor. HIF plays a critical role in pVHL-defective tumor formation, raising the possibility that drugs directed against HIF or its downstream targets (such as vascular endothelial growth factor) may one day play a role in the treatment of RCC. Moreover, a number of drugs have been developed that target HIF-responsive gene products, many of these targeted therapies have demonstrated significant activity in kidney cancer clinical trials and signify substantive advances in the treatment of this disease.