Metabolic adaptation to a disruption in oxygen supply during myocardial ischemia and reperfusion is underpinned by temporal and quantitative changes in the cardiac proteome

Despite decades of intensive research, there is still no effective treatment for ischemia/reperfusion (I/R) injury, an important corollary in the treatment of ischemic disease. I/R injury is initiated when the altered biochemistry of cells after ischemia is no longer compatible with oxygenated microenvironment (or reperfusion). To better understand the molecular basis of this alteration and subsequent incompatibility, we assessed the temporal and quantitative alterations in the cardiac proteome of a mouse cardiac I/R model by an iTRAQ approach at 30 min of ischemia, and at 60 or 120 min reperfusion after the ischemia using sham-operated mouse heart as the baseline control. Of the 509 quantified proteins identified, 121 proteins exhibited significant changes (p-value<0.05) over time and were mostly clustered in eight functional groups: Fatty acid oxidation, Glycolysis, TCA cycle, ETC (electron transport chain), Redox Homeostasis, Glutathione S-transferase, Apoptosis related, and Heat Shock proteins. The first four groups are intimately involved in ATP production and the last four groups are known to be important in cellular antioxidant activity. During ischemia and reperfusion, the short supply of oxygen precipitates a pivotal metabolic switch from aerobic metabolism involving fatty acid oxidation, TCA, and phosphorylation to anaerobic metabolism for ATP production and this, in turn, increases reactive oxygen species (ROS) formation. Therefore the implication of these 8 functional groups suggested that ischemia-reperfusion injury is underpinned in part by proteomic alterations. Reversion of these alterations to preischemia levels took at least 60 min, suggesting a refractory period in which the ischemic cells cannot adjust to the presence of oxygen. Therefore, therapeutics that could compensate for these proteomic alterations during this interim refractory period could alleviate ischemia-reperfusion injury to enhance cellular recovery from an ischemic to a normoxic microenvironment. Among the perturbed proteins, Park7 and Ppia were selected for further investigation of their functions under hypoxia. The results show that Park7 plays a key role in regulating antioxidative stress and cell survival, and Ppia may function in coping with the unfolded protein stress in the I/R condition.

Glycomic and proteomic profiling of pancreatic cyst fluids identifies hyperfucosylated lactosamines on the N-linked glycans of overexpressed glycoproteins

Pancreatic cancer is now the fourth leading cause of cancer deaths in the United States, and it is associated with an alarmingly low 5-year survival rate of 5%. However, a patient's prognosis is considerably improved when the malignant lesions are identified at an early stage of the disease and removed by surgical resection. Unfortunately, the absence of a practical screening strategy and clinical diagnostic test for identifying premalignant lesions within the pancreas often prevents early detection of pancreatic cancer. To aid in the development of a molecular screening system for early detection of the disease, we have performed glycomic and glycoproteomic profiling experiments on 21 pancreatic cyst fluid samples, including fluids from mucinous cystic neoplasms and intraductal papillary mucinous neoplasms, two types of mucinous cysts that are considered high risk to undergo malignant transformation. A total of 80 asparagine-linked (N-linked) glycans, including high mannose and complex structures, were identified. Of special interest was a series of complex N-linked glycans containing two to six fucose residues, located predominantly as substituents on β-lactosamine extensions. Following the observation of these "hyperfucosylated" glycans, bottom-up proteomics experiments utilizing a label-free quantitative approach were applied to the investigation of two sets of tryptically digested proteins derived from the cyst fluids: 1) all soluble proteins in the raw samples and 2) a subproteome of the soluble cyst fluid proteins that were selectively enriched for fucosylation through the use of surface-immobilized Aleuria aurantia lectin. A comparative analysis of these two proteomic data sets identified glycoproteins that were significantly enriched by lectin affinity. Several candidate glycoproteins that appear hyperfucosylated were identified, including triacylglycerol lipase and pancreatic α-amylase, which were 20- and 22-fold more abundant, respectively, following A. aurantia lectin enrichment.

Mammalian X chromosome inactivation evolved as a dosage-compensation mechanism for dosage-sensitive genes on the X chromosome

How and why female somatic X-chromosome inactivation (XCI) evolved in mammals remains poorly understood. It has been proposed that XCI is a dosage-compensation mechanism that evolved to equalize expression levels of X-linked genes in females (2X) and males (1X), with a prior twofold increase in expression of X-linked genes in both sexes ("Ohno's hypothesis"). Whereas the parity of X chromosome expression between the sexes has been clearly demonstrated, tests for the doubling of expression levels globally along the X chromosome have returned contradictory results. However, changes in gene dosage during sex-chromosome evolution are not expected to impact on all genes equally, and should have greater consequences for dosage-sensitive genes. We show that, for genes encoding components of large protein complexes (≥ 7 members)--a class of genes that is expected to be dosage-sensitive--expression of X-linked genes is similar to that of autosomal genes within the complex. These data support Ohno's hypothesis that XCI acts as a dosage-compensation mechanism, and allow us to refine Ohno's model of XCI evolution. We also explore the contribution of dosage-sensitive genes to X aneuploidy phenotypes in humans, such as Turner (X0) and Klinefelter (XXY) syndromes. X aneuploidy in humans is common and is known to have mild effects because most of the supernumerary X genes are inactivated and not affected by aneuploidy. Only genes escaping XCI experience dosage changes in X-aneuploidy patients. We combined data on dosage sensitivity and XCI to compute a list of candidate genes for X-aneuploidy syndromes.

GPEC: a Cytoscape plug-in for random walk-based gene prioritization and biomedical evidence collection

Finding genes associated with a disease is an important issue in the biomedical area and many gene prioritization methods have been proposed for this goal. Among these, network-based approaches are recently proposed and outperformed functional annotation-based ones. Here, we introduce a novel Cytoscape plug-in, GPEC, to help identify putative genes likely to be associated with specific diseases or pathways. In the plug-in, gene prioritization is performed through a random walk with restart algorithm, a state-of-the art network-based method, along with a gene/protein relationship network. The plug-in also allows users efficiently collect biomedical evidence for highly ranked candidate genes. A set of known genes, candidate genes and a gene/protein relationship network can be provided in a flexible way.

The role of structural disorder in the rewiring of protein interactions through evolution

Structurally disordered regions play a key role in protein-protein interaction networks and the evolution of highly connected proteins, enabling the molecular mechanisms for multiple binding. However, the role of protein disorder in the evolution of interaction networks has only been investigated through the analysis of individual proteins, making it impossible to distinguish its specific impact in the (re)shaping of their interaction environments. Now, the availability of large interactomes for several model organisms permits exploration of the role of disorder in protein interaction networks not only at the level of the interacting proteins but of the interactions themselves. By comparing the interactomes of human, fly, and yeast, we discovered that, despite being much more abundant, disordered interactions are significantly less conserved than their ordered counterparts. Furthermore, our analyses provide evidence that this happens not only because disordered proteins are less conserved but also because they display a higher capacity to rewire their interaction neighborhood through evolution. Overall, our results support the hypothesis that conservation of disorder gives a clear evolutionary advantage, facilitating the change of interaction partners during evolution. Moreover, this mechanism is not exclusive of a few anecdotal cases but a global feature present in the interactome networks of entire organisms.

A placental sub-proteome: the apical plasma membrane of the syncytiotrophoblast

As a highly vascularized tissue, the placenta mediates gas and solute exchange between maternal and fetal circulations. In the human placenta, the interface with maternal blood is a unique epithelial structure known as the syncytiotrophoblast. Previously we developed a colloidal-silica based method to generate highly enriched preparations of the apical plasma membrane of the syncytiotrophoblast. Using similar preparations, a proteomics assessment of this important sub-proteome has identified 340 proteins as part of this apical membrane fraction. The expression of 38 of these proteins was previously unknown in the human placental syncytiotrophoblast. Together with previous studies, the current proteomic database expands our knowledge of the proteome of the syncytiotrophoblast apical plasma membrane from normal placentas to include more than 500 proteins. This database is a valuable resource for future comparisons to diseased placentas. Additionally, this data set provides a basis for further experimental studies of placenta and trophoblast function.

Mass spectrometry-based proteomics: qualitative identification to activity-based protein profiling

Mass spectrometry has become the method of choice for proteome characterization, including multicomponent protein complexes (typically tens to hundreds of proteins) and total protein expression (up to tens of thousands of proteins), in biological samples. Qualitative sequence assignment based on MS/MS spectra is relatively well-defined, while statistical metrics for relative quantification have not completely stabilized. Nonetheless, proteomics studies have progressed to the point whereby various gene-, pathway-, or network-oriented computational frameworks may be used to place mass spectrometry data into biological context. Despite this progress, the dynamic range of protein expression remains a significant hurdle, and impedes comprehensive proteome analysis. Methods designed to enrich specific protein classes have emerged as an effective means to characterize enzymes or other catalytically active proteins that are otherwise difficult to detect in typical discovery mode proteomics experiments. Collectively, these approaches will facilitate identification of biomarkers and pathways relevant to diagnosis and treatment of human disease.

Large-scale mapping of human protein interactome using structural complexes

Although the identification of protein interactions by high-throughput (HTP) methods progresses at a fast pace, 'interactome' data sets still suffer from high rates of false positives and low coverage. To map the human protein interactome, we describe a new framework that uses experimental evidence on structural complexes, the atomic details of binding interfaces and evolutionary conservation. The structurally inferred interaction network is highly modular and more functionally coherent compared with experimental interaction networks derived from multiple literature citations. Moreover, structurally inferred and high-confidence HTP networks complement each other well, allowing us to construct a merged network to generate testable hypotheses and provide valuable experimental leads.

Proteomic analysis of stem cell differentiation and early development

Genomics methodologies have advanced to the extent that it is now possible to interrogate the gene expression in a single cell but proteomics has traditionally lagged behind and required much greater cellular input and was not quantitative. Coupling protein with gene expression data is essential for understanding how cell behavior is regulated. Advances primarily in mass spectrometry have, however, greatly improved the sensitivity of proteomics methods over the last decade and the outcome of proteomic analyses can now also be quantified. Nevertheless, it is still difficult to obtain sufficient tissue from staged mammalian embryos to combine proteomic and genomic analyses. Recent developments in pluripotent stem cell biology have in part addressed this issue by providing surrogate scalable cell systems in which early developmental events can be modeled. Here we present an overview of current proteomics methodologies and the kind of information this can provide on the biology of human and mouse pluripotent stem cells.

Extensive up-regulation of gene expression in cancer: the normalised use of microarray data

Based on the assumption that only a few genes are differentially expressed in a disease and have balanced upward and downward expression level changes, researchers usually normalise microarray data by forcing all of the arrays to have the same probe intensity distributions to remove technical variations in the data. However, accumulated evidence suggests that gene expressions could be widely altered in cancer, so we need to evaluate the sensitivities of biological discoveries to violation of the normalisation assumption. Here, we show that the medians of the original probe intensities increase in most of the ten cancer types analyzed in this paper, indicating that genes may be widely up-regulated in many cancer types. Thus, at least for cancer study, normalising all arrays to have the same distribution of probe intensities regardless of the state (diseased vs. normal) tends to falsely produce many down-regulated differentially expressed (DE) genes while missing many truly up-regulated DE genes. We also show that the DE genes solely detected in the non-normalised data for cancers are highly reproducible across different datasets for the same cancers, indicating that effective biological signals naturally exist in the non-normalised data. Because the powers of current statistical analyses using the non-normalised data tend to be low, we suggest selecting DE genes in both normalised and non-normalised data and then filter out the false DE genes extracted from the normalised data that show opposite deregulation directions in the non-normalised data.

A novel framework for the comparative analysis of biological networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes.

A systematic survey of loss-of-function variants in human protein-coding genes

Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease-causing variants, as well as common LoF variants in nonessential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.

RNA-Seq Atlas--a reference database for gene expression profiling in normal tissue by next-generation sequencing

MOTIVATION

Next-generation sequencing technology enables an entirely new perspective for clinical research and will speed up personalized medicine. In contrast to microarray-based approaches, RNA-Seq analysis provides a much more comprehensive and unbiased view of gene expression. Although the perspective is clear and the long-term success of this new technology obvious, bioinformatics resources making these data easily available especially to the biomedical research community are still evolving.

RESULTS

We have generated RNA-Seq Atlas, a web-based repository of RNA-Seq gene expression profiles and query tools. The website offers open and easy access to RNA-Seq gene expression profiles and tools to both compare tissues and find genes with specific expression patterns. To enlarge the scope of the RNA-Seq Atlas, the data were linked to common functional and genetic databases, in particular offering information on the respective gene, signaling pathway analysis and evaluation of biological functions by means of gene ontologies. Additionally, data were linked to several microarray gene profiles, including BioGPS normal tissue profiles and NCI60 cancer cell line expression data. Our data search interface allows an integrative detailed comparison between our RNA-Seq data and the microarray information. This is the first database providing data mining tools and open access to large scale RNA-Seq expression profiles. Its applications will be versatile, as it will be beneficial in identifying tissue specific genes and expression profiles, comparison of gene expression profiles among diverse tissues, but also systems biology approaches linking tissue function to gene expression changes.

AVAILABILITY AND IMPLEMENTATION

http://medicalgenomics.org/rna_seq_atlas.

TSLP signaling network revealed by SILAC-based phosphoproteomics

Thymic stromal lymphopoietin (TSLP) is a cytokine that plays diverse roles in the regulation of immune responses. TSLP requires a heterodimeric receptor complex consisting of IL-7 receptor α subunit and its unique TSLP receptor (gene symbol CRLF2) to transmit signals in cells. Abnormal TSLP signaling (e.g. overexpression of TSLP or its unique receptor TSLPR) contributes to the development of a number of diseases including asthma and leukemia. However, a detailed understanding of the signaling pathways activated by TSLP remains elusive. In this study, we performed a global quantitative phosphoproteomic analysis of the TSLP signaling network using stable isotope labeling by amino acids in cell culture. By employing titanium dioxide in addition to antiphosphotyrosine antibodies as enrichment methods, we identified 4164 phosphopeptides on 1670 phosphoproteins. Using stable isotope labeling by amino acids in cell culture-based quantitation, we determined that the phosphorylation status of 226 proteins was modulated by TSLP stimulation. Our analysis identified activation of several members of the Src and Tec families of kinases including Btk, Lyn, and Tec by TSLP for the first time. In addition, we report TSLP-induced phosphorylation of protein phosphatases such as Ptpn6 (SHP-1) and Ptpn11 (Shp2), which has also not been reported previously. Co-immunoprecipitation assays showed that Shp2 binds to the adapter protein Gab2 in a TSLP-dependent manner. This is the first demonstration of an inducible protein complex in TSLP signaling. A kinase inhibitor screen revealed that pharmacological inhibition of PI-3 kinase, Jak family kinases, Src family kinases or Btk suppressed TSLP-dependent cellular proliferation making them candidate therapeutic targets in diseases resulting from aberrant TSLP signaling. Our study is the first phosphoproteomic analysis of the TSLP signaling pathway that greatly expands our understanding of TSLP signaling and provides novel therapeutic targets for TSLP/TSLPR-associated diseases in humans.

HIPPIE: Integrating protein interaction networks with experiment based quality scores

Protein function is often modulated by protein-protein interactions (PPIs) and therefore defining the partners of a protein helps to understand its activity. PPIs can be detected through different experimental approaches and are collected in several expert curated databases. These databases are used by researchers interested in examining detailed information on particular proteins. In many analyses the reliability of the characterization of the interactions becomes important and it might be necessary to select sets of PPIs of different confidence levels. To this goal, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool (available at http://cbdm.mdc-berlin.de/tools/hippie) allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level.

Neurotrophin-4 regulates the survival of gustatory neurons earlier in development using a different mechanism than brain-derived neurotrophic factor

The number of neurons in the geniculate ganglion that are available to innervate taste buds is regulated by neurotrophin-4 (NT-4) and brain-derived neurotrophic factor (BDNF). Our goal for the current study was to examine the timing and mechanism of NT-4-mediated regulation of geniculate neuron number during development. We discovered that NT-4 mutant mice lose 33% of their geniculate neuronal cells between E10.5 and E11.5. By E11.5, geniculate axons have just reached the tongue and do not yet innervate their gustatory targets; thus, NT-4 does not function as a target-derived growth factor. At E11.5, no difference was observed in proliferating cells or the rate at which cells exit the cell cycle between NT-4 mutant and wild type ganglia. Instead, there was an increase in TUNEL-labeling, indicating an increase in cell death in Ntf4(-/-) mice compared with wild types. However, activated caspase-3, which is up-regulated in the absence of BDNF, was not increased. This finding indicates that cell death initiated by NT-4-removal occurs through a different cell death pathway than BDNF-removal. We observed no additional postnatal loss of taste buds or neurons in Ntf4(-/-) mice. Thus, during early embryonic development, NT-4 produced in the ganglion and along the projection pathway inhibits cell death through an activated caspase-3 independent mechanism. Therefore, compared to BDNF, NT-4 plays distinct roles in gustatory development; differences include timing, source of neurotrophin, and mechanism of action.

Rare coding SNP in DZIP1 gene associated with late-onset sporadic Parkinson's disease

An association between a rare, coding, non-synonymous SNP variant in the gene DZIP1 and Parkinson's disease was found, based on an analysis of the existing NGRC genome-wide association study dataset. The statistical analysis utilized the hypothesis-rich, targeted search unbiased assessment approach, rather than the hypothesis-free, genome-wide agnostic search paradigm. The association of DZIP1 with Parkinson's disease is discussed in the context of a Parkinson's disease stem-cell ageing theory.

PPISEARCHENGINE: gene ontology-based search for protein-protein interactions

This paper presents a new search engine called PPISearchEngine which finds protein-protein interactions (PPIs) using the gene ontology (GO) and the biological relations of proteins. For efficient retrieval of PPIs, each GO term is assigned a prime number and the relation between the terms is represented by the product of prime numbers. This representation is hidden from users but facilitates the search for the interactions of a query protein by unique prime factorisation of the number that represents the query protein. For a query protein, PPISearchEngine considers not only the GO term associated with the query protein but also the GO terms at the lower level than the GO term in the GO hierarchy, and finds all the interactions of the query protein which satisfy the search condition. In contrast, the standard keyword-matching or ID-matching search method cannot find the interactions of a protein unless the interactions involve a protein with explicit annotations. To the best of our knowledge, this search engine is the first method that can process queries like 'for protein p with GO [Formula: see text], find p's interaction partners with GO [Formula: see text]'. PPISearchEngine is freely available to academics at http://search.hpid.org/.

MOfinder: a novel algorithm for detecting overlapping modules from protein-protein interaction network

Since organism development and many critical cell biology processes are organized in modular patterns, many algorithms have been proposed to detect modules. In this study, a new method, MOfinder, was developed to detect overlapping modules in a protein-protein interaction (PPI) network. We demonstrate that our method is more accurate than other 5 methods. Then, we applied MOfinder to yeast and human PPI network and explored the overlapping information. Using the overlapping modules of human PPI network, we constructed the module-module communication network. Functional annotation showed that the immune-related and cancer-related proteins were always together and present in the same modules, which offer some clues for immune therapy for cancer. Our study around overlapping modules suggests a new perspective on the analysis of PPI network and improves our understanding of disease.

Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study

Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.

A Resource of Quantitative Functional Annotation for Homo sapiens Genes

The body of human genomic and proteomic evidence continues to grow at ever-increasing rates, while annotation efforts struggle to keep pace. A surprisingly small fraction of human genes have clear, documented associations with specific functions, and new functions continue to be found for characterized genes. Here we assembled an integrated collection of diverse genomic and proteomic data for 21,341 human genes and make quantitative associations of each to 4333 Gene Ontology terms. We combined guilt-by-profiling and guilt-by-association approaches to exploit features unique to the data types. Performance was evaluated by cross-validation, prospective validation, and by manual evaluation with the biological literature. Functional-linkage networks were also constructed, and their utility was demonstrated by identifying candidate genes related to a glioma FLN using a seed network from genome-wide association studies. Our annotations are presented—alongside existing validated annotations—in a publicly accessible and searchable web interface.

SCPC: a method to structurally compare protein complexes

MOTIVATION

Protein-protein interactions play vital functional roles in various biological phenomena. Physical contacts between proteins have been revealed using experimental approaches that have solved the structures of protein complexes at atomic resolution. To examine the huge number of protein complexes available in the Protein Data Bank, an efficient automated method that compares protein complexes is required.

RESULTS

We have developed Structural Comparison of Protein Complexes (SCPC), a novel method to structurally compare protein complexes. SCPC compares the spatial arrangements of subunits in a complex with those in another complex using secondary structure elements. Similar substructures are detected in two protein complexes and the similarity is scored. SCPC was applied to dimers, homo-oligomers and haemoglobins. SCPC properly estimated structural similarities between the dimers examined as well as an existing method, MM-align. Conserved substructures were detected in a homo-tetramer and a homo-hexamer composed of homologous proteins. Classification of quaternary structures of haemoglobins using SCPC was consistent with the conventional classification. The results demonstrate that SCPC is a valuable tool to investigate the structures of protein complexes.

AVAILABILITY

SCPC is available at http://idp1.force.cs.is.nagoya-u.ac.jp/scpc/.

CONTACT

rkoike@is.nagoya-u.ac.jp

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Quantitative molecular profiling of biomarkers for pancreatic cancer with functionalized quantum dots

Applications in nanomedicine, such as diagnostics and targeted therapeutics, rely on the detection and targeting of membrane biomarkers. In this article we demonstrate absolute quantitative profiling, spatial mapping, and multiplexing of cancer biomarkers using functionalized quantum dots (QDs). We demonstrate highly selective targeting molecular markers for pancreatic cancer with extremely low levels of nonspecific binding. We confirm that we have saturated all biomarkers on the cell surface, and, in conjunction with control experiments, extract absolute quantitative values for the biomarker density in terms of the number of molecules per square micron on the cell surface. We show that we can obtain quantitative spatial information of biomarker distribution on a single cell, important because tumors' cell populations are inherently heterogeneous. We validate our quantitative measurements (number of molecules per square micron) using flow cytometry and demonstrate multiplexed quantitative profiling using color-coded QDs.

FROM THE CLINICAL EDITOR

This paper demonstrates a nice example for quantum dot-based molecular targeting of pancreatic cancer cells for advanced high sensitivity diagnostics and potential future selective therapeutic purposes.

Mutual exclusivity analysis identifies oncogenic network modules

Although individual tumors of the same clinical type have surprisingly diverse genomic alterations, these events tend to occur in a limited number of pathways, and alterations that affect the same pathway tend to not co-occur in the same patient. While pathway analysis has been a powerful tool in cancer genomics, our knowledge of oncogenic pathway modules is incomplete. To systematically identify such modules, we have developed a novel method, Mutual Exclusivity Modules in cancer (MEMo). The method uses correlation analysis and statistical tests to identify network modules by three criteria: (1) Member genes are recurrently altered across a set of tumor samples; (2) member genes are known to or are likely to participate in the same biological process; and (3) alteration events within the modules are mutually exclusive. Applied to data from the Cancer Genome Atlas (TCGA), the method identifies the principal known altered modules in glioblastoma (GBM) and highlights the striking mutual exclusivity of genomic alterations in the PI(3)K, p53, and Rb pathways. In serous ovarian cancer, we make the novel observation that inactivation of BRCA1 and BRCA2 is mutually exclusive of amplification of CCNE1 and inactivation of RB1, suggesting distinct alternative causes of genomic instability in this cancer type; and, we identify RBBP8 as a candidate oncogene involved in Rb-mediated cell cycle control. When applied to any cancer genomics data set, the algorithm can nominate oncogenic alterations that have a particularly strong selective effect and may also be useful in the design of therapeutic combinations in cases where mutual exclusivity reflects synthetic lethality.

TranscriptomeBrowser 3.0: introducing a new compendium of molecular interactions and a new visualization tool for the study of gene regulatory networks

Background

Deciphering gene regulatory networks by in silico approaches is a crucial step in the study of the molecular perturbations that occur in diseases. The development of regulatory maps is a tedious process requiring the comprehensive integration of various evidences scattered over biological databases. Thus, the research community would greatly benefit from having a unified database storing known and predicted molecular interactions. Furthermore, given the intrinsic complexity of the data, the development of new tools offering integrated and meaningful visualizations of molecular interactions is necessary to help users drawing new hypotheses without being overwhelmed by the density of the subsequent graph.

Results

We extend the previously developed TranscriptomeBrowser database with a set of tables containing 1,594,978 human and mouse molecular interactions. The database includes: (i) predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices), (ii) potential regulatory interactions inferred from systematic analysis of ChIP-seq experiments, (iii) regulatory interactions curated from the literature, (iv) predicted post-transcriptional regulation by micro-RNA, (v) protein kinase-substrate interactions and (vi) physical protein-protein interactions. In order to easily retrieve and efficiently analyze these interactions, we developed In-teractomeBrowser, a graph-based knowledge browser that comes as a plug-in for Transcriptome-Browser. The first objective of InteractomeBrowser is to provide a user-friendly tool to get new insight into any gene list by providing a context-specific display of putative regulatory and physical interactions. To achieve this, InteractomeBrowser relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology to map gene products onto relevant cell compartments. This layout is particularly powerful for visual integration of heterogeneous biological information and is a productive avenue in generating new hypotheses. The second objective of InteractomeBrowser is to fill the gap between interaction databases and dynamic modeling. It is thus compatible with the network analysis software Cytoscape and with the Gene Interaction Network simulation software (GINsim). We provide examples underlying the benefits of this visualization tool for large gene set analysis related to thymocyte differentiation.

Conclusions

The InteractomeBrowser plugin is a powerful tool to get quick access to a knowledge database that includes both predicted and validated molecular interactions. InteractomeBrowser is available through the TranscriptomeBrowser framework and can be found at: http://tagc.univ-mrs.fr/tbrowser/. Our database is updated on a regular basis.

Computational approaches to disease-gene prediction: rationale, classification and successes

The identification of genes involved in human hereditary diseases often requires the time-consuming and expensive examination of a great number of possible candidate genes, since genome-wide techniques such as linkage analysis and association studies frequently select many hundreds of 'positional' candidates. Even considering the positive impact of next-generation sequencing technologies, the prioritization of candidate genes may be an important step for disease-gene identification. In this paper we develop a basic classification scheme for computational approaches to disease-gene prediction and apply it to exhaustively review bioinformatics tools that have been developed for this purpose, focusing on conceptual aspects rather than technical detail and performance. Finally, we discuss some past successes obtained by computational approaches to illustrate their beneficial contribution to medical research.

Short Co-occurring Polypeptide Regions Can Predict Global Protein Interaction Maps

A goal of the post-genomics era has been to elucidate a detailed global map of protein-protein interactions (PPIs) within a cell. Here, we show that the presence of co-occurring short polypeptide sequences between interacting protein partners appears to be conserved across different organisms. We present an algorithm to automatically generate PPI prediction method parameters for various organisms and illustrate that global PPIs can be predicted from previously reported PPIs within the same or a different organism using protein primary sequences. The PPI prediction code is further accelerated through the use of parallel multi-core programming, which improves its usability for large scale or proteome-wide PPI prediction. We predict and analyze hundreds of novel human PPIs, experimentally confirm protein functions and importantly predict the first genome-wide PPI maps for S. pombe (∼9,000 PPIs) and C. elegans (∼37,500 PPIs).

SynaptomeDB: an ontology-based knowledgebase for synaptic genes

MOTIVATION

The synapse is integral to the function of the brain and may be an important source of dysfunction underlying many neuropsychiatric disorders. Consequently, it is an excellent candidate for large-scale genomic and proteomic study. However, while the tools and databases available for the annotation of high-throughput DNA and protein are generally robust, a comprehensive resource dedicated to the integration of information about the synapse is lacking.

RESULTS

We present an integrated database, called SynaptomeDB, to retrieve and annotate genes comprising the synaptome. These genes encode components of the synapse including neurotransmitters and their receptors, adhesion/cytoskeletal proteins, scaffold proteins, membrane transporters. SynaptomeDB integrates various and complex data sources for synaptic genes and proteins.

Mutational robustness of gene regulatory networks

Mutational robustness of gene regulatory networks refers to their ability to generate constant biological output upon mutations that change network structure. Such networks contain regulatory interactions (transcription factor – target gene interactions) but often also protein-protein interactions between transcription factors. Using computational modeling, we study factors that influence robustness and we infer several network properties governing it. These include the type of mutation, i.e. whether a regulatory interaction or a protein-protein interaction is mutated, and in the case of mutation of a regulatory interaction, the sign of the interaction (activating vs. repressive). In addition, we analyze the effect of combinations of mutations and we compare networks containing monomeric with those containing dimeric transcription factors. Our results are consistent with available data on biological networks, for example based on evolutionary conservation of network features. As a novel and remarkable property, we predict that networks are more robust against mutations in monomer than in dimer transcription factors, a prediction for which analysis of conservation of DNA binding residues in monomeric vs. dimeric transcription factors provides indirect evidence.

Thrombotic microangiopathy: a role for magnesium?

Despite advances in more recent years, the pathophysiology and especially treatment modalities of thrombotic microangiopathy (TMA) largely remain enigmatic. Disruption of endothelial homeostasis plays an essential role in TMA. Considering the proven causal association between magnesium and both endothelial function and platelet aggregability, we speculate that a magnesium deficit could influence the course of TMA and the related haemolytic uraemic syndrome and thrombotic thrombocytopenic purpura. A predisposition towards TMA is seen in many conditions with both extracellular and intracellular magnesium deficiency. We propose a rationale for magnesium supplementation in TMA, in analogy with its evidence-based therapeutic application in pre-eclampsia and suggest, based on theoretical grounds, that it might attenuate the development of TMA, minimise its severity and prevent its recurrence. This is based on several lines of evidence from both in vitro and in vivo data showing dose-dependent effects of magnesium supplementation on nitric oxide production, platelet aggregability and inflammation. Our hypothesis, which is further amenable to assessment in animal models before therapeutic applications in humans are implemented, could be explored both in vitro and in vivo to decipher the potential role of magnesium deficit in TMA and of the effects of its supplementation.

PlateletWeb: a systems biologic analysis of signaling networks in human platelets

Understanding the cellular mechanisms of platelet activation and their pharmacologic modulation is of major interest for basic and clinical research. Here we introduce a comprehensive human platelet repository (PlateletWeb) for systems biologic analysis of platelets in the functional context of integrated networks. Functional, drug, and pathway associations provide a first systemic insight into various aspects of platelet functionality and pharmacologic regulation. Detailed manual curation of recent platelet proteome and transcriptome studies yielded more than 5000 platelet proteins. Integration of protein-protein interactions with kinase-substrate relationships unraveled the platelet signaling network involving more than 70% of all platelet proteins. Analysis of the platelet kinome in the context of the kinase phylogenetic background revealed an over-representation of tyrosine kinase substrates. The extraction and graphical visualization of specific subnetworks allow identification of all major signaling modules involved in activation and inhibition. An in-depth analysis of DOK1 signaling identifies putative signal modulators of the integrin network. Through integration of various information sources and high curation standards, the PlateletWeb knowledge base offers the systems biologic background for the investigation of signal transduction in human platelets (http://plateletweb.bioapps.biozentrum.uni-wuerzburg.de).

Oncogene-specific activation of tyrosine kinase networks during prostate cancer progression

Dominant mutations or DNA amplification of tyrosine kinases are rare among the oncogenic alterations implicated in prostate cancer. We demonstrate that castration-resistant prostate cancer (CRPC) in men exhibits increased tyrosine phosphorylation, raising the question of whether enhanced tyrosine kinase activity is observed in prostate cancer in the absence of specific tyrosine kinase mutation or DNA amplification. We generated a mouse model of prostate cancer progression using commonly perturbed non-tyrosine kinase oncogenes and pathways and detected a significant up-regulation of tyrosine phosphorylation at the carcinoma stage. Phosphotyrosine peptide enrichment and quantitative mass spectrometry identified oncogene-specific tyrosine kinase signatures, including activation of EGFR, ephrin type-A receptor 2 (EPHA2), and JAK2. Kinase:substrate relationship analysis of the phosphopeptides also revealed ABL1 and SRC tyrosine kinase activation. The observation of elevated tyrosine kinase signaling in advanced prostate cancer and identification of specific tyrosine kinase pathways from genetically defined tumor models point to unique therapeutic approaches using tyrosine kinase inhibitors for advanced prostate cancer.

Differential network biology

Protein and genetic interaction maps can reveal the overall physical and functional landscape of a biological system. To date, these interaction maps have typically been generated under a single condition, even though biological systems undergo differential change that is dependent on environment, tissue type, disease state, development or speciation. Several recent interaction mapping studies have demonstrated the power of differential analysis for elucidating fundamental biological responses, revealing that the architecture of an interactome can be massively re-wired during a cellular or adaptive response. Here, we review the technological developments and experimental designs that have enabled differential network mapping at very large scales and highlight biological insight that has been derived from this type of analysis. We argue that differential network mapping, which allows for the interrogation of previously unexplored interaction spaces, will become a standard mode of network analysis in the future, just as differential gene expression and protein phosphorylation studies are already pervasive in genomic and proteomic analysis.

Oxidative stress contributes to sex differences in angiotensin II-mediated hypertension in spontaneously hypertensive rats

NADPH oxidase has been implicated in ANG II-induced oxidative stress and hypertension in males; however, the contribution of oxidative stress to ANG II hypertension in females is unknown. In the present study, we tested the hypothesis that greater antioxidant capacity in female spontaneously hypertensive rats (SHR) blunts ANG II-induced oxidative stress and hypertension relative to males. Whole body and renal cortical oxidative stress levels were assessed in female and male SHR left untreated or following 2 wk of chronic ANG II infusion. Chronic ANG II infusion increased NADPH oxidase enzymatic activity in the renal cortex of both sexes; however, this increase only reached significance in female SHR. In contrast, male SHR demonstrated a greater increase in all measurements of reactive oxygen species production in response to chronic ANG II infusion. ANG II infusion increased plasma superoxide dismutase activity only in female SHR (76 ± 9 vs. 190 ± 7 Units·ml(-1)·mg(-1), P < 0.05); however, cortical antioxidant capacity was unchanged by ANG II in either sex. To assess the functional implication of alterations in NADPH enzymatic activity and oxidative stress levels following ANG II infusion, additional experiments assessed the ability of the in vivo antioxidant apocynin to modulate ANG II hypertension. Apocynin significantly blunted ANG II hypertension in male SHR (174 ± 2 vs. 151 ± 1 mmHg, P < 0.05), with no effect in females (160 ± 11 vs. 163 ± 10 mmHg). These data suggest that ANG II hypertension in male SHR is more dependent on increases in oxidative stress than in female SHR.

Three-dimensional reconstruction of protein networks provides insight into human genetic disease

To better understand the molecular mechanisms and genetic basis of human disease, we systematically examine relationships between 3,949 genes, 62,663 mutations and 3,453 associated disorders by generating a three-dimensional, structurally resolved human interactome. This network consists of 4,222 high-quality binary protein-protein interactions with their atomic-resolution interfaces. We find that in-frame mutations (missense point mutations and in-frame insertions and deletions) are enriched on the interaction interfaces of proteins associated with the corresponding disorders, and that the disease specificity for different mutations of the same gene can be explained by their location within an interface. We also predict 292 candidate genes for 694 unknown disease-to-gene associations with proposed molecular mechanism hypotheses. This work indicates that knowledge of how in-frame disease mutations alter specific interactions is critical to understanding pathogenesis. Structurally resolved interaction networks should be valuable tools for interpreting the wealth of data being generated by large-scale structural genomics and disease association studies.

A dynamic view of domain-motif interactions

Many protein-protein interactions are mediated by domain-motif interaction, where a domain in one protein binds a short linear motif in its interacting partner. Such interactions are often involved in key cellular processes, necessitating their tight regulation. A common strategy of the cell to control protein function and interaction is by post-translational modifications of specific residues, especially phosphorylation. Indeed, there are motifs, such as SH2-binding motifs, in which motif phosphorylation is required for the domain-motif interaction. On the contrary, there are other examples where motif phosphorylation prevents the domain-motif interaction. Here we present a large-scale integrative analysis of experimental human data of domain-motif interactions and phosphorylation events, demonstrating an intriguing coupling between the two. We report such coupling for SH3, PDZ, SH2 and WW domains, where residue phosphorylation within or next to the motif is implied to be associated with switching on or off domain binding. For domains that require motif phosphorylation for binding, such as SH2 domains, we found coupled phosphorylation events other than the ones required for domain binding. Furthermore, we show that phosphorylation might function as a double switch, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. Evolutionary analysis shows that co-evolution of the motif and the proximal residues capable of phosphorylation predominates over other evolutionary scenarios, in which the motif appeared before the potentially phosphorylated residue, or vice versa. Our findings provide strengthening evidence for coupled interaction-regulation units, defined by a domain-binding motif and a phosphorylated residue.

Domain-motif interactions are instrumental for many central cellular processes, and are therefore tightly regulated. Phosphorylation events are known modulators of protein-protein interactions in general, including domain-motif interactions. Here, we addressed the association of phosphorylation and domain-motif interaction taking a motif-centred view. We integrated human domain-motif interaction and phosphorylation data for four representative domains (SH2, WW, SH3 and PDZ), and showed that the adjacency between phosphorylation and domain-motif interactions is extensive, suggesting interesting functional links between them that extend the classical and widely studied phospho-regulation of SH2 or WW domain-motif interactions. Furthermore, we show that such interaction-regulation units may function as double switches, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. These latter interaction-regulation units are more conserved in evolution than the individual units comprising them. Assuming that the four analyzed domain-motif interaction types are reliable representatives of such interactions, our results support the existence of units comprising motifs and associated phosphorylation sites, in which the regulation of domain-motif interaction is inherent.

Interactome-transcriptome integration for predicting distant metastasis in breast cancer

MOTIVATION

High-throughput gene expression profiling yields genomic signatures that allow the prediction of clinical conditions including patient outcome. However, these signatures have limitations, such as dependency on the training set, and worse, lack of generalization.

RESULTS

We propose a novel algorithm called ITI (interactome-transcriptome integration), to extract a genomic signature predicting distant metastasis in breast cancer by superimposition of large-scale protein-protein interaction data over a compendium of several gene expression datasets. Training on two different compendia showed that the estrogen receptor-specific signatures obtained are more stable (11-35% stability), can be generalized on independent data and performs better than previously published methods (53-74% accuracy).

AVAILABILITY

The ITI algorithm source code from analysis are available under CeCILL from the ITI companion website: http://bioinformatique.marseille.inserm.fr/iti.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

IMID: integrated molecular interaction database

MOTIVATION

Molecular interaction information, such as protein-protein interactions and protein-small molecule interactions, is indispensable for understanding the mechanism of biological processes and discovering treatments for diseases. Many databases have been built by manual annotation of literature to organize such information into structured form. However, most databases focus on only one type of interactions, which are often not well annotated and integrated with related functional information.

RESULTS

In this study, we integrate molecular interaction information from literature by automatic information extraction and from manually annotated databases. We further integrate the relationships between protein/gene and other bio-entity terms including gene ontology terms, pathways, species and diseases to build an integrated molecular interaction database (IMID). Interactions can be selected by their associated probabilities. IMID allows complex and versatile queries for context-specific molecular interactions, which are not available currently in other molecular interaction databases.

AVAILABILITY

The database is located at www.integrativebiology.org.

Prediction and characterization of protein-protein interaction networks in swine

Background

Studying the large-scale protein-protein interaction (PPI) network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes.

Results

We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively.

Conclusion

The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/).

Identification of human protein complexes from local sub-graphs of protein-protein interaction network based on random forest with topological structure features

In the post-genomic era, one of the most important and challenging tasks is to identify protein complexes and further elucidate its molecular mechanisms in specific biological processes. Previous computational approaches usually identify protein complexes from protein interaction network based on dense sub-graphs and incomplete priori information. Additionally, the computational approaches have little concern about the biological properties of proteins and there is no a common evaluation metric to evaluate the performance. So, it is necessary to construct novel method for identifying protein complexes and elucidating the function of protein complexes. In this study, a novel approach is proposed to identify protein complexes using random forest and topological structure. Each protein complex is represented by a graph of interactions, where descriptor of the protein primary structure is used to characterize biological properties of protein and vertex is weighted by the descriptor. The topological structure features are developed and used to characterize protein complexes. Random forest algorithm is utilized to build prediction model and identify protein complexes from local sub-graphs instead of dense sub-graphs. As a demonstration, the proposed approach is applied to protein interaction data in human, and the satisfied results are obtained with accuracy of 80.24%, sensitivity of 81.94%, specificity of 80.07%, and Matthew's correlation coefficient of 0.4087 in 10-fold cross-validation test. Some new protein complexes are identified, and analysis based on Gene Ontology shows that the complexes are likely to be true complexes and play important roles in the pathogenesis of some diseases. PCI-RFTS, a corresponding executable program for protein complexes identification, can be acquired freely on request from the authors.

Quantitative proteomic analysis of membrane proteins involved in astroglial differentiation of neural stem cells by SILAC labeling coupled with LC-MS/MS

Membrane proteins play a critical role in the process of neural stem cell self-renewal and differentiation. Here, we apply the SILAC (stable isotope labeling by amino acids in cell culture) approach to quantitatively compare the membrane proteome of the self-renewing and the astroglial differentiating cells. High-resolution analysis on a linear ion trap-Orbitrap instrument (LTQ-Orbitrap) at sub-ppm mass accuracy resulted in confident identification and quantitation of more than 700 distinct membrane proteins during the astroglial differentiation. Of the 735 quantified proteins, seven cell surface proteins display significantly higher expression levels in the undifferentiated state membrane compared to astroglial differentiating membrane. One cell surface protein transferrin receptor protein 1 may serve as a new candidate for NSCs surface markers. Functional clustering of differentially expressed proteins by Ingenuity Pathway Analysis revealed that most of overexpressed membrane proteins in the astroglial differentiation neural stem cells are involved in cellular growth, nervous system development, and energy metabolic pathway. Taken together, this study increases our understanding of the underlying mechanisms that modulate complex biological processes of neural stem cell proliferation and differentiation.

LC-MS/MS analysis of differentially expressed glioblastoma membrane proteome reveals altered calcium signaling and other protein groups of regulatory functions

Membrane proteins play key roles in the development and progression of cancer. We have studied differentially expressed membrane proteins in glioblastoma multiforme (GBM), the most common and aggressive type of primary brain tumor, by high resolution LC-MS/MS mass spectrometry and quantitation by iTRAQ. A total of 1834 membrane proteins were identified with high confidence, of which 356 proteins were found to be altered by 2-fold change or more (198 up- and 158 down-regulated); 56% of them are known membrane proteins associated with major cellular processes. Mass spectrometry results were confirmed for representative proteins on individual specimens by immunohistochemistry. On mapping of the differentially expressed proteins to cellular pathways and functional networks, we notably observed many calcium-binding proteins to be altered, implicating deregulation of calcium signaling and homeostasis in GBM, a pathway also found to be enriched in the report (Dong, H., Luo, L., Hong, S., Siu, H., Xiao, Y., Jin, L., Chen, R., and Xiong, M. (2010) Integrated analysis of mutations, miRNA and mRNA expression in glioblastoma. BMC Syst. Biol. 4, 163) based on The Cancer Genome Atlas analysis of GBMs. Annotations of the 356 proteins identified by us with The Cancer Genome Atlas transcriptome data set indicated overlap with 295 corresponding transcripts, which included 49 potential miRNA targets; many transcripts correlated with proteins in their expression status. Nearly 50% of the differentially expressed proteins could be classified as transmembrane domain or signal sequence-containing proteins (159 of 356) with potential of appearance in cerebrospinal fluid or plasma. Interestingly, 75 of them have been already reported in normal cerebrospinal fluid or plasma along with other proteins. This first, in-depth analysis of the differentially expressed membrane proteome of GBM confirms genes/proteins that have been implicated in earlier studies, as well as reveals novel candidates that are being reported for the first time in GBM or any other cancer that could be investigated further for clinical applications.

Phosphoproteomics-based systems analysis of signal transduction networks

Signal transduction systems coordinate complex cellular information to regulate biological events such as cell proliferation and differentiation. Although the accumulating evidence on widespread association of signaling molecules has revealed essential contribution of phosphorylation-dependent interaction networks to cellular regulation, their dynamic behavior is mostly yet to be analyzed. Recent technological advances regarding mass spectrometry-based quantitative proteomics have enabled us to describe the comprehensive status of phosphorylated molecules in a time-resolved manner. Computational analyses based on the phosphoproteome dynamics accelerate generation of novel methodologies for mathematical analysis of cellular signaling. Phosphoproteomics-based numerical modeling can be used to evaluate regulatory network elements from a statistical point of view. Integration with transcriptome dynamics also uncovers regulatory hubs at the transcriptional level. These omics-based computational methodologies, which have firstly been applied to representative signaling systems such as the epidermal growth factor receptor pathway, have now opened up a gate for systems analysis of signaling networks involved in immune response and cancer.

Translational bioinformatics in psychoneuroimmunology: methods and applications

Translational bioinformatics plays an indispensable role in transforming psychoneuroimmunology (PNI) into personalized medicine. It provides a powerful method to bridge the gaps between various knowledge domains in PNI and systems biology. Translational bioinformatics methods at various systems levels can facilitate pattern recognition, and expedite and validate the discovery of systemic biomarkers to allow their incorporation into clinical trials and outcome assessments. Analysis of the correlations between genotypes and phenotypes including the behavioral-based profiles will contribute to the transition from the disease-based medicine to human-centered medicine. Translational bioinformatics would also enable the establishment of predictive models for patient responses to diseases, vaccines, and drugs. In PNI research, the development of systems biology models such as those of the neurons would play a critical role. Methods based on data integration, data mining, and knowledge representation are essential elements in building health information systems such as electronic health records and computerized decision support systems. Data integration of genes, pathophysiology, and behaviors are needed for a broad range of PNI studies. Knowledge discovery approaches such as network-based systems biology methods are valuable in studying the cross-talks among pathways in various brain regions involved in disorders such as Alzheimer's disease.