The Reactome pathway knowledgebase

Peripheral blood mononuclear cell proteome changes in patients with myelodysplastic syndrome

Our aim was to search for proteome changes in peripheral blood mononuclear cells (PBMCs) of MDS patients with refractory cytopenia with multilineage dysplasia. PBMCs were isolated from a total of 12 blood samples using a Histopaque-1077 solution. The proteins were fractioned, separated by 2D SDS-PAGE (pI 4–7), and double-stained. The proteomes were compared and statistically processed with Progenesis SameSpots; then proteins were identified by nano-LC-MS/MS. Protein functional association and expression profiles were analyzed using the EnrichNet application and Progenesis SameSpots hierarchical clustering software, respectively. By comparing the cytosolic, membrane, and nuclear fractions of the two groups, 178 significantly (P < 0.05, ANOVA) differing spots were found, corresponding to 139 unique proteins. Data mining of the Reactome and KEGG databases using EnrichNet highlighted the possible involvement of the identified protein alterations in apoptosis, proteasome protein degradation, heat shock protein action, and signal transduction. Western blot analysis revealed underexpression of vinculin and advanced fragmentation of fermitin-3 in MDS patients. To the best of our knowledge, this is the first time that proteome changes have been identified in the mononuclear cells of MDS patients. Vinculin and fermitin-3, the proteins involved in cell adhesion and integrin signaling, have been shown to be dysregulated in MDS.

Inference of Evolutionary Forces Acting on Human Biological Pathways

Because natural selection is likely to act on multiple genes underlying a given phenotypic trait, we study here the potential effect of ongoing and past selection on the genetic diversity of human biological pathways. We first show that genes included in gene sets are generally under stronger selective constraints than other genes and that their evolutionary response is correlated. We then introduce a new procedure to detect selection at the pathway level based on a decomposition of the classical McDonald-Kreitman test extended to multiple genes. This new test, called 2DNS, detects outlier gene sets and takes into account past demographic effects and evolutionary constraints specific to gene sets. Selective forces acting on gene sets can be easily identified by a mere visual inspection of the position of the gene sets relative to their two-dimensional null distribution. We thus find several outlier gene sets that show signals of positive, balancing, or purifying selection but also others showing an ancient relaxation of selective constraints. The principle of the 2DNS test can also be applied to other genomic contrasts. For instance, the comparison of patterns of polymorphisms private to African and non-African populations reveals that most pathways show a higher proportion of nonsynonymous mutations in non-Africans than in Africans, potentially due to different demographic histories and selective pressures.

NaviCell Web Service for network-based data visualization

Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of ‘omics’ data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases.

The SwissLipids knowledgebase for lipid biology

MOTIVATION

Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it.

RESULTS

To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology-SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge.

AVAILABILITY

SwissLipids is freely available at http://www.swisslipids.org/.

CONTACT

alan.bridge@isb-sib.ch

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Galahad: a web server for drug effect analysis from gene expression

Galahad (https://galahad.esat.kuleuven.be) is a web-based application for analysis of drug effects. It provides an intuitive interface to be used by anybody interested in leveraging microarray data to gain insights into the pharmacological effects of a drug, mainly identification of candidate targets, elucidation of mode of action and understanding of off-target effects. The core of Galahad is a network-based analysis method of gene expression. As an input, Galahad takes raw Affymetrix human microarray data from treatment versus control experiments and provides quality control and data exploration tools, as well as computation of differential expression. Alternatively, differential expression values can be uploaded directly. Using these differential expression values, drug target prioritization and both pathway and disease enrichment can be calculated and visualized. Drug target prioritization is based on the integration of the gene expression data with a functional protein association network. The web site is free and open to all and there is no login requirement.

FNTM: a server for predicting functional networks of tissues in mouse

Functional Networks of Tissues in Mouse (FNTM) provides biomedical researchers with tissue-specific predictions of functional relationships between proteins in the most widely used model organism for human disease, the laboratory mouse. Users can explore FNTM-predicted functional relationships for their tissues and genes of interest or examine gene function and interaction predictions across multiple tissues, all through an interactive, multi-tissue network browser. FNTM makes predictions based on integration of a variety of functional genomic data, including over 13 000 gene expression experiments, and prior knowledge of gene function. FNTM is an ideal starting point for clinical and translational researchers considering a mouse model for their disease of interest, researchers already working with mouse models who are interested in discovering new genes related to their pathways or phenotypes of interest, and biologists working with other organisms to explore the functional relationships of their genes of interest in specific mouse tissue contexts. FNTM predicts tissue-specific functional relationships in 200 tissues, does not require any registration or installation and is freely available for use at http://fntm.princeton.edu.

Genetic conflict reflected in tissue-specific maps of genomic imprinting in human and mouse

Genomic imprinting is an epigenetic process that restricts gene expression to either the maternally or paternally inherited allele. Many theories have been proposed to explain its evolutionary origin, but understanding has been limited by a paucity of data mapping the breadth and dynamics of imprinting within any organism. We generated an atlas of imprinting spanning 33 mouse and 45 human developmental stages and tissues. Nearly all imprinted genes were imprinted in early development and either retained their parent-of-origin expression in adults or lost it completely. Consistent with an evolutionary signature of parental conflict, imprinted genes were enriched for coexpressed pairs of maternally and paternally expressed genes, showed accelerated expression divergence between human and mouse, and were more highly expressed than their non-imprinted orthologs in other species. Our approach demonstrates a general framework for the discovery of imprinting in any species and sheds light on the causes and consequences of genomic imprinting in mammals.

The unfolded protein response and its potential role in Huntington's disease elucidated by a systems biology approach

Huntington ´s disease (HD) is a progressive, neurodegenerative disease with a fatal outcome. Although the disease-causing gene (huntingtin) has been known for over 20 years, the exact mechanisms leading to neuronal cell death are still controversial. One potential mechanism contributing to the massive loss of neurons observed in the brain of HD patients could be the unfolded protein response (UPR) activated by accumulation of misfolded proteins in the endoplasmic reticulum (ER). As an adaptive response to counter-balance accumulation of un- or misfolded proteins, the UPR upregulates transcription of chaperones, temporarily attenuates new translation, and activates protein degradation via the proteasome. However, persistent ER stress and an activated UPR can also cause apoptotic cell death. Although different studies have indicated a role for the UPR in HD, the evidence remains inconclusive. Here, we present extensive bioinformatic analyses that revealed UPR activation in different experimental HD models based on transcriptomic data. Accordingly, we have identified 53 genes, including RAB5A, HMGB1, CTNNB1, DNM1, TUBB, TSG101, EEF2, DYNC1H1, SLC12A5, ATG5, AKT1, CASP7 and SYVN1 that provide a potential link between UPR and HD. To further elucidate the potential role of UPR as a disease-relevant process, we examined its connection to apoptosis based on molecular interaction data, and identified a set of 40 genes including ADD1, HSP90B1, IKBKB, IKBKG, RPS3A and LMNB1, which seem to be at the crossroads between these two important cellular processes. Remarkably, we also found strong correlation of UPR gene expression with the length of the polyglutamine tract of Huntingtin, which is a critical determinant of age of disease onset in human HD patients pointing to the UPR as a promising target for therapeutic intervention. The study is complemented by a newly developed web-portal called UPR-HD (http://uprhd.sysbiolab.eu) that enables visualization and interactive analysis of UPR-associated gene expression across various HD models.

Development of data representation standards by the human proteome organization proteomics standards initiative

OBJECTIVE

To describe the goals of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization, the methods that the PSI has employed to create data standards, the resulting output of the PSI, lessons learned from the PSI's evolution, and future directions and synergies for the group.

MATERIALS AND METHODS

The PSI has 5 categories of deliverables that have guided the group. These are minimum information guidelines, data formats, controlled vocabularies, resources and software tools, and dissemination activities. These deliverables are produced via the leadership and working group organization of the initiative, driven by frequent workshops and ongoing communication within the working groups. Official standards are subjected to a rigorous document process that includes several levels of peer review prior to release.

RESULTS

We have produced and published minimum information guidelines describing what information should be provided when making data public, either via public repositories or other means. The PSI has produced a series of standard formats covering mass spectrometer input, mass spectrometer output, results of informatics analysis (both qualitative and quantitative analyses), reports of molecular interaction data, and gel electrophoresis analyses. We have produced controlled vocabularies that ensure that concepts are uniformly annotated in the formats and engaged in extensive software development and dissemination efforts so that the standards can efficiently be used by the community.Conclusion In its first dozen years of operation, the PSI has produced many standards that have accelerated the field of proteomics by facilitating data exchange and deposition to data repositories. We look to the future to continue developing standards for new proteomics technologies and workflows and mechanisms for integration with other omics data types. Our products facilitate the translation of genomics and proteomics findings to clinical and biological phenotypes. The PSI website can be accessed at http://www.psidev.info.

The unfolded protein response and its potential role in Huntington's disease elucidated by a systems biology approach

Huntington ´s disease (HD) is a progressive, neurodegenerative disease with a fatal outcome. Although the disease-causing gene (huntingtin) has been known for over 20 years, the exact mechanisms leading to neuronal cell death are still controversial. One potential mechanism contributing to the massive loss of neurons observed in the brain of HD patients could be the unfolded protein response (UPR) activated by accumulation of misfolded proteins in the endoplasmic reticulum (ER). As an adaptive response to counter-balance accumulation of un- or misfolded proteins, the UPR upregulates transcription of chaperones, temporarily attenuates new translation, and activates protein degradation via the proteasome. However, persistent ER stress and an activated UPR can also cause apoptotic cell death. Although different studies have indicated a role for the UPR in HD, the evidence remains inconclusive. Here, we present extensive bioinformatic analyses that revealed UPR activation in different experimental HD models based on transcriptomic data. Accordingly, we have identified 53 genes, including RAB5A, HMGB1, CTNNB1, DNM1, TUBB, TSG101, EEF2, DYNC1H1, SLC12A5, ATG5, AKT1, CASP7 and SYVN1 that provide a potential link between UPR and HD. To further elucidate the potential role of UPR as a disease-relevant process, we examined its connection to apoptosis based on molecular interaction data, and identified a set of 40 genes including ADD1, HSP90B1, IKBKB, IKBKG, RPS3A and LMNB1, which seem to be at the crossroads between these two important cellular processes. Remarkably, we also found strong correlation of UPR gene expression with the length of the polyglutamine tract of Huntingtin, which is a critical determinant of age of disease onset in human HD patients pointing to the UPR as a promising target for therapeutic intervention. The study is complemented by a newly developed web-portal called UPR-HD (http://uprhd.sysbiolab.eu) that enables visualization and interactive analysis of UPR-associated gene expression across various HD models.

Human Cytomegalovirus miR-UL112-3p Targets TLR2 and Modulates the TLR2/IRAK1/NFκB Signaling Pathway

Human Cytomegalovirus (HCMV) encodes multiple microRNAs (miRNAs) whose functions are just beginning to be uncovered. Using in silico approaches, we identified the Toll-Like Receptor (TLR) innate immunity pathway as a possible target of HCMV miRNAs. Luciferase reporter assay screens further identified TLR2 as a target of HCMV miR-UL112-3p. TLR2 plays a major role in innate immune response by detecting both bacterial and viral ligands, including HCMV envelope proteins gB and gH. TLR2 activates a variety of signal transduction routes including the NFκB pathway. Furthermore, TLR2 plays an important role in controlling CMV infection both in humans and in mice. Immunoblot analysis of cells transfected with a miR-UL112-3p mimic revealed that endogenous TLR2 is down-regulated by miR-UL112-3p with similar efficiency as a TLR2-targeting siRNA (siTLR2). We next found that TLR2 protein level decreases at late times during HCMV infection and correlates with miR-UL112-3p accumulation in fibroblasts and monocytic THP1 cells. Confirming direct miR-UL112-3p targeting, down-regulation of endogenous TLR2 was not observed in cells infected with HCMV mutants deficient in miR-UL112-3p expression, but transfection of miR-UL112-3p in these cells restored TLR2 down-regulation. Using a NFκB reporter cell line, we found that miR-UL112-3p transfection significantly inhibited NFκB-dependent luciferase activity with similar efficiency as siTLR2. Consistent with this observation, miR-UL112-3p transfection significantly reduced the expression of multiple cytokines (IL-1β, IL-6 and IL-8) upon stimulation with a TLR2 agonist. Finally, miR-UL112-3p transfection significantly inhibited the TLR2-induced post-translational activation of IRAK1, a kinase located in the upstream section of the TLR2/NFκB signaling axis. To our knowledge, this is the first identified mechanism of TLR2 modulation by HCMV and is the first report of functional targeting of TLR2 by a viral miRNA. These results provide a novel mechanism through which a HCMV miRNA regulates the innate immune response by down-regulating TLR-2 expression.

Pathways with PathWhiz

PathWhiz (http://smpdb.ca/pathwhiz) is a web server designed to create colourful, visually pleasing and biologically accurate pathway diagrams that are both machine-readable and interactive. As a web server, PathWhiz is accessible from almost any place and compatible with essentially any operating system. It also houses a public library of pathways and pathway components that can be easily viewed and expanded upon by its users. PathWhiz allows users to readily generate biologically complex pathways by using a specially designed drawing palette to quickly render metabolites (including automated structure generation), proteins (including quaternary structures, covalent modifications and cofactors), nucleic acids, membranes, subcellular structures, cells, tissues and organs. Both small-molecule and protein/gene pathways can be constructed by combining multiple pathway processes such as reactions, interactions, binding events and transport activities. PathWhiz's pathway replication and propagation functions allow for existing pathways to be used to create new pathways or for existing pathways to be automatically propagated across species. PathWhiz pathways can be saved in BioPAX, SBGN-ML and SBML data exchange formats, as well as PNG, PWML, HTML image map or SVG images that can be viewed offline or explored using PathWhiz's interactive viewer. PathWhiz has been used to generate over 700 pathway diagrams for a number of popular databases including HMDB, DrugBank and SMPDB.

Pathway reporter genes define molecular phenotypes of human cells

Background

The phenotype of a living cell is determined by its pattern of active signaling networks, giving rise to a “molecular phenotype” associated with differential gene expression. Digital amplicon based RNA quantification by sequencing is a useful technology for molecular phenotyping as a novel tool to characterize the state of biological systems.

Results

We show here that the activity of signaling networks can be assessed based on a set of established key regulators and expression targets rather than the entire transcriptome. We compiled a panel of 917 human pathway reporter genes, representing 154 human signaling and metabolic networks for integrated knowledge- and data-driven understanding of biological processes. The reporter genes are significantly enriched for regulators and effectors covering a wide range of biological processes, and faithfully capture gene-level and pathway-level changes. We apply the approach to iPSC derived cardiomyocytes and primary human hepatocytes to describe changes in molecular phenotype during development or drug response. The reporter genes deliver an accurate pathway-centric view of the biological system under study, and identify known and novel modulation of signaling networks consistent with literature or experimental data.

Conclusions

A panel of 917 pathway reporter genes is sufficient to describe changes in the molecular phenotype defined by 154 signaling cascades in various human cell types. AmpliSeq-RNA based digital transcript imaging enables simultaneous monitoring of the entire pathway reporter gene panel in up to 150 samples. We propose molecular phenotyping as a useful approach to understand diseases and drug action at the network level.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1532-2) contains supplementary material, which is available to authorized users.

The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits

Background

Genetic heritability and expression study have shown that different diabetes traits have common genetic components and pathways. A computationally efficient pathway analysis of GWAS results will benefit post-GWAS study of SNP associations and identification of common genetic pathways from diabetes GWAS can help to improve understanding of the disease pathogenesis.

Results

We proposed a uniform-score gene-set analysis (USGSA) with implemented package to unify different gene measures by a uniform score for identifying pathways from GWAS data, and use a pre-generated permutation distribution table to quickly obtain multiple-testing adjusted p-value. Simulation studies of uniform score for four gene measures (minP, 2ndP, simP and fishP) have shown that USGSA has strictly controlled family-wise error rate. The power depends on types of gene measure. USGSA with a two-stage study strategy was applied to identify common pathways associated with diabetes traits based on public dbGaP GWAS results. The study identified 7 gene sets that contain binding motifs at promoter region of component genes for 5 transcription factors (TFs) of FOXO4, TCF3, NFAT, VSX1 and POU2F1, and 1 microRNA of mir-218. These gene sets include 25 common genes that are among top 5% of the gene associations over genome for all GWAS. Previous evidences showed that nearly all of these genes are mainly expressed in the brain.

Conclusions

USGSA is a computationally efficient approach for pathway analysis of GWAS data with promoted interpretability and comparability. The pathway analysis suggested that different diabetes traits share common pathways and component genes are potentially regulated by common TFs and microRNA. The result also indicated that the central nervous system has a critical role in diabetes pathogenesis. The findings will be important in formulating novel hypotheses for guiding follow-up studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1515-3) contains supplementary material, which is available to authorized users.

Toll-like receptor signaling in vertebrates: testing the integration of protein, complex, and pathway data in the protein ontology framework

The Protein Ontology (PRO) provides terms for and supports annotation of species-specific protein complexes in an ontology framework that relates them both to their components and to species-independent families of complexes. Comprehensive curation of experimentally known forms and annotations thereof is expected to expose discrepancies, differences, and gaps in our knowledge. We have annotated the early events of innate immune signaling mediated by Toll-Like Receptor 3 and 4 complexes in human, mouse, and chicken. The resulting ontology and annotation data set has allowed us to identify species-specific gaps in experimental data and possible functional differences between species, and to employ inferred structural and functional relationships to suggest plausible resolutions of these discrepancies and gaps.

Capturing domain knowledge from multiple sources: the rare bone disorders use case

Background

Lately, ontologies have become a fundamental building block in the process of formalising and storing complex biomedical information. The community-driven ontology curation process, however, ignores the possibility of multiple communities building, in parallel, conceptualisations of the same domain, and thus providing slightly different perspectives on the same knowledge. The individual nature of this effort leads to the need of a mechanism to enable us to create an overarching and comprehensive overview of the different perspectives on the domain knowledge.

Results

We introduce an approach that enables the loose integration of knowledge emerging from diverse sources under a single coherent interoperable resource. To accurately track the original knowledge statements, we record the provenance at very granular levels. We exemplify the approach in the rare bone disorders domain by proposing the Rare Bone Disorders Ontology (RBDO). Using RBDO, researchers are able to answer queries, such as: “What phenotypes describe a particular disorder and are common to all sources?” or to understand similarities between disorders based on divergent groupings (classifications) provided by the underlying sources.

Availability

RBDO is available at http://purl.org/skeletome/rbdo. In order to support lightweight query and integration, the knowledge captured by RBDO has also been made available as a SPARQL Endpoint at http://bio-lark.org/se_skeldys.html.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation.

Database URL:http://causalbionet.com

Assessing the impact of mutations found in next generation sequencing data over human signaling pathways

Modern sequencing technologies produce increasingly detailed data on genomic variation. However, conventional methods for relating either individual variants or mutated genes to phenotypes present known limitations given the complex, multigenic nature of many diseases or traits. Here we present PATHiVar, a web-based tool that integrates genomic variation data with gene expression tissue information. PATHiVar constitutes a new generation of genomic data analysis methods that allow studying variants found in next generation sequencing experiment in the context of signaling pathways. Simple Boolean models of pathways provide detailed descriptions of the impact of mutations in cell functionality so as, recurrences in functionality failures can easily be related to diseases, even if they are produced by mutations in different genes. Patterns of changes in signal transmission circuits, often unpredictable from individual genes mutated, correspond to patterns of affected functionalities that can be related to complex traits such as disease progression, drug response, etc. PATHiVar is available at: http://pathivar.babelomics.org.

DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380 000 associations between >16 000 genes and 13 000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/

A review on computational systems biology of pathogen-host interactions

Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.

Transcriptome markers of viral persistence in naturally-infected andes virus (bunyaviridae) seropositive long-tailed pygmy rice rats

Long-tailed pygmy rice rats (Oligoryzomys longicaudatus) are principal reservoir hosts of Andes virus (ANDV) (Bunyaviridae), which causes most hantavirus cardiopulmonary syndrome cases in the Americas. To develop tools for the study of the ANDV-host interactions, we used RNA-Seq to generate a de novo transcriptome assembly. Splenic RNA from five rice rats captured in Chile, three of which were ANDV-infected, was used to generate an assembly of 66,173 annotated transcripts, including noncoding RNAs. Phylogenetic analysis of selected predicted proteins showed similarities to those of the North American deer mouse (Peromyscus maniculatus), the principal reservoir of Sin Nombre virus (SNV). One of the infected rice rats had about 50-fold more viral burden than the others, suggesting acute infection, whereas the remaining two had levels consistent with persistence. Differential expression analysis revealed distinct signatures among the infected rodents. The differences could be due to 1) variations in viral load, 2) dimorphic or reproductive differences in splenic homing of immune cells, or 3) factors of unknown etiology. In the two persistently infected rice rats, suppression of the JAK-STAT pathway at Stat5b and Ccnot1, elevation of Casp1, RIG-I pathway factors Ppp1cc and Mff, and increased FC receptor-like transcripts occurred. Caspase-1 and Stat5b activation pathways have been shown to stimulate T helper follicular cell (T_FH) development in other species. These data are also consistent with reports suggestive of T_FH stimulation in deer mice experimentally infected with hantaviruses. In the remaining acutely infected rice rat, the apoptotic pathway marker Cox6a1 was elevated, and putative anti-viral factors Abcb1a, Fam46c, Spp1, Rxra, Rxrb, Trmp2 and Trim58 were modulated. Transcripts for preproenkephalin (Prenk) were reduced, which may be predictive of an increased T cell activation threshold. Taken together, this transcriptome dataset will permit rigorous examination of rice rat-ANDV interactions and may lead to better understanding of virus ecology.

The EBI Search engine: providing search and retrieval functionality for biological data from EMBL-EBI

The European Bioinformatics Institute (EMBL-EBI-https://www.ebi.ac.uk) provides free and unrestricted access to data across all major areas of biology and biomedicine. Searching and extracting knowledge across these domains requires a fast and scalable solution that addresses the requirements of domain experts as well as casual users. We present the EBI Search engine, referred to here as 'EBI Search', an easy-to-use fast text search and indexing system with powerful data navigation and retrieval capabilities. API integration provides access to analytical tools, allowing users to further investigate the results of their search. The interconnectivity that exists between data resources at EMBL-EBI provides easy, quick and precise navigation and a better understanding of the relationship between different data types including sequences, genes, gene products, proteins, protein domains, protein families, enzymes and macromolecular structures, together with relevant life science literature.

Identification of endogenous metabolites in human sperm cells using proton nuclear magnetic resonance ((1) H-NMR) spectroscopy and gas chromatography-mass spectrometry (GC-MS)

The objective of this study was to contribute to the first comprehensive metabolomic characterization of the human sperm cell through the application of two untargeted platforms based on proton nuclear magnetic resonance ((1) H-NMR) spectroscopy and gas chromatography coupled to mass spectrometry (GC-MS). Using these two complementary strategies, we were able to identify a total of 69 metabolites, of which 42 were identified using NMR, 27 using GC-MS and 4 by both techniques. The identity of some of these metabolites was further confirmed by two-dimensional (1) H-(1) H homonuclear correlation spectroscopy (COSY) and (1) H-(13) C heteronuclear single-quantum correlation (HSQC) spectroscopy. Most of the metabolites identified are reported here for the first time in mature human spermatozoa. The relationship between the metabolites identified and the previously reported sperm proteome was also explored. Interestingly, overrepresented pathways included not only the metabolism of carbohydrates, but also of lipids and lipoproteins. Of note, a large number of the metabolites identified belonged to the amino acids, peptides and analogues super class. The identification of this initial set of metabolites represents an important first step to further study their function in male gamete physiology and to explore potential reasons for dysfunction in future studies. We also demonstrate that the application of NMR and MS provides complementary results, thus constituting a promising strategy towards the completion of the human sperm cell metabolome.

Wnt directs the endosomal flux of LDL-derived cholesterol and lipid droplet homeostasis

The Wnt pathway, which controls crucial steps of the development and differentiation programs, has been proposed to influence lipid storage and homeostasis. In this paper, using an unbiased strategy based on high-content genome-wide RNAi screens that monitored lipid distribution and amounts, we find that Wnt3a regulates cellular cholesterol. We show that Wnt3a stimulates the production of lipid droplets and that this stimulation strictly depends on endocytosed, LDL-derived cholesterol and on functional early and late endosomes. We also show that Wnt signaling itself controls cholesterol endocytosis and flux along the endosomal pathway, which in turn modulates cellular lipid homeostasis. These results underscore the importance of endosome functions for LD formation and reveal a previously unknown regulatory mechanism of the cellular programs controlling lipid storage and endosome transport under the control of Wnt signaling.

Sample multiplexing with cysteine-selective approaches: cysDML and cPILOT

Cysteine-selective proteomics approaches simplify complex protein mixtures and improve the chance of detecting low abundant proteins. It is possible that cysteinyl-peptide/protein enrichment methods could be coupled to isotopic labeling and isobaric tagging methods for quantitative proteomics analyses in as few as two or up to 10 samples, respectively. Here we present two novel cysteine-selective proteomics approaches: cysteine-selective dimethyl labeling (cysDML) and cysteine-selective combined precursor isotopic labeling and isobaric tagging (cPILOT). CysDML is a duplex precursor quantification technique that couples cysteinyl-peptide enrichment with on-resin stable-isotope dimethyl labeling. Cysteine-selective cPILOT is a novel 12-plex workflow based on cysteinyl-peptide enrichment, on-resin stable-isotope dimethyl labeling, and iodoTMT tagging on cysteine residues. To demonstrate the broad applicability of the approaches, we applied cysDML and cPILOT methods to liver tissues from an Alzheimer's disease (AD) mouse model and wild-type (WT) controls. From the cysDML experiments, an average of 850 proteins were identified and 594 were quantified, whereas from the cPILOT experiment, 330 and 151 proteins were identified and quantified, respectively. Overall, 2259 unique total proteins were detected from both cysDML and cPILOT experiments. There is tremendous overlap in the proteins identified and quantified between both experiments, and many proteins have AD/WT fold-change values that are within ~20% error. A total of 65 statistically significant proteins are differentially expressed in the liver proteome of AD mice relative to WT. The performance of cysDML and cPILOT are demonstrated and advantages and limitations of using multiple duplex experiments versus a single 12-plex experiment are highlighted.

Multiset Statistics for Gene Set Analysis

An important data analysis task in statistical genomics involves the integration of genome-wide gene-level measurements with preexisting data on the same genes. A wide variety of statistical methodologies and computational tools have been developed for this general task. We emphasize one particular distinction among methodologies, namely whether they process gene sets one at a time (uniset) or simultaneously via some multiset technique. Owing to the complexity of collections of gene sets, the multiset approach offers some advantages, as it naturally accommodates set-size variations and among-set overlaps. However, this approach presents both computational and inferential challenges. After reviewing some statistical issues that arise in uniset analysis, we examine two model-based multiset methods for gene list data.

Properties of protein drug target classes

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein's sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein's sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins' hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme.

Increased expression of interferon signaling genes in the bone marrow microenvironment of myelodysplastic syndromes

Introduction

The bone marrow (BM) microenvironment plays an important role in the pathogenesis of myelodysplastic syndromes (MDS) through a reciprocal interaction with resident BM hematopoietic cells. We investigated the differences between BM mesenchymal stromal cells (MSCs) in MDS and normal individuals and identified genes involved in such differences.

Materials and Methods

BM-derived MSCs from 7 MDS patients (3 RCMD, 3 RAEB-1, and 1 RAEB-2) and 7 controls were cultured. Global gene expression was analyzed using a microarray.

Result

We found 314 differentially expressed genes (DEGs) in RCMD vs. control, 68 in RAEB vs. control, and 51 in RAEB vs. RCMD. All comparisons were clearly separated from one another by hierarchical clustering. The overall similarity between differential expression signatures from the RCMD vs. control comparison and the RAEB vs. control comparison was highly significant (p = 0), which indicates a common transcriptomic response in these two MDS subtypes. RCMD and RAEB simultaneously showed an up-regulation of interferon alpha/beta signaling and the ISG15 antiviral mechanism, and a significant fraction of the RAEB vs. control DEGs were also putative targets of transcription factors IRF and ICSBP. Pathways that involved RNA polymerases I and III and mitochondrial transcription were down-regulated in RAEB compared to RCMD.

Conclusion

Gene expression in the MDS BM microenvironment was different from that in normal BM and exhibited altered expression according to disease progression. The present study provides genetic evidence that inflammation and immune dysregulation responses that involve the interferon signaling pathway in the BM microenvironment are associated with MDS pathogenesis, which suggests BM MSCs as a possible therapeutic target in MDS.

Proteome-wide analysis of mutant p53 targets in breast cancer identifies new levels of gain-of-function that influence PARP, PCNA, and MCM4

The gain-of-function mutant p53 (mtp53) transcriptome has been studied, but, to date, no detailed analysis of the mtp53-associated proteome has been described. We coupled cell fractionation with stable isotope labeling with amino acids in cell culture (SILAC) and inducible knockdown of endogenous mtp53 to determine the mtp53-driven proteome. Our fractionation data highlight the underappreciated biology that missense mtp53 proteins R273H, R280K, and L194F are tightly associated with chromatin. Using SILAC coupled to tandem MS, we identified that R273H mtp53 expression in MDA-MB-468 breast cancer cells up- and down-regulated multiple proteins and metabolic pathways. Here we provide the data set obtained from sequencing 73,154 peptide pairs that then corresponded to 3,010 proteins detected under reciprocal labeling conditions. Importantly, the high impact regulated targets included the previously identified transcriptionally regulated mevalonate pathway proteins but also identified two new levels of mtp53 protein regulation for nontranscriptional targets. Interestingly, mtp53 depletion profoundly influenced poly(ADP ribose) polymerase 1 (PARP1) localization, with increased cytoplasmic and decreased chromatin-associated protein. An enzymatic PARP shift occurred with high mtp53 expression, resulting in increased poly-ADP-ribosylated proteins in the nucleus. Mtp53 increased the level of proliferating cell nuclear antigen (PCNA) and minichromosome maintenance 4 (MCM4) proteins without changing the amount of pcna and mcm4 transcripts. Pathway enrichment analysis ranked the DNA replication pathway above the cholesterol biosynthesis pathway as a R273H mtp53 activated proteomic target. Knowledge of the proteome diversity driven by mtp53 suggests that DNA replication and repair pathways are major targets of mtp53 and highlights consideration of combination chemotherapeutic strategies targeting cholesterol biosynthesis and PARP inhibition.

Resources, challenges and way forward in rare mitochondrial diseases research

Over 300 million people are affected by about 7000 rare diseases globally. There are tremendous resource limitations and challenges in driving research and drug development for rare diseases. Hence, innovative approaches are needed to identify potential solutions. This review focuses on the resources developed over the past years for analysis of genome data towards understanding disease biology especially in the context of mitochondrial diseases, given that mitochondria are central to major cellular pathways and their dysfunction leads to a broad spectrum of diseases. Platforms for collaboration of research groups, clinicians and patients and the advantages of community collaborative efforts in addressing rare diseases are also discussed. The review also describes crowdsourcing and crowdfunding efforts in rare diseases research and how the upcoming initiatives for understanding disease biology including analyses of large number of genomes are also applicable to rare diseases.

Effects of statin on circulating microRNAome and predicted function regulatory network in patients with unstable angina

BACKGROUND

Statin therapy plays a pivotal role in stabilizing the plaque for unstable angina (UA) patients although its mechanism(s) remains largely unexplored. Here we aim to identify microRNAs (miRNAs) mediating the protective effect of statins in UA patients.

METHODS

MiRNAs Array was carried out to compare the circulating whole blood miRNA profile of UA patients treated with (n = 10) and without statin (n = 10) and plasma miRNA profile UA patients treated with (n = 5) and without statin (n = 5). 22 whole blood miRNAs and 19 plasma miRNAs were found significantly upregulated in statin group. Targets of these miRNAs were predicted by algoritms: Targetscan, Miranda and Diana microT, then clustered according to functions and cell types by using the Database for Annotation, Visualization and Integrated Discovery (DAVID). To reveal the enriched function pathways in human atherosclerotic plaque, we analyzed microarray data from GEO database, Coronary atherosclerotic plaque (n = 80); macrophages in ruptured plaque (n = 11); carotid atheroma plaque (n = 64); advanced carotid atherosclerotic plaque (n = 29) using Reactome database. Integrated analysis indicated that statin induced miRNAs mainly regulate the signaling pathways of Rho GTPase and hemostasis in human atherosclerotic lesion. In vulnerable plaque, additional immune system signaling was also targeted.

RESULTS

The data showed target genes regulated by these statin induced miRNAs majorly expressed in i) plaque macrophage and platelet, where they were involved in hemostasis process; ii) in monocyte to regulate NGF apoptosis; iii) and in endothelial cell function in Rho GTPase pathway. Integrate analysis indicated that statin induced miRNAs mainly regulate the signaling pathways of Rho GTPase and hemostasis in human atherosclerotic lesion.

CONCLUSIONS

Our study suggest that statin induces the expression of multiple miRNAs in the circulation of UA patient, which play important roles by regulating signal pathways critical for the pathogenesis of UA.

Developing novel methods to search for substrates of protein kinases such as Rho-kinase

Protein phosphorylation is a major and essential post-translational modification in eukaryotic cells that plays a critical role in various cellular processes. Recent progresses in mass spectrometry techniques have enabled the effective identification and analysis of protein phosphorylation. Mass spectrometry-based approaches in investigating protein phosphorylation are very powerful and informative and can further improve our understanding of protein phosphorylation as a whole, but they cannot determine the upstream kinases involved. We introduce several studies that attempted to uncover the relationships between various kinases of interest and substrates, including two methods we developed: an in vitro approach termed the kinase-interacting substrate screening (KISS) method and an in vivo approach termed the phosphatase inhibitor and kinase inhibitor substrate screening (PIKISS) method. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.

Reciprocal interaction of Wnt and RXR-α pathways in hepatocyte development and hepatocellular carcinoma

Genomic analysis of human hepatocellular carcinoma (HCC) is potentially confounded by the differentiation state of the hepatic cell-of-origin. Here we integrated genomic analysis of mouse HCC (with defined cell-of-origin) along with normal development. We found a major shift in expression of Wnt and RXR-α pathway genes (up and down, respectively) coincident with the transition from hepatoblasts to hepatocytes. A combined Wnt and RXR-α gene signature categorized HCCs into two subtypes (high Wnt, low RXR-α and low Wnt, high RXR-α), which matched cell-of-origin in mouse models and the differentiation state of human HCC. Suppression of RXR-α levels in hepatocytes increased Wnt signaling and enhanced tumorigenicity, whereas ligand activation of RXR-α achieved the opposite. These results corroborate that there are two main HCC subtypes that correspond to the degree of hepatocyte differentation and that RXR-α, in part via Wnt signaling, plays a key functional role in the hepatocyte-like subtype and potentially could serve as a selective therapeutic target.

The shortest path is not the one you know: application of biological network resources in precision oncology research

Several decades of molecular biology research have delivered a wealth of detailed descriptions of molecular interactions in normal and tumour cells. This knowledge has been functionally organised and assembled into dedicated biological pathway resources that serve as an invaluable tool, not only for structuring the information about molecular interactions but also for making it available for biological, clinical and computational studies. With the advent of high-throughput molecular profiling of tumours, close to complete molecular catalogues of mutations, gene expression and epigenetic modifications are available and require adequate interpretation. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular profiles of tumours. Making sense out of these descriptions requires biological pathway resources for functional interpretation of the data. In this review, we describe the available biological pathway resources, their characteristics in terms of construction mode, focus, aims and paradigms of biological knowledge representation. We present a new resource that is focused on cancer-related signalling, the Atlas of Cancer Signalling Networks. We briefly discuss current approaches for data integration, visualisation and analysis, using biological networks, such as pathway scoring, guilt-by-association and network propagation. Finally, we illustrate with several examples the added value of data interpretation in the context of biological networks and demonstrate that it may help in analysis of high-throughput data like mutation, gene expression or small interfering RNA screening and can guide in patients stratification. Finally, we discuss perspectives for improving precision medicine using biological network resources and tools. Taking into account the information about biological signalling machinery in cells may help to better interpret molecular patterns of tumours and enable to put precision oncology into general clinical practice.

Quantitative and logic modelling of molecular and gene networks

Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.

Biological databases for human research

The completion of the Human Genome Project lays a foundation for systematically studying the human genome from evolutionary history to precision medicine against diseases. With the explosive growth of biological data, there is an increasing number of biological databases that have been developed in aid of human-related research. Here we present a collection of human-related biological databases and provide a mini-review by classifying them into different categories according to their data types. As human-related databases continue to grow not only in count but also in volume, challenges are ahead in big data storage, processing, exchange and curation.

Biochemical functional predictions for protein structures of unknown or uncertain function

With the exponential growth in the determination of protein sequences and structures via genome sequencing and structural genomics efforts, there is a growing need for reliable computational methods to determine the biochemical function of these proteins. This paper reviews the efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins. While sequence- and three-dimensional-structure-based methods for protein function prediction have been reviewed previously, the recent trends in local structure-based methods have received less attention. These local structure-based methods are the primary focus of this review. Computational methods have been developed to predict the residues important for catalysis and the local spatial arrangements of these residues can be used to identify protein function. In addition, the combination of different types of methods can help obtain more information and better predictions of function for proteins of unknown function. Global initiatives, including the Enzyme Function Initiative (EFI), COMputational BRidges to EXperiments (COMBREX), and the Critical Assessment of Function Annotation (CAFA), are evaluating and testing the different approaches to predicting the function of proteins of unknown function. These initiatives and global collaborations will increase the capability and reliability of methods to predict biochemical function computationally and will add substantial value to the current volume of structural genomics data by reducing the number of absent or inaccurate functional annotations.

Proteomic analysis and identification of cellular interactors of the giant ubiquitin ligase HERC2

HERC2 is a large E3 ubiquitin ligase with multiple structural domains that has been implicated in an array of cellular processes. Mutations in HERC2 are linked to developmental delays and impairment caused by nervous system dysfunction, such as Angelman Syndrome and autism-spectrum disorders. However, HERC2 cellular activity and regulation remain poorly understood. We used a broad proteomic approach to survey the landscape of cellular proteins that interact with HERC2. We identified nearly 300 potential interactors, a subset of which we validated binding to HERC2. The potential HERC2 interactors included the eukaryotic translation initiation factor 3 complex, the intracellular transport COPI coatomer complex, the glycogen regulator phosphorylase kinase, beta-catenin, PI3 kinase, and proteins involved in fatty acid transport and iron homeostasis. Through a complex bioinformatic analysis of potential interactors, we linked HERC2 to cellular processes including intracellular protein trafficking and transport, metabolism of cellular energy, and protein translation. Given its size, multidomain structure, and association with various cellular activities, HERC2 may function as a scaffold to integrate protein complexes and bridge critical cellular pathways. This work provides a significant resource with which to interrogate HERC2 function more deeply and evaluate its contributions to mechanisms governing cellular homeostasis and disease.

Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study

The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in a cellular environment. Merging and scoring of data are commonly required operations after querying for the details of specific molecular interactions, to remove redundancy and assess the strength of accompanying experimental evidence. We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative-molecular interaction standards. In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.

A genomic approach to study down syndrome and cancer inverse comorbidity: untangling the chromosome 21

Down syndrome (DS), one of the most common birth defects and the most widespread genetic cause of intellectual disabilities, is caused by extra genetic material on chromosome 21 (HSA21). The increased genomic dosage of trisomy 21 is thought to be responsible for the distinct DS phenotypes, including an increased risk of developing some types of childhood leukemia and germ cell tumors. Patients with DS, however, have a strikingly lower incidence of many other solid tumors. We hypothesized that the third copy of genes located in HSA21 may have an important role on the protective effect that DS patients show against most types of solid tumors. Focusing on Copy Number Variation (CNV) array data, we have generated frequencies of deleted regions in HSA21 in four different tumor types from which DS patients have been reported to be protected. We describe three different regions of deletion pointing to a set of candidate genes that could explain the inverse comorbidity phenomenon between DS and solid tumors. In particular we found RCAN1 gene in Wilms tumors and a miRNA cluster containing miR-99A, miR-125B2 and miR-LET7C in lung, breast, and melanoma tumors as the main candidates for explaining the inverse comorbidity observed between solid tumors and DS.

Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin

BACKGROUND

Although analysis pipelines have been developed to use RNA-seq to identify long non-coding RNAs (lncRNAs), inference of their biological and pathological relevance remains a challenge. As a result, most transcriptome studies of autoimmune disease have only assessed protein-coding transcripts.

RESULTS

We used RNA-seq data from 99 lesional psoriatic, 27 uninvolved psoriatic, and 90 normal skin biopsies, and applied computational approaches to identify and characterize expressed lncRNAs. We detect 2,942 previously annotated and 1,080 novel lncRNAs which are expected to be skin specific. Notably, over 40% of the novel lncRNAs are differentially expressed and the proportions of differentially expressed transcripts among protein-coding mRNAs and previously-annotated lncRNAs are lower in psoriasis lesions versus uninvolved or normal skin. We find that many lncRNAs, in particular those that are differentially expressed, are co-expressed with genes involved in immune related functions, and that novel lncRNAs are enriched for localization in the epidermal differentiation complex. We also identify distinct tissue-specific expression patterns and epigenetic profiles for novel lncRNAs, some of which are shown to be regulated by cytokine treatment in cultured human keratinocytes.

CONCLUSIONS

Together, our results implicate many lncRNAs in the immunopathogenesis of psoriasis, and our results provide a resource for lncRNA studies in other autoimmune diseases.

Network representations of immune system complexity

The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single‐cell responses to increasingly complex networks of in vivo cellular interaction, positioning, and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather nonlinear behaviors arising from dynamic, feedback‐regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels, while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular‐ and organism‐level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. WIREs Syst Biol Med 2015, 7:13–38. doi: 10.1002/wsbm.1288

This article is categorized under: 1

Analytical and Computational Methods > Computational Methods

2

Laboratory Methods and Technologies > Macromolecular Interactions, Methods

Systems Biology and Synthetic Biology: A New Epoch for Toxicology Research

Systems biology and synthetic biology are emerging disciplines which are becoming increasingly utilised in several areas of bioscience. Toxicology is beginning to benefit from systems biology and we suggest in the future that is will also benefit from synthetic biology. Thus, a new era is on the horizon. This review illustrates how a suite of innovative techniques and tools can be applied to understanding complex health and toxicology issues. We review limitations confronted by the traditional computational approaches to toxicology and epidemiology research, using polycyclic aromatic hydrocarbons (PAHs) and their effects on adverse birth outcomes as an illustrative example. We introduce how systems toxicology (and their subdisciplines, genomic, proteomic, and metabolomic toxicology) will help to overcome such limitations. In particular, we discuss the advantages and disadvantages of mathematical frameworks that computationally represent biological systems. Finally, we discuss the nascent discipline of synthetic biology and highlight relevant toxicological centred applications of this technique, including improvements in personalised medicine. We conclude this review by presenting a number of opportunities and challenges that could shape the future of these rapidly evolving disciplines.

Evolutionary constraint and disease associations of post-translational modification sites in human genomes

Interpreting the impact of human genome variation on phenotype is challenging. The functional effect of protein-coding variants is often predicted using sequence conservation and population frequency data, however other factors are likely relevant. We hypothesized that variants in protein post-translational modification (PTM) sites contribute to phenotype variation and disease. We analyzed fraction of rare variants and non-synonymous to synonymous variant ratio (Ka/Ks) in 7,500 human genomes and found a significant negative selection signal in PTM regions independent of six factors, including conservation, codon usage, and GC-content, that is widely distributed across tissue-specific genes and function classes. PTM regions are also enriched in known disease mutations, suggesting that PTM variation is more likely deleterious. PTM constraint also affects flanking sequence around modified residues and increases around clustered sites, indicating presence of functionally important short linear motifs. Using target site motifs of 124 kinases, we predict that at least ∼180,000 motif-breaker amino acid residues that disrupt PTM sites when substituted, and highlight kinase motifs that show specific negative selection and enrichment of disease mutations. We provide this dataset with corresponding hypothesized mechanisms as a community resource. As an example of our integrative approach, we propose that PTPN11 variants in Noonan syndrome aberrantly activate the protein by disrupting an uncharacterized cluster of phosphorylation sites. Further, as PTMs are molecular switches that are modulated by drugs, we study mutated binding sites of PTM enzymes in disease genes and define a drug-disease network containing 413 novel predicted disease-gene links.

Prediction of signaling cross-talks contributing to acquired drug resistance in breast cancer cells by Bayesian statistical modeling

BACKGROUND

Initial success of inhibitors targeting oncogenes is often followed by tumor relapse due to acquired resistance. In addition to mutations in targeted oncogenes, signaling cross-talks among pathways play a vital role in such drug inefficacy. These include activation of compensatory pathways and altered activities of key effectors in other cell survival and growth-associated pathways.

RESULTS

We propose a computational framework using Bayesian modeling to systematically characterize potential cross-talks among breast cancer signaling pathways. We employed a fully Bayesian approach known as the p 1-model to infer posterior probabilities of gene-pairs in networks derived from the gene expression datasets of ErbB2-positive breast cancer cell-lines (parental, lapatinib-sensitive cell-line SKBR3 and the lapatinib-resistant cell-line SKBR3-R, derived from SKBR3). Using this computational framework, we searched for cross-talks between EGFR/ErbB and other signaling pathways from Reactome, KEGG and WikiPathway databases that contribute to lapatinib resistance. We identified 104, 188 and 299 gene-pairs as putative drug-resistant cross-talks, respectively, each comprised of a gene in the EGFR/ErbB signaling pathway and a gene from another signaling pathway, that appear to be interacting in resistant cells but not in parental cells. In 168 of these (distinct) gene-pairs, both of the interacting partners are up-regulated in resistant conditions relative to parental conditions. These gene-pairs are prime candidates for novel cross-talks contributing to lapatinib resistance. They associate EGFR/ErbB signaling with six other signaling pathways: Notch, Wnt, GPCR, hedgehog, insulin receptor/IGF1R and TGF- β receptor signaling. We conducted a literature survey to validate these cross-talks, and found evidence supporting a role for many of them in contributing to drug resistance. We also analyzed an independent study of lapatinib resistance in the BT474 breast cancer cell-line and found the same signaling pathways making cross-talks with the EGFR/ErbB signaling pathway as in the primary dataset.

CONCLUSIONS

Our results indicate that the activation of compensatory pathways can potentially cause up-regulation of EGFR/ErbB pathway genes (counteracting the inhibiting effect of lapatinib) via signaling cross-talk. Thus, the up-regulated members of these compensatory pathways along with the members of the EGFR/ErbB signaling pathway are interesting as potential targets for designing novel anti-cancer therapeutics.

What's that gene (or protein)? Online resources for exploring functions of genes, transcripts, and proteins

The genomic era has enabled research projects that use approaches including genome-scale screens, microarray analysis, next-generation sequencing, and mass spectrometry-based proteomics to discover genes and proteins involved in biological processes. Such methods generate data sets of gene, transcript, or protein hits that researchers wish to explore to understand their properties and functions and thus their possible roles in biological systems of interest. Recent years have seen a profusion of Internet-based resources to aid this process. This review takes the viewpoint of the curious biologist wishing to explore the properties of protein-coding genes and their products, identified using genome-based technologies. Ten key questions are asked about each hit, addressing functions, phenotypes, expression, evolutionary conservation, disease association, protein structure, interactors, posttranslational modifications, and inhibitors. Answers are provided by presenting the latest publicly available resources, together with methods for hit-specific and data set-wide information retrieval, suited to any genome-based analytical technique and experimental species. The utility of these resources is demonstrated for 20 factors regulating cell proliferation. Results obtained using some of these are discussed in more depth using the p53 tumor suppressor as an example. This flexible and universally applicable approach for characterizing experimental hits helps researchers to maximize the potential of their projects for biological discovery.

Copy number variable microRNAs in schizophrenia and their neurodevelopmental gene targets

BACKGROUND

MicroRNAs (miRNAs) are key regulators of gene expression in the human genome and may contribute to risk for neuropsychiatric disorders. miRNAs play an acknowledged role in the strongest of genetic risk factors for schizophrenia, 22q11.2 deletions. We hypothesized that in schizophrenia there would be an enrichment of other rare copy number variants (CNVs) that overlap miRNAs.

METHODS

Using high-resolution genome-wide microarrays and rigorous methods, we compared the miRNA content of rare CNVs in well-characterized cohorts of schizophrenia cases (n = 420) and comparison subjects, excluding 22q11.2 CNVs. We also performed a gene-set enrichment analysis of the predicted miRNA target genes.

RESULTS

The schizophrenia group was enriched for the proportion of individuals with a rare CNV overlapping a miRNA (3.29-fold increase over comparison subjects, p < .0001). The presence of a rare CNV overlapping a miRNA remained a significant predictor of schizophrenia case status (p = .0072) in a multivariate logistic regression model correcting for total CNV size. In contrast, comparable analyses correcting for CNV size showed no enrichment of rare CNVs overlapping protein-coding genes. A gene-set enrichment analysis indicated that predicted target genes of recurrent CNV-overlapped miRNAs in schizophrenia may be functionally enriched for neurodevelopmental processes, including axonogenesis and neuron projection development. Predicted gene targets driving these results included CAPRIN1, NEDD4, NTRK2, PAK2, RHOA, and SYNGAP1.

CONCLUSIONS

These data are the first to demonstrate a genome-wide role for CNVs overlapping miRNAs in the genetic risk for schizophrenia. The results provide support for an expanded multihit model of causation, with potential implications for miRNA-based therapeutics.

Identification of B cells participated in the mechanism of postmenopausal women osteoporosis using microarray analysis

To further understand the molecular mechanism of lymphocytes B cells in postmenopausal women osteoporosis. Microarray data (GSE7429) were downloaded from Gene Expression Omnibus, in which B cells were separated from the whole blood of postmenopausal women, including 10 with high bone mineral density (BMD) and 10 with low BMD. Differentially expressed genes (DEGs) between high and low BMD women were identified by Student's t-test, and P < 0.01 was used as the significant criterion. Functional enrichment analysis was performed for up- and down-regulated DEGs using KEGG, REACTOME, and Gene Ontology (GO) databases. Protein-protein interaction network (PPI) of up- and down-regulated DEGs was respectively constructed by Cytoscape software using the STRING data. Total of 169 up-regulated and 69 down-regulated DEGs were identified. Functional enrichment analysis indicated that the genes (ITPA, ATIC, UMPS, HPRT1, COX10 and COX15) might participate in metabolic pathways, MAP3K10 and MAP3K9 might participate in the activation of JNKK activity, COX10 and COX15 might involve in mitochondrial electron transport, and ATIC, UMPS and HPRT1 might involve in transferase activity. MAPK3, ITPA, ATIC, UMPS and HPRT1 with a higher degree in PPI network were identified. MAPK3, MAP3K10, MAP3K9, COX10, COX15, ATIC, UMPS and HPRT1 might participate in the pathogenesis of osteoporosis.