The ConsensusPathDB interaction database: 2013 update

Characterization of piRNAs across postnatal development in mouse brain

PIWI-interacting RNAs (piRNAs) are responsible for maintaining the genome stability by silencing retrotransposons in germline tissues– where piRNAs were first discovered and thought to be restricted. Recently, novel functions were reported for piRNAs in germline and somatic cells. Using deep sequencing of small RNAs and CAGE of postnatal development of mouse brain, we identified piRNAs only in adult mouse brain. These piRNAs have similar sequence length as those of MILI-bound piRNAs. In addition, we predicted novel candidate regulators and putative targets of adult brain piRNAs.

Hypoxia regulates global membrane protein endocytosis through caveolin-1 in cancer cells

Hypoxia promotes tumour aggressiveness and resistance of cancers to oncological treatment. The identification of cancer cell internalizing antigens for drug targeting to the hypoxic tumour niche remains a challenge of high clinical relevance. Here we show that hypoxia down-regulates the surface proteome at the global level and, more specifically, membrane proteome internalization. We find that hypoxic down-regulation of constitutive endocytosis is HIF-independent, and involves caveolin-1-mediated inhibition of dynamin-dependent, membrane raft endocytosis. Caveolin-1 overexpression inhibits protein internalization, suggesting a general negative regulatory role of caveolin-1 in endocytosis. In contrast to this global inhibitory effect, we identify several proteins that can override caveolin-1 negative regulation, exhibiting increased internalization at hypoxia. We demonstrate antibody-mediated cytotoxin delivery and killing specifically of hypoxic cells through one of these proteins, carbonic anhydrase IX. Our data reveal that caveolin-1 modulates cell-surface proteome turnover at hypoxia with potential implications for specific targeting of the hypoxic tumour microenvironment.

Hypoxia promotes tumour aggressiveness and resistance of cancers to oncological treatment. Here, the authors show that caveolin-1 can down-regulate global membrane protein endocytosis in hypoxic cells with potential implications for targeting the hypoxic 3microenvironment of aggressive tumours.

Discovery of Metabolic Biomarkers for Duchenne Muscular Dystrophy within a Natural History Study

Serum metabolite profiling in Duchenne muscular dystrophy (DMD) may enable discovery of valuable molecular markers for disease progression and treatment response. Serum samples from 51 DMD patients from a natural history study and 22 age-matched healthy volunteers were profiled using liquid chromatography coupled to mass spectrometry (LC-MS) for discovery of novel circulating serum metabolites associated with DMD. Fourteen metabolites were found significantly altered (1% false discovery rate) in their levels between DMD patients and healthy controls while adjusting for age and study site and allowing for an interaction between disease status and age. Increased metabolites included arginine, creatine and unknown compounds at m/z of 357 and 312 while decreased metabolites included creatinine, androgen derivatives and other unknown yet to be identified compounds. Furthermore, the creatine to creatinine ratio is significantly associated with disease progression in DMD patients. This ratio sharply increased with age in DMD patients while it decreased with age in healthy controls. Overall, this study yielded promising metabolic signatures that could prove useful to monitor DMD disease progression and response to therapies in the future.

ToxDB: pathway-level interpretation of drug-treatment data

Motivation: Extensive drug treatment gene expression data have been generated in order to identify biomarkers that are predictive for toxicity or to classify compounds. However, such patterns are often highly variable across compounds and lack robustness. We and others have previously shown that supervised expression patterns based on pathway concepts rather than unsupervised patterns are more robust and can be used to assess toxicity for entire classes of drugs more reliably.

Results: We have developed a database, ToxDB, for the analysis of the functional consequences of drug treatment at the pathway level. We have collected 2694 pathway concepts and computed numerical response scores of these pathways for 437 drugs and chemicals and 7464 different experimental conditions. ToxDB provides functionalities for exploring these pathway responses by offering tools for visualization and differential analysis allowing for comparisons of different treatment parameters and for linking this data with toxicity annotation and chemical information.

Database URL:http://toxdb.molgen.mpg.de

Whole-exome sequencing in an isolated population from the Dalmatian island of Vis

We have whole-exome sequenced 176 individuals from the isolated population of the island of Vis in Croatia in order to describe exonic variation architecture. We found 290 577 single nucleotide variants (SNVs), 65% of which are singletons, low frequency or rare variants. A total of 25 430 (9%) SNVs are novel, previously not catalogued in NHLBI GO Exome Sequencing Project, UK10K-Generation Scotland, 1000Genomes Project, ExAC or NCBI Reference Assembly dbSNP. The majority of these variants (76%) are singletons. Comparable to data obtained from UK10K-Generation Scotland that were sequenced and analysed using the same protocols, we detected an enrichment of potentially damaging variants (non-synonymous and loss-of-function) in the low frequency and common variant categories. On average 115 (range 93–140) genotypes with loss-of-function variants, 23 (15–34) of which were homozygous, were identified per person. The landscape of loss-of-function variants across an exome revealed that variants mainly accumulated in genes on the xenobiotic-related pathways, of which majority coded for enzymes. The frequency of loss-of-function variants was additionally increased in Vis runs of homozygosity regions where variants mainly affected signalling pathways. This work confirms the isolate status of Vis population by means of whole-exome sequence and reveals the pattern of loss-of-function mutations, which resembles the trails of adaptive evolution that were found in other species. By cataloguing the exomic variants and describing the allelic structure of the Vis population, this study will serve as a valuable resource for future genetic studies of human diseases, population genetics and evolution in this population.

PRR14 is a novel activator of the PI3K pathway promoting lung carcinogenesis

Chromosomal focal amplifications often cause an increase in gene copy number, contributing to the pathogenesis of cancer. PRR14 overexpression is associated with recurrent locus amplification in lung cancer, and it correlates with a poor prognosis. We show that increased PRR14 expression promoted and reduced PRR14 expression impeded lung cancer cell proliferation. Interestingly, PRR14 cells were markedly enlarged in size and exhibited an elevated activity of the PI3-kinase/Akt/mTOR pathway, which was associated with a heightened sensitivity to the inhibitors of PI3K and mammalian target of rapamycin (mTOR). Biochemical analysis revealed that PRR14, as a proline-rich protein, binds to the Src homology 3 (SH3) domains of GRB2 resulting in PI3K activation. Significantly, two cancer patient-derived PRR14 mutants displayed considerably augmented GRB2-binding and an enhanced ability of promoting cell proliferation. Together with the in vivo data demonstrating a strong tumor-promoting activity of PRR14 and the mutants, our work uncovered this proline-rich protein as a novel activator of the PI3K pathway that promoted tumorigenesis in lung cancer.

Transcriptional signature induced by a metastasis-promoting c-Src mutant in a human breast cell line

Deletions at the C-terminus of the proto-oncogene protein c-Src kinase are found in the viral oncogene protein v-Src as well as in some advanced human colon cancers. They are associated with increased kinase activity and cellular invasiveness. Here, we analyzed the mRNA expression signature of a constitutively active C-terminal mutant of c-Src, c-Src(mt), in comparison with its wild-type protein, c-Src(wt), in the human non-transformed breast epithelial cell line MCF-10A. We demonstrated previously that the mutant altered migratory and metastatic properties. Genome-wide transcriptome analysis revealed that c-Src(mt) de-regulated the expression levels of approximately 430 mRNAs whose gene products are mainly involved in the cellular processes of migration and adhesion, apoptosis and protein synthesis. 82.9% of these genes have previously been linked to cellular migration, while the others play roles in RNA transport and splicing processes, for instance. Consistent with the transcriptome data, cells expressing c-Src(mt), but not those expressing c-Src(wt), showed the capacity to metastasize into the lungs of mice in vivo. The mRNA expression profile of c-Src(mt)-expressing cells shows significant overlap with that of various primary human tumor samples, possibly reflecting elevated Src activity in some cancerous cells. Expression of c-Src(mt) led to elevated migratory potential. We used this model system to analyze the transcriptional changes associated with an invasive cellular phenotype. These genes and pathways de-regulated by c-Src(mt) may provide suitable biomarkers or targets of therapeutic approaches for metastatic cells.

DATABASE

This project was submitted to the National Center for Biotechnology Information BioProject under ID PRJNA288540. The Illumina RNA-Seq reads are available in the National Center for Biotechnology Information Sequence Read Archive under study ID SRP060008 with accession numbers SRS977414 for MCF-10A cells, SRS977717 for mock cells, SRS978053 for c-Src(wt) cells and SRS978046 for c-Src(mt) cells.

DNA methylation levels and long-term trihalomethane exposure in drinking water: an epigenome-wide association study

Trihalomethanes (THM) are undesired disinfection byproducts (DBPs) formed during water treatment. Mice exposed to DBPs showed global DNA hypomethylation and c-myc and c-jun gene-specific hypomethylation, while evidence of epigenetic effects in humans is scarce. We explored the association between lifetime THM exposure and DNA methylation through an epigenome-wide association study. We selected 138 population-based controls from a case-control study of colorectal cancer conducted in Barcelona, Spain, exposed to average lifetime THM levels ≤85 μg/L vs. >85 μg/L (N = 68 and N = 70, respectively). Mean age of participants was 70 years, and 54% were male. Average lifetime THM level in the exposure groups was 64 and 130 µg/L, respectively. DNA was extracted from whole blood and was bisulphite converted to measure DNA methylation levels using the Illumina HumanMethylation450 BeadChip. Data preprocessing was performed using RnBeads. Methylation was compared between exposure groups using empirical Bayes moderated linear regression for CpG sites and Gaussian kernel for CpG regions. ConsensusPathDB was used for gene set enrichment. Statistically significant differences in methylation between exposure groups was found in 140 CpG sites and 30 gene-related regions, after false discovery rate <0.05 and adjustment for age, sex, methylation first principal component, and blood cell proportion. The annotated genes were localized to several cancer pathways. Among them, 29 CpGs had methylation levels associated with THM levels (|Δβ|≥0.05) located in 11 genes associated with cancer in other studies. Our results suggest that THM exposure may affect DNA methylation in genes related to tumors, including colorectal and bladder cancers. Future confirmation studies are required.

The landscape of somatic mutations in protein coding genes in apparently benign human tissues carries signatures of relaxed purifying selection

Mutations acquired during development and aging lead to inter- and intra-tissue genetic variations. Evidence linking such mutations to complex traits and diseases is rising. We detected somatic mutations in protein-coding regions in 140 benign tissue samples representing nine tissue-types (bladder, breast, liver, lung, prostate, stomach, thyroid, head and neck) and paired blood from 70 donors. A total of 80% of the samples had 2–39 mutations detectable at tissue-level resolution. Factors such as age and smoking were associated with increased burden of detectable mutations, and tissues carried signatures of distinct mutagenic processes such as oxidative DNA damage and transcription-coupled repair. Using mutational signatures, we predicted that majority of the mutations in blood originated in hematopoietic stem and early progenitor cells. Missense to silent mutations ratio and the persistence of potentially damaging mutations in expressed genes carried signatures of relaxed purifying selection. Our findings have relevance for etiology, diagnosis and treatment of diseases including cancer.

MYC-induced apoptosis in mammary epithelial cells is associated with repression of lineage-specific gene signatures

Apoptosis caused by deregulated MYC expression is a prototype example of intrinsic tumor suppression. However, it is still unclear how supraphysiological MYC expression levels engage specific sets of target genes to promote apoptosis. Recently, we demonstrated that repression of SRF target genes by MYC/MIZ1 complexes limits AKT-dependent survival signaling and contributes to apoptosis induction. Here we report that supraphysiological levels of MYC repress gene sets that include markers of basal-like breast cancer cells, but not luminal cancer cells, in a MIZ1-dependent manner. Furthermore, repressed genes are part of a conserved gene signature characterizing the basal subpopulation of both murine and human mammary gland. These repressed genes play a role in epithelium and mammary gland development and overlap with genes mediating cell adhesion and extracellular matrix organization. Strikingly, acute activation of oncogenic MYC in basal mammary epithelial cells is sufficient to induce luminal cell identity markers. We propose that supraphysiological MYC expression impacts on mammary epithelial cell identity by repressing lineage-specific target genes. Such abrupt cell identity switch could interfere with adhesion-dependent survival signaling and thus promote apoptosis in pre-malignant epithelial tissue.

Cell type-specific properties and environment shape tissue specificity of cancer genes

One of the biggest mysteries in cancer research remains why mutations in certain genes cause cancer only at specific sites in the human body. The poor correlation between the expression level of a cancer gene and the tissues in which it causes malignant transformations raises the question of which factors determine the tissue-specific effects of a mutation. Here, we explore why some cancer genes are associated only with few different cancer types (i.e., are specific), while others are found mutated in a large number of different types of cancer (i.e., are general). We do so by contrasting cellular functions of specific-cancer genes with those of general ones to identify properties that determine where in the body a gene mutation is causing malignant transformations. We identified different groups of cancer genes that did not behave as expected (i.e., DNA repair genes being tissue specific, immune response genes showing a bimodal specificity function or strong association of generally expressed genes to particular cancers). Analysis of these three groups demonstrates the importance of environmental impact for understanding why certain cancer genes are only involved in the development of some cancer types but are rarely found mutated in other types of cancer.

An Impaired Respiratory Electron Chain Triggers Down-regulation of the Energy Metabolism and De-ubiquitination of Solute Carrier Amino Acid Transporters

Hundreds of genes have been associated with respiratory chain disease (RCD), the most common inborn error of metabolism so far. Elimination of the respiratory electron chain by depleting the entire mitochondrial DNA (mtDNA, ρ(0) cells) has therefore one of the most severe impacts on the energy metabolism in eukaryotic cells. In this study, proteomic data sets including the post-translational modifications (PTMs) phosphorylation and ubiquitination were integrated with metabolomic data sets and selected enzyme activities in the osteosarcoma cell line 143B.TK(-) A shotgun based SILAC LC-MS proteomics and a targeted metabolomics approach was applied to elucidate the consequences of the ρ(0) state. Pathway and protein-protein interaction (PPI) network analyses revealed a nonuniform down-regulation of the respiratory electron chain, the tricarboxylic acid (TCA) cycle, and the pyruvate metabolism in ρ(0) cells. Metabolites of the TCA cycle were dysregulated, such as a reduction of citric acid and cis-aconitic acid (six and 2.5-fold), and an increase of lactic acid, oxalacetic acid (both twofold), and succinic acid (fivefold) in ρ(0) cells. Signaling pathways such as GPCR, EGFR, G12/13 alpha, and Rho GTPases were up-regulated in ρ(0) cells, which could be indicative for the mitochondrial retrograde response, a pathway of communication from mitochondria to the nucleus. This was supported by our phosphoproteome data, which revealed two main processes, GTPase-related signal transduction and cytoskeleton organization. Furthermore, a general de-ubiquitination in ρ(0) cells was observed, for example, 80S ribosomal proteins were in average threefold and SLC amino acid transporters fivefold de-ubiquitinated. The latter might cause the observed significant increase of amino acid levels in ρ(0) cells. We conclude that an elimination of the respiratory electron chain, e.g. mtDNA depletion, not only leads to an uneven down-regulation of mitochondrial energy pathways, but also triggers the retrograde response.

Methylomic analysis of salivary DNA in childhood ADHD identifies altered DNA methylation in VIPR2

BACKGROUND

Peripheral epigenetic marks hold promise for understanding psychiatric illness and may represent fingerprints of gene-environment interactions. We conducted an initial examination of CpG methylation variation in children with or without attention-deficit/hyperactivity disorder (ADHD).

METHODS

Children age 7-12 were recruited, screened, evaluated and assigned to ADHD or non-ADHD groups by defined research criteria. Two independent age-matched samples were examined, a discovery set (n = 92, all boys, half control, half ADHD) and a confirmation set (n = 20, half ADHD, all boys). 5-methylcytosine levels were quantified in salivary DNA using the Illumina 450 K HumanMethylation array. Genes for which multiple probes were nominally significant and had a beta difference of at least 2% were evaluated for biological relevance and prioritized for confirmation and sequence validation. Gene pathways were explored and described.

RESULTS

Two genes met the criteria for confirmation testing, VIPR2 and MYT1L; both had multiple probes meeting cutoffs and strong biological relevance. Probes on VIPR2 passed FDR correction in the confirmation set and were confirmed through bisulfite sequencing. Enrichment analysis suggested involvement of gene sets or pathways related to inflammatory processes and modulation of monoamine and cholinergic neurotransmission.

CONCLUSIONS

Although it is unknown to what extent CpG methylation seen in peripheral tissue reflect transcriptomic changes in the brain, these initial results indicate that peripheral DNA methylation markers in ADHD may be promising and suggest targeted hypotheses for future study in larger samples.

The Genetic Architecture of Murine Glutathione Transferases

Glutathione S-transferase (GST) genes play a protective role against oxidative stress and may influence disease risk and drug pharmacokinetics. In this study, massive multiscalar trait profiling across a large population of mice derived from a cross between C57BL/6J (B6) and DBA2/J (D2)—the BXD family—was combined with linkage and bioinformatic analyses to characterize mechanisms controlling GST expression and to identify downstream consequences of this variation. Similar to humans, mice show a wide range in expression of GST family members. Variation in the expression of Gsta4, Gstt2, Gstz1, Gsto1, and Mgst3 is modulated by local expression QTLs (eQTLs) in several tissues. Higher expression of Gsto1 in brain and liver of BXD strains is strongly associated (P < 0.01) with inheritance of the B6 parental allele whereas higher expression of Gsta4 and Mgst3 in brain and liver, and Gstt2 and Gstz1 in brain is strongly associated with inheritance of the D2 parental allele. Allele-specific assays confirmed that expression of Gsto1, Gsta4, and Mgst3 are modulated by sequence variants within or near each gene locus. We exploited this endogenous variation to identify coexpression networks and downstream targets in mouse and human. Through a combined systems genetics approach, we provide new insight into the biological role of naturally occurring variants in GST genes.

Selective inhibition of esophageal cancer cells by combination of HDAC inhibitors and Azacytidine

Esophageal cancers are highly aggressive tumors with poor prognosis despite some recent advances in surgical and radiochemotherapy treatment options. This study addressed the feasibility of drugs targeting epigenetic modifiers in esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) cells. We tested inhibition of histone deacetylases (HDACs) by SAHA, MS-275, and FK228, inhibition of DNA methyltransferases by Azacytidine (AZA) and Decitabine (DAC), and the effect of combination treatment using both types of drugs. The drug targets, HDAC1/2/3 and DNMT1, were expressed in normal esophageal epithelium and tumor cells of ESCC or EAC tissue specimens, as well as in non-neoplastic esophageal epithelial (Het-1A), ESCC (OE21, Kyse-270, Kyse-410), and EAC (OE33, SK-GT-4) cell lines. In vitro, HDAC activity, histone acetylation, and p21 expression were similarly affected in non-neoplastic, ESCC, and EAC cell lines post inhibitor treatment. Combined MS-275/AZA treatment, however, selectively targeted esophageal cancer cell lines by inducing DNA damage, cell viability loss, and apoptosis, and by decreasing cell migration. Non-neoplastic Het-1A cells were protected against HDACi (MS-275)/AZA treatment. RNA transcriptome analyses post MS-275 and/or AZA treatment identified novel regulated candidate genes (up: BCL6, Hes2; down: FAIM, MLKL), which were specifically associated with the treatment responses of esophageal cancer cells. In summary, combined HDACi/AZA treatment is efficient and selective for the targeting of esophageal cancer cells, despite similar target expression of normal and esophageal cancer epithelium, in vitro and in human esophageal carcinomas. The precise mechanisms of action of treatment responses involve novel candidate genes regulated by HDACi/AZA in esophageal cancer cells. Together, targeting of epigenetic modifiers in esophageal cancers may represent a potential future therapeutic approach.

High-density genotyping of immune-related loci identifies new SLE risk variants in individuals with Asian ancestry

Systemic lupus erythematosus (SLE) has a strong but incompletely understood genetic architecture. We conducted an association study with replication in 4,478 SLE cases and 12,656 controls from six East Asian cohorts to identify new SLE susceptibility loci and better localize known loci. We identified ten new loci and confirmed 20 known loci with genome-wide significance. Among the new loci, the most significant locus was GTF2IRD1-GTF2I at 7q11.23 (rs73366469, Pmeta = 3.75 × 10(-117), odds ratio (OR) = 2.38), followed by DEF6, IL12B, TCF7, TERT, CD226, PCNXL3, RASGRP1, SYNGR1 and SIGLEC6. We identified the most likely functional variants at each locus by analyzing epigenetic marks and gene expression data. Ten candidate variants are known to alter gene expression in cis or in trans. Enrichment analysis highlights the importance of these loci in B cell and T cell biology. The new loci, together with previously known loci, increase the explained heritability of SLE to 24%. The new loci share functional and ontological characteristics with previously reported loci and are possible drug targets for SLE therapeutics.

Specific and redundant activities of ETV1 and ETV4 in prostate cancer aggressiveness revealed by co-overexpression cellular contexts

Genomic rearrangements involving ETS transcription factors are found in 50–70% of prostate carcinomas. While the large majority of the rearrangements involve ERG, around 10% involve members of the PEA3 subfamily (ETV1, ETV4 and ETV5). Using a panel of prostate cancer cell lines we found co-overexpression of ETV1 and ETV4 in two cell line models of advanced prostate cancer (MDA-PCa-2b and PC3) and questioned whether these PEA3 family members would cooperate in the acquisition of oncogenic properties or show functional redundancy. Using shRNAs we found that ETV1 and ETV4 have partially overlapping functions, with ETV1 being more relevant for cell invasion and ETV4 for anchorage-independent growth. In vitro expression signatures revealed the regulation of both specific and shared candidate targets that may resemble cellular mechanisms in vivo by interaction with the same intermediate partners. By combining the phenotypic impact data and the gene expression profiles of in vitro models with clinico-pathological features and gene expression profiles of ETS-subtyped tumors, we identified a set of eight genes associated with advanced stage and a set of three genes associated with higher Gleason score, supporting an oncogenic role of ETV1 and ETV4 overexpression and revealing gene sets that may be useful as prognostic markers.

Whole exome sequencing identifies novel candidate genes that modify chronic obstructive pulmonary disease susceptibility

Background

Chronic obstructive pulmonary disease (COPD) is characterized by an irreversible airflow limitation in response to inhalation of noxious stimuli, such as cigarette smoke. However, only 15–20 % smokers manifest COPD, suggesting a role for genetic predisposition. Although genome-wide association studies have identified common genetic variants that are associated with susceptibility to COPD, effect sizes of the identified variants are modest, as is the total heritability accounted for by these variants. In this study, an extreme phenotype exome sequencing study was combined with in vitro modeling to identify COPD candidate genes.

Results

We performed whole exome sequencing of 62 highly susceptible smokers and 30 exceptionally resistant smokers to identify rare variants that may contribute to disease risk or resistance to COPD. This was a cross-sectional case-control study without therapeutic intervention or longitudinal follow-up information. We identified candidate genes based on rare variant analyses and evaluated exonic variants to pinpoint individual genes whose function was computationally established to be significantly different between susceptible and resistant smokers. Top scoring candidate genes from these analyses were further filtered by requiring that each gene be expressed in human bronchial epithelial cells (HBECs). A total of 81 candidate genes were thus selected for in vitro functional testing in cigarette smoke extract (CSE)-exposed HBECs. Using small interfering RNA (siRNA)-mediated gene silencing experiments, we showed that silencing of several candidate genes augmented CSE-induced cytotoxicity in vitro.

Conclusions

Our integrative analysis through both genetic and functional approaches identified two candidate genes (TACC2 and MYO1E) that augment cigarette smoke (CS)-induced cytotoxicity and, potentially, COPD susceptibility.

Electronic supplementary material

The online version of this article (doi:10.1186/s40246-015-0058-7) contains supplementary material, which is available to authorized users.

HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species

HitPredict is a consolidated resource of experimentally identified, physical protein–protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein–protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein–protein interactions from several species for the study of gene groups.

Database URL: http://hintdb.hgc.jp/htp

Dintor: functional annotation of genomic and proteomic data

Background

During the last decade, a great number of extremely valuable large-scale genomics and proteomics datasets have become available to the research community. In addition, dropping costs for conducting high-throughput sequencing experiments and the option to outsource them considerably contribute to an increasing number of researchers becoming active in this field. Even though various computational approaches have been developed to analyze these data, it is still a laborious task involving prudent integration of many heterogeneous and frequently updated data sources, creating a barrier for interested scientists to accomplish their own analysis.

Results

We have implemented Dintor, a data integration framework that provides a set of over 30 tools to assist researchers in the exploration of genomics and proteomics datasets. Each of the tools solves a particular task and several tools can be combined into data processing pipelines. Dintor covers a wide range of frequently required functionalities, from gene identifier conversions and orthology mappings to functional annotation of proteins and genetic variants up to candidate gene prioritization and Gene Ontology-based gene set enrichment analysis. Since the tools operate on constantly changing datasets, we provide a mechanism to unambiguously link tools with different versions of archived datasets, which guarantees reproducible results for future tool invocations. We demonstrate a selection of Dintor’s capabilities by analyzing datasets from four representative publications. The open source software can be downloaded and installed on a local Unix machine. For reasons of data privacy it can be configured to retrieve local data only. In addition, the Dintor tools are available on our public Galaxy web service at http://dintor.eurac.edu.

Conclusions

Dintor is a computational annotation framework for the analysis of genomic and proteomic datasets, providing a rich set of tools that cover the most frequently encountered tasks. A major advantage is its capability to consistently handle multiple versions of tool-associated datasets, supporting the researcher in delivering reproducible results.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2279-5) contains supplementary material, which is available to authorized users.

A novel analysis strategy for integrating methylation and expression data reveals core pathways for thyroid cancer aetiology

Background

Recently, a wide range of diseases have been associated with changes in DNA methylation levels, which play a vital role in gene expression regulation. With ongoing developments in technology, attempts to understand disease mechanism have benefited greatly from epigenetics and transcriptomics studies. In this work, we have used expression and methylation data of thyroid carcinoma as a case study and explored how to optimally incorporate expression and methylation information into the disease study when both data are available. Moreover, we have also investigated whether there are important post-translational modifiers which could drive critical insights on thyroid cancer genetics.

Results

In this study, we have conducted a threshold analysis for varying methylation levels to identify whether setting a methylation level threshold increases the performance of functional enrichment. Moreover, in order to decide on best-performing analysis strategy, we have performed data integration analysis including comparison of 10 different analysis strategies. As a result, combining methylation with expression and using genes with more than 15% methylation change led to optimal detection rate of thyroid-cancer associated pathways in top 20 functional enrichment results. Furthermore, pooling the data from different experiments increased analysis confidence by improving the data range. Consequently, we have identified 207 transcription factors and 245 post-translational modifiers with more than 15% methylation change which may be important in understanding underlying mechanisms of thyroid cancer.

Conclusion

While only expression or only methylation information would not reveal both primary and secondary mechanisms involved in disease state, combining expression and methylation led to a better detection of thyroid cancer-related genes and pathways that are found in the recent literature. Moreover, focusing on genes that have certain level of methylation change improved the functional enrichment results, revealing the core pathways involved in disease development such as; endocytosis, apoptosis, glutamatergic synapse, MAPK, ErbB, TGF-beta and Toll-like receptor pathways. Overall, in addition to novel analysis framework, our study reveals important thyroid-cancer related mechanisms, secondary molecular alterations and contributes to better knowledge of thyroid cancer aetiology.

Challenges and Strategies for Proteome Analysis of the Interaction of Human Pathogenic Fungi with Host Immune Cells

Opportunistic human pathogenic fungi including the saprotrophic mold Aspergillus fumigatus and the human commensal Candida albicans can cause severe fungal infections in immunocompromised or critically ill patients. The first line of defense against opportunistic fungal pathogens is the innate immune system. Phagocytes such as macrophages, neutrophils and dendritic cells are an important pillar of the innate immune response and have evolved versatile defense strategies against microbial pathogens. On the other hand, human-pathogenic fungi have sophisticated virulence strategies to counteract the innate immune defense. In this context, proteomic approaches can provide deeper insights into the molecular mechanisms of the interaction of host immune cells with fungal pathogens. This is crucial for the identification of both diagnostic biomarkers for fungal infections and therapeutic targets. Studying host-fungal interactions at the protein level is a challenging endeavor, yet there are few studies that have been undertaken. This review draws attention to proteomic techniques and their application to fungal pathogens and to challenges, difficulties, and limitations that may arise in the course of simultaneous dual proteome analysis of host immune cells interacting with diverse morphotypes of fungal pathogens. On this basis, we discuss strategies to overcome these multifaceted experimental and analytical challenges including the viability of immune cells during co-cultivation, the increased and heterogeneous protein complexity of the host proteome dynamically interacting with the fungal proteome, and the demands on normalization strategies in terms of relative quantitative proteome analysis.

Constructing a molecular interaction network for thyroid cancer via large-scale text mining of gene and pathway events

Background

Biomedical studies need assistance from automated tools and easily accessible data to address the problem of the rapidly accumulating literature. Text-mining tools and curated databases have been developed to address such needs and they can be applied to improve the understanding of molecular pathogenesis of complex diseases like thyroid cancer.

Results

We have developed a system, PWTEES, which extracts pathway interactions from the literature utilizing an existing event extraction tool (TEES) and pathway named entity recognition (PathNER). We then applied the system on a thyroid cancer corpus and systematically extracted molecular interactions involving either genes or pathways. With the extracted information, we constructed a molecular interaction network taking genes and pathways as nodes. Using curated pathway information and network topological analyses, we highlight key genes and pathways involved in thyroid carcinogenesis.

Conclusions

Mining events involving genes and pathways from the literature and integrating curated pathway knowledge can help improve the understanding of molecular interactions of complex diseases. The system developed for this study can be applied in studies other than thyroid cancer. The source code is freely available online at https://github.com/chengkun-wu/PWTEES.

Deep sequencing of small RNA libraries from human prostate epithelial and stromal cells reveal distinct pattern of microRNAs primarily predicted to target growth factors

Complex epithelial and stromal cell interactions are required during the development and progression of prostate cancer. Regulatory small non-coding microRNAs (miRNAs) participate in the spatiotemporal regulation of messenger RNA (mRNA) and regulation of translation affecting a large number of genes involved in prostate carcinogenesis. In this study, through deep-sequencing of size fractionated small RNA libraries we profiled the miRNAs of prostate epithelial (PrEC) and stromal (PrSC) cells. Over 50 million reads were obtained for PrEC in which 860,468 were unique sequences. Similarly, nearly 76 million reads for PrSC were obtained in which over 1 million were unique reads. Expression of many miRNAs of broadly conserved and poorly conserved miRNA families were identified. Sixteen highly expressed miRNAs with significant change in expression in PrSC than PrEC were further analyzed in silico. ConsensusPathDB showed the target genes of these miRNAs were significantly involved in adherence junction, cell adhesion, EGRF, TGF-β and androgen signaling. Let-7 family of tumor-suppressor miRNAs expression was highly pervasive in both, PrEC and PrSC cells. In addition, we have also identified several miRNAs that are unique to PrEC or PrSC cells and their predicted putative targets are a group of transcription factors. This study provides perspective on the miRNA expression in PrEC and PrSC, and reveals a global trend in miRNA interactome. We conclude that the most abundant miRNAs are potential regulators of development and differentiation of the prostate gland by targeting a set of growth factors. Additionally, high level expression of the most members of let-7 family miRNAs suggests their role in the fine tuning of the growth and proliferation of prostate epithelial and stromal cells.

Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity

To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ∼2.4 million (Stage 1) or ∼200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the 'VErsatile Gene-based Association Study' (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10(-6) for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity.

Combined ultra-low input mRNA and whole-genome sequencing of human embryonic stem cells

Background

Next Generation Sequencing has proven to be an exceptionally powerful tool in the field of genomics and transcriptomics. With recent development it is nowadays possible to analyze ultra-low input sample material down to single cells. Nevertheless, investigating such sample material often limits the analysis to either the genome or transcriptome. We describe here a combined analysis of both types of nucleic acids from the same sample material.

Methods

The method described enables the combined preparation of amplified cDNA as well as amplified whole-genome DNA from an ultra-low input sample material derived from a sub-colony of in-vitro cultivated human embryonic stem cells. cDNA is prepared by the application of oligo-dT coupled magnetic beads for mRNA capture, first strand synthesis and 3’-tailing followed by PCR. Whole-genome amplified DNA is prepared by Phi29 mediated amplification. Illumina sequencing is applied to short fragment libraries prepared from the amplified samples.

Results

We developed a protocol which enables the combined analysis of the genome as well as the transcriptome by Next Generation Sequencing from ultra-low input samples. The protocol was evaluated by sequencing sub-colony structures from human embryonic stem cells containing 150 to 200 cells. The method can be adapted to any available sequencing system.

Conclusions

To our knowledge, this is the first report where sub-colonies of human embryonic stem cells have been analyzed both at the genomic as well as transcriptome level. The method of this proof of concept study may find useful practical applications for cases where only a limited number of cells are available, e.g. for tissues samples from biopsies, tumor spheres, circulating tumor cells and cells from early embryonic development. The results we present demonstrate that a combined analysis of genomic DNA and messenger RNA from ultra-low input samples is feasible and can readily be applied to other cellular systems with limited material available.

WITHDRAWN--a resource for withdrawn and discontinued drugs

Post-marketing drug withdrawals can be associated with various events, ranging from safety issues such as reported deaths or severe side-effects, to a multitude of non-safety problems including lack of efficacy, manufacturing, regulatory or business issues. During the last century, the majority of drugs voluntarily withdrawn from the market or prohibited by regulatory agencies was reported to be related to adverse drug reactions. Understanding the underlying mechanisms of toxicity is of utmost importance for current and future drug discovery. Here, we present WITHDRAWN, a resource for withdrawn and discontinued drugs publicly accessible at http://cheminfo.charite.de/withdrawn. Today, the database comprises 578 withdrawn or discontinued drugs, their structures, important physico-chemical properties, protein targets and relevant signaling pathways. A special focus of the database lies on the drugs withdrawn due to adverse reactions and toxic effects. For approximately one half of the drugs in the database, safety issues were identified as the main reason for withdrawal. Withdrawal reasons were extracted from the literature and manually classified into toxicity types representing adverse effects on different organs. A special feature of the database is the presence of multiple search options which will allow systematic analyses of withdrawn drugs and their mechanisms of toxicity.

Integrated interactions database: tissue-specific view of the human and model organism interactomes

IID (Integrated Interactions Database) is the first database providing tissue-specific protein–protein interactions (PPIs) for model organisms and human. IID covers six species (S. cerevisiae (yeast), C. elegans (worm), D. melonogaster (fly), R. norvegicus (rat), M. musculus (mouse) and H. sapiens (human)) and up to 30 tissues per species. Users query IID by providing a set of proteins or PPIs from any of these organisms, and specifying species and tissues where IID should search for interactions. If query proteins are not from the selected species, IID enables searches across species and tissues automatically by using their orthologs; for example, retrieving interactions in a given tissue, conserved in human and mouse. Interaction data in IID comprises three types of PPI networks: experimentally detected PPIs from major databases, orthologous PPIs and high-confidence computationally predicted PPIs. Interactions are assigned to tissues where their proteins pairs or encoding genes are expressed. IID is a major replacement of the I2D interaction database, with larger PPI networks (a total of 1,566,043 PPIs among 68,831 proteins), tissue annotations for interactions, and new query, analysis and data visualization capabilities. IID is available at http://ophid.utoronto.ca/iid.

A survey of computational tools for downstream analysis of proteomic and other omic datasets

Proteomics is an expanding area of research into biological systems with significance for biomedical and therapeutic applications ranging from understanding the molecular basis of diseases to testing new treatments, studying the toxicity of drugs, or biotechnological improvements in agriculture. Progress in proteomic technologies and growing interest has resulted in rapid accumulation of proteomic data, and consequently, a great number of tools have become available. In this paper, we review the well-known and ready-to-use tools for classification, clustering and validation, interpretation, and generation of biological information from experimental data. We suggest some rules of thumb for the reader on choosing the best suitable learning method for a particular dataset and conclude with pathway and functional analysis and then provide information about submitting final results to a repository.

DIDA: A curated and annotated digenic diseases database

DIDA (DIgenic diseases DAtabase) is a novel database that provides for the first time detailed information on genes and associated genetic variants involved in digenic diseases, the simplest form of oligogenic inheritance. The database is accessible via http://dida.ibsquare.be and currently includes 213 digenic combinations involved in 44 different digenic diseases. These combinations are composed of 364 distinct variants, which are distributed over 136 distinct genes. The web interface provides browsing and search functionalities, as well as documentation and help pages, general database statistics and references to the original publications from which the data have been collected. The possibility to submit novel digenic data to DIDA is also provided. Creating this new repository was essential as current databases do not allow one to retrieve detailed records regarding digenic combinations. Genes, variants, diseases and digenic combinations in DIDA are annotated with manually curated information and information mined from other online resources. Next to providing a unique resource for the development of new analysis methods, DIDA gives clinical and molecular geneticists a tool to find the most comprehensive information on the digenic nature of their diseases of interest.

Vanno: a visualization-aided variant annotation tool

Next-generation sequencing (NGS) technologies have revolutionized the field of genetics and are trending toward clinical diagnostics. Exome and targeted sequencing in a disease context represent a major NGS clinical application, considering its utility and cost-effectiveness. With the ongoing discovery of disease-associated genes, various gene panels have been launched for both basic research and diagnostic tests. However, the fundamental inconsistencies among the diverse annotation sources, software packages, and data formats have complicated the subsequent analysis. To manage disease-associated NGS data, we developed Vanno, a Web-based application for in-depth analysis and rapid evaluation of disease-causative genome sequence alterations. Vanno integrates information from biomedical databases, functional predictions from available evaluation models, and mutation landscapes from TCGA cancer types. A highly integrated framework that incorporates filtering, sorting, clustering, and visual analytic modules is provided to facilitate exploration of oncogenomics datasets at different levels, such as gene, variant, protein domain, or three-dimensional structure. Such design is crucial for the extraction of knowledge from sequence alterations and translating biological insights into clinical applications. Taken together, Vanno supports almost all disease-associated gene tests and exome sequencing panels designed for NGS, providing a complete solution for targeted and exome sequencing analysis. Vanno is freely available at http://cgts.cgu.edu.tw/vanno.

Gene set analysis: A step-by-step guide

To maximize the potential of genome-wide association studies, many researchers are performing secondary analyses to identify sets of genes jointly associated with the trait of interest. Although methods for gene-set analyses (GSA), also called pathway analyses, have been around for more than a decade, the field is still evolving. There are numerous algorithms available for testing the cumulative effect of multiple SNPs, yet no real consensus in the field about the best way to perform a GSA. This paper provides an overview of the factors that can affect the results of a GSA, the lessons learned from past studies, and suggestions for how to make analysis choices that are most appropriate for different types of data. © 2015 Wiley Periodicals, Inc.

Cytoscape.js: a graph theory library for visualisation and analysis

Summary: Cytoscape.js is an open-source JavaScript-based graph library. Its most common use case is as a visualization software component, so it can be used to render interactive graphs in a web browser. It also can be used in a headless manner, useful for graph operations on a server, such as Node.js.

Availability and implementation: Cytoscape.js is implemented in JavaScript. Documentation, downloads and source code are available at http://js.cytoscape.org.

Contact:gary.bader@utoronto.ca

Altered glycolipid and glycerophospholipid signaling drive inflammatory cascades in adrenomyeloneuropathy

X-linked adrenomyeloneuropathy (AMN) is an inherited neurometabolic disorder caused by malfunction of the ABCD1 gene, characterized by slowly progressing spastic paraplegia affecting corticospinal tracts, and adrenal insufficiency. AMN is the most common phenotypic manifestation of adrenoleukodystrophy (X-ALD). In some cases, an inflammatory cerebral demyelination occurs associated to poor prognosis in cerebral AMN (cAMN). Though ABCD1 codes for a peroxisomal transporter of very long-chain fatty acids, the molecular mechanisms that govern disease onset and progression, or its transformation to a cerebral, inflammatory demyelinating form, remain largely unknown. Here we used an integrated -omics approach to identify novel biomarkers and altered network dynamic characteristic of, and possibly driving, the disease. We combined an untargeted metabolome assay of plasma and peripheral blood mononuclear cells (PBMC) of AMN patients, which used liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (LC-Q-TOF), with a functional genomics analysis of spinal cords of Abcd1(-) mouse. The results uncovered altered nodes in lipid-driven proinflammatory cascades, such as glycosphingolipid and glycerophospholipid synthesis, governed by the β-1,4-galactosyltransferase (B4GALT6), the phospholipase 2γ (PLA2G4C) and the choline/ethanolamine phosphotransferase (CEPT1) enzymes. Confirmatory investigations revealed a non-classic, inflammatory profile, consisting on the one hand of raised plasma levels of several eicosanoids derived from arachidonic acid through PLA2G4C activity, together with also the proinflammatory cytokines IL6, IL8, MCP-1 and tumor necrosis factor-α. In contrast, we detected a more protective, Th2-shifted response in PBMC. Thus, our findings illustrate a previously unreported connection between ABCD1 dysfunction, glyco- and glycerolipid-driven inflammatory signaling and a fine-tuned inflammatory response underlying a disease considered non-inflammatory.

In vivo hematopoietic Myc activation directs a transcriptional signature in endothelial cells within the bone marrow microenvironment

Cancer pathogenesis involves tumor-intrinsic genomic aberrations and tumor-cell extrinsic mechanisms such as failure of immunosurveillance and structural and functional changes in the microenvironment. Using Myc as a model oncogene we established a conditional mouse bone marrow transduction/transplantation model where the conditional activation of the oncoprotein Myc expressed in the hematopoietic system could be assessed for influencing the host microenvironment. Constitutive ectopic expression of Myc resulted in rapid onset of a lethal myeloproliferative disorder with a median survival of 21 days. In contrast, brief 4-day Myc activation by means of the estrogen receptor (ER) agonist tamoxifen did not result in gross changes in the percentage/frequency of hematopoietic lineages or hematopoietic stem/progenitor cell (HSPC) subsets, nor did Myc activation significantly change the composition of the non-hematopoietic microenvironment defined by phenotyping for CD31, ALCAM, and Sca-1 expression. Transcriptome analysis of endothelial CD45- Ter119- cells from tamoxifen-treated MycER bone marrow graft recipients revealed a gene expression signature characterized by specific changes in the Rho subfamily pathway members, in the transcription-translation-machinery and in angiogenesis. In conclusion, intra-hematopoietic Myc activation results in significant transcriptome alterations that can be attributed to oncogene-induced signals from hematopoietic cells towards the microenvironment, e. g. endothelial cells, supporting the idea that even pre-leukemic HSPC highjack components of the niche which then could protect and support the cancer-initiating population.

Integrated analysis of mRNA and miRNA expression in response to interleukin-6 in hepatocytes

Understanding the interactions between miRNAs and genes they regulate during the acute phase response is crucial to our understanding of inflammatory diseases and processes. Inducing the acute phase response in hepatocytes by stimulating them with interleukin-6 [1] and then examining global changes in mRNA and miRNA expression can provide insight into the timing and dynamics of these interactions. Here we provide additional data for our study, Ref. [2]. In this data, we identify and validate IL-6-induced changes in gene expression [3-6] and their functional relationships over time and between cell types by gene ontology [7,8]. We also provide data showing the enrichment of miRNA binding motifs in the 3׳UTRs of differentially expressed genes [9], and their predicted gene targets derived from our RNA-seq data [10].

Prediction of Causal Candidate Genes in Coronary Artery Disease Loci

OBJECTIVE

Genome-wide association studies have to date identified 159 significant and suggestive loci for coronary artery disease (CAD). We now report comprehensive bioinformatics analyses of sequence variation in these loci to predict candidate causal genes.

APPROACH AND RESULTS

All annotated genes in the loci were evaluated with respect to protein-coding single-nucleotide polymorphism and gene expression parameters. The latter included expression quantitative trait loci, tissue specificity, and miRNA binding. High priority candidate genes were further identified based on literature searches and our experimental data. We conclude that the great majority of causal variations affecting CAD risk occur in noncoding regions, with 41% affecting gene expression robustly versus 6% leading to amino acid changes. Many of these genes differed from the traditionally annotated genes, which was usually based on proximity to the lead single-nucleotide polymorphism. Indeed, we obtained evidence that genetic variants at CAD loci affect 98 genes which had not been linked to CAD previously.

CONCLUSIONS

Our results substantially revise the list of likely candidates for CAD and suggest that genome-wide association studies efforts in other diseases may benefit from similar bioinformatics analyses.

Computational analysis of protein interaction networks for infectious diseases

Infectious diseases caused by pathogens, including viruses, bacteria and parasites, pose a serious threat to human health worldwide. Frequent changes in the pattern of infection mechanisms and the emergence of multidrug-resistant strains among pathogens have weakened the current treatment regimen. This necessitates the development of new therapeutic interventions to prevent and control such diseases. To cater to the need, analysis of protein interaction networks (PINs) has gained importance as one of the promising strategies. The present review aims to discuss various computational approaches to analyse the PINs in context to infectious diseases. Topology and modularity analysis of the network with their biological relevance, and the scenario till date about host–pathogen and intra-pathogenic protein interaction studies were delineated. This would provide useful insights to the research community, thereby enabling them to design novel biomedicine against such infectious diseases.

Computational genomic analysis of PARK7 interactome reveals high BBS1 gene expression as a prognostic factor favoring survival in malignant pleural mesothelioma

The aim of our study was to assess the differential gene expression of Parkinson protein 7 (PARK7) interactome in malignant pleural mesothelioma (MPM) using data mining techniques to identify novel candidate genes that may play a role in the pathogenicity of MPM. We constructed the PARK7 interactome using the ConsensusPathDB database. We then interrogated the Oncomine Cancer Microarray database using the Gordon Mesothelioma Study, for differential gene expression of the PARK7 interactome. In ConsensusPathDB, 38 protein interactors of PARK7 were identified. In the Gordon Mesothelioma Study, 34 of them were assessed out of which SUMO1, UBC3, KIAA0101, HDAC2, DAXX, RBBP4, BBS1, NONO, RBBP7, HTRA2, and STUB1 were significantly overexpressed whereas TRAF6 and MTA2 were significantly underexpressed in MPM patients (network 2). Furthermore, Kaplan-Meier analysis revealed that MPM patients with high BBS1 expression had a median overall survival of 16.5 vs. 8.7 mo of those that had low expression. For validation purposes, we performed a meta-analysis in Oncomine database in five sarcoma datasets. Eight network 2 genes (KIAA0101, HDAC2, SUMO1, RBBP4, NONO, RBBP7, HTRA2, and MTA2) were significantly differentially expressed in an array of 18 different sarcoma types. Finally, Gene Ontology annotation enrichment analysis revealed significant roles of the PARK7 interactome in NuRD, CHD, and SWI/SNF protein complexes. In conclusion, we identified 13 novel genes differentially expressed in MPM, never reported before. Among them, BBS1 emerged as a novel predictor of overall survival in MPM. Finally, we identified that PARK7 interactome is involved in novel pathways pertinent in MPM disease.

Genomic landscape of adenoid cystic carcinoma of the breast

Adenoid cystic carcinoma (AdCC) is a rare type of triple-negative breast cancer (TNBC) characterized by the presence of the MYB-NFIB fusion gene. The molecular underpinning of breast AdCCs other than the MYB-NFIB fusion gene remains largely unexplored. Here we sought to define the repertoire of somatic genetic alterations of breast AdCCs. We performed whole-exome sequencing, followed by orthogonal validation, of 12 breast AdCCs to determine the landscape of somatic mutations and gene copy number alterations. Fluorescence in situ hybridization and reverse-transcription PCR were used to define the presence of MYB gene rearrangements and MYB-NFIB chimeric transcripts. Unlike common forms of TNBC, we found that AdCCs have a low mutation rate (0.27 non-silent mutations/Mb), lack mutations in TP53 and PIK3CA and display a heterogeneous constellation of known cancer genes affected by somatic mutations, including MYB, BRAF, FBXW7, SMARCA5, SF3B1 and FGFR2. MYB and TLN2 were affected by somatic mutations in two cases each. Akin to salivary gland AdCCs, breast AdCCs were found to harbour mutations targeting chromatin remodelling, cell adhesion, RNA biology, ubiquitination and canonical signalling pathway genes. We observed that, although breast AdCCs had rather simple genomes, they likely display intra-tumour genetic heterogeneity at diagnosis. Taken together, these findings demonstrate that the mutational burden and mutational repertoire of breast AdCCs are more similar to those of salivary gland AdCCs than to those of other types of TNBCs, emphasizing the importance of histological subtyping of TNBCs. Furthermore, our data provide direct evidence that AdCCs harbour a distinctive mutational landscape and genomic structure, irrespective of the disease site of origin.

Using gene expression signatures to identify novel treatment strategies in gulf war illness

Background

Gulf War Illness (GWI) is a complex multi-symptom disorder that affects up to one in three veterans of this 1991 conflict and for which no effective treatment has been found. Discovering novel treatment strategies for such a complex chronic illness is extremely expensive, carries a high probability of failure and a lengthy cycle time. Repurposing Food and Drug Administration approved drugs offers a cost-effective solution with a significantly abbreviated timeline.

Methods

Here, we explore drug re-purposing opportunities in GWI by combining systems biology and bioinformatics techniques with pharmacogenomic information to find overlapping elements in gene expression linking GWI to successfully treated diseases. Gene modules were defined based on cellular function and their activation estimated from the differential expression of each module’s constituent genes. These gene modules were then cross-referenced with drug atlas and pharmacogenomic databases to identify agents currently used successfully for treatment in other diseases. To explore the clinical use of these drugs in illnesses similar to GWI we compared gene expression patterns in modules that were significantly expressed in GWI with expression patterns in those same modules in other illnesses.

Results

We found 19 functional modules with significantly altered gene expression patterns in GWI. Within these modules, 45 genes were documented drug targets. Illnesses with highly correlated gene expression patterns overlapping considerably with GWI were found in 18 of the disease conditions studied. Brain, muscular and autoimmune disorders composed the bulk of these.

Conclusion

Of the associated drugs, immunosuppressants currently used in treating rheumatoid arthritis, and hormone based therapies were identified as the best available candidates for treating GWI symptoms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0111-3) contains supplementary material, which is available to authorized users.

Enrichment of the Cancer Stem Phenotype in Sphere Cultures of Prostate Cancer Cell Lines Occurs through Activation of Developmental Pathways Mediated by the Transcriptional Regulator ΔNp63α

Background

Cancer stem cells (CSC) drive prostate cancer tumor survival and metastasis. Nevertheless, the development of specific therapies against CSCs is hindered by the scarcity of these cells in prostate tissues. Suspension culture systems have been reported to enrich CSCs in primary cultures and cell lines. However, the molecular mechanisms underlying this phenomenon have not been fully explored.

Methodology/Principal Findings

We describe a prostasphere assay for the enrichment of CD133⁺ CSCs in four commercial PCa cell lines: 22Rv1, DU145, LNCaP, and PC3. Overexpression of CD133, as determined by flow cytometric analysis, correlated with an increased clonogenic, chemoresistant, and invasive potential in vitro. This phenotype is concordant to that of CSCs in vivo. Gene expression profiling was then carried out using the Cancer Reference panel and the nCounter system from NanoString Technologies. This analysis revealed several upregulated transcripts that can be further explored as potential diagnostic markers or therapeutic targets. Furthermore, functional annotation analysis suggests that ΔNp63α modulates the activation of developmental pathways responsible for the increased stem identity of cells growing in suspension cultures.

Conclusions/Significance

We conclude that profiling the genetic mechanisms involved in CSC enrichment will help us to better understand the molecular pathways that underlie CSC pathophysiology. This platform can be readily adapted to enrich and assay actual patient samples, in order to design patient-specific therapies that are aimed particularly against CSCs.

Interindividual variation in gene expression responses and metabolite formation in acetaminophen-exposed primary human hepatocytes

Acetaminophen (APAP) is a readily available over-the-counter drug and is one of the most commonly used analgesics/antipyretics worldwide. Large interindividual variation in susceptibility toward APAP-induced liver failure has been reported. However, the exact underlying factors causing this variability in susceptibility are still largely unknown. The aim of this study was to better understand this variability in response to APAP by evaluating interindividual differences in gene expression changes and APAP metabolite formation in primary human hepatocytes (PHH) from several donors (n = 5) exposed in vitro to a non-toxic to toxic APAP dose range. To evaluate interindividual variation, gene expression data/levels of metabolites were plotted against APAP dose/donor. The correlation in APAP dose response between donors was calculated by comparing data points from one donor to the data points of all other donors using a Pearson-based correlation analysis. From that, a correlation score/donor for each gene/metabolite was defined, representing the similarity of the omics response to APAP in PHH of a particular donor to all other donors. The top 1 % highest variable genes were selected for further evaluation using gene set overrepresentation analysis. The biological processes in which the genes with high interindividual variation in expression were involved include liver regeneration, inflammatory responses, mitochondrial stress responses, hepatocarcinogenesis, cell cycle, and drug efficacy. Additionally, the interindividual variation in the expression of these genes could be associated with the variability in expression levels of hydroxyl/methoxy-APAP and C8H13O5N-APAP-glucuronide. The before-mentioned metabolites or their derivatives have also been reported in blood of humans exposed to therapeutic APAP doses. Possibly these findings can contribute to elucidating the causative factors of interindividual susceptibility toward APAP.

Integrated web visualizations for protein-protein interaction databases

Background

Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks.

Results

We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015.

Conclusions

Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.

Network-based approaches for drug response prediction and targeted therapy development in cancer

Signaling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways. This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of cell signaling network, cancer treatment should be extended to a combination therapy approach. Integrating and analyzing patient high-throughput data together with the information about biological signaling machinery may help deciphering molecular patterns specific to each patient and finding the best combinations of candidates for therapeutic targeting. We review state of the art in the field of targeted cancer medicine from the computational systems biology perspective. We summarize major signaling network resources and describe their characteristics with respect to applicability for drug response prediction and intervention targets suggestion. Thus discuss methods for prediction of drug sensitivity and intervention combinations using signaling networks together with high-throughput data. Gradual integration of these approaches into clinical routine will improve prediction of response to standard treatments and adjustment of intervention schemes.

SPECTRA: An Integrated Knowledge Base for Comparing Tissue and Tumor-Specific PPI Networks in Human

Protein–protein interaction (PPI) networks available in public repositories usually represent relationships between proteins within the cell. They ignore the specific set of tissues or tumors where the interactions take place. Indeed, proteins can form tissue-selective complexes, while they remain inactive in other tissues. For these reasons, a great attention has been recently paid to tissue-specific PPI networks, in which nodes are proteins of the global PPI network whose corresponding genes are preferentially expressed in specific tissues. In this paper, we present SPECTRA, a knowledge base to build and compare tissue or tumor-specific PPI networks. SPECTRA integrates gene expression and protein interaction data from the most authoritative online repositories. We also provide tools for visualizing and comparing such networks, in order to identify the expression and interaction changes of proteins across tissues, or between the normal and pathological states of the same tissue. SPECTRA is available as a web server at http://alpha.dmi.unict.it/spectra.