Linking microarray reporters with protein functions

<p>Abstract</p> <p>Background</p> <p>The analysis of microarray experiments requires accurate and up-to-date functional annotation of the microarray reporters to optimize the interpretation of the biological processes involved. Pathway visualization tools are used to connect gene expression data with existing biological pathways by using specific database identifiers that link reporters with elements in the pathways.</p> <p>Results</p> <p>This paper proposes a novel method that aims to improve microarray reporter annotation by BLASTing the original reporter sequences against a species-specific EMBL subset, that was derived from and crosslinked back to the highly curated UniProt database. The resulting alignments were filtered using high quality alignment criteria and further compared with the outcome of a more traditional approach, where reporter sequences were BLASTed against EnsEMBL followed by locating the corresponding protein (UniProt) entry for the high quality hits. Combining the results of both methods resulted in successful annotation of > 58% of all reporter sequences with UniProt IDs on two commercial array platforms, increasing the amount of Incyte reporters that could be coupled to Gene Ontology terms from 32.7% to 58.3% and to a local GenMAPP pathway from 9.6% to 16.7%. For Agilent, 35.3% of the total reporters are now linked towards GO nodes and 7.1% on local pathways.</p> <p>Conclusion</p> <p>Our methods increased the annotation quality of microarray reporter sequences and allowed us to visualize more reporters using pathway visualization tools. Even in cases where the original reporter annotation showed the correct description the new identifiers often allowed improved pathway and Gene Ontology linking. These methods are freely available at http://www.bigcat.unimaas.nl/public/publications/Gaj_Annotation/.</p

High-throughput data integration of RNA-miRNA-circRNA reveals novel insights into mechanisms of benzo[a]pyrene-induced carcinogenicity

The chain of events leading from a toxic compound exposure to carcinogenicity is still barely understood. With the emergence of high-throughput sequencing, it is now possible to discover many different biological components simultaneously. Using two different RNA libraries, we sequenced the complete transcriptome of human HepG2 liver cells exposed to benzo[a]pyrene, a potent human carcinogen, across six time points. Data were integrated in order to reveal novel complex chemical–gene interactions. Notably, we hypothesized that the inhibition of MGMT, a DNA damage response enzyme, by the over-expressed miR-181a-1_3p induced by BaP, may lead to liver cancer over time

Benzo[a]pyrene-Induced Changes in MicroRNA-mRNA Networks

Toxicological studies assessing the safety of compounds for humans frequently use in vitro systems to characterize toxic responses in combination with transcriptomic analyses. Thus far, changes have mostly been investigated at the mRNA level. Recently, microRNAs have attracted attention because they are powerful negative regulators of mRNA levels and, thus, may be responsible for the modulation of important mRNA networks implicated in toxicity. This study aimed to identify possible microRNA-mRNA networks as novel interactions on the gene expression level after a genotoxic insult. We used benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon, as a model genotoxic/carcinogenic compound. We analyzed time-dependent effects on mRNA and microRNA profiles in HepG2 cells, a widely used human liver cell line that expresses active p53 and is competent for the biotransformation of BaP. Changes in microRNA expression in response to BaP, in combination with multiple alterations of mRNA levels, were observed. Many of these altered mRNAs are targets of altered microRNAs. Using pathway analysis, we evaluated the relevance of such microRNA deregulations to genotoxicity. This revealed eight microRNAs that appear to participate in specific BaP-responsive pathways relevant to genotoxicity, such as apoptotic signaling, cell cycle arrest, DNA damage response, and DNA damage repair. Our results particularly highlight the potential of microRNA-29b, microRNA-26a-1*, and microRNA-122* as novel players in the BaP response. Therefore, this study demonstrates the added value of an integrated microRNA-mRNA approach for identifying molecular mechanisms induced by BaP in an in vitro human model

Creating and improving detoxification pathways for interpretation of toxicogenomics data

Current high-throughput technology in genomics creates a large amount of biological data. Bioinformatics approaches are directed towards understanding such data on a systems biology level. Advanced mathematical methods like principal component analysis, clustering, neural networks, support vector machine (SVM) approaches and neural networks can help to find patterns in the data. However, to really understand the data the patterns must be combined with existing knowledge. One of the approaches to do so is to associate these data to functional classifications such as can be found in the Gene Ontology. Other methods focus on using biological pathways coming from both public and private pathway databases like KEGG, WikiPathways, Reactome, and MetaCore. Some of these pathway databases contain rather aspecific information about which genes are involved in reactions. A single identifier, like an Enzyme Code, may correspond to a full family of enzymes, whereas only one family member is responsible for the reaction in the given biological context. Another problem arises in the availability of relevant biological pathways for both rodents and humans. This is especially true in the field of toxicology, where many pathways are still lacking. The focus of this project was to create phase I and phase II detoxification pathways using PathVisio, the pathway editor of WikiPathways. New pathway content was generated using information obtained from several trusted resources: a) handbooks, b) manual/automated PubMed literature searches; c) online pathway resources; d) UniProt Knowledgebase; e) EnsEMBL; f) GeneCards. In addition, the content of some existing toxicology pathways was improved. To illustrate the usefulness of the new pathways data from experiments with carcinogenic compounds were retrieved from the main online microarray data repositories Gene Expression Omnibus (GEO) and ArrayExpress. The resulting data were visualized on the new pathways using PathVisio. All these pathways are made available to the community at WikiPathways, where they can be further used for statistical pathway analysis and visualization in the (toxico)genomics field

User-friendly solutions for microarray quality control and pre-processing on ArrayAnalysis.org

Quality control (QC) is crucial for any scientific method producing data. Applying adequate QC introduces new challenges in the genomics field where large amounts of data are produced with complex technologies. For DNA microarrays, specific algorithms for QC and pre-processing including normalization have been developed by the scientific community, especially for expression chips of the Affymetrix platform. Many of these have been implemented in the statistical scripting language R and are available from the Bioconductor repository. However, application is hampered by lack of integrative tools that can be used by users of any experience level. To fill this gap, we developed a freely available tool for QC and pre-processing of Affymetrix gene expression results, extending, integrating and harmonizing functionality of Bioconductor packages. The tool can be easily accessed through a wizard-like web portal at http://www.arrayanalysis.org or downloaded for local use in R. The portal provides extensive documentation, including user guides, interpretation help with real output illustrations and detailed technical documentation. It assists newcomers to the field in performing state-of-the-art QC and pre-processing while offering data analysts an integral open-source package. Providing the scientific community with this easily accessible tool will allow improving data quality and reuse and adoption of standards

GO-Elite: a flexible solution for pathway and ontology over-representation.

UnlabelledWe introduce GO-Elite, a flexible and powerful pathway analysis tool for a wide array of species, identifiers (IDs), pathways, ontologies and gene sets. In addition to the Gene Ontology (GO), GO-Elite allows the user to perform over-representation analysis on any structured ontology annotations, pathway database or biological IDs (e.g. gene, protein or metabolite). GO-Elite exploits the structured nature of biological ontologies to report a minimal set of non-overlapping terms. The results can be visualized on WikiPathways or as networks. Built-in support is provided for over 60 species and 50 ID systems, covering gene, disease and phenotype ontologies, multiple pathway databases, biomarkers, and transcription factor and microRNA targets. GO-Elite is available as a web interface, GenMAPP-CS plugin and as a cross-platform application.Availabilityhttp://www.genmapp.org/go_elit

GO-Elite: a flexible solution for pathway and ontology over-representation.

UnlabelledWe introduce GO-Elite, a flexible and powerful pathway analysis tool for a wide array of species, identifiers (IDs), pathways, ontologies and gene sets. In addition to the Gene Ontology (GO), GO-Elite allows the user to perform over-representation analysis on any structured ontology annotations, pathway database or biological IDs (e.g. gene, protein or metabolite). GO-Elite exploits the structured nature of biological ontologies to report a minimal set of non-overlapping terms. The results can be visualized on WikiPathways or as networks. Built-in support is provided for over 60 species and 50 ID systems, covering gene, disease and phenotype ontologies, multiple pathway databases, biomarkers, and transcription factor and microRNA targets. GO-Elite is available as a web interface, GenMAPP-CS plugin and as a cross-platform application.Availabilityhttp://www.genmapp.org/go_elit

Abstract 4388: Characterisation of molecular events across the colorectal cancer progression axis

Abstract Uniquely amongst the major tumor types, the premalignant state in colorectal cancer (CRC) is readily detectable and diagnosed. Indeed, a multi-step process of CRC genesis was defined by the seminal work of Vogelstein et al, who described the mutational events leading from adenoma to adenocarcinoma. In addition to genetic aberrations, recent evidence has highlighted the importance of the gut microbiome and associated inflammation in predicting CRC progression. To date however, these insights have not led to significant changes in the treatment paradigm because efforts continue to be focused on the later stages of disease. We believe CRC presents an exceptional opportunity for disease interception but to design effective interventional strategies, the exact sequence of molecular events underlying progression needs to be better defined and understood. To address these needs, we have assembled a clinically annotated sample database encompassing all stages of CRC (healthy colon, adjacent mucosa, adenomas, high-grade dysplasia, primary CRC and liver metastases), including samples from the conventional, microsatellite stable subtype, as well as from the serrated, microsatellite instable pathway. The progression status of each sample was characterized using standard pathology criteria. In addition, molecular progression was determined by targeted mutation profiling and targeted copy number profiling, as well as genome-wide expression profiling. This analysis confirms previous observations of early mutational events at the adenoma stage, including known tumor suppressor and oncogene driver mutations, e.g. KRAS G12 and G13 are mutated in 15% of the conventional adenomas but not in sessile serrated adenomas, as well as in 23% of the colorectal tumors. Copy number aberrations were observed at the adenoma and carcinoma stage, but with a lower prevalence then somatic mutations. Furthermore, genome wide expression analysis indicates that several pathways known to be affected in colorectal cancer are already disregulated at the adenoma stage. These pathways include Wnt signaling, mucosal barrier defects, bile acid metabolism, and several immune respons genes, again fingerpointing at the interplay between local inflammation, the microbiome, and epithelial events. In addition, we observed that while genetic events are very dissimilar between the serrated/MSI and conventional/MSS pathway, the transcriptional regulation has many similarities, indicating at a possibility at targeting these disease subtypes using the same therapeutics. These initial findings provide rational avenues to intercept CRC at the adenoma stage and efforts are now focused on exploring the added role of the colonic microbiota and immune system modulation. A more comprehensive and integrated view of the changes associated with disease initiation will lead to the identification of new paths for prevention, interception and cure. Citation Format: Joke Reumers, Liesbeth Van Wesenbeeck, Eric Ciamporcero, Gerald Chu, Stan Gaj, Emanuele Palescandolo, Carl Van Hove, Karin Verstraeten, Gary Borzillo, Dianna Wu, Pieter Peeters, Janine Arts. Characterisation of molecular events across the colorectal cancer progression axis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4388. doi:10.1158/1538-7445.AM2017-4388</jats:p