Cell division is a highly regulated process and periodic gene expression is crucial to this regulation. Gene subsets are induced at specific times during the cell cycle, when their function is required, and are then repressed when they are no longer needed; mistakes in this regulation can lead to alterations in cell proliferation which will in turn result in diseases such as cancer.
In humans, as well as in lower eukaryotes, a well-known regulatory complex is responsible for controlling the onset of DNA replication by inducing genes required for this transition. We show that in fission yeast this regulatory complex (MBF) also induces a gene whose encoded protein (Yox1p) in turn binds to MBF and represses MBF-regulated genes. In the absence of Yox1p, the MBF-regulated genes do not fluctuate during the cell cycle but remain constantly induced. Thus, MBF sets up not only the induction but also the timely repression of its target genes via Yox1p. We also provide a global analysis of all the genes regulated by Yox1p and MBF. Together, our data uncover a new negative control loop, further highlighting the sophistication of gene regulation during the cell cycle, and illustrating regulatory similarities and differences between organisms. This work was done in collaboration with Jürg Bähler’s group at UCL and the work is published in PLoS Genetics (Aligianni et al., 2009).
Transcriptional and post-transcriptional regulation of gene expression: computational analysis of microarray studies in fungal species
Katherine Lawler, a PhD student in the group, finished and submitted her PhD thesis. The thesis presents two related computational studies of the genome-wide regulation of gene expression based on the analysis of microarray datasets. The first study concerns the dynamics of a global gene expression response. The regulation of mRNA abundance by both transcriptional and post-transcriptional control implies a range of possible strategies for shaping gene expres- sion in response to a stimulus. Katerine’s work investigated strategies for shaping gene expression and the strength of evidence for regulated mRNA stability from microarray time series in the fission yeast Schizosaccharomyces pombe. A dynamic model of mRNA abundance was applied to simultaneous time series of mRNA abundance (DNA microarray) and transcription rate (RNA polymerase II ChIP-chip) datasets. Candidate genes were identified for which the gene expression response appears to be driven by a change in mRNA stability rather than by transcriptional control. The second study used expression analysis combined with recently predicted transcription associated proteins to identify genes co-expressed with putative DNA binding transcription factors in the recently sequenced fungal crop pathogen
Fusarium graminearum.
TRAINING
Gabriella Rustici, Tomasz Adamusiak, Ibrahim Emam, Margus Lukk, Misha Kapushesky, Maria Krestyaninova, Helen Parkinson, Susanna-Assunta Sansone, Eleanor Williams
We have organised or participated in over 25 training events in the past year. The EMBO course on Analysis and Informatics of Microarray Data was one of the most successful training workshops at EMBL-EBI in 2009. Funding to repeat this course next year has been secured.
FUTURE PROJECTS AND GOALS
Our main goals for the foreseeable future will be
to develop and release the fully functional EBI Sample Database and populate it with sample information from the •
existing core databases at EMBL-EBI;
to continue developing the Gene Expression Atlas, enriching it with new functionality, new data including the next- •
generation sequencing experiments and data from protein expression;
to increase the robustness of the established links and pipelines with ENA and EGA, with regards to the shared •
sequencing and genotyping data, and full metadata exchange with GEO;
to continue our involvement in medically relevant collaborative projects to develop tools for data management, •
representation and analysis and to contribute to data analysis in these projects;
to continue research in integrative data analysis, in particular using next-generation sequencing data and integrating •
genotype and gene expression data, and building systems biology models.
Among the concrete plans is organising a Wellcome Trust sponsored conference ‘Bridging the gap between bioin- formatics and medical informatics’ in 2010, to work on genotype imputations using the 1000 Genomes Project data jointly with the EGA and the ENGAGE project; to work on next-generation sequencing-based transcriptomics data analysis jointly with the Cancer Research UK in Cambridge; and to start working on new collaborative projects, including SYBARIS (biomarker discovery for fungal diseases), and CAGEKID (kidney cancer).
Services in 2009 – The Micr oarray Informatics Team
84
Team Members Technical Team Leader
Ugis Sarkans Coordinators Misha Kapushesky Maria Krestyaninova Helen Parkinson Susanna-Assunta Sansone Technical Coordinator Philippe Rocca-Serra Software Developers Tony Burdett Marco Brandizi Mike Gostev Pavel Kurnosov Eamonn Maguire Nataliya Sklyar Andrew Tikhonov Andrey Zorin Anna Farne Scientists Johan Rung Gabriela Rustici Scientific Curators/ Bioinformaticians Tomasz Adamusiak Ele Holloway Natalja Kurbatova Chris Taylor Anna Farne Margus Lukk Eleanor Williams Holly Zheng-Bradley* James Malone PhD Students Nils Gehlenborg Angela Gonzalves Katherine Lawler* Visitors Vincenzo Belcastro Juok Cho Richard Evans Talay Djumabaev Morris Swertz Anna Zhukova Personal Assistant Lynn French
* Indicates part of the year only
Publications 2008
Rustici, G., et al. (2008). Data stor- age and analysis in ArrayExpress and expression profiler. Curr. Protoc. Bioinformatics, unit 7.13, Suppl 23,1-27
Schmidt, A., et al. (2008). An inte- grated, directed mass spectrometric
approach for in-depth characteriza- tion of complex peptide mixtures. Mol. Cell Proteomics, 7, 2138-2150 Vinken, M., et al. (2008). The carcino- GENOMICS project: Critical selec- tion of model compounds for the development of omics-based in vitro carcinogenicity screening assays. Mutat. Res.-Rev. Mutat. Res., 659, 202-210
2009
Aebersold, R., et al. (2009). Report on EU-USA Workshop: How Systems Biology Can Advance Cancer Research (27 October 2008). Mol. Oncol., 3, 9-17
Aligianni, S., et al. (2009). The fission yeast homeodomain protein Yox1p binds to MBF and confines MBF- dependent cell-cycle transcription to G1-S via negative feedback. PLoS Genet., 5, 1-12
Brazma, A. (2009). Minimum Information About a Microarray Experiment (MIAME) – suc- cesses, failures, challenges. TheScientificWorldJournal, 9, 420- 423
Brazma, A., et al. (2009).
Introduction. J. Bioinform. Comput. Biol., 7, 5
Caldas, J., et al. (2009). Probabilistic retrieval and visualization of bio- logically relevant microarray experi- ments. Bioinformatics, 25, i145-i153 Field, D., et al. (2009). Omics data sharing. Science, 326, 234-236 Gehlenborg, N., et al. (2009). Prequips – An extensible software platform for integration, visualization and analysis of LC-MS-MS proteom- ics data. Bioinformatics, 25, 682-683 Harttig, U., et al. (2009). Owner con- trolled data exchange in nutrigenom- ic collaborations: the NuGO informa- tion network. Genes Nutr., 1-10 Hwang, D., et al. (2009). A systems approach to prion disease. Mol. Syst. Biol., 5, 1-23
Kauffmann, A., et al. (2009). Importing ArrayExpress datasets into R/Bioconductor. Bioinformatics, 25, 2092-2094
Krestyaninova, M., et al. (2009). A System for Information Management in BioMedical Studies – SIMBioMS. Bioinformatics, 20, 2768-2769 Lefever, S., et al. (2009). RDML:
Structured language and reporting guidelines for real-time quantitative PCR data. Nucleic Acids Res., 37, 2065-2069
Orchard, S. & Taylor, C.F. (2009). Debunking minimum information myths: one hat need not fit all. Nat. Biotechnol., 25, 171-172
Parkinson, H., et al. (2009). ArrayExpress update – from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res., 37, D868-872
Prokopenko, I., et al. (2009). Variants in MTNR1B influence fasting glucose levels. Nat. Genet., 41, 77-81 Rayner, T.F., et al. (2009). MAGETabulator, a suite of tools to support the microarray data format MAGE-TAB. Bioinformatics, 25, 279-280
Rung, J., et al. (2009). Genetic vari- ant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat. Genet., in press
Schober, D., et al. (2009). Survey- based naming conventions for use in OBO Foundry ontology development. BMC Bioinformatics, 10, 125 Vingron, M., et al. (2009). Integrating sequence, evolution and functional genomics in regulatory genomics. Genome Biol., 10, 8
Other EMBL publications
Brazma, A., et al. (1998). Predicting gene regulatory elements in silico on a genomic scale. Genome Res., 8, 1202-1215
Rayner, T.F., et al. (2006). A simple spreadsheet-based, MIAME-supportive format for microarray data: MAGE-TAB. BMC Bioinformatics, 7, 489
Rustici, G., et al. (2004). Periodic gene expression program of the fis- sion yeast cell cycle. Nat. Genet., 36, 809-817
Sansone, S. A., et al., (2008). The first RSBI (ISA-TAB) workshop: 'CAn a simple format work for com- plex studies?' OMICS A Journal of Integrative Biology, 12, 143-149 Schlitt, T. & Brazma, A. (2006). Modelling in molecular biology: describing transcription regulatory networks. Philos. Trans. R. Soc. Lond., B, 361, 483-494 Services in 2009 – The Micr oarray Informatics Team
85 Services in 2009 –