Division of Functional Genome Analysis

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					Functional Genome Analysis (B070) Deutsches Krebsforschungszentrum, Im Neuenheimer Feld 580 D-69120 Heidelberg, Germany.

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Archive

Global Transcriptional Profiling Analyses

eukaryotic:

microbial:

current projects

Saccharomyces cerevisiae

Drosophila melanogaster

Arabidopsis thaliana

Neurospora crassa

Bacillus subtilis

Pseudomonas putida

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Collaborators:
Frank Sauer
and
Renato Paro

FINISHED PROJECT:
Functional analysis of gene and protein networks in Drosophila
with microarray based expression studies

The complete genomic sequence of several metazoan organisms such as Drosophila melanogaster and Caenorhabditis elegans are available. The next task arising from this sequence data is the deciphering of the role and function of the identified genes and their corresponding protein products in the context of an entire organism. As often the expression pattern of a gene provides clues to its function, we produced a DNA-microarray that enabled us to monitor gene expression in the context of the entire Drosophila genome. Such a system should enable us to identify genes whose activities are required for the execution of complex developmental gene networks and signal transduction pathways. As such networks and pathways are evolutionary highly conserved among metazoans, the analyses of gene and protein function in Drosophila should also provide valuable clues for a better knowledge of corresponding pathways in vertebrates.

While the genome sequences for a variety of organisms are now available, the precise number of genes encoded is still a matter of debate. We based our whole-transcriptome microarray, the Heidelberg FlyArray, on the combination of the BDGP annotation and a novel ab initio gene prediction of lower stringency using the Fgenesh software. A microarray was established with altogether some 24,000 different features, each actually present in duplicate. The primer set used to produce the PCR-products was from Eurogentec.

Apart from being used for the production of the microarray, the very primer set was also applied to the generation of a genome-wide dsRNA library, the actual work being performed in the laboratory of Norbert Perrimon at Harvard Medical School in Boston (USA). This molecule set allows the identification of gene functions by cell-based RNAi-screens.

To assess the overall quality of our array design as well as to validate the novel predictions, we performed developmental profiling of the Drosophila lifecycle using 9 different stages. We were able to provide evidence for the transcription of ~2,600 additional genes predicted by Fgenesh. Validation of the developmental profiling data by RT-PCR and in situ hybridization indicates a lower limit of 2,000 novel annotations, thus substantially raising the number of genes that make a fly. The successful design and application of this Drosophila microarray confirms our expectation that mere in silico approaches will always tend to be incomplete. The identification of at least 2,000 novel genes highlights the importance of gathering experimental evidence to discover all genes within a genome. Moreover, as such an approach is independent of homology criteria, it will allow the discovery of novel genes unrelated to known protein families or which have not been strictly conserved between species.

We are participating in the International Drosophila Array Consortium (INDAC; www.indac.net), which aims at establishing a common, standardised microarray and a corresponding set of controls for the entire Drosophila community.

figure of chip hybridisation

Two-colour hybridisation (green: adult stage; red: 4-8h old embryo) on the Heidelberg FlyArray directly showing the expression of genes unique to the Heidelberg prediction (spots within the green rectangle).

figure of data analysis

Figure to the left. Correspondence cluster analysis of the developmental profiling. Samples from 9 different stages of the Drosophila lifecycle were hybridised. Each hybridisation of an individual developmental stage is depicted as coloured square. They all form distinct clusters – but for the larval stage – indicating the degree of reproducibility and specificity between them. As a consequence of the normalisation process, only the median of all control hybridisations (0-4 h) is shown in the diagram as a single red square. Genes are shown as grey dots, if they exhibited significant differential transcription levels. The distance between dots is low when their expression profiles show similar shape, independent of their absolute values.


	Diehl et al.	(2002)	Nucleic Acids Res. 30, e79.
	Hild et al.	(2003)	Genome Biol. 5, R3.
	Boutros et al.	(2004)	Science 303, 382-385.
	Altenhein et al.	(2006)	Develop. Biol. 296, 545-560.

FINISHED PROJECT:
Creation of a minimal tiling path of genomic clones for Drosophila melanogaster;
provision of a common resource

On the basis of shotgun subclone libraries used in the sequencing of the Drosophila melanogaster genome, a minimal tiling path of subclones across much of the genome was determined. About 320,000 shotgun clones for chromosomes X(12-20), 2R, 2L, 3R and 4 were available from the Berkeley Drosophila Genome Project. The clone inserts have an average length of 3.4 kb and are amenable to standard PCR-amplification. The resulting tiling path covers 86.2% of chromosome X(12-20), 86.2% of chromosomal arm 2R, 79.0% of 2L, 89.6% of 3R and 80.5% of chromosome 4. In total, the 25,135 clones represent 76.7 Mb – equivalent to about 67% of the genome – and would be suitable for producing a microarray on a single slide.

This work was performed in collaboration with Susan Celniker (Berkeley), Eric Johnson (University of Oregon) and Eileen Furlong (EMBL).

Hollich et al. (2004) Biotechniques 37, 282-284.

figure of shotgun clone genome coverage

Schematic representation of the tiling path’s genome coverage. Horizontal lines indicate the chromosomes of the 115 Mb Drosophila genome. The genomic regions that are covered by the minimal tiling path of 25,135 shotgun clones are represented as blue and green coloured areas. Interruption of the colouring depicts large gaps. Any change in colour from blue to green or visa versa indicates the existence of a gap that is too small to be visible. Below, a table presents the relevant numbers.

FINISHED PROJECTS:
Transcriptional profiling of Saccharomyces cerevisiae

- EUROFAN

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Based on our involvement in the EU yeast genome sequencing, expression profiling on all yeast genes was started as part of the German and European (EUROFAN) yeast functional analysis networks. The relatively small number of some 6200 genes makes the unravelling of the basic processes of expression control in a eukaryotic cell much easier or even at all possible. Since there exists a surprising degree of structural and partially even functional homology between some human (disease and cancer) genes and their yeast equivalents, an analysis of the expression patterns of this complete gene set is not only very informative for the analysis of yeast gene expression and regulation itself but also very much of relevance to the grasp of such mechanisms in higher eukaryotes.

References, see below.

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- Eurocellwall .

The main objective of this project was the exploitation of the molecular knowledge of the Saccharomyces cerevisiae cell wall for high throughput screening of anti-microbial agents. To this end, a consortium of 10 laboratories was converting the molecular data on essential gene targets involved in cell wall cross-linking, remodelling and chitin pathways into assays amenable to drug-discovery programmes. Also, genomics, proteomics and bio-informatics were applied to identify new targets through the characterisation of the cell wall compensatory mechanism, which is induced when cell wall is weakened by drug treatment, stress or mutations.

References, see below.

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Hauser et al.	(1998) . Yeast 14, 1209-1221.
Hauser et al.	(1998) .Meth. Microbiol.28, 193-204.
Beissbarth et al.	(2000) .Bioinformatics 16, 1014-1022.
Hoheisel & Vingron	(2000) .Res. Microbiol. 151, 113 -119.
Fellenberg et al.	(2001) .Proc. Natl. Acad. Sci. USA 98, 10781-10786.
Hauser et al.	(2001) .Comp. Funct. Genomics 2, 69-79.
Fellenberg et al.	(2002) .Bioinformatics 18, 423-433.
Becerra et al.	(2002) .Mol. Microbiol. 43, 545-555.
Lombardia et al.	(2002) .Cell Calcium 32, 83-91.

Hauser et al.	(2002) .Screening 3/02, 28-31.
Yin et al.	(2003) .Mol. Microbiol. 48, 713-724.
Lagorce et al.	(2003) J. Biol. Chem. 278, 20345-20357.
Fellenberg et al.	(2003) Perspectives in Gene Expression, Eaton Publishing, 307-343.
Becerra et al.	(2003) .Comp. Funct. Genomics 4, 366-375.
Hagen et al.	(2004) Mol. Microbiol. 52, 1413 -1425.
Busold et al.	(2005) Bioinformatics 21, 2424-2429.
Fellenberg et al.	(2006) BMC Genomics 7, 319
Hauser et al.	(2007) FEMS Yeast Res. 7, 84-92.

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FINISHED PROJECT:
"MouseExpress": In silico analysis of expression profiles in mouse-mutants

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overall picture of mouse array

Collaborators:
Johannes Beckers , Martin Hrabe de Angelis , GSF, Munich; Werner Mewes , GSF, Munich; Martin Vingron , MPIMG, Berlin.

The sequencing of the mouse and human genomes have basically been accomplished. The next step for the integration of this genomic information into biological and biomedical research will be the systematic analysis of gene function. The similarities between man and mouse in their genomes, molecular pathways, physiology and developmental mechanisms make the mouse the most important model organism for the study of inherited diseases in man.

To discover new mutants that serve as models for human diseases or that have developmental defects, mice that have been subjected to ENU mutagenesis are routinely examined for clinical parameters, behaviour and dysmorphologies (Mouse ENU Mutagenesis Screen, Institute of Experimental Genetics, GSF, Neuherberg-Munich, Germany). In the 'MouseExpress' project, we extended phenotypic description to a molecular level. Combining DNA microarrays and appropriate phenotypical data, we compared RNA expression profiles of thousands of genes from tissues or embryos of mutant and wildtype mice.

We used the RNA expression profiles to identify molecular pathways that are affected in disease and analysed interdependencies between pathways within a molecular network. Expression profiles of mutant and wildtype mouse strains are filed in a database and will be linked to the phenotype and mutant databases of the Mouse ENU Mutagenesis Screen of the GSF.

Beckers et al. (2002) Curr. Genet. 3, 121-129. .

other publications

FINISHED PROJECT:
Transcriptional profiling of Arabidopsis thaliana

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Global transcriptional profiling in Arabidopsis thaliana was started in the EU-funded PPMdb-network and extended as part of the German ZIGIA consortium. Analyses were initially performed on a set of some 13,000 non-redundant EST-clones combined from the EST-clone collection of the Institut National de la Recherche Agronomique (Versailles, France) and the MSU EST-clone collection obtained from the Arabidopsis Biological Resource Center at the Ohio State University (Columbus, USA). Current analyses are performed on a set of 50mer oligonucleotides, representing the relevant gene set.

Various conditions have been studied. One initial area of emphasis was the analysis of pathogen responses, done in collaboration with Nikolaus Schlaich and Alan Slusarenko of the RWTH Aachen.

Current studies are performed in a local collaboration with Florian Haas and Rüdiger Hell of the Heidelberg Institute for Plant Science (HIP) at Heidelberg University. They aim at the elucidation of the effects of mitochondrial serine acetyltransferase functions on cysteine synthesis in plant cells. At the cellular level, cysteine synthesis in plants is entirely different from that in non-photosynthetic eukaryotes.

Scheideler et al. (2002) J. Biol. Chem. 277, 10555-10561. .

Wambutt et al. (2000) J. Biotechnol. 78, 281-292. .

Haas et al. (2008) Plant Physiol. 148, 1055-1067.
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other publications and patents

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FINISHED PROJECTS:
Transcription analysis in microbial organisms

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Bacillus subtilis .

Within a 'Leitmotiv Medizin' project, comparative studies were performed in collaboration with Michael Hecker of the University of Greifswald on the variation of all transcripts of Bacillus subtilis - carried out by microarray analysis - and the actual protein levels as identified in 2D-electrophoresis.

Petersohn et al. (2001) J. Bacteriol. 183, 5617-5631. .

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Neurospora crassa .

Ever since Tatum and Beadle formulated their one-gene-one-enzyme hypothesis on the basis of studies with Neurospora crassa, this filamentous fungus served as a model organism not only in genetics but also many other fields of basic research. Despite a lot of successful research, only about one tenth of the genes of Neurospora crassa had been described and localised on the seven chromosomes prior to genome initiatives. Genome analysis started by ordering cosmid and BAC clones along individual chromosomes. Based on the physical clone maps of linkage groups II and V, sequencing of the two chromosomes was done as part of the German Neurospora Genome Project. Simultaneously, a whole-genome shotgun approach was taken at the Whitehead Genome Center, Cambridge,

USA

, recently yielding the complete genomic sequence.
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For an initial insight into transcriptional variations in Neurospora crassa, we started with the creation of a microarray prior to sequence assembly and annotation, however. Some 4,700 EST-clones were arrayed on glass slides and used to monitor nutrient-dependent functional phenomena in Neurospora crassa. Upon availability of the sequence, also arrays made by in situ synthesis of oligonucleotides were used in other analyses.

Aign & Hoheisel (2003) Fungal Genet. Biol. 40, 225-233.

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Pseudomonas putida .

As part of our participation in the sequencing of the Pseudomonas putida genome, we selected in collaboration with our partners a tiling path of shotgun clones across the entire genome. DNA-microarrays were produced with PCR-amplified material of these genomic fragments and used in transcriptional studies.

Stjepandic et al. (2002) Environ. Microbiol. 4, 819-823. .

Nelson et al. (2002) Environ. Microbiol. 4, 799-808. .

Reva et al. (2006) J. Bacteriol. 188, 4079-4092. .

Vingron & Hoheisel		(1999)		J. Mol. Med. 77, 3-7.
Beissbarth et al.		(2000)		Bioinformatics 16, 1014-1022.
Fellenberg et al.		(2001)		Proc. Natl. Acad. Sci. USA 98, 10781-10786.
Hoheisel et al.		(2001)		Bioforum 12/01, 908-910.
Fellenberg et al.		(2002)		Bioinformatics 18, 423-433.
Brazma et al.		(2002)		Adv. Biochem. Biotechnol. 77, 113-139.
Fellenberg et al.		(2003)		Perspectives in Gene Expression, Eaton Publishing, Westborough, 307-343.
Busold et al.		(2005)		Bioinformatics 21, 2424-2429.
Fellenberg et al.		(2006)		BMC Genomics 7, 319.
Moghaddas Gholami & Fellenberg		(2010)		Bioinformatics 26, 1082-1090.
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FINISHED PROJECT:
Multi-Conditional Hybridisation Intensity Processing System (M-CHiPS)

Initial data analysis software tools and an appropriately structured database were developed by Kurt Fellenberg in close collaboration with Martin Vingron and went live in 1999. From this work resulted the M-CHiPS data warehouse and analysis software package, which was designed and implemented by Kurt Fellenberg. Apart from our own projects, the package is being used by various external partners and other groups elsewhere.
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Currently, the data warehouse holds results of more than 13,000 experiments.
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The Multi-Conditional Hybridisation Intensity Processing System (M-CHiPS) is a data warehouse, which provides a structure suitable for statistical analysis of a microarray database's entire content, including components such as the experimental and clinical annotations, for example. The storage concept is flexible and accounts for future developments. For each organism, there is a specific database. Although these databases may contain different ontologies of experimental and other annotations, they share the same structure and therefore can be accessed by the very same statistical algorithms. An ontology-independent structure enables ontology-updates during normal database operation, avoiding structure-alterations.
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For overall data analyses as well as the identification of associations between transcriptional variations and annotated factors, including clinical information, GO-terms, mapping data and such alike, correspondence analysis is used extensively. It is an explorative computational method for the study of associations between variables and proved its usefulness for identifying factors, which are associated to certain phenotypes, for example. Much like principle component analysis, it displays a low-dimensional projection of the overall data matrix. One major advantage of the process is its ability to present different parameters of a multi-dimensional data matrix (e.g., genes and experimental conditions) in a single plot. Localisation of genes and individual experiments is an indicator for an association between them. Moreover, additional information, such as GO-term or clinical annotations, can be displayed also, permitting an immediate identification of regulated functional groups or pathways). In addition, algorithms have been established to identify from the annotated data the factors, which are likely to be causative for the establishment of certain sub-groups (clusters) of factors (e.g., genes or patient groups) by statistical data evaluation.

.Overview

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