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

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  Transcriptional Profiling Analyses
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For the understanding of the complex regulative mechanisms and the investigation of the cellular control management, a simultaneous analysis of the expression of all genes of an organism under various conditions is indispensable. On the basis of early work in yeast, we proceeded to analyses in a large number of organisms. Next to the analyses performed on the particular organism, we also worked at comparative studies, utilising similar types of data obtained from different organisms.

Today, our emphasis is on studies of human cancer material
. Analyses are performed at the level of mRNA and microRNA. Information is gathered for enabling early diagnosis and accurate prognosis, the identification of potentially interesting avenues for therapy as well as the evaluation of the success of disease treatment. For this, the measurements of transcriptional variations are combined with the analysis of epigenetic modulation of the genomic DNA, studies on the changes in transcription factor binding, and actual protein expression. In addition, we try to combine this kind of information on a single universal microarray analysis platform.



human:
eukaryotic:
microbial:
M-CHiPS:

Pancreatic cancer
  - Malignancy factors
  - Effects of drugs & compounds

Other tumour entities

Arabidopsis thaliana
 
Archive: Saccharomyces cerevisiae
Archive: Mouse
Archive: Drosophila melanogaster

Trypanosoma brucei
  
Archive: Neurospora crassa
Archive: Bacillus subtilis
Archive: Pseudomonas putida

Data warehouse and
analysis algorithms
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Currently, there are more than 13,000 hybridisation experiments stored.

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Prevention and diagnosis of pancreatic cancer

                                            logo NGFN

basic transcriptional chip data of cancer samplePancreatic cancer has a dismal prognosis due the late presentation of the tumours and the absence of satisfactory therapeutic options for advanced disease. Surgical resection of early tumours or preneoplastic lesions represents the only curative approach. It is currently difficult to identify early stages of the tumour and preneoplastic lesions. Once in an advanced stage, there are no diagnostic means for a risk stratification of patients concerning prognosis or responsiveness to therapy.

Joining forces with leading groups in German and European pancreatic cancer research and industry, we aim at establishing novel molecular diagnostic approaches for the prevention, early diagnosis and risk stratification of pancreatic cancer. These approaches will be developed based on large-scale transcriptome, genome and proteome analyses that have been performed by members of the consortium in the recent years, e.g. in two subsequent EU-funded Concerted Actions. During these earlier projects, the relevant protocols and processes have been optimised and adjusted between the partners and common standards have been established. In addition, the current network of clinicians, clinical researchers and basic research groups was formed.

On this basis, molecular techniques are used as part of a close local collaboration with the Surgery Department of Heidelberg University, within the national NGFN translational PaCaNet consortium on pancreatic cancer and the EU-funded MolDiagPaCa
consortium for the detection and characterisation of cancer cells or preneoplastic cells in minimal amounts of clinical tissue (fine needle biopsies) or fluidic (pancreatic/duodenal juice or serum) samples. The tools applied in these studies include transcript and epigenetic analyses, single or multiple marker protein studies, DNA/RNA PCR analyses, serum proteomics and molecular imaging (see also tumour analyses). The projects take advantage of well characterised clinical samples such as serum, urine, fine needle aspirates and surgically resected materials of pancreatic cancer patients that were and are collected in participating clinical centers as well as multi-national European trials such as ESPAC or EUROPAC. Eventually, prospective clinical trials of novel diagnostic tools developed in the projects will be designed and started.


Gress et al. .(1997) Genes Chrom. Cancer 19, 97-103.
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Wallrapp et al.  (1999) Ann. Oncology 10, suppl. 4, 64-68.

Geng et al. .(1999) Expression Genetics, Biotechniques Books, 145-153.
Frohme et al. .(2000) Mol. Pathog. Pancreatic Cancer, IOS Press, 88-94.
Löhr & Hoheisel .(2003)   Z. Gastroenterol. 41, 623-624.
Bauer et al. .(2003) Comp. Funct. Genom. 4, 520-524.
Hoheisel, J.D.
.(2003) Forschung & Diagnostik 1, 34-35.
Esposito et al. .(2004) Virchows Arch. 444, 447-453.
Brandt et al. .(2004) Pancreatology 4, 587-597.
Busold et al. .(2005) 
Bioinformatics 21, 2424-2429. pdf icon
Beier et al. .(2005)  Exocrine Pancreas Cancer, Solvay, 254-260. pdf icon
Buchholz et al. .(2005) Exocrine Pancreas Cancer, Solvay, 396-403. pdf icon
Buchholz et al. .(2005) Clin. Cancer Res. 11, 8048-8054. pdf icon
Frohme & Hoheisel .(2006) Cell Biology 3rd ed., Elsevier, 113-120.
Fellenberg et al. .(2006). BMC Genomics 7, 319. pdf icon
Jesnowski et al. .(2006) Neoplesia 9, 136-146. pdf icon
Kusumawidjaja et al. .(2007) Cancer Biol. Ther. 6, 367-376. pdf icon
Bauer et al. .(2007) Grundlagen Mol. Medizin, Springer, 346-362. 
Loos et al. .(2007) Virchows Arch. 450, 719-726. pdf icon
Bauer et al.  (2009) Pancreatology 9, 34-44.

Börner et al.  (2009) Biotechniques 46, 297-304. pdf icon
Ketterer et al.  (2009) Cancer Lett. 277, 72-81.
Youns et al.  (2009) Biochem. Pharmacol. 78, 273-283.
Alhamdani et al.  (2010) J. Prot. Res. 9, 963-971.
Schröder et al.  (2010) Mol. Cell. Prot., in press.
Youns et al.  (2010) Planta Med. in press.



 
 

Identification of malignancy factors by analysing cystic tumours of the pancreas

          logo NGFN             

figureThe diversity in the aggressiveness of cystic tumours of the pancreas – ranging from the usually benign serous cystadenoma to lesions of variable degrees of malignancy – was utilised for the identification of molecular factors that are involved in the occurrence of malignancy. We analysed the transcript profiles of different cystic tumour types. Variations could be identified that could be critical for the regulation of malignancy and thus relevant to the treatment of also the majority of pancreatic tumours. The results were confirmed at the protein level by immunohistochemistry. Also, functional studies with siRNA silencing were performed. Expression variations at the RNA and protein level were identified that are closely correlated with the degree of malignancy. Besides, all tumours could be classified effectively by this means. Many of the identified factors had not previously been known to be associated with malignant cystic lesions. SiRNA silencing of the gene with the most prominent variation – the anti-apoptotic factor FASTK (Fas-activated Serine/Threonine Kinase) – revealed a regulative effect on several genes known to be relevant to the development of tumours.


Figure legend: correspondence cluster analysis of transcript profiles. In the resulting biplot, each hybridisation of an individual sample is depicted as a coloured square. Genes that exhibited significantly differential transcription levels are shown as black dots. The closer the co-localisation of two spots (both genes and tumours) the higher is the degree of association between them. Also, guidelines are displayed in the diagram. They are calculated from the data and point to the positions of virtual genes, which exhibit a variation in one tumour entity only. The closer a depicted gene lies to one of these guidelines and the further its distance to the centroid the better its expression is described by the respective ideal profile. All genes that are not significantly differentially transcribed are located close to the centroid of the lines but are not shown for clarity. In (a), a cluster analysis is shown of normal pancreatic tissue, all cystic tumours combined and ductal adenocarcinoma. Panel (b) presents the results obtained for the cystic tumours alone. As a consequence of the normalisation process, only the median of the controls is shown in the diagram as a single red circle instead of the individual hybridisation events. In (d), a close-up of the data is shown, which were generated with the IPMC, MCA and MCAC samples. Panel (c), finally, presents a combination of the data with a colour-code added that indicated the tendency of malignancy of the respective tumour types: blue, non-malignant; red, highly malignant.


Bauer et al. (2009) Pancreatology 9, 34-44.
more info


 
 

Effect of Artesunate on pancreatic cancer cells

          logo NGFN                             logo of Helwan University, Cairo

The paucity of curative therapies for pancreatic cancer has translated into an overall 5-year survival rate of less than 5%, underscoring a desperate need for new therapeutic options. Artesunate (ART) is clinically used as anti-malarial agent. It has recently revealed remarkable anti-tumour activity. However, the mechanisms underlying those activities in pancreatic cancer were not yet known. We evaluated the anti-tumour activity of Artesunate and the possible underlying mechanisms in pancreatic cancer. MiaPaCa-2 (poorly differentiated) and BxPC-3 (moderately differentiated) pancreatic cancer cell lines were treated with Artesunate. The effect was monitored by evaluating cell viability, apoptosis and the generation of transcript profiles.

Our results provide in vitro evidence for the therapeutic utility of Artesunate in pancreatic cancer. Moreover, we identified Artesunate as a novel topoisomerase II-alpha inhibitor that inhibits pancreatic cancer growth through modulation of multiple signaling pathways. The analysis is a starting point for the generation of hypotheses and a more detailed dissection of the functional role of individual proteins for the activity of Artesunate in tumour cells.

Youns et al. (2009) Biochem. Pharmacol. 78, 273-283.
Youns et al. (2009) Drug Discov. Ther. 3, 200-207.
Youns et al. (2010) Curr. Drug Discov. Technol. 7, 37-45.
 





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Transcriptional Analyses in Trypanosoma brucei                     logo DFG          logo DAAD

The life cycle of Trypanosoma brucei involves adaptation to a variety of conditions in the host and Tsetse fly. The successive changes in morphology, biochemistry and plasma membrane proteins, some of which also involve cell cycle arrest, are still very poorly understood. Many of these changes are likely to be directed by changes in mRNA abundance and translation, and over two decades of effort have now been expended in the identification of stage-specific mRNAs. Because of technical limitations, however, most of the transcripts identified have been rather abundant, and the regulation studied has mainly been restricted to the rapidly-dividing long slender bloodstream and procyclic forms.

Given the relatively small size of the T. brucei genome, there is a good prospect of a complete exploration of its genome by microarray analyses. This will allow identification of lower-abundance regulated transcripts, and (using amplification methods) the study of the transcriptome of the less accessible forms found in the Tsetse fly. One format for the analysis is to perform genome-wide expression studies on genomic instead of gene-specific fragments. Such arrays have several intrinsic advantages. Overall genome representation is usually good in shotgun libraries with comparatively little variation across the genome. Thus, even if randomly selected clone inserts are used as probes, there should be good coverage and relatively little redundancy. Also, not only coding but also intergenic regions can be studied, for example in chromatin immunoprecipitation experiments. Insert amplification can be performed with a single primer pair and functional analyses can actually precede sequencing.

In collaboration with the group of Christine Clayton, we have produced DNA-microarrays containing more than 21,000 PCR-products of 2 to 2.5 kb long genomic fragments of T. brucei strain TREU927/4, which are being used in several projects. More recently, we also applied oligonucleotide microarrays provided by TIGR
figure of T. brucei chip


Diehl et al.  (2002)
Mol. Biochem. Paras. 123, 115-123.
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Queiroz et al. .(2009) BMC Genomics 10, 495 pdf icon
Archer et al. .(2009) PLoS Pathogen 5, e1000565. pdf icon
Kramer et al. .(2010) J. Cell. Biol., in press.







 
 
logo Humboldt-StiftungAnalysis of gene expression in Trypanosoma brucei gambiense

Human African Trypanosomiasis (HAT) is a disease that exists in two forms. The acute form is caused by T. b. rhodesiense (observed in East Africa) while the chronic form is due to T. b. gambiense (found in West and central Africa). Around 60 million persons are exposed to this disease with some 500,000 current infections. Despite a relentless fight against HAT during the last century, it is currently resurgent in epidemic form in several countries.

The low sensitivity of the diagnostic techniques associated to the low parasitaemia that characterizes T. b. gambiense infections lead permanently to residual human reservoir of trypanosomes in HAT foci after medical surveys. Moreover, the treatment of T. b. gambiense infections requires toxic drugs that are also less effective in the late stage of the disease. This ineffective treatment is strengthens by the emergency of parasite strains resistant to the most available drug (Melarsoprol). Since treatment of HAT is harsh and sometimes ineffective, the vaccine approach is probably a good perspective for trypanosomiasis prevention. Until now, there is no prospective vaccine for HAT despite the fact that investigations on vaccine have been a goal for nearly a century.

With the resurgence of HAT, it is important to improve the control of this disease by undertaking studies that may lead to the discovery of new genes essential for the survival of trypanosomes. These genes could be targeted further for drugs, vaccine or diagnostic tools. The considerable differences between T. b. gambiense and the others T. brucei sub-species should be most likely the result of changes at the nucleotide sequences or in gene expression. We are analysing gene expression in T. b. gambiense using DNA-microarrays, which were produced on the basis of the shotgun clones made for sequencing. The identification of sub-species specific gene expression variations could greatly facilitate the generation and testing of hypotheses on the mechanism of human serum resistance in T. b. gambiense, the key factors to its ability to infect human.

Simo et al. (2010) Infect. Genet. Evol. 10, 229-237.








Transcriptional profiling of Arabidopsis thaliana

                 logo BMBF        .

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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Vingron & Hoheisel   
(1999)   
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(2000)
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(2001)
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(2001)
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(2002)
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(2002)
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(2003)
Perspectives in Gene Expression, Eaton Publishing, Westborough, 307-343.
Busold et al.
(2005)
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Fellenberg et al.
(2006)
BMC Genomics 7, 319.
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Moghaddas Gholami & Fellenberg
(2010)
Bioinformatics 26, 1082-1090.
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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. 







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