Analysis of transcription factor CREM dependent gene expression during mouse spermatogenesis [Elektronische Ressource] / vorgelegt von Tim Beißbarth

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INAUGURAL DISSERTATIONzurErlangung der Doktorwurde¨derNaturwissenschaftlich Mathematischen Gesamtfakultat¨derRuprecht Karls Universit at¨Heidelbergvorgelegt vonDiplom Biologe Tim Beißbarthaus Koln,¨ DeutschlandTag der mundlichen¨ Prufung:¨ThemaAnalysis of transcription factor CREM dependentgene expression during mouse spermatogenesisGutachter:Prof. Dr. Gunther¨ Schutz¨Prof. Dr. Richard HerrmannContents1 Summary 92 Introduction 112.1 CREB, ATF 1 and CREM are members of a family of transcription factors . . . . . . 112.1.1 Transcriptional regulation by factors responsive to cyclic AMP . . . . . . . . 112.1.2 The transcription factor CREM . . . . . . . . . . . . . . . . . . . . . . . . 132.1.2.1 Splice isoforms of CREM . . . . . . . . . . . . . . . . . . . . . . 132.1.2.2 Different types of activation of the CREM protein . . . . . . . . . 152.1.2.3 Functions of CREM in different tissues . . . . . . . . . . . . . . . 162.2 Overview over the course of spermatogenesis . . . . . . . . . . . . . . . . . . . . . 172.2.1 Stages of germ cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.2 Hormonal control of spermatogenesis . . . . . . . . . . . . . . . . . . . . . 182.2.3 Gene expression at various stages of germ cell differentiation . . . . . . . . . 202.2.4 Possible role of CREM in spermatogenesis . . . . . . . . . . . . . . . . . . 212.3 Aims and structure of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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INAUGURAL DISSERTATION
zur
Erlangung der Doktorwurde¨
der
Naturwissenschaftlich Mathematischen Gesamtfakultat¨
der
Ruprecht Karls Universit at¨
Heidelberg
vorgelegt von
Diplom Biologe Tim Beißbarth
aus Koln,¨ Deutschland
Tag der mundlichen¨ Prufung:¨Thema
Analysis of transcription factor CREM dependent
gene expression during mouse spermatogenesis
Gutachter:
Prof. Dr. Gunther¨ Schutz¨
Prof. Dr. Richard HerrmannContents
1 Summary 9
2 Introduction 11
2.1 CREB, ATF 1 and CREM are members of a family of transcription factors . . . . . . 11
2.1.1 Transcriptional regulation by factors responsive to cyclic AMP . . . . . . . . 11
2.1.2 The transcription factor CREM . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.2.1 Splice isoforms of CREM . . . . . . . . . . . . . . . . . . . . . . 13
2.1.2.2 Different types of activation of the CREM protein . . . . . . . . . 15
2.1.2.3 Functions of CREM in different tissues . . . . . . . . . . . . . . . 16
2.2 Overview over the course of spermatogenesis . . . . . . . . . . . . . . . . . . . . . 17
2.2.1 Stages of germ cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.2 Hormonal control of spermatogenesis . . . . . . . . . . . . . . . . . . . . . 18
2.2.3 Gene expression at various stages of germ cell differentiation . . . . . . . . . 20
2.2.4 Possible role of CREM in spermatogenesis . . . . . . . . . . . . . . . . . . 21
2.3 Aims and structure of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 Methods 23
3.1 Cloning differential messages via Suppression Subtractive Hybridization . . . . . . . 23
3.1.1 Principle of suppression subtractive hybridization . . . . . . . . . . . . . . . 24
3.2 DNA microarrays for large scale expression profiling . . . . . . . . . . . . . . . . . 26
3.2.1 Methods to determine the expression levels of thousands of genes simulta
neously . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5Contents
3.2.2 Finding differentially expressed genes in pairs of conditions . . . . . . . . . 28
3.2.3 Detecting correlations between different genes or between different experi
mental conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.3.1 Distance measure . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2.3.2 Unsupervised analysis . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.3.3 Supervised analysis . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3 Construction of a microarray containing the sequences of the CREM SSH library . . 32
3.3.1 Selection of representative clones from the CREM SSH library . . . . . . . . 33
3.3.2 Selection of hybridization controls . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 Mining information from databases of genomic or EST sequences . . . . . . . . . . 36
3.5 Software used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4 Results 39
4.1 Developments in Bioinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.1.1 Use of expressed sequence tags to generate gene index databases . . . . . . . 39
4.1.1.1 Generation of gene indices . . . . . . . . . . . . . . . . . . . . . 39
4.1.1.2 Update of the gene index databases . . . . . . . . . . . . . . . . . 40
4.1.1.3 Querying of gene indices and storage of the data . . . . . . . . . . 40
4.1.1.4 Visualization of gene index data . . . . . . . . . . . . . . . . . . . 41
4.1.2 Analysis of cDNA microarray expression data . . . . . . . . . . . . . . . . . 43
4.1.2.1 Extracting numerical data from an image . . . . . . . . . . . . . . 43
4.1.2.2 Separating expressed and non expressed genes of one microarray
hybridization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.1.2.3 Standardization procedure to compare pairs of experiments . . . . 45
4.1.2.4 Expression profiling with series of experiments . . . . . . . . . . 49
4.2 Analysis of expression data from a CREM SSH library . . . . . . . . . . . . . . . . 52
4.2.1 Analysis of DNA sequences from the CREM SSH library . . . . . . . . . . 52
4.2.1.1 Processing of DNA sequences from the sequencer . . . . . . . . . 53
4.2.1.2 Clusters of sequences in the CREM SSH library . . . . . . . . . . 54
6Contents
4.2.1.3 Finding homologies in databases of known sequences . . . . . . . 54
4.2.1.4 Ontology of the genes . . . . . . . . . . . . . . . . . . . . . . . . 56
4.2.2 Integration of sequencing and hybridization data in a database . . . . . . . . 57
4.2.3 Expression analysis of CREM dependent sequences . . . . . . . . . . . . . 58
4.2.3.1 Comparison of wild type versus CREM ( / ) testes . . . . . . . . . 58
4.2.3.2 Expression profiles of sequences found in the CREM SSH library
in testes of prepubertal mice . . . . . . . . . . . . . . . . . . . . . 63
4.2.4 Availability of results via a web based interface . . . . . . . . . . . . . . . . 68
5 Discussion 71
5.1 Analysis of CREM dependent genes . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2 Comparison of the methods used for the identification of differentially expressed genes 75
5.3 Computational methods to analyze gene expression profiles . . . . . . . . . . . . . . 76
5.4 Perspectives for Bioinformatics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.5 Accomplishments of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6 Acknowledgments 81
7 Abbreviations 83
Bibliography 85
71 Summary
Computational methods are getting increasingly important for the analysis of large data sets in
molecular biology. The data sets analyzed in this thesis are derived from experiments measuring
the changes of expression levels in response to the transcription factor CREM (cAMP Responsive
Element Modulator) during mouse spermatogenesis. In the course of this analysis new computa
tional methods were developed and used that will also be of value in other projects in Bioinformat
ics.
CREM belongs to a family of cAMP responsive nuclear factors. The activator splice isoform CREM
is exclusively expressed at high levels in post meiotic germ cells during mouse spermiogenesis. Mu
tant male mice lacking CREM expression are sterile due to lack of maturation of the germ cells.
In order to find CREM target genes the mRNA expression levels in testes of CREM deficient mice
and wild type mice were compared using the suppression subtractive hybridization (SSH) technique
as well as oligonucleotide DNA microarrays.
SSH was used to selectively amplify the differentially expressed genes. 12,000 clones, which contain
sequence fragments of genes expressed stronger in wild type as in the CREM ( / ) mutant, were
analyzed by a combination of sequencing and hybridization.
Sequence analysis methods were used to characterize 956 unique sequences. Homologies to 158
known mouse genes and 99 known genes from other organisms were detected. 296 sequences show
homologies to sequences of expressed sequence tags (ESTs). 199 novel sequences have been found.
The sequences not corresponding to full length genes of known function were characterized using
publicly available EST data. To make EST databases useful for data analysis all of the publicly
available ESTs have been grouped into clusters and methods to analyze and visualize EST data were
developed.
Nylon cDNA microarrays containing the unique sequences from the CREM SSH library were con
structed to determine expression levels of those sequences. Most of the sequences from the CREM
SSH library are shown to be expressed in wild type but are down regulated in CREM deficient mice.
91 Summary
Statistical methods to standardize microarray expression data were developed and software was im
plemented to perform comparisons.
Further CREM dependent genes were detected comparing the mRNA expression levels in testes of
CREM deficient mice and wild type mice using Affymetrix oligonucleotide microarrays containing
10,000 mouse sequences. Comparison of the different techniques (SSH, nylon cDNA arrays and
Affymetrix oligonucleotide microarrays) shows that the results are complementing each other.
The unique sequences from the CREM SSH library were further analyzed by determining the sper-
matogenic stage specific expression profiles. cDNA from prepubertal mice at certain stages of sper-
matogenesis were hybridized on nylon cDNA arrays. Several important functional groups of genes
like transcription factors, signal transduction proteins and metabolic enzymes are shown to be coex
pressed at the latest stages of spermatogenesis.
Expression profiles were arranged to find similar profile shapes and co regulation of functionally
related genes. An algorithm to arrange the profiles in an optimal linear order was developed. The
linear order is constructed in a way that similar expression profiles end up close together in the linear
order, i.e. the sum over all distances of neighboring profiles is minimized. This corresponds to the
solution of a traveling salesman problem (TSP), which is well known in computer science. A fast
algorithm that computes a heuristic solution to a TSP was adapted to be used in expression profile
analysis.
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