Genetic networks of antibacterial responses of eukaryotic cells [Elektronische Ressource] : bioinformatics analysis and modeling / von Ekaterina Shelest

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Sujets

• Biologie

Informations

• Publié par: technische_universitat_carolo-wilhelmina_zu_braunschweig
• Publié le: 01 janvier 2006
• Nombre de lectures: 14
• Langue: English

Extrait

Genetic networks of antibacterial responses of eukaryotic cells.
Bioinformatics analysis and modeling

Vom Fachbereich für Biowissenschaften und Psychologie
der Technischen Universität Carolo-Wilhelmina
zu Braunschweig

zur Erlangung des Grades einer
Doktorin der Naturwissenschaften
(Dr.rer.nat.)

genehmigte

D i s s e r t a t i o n









von Ekaterina Shelest
aus Novosibirsk

















1. Referent: Prof. Dr. D. Jahn
2. Referent: Prof. Dr. E. Wingender.
eingereicht am: 23.11.2005
mündliche Prüfung (Disputation) am: 7.02.2006

2006
(Druckjahr)




























Vorveröffentlichungen der Dissertation

Teilergebnisse aus dieser Arbeit wurden mit Genehmigung der Gemeinsamen
Naturwissenschaflichen Fakultät, vertreten durch den Mentor die Betreuerin der Arbeit, in
folgenden Beiträgen vorab veröffentlicht:

Publikationen
Shelest, E., Kel, A.E., Goessling, E. & Wingender, E. Prediction of potential C/EBP/NF-
kappaB composite elements using matrix-based search methods. In Silico Biol. 3: 71-
79. (2003).
Shelest, E. & Wingender, E. Construction of predictive promoter models on the example of
antibacterial response of human epithelial cells. Theor Biol Med Model. 2(1):2.
(2005).

Tagungsbeiträge
Shelest, E., Kel-Margoulis, O., Kel, A. & Wingender, E.: Bioinformatics representation of
cellular responses to bacterial infection. (Vortrag). Cell signaling, transcription and
translation as therapeutic targets. Luxembourg (2002).
Shelest, E., Kel, A.E., Gößling, E. & Wingender E.: Prediction of potential C/EBP/NF-
kappaB composite elements using matrix-based search methods. (Vortrag). The 3rd
International Conference on Bioinformatics of Genome Regulation and Structure,
BGRS 2002, Novosibirsk, Russia (2002).
Shelest, E., Kel, A., Gößling, E. & Wingender, E.: Composing a promoter model for
antibacterial response of epithelial cells. (Poster). European Conference on
Computational Biology, ECCB 2002, Saarbruecken, Germany (2002).
Shelest E., Kel A.E. & Wingender E.: Constructing a promoter model for antibacterial
response of lung epithelial cells. (Poster). Gordon Research Conference
“Bioinformatics: from predictive models to inference”. Oxford UK (2003).
Shelest, E., Kel, A.E. & Wingender, E.: Constructing a promoter model for antibacterial
response of lung epithelial cells. (Poster). European Conference on Computational
Biology, ECCB 2003, Paris, France (2003).
Shelest E., Sauer T. & Wingender E.: Regulatory networks of antibacterial response. (Poster).
Symposium NGFN, Tübingen, Germany (2003).
Shelest E. & Wingender E.: Identification of immune-related target genes by application of a

promoter model. (Vortrag). 6th EMBL Transcription Meeting, Heidelberg, Germany
(2004).
Shelest, E. & Wingender, E.: Investigation of distances in transcription factor binding site
pairs. (Poster). European Conference on Computational Biology, ECCB 2005,
Madrid, Spain. (2005)
















































All models are wrong—but some are useful.
George E.P. Box, 1979

Knowledge of some principles easily compensates for the
ignorance of some facts.
Claude Adrien Helvetius
(1715-1771)



























I

CONTENTS

1. INTRODUCTION 1
1.1. Gene regulatory networks, transcription networks and promoter models 1
1.1.1. Biological networks 1
1.1.2. Definition of a promoter model 3
1.2. Biological systems addressed 5
1.2.1. Antibacterial response: Innate immunity 5
1.2.2. Pseudomonas aeruginosa 6
1.2.2.1. General characteristics, virulence and biofilm formation 6
1.2.2.2. Mucoid phenotype, PMNs and oxygen radicals 7
1.2.3. Antibacterial response: bacterial agents, receptors and pathways 8
1.2.3.1. Pyocyanin and autoinducer 1 8
1.2.3.2. Pilin and asialoGM1 9
1.2.3.3. Lipopolysaccharides (LPS) 10
1.2.3.4. Toll-like receptors and the triggered pathways. Short overview 11
1.2.4. General scheme of interactions triggered by binding of P. aeruginosa to human epithelial cells 16
1.3. Bioinformatics: databases, methods and tools for computational approaches in biology 17
1.3.1. Databases 17
1.3.1.1. Databases used in sequence analysis 17
1.3.1.2. Databases on signal transduction 18
1.3.2. Methods and algorithms used for promoter model construction 20
1.3.3. Tools for promoter modeling 24
1.3.3.1. Tools for motif and TFBS search 24
1.3.3.2. Tools for further promoter analysis 27
2. RESULTS 29
2.1. Subtractive approach to positional weight matrix generation 29
2.1.1. Motivation 29
2.1.2. Description of the approach 30
2.1.2.1. Subtractive approach to matrix generation 30
2.1.2.2. Defining thresholds for a set of PWMs. 31
2.1.3. Application to C/EBP matrix re-evaluation 32
2.2. Distance distributions 35
2.2.1. Motivation 35
2.2.2. Calculation of theoretical distance distribution 36
2.2.3. Comparison of random distance distributions with the distance distributions in the control set of
random sequences 38
2.2.4. Application of the distance distribution approach 38
2.2.4.1. Distance distributions in composite elements 39
2.2.4.2. Coincidence of the dominating peaks and the true positive distances 39
2.2.4.3. Potentially false predictions 42
2.3. Other anti-false-positive measures 42
2.3.1. “Seed” sequences 43
2.3.2. Complementary pairs 45
2.3.3. Phylogenetic conservation 46
2.4. Promoter model construction 47
2.4.1. Identification of pairs with defined mutual orientation 47
2.4.2. Defining complementary pairs (pairs of pairs) 49
2.5. Application of the methodology 50
2.5.1. Epithelial cells’ response to Pseudomonas aeruginosa binding 52
2.5.1.1. Selection of the “seed” set 52
2.5.1.2. Selected TFs and conditions of the search 53
2.5.1.3. Promoter model 57
2.5.1.4. Identification of potential target genes 58
2.5.2. LPS triggering: promoter model for immediate early response 58
2.5.2.1. Selection of the relevant TFs 58
2.5.2.2. Search for combinations 59
2.5.2.3. Promoter models 60
II

2.5.3. MyD88-dependent and -independent pathways in TLR4 triggering 63
2.5.3.1. Promoter model for MyD88–independent pathway. Re-identification of the NF-kappaB/IRF
composite element as playing the main role in the regulation of this pathway. 63
2.5.3.2. Re-identification of the MALP-2 subset 64
2.5.3.3. Re-identification of the IRF subset 65
3. DISCUSSION 69
3.1. Development of methods 70
3.1.1. Subtractive approach to matrix generation 70
3.1.2. Distance distributions 73
3.1.2.1. The method: main idea, some methodological premise and the result 73
3.1.2.2. Application of the distance distribution approach 75
3.1.3. Other anti-false-positive measures 77
3.2. Applications 79
3.3. Shortcomings 82
3.4. Related work 83
3.5. Perspectives 85
4. MATERIALS AND METHODS 87
4.1. Software 87
4.2. Tools 87
4.3. Databases 87
4.4. Training sequence sets 88
4.4.1. Positive training sets 88
4.4.2. Negative training (Control) set 92
4.5. Defining the sets of transcription factors (potential constituents of the model) 93
4.5.1. Model for P.aeruginosa triggering 93
4.5.2. Models for LPS and MALP-2 93
4.6. Search for the potential transcription factor binding sites 94
4.6.1. For promoter model construction 94
4.6.2. In the set of CE-containing sequences (application of distance distribution approach) 94
4.7. Identification of pairs 94
5. SUMMARY 95
6. REFERENCES 97
APPENDIX 1. SUBTRACTIVE APPROACH. 117
APPENDIX 2. DISTANCE DISTRIBUTIONS 121
APPENDIX 3. P.A. PROMOTER MODEL 125
APPENDIX 4. LP