Integrative transcriptomic approaches to analyzing plant co-expression networks [Elektronische Ressource] / Marek Mutwil

Marek Mutwil

Sujets

• Biologie

Informations

• Publié par: universitat_potsdam
• Publié le: 01 janvier 2010
• Nombre de lectures: 7
• Langue: English
• Poids de l'ouvrage: 8 Mo

Extrait

Integrative transcriptomic approaches to
analyzing plant co-expression networks
Dissertation

zur Erlangung des akademischen Grades
"doctor rerum naturalium" (Dr. rer. nat.)

eingereicht im
Institut für Biochemie und Biologie an der
Mathematisch-Naturwissenschaftlichen Fakultät der
Universität Potsdam



Marek Mutwil

Arbeitsgruppe Persson
Max-Plank-Institut für Molekulare Pflanzenphysiologie

Potsdam, den 25.07.2010
This work is licensed under a Creative Commons License:
Attribution - Noncommercial - Share Alike 3.0 Unported
To view a copy of this license visit
http://creativecommons.org/licenses/by-nc-sa/3.0/










































Published online at the
Institutional Repository of the University of Potsdam:
URL http://opus.kobv.de/ubp/volltexte/2011/5075/
URN urn:nbn:de:kobv:517-opus-50752
http://nbn-resolving.org/urn:nbn:de:kobv:517-opus-50752
Acknowledgments
Staffan Persson is certainly one of the best supervisors I’ve ever had. He gave me the
freedom to pursue my own ideas and goals whilst providing all the necessary help and
support when needed. To work at such an institute, in the company of great scientific minds,
has helped my scientific thinking through a colossal improvement. I still have far to go, but I
start my post-doctoral efforts from a very solid basis.
I would also like to thank my official university supervisor Ralph Bock and progress
commity member Dirk Walther, for superb guidance during my PHD. Thanks to the whole of
AG Persson for the good times and great company.
I thank my family for their support; Mum, Jens and the rest of my family, in Denmark and
Poland.




Contents
1. Introduction ......................................................................................................................................... 1
1.1 Transcriptomics . 3
1.2 Co-expression analysis ...................................................................................................................... 5
1.2.1 Expression similarity metrics ..................................................................................................... 8
1.2.2 Setting threshold for biologically relevant co-expression .......................... 9
1.2.3 Meta-analysis of co-expression relationships. ......... 10
1.3 Functional gene ontology ................................................................................................................ 12
1.4 Graphs and graphs theory ............... 12
1.5 Clustering ........................................................................................................................................ 13
1.6 Outline and contributions ................ 13

2. GeneCAT - Novel webtools that combine BLAST and co-expression analyses .............................. 15
2.1 Abstract ........................................................................................................................................... 15
2.2 Introduction ..... 15
2.3 Results and Discussion ................................................................................................................... 16
2.3.1 Expression Profiling and Tree View - Cellulose synthases ..................... 16
2.3.2 Co-expression using multiple bait genes – Suberin biosynthesis ............ 19
2.3.4 Forward genetics predictions using Map-O-Matic: photosynthesis ......................................... 23
2.3.5 Combining BLAST and Co-expression using Rosetta - Cellulose synthases .......................... 26
2.4 Concluding Remarks ....................................................................................................................... 29
2.5 Materials and Methods .................... 30

3. Assembly of an Interactive Correlation Network for the Arabidopsis Genome Using a Novel
Heuristic Clustering Algorithm ............................................................................................................. 31
3.1 Abstract ........................................... 31
3.2 Introduction ..................................................................................................... 32
3.3 Results and Discussion ................... 34
3.3.1 Calculation of Pearson-Based Correlation Networks ............................... 34
3.3.2 Centrality vs. Essentiality ........................................................................................................ 35
3.3.3 Construction of a Highest Reciprocal Rank - Based Correlation Network in Arabidopsis ..... 36
3.3.4 Designing the HCCA ............... 37
3.3.5 Visual Inspection of the Network Solutions ............................................................................ 38
3.3.6 Estimates of Clustering Solutions ............................................................................................ 39
3.3.7 Robustness of Clustering Towards Node Removal and to Different HRR Cut-offs ................ 41

3.3.9 Construction of an Interactive Correlation Network for the Arabidopsis Genome ................. 42
3.3.10 Phenotype and Ontology Mapping onto Network ................................................................. 44
3.3.11 Prediction and Verification of Essential Genes in the Network ............. 45
3.3.12 Associations of Functional Annotations Using MapMan Ontology ...... 48
3.4 Conclusions ..................................................................................................................................... 50
3.5 Materials and Methods .................................................................................................................... 51

4. PlaNet: Combined sequence and expression comparisons across seven plant species ..................... 57
4.1 Abstract ........................................................................................................................................... 57
4.2 Introduction ..... 58
4.3 Data sources, construction and structure of PlaNet ........ 60
4.4 Comparative co-expression relationships across seven plant species. ............................................ 65
4.4.1 Photosynthesis – AtPSA-D1 and AtPSA-D2 ........................................... 66
4.4.2 Flavonol and flavonoid synthesis - Chalcone Synthases ......................................................... 71
4.5 MapMan ontology networks ........................................... 79
4.6 Summary and future prospects. ....................................................................... 80

5. General discussion ............................................................................................................................ 82
5.1 Prediction of gene function ............................................................................................................ 83
5.2 Organization of biological processes .............................. 84
5.3 Prediction of functional homologs. ................................................................................................ 84
5.4 Future work .................................... 85
5.4.1 Improved algorithm for comparing network structures .......................... 85
5.4.2 Further comparative analyses .................................................................................................. 87
5.4.3 Transcriptomic associations between gene families. ............................... 88
5.5 Conclusion ..................................... 89

Publications ........................................................................................................... 91
Curriculum Vitae .................................. 92
Selbständigkeitserklärung ..................................................................................... 93
Bibliography ......................................... 94



Abstract
It is well documented that transcriptionally coordinated genes tend to be functionally related,
and that such relationships may be conserved across different species, and even kingdoms.
(Ihmels et al., 2004). Such relationships was initially utilized to reveal functional gene
modules in yeast and mammals (Ihmels et al., 2004), and to explore orthologous gene
functions between different species and kingdoms (Stuart et al., 2003; Bergmann et al.,
2004).
Model organisms, such as Arabidopsis, are readily used in basic research due to
resource availability and relative speed of data acquisition. A major goal is to transfer the
acquired knowledge from these model organisms to species that are of greater importance to
our society. However, due to large gene families in plants, the identification of functional
equivalents of well characterized Arabidopsis genes in other plants is a non-trivial task,
which often returns erroneous or inconclusive results.
In this thesis, concepts of utilizing co-expression networks to help infer (i) gene
function, (ii) organization of biological processes and (iii) knowledge transfer between