Expression-based reverse engineering of plant transcriptional networks [Elektronische Ressource] / Federico Manuel Giorgi. Betreuer: Mark Stitt

Federico Manuel Giorgi

Sujets

• Biologie

Informations

• Publié par: universitat_potsdam
• Publié le: 01 janvier 2011
• Nombre de lectures: 82
• Langue: English
• Poids de l'ouvrage: 10 Mo

Extrait

Max-Planck-Institut für Molekulare Pflanzenphysiologie
Arbeitsgruppe Usadel
______________________________________________________________________________

Expression-based Reverse Engineering
of Plant Transcriptional Networks
Dissertation



zur Erlangung des akademischen Grades
"doctor rerum naturalium"
(Dr. rer. nat.)
in der Wissenschaftsdisziplin "Molekulare Pflanzenphysiologie"

eingereicht an der
Mathematisch-Naturwissenschaftlichen Fakultät
der Universität Potsdam




von

Federico Manuel Giorgi





Potsdam, den 28.07.2011
This work is licensed under a Creative Commons License:
Attribution - Noncommercial - No Derivative Works 3.0 Unported
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Published online at the
Institutional Repository of the University of Potsdam:
URL http://opus.kobv.de/ubp/volltexte/2011/5676/
URN urn:nbn:de:kobv:517-opus-56760
http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-56760
A big computer, a complex algorithm and a long time does not equal science
Robert Gentleman, SSC 2003, Halifax (June 2003)
III

IV

Abstract
Regulation of gene transcription plays a major role in mediating cellular responses and physiological behavior
in all known organisms. The finding that similar genes are often regulated in a similar manner (co-regulated or
"co-expressed") has directed several "guilt-by-association" approaches in order to reverse-engineer the
cellular transcriptional networks using gene expression data as a compass. This kind of studies has been
considerably assisted in the recent years by the development of high-throughput transcript measurement
platforms, specifically gene microarrays and next-generation sequencing.
In this thesis, I describe several approaches for improving the extraction and interpretation of the information
contained in microarray based gene expression data, through four steps: (1) microarray platform design, (2)
microarray data normalization, (3) gene network reverse engineering based on expression data and (4)
experimental validation of expression-based guilt-by-association inferences. In the first part test case is shown
aimed at the generation of a microarray for Thellungiella salsuginea, a salt and drought resistant close relative
to the model plant Arabidopsis thaliana; the transcripts of this organism are generated on the combination of
publicly available ESTs and newly generated ad-hoc next-generation sequencing data. Since the design of a
microarray platform requires the availability of highly reliable and non-redundant transcript models, these
issues are addressed consecutively, proposing several different technical solutions. In the second part I
describe how inter-array correlation artifacts are generated by the common microarray normalization methods
RMA and GCRMA, together with the technical and mathematical characteristics underlying the problem. A
solution is proposed in the form of a novel normalization method, called tRMA. The third part of the thesis
deals with the field of expression-based gene network reverse engineering. It is shown how different centrality
measures in reverse engineered gene networks can be used to distinguish specific classes of genes, in
particular essential genes in Arabidopsis thaliana, and how the use of conditional correlation can add a layer
of understanding over the information flow processes underlying transcript regulation. Furthermore, several
network reverse engineering approaches are compared, with a particular focus on the LASSO, a linear
regression derivative rarely applied before in global gene network reconstruction, despite its theoretical
advantages in robustness and interpretability over more standard methods. The performance of LASSO is
assessed through several in silico analyses dealing with the reliability of the inferred gene networks. In the
final part, LASSO and other reverse engineering methods are used to experimentally identify novel genes
involved in two independent scenarios: the seed coat mucilage pathway in Arabidopsis thaliana and the
hypoxic tuber development in Solanum tuberosum. In both cases an interesting method complementarity is
shown, which strongly suggests a general use of hybrid approaches for transcript expression-based
inferences.
In conclusion, this work has helped to improve our understanding of gene transcription regulation through a
better interpretation of high-throughput expression data. Part of the network reverse engineering methods
described in this thesis have been included in a tool (CorTo) for gene network reverse engineering and
annotated visualization from custom transcription datasets.
V
Contents
1. Introduction................................................................................................ 1
1.1 Regulation of Transcription and Systems Biology ... 1
1.2 Transcriptomics ...................................................................................... 2
1.2.1 Transcriptomics from Northern blot to microarrays ............................................. 2
1.2.2 Microarray data preprocessing ............................................ 5
1.2.3 The future of Transcriptomics .............. 6
1.3 Gene network reverse engineering ......................................................... 7
1.4 Biological scenarios of gene network reverse engineering .................... 12
1.4.1 The seed coat mucilage pathway ...................................................................... 12
1.4.2 Hypoxic tuber development in Solanum tuberosum .......................................... 14
1.4.3 Essential genes ................................. 16
1.5 Summary of the aims of this thesis ....................... 17
2. Results ..................................................................................................... 18
2.1 Generation of a custom microarray platform from next-generation mRNA
sequencing data: the Thellungiella salsuginea transcriptome ..................... 18
2.1.1 Collecting Thellungiella sequences ................................................................................................... 18
2.1.2 Transcript assembly ........................................................... 19
2.1.3 Transcriptome completion ................................................................................. 23
2.1.4 Comparative transcriptome considerations between Thellungiella and Arabidopsis ........................ 23
2.2 Algorithm-driven Artifacts in median polish summarization of microarray
data: tRMA .................................................................................................. 26
2.2.1 Multi-array preprocessing effects ...... 26
2.2.2 Causes of RMA and GCRMA artifact generation .............................................. 28
2.2.3 Median polish inconsistency ................................ 30
2.2.4 Comparison between RMA and tRMA in biological contexts ............................................................ 34
2.2.5 Conclusions on median polish based microarrays normalization methods ....................................... 36
2.3 Combining network centrality analysis and conditional correlation:
application to essential gene prediction ...................................................... 37
2.3.1 Definition of Breaking Potential.......................................................................... 37
VI

2.3.2 Comparison between Breaking Potential and other centralities in Arabidopsis thaliana coexpression
networks ...................................................................................................................................................... 38
2.3.3 Breaking Potential is a positive predictor for gene essentiality in Arabidopsis thaliana .................... 38
2.3.4 Conclusions on Breaking Potential as an essential gene predictor and future perspectives ............ 41
2.4 Expression-based gene network reverse engineering ........................... 42
2.4.1 Custom network reverse engineering and method comparison: the CorTo tool ............................... 42
2.4.2 Application of the LASSO to gene expression-based modeling ........................................................ 44
2.4.3 Comparative analysis of expression-based methods for gene network reverse engineering ........... 45
2.5 LASSO and correlation for reverse engineering the seed coat mucilage
pathway in Arabidopsis thaliana .................................................................. 58
2.5.1 RHM2 expression network analysis ... 58
2.5.2 Network reconstruction based on several mucilage genes ............................... 60
2.6 LASSO and correlation for reverse engineering the hypoxia-regulated
tuber development pathway in Solanum tuberosum .................................... 69
2.6.1 Identification of hypoxia responsive ERFs in Solanum tuberosum .................... 69
2.6