Bioinformatic approaches for identification of genetic modules in response to abiotic stresses in the Poaceae family

다국어 초록 (Multilingual Abstract)

The rapid growth of public genome databases together with computational tools appears to promoter to identify and characterize interest genes. In particular,
the advent and the rapid growth of public microarray databases, e.g. the gene expression omnibus (GEO) of National Center for Biotechnology Information
(NCBI), allows us to be able to monitor simultaneously the expression levels of thousands of genes over a range of biological issues, further providing some clues
on the entire genome’s networks. In order to uncover gene regulatory networks clustering of co-expressing genes was performed using a rice microarray dataset of
155 gene expression omnibus sample (GSM) plates in NCBI, generating a total of 1660 clusters. Expression profiles of co-expressing genes revealed some degree of
tissue-specific expression patterns, probably reflecting the existence of, at least partial, parallel versions of stress-related networks with evolutionary process, such
as subfuntionalization. The finding that several cis-elements related to abiotic stresses was detected by differences in frequency between co-expressing genes and
randomly selected genes. Together, clustering, expression profiles, and putative cisacting regulatory elements of co-expressing genes related to abiotic stresses may
provide clues to shed further light on the gene regulatory network of stressresponsive pathway.
Duplicate genes are believed to be a major source of new gene functions over evolutionary time. In order to evaluate evolutionary dynamics of rice duplicate genes, mainly formed by the paleoployploidizaiton before speciation of the Poaceae family, we have employed a public microarray data set including 155 gene expression omnibus sample (GSM) plates and bioinformatics tools. For seeking diversity and evolutionary dynamics of the rice duplicate genes formed by the whole genome duplication event as well as the segmental duplication event, we
have employed the rice microarray dataset and bioinformatics tools, further dividing non- and divergence duplicate genes. In addition, we have attempted to evaluate
differences in values of non-synonymous substitution (Ka), synonymous substitution (Ks) and Ka/Ks, and in frequencies of insertions and deletions (indels)
and conserved cis-elements between three groups, such as non-, ongoing- and divergence group. We have also tried to evaluate evolutionary dynamics of 28 rice
gene families via in silico analysis, suggesting a rapid functional diversity between members of each of rice gene families As grouping the rice duplicate genes according to functional categories there was striking and significant enrichment of divergence duplicate genes related to metabolism as compared to that of nondivergence
duplicate genes. While both synonymous substitution (Ka) and nonsynonymous substitution (Ks) values between non- and divergence duplicate gene pairs exhibited significant differences, Ka/Ks values between them showed no significant differences. Interestingly, the average numbers of conserved motifs of duplicate gene pairs revealed a decline pattern along with increasing of expression diversity, partially supporting the subfunctionalization model with degenerative
complementation in regulatory motifs.
The PLANt co-EXpression database (PLANEX) is a new internet-based database for plant gene analysis. PLANEX is based on publicly available GeneChip data obtained from GEO of NCBI. PLANEX (http://planex.plantbioinformatics.org)
is a genome-wide co-expression database and can be used for broad experimental designs, such as characterization of genes for functional identification and analysis of their dependency among genes. A database for co-expressed genes can provide functional identification from a wide variety of experimental designs. Therefore, we
constructed PLANEX as a co-expressed gene list and functional annotation for Arabidopsis thaliana, Glycine max, Hordeum vulgare, Oryza sativa, Solanum
lycopersicum, Triticum aestivum, Vitis vinifera and Zea mays.
Wheat is the third largest crop in the world, after corn and rice. Wheat is grown over a wide range of different agricultural conditions, and as one of the principle sources of carbohydrate, and an essential ingredient throughout the food industry, it critical that the supply of wheat is maintained, regardless of varying environmental conditions. However, the genomics of wheat, a non-model species, is still challenging despite the advances that have been made with corn and rice. The recent advent of RNA-Sequencing (RNA-Seq), a highly applicable method for the
sequencing and analysis of transcriptome provides an opportunity for gene identification, as well as that of the molecular mechanisms of cellular processes. We
performed a RNA-Seq experiment to find differentially expressed genes under conditions of high temperature. More than 344 million shot reads were generated
using Illumina HiSeq technology. A comprehensive and integrated 285,324 transcripts were assembled via Trinity by combining tentative consensus sequences.
Transcripts were annotated by BLAST2Go, and differently expressed transcripts were analyzed. Our results demonstrate that RNA-Seq can be successfully used for
gene identification and transcript profiling in wheat. Furthermore these sequences will provide valuable resources for wheat researchers.

목차 (Table of Contents)

• CHAPTER I: Discovery of gene regulatory networks via in silico analysis and their application in abiotic stress responses = 1
• INTRODUCTION = 2
• MATERIALS AND METHODS = 5
• Clustering of co-expressing genes = 5
• Genes of interest related to abiotic stresses = 5
• Putative promoter analysis of co-expressing genes = 6
• RESULTS = 7
• Clustering of co-expressing genes = 7
• Expression profiles of co-expressing genes related to abiotic stresses = 7
• Drought stress = 8
• Salt stress = 8
• Cold stress = 14
• Comparison of putative cis-acting regulatory elements = 16
• DISCUSSION = 19
• REFERENCES = 22
• CHAPTER II: Diversity and evolutionary dynamics of rice duplicate genes = 26
• INTRODUCTION = 27
• MATERIALS AND METHODS = 31
• Rice duplicate genes = 31
• Functional categories = 32
• Expression correlation, structural alternation, and conserved motifs of duplicate genes = 32
• Rice gene families = 34
• RESULTS = 35
• Divergence of expression profiles of duplicate genes = 35
• Functional categories of duplicate genes = 39
• Structural changes of duplicate genes = 43
• Duplicate gene pairs with the high-local-similarity segments = 46
• Expression diversity of members of each of rice gene family = 49
• DISCUSSION = 56
• REFERENCES = 61
• CHAPTER III: PLANEX: the plant co-expression database = 65
• INTRODUCTION = 66
• MATERIALS AND METHODS = 69
• Expression data = 69
• Implementation = 72
• Clustering = 72
• Mapping gene identifiers onto probe set IDs = 73
• Gene Ontology term assignment = 73
• Comparative analysis of co-expressed gene sets = 74
• System development = 75
• RESULTS
• Web interface = 78
• DISCUSSION = 84
• REFERENCES = 87
• CHAPTER IV: Gene expression analysis based on de novo assembly in wheat RNAsequencing = 93
• INTRODUCTION = 94
• MATERIALS AND METHODS = 99
• Plant material = 99
• cDNA library construction and sequencing = 100
• De novo transcriptome assembly and evaluation = 101
• Sequence analysis and functional annotation = 104
• RESULTS = 105
• RNA-Seq and de novo assembly = 105
• Validation of assembled transcripts = 109
• Analysis of different gene expression = 112
• Functional annotation and classification of differentially expressed transcripts = 114
• DISCUSSION = 120
• REFERENCES = 125