In this thesis we propose some new approaches to the study of complex networks, and apply them to multiple domains, focusing in particular on protein-protein interaction networks. We begin by examining the roles of individual proteins; specifically, the influential idea of 'date' and 'party' hubs. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. We show that the observations underlying this proposal appear to have been largely illusory, and that topological properties of hubs do not in general correlate with interactor co-expression, thus undermining the primary basis for the categorisation. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins, indicating that it might be useful to conceive of roles for protein-protein interactions, as opposed to individual proteins. The observation that examining just one or a few network properties can be misleading motivates us to attempt to develop a more holistic methodology for network investigation. A wide variety of diagnostics of network structure exist, but studies typically employ only small, largely arbitrarily selected subsets of these. Here we simultaneously investigate many networks using many diagnostics in a data-driven fashion, and demonstrate how this approach serves to organise both networks and diagnostics, as well as to relate network structure to functionally relevant characteristics in a variety of settings. These include finding fast estimators for the solution of hard graph problems, discovering evolutionarily significant aspects of metabolic networks, detecting structural constraints on particular network types, and constructing summary statistics for efficient model-fitting to networks. We use the last mentioned to suggest that duplication-divergence is a feasible mechanism for protein-protein interaction evolution, and that interactions may rewire faster in yeast than in larger genomes like human and fruit fly. Our results help to illuminate protein-protein interaction networks in multiple ways, as well as providing some insight into structure-function relationships in other types of networks. We believe the methodology outlined here can serve as a general-purpose, data-driven approach to aid in the understanding of networked systems.

Doctor of Philosophy / Department of Entomology / Larry L. Buschman / Kun Yan Zhu / European corn borer, Ostrinia nubilalis, is one of the most destructive insect pests of corn in the Midwest corn belt of the United States. The crystal protein toxin (Cry1Ab) expressed by the bacterium, Bacillus thuriginesis (Bt), specifically targets O. nubilalis gut and functions as “stomach poison”. Transgenic corn expressing Cry1Ab can effectively control O. nubilalis larval infestation. However, O. nubilalis has the potential to develop resistance to Bt toxins which prompts concerns that transgenic corn will lose its control efficacy. Previous studies found that O. nubilalis gut serine proteases and membrane proteins were involved in Bt toxicity and resistance. Therefore, this study was to identify and characterize gut transcripts potentially involved in Bt toxicity and resistance, and to compare their transcriptional responses to the ingestion of Cry1Ab protoxin and transgenic corn leaves expressing Cry1Ab toxin. We identified and characterized 34 cDNAs encoding putative trypsins, chymotrypsins, and trypsin- and chymotrypsin-like protease homologs from O. nubilalis gut-specific expressed sequence tags (ESTs). Blast and phylogenetic analysis of their deduced amino acid sequences indicated that 15 were putative trypsins belonging to Try-G2 and Try-G3 groups (none of them was grouped in Try-G1), another 15 were putative chymotrypsins in one large group (CTP-G1), and the remaining four were serine protease homologs in Try-G4 and CTP-G2 groups, respectively. The existence of diverse trypsins, chymotrypsins and serine protease homologs in O. nubilalis could be an adaptation to different food sources and also a defense mechanism against plant-specific protease inhibitors and Cry toxins from transgenic corn. The expressions of four putative trypsins (OnTry4, OnTry5, OnTry6 and OnTry14) were up-regulated in O. nubilalis larvae after the ingestion of Cry1Ab protoxin. The differential expressions of these protease transcripts may implicate a link to Cry1Ab intoxication. To better understand the basic physiology of insect gut and Bt toxin interactions, we developed a high-resolution 8×15K cDNA microarray chips based on the larval gut specific ESTs. Each microarray contains 12,797 probes representing 2,895 unique larval gut transcripts. The expressions of 174 transcripts were differentially regulated at least 2-fold (P-value ≤0.05) after the larvae fed Cry1Ab protoxin for 6 hours. Among them, 13 transcripts, putatively encoding eight serine protease, three aminopeptidase, one alkaline phosphatase, and one cadherin-like protein, were identified and further validated their expression ratios by quantitative PCR (qPCR). Three trypsin transcripts were up-regulated by more than 5-fold in larvae fed Cry1Ab protoxin. Sequence analysis suggests that they may have role in protoxin activation and toxin degradation. The transcriptional responses of laboratory-selected Cry1Ab resistant (R) and susceptible (S) strains of O. nubilalis to the ingestion of transgenic corn (MON811) leaves expressing Cry1Ab toxin were also examined. Even though R-strain larvae showed 200-fold resistance to Cry1Ab protoxin as compared with S strain, the larvae from both strains eventually died after fed transgenic corn leaves. However, the survival time of R-strain larvae was significantly different from that of S-strain larvae. The median lethal time (LT50) for the early third-instar larvae of R- and S-strains were 5.4 and 3.6 days, respectively. Furthermore, we identified 398 and 264 transcripts from the larvae of the S and R strains, respectively, with a significantly increased or decreased expression (expression ratio cut off ≥2.0 fold with p-value ≤0.05) as compared with those in the larvae fed on non-transgenic corn leaves. The number of transcripts and their expression ratios of S-strain larvae are larger than these of R-strain larvae. These significantly differentially expressed transcripts may play important roles in influencing Cry1Ab toxicity from toxin degradation, toxin binding, to intracellular defense. Seventeen transcripts including serine protease and aminopeptdiase in S strain and nine in R strain were further analyzed by qPCR to validate their expression ratios. This study not only revealed information about the difference in the transcriptional responses of these genes to Cry1Ab between Bt-resistant and susceptible strains of O. nubilalis, but also provided new insights into potential interactions of the protoxin, toxin from transgenic corn with important proteins in the gut of O. nubilalis larvae.

Dorsal root ganglion (DRG) neurons are responsive to altered neuromuscular activity and play a role in diabetic peripheral neuropathy (DPN). We present evidence that small and large DRG neurons are differentially affected by exercise and diabetes. We examined gene expression in samples of small and large neurons of rat L4/L5 DRG, and the specific responses after exercise and diabetes, to identify potential molecular processes involved in activity-dependent changes. Small and large DRG neurons were collected using laser capture microdissection. Relative mRNA levels were determined using real-time polymerase chain reaction experiments. In study 1, healthy adult rats received treadmill exercise for 1 or 17 weeks, or voluntary wheel exercise for 16 weeks. In study 2, STZ-induced diabetic rats received 15 weeks of sedentary treatment or voluntary wheel exercise. Behavioural testing of thermal latency response was performed on all animals in study 2. In study 1, there were no significant changes in small or large DRG neuron gene expression after acute treadmill exercise. After chronic treadmill exercise, mRNA levels changed relative to healthy sedentary rats in small (↑ 5HT1D; ↓5HT1F) and large (↓ 5HT1A, TrkC, SYN1) DRG neurons. After chronic voluntary wheel exercise, mRNA levels changed relative to healthy sedentary rats in small (↓ 5HT1D, OPRD1, TrkA; ↑ GAP43) and large (↓ 5HT1D, Nav1.6, OPRD1, TrkA, TrkC, SYN1; ↑ 5HT3A, GAP43) DRG neurons. In study 2, there were no significant changes in large DRG neuron gene expression. In small DRG neurons, mRNA levels were changed in the diabetic sedentary group (↓TrkB; ↑5HT1F) as well as the diabetic wheel group (↓ CGRP) relative to healthy sedentary rats. 5HT1A receptor mRNA levels were higher in diabetic sedentary rats relative to diabetic wheel rats. Our results demonstrate that small and large DRG neurons respond, but in different ways, to the duration and intensity of exercise. DRG neurons show a greater response to voluntary compared to forced exercise, and chronic compared to acute exercise. The genetic changes in small DRG neurons of rats with DPN that exercise may be correlated with the positive change in progression of thermal hypoalgesia associated with exercise. / May 2016

Recent developments in automation and novel experimental techniques have led to the accumulation of vast amounts of biological data and the emergence of numerous databases to store the wealth of information. Consequentially, bioinformatics have drawn considerable attention, accompanied by the development of a plethora of tools for the analysis of biological data. DNA microarrays constitute a prominent example of a high-throughput experimental technique that has required substantial contribution of bioinformatics tools. Following its popularity there is an on-going effort to integrate gene expression with other types of data in a common analytical approach. Pathway based microarray analysis seeks to facilitate microarray data in conjunction with biochemical pathway data and look for a coordinated change in the expression of genes constituting a pathway. However, it has been observed that genes in a pathway may show variable expression, with some appearing activated while others repressed. This thesis aims to add some contribution to pathway based microarray analysis and assist the interpretation of such observations, based on the fact that in all organisms a substantial number of genes take part in more than one biochemical pathway. It explores the hypothesis that the expression of such genes represents a net effect of their contribution to all their constituent pathways, applying statistical and data mining approaches. A heuristic search methodology is proposed to manipulate the pathway contribution of genes to follow underlying trends and interpret microarray results centred on pathway behaviour. The methodology is further refined to account for distinct genes encoding enzymes that catalyse the same reaction, and applied to modules, shorter chains of reactions forming sub-networks within pathways. Results based on various datasets are discussed, showing that the methodology is promising and may assist a biologist to decipher the biochemical state of an organism, in experiments where pathways exhibit variable expression.

A diapausa é um fenômeno amplamente presente nos artrópodes e é considerada como primordial para o sucesso evolutivo da Classe Insecta, pois possibilita a sobrevivência em condições adversas, como estações frias e secas. Sabe-se que durante a diapausa ocorre o silenciamento de muitos genes e que outros são unicamente expressos nesta fase. Embora existam evidências de que o processo da diapausa tenha se mantido conservado durante a evolução das espécies, ainda há lacunas no conhecimento sobre o nível de conservação dos padrões metabólicos. Um bom modelo para se estudar a diapausa é Tetrapedia diversipes, uma espécie bivoltina de abelha solitária. Os indivíduos que nascem na primeira geração seguem o desenvolvimento desde ovo até adulto em tempo bem menor do que aqueles que nascem na segunda geração; estes retardam o desenvolvimento na fase larval. Além disso, essa espécie é de fácil obtenção no seu ambiente natural, pois apresenta alta taxa de nidificação em ninhos-armadilha. O objetivo deste trabalho foi comparar o perfil de expressão de genes entre as larvas da 1ª geração (que não entram em diapausa), larvas da 2ª geração (que entrariam em diapausa) e das larvas em diapausa. Foram identificados 196 genes diferencialmente expressos, destes 87 foram anotados. Muitos destes genes já foram descritos na literatura como relacionados à diapausa em outras espécies, no entanto, o padrão de expressão não é conservado. Os genes aqui identificados foram divididos em cinco grupos: relacionados à desintoxicação celular, cutícula e citoesqueleto, metabolismo de lipídeos e esteróis, ciclo celular e outros genes relacionados à diapausa / The diapause is broadly distributed among the arthropods and has had an important role for the evolutionary success of the Class Insecta, mainly because this process permits insects to explore adverse conditions, such as cold and dry seasons. It is known that there are many genes being silenced and others being uniquely expressed during diapause. And although there are evidences that the diapause process has remained conserved during the evolution of species, it is still not clear how conserved are the metabolic patterns involved in this behavior. Tetrapedia diversipes is a solitary bee and a good model to study diapause. Individuals from the first generation do not enter in diapause and develop faster than individuals from the second generation, which enter in diapause during the winter. Moreover, this species is easy to capture in natural conditions due to the high rate of nesting in trap nests. The aim of this work was to compare the gene expression profile among non-diapause larvae from first and second generation (about to enter diapause) and larvae already in diapause, trough transcriptome data. One hundred ninety-four genes were identified as differentially expressed and 87 of them were annotated. Many of these genes have already been described as related to diapause in others species, but the expression pattern was not conserved. These genes were divided in five groups: related to cellular detoxification, cuticle and cytoskeleton, lipids and steroids metabolism, cell cycle and other genes related to diapause

Tsang Sup-Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 176-185). / Abstracts in English and Chinese. / Acknowledgments --- p.1 / Abstract --- p.2 / 摘要 --- p.4 / List of Abbreviation --- p.6 / Chapter Chapter 1 --- General Introduction / Chapter 1.1 --- Isolation and activation of mas oncogene --- p.11 / Chapter 1.2 --- Amino acid sequence of mas oncogene --- p.14 / Chapter 1.3 --- Expression of mas oncogene --- p.18 / Chapter 1.4 --- Possible physiological role of mas oncogene --- p.20 / Chapter 1.5 --- Gene related to mas family --- p.23 / Chapter 1.6 --- Aims of study --- p.26 / Chapter Chapter 2 --- Over-expression of mas oncogene / Chapter 2.1 --- Introduction --- p.28 / Chapter 2.2 --- Materials and Methods --- p.29 / Chapter 2.2.1 --- Materials --- p.30 / Chapter 2.2.1.1 --- Chemicals --- p.30 / Chapter 2.2.1.2 --- Enzyme --- p.30 / Chapter 2.2.1.3 --- DNA Purification Kit --- p.31 / Chapter 2.2.1.4 --- Others --- p.31 / Chapter 2.2.2 --- Methods --- p.31 / Chapter 2.2.2.1 --- Strategy of construct preparation --- p.31 / Chapter 2.2.2.2 --- "Preparation of linearized vector, pFRSV" --- p.32 / Chapter 2.2.2.2.1 --- Cloning of vectors --- p.32 / Chapter 2.2.2.2.2 --- Restriction enzyme digestion and DNA dephosphorylation --- p.34 / Chapter 2.2.2.2.3 --- DNA purification by agarose gel electro-elution --- p.34 / Chapter 2.2.2.3 --- Preparation of pFRSV/mas construct --- p.35 / Chapter 2.2.2.3.1 --- PCR amplification --- p.35 / Chapter 2.2.2.3.2 --- Restriction enzyme digestion --- p.36 / Chapter 2.2.2.4 --- Ligation and analysis --- p.37 / Chapter 2.2.2.5 --- Purification of DNA by cesium chloride --- p.38 / Chapter 2.2.2.5.1 --- Large-scale bacterial culturing --- p.38 / Chapter 2.2.2.5.2 --- Ethanol precipitation --- p.39 / Chapter 2.2.2.5.3 --- Cesium chloride purification --- p.39 / Chapter 2.2.2.5.4 --- Removal of DNA dye by dialysis and ethanol precipitation --- p.40 / Chapter 2.2.2.6 --- Transfection by electroporation --- p.41 / Chapter 2.2.2.7 --- Screening for the stably transfected cells --- p.42 / Chapter 2.2.2.8 --- RT-PCR analysis of the mas transfectant --- p.43 / Chapter 2.2.2.8.1 --- Isolation of the total RNA from the mas transfectants by TRIzol ® Reagent --- p.43 / Chapter 2.2.2.8.2 --- Reverse transcription of the total RNA into cDNA --- p.44 / Chapter 2.2.2.8.3 --- Analysis of the transfected mas expression by PCR --- p.44 / Chapter 2.2.2.8.4 --- Analysis of the transfected DHFR expression by PCR --- p.45 / Chapter 2.2.2.8.5 --- Analysis of endogenous GAPDH expression by PCR --- p.46 / Chapter 2.2.2.9 --- Amplification of mas transgene by using methotrexate --- p.47 / Chapter 2.2.2.9.1 --- Amplification by low dosage MTX treatment --- p.47 / Chapter 2.2.2.9.2 --- Amplification by high dosage MTX treatment --- p.49 / Chapter 2.2.2.10 --- Southern blot analysis --- p.50 / Chapter 2.2.2.10.1 --- Preparation of DIG-labelled mas probe --- p.51 / Chapter 2.2.2.10.2 --- Preparation of DIG-labelled DHFR probe --- p.51 / Chapter 2.2.2.10.3 --- Preparation of DIG-labelled GAPDH probe --- p.52 / Chapter 2.2.2.10.4 --- Isolation of Genomic DNA from the mas transfectants by DNAzol® Reagent / Chapter 2.2.2.10.5 --- Enzymatic restriction of genomic DNA and Gel electrophoresis --- p.54 / Chapter 2.2.2.10.6 --- DNA transferring to positive charged Nylon membrane --- p.54 / Chapter 2.2.2.10.7 --- Pre-hybridization and hybridization --- p.56 / Chapter 2.2.2.10.8 --- Post-hybridization washing and blocking --- p.56 / Chapter 2.2.2.10.9 --- Detection --- p.57 / Chapter 2.2.2.11 --- Northern blot analysis --- p.57 / Chapter 2.2.2.11.1 --- Preparation of the agarose gel containing formaldehyde --- p.58 / Chapter 2.2.2.11.2 --- Preparation of the RNA sample --- p.58 / Chapter 2.2.2.11.3 --- Gel electrophoresis and transferring --- p.59 / Chapter 2.2.2.11.5 --- Pre-hybridization and hybridization --- p.60 / Chapter 2.2.2.11.4 --- Post-hybridization washing and blocking --- p.60 / Chapter 2.2.2.11.6 --- Detection --- p.61 / Chapter 2.2.2.11.7 --- Stripping and rehybridization --- p.61 / Chapter 2.3 --- Results --- p.62 / Chapter 2.3.1 --- RT-PCR analysis of gene expression in the stably transfectant --- p.62 / Chapter 2.3.2 --- Morphology of the mas trasnfectant --- p.64 / Chapter 2.3.3 --- Determination of mas gene copy number by Southern blot analysis in the mas transfectants --- p.66 / Chapter 2.3.4 --- Northern blot analysis of the transcriptional level of mas transcriptsin mas transfectants --- p.76 / Chapter 2.4 --- Discussion --- p.87 / Chapter Chapter 3 --- In vivo study of physiological effect of over-expression of mas / Chapter 3.1 --- Introduction --- p.92 / Chapter 3.2 --- Materials and Methods --- p.93 / Chapter 3.2.1 --- Materials --- p.93 / Chapter 3.2.2 --- Methods --- p.93 / Chapter 3.2.2.1 --- Cell culture --- p.93 / Chapter 3.2.2.2 --- Subcutaneous injection of nude mice --- p.94 / Chapter 3.2.2.3 --- Isolation of the total RNA from the tumor tissues --- p.95 / Chapter 3.2.2.4 --- Northern blot analysis --- p.96 / Chapter 3.3 --- Results --- p.96 / Chapter 3.3.1 --- Tumorgenicity assay of mas oncogene in nude mice --- p.96 / Chapter 3.3.2 --- Northern blot analysis of mas expression in the tumor tissues --- p.103 / Chapter 3.4 --- Discussion --- p.109 / Chapter Chapter 4 --- Fluorescent differential display analysis of mas transfectants / Chapter 4.1 --- Introduction --- p.111 / Chapter 4.2 --- Materials and Methods --- p.112 / Chapter 4.2.1 --- Materials --- p.112 / Chapter 4.2.1.1 --- Chemicals --- p.112 / Chapter 4.2.1.2 --- Enzyme --- p.113 / Chapter 4.2.1.3 --- Kits --- p.113 / Chapter 4.2.1.4 --- Others --- p.114 / Chapter 4.2.2 --- Methods --- p.114 / Chapter 4.2.2.1 --- Isolation of the total RNA from the mas transfectants by TRIzol ® Reagent --- p.114 / Chapter 4.2.2.2 --- DNase I treatment --- p.115 / Chapter 4.2.2.3 --- Reverse transcription (RT) and non-fluorescent PCR --- p.116 / Chapter 4.2.2.4 --- Reverse transcription and fluorescent differential display-PCR --- p.118 / Chapter 4.2.2.5 --- High resolution fluorescent differential display (Fluoro DD) gel --- p.118 / Chapter 4.2.2.6 --- Gel band excision of differentially expressed cDNA fragments --- p.120 / Chapter 4.2.2.7 --- Gel band reamplification --- p.120 / Chapter 4.2.2.8 --- Subcloning of reamplified cDNA fragments --- p.121 / Chapter 4.2.2.9 --- Purification of plasmid DNA from recombinant clones for reverse dot blot analysis --- p.122 / Chapter 4.2.2.10 --- Reverse dot blot analysis --- p.123 / Chapter 4.2.2.10.1 --- Preparation of cDNA dot blot --- p.123 / Chapter 4.2.2.10.2 --- Preparation of DIG-labeled cDNA library probes --- p.124 / Chapter 4.2.2.10.3 --- Hybridization --- p.126 / Chapter 4.2.2.11 --- Northern blot analysis --- p.127 / Chapter 4.3 --- Results --- p.128 / Chapter 4.3.1 --- Fluorescent differential display (FluoroDD) --- p.128 / Chapter 4.3.2 --- Reverse dot blot analysis --- p.135 / Chapter 4.3.3 --- DNA sequencing analysis of the clones --- p.141 / Chapter 4.3.4 --- Confirmation of differential display pattern of the subclones by Northern blot analysis --- p.160 / Chapter 4.4 --- Discussion --- p.166 / Chapter Chapter 5 --- General Discussion / Chapter 5.1 --- General model for mos-induced tumor formation --- p.169 / Chapter 5.2 --- Future aspect --- p.174 / References --- p.176 / Appendix I Buffer composition --- p.186 / Appendix II Sequences of fluoroDD TMR-Anchored primers and arbitrary primers --- p.190

During the development of many diseases such as cancer and diabetes, the pattern of gene expression within certain cells changes. A vital part of understanding these diseases will come from understanding the factors governing gene expression. This thesis work focused on mining association rules in the context of gene expression. We designed and developed a tool that enables domain experts to interactively analyze association rules that describe relationships in genetic data. Association rules in their native form deal with sets of items and associations among them. But domain experts hypothesize that additional factors like relative ordering and spacing of these items are important aspects governing gene expression. We proposed hypothesis-based specializations of association rules to identify biologically significant relationships. Our approach also alleviates the limitations inherent in the conventional association rule mining that uses a support-confidence framework by providing filtering and reordering of association rules according to other measures of interestingness in addition to support and confidence. Our tool supports visualization of genetic data in the context of a rule, which facilitates rule analysis and rule specialization. The improvement in different measures of interestingness (e.g., confidence, lift, and p-value) enabled by our approach is used to evaluate the significance of the specialized rules.

Recently, large amounts of experimental data for complex biological systems have become available. We use tools and algorithms from machine learning to build data-driven predictive models. We first present a novel algorithm to discover gene sequence motifs associated with temporal expression patterns of genes. Our algorithm, which is based on partial least squares (PLS) regression, is able to directly model the flow of information, from gene sequence to gene expression, to learn cis regulatory motifs and characterize associated gene expression patterns. Our algorithm outperforms traditional computational methods e.g. clustering in motif discovery. We then present a study of extending a machine learning model for transcriptional regulation predictive of genetic regulatory response to Caenorhabditis elegans. We show meaningful results both in terms of prediction accuracy on the test experiments and biological information extracted from the regulatory program. The model discovers DNA binding sites ab intio. We also present a case study where we detect a signal of lineage-specific regulation. Finally we present a comparative study on learning predictive models for motif discovery, based on different boosting algorithms: Adaptive Boosting (AdaBoost), Linear Programming Boosting (LPBoost) and Totally Corrective Boosting (TotalBoost). We evaluate and compare the performance of the three boosting algorithms via both statistical and biological validation, for hypoxia response in Saccharomyces cerevisiae.

No description available.

Ma Junhao. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 111-123). / Abstracts in English and Chinese. / Thesis Committee --- p.i / Statement --- p.ii / Abstract --- p.iii / Acknowledgements --- p.vii / General Abbreviations --- p.ix / Abbreviations of Chemicals --- p.xi / Table of Contents --- p.xii / List of Figures --- p.xvi / List of Tables --- p.xix / Chapter 1. --- Literature review --- p.1 / Chapter 1.1 --- Importance of amino acid metabolism --- p.1 / Chapter 1.1.1 --- Rice as the important source of essential amino acids --- p.1 / Chapter 1.1.2 --- Rice seeds are nutritionally incomplete --- p.2 / Chapter 1.1.3 --- The nitrogen source for aspartate family amino acid synthesis --- p.3 / Chapter 1.1.4 --- Synthesis of aspartate family amino acids in plants --- p.4 / Chapter 1.1.5 --- Regulation of the aspartate pathway at free amino acid level --- p.12 / Chapter 1.2 --- Regulation of amino acid metabolism during seed development --- p.14 / Chapter 1.3 --- Signalling system for nitrogen metabolism --- p.17 / Chapter 1.3.1 --- Nitrogen signalling in plants --- p.18 / Chapter 1.3.2 --- GCN signalling: another nitrogen signalling pathway --- p.21 / Chapter 1.3.2.1 --- Mechanism of GCN signalling pathway in yeast --- p.21 / Chapter 1.3.2.2 --- GCN in mammalian --- p.24 / Chapter 1.3.2.3 --- GCN in higher plant --- p.24 / Chapter 1.3.3 --- Relationship between carbon and nitrogen metabolic signaling in plants --- p.26 / Chapter 1.3.4 --- Paradigm for elucidating new signal transduction pathways --- p.29 / Chapter 1.4 --- Hypothesis of this thesis work --- p.30 / Chapter 2. --- Materials and Methods --- p.32 / Chapter 2.1. --- Materials --- p.32 / Chapter 2.1.1 --- Plants --- p.32 / Chapter 2.1.2 --- Bacterial strains and vectors --- p.32 / Chapter 2.1.3 --- Chemicals and reagents --- p.33 / Chapter 2.1.4 --- Buffer，solution and gel --- p.33 / Chapter 2.1.5 --- Commercial kits --- p.33 / Chapter 2.1.6 --- Equipments and facilities used --- p.33 / Chapter 2.1.6 --- Growth medium --- p.34 / Chapter 2.2 --- Methods --- p.34 / Chapter 2.2.1 --- Profiling genes expression pattern in developing rice seeds --- p.34 / Chapter 2.2.1.1 --- Growth conditions of rice --- p.34 / Chapter 2.2.1.2 --- Collection of developing rice seeds --- p.35 / Chapter 2.2.1.3 --- Total RNA extraction from rice seeds --- p.37 / Chapter 2.2.1.4 --- Total RNA extraction from plant leaf --- p.37 / Chapter 2.2.1.5 --- Gel electrophoresis --- p.38 / Chapter 2.2.1.6 --- First strand cDNA synthesis from rice total RNA --- p.39 / Chapter 2.2.1.7 --- Search for the coding sequence of rice genes related to amino acid metabolism --- p.39 / Chapter 2.2.1.8 --- Alignment of homologous coding sequence between family member genes --- p.42 / Chapter 2.2.1.9 --- Primer design --- p.42 / Chapter 2.2.1.10 --- Quantitation of total RNA and determination of internal control --- p.45 / Chapter 2.2.1.11 --- PCR to amplify the DNA fragments --- p.45 / Chapter 2.2.1.12 --- DNA Sequencing --- p.46 / Chapter 2.2.1.13 --- Generation and testing of single-stranded DIG-labelled DNA probes --- p.46 / Chapter 2.2.1.14 --- Northern blot --- p.47 / Chapter 2.2.1.15 --- RT-PCR (Reverse-transcription polymerase chain reaction) --- p.48 / Chapter 2.2.2 --- Expression assay of selected genes in herbicide treated plants --- p.49 / Chapter 2.2.2.1 --- Growing conditions and herbicide treatments --- p.49 / Chapter 2.2.2.2 --- GCN2 homologue in Arabidopsis and rice --- p.51 / Chapter 2.2.2.3 --- RT-PCR to analyze the change in expression level of selected genes in herbicide treated plants --- p.53 / Chapter 2.2.3 --- Generation of transgenic Arabidopsis --- p.56 / Chapter 2.2.3.1 --- Preparation of T-vector for T-ligation --- p.56 / Chapter 2.2.3.2 --- Cloning of Arabidopsis GCN2 gene --- p.56 / Chapter 2.2.3.3 --- Transformation of the plasmid into DH5a competent cell --- p.57 / Chapter 2.2.3.4 --- Screening of right recombinants --- p.58 / Chapter 2.2.3.5 --- Construction of chimeric AtGCN2 genes --- p.59 / Chapter 2.2.3.6 --- Transformation of electro-competent Agrobacterium cell --- p.60 / Chapter 2.2.3.7 --- Transformation of Arabidopsis by vacuum infiltration --- p.61 / Chapter 2.2.3.8 --- Selection of hemizygous and homozygous transgenic plants --- p.61 / Chapter 2.2.3.9 --- Screening of the T3 transformants --- p.63 / Chapter 2.2.3.10 --- Expression analysis of homozygous AtGCN2 transgenic Arabidopsis --- p.63 / Chapter 3. --- Results --- p.63 / Chapter 3.1 --- Profiling genes expression pattern in developing rice seeds --- p.63 / Chapter 3.1.1 --- Quantification of total RNA from seeds at different developing stages --- p.63 / Chapter 3.1.2 --- DNA sequence analysis --- p.66 / Chapter 3.1.3 --- Profiling the gene expression in developing rice seeds --- p.67 / Chapter 3.1.3.1 --- Expression profiles of nitrogen assimilation related genes --- p.67 / Chapter 3.1.3.2 --- Expression profiles of aspartate pathway genes --- p.72 / Chapter 3.1.3.3 --- Expression profiles of branched-chain amino acid synthesis pathway genes --- p.78 / Chapter 3.2 --- Relationship between GCN2 and amino acid metabolism in plants --- p.82 / Chapter 3.2.1 --- GCN2 homologue in A. thaliana and rice --- p.82 / Chapter 3.2.2 --- GCN2 and amino acid starvation --- p.85 / Chapter 3.2.3 --- Effects of amino acid starvation on GCN2 expression --- p.90 / Chapter 3.2.3 --- Changes in the expression level AK and BCAT genes in herbicide treated rice and A. thaliana --- p.93 / Chapter 3.3 --- Characterization of GCN2 transgenic A. thaliana --- p.96 / Chapter 3.3.1 --- Construct of pBI121-AtGCN2 --- p.96 / Chapter 3.3.2 --- Construction of GCN2 transgenic A. thaliana --- p.96 / Chapter 3.3.3 --- Expression of GCN2 in transgenic A. thaliana --- p.97 / Chapter 3.3.4 --- Expression level changes of AK and BCAT in transgenic A. thaliana --- p.99 / Chapter 4. --- Discussions --- p.101 / Chapter 4.1 --- Expression pattern of selected metabolic genes in developing plant seeds --- p.101 / Chapter 4.1.1 --- Most genes studied displayed a similar pattern --- p.101 / Chapter 4.1.2 --- Regulation of gene expression in developing rice seeds --- p.105 / Chapter 4.2 --- GCN2 and its role in higher plants --- p.106 / Chapter 4.2.1 --- The existence of the GCN2 gene in rice --- p.106 / Chapter 4.2.2 --- GCN2 responses solely to amino acid starvation --- p.106 / Chapter 5. --- Conclusion and Prospective --- p.109 / Reference --- p.111 / Appendix I: Chemicals and reagents --- p.124 / "Appendix II: Buffer, solution and gel" --- p.126 / Appendix III: Commercial kits --- p.128 / Appendix IV: Equipments and facilities used --- p.128