Cheuk, Wai Ka. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 96-108). / Abstracts in English and Chinese. / Abstract --- p.i / 摘要 --- p.iii / Acknowledgements --- p.v / Table of contents --- p.vi / List of Tables --- p.ix / List of Figures --- p.x / Abbreviations --- p.xii / Chapter CHAPTER 1 --- General introduction / Chapter 1.1 --- Metal Contaminations in the environment --- p.1 / Chapter 1.2 --- Biology of Heavy Metal Ions --- p.3 / Chapter 1.2.1 --- Essential and non-essential metal ions --- p.3 / Chapter 1.2.2 --- Toxicities and origins of heavy metal ions --- p.5 / Chapter 1.3 --- Monitoring Of Heavy Metal Contaminations In Aquatic Environment --- p.9 / Chapter 1.3.1 --- Monitoring in chemical approach --- p.9 / Chapter 1.3.2 --- Monitoring in biological approach: biomarkers --- p.11 / Chapter 1.4 --- Metallothionein (MT) --- p.12 / Chapter 1.4.1 --- Biological functions of MT and its regulation --- p.12 / Chapter 1.4.2 --- MT isoforms --- p.14 / Chapter 1.4.3 --- Mechanisms of MT gene regulation --- p.15 / Chapter 1.4.3.1 --- Zinc pool hypothesis --- p.20 / Chapter 1.4.3.2 --- Protein kinase cascade --- p.21 / Chapter 1.5 --- Metal responsive element (MRE) --- p.22 / Chapter 1.6 --- MRE-Binding Transcription Factor-1 (MTF-1) --- p.30 / Chapter 1.6.1 --- Structure of MTF-1 --- p.30 / Chapter 1.6.2 --- Physiological functions of MTF-1 --- p.32 / Chapter 1.6.3 --- The role of MTF-1 in MT gene regulation --- p.33 / Chapter 1.6.4 --- Regulation of MTF-1 by various heavy metals --- p.34 / Chapter 1.7 --- Zebrafish (Daino reio) --- p.36 / Chapter 1.8 --- Project aim --- p.37 / Chapter CHAPTER 2 --- Materials and Methods / Chapter 2.1 --- Cell Culture --- p.40 / Chapter 2.1.1 --- ZFL cell line --- p.40 / Chapter 2.1.2 --- SJD cell line --- p.41 / Chapter 2.2 --- Alarmar blue̐ưؤ M assay --- p.41 / Chapter 2.3 --- First strand cDNA synthesis --- p.42 / Chapter 2.3.1 --- Metal treatment of the SJD and ZFL cell lines --- p.42 / Chapter 2.3.2 --- Isolation of total RNA --- p.43 / Chapter 2.3.3 --- Quantification of mRNA by spectrophotometer --- p.43 / Chapter 2.3.4 --- Reverse Transcription --- p.44 / Chapter 2.4 --- Quantifications of mRNA levels by using real-time PCR technique --- p.44 / Chapter 2.4.1 --- Primer design --- p.44 / Chapter 2.4.2 --- PCR components and cycling condition --- p.45 / Chapter 2.4.3 --- Determination of relative amount of target gene present in the samples --- p.49 / Chapter 2.5 --- Cloning of zMT-II gene promoter and its transient expression studies --- p.50 / Chapter 2.5.1 --- Purification of genomic DNA --- p.50 / Chapter 2.5.2 --- Preparation of Escherichia coli competent cell --- p.51 / Chapter 2.5.3 --- PCR-Cloning of a 1.4 kb zMT-II gene promoter --- p.51 / Chapter 2.5.4 --- Purification of plasmid DNA --- p.53 / Chapter 2.5.5 --- Transient transfection of plasmid into SJD and ZFL cells --- p.54 / Chapter 2.5.6 --- Heavy metal treatments and measurement of luciferase activities --- p.54 / Chapter CHAPTER 3 --- Results / Chapter 3.1 --- Toxicities of various heavy metal ions --- p.56 / Chapter 3.2 --- Relative mRNA fold induction of zMT in SJD and ZFL cell lines --- p.59 / Chapter 3.3 --- The zMT-II gene and its induction by metal ions in zebrafish cell-lines --- p.63 / Chapter 3.4 --- MTF-1 mRNA levels in SJD and ZFL cell lines exposed to heavy metal ions --- p.74 / Chapter CHAPTER 4 --- Discussion / Chapter 4.1 --- Comparison of metal toxicities in the two cell lines studied --- p.78 / Chapter 4.2 --- zMT gene expression study --- p.80 / Chapter 4.2.1 --- zMT mRNA regulation by heavy metal ions in the two cell lines --- p.80 / Chapter 4.2.2 --- The potential use of MT regulation as exposure biomarker --- p.82 / Chapter 4.3 --- Structure of the zMT-II gene promoter region --- p.82 / Chapter 4.4 --- Metal responsiveness of zMT-II promoter --- p.84 / Chapter 4.5 --- Mechanism of MT gene expression and the MTF-1 mRNA inductions in SJD and ZFL cell lines --- p.86 / Chapter 4.6 --- Concluding Remarks --- p.93 / References --- p.96

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

microRNAs (miRNAs), ~21-24 nucleotide-long RNAs that post-transcriptionally regulate gene expression, have rapidly become one of the most extensively studied mechanisms of the past decade. Since their discovery as temporal regulators of post-embryonic development in C. elegans, miRNAs have been functionally implicated in almost every cellular process investigated to date. miRNAs are integral to the complex biological processes of embryonic development and aging. In this research, we sought to determine whether misregulation of miRNAs could be responsible for eliciting adverse effects during these two distinct developmental stages. First, to uncover the potential role of miRNAs in teratogenicity, we investigated whether miRNAs were involved in regulation of retinoic acid (RA) induced vertebrate axis defects. Global miRNA expression profiling revealed that RA exposure suppressed the expression of miR-19 family members during zebrafish somitogenesis. Bioinformatics analyses predict that miR-19 targets cyp26a1, a key RA detoxifying enzyme, and a physiological reporter assay confirmed that cyp26a1 is a bona fide target of miR-19. Transient knockdown of miR-19 phenocopied RA-induced body axis defects. In gain-of-function studies, exogenous miR-19 rescued the axis defects caused by RA exposure. Our findings indicate that the teratogenic effects of RA exposure result, in part, from repression of miR-19 and the subsequent misregulation of cyp26a1. This highlights a previously unidentified role of miR-19 in facilitating vertebrate axis development. Next, to explore whether age-related changes in miRNAs trigger deficits in regeneration capacity, we performed mRNA and small RNA sequencing on regenerating and non-regenerating caudal fin tissue from aged, adult and juvenile zebrafish. An unbiased approach identified cbx7 as the most abundant transcript with significantly increased expression in regenerative-competent adult and juvenile tissue and decreased expression in regenerative-compromised aged tissue. While cbx7 is a known regulator of aging, this is the first report of its role in tissue regeneration. A computational approach was used to discover mRNAs expressed during regeneration, which are potential targets of the significantly expressed miRNAs in regenerating tissue. miR-21 was one of the most abundant and significantly increased miRNAs in regenerating tissue and exhibited an aberrant age-dependent expression profile. Bioinformatics predicts miR-21 to target the 3' UTR of cbx7 and a reporter assay confirmed that miR-21 targets cbx7 in vivo. Transient knockdown of miR-21 inhibited tissue regeneration, suggesting a role for miRNA mediated regulation of cbx7 during regeneration. These findings reveal a novel, age-dependent regenerative function of cbx7 and emphasize the importance of miR-21 as a master regulator of vertebrate regenerative responses. This research, when combined, underscores the negative consequences misregulation of miRNAs has on embryonic development and aging. / Graduation date: 2013 / Access restricted to the OSU Community at author's request from Dec. 5, 2012 - Dec. 5, 2013

Thesis (Ph. D.)--University of Missouri--Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 107-112). Also available on the Internet.

La peridermis és una estructura complexa que protegeix els òrgans vegetals madurs (secundaris) i les zones que han sofert ferides de la pèrdua d'aigua i dels patògens. Aquesta funció barrera és deguda al fel·lema o súber, un teixit format per cèl·lules suberificades. Tant el fel·lema com la suberina són crucials per la vida de les plantes terrestres, però pràcticament no es coneix res dels processos moleculars que regulen la seva formació, probablement degut a la manca de models adequats. En aquesta tesi s'han identificat i caracteritzat gens induïts al fel·lema mitjançant la combinació de dues plantes models. L'escorça d'alzina surera (Quercus suber) s'ha utilitzat per aïllar gens candidats de la formació del fel·lema i per investigar el comportament d'alguns d'aquests gens durant l'estació de creixement, mentre que la pela de la patata (Solanum tuberosum) s'ha utilitzat en estudis de genètica reversa per demostrar la funció d'alguns gens reguladors al fel·lema. / The periderm is a complex structure that protects plant mature (secondary) organs and wounded tissues from water loss, injuries and pathogens. This barrier capacity is accomplished by the cork layer of the periderm, a tissue made of dead cells with suberin deposited into cell walls. Although cork and suberin are critical for the survival of land plants, very few is known about the molecular processes involved in their biosynthesis and differentiation, probably due to the lack of appropriate plant models. Here we developed a strategy to identify and characterize cork candidate genes using a combination of two model plants for periderm studies. The bark of cork oak (Quercus suber) was used to identify candidate genes and to analyze the seasonal behaviour of some of these genes. The potato (Solanum tuberosum) tuber was used to demonstrate the role of some selected candidates in the regulation of cork by reverse genetic analyses.

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007

Complex systems are those which exhibit one or more of the following inter-related behaviours: 1. Nonlinear behaviour: the component parts do not act in linear ways, that is the superposition of the actions of the parts is not the output of the system. 2. Emergent behaviour: the output of the system may be inexpressible in terms of the rules or equations of the component parts. 3. Self-organisation: order appears from the chaotic interactions of individuals and the rules they obey. 4. Layers of description: in which a rule may apply at some higher levels of description but not at lower layers. 5. Adaptation: in which the environment becomes encoded in the rules governing the structure and/or behaviour of the parts (in this case strictly agents) that undergo selection in which those that are by some measure better become more numerous than those that are not as “fit”. A single cell is a complex system: we cannot explain all of its behaviour as simply the sum of its parts. Similarly, DNA structures, social networks, cancers, the brain, and living beings are intricate complex systems. This thesis tackles all of these topics from a complex systems approach. I have skirted some of the philosophical issues of complex systems and mainly focussed on appropriate tools to analyse these systems, addressing important questions such as: • What is the best way to extract information from DNA? • How can we model and analyse mutations in DNA? • Can we determine the likely spread of both viruses and ideas in social networks? • How can we model the growth of cancer? • How can we model and analyse interactions between genes in such living systems as the fruit fly, cancers, and humans? • Can complex systems techniques give us some insight into the human brain? / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1290759 / Thesis (Ph.D.)-- School of Electrical and Electronic Engineering, 2007