The Pax6 encodes a highly conserved transcriptional regulator that contains two DNA binding domains, the paired domain (PD) and homeodomain (HD). In mammals, Pax6 is widely expressed in a complex spatiotemporal pattern during the development of the eye, olfactory bulbs and central nervous system and plays important roles in pattern formation, cell fate determination and cell cycle progression in these regions. Normal development requires Pax6 to be present in certain cells with correct levels, which implies that Pax6 expression is tightly controlled and that different levels need to be maintained across different regions as they develop. To gain better insight into the regulatory mechanisms of Pax6 expression, a series of tauGFP-Pax6 transgenic reporter mouse lines was previously generated in which the expression of tauGFP is under the control of putative Pax6 regulatory elements. Here, I have characterized the functional importance these regulatory elements by comparing the pattern of tauGFP expression with endogenous Pax6 expression in transgenic mice containing either complete or truncated versions of the reporter. I showed that the expression of tauGFP reporter exhibits the known Pax6 expression pattern in forebrain and eye, except for some minor discrepancy within the telecephalon. The loss of tauGFP expression within the eye and thalamus was observed in transgenic lines carrying truncated reporter sequences lacking the downstream regulatory region (DDR) of Pax6. Analysis of the pattern of GFP reporter expression in transgenic lines that vary in the extent of their putative Pax6 regulatory elements revealed the functional significance of these elements and also implied the existence of unknown distal regulatory elements, outside of the reporter sequences, which control Pax6 expression in the telecephalon. I went on to study a Pax6-dependent signaling pathway through which Pax6 controls progenitor cell proliferation in the developing telencephalon. Comparison of cell cycle parameters between Pax6+/+ and Pax6sey/sey progenitors suggested that correct levels of Pax6 are crucial in regulating progenitor cell proliferation. To address the possible molecular basis of the cell cycle defect observed in Pax6sey/sey embryos, the expression of a number of cell cycle genes was analyzed by qRT-PCR in the lateral cortex of Pax6+/+ and Pax6sey/sey embryos, which confirmed the significantly altered expression levels of these genes. Of them, Cdk6 was further identified as a direct target of Pax6 and the interaction of putative binding sites with Pax6 protein was confirmed by EMSA in vitro and by qChIP in vivo. In addition, the functional role of these Pax6 binding sites, through which Pax6 represses the transcription of Cdk6, was further evaluated by luciferase assays. Activation of Cdk6 is required for pRb phosphorylation as well as induction of the pRB/E2F pathway, and in turn promotes the G1-S cell-cycle transition. An increase in pRb phosphorylation accompanied by changes in pRb subcellular distribution and up-regulation of E2F downstream targets were observed in the cortex of Pax6sey/sey embryos. In contrast, a reduction of Cdk6 expression and pRb phosphorylation was found in HEK293 cells overexpressing Pax6. Collectively, these findings provided new insight into the molecular mechanism of Pax6-dependent regulation of progenitor cell proliferation in the developing telecephalon.

This thesis considers the problem of learning the structure of gene regulatory networks using gene expression time series data. A more realistic scenario where the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter based state estimation algorithm is studied instead of the contemporary linear approximation based approaches. The parameters signifying the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then fed to a LASSO based least squares regression operation, which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with Extended Kalman filtering (EKF), employing Mean Square Error as fidelity criterion using synthetic data and real biological data. Extensive computer simulations illustrate that the particle filter based gene network inference algorithm outperforms EKF and therefore, it can serve as a natural framework for modeling gene regulatory networks.

Microarray gene expression data for thousands of genes in many organisms is quickly becoming available. The information this data can provide the experimental biologist is powerful. This data may provide information clarifying the regulatory linkages between genes within a single metabolic pathway, or alternative pathway routes under different environmental conditions, or provide information leading to the identification of genes for selection in animal and plant genetic improvement programs or targets for drug therapy. Many analysis methods to unlock this information have been both proposed and utilized, but not evaluated under known conditions (e.g. simulations). Within this dissertation, an analysis method is proposed and evaluated for identifying independent and linked metabolic pathways and compared to a popular analysis method. Also, this same analysis method is investigated for its ability to identify regulatory linkages within a single metabolic pathway. Lastly, a variant of this same method is used to analyze time series microarray data. In Chapter 2, Factor Analysis is shown to identify and group genes according to membership within independent metabolic pathways for steady state microarray gene expression data. There were cases, however, where the allocation of all genes to a pathway was not complete. A competing analysis method, Hierarchical Clustering, was shown to perform poorly when negatively correlated genes are assumed unrelated, but performance improved when the sign of the correlation coefficient was ignored. In Chapter 3, Factor Analysis is shown to identify regulatory relationships between genes within a single metabolic pathway. These relationships can be explained using metabolic control analysis, along with external knowledge of the pathway structure and activation and inhibition of transcription regulation. In this chapter, it is also shown why factor analysis can group genes by metabolic pathway using metabolic control analysis. In Chapter 4, a Bayesian exploratory factor analysis is developed and used to analyze microarray gene expression data. This Bayesian model differs from a previous implementation in that it is purely exploratory and can be used with vague or uninformative priors. Additionally, 95% highest posterior density regions can be calculated for each factor loading to aid in interpretation of factor loadings. A correlated Bayesian exploratory factor analysis model is also developed in this chapter for application to time series microarray gene expression data. While this method is appropriate for the analysis of correlated observation vectors, it fails to group genes by metabolic pathway for simulated time series data. / Ph. D.

Following advancements in the "omics" fields of molecular biology and genetics, much attention has been focused on categorising and annotating the large volume of data that has been produced since the sequencing of human and model genomes. With high-throughput data generated from these "omics" experiments and the increasing deposition of information from genetics experiments in biological databases, our understanding of the mechanisms that bridge the gap from genotype to phenotype can be explored in a holistic context. This is one of the aims of the relatively new field of systems biology, which aims to understand the complexity of biological systems in a holistic manner by studying the system as an ensemble of interacting parts. With increased volume and comprehensiveness of biological data, prediction of gene function and automatic identification of potential models for human diseases have become important aspects of systems-level analysis for wet-lab geneticists and clinicians. Here, I describe an integrated analysis of mouse phenotype data with high-throughput experiments to give genome-wide information about gene relationships and their function in a systems biology context. I show a functional dissection of mouse gene and phenotype networks and investigate the potential that ontology-compliant phenotype annotations can offer for functional classification of genes. The mouse genome and phenome show modularity at higher levels of cellular, physiological and organismal function. Using high-throughput protein-protein interaction data, the mouse proteome was dissected and computationally extracted communities were used to predict phenotypes of mouse gene ablation. Precision and recall curves show comparable performance for higher levels of the MP ontology to those undertaken by comprehensive mouse gene function prediction such as the Mouse Function Project which predicted Gene Ontology terms. I also developed and tested an automatic procedure that relates mouse phenotypes to human diseases and demonstrate its application to the use cases of identifying mouse models given a query consisting of a set of mouse phenotypes and breaking down human diseases into mouse phenotypes. Taken together, my results may be useful as a map for candidate gene discovery, finding how mouse networks relate to human networks and investigating the evolutionary origins of their components at higher levels of gene function.

Cells of the ventrolateral dermomyotome delaminate and migrate into the limb buds where they give rise to all muscles of the limbs. The migratory cells proliferate and form myoblasts, which withdraw from the cell cycle to become terminally differentiated myocytes. The regulatory mechanisms that control the later steps of this myogenic program are not well understood. The homeodomain transcription factor Pitx2 is expressed specifically in the muscle lineage from the migration of precursors to adult muscle. Ablation of Pitx2 results in distortion, rather than loss, of limb muscle anlagen, suggesting that its function becomes critical during the colonization of, and/or fiber assembly in, the anlagen. Microarrays were used to identify changes in gene expression in flow-sorted migratory muscle precursors from Wild type and Pitx2 null mice. Changes in gene expression were observed in genes encoding cytoskeletal, adhesion and fusion proteins which play a role in cell motility and myoblast fusion. We observed decreased cellular motility, disrupted cytoskeleton organization and focal adhesion distribution, decreased fusion of mononucleated myoblasts into multinucleated myotubes and decreased proliferation in presence of Ptix2. These studies suggest that Pitx2 plays a critical role in regulating the timing of myoblast filling the limb anlagen which may have detrimental consequences for higher order muscle architecture. / Graduation date: 2013

abstract: Synthetic biology is an emerging field which melds genetics, molecular biology, network theory, and mathematical systems to understand, build, and predict gene network behavior. As an engineering discipline, developing a mathematical understanding of the genetic circuits being studied is of fundamental importance. In this dissertation, mathematical concepts for understanding, predicting, and controlling gene transcriptional networks are presented and applied to two synthetic gene network contexts. First, this engineering approach is used to improve the function of the guide ribonucleic acid (gRNA)-targeted, dCas9-regulated transcriptional cascades through analysis and targeted modification of the RNA transcript. In so doing, a fluorescent guide RNA (fgRNA) is developed to more clearly observe gRNA dynamics and aid design. It is shown that through careful optimization, RNA Polymerase II (Pol II) driven gRNA transcripts can be strong enough to exhibit measurable cascading behavior, previously only shown in RNA Polymerase III (Pol III) circuits. Second, inherent gene expression noise is used to achieve precise fractional differentiation of a population. Mathematical methods are employed to predict and understand the observed behavior, and metrics for analyzing and quantifying similar differentiation kinetics are presented. Through careful mathematical analysis and simulation, coupled with experimental data, two methods for achieving ratio control are presented, with the optimal schema for any application being dependent on the noisiness of the system under study. Together, these studies push the boundaries of gene network control, with potential applications in stem cell differentiation, therapeutics, and bio-production. / Dissertation/Thesis / Doctoral Dissertation Biomedical Engineering 2018

The goal of this research is to construct causal gene networks for genetical genomics experiments using expression Quantitative Trait Loci (eQTL) mapping and Structural Equation Modeling (SEM). Unlike Bayesian Networks, this approach is able to construct cyclic networks, while cyclic relationships are expected to be common in gene networks. Reconstruction of gene networks provides important knowledge about the molecular basis of complex human diseases and generally about living systems. In genetical genomics, a segregating population is expression profiled and DNA marker genotyped. An Encompassing Directed Network (EDN) of causal regulatory relationships among genes can be constructed with eQTL mapping and selection of candidate causal regulators. Several eQTL mapping approaches and local structural models were evaluated in their ability to construct an EDN. The edges in an EDN correspond to either direct or indirect causal relationships, and the EDN is likely to contain cycles or feedback loops. We implemented SEM with genetics algorithms to produce sub-models of the EDN containing fewer edges and being well supported by the data. The EDN construction and sparsification methods were tested on a yeast genetical genomics data set, as well as the simulated data. For the simulated networks, the SEM approach has an average detection power of around ninety percent, and an average false discovery rate of around ten percent. / Ph. D.

Chez le riz, la céréale modèle, le système racinaire est principalement constitué de racines issues de la tige, nommées racines coronaires (RC). Peu de gènes contrôlant le développement des RC sont connus, parmi eux CROWN ROOTLESS1 (CRL1) code une protéine à domaine AS2/LOB (ASL/LBD), qui est probablement un facteur de transcription. Le gène CRL1 est nécessaire à l'initiation des primordia de RC, il est directement activé par l'auxine et est situé en amont du réseau de gènes contrôlant le programme de différentiation des RC. Afin de mieux connaître les processus génétiques impliqués dans l'initiation des RC, l!objectif principal de cette thèse est de comprendre la fonction moléculaire de la protéine CRL1 en validant sa fonction de facteur de transcription et en identifiant ses gènes cibles. L'interaction de la protéine CRL1 avec l!ADN a été montrée in vitro et une expérience de SELEX a permis d'identifier sa séquence de fixation à l!ADN : CACA(A/C)C (CRL1-box). Des expériences en levure ont permis de montrer que CRL1 est un activateur de la transcription. Une comparaison entre le sauvage et le mutant crl1, ainsi que l'élaboration d!un système inductible à la dexaméthasone permettant d'activer l'expression de CRL1 dans le fond génétique mutant crl1, ont été utilisés pour identifier des gènes cibles précoces de CRL1 grâce à des analyse de transcriptome. 277 gènes sont activés dès quatre heures après induction de CRL1, les deux tiers contiennent au moins une CRL1-box dans leur promoteur et peuvent donc être des cibles directes de CRL1. CRL1 induit l'expression d!un ensemble de gènes permettant la mise en place des processus de régulation de l'information génétique, de division, de croissance et de différenciation cellulaires nécessaires à la création d'un méristème de racine coronaire organisé et fonctionnel. Parmi eux, QHB code un facteur de transcription clé nécessaire au maintien des cellules souches des méristèmes racinaires. Ce résultat établit pour la première fois un lien moléculaire entre la signalisation de l!auxine et des gènes impliqués dans la mise en place ou le maintien des cellules souches lors de la formation d!un nouveau méristème racinaire au cours du développement post-embryonnaire. Par ailleurs, une étude histologique a permis de révéler que les RC sont issues d!une couche de péricycle dans la tige, un tissu équivalent en termes de localisation et de potentiel rhizogène au péricycle de la racine à partir duquel sont initiées les racines latérales. Les données acquises suggèrent de fortes similarités dans les processus cellulaires et génétiques de la différentiation des méristèmes racinaires au cours du développement post-embryonnaire chez les monocotylédones et les dicotylédones. La découverte de gènes spécifiques au développement de racines issues de la tige ouvre une voie importante vers la compréhension du déterminisme génétique de l!architecture du système racinaire chez les céréales et offre un nouveau potentiel de ressources génétiques pour l!amélioration variétale. / In rice, the model cereal, the root system is mainly composed of stem-derived roots, named crown roots (CR). Very few genes that control the root system development are known, among them CROWN ROOTLESS1 (CRL1) encodes an AS2/LOB-domain protein that is a putative transcription factor (TF). CRL1 is necessary for CR primordium initiation, it is directly activated by auxin and is situated upstream of the gene regulatory network that control the CR differentiation programme. To better known the genetic processes involved in CR initiation, the main objective of this thesis is to understand the molecular function of the CRL1 protein by validating its function of TF and by identifying its target genes. The interaction of CRL1 with DNA was shown in vitro and a consensus CRL1 DNA-binding motif was identified with a SELEX method : CACA(A/C)C named CRL1-box. A yeast assay showed that CRL1 is a transcriptional activator. A comparison between wild type and c rl1 mutant, and the development of a dexamethasone inducible system to ectopically express CRL1 in the crl1 background, were used to identify CRL1 early target genes by transcript profiling. 277 genes were induced from four hours following CRL1 activation, the two-thirds possess at least one CRL1-box and may be CRL1 direct target genes. CRL1 activates the expression of a broad range of genes that allow to orientate genome expression and to initiate cell division, growth and differentiation mechanisms required for the building of an organized and functional CR meristem. Among these genes, QHB encodes a key TF required for the maintenance of root meristem stem cells. This result evidences for the first time a molecular link between auxin signalling and major genes involved in stem cell patterning and maintenance in the formation of a new root meristem during post-embryonic development. Otherwise, an histological study showed that CR are derived from a shoot pericycle, a tissue equivalent to the root pericycle, from which lateral roots develop, in terms of location and rhizogenic potential. All these data suggest strong similarities between monocots and dicots in cellular and genetic mechanisms that control root meristem differentiation during post-embryonic development. The identification of genes specifically involved in stem-derived root development pave the way towards the understanding of the genetic control of root system architecture in cereals and offer a new potential of genetic resources for plant breeding.

Heterosis, or hybrid vigor, refers to the superiority of F₁hybrid performance over the mean of its parents (mid-parent heterosis) theoretically, or the performance of better parents. It has been discovered in many species of plants and animals as well as in humans, and played an important role in enhanced agricultural production, especially in maize, rice and sorghum although the mechanism have not been elucidated. We studied the molecular basis of heterosis with a combined genomics and systems biology approach using model organism maize. We profiled the expression of 39 genes that were most differentially expressed (DG) between the mid-parents and their F1 hybrid (Mo17 x B73) in the 13V-satged, developed whole ear shoots of 13 inbred lines and their 22 F1 hybrids grown in the field trails and phenotyped their 13 traits significant for grain yield. The results showed that gene expression varies significantly among inbreds, among hybrids and in heterosis. The gene clustering heat map and gene action networks in inbreds and hybrids were constructed respectively based on their gene expression profile. According to these pattern analyses, we find dramatically difference between inbreds and their hybrids, although the differential expression varies across different hybrids. Our results also suggest that gene networks are altered from inbreds to hybrids, including their gene contents and wire structures. Last but not least, we have determined the genetic variation correlations between the gene expression and trait performance and constructed the gene networks for the development of 12 of the 13 traits that varied significantly among genotypes. This has led to identification of genes significantly contributing to the performances of the traits, with 1 – 16 genes per trait. These results have indicated that heterosis results not only from altered expression level of corresponding genes between inbreds and their hybrids, importantly, also from the altered gene action networks and expression patterns. These alternations could be derived from gene actions in a manner of additivity, dominance, over dominance, pseudo-overdominance, epistasis and/or their combinations. Therefore, our findings provide a better understanding of the underlying molecular basis of heterosis. The genes identified for the traits will provide tools for advanced studies of the trait heterosis and could be used as tools for their heterosis breeding in maize. The strategy developed in this study will provide an effective tool for studies of other complicated, quantitative traits in maize and other species.

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