Indiana University-Purdue University Indianapolis (IUPUI) / In systems biology study, biological networks were used to gain insights into biological systems. While the traditional approach to studying biological networks is based on the identification of interactions among genes or the identification of a gene set ranking according to differentially expressed gene lists, little is known about interactions between higher order biological systems, a network of gene sets. Several types of gene set network have been proposed including co-membership, linkage, and co-enrichment human gene set networks. However, to our knowledge, none of them contains directionality information. Therefore, in this study we proposed a method to construct a regulatory gene set network, a directed network, which reveals novel relationships among gene sets. A regulatory gene set network was constructed by using publicly available gene regulation data. A directed edge in regulatory gene set networks represents a regulatory relationship from one gene set to the other gene set. A regulatory gene set network was compared with another type of gene set network to show that the regulatory network provides additional information. In order to show that a regulatory gene set network is useful for understand the underlying mechanism of a disease, an Alzheimer's disease (AD) regulatory gene set network was constructed. In addition, we developed Pathway and Annotated Gene-set Electronic Repository (PAGER), an online systems biology tool for constructing and visualizing gene and gene set networks from multiple gene set collections. PAGER is available at http://discern.uits.iu.edu:8340/PAGER/. Global regulatory and global co-membership gene set networks were pre-computed. PAGER contains 166,489 gene sets, 92,108,741 co-membership edges, 697,221,810 regulatory edges, 44,188 genes, 651,586 unique gene regulations, and 650,160 unique gene interactions. PAGER provided several unique features including constructing regulatory gene set networks, generating expanded gene set networks, and constructing gene networks within a gene set. However, tissue specific or disease specific information was not considered in the disease specific network constructing process, so it might not have high accuracy of presenting the high level relationship among gene sets in the disease context. Therefore, our framework can be improved by collecting higher resolution data, such as tissue specific and disease specific gene regulations and gene sets. In addition, experimental gene expression data can be applied to add more information to the gene set network. For the current version of PAGER, the size of gene and gene set networks are limited to 100 nodes due to browser memory constraint. Our future plans is integrating internal gene or proteins interactions inside pathways in order to support future systems biology study.

Indiana University-Purdue University Indianapolis (IUPUI) / In response to different environmental stresses, phosphorylation of eIF2 (eIF2P) represses global translation coincident with preferential translation of ATF4. ATF4 is a transcriptional activator of the integrated stress response, a program of gene expression involved in metabolism, nutrient uptake, anti-oxidation, and the activation of additional transcription factors, such as CHOP/GADD153, that can induce apoptosis. Although eIF2P elicits translational control in response to many different stress arrangements, there are selected stresses, such as exposure to UV irradiation, that do not increase ATF4 expression despite robust eIF2P. In this study we addressed the underlying mechanism for variable expression of ATF4 in response to eIF2P during different stress conditions and the biological significance of omission of enhanced ATF4 function. We show that in addition to translational control, ATF4 expression is subject to transcriptional regulation. Stress conditions such as endoplasmic reticulum stress induce both transcription and translation of ATF4, which together enhance expression of ATF4 and its target genes in response to eIF2P. By contrast, UV irradiation represses ATF4 transcription, which diminishes ATF4 mRNA available for translation during eIF2∼P. eIF2P enhances cell survival in response to UV irradiation. However, forced expression of ATF4 and its target gene CHOP leads to increased sensitivity to UV irradiation. In this study, we also show that C/EBPβ is a transcriptional repressor of ATF4 during UV stress. C/EBPβ binds to critical elements in the ATF4 promoter resulting in its transcriptional repression. The LIP isoform of C/EBPβ, but not the LAP version is regulated following UV exposure and directly represses ATF4 transcription. Loss of the LIP isoform results in increased ATF4 mRNA levels in response to UV irradiation, and subsequent recovery of ATF4 translation, leading to enhanced expression of its target genes. Together these results illustrate how eIF2P and translational control, combined with transcription factors regulated by alternative signaling pathways, can direct programs of gene expression that are specifically tailored to each environmental stress.

Indiana University-Purdue University Indianapolis (IUPUI) / Photoreceptors are a class of sensory neuronal cells that are deleteriously affected in many disorders and injuries of the visual system. Significant injury or loss of these cells often results in a partial or complete loss of vision. While previous studies have determined many necessary components of the gene regulatory network governing the establishment, development, and maintenance of these cells, the necessary and sufficient profile and timecourse of gene expression and/or silencing has yet to be elucidated. Arduous protocols do exist to derive photoreceptors in vitro utilizing pluripotent stem cells, but only recently have been able to yield cells that are disease- and/or patient-specific. The discovery that mammalian somatic cells can be directly reprogrammed to another terminally-differentiated cell phenotype has inspired an explosion of research demonstrating the successful genetic reprogramming of one cell type to another, a process which is typically both more timely and efficient than those used to derive the same cells from pluripotent stem cell sources. Therefore, the emphasis of this study was to establish a novel system to be used to determine a minimal transcriptional network capable of directly reprogramming mouse embryonic fibroblasts (MEFs) to rod photoreceptors. The tools, assays, and experimental design chosen and established herein were designed and characterized to facilitate this determination, and preliminary data demonstrated the utility of this approach for accomplishing this aim.

Des techniques expérimentales récentes ont donné la possibilité d'acquérir un très grand nombre de données concernant des réseaux biologiques complexes, comme des réseaux de neurones, des réseaux de gènes et des réseaux d'interactions de protéines. Ces techniques sont capables d'enregistrer les états des composantes individuelles de ces réseaux (neurones, gènes, protéines) pour un grand nombre de configurations. Cependant, l'information la plus pertinente biologiquement se trouve dans la connectivité de ces systèmes et dans la façon précise avec laquelle ces composantes interagissent, information que les techniques expérimentales ne sont pas au point d'observer directement. Le bût de cette thèse est d'étudier les méthodes statistiques nécessaires pour inférer de l'information sur la connectivité des réseaux complexes en partant des données expérimentales. Ce sujet est traité par le point de vue de la physique statistique, en puisant de l'arsenal de méthodes théoriques qui ont été développées pour l'étude des verres de spins. Les verres de spins sont des exemples de réseaux à variables discrètes qui interagissent de façon complexe et sont souvent utilisés pour modéliser des réseaux biologiques. Après une introduction sur les modèles utilisés ainsi qu'une discussion sur la motivation biologique de cette thèse, toutes les méthodes d'inférence de réseaux connues sont présentées et analysées du point de vue de leur performance. Par la suite, dans la troisième partie de la thèse, un nouvelle méthode est proposée qui s'appuie sur la remarque que les interactions en biologie ne sont pas nécessairement symétriques (c'est-à-dire l'interaction entre les noeuds A et B n'est pas la même dans les deux directions). Il est démontré que cette assomption conduit à des méthodes qui sont capables de prédire les interactions de façon exacte, étant donné un nombre suffisant de données, tout en utilisant un temps de calcul polynomial. Ceci est un résultat original important car toutes les autres méthodes connues sont soit exactes et non-polynomiales soit inexactes et polynomiales. / Recent experimental techniques in biology made possible the acquisition of overwhelming amounts of data concerning complex biological networks, such as neural networks, gene regulation networks and protein-protein interaction networks. These techniques are able to record states of individual components of such networks (neurons, genes, proteins) for a large number of configurations. However, the most biologically relevantinformation lies in their connectivity and in the way their components interact, information that these techniques aren't able to record directly. The aim of this thesis is to study statistical methods for inferring information about the connectivity of complex networks starting from experimental data. The subject is approached from a statistical physics point of view drawing from the arsenal of methods developed in the study of spin glasses. Spin-glasses are prototypes of networks of discrete variables interacting in a complex way and are widely used to model biological networks. After an introduction of the models used and a discussion on the biological motivation of the thesis, all known methods of network inference are introduced and analysed from the point of view of their performance. Then, in the third part of the thesis, a new method is proposed which relies in the remark that the interactions in biology are not necessarily symmetric (i.e. the interaction from node A to node B is not the same as the one from B to A). It is shown that this assumption leads to methods that are both exact and efficient. This means that the interactions can be computed exactly, given a sufficient amount of data, and in a reasonable amount of time. This is an important original contribution since no other method is known to be both exact and efficient.

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X-Chromosom-Inaktivierung (XCI) ist der Mechanismus, den Säuger zur Dosiskompensierung zwischen weiblichen und männlichen Zellen verwenden. XCI wird ausgelöst durch die monoallelische Hochregulation der langen nicht-kodierenden RNA Xist von einem der zwei X-Chromosomen in weiblichen Zellen. Die Xist RNA vermittelt dann das Ausschalten der Gene auf diesem X-Chromosom. Das wirft einige interessante Fragen auf: Wie zählen Zellen ihre X-Chromosomen und stellen sicher, dass genau eines aktiv bleibt? Wie entscheiden sie, welches X-Chromosom aktiv bleibt und welches ausgeschaltet wird? Und wie erinnern sie sich an diese Entscheidung und behalten sie stabil bei durch alle weiteren Zellteilungen? Mithilfe eines stochastischen Modells zeigen wir, dass diese XCI Regulation prinzipiell durch nur zwei Regulatoren erklärt werden kann: Ein global (in trans) agierender XCI Aktivator und ein lokal (in cis) agierender XCI Repressor. Dieses Netzwerk aus nur zwei Regulatoren kann die Xist Expressionsmuster in verschiedenen Säugerspezies reproduzieren, von der Maus bis zum Mensch. Es sagt außerdem voraus, dass Zellen in der Lage sind, biallelische zu monoallelischer Xist Expression zu korrigieren, eine Vorhersage, für die wir tatsächlich experimentelle Belege finden. Mit einem mechanistischen Modell zeigen wir, dass das cis-Gedächtnis über den Xist Expressionszustand durch Antisense-Transkription zustande kommen könnte. Auf dieser Hypothese aufbauend untersucht der zweite Teil der Arbeit das Potential von Antisense-Transkription, ein lokales Gedächtnis über den Expressionszustand eines Gens zu generieren, genauer. Diese Analyse sagt vorher, dass Antisense-Repression den Expressionszustand eines Lokus tatsächlich für einige Tage stabil erhalten kann. / X-chromosome inactivation (XCI) is the mechanism for dosage compensation between the sexes in mammals. It is initiated through monoallelic upregulation of the long non-coding RNA Xist from one X chromosome, which mediates almost complete transcriptional silencing of this X chromosome. XCI regulation raises intriguing and thus far unanswered questions: How do cells count their X chromosomes and ensure that exactly one stays active? How do they make a mutually exclusive choice for one inactive X chromosome, and how do they then stably maintain this choice throughout subsequent cell divisions? Using stochastic modeling, we show that XCI onset only requires two regulators: A trans-acting Xist activator that ensures female specificity and a cis-acting Xist repressor that allows stable maintenance of alternative Xist expression states. This two-regulator network can recapitulate Xist expression patterns across different species and makes a novel prediction that is validated experimentally: Cells are able to revert biallelic Xist expression to monoallelic expression. With a mechanistic stochastic model we show that Xist's antisense transcript Tsix might be the cis-acting Xist repressor, uncovering the molecular mechanism behind the stabilization of the alternative Xist expression states. Building upon Tsix' possible functional role in stabilizing alternative Xist expression states on the active and inactive X chromosome, the second part of this thesis investigates the potential of antisense transcription to maintain a transient transcriptional memory. We find that mutual repression between a pair of antisense genes can allow the locus to remember the transcription state it has acquired due to a past signal for several days.