The threat from infectious diseases dates as far back as prehistoric times. Pathogens continue to pose serious challenges to human health. The emergence and spread of diseases such as HIV/AIDS, Severe Acute Respiratory Syndrome (SARS), avian influenza, and the threats of bioterrorism have made infectious diseases major public health concerns. Despite many successes in the discovery of anti-infective medications, the treatment of infectious diseases faces serious challenges, which include (i) the emergence and reemergence of infectious pathogens, (ii) the ability of pathogens to adapt and develop resistance to drugs, and (ii) a shortage in the development and discovery of new anti-infective drugs. Host-Oriented Broad-Spectrum (HOBS) treatments have the promising potential to alleviate these problems. The HOBS treatment paradigm focuses on finding drug targets in human host that are simultaneously effective against a wide variety of infectious agents and toxins. In this dissertation, we present a computational approach to predict HOBS treatments by integrative analysis of three types of data, namely, (a) gene expression data representing host responses upon infection by a pathogen, (b) annotations of genes to pre-defined biological pathways and processes, and (iii) genes that are targets of known drugs. Our methods combine gene set-level enrichment with biclustering. We applied our approach to a compendium of gene expression data sets derived from host cells exposed to bacterial or to fungal pathogens, to functional annotation data from multiple databases, and to drug targets from DrugBank. We present putative host drug targets and drugs with extensive support in the literature for their potential to treat multiple bacterial and fungal infections. These results showcase the potential of our computational approach to predict HOBS drug targets that may be effective against two or more pathogens. Our study takes a clean-slate approach that promises to yield unsuspected or unknown associations between pathogens and biological processes, and thus discern candidate gene/proteins to be further probed as HOBS targets. Furthermore, by focusing on host responses to pathogens as captured by transcriptional data, our proposed approach stimulates host-oriented drug target identification, which has potential to alleviate the problem of drug resistance. / Ph. D.

Die Signalwege des transformierenden Wachstumsfaktors β (TGF-β) spielen eine entscheidende Rolle bei der Zellproliferation, -migration und -apoptose durch die Aktivierung von Smad-Proteinen. Untersuchungen haben gezeigt, dass die biologischen Wirkungen des TGF-β-Signalwegs stark vom Zellkontext abhängen. In dieser Arbeit ging es darum zu verstehen, wie TGF-β-Signale Zielgene unterschiedlich regulieren können, wie unterschiedliche Dynamiken der Genexpression durch TGF-β-Signale induziert werden und auf welche Weise Smad-Proteine zu unterschiedlichen Expressionsmustern von TGF- β-Zielgenen beitragen. Der Fokus dieser Studie liegt auf den transkriptionsregulatorischen Effekten des Nodal / Activin-Liganden, der zur TGF-β-Superfamilie gehört und ein wichtiger Faktor in der frühen embryonalen Entwicklung ist. Um diese Effekte zu analysieren, habe ich kinetische Modelle entwickelt und mit den Zeitverlaufsdaten von RNA-Polymerase II (Pol II) und Smad2-Chromatin-Bindungsprofilen für die Zielgene kalibriert. Unter Verwendung des Akaike-Informationskriteriums (AIC) zur Bewertung verschiedener kinetischer Modelle stellten wir fest, dass der Nodal / Activin-Signalweg Zielgene über verschiedene Mechanismen reguliert. Im Nodal / Activin-Smad2-Signalweg spielt Smad2 für verschiedene Zielgene unterschiedliche regulatorische Rollen. Wir zeigen, wie Smad2 daran beteiligt ist, die Transkriptions- oder Abbaurate jedes Zielgens separat zu regulieren. Darüber hinaus werden eine Reihe von Merkmalen, die die Transkriptionsdynamik von Zielgenen vorhersagen können, durch logistische Regression ausgewählt. Der hier vorgestellte Ansatz liefert quantitative Beziehungen zwischen der Dynamik des Transkriptionsfaktors und den Transkriptionsantworten. Diese Arbeit bietet auch einen allgemeinen mathematischen Rahmen für die Untersuchung der Transkriptionsregulation anderer Signalwege. / Transforming growth factor-β (TGF-β) signaling pathways play a crucial role in cell proliferation, migration, and apoptosis through the activation of Smad proteins. Research has shown that the biological effects of TGF-β signaling pathway are highly cellular-context-dependent. In this thesis work, I aimed at understanding how TGF-β signaling can regulate target genes differently, how different dynamics of gene expressions are induced by TGF-β signal, and what is the role of Smad proteins in differing the profiles of target gene expression. In this study, I focused on the transcriptional responses to the Nodal/Activin ligand, which is a member of the TGF-β superfamily and a key regulator of early embryonic development. Kinetic models were developed and calibrated with the time course data of RNA polymerase II (Pol II) and Smad2 chromatin binding profiles for the target genes. Using the Akaike information criterion (AIC) to evaluate different kinetic models, we discovered that Nodal/Activin signaling regulates target genes via different mechanisms. In the Nodal/Activin-Smad2 signaling pathway, Smad2 plays different regulatory roles on different target genes. We show how Smad2 participates in regulating the transcription or degradation rate of each target gene separately. Moreover, a series of features that can predict the transcription dynamics of target genes are selected by logistic regression. The approach we present here provides quantitative relationships between transcription factor dynamics and transcriptional responses. This work also provides a general computational framework for studying the transcription regulations of other signaling pathways.