Evolutionary analysis of mammalian genomes and associations to human disease

Statistical models of DNA sequence evolution for analysing protein-coding genes can be used to estimate rates of molecular evolution and to detect signals of natural selection. Genes that have undergone positive selection during evolution are indicative of functional adaptations that drive species differences. Genes that underwent positive selection during the evolution of humans and four mammals used to model human diseases (mouse, rat, chimpanzee and dog) were identified, using maximum likelihood methods. I show that genes under positive selection during human evolution are implicated in diseases such as epithelial cancers, schizophrenia, autoimmune diseases and Alzheimer’s disease. Comparisons of humans with great apes have shown such diseases to display biomedical disease differences, such as varying degrees of pathology, differing symptomatology or rates of incidence. The chimpanzee lineage was found to have more adaptive genes than any of the other lineages. In addition, evidence was found to support the hypothesis that positively selected genes tend to interact with each other. This is the first such evidence to be detected among mammalian genes and may be important in identifying molecular pathways causative of species differences. The genome scan analysis spurred an in*depth evolutionary analysis of the nuclear receptors, a family of transcription factors. 12 of the 48 nuclear receptors were found to be under positive selection in mammalia. The androgen receptor was found to have undergone positive selection along the human lineage. Positively selected sites were found to be present in the major activation domain, which has implications for ligand recognition and binding. Studying the evolution of genes which are associated with biomedical disease differences between species is an important way to gain insight into the molecular causes of diseases and may provide a method to predict when animal models do not mirror human biology

Study protein-protein interaction in Methyl-directed DNA mismatch repair in E. coli: Exonuclease I (Exo I) and DNA helicas II (UvrD) & A Minimal Exonuclease Domain of WRN Forms a Hexamer on DNA and Possesses Both 3’-5’ Exonuclease and 5’-Protruding Strand Endonuclease Activities & Solving the Structure of the Ligand-Binding Domain of the Pregnane-Xenobiotic-Receptor with 17β Estradiol and T1317

Exonuclease I (ExoI) from Escherichia coli is a monomeric enzyme that processively degrades single stranded DNA in the 3′ to 5′ direction and has been implicated in DNA recombination and repair. It functions in numerous genome maintenance pathways, with particularly well defined roles in methyl-directed mismatch repair (MMR). The Escherichia coli MMR pathway can be reconstituted in vitro with the activities of eight proteins (8). MutS, MutL and MutH are involved in initiation of repair including mismatch recognition and generation of a nick at a nearby GATC sequence (53, 54, 55, 56). The hemimethylated state of GATC sequences immediately following replication serves as a signal to direct repair to the nascent strand of the DNA duplex (57, 58). DNA helicase II and one of several exonucleases (Exonucleas I, Exonuclease VII and RecJ) are required to excise the error-containing DNA strand beginning at the nicked GATC site (34, 35). Restoration of the correct DNA sequence by repair synthesis involves DNA polymerase III holoenzyme and SSB, and the final nick is sealed by DNA ligase (34). To identify interactions with ExoI involved in MMR repair system, we used the yeast two-hybrid system with ExoI as bait. By screening an E.coli genomic library, E. coli DNA helicase II (UvrD) was identified as a potential interacting protein. UvrD has been shown to be required for DNA excision repair, methyl-directed mismatch repair and has some undefined, role in DNA replication and recombination. In this report, in vitro experiments confirm that UvrD and ExoI make a direct physical interaction that may be required for function of the methyl-directed mismatch repair. Werner Syndrome is a rare autosomal recessive disease characterized by a premature aging phenotype, genomic instability and a dramatically increased incidence of cancer and heart disease. Mutations in a single gene encoding a 1,432 amino-acid helicase/exonuclease (hWRN) have been shown to be responsible for the development of this disease. We have cloned, over-expressed and purified a minimal, 171-amino acid fragment of hWRN that functions as an exonuclease. This fragment, encompassing residues 70-240 of hWRN (hWRN-N70-240), exhibits the same level of 3’-5’ exonuclease activity as the previously described exonuclease fragment encompassing residues 1-333 of the full-length protein. The fragment also contains a 5’-protruding DNA strand endonuclease activity at a single-strand/double-strand DNA junction and within singlestranded DNA, as well as a 3’-5’ exonuclease activity on single-stranded DNA. We find hWRN-N70-240 is in a trimer-hexamer equilibrium in the absence of DNA when examined by gel filtration chromatography and atomic force microscopy (AFM). Upon the addition of DNA substrate, hWRN-N70-240 forms a hexamer and interacts with the recessed 3’-end of the DNA. Moreover, we find that the interaction of hWRN-N70-240 with the replication protein PCNA also causes this minimal, 171-amino acid exonuclease region to form a hexamer. Thus, the active form of this minimal exonuclease fragment of human WRN appears to be a hexamer. The implications the results presented here have on our understanding of hWRN’s roles in DNA replication and repair are discussed. The pregnane X receptor (PXR) is a nuclear xenobiotic receptor which acts as a molecular sentry that detects potentially toxic foreign chemicals and activates genes to initiate their breakdown and removal. PXR fills this role by its ability to promiscuously bind to a diverse array of structurally distinct ligands which in turn enables it to activate a wide array of genes such as CYP3A, a monooxygenase involved in breaking down greater than 50 percent of all drugs and MDR1, a drug and xenobiotic efflux pump. Activation of PXR has the potentially deadly side effect of causing drug-drug interactions. Crystal structures of the human PXR ligand binding domain (LBD) have revealed a number of unique features which could facilitate PXR’s promiscuous binding activity. Chief among these is a very large and highly conformable hydrophobic ligand binding cavity. The overall shapes of the ligand binding cavities of hPXR-LBD without ligand and bound to endogenous compound 17β estradiol and the LXR ligand T1317 are distinct. Several structural features of PXR contribute to the plasticity of its binding cavity including an extended beta-sheet region and two novel helices. One of the novel helices and the extended beta-sheet frames the critical second unique helix. This highly flexible helix, called the pseudo-helix due to its variance from the canonical alpha-helical conformation, adopts distinct orientations in every structure solved and plays the single most important role in adapting the shape of the binding cavity to fit different ligand orientations. The accumulating structural data provides important insights into how PXR detects xenobiotics and endobiotics and may prove useful in structure based drug design

Ritonavir - A Novel Multidrug Resistance Modulator in Cancer Chemotherapy and Ocular Neovascular Diseases

Title from PDF of title page, viewed on July 15, 2015Dissertation advisor: Ashim K. MitraVitaIncludes bibliographic references (pages 195-222)Thesis (Ph.D.)--School of Pharmacy and Department of Chemistry. University of Missouri--Kansas City, 2014Multidrug resistance (MDR), a clinical outcome characterized by subtherapeutic intracellular drug concentration, is one of the predominant factors limiting effective cancer chemotherapy. Several possible mechanisms and molecular alterations have been implicated in the development of MDR, including activation of efflux transporters and metabolizing enzymes in response to therapeutic agents. Therefore, the primary objective of my dissertation project is to develop strategies for overcoming drug resistance in cancer chemotherapy. Human adenocarcinoma cells (LS-180) were treated for 72 hours with vinblastine alone and in the presence of ritonavir. The expression of efflux transporters (MDR1 and MRP2), metabolizing enzyme (CYP3A4) and nuclear hormone receptor (PXR) was induced in response to vinblastine. This overexpression was completely neutralized when cells were cotreated with ritonavir. Uptake of [3H] lopinavir and Vivid™ assay further confirmed the functional activity of transcribed genes upon cotreatment. Reduced cell proliferation, migration and increased apoptosis of cancer cells were further indicative of enhanced activity of chemotherapeutics (doxorubicin, paclitaxel, tamoxifen and vinblastine) in the presence of ritonavir. Combination therapy of anticancer drug with ritonavir may overcome drug resistance by neutralizing overexpression of efflux transporters and metabolizing enzymes. Hypoxia leading to neovascularization has also been implicated in the development of MDR and ocular neovascular diseases. Despite introduction of novel therapeutics, treatment of retinal disorders remains challenging, possibly due to complex nature of hypoxia signaling. This study demonstrates for the first time that hypoxic conditions may alter expression of efflux and influx transporters in retinal pigment epithelial (RPE) cells. These findings suggest that hypoxia may further alter disposition of ophthalmic drugs. Inhibiting this signaling mechanism with an already approved therapeutic molecule may have promising antiangiogenic role with fewer side effects. Our studies (quantitative PCR, immunoblot analysis, ELISA and angiogenic assay) have demonstrated that ritonavir inhibits the expression of hypoxia-inducible factor-1α (HIF-1α) mediated vascular endothelial growth factor (VEGF) expression in RPE cells probably via inhibition of PI3K/AKT pathway. This inhibition may reduce retinal neovascularization. These findings shed new light on the possibility of incorporating ritonavir in the treatment regimen of ocular angiogenic diseases. Although many inhibitors of HIF-1α are in clinical trials, additional benefit of using ritonavir is that it has been given to HIV patients with relatively low toxicity. The process of traditional drug development could be fast tracked since ritonavir is clinically approved for human use. However, further preclinical and clinical experiments are necessary to determine the repositioning of ritonavir in the treatment of ocular neovascular diseasesMechanisms of drug resistance in cancer chemotherapy: Coordinated role and regulation of efflux transporters and metabolizing enzymes -- Differential effect of MDR1 and MRP2 in cellular translocation of gemifloxacin -- PXR mediated induction of efflux transporters by fluoroquinolones: a possible mechanism for development of multidrug resistance -- Rotonavir: a novel therapeutic for overcoming drug resistance in cancer chemotherapy -- Hypoxia-inducible factor 1 (HIF-1): a potential target intervention in ocular neovascular diseases -- Molecular expression and functional activity of efflux and influx transporters in hypoxia induced retinal pigment epithelial cells -- Ritonavir inhibits HIF-1a mediated VEGF expression in retinal pigment epithelial cells -- Summary and recommendations -- Appendi

Kern-basierte Lernverfahren für das virtuelle Screening

We investigate the utility of modern kernel-based machine learning methods for ligand-based virtual screening. In particular, we introduce a new graph kernel based on iterative graph similarity and optimal assignments, apply kernel principle component analysis to projection error-based novelty detection, and discover a new selective agonist of the peroxisome proliferator-activated receptor gamma using Gaussian process regression. Virtual screening, the computational ranking of compounds with respect to a predicted property, is a cheminformatics problem relevant to the hit generation phase of drug development. Its ligand-based variant relies on the similarity principle, which states that (structurally) similar compounds tend to have similar properties. We describe the kernel-based machine learning approach to ligand-based virtual screening; in this, we stress the role of molecular representations, including the (dis)similarity measures defined on them, investigate effects in high-dimensional chemical descriptor spaces and their consequences for similarity-based approaches, review literature recommendations on retrospective virtual screening, and present an example workflow. Graph kernels are formal similarity measures that are defined directly on graphs, such as the annotated molecular structure graph, and correspond to inner products. We review graph kernels, in particular those based on random walks, subgraphs, and optimal vertex assignments. Combining the latter with an iterative graph similarity scheme, we develop the iterative similarity optimal assignment graph kernel, give an iterative algorithm for its computation, prove convergence of the algorithm and the uniqueness of the solution, and provide an upper bound on the number of iterations necessary to achieve a desired precision. In a retrospective virtual screening study, our kernel consistently improved performance over chemical descriptors as well as other optimal assignment graph kernels. Chemical data sets often lie on manifolds of lower dimensionality than the embedding chemical descriptor space. Dimensionality reduction methods try to identify these manifolds, effectively providing descriptive models of the data. For spectral methods based on kernel principle component analysis, the projection error is a quantitative measure of how well new samples are described by such models. This can be used for the identification of compounds structurally dissimilar to the training samples, leading to projection error-based novelty detection for virtual screening using only positive samples. We provide proof of principle by using principle component analysis to learn the concept of fatty acids. The peroxisome proliferator-activated receptor (PPAR) is a nuclear transcription factor that regulates lipid and glucose metabolism, playing a crucial role in the development of type 2 diabetes and dyslipidemia. We establish a Gaussian process regression model for PPAR gamma agonists using a combination of chemical descriptors and the iterative similarity optimal assignment kernel via multiple kernel learning. Screening of a vendor library and subsequent testing of 15 selected compounds in a cell-based transactivation assay resulted in 4 active compounds. One compound, a natural product with cyclobutane scaffold, is a full selective PPAR gamma agonist (EC50 = 10 +/- 0.2 muM, inactive on PPAR alpha and PPAR beta/delta at 10 muM). The study delivered a novel PPAR gamma agonist, de-orphanized a natural bioactive product, and, hints at the natural product origins of pharmacophore patterns in synthetic ligands.Wir untersuchen moderne Kern-basierte maschinelle Lernverfahren für das Liganden-basierte virtuelle Screening. Insbesondere entwickeln wir einen neuen Graphkern auf Basis iterativer Graphähnlichkeit und optimaler Knotenzuordnungen, setzen die Kernhauptkomponentenanalyse für Projektionsfehler-basiertes Novelty Detection ein, und beschreiben die Entdeckung eines neuen selektiven Agonisten des Peroxisom-Proliferator-aktivierten Rezeptors gamma mit Hilfe von Gauß-Prozess-Regression. Virtuelles Screening ist die rechnergestützte Priorisierung von Molekülen bezüglich einer vorhergesagten Eigenschaft. Es handelt sich um ein Problem der Chemieinformatik, das in der Trefferfindungsphase der Medikamentenentwicklung auftritt. Seine Liganden-basierte Variante beruht auf dem Ähnlichkeitsprinzip, nach dem (strukturell) ähnliche Moleküle tendenziell ähnliche Eigenschaften haben. In unserer Beschreibung des Lösungsansatzes mit Kern-basierten Lernverfahren betonen wir die Bedeutung molekularer Repräsentationen, einschließlich der auf ihnen definierten (Un)ähnlichkeitsmaße. Wir untersuchen Effekte in hochdimensionalen chemischen Deskriptorräumen, ihre Auswirkungen auf Ähnlichkeits-basierte Verfahren und geben einen Literaturüberblick zu Empfehlungen zur retrospektiven Validierung, einschließlich eines Beispiel-Workflows. Graphkerne sind formale Ähnlichkeitsmaße, die inneren Produkten entsprechen und direkt auf Graphen, z.B. annotierten molekularen Strukturgraphen, definiert werden. Wir geben einen Literaturüberblick über Graphkerne, insbesondere solche, die auf zufälligen Irrfahrten, Subgraphen und optimalen Knotenzuordnungen beruhen. Indem wir letztere mit einem Ansatz zur iterativen Graphähnlichkeit kombinieren, entwickeln wir den iterative similarity optimal assignment Graphkern. Wir beschreiben einen iterativen Algorithmus, zeigen dessen Konvergenz sowie die Eindeutigkeit der Lösung, und geben eine obere Schranke für die Anzahl der benötigten Iterationen an. In einer retrospektiven Studie zeigte unser Graphkern konsistent bessere Ergebnisse als chemische Deskriptoren und andere, auf optimalen Knotenzuordnungen basierende Graphkerne. Chemische Datensätze liegen oft auf Mannigfaltigkeiten niedrigerer Dimensionalität als der umgebende chemische Deskriptorraum. Dimensionsreduktionsmethoden erlauben die Identifikation dieser Mannigfaltigkeiten und stellen dadurch deskriptive Modelle der Daten zur Verfügung. Für spektrale Methoden auf Basis der Kern-Hauptkomponentenanalyse ist der Projektionsfehler ein quantitatives Maß dafür, wie gut neue Daten von solchen Modellen beschrieben werden. Dies kann zur Identifikation von Molekülen verwendet werden, die strukturell unähnlich zu den Trainingsdaten sind, und erlaubt so Projektionsfehler-basiertes Novelty Detection für virtuelles Screening mit ausschließlich positiven Beispielen. Wir führen eine Machbarkeitsstudie zur Lernbarkeit des Konzepts von Fettsäuren durch die Hauptkomponentenanalyse durch. Der Peroxisom-Proliferator-aktivierte Rezeptor (PPAR) ist ein im Zellkern vorkommender Rezeptor, der den Fett- und Zuckerstoffwechsel reguliert. Er spielt eine wichtige Rolle in der Entwicklung von Krankheiten wie Typ-2-Diabetes und Dyslipidämie. Wir etablieren ein Gauß-Prozess-Regressionsmodell für PPAR gamma-Agonisten mit chemischen Deskriptoren und unserem Graphkern durch gleichzeitiges Lernen mehrerer Kerne. Das Screening einer kommerziellen Substanzbibliothek und die anschließende Testung 15 ausgewählter Substanzen in einem Zell-basierten Transaktivierungsassay ergab vier aktive Substanzen. Eine davon, ein Naturstoff mit Cyclobutan-Grundgerüst, ist ein voller selektiver PPAR gamma-Agonist (EC50 = 10 +/- 0,2 muM, inaktiv auf PPAR alpha und PPAR beta/delta bei 10 muM). Unsere Studie liefert einen neuen PPAR gamma-Agonisten, legt den Wirkmechanismus eines bioaktiven Naturstoffs offen, und erlaubt Rückschlüsse auf die Naturstoffursprünge von Pharmakophormustern in synthetischen Liganden

Understanding the Metabolic and Genetic Regulation of Breast Cancer Recurrence Using Magnetic Resonance-Based Integrative Metabolomics

Breast cancer is the most commonly diagnosed malignancy in women and is the leading cause of cancer-related death in the female population worldwide. In these women, breast cancer recurrence--local, regional, or distant--represents the principal cause of death from this disease. The mechanisms underlying tumor recurrence remain largely unknown. To dissect those mechanisms, our laboratory has developed inducible transgenic mouse models that accurately recapitulate key features of the natural history of human breast cancer progression: primary tumor development, tumor dormancy and recurrence. Dysregulated metabolism has long been known to be a key feature in tumorigenesis. Yet, very little is known about the connection, if any, between cellular metabolic changes and breast cancer recurrence. In this work, I design and implement a systems engineering-based approach, magnetic resonance-based integrative metabolomics, to better understand the metabolic and genetic regulation of breast cancer recurrence. Through a combination of 1H and 13C magnetic resonance spectroscopy (MRS), mass spectrometry (MS) as well as gene expression profiling and functional metabolic and genetic studies, I aim to identify the metabolic profile of mammary tumors during breast cancer progression, identify the molecular basis and role of differential glutamine uptake and metabolism in breast cancer recurrence and finally, investigate the molecular basis and role of differential lactate production in breast cancer recurrence. The findings suggest an evolving metabolic phenotype of tumors during breast cancer progression as well as metabolic dysregulation in some of the key regulatory nodes that control that evolution. Identifying the metabolic changes associated with tumor recurrence can pave the way for identifying novel diagnostic strategies and therapeutic targets that can contribute to improved clinical management and outcome for breast cancer patients