Characterization of the functional role of the transketolase-like 1 (TKTL1) gene for glucose metabolism, cellular growth, and malignant transformation in vitro and in vivo

Veränderungen im Zellstoffwechsel gehören zu den konsistentesten Charakteristika von Tumoren. Der nicht-oxidative Teil des Pentose-Phosphat-Wegs wird durch Transketolase Enzyme kontrolliert. Die TKTL1-Isoform ist speziell in Tumoren überexprimiert und ein Marker einer schlechten Prognose der Krebspatienten. TKTL1-supprimierte HCT116 Zellklone wurden mittels shRNA-Technologie etabliert, um die Rolle der TKTL1 in der Tumor-Propagation zu untersuchen. Wie erwartet waren in TKTL1-supprimierten Zellen der D-Glukose-Verbrauch und die L-Milchsäure-Produktion reduziert. Zudem proliferierten diese Klone langsamer, was vermuten lässt, dass die TKTL1 eine entscheidende Rolle beim Zellwachstum und der Proliferation von Krebszellen spielt. Wir stellten zudem fest, dass die mitochondriale Atmung TKTL1-supprimierter Zellen unterdrückt ist. Als Folge einer aktivierten aeroben Atmung mach TKTL1-Suppression wiesen diese Zellen einen höheren Sauerstoff-Verbrauch und Glukose-Abbau auf, was zu einem Anstieg reaktiver Sauerstoffspezies (ROS) durch die intensiver genutzte Elektronen-Transportkette führen könnte. Parallel war die Expression einiger ROS-inaktivierender Enzyme in den TKTL1-supprimierten Zellen reduziert, was eine erhöhte Apoptose-Sensitivität bei oxidativem Stress bedingte. Die Induktion Wachstums-supprimierender Gene (z. B. p21) sowie die erhöhte Menge intrazellulärer ROS in TKTL1-supprimierten Zellen können einen Beitrag an der erhöhten zellulären Seneszenz haben. Der Hypoxie-induzierbare Faktor 1α (HIF-1α) aktiviert die Transkription Krebs-relevanter Genen, die wesentliche Prozesse der Krebsentstehung wie Angiogenese, Zellvitalität, Glukose-Stoffwechsel und Zellinvasion beeinflussen. TKTL1-supprimierte HCT116 Zellen wiesen reduzierte HIF-1α-Level mit verminderter Stabilität auf und zeigten eine reduzierte Fähigkeit zur Invasion, Migration und Transformation in vitro, was zeigt, dass die TKTL1 an der Aufrechterhaltung des malignen Phänotyps des Tumor beteiligt ist. In einem murinen Tumor-Implantationsmodell zeigten TKTL1-supprimierte Zellen ein geringeres Tumor-Wachstum und eine geringere Tumorinzidenz, was eventuell die Konsequenz einer reduzierten Expression Tumor-relevanter Gene ist Mäuse mit TKTL1-supprimierten Tumore wiesen einen milderen Kachexie-Phänotyp auf. Die von TKTL1-supprimierten Zellen gebildeten Tumore hatten zudem eine schlechtere Vaskularisierung und wiesen weniger Nekrosen auf, ihr maligner Phänotyp war im Vergleich zu den entsprechenden Kontrollen attenuiert. Diese Daten liefern neue Hinweise auf die Bedeutung der TKTL1 in Tumoren wie dies bereits in klinischen Studien beobachtet wurde. Da die andauernde Aktivierung der TKTL1 das Tumorwachstum und die Metastasierung fördert, könnte die TKTL1 ein geeignetes Ziel einer innovativen Krebs-Therapie darstellen

Magnetic Resonance Imaging of 3He Apparent Diffusion Coefficient Anisotropy in Emphysema

Perhaps one of the most unique implementations of hyperpolarized 3He is diffusion- weighted MR imaging, taking advantage of the much larger apparent diffusion coefficient (ADC) of 3He ( 0.2 cm2∕s), compared to 1H (1.1 × 105 cm2∕s). 3He diffusion in the lungs is restricted by airway and alveoli walls and therefore is highly dependent on lung microstructure. 3He ADC has been shown to be sensitive to changes in terminai airway anatomy, specifically alveolar damage due to emphysema. At the terminai airway, 3He diffusion has been demonstrated to be anisotropic and described by two components: (i) a longitudinal diffusion coefficient (Dl)i reflective of diffusion along the length of the duct, and (ii) a transverse diffusion coefficient (Dτ), reflective of diffusion perpendicular to the duct. The purpose of this thesis was to first determine the sensitivity of Dτ and Dl to emphysema through a finite difference simulation in a budded cylinder model at submillisecond diffusion times. In-vivo experiments were performed in elastase-instilled rats to compare Dτ and Dl to those of sham-instilled rats. MR imaging was performed at 3T using a custom-built high performance insert gradient. D1 and DT were then mapped pixel-by-pixel by fitting a multi-component function to the data. Following imaging, the rats were euthanized and lungs were extracted and fixed under inflation at the end of each experiment for histological measurement of mean linear intercept. We hypothesized that Dτ measured at sub-millisecond diffusion times would be more sensitive than Dl for discriminated damage. Results from both simulations and in-vivo experiments supported this hypothesis. Overall, anisotropy measurement of the diffusion coefficient in Hyperpolarized Noble Gas (HNG) MR imaging could be a way to detect emphysema in its early stages

Modeling Assemblies and Interactions at the Replication Fork: Sliding Clamps and Clamp Interacting Enzymes

DNA sliding clamps are structurally conservative toroid-shape proteins that encircle and slide along DNA, serving as scaffold for other functional enzymes to act on DNA and ensuring the replication proccessivity, thereby, of fundamental biological significance across domains of life. Mechanistic details and related functional implications concerning clamp opening, interaction between clamp and clamp-interactive proteins, post-translational modification of sliding clamp remain largely elusive due to technical difficulties in single molecule level manipulations and structural studies on large biological complex. Toward the end of providing a unified molecular-level description on clamp loading that would account for all available experimental observations, we calculated the interface binding energy and depicted the residue pair contributions using MM/PBSA and MM/GBSA calculations to compare the different interfaces of sliding clamps, and dissolved the uncertainty in comparative stability of different sliding clamp interfaces. The possible interface breaking pathways were investigated by sampling the opening state of interfaces using SMD simulations. Functioning as a polymerase accessory factor, sliding clamp associates with the dual enzymatic functional polymerase B (PolB) as DNA replication occurs. The massive conformational switch of PolB between replicating and editing mode is recognized for its functional significance but little is understood in the context of the PCNA/PolB/DNA complex. We integrated the structural informations from individual and binary crystal structures, as well as low-resolution structural information and other functional assay results, to build the complex atomistic models in both modes and refine them through atomistic simulations. The transition process was probed using TMD and ENM to reveal the structural characteristics and determinants of the transition. Sliding clamp is also a master coordinator of cellular responses to DNA damage. Efforts with the same methodology were made on human PCNA/FEN1/DNA ternary complex to investigate the reversible associations of FEN1 to PCNA and the conformational switching leading to exchange of repair intermediates. In the third thrust, we modeled the ubiquitin-modified and SUMO-modified PCNA using protein-protein docking and atomic simulation. Alongside with the SAXS data, our results revealed the structural basis for the distinct functional outcomes upon different posttranslational modification of PCNA

A Neural Stochastic Volatility Model

In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance. The model comprises a pair of complementary stochastic recurrent neural networks: the generative network models the joint distribution of the stochastic volatility process; the inference network approximates the conditional distribution of the latent variables given the observables. Our focus here is on the formulation of temporal dynamics of volatility over time under a stochastic recurrent neural network framework. Experiments on real-world stock price datasets demonstrate that the proposed model generates a better volatility estimation and prediction that outperforms mainstream methods, e.g., deterministic models such as GARCH and its variants, and stochastic models namely the MCMC-based model \emph{stochvol} as well as the Gaussian process volatility model \emph{GPVol}, on average negative log-likelihood