Understanding Molecular Mechanisms of Protein Kinases Regulation and Inhibition

Protein kinases (PKs) play a key role in regulating cellular processes. Kinase dysfunction can lead to disease, thus kinases are important targets for drug design and a fundamental class of pharmacological targets for anti-cancer therapy. Among protein kinases, B-Raf and c-Src are remarkably interesting as anticancer drug targets because of their important role in cancer onset (B-Raf) and progression (c-Src). This thesis is mainly focused on the characterization of the molecular mechanism at the basis of the regulation and inhibition of these remarkable PKs. By using nuclear magnetic resonance (NMR) and molecular dynamics simulations (MD) we have studied in great details their activation dynamics, their inhibition and the effect of clinically-relevant oncogenic mutations on their structure and dynamics. C-Scr was the first viral oncogenic protein discovered, is involved in metastasis and is mutated in 50% of colon, liver, lung, breast and pancreas tumours. Upon phosphorylation, various conserved structural elements, including the activation loop, switch from an inactive to an active form able to bind ATP and phosphorylate a substrate in a cellular signalling process leading to cell replication. In this thesis, we will discuss how phosphorylation drastically changes the dynamics of the C-lobe in c-Src by NMR analysis, a phenomenon not easily accessible by static crystallographic studies. The second part of the thesis will be focused on B-Raf, a protein serine/threonine kinase. B-Raf kinase is a key target for the treatment of melanoma, since a single mutation (V600E) is found in more than 50% of all malignant melanomas. Despite their importance, the molecular mechanisms explaining the increased kinase activity in this mutant remains elusive. As kinase activity is often tightly regulated by one or more conformational transitions between an active and an inactive state, which are difficult to be observed experimentally, molecular dynamics simulations are often useful to interpret the experimental results. In this project, we will examine the mechanism by which the V600E mutation enhances the activity of the B-Raf monomer. We will also employ a combination of MD techniques with NMR experiments to fully map the effects of the mutation on the conformational landscape of B-Raf. An understanding at the atomic level of the mechanisms leading to their activation and inhibition is an extremely important goal in anti-cancer drug discovery. A better understanding of these proteins' mechanisms might lead to more potent and less toxic drugs. Finally, I report on the studies of a much small domain often associated with PKs in regulatory pathways: the WW domain. By using a combination of MD simulations and NMR, we have characterized the effect of a pathogenic mutation on its folding landscape

Towards elucidating the molecular modes of action of two types of antidepressant drugs using proteomics

Major depressive disorder (MDD) is a common mental disorder that globally millions of people of all ages suffer from. Despite the large-scale and long-term research that has been carried out, the etiology of MDD has not been fully elucidated. A number of antidepressants have been developed for pharmacotherapy of MDD with considerable efficacy in many patients. Nevertheless, currently used antidepressants are still limited by their undesirable side effects and other drawbacks, including insufficiency in the therapy of treatment-resistant depression (TRD). A comprehensive mechanistic study of the side effects of clinically used antidepressants and the development of novel antidepressants free from these limitations are of importance and in great demand.In my PhD work, I have aimed at elucidating the molecular mechanism of action of two drugs that represent different classes of antidepressants. First, in order to investigate side effects caused by chronic treatment with fluoxetine, one of the most widely prescribed selective serotonin reuptake inhibitors (SSRIs), I subjected brain tissue from juvenile macaques that had been treated with fluoxetine for two years to proteome and phosphoproteome profiling using quantitative mass spectrometry. The proteomics data indicate that GABAergic synapse pathways are associated with the increased impulsivity observed in the juvenile macaques after chronic fluoxetine treatment.In the second study, I attempted to unveil novel protein targets for the fast-acting antidepressant ketamine. Using several mass spectrometry-based strategies to uncover drug-protein interactions, I have identified novel binding partners of ketamine and its metabolites, which includes pyruvate kinase, and implicate the involvement of energy metabolism in ketamine?s mode of action.In summary, this work reveals that GABAergic synapse pathways are affected by fluoxetine treatment in non-human primate macaques, and suggests new protein targets and associated mechanisms of ketamine as an antidepressant. My project data provide leads for pharmacology, and drug targets for the development of novel antidepressants with greater efficacy and fewer side effects

Towards elucidating the molecular modes of action of two types of antidepressant drugs using proteomics

Major depressive disorder (MDD) is a common mental disorder that globally millions of people of all ages suffer from. Despite the large-scale and long-term research that has been carried out, the etiology of MDD has not been fully elucidated. A number of antidepressants have been developed for pharmacotherapy of MDD with considerable efficacy in many patients. Nevertheless, currently used antidepressants are still limited by their undesirable side effects and other drawbacks, including insufficiency in the therapy of treatment-resistant depression (TRD). A comprehensive mechanistic study of the side effects of clinically used antidepressants and the development of novel antidepressants free from these limitations are of importance and in great demand. In my PhD work, I have aimed at elucidating the molecular mechanism of action of two drugs that represent different classes of antidepressants. First, in order to investigate side effects caused by chronic treatment with fluoxetine, one of the most widely prescribed selective serotonin reuptake inhibitors (SSRIs), I subjected brain tissue from juvenile macaques that had been treated with fluoxetine for two years to proteome and phosphoproteome profiling using quantitative mass spectrometry. The proteomics data indicate that GABAergic synapse pathways are associated with the increased impulsivity observed in the juvenile macaques after chronic fluoxetine treatment. In the second study, I attempted to unveil novel protein targets for the fast-acting antidepressant ketamine. Using several mass spectrometry-based strategies to uncover drug-protein interactions, I have identified novel binding partners of ketamine and its metabolites, which includes pyruvate kinase, and implicate the involvement of energy metabolism in ketamine’s mode of action. In summary, this work reveals that GABAergic synapse pathways are affected by fluoxetine treatment in non-human primate macaques, and suggests new protein targets and associated mechanisms of ketamine as an antidepressant. My project data provide leads for pharmacology, and drug targets for the development of novel antidepressants with greater efficacy and fewer side effects

Proteomic identification of putative biomarkers of radiotherapy resistance

BackgroundCurrently, tumour response to radiotherapy cannot be predicted meaning that those patients with tumours resistant to the therapy endure the harmful side effects associated with ionising radiation in the absence of therapeutic gain. The aim of this project was to identify protein biomarkers predictive of radiotherapy response using comparative proteomic platforms to study radioresistant cell line models. The identification of such biomarkers will enable radiotherapy to be tailored on an individual patient basis and hence increase treatment efficacy.MethodsSeven radioresistant (RR) cell line models derived from breast, head and neck (oral), and rectal cancers were investigated to identify differentially expressed proteins (DEPs) associated with radiotherapy resistance. This included the establishment of 2 RR rectal cancer cell line models and the proteomic analysis of 2 RR oral cancer cell lines and 2 RR rectal cancer cell lines. Proteomic analysis included 3 different platforms, namely antibody microarray, 2D MS and iTRAQ. Data mining of all biomarker discovery data, from all 7 novel RR cell lines was carried out using Ingenuity Pathway Analysis (IPA) which identified canonical pathways associated with the data. Protein candidates from selected canonical pathways were confirmed by western blotting and assessed clinically using immunohistochemistry.ResultsFollowing the combination of all biomarker discovery data for all 7 RR cell lines, 373 unique DEPs were successfully mapped onto the Ingenuity Knowledge Base, generating 339 canonical pathways. Of these, 13 of the most relevant pathways were selected for further interpretation. Several proteasomal subunits were identified during the biomarker discovery phase and were mapped onto the protein ubiquitination pathway by IPA. DR4, was identified in 4/7 RR cell lines and was mapped onto the death receptor signalling pathway by IPA. Radiotherapy is typically thought to induce cellular apoptosis via the intrinsic (mitochondrial) pathway, therefore the repeated identification of the DR4 protein involved in the extrinsic apoptotic pathway has potentially lead to the discovery of a novel relationship between radiotherapy and the extrinsic death receptor pathway. The differential expression of both the 26S Proteasome and DR4 were confirmed by western blotting. Clinical assessment using immunohistochemistry revealed a significant association between expression of the 26S Proteasome and radioresistance in breast cancer.DiscussionA large number of DEPs which may be associated with radiotherapy resistance in breast, oral and rectal cancers have been identified using comparative proteomic platforms. The protein ubiquitination pathway and the death receptor signalling pathway may play a significant role in radioresistance and proteins within these pathways may be putative biomarkers of radiotherapy response

A PARADIGM SHIFTING APPROACH IN SON FOR FUTURE CELLULAR NETWORKS

The race to next generation cellular networks is on with a general consensus in academia and industry that massive densification orchestrated by self-organizing networks (SONs) is the cost-effective solution to the impending mobile capacity crunch. While the research on SON commenced a decade ago and is still ongoing, the current form (i.e., the reactive mode of operation, conflict-prone design, limited degree of freedom and lack of intelligence) hinders the current SON paradigm from meeting the requirements of 5G. The ambitious quality of experience (QoE) requirements and the emerging multifarious vision of 5G, along with the associated scale of complexity and cost, demand a significantly different, if not totally new, approach to SONs in order to make 5G technically as well as financially feasible. This dissertation addresses these limitations of state-of-the-art SONs. It first presents a generic low-complexity optimization framework to allow for the agile, on-line, multi-objective optimization of future mobile cellular networks (MCNs) through only top-level policy input that prioritizes otherwise conflicting key performance indicators (KPIs) such as capacity, QoE, and power consumption. The hybrid, semi-analytical approach can be used for a wide range of cellular optimization scenarios with low complexity. The dissertation then presents two novel, user-mobility, prediction-based, proactive self-optimization frameworks (AURORA and OPERA) to transform mobility from a challenge into an advantage. The proposed frameworks leverage mobility to overcome the inherent reactiveness of state-of-the-art self-optimization schemes to meet the extremely low latency and high QoE expected from future cellular networks vis-à-vis 5G and beyond. The proactiveness stems from the proposed frameworks’ novel capability of utilizing past hand-over (HO) traces to determine future cell loads instead of observing changes in cell loads passively and then reacting to them. A semi-Markov renewal process is leveraged to build a model that can predict the cell of the next HO and the time of the HO for the users. A low-complexity algorithm has been developed to transform the predicted mobility attributes to a user-coordinate level resolution. The learned knowledge base is used to predict the user distribution among cells. This prediction is then used to formulate a novel (i) proactive energy saving (ES) optimization problem (AURORA) that proactively schedules cell sleep cycles and (ii) proactive load balancing (LB) optimization problem (OPERA). The proposed frameworks also incorporate the effect of cell individual offset (CIO) for balancing the load among cells, and they thus exploit an additional ultra-dense network (UDN)-specific mechanism to ensure QoE while maximizing ES and/or LB. The frameworks also incorporates capacity and coverage constraints and a load-aware association strategy for ensuring the conflict-free operation of ES, LB, and coverage and capacity optimization (CCO) SON functions. Although the resulting optimization problems are combinatorial and NP-hard, proactive prediction of cell loads instead of reactive measurement allows ample time for combination of heuristics such as genetic programming and pattern search to find solutions with high ES and LB yields compared to the state of the art. To address the challenge of significantly higher cell outage rates in anticipated in 5G and beyond due to higher operational complexity and cell density than legacy networks, the dissertation’s fourth key contribution is a stochastic analytical model to analyze the effects of the arrival of faults on the reliability behavior of a cellular network. Assuming exponential distributions for failures and recovery, a reliability model is developed using the continuous-time Markov chains (CTMC) process. Unlike previous studies on network reliability, the proposed model is not limited to structural aspects of base stations (BSs), and it takes into account diverse potential fault scenarios; it is also capable of predicting the expected time of the first occurrence of the fault and the long-term reliability behavior of the BS. The contributions of this dissertation mark a paradigm shift from the reactive, semi-manual, sub-optimal SON towards a conflict-free, agile, proactive SON. By paving the way for future MCN’s commercial and technical viability, the new SON paradigm presented in this dissertation can act as a key enabler for next-generation MCNs

The role of the monomeric GTPase RhoA in cardiac fibroblasts

Der spezifische Knockdown von RhoA in neonatalen kardialen Rattenfibroblasten führte auf molekularem Level zu einer Reduktion des Myofibroblastenmarkers α-Glattmuskelaktin und zu einem Anstieg im modifizierten acetylierten Tubulin. Auf subzellulärer Ebene konnte ein Verlust von Stressfasern, Aktinstrukturen höherer Ordnung und eine erhöhte Dichte des Golgi-Apparats beobachtet werden. Außerdem waren die Fokaladhäsionen kürzer und zufällig verteilt, was auf einen Verlust der Zellpolarität hinweist. Auf dem zellulären Level erhöhte der Knockdown von RhoA die Zellfläche aber nicht das Volumen. Diese Veränderungen führten zu einer schnelleren Adhäsion unabhängig vom Substrat, eine Reduktion der Migration in 2D und im Gegensatz dazu eine verbesserte Migration durch eine poröse Membran. Außerdem war die mitogene Antwort der Zellen auf einen Serumstimulus stark reduziert. Eine Veränderung in Zellviabilität konnte zudem nicht beobachtet werden. Die Expression und Sekretion des Fibrose-assoziierten Faktors CTGF war in gehungerten Zellen mit einer Reduktion in RhoA Expression signifikant vermindert, was jedoch in der Anwesenheit eines Serumstimulus aufgehoben werden konnte. Auf einer heterogenen multizellulären Ebene verringerte der Knockdown von RhoA die kontraktile Funktion von generierten künstlichen Herzgeweben unter Kalziumstimulation. Dies ging einher mit einer Reduktion der Expression von α-Glattmuskelaktin und Calsequestrin. Durch die Verwendung spezifischer Inhibitoren der Rho-assoziierten Kinase (ROCK) und HDAC6 konnten einige dieser zellulären Veränderungen imitiert und demensprechend einem Effektorprotein zugeordnet werden. Der ROCK Inhibitor Fasudil konnte die morphologischen Veränderungen und die reduzierte Migrationskapazität in Wildtyp-Fibroblasten abbilden, wobei eine Reduktion der Proliferation nach der Verwendung des HDAC6 Inhibitors Tubastatin A beobachtet wurde