De acido carbonico

Dedicatio: Elias Alcenius.

Biomolecular screening for inhibitors of butyrylcholinesterase : identification and characterization using in vitro and in silico tools

Drug discovery is a continuous process where researchers are constantly trying to find new and better drugs for the treatment of various conditions. Alzheimer’s disease, a neurodegenerative disease mostly affecting the elderly, has a complex etiology with several possible drug targets. Some of these targets have been known for years while other new targets and theories have emerged more recently. Cholinesterase inhibitors are the major class of drugs currently used for the symptomatic treatment of Alzheimer’s disease. In the Alzheimer’s disease brain there is a deficit of acetylcholine and an impairment in signal transmission. Acetylcholinesterase has therefore been the main target as this is the main enzyme hydrolysing acetylcholine and ending neurotransmission. It is believed that by inhibiting acetylcholinesterase the cholinergic signalling can be enhanced and the cognitive symptoms that arise in Alzheimer’s disease can be improved. Butyrylcholinesterase, the second enzyme of the cholinesterase family, has more recently attracted interest among researchers. Its function is still not fully known, but it is believed to play a role in several diseases, one of them being Alzheimer’s disease. In this contribution the aim has primarily been to identify butyrylcholinesterase inhibitors to be used as drug molecules or molecular probes in the future. Both synthetic and natural compounds in diverse and targeted screening libraries have been used for this purpose. The active compounds have been further characterized regarding their potencies, cytotoxicity, and furthermore, in two of the publications, the inhibitors ability to also inhibit Aβ aggregation in an attempt to discover bifunctional compounds. Further, in silico methods were used to evaluate the binding position of the active compounds with the enzyme targets. Mostly to differentiate between the selectivity towards acetylcholinesterase and butyrylcholinesterase, but also to assess the structural features required for enzyme inhibition. We also evaluated the compounds, active and non-active, in chemical space using the web-based tool ChemGPS-NP to try and determine the relevant chemical space occupied by cholinesterase inhibitors. In this study, we have succeeded in finding potent butyrylcholinesterase inhibitors with a diverse set of structures, nine chemical classes in total. In addition, some of the compounds are bifunctional as they also inhibit Aβ aggregation. The data gathered from all publications regarding the chemical space occupied by butyrylcholinesterase inhibitors we believe will give an insight into the chemically active space occupied by this type of inhibitors and will hopefully facilitate future screening and result in an even deeper knowledge of butyrylcholinesterase inhibitors.

In silico metabolic modelling of Saccharomyces cerevisiae for the overproduction of succinate using OptKnock and OptGene

Succinate is a naturally occurring metabolite in organism’s cell and is industrially important chemical with various applications in food and pharmaceutical industry. It is also widely used to produce bio-degradable plastics, surfactants, detergents etc. In last decades, emphasis has been given to bio-based chemical production using industrial biotechnology route rather than fossil-based production considering sustainability and environment friendly economy. In this thesis I am presenting a computational model for silico metabolic engineering of Saccharomyces cerevisiae for large scale production of succinate. For metabolic modelling, I have used OptKnock and OptGene optimization algorithms to identify the reactions to delete from the genome-scale metabolic model of S. cerevisiae to overproduce succinate by coupling with organism’s growth. Both OptKnock and OptGene proposed numerous straightforward and non-intuitive deletion strategies when number of constraints including growth constraint to the model were applied. The most interesting strategy identified by both algorithms was deletion combination of pyruvate decarboxylase and Ubiquinol:ferricytochrome c reductase(respiratory enzyme) reactions thereby also suggesting anaerobic fermentation of the organism in glucose medium. Such strategy was never reported earlier for growth-coupled succinate production in S.cerevisiae.

Molecular Characteristics of Neuroblastoma with Special Reference to Novel Prognostic Factors and Diagnostic Applications

Molecular Characteristics of Neuroblastoma with Special Reference to Novel Prognostic Factors and Diagnostic Applications Department of Medical Biochemistry and Genetics Annales Universitatis Turkuensis, Medica-Odontologica, 2009, Turku, Finland Painosalama Oy, Turku, Finland 2009 Background: Neuroblastoma, which is the most common and extensively studied childhood solid cancer, shows a great clinical and biological heterogeneity. Most of the neuroblastoma patients older than one year have poor prognosis despite intensive therapies. The hallmark of neuroblastoma, biological heterogeneity, has hindered the discovery of prognostic tumour markers. At present, few molecular markers, such as MYCN oncogene status, have been adopted into clinical practice. Aims: The aim of the study was to improve the current prognostic methodology of neuroblastoma, especially by taking cognizance of the biological heterogeneity of neuroblastoma. Furthermore, unravelling novel molecular characteristics which associate with neuroblastoma tumour progression and cell differentiation was an additional objective. Results: A new strictly defined selection of neuroblastoma tumour spots of highest proliferation activity, hotspots, appeared to be representative and reliable in an analysis of MYCN amplification status using a chromogenic in situ hybridization technique (CISH). Based on the hotspot tumour tissue microarray immunohistochemistry and high-resolution oligo-array-based comparative genomic hybridization, which was integrated with gene expression and in silico analysis of existing transcriptomics, a polysialylated neural cell adhesion molecule (NCAM) and poorly characterized amplicon at 12q24.31 were discovered to associate with outcome. In addition, we found that a previously considered new neuroblastoma treatment target, the mutated c-kit receptor, was not mutated in neuroblastoma samples. Conclusions: Our studies indicate polysialylated NCAM and 12q24.31 amplicon to be new molecular markers with important value in prognostic evaluation of neuroblastoma. Moreover, the presented hotspot tumour tissue microarray method together with the CISH technique of the MYCN oncogene copy number is directly applicable to clinical use. Key words: neuroblastoma, polysialic acid, neural cell adhesion molecule, MYCN, c-kit, chromogenic in situ hybridization, hotspot

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.

Characterization of novel genes predominantly expressed in the epididymis – from genome analyses to genetically modified mice

In mammals, post-testicular sperm maturation taking place in the epididymis is required for the spermatozoa to acquire the abilities required to fertilize the egg in vivo. The epididymal epithelial cells secrete proteins and other small molecules into the lumen, where they interact with the spermatozoa and enable necessary maturational changes. In this study different in silico, in vitro and in vivo approaches were utilized in order to find novel genes responsible for the function of the epididymis and post-testicular sperm maturation in the mouse. Available online genomic databases were analyzed to identify genes potentially expressed in the epididymis, gene expression profiling was performed by studying their expression in different mouse tissues, and significance of certain genes to fertility was assessed by generating genetically modified mouse models. A recently discovered Pate (prostate and testis expression) gene family was found to be predominantly expressed in the epididymis. It represents one of the largest known gene families expressed in the epididymis, and the members code for proteins potentially involved in defense against microorganisms. Through genetically modified mouse models CRISP4 (cysteine-rich secretory protein 4) was identified to regulate sperm acrosome reaction, and BMYC to inhibit the expression of the Myc proto-oncogene in the developing testis. A mouse line expressing iCre recombinase specifically in the epididymis was also generated. This model can be used to generate conditional, epididymis-specific knock-out models, and will be a valuable tool in fertility studies.

High-Throughput Screening for Novel Prostate Cancer Drug Targets –Getting Personal

Prostate cancers form a heterogeneous group of diseases and there is a need for novel biomarkers, and for more efficient and targeted methods of treatment. In this thesis, the potential of microarray data, RNA interference (RNAi) and compound screens were utilized in order to identify novel biomarkers, drug targets and drugs for future personalized prostate cancer therapeutics. First, a bioinformatic mRNA expression analysis covering 9873 human tissue and cell samples, including 349 prostate cancer and 147 normal prostate samples, was used to distinguish in silico prevalidated putative prostate cancer biomarkers and drug targets. Second, RNAi based high-throughput (HT) functional profiling of 295 prostate and prostate cancer tissue specific genes was performed in cultured prostate cancer cells. Third, a HT compound screen approach using a library of 4910 drugs and drug-like molecules was exploited to identify potential drugs inhibiting prostate cancer cell growth. Nine candidate drug targets, with biomarker potential, and one cancer selective compound were validated in vitro and in vivo. In addition to androgen receptor (AR) signaling, endoplasmic reticulum (ER) function, arachidonic acid (AA) pathway, redox homeostasis and mitosis were identified as vital processes in prostate cancer cells. ERG oncogene positive cancer cells exhibited sensitivity to induction of oxidative and ER stress, whereas advanced and castrate-resistant prostate cancer (CRPC) could be potentially targeted through AR signaling and mitosis. In conclusion, this thesis illustrates the power of systems biological data analysis in the discovery of potential vulnerabilities present in prostate cancer cells, as well as novel options for personalized cancer management.

From protein structure to function with bioinformatics

It has long been known that amino acids are the building blocks for proteins and govern their folding into specific three-dimensional structures. However, the details of this process are still unknown and represent one of the main problems in structural bioinformatics, which is a highly active research area with the focus on the prediction of three-dimensional structure and its relationship to protein function. The protein structure prediction procedure encompasses several different steps from searches and analyses of sequences and structures, through sequence alignment to the creation of the structural model. Careful evaluation and analysis ultimately results in a hypothetical structure, which can be used to study biological phenomena in, for example, research at the molecular level, biotechnology and especially in drug discovery and development. In this thesis, the structures of five proteins were modeled with templatebased methods, which use proteins with known structures (templates) to model related or structurally similar proteins. The resulting models were an important asset for the interpretation and explanation of biological phenomena, such as amino acids and interaction networks that are essential for the function and/or ligand specificity of the studied proteins. The five proteins represent different case studies with their own challenges like varying template availability, which resulted in a different structure prediction process. This thesis presents the techniques and considerations, which should be taken into account in the modeling procedure to overcome limitations and produce a hypothetical and reliable three-dimensional structure. As each project shows, the reliability is highly dependent on the extensive incorporation of experimental data or known literature and, although experimental verification of in silico results is always desirable to increase the reliability, the presented projects show that also the experimental studies can greatly benefit from structural models. With the help of in silico studies, the experiments can be targeted and precisely designed, thereby saving both money and time. As the programs used in structural bioinformatics are constantly improved and the range of templates increases through structural genomics efforts, the mutual benefits between in silico and experimental studies become even more prominent. Hence, reliable models for protein three-dimensional structures achieved through careful planning and thoughtful executions are, and will continue to be, valuable and indispensable sources for structural information to be combined with functional data.