Expression of the repressor element-1 silencing transcription factor (REST) is regulated by IGF-I and PKC in human neuroblastoma cells

The repressor element 1-silencing transcription factor (REST) was first identified as a protein that binds to a 21-bp DNA sequence element (known as repressor element 1 (RE1)) resulting in transcriptional repression of the neural-specific genes [Chong et al., 1995; Schoenherr and Anderson, 1995]. The original proposed role for REST was that of a factor responsible for restricting neuronal gene expression to the nervous system by silencing expression of these genes in non-neuronal cells. Although it was initially thought to repress neuronal genes in non-neuronal cells, the role of REST is complex and tissue dependent. In this study I investigated any role played by REST in the induction and patterning of differentiation of SH-SY5Y human neuroblastoma cells exposed to IGF-I. and phorbol 12- myristate 13-acetate (PMA) To down-regulate REST expression we developed an antisense (AS) strategy based on the use of phosphorothioate oligonucleotides (ODNs). In order to evaluate REST mRNA levels, we developed a real-time PCR technique and REST protein levels were evaluated by western blotting. Results showed that nuclear REST is increased in SH-SY5Y neuroblastoma cells cultured in SFM and exposed to IGF-I for 2-days and it then declines in 5-day-treated cells concomitant with a progressive neurite extension. Also the phorbol ester PMA was able to increase nuclear REST levels after 3-days treatment concomitant to neuronal differentiation of neuroblastoma cells, whereas, at later stages, it is down-regulated. Supporting these data, the exposure to PKC inhibitors (GF10923X and Gö6976) and PMA (16nM) reverted the effects observed with PMA alone. REST levels were related to morphological differentiation, expression of growth coneassociated protein 43 (GAP-43; a gene not regulated by REST) and of synapsin I and βIII tubulin (genes regulated by REST), proteins involved in the early stage of neuronal development. We observed that differentiation of SH-SY5Y cells by IGF-I and PMA was accompanied by a significant increase of these neuronal markers, an effect that was concomitant with REST decrease. In order to relate the decreased REST expression with a progressive neurite extension, I investigated any possible involvement of the ubiquitin–proteasome system (UPS), a multienzymatic pathway which degrades polyubiquinated soluble cytoplasmic proteins [Pickart and Cohen, 2004]. For this purpose, SH-SY5Y cells are concomitantly exposed to PMA and the proteasome inhibitor MG132. In SH-SY5Y exposed to PMA and MG 132, we observed an inverse pattern of expression of synapsin I and β- tubulin III, two neuronal differentiation markers regulated by REST. Their cytoplasmic levels are reduced when compared to cells exposed to PMA alone, as a consequence of the increase of REST expression by proteasome inhibitor. The majority of proteasome substrates identified to date are marked for degradation by polyubiquitinylation; however, exceptions to this principle, are well documented [Hoyt and Coffino, 2004]. Interestingly, REST degradation seems to be completely ubiquitin-independent. The expression pattern of REST could be consistent with the theory that, during early neuronal differentiation induced by IGF-I and PKC, it may help to repress the expression of several genes not yet required by the differentiation program and then it declines later. Interestingly, the observation that REST expression is progressively reduced in parallel with cell proliferation seems to indicate that the role of this transcription factor could also be related to cell survival or to counteract apotosis events [Lawinger et al., 2000] although, as shown by AS-ODN experiments, it does not seem to be directly involved in cell proliferation. Therefore, the decline of REST expression is a comparatively later event during maturation of neuroroblasts in vitro. Thus, we propose that REST is regulated by growth factors, like IGF-I, and PKC activators in a time-dependent manner: it is elevated during early steps of neural induction and could contribute to down-regulate genes not yet required by the differentiation program while it declines later for the acquisition of neural phenotypes, concomitantly with a progressive neurite extension. This later decline is regulated by the proteasome system activation in an ubiquitin-indipendent way and adds more evidences to the hypothesis that REST down-regulation contributes to differentiation and arrest of proliferation of neuroblastoma cells. Finally, the glycosylation pattern of the REST protein was analysed, moving from the observation that the molecular weight calculated on REST sequence is about 116 kDa but using western blotting this transcription factor appears to have distinct apparent molecular weight (see Table 1.1): this difference could be explained by post-translational modifications of the proteins, like glycosylation. In fact recently, several studies underlined the importance of O-glycosylation in modulating transcriptional silencing, protein phosphorylation, protein degradation by proteasome and protein–protein interactions [Julenius et al., 2005; Zachara and Hart, 2006]. Deglycosilating analysis showed that REST protein in SH-SY5Y and HEK293 cells is Oglycosylated and not N-glycosylated. Moreover, using several combination of deglycosilating enzymes it is possible to hypothesize the presence of Gal-β(1-3)-GalNAc residues on the endogenous REST, while β(1-4)-linked galactose residues may be present on recombinant REST protein expressed in HEK293 cells. However, the O-glycosylation process produces an immense multiplicity of chemical structures and monosaccharides must be sequentially hydrolyzed by a series of exoglycosidase. Further experiments are needed to characterize all the post-translational modification of the transcription factor REST.

Computational methods for the analysis of protein structure and function

The vast majority of known proteins have not yet been experimentally characterized and little is known about their function. The design and implementation of computational tools can provide insight into the function of proteins based on their sequence, their structure, their evolutionary history and their association with other proteins. Knowledge of the three-dimensional (3D) structure of a protein can lead to a deep understanding of its mode of action and interaction, but currently the structures of <1% of sequences have been experimentally solved. For this reason, it became urgent to develop new methods that are able to computationally extract relevant information from protein sequence and structure. The starting point of my work has been the study of the properties of contacts between protein residues, since they constrain protein folding and characterize different protein structures. Prediction of residue contacts in proteins is an interesting problem whose solution may be useful in protein folding recognition and de novo design. The prediction of these contacts requires the study of the protein inter-residue distances related to the specific type of amino acid pair that are encoded in the so-called contact map. An interesting new way of analyzing those structures came out when network studies were introduced, with pivotal papers demonstrating that protein contact networks also exhibit small-world behavior. In order to highlight constraints for the prediction of protein contact maps and for applications in the field of protein structure prediction and/or reconstruction from experimentally determined contact maps, I studied to which extent the characteristic path length and clustering coefficient of the protein contacts network are values that reveal characteristic features of protein contact maps. Provided that residue contacts are known for a protein sequence, the major features of its 3D structure could be deduced by combining this knowledge with correctly predicted motifs of secondary structure. In the second part of my work I focused on a particular protein structural motif, the coiled-coil, known to mediate a variety of fundamental biological interactions. Coiled-coils are found in a variety of structural forms and in a wide range of proteins including, for example, small units such as leucine zippers that drive the dimerization of many transcription factors or more complex structures such as the family of viral proteins responsible for virus-host membrane fusion. The coiled-coil structural motif is estimated to account for 5-10% of the protein sequences in the various genomes. Given their biological importance, in my work I introduced a Hidden Markov Model (HMM) that exploits the evolutionary information derived from multiple sequence alignments, to predict coiled-coil regions and to discriminate coiled-coil sequences. The results indicate that the new HMM outperforms all the existing programs and can be adopted for the coiled-coil prediction and for large-scale genome annotation. Genome annotation is a key issue in modern computational biology, being the starting point towards the understanding of the complex processes involved in biological networks. The rapid growth in the number of protein sequences and structures available poses new fundamental problems that still deserve an interpretation. Nevertheless, these data are at the basis of the design of new strategies for tackling problems such as the prediction of protein structure and function. Experimental determination of the functions of all these proteins would be a hugely time-consuming and costly task and, in most instances, has not been carried out. As an example, currently, approximately only 20% of annotated proteins in the Homo sapiens genome have been experimentally characterized. A commonly adopted procedure for annotating protein sequences relies on the "inheritance through homology" based on the notion that similar sequences share similar functions and structures. This procedure consists in the assignment of sequences to a specific group of functionally related sequences which had been grouped through clustering techniques. The clustering procedure is based on suitable similarity rules, since predicting protein structure and function from sequence largely depends on the value of sequence identity. However, additional levels of complexity are due to multi-domain proteins, to proteins that share common domains but that do not necessarily share the same function, to the finding that different combinations of shared domains can lead to different biological roles. In the last part of this study I developed and validate a system that contributes to sequence annotation by taking advantage of a validated transfer through inheritance procedure of the molecular functions and of the structural templates. After a cross-genome comparison with the BLAST program, clusters were built on the basis of two stringent constraints on sequence identity and coverage of the alignment. The adopted measure explicity answers to the problem of multi-domain proteins annotation and allows a fine grain division of the whole set of proteomes used, that ensures cluster homogeneity in terms of sequence length. A high level of coverage of structure templates on the length of protein sequences within clusters ensures that multi-domain proteins when present can be templates for sequences of similar length. This annotation procedure includes the possibility of reliably transferring statistically validated functions and structures to sequences considering information available in the present data bases of molecular functions and structures.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Algal wastewater treatment and biomass producing potential: nutrient removal efficiency and cell physiological responses

Microalgae are sun - light cell factories that convert carbon dioxide to biofuels, foods, feeds, and other bioproducts. The concept of microalgae cultivation as an integrated system in wastewater treatment has optimized the potential of the microalgae - based biofuel production. These microorganisms contains lipids, polysaccharides, proteins, pigments and other cell compounds, and their biomass can provide different kinds of biofuels such as biodiesel, biomethane and ethanol. The algal biomass application strongly depends on the cell composition and the production of biofuels appears to be economically convenient only in conjunction with wastewater treatment. The aim of this research thesis was to investigate a biological wastewater system on a laboratory scale growing a newly isolated freshwater microalgae, Desmodesmus communis, in effluents generated by a local wastewater reclamation facility in Cesena (Emilia Romagna, Italy) in batch and semi - continuous cultures. This work showed the potential utilization of this microorganism in an algae - based wastewater treatment; Desmodesmus communis had a great capacity to grow in the wastewater, competing with other microorganisms naturally present and adapting to various environmental conditions such as different irradiance levels and nutrient concentrations. The nutrient removal efficiency was characterized at different hydraulic retention times as well as the algal growth rate and biomass composition in terms of proteins, polysaccharides, total lipids and total fatty acids (TFAs) which are considered the substrate for biodiesel production. The biochemical analyses were coupled with the biomass elemental analysis which specified the amount of carbon and nitrogen in the algal biomass. Furthermore photosynthetic investigations were carried out to better correlate the environmental conditions with the physiology responses of the cells and consequently get more information to optimize the growth rate and the increase of TFAs and C/N ratio, cellular compounds and biomass parameter which are fundamental in the biomass energy recovery.

Models and Algorihtm for the Optimization of Real-World Routing and Logistics Problems

Logistics involves planning, managing, and organizing the flows of goods from the point of origin to the point of destination in order to meet some requirements. Logistics and transportation aspects are very important and represent a relevant costs for producing and shipping companies, but also for public administration and private citizens. The optimization of resources and the improvement in the organization of operations is crucial for all branches of logistics, from the operation management to the transportation. As we will have the chance to see in this work, optimization techniques, models, and algorithms represent important methods to solve the always new and more complex problems arising in different segments of logistics. Many operation management and transportation problems are related to the optimization class of problems called Vehicle Routing Problems (VRPs). In this work, we consider several real-world deterministic and stochastic problems that are included in the wide class of the VRPs, and we solve them by means of exact and heuristic methods. We treat three classes of real-world routing and logistics problems. We deal with one of the most important tactical problems that arises in the managing of the bike sharing systems, that is the Bike sharing Rebalancing Problem (BRP). We propose models and algorithms for real-world earthwork optimization problems. We describe the 3DP process and we highlight several optimization issues in 3DP. Among those, we define the problem related to the tool path definition in the 3DP process, the 3D Routing Problem (3DRP), which is a generalization of the arc routing problem. We present an ILP model and several heuristic algorithms to solve the 3DRP.