Structural and biophysical characterization of RAD52 as novel pharmacological target for synthetic lethality

RAD52 is a protein involved in various DNA reparation mechanisms. In the last few years, RAD52 has been proposed as a novel pharmacological target for cancer synthetic lethality strategies. Hence, this work has the purpose to investigate RAD52 protein, with biophysical and structural tools to shed light on proteins features and mechanistic details that are, up to now poorly described, and to design novel strategies for its inhibition. My PhD work had two goals: the structural and functional characterization of RAD52 and the identification of novel RAD52 inhibitors. For the first part, RAD52 was characterized both for its DNA interaction and oligomerization state together with its propensity to form high molecular weight superstructures. Moreover, using EM and Cryo-EM techniques, additional RAD52 structural hallmarks were obtained, valuable both for understanding protein mechanism of action and for drug discovery purpose. The second part of my PhD project focused on the design and characterization of novel RAD52 inhibitors to be potentially used in combination therapies with PARPi to achieve cancer cells synthetic lethality, avoiding resistance occurrence and side effects. With this aim we selected and characterized promising RAD52 inhibitors through three different approaches: 19F NMR fragment-based screening; virtual screening campaign; aptamers computational design. Selected hits (fragments, molecules and aptamers) were investigated for their binding to RAD52 and for their mechanism of inhibition. Collected data highlighted the identification of hits worthy to be developed into more potent and selective RAD52 inhibitors. Finally, a side project carried out during my PhD is reported. GSK-3β protein, an already validated pharmacological target was investigated using biophysical and structural biology tools. Here, an innovative and adaptable drug discovery screening pipeline able to directly identify selective compounds with binding affinities not higher than a reference binder was developed.

Molecular changes induced by a centrally-induced state of synthetic torpor in multiple organs:a new strategy for radioprotection.

Torpor is a successful survival strategy displayed by several mammalian species to cope with harsh environmental conditions. A complex interplay of ambient, genetic and circadian stimuli acts centrally to induce a severe suppression of metabolic rate, usually followed by an apparently undefended reduction of body temperature. Some animals, such as marmots, are able to maintain this physiological state for months (hibernation), during which torpor bouts are periodically interrupted by short interbouts of normothermia (arousals). Interestingly, torpor adaptations have been shown to be associated with a large resistance towards stressors, such as radiation: indeed, if irradiated during torpor, hibernators can tolerate higher doses of radiation, showing an increased survival rate. New insights for radiotherapy and long-term space exploration could arise from the induction of torpor in non-hibernators, like humans. The present research project is centered on synthetic torpor (ST), a hypometabolic/hypothermic condition induced in a non-hibernator, the rat, through the pharmacological inhibition of the Raphe Pallidus, a key brainstem area controlling thermogenic effectors. By exploiting this procedure, this thesis aimed at: i) providing a multiorgan description of the functional cellular adaptations to ST; ii) exploring the possibility, and the underpinning molecular mechanisms, of enhanced radioresistance induced by ST. To achieve these aims, transcriptional and histological analysis have been performed in multiple organs of synthetic torpid rats and normothermic rats, either exposed or not exposed to 3 Gy total body of X-rays. The results showed that: i) similarly to natural torpor, ST induction leads to the activation of survival and stress resistance responses, which allow the organs to successfully adapt to the new homeostasis; ii) ST provides tissue protection against radiation damage, probably mainly through the cellular adaptations constitutively induced by ST, even though the triggering of specific responses when the animal is irradiated during hypothermia might play a role.

Study of the urinary biomarkers of synthetic cannabinoid receptor agonists (SCRAs) for the forensic unequivocal proof of consumption: medico-legal implications for drug abuse prevention

Introduction. Synthetic cannabinoid receptor agonists (SCRAs) represent the widest group of New Psychoactive Substances (NPS) and, around 2021-2022, new compounds emerged on the market. The aims of the present research were to identify suitable urinary markers of Cumyl-CB-MEGACLONE, Cumyl-NB-MEGACLONE, Cumyl-NB-MINACA, 5F-EDMB-PICA, EDMB-PINACA and ADB-HEXINACA, to present data on their prevalence and to adapt the methodology from the University of Freiburg to the University of Bologna. Materials and methods. Human phase-I metabolites detected in 46 authentic urine samples were confirmed in vitro with pooled human liver microsomes (pHLM) assays, analyzed by liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-qToF-MS). Prevalence data were obtained from urines collected for abstinence control programs. The method to study SCRAs metabolism in use at the University of Freiburg was adapted to the local facilities, tested in vitro with 5F-EDMB-PICA and applied to the study of ADB-HEXINACA metabolism. Results. Metabolites built by mono, di- and tri-hydroxylation were recommended as specific urinary biomarkers to monitor the consumption of SCRAs bearing a cumyl moiety. Monohydroxylated and defluorinated metabolites were suitable proof of 5F-EDMB-PICA consumption. Products of monohydroxylation and amide or ester hydrolysis, coupled to monohydroxylation or ketone formation, were recognized as specific markers for EDMB-PINACA and ADB-HEXINACA. The LC-qToF-MS method was successfully adapted to the University of Bologna, as tested with 5F-EDMB-PICA in vitro metabolites. Prevalence data showed that 5F-EDMB-PINACA and EDMB-PINACA were more prevalent than ADB-HEXINACA, but for a limited period. Conclusion. Due to undetectability of parent compounds in urines and to shared metabolites among structurally related compounds, the identification of specific urinary biomarkers as unequivocal proofs of SCRAs consumption remains challenging for forensic laboratories. Urinary biomarkers are necessary to monitor SCRAs abuse and prevalence data could help in establishing tailored strategies to prevent their spreading, highlighting the role for legal medicine as a service to public health.

Production Lot Detection through Synthetic Generated Data

Correctness of information gathered in production environments is an essential part of quality assurance processes in many industries, this task is often performed by human resources who visually take annotations in various steps of the production flow. Depending on the performed task the correlation between where exactly the information is gathered and what it represents is more than often lost in the process. The lack of labeled data places a great boundary on the application of deep neural networks aimed at object detection tasks, moreover supervised training of deep models requires a great amount of data to be available. Reaching an adequate large collection of labeled images through classic techniques of data annotations is an exhausting and costly task to perform, not always suitable for every scenario. A possible solution is to generate synthetic data that replicates the real one and use it to fine-tune a deep neural network trained on one or more source domains to a different target domain. The purpose of this thesis is to show a real case scenario where the provided data were both in great scarcity and missing the required annotations. Sequentially a possible approach is presented where synthetic data has been generated to address those issues while standing as a training base of deep neural networks for object detection, capable of working on images taken in production-like environments. Lastly, it compares performance on different types of synthetic data and convolutional neural networks used as backbones for the model.

Synthetic data generation for the assessment of antimicrobial resistance through machine learning

As a consequence of the diffusion of next generation sequencing techniques, metagenomics databases have become one of the most promising repositories of information about features and behavior of microorganisms. One of the subjects that can be studied from those data are bacteria populations. Next generation sequencing techniques allow to study the bacteria population within an environment by sampling genetic material directly from it, without the needing of culturing a similar population in vitro and observing its behavior. As a drawback, it is quite complex to extract information from those data and usually there is more than one way to do that; AMR is no exception. In this study we will discuss how the quantified AMR, which regards the genotype of the bacteria, can be related to the bacteria phenotype and its actual level of resistance against the specific substance. In order to have a quantitative information about bacteria genotype, we will evaluate the resistome from the read libraries, aligning them against CARD database. With those data, we will test various machine learning algorithms for predicting the bacteria phenotype. The samples that we exploit should resemble those that could be obtained from a natural context, but are actually produced by a read libraries simulation tool. In this way we are able to design the populations with bacteria of known genotype, so that we can relay on a secure ground truth for training and testing our algorithms.

Synthetic generation of amyloid PET images by non-linear dimensionality reduction inversion

Privacy issues and data scarcity in PET field call for efficient methods to expand datasets via synthetic generation of new data that cannot be traced back to real patients and that are also realistic. In this thesis, machine learning techniques were applied to 1001 amyloid-beta PET images, which had undergone a diagnosis of Alzheimer’s disease: the evaluations were 540 positive, 457 negative and 4 unknown. Isomap algorithm was used as a manifold learning method to reduce the dimensions of the PET dataset; a numerical scale-free interpolation method was applied to invert the dimensionality reduction map. The interpolant was tested on the PET images via LOOCV, where the removed images were compared with the reconstructed ones with the mean SSIM index (MSSIM = 0.76 ± 0.06). The effectiveness of this measure is questioned, since it indicated slightly higher performance for a method of comparison using PCA (MSSIM = 0.79 ± 0.06), which gave clearly poor quality reconstructed images with respect to those recovered by the numerical inverse mapping. Ten synthetic PET images were generated and, after having been mixed with ten originals, were sent to a team of clinicians for the visual assessment of their realism; no significant agreements were found either between clinicians and the true image labels or among the clinicians, meaning that original and synthetic images were indistinguishable. The future perspective of this thesis points to the improvement of the amyloid-beta PET research field by increasing available data, overcoming the constraints of data acquisition and privacy issues. Potential improvements can be achieved via refinements of the manifold learning and the inverse mapping stages during the PET image analysis, by exploring different combinations in the choice of algorithm parameters and by applying other non-linear dimensionality reduction algorithms. A final prospect of this work is the search for new methods to assess image reconstruction quality.

Aquatic Toxicity Of Dyes Before And After Photo-fenton Treatment.

This study evaluated the ecotoxicity of five dyes to freshwater organisms before and during their photo-Fenton degradation. EC50 (48h) of the five tested dyes ranged from of 6.9 to >1000mgL(-1) for Daphnia similis. In the chronic tests IC50 (72h) varied from 65 to >100mgL(-1) for Pseudokirchneriella subcapitata and IC50 (8 days) from 0.5 to 410mgL(-1) for Ceriodaphnia dubia. Toxicity tests revealed that although the applied treatment was effective for decolorization of the dye, the partial mineralization may be responsible for the presence of degradation products which can be either more toxic than the original dye, as is the case of Vat Green 3 and Reactive Black 5, lead to initially toxic products which may be further degraded to non toxic products (acid Orange 7 and Food Red 17), or generate non toxic products as in the case of Food Yellow 3. The results highlighted the importance of assessing both acute and chronic toxicity tests of treated sample before effluent discharge.

De Novo Assembly And Transcriptome Analysis Of The Rubber Tree (hevea Brasiliensis) And Snp Markers Development For Rubber Biosynthesis Pathways.

Hevea brasiliensis (Willd. Ex Adr. Juss.) Muell.-Arg. is the primary source of natural rubber that is native to the Amazon rainforest. The singular properties of natural rubber make it superior to and competitive with synthetic rubber for use in several applications. Here, we performed RNA sequencing (RNA-seq) of H. brasiliensis bark on the Illumina GAIIx platform, which generated 179,326,804 raw reads on the Illumina GAIIx platform. A total of 50,384 contigs that were over 400 bp in size were obtained and subjected to further analyses. A similarity search against the non-redundant (nr) protein database returned 32,018 (63%) positive BLASTx hits. The transcriptome analysis was annotated using the clusters of orthologous groups (COG), gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pfam databases. A search for putative molecular marker was performed to identify simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs). In total, 17,927 SSRs and 404,114 SNPs were detected. Finally, we selected sequences that were identified as belonging to the mevalonate (MVA) and 2-C-methyl-D-erythritol 4-phosphate (MEP) pathways, which are involved in rubber biosynthesis, to validate the SNP markers. A total of 78 SNPs were validated in 36 genotypes of H. brasiliensis. This new dataset represents a powerful information source for rubber tree bark genes and will be an important tool for the development of microsatellites and SNP markers for use in future genetic analyses such as genetic linkage mapping, quantitative trait loci identification, investigations of linkage disequilibrium and marker-assisted selection.

Molecularly Imprinted Solid Phase Extraction Of Fluconazole From Pharmaceutical Formulations.

This work encompasses a direct and coherent strategy to synthesise a molecularly imprinted polymer (MIP) capable of extracting fluconazole from its sample. The MIP was successfully prepared from methacrylic acid (functional monomer), ethyleneglycoldimethacrylate (crosslinker) and acetonitrile (porogenic solvent) in the presence of fluconazole as the template molecule through a non-covalent approach. The non-imprinted polymer (NIP) was prepared following the same synthetic scheme, but in the absence of the template. The data obtained from scanning electronic microscopy, infrared spectroscopy, thermogravimetric and nitrogen Brunauer-Emmett-Teller plot helped to elucidate the structural as well as the morphological characteristics of the MIP and NIP. The application of MIP as a sorbent was demonstrated by packing it in solid phase extraction cartridges to extract fluconazole from commercial capsule samples through an offline analytical procedure. The quantification of fluconazole was accomplished through UPLC-MS, which resulted in LOD≤1.63×10(-10) mM. Furthermore, a high percentage recovery of 91±10% (n=9) was obtained. The ability of the MIP for selective recognition of fluconazole was evaluated by comparison with the structural analogues, miconazole, tioconazole and secnidazole, resulting in percentage recoveries of 51, 35 and 32%, respectively.

Cheaper Faster Drug Development Validated By The Repositioning Of Drugs Against Neglected Tropical Diseases.

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist 'Eve' designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax.

Explosive Synchronization With Partial Degree-frequency Correlation.

Networks of Kuramoto oscillators with a positive correlation between the oscillators frequencies and the degree of their corresponding vertices exhibit so-called explosive synchronization behavior, which is now under intensive investigation. Here we study and discuss explosive synchronization in a situation that has not yet been considered, namely when only a part, typically a small part, of the vertices is subjected to a degree-frequency correlation. Our results show that in order to have explosive synchronization, it suffices to have degree-frequency correlations only for the hubs, the vertices with the highest degrees. Moreover, we show that a partial degree-frequency correlation does not only promotes but also allows explosive synchronization to happen in networks for which a full degree-frequency correlation would not allow it. We perform a mean-field analysis and our conclusions were corroborated by exhaustive numerical experiments for synthetic networks and also for the undirected and unweighed version of a typical benchmark biological network, namely the neural network of the worm Caenorhabditis elegans. The latter is an explicit example where partial degree-frequency correlation leads to explosive synchronization with hysteresis, in contrast with the fully correlated case, for which no explosive synchronization is observed.

Development Plates For Stable Internal Fixation: Study Of Mechanical Resistance In Simulated Fractures Of The Mandibular Condyle.

To develop Y-shaped plates with different thicknesses to be used in simulated fractures of the mandibular condyle. Ten plates were developed in Y shape, containing eight holes, and 30 synthetic polyurethane mandible replicas were developed for the study. The load test was performed on an Instron Model 4411 universal testing machine, applying load in the mediolateral and anterior-posterior positions on the head of the condyle. Two-way ANOVA with Tukey testing with a 5% significance level was used. It was observed that when the load was applied in the medial-lateral plate of greater thickness (1.5 mm), it gave the highest strength, while in the anteroposterior direction, the plate with the highest resistance was of the lesser thickness (0.6 mm). A plate with a thickness of 1.5 mm was the one with the highest average value for all displacements. In the anteroposterior direction, the highest values of resistance were seen in the displacement of 15 mm. After comparing the values of the biomechanical testing found in the scientific literature, it is suggested that the use of Y plates are suitable for use in subcondylar fractures within the limitations of the study.

Factors Controlling The Deposition Of Silk Fibroin Nanofibrils During Layer-by-layer Assembly.

The layer-by-layer technique has been used as a powerful method to produce multilayer thin films with tunable properties. When natural polymers are employed, complicated phenomena such as self-aggregation and fibrilogenesis can occur, making it more difficult to obtain and characterize high-quality films. The weak acid and base character of such materials provides multilayer systems that may differ from those found with synthetic polymers due to strong self-organization effects. Specifically, LbL films prepared with chitosan and silk fibroin (SF) often involve the deposition of fibroin fibrils, which can influence the assembly process, surface properties, and overall film functionality. In this case, one has the intriguing possibility of realizing multilayer thin films with aligned nanofibers. In this article, we propose a strategy to control fibroin fibril formation by adjusting the assembly partner. Aligned fibroin fibrils were formed when chitosan was used as the counterpart, whereas no fibrils were observed when poly(allylamine hydrochloride) (PAH) was used. Charge density, which is higher in PAH, apparently stabilizes SF aggregates on the nanometer scale, thereby preventing their organization into fibrils. The drying step between the deposition of each layer was also crucial for film formation, as it stabilizes the SF molecules. Preliminary cell studies with optimized multilayers indicated that cell viability of NIH-3T3 fibroblasts remained between 90 and 100% after surface seeding, showing the potential application of the films in the biomedical field, as coatings and functional surfaces.

The Influence Of The Buffering Capacity On The Production Of Organic Acids And Alcohols From Wastewater In Anaerobic Reactor.

Some bacteria common in anaerobic digestion process can ferment a broad variety of organic compounds to organic acids, alcohols, and hydrogen, which can be used as biofuels. Researches are necessary to control the microbial interactions in favor of the alcohol production, as intermediary products of the anaerobic digestion of organic compounds. This paper reports on the effect of buffering capacity on the production of organic acids and alcohols from wastewater by a natural mixed bacterial culture. The hypothesis tested was that the increase of the buffering capacity by supplementation of sodium bicarbonate in the influent results in benefits for alcohol production by anaerobic fermentation of wastewater. When the influent was not supplemented with sodium bicarbonate, the chemical oxygen demand (COD)-ethanol and COD-methanol detected in the effluent corresponded to 22.5 and 12.7 % of the COD-sucrose consumed. Otherwise, when the reactor was fed with influent containing 0.5 g/L of sodium bicarbonate, the COD-ethanol and COD-methanol were effluents that corresponded to 39.2 and 29.6 % of the COD-sucrose consumed. Therefore, the alcohol production by supplementation of the influent with sodium bicarbonate was 33.6 % higher than the fermentation of the influent without sodium bicarbonate.