Mass spectrometry-based protein profiling strategies for biomarker discovery in liver and inflammatory bowel diseases

The study of protein expression profiles for biomarker discovery in serum and in mammalian cell populations needs the continuous improvement and combination of proteins/peptides separation techniques, mass spectrometry, statistical and bioinformatic approaches. In this thesis work two different mass spectrometry-based protein profiling strategies have been developed and applied to liver and inflammatory bowel diseases (IBDs) for the discovery of new biomarkers. The first of them, based on bulk solid-phase extraction combined with matrix-assisted laser desorption/ionization - Time of Flight mass spectrometry (MALDI-TOF MS) and chemometric analysis of serum samples, was applied to the study of serum protein expression profiles both in IBDs (Crohn’s disease and ulcerative colitis) and in liver diseases (cirrhosis, hepatocellular carcinoma, viral hepatitis). The approach allowed the enrichment of serum proteins/peptides due to the high interaction surface between analytes and solid phase and the high recovery due to the elution step performed directly on the MALDI-target plate. Furthermore the use of chemometric algorithm for the selection of the variables with higher discriminant power permitted to evaluate patterns of 20-30 proteins involved in the differentiation and classification of serum samples from healthy donors and diseased patients. These proteins profiles permit to discriminate among the pathologies with an optimum classification and prediction abilities. In particular in the study of inflammatory bowel diseases, after the analysis using C18 of 129 serum samples from healthy donors and Crohn’s disease, ulcerative colitis and inflammatory controls patients, a 90.7% of classification ability and a 72.9% prediction ability were obtained. In the study of liver diseases (hepatocellular carcinoma, viral hepatitis and cirrhosis) a 80.6% of prediction ability was achieved using IDA-Cu(II) as extraction procedure. The identification of the selected proteins by MALDITOF/ TOF MS analysis or by their selective enrichment followed by enzymatic digestion and MS/MS analysis may give useful information in order to identify new biomarkers involved in the diseases. The second mass spectrometry-based protein profiling strategy developed was based on a label-free liquid chromatography electrospray ionization quadrupole - time of flight differential analysis approach (LC ESI-QTOF MS), combined with targeted MS/MS analysis of only identified differences. The strategy was used for biomarker discovery in IBDs, and in particular of Crohn’s disease. The enriched serum peptidome and the subcellular fractions of intestinal epithelial cells (IECs) from healthy donors and Crohn’s disease patients were analysed. The combining of the low molecular weight serum proteins enrichment step and the LCMS approach allowed to evaluate a pattern of peptides derived from specific exoprotease activity in the coagulation and complement activation pathways. Among these peptides, particularly interesting was the discovery of clusters of peptides from fibrinopeptide A, Apolipoprotein E and A4, and complement C3 and C4. Further studies need to be performed to evaluate the specificity of these clusters and validate the results, in order to develop a rapid serum diagnostic test. The analysis by label-free LC ESI-QTOF MS differential analysis of the subcellular fractions of IECs from Crohn’s disease patients and healthy donors permitted to find many proteins that could be involved in the inflammation process. Among them heat shock protein 70, tryptase alpha-1 precursor and proteins whose upregulation can be explained by the increased activity of IECs in Crohn’s disease were identified. Follow-up studies for the validation of the results and the in-depth investigation of the inflammation pathways involved in the disease will be performed. Both the developed mass spectrometry-based protein profiling strategies have been proved to be useful tools for the discovery of disease biomarkers that need to be validated in further studies.

Bioanalytical applications of multicolour bioluminescence imaging: new tools for drug discovery and development

The subject of this thesis is multicolour bioluminescence analysis and how it can provide new tools for drug discovery and development.The mechanism of color tuning in bioluminescent reactions is not fully understood yet but it is object of intense research and several hypothesis have been generated. In the past decade key residues of the active site of the enzyme or in the surface surrounding the active site have been identified as responsible of different color emission. Anyway since bioluminescence reaction is strictly dependent from the interaction between the enzyme and its substrate D-luciferin, modification of the substrate can lead to a different emission spectrum too. In the recent years firefly luciferase and other luciferases underwent mutagenesis in order to obtain mutants with different emission characteristics. Thanks to these new discoveries in the bioluminescence field multicolour luciferases can be nowadays employed in bioanalysis for assay developments and imaging purposes. The use of multicolor bioluminescent enzymes expanded the potential of a range of application in vitro and in vivo. Multiple analysis and more information can be obtained from the same analytical session saving cost and time. This thesis focuses on several application of multicolour bioluminescence for high-throughput screening and in vivo imaging. Multicolor luciferases can be employed as new tools for drug discovery and developments and some examples are provided in the different chapters. New red codon optimized luciferase have been demonstrated to be improved tools for bioluminescence imaging in small animal and the possibility to combine red and green luciferases for BLI has been achieved even if some aspects of the methodology remain challenging and need further improvement. In vivo Bioluminescence imaging has known a rapid progress since its first application no more than 15 years ago. It is becoming an indispensable tool in pharmacological research. At the same time the development of more sensitive and implemented microscopes and low-light imager for a better visualization and quantification of multicolor signals would boost the research and the discoveries in life sciences in general and in drug discovery and development in particular.

MetaVaccinology: a new vaccine discovery tool

In the last decade, the reverse vaccinology approach shifted the paradigm of vaccine discovery from conventional culture-based methods to high-throughput genome-based approaches for the development of recombinant protein-based vaccines against pathogenic bacteria. Besides reaching its main goal of identifying new vaccine candidates, this new procedure produced also a huge amount of molecular knowledge related to them. In the present work, we explored this knowledge in a species-independent way and we performed a systematic in silico molecular analysis of more than 100 protective antigens, looking at their sequence similarity, domain composition and protein architecture in order to identify possible common molecular features. This meta-analysis revealed that, beside a low sequence similarity, most of the known bacterial protective antigens shared structural/functional Pfam domains as well as specific protein architectures. Based on this, we formulated the hypothesis that the occurrence of these molecular signatures can be predictive of possible protective properties of other proteins in different bacterial species. We tested this hypothesis in Streptococcus agalactiae and identified four new protective antigens. Moreover, in order to provide a second proof of the concept for our approach, we used Staphyloccus aureus as a second pathogen and identified five new protective antigens. This new knowledge-driven selection process, named MetaVaccinology, represents the first in silico vaccine discovery tool based on conserved and predictive molecular and structural features of bacterial protective antigens and not dependent upon the prediction of their sub-cellular localization.

Social learning and action understanding in human observers: contributions of sensori-motor constraints and prior information

The aim of this thesis was to investigate the respective contribution of prior information and sensorimotor constraints to action understanding, and to estimate their consequences on the evolution of human social learning. Even though a huge amount of literature is dedicated to the study of action understanding and its role in social learning, these issues are still largely debated. Here, I critically describe two main perspectives. The first perspective interprets faithful social learning as an outcome of a fine-grained representation of others’ actions and intentions that requires sophisticated socio-cognitive skills. In contrast, the second perspective highlights the role of simpler decision heuristics, the recruitment of which is determined by individual and ecological constraints. The present thesis aims to show, through four experimental works, that these two contributions are not mutually exclusive. A first study investigates the role of the inferior frontal cortex (IFC), the anterior intraparietal area (AIP) and the primary somatosensory cortex (S1) in the recognition of other people’s actions, using a transcranial magnetic stimulation adaptation paradigm (TMSA). The second work studies whether, and how, higher-order and lower-order prior information (acquired from the probabilistic sampling of past events vs. derived from an estimation of biomechanical constraints of observed actions) interacts during the prediction of other people’s intentions. Using a single-pulse TMS procedure, the third study investigates whether the interaction between these two classes of priors modulates the motor system activity. The fourth study tests the extent to which behavioral and ecological constraints influence the emergence of faithful social learning strategies at a population level. The collected data contribute to elucidate how higher-order and lower-order prior expectations interact during action prediction, and clarify the neural mechanisms underlying such interaction. Finally, these works provide/open promising perspectives for a better understanding of social learning, with possible extensions to animal models.

Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Development of advanced analytical methodologies for Alzheimer’s disease drug discovery

Advanced analytical methodologies were developed to characterize new potential active MTDLs on isolated targets involved in the first stages of Alzheimer’s disease (AD). In addition, the methods investigated drug-protein bindings and evaluated protein-protein interactions involved in the neurodegeneration. A high-throughput luminescent assay allowed the study of the first in class GSK-3β/ HDAC dual inhibitors towards the enzyme GSK-3β. The method was able to identify an innovative disease-modifying agent with an activity in the micromolar range both on GSK-3β, HDAC1 and HDAC6. Then, the same assay reliably and quickly selected true positive hit compounds among natural Amaryllidaceae alkaloids tested against GSK-3β. Hence, given the central role of the amyloid pathway in the multifactorial nature of AD, a multi-methodological approach based on mass spectrometry (MS), circular dichroism spectroscopy (CD) and ThT assay was applied to characterize the potential interaction of CO releasing molecules (CORMs) with Aβ1-42 peptide. The comprehensive method provided reliable information on the different steps of the fibrillation process and regarding CORMs mechanism of action. Therefore, the optimal CORM-3/Aβ1−42 ratio in terms of inhibitory effect was identified by mass spectrometry. CD analysis confirmed the stabilizing effect of CORM-3 on the Aβ1−42 peptide soluble form and the ThT Fluorescent Analysis ensured that the entire fibrillation process was delayed. Then the amyloid aggregation process was studied in view of a possible correlation with AD lipid brain alterations. Therefore, SH-SY5Y cells were treated with increasing concentration of Aß1-42 at different times and the samples were analysed by a RP-UHPLC system coupled with a high-resolution quadrupole TOF mass spectrometer in comprehensive data-independent SWATH acquisition mode. Each lipid class profiling in SH-SY5Y cells treated with Aß1-42 was compared to the one obtained from the untreated. The approach underlined some peculiar lipid alterations, suitable as biomarkers, that might be correlated to Aß1-42 different aggregation species.