Target-cell-specific synaptic properties in neocortical microcircuits

A major focus of modern neuroscience is to establish the links between structure, physiology and function in neural cells and circuits. One key strand of this effort is in establishing the number and properties of cardinal cell types; increasing evidence suggests that many physiological and functional properties of neural circuits may be cell- and synapse-specific. Cortical interneurons are one group of cells which may be comprised of a large number of distinct classes with differing genetic, physiological and functional properties. Studies suggest that axonal morphology may be one of the most useful and simple indicators of these interneuronal types. The results presented in this thesis contribute to knowledge of both anatomical cell-type classification and the function of presynaptic NMDA receptors in visual cortex. Firstly, the utility of two-photon microscopy to create neural reconstructions suitable for cell-type classification is validated. However, reconstructions created from two-photon imaging suffer from errors when used in computer modelling due to overestimation of neurite diameters when compared to biocytin reconstructions of the same cells. Cell-type classification from two-photon imaging is then utilised in elucidating the target-cell-specific expression and function of presynaptic NMDA receptors (preNMDARs) in layer 5 of visual cortex; controversy regarding the existence of these receptors may be explained by their selective expression at synapses from pyramidal cells onto particular postsynaptic cell types. The target-specific expression of preN M DARs, along with synapse-specific differences in short-term plasticity, contributes to the spatiotemporal remapping of inhibition across the somatodendritic axis of pyramidal cells during high-frequency firing, mediated by somatostatin and parvalbumin - expressing interneurons. Finally, the reconstructions, cell types and results from this work are used to develop and validate a time-saving approach based on Sholl analysis to classify cells from bitmap images without the need for laborious manual reconstructions – something which should facilitate high-throughput future studies of neural anatomy and morphology

Structural Cheminformatics for Kinase-Centric Drug Design

Drug development is a long, expensive, and iterative process with a high failure rate, while patients wait impatiently for treatment. Kinases are one of the main drug targets studied for the last decades to combat cancer, the second leading cause of death worldwide. These efforts resulted in a plethora of structural, chemical, and pharmacological kinase data, which are collected in the KLIFS database. In this thesis, we apply ideas from structural cheminformatics to the rich KLIFS dataset, aiming to provide computational tools that speed up the complex drug discovery process. We focus on methods for target prediction and fragment-based drug design that study characteristics of kinase binding sites (also called pockets). First, we introduce the concept of computational target prediction, which is vital in the early stages of drug discovery. This approach identifies biological entities such as proteins that may (i) modulate a disease of interest (targets or on-targets) or (ii) cause unwanted side effects due to their similarity to on-targets (off-targets). We focus on the research field of binding site comparison, which lacked a freely available and efficient tool to determine similarities between the highly conserved kinase pockets. We fill this gap with the novel method KiSSim, which encodes and compares spatial and physicochemical pocket properties for all kinases (kinome) that are structurally resolved. We study kinase similarities in the form of kinome-wide phylogenetic trees and detect expected and unexpected off-targets. To allow multiple perspectives on kinase similarity, we propose an automated and production-ready pipeline; user-defined kinases can be inspected complementarily based on their pocket sequence and structure (KiSSim), pocket-ligand interactions, and ligand profiles. Second, we introduce the concept of fragment-based drug design, which is useful to identify and optimize active and promising molecules (hits and leads). This approach identifies low-molecular-weight molecules (fragments) that bind weakly to a target and are then grown into larger high-affinity drug-like molecules. With the novel method KinFragLib, we provide a fragment dataset for kinases (fragment library) by viewing kinase inhibitors as combinations of fragments. Kinases have a highly conserved pocket with well-defined regions (subpockets); based on the subpockets that they occupy, we fragment kinase inhibitors in experimentally resolved protein-ligand complexes. The resulting dataset is used to generate novel kinase-focused molecules that are recombinations of the previously fragmented kinase inhibitors while considering their subpockets. The KinFragLib and KiSSim methods are published as freely available Python tools. Third, we advocate for open and reproducible research that applies FAIR principles ---data and software shall be findable, accessible, interoperable, and reusable--- and software best practices. In this context, we present the TeachOpenCADD platform that contains pipelines for computer-aided drug design. We use open source software and data to demonstrate ligand-based applications from cheminformatics and structure-based applications from structural bioinformatics. To emphasize the importance of FAIR data, we dedicate several topics to accessing life science databases such as ChEMBL, PubChem, PDB, and KLIFS. These pipelines are not only useful to novices in the field to gain domain-specific skills but can also serve as a starting point to study research questions. Furthermore, we show an example of how to build a stand-alone tool that formalizes reoccurring project-overarching tasks: OpenCADD-KLIFS offers a clean and user-friendly Python API to interact with the KLIFS database and fetch different kinase data types. This tool has been used in this thesis and beyond to support kinase-focused projects. We believe that the FAIR-based methods, tools, and pipelines presented in this thesis (i) are valuable additions to the toolbox for kinase research, (ii) provide relevant material for scientists who seek to learn, teach, or answer questions in the realm of computer-aided drug design, and (iii) contribute to making drug discovery more efficient, reproducible, and reusable

Novel cell models to study breast tumour microenvironment and disease progression

Breast cancer is the most prevalent and deadly in woman. ER+ breast cancer represents around two-thirds of all cases and has a favourable prognosis due to good response to endocrine therapy. However, these tumours present 25% of disease relapse due to drug resistance and metastatic behaviour. Tumour progression and acquired drug resistance are modulated by the interactions between tumour cells and the surrounding microenvironment. Most models employed to address these mechanisms fail to reflect the complex tumour microenvironment and do not allow long-term monitoring of tumour progression. (...

New Strategies in Quantitative Structure-Activity Relationships. Applications to Adenosine Receptor Ligands

Over the last years, an increasing number of applications of QSAR (Quantitative Structure-Activity Relationships) appeared in the literature, not only in lead finding and lead optimization, but also in other fields related to drug discovery, such as ADMET predictions. Neverthless, many questions are still open and they supplied the starting point of this research. Attention was focused on characteristics usually intended as “pitfalls” of QSAR itself. In this work, each step of the QSAR model development process was handled with a rational and rigorous approach, and the classic QSAR strategies were implemented with new protocol

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Tissue-specific gene expression and promoter characterization in triticale

xxiii, 425 leaves : col. ill. ; 29 cmTriticale (x Triticosecale Whitm.) is a cereal with favorable agronomic traits for a Canadian bioproduction platform crop. Appropriate tissue sampling times were determined and gene expression profiles were evaluated in five triticale seed tissues and eleven vegetative tissues using the Affymetrix Wheat GeneChip®. Genes that were expressed, not expressed, tissue-specific, tissue-enriched and developmentally regulated were identified. The percentage of probe sets on the wheat GeneChip with gene ontology annotations was improved from less than 3% to over 76% using homologous sequence identification and annotation transfer. This information was used to determine functions and processes over-represented within the identified gene lists and provide biological meaning to the results. Expression of candidate genes was further evaluated using qRT-PCR, RNA in situ hybridization and promoter characterization. This study has provided a comprehensive triticale gene expression atlas; knowledge regarding triticale development, gene function, expression and regulation; and tools enabling further triticale research and development

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

New Insights into Food Fermentation

This reprint is dedicated to new insights into food fermentation. The goal of this reprint was to broaden the current knowledge on advanced approaches concerning food fermentation, gathering studies on conventional and unconventional food matrix fermentation, functional compounds obtained through fermentation, fermentations increasing quality and safety standards, as well as papers presenting innovative approaches shedding light on the microbial community that characterizes fermented foods

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications