Application of nuclear magnetic resonance spectroscopy in the study of complex matrices

The aim of this PhD work was to apply the NMR based metabolomic approach to the study of complex matrices such as several food plants (pepper, celery, tomatoes, hemp, baobab, teas, blueberries and olive oils). A comprehensive description of the chemical composition in term of primary and secondary metabolites obtained by means of 1D and 2D experiments was reported and information regarding specific aspects (variety, type of production etc) were obtained. The study of stool samples of patients with liver cirrhosis was also carried out confirming the important contribution of the NMR approach in the disease investigation

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

CHARMM: The biomolecular simulation program

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983. © 2009 Wiley Periodicals, Inc.J Comput Chem, 2009.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63074/1/21287_ftp.pd

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Bioinformatic approaches to study the metabolic effect on Gene Regulation

La adaptación celular a ambientes dinámicos constituye un mecanismo esencial para la supervivencia celular. Las células responden a condiciones externas modulando los mecanismos moleculares que regulan expresión génica o la actividad proteica, confiriendo una respuesta rápida a cambios metabólicos externos. Por ello, los mecanismos celulares que captan los cambios metabólicos consistuyen un paso importante en adaptación celular, siendo la epigenética el mecanismo que une el metabolismo con la regulación génica. Las marcas epigenéticas confieren a la célula la capacidad de moldear la conformacion de la cromatina, lo que permite la regulación de la expresión génica. Por tanto, un correcto funcionamiento de la regulación epigenética de la célula, es crucial para la adaptación celular a ambientes con cambios metabólicos. Los moduladores epigenéticos dependen de la disponibilidad meta\-bólica para poder modificar la epigenética de la célula. Estudios recientes han señalado que la acumulación de ciertos metabolitos es clave para que moduladores epigenéticos actúen sobre las marcas de la cromatina. Un ejemplo claro se ve en los ritmos circadianos, donde los mecanismos epigenéticos median la relación que existe entre las oscilaciones metabólicas y los cambios en expresión génica; la falta de mecanismos epigenéticos desconecta estos relojes moleculares, provocando enfermedades como en el caso del síndrome metabólico. El estudio del control metabólico del epigenoma y el transcriptoma es un área de conocimiento emergente. Muchos estudios han generado información a través de las tecnologías de alto rendimiento, que miden la expresión génica, los metabolitos o las modificaciones de histonas entre otros tipos de moleculas para medir esta conexión, y aunque se ha desarrollado mucha literatura al respecto, los mecanismos que ejercen la regulación de distintos tipos moleculares es todavía desconocida. Una necesidad en el ámbito de la bioinformática es el análisis integrativo de datos moleculares que propongan modelos de regulación detallados para conocer la relación entre metabolismo, cromatina y la transcripción. En este trabajo se ha aproximado la integración estadística de meta\-bolómica y distintos datos epigenéticos con la expresión génica. Hemos realizado estos análisis integrativos en el sistema modelo del ciclo metabólico de la levadura (YMC), en el cual la expresión génica se coordina con cambios en modificaciones de histonas y oscilaciones metabólicas. Primero analizamos el impacto de las modificaciones de histonas sobre la expresión génica, lo cual nos permitió identificar las marcas de histonas que coordinan los cambios en expresión. Después creamos un conjunto de datos multiómico obteniendo muestras de metabolómica y ATAC-seq en el YMC, e incorporamos un set de datos de NET-seq. Estos datos fueron usados para modelar el impacto de los cambios metabólicos y de la cromatina en la expresión génica y, por primera vez en ritmos biológicos, integramos los tres tipos de datos moleculares en un solo modelo usando PLS-Path Modelling, una estrategia multivariante que permite encontrar relaciones entre muchos conjuntos de datos multi dimensionales. Esta herramienta nos ha permitido conocer que la expresión génica en la fase oxidativa está regulada principalmente por la marca de histona H3K9ac, y la acumulación de ATP en esta parte del ciclo sugiere una regulación de la cromatina activando la enzima dependiente de ATP INO80. El resultado de PLS-PM también nos muestra que los derivados de la nicotinamida podrían afectar los niveles de H3K18ac en a fase RC del ciclo a través de la regulación de las sirtuinas, activando la respuesta de degradación de ácidos grasos. El aspartato también se ha asociado a la regulación epigenética de la fase RC, pero los mecanismos por los que esta asociación novedosa tienen lugar son aún desconocidos. Finalmente, hemos creado Padhoc, una herramienta computacional capaz de combinar el conocimiento existente en nuevos ámbitos de investigación -como el de este trabajo- para proponer modelos de redes metabólicas que compleneten el conocimiento de las bases de datos actuales. Esta tesis recopila la extracción de un conjunto de datos multiómicos que cubre metabolismo, epigenética y expresión génica, así como su análisis integrativo usando estrategias multivariantes novedosas que modelan la coordinación de las distintas moléculas estudiadas. Además, incluimos una herramienta para la reconstrucción de redes biológicas. En conjunto, esta tesis presenta distintas herramientas para estudiar el impacto metabólico en la expresión génica usando la biología computacional.Cellular adaptation to changing environments constitutes a critical mechanism for cell survival. Cells primarily respond to external conditions by modulating the molecular mechanisms that regulate gene expression or protein activity, granting a rapid response to external metabolic changes. Therefore, metabolic sensing constitutes an important step in cell adaptation, and epigenetics is now considered the mechanism that connects metabolic shifts with gene regulation. Epigenetic marks give cells the capability of shaping chromatin conformation, which in turn regulates gene expression. Consequently, the correct functioning of a cell's epigenetic program is critical for cellular adaptation to changing conditions. Different epigenetic modifiers rely on metabolite availability to modify the cell's epigenetic landscape. Recent studies point towards the accumulation of key metabolites as the critical mechanism by which epigenetic modifiers modulate the chromatin marks. This can be appreciated in circadian rhythms, where epigenetic changes mediate the cross-talk between metabolic oscillations and gene expression. Deficiencies that disconnect this molecular regulation lead to diseases, such as metabolic syndrome. The study of the metabolic control of the epigenome and transcriptome is an emerging field of research. Multiple studies have generated large, high-throughput datasets that measure gene expression, metabolites and histone modifications, among others, to study these interconnections; although a wealth of literature is accumulating, the precise mechanisms of these multi-layered regulations are still to be fully elucidated. Also, a consensus pathway describing these processes cannot yet be found in any of the common biological pathway databases. One critical need in the field is the integrative analysis of existing molecular data to propose detailed regulatory models for the interplay between metabolism, chromatin state and transcription. This thesis addresses the statistical integration of metabolomics and epigenetics measurements with gene expression. We approached this data analysis challenge using the Yeast Metabolic Cycle (YMC) as a model system. Gene expression at the YMC can be divided into three, well-defined phases where transcription is coordinated with histone modifications and metabolomics oscillations. First, we analyzed the impact of histone modifications on gene expression, which led to the identification of the histone marks that have a higher impact on gene expression changes. Next, we created a comprehensive, multi-layered, multi-omics dataset for this system by obtaining metabolomics and ATAC-Seq data of the YMC and incorporating an existing nascent transcription (NET-seq) dataset. Moreover, we modeled the impact of chromatin conformation and metabolic changes on gene expression, and created a regulatory model for gene expression, epigenetics and metabolomics by applying PLS Path Modeling, a multivariate strategy suitable for finding relationships across multiple high-dimensional datasets. To our knowledge, this is the first time that PLS-PM is used for the modelling of molecular regulatory layers. We found that gene expression in OX phase was mainly controlled by H3K9ac histone mark and ATP accumulation at this phase, suggesting INO80 ATP-dependent chromatin remodeling activity. We also found an enrichment of H3K18ac during RC phase, together with accumulation of nicotinamide and its derivatives, suggesting that sirtuins may regulate H3K18ac levels at RC to activate fatty acid oxidation response. Aspartate was also associated with RC phase epigenetic regulation, but the mechanisms by which this amino acid may control the epigenome are still unanswered. Finally, in this work, we have also created Padhoc, a computational pipeline to integrate the existing published knowledge in emerging research fields -such as those studied in this thesis- to propose pathway models that can complement current pathway databases. Altogether, this thesis involves the generation of a multi-omics dataset that covers metabolic, epigenetic and gene expression information, and their integrative analysis using novel multivariate strategies that model their mechanistic coordination. Moreover, it includes a framework for the reconstruction of biological pathways. All in all, we have presented different strategies by which to study the impact of metabolic changes in chromatin using computational biology approaches

Innovations in the Food System: Exploring the Future of Food

Innovations in Food Systems should be: Inclusive: ensuring economic and social inclusion for all food system actors, especially smallholders, women, and youth; Sustainable: minimizing negative environmental impacts, conserving scarce natural resources, and strengthening resiliency against future shocks; Efficient: producing adequate quantities of food for global needs while minimizing postharvest loss and consumer waste; Nutritious and healthy: enabling the consumption of a diverse range of healthy, nutritious, and safe foods. These are ambitious goals that will require multidisciplinary effort—from engineering to life sciences, biotechnology, medical sciences, social sciences, and economic sciences. New technologies and scientific discoveries are the solutions to the increasing demand for sufficient, safe, healthy, and sustainable foods influenced by the increased public awareness of their importance

Plasma Biology

Irving Langmuir coined the name “plasma” to describe an ionized gas back in 1927. Just over 90 years later, plasma technology is becoming increasingly important in our daily life. For example, in the medical field and dentistry, plasma is used as a method of disinfection and sterilization. Moreover, additional potential novel applications of this technology in different forms of therapy have been proposed. In the agricultural sector, plasma technology could contribute to higher crop yields by enhancing seed germination and the growth of plants, as well as the preservation of foods by disinfection. Plasma technology could also be utilized in environmental applications, including water treatment and remediation, as well as treatment of exhaust gases. Although recent extensive studies have uncovered the broad potential of plasma technology, its mechanisms of action remain unclear. Therefore, further studies aimed at elucidating the molecular mechanisms of plasma technology are required. This book is composed of original articles and reviews investigating the molecular mechanisms of plasma biology. Relevant areas of study include applications in plasma medicine, plasma agriculture, as well as plasma chemistry. Studies on potential therapeutic approaches using plasma itself and plasma-treated solutions are also included