Totum Enim Quod Intelligo, Volo Ut Qui Me Audit Intelligat: An Examiation of S. Augustine as a Teacher of Catechumens in the De Catechizandis Rudibus

Many individuals in the ancient western world, whether Greek, Roman or otherwise, have captured the hearts and minds of their contemporaries and us moderns alike. Few, however, have captivated quite as many as Saint Augustine, Bishop of Hippo Regius in the North of Africa. Inarguably one of the brightest thinkers of his age, even of all human history, Augustine’s influences on philosophy and Christian theology have changed the intellectual landscape of the western world even into our time. One can scarcely handle either of those topics without at least coming across his name or ideas, and yet how deeply has any man, even one entirely devoted to the saint’s work, really understood him, that creator of, as Dr. Philip Cary put it, ‘the inner self’1

Palmitate-induced lipotoxicity alters acetylation of multiple proteins in clonal β cells and human pancreatic islets

Type 2 diabetes is characterized by progressive β cell dysfunction, with lipotoxicity playing a possible pathogenetic role. Palmitate is often used to examine the direct effects of lipotoxicity and it may cause mitochondrial alterations by activating protein acetylation. However, it is unknown whether palmitate influences protein acetylation in β cells. We investigated lysine acetylation in mitochondrial proteins from INS-1E β cells (INS-1E) and in proteins from human pancreatic islets (HPI) after 24 h palmitate exposure. First, we confirmed that palmitate damages β cells and demonstrated that chemical inhibition of deacetylation also impairs INS-1E function and survival. Then, by 2-D gel electrophoresis, Western Blot and Liquid Chromatography-Mass Spectrometry we evaluated the effects of palmitate on protein acetylation. In mitochondrial preparations from palmitate-treated INS-1E, 32 acetylated spots were detected, with 13 proteins resulting over-acetylated. In HPI, 136 acetylated proteins were found, of which 11 were over-acetylated upon culture with palmitate. Interestingly, three proteins, glutamate dehydrogenase, mitochondrial superoxide dismutase, and SREBP-1, were over-acetylated in both INS-1E and HPI. Therefore, prolonged exposure to palmitate induces changes in β cell protein lysine acetylation and this modification could play a role in causing β cell damage. Dysregulated acetylation may be a target to counteract palmitate-induced β cell lipotoxicity

Integrative relational machine-learning for understanding drug side-effect profiles

International audienceBackgroundDrug side effects represent a common reason for stopping drug development during clinical trials. Improving our ability to understand drug side effects is necessary to reduce attrition rates during drug development as well as the risk of discovering novel side effects in available drugs. Today, most investigations deal with isolated side effects and overlook possible redundancy and their frequent co-occurrence.ResultsIn this work, drug annotations are collected from SIDER and DrugBank databases. Terms describing individual side effects reported in SIDER are clustered with a semantic similarity measure into term clusters (TCs). Maximal frequent itemsets are extracted from the resulting drug x TC binary table, leading to the identification of what we call side-effect profiles (SEPs). A SEP is defined as the longest combination of TCs which are shared by a significant number of drugs. Frequent SEPs are explored on the basis of integrated drug and target descriptors using two machine learning methods: decision-trees and inductive-logic programming. Although both methods yield explicit models, inductive-logic programming method performs relational learning and is able to exploit not only drug properties but also background knowledge. Learning efficiency is evaluated by cross-validation and direct testing with new molecules. Comparison of the two machine-learning methods shows that the inductive-logic-programming method displays a greater sensitivity than decision trees and successfully exploit background knowledge such as functional annotations and pathways of drug targets, thereby producing rich and expressive rules. All models and theories are available on a dedicated web site.ConclusionsSide effect profiles covering significant number of drugs have been extracted from a drug ×side-effect association table. Integration of background knowledge concerning both chemical and biological spaces has been combined with a relational learning method for discovering rules which explicitly characterize drug-SEP associations. These rules are successfully used for predicting SEPs associated with new drugs

Contribuições recentes da reação de hidroformilação na síntese de produtos farmacêuticos: parte II Current improvement to the synthesis of pharmaceuticals by hydrofor-mylation reaction: part II

<abstract language="eng">The hydroformylation reaction represents one of the most important C1-chemistry area in the chemical industry. This catalytic process, which has been developed up to now mainly to the production of commodities chemicals, has shown a remarkable potential for the preparation of several categories of specialty chemicals and in particular pharmaceutical compounds. Arylpropanoic acids, various amines containing aryl groups, and intermediates for the preparation of vitamins, carbocyclic and heterocyclic compounds and many other classes of organic molecules endowed with pharmacological activity are currently accessible in good-to-high yields through hydroformylation of selected olefinic substrates. The asymmetric hydroformylation is going to reach the stage of maturity and hence to contribute in solving many troublesome synthetic problems connected with the preparation of pharmacologically active compounds with very high enantiomeric purity. The present survey emphasizes the usefulness of synthesis gas as a starting material in fine chemistry, which is expected to be important for industry

Modélisation moléculaire du phénomène du transport du calcium dans la protéine ATPase-Ca2+ (SERCA1a) : une première étude

Cette thèse propose l étude par Dynamique Moléculaire (DM) de la structure de l ATPase-Ca2+ (SERCA1a) pendant le processus de transport des ions à travers la membrane. On a considéré trois structures, représentant trois étapes conformationnelles successives du processus de pompage. Les simulations sur les structures ont été faites en utilisant deux types de recouvrement de la région transmembranaire: en monocouche de surfactant (LDAO) et en bicouche lipidique (POPC). L étude structurale s est intéressée à la mobilité relative des domaines cytoplasmiques ainsi qu à celle des hélices transmembranaires. Une étude sur l hydratation des différentes régions transmembranaires a révélé la formation de canaux de solvant à l intérieur de la protéine. Le surfactant LDAO a été séparément étudié par DM dans sa forme micellaire. L étude de l hydratation de la micelle a été présentée dans un article publié dans J. Phys. Chem. B 110: 11504 (2006) comparant la micelle de LDAO avec une micelle de C12E6.PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Benchmarking of HPCC: A novel 3D molecular representation combining shape and pharmacophoric descriptors for efficient molecular similarity assessments

International audienceSince 3D molecular shape is an important determinant of biological activity, designing accurate 3D molecular representations is still of high interest. Several chemoinformatic approaches have been developed to try to describe accurate molecular shapes. Here, we present a novel 3D molecular description, namely harmonic pharma chemistry coefficient (HPCC), combining a ligand-centric pharmacophoric description projected onto a spherical harmonic based shape of a ligand. The performance of HPCC was evaluated by comparison to the standard ROCS software in a ligand-based virtual screening (VS) approach using the publicly available directory of useful decoys (DUD) data set comprising over 100,000 compounds distributed across 40 protein targets. Our results were analyzed using commonly reported statistics such as the area under the curve (AUC) and normalized sum of logarithms of ranks (NSLR) metrics. Overall, our HPCC 3D method is globally as efficient as the state-of-the-art ROCS software in terms of enrichment and slightly better for more than half of the DUD targets. Since it is largely admitted that VS results depend strongly on the nature of the protein families, we believe that the present HPCC solution is of interest over the current ligand-based VS methods

eTRIKS platform: Conception and operation of a highly scalable cloud-based platform for translational research and applications development

International audienceWe describe the genesis, design and evolution of a computing platform designed and built to improve the success rate of biomedical translational research. The eTRIKS project platform was developed with the aim of building a platform that can securely host heterogeneous types of data and provide an optimal environment to run tranSMART analytical applications. Many types of data can now be hosted, including multi-OMICS data, preclinical laboratory data and clinical information, including longitudinal data sets. During the last two years, the platform has matured into a robust translational research knowledge management system that is able to host other data mining applications and support the development of new analytical tools