A Boolean probabilistic model of metabolic adaptation to oxygen in relation to iron homeostasis and oxidative stress

<p>Abstract</p> <p>Background</p> <p>In aerobically grown cells, iron homeostasis and oxidative stress are tightly linked processes implicated in a growing number of diseases. The deregulation of iron homeostasis due to gene defects or environmental stresses leads to a wide range of diseases with consequences for cellular metabolism that remain poorly understood. The modelling of iron homeostasis in relation to the main features of metabolism, energy production and oxidative stress may provide new clues to the ways in which changes in biological processes in a normal cell lead to disease.</p> <p>Results</p> <p>Using a methodology based on probabilistic Boolean modelling, we constructed the first model of yeast iron homeostasis including oxygen-related reactions in the frame of central metabolism. The resulting model of 642 elements and 1007 reactions was validated by comparing simulations with a large body of experimental results (147 phenotypes and 11 metabolic flux experiments). We removed every gene, thus generating <it>in silico </it>mutants. The simulations of the different mutants gave rise to a remarkably accurate qualitative description of most of the experimental phenotype (overall consistency > 91.5%). A second validation involved analysing the anaerobiosis to aerobiosis transition. Therefore, we compared the simulations of our model with different levels of oxygen to experimental metabolic flux data. The simulations reproducted accurately ten out of the eleven metabolic fluxes. We show here that our probabilistic Boolean modelling strategy provides a useful description of the dynamics of a complex biological system. A clustering analysis of the simulations of all <it>in silico </it>mutations led to the identification of clear phenotypic profiles, thus providing new insights into some metabolic response to stress conditions. Finally, the model was also used to explore several new hypothesis in order to better understand some unexpected phenotypes in given mutants.</p> <p>Conclusions</p> <p>All these results show that this model, and the underlying modelling strategy, are powerful tools for improving our understanding of complex biological problems.</p

Combined comparative genomic hybridization and transcriptomic analyses of ovarian granulosa cell tumors point to novel candidate driver genes

Background: Ovarian granulosa cell tumors (GCTs) are the most frequent sex cord-stromal tumors. Several studies have shown that a somatic mutation leading to a C134W substitution in the transcription factor FOXL2 appears in more than 95% of adult-type GCTs. Its pervasive presence suggests that FOXL2 is the main cancer driver gene. However, other mutations and genomic changes might also contribute to tumor formation and/or progression. Methods: We have performed a combined comparative genomic hybridization and transcriptomic analyses of 10 adult-type GCTs to obtain a picture of the genomic landscape of this cancer type and to identify new candidate co-driver genes. Results: Our results, along with a review of previous molecular studies, show the existence of highly recurrent chromosomal imbalances (especially, trisomy 14 and monosomy 22) and preferential co-occurrences (i.e. trisomy 14/monosomy 22 and trisomy 7/monosomy 16q). In-depth analyses showed the presence of recurrently broken, amplified/duplicated or deleted genes. Many of these genes, such as AKT1, RUNX1 and LIMA1, are known to be involved in cancer and related processes. Further genomic explorations suggest that they are functionally related. Conclusions: Our combined analysis identifies potential candidate genes, whose alterations might contribute to adult-type GCT formation/progression together with the recurrent FOXL2 somatic mutation.Peer reviewe

Modélisations booléennes probabilistes de l'homéostasie du fer pour l'exploration des liens entre stress oxydant et perturbations métaboliques (de la cellulle à l'organisme)

Les liens entre l'homéostasie du fer, le stress oxydant et le métabolisme (en particulier le métabolisme énergétique) sont mis en cause dans un nombre croissant de maladies. Pourtant, les mécanismes de régulations, les liens entre ces éléments ainsi que les conséquences des dérégulations de ce système complexe sont encore mal compris. Un modèle de l'homéostasie du fer connecté au stress oxydant et au métabolisme permettrait une meilleure compréhension de ce syste me. Un modèle multi-échelle permettra d'explorer les conséquences des dérégulations de l'homéostasie du fer sur différents types cellulaires. Cette thèse présente une méthode et des outils permettant de modéliser un tel système complexe à partir de la littérature. Avec cette méthode, nous avons construit un premier modèle booléen probabiliste qui décrit ce système au niveau cellulaire chez la levure Saccharomyces cerevisiae. Ce modèle est analysé par simulations. De nombreux mutants in silico ont été comparés à des données expérimentales. Puis ce modèle est utilisé pour analyser des hypothèses expliquant un phénotype mal compris : l'accumulation d'agrégats fer-phosphate dans les mitochondries de certains mutants. Le second modèle décrit l'homéostasie du fer au niveau de l'organisme humain. Ce modèle nous permet de reproduire certaines pathologies, montrant ainsi que notre méthodologie peut être utilisée pour modéliser des systèmes physiologiques. En perspectives, un modèle cellulaire humain, construit à partir du modèle levure, pourrait être intégré au modèle physiologique présenté ici.PARIS-BIUSJ-Physique recherche (751052113) / SudocSudocFranceF

Ein Beitrag zur Einfuehrung moderner Steuerungskonzepte und Entwicklungsmethoden in die Umformtechnik am Beispiel des Radial-Axial-Ringwalzens

TIB: RN 9478(89,2) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Human Colonic Microbiota and Short-Term Postoperative Outcomes in Colorectal Cancer Patients: A Pilot Study

Despite the advances in surgical techniques and perioperative care, the complication rates after colorectal cancer surgery have remained stable. Recently, it has been suggested that colon microbiota may be implicated in several pathways that can lead to impaired colonic homeostasis and, thereby, to the development of complications after colorectal surgery. The aim of this study was to evaluate the potential impact of colonic dysbiosis on postoperative course. This prospective human clinical study recruited patients operated on for left colon, sigmoid colon or rectal cancer. Colon mucosa and fecal samples were collected to study mucosa associated microbiota (MAM) and luminal microbiota (LM), accordingly. Preliminary analysis for the first 25 consecutive patients with V3–V4 16S rRNA metagenomic analysis was performed. Bacterial composition and abundance in patients who developed postoperative complications over a 90-day follow-up period were compared to those without postoperative complications. Abundance and distribution of genera in MAM differed significantly when compared to LM with a significant impact on neoadjuvant therapy on bacterial composition. Preliminary analysis revealed no statistically significant differences in LM nor in MAM composition when individuals with and without postoperative surgical complications were compared. In cases of postoperative complications, LM and MAM showed significantly decreased diversity. Composition of the colonic microbiota is altered by neoadjuvant therapy. Results on the impact of colonic dysbiosis on postoperative complications are pending the end of the present study, with 50 patients enrolled