Metabolic Network Modeling to Unravel the Impact of Copper Deficiency and High-fat Diet on Liver Metabolism

Fatty liver disease is a common health problem caused by an imbalance in the intake, utilization, and distribution of energy. High-fat and high-carbohydrate diets have been identified as major risk factors for fatty liver disease. Other contributors and modifiers can also influence the development of liver damage. Intriguingly, inappropriate copper (Cu) levels in the diet and organs are associated with fatty liver disease, though the exact way in which Cu deficiency or toxicity contributes to metabolic issues is not yet clear. Cu is necessary for several biological processes, including energy production and redox balance. Further research should be taken to determine exactly how Cu deficiency and toxicity cause metabolic complications. To fill in this knowledge gap, this research aimed to use human genome-scale metabolic modeling with incorporating transcriptomic data and determine how Cu deficiency and high-fat diet affect metabolism in the liver. Results revealed that high-fat diet combining with Cu deficiency induces dramatic effects on fatty acid catabolism, energy metabolism, and NADPH balance, which is consistent with gene expression changes. In addition, the model proposed new findings in reduced ascorbate recycling, oxalate synthesis, and betaine synthesis which can affect other minerals or organs. These results presented Cu limitation-induced exacerbation of fatty liver disease and propose underlying mechanisms. Advisors: Hyun-Seob Song and Jaekwon Le

Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations

International audienceMotivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability: Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro ) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes

Winter moth adaptation to climate change:Genetic changes in thermal plasticity of embryonic development rate

Timing of winter moth egg hatching shows rapid genetic adaptation to climate change. The reaction norm of egg development rate versus temperature has shifted up compared to 10 years ago. This later hatching for a given temperature has led to a better match with timing of their food source, young oak leaves. To identify the genes underlying the genetic adaptation of winter moth egg hatching, we used an evo-eco-devo approach: eggs collected from the field were used in a split-brood experiment. At different times during development, we measured embryonic development in, and obtained transcriptomes of, eggs before and after transfer to a colder or warmer temperature compared to a baseline. Stages of embryonic development in the winter moth were determined by imagining eggs using epifluorescence microscopy. These images were then used to map the thermal sensitivity of winter moth embryonic development over time, enabling us to focus on the transcriptomes taken during thermally sensitive stages of development. Ultimately, we aim to compare the genes identified this way with genes that show changes in allele frequency over the past 20 years, using our DNA record of four natural populations that adapted to climate change. As winter moths are one of the few species showing genetic adaptation under climate change, this study of winter moth embryonic development can advance our understanding of the genetic basis of adaptive evolutionary change in a natural population

Winter moth adaptation to climate change:Genetic changes in thermal plasticity of embryonic development rate

Timing of winter moth egg hatching shows rapid genetic adaptation to climate change. The reaction norm of egg development rate versus temperature has shifted up compared to 10 years ago. This later hatching for a given temperature has led to a better match with timing of their food source, young oak leaves. To identify the genes underlying the genetic adaptation of winter moth egg hatching, we used an evo-eco-devo approach: eggs collected from the field were used in a split-brood experiment. At different times during development, we measured embryonic development in, and obtained transcriptomes of, eggs before and after transfer to a colder or warmer temperature compared to a baseline. Stages of embryonic development in the winter moth were determined by imagining eggs using epifluorescence microscopy. These images were then used to map the thermal sensitivity of winter moth embryonic development over time, enabling us to focus on the transcriptomes taken during thermally sensitive stages of development. Ultimately, we aim to compare the genes identified this way with genes that show changes in allele frequency over the past 20 years, using our DNA record of four natural populations that adapted to climate change. As winter moths are one of the few species showing genetic adaptation under climate change, this study of winter moth embryonic development can advance our understanding of the genetic basis of adaptive evolutionary change in a natural population

Modélisation mathématique des impacts de l'environnement à l'aide de réseaux métaboliques et de la théorie des jeux

Le sujet général de cette thèse est la modélisation mathématique des systèmes biologiques. Le principal modèle étudié est le réseau métabolique: une collection d'objets - métabolites, réactions biochimiques, enzymes et gènes - et les relations entre eux, généralement organisées sous forme de graphe.Trois sujets distincts sont couverts. Dans le premier chapitre principal, un algorithme appelé MOOMIN pour «Mathematical explOration of Omics data on a MetabolIc Network» est présenté. C'est un outil informatique permettant d'interpréter les résultats d'une analyse d'expression différentielle à l'aide d'un réseau métabolique. Le résultat de l'algorithme est un changement métabolique, exprimé en termes de réactions supposées avoir subi un changement d'activité, qui correspond le mieux aux données d'expression génique. Le deuxième chapitre principal traite de l'intersection de la théorie des jeux et de l'étude du métabolisme cellulaire. Un nouveau type de modèle est proposé, combinant les principes de la théorie des jeux évolutive à la modélisation par contraintes pour prédire le comportement métabolique. Dans le troisième et dernier chapitre principal, un modèle épidémiologique de l'agent pathogène de la vigne Xylella fastidiosa est présenté et analysé. À l'aide d'une analyse de sensibilité, l'importance relative des paramètres du modèle est évaluée et les résultats sont discutés du point de vue de la lutte contre la maladieThe overall subject of this thesis is mathematical modelling of biological systems. The main model under study is the metabolic network: a collection of objects — metabolites, biochemical reactions, enzymes, and genes — and the relations amongst them, usually organised to form a graph.Three distinct topics are covered. In the first main chapter, an algorithm called MOOMIN for “Mathematical explOration of Omics data on a MetabolIc Network” is presented. It is a computational tool to interpret the results of a differential expression analysis with the help of a metabolic network. The output of the algorithm is a metabolic shift, expressed in terms of reactions that were inferred to have undergone a change in activity, that best aligns with the gene expression data. In the second main chapter, the intersection of game theory and the study of cellular metabolism is discussed. A new type of model is proposed, one that combines the principles behind evolutionary game theory with constraint-based modelling to predict metabolic behaviour. In the third and last main chapter, an epidemiological model of the Xylella fastidiosa grapevine pathogen is presented and analysed. Using sensitivity analysis, the relative importance of the model parameters is evaluated, and the results discussed from the point of view of disease contro

Mathematical modelling of the impacts of environment using metabolic networks and game theory

The overall subject of this thesis is mathematical modelling of biological systems. The main model under study is the metabolic network: a collection of objects — metabolites, biochemical reactions, enzymes, and genes — and the relations amongst them, usually organised to form a graph.Three distinct topics are covered. In the first main chapter, an algorithm called MOOMIN for “Mathematical explOration of Omics data on a MetabolIc Network” is presented. It is a computational tool to interpret the results of a differential expression analysis with the help of a metabolic network. The output of the algorithm is a metabolic shift, expressed in terms of reactions that were inferred to have undergone a change in activity, that best aligns with the gene expression data. In the second main chapter, the intersection of game theory and the study of cellular metabolism is discussed. A new type of model is proposed, one that combines the principles behind evolutionary game theory with constraint-based modelling to predict metabolic behaviour. In the third and last main chapter, an epidemiological model of the Xylella fastidiosa grapevine pathogen is presented and analysed. Using sensitivity analysis, the relative importance of the model parameters is evaluated, and the results discussed from the point of view of disease controlLe sujet général de cette thèse est la modélisation mathématique des systèmes biologiques. Le principal modèle étudié est le réseau métabolique: une collection d'objets - métabolites, réactions biochimiques, enzymes et gènes - et les relations entre eux, généralement organisées sous forme de graphe.Trois sujets distincts sont couverts. Dans le premier chapitre principal, un algorithme appelé MOOMIN pour «Mathematical explOration of Omics data on a MetabolIc Network» est présenté. C'est un outil informatique permettant d'interpréter les résultats d'une analyse d'expression différentielle à l'aide d'un réseau métabolique. Le résultat de l'algorithme est un changement métabolique, exprimé en termes de réactions supposées avoir subi un changement d'activité, qui correspond le mieux aux données d'expression génique. Le deuxième chapitre principal traite de l'intersection de la théorie des jeux et de l'étude du métabolisme cellulaire. Un nouveau type de modèle est proposé, combinant les principes de la théorie des jeux évolutive à la modélisation par contraintes pour prédire le comportement métabolique. Dans le troisième et dernier chapitre principal, un modèle épidémiologique de l'agent pathogène de la vigne Xylella fastidiosa est présenté et analysé. À l'aide d'une analyse de sensibilité, l'importance relative des paramètres du modèle est évaluée et les résultats sont discutés du point de vue de la lutte contre la maladi