A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189

With a genome size of ∼580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms

Modélisation métabolique à l’échelle du génome de la bactérie quasi-minimale Mesoplasma florum

Des avancées significatives au niveau de la synthèse et de l’assemblage de fragments d’acide désoxyribonucléique (ADN), le support physique des fonctions cellulaires encodées dans une cellule vivante, permettent maintenant la construction de génomes entiers. Ce progrès permet d’imaginer que la conception d’organismes synthétiques deviendra routinière au cours des prochaines années. Cette capacité promet de transformer radicalement le domaine de la biologie en formant une nouvelle discipline d’ingénierie biologique. Parmi les retombées anticipées, on note le remplacement de synthèses chimiques par des procédés biologiques renouvelables tels que la production de biocarburants, la synthèse de médicaments microbiens, ou des approches alternatives pour le traitement des maladies. Dans ce contexte, il devient particulièrement important d’arriver à prédire correctement le phénotype résultant des génomes qui seront générés. Pour y arriver, il convient de réduire la complexité biologique en travaillant d’abord avec les cellules les plus simples possibles. Ce type d’organisme ayant subi un processus de réduction de génome et dont la majorité des gènes sont essentiels afin de survivre en conditions définies se nomme une cellule minimale. Le groupe phylogénétique des mollicutes, bactéries dépourvues de paroi cellulaire, contient les espèces vivant avec les plus petits génomes connus à ce jour. Membre de ce groupe, le pathogène humain Mycoplasma genitalium possède le plus petit génome capable de croissance autonome (560kbp codant pour 482 protéines. Cependant, sa pathogénicité et sa vitesse de croissance réduite (~24h) limitent l’applicabilité de M. genitalium en biologie synthétique. Pour remédier à ce problème, notre laboratoire a choisi de travailler avec Mesoplasma florum dont le temps de doublement est très rapide (~32 min) et qui ne cause pas de maladies chez l’humain. Les travaux effectués chez M. florum permettent maintenant le clonage et la transplantation de son génome et des travaux récents ont permis de caractériser les propriétés physico-chimiques de sa cellule ainsi que plusieurs paramètres biologiques. Afin de permettre la conception de génomes synthétiques basés sur M. florum, il convient d’intégrer un maximum de connaissances dans un cadre informatique structuré capable de générer des prédictions phénotypiques. Un modèle métabolique à l’échelle du génome (GEM) reposant sur la méthode d’analyse des flux à l’équilibre (FBA) représente un format particulièrement intéressant pour initier ces travaux de biologie des systèmes. La qualité des prédictions générées par ce type de modèle est dépendante de la précision de l’objectif à atteindre. Pour simuler la croissance, les GEMs doivent satisfaire un objectif nommé “fonction objective de biomasse” (BOF) qui contient l’ensemble des métabolites nécessaires à la production d’une nouvelle cellule avec des coefficients stœchiométriques représentatifs de l’abondance de ces composantes dans la cellule. Pendant mon parcours de doctorat, j’ai développé le logiciel BOFdat qui permet la définition d’une BOF représentative de la composition cellulaire spécifique à une espèce avec les données expérimentales associées. Les deux premières des trois étapes de BOFdat déterminent les coefficients stoechiométriques de molécules connues pour faire partie de la composition cellulaire telles que les macromolécules principales (étape 1, ADN, ARN et protéines) et les coenzymes essentiels (étape 2). L’étape 3 de BOFdat propose une méthode non-biaisée pour déterminer les métabolites susceptibles d’améliorer la prédiction d’essentialité des gènes formulée par le modèle. Pour ce faire, un algorithme génétique maximise la composition de la biomasse en fonction des données d’essentialité expérimentales à l’échelle du génome. BOFdat a été validé en reconstruisant la BOF du modèle iML1515 de la bactérie modèle Escherichia coli. L’utilisation de BOFdat a permis de récapituler le taux de croissance prédit avec la BOF originale tout en améliorant la qualité des prédictions d’essentialité de gènes de iML1515. BOFdat est disponible en libre accès pour quiconque désire construire une BOF pour un modèle métabolique. Ensuite, un GEM nommé iJL208 a été produit et contient 208 des 676 protéines représentant l’ensemble du métabolisme de M. florum. La qualité de l’annotation du génome a d’abord été évaluée en intégrant l’information obtenue par trois approches bio-informatiques, révélant que la majorité des protéines (418/676) ont une qualité suffisante pour être incorporées dans le modèle. Ensuite, les réactions ont été identifiées et rigoureusement incorporées une à la fois afin de construire le réseau métabolique de cette bactérie quasi-minimale. L’étude de la carte métabolique reconstruite révèle une dépendance prononcée pour l’import de composantes à partir du milieu de culture ainsi que l’importance des mécanismes de recyclage des métabolites. Pour sa production d’énergie, M. florum est entièrement dépendante de la glycolyse et ne possède pas la machinerie nécessaire à la respiration cellulaire. L’élaboration d’un milieu de culture semi-défini a réduit la présence de sucres contaminants dans le milieu de culture initial et ainsi de distinguer la croissance avec ou sans supplémentation de sucrose. Cette avancée importante a permis de mesurer les taux d’assimilation de sucrose et de production des déchets métaboliques lactate et acétate. Ces paramètres ont été utilisés afin de contraindre le modèle et de mieux comprendre la sensibilité du modèle à une variété de paramètres. Aussi, la croissance de M. florum a pu être validée expérimentalement avec différents sucres. L’information contextuelle obtenue, combinée à une analyse de structures tridimensionnelles de protéines clés, a permis de suggérer des hypothèses crédibles supportant l’assimilation de ces sucres par M. florum. Finalement, iJL208 a été utilisé afin de formuler une prédiction de génome minimal pour M. florum en simulant itérativement de larges délétions dans son génome. Combiner l’intégration de données expérimentales avec les prédictions du modèle constitue une voie d’avenir pour la conception de génomes synthétiques qui rejoint les capacités techniques d’assemblage de chromosomes en biologie synthétique. Globalement, les projets réalisés au cours de mon doctorat contribuent à l’avancement de la biologie des systèmes chez M. florum dans le but de prédire efficacement les phénotypes de la souche naturelle et de variants synthétiques qui pourront être produits au cours des prochaines années

Life on Arginine for Mycoplasma hominis: Clues from Its Minimal Genome and Comparison with Other Human Urogenital Mycoplasmas

Mycoplasma hominis is an opportunistic human mycoplasma. Two other pathogenic human species, M. genitalium and Ureaplasma parvum, reside within the same natural niche as M. hominis: the urogenital tract. These three species have overlapping, but distinct, pathogenic roles. They have minimal genomes and, thus, reduced metabolic capabilities characterized by distinct energy-generating pathways. Analysis of the M. hominis PG21 genome sequence revealed that it is the second smallest genome among self-replicating free living organisms (665,445 bp, 537 coding sequences (CDSs)). Five clusters of genes were predicted to have undergone horizontal gene transfer (HGT) between M. hominis and the phylogenetically distant U. parvum species. We reconstructed M. hominis metabolic pathways from the predicted genes, with particular emphasis on energy-generating pathways. The Embden–Meyerhoff–Parnas pathway was incomplete, with a single enzyme absent. We identified the three proteins constituting the arginine dihydrolase pathway. This pathway was found essential to promote growth in vivo. The predicted presence of dimethylarginine dimethylaminohydrolase suggested that arginine catabolism is more complex than initially described. This enzyme may have been acquired by HGT from non-mollicute bacteria. Comparison of the three minimal mollicute genomes showed that 247 CDSs were common to all three genomes, whereas 220 CDSs were specific to M. hominis, 172 CDSs were specific to M. genitalium, and 280 CDSs were specific to U. parvum. Within these species-specific genes, two major sets of genes could be identified: one including genes involved in various energy-generating pathways, depending on the energy source used (glucose, urea, or arginine) and another involved in cytadherence and virulence. Therefore, a minimal mycoplasma cell, not including cytadherence and virulence-related genes, could be envisaged containing a core genome (247 genes), plus a set of genes required for providing energy. For M. hominis, this set would include 247+9 genes, resulting in a theoretical minimal genome of 256 genes

Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data

<p>Abstract</p> <p>Background</p> <p><it>Saccharomyces cerevisiae </it>is the first eukaryotic organism for which a multi-compartment genome-scale metabolic model was constructed. Since then a sequence of improved metabolic reconstructions for yeast has been introduced. These metabolic models have been extensively used to elucidate the organizational principles of yeast metabolism and drive yeast strain engineering strategies for targeted overproductions. They have also served as a starting point and a benchmark for the reconstruction of genome-scale metabolic models for other eukaryotic organisms. In spite of the successive improvements in the details of the described metabolic processes, even the recent yeast model (i.e., <it>i</it>MM904) remains significantly less predictive than the latest <it>E. coli </it>model (i.e., <it>i</it>AF1260). This is manifested by its significantly lower specificity in predicting the outcome of grow/no grow experiments in comparison to the <it>E. coli </it>model.</p> <p>Results</p> <p>In this paper we make use of the automated GrowMatch procedure for restoring consistency with single gene deletion experiments in yeast and extend the procedure to make use of synthetic lethality data using the genome-scale model <it>i</it>MM904 as a basis. We identified and vetted using literature sources 120 distinct model modifications including various regulatory constraints for minimal and YP media. The incorporation of the suggested modifications led to a substantial increase in the fraction of correctly predicted lethal knockouts (i.e., specificity) from 38.84% (87 out of 224) to 53.57% (120 out of 224) for the minimal medium and from 24.73% (45 out of 182) to 40.11% (73 out of 182) for the YP medium. Synthetic lethality predictions improved from 12.03% (16 out of 133) to 23.31% (31 out of 133) for the minimal medium and from 6.96% (8 out of 115) to 13.04% (15 out of 115) for the YP medium.</p> <p>Conclusions</p> <p>Overall, this study provides a roadmap for the computationally driven correction of multi-compartment genome-scale metabolic models and demonstrates the value of synthetic lethals as curation agents.</p

Automation on the generation of genome scale metabolic models

Background: Nowadays, the reconstruction of genome scale metabolic models is a non-automatized and interactive process based on decision taking. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. Results: This work presents the automation of a methodology for the reconstruction of genome scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome scale metabolic model of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. Conclusions: For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models like connectivity and average shortest mean path of the different models have been compared and analyzed.Comment: 24 pages, 2 figures, 2 table

Analysis of minimal metabolic networks through whole-cell in silico modelling of prokaryotes

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Reconstruction of an in silico metabolic model of _Arabidopsis thaliana_ through database integration

The number of genome-scale metabolic models has been rising quickly in recent years, and the scope of their utilization encompasses a broad range of applications from metabolic engineering to biological discovery. However the reconstruction of such models remains an arduous process requiring a high level of human intervention. Their utilization is further hampered by the absence of standardized data and annotation formats and the lack of recognized quality and validation standards.&#xd;&#xa;&#xd;&#xa;Plants provide a particularly rich range of perspectives for applications of metabolic modeling. We here report the first effort to the reconstruction of a genome-scale model of the metabolic network of the plant _Arabidopsis thaliana_, including over 2300 reactions and compounds. Our reconstruction was performed using a semi-automatic methodology based on the integration of two public genome-wide databases, significantly accelerating the process. Database entries were compared and integrated with each other, allowing us to resolve discrepancies and enhance the quality of the reconstruction. This process lead to the construction of three models based on different quality and validation standards, providing users with the possibility to choose the standard that is most appropriate for a given application. First, a _core metabolic model_ containing only consistent data provides a high quality model that was shown to be stoichiometrically consistent. Second, an _intermediate metabolic model_ attempts to fill gaps and provides better continuity. Third, a _complete metabolic model_ contains the full set of known metabolic reactions and compounds in _Arabidopsis thaliana_.&#xd;&#xa;&#xd;&#xa;We provide an annotated SBML file of our core model to enable the maximum level of compatibility with existing tools and databases. We eventually discuss a series of principles to raise awareness of the need to develop coordinated efforts and common standards for the reconstruction of genome-scale metabolic models, with the aim of enabling their widespread diffusion, frequent update, maximum compatibility and convenience of use by the wider research community and industry

Reconstruction of a Bacterial Genome from DNA Cassettes

This basic research program comprised two major areas: (1) acquisition and analysis of marine microbial metagenomic data and development of genomic analysis tools for broad, external community use; (2) development of a minimal bacterial genome. Our Marine Metagenomic Diversity effort generated and analyzed shotgun sequencing data from microbial communities sampled from over 250 sites around the world. About 40% of the 26 Gbp of sequence data has been made publicly available to date with a complete release anticipated in six months. Our results and those mining the deposited data have revealed a vast diversity of genes coding for critical metabolic processes whose phylogenetic and geographic distributions will enable a deeper understanding of carbon and nutrient cycling, microbial ecology, and rapid rate evolutionary processes such as horizontal gene transfer by viruses and plasmids. A global assembly of the generated dataset resulted in a massive set (5Gbp) of genome fragments that provide context to the majority of the generated data that originated from uncultivated organisms. Our Synthetic Biology team has made significant progress towards the goal of synthesizing a minimal mycoplasma genome that will have all of the machinery for independent life. This project, once completed, will provide fundamentally new knowledge about requirements for microbial life and help to lay a basic research foundation for developing microbiological approaches to bioenergy