Letteratura scientifica selezionata sul tema "Genome-Scale Metabolic Network (GSMN)"

Abstract MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54–1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.

Cordyceps militaris is currently exploited for commercial production of specialty products as its biomass constituents are enriched in bioactive compounds, such as cordycepin. The rational process development is important for economically feasible production of high quality bioproducts. Light is an abiotic factor affecting the cultivation process of this entomopathogenic fungus, particularly in its carotenoid formation. To uncover the cell response to light exposure, this study aimed to systematically investigate the metabolic responses of C. militaris strain TBRC6039 using integrative genome-wide transcriptome and genome-scale metabolic network (GSMN)-driven analysis. The genome-wide transcriptome analysis showed 8747 expressed genes in the glucose and sucrose cultures grown under light-programming and dark conditions. Of them, 689 differentially expressed genes were significant in response to the light-programming exposure. Through integration with the GSMN-driven analysis using the improved network (iRT1467), the reporter metabolites, e.g., adenosine-5′-monophosphate (AMP) and 2-oxoglutarate, were identified when cultivated under the carotenoid-producing condition controlled by light-programming exposure, linking to up-regulations of the metabolic genes involved in glyoxalase system, as well as cordycepin and carotenoid biosynthesis. These results indicated that C. militaris had a metabolic control in acclimatization to light exposure through transcriptional co-regulation, which supported the cell growth and cordycepin production in addition to the accumulation of carotenoid as a photo-protective bio-pigment. This study provides a perspective in manipulating the metabolic fluxes towards the target metabolites through either genetic or physiological approaches.

Understanding growth mechanisms in brown algae is a current scientific and economic challenge that can benefit from the modeling of their metabolic networks. The sequencing of the genomes of Saccharina japonica and Cladosiphon okamuranus has provided the necessary data for the reconstruction of Genome–Scale Metabolic Networks (GSMNs). The same in silico method deployed for the GSMN reconstruction of Ectocarpus siliculosus to investigate the metabolic capabilities of these two algae, was used. Integrating metabolic profiling data from the literature, we provided functional GSMNs composed of an average of 2230 metabolites and 3370 reactions. Based on these GSMNs and previously published work, we propose a model for the biosynthetic pathways of the main carotenoids in these two algae. We highlight, on the one hand, the reactions and enzymes that have been preserved through evolution and, on the other hand, the specificities related to brown algae. Our data further indicate that, if abscisic acid is produced by Saccharina japonica, its biosynthesis pathway seems to be different in its final steps from that described in land plants. Thus, our work illustrates the potential of GSMNs reconstructions for formalizing hypotheses that can be further tested using targeted biochemical approaches.

The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.

Summary Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs) approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph,Methylococcus capsulatusstr. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is namediMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner–Duodoroff pathway to be essential for the growth of this bacterium, whereas the Embden–Meyerhof–Parnas pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found thatM. capsulatuscan grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis, 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a GSMM of a methanotrophMethylococcus capsulatusstr. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential ofM. capsulatusBath.

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized, and so name our tool MetConSIN: Metabolically Contextualized Species Interaction Networks.

Actinosynnema pretiosum ATCC 31280 is the producer of antitumor agent ansamitocin P-3 (AP-3). Understanding of the AP-3 biosynthetic pathway and the whole metabolic network in A. pretiosum is important for the improvement of AP-3 titer. In this study, we reconstructed the first complete Genome-Scale Metabolic Model (GSMM) Aspm1282 for A. pretiosum ATCC 31280 based on the newly sequenced genome, with 87% reactions having definite functional annotation. The model has been validated by effectively predicting growth and the key genes for AP-3 biosynthesis. Then we built condition-specific models for an AP-3 high-yield mutant NXJ-24 by integrating Aspm1282 model with time-course transcriptome data. The changes of flux distribution reflect the metabolic shift from growth-related pathway to secondary metabolism pathway since the second day of cultivation. The AP-3 and methionine metabolisms were both enriched in active flux for the last two days, which uncovered the relationships among cell growth, activation of methionine metabolism, and the biosynthesis of AP-3. Furthermore, we identified four combinatorial gene modifications for overproducing AP-3 by in silico strain design, which improved the theoretical flux of AP-3 biosynthesis from 0.201 to 0.372 mmol/gDW/h. Upregulation of methionine metabolic pathway is a potential strategy to improve the production of AP-3.

Face à la résistance accrue aux antibiotiques menaçant la santé publique, la prospection de nouvelles molécules biologiquement actives est pressante. Les champignons filamenteux se distinguent par leur capacité à synthétiser une large gamme de produits naturels, sous l’influence de clusters de gènes biosynthétiques (BGC) qui orchestrent la production de métabolites spécialisés. Toutefois, de nombreux produits issus de ces BGCs n’ont pas encore été caractérisés et leur chimiodiversité demeure sous-explorée en raison de l’incapacité à activer l’ensemble de leur potentiel en laboratoire. L’approche OSMAC (One Strain Many Compound) permet de solliciter ce potentiel en variant les conditions de culture. Cependant, cette méthode reste complexe et coûteuse en raison de son caractère aléatoire et du grand nombre d’expérimentations nécessaires. L’optimisation de ces processus nécessite l’intégration de stratégies plus rationnelles et efficaces. A l’aide d’approches systémiques liées à la biologie des systèmes, les réseaux métaboliques à l’échelle du génome (GSMN) offrent une modélisation détaillée des voies métaboliques, des enzymes impliquées et des gènes associés, fournissant un aperçu précis du métabolisme. Dans ce cadre, nous proposons une stratégie alternative: l’OSMAC in silico. En reconstruisant un GSMN actualisé pour Penicillium rubens, nous avons pu étudier les réponses de son métabolisme sous divers scénarios nutritionnels. Cette modélisation a permis d’évaluer l’influence de différentes sources de carbone et d’azote sur sa croissance et la production de métabolites spécialisés, ouvrant ainsi de nouvelles perspectives pour optimiser la production de produits naturels
Given the pressing issue of increasing antibiotic resistance threatening public health, new biologically active molecule research is urgent. Filamentous fungi are charcterised by their ability to synthesise a wide range of natural products, driven by biosynthetic gene clusters (BGCs) that orchestrate the production of specialised metabolites. However, many products derived from these BGCs remain uncharacterised, and their chemodiversity is underexplored due to the inability to activate their full potential in laboratory settings. The OSMAC (One Strain Many Compounds) approach seeks to harness this potential through culture condition variations. Nevertheless, this method remains complex and costly due to its randomness and vast number of experiments required. Therefore, optimising these processes needs the integration of more rational and efficient strategies. Using systems biology approaches, genome-scale metabolic networks (GSMNs) provide detailed modeling of metabolic pathways, involved enzymes, and associated genes, offering a precise overview of metabolism. In this context, we propose an alternative strategiy: in silico OSMAC. By reconstructing an updated GSMN for Penicillium rubens , we studied its metabolic responses under various nutritional scenarios. This modelling enabled us to assess the influence of different carbon and nitrogen sources on growth and the production of specialised metabolites, thereby opening new prospects for optimising the production of natural products

La compréhension des organismes vivant a été une quête pendant longtemps. Depuisles premiers progrès des derniers siècles, nous sommes arrivés jusqu’au point où desquantités massives de données et d’information sont constamment générées. Bien que,jusqu’au présent la plupart du travail a été concentré sur la génération d’un catalogued’éléments biologiques, ce n’est pas que récemment qu’un effort coordonné pour découvrirles réseaux de relations entre ces parties a été constaté. Nous nous sommes intéressésà comprendre non pas seulement ces réseaux, mais aussi la façon dont, à partir de sesconnexions, émergent des fonctions biologiques.Ce travail se concentre sur la modélisation et l’exploitation d’un de ces réseaux :le métabolisme. Un réseau métabolique est un ensemble des réactions biochimiquesinterconnectées qui se produisent à l’intérieur, ou dans les proximité d’une cellulevivante. Une nouvelle méthode de découverte, ou de reconstruction des réseaux métaboliquesest proposée dans ce travail, avec une emphase particulière sur les organismeseucaryotes.Cette nouvelle méthode est divisée en deux parties : une nouvelle approche pour lamodélisation de la reconstruction basée sur l’instanciation des éléments d’un modèlesquelette existant, et une nouvelle méthode de réécriture d’association des gènes. Cetteméthode en deux parties permet des reconstructions qui vont au-delà de la capacitédes méthodes de l’état de l’art, permettant la reconstruction de modèles métaboliquesdes organismes eucaryotes, et fournissant une relation détaillée entre ses réactions etses gènes, des connaissances cruciales pour des applications biotechnologiques.Les méthodes de reconstruction développées dans ce travail, ont été complétéespar un workflow itératif d’édition, de vérification et d’amélioration du modèle. Ceworkflow a été implémenté dans un logiciel, appelé Pathtastic.Comme une étude de cas de la méthode développée et implémentée dans le présenttravail, le réseau métabolique de la levure oléagineuse Yarrowia lipolytica, connucomme contaminant alimentaire et utilisé pour la biorestauration et comme usinecellulaire, a été reconstruit. Une version préliminaire du modèle a été générée avecPathtastic, laquelle a été améliorée par curation manuelle, à travers d’un travail avecdes spécialistes dans le domaine de cette espèce. Les données expérimentales, obtenuesà partir de la littérature, ont été utilisées pour évaluer la qualité du modèle produit.La méthode de reconstruction chez les eucaryotes, et le modèle reconstruit deY. lipolytica peuvent être utiles pour les communautés scientifiques respectives, lepremier comme un pas vers une meilleure reconstruction automatique des réseauxmétaboliques, et le deuxième comme un soutien à la recherche, un outil pour desapplications biotechnologiques et comme un étalon-or pour les reconstructions futures
Understanding living organisms has been a quest for a long time. Since the advancesof the last centuries, we have arrived to a point where massive quantities of data andinformation are constantly generated. Even though most of the work so far has focusedon generating a parts catalog of biological elements, only recently have we seena coordinated effort to discover the networks of relationships between those parts. Notonly are we trying to understand these networks, but also the way in which, from theirconnections, emerge biological functions.This work focuses on the modeling and exploitation of one of those networks:metabolism. A metabolic network is a net of interconnected biochemical reactionsthat occur inside, or in the proximity of, a living cell. A new method of discovery, orreconstruction, of metabolic networks is proposed in this work, with special emphasison eukaryote organisms.This new method is divided in two parts: a novel approach to reconstruct metabolicmodels, based on instantiation of elements of an existing scaffold model, and a novelmethod of assigning gene associations to reactions. This two-parts method allows reconstructionsthat are beyond the capacity of the state-of-the-art methods, enablingthe reconstruction of metabolic models of eukaryotes, and providing a detailed relationshipbetween its reactions and genes, knowledge that is crucial for biotechnologicalapplications.The reconstruction methods developed for the present work were complementedwith an iterative workflow of model edition, verification and improvement. This workflowwas implemented as a software package, called Pathtastic.As a case study of the method developed and implemented in the present work,we reconstructed the metabolic network of the oleaginous yeast Yarrowia lipolytica,known as food contaminant and used for bioremediation and as a cell factory. A draftversion of the model was generated using Pathtastic, and further improved by manualcuration, working closely with specialists in that species. Experimental data, obtainedfrom the literature, were used to assess the quality of the produced model.Both, the method of reconstruction in eukaryotes, and the reconstructed model ofY. lipolytica can be useful for their respective research communities, the former as astep towards better automatic reconstructions of metabolic networks, and the latteras a support for research, a tool in biotechnological applications and a gold standardfor future reconstructions

Mycobacterium tuberculosis (Mtb), a pathogenic bacterium, is the causative agent in the vast majority of human tuberculosis (TB) cases. Nearly one-third of the world’s population has been affected by TB and annually two million deaths result from the disease. Because of the high cost of medication for a long term treatment with multiple drugs and the increase of multidrug-resistant Mtb strains, faster-acting drugs and more effective vaccines are urgently demanded. Several metabolic pathways of Mtb are attractive for identifying novel drug targets against TB. Hence, a high quality genome-scale metabolic network of Mtb (HQMtb) was reconstructed to investigate its whole metabolism and explore for new drug targets. The HQMtb metabolic network was constructed using an unbiased approach by extracting gene annotation information from various databases and consolidating the data with information from literature. The HQMtb consists of 686 genes, 607 intracellular reactions, 734 metabolites and 471 E.C. numbers, 27 of which are incomplete. The HQMtb was compared with two recently published Mtb metabolic models, GSMN-TB by Beste et al. and iNJ661 model by Jamshidi and Palsson. Due to the different reconstruction methods used, the three models have different characteristics. The 68 new genes and 80 new E.C. numbers were found only in the HQMtb and resulting in approximately 52 new metabolic reactions located in various metabolic pathways, for example biosynthesis of steroid, fatty acid metabolism, and TCA cycle. Through a comparison of HQMtb with a previously published human metabolic network (EHMN) in terms of protein signatures, 42 Mtb metabolic genes were proposed as new drug targets based on two criteria: (a) their protein functional sites do not match with any human protein functional sites; (b) they are essential genes. Interestingly, 13 of them are found in a list of current validated drug targets. Among all proposed drug targets, Rv0189c, Rv3001c and Rv3607c are of interest to be tested in the laboratory because they were also proposed as drug target candidates from two research groups using different methods.

In this thesis, we presented three different methods for enumerating special subnetworks containedin a metabolic network: metabolic stories, minimal precursor sets and chemical organisations. Foreach of the three methods, we gave theoretical results, and for the two first ones, we further providedan illustration on how to apply them in order to study the metabolic behaviour of living organisms.Metabolic stories are defined as maximal directed acyclic graphs whose sets of sources and targets arerestricted to a subset of the nodes. The initial motivation of this definition was to analyse metabolomicsexperimental data, but the method was also explored in a different context. Metabolic precursor setsare minimal sets of nutrients that are able to produce metabolites of interest. We present threedifferent methods for enumerating minimal precursor sets and we illustrate the application in a studyof the metabolic exchanges in a symbiotic system. Chemical organisations are sets of metabolites thatare simultaneously closed and self-maintaining, which captures some stability feature in the

Clostridium botulinum (C. botulinum) produces a neurotoxin which can be used in a clinical environment to treat diseases and disorders characterised by muscle hypertension or spasm. However, previous research has mostly focused on the biochemical mode of action of the toxin and the disease it manifests. In order to increase our understanding of the process further, this study aimed to investigate the metabolism of various biomarkers, thought to be correlated with neurotoxin biosynthesis. The objective was to increase our understanding of the metabolism which drives the production of C. botulinum toxin using a Genome Scale Metabolic Network (GSMN) enhanced surrogate system. A linear correlation was established between the accumulation of intracellular Poly β hydroxybutyrate (PHB) and neurotoxin in silico (R2 = 0.988). This correlation was confirmed by chemostat experiments in C. sporogenes demonstrating that increased supply of gaseous carbon dioxide (CO2) to the culture results in increased accumulation of PHB and in silico neurotoxin in C. botulinum. Experiments revealed the correlation is a result of modulation of carbon flux partitioning between glycolysis and the TCA cycle, ultimately increasing the availability of carbon for storage as PHB. Phosphate limitation and supplementation with Homoserine and other oxaloacetate derived amino acids, gave rise to increased PHB, owing to reduced activity and/or demand of the TCA cycle increasing the availability of acetyl CoA, the energy storage polymer’s precursor. Altering the growth medium to decrease TCA activity also resulted in decreased flagellin biosynthesis. The results of this study can be used to design a C. botulinum production process based on experimentally proven correlations and pathway analysis to yield a process which promotes neurotoxin biosynthesis over competing pathways, such as flagellin biosynthesis.

The current investigation is aimed at the reconstruction and analysis of genome-scale metabolic models. Specifically, it is focused on the use of mathematical-computational simulations to predict the cellular metabolism behavior towards bio-products production. The photosynthetic cyanobacterium Synechococcus elongatus PCC7942 was studied as biological system. This prokaryotic has been used in several studies as a biological platform for the synthesis of several substances for industrial interest. These studies are based on the advantage of autotrophic systems, which basically requires light and CO2 for growth. The main objective of this thesis is the integration of different types of biological information, whose interaction can be extract applicable knowledge for economic interests. To this end, our study was addressed to the use of methods for modeling, analyzing and predicting the behavior of metabolic phenotypes of cyanobacterium. The work has been divided into chapters organized sequentially, where the starting point was the in silico metabolic reconstruction network. This process intent to join in a metabolic model of all chemical reactions codified in genome. The stoichiometric coefficients of each reactions, can be arranged into a sparse matrix (stoichiometric matrix), where the columns corresponds to reactions and rows to metabolites. As a result of this process the first model was obtained (iSyf646) than later was updated to another (iSyf714). Both were generated from data ¿omics published in databases, scientific reviews as well as textbooks. To validate them, each one of the stoichiometric matrix together with relevant constraints were used by simulation techniques based on linear programming. These reconstructions have to be flexible enough to allow autotrophic growth under which the organism grows in nature. Once the reconstructions were validated, environmental variations can be simulated and we were able to study its effects through changes in outline system parameters. Subsequently, synthetic capabilities were evaluated from the in silico models in order to design metabolic engineering strategies. To do this a genetic variation was simulated in reactions network, where the disturbed stoichiometric matrix was the object of the quadratic optimization methods. As a results sets of optimal solutions were generated to enhanced production of various metabolites of energetic interest such as: ethanol, n-butanol isomers, lipids and hydrogen, as well as lactic acid as the compound which is an interest to the industry. Furthermore, functionally coupled reactions have been studied and have been weighted to the importance in the production of metabolites. Finally, genome-scale metabolic models allow us to establish criteria to integrate different types of data to help of find important points of regulation that may be subject to genetic modification. These regulatory centers have been investigated under drastic changes of illumination and have been inferred operational principles of cyanobacterium metabolism. In general, this thesis presents the metabolic capabilities of photosynthetic cyanobacterium Synechococcus elongatus PCC7942 to produce substances of interest, being a potential biological platform for clean and sustainable production.
Triana Dopico, J. (2014). Model-based analysis and metabolic design of a cyanobacterium for bio-products synthesis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/39351
TESIS

碩士
國立中正大學
化學工程研究所
106
This study used the liver as the main axis to exploit the significant flux distribution differences between normal cells and cancer cells to find biomarkers or oncogenes under the case of cancer. Based on the human metabolic network model Recon2.2, and use tissue-specific data from the Human Protein Atlas (HPA), the Virtual Metabolic Human (VMH) to provide the nutrients and Cost Optimization Reaction Dependency Assessment (CORDA) algorithm to generate the model reconstructs of liver health and cancer. Through the use of the Nested Hybrid Differential Evolution (NHDE) and Mutant Flux Balance Analysis (mFBA) developed in the laboratory, the phenomenon of the Warburg Effect as indicators of research to simulate the reprogramming of liver cancer metabolism. Most of the found oncogenes are related to the enzyme enzymes involved in fat metabolism. They will induce human body metabolic network disorders to promote the growth of tumors and the development of cancer. The studies of liver cancer, predict biomarkers, give medical care a clear direction in the further .

System Biology proposes to study biological components, as well as the interactions between them, to understand and predict systems’ behaviour through the use of mathematical models. Under this scope, Genome-Scale Metabolic Models (GSMMs) can be regarded as mathematical representations of the intrinsic metabolic capabilities of a given organism, encoded in its genome, and can be used in a variety of applications like predicting the phenotypical behaviour of a given organism in different environmental and genetic perturbations. The reconstruction of these models comprehends four fundamental stages, namely Genome Annotation, Assembling of a Metabolic Network from the Genome, the Conversion of the Network to a Stoichiometric Model and finally the Validation of the Metabolic Model. Although this procedure is currently relatively standardized in some stages, a significant amount of work still needs to be done by the community before the reconstruction process becomes semi-automated and reproducible. The present work aims at contributing to this field through the development of several tools for aiding the reconstruction process, while simultaneously applying some of those tools to an industrially relevant organism, the yeast Kluyveromyces lactis. The genome annotation stage is critical, as an inadequate annotation may delay, or even impair, the development of the model. The genome metabolic annotation consists on identifying and attributing functions to metabolic genes, i.e., genes encoding enzymes and transport proteins. While the identification of enzyme encoding genes can be performed by assigning Enzyme Commission numbers to the proteins encoded in the genes, the transport proteins encoding genes annotation is not straightforward. In this work, an automatic system to detect and classify all potential transport proteins from a given genome and integrate the related reactions into GSMMs is proposed, based on the identification and classification of genes that encode transmembrane proteins. The integration of the data provided by this methodology with highly curated models allowed the identification of new transport reactions. This tool was included in the merlin tool, a user-friendly Java application developed under the scope of this thesis that performs the reconstruction of GSMMs for any organism that has its genome sequenced. It performs several steps of the reconstruction process, including the functional genomic annotation of the whole genome. merlin 2.0 also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome, and thus the localisation of the metabolites involved in the reactions induced by such proteins. Finally, merlin 2.0 expedites the transition from genome-scale data to SBML (the standard Systems Biology Markup Language) metabolic models, allowing the user to have a preliminary view of the biochemical network. The yeast Kluyveromyces lactis has long been considered a model organism for studies in genetics and physiology, mainly due to its ability to metabolize lactose and to express recombinant proteins. Although the genome of Kluyveromyces lactis has been publicly available for some years, until now no complete metabolic functional annotation has been performed to the proteins encoded in the Kluyveromyces lactis genome and consequently no GSMM has been made available. In this work, a new metabolic genome-wide functional re-annotation of the proteins encoded in the Kluyveromyces lactis genome was performed, resulting in the annotation of 1759 genes with metabolic functions, and the development of a methodology supported by merlin. The new annotation includes novelties, such as the assignment of transporter superfamily numbers to genes identified as transporter proteins. The methodology developed throughout this work can be used to re-annotate any yeast or, with a little tweak of the reference organism, the proteins encoded in any sequenced genome. The new annotation provided by this study served as the basis for the reconstruction of a compartmentalized, genome-scale metabolic model for Kluyveromyces lactis. The partially compartmentalised (4 compartments) genome-scale metabolic model of Kluyveromyces lactis, the iOD962 metabolic model, comprises 962 genes, 2038 reactions and 1561 metabolites. Previous chemostat experiments were used to adjust both growth and non-growth associated energy requirements, and the model proved accurate when predicting the biomass, oxygen and carbon dioxide yields. Also, the in silico knockouts predicted accurately the in vivo phenotypes, when compared to published experiments. This model allowed determining a minimal medium for cultivating Kluyveromyces lactis and will surely bring new insights on the milk yeast metabolism, identifying engineering targets for the improvement of the yields of products of interest by performing in silico simulations.
A Biologia de Sistemas propõe-se estudar os componentes biológicos e as interações entre eles, para compreender e prever o comportamento dos sistemas através do uso de modelos matemáticos. Nesse âmbito, os Modelos Metabólicos à Escala Genómica (MMEGs) podem ser considerados representações matemáticas das capacidades metabólicas intrínsecas de um dado organismo, codificadas no seu genoma, e podem ser usados numa grande variedade de aplicações tais como a previsão do comportamento fenotípico de um determinado organismo face a diferentes perturbações ambientais e genéticas. O processo de reconstrução destes modelos compreende quatro fases fundamentais: anotação do genoma, desenvolvimento da rede metabólica, conversão da rede num modelo estequiométrico e, finalmente, a validação do modelo metabólico. Apesar de algumas destas fases estarem já relativamente normalizadas, existe ainda uma lacuna significativa na comunidade no que se refere à (semi-) automação e reprodutibilidade deste processo. O presente trabalho apresenta-se como uma contribuição para esta área, através do desenvolvimento de várias ferramentas de apoio à construção de modelos metabólicos e, simultaneamente da sua aplicação ao organismo Kluyveromyces lactis, uma levedura de elevado interesse industrial. A fase de anotação do genoma é uma fase crítica, pois uma anotação inadequada pode atrasar, ou mesmo comprometer o desenvolvimento de um modelo metabólico. A anotação metabólica do genoma consiste na identificação e atribuição de funções aos genes metabólicos, ou seja, genes que codificam enzimas e proteínas de transporte. Enquanto que a identificação de enzimas codificadas nos genes pode ser realizada através da atribuição de números da Comissão para as Enzimas, a anotação de genes que codificam as proteínas de transporte é um processo mais complexo. Neste trabalho é proposto um sistema automático para a deteção e classificação de proteínas de transporte. Este sistema é baseado na identificação e classificação dos genes que codificam proteínas transmembranares. A integração dos dados fornecidos por esta metodologia com modelos metabólicos curados permitiu a identificação de novas reações de transporte em organismos bem estudados. Esta ferramenta está incluída na ferramenta bioinformática merlin desenvolvida no âmbito desta tese, que é uma aplicação Java de fácil utilização, direcionada para a reconstrução de modelos metabólicos à escala genómica. Esta aplicação executa várias etapas do processo de reconstrução, incluindo a anotação funcional do genoma. O merlin 2.0 também efetua a compartimentação do modelo, prevendo a localização das proteínas codificadas no genoma, e consequentemente dos metabolitos envolvidos nas reações induzidas por essas proteínas. Finalmente, merlin 2.0 acelera a transição de dados do genoma para modelos metabólicos no formato SBML (Systems Biology Markup Language), possibilitando uma visão preliminar da rede bioquímica. A levedura Kluyveromyces lactis tem sido considerada um organismo modelo para estudos de genética e fisiologia, principalmente devido à sua capacidade de metabolizar a lactose e pela sua capacidade de expressar proteínas recombinantes. Apesar de o genoma da Kluyveromyces lactis ter sido disponibilizado publicamente há alguns anos, até agora não foi efetuada uma anotação funcional completa para identificar as proteínas codificadas no genoma da Kluyveromyces lactis. Consequentemente, não existe ainda nenhum MMEG para esta levedura. Neste trabalho foi efetuada uma re-anotação funcional das proteínas codificadas no genoma da Kluyveromyces lactis, resultando na anotação de 1759 genes com funções metabólicas, e no desenvolvimento de uma metodologia apoiada na aplicação merlin. A nova anotação do genoma inclui novidades, tais como a atribuição de números de superfamílias de transportadores a genes que codificam proteínas de transporte. A metodologia desenvolvida ao longo deste trabalho pode ser usada para reanotar qualquer levedura ou, com um ajuste do organismo de referência, as proteínas codificadas em qualquer genoma sequenciado. A nova anotação fornecida por este estudo serviu de base para a reconstrução de um modelo metabólico à escala genómica da Kluyveromyces lactis. Este modelo metabólico, parcialmente compartimentado (4 compartimentos), designado iOD962, inclui 962 genes, 2038 reações e 1561 metabolitos. Foram utilizadas experiências em quimiostato publicadas anteriormente para ajustar os requisitos energéticos associados à manutenção celular, e o modelo mostrou precisão na previsão dos rendimentos de biomassa, de dióxido de carbono e de oxigénio. Além disso, as simulações in silico previram com precisão os fenótipos in vivo, quando comparadas com as experiências publicadas. Este modelo permitiu determinar um meio mínimo para o cultivo de Kluyveromyces lactis e certamente trará novas perspectivas sobre o metabolismo desta levedura, identificando alvos de engenharia metabólica para a melhoria dos rendimentos dos produtos de interesse através da realização de simulações in silico.

AbstractMicrobial community simulations using genome scale metabolic networks (GSMs) are relevant for many application areas, such as the analysis of the human microbiome. Such simulations rely on assumptions about the culturing environment, affecting if the culture may reach a metabolically stationary state with constant microbial concentrations. They also require assumptions on decision making by the microbes: metabolic strategies can be in the interest of individual community members or of the whole community. However, the impact of such common assumptions on community simulation results has not been investigated systematically. Here, we investigate four combinations of assumptions, elucidate how they are applied in literature, provide novel mathematical formulations for their simulation, and show how the resulting predictions differ qualitatively. Crucially, our results stress that different assumption combinations give qualitatively different predictions on microbial coexistence by differential substrate utilization. This fundamental mechanism is critically under explored in the steady state GSM literature with its strong focus on coexistence states due to crossfeeding (division of labor).

AbstractCohesive network modelling and systems biology have emerged as extremely potent tools which helps understanding the combinatorial effects of biomolecules. Synergistic modulation among biomolecules (e.g., enzymes, transcription factors, microRNAs, drugs, etc.) are significant in finding out complex regulatory mechanisms in biological networks and pathways. In some cases, although combinatorial interactions among some biomolecules in specific biological networks is available, our knowledge in that particular domain is very limited with context to a genomic scale. Here we explore the pathway-pathway network to identify and understand the network architecture of metabolic pathway mediated regulations at genomic and co-functional levels, in rice. Using network transformation methods, a genome scale pathway-pathway co-functional synergistic network (PcFSN) was constructed. Finally, the PcFSN modules are extracted. This in turn helps to identify the miRNAs and genes associated with the pathways, especially linked to the central metabolic network in rice.