A Practical Protocol for Integration of Transcriptomics Data into Genome-Scale Metabolic Reconstructions

2015 ◽  
pp. 135-152 ◽  
Author(s):  
Juan Nogales ◽  
Lucía Agudo
Keyword(s):  
Transcriptomics Data ◽  
Genome Scale

scholarly journals Revealing the Metabolic Alterations during Biofilm Development of Burkholderia cenocepacia Based on Genome-Scale Metabolic Modeling

Metabolites ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 221
Author(s):  
Ozlem Altay ◽  
Cheng Zhang ◽  
Hasan Turkez ◽  
Jens Nielsen ◽  
Mathias Uhlén ◽  
...  
Keyword(s):  
Systems Biology ◽  
Alternative Pathway ◽  
Growth Conditions ◽  
Pentose Phosphate ◽  
Burkholderia Cenocepacia ◽  
Bacterial Cells ◽  
Metabolic Remodeling ◽  
Transcriptomics Data ◽  
Biofilm Development ◽  
Genome Scale

Burkholderia cenocepacia is among the important pathogens isolated from cystic fibrosis (CF) patients. It has attracted considerable attention because of its capacity to evade host immune defenses during chronic infection. Advances in systems biology methodologies have led to the emergence of methods that integrate experimental transcriptomics data and genome-scale metabolic models (GEMs). Here, we integrated transcriptomics data of bacterial cells grown on exponential and biofilm conditions into a manually curated GEM of B. cenocepacia. We observed substantial differences in pathway response to different growth conditions and alternative pathway susceptibility to extracellular nutrient availability. For instance, we found that blockage of the reactions was vital through the lipid biosynthesis pathways in the exponential phase and the absence of microenvironmental lysine and tryptophan are essential for survival. During biofilm development, bacteria mostly had conserved lipid metabolism but altered pathway activities associated with several amino acids and pentose phosphate pathways. Furthermore, conversion of serine to pyruvate and 2,5-dioxopentanoate synthesis are also identified as potential targets for metabolic remodeling during biofilm development. Altogether, our integrative systems biology analysis revealed the interactions between the bacteria and its microenvironment and enabled the discovery of antimicrobial targets for biofilm-related diseases.

scholarly journals Exploring the associations between transcript levels and fluxes in constraint-based models of metabolism

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Neeraj Sinha ◽  
Evert M. van Schothorst ◽  
Guido J. E. J. Hooiveld ◽  
Jaap Keijer ◽  
Vitor A. P. Martins dos Santos ◽  
...  
Keyword(s):  
Metabolic Flux ◽  
Transcript Levels ◽  
Proportionality Constant ◽  
Model Predictions ◽  
Physiological Information ◽  
Highly Expressed Genes ◽  
Transcriptomics Data ◽  
Genome Scale ◽  
The Impact ◽  
Constraint Based Models

Abstract Background Several computational methods have been developed that integrate transcriptomics data with genome-scale metabolic reconstructions to increase accuracy of inferences of intracellular metabolic flux distributions. Even though existing methods use transcript abundances as a proxy for enzyme activity, each method uses a different hypothesis and assumptions. Most methods implicitly assume a proportionality between transcript levels and flux through the corresponding function, although these proportionality constant(s) are often not explicitly mentioned nor discussed in any of the published methods. E-Flux is one such method and, in this algorithm, flux bounds are related to expression data, so that reactions associated with highly expressed genes are allowed to carry higher flux values. Results Here, we extended E-Flux and systematically evaluated the impact of an assumed proportionality constant on model predictions. We used data from published experiments with Escherichia coli and Saccharomyces cerevisiae and we compared the predictions of the algorithm to measured extracellular and intracellular fluxes. Conclusion We showed that detailed modelling using a proportionality constant can greatly impact the outcome of the analysis. This increases accuracy and allows for extraction of better physiological information.

scholarly journals RMetD2: a tool for integration of relative transcriptomics data into Genome-scale metabolic models

2019 ◽  
Author(s):  
Cheng Zhang ◽  
Sunjae Lee ◽  
Gholamreza Bidkhori ◽  
Rui Benfeitas ◽  
Alen Lovric ◽  
...  
Keyword(s):  
Case Studies ◽  
State Of The Art ◽  
Differentially Expressed ◽  
Systems Medicine ◽  
Transcriptomics Data ◽  
Altered Metabolism ◽  
Metabolic Models ◽  
Genome Scale ◽  
Potential Use ◽  
Single Set

AbstractRelative Metabolic Differences version 2 (RMetD2) is a tool for integration of differentially expressed (DE) genes into genome-scale metabolic models (GEMs) for revealing the altered metabolism between two biological conditions. This method provides a robust evaluation of the metabolism by using flux ranges instead of a single set of flux distributions. RMetD2 classifies reactions into three different groups, namely up-regulated, down-regulated and unchanged, which enables systematic interpretation of the metabolic differences between two different conditions. We employed this method in three different case studies using mice and human datasets, and compared it with state-of-the-art methods used for studying condition-specific metabolic differences using GEMs. We observed that RMetD2 is capable of capturing experimentally-observed features that are missed by other methods, highlighting its potential use in biotechnology and systems medicine applications. RMetD2 is implemented in Matlab and it is available without any limitation at https://sourceforge.net/projects/rmetd.

scholarly journals iEC7871 Quercus suber model: the first multi-tissue diel cycle genome-scale metabolic model of a woody tree

2021 ◽  
Author(s):  
Emanuel Cunha ◽  
Miguel Silva ◽  
Ines Chaves ◽  
Huseyin Demirci ◽  
Davide Lagoa ◽  
...  
Keyword(s):  
Metabolic Model ◽  
Tissue Level ◽  
Quercus Suber ◽  
Biomass Composition ◽  
Improve Model ◽  
Transcriptomics Data ◽  
Oak Tree ◽  
Metabolic Models ◽  
Genome Scale ◽  
Metabolic Behaviour

AbstractIn the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level in different environmental conditions. Quercus suber (Q. suber), also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871), the first of a woody plant. The metabolic model comprises 7871 genes, 6230 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen. Each tissue’s biomass composition was determined to improve model accuracy and merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyze the pathways associated with the synthesis of suberin monomers. Nevertheless, the models developed in this work can provide insights about other aspects of the metabolism of Q. suber, such as its secondary metabolism and cork formation.

scholarly journals Plant Metabolic Network: A multi-species resource of plant metabolic information

2021 ◽  
Author(s):  
Charles Hawkins ◽  
Daniel Ginzburg ◽  
Kangmei Zhao ◽  
William Dwyer ◽  
Bo Xue ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Multiple Correspondence Analysis ◽  
Model Organisms ◽  
Plant Metabolism ◽  
Online Resource ◽  
Transcriptomics Data ◽  
Cell Type Specific ◽  
Genome Scale ◽  
Metabolic Information

AbstractPlant metabolism is a pillar of our ecosystem, food security, and economy. To understand and engineer plant metabolism, we first need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we previously created the Plant Metabolic Network (PMN), an online resource of curated and computationally predicted information about the enzymes, compounds, reactions, and pathways that make up plant metabolism. Here we report PMN 15, which contains genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants, and new tools for analyzing and viewing metabolism information across species and integrating omics data in a metabolic context. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell-type specific metabolic pathways. This work shows the continued growth and refinement of the PMN resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.One-sentence SummaryThe Plant Metabolic Network is a collection of databases containing experimentally-supported and predicted information about plant metabolism spanning many species.

scholarly journals Combined Metabolic Activators decrease liver steatosis by activating mitochondrial metabolism in a Golden Syrian hamster study

2021 ◽  
Author(s):  
Hong Yang ◽  
Jordi Mayneris-Perxachs ◽  
Noemí Boqué ◽  
Josep M del Bas ◽  
Lluís Arola ◽  
...  
Keyword(s):  
Systems Analysis ◽  
Syrian Hamster ◽  
Liver Tissue ◽  
Liver Steatosis ◽  
Metabolic Model ◽  
Golden Syrian Hamster ◽  
Nicotinamide Riboside ◽  
Before And After ◽  
Transcriptomics Data ◽  
Genome Scale

AbstractThe prevalence of non-alcohol fatty liver disease (NAFLD), defined as the liver’s excessive fat accumulation, continues to increase dramatically. We have recently revealed the molecular mechanism underlying NAFLD using in-depth multi-omics profiling and identified that combined metabolic activators (CMA) could be administered to decrease the amount of hepatic steatosis (HS) in mouse model and NAFLD patients based on systems analysis. Here, we investigated the effects of a CMA including L-carnitine, N-acetyl-l-cysteine, nicotinamide riboside and betaine on a Golden Syrian hamster NAFLD model fed with HFD, and found that HS was decreased with the administration of CMA. To explore the mechanisms involved in the clearance HS, we generated liver transcriptomics data before and after CMA administration, and integrated these data using liver-specific genome-scale metabolic model of liver tissue. We systemically determined the molecular changes after the supplementation of CMAs and found that it activates mitochondria in the liver tissue by modulating the global fatty acid, amino acids, antioxidant and folate metabolism.

scholarly journals TEX-FBA: A constraint-based method for integrating gene expression, thermodynamics, and metabolomics data into genome-scale metabolic models

2019 ◽  
Author(s):  
Vikash Pandey ◽  
Daniel Hernandez Gardiol ◽  
Anush Chiappino-Pepe ◽  
Vassily Hatzimanikatis
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Solution Space ◽  
Metabolic Model ◽  
Scale Models ◽  
Different Types ◽  
Transcriptomics Data ◽  
Metabolic Reactions ◽  
Metabolic Models ◽  
Genome Scale

AbstractA large number of genome-scale models of cellular metabolism are available for various organisms. These models include all known metabolic reactions based on the genome annotation. However, the reactions that are active are dependent on the cellular metabolic function or environmental condition. Constraint-based methods that integrate condition-specific transcriptomics data into models have been used extensively to investigate condition-specific metabolism. Here, we present a method (TEX-FBA) for modeling condition-specific metabolism that combines transcriptomics and reaction thermodynamics data to generate a thermodynamically-feasible condition-specific metabolic model. TEX-FBA is an extension of thermodynamic-based flux balance analysis (TFA), which allows the simultaneous integration of different stages of experimental data (e.g., absolute gene expression, metabolite concentrations, thermodynamic data, and fluxomics) and the identification of alternative metabolic states that maximize consistency between gene expression levels and condition-specific reaction fluxes. We applied TEX-FBA to a genome-scale metabolic model ofEscherichia coliby integrating available condition-specific experimental data and found a marked reduction in the flux solution space. Our analysis revealed a marked correlation between actual gene expression profile and experimental flux measurements compared to the one obtained from a randomly generated gene expression profile. We identified additional essential reactions from the membrane lipid and folate metabolism when we integrated transcriptomics data of the given condition on the top of metabolomics and thermodynamics data. These results show TEX-FBA is a promising new approach to study condition-specific metabolism when different types of experimental data are available.Author summaryCells utilize nutrients via biochemical reactions that are controlled by enzymes and synthesize required compounds for their survival and growth. Genome-scale models of metabolism representing these complex reaction networks have been reconstructed for a wide variety of organisms ranging from bacteria to human cells. These models comprise all possible biochemical reactions in a cell, but cells choose only a subset of reactions for their immediate needs and functions. Usually, these models allow for a large flux solution space and one can integrate experimental data in order to reduce it and potentially predict the physiology for a specific condition. We developed a method for integrating different types of omics data, such as fluxomics, transcriptomics, metabolomics into genome-scale metabolic models that reduces the flux solution space. Using gene expression data, the algorithm maximizes the consistency between the predicted and experimental flux for the reactions and predicts biologically relevant flux ranges for the remaining reactions in the network. This method is useful for determining fluxes of metabolic reactions with reduced uncertainty and suitable for performing context- and condition-specific analysis in metabolic models using different types of experimental data.

scholarly journals Combined Metabolic Activators Improves Cognitive Functions in Alzheimer's Disease

2021 ◽  
Author(s):  
BURAK YULUG ◽  
OZLEM ALTAY ◽  
XIANGYU LI ◽  
LUTFU HANOGLU ◽  
SEYDA CANKAYA ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Functions ◽  
Supporting Evidence ◽  
Critical Elements ◽  
Therapeutic Regime ◽  
Plasma Metabolome ◽  
Transcriptomics Data ◽  
Double Blinded ◽  
Genome Scale

Alzheimer's disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. Here, we analyzed the brain transcriptomics data of more than 600 AD patients using genome-scale metabolic models and provided supporting evidence of mitochondrial dysfunction related to the pathophysiologic mechanisms of AD progression. Subsequently, we investigated, in an animal model of AD, the oral administration of Combined Metabolic Activators (CMAs), consisting of NAD+ and glutathione precursors, to explore the effect for improvement of biological functions in AD. CMAs includes L-serine, nicotinamide riboside, N-acetyl-L-cysteine, and L-carnitine tartrate, salt form of L-carnitine. The study revealed that supplementation of the CMAs improved the AD-associated histological parameters in the animals. Finally, we designed a randomized, double-blinded, placebo-controlled human phase 2 study and showed that the administration of CMAs improves cognitive functions in AD patients. A comprehensive analysis of the human plasma metabolome and proteome revealed that plasma levels of proteins and metabolites associated with redox metabolism are significantly improved after treatment. In conclusion, our results show that treating AD patients with metabolic activators leads to enhanced cognitive functions, suggesting a role for such a therapeutic regime in treating AD and other neurodegenerative diseases.

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