scholarly journals Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

2021 ◽  
Author(s):  
Iván Domenzain ◽  
Benjamín Sánchez ◽  
Mihail Anton ◽  
Eduard J. Kerkhoven ◽  
Aarón Millán-Oropeza ◽  
...  
Keyword(s):  
Microbial Growth ◽  
Acid Metabolism ◽  
Homo Sapiens ◽  
Stress Factors ◽  
Proteomics Data ◽  
Metabolic Robustness ◽  
Metabolic Models ◽  
Constrained Models ◽  
Genome Scale

AbstractGenome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into GEMs was first enabled by the GECKO method, allowing the study of phenotypes constrained by protein limitations. Here, we upgraded the GECKO toolbox in order to enhance models with enzyme and proteomics constraints for any organism with an available GEM reconstruction. With this, enzyme-constrained models (ecModels) for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus were generated, aiming to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions revealed that upregulation and high saturation of enzymes in amino acid metabolism were found to be common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO was further developed by the implementation of an automated framework for continuous and version-controlled update of ecModels, which was validated by producing additional high-quality ecModels for Escherichia coli and Homo sapiens. These efforts aim to facilitate the utilization of ecModels in basic science, metabolic engineering and synthetic biology purposes.

scholarly journals Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

2021 ◽  
Author(s):  
Iván Domenzain ◽  
Benjamín Sánchez ◽  
Mihail Anton ◽  
Eduard J Kerkhoven ◽  
Aarón Millán-Oropeza ◽  
...  
Keyword(s):  
Microbial Growth ◽  
Acid Metabolism ◽  
Homo Sapiens ◽  
Stress Factors ◽  
Proteomics Data ◽  
Metabolic Robustness ◽  
Metabolic Models ◽  
Constrained Models ◽  
Genome Scale

Abstract Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into GEMs was first enabled by the GECKO method, allowing the study of phenotypes constrained by protein limitations. Here, we upgraded the GECKO toolbox in order to enhance models with enzyme and proteomics constraints for any organism with an available GEM reconstruction. With this, enzyme-constrained models (ecModels) for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus were generated, aiming to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions revealed that upregulation and high saturation of enzymes in amino acid metabolism were found to be common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO was further developed by the implementation of an automated framework for continuous and version-controlled update of ecModels, which was validated by producing additional high-quality ecModels for Escherichia coli and Homo sapiens. These efforts aim to facilitate the utilization of ecModels in basic science, metabolic engineering and synthetic biology purposes.

scholarly journals Environmental flexibility does not explain metabolic robustness

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Julian Libiseller-Egger ◽  
Ben Coltman ◽  
Matthias P. Gerstl ◽  
Jürgen Zanghellini
Keyword(s):  
Acid Synthesis ◽  
Fault Analysis ◽  
Fungal Genome ◽  
Loss Of Function ◽  
Amino Acid Synthesis ◽  
Theoretical Understanding ◽  
Synthetic Lethal ◽  
Metabolic Robustness ◽  
Metabolic Models ◽  
Genome Scale

AbstractCells show remarkable resilience against genetic and environmental perturbations. However, its evolutionary origin remains obscure. In order to leverage methods of systems biology for examining cellular robustness, a computationally accessible way of quantification is needed. Here, we present an unbiased metric of structural robustness in genome-scale metabolic models based on concepts prevalent in reliability engineering and fault analysis. The probability of failure (PoF) is defined as the (weighted) portion of all possible combinations of loss-of-function mutations that disable network functionality. It can be exactly determined if all essential reactions, synthetic lethal pairs of reactions, synthetic lethal triplets of reactions etc. are known. In theory, these minimal cut sets (MCSs) can be calculated for any network, but for large models the problem remains computationally intractable. Herein, we demonstrate that even at the genome scale only the lowest-cardinality MCSs are required to efficiently approximate the PoF with reasonable accuracy. Building on an improved theoretical understanding, we analysed the robustness of 489 E. coli, Shigella, Salmonella, and fungal genome-scale metabolic models (GSMMs). In contrast to the popular “congruence theory”, which explains the origin of genetic robustness as a byproduct of selection for environmental flexibility, we found no correlation between network robustness and the diversity of growth-supporting environments. On the contrary, our analysis indicates that amino acid synthesis rather than carbon metabolism dominates metabolic robustness.

scholarly journals The view of microbes as energy converters illustrates the trade-off between growth rate and yield

2021 ◽  
Author(s):  
St. Elmo Wilken ◽  
Victor Vera Frazão ◽  
Nima P. Saadat ◽  
Oliver Ebenhoeh
Keyword(s):  
Growth Rate ◽  
Microbial Growth ◽  
Black Box ◽  
Trade Off ◽  
Box Models ◽  
Metabolic Models ◽  
Genome Scale ◽  
Self Replication ◽  
Black Box Models ◽  
Energy Converters

The application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy, but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models provide us with unprecedented detail about metabolic processes inside the cell. However, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black box models and modern approaches that integrate thermodynamics into genome-scale metabolic models. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield.

scholarly journals Environmental flexibility does not explain metabolic robustness

2020 ◽  
Author(s):  
Julian Libiseller-Egger ◽  
Ben Coltman ◽  
Matthias P. Gerstl ◽  
Jürgen Zanghellini
Keyword(s):  
Acid Synthesis ◽  
Fault Analysis ◽  
Fungal Genome ◽  
Loss Of Function ◽  
Amino Acid Synthesis ◽  
Synthetic Lethal ◽  
Genetic Robustness ◽  
Metabolic Robustness ◽  
Metabolic Models ◽  
Genome Scale

Cells show remarkable resilience against genetic and environmental perturbations. However, its evolutionary origin remains obscure. In order to leverage methods of systems biology for examining cellular robustness, a computationally accessible way of quantification is needed. Here, we present an unbiased metric of structural robustness in genome-scale metabolic models based on concepts prevalent in reliability engineering and fault analysis.The probability of failure (PoF) is defined as the (weighted) portion of all possible combinations of loss-of-function mutations that disable network functionality. It can be exactly determined, if all essential reactions, synthetic lethal pairs of reactions, synthetic lethal triplets of reactions etc., are known. In theory, these minimal cut sets (MCSs) can be calculated for any network, but for large models the problem remains computationally intractable. Herein, we demonstrate that even at the genome scale only the lowest-cardinality MCSs are required to efficiently approximate the PoF with reasonable accuracy.We analysed the robustness of 489 E. coli, Shigella, Salmonella, and fungal genome-scale metabolic models (GSMMs). In contrast to the popular “congruence theory”, which explains the origin of genetic robustness as a byproduct of selection for environmental flexibility, we found no correlation between network robustness and the diversity of growth-supporting environments. On the contrary, our analysis indicates that amino acid synthesis rather than carbon metabolism dominates metabolic robustness.

scholarly journals Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lea Seep ◽  
Zahra Razaghi-Moghadam ◽  
Zoran Nikoloski
Keyword(s):  
Free Energy ◽  
Gibbs Free Energy ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Homo Sapiens ◽  
Flux Analysis ◽  
Combined Procedure ◽  
Metabolic Models ◽  
Systematic Identification ◽  
Genome Scale

AbstractThermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation $$, {\Delta }_{f} G^{0}$$ , Δ f G 0 , of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown $${\Delta }_{f} G^{0}$$ Δ f G 0 . However, the lumping procedure has not been formalized nor implemented for systematic identification of lumped reactions. Here, we propose, implement, and test a combined procedure for reaction lumping, applicable to genome-scale metabolic models. It is based on identification of groups of metabolites with unknown $${\Delta }_{f} G^{0}$$ Δ f G 0 whose elimination can be conducted independently of the others via: (1) group implementation, aiming to eliminate an entire such group, and, if this is infeasible, (2) a sequential implementation to ensure that a maximal number of metabolites with unknown $${\Delta }_{f} G^{0}$$ Δ f G 0 are eliminated. Our comparative analysis with genome-scale metabolic models of Escherichia coli, Bacillus subtilis, and Homo sapiens shows that the combined procedure provides an efficient means for systematic identification of lumped reactions. We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA.

Strategies to Protect Hematopoietic Stem Cells from Culture-Induced Stress Conditions

2020 ◽  
Vol 15 ◽  
Author(s):  
Fatima Aerts-Kaya
Keyword(s):  
Stem Cells ◽  
Hematopoietic Stem Cells ◽  
Ex Vivo ◽  
Stress Conditions ◽  
Stress Factors ◽  
Hematopoietic Stem ◽  
Induced Stress

: In contrast to their almost unlimited potential for expansion in vivo and despite years of dedicated research and optimization of expansion protocols, the expansion of Hematopoietic Stem Cells (HSCs) in vitro remains remarkably limited. Increased understanding of the mechanisms that are involved in maintenance, expansion and differentiation of HSCs will enable the development of better protocols for expansion of HSCs. This will allow procurement of HSCs with long-term engraftment potential and a better understanding of the effects of the external influences in and on the hematopoietic niche that may affect HSC function. During collection and culture of HSCs, the cells are exposed to suboptimal conditions that may induce different levels of stress and ultimately affect their self-renewal, differentiation and long-term engraftment potential. Some of these stress factors include normoxia, oxidative stress, extra-physiologic oxygen shock/stress (EPHOSS), endoplasmic reticulum (ER) stress, replicative stress, and stress related to DNA damage. Coping with these stress factors may help reduce the negative effects of cell culture on HSC potential, provide a better understanding of the true impact of certain treatments in the absence of confounding stress factors. This may facilitate the development of better ex vivo expansion protocols of HSCs with long-term engraftment potential without induction of stem cell exhaustion by cellular senescence or loss of cell viability. This review summarizes some of available strategies that may be used to protect HSCs from culture-induced stress conditions.

scholarly journals Tree Vitality and Forest Health: Can Tree-Ring Stable Isotopes Be Used as Indicators?

2021 ◽  
Author(s):  
Paolo Cherubini ◽  
Giovanna Battipaglia ◽  
John L. Innes
Keyword(s):  
Air Pollution ◽  
Stable Isotopes ◽  
Tree Ring ◽  
Forest Decline ◽  
Forest Health ◽  
Stress Factors ◽  
Tree Vitality ◽  
The Past ◽  
Physiological Processes

Abstract Purpose of Review Society is concerned about the long-term condition of the forests. Although a clear definition of forest health is still missing, to evaluate forest health, monitoring efforts in the past 40 years have concentrated on the assessment of tree vitality, trying to estimate tree photosynthesis rates and productivity. Used in monitoring forest decline in Central Europe since the 1980s, crown foliage transparency has been commonly believed to be the best indicator of tree condition in relation to air pollution, although annual variations appear more closely related to water stress. Although crown transparency is not a good indicator of tree photosynthesis rates, defoliation is still one of the most used indicators of tree vitality. Tree rings have been often used as indicators of past productivity. However, long-term tree growth trends are difficult to interpret because of sampling bias, and ring width patterns do not provide any information about tree physiological processes. Recent Findings In the past two decades, tree-ring stable isotopes have been used not only to reconstruct the impact of past climatic events, such as drought, but also in the study of forest decline induced by air pollution episodes, and other natural disturbances and environmental stress, such as pest outbreaks and wildfires. They have proven to be useful tools for understanding physiological processes and tree response to such stress factors. Summary Tree-ring stable isotopes integrate crown transpiration rates and photosynthesis rates and may enhance our understanding of tree vitality. They are promising indicators of tree vitality. We call for the use of tree-ring stable isotopes in future monitoring programmes.

scholarly journals Coordinated multitissue transcriptional and plasma metabonomic profiles following acute caloric restriction in mice

2006 ◽  
Vol 27 (3) ◽  
pp. 187-200 ◽  
Author(s):  
Colin Selman ◽  
Nicola D. Kerrison ◽  
Anisha Cooray ◽  
Matthew D. W. Piper ◽  
Steven J. Lingard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Fatty Acid ◽  
Caloric Restriction ◽  
Fatty Acid Metabolism ◽  
Acid Metabolism ◽  
Proton Nuclear Magnetic Resonance ◽  
Cellular Transport ◽  
Expression Of Genes ◽  
Transcriptional Changes

Caloric restriction (CR) increases healthy life span in a range of organisms. The underlying mechanisms are not understood but appear to include changes in gene expression, protein function, and metabolism. Recent studies demonstrate that acute CR alters mortality rates within days in flies. Multitissue transcriptional changes and concomitant metabolic responses to acute CR have not been described. We generated whole genome RNA transcript profiles in liver, skeletal muscle, colon, and hypothalamus and simultaneously measured plasma metabolites using proton nuclear magnetic resonance in mice subjected to acute CR. Liver and muscle showed increased gene expressions associated with fatty acid metabolism and a reduction in those involved in hepatic lipid biosynthesis. Glucogenic amino acids increased in plasma, and gene expression for hepatic gluconeogenesis was enhanced. Increased expression of genes for hormone-mediated signaling and decreased expression of genes involved in protein binding and development occurred in hypothalamus. Cell proliferation genes were decreased and cellular transport genes increased in colon. Acute CR captured many, but not all, hepatic transcriptional changes of long-term CR. Our findings demonstrate a clear transcriptional response across multiple tissues during acute CR, with congruent plasma metabolite changes. Liver and muscle switched gene expression away from energetically expensive biosynthetic processes toward energy conservation and utilization processes, including fatty acid metabolism and gluconeogenesis. Both muscle and colon switched gene expression away from cellular proliferation. Mice undergoing acute CR rapidly adopt many transcriptional and metabolic changes of long-term CR, suggesting that the beneficial effects of CR may require only a short-term reduction in caloric intake.

Critical assessment of genome-scale metabolic models of Arabidopsis thaliana

2022 ◽  
Author(s):  
Javad Zamani ◽  
Sayed-Amir Marashi ◽  
Tahmineh Lohrasebi ◽  
Mohammad-Ali Malboobi ◽  
Esmail Foroozan
Keyword(s):  
Arabidopsis Thaliana ◽  
Flux Balance Analysis ◽  
Critical Assessment ◽  
Flux Balance ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Living Organisms ◽  
Genome Scale

Genome-scale metabolic models (GSMMs) have enabled researchers to perform systems-level studies of living organisms. As a constraint-based technique, flux balance analysis (FBA) aids computation of reaction fluxes and prediction of...

Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111

2017 ◽  
Vol 9 (10) ◽  
pp. 830-835 ◽  
Author(s):  
Xingxing Jian ◽  
Ningchuan Li ◽  
Qian Chen ◽  
Qiang Hua
Keyword(s):  
Escherichia Coli ◽  
Metabolic Engineering ◽  
Gene Amplification ◽  
Metabolic Networks ◽  
Computational Analysis ◽  
Succinate Production ◽  
Novel Gene ◽  
Metabolic Models ◽  
Genome Scale

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed.

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