scholarly journals Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments

2020 ◽  
Author(s):  
Yu Chen ◽  
Eunice van Pelt-KleinJan ◽  
Berdien van Olst ◽  
Sieze Douwenga ◽  
Sjef Boeren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Proteomics Data ◽  
Available Nitrogen ◽  
Regulatory Strategies ◽  
Trade Offs ◽  
Evolutionary Changes ◽  
Genome Scale

Cells adapt to different conditions via gene expression that tunes metabolism and stress resistance for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade-offs1; Resource allocation under proteome constraints has emerged as a powerful paradigm to explain regulatory strategies in bacteria2. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient-rich conditions, i.e., multiple available nitrogen sources, such as the lactic acid bacterium Lactococcus lactis. Here we present an approach to identify preferred nutrients from integration of experimental data with a proteome-constrained genome-scale metabolic model of L. lactis (pcLactis), which explicitly accounts for gene expression processes and associated constraints. Using glucose-limited chemostat data3, we identified the uptake of glucose and arginine as dominant constraints, whose pathway proteins were indeed upregulated in evolved mutants. However, above a growth rate of 0.5 h-1, pcLactis suggests that available enzymes function at their maximum capacity, which allows an increase in growth rate only by altering gene expression to change metabolic fluxes, as was mainly observed for arginine metabolism. Thus, our integrative analysis of flux and proteomics data with a proteome-constrained model is able to identify and explain the constraints that form targets of regulation and fitness improvement in nutrient-rich growth environments.

scholarly journals A mechanistic link between cellular trade-offs, gene expression and growth

2015 ◽  
Author(s):  
Andrea Y. Weisse ◽  
Diego A. Oyarzun ◽  
Vincent Danos ◽  
Peter S. Swain
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Metabolic Pathways ◽  
Mechanistic Model ◽  
Living Cells ◽  
Coarse Grained ◽  
Empirical Relationships ◽  
Cellular Energy ◽  
Modelling Framework ◽  
Trade Offs

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that because of limitations in levels of cellular energy, free ribosomes, and proteins are faced by all living cells and construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod's law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth related effects in dosage compensation by paralogs and predict host-circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modelling framework has potentially wide application, including in both biotechnology and medicine.

scholarly journals Genome-Scale reconstruction ofPaenarthrobacter aurescensTC1 metabolic model towards the study of atrazine bioremediation

2019 ◽  
Author(s):  
Shany Ofaim ◽  
Raphy Zarecki ◽  
Seema Porob ◽  
Daniella Gat ◽  
Tamar Lahav ◽  
...  
Keyword(s):  
Environmental Concern ◽  
Subsequent Development ◽  
Biomass Accumulation ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Strain Design ◽  
A Genome ◽  
Genome Scale ◽  
Atrazine Degradation

ABSTRACTAtrazine is an herbicide and pollutant of great environmental concern that is naturally biodegraded by microbial communities. The efficiency of biodegradation can be improved through the stimulating addition of fertilizers, electron acceptors, etc. In recent years, metabolic modelling approaches have become widely used as anin silicotool for organism-level phenotyping and the subsequent development of metabolic engineering strategies including biodegradation improvement. Here, we constructed a genome scale metabolic model,iRZ960, forPaenarthrobacter aurescensTC1 – a widely studied atrazine degrader - aiming at simulating its degradation activity. A mathematical stoichiometric metabolic model was constructed based on a published genome sequence ofP. aurescensTC1. An Initial draft model was automatically constructed using the RAST and KBase servers. The draft was developed into a predictive model through semi-automatic gap-filling procedures including manual curation. In addition to growth predictions under different conditions, model simulations were used to identify optimized media for enhancing the natural degradation of atrazine without a need in strain design via genetic modifications. Model predictions for growth and atrazine degradation efficiency were tested in myriad of media supplemented with different combinations of carbon and nitrogen sources that were verifiedin vitro. Experimental validations support the reliability of the model’s predictions for both bacterial growth (biomass accumulation) and atrazine degradation. Predictive tools, such as the presented model, can be applied for achieving optimal biodegradation efficiencies and for the development of ecologically friendly solutions for pollutant degradation in changing environments.

scholarly journals On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models

2019 ◽  
Author(s):  
Miguel Ponce-de-León ◽  
Iñigo Apaolaza ◽  
Alfonso Valencia ◽  
Francisco J. Planes
Keyword(s):  
Gene Expression ◽  
Metabolic Model ◽  
Specific Model ◽  
Omics Data ◽  
Model Reconstruction ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Metabolic Models ◽  
Context Specific ◽  
Genome Scale

ABSTRACTWith the publication of high-quality genome-scale metabolic models for several organisms, the Systems Biology community has developed a number of algorithms for their analysis making use of ever growing –omics data. In particular, the reconstruction of the first genome-scale human metabolic model, Recon1, promoted the development of Context-Specific Model (CS-Model) reconstruction methods. This family of algorithms aims to identify the set of metabolic reactions that are active in a cell in a given condition using omics data, such as gene expression levels. Different CS-Model reconstruction algorithms have their own strengths and weaknesses depending on the problem under study and omics data available. However, after careful inspection, we found that all of these algorithms share common issues in the way GPR rules and gene expression data are treated. The first issue is related with how gapfilling reactions are managed after the reconstruction is conducted. The second issue concerns the molecular context, which is used to build the CS-model but neglected for posterior analyses. To evaluate the effect of these issues, we reconstructed ∼400 CS-Models of cancer cell lines and conducted gene essentiality analysis, using CRISPR–Cas9 essentiality data for validation purposes. Altogether, our results illustrate the importance of correcting the errors introduced during the GPR translation in many of the published metabolic reconstructions.

scholarly journals The interplay between growth rate and nutrient quality defines gene expression capacity

2021 ◽  
Author(s):  
Juhyun Kim ◽  
Alexander P.S. Darlington ◽  
Declan G Bates ◽  
Jose Ignacio Jimenez
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Physiological Parameter ◽  
Bacterial Cells ◽  
Heterologous Proteins ◽  
Efficient Design ◽  
Translational Machinery ◽  
Trade Offs ◽  
Individual Contributions ◽  
The Individual

The gene expression capacity of bacterial cells depends on the interplay between growth and the availability of the transcriptional and translational machinery. Growth rate is widely accepted as the global physiological parameter controlling the allocation of cell resources. This allocation has an impact on the ability of the cell to produce both host and heterologous proteins required for synthetic circuits and pathways. Understanding the relationship between growth and resources is key for the efficient design of artificial genetic constructs, however, it is obscured by the mutual dependence of growth and gene expression on each other. In this work, we investigate the individual contributions of molecular factors, growth rate and metabolism to gene expression by investigating the behaviour of bacterial cells growing in chemostats in growth-limited conditions. We develop a model of the whole cell that captures trade-offs in gene expression arising from the individual contributions of different factors, and validate it by analysing gene couplings which emerge from competition for the gene expression machinery. Our results show that while growth rate and molecular factors, such as the number of rRNA operons, set the abundance of transcriptional and translational machinery available, it is metabolism that governs the usage of those resources by tuning elongation rates. We show that synthetic gene expression capacity can be maximised by using low growth in a high-quality medium. These findings provide valuable insights into fundamental trade-offs in microbial physiology that will inform future strain and bioprocesses optimisation.

scholarly journals Noise propagation in an integrated model of bacterial gene expression and growth

2018 ◽  
Author(s):  
Istvan T. Kleijn ◽  
Laurens H. J. Krah ◽  
Rutger Hermsen
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Integrated Approach ◽  
Control Analysis ◽  
Instantaneous Growth Rate ◽  
Bacterial Cells ◽  
Noise Propagation ◽  
Bacterial Gene ◽  
Trade Offs ◽  
Cross Correlations

AbstractIn bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing > 1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements.

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 Strategies for Enhancing in vitro Degradation of Linuron by Variovorax sp. Strain SRS 16 Under the Guidance of Metabolic Modeling

2021 ◽  
Vol 9 ◽  
Author(s):  
Kusum Dhakar ◽  
Raphy Zarecki ◽  
Daniella van Bommel ◽  
Nadav Knossow ◽  
Shlomit Medina ◽  
...  
Keyword(s):  
Genetic Manipulation ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Scale Model ◽  
Promoting Effect ◽  
Metabolic Potential ◽  
Carbon And Nitrogen ◽  
A Genome ◽  
Genome Scale

Phenyl urea herbicides are being extensively used for weed control in both agricultural and non-agricultural applications. Linuron is one of the key herbicides in this family and is in wide use. Like other phenyl urea herbicides, it is known to have toxic effects as a result of its persistence in the environment. The natural removal of linuron from the environment is mainly carried through microbial biodegradation. Some microorganisms have been reported to mineralize linuron completely and utilize it as a carbon and nitrogen source. Variovorax sp. strain SRS 16 is one of the known efficient degraders with a recently sequenced genome. The genomic data provide an opportunity to use a genome-scale model for improving biodegradation. The aim of our study is the construction of a genome-scale metabolic model following automatic and manual protocols and its application for improving its metabolic potential through iterative simulations. Applying flux balance analysis (FBA), growth and degradation performances of SRS 16 in different media considering the influence of selected supplements (potential carbon and nitrogen sources) were simulated. Outcomes are predictions for the suitable media modification, allowing faster degradation of linuron by SRS 16. Seven metabolites were selected for in vitro validation of the predictions through laboratory experiments confirming the degradation-promoting effect of specific amino acids (glutamine and asparagine) on linuron degradation and SRS 16 growth. Overall, simulations are shown to be efficient in predicting the degradation potential of SRS 16 in the presence of specific supplements. The generated information contributes to the understanding of the biochemistry of linuron degradation and can be further utilized for the development of new cleanup solutions without any genetic manipulation.

scholarly journals Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotrophMethylococcus capsulatusstr. Bath

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6685 ◽  
Author(s):  
Ankit Gupta ◽  
Ahmad Ahmad ◽  
Dipesh Chothwe ◽  
Midhun K. Madhu ◽  
Shireesh Srivastava ◽  
...  
Keyword(s):  
Amino Acids ◽  
Nitrogen Sources ◽  
Metabolic Model ◽  
Metabolic Reconstruction ◽  
Methylococcus Capsulatus ◽  
Double Knockout ◽  
Metabolic Genes ◽  
Metabolic Versatility ◽  
Balance Analysis ◽  
Genome Scale

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.

scholarly journals Mechanistic links between cellular trade-offs, gene expression, and growth

2015 ◽  
Vol 112 (9) ◽  
pp. E1038-E1047 ◽  
Author(s):  
Andrea Y. Weiße ◽  
Diego A. Oyarzún ◽  
Vincent Danos ◽  
Peter S. Swain
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Metabolic Pathways ◽  
Mechanistic Model ◽  
Living Cells ◽  
Coarse Grained ◽  
Modeling Framework ◽  
Empirical Relationships ◽  
Cellular Energy ◽  
Trade Offs

Intracellular processes rarely work in isolation but continually interact with the rest of the cell. In microbes, for example, we now know that gene expression across the whole genome typically changes with growth rate. The mechanisms driving such global regulation, however, are not well understood. Here we consider three trade-offs that, because of limitations in levels of cellular energy, free ribosomes, and proteins, are faced by all living cells and we construct a mechanistic model that comprises these trade-offs. Our model couples gene expression with growth rate and growth rate with a growing population of cells. We show that the model recovers Monod’s law for the growth of microbes and two other empirical relationships connecting growth rate to the mass fraction of ribosomes. Further, we can explain growth-related effects in dosage compensation by paralogs and predict host–circuit interactions in synthetic biology. Simulating competitions between strains, we find that the regulation of metabolic pathways may have evolved not to match expression of enzymes to levels of extracellular substrates in changing environments but rather to balance a trade-off between exploiting one type of nutrient over another. Although coarse-grained, the trade-offs that the model embodies are fundamental, and, as such, our modeling framework has potentially wide application, including in both biotechnology and medicine.

Genome scale metabolic model driven strategy to delineate host response to Mycobacterium tuberculosis infection

2021 ◽  
Author(s):  
Ankur Gupta ◽  
Ajay Kumar ◽  
Rajat Anand ◽  
Nandadulal Bairagi ◽  
Samrat Chatterjee
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Cell Line ◽  
High Throughput ◽  
Host Response ◽  
Metabolic Model ◽  
Thp1 Cell ◽  
Proteomics Data ◽  
Mycobacterium Tuberculosis Infection ◽  
Model Driven ◽  
Genome Scale

We analyzed high throughput proteomics data reflecting the response of the Mϕ-like THP1 cell line to Mycobacterium tuberculosis (M. tuberculosis) infection.

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