scholarly journals Maintaining maximal metabolic flux by gene expression control

2017 ◽  
Author(s):  
Robert Planqué ◽  
Josephus Hulshof ◽  
Bas Teusink ◽  
Johan Hendriks ◽  
Frank J. Bruggeman
Keyword(s):  
Gene Expression ◽  
Metabolic Flux ◽  
Metabolic Enzymes ◽  
Reaction Rates ◽  
Robust Adaptive Control ◽  
High Rate ◽  
Metabolic Reaction ◽  
Control Logic ◽  
Gene Expression Control ◽  
Regulation Of Metabolism

SummaryMany evolutionarily successful bacteria attain high growth rates across growth-permissive conditions. They express metabolic networks that synthesise all cellular components at a high rate. Metabolic reaction rates are bounded by the concentration of the catalysing enzymes and cells have finite resources available for enzyme synthesis. Therefore, bacteria that grow fast should express needed metabolic enzymes at precisely tuned concentrations. To maintain fast growth in a dynamic environment, cells should adjust gene expression of metabolic enzymes. The activity of many of the associated transcription factors is regulated by their binding to intracellular metabolites. We study optimal metabolite-mediated regulation of metabolic-gene expression that preserves maximisation of metabolic fluxes across varying conditions. We logically derive the underlying control logic of this type of optimal regulation, which we term ‘Specific Flux (q) Optimization by Robust Adaptive Control’ (qORAC), and illustrate it with several examples. We show that optimal metabolic flux can be maintained in the face of K changing parameters only if the number of transcription-factor-binding metabolites is at least equal to K. qORAC-regulation of metabolism can generally be achieved with basic biochemical interactions, indicating that metabolism can operate close to optimality. The theory that we present is directly applicable to synthetic biology, biotechnology and fundamental studies of the regulation of metabolism.

scholarly journals Bayesian metabolic flux analysis reveals intracellular flux couplings

2019 ◽  
Vol 35 (14) ◽  
pp. i548-i557 ◽  
Author(s):  
Markus Heinonen ◽  
Maria Osmala ◽  
Henrik Mannerström ◽  
Janne Wallenius ◽  
Samuel Kaski ◽  
...  
Keyword(s):  
Steady State ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Reaction Network ◽  
Reaction Rates ◽  
Supplementary Information ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Metabolic Systems ◽  
Genome Scale

AbstractMotivationMetabolic flux balance analysis (FBA) is a standard tool in analyzing metabolic reaction rates compatible with measurements, steady-state and the metabolic reaction network stoichiometry. Flux analysis methods commonly place model assumptions on fluxes due to the convenience of formulating the problem as a linear programing model, while many methods do not consider the inherent uncertainty in flux estimates.ResultsWe introduce a novel paradigm of Bayesian metabolic flux analysis that models the reactions of the whole genome-scale cellular system in probabilistic terms, and can infer the full flux vector distribution of genome-scale metabolic systems based on exchange and intracellular (e.g. 13C) flux measurements, steady-state assumptions, and objective function assumptions. The Bayesian model couples all fluxes jointly together in a simple truncated multivariate posterior distribution, which reveals informative flux couplings. Our model is a plug-in replacement to conventional metabolic balance methods, such as FBA. Our experiments indicate that we can characterize the genome-scale flux covariances, reveal flux couplings, and determine more intracellular unobserved fluxes in Clostridium acetobutylicum from 13C data than flux variability analysis.Availability and implementationThe COBRA compatible software is available at github.com/markusheinonen/bamfa.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals iMTBGO: An Algorithm for Integrating Metabolic Networks with Transcriptomes Based on Gene Ontology Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 252-259
Author(s):  
Zhitao Mao ◽  
Hongwu Ma
Keyword(s):  
Gene Expression ◽  
Metabolic Networks ◽  
Metabolic Flux ◽  
Network Models ◽  
Reaction Rates ◽  
Marker Genes ◽  
Published Data ◽  
Flux Analysis ◽  
Prediction Errors ◽  
Balance Analysis

Background:Constraint-based metabolic network models have been widely used in phenotypic prediction and metabolic engineering design. In recent years, researchers have attempted to improve prediction accuracy by integrating regulatory information and multiple types of “omics” data into this constraint-based model. The transcriptome is the most commonly used data type in integration, and a large number of FBA (flux balance analysis)-based integrated algorithms have been developed.Methods and Results:We mapped the Kcat values to the tree structure of GO terms and found that the Kcat values under the same GO term have a higher similarity. Based on this observation, we developed a new method, called iMTBGO, to predict metabolic flux distributions by constraining reaction boundaries based on gene expression ratios normalized by marker genes under the same GO term. We applied this method to previously published data and compared the prediction results with other metabolic flux analysis methods which also utilize gene expression data. The prediction errors of iMTBGO for both growth rates and fluxes in the central metabolic pathways were smaller than those of earlier published methods.Conclusion:Considering the fact that reaction rates are not only determined by genes/expression levels, but also by the specific activities of enzymes, the iMTBGO method allows us to make more precise predictions of metabolic fluxes by using expression values normalized based on GO.

scholarly journals Maintaining maximal metabolic flux by gene expression control

2018 ◽  
Vol 14 (9) ◽  
pp. e1006412 ◽  
Author(s):  
Robert Planqué ◽  
Josephus Hulshof ◽  
Bas Teusink ◽  
Johannes C. Hendriks ◽  
Frank J. Bruggeman
Keyword(s):  
Gene Expression ◽  
Metabolic Flux ◽  
Expression Control ◽  
Gene Expression Control

scholarly journals Robustifying Experimental Tracer Design for13C-Metabolic Flux Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Martin Beyß ◽  
Victor D. Parra-Peña ◽  
Howard Ramirez-Malule ◽  
Katharina Nöh
Keyword(s):  
Experimental Design ◽  
Prior Knowledge ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Streptomyces Clavuligerus ◽  
Reaction Rates ◽  
Computational Method ◽  
Flux Analysis ◽  
Metabolic Reaction ◽  
Diverse Range

13C metabolic flux analysis (MFA) has become an indispensable tool to measure metabolic reaction rates (fluxes) in living organisms, having an increasingly diverse range of applications. Here, the choice of the13C labeled tracer composition makes the difference between an information-rich experiment and an experiment with only limited insights. To improve the chances for an informative labeling experiment, optimal experimental design approaches have been devised for13C-MFA, all relying on some a priori knowledge about the actual fluxes. If such prior knowledge is unavailable, e.g., for research organisms and producer strains, existing methods are left with a chicken-and-egg problem. In this work, we present a general computational method, termed robustified experimental design (R-ED), to guide the decision making about suitable tracer choices when prior knowledge about the fluxes is lacking. Instead of focusing on one mixture, optimal for specific flux values, we pursue a sampling based approach and introduce a new design criterion, which characterizes the extent to which mixtures are informative in view of all possible flux values. The R-ED workflow enables the exploration of suitable tracer mixtures and provides full flexibility to trade off information and cost metrics. The potential of the R-ED workflow is showcased by applying the approach to the industrially relevant antibiotic producer Streptomyces clavuligerus, where we suggest informative, yet economic labeling strategies.

Decreased carO gene expression and OXA-type carbapenemases among extensively drug-resistant Acinetobacter baumannii strains isolated from burn patients in Tehran, Iran

2020 ◽  
Author(s):  
Elham Abbasi ◽  
Hossein Goudarzi ◽  
Ali Hashemi ◽  
Alireza Salimi Chirani ◽  
Abdollah Ardebili ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acinetobacter Baumannii ◽  
High Rate ◽  
Burn Patients ◽  
Drug Resistant ◽  
Disc Diffusion ◽  
Carbapenem Resistant ◽  
Extensively Drug Resistant ◽  
Sds Page ◽  
Extensively Drug Resistance

AbstractA major challenge in the treatment of infections has been the rise of extensively drug resistance (XDR) and multidrug resistance (MDR) in Acinetobacter baumannii. The goals of this study were to determine the pattern of antimicrobial susceptibility, blaOXA and carO genes among burn-isolated A. baumannii strains. In this study, 100 A. baumannii strains were isolated from burn patients and their susceptibilities to different antibiotics were determined using disc diffusion testing and broth microdilution. Presence of carO gene and OXA-type carbapenemase genes was tested by PCR and sequencing. SDS-PAGE was done to survey CarO porin and the expression level of carO gene was evaluated by Real-Time PCR. A high rate of resistance to meropenem (98%), imipenem (98%) and doripenem (98%) was detected. All tested A. baumannii strains were susceptible to colistin. The results indicated that 84.9% were XDR and 97.9% of strains were MDR. In addition, all strains bore blaOXA-51 like and blaOXA-23 like and carO genes. Nonetheless, blaOXA-58 like and blaOXA-24 like genes were harbored by 0 percent and 76 percent of strains, respectively. The relative expression levels of the carO gene ranged from 0.06 to 35.01 fold lower than that of carbapenem-susceptible A. baumannii ATCC19606 and SDS – PAGE analysis of the outer membrane protein showed that all 100 isolates produced CarO. The results of current study revealed prevalence of blaOXA genes and changes in carO gene expression in carbapenem resistant A.baumannii.

scholarly journals The [KIL-d] Element Specifically Regulates Viral Gene Expression in Yeast

Genetics ◽  
2000 ◽  
Vol 155 (2) ◽  
pp. 601-609 ◽  
Author(s):  
Zsolt Tallóczy ◽  
Rebecca Mazar ◽  
Denise E Georgopoulos ◽  
Fausto Ramos ◽  
Michael J Leibowitz
Keyword(s):  
Gene Expression ◽  
Phenotypic Expression ◽  
Viral Gene Expression ◽  
Virus Genome ◽  
Viral Rna ◽  
High Rate ◽  
Viral Gene ◽  
Double Stranded Rna ◽  
Yeast Prions ◽  
Killer Virus

Abstract The cytoplasmically inherited [KIL-d] element epigenetically regulates killer virus gene expression in Saccharomyces cerevisiae. [KIL-d] results in variegated defects in expression of the M double-stranded RNA viral segment in haploid cells that are “healed” in diploids. We report that the [KIL-d] element is spontaneously lost with a frequency of 10−4–10−5 and reappears with variegated phenotypic expression with a frequency of ≥10−3. This high rate of loss and higher rate of reappearance is unlike any known nucleic acid replicon but resembles the behavior of yeast prions. However, [KIL-d] is distinct from the known yeast prions in its relative guanidinium hydrochloride incurability and independence of Hsp104 protein for its maintenance. Despite its transmissibility by successive cytoplasmic transfers, multiple cytoplasmic nucleic acids have been proven not to carry the [KIL-d] trait. [KIL-d] epigenetically regulates the expression of the M double-stranded RNA satellite virus genome, but fails to alter the expression of M cDNA. This specificity remained even after a cycle of mating and meiosis. Due to its unique genetic properties and viral RNA specificity, [KIL-d] represents a new type of genetic element that interacts with a viral RNA genome.

scholarly journals High-performance chemical- and light-inducible recombinases in mammalian cells and mice

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Benjamin H. Weinberg ◽  
Jang Hwan Cho ◽  
Yash Agarwal ◽  
N. T. Hang Pham ◽  
Leidy D. Caraballo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mammalian Cells ◽  
High Performance ◽  
Genome Engineering ◽  
Genetic Circuits ◽  
Control Of Gene Expression ◽  
Expression Control ◽  
Gene Expression Control ◽  
High Performance Systems ◽  
Spatiotemporal Control

Abstract Site-specific DNA recombinases are important genome engineering tools. Chemical- and light-inducible recombinases, in particular, enable spatiotemporal control of gene expression. However, inducible recombinases are scarce due to the challenge of engineering high performance systems, thus constraining the sophistication of genetic circuits and animal models that can be created. Here we present a library of >20 orthogonal inducible split recombinases that can be activated by small molecules, light and temperature in mammalian cells and mice. Furthermore, we engineer inducible split Cre systems with better performance than existing systems. Using our orthogonal inducible recombinases, we create a genetic switchboard that can independently regulate the expression of 3 different cytokines in the same cell, a tripartite inducible Flp, and a 4-input AND gate. We quantitatively characterize the inducible recombinases for benchmarking their performances, including computation of distinguishability of outputs. This library expands capabilities for multiplexed mammalian gene expression control.

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