iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models

Sajitz-Hermstein, Max; Töpfer, Nadine; Kleeßen, Sabrina; Fernie, Alisdair R.; Nikoloski, Zoran

doi:10.1093/bioinformatics/btw465

Cited by 31 publications

(24 citation statements)

References 30 publications

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“…However, the application of TERM-FLUX is limited to studies with time-series data, which are not widely available. More recently, a method for the integration of relative metabolite levels for flux prediction, iReMet-flux, has been introduced to predict differential fluxes at the genome-scale [17], and it requires an assessment of the differential changes of all existing metabolites in a 4 GEM. This limits its application, as metabolomic data are mostly measured not at a genome-wide level but rather for only a few metabolites in a system.…”

Section: Introductionmentioning

confidence: 99%

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

Pandey

Hadadi

Hatzimanikatis

2018

Preprint

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The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI is the first method that integrates thermodynamics together with relative gene-expression and metabolomic data as constraints for FBA. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was also able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any 2 two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology. Author SummaryThe recent advances in omics technologies have provided us with an unprecedented abundance of data spanning genomes, global gene expression, and metabolomes. Though these advancements in highthroughput data collection offer an excellent opportunity for a more thorough understanding of metabolic capacities of a wide range of species, they have caused a considerable gap between "data generation" and "data integration." reconstructed model to predict the observed physiology, e.g., growth phase through omics data integration. In this study, we present a new method named REMI (Relative Expression and Metabolomic Integrations) that enables the co-integration of gene expression, metabolomics and thermodynamics data as constraints in genome-scale models. This not only allows the better understanding of how different phenotypes originate from a given genotype but also aid...

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Section: Introductionmentioning

confidence: 99%

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

Pandey

Hadadi

Hatzimanikatis

2018

Preprint

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“…:

{bold-italicv}_{b i o m a s s, l} = R G R_{l} {bold-italicv}_{b i o m a s s, r e f}, \forall l \in L

where

v_{b i o m a s s, r e f}

is the maximum biomass production flux. Metabolite levels obtained from metabolomics data are incorporated using mass‐action kinetics (Sajitz‐Hermstein et al , 2016), where the flux

v_{jl}

through an irreversible reaction j in a genotype

l

is expressed as (Equation 4):

{bold-italicv}_{bold-italicjl} = k_{italicjl} E_{italicjl} \underset{i false| {bold-italicS}_{bold-italicij} < 0}{false\prod} {()(, C_{italicil})}^{(||, S_{bold-italicij})}

…”

Section: Genome‐scale Metabolic Model Reconstructionmentioning

confidence: 99%

“…Equation (3) expressed with respect to the reference genotype is (Equation 5):

v_{jl} = v_{bold-italicj, bold-italicref} \frac{k_{jl} E_{jl}}{k_{j, r e f} E_{j, r e f}} \underset{i false| S_{ij} < 0}{false\prod} {()(, C_{il}^{rel})}^{(||, S_{ij})} = v_{bold-italicj, bold-italicref} \frac{V_{italicjl}^{italicmax}}{V_{j, r e f}^{italicmax}} \underset{i false| S_{ij} < 0}{false\prod} {()(, C_{il}^{rel})}^{(||, S_{ij})}

where

V_{jl}^{\max}

is the maximum rate of reaction

j

, and

C_{il}^{rel}

is a parameter representing the amount of metabolite

i

in genotype

l

(relative to the reference genotype) whose value is obtained from genotype‐specific metabolomics data. Similar to (Sajitz‐Hermstein et al , 2016), the largest and smallest metabolite ratios (over all measured metabolites for a given genotype) were used for metabolites not present in the metabolomics dataset.…”

Section: Genome‐scale Metabolic Model Reconstructionmentioning

confidence: 99%

“…substrate concentrations are lower than the enzyme’s K m ) (Harris and Königer, 1997; Mettler et al , 2014), to which our approach is readily applicable. However, this may not be the case for all enzymes considered in a large‐scale model (Sajitz‐Hermstein et al , 2016). In this case, the imposed metabolite ratio constraints are expected to lead to infeasibilities, especially for reactions catalyzed by enzymes without any SNPs in the corresponding genes.…”

Section: Genome‐scale Metabolic Model Reconstructionmentioning

confidence: 99%

“…In this case, the imposed metabolite ratio constraints are expected to lead to infeasibilities, especially for reactions catalyzed by enzymes without any SNPs in the corresponding genes. Thus, for a reaction with no SNPs in the corresponding genes, the reactions bounds are constrained as (Equation 7):

L B_{j, r e f} \underset{i false| S_{ij} < 0}{false\prod} {()(, C_{il}^{rel})}^{false| S_{ij} false|} \leq v_{jl} + {dev}_{jl}^{neg} - {dev}_{jl}^{pos} \leq U B_{j, r e f} \underset{i false| S_{ij} < 0}{false\prod} {()(, C_{il}^{rel})}^{false| S_{ij} false|}, \forall j \in bold-italicJ & \forall l \in bold-italicL

where

{bold-italicdev}_{italicjl}^{bold-italicpos}, and {bold-italicdev}_{italicjl}^{bold-italicneg}

represent deviations from mass‐action kinetics (Sajitz‐Hermstein et al , 2016).…”

Section: Genome‐scale Metabolic Model Reconstructionmentioning

confidence: 99%

See 2 more Smart Citations

SNPeffect: identifying functional roles of SNPs using metabolic networks

Sarkar

Maranas

2020

The Plant Journal

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Summary Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome‐wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single‐nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype‐to‐phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme‐coding genes. To this end, we also constructed a genome‐scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth‐determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.

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Genome-Scale Modeling of Photorespiratory Pathway Manipulation

Küken

Nikoloski

2017

Methods in Molecular Biology

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Quantifying the redistribution of metabolic reaction fluxes under experimental scenarios that affect the photorespiratory pathway can provide insights about the coupling of this pathway with other parts of metabolism. However, differential flux profiling on a genome-scale level remains the biggest challenge in modern systems biology. Here we present a protocol for applying a constraint-based approach, termed iReMet-Flux, that integrates data about relative metabolite levels in a stoichiometric metabolic model to predict differential fluxes at a genome-scale level under mild modeling assumptions. We demonstrate how iReMet-Flux can be employed to investigate the interplay between photorespiration and other pathways at a genome-scale level, and complements flux profiling methods based on radioactive tracer labeling.

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iReMet-flux: constraint-based approach for integrating relative metabolite levels into a stoichiometric metabolic models

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 31 publications

References 30 publications

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genome-Scale Modeling of Photorespiratory Pathway Manipulation

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