Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

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.

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Advances in metabolic flux analysis toward genome-scale profiling of higher organisms

Bioscience Reports ◽

10.1042/bsr20170224 ◽

2018 ◽

Vol 38 (6) ◽

Cited By ~ 11

Author(s):

Georg Basler ◽

Alisdair R. Fernie ◽

Zoran Nikoloski

Keyword(s):

Large Scale ◽

Metabolic Flux ◽

Cell Types ◽

Flux Analysis ◽

Metabolic Labeling ◽

Modeling Framework ◽

Metabolomics Data ◽

Technological Advances ◽

A Genome ◽

Genome Scale

Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies.

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State-of-the-Art Genetic Modalities to Engineer Cyanobacteria for Sustainable Biosynthesis of Biofuel and Fine-Chemicals to Meet Bio–Economy Challenges

Life ◽

10.3390/life9030054 ◽

2019 ◽

Vol 9 (3) ◽

pp. 54 ◽

Cited By ~ 4

Author(s):

Aqib Zafar Khan ◽

Muhammad Bilal ◽

Shahid Mehmood ◽

Ashutosh Sharma ◽

Hafiz M. N. Iqbal

Keyword(s):

High Throughput ◽

Metabolic Flux Analysis ◽

Large Scale ◽

Metabolic Flux ◽

Flux Analysis ◽

Fine Chemicals ◽

Carbon Regulation ◽

Regulation Mechanisms ◽

Large Scale Cultivation ◽

Genome Scale

In recent years, metabolic engineering of microorganisms has attained much research interest to produce biofuels and industrially pertinent chemicals. Owing to the relatively fast growth rate, genetic malleability, and carbon neutral production process, cyanobacteria has been recognized as a specialized microorganism with a significant biotechnological perspective. Metabolically engineering cyanobacterial strains have shown great potential for the photosynthetic production of an array of valuable native or non-native chemicals and metabolites with profound agricultural and pharmaceutical significance using CO2 as a building block. In recent years, substantial improvements in developing and introducing novel and efficient genetic tools such as genome-scale modeling, high throughput omics analyses, synthetic/system biology tools, metabolic flux analysis and clustered regularly interspaced short palindromic repeats (CRISPR)-associated nuclease (CRISPR/cas) systems have been made for engineering cyanobacterial strains. Use of these tools and technologies has led to a greater understanding of the host metabolism, as well as endogenous and heterologous carbon regulation mechanisms which consequently results in the expansion of maximum productive ability and biochemical diversity. This review summarizes recent advances in engineering cyanobacteria to produce biofuel and industrially relevant fine chemicals of high interest. Moreover, the development and applications of cutting-edge toolboxes such as the CRISPR-cas9 system, synthetic biology, high-throughput “omics”, and metabolic flux analysis to engineer cyanobacteria for large-scale cultivation are also discussed.

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Genome-scale metabolic flux analysis of Streptomyces lividans growing on a complex medium

Journal of Biotechnology ◽

10.1016/j.jbiotec.2012.04.010 ◽

2012 ◽

Vol 161 (1) ◽

pp. 1-13 ◽

Cited By ~ 31

Author(s):

Pieter-Jan D’Huys ◽

Ivan Lule ◽

Dominique Vercammen ◽

Jozef Anné ◽

Jan F. Van Impe ◽

...

Keyword(s):

Metabolic Flux Analysis ◽

Metabolic Flux ◽

Streptomyces Lividans ◽

Complex Medium ◽

Flux Analysis ◽

Genome Scale

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Pathway-flux based biomarker for immunotherapy response in melanoma.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e22064 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e22064-e22064

Author(s):

Jian Li ◽

Qi Mei ◽

Weiting Cheng ◽

Guangyuan Hu ◽

Xianglin Yuan ◽

...

Keyword(s):

Immune System ◽

Metabolic Flux ◽

Expression Profiles ◽

Objective Response ◽

Superior Performance ◽

Flux Analysis ◽

Signaling Transduction ◽

A Genome ◽

Genome Scale ◽

Pathway Flux

e22064 Background: Immune checkpoint therapy (ICT) refers to therapeutic interventions that specifically target immune evasion mechanisms to restore the host immunity with anti-tumor ability. The ICT has revolutionized the immune-based treatment across > 30 different cancers including solid tumors and hematopoietic malignancies, with an ORR of 30% and a 7%-12% grades 3-5 irAEs in average. However, a substantial unmet point is the development of a biomarker with which response of ICT can be predicted before treatment for individual patients. Methods: In order to face this challenge this study developed an advanced genome-scale pathway flux analysis (GPFA) to evaluate the strength of signaling transduction and metabolic flux in immune system. The input of GPFA is the gene expression profiles of individual objects and the output of GPFA can be summarized in a index system, IM.Index, with following definition: IM.Index = 1.78E-4 * Σ flux(P) + 2.37E-4 * Σ flux(P) p ∈ signaling transduction p ∈ energy metabolism. Subsequently, the IM.Index was applied to analyze genetic data of two independent cohorts of melanoma patients treated with anti-PD-1 therapy (nivolumab a. pembrolizumab). Results: The IM.Index predicted the response of anti-PD-1 therapy (nivolumab) in the first cohort with an odds ratio (OR) of 3.14 (95%CI: 1.16-8.45; p = 3.10E-3; AUC = 0.82) and with a sensitivity 89% and specificity 76%. The prediction on overall survival (OS) of this cohort achieved an hazard ratio (HR) of 1.53 (95%CI: 1.22-1.92; p = 7.8E-3). Subsequently, the prediction result for the anti-PD-1 therapy (pembrolizumab) in the second cohort achieved an OR of 2.12 (95%CI: 1.22-3.66; p = 4.50E-4; AUC = 0.87) and the OS prediction in this cohort reached an HR of 1.24 (95%CI: 1.04-1.47; p = 1.40E-2). Comparison with other four potential biomarkers (TMB, TNB, neo-peptide load and cytolytic score) related to immunotherapy showed a comparative outcome of the IM.Index regarding diagnosis and prognosis in melanoma. For instance, IM.Index showed a superior performance on objective response rate (ORR) of 70% and AUC of 0.83. Conclusions: In conclusion this study demonstrated that a pathway flux analysis at a genome-scale may be explorative in biomarker research in immunotherapy, since this type of analysis could reflect the strength or functional status of the immune system. The IM.Index developed in this study may also be applied to investigation the treatment response of immunotherapy in other types of cancer.

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Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

Metabolites ◽

10.3390/metabo8010003 ◽

2018 ◽

Vol 8 (1) ◽

pp. 3 ◽

Cited By ~ 2

Author(s):

Tyler Backman ◽

David Ando ◽

Jahnavi Singh ◽

Jay Keasling ◽

Héctor García Martín

Keyword(s):

Metabolic Flux Analysis ◽

Metabolic Flux ◽

Flux Analysis ◽

13C Metabolic Flux Analysis ◽

Scale Models ◽

Genome Scale ◽

Bow Tie

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Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

10.21203/rs.2.16651/v2 ◽

2020 ◽

Author(s):

Piyush Nanda ◽

Pradipta Patra ◽

Manali Das ◽

Amit Ghosh

Keyword(s):

Ethyl Acetate ◽

Metabolic Flux ◽

Carbon Sources ◽

Growth Dynamics ◽

Dry Weight ◽

Metabolic Model ◽

Flux Analysis ◽

Specific Rate ◽

A Genome ◽

Genome Scale

Abstract Background Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to optimal fermentation only in oxygen limiting conditions that render it a suitable host for industrial-scale protein production. Ethyl acetate, an important industrial chemical, has been demonstrated to be a major overflow metabolite during aerobic batch cultivation with a specific rate of 0.12 g per g dry weight per hour. Here, we reconstruct a genome-scale metabolic model of the yeast to better explain the observed phenotypes and aid further hypothesis generation. Results We report the first genome-scale metabolic model, iPN730, using Build Fungal Model in KBase workspace. The inconsistencies in the draft model were semi-automatically corrected using literature and published datasets. The curated model comprises of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments (organelles). The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. The URC pyrimidine degradation pathway incorporated into the model enables it to grow on uracil or urea as the sole nitrogen source. Conclusion The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism, particularly that of pyrimidine metabolism and ethyl acetate production. Metabolic flux analysis using the model will enable hypotheses generation to gain a deeper understanding of metabolism in weakly Crabtree positive yeast and in fungal biodiversity in general.

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METAMDB: METABOLIC ATOM MAPPING DATABASE

10.1101/2021.10.05.463172 ◽

2021 ◽

Author(s):

Collin Starke ◽

Andre Wegner

Keyword(s):

Metabolic Flux Analysis ◽

Metabolic Networks ◽

Metabolic Flux ◽

Flux Analysis ◽

Analysis Software ◽

13C Metabolic Flux Analysis ◽

Atom Mapping ◽

Metabolic Reactions ◽

Genome Scale ◽

Mapping Models

MetAMDB (https://metamdb.tu-bs.de/) is an open source metabolic atom mapping database, providing atom mappings for around 75000 metabolic reactions. Each atom mapping can be inspected and downloaded either as a RXN file or as a graphic in SVG format. In addition, MetAMDB offers the possibility of automatically creating atom mapping models based on user-specified metabolic networks. These models can be of any size (small to genome scale) and can subsequently be used in standard 13C metabolic flux analysis software.

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

Scientific Reports ◽

10.1038/s41598-021-87643-8 ◽

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.

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