Multi-objective experimental design for 13 C-based metabolic flux analysis

Bouvin, Jeroen; Cajot, S.; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Impe, Jan Van; Bernaerts, Kristel

doi:10.1016/j.mbs.2015.08.002

Cited by 17 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For -based fluxomics experiments, a mixture of 56% uniformly labeled glucose (U-GLC) and 44% position 1-labeled glucose (1-GLC) was used (Cambridge Isotope Laboratories, Tewksbury, Massachusetts, United States). This optimal mixture was determined based on the network model and d -optimal design method in [17]. For expression-based gene clustering, additional samples for transcriptomics were harvested from growth in MMGLC supplemented with 5 g/L BD Bacto™Casamino Acids, Technical (MMGLC+CAS).…”

Section: Methodsmentioning

confidence: 99%

Transcriptomic and fluxomic changes in Streptomyces lividans producing heterologous protein

et al. 2018

Self Cite

View full text Add to dashboard Cite

BackgroundThe Gram-positive Streptomyces lividans TK24 is an attractive host for heterologous protein production because of its high capability to secrete proteins—which favors correct folding and facilitates downstream processing—as well as its acceptance of methylated DNA and its low endogeneous protease activity. However, current inconsistencies in protein yields urge for a deeper understanding of the burden of heterologous protein production on the cell. In the current study, transcriptomics and -based fluxomics were exploited to uncover gene expression and metabolic flux changes associated with heterologous protein production. The Rhodothermus marinus thermostable cellulase A (CelA)—previously shown to be successfully overexpressed in S. lividans—was taken as an example protein.ResultsRNA-seq and -based metabolic flux analysis were performed on a CelA-producing and an empty-plasmid strain under the same conditions. Differential gene expression, followed by cluster analysis based on co-expression and co-localization, identified transcriptomic responses related to secretion-induced stress and DNA damage. Furthermore, the OsdR regulon (previously associated with hypoxia, oxidative stress, intercellular signaling, and morphological development) was consistently upregulated in the CelA-producing strain and exhibited co-expression with isoenzymes from the pentose phosphate pathway linked to secondary metabolism. Increased expression of these isoenzymes matches to increased fluxes in the pentose phosphate pathway. Additionally, flux maps of the central carbon metabolism show increased flux through the tricarboxylic acid cycle in the CelA-producing strain. Redirection of fluxes in the CelA-producing strain leads to higher production of NADPH, which can only partly be attributed to increased secretion.ConclusionsTranscriptomic and fluxomic changes uncover potential new leads for targeted strain improvement strategies which may ease the secretion stress and metabolic burden associated with heterologous protein synthesis and secretion, and may help create a more consistently performing S. lividans strain. Yet, links to secondary metabolism and redox balancing should be further investigated to fully understand the S. lividans metabolome under heterologous protein production.Electronic supplementary materialThe online version of this article (10.1186/s12934-018-1040-6) contains supplementary material, which is available to authorized users.

show abstract

Section: Methodsmentioning

confidence: 99%

Transcriptomic and fluxomic changes in Streptomyces lividans producing heterologous protein

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Still, in these and other theoretical and practical studies a part of the fluxes remained non-identifiable [27,44]. Interestingly, a study of Bouvin et al [45] exemplified, also using a MO-ED approach, that it is indeed possible to find CLEs with comparable information content, but considerably different tracer costs.…”

Section: Methods and Modelsmentioning

confidence: 99%

“…Till now, if at all, only 13 C labeled tracers have been considered in CLE costs examinations while further experimental-analytical efforts were neglected so far (see e.g. [45]), meaning that a fine-grained cost function which relates all cost factors to the design parameters has to be set up. The overall cost function of a 13 C MFA study is composed of three parts, the experimental, the analytical, and the modeling part.…”

Section: Methods and Modelsmentioning

confidence: 99%

A Pareto approach to resolve the conflict between information gain and experimental costs: Multiple-criteria design of carbon labeling experiments

Nöh¹,

Niedenführ²,

Beyß³

et al. 2018

PLoS Comput Biol

View full text Add to dashboard Cite

Science revolves around the best way of conducting an experiment to obtain insightful results. Experiments with maximal information content can be found by computational experimental design (ED) strategies that identify optimal conditions under which to perform the experiment. Several criteria have been proposed to measure the information content, each emphasizing different aspects of the design goal, i.e., reduction of uncertainty. Where experiments are complex or expensive, second sight is at the budget governing the achievable amount of information. In this context, the design objectives cost and information gain are often incommensurable, though dependent. By casting the ED task into a multiple-criteria optimization problem, a set of trade-off designs is derived that approximates the Pareto-frontier which is instrumental for exploring preferable designs. In this work, we present a computational methodology for multiple-criteria ED of information-rich experiments that accounts for virtually any set of design criteria. The methodology is implemented for the case of 13C metabolic flux analysis (MFA), which is arguably the most expensive type among the ‘omics’ technologies, featuring dozens of design parameters (tracer composition, analytical platform, measurement selection etc.). Supported by an innovative visualization scheme, we demonstrate with two realistic showcases that the use of multiple criteria reveals deep insights into the conflicting interplay between information carriers and cost factors that are not amendable to single-objective ED. For instance, tandem mass spectrometry turns out as best-in-class with respect to information gain, while it delivers this information quality cheaper than the other, routinely applied analytical technologies. Therewith, our Pareto approach to ED offers the investigator great flexibilities in the conception phase of a study to balance costs and benefits.

show abstract

“…As an alternative of the D-value, a precision metric based on confidence intervals of individual fluxes is also used to assess different 13 C tracers’ ability to provide information in flux analysis (Metallo et al, 2009). One strategy to improve the tracer selection is to consider a mixture of several tracers and then search for the optimal fraction of each tracer which minimizes the flux uncertainty metric (Bouvin et al, 2015; Crown and Antoniewicz, 2012; Möllney et al, 1999; Nöh and Wiechert, 2006; Walther et al, 2012). Other factors including the cost of the tracer (U-13C glucose is relatively cheap) and number of experiments possible are also taken into consideration.…”

Section: Advanced Issues For 13c-mfamentioning

confidence: 99%

Understanding metabolism with flux analysis: From theory to application

Dai

Locasale

2017

Metabolic Engineering

View full text Add to dashboard Cite

Quantitative and qualitative knowledge of metabolic rates (i.e. fluxes) over a metabolic network and in specific cellular compartments gives insights into the regulation of metabolism and helps to understand the contribution of metabolic alterations to pathology. In this review we introduce methodology to resolve metabolic fluxes from stable isotope labeling and relevant techniques in model development, model simplification, flux uncertainty analysis and experimental design that together is termed metabolic flux analysis. Finally we discuss applications using metabolic flux analysis to elucidate mechanisms pertinent to tumor cell metabolism. We hope that this review gives the readers a brief introduction of how flux analysis is conducted, how technical issues related to it are addressed, and how its application has contributed to our knowledge of tumor cell metabolism.

show abstract

Multi-objective experimental design for 13 C-based metabolic flux analysis

Cited by 17 publications

References 24 publications

Transcriptomic and fluxomic changes in Streptomyces lividans producing heterologous protein

Transcriptomic and fluxomic changes in Streptomyces lividans producing heterologous protein

A Pareto approach to resolve the conflict between information gain and experimental costs: Multiple-criteria design of carbon labeling experiments

Understanding metabolism with flux analysis: From theory to application

Contact Info

Product

Resources

About