Metabolic Flux Analysis: Methods and Protocols

329 Pages·2014·7.65 MB·English

by  Jens O. Krömer

Methods in
Molecular Biology 1191
Jens O. Krömer
Lars K. Nielsen
Lars M. Blank Editors
Metabolic
Flux Analysis
Methods and Protocols
M M B
ETHODS IN OLECULAR IOLOGY
Series Editor
John M. Walker
School of Life Sciences
University of Hertfordshire
Hat fi eld, Hertfordshire, AL10 9AB, UK
For further volumes:
http://www.springer.com/series/7651

Metabolic Flux Analysis
Methods and Protocols
Edited by
Jens O. Krömer
Centre for Microbial Electrosynthesis (CEMES),
Advanced Water Management Centre, The University of Queensland,
Brisbane, QLD, Australia
Lars K. Nielsen
Australian Institute for Bioengineering and Nanotechnology (AIBN),
The University of Queensland, Brisbane, QLD, Australia
Lars M. Blank
Institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany
Editors
Jens O. K römer Lars K. N ielsen
Centre for Microbial Electrosynthesis (CEMES), Australian Institute for Bioengineering
Advanced Water Management Centre and Nanotechnology (AIBN)
The University of Queensland The University of Queensland
Brisbane, QLD, Australia Brisbane, QLD, Australia
Lars M. B lank
Institute of Applied Microbiology
RWTH Aachen University
Aachen, Germany
ISSN 1064-3745 ISSN 1940-6029 (electronic)
ISBN 978-1-4939-1169-1 ISBN 978-1-4939-1170-7 (eBook)
DOI 10.1007/978-1-4939-1170-7
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Prefa ce
Systems Biology has profoundly changed the way we approach the fundamental problem of
understanding cellular processes. Besides this, it has transformed the way we design new
strains for industrial applications in the fi eld of metabolic engineering. Within this systems
biology framework, we heavily rely on the so-called omics tools. These range from sequenc-
ing of an organism’s genome ( genomics ), the measurement of gene transcription ( transcrip-
tomics ) and protein abundance (p roteomics ) to the product and substrate concentrations in
metabolism ( metabolomics ). Each of these tools delivers a dataset that represents a snapshot
of information storage, information fl ow, available machinery, and building materials at a
given time. However, they mostly fail to describe the current activities in the systems that
ultimately represent the metabolic phenotype. In order for these tools to be quantitative,
the absolute concentrations of enzymes and metabolites would be needed alongside the
in vivo thermodynamic and kinetic parameters for all reactions in the network, including
information about the regulatory status of each step along a pathway. The reason for this
very complex puzzle is the fact that metabolic activity of (or fl ux through) a reaction is
described as a function of capacity-based (enzyme abundance) and k inetics-based (enzyme
activity) regulation:

Where the enzyme concentration, E , regulates the fl ux on a capacity basis. The capacity
i
is dictated by the regulation on transcriptional and translational level and also through the
stability and activity of the protein (degradation, phosphorylation). E can be measured for
i
many proteins with quantitative proteomics. The analysis of the kinetic parameters, how-
ever, is far more challenging. Current technology allows for the measurement of some of
substrates, S , products, P , and effector molecules, I . But despite the technical progress in
i i i
quantitative metabolite analysis not all concentrations are accessible with today’s technol-
ogy. More importantly, the in vivo (rather than i n-vitro ) kinetic parameters of enzymes, k ,
i
are only available for very few enzymes.
Because of this current limitation for the prediction of metabolic fl uxes from a mea-
sured inventory of the cell, an alternative technology emerged and matured over the last
decades: Metabolic Flux Analysis.
Metabolic fl uxes are the end result of the interplay of gene expression, protein concen-
tration, protein kinetics, regulation, and metabolite concentrations (thermodynamic driv-
ing forces)—basically the metabolic phenotype. A quantitative representation of this
phenotype can be estimated with metabolic fl ux analysis, which was in analogy to other
“omics-type” analyses, termed “fl uxomics.” The term fl uxomics bundles a wide variety of
v
vi Preface
tools that are at different stages of development. While some technology is well developed
(steady-state fl ux analysis using fl ux balancing (FBA) or stable isotope metabolic fl ux analy-
sis ( 13 C-MFA)) others are still specialized tools that only a few expert groups are currently
able to apply (non-steady-state approaches).

This book is dedicated to Metabolic Flux Analysis in systems that can be studied under
metabolic steady-state or pseudo-steady-state conditions, also called stationary approaches.
Under these conditions the change of intracellular metabolite concentrations ( C ) as a
MET
function of the stoichiometric matrix S and the fl ux vector v can be approximated to zero.
This approximation converts the equations system from a differential to a linear one and
greatly simplifi es its solution.
We hope that this book will open the fi eld of metabolic fl ux analysis to a wider scientifi c
community by providing helpful tricks to those who want to start a new fl ux analysis project
but are overwhelmed by the complexity of the approach. We tried to break it up into man-
ageable bits of information that can be implemented step by step following the methods
chapters provided. The overview chapters are there to explain the necessary basics to get
started and also to review what has been achieved within a few model organisms.
The book is divided into several thematic parts. Part I focuses on the fundamental char-
acteristics of the underlying networks and lays the basis for the new academic and student
in the fi eld to start developing their own metabolic networks for fl ux balance analysis. There
has been signifi cant progress in this area, and new algorithms (such as SEED) allow to
automate the network building to a large extent. Part II then looks into the application of
quantitative metabolite data and thermodynamic principles to constrain the solution space
for fl ux balance analysis (FBA) in order to obtain more realistic results. The third and larg-
est part of book (Part III) provides the experimental toolbox to conduct different types of
fl ux analysis experiments. To start with, we highlight how to determine the biomass com-
position of a complex system, the marine sponge A mphimedon queenslandica . Determining
the biomass composition is one of the most important steps in FBA, since many net-fl uxes
in a system get determined by growth rate and biomass composition. A prerequisite for
thermodynamically constraining the solution space (Part II) is the conduction of quantita-
tive metabolomics experiments. Special consideration is given to challenges that the experi-
menter faces in complex environments such as mammalian cell cultures or when trying to
downsize fl ux analysis for high-throughput screening of S accharomyces cerevisiae using
Flux-P (a FIAT-FLUX automation). Finally three different ways to analyze labelling for
13 C-fl uxomics are presented: Gas Chromatography-Mass Spectrometry (GC-MS), high-
lighted for Corynebacterium glutamicum , Nuclear Magnetic Resonance (NMR) and
Membrane Inlet Mass Spectrometry (MI–MS) for in situ CO labelling analysis for
2
1 3 C-fl uxomics.
Preface vii
Part IV is dedicated to the processing of data from 1 3 C experiments in order to achieve
fl ux distributions. Here it is important to correct for naturally occurring isotopes in the
analytes. One state-of-the-art tool for the estimation of the fl uxes (OpenFlux) is demon-
strated in detail, while a new tool to quickly create fl ux maps for visualization rounds of this
part.
Finally, Part V provides three overview chapters that summarize some key fi ndings
through FBA and 1 3 C-fl uxomics in the important biotechnological organism E scherichia
coli as well as recent progress with 1 3 C-fl uxomics in the yeast P ichia pastoris .
The fi eld is fast developing and we are sure while writing this, some other useful tools
are appearing. Nevertheless, we are confi dent that this book will provide a solid basis to
everybody interested in Metabolic Flux Analysis and provides protocols that cover a range
of relevant organisms currently used in the fi eld.
We thank all the contributors to this book and thank all our colleagues for their useful
discussions and support during the journey of fi nalizing this book.
Brisbane, QLD, Australia Jens O. K römer
Brisbane, QLD, Australia L ars K. Nielsen
Aachen, Germany L ars M . Blank

Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
PART I MODELS: STOICHIOMETRY, NETWORK GENERATION
1 Stoichiometric Modelling of Microbial Metabolism. . . . . . . . . . . . . . . . . . . . . 3
Lars Kuepfer
2 T apping the Wealth of Microbial Data in High-Throughput Metabolic
Model Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Ric C olasanti, Janaka N. Edirisinghe, T ahmineh Khazaei,
José P. F aria, S am S eaver, Fangfang X ia, and C hristopher Henry
PART II THERMODYNAMICS: A STEP BEYOND MFA
3 Constraining the Flux Space Using Thermodynamics
and Integration of Metabolomics Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 9
Keng C her S oh and Vassily H atzimanikatis
4 N ExT: Integration of Thermodynamic Constraints and Metabolomics
Data into a Metabolic Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Verónica S ofía Martínez and Lars K . Nielsen
PART III EXPERIMENTS: DESIGN CONSIDERATIONS,
FERMENTATION, ANALYTICS
5 Customization of 13C-MFA Strategy According to Cell Culture System. . . . . . 8 1
Lake-Ee Q uek and L ars K . N ielsen
6 Q uantitative Metabolomics Using ID-MS. . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
S. Aljoscha Wahl, R eza M aleki S eifar, Angela ten Pierick,
Cor Ras, Jan C . van Dam, J oseph J. Heijnen, and W alter M . van Gulik
7 D etermining the Biomass Composition of a Sponge Holobiont
for Flux Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 07
Jabin W atson, B ernard D egnan, Sandie D egnan,
and Jens O . Krömer
8 S uccessful Downsizing for High-Throughput 13C-MFA Applications. . . . . . . . 1 27
Birgitta E. Ebert and Lars M . B lank
9 Labelling Analysis for 13C MFA Using NMR Spectroscopy . . . . . . . . . . . . . . . 1 43
Paula Jouhten and Hannu M aaheimo
10 GC-MS-Based 13C Metabolic Flux Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . 165
Judith B ecker and Christoph Wittmann
ix