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Fluxomics: Mass spectrometry versus quantitative imaging


Abstract and Figures

The recent development of analytic high-throughput technologies enables us to take a bird's view of how metabolism is regulated in real time. We have known for a long time that metabolism is highly regulated at all levels, including transcriptional, posttranslational and allosteric controls. Flux through a metabolic or signaling pathway is determined by the activity of its individual components. Fluxomics aims to define the genes involved in regulation by following the flux. Two technologies are used to monitor fluxes. Pulse labeling of the organism or cell with a tracer, such as 13C, followed by mass spectrometric analysis of the partitioning of label into different compounds provides an efficient tool to study flux and to compare the effect of mutations on flux. The second approach is based on the use of flux sensors, proteins that respond with a conformational change to ligand binding. Fluorescence resonance energy transfer (FRET) detects the conformational change and serves as a proxy for ligand concentration. In contrast to the mass spectrometry assays, FRET nanosensors monitor only a single compound. Both methods provide high time resolution. The major advantages of FRET nanosensors are that they yield data with cellular and subcellular resolution and the method is minimally invasive.
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Fluxomics: mass spectrometry versus quantitative imaging
Wolfgang Wiechert1, Oliver Schweissgut1, Hitomi Takanaga2, and Wolf B Frommer2
1 University of Siegen, Paul-Bonatz Strasse 9–11, 57068 Siegen, Germany
2 Carnegie Institution, 260 Panama Street, California 94305, USA
The recent development of analytic high-throughput technologies enables us to take a bird’s view
of how metabolism is regulated in real time. We have known for a long time that metabolism is
highly regulated at all levels, including transcriptional, posttranslational and allosteric controls.
Flux through a metabolic or signaling pathway is determined by the activity of its individual
components. Fluxomics aims to define the genes involved in regulation by following the flux.
Two technologies are used to monitor fluxes. Pulse labeling of the organism or cell with a tracer,
such as 13C, followed by mass spectrometric analysis of the partitioning of label into different
compounds provides an efficient tool to study flux and to compare the effect of mutations on flux.
The second approach is based on the use of flux sensors, proteins that respond with a
conformational change to ligand binding. Fluorescence resonance energy transfer (FRET) detects
the conformational change and serves as a proxy for ligand concentration. In contrast to the mass
spectrometry assays, FRET nanosensors monitor only a single compound. Both methods provide
high time resolution. The major advantages of FRET nanosensors are that they yield data with
cellular and subcellular resolution and the method is minimally invasive.
Cells and organisms dynamically acclimate their metabolism to changing conditions, such as
nutrient availability, temperature or stress. Flux across the plasma membrane and through
metabolic pathways is continuously optimized. Sensory systems that are coupled with
complex signaling networks adjust the flux and its direction via regulatory circuits. The
sensory systems measure the extracellular availability of a given metabolite and other
external cues, as well as the intracellular level of the metabolite or subsequent intermediates,
and send signals to regulate transporter and enzyme activities by posttranslational
modification, protein turnover, or changes in the rate of their biosynthesis. At present, we
know little about the receptors that detect the signals, the signaling networks that transmit
the information, nor their integration. A new discipline, fluxomics (Box 1), aims to
systematically analyze the fluxes occurring within a cell, and at some point even to unravel
these networks in a multicellular organism. The availability of large mutant collections,
RNA interference (RNAi) or overexpression line libraries, and large chemical libraries now
puts us in a position to unravel these networks, provided we have the tools to measure
metabolite flux, especially in high throughput. This review discusses two sets of
methodologies that have been developed to measure flux: first, mass spectrometric analysis
of changes in metabolite levels after pulse labeling, and second, quantitative imaging using
fluorescence resonance energy transfer (FRET)-based nanosensors. These recent
developments are contrasted to established isotope labeling techniques for metabolic steady-
state systems.
Corresponding author: Frommer, Wolf B (
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Published in final edited form as:
Curr Opin Plant Biol
. 2007 June ; 10(3): 323–330. doi:10.1016/j.pbi.2007.04.015.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
Box 1
Flux in analogy to electric current, flux is the passage of molecules (moles
of a particular metabolite) through a metabolic or transport step per
unit cell mass per unit time. Defined by the concentrations of
compounds participating in a reaction, enzyme level and enzyme
Fluxome commonly referred to as the totality of all fluxes in a system, e.g. a
cell. The term fluxome was defined by Sauer et al. [55] as the array of
fluxes (reaction rates on a per unit cell volume or per unit cell mass
basis) for all of the reactions that occur in the organism. This term is
typically used in the context of pulse labeling with 13C-labelled
metabolites, followed by mass spectrometry to analyze multiple
(typically hundreds of) metabolites in parallel. Such analyses have
been expanded to the comparison of fluxes in microorganismal
mutants. The term fluxome can also be used to describe the factors
(gene networks) that define a specific flux rate.
Fluxomics the discipline that analyzes the fluxome as one part of systems
biology [56]. Provides mathematically defined networks of metabolic
reactions and their regulation. Fluxomics are defined here as an
approach to identify the genes that affect the fluxes which control the
steady state of a single metabolite (using FRET nanosensors).
The importance of fluxes
All kinds of physical systems — living cells are no exception — are governed by a dualism
between potentials and flows. Potential quantities are related to energy levels whereas flow
quantities are related to transport or conversion phenomena. In mechanics, potentials and
flows are represented by potential energy and force (a force is a flow of momentum), in
hydraulics by pressure and liquid flow, and in electrical systems by voltage and current. For
example, voltage can be directly measured by using electrostatic forces, whereas current,
that is the flux of electrons, can be measured using a light bulb: the number of collisions per
time unit determines the intensity of light emitted. In biological networks, the dualism is
given by chemical activities and reaction rates, also called metabolic fluxes. At present, the
potential, for example the concentration gradient, can be determined but fluxes must be
derived. The precise relation between potentials and flows is described by constitutive laws
such as Hooke’s law in mechanics, Ohm’s law for electrical systems, Fick’s law for
diffusion, the Nernst equation for electrochemistry, or mass action kinetics in chemistry.
However, these are just the simplest examples.
The dualism between substance concentrations, or more precisely chemical potentials, and
metabolic fluxes is expressed by the fact that one part is causal for the other. On the one
hand, chemical potentials of reactants constitute the driving forces for fluxes. On the other
hand, metabolic fluxes change the potentials of metabolite pools. Usually, flows in physical
systems are closely related to system functions (i.e. dynamics), whereas potentials rather
describe their stock-keeping aspects (i.e. statics). In the field of biochemical networks, in
particular, metabolic fluxes are the ultimate manifestation of the cell’s function under certain
physiological conditions.
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It might appear surprising that flux, such as that across the plasma membrane, is not
optimized for a wide spectrum of conditions. The plasma membrane provides a limited
compartment [1], however, and a given transporter (at least if not modified), has defined
kinetic properties, such as high affinity (typically coupled with low capacity) optimized for
importing nutrients present in low abundance or high capacity (typically coupled with low
affinity). Thus, when nutrient availability or demand change, either a different set of
transporters is required or the properties of the transporter have to be adjusted to allow for
optimal flux [2–4]. The same is essentially true for many isozymes. It is thus important to
determine both the flux potential and the actual flow, and to compare the system’s behavior
under different conditions, for example, different levels of nutrient availability.
In recent years, fluxome analysis under steady-state conditions has become a widely used
tool. It is applied to characterize an organism [5,6], to diagnose the effect of genetic
manipulations [7], to compare the behavior of one organism under different physiological
conditions [8], to detect the presence of certain metabolic pathways [9,10], to compare
mutant libraries [11••], or to monitor different growth phases [12]. This tool has been applied
to all classes of organisms, including bacteria, unicellular eukaryotes, animal and plant cells,
and even whole organs. Some recent reviews overlook the general methodology at the state
of the art [13,14–17]. Particular problems of plant fluxome analysis have been addressed by
Ratcliffe and Shachar-Hill [18] and by Schwender et al. [19], and its application in plants is
exemplified in a number of papers [20–22].
Methods in fluxomics
Fluxes are usually determined under metabolic steady-state conditions. There is, however,
no reason why fluxomics should be restricted to this case. In fact, the rapid redirection of
fluxes under dynamic conditions is an important capability of living organisms.
Consequently, a new aspect in fluxomics is the consideration of time-dependent fluxes. As
we discover below, the experimental methods required to determine these fluxes are quite
different from steady-state methods. Interestingly, some current developments are
combining methods from dynamic and steady-state approaches to reach new ambitious
Although, the description of living cells by fluxes seems to be more natural than a
description by substance concentrations, cells have predominantly been characterized by
their intracellular pool sizes. The reason is rather simple: direct measurements of flows are
possible in other physical systems but there is almost no known measurement procedure that
directly yields information on metabolic reaction rates (an exotic exception might be the
light emission in the luciferase reaction [Figure 1a]). This makes fluxomics a rather singular
discipline in the ‘omics’ field.
As an example that illustrates this difference, electrical currents can be measured not only
directly by magnetic induction but also indirectly from the voltage change of a capacitor. In
the latter case, the signal must be differentiated with respect to time to obtain the time-
dependent current. For an electrical system, this is usually not crucial because the measured
signal has a high precision. This situation is completely different in the biological case,
where measurement noise is significant and a direct differentiation leads to error
amplification. To solve this problem, only initial velocities are calculated or signal
smoothing algorithms are applied (Figure 1b). However, this helps only in case of a high
data density or slowly changing pool sizes. If both approaches are not applicable, a model-
based data evaluation is preferred in which a constitutive law is assumed (e.g. Michaelis–
Menten kinetics) and only the parameters of this law are estimated (Figure 1c). This strongly
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reduces the required amount of measured data, but the interpretation of the data is
‘prejudiced’ by the assumed law.
The present review focuses on the comparison and classification of different fluxomics
methods for dynamic and static conditions and their interrelations. The flow-potential
viewpoint will serve as a guideline. As the dual quantity of fluxes is given by the metabolite
pool sizes, methods from metabolomics play an important role here by supplying the raw
data for flux determination.
Flux analysis under highly dynamic conditions
The classical idea of measuring enzyme kinetic data from initial slopes of NAD signals
immediately leads to indirect flux analysis methods that are based on time-resolved pool-
size signals. A well-known class of experiments to investigate metabolic networks under
highly dynamic conditions are represented by stimulus–response experiments ([23,24;
Figure 2a). Here, an external stimulus is imposed on the system by suddenly raising the
extracellular concentration of a substance at time zero. The intracellular pathways
immediately respond to the stimulus, usually within seconds or minutes. Under these highly
dynamic conditions, the most difficult analytical problem is clearly the reliable quantitative
measurement of intracellular pool sizes within short time intervals and with minimal
interference with cellular functions.
Two methods have been developed to obtain dynamic flux information from measurements
of pool size. In the first, a pulse stimulus in combination with rapid sampling serves to draw
samples from a cell culture at high sampling frequency, up to several samples per second
[23,25]. Subsequently, each sample is rapidly inactivated, for example by cold methanol
quenching. Cell disruption and separating the intercellular metabolites allows pool sizes to
be determined using modern high performance liquid chromatography (LC)-MS instruments
[26]. In essence, this is a technical improvement over the method originally introduced to
biology by Calvin and Benson [27] to determine the carbon path in photosynthesis. The
reliability of this procedure is still a controversy, however, because of the leakage of cell
membranes when using methanol quenching. Moreover, it does not provide spatial
information when applied to tissues or organs. Neither does it provide subcellular spatial
resolution, which is of primary relevance in eukaryotic systems due to their high level of
compartmentation. Finally, because of the destructive nature of this approach, the analysis of
tissues is limited to parallel sampling. Thus, to date, fluxomics had been restricted to the
analysis of unicellular systems in the population average.
An exciting new alternative to this method is given by the direct expression of sensors for
the concentration of molecular metabolites in the cell. Certain proteins, specifically
chemoreceptors, respond to ligand binding with a conformational change [28]. Protein
conformation can be measured directly, using fluorescence resonance energy transfer [29].
FRET reporters in which recognition elements from diverse bacterial chemoreceptors are
combined with green fluorescent protein (GFP) variants have permitted the development of
genetically encoded flux-sensors for a variety of small molecules, such as calcium,
phosphate, carbohydrates (ribose, glucose, maltose and sucrose) and amino acids [30,31••,
32,33,34,35]. These sensors can detect flux changes in vivo because they monitor steady-
state levels of a given ion or metabolite.
A single sensor can already be used to analyze large mutant collections from any organism.
The major advantage of these sensors over any other technology lies in the time resolution
that they can provide (up to millisecond range as shown for calcium [36,37]). These sensors
also provide high spatial resolution; for example, they have successfully been applied to
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analyze metabolite levels in individual cells within intact organs [38,39,40••]. On the one
hand, a single sensor is able to monitor fewer substances than a MS approach.
Since these FRET sensors are genetically encoded, they provide subcellular resolution. This
subcellular resolution is not achieved by optical methods, but genetically by targeting. The
addition of targeting signals has successfully been used to monitor glucose flux across the
membrane of the endoplasmic reticulum (ER) or nuclear fluxes [31••,41. Thus, local fluxes
can be monitored by anchoring the sensors in specific membranes, for example at the cell
surface [32••], and it is conceivable that even flux in localized domains, such as rafts, can be
monitored this way. Finally, and most importantly, the sensor approach is non-destructive,
making it possible to monitor an intact organ (or organism) with minimal invasion.
Obviously, the expression of the sensors with a cell adds a new buffer for the analyte of
interest that may affect metabolic flux.
The computer evaluation of dynamic experiments to estimate the time-dependent
intracellular fluxes between the metabolite pools must currently rely on the assumption of a
reaction kinetic mechanism (i.e. constitutive laws) for all of the involved transport and
reaction steps. This gives rise to a differential equation model that describes the dynamics of
intracellular pool sizes. By fitting this model to the measured data, the in vivo parameters of
the enzyme kinetics are estimated ([42]; Figure 3). Finally, the time-dependent metabolic
fluxes (as a function of time) can be directly computed from the fitted model [43,44].
13C metabolic flux analysis in steady state
13C metabolic flux analysis (13C-MFA) is currently the best-established fluxomics
technology. In contrast to flux analysis under highly dynamic conditions, the 13C-MFA
method always requires the assumption of metabolic steady state for the entire duration of
the experiment. This limits the possible applications, but on the other hand, no assumptions
on reaction kinetics are needed to derive the fluxes from the available measurements.
Clearly, in this situation, knowledge of the intracellular metabolite pool sizes does not help
to determine the fluxes.
This major source of information for computing the fluxes is the isotope labeling of
substrates that are fed into the system (usually with 13C but other isotopes are discussed
[45]). After switching the feed to a labeled substrate, the isotopes are distributed over the
intercellular network by metabolic activities (Figure 2b). After some time, both the
metabolic fluxes and the fluxes and fractions of labeled material in the metabolite pools can
be shown to have reached a steady state [44]. This fractional information can be obtained
using NMR or MS. Interestingly, once again, the intracellular fluxes have to be determined
from a concentration-like quantity.
The measured fractions of isotopic labeling in intracellular pools form the basis of a rather
complex mathematical model that describes the distribution of labeled material over cellular
metabolism and relates the unknown fluxes with the given measurements. This is not a real
biological model but rather a physical model that provides probabilistic rules describing the
distribution of isotope label. Thus, the validity of this complex model is non-critical, in
contrast to mechanistic reaction kinetic models for biochemical networks in a dynamic
metabolic state.
By fitting the isotope distribution model to the measured data, intracellular fluxes can finally
be determined [17]. Additional information, given by the knowledge of forward and
backward fluxes in bidirectional reaction steps, is generated by this method. This
information can also be obtained from dynamic models that are based on reaction kinetics, if
all reversible reaction steps are consequently modeled with reversible reaction kinetic
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formulae. This, in turn, increases the number of parameters to be estimated from the rapid-
sampling data.
13C metabolic flux analysis on the ultra-short time scale
The standard 13C-MFA method takes rather long experimental durations, approximately 2–3
times the doubling times of the microorganism, because the label must first accumulate in
the biomass before being measured. In many cases, it is not possible to keep a living system
in a metabolic steady state for such a long time. Examples in which this has been achieved,
however, include cells in transient growth phases, cells in industrial fermentation conditions
(fed batch) or genetically unstable recombinant cells. Application to slowly growing or non-
growing cells is of particular interest for plant physiology. An application that is unique to
plants is given by studies on C1-metabolism in photoautotrophic plants because, in this case,
the isotopic steady state contains no information on metabolic fluxes if the substrate carries
only a single carbon atom [46].
Nevertheless, most physiological states of a cellular system can be considered to be in at
least a quasi metabolic steady state, which means that, for short time durations (minutes to
hours), metabolism is approximately in a steady state. In this case, a novel 13C-flux analysis
method can be applied that does not rely on the steady-state assumption for label enrichment
in metabolite pools. By contrast, the enrichment of labeled material in the metabolite pools
is now observed under isotopically transient conditions ([47]; Figure 2c).
The new isotopically non-stationary method represents an interesting fusion of the methods
for MFA under highly dynamic conditions that are based on rapid sampling and 13C-MFA
under steady-state conditions. Metabolite pools are kept constant but must be measured
because they represent the system’s capacity for labeled material.
This isotopically non-stationary method (INST-MFA) became possible only recently
because mass spectrometers are now able to determine reliable fractional label information
from low-concentration pools of intermediate metabolites [48]. The processes for the
computational evaluation of such transient data are substantially more difficult than those
for the evaluation of data describing the isotopic steady state because the accumulation of
labeled materials in the intracellular pools is now described by a dynamic model. Thus, the
system of equations that describes the distribution of label over the network has to be
replaced by differential equations [49].
A first application of INST-MFA to Escherichia coli proved that information on all
intracellular fluxes could be obtained in just 15 s, during which about 20 samples were
drawn from the culture [50••]. This makes isotopically non-stationary metabolic flux
analysis a promising candidate for the analysis of slowly growing plant or animal cells.
Several teams are currently working to establish the new method from experimental and
theoretical view-points [45,49,51,52].
Conclusions and new horizons
A still-speculative extension of the INST-MFA method may be the use of experiments under
both metabolic and isotopic steady states (Figure 2d). An exploratory simulation study [24]
has already shown that INST-MFA would significantly improve the available information
on reaction kinetic parameters in vivo and, ultimately, on the dynamic fluxes.
Another challenging development is the model-free determination of flux information from
pool-size data by smoothing and differentiation of high-density data (Figure 1b). It should
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be noticed that this is already possible in other chemical disciplines, as has been
demonstrated with the determination of diffusion flows [53].
Altogether, a detailed theoretical basis has been developed for fluxome analysis using 13C-
labeling. These mathematical tools are currently adapted for fluxome studies using the
FRET flux sensors. FRET sensors can provide direct information on steady-state levels and
fluxes in a specific subcellular compartment. Moreover, these sensors provide information
on flux in individual cells and thus significantly expand the potential of metabolomics. The
combination of FRET sensors with microfluidics and modeling promises new insights into
the regulation of metabolism in response to a changing environment, and into the underlying
signaling networks not only in multicellular, eukaryotic systems but also in microorganisms
from E. coli to yeast. Both 13C and nano-sensor MFA technologies will rapidly be applied to
fluxomics, as exemplified in the case of yeast mutants that are affected in sugar signaling
This work was supported by grants from Bundesministerium für Bildung und Forschung (BMBF) (SysMAP
Project) to WW and from the US National Institute of Health (Roadmap Initiative ‘Metabolomics technology
development’ [R33DK070272]) and US Department of Energy (DE-FG02-04ER15542) to WBF.
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Figure 1.
Time-dependent flux determination from direct and indirect measurement data. (a) Direct
flux measurement, the ideal case. (b) Computation of time derivatives from high-density
pool size data. (c) Fitting of a kinetic model to time resolved pool size data.
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Figure 2.
Conceptionally different approaches to obtain flux information from measured pool size
and/or isotope labeling data. (a) Rapid sampling of intracellular pools under highly dynamic
conditions of a stimulus response. (b) Standard 13C-labeling experiment under metabolic
and isotopic steady-state conditions. (c) Isotopically non-stationary 13C-labeling experiment
under metabolic steady-state conditions. (d) Fictive combined experiment under
metabolically and isotopically non-stationary conditions.
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Figure 3.
Changes in glucose concentration in HepG2 cells stably expressing FLIPglu600 μ. (a)
External perfused glucose concentration. (b) Measured yellow fluorescent protein (YFP)/
cyan fluorescent protein (CFP) emission ratios and simulation curve based on a kinetic
model. (c) Cytosolic glucose concentration from simulation. (d) Underlying compartment
model for analysis of glucose homeostasis in HepG2 cells (modified from Fehr et al. [31••]).
Glucose is transported reversibly across the plasma and ER membranes and is
phosphorylated irreversibly in the cytosol.
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... Similar to other -omics fields, fluxomics is a technology driven field where recent advances in instrumentation, software and databases have significantly contributed to development. Different analytical tools and approaches in fluxomics have been reviewed recently (Wiechert et al., 2007;Niittylae et al., 2009;Klein and Heinzle, 2012;Winter and Krömer, 2013;Niedenführ et al., 2015). Even if different analytical tools are utilized in fluxomics/metabolomics research, nuclear magnetic resonance (NMR) spectroscopy (Giraudeau, 2020) and mass spectrometry (MS) (Wiechert et al., 2007; Antoniewicz, 2019; Babele and Young, 2020) are the most commonly used tools in metabolomic studies. ...
... Different analytical tools and approaches in fluxomics have been reviewed recently (Wiechert et al., 2007;Niittylae et al., 2009;Klein and Heinzle, 2012;Winter and Krömer, 2013;Niedenführ et al., 2015). Even if different analytical tools are utilized in fluxomics/metabolomics research, nuclear magnetic resonance (NMR) spectroscopy (Giraudeau, 2020) and mass spectrometry (MS) (Wiechert et al., 2007; Antoniewicz, 2019; Babele and Young, 2020) are the most commonly used tools in metabolomic studies. ...
Full-text available
Fluxomics is a new omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics is one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways. By determining metabolic pathways, researchers can probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics can be used to understand metabolic pathways and has many applications in biotechnology and pharmacology. Here, we briefly introduce fluxomics with a review of the most recent studies and common analytical tools.
... Metabolomics is analysis of all the cellular metabolites in a cell and their interactions in the microbial community. Analysis of cellular metabolites within a cell and community over a time period in real time is known as fluxomics (Wiechert et al. 2007). Information on factors regulating growth and metabolism of microbial communities can be accessed by metabolomics, and fluxomics can provide the missing links in the regulatory pathways involved in metabolism of environmental pollutants. ...
... Bioinformatics has also led to finding possible cures for detoxification of the environment. Scientists expert in analyzing biological data can use the computational tools to solve the problems of bioremediation (Westhead et al. 2003). The important branches of bioinformatics are genomics, transcriptomics, proteomics, organic databases, molecular phylogenetics, and microarray informatics, which are crucial in understanding bioinformatics. ...
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Among the various microbial biodegradation techniques, molecular microbiology methods have revolutionized microbial biotechnology, thus leading to rapid and high-throughput methods for culture-independent assessment and exploitation of microbes present in polluted environments. Whether organic or inorganic, pollutants present in contaminated sites can cause an imbalance in the ecosystem by affecting the flora and fauna. The efficiency of naturally occurring microorganisms for field bioremediation could be significantly improved by the microbial molecular biology approach for its comparatively high efficiency and safety. Many techniques, including polymerase chain reaction (PCR), fluorescent in situ hybridization (FISH), denaturing gradient gel electrophoresis (DGGE), ribosomal intergenic spacer analysis (RISA), amplified ribosomal DNA restriction analysis (ARDRA), terminal-restriction fragment length polymorphism (TRFLP), single-strand conformation polymorphism (SSCP), and ribosomal intergenic spacer analysis (RISA) can be selectively employed in microbial flora and ecology research. Recent methods such as genotypic profiling, metagenomics, ultrafast genome pyrosequencing, metatranscriptomics, metaproteomics, and metabolomics have provided exemplary knowledge about microbial communities and their role in the bioremediation of environmental pollutants.
... Desiccation-tolerance plants are able to survive in the dried condition with low water content (< 50%) and retain little intracellular water concentration. The biochemical analysis revealed the production and accumulation of various metabolites and gives the wide information about the biochemical tolerance mechanism of the plants (Cascante and Marin 2008;Wiechert et al. 2007). The results of biochemical analysis confirmed the varied amount of accumulation of various metabolites in the dehydrated and rehydrated samples of S. wightii and S. involvens. ...
To better understand the mechanism of desiccation tolerance of resurrection fern allies Selaginella wightii Hieron and Selaginella involvens (S.W.) Spring, the present study was planned to compare the quantitative profile of photosynthetic pigments, carotenoids, amino acids, proline, protein, IAA and non reducing sugar content of desiccated, rehydrated and fresh samples of Selaginella wightii Hieron and Selaginella involvens (S.W.) Spring. The variation of photosynthetic pigments, amino acids, proline, protein, indole acetic acid (IAA) and reducing sugar content of desiccated and rehydrated samples of S. wightii and S. involvens were estimated using the standard procedure. The ten months dehydrated samples of S. wightii and S. involvens showed high concentration of chlorophyll, carotenoids, proteins, amino acids, proline and IAA. The observed biochemical results confirm the resurrection and desiccation tolerant potential of Selaginella wightii Hieron and Selaginella involvens (S.W.) Spring. The observed chlorophyll results suggested that the studied two fern allies are homoiochlorophyllous plants. The reducing sugar content was high in fresh samples and low amount in the ten months dehydrated samples of S. wightii and S. involvens. It may be due to the conversion of reducing sugar to non-reducing sugar to tolerate the desiccation. The biochemical composition change confirms the resurrection potential of S. wightii and S. involvens.
... Previously, stable isotope techniques and radiotracers have been employed for flux analysis and cellular transport processes, but they lack cellular and subcellular spatio-temporal resolution. 29,30 FRET-based genetically encoded nanosensors pave the way to dynamic quantification of metabolite with cellular and subcellular resolution in a nondestructive manner. 31,32 The response of our FLIP-NT nanosensor elucidated that the designed nanosensor has the potential for the quantification of flux or to measure transport across the intracellular membranes that could help the researchers to identify the dynamic of nitrate, underlying processes, and their regulatory switch. ...
Full-text available
Nitrate (NO3–) is a critical source of nitrogen (N) available to microorganisms and plants. Nitrate sensing activates signaling pathways in the plant system that impinges upon, developmental, molecular, metabolic, and physiological responses locally, and globally. To sustain, the high crop productivity and high nutritional value along with the sustainable environment, the study of rate-controlling steps of a metabolic network of N assimilation through fluxomics becomes an attractive strategy. To monitor the flux of nitrate, we developed a non-invasive genetically encoded fluorescence resonance energy transfer (FRET)-based tool named “FLIP-NT” that monitors the real-time uptake of nitrate in the living cells. The developed nanosensor is suitable for real-time monitoring of nitrate flux in living cells at subcellular compartments with high spatio-temporal resolution. The developed FLIP-NT nanosensor was not affected by the pH change and have specificity for nitrate with an affinity constant (Kd) of ∼5 μM. A series of affinity mutants have also been generated to expand the physiological detection range of the sensor protein with varying Kd values. It has been found that this sensor successfully detects the dynamics of nitrate fluctuations in bacteria and yeast, without the disruption of cellular organization. This FLIP-NT nanosensor could be a very important tool that will help us to advance the understanding of nitrate signaling.
... Fluxomics studies capture metabolic fluxes in an organism. In a given time period the real time flux of cellular metabolites is called fluxomics (Wiechert et al., 2007). The fluxome or metabolic fluxes gives information about how the genome and environment affect metabolic fluxes. ...
Bioremediation is an emerging technology that can clean-up contaminated environments in an effective and economically feasible way. However, gaps exist as the growth and regulatory mechanisms operating in these microorganisms are not completely known. Earlier approaches in bioremediation included treatability via microbial culture but it has several drawbacks, which can be overcome by metagenomics approaches via studying and exploring un-cultured microorganisms present in the environment at the contaminated site. Recent advances in omics technologies, however, are making it possible to understand microbial physiology and their metabolic functioning as well. Availability of genome sequences of most of the microorganisms involved in bioremediation and advancement in the omics approaches including transcriptomics, proteomics and metabolomics have led to much progress in the understanding of metabolic functioning at the level of microbial cell to that of the entire microbial population. It is expected that the omics tools will further help in understanding the response of microbial communities to the expanding list of contaminants and xenobiotics which would lead to successful bioremediation initiatives worldwide. The present chapter is small effort to discuss the omics approaches in bioremediation.
... The results from their study showed the presence of more than 4,776 metabolite in these polluted sites, thereby revealing the high metabolic heterogeneity within the study sites. A number of other studies that have used metabolomic and fluxomics approaches to study the biodegradation of anthropogenic environmental pollutants were carried out by Villas-Bôas and Bruheim (2007), Wiechert et al. (2007), Keum et al. (2008), Tang et al. (2009), Brune and Bayer (2012). ...
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Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation using single omics-approach, however the present review specifically addresses the integrative role of the multi-omics approaches in microbial-mediated bioremediation. Additionally, we discussed how the multi-omics approaches help to comprehend and explore the structural and functional aspects of the microbial consortia in response to the different environmental pollutants and presented some success stories by using these approaches.
... Since the phenotype is the net result of these interactions, it is immensely important to unveil the different cell components and their interactions, not only for an integrated understanding of physiology, but also for the practical applications of biological systems as cell factories [2][3][4]. High-throughput omics data provides quantitative readouts of these cell components, including the cell's DNA sequence (i.e., genomics [5,6]), mRNA expression (i.e., transcriptomics [7]), metabolite abundance (i.e., metabolomics [8,9]), protein composition (i.e., proteomics [10][11][12]), and in vivo enzyme activities (i.e., fluxomics [13,14]). This valuable biological information enables the identification and quantification of individual components of a biological system, and we are now facing the challenge of understanding the interactions among these components [1,15] by appropriately analyzing and interpreting the omics data. ...
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Constraint-based metabolic modeling such as flux balance analysis (FBA) has been widely used to simulate cell metabolism. Thanks to its simplicity and flexibility, numerous algorithms have been developed based on FBA and successfully predicted the phenotypes of various biological systems. However, their phenotype predictions may not always be accurate in FBA because of using the objective function that is assumed for cell metabolism. To overcome this challenge, we have developed a novel computational framework, namely omFBA, to integrate multi-omics data (e.g. transcriptomics) into FBA to obtain omics-guided objective functions with high accuracy. In general, we first collected transcriptomics data and phenotype data from published database (e.g. GEO database) for different microorganisms such as Saccharomyces cerevisiae. We then developed a "Phenotype Match" algorithm to derive an objective function for FBA that could lead to the most accurate estimation of the known phenotype (e.g. ethanol yield). The derived objective function was next correlated with the transcriptomics data via regression analysis to generate the omics-guided objective function, which was next used to accurately simulate cell metabolism at unknown conditions. We have applied omFBA in studying sugar metabolism of S. cerevisiae and found that the ethanol yield could be accurately predicted in most of the cases tested (>80%) by using transcriptomics data alone, and revealed valuable metabolic insights such as the dynamics of flux ratios. Overall, omFBA presents a novel platform to potentially integrate multi-omics data simultaneously and could be incorporated with other FBA-derived tools by replacing the arbitrary objective function with the omics-guided objective functions.
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Microbial cell factory development often faces bottlenecks after initial rounds of design-build-test-learn (DBTL) cycles as engineered producers respond unpredictably to further genetic modifications. Thus, deciphering metabolic flux and correcting bottlenecks are key components of DBTL cycles. Here, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using both ¹³ C-metabolic flux analysis ( ¹³ C-MFA) and metabolite measurements. The results indicated the conservation of the cyclic Entner-Doudoroff (ED)-EMP pathway flux, downregulation of the TCA cycle and pyruvate shunt, and glyoxylate shunt activation. At the metabolite level, the CRISPR/dCpf1-interference mediated multiplex repression decreased gluconate/2-ketogluconate secretion and altered several intracellular TCA metabolite concentrations, leading to succinate overflow. Further strain engineering based on the metabolic knowledge first employed an optimal ribosome binding site (RBS) to achieve stronger product-substrate growth coupling (1.6-fold increase). Then, deletion strains were constructed using ssDNA recombineering. Of the five strains tested, deletion of the PHA operon (Δ phaAZC-IID ) resulted in a 2.2-fold increase in growth phase production compared to the optimal RBS construct. After 72 h of batch cultivation, the Δ phaAZC-IID strain had 1.5-fold and 1.8-fold increases of indigoidine titer compared to the improved RBS construct and the original strain, respectively. Overall, the findings provided actionable DBTL targets as well as insights into physiological responses and flux buffering when new recombineering tools were used for engineering P. putida KT2440.
With several complete plant genomes currently available and with massive amounts of transcriptomic, proteomic, and metabolomic data being generated by plant scientists, one could assume that making sense of all this data is only a minor problem. However, it has been pointed out by many leading plant scientists that this is not the case and that developing appropriate modeling skills and tools may lack behind the technological progress that allows the data generation. In recent years, the plant science community became more and more aware of the importance of different kinds of analysis and modeling approaches, like metabolic flux analysis. Accordingly, in this book, contributions from different expert authors have been assembled to give a current view on plant metabolic networks, from the analysis of the molecular parts to approaches of mathematical modeling of plant metabolic networks at the cellular level. Other processes like gene regulation, cell signaling or models at the whole plant or ecosystem level certainly have their justification [1], but have been mostly excluded here to give cellular metabolism special attention.
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Diffusion in liquids can still be predicted only with high uncertainty due to the lack of sufficient experimental data. Diffusion experiments are complex and time-consuming. Furthermore, the concentration dependence of the diffusion coefficients requires usually several experiments even for binary mixtures. The possibility to extract this information from one short Raman diffusion experiment is explored here. A general identification framework is provided which does not require the a priori specification of a diffusion coefficient model structure but establishes the concentration dependence directly from the data. The methodology is used to determine the diffusion coefficient in the mixture ethyl acetate–cyclohexane in a wide concentration range.
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Cytosolic calcium oscillations control signaling in animal cells, whereas in plants their importance remains largely unknown. In wild-type Arabidopsis guard cells abscisic acid, oxidative stress, cold, and external calcium elicited cytosolic calcium oscillations of differing amplitudes and frequencies and induced stomatal closure. In guard cells of the V-ATPase mutantdet3, external calcium and oxidative stress elicited prolonged calcium increases, which did not oscillate, and stomatal closure was abolished. Conversely, cold and abscisic acid elicited calcium oscillations in det3, and stomatal closure occurred normally. Moreover, in det3 guard cells, experimentally imposing external calcium-induced oscillations rescued stomatal closure. These data provide genetic evidence that stimulus-specific calcium oscillations are necessary for stomatal closure.
Model-based analysis of enzyme kinetics allows the determination of optimal conditions for their use in biocatalysis. For biotransformations or fermentative approaches the modeling of metabolic pathways or complex metabolic networks is necessary to obtain model-based predictions of steps which limit product formation within the network. To set up adequate kinetic models, relevant mechanistic information about enzyme properties is required and can be taken from in vitro studies with isolated enzymes or from in vivo investigations using stimulus-response experiments which provide a lot of kinetic information about the metabolic network. But with increasing number of reaction steps and regulatory interdependencies in the network structure the amount of simulation data dramatically increases and the simulation results from the dynamic models become difficult to analyze and interpret. Demonstrated for an Escherichia coli model of the central carbon metabolism, methods for visualization and animation of simulation data were applied and extended to facilitate model analysis and biological interpretation. The dynamic metabolite pool and metabolic flux changes were visualized simultaneously by a software tool. In addition, a new quantification method for enzyme activation/inhibition was proposed, and this information was implemented in the metabolic visualization.
A dynamic model describing carbon atom transitions in the central metabolism of Saccharomyces cerevisiae is used to investigate the influence of transamination reactions and protein turnover on the transient behavior of 13C-labeling chemostat experiments. The simulations performed suggest that carbon exchange due to transamination and protein turnover can significantly increase the required time needed for metabolites in the TCA cycle to reach isotopic steady state, which is in agreement with published experimental observations. On the other hand, transamination and protein turnover will speed-up the net rate of incorporation of labeled carbon into some free and protein-bound amino acids. The simulation results indicate that the pattern of labeled carbon incorporation into amino acids obtained from biomass hydrolysate shows significant deviation from the commonly assumed first-order kinetics behavior until after three residence times. These observations suggest that greater caution should be used while also pointing to new opportunities in the design and interpretation of 13C-labeling experiments. © 2004 Wiley Periodicals, Inc.
Fluxes through metabolic networks are crucial for cell function, and a knowledge of these fluxes is essential for understanding and manipulating metabolic phenotypes. Labeling provides the key to flux measurement, and in network flux analysis the measurement of multiple fluxes allows a flux map to be superimposed on the metabolic network. The principles and practice of two complementary methods, dynamic and steady-state labeling, are described, emphasizing best practice and illustrating their contribution to network flux analysis with examples taken from the plant and microbial literature. The principal analytical methods for the detection of stable isotopes are also described, as well as the procedures for obtaining flux maps from labeling data. A series of boxes summarizing the key concepts of network flux analysis is provided for convenience.
The goal of this work was to obtain rapid sampling technique to measure transient metabolites in vivo. First, a pulse of glucose was added to a culture of the yeast Saccharomyces cerevisiae growing aerobically under glucose limitation. Next, samples were removed at 2 to 5 s intervals and quenched using methods that depend on the metabolite measured. Extracellular glucose, excreted products, as well as glycolytic intermediates (G6P, F6P, FBP, GAP, 3-PG, PEP, Pyr) and cometabolites (ATP, ADP, AMP, NAD(+), NADH) were measured using enzymatic or HPLC methods. Significant differences between the adenine nucleotide concentrations in the cytoplasm and mitochondria indicated the importance of compartmentation for the regulation of the glycolysis. Changes in the intra- and extracellular levels of metabolites confirmed that glycolysis is regulated on a time scale of seconds. (c) 1997 John Wiley & Sons, Inc. Biotechnol Bioeng 55: 305-316, 1997.