Kinetic flux profiling (KFP) aims to provide a practical experimental approach for measuring metabolic fluxes in live cells. The central idea of KFP is that larger metabolic fluxes are associated with faster transmission of isotopic label from added nutrient to downstream metabolites. The half-time of labeling of a metabolite will depend directly on the speed of transmission of label into the metabolite (i.e., the flux) and inversely on the size of the metabolite pool (i.e., the intracellular metabolite concentration). The KFP approach has been used to investigate nitrogen assimilation fluxes in exponentially growing E. coli1
, metabolic flux changes accompanying onset of carbon starvation in E. coli2
, carbon fluxes in E. coli
fed with glucose versus acetate (Daniel Amador-Noguez and J.D.R., unpublished data), aromatic amino-acid pathway flux in nitrogen-starved E. coli
and carbon flux in growing, quiescent and virally infected human fibroblasts (Hilary Coller, Johanna Scarino, Joshua Munger, Thomas Shenk, B.D.B., J.D.R., unpublished data).
This concept is illustrated in . During exponential growth, the rate of production of each metabolite (influx) should match its rate of consumption (efflux) so that the intracellular concentration remains constant. Under this pseudosteady state, if an external nutrient is instantaneously switched from natural to isotopically labeled, for a metabolite X
directly downstream of nutrient assimilation, unlabeled X
) will be replaced over time by its labeled counterpart (X
*) and the fraction of unlabeled X
) will decay exponentially (). The rate constant of this decay (kX
) is determined by the ratio of the flux through X
) to the total pool size of X
) as shown in . Therefore, one can calculate the flux through X
) from kX
can be obtained experimentally by the protocol described here and XT
by a diversity of literature approaches4-6
, including the protocol of Bennett et al
. For an example of quantitative analysis of data from more complex cases (which realistically involve metabolites not immediately downstream of labeled nutrient and being formed by multiple reactions), see the ANTICIPATED RESULTS section.
Figure 1 Illustration of the basic concept of KFP. Metabolite X is generated directly from nutrient and is consumed during biosynthesis (eventually leading to biomass production). At metabolic steady state, the influx and efflux of X pool are both fX. The differential (more ...)
Accurately measuring kX
is most easily achieved by rapid and complete switch of the nutrient of interest from unlabeled to isotope-labeled form, followed by fast sampling of cells1
. For reliable flux measurements, both steps must be accomplished without perturbing cellular metabolism. Otherwise, the artifacts induced by the handling steps will mask the true cellular metabolic state8-12
. To meet this need for nonadherent cells (e.g., E. coli1-3,13
, Saccharomyces cerevisiae3
), we developed a filter culture technique in which cells are grown on a membrane filter sitting on top of agarose plates loaded with media. The cells are fed by nutrient diffusion from the underlying medium up through the filter. This technique enables isotope switching by transferring the filter between agarose plates of different composition. It also allows fast metabolic quenching by transferring the filter into cold organic solvent, which stops metabolism (initially due to the temperature drop and subsequently by denaturing enzymes) and simultaneously initiates the extraction process by disrupting the cell membrane. Movements of the filter can be done in ~1 s. Minor deviations in the transfer time tend to have minimal impact, as the cells continue to receive nutrients from the filter during the transfer.
Although the overall strategy is the same, the filter culture approach is not necessary for adherent cell types like human fibroblasts. Instead, medium removal via quick aspiration is followed by addition of isotope-labeled medium for isotope switch or cold organic solvent for metabolic quenching and extraction. Specific cell-handling steps are provided in the protocol.
Overview of the workflow and experimental setups for KFP are shown in . The step of quantifying heavy and light (i.e., labeled and unlabeled) isotopic forms can be achieved by any appropriate form of mass spectrometry (MS). Typically, we use liquid chromatography (LC)–electrospray ionization (ESI)–triple-quadrupole MS operating in multiple reaction monitoring (MRM) mode. MRM is a targeted form of tandem mass spectrometry (MS/MS) with the advantage of excellent sensitivity and linear dynamic range. LC separation before the MS/MS analysis is valuable for separating isobaric compounds. Other forms of chromatography-MS should also be applicable. These include gas chromatography–MS and LC–time-of-flight–MS. For further information on analytical options, see the protocol of Bennett et al
. The rate constants obtained from KFP can then be combined with intracellular concentrations of metabolites determined separately to calculate fluxes.
Overview of experimental procedure for KFP (Steps 1–8).
Typically, a single replicate of KFP is informative (as even a single replicate contains multiple time points). For quantitative work where small differences between conditions are involved or precise flux estimates are desired, 3–4 replicates are preferred. As KFP experiments are internally controlled (by the multiple isotopic forms), external controls are not required. It is often useful, however, to conduct switches from unlabeled to unlabeled media to ensure against unanticipated metabolic shifts due to media change rather than isotope switch.
Application and limitations
Kinetic flux profiling provides an approach for experimentally quantifying metabolic fluxes in live cells. The principal starting information required is the structure (connectivity) of the metabolic network being investigated (see ANTICIPATED RESULTS for an example). An advantage of the technique is that the experimental data provide a check on the pathway architecture, based on the requirement for precursor metabolites to become labeled before their downstream products. Quantitative analysis of fluxes by KFP is facilitated when the fluxes are at steady state; however, differential KFP (described below) can provide quantitative data even outside of the steady-state condition. Further quantitative assumptions are not required, as fluxes are calculated directly from the experimental results obtained as described here (kX
) and in the companion protocol of Bennett et al
One can choose to use basic nutrient(s) (e.g., glucose, ammonia, etc.) to introduce isotopic labels or to use special tracers for a specific pathway. We have successfully applied KFP to measure fluxes involved in nitrogen metabolism in E. coli1,2
, and for this reason, this protocol uses the example of 15
Cl as the labeled nutrient in this model organism. The KFP approach is, however, also widely applicable to other labeled nutrients and cell types, as demonstrated by published work from our laboratory using 13
C-glucose as the tracer in both E. coli
and S. cerevisiae3
and unpublished data from our laboratory with cultured human cells.
It is important to note that KFP measures gross fluxes: the labeling kinetics is related to the sum of all fluxes feeding into (or, equivalently at steady state, flowing out of) a metabolite. In the case of reversible reactions, this means that the flux measured by KFP may differ significantly from the net pathway flux.
A limitation to the KFP approach is that quantitative flux information is reliably obtained only for metabolites that turn over slowly relative to their upstream precursor. Consider a case in which the isotope label is relayed from a precursor metabolite X to a downstream metabolite Y. If X turns over faster than Y, then the labeling kinetics of Y will reliably reflect the flux through Y, based on the magnitude of kY. In contrast, if X turns over at a much slower rate than Y, then the labeling kinetics of Y will mainly depend upon the labeling of X, rendering measurement of kY, and accordingly flux through Y, imprecise (a more quantitative treatment of this important case is provided in the ANTICIPATED RESULTS). For a similar reason, the uptake of the nutrient or the tracer needs to be efficient, and the nutrient must not accumulate significantly internally. Otherwise, the rate of labeling of metabolites will be largely determined by the rate of turnover of the internal pool of the nutrient/tracer, rendering it impossible to accurately quantitate the downstream fluxes.
Differential KFP provides an example of a KFP variant suitable for quantitative analysis of dynamically changing fluxes. It was developed to evaluate changes in biosynthesis and macromolecule degradation when cells are exposed to an environmental perturbation. It has been used to demonstrate that carbon starvation in E. coli
results in rapid turning off of de novo
biosynthetic fluxes, with protein degradation becoming the major source of intracellular amino acids2
. As shown in , differential KFP consists of two (or more) sets of the KFP experiment, with the isotope switch initiated at different times with respect to the perturbation (e.g., preceding or following the perturbation). The kinetic patterns of isotope incorporation into metabolites obtained from these experiments, in addition to the knowledge of metabolite concentration changes triggered by the perturbation, can often be used to determine the effect of the perturbation on metabolic fluxes. Carbon source withdrawal in E. coli
is used as an example of a perturbation in this protocol.
Overview of experimental procedure for differential KFP (Steps 9–16, optional).
Efforts at measuring cellular metabolic fluxes have been ongoing for decades and a diversity of valuable tools have been developed14-20
. Several of these contain elements similar to the current KFP approach. Instead of detailing these related approaches, here we focus on two conceptually distinct alternatives: flux balance analysis (FBA)21
and metabolic flux analysis (MFA)22
Flux balance analysis is a constraints-based computational approach that requires little experimental data and offers an estimation of the range of feasible flux distributions in steadily growing cells21,23
. Although it has proven powerful, especially for E. coli24
, the precise determination of fluxes by FBA relies on an objective function and related assumptions (e.g., that E. coli
maximizes biomass yield per molecule of carbon source consumed23-25
). For most organisms, a validated objective function is not available, limiting the ability of FBA to make quantitative flux predictions in the absence of experimental data26-28
Metabolic flux analysis is an experimental approach that typically involves feeding cells with a mixture of different 13
C-labeled glucose species (e.g., uniformly labeled and only one carbon labeled) for a prolonged period under metabolic steady state29
, until the isotopic labeling pattern of the compounds to be measured (most typically proteic amino acids) reaches a steady state (>1 h). From the labeling pattern of proteic amino acids30
or primary free metabolites31
, metabolic fluxes (mostly of central carbon metabolism) are then deconvolved with the aid of computer modeling32,33
To assist in the choice of the appropriate experimental approach, MFA and KFP are compared here. MFA is well suited to measuring the ratio of fluxes at branch points when the alternative branches yield different labeling patterns of a downstream metabolite30,31
. It is also suitable for large-scale studies with respect to the number of species/strains29
, as the fluxes (relative to glucose uptake) of multiple pathways can be obtained from a single sample. No time courses or pool size data are needed, which reduces the experimental demand compared with KFP. However, MFA (at least in its most commonly practiced form) is largely limited to carbon metabolism, as labeling by other elements rarely produces the rich spectrum of labeling patterns of metabolites required for flux deconvolution (e.g., there are 32 theoretical C-labeling states but only 2 possible N-labeling states of glutamate—labeled or unlabeled). In contrast, KFP is more versatile in terms of what pathways can be monitored. In addition, KFP also has the following strengths compared with MFA: easy data deconvolution (in many cases, the differential equations of KFP have analytical solutions in the form of exponential functions with few free parameters, enabling direct parameter determination); short labeling time (no requirement for incorporation of isotope labels to reach steady state, which allows effective probing of dynamically changing fluxes using variants like differential KFP); and KFP can provide absolute fluxes instead of just split ratios. Each individual approach has limitations, and combining multiple approaches will generally yield the most complete understanding.