Engineering Genetically Encoded Nanosensors for Real-
Time In Vivo Measurements of Citrate Concentrations
Jennifer C. Ewald1,2, Sabrina Reich1, Stephan Baumann1, Wolf B. Frommer3, Nicola Zamboni1*
1Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland, 2PhD Program in Systems Biology of Complex Diseases, ETH Zurich, Zurich, Switzerland,
3Department of Plant Biology, Carnegie Institution for Science, Stanford, California, United States of America
Citrate is an intermediate in catabolic as well as biosynthetic pathways and is an important regulatory molecule in the
control of glycolysis and lipid metabolism. Mass spectrometric and NMR based metabolomics allow measuring citrate
concentrations, but only with limited spatial and temporal resolution. Methods are so far lacking to monitor citrate levels in
real-time in-vivo. Here, we present a series of genetically encoded citrate sensors based on Fo ¨rster resonance energy transfer
(FRET). We screened databases for citrate-binding proteins and tested three candidates in vitro. The citrate binding domain
of the Klebsiella pneumoniae histidine sensor kinase CitA, inserted between the FRET pair Venus/CFP, yielded a sensor highly
specific for citrate. We optimized the peptide linkers to achieve maximal FRET change upon citrate binding. By modifying
residues in the citrate binding pocket, we were able to construct seven sensors with different affinities spanning a
concentration range of three orders of magnitude without losing specificity. In a first in vivo application we show that E. coli
maintains the capacity to take up glucose or acetate within seconds even after long-term starvation.
Citation: Ewald JC, Reich S, Baumann S, Frommer WB, Zamboni N (2011) Engineering Genetically Encoded Nanosensors for Real-Time In Vivo Measurements of
Citrate Concentrations. PLoS ONE 6(12): e28245. doi:10.1371/journal.pone.0028245
Editor: Dafydd Jones, Cardiff University, United Kingdom
Received July 8, 2011; Accepted November 4, 2011; Published December 2, 2011
Copyright: ? 2011 Ewald et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: We gratefully acknowledge support from the National Institutes of Health (NIDDK 1RO1DK079109) and the National Science Foundation (NSF 1045185)
to WBF and a travel grant from the commercial funder Boehringer Ingelheim Fonds (http://www.boehringer-ingelheim.com/research_development/
awards_fellowships/boehringer_ingelheimfonds.html#) to JCE. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript, and do not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
Metabolite concentrations are a functional read out of the state
of metabolism . In recent years, studies have exploited mass
spectrometry or nuclear magnetic resonance based metabolomics
approaches to elucidate gene functions and regulation in microbes
and higher cells [2,3,4]. However, available metabolomics
techniques have a limited temporal and spatial resolution due to
disruptive sample preparation and the required sample amount,
resprectively. Therefore, techniques allowing in vivo monitoring of
metabolites are a valuable contribution to studying metabolism.
Fluorescent indicator proteins (FLIPs) have been developed to
monitor metabolites in vivo. These genetically encoded sensors
employ the concept of Fo ¨rster resonance energytransfer (FRET) 
to emit a fluorescent signal dependent on a ligand concentration.
FLIPs consist of a protein which specifically binds a ligand and is
sandwiched between two variants of green fluorescent protein
(typically CFP and YFP). The efficiency of fluorescence energy
transfer between the two fluorophores is highly dependent on their
distance and orientation (reviewed in [6,7,8]). If the binding of a
ligand induces a conformational change inthe protein and altersthe
Miyawaki et al.  firstly demonstrated this technique by
measuring intracellular calcium during signalling events with the
help of a FRET sensor based on calmodulin. Fehr et al. 
presented the first FRET sensor for an organic molecule in 2002.
Exploiting a periplasmic binding protein, they were able to develop
a sensor for maltose and visualize its uptake in single yeast cells by
fluorescence microscopy. To date, sensor proteins have been
developed for approximately a dozen compounds and applied in
variousorganismsincludingCOS cells, Arabidopsis  , yeast
[10,13] and Escherichia coli . Metabolite sensors have been
developed thus far for signalling intermediates [15,16], nucleotides
, sugars [10,11] and amino acids  (recently reviewed in ).
Their main application has been to monitor exchange between cells
and their environment. In this work we set out to develop a FRET
sensor which allows monitoring of events downstream in intracel-
lular carbon metabolism.
Citrate is an organic acid at the heart of central carbon
metabolism. Its production is the first committed step of the
tricarboxylic acid (TCA) cycle. This molecule is not only involved in
catabolic processes, but is also a biosynthetic precursor and exhibits
regulatory functions. It serves as substrate and as regulator for fatty
acid synthesis [19,20], which is of core interest in biomedical
research. Citrate also regulates the glycolytic/gluconeogenic switch
by allosteric control of the enzyme phosphofructokinase . In
eukaryotic cells the different functions of citrate take place in
different compartments. Hence, the analysis of citrate dynamics calls
for resolving concentrations in different compartments of the cell,
which to date can only be achieved by genetically encoded sensors.
Here, we developed a series of FRET-based nanosensors for
citrate. In a first in vivo application we monitor the dynamic
responses of E. coli to addition of different carbon sources and
show that cells maintain the capacity to immediately take up
glucose and acetate even after 24 h of starvation.
PLoS ONE | www.plosone.org1 December 2011 | Volume 6 | Issue 12 | e28245
We aimed at developing a FRET sensor for in vivo, real-time
monitoring of citrate. Though several metabolite sensors have
been constructed the design of novel sensors remains empirical.
The key characteristics of a useful sensor are specificity and an
affinity for the ligand in a physiological range. Since citric acid is
only taken up and metabolized by a limited amount of organisms,
natural citrate binding proteins were scarcer than for e.g. sugars or
amino acids. We found three promising candidates: CitX
(periplasmic binding protein from Salmonella typhimurium) and the
binding domains of DpiB (a sensor histidine kinase from E. coli)
and CitA (a sensor histidine kinase from Klebsiella pneumoniae). We
cloned each of the candidate recognition elements between CFP
and Venus (modified YFP ), inserting the sensors proteins
directly between the fluorophores without additional linkers. We
expressed the constructs in E. coli BL21 and analyzed the purified
proteins in vitro. We recorded the fluorescence spectrum at an
excitation of 433 nm and determined the ratio of the Venus
(530 nm) to CFP (488 nm) peaks. All three candidates showed
energy transfer from CFP to Venus. However, only CitA gave a
small but reproducible change in fluorescence upon addition of
500 mM citrate (Figure 1). This sensor displayed a 530/488 nm
fluorescence intensity ratio of 3.6 which increased to 3.9 upon
ligand binding, which is in a similar range as most other non-
optimized metabolite FRET sensors .
Optimization and characterization in vitro
This observed change is small and may therefore limit practical
application for in vivo measurements, where more background
fluorescence and interference, e.g. light scattering, is expected. We
therefore set out to increase the total FRET change upon citrate
binding. This depends on the transduction of a conformational
change in the receptor upon ligand binding into a positional
change of the fluorophores. The CitA domain is a 132 amino acid
protein that has been structurally and genetically characterized
[24,25,26] (pdb file 2J80). We designed several truncations of CitA
to remove potentially flexible elements in the peripheral areas of
the protein. By combining three C-terminal and five N-terminal
truncations we created a total of 15 CitA derivatives. We purified
the modified proteins and determined the Venus/CFP ratio with
or without addition of 500 mM citrate. While some truncations
decreased or completely abolished ligand-induced change of
FRET ratio, others significantly improved the maximal emission
ratio change (Figure S1). The biggest improvement was obtained
by removing residues 1–5 and 131–132, which constitute the
unstructured regions preceding and following a-helices, respec-
tively. The resulting nanosensor displayed a change in FRET ratio
of 2.3 (55% increase), which is more than sixfold enhancement
compared to the original construct. Further reducing the protein
and removing the first three helices (which are not directly
involved in citrate binding) had an adverse effect.
Next, we thoroughly tested the newly created nanosensor CFP-
CitA6-130-Venus in vitro. We determined the binding properties
by mixing the sensor with 0.5 to 1000 mM citrate. The obtained
ratios were fitted to a single site binding curve as described in the
methods section. We determined the KD, the ligand concentration
at half-maximal saturation, to be 8 mM, which is very close to the
value determined by Gerharz et al (2003) using isothermal titration
calorimetry to characterize the purified protein . The
reproducibility between different batches of proteins extraction
was excellent, with ratios in general not differing by more than
5%. A crucial feature for in vivo application is specificity. We thus
exposed the nanosensor to a variety of structurally related organic
acids including isocitrate, aconitate, a-ketoglutarate, succinate and
pyruvate in presence or absence of citrate. With the exception of
isocitrate, none of the tested compounds induced a detectable
change in FRET ratio. In the case of isocitrate, we observed an
effect only at concentrations above 1 mM (Figure 2 B) in absence
of citrate, which by far exceeds the concentration typically
observed by metabolomics in microbes . Also, the apparent
affinity for citrate was not modified in the presence of isocitrate or
aconitate (data not shown), which excludes competitive binding.
After confirming the high specificity of the sensor we tested pH
stability, which is crucial when aiming to study certain intracellular
compartments (Figure S2 A). An increase of pH only marginally
affected the sensor and the binding curve at pH 7.5 was almost
identical to pH 7. At pH 6.5 the FRET efficiency (Venus/CFP
ratio) was reduced to a ratio of 3.5 (215%), though the dynamic
range and affinity were not affected. A further decrease of pH
severely reduced FRET efficiency. Therefore, the new nanosensor
should be functional in most compartments, however it is not
suitable for highly acidic compartments such as endosomes or
vacuoles . We also tested the effect of high salt concentrations
as typically observed in cells. Addition of 100 mM sodium chloride
led to a major decrease of the FRET efficiency (Figure S2 B).
Thus, as has been previously observed when comparing
nanosensors in vitro and in vivo [13,23], we can expect the observed
fluorescence intensity ratio in vivo to be lower than under assay
conditions. The KDand the dynamic range, however, were not
altered, and thus relative changes in citrate concentrations can still
be monitored in vivo.
The natural receptor CitA is highly sensitive and detects low
concentrations of citrate in the periplasmic environment and thus
has a very high affinity for its substrate. For application as an
intracellular sensor a saturation concentration of approximately
100 mM appeared too low given the estimates of intracellular
concentrations of citrate by metabolomics approaches in the mid
micromolar to low millimolar range . We therefore engineered
the affinity of the nanosensor for in vivo applications. Gerharz et al
Figure 1. Fluorescence emission spectrum of CitA with and
without 500 m mM citrate. The FRET sensor CFP-CitA-Venus was
purified and the fluorescence emission recorded at an excitation of
433 nm in a fluorescence plate reader. The dotted line represents the
spectrum of the protein in buffer, the dashed line the protein after
addition of 500 mM citrate.
Nanosensors for In Vivo Measurements of Citrate
PLoS ONE | www.plosone.org2December 2011 | Volume 6 | Issue 12 | e28245
 systematically investigated residues of the CitA sensor domain
involved in citrate binding. Replacement of positively charged
amino acids with alanine led to decreased affinity. We chose five
of the reported point mutations and introduced them into the
sensor CitA6-130. We determined binding constants and total
FRET ratios from two independent extractions (Table 1). The
binding constants were qualitatively in good agreement with the
observations of Gerharz et al. , though in some cases we
observed a much larger decrease of affinity. This might be due to
the different measurement techniques, a different behaviour of the
truncated and sandwiched protein compared to the isolated full-
length protein, or to the minor differences we found in the
sequence compared to the published Klebsiella CitA gene (sequence
of the final sensor can be found in Document S1). The mutation
R66A completely abolished citrate-induced FRET change. The
others increased the apparent KD 2–250 fold up to 1.8 mM
(R107A). To further increase the coverage of affinities we
combined the two mutations K77A and R49A in one sensor
and thereby obtained an affinity of 470 mM. In total, our set of six
citrate nanosensors cover an affinity range of 8 mM to 1.8 mM,
and a dynamic measurement range of 1 mM–15 mM (10–90%
saturation). Decreasing the affinity for the designated substrate
bears the risk of also reducing specificity. We therefore tested all
mutated sensors for binding of isocitrate and aconitate, the most
closely related compounds. All of the sensors maintained their
specificity and showed no FRET change in response to addition of
either compound. Also, pH and salt sensitivity were similar to the
original sensor (data not shown).
Monitoring the dynamic response of E. coli to different
substrates after starvation
One of the major advantages of using FRET sensors is the
ability to collect real time data to study the kinetics of metabolite
accumulation. In a first in vivo application we monitored the
metabolic response of E. coli to different substrates after starvation.
After starving cells harbouring different versions of the citrate
sensor in carbon-free M9 media for four hours we added glucose,
acetate, citrate or buffer control to the cells and monitored their
response over time. Time profiles are shown in Figure 3. We
observed an immediate increase of the FRET ratio in bacteria
supplied with either glucose or acetate, but not with citrate or the
carbon-free control. The addition of substrate did not elicit a ratio
change in the sensor CIT0, which does not bind citrate in vitro.
This confirms that the observed effect is specific to a change in
citrate concentration. The external supply of citrate did not lead to
an increase of intracellular citrate concentration, consistent with
the observation that E. coli does not metabolize citrate as a carbon
source under aerobic conditions .
While in acetate medium citrate concentrations remain high, on
glucose a transient peak is followed by a drop in concentration.
This observation fits well to those made by Bennett et al , who
show that citrate concentrations during growth on acetate
exceeded the concentrations during growth on glucose more than
fivefold. The initial citrate peak upon glucose uptake might be due
to a higher glycolytic than TCA cycle capacity during starvation.
Thus, citrate is accumulated until expression of the aconitate
hydratase and downstream enzymes is induced. Whether the small
Figure 2. Binding curve of citrate to the FRET nanosensor CIT8m. The FRET sensor CFP-truncated CitA-Venus was purified and the
fluorescence emission of CFP and Venus recorded at an excitation of 433 nm in a fluorescence plate reader. The emission ratio 530/488 nm was
determined at different citrate (A) and isocitrate (B) concentrations. Data points are averages of three independent protein extractions. Error bars
indicate standard deviations. Data were fitted to a single site binding curve (black line) as described in the method section.
Table 1. Binding properties of seven citrate sensors.
[m mM] (in % Rapo)
truncation8 mM 4.1 6.455
R49A50 mM 4.8 6.229
CIT0R66A no binding 4.34.30
K77A 96 mM 4.1 6.455
K92A 32 mM 4.56.544
CIT1.8m R107A1765 mM 4.4 5.320
CIT0.5mR49A-K77A 470 mM 4.35.835
Values are averages of two independent protein extractions, measured twice
each. Rapo: Venus/CFP ratio at 0 mM citrate; KD: concentration at half-maximal
Nanosensors for In Vivo Measurements of Citrate
PLoS ONE | www.plosone.org3 December 2011 | Volume 6 | Issue 12 | e28245
differences in the speed of the glucose or acetate pulse to manifest
in the citrate pool results from slower uptake or lower capacity of
the downstream enzymes remains to be investigated.
The FRET ratios in vivo differ from those determined under
assay conditions and therefore the ratio does not directly allow
inferring the absolute concentrations. However, using sensors with
different affinities allows roughly estimating the concentration
range. Upon a glucose pulse, both the sensor CIT8m and the
sensor CIT96m, but not the sensor CIT1.8m respond, indicating a
concentration in the mid micromolar range. The initial ratio
varied by about 0.1 around the midpoint, which could either be
caused by variations in the base level of citrate or could be caused
by other parameters such as optical density, sedimentation etc. It
was thus not possible to directly compare steady state levels,
however under optimized conditions FRET sensors can be used to
monitor steady state levels as shown by Bermejo et al. .
Next, we investigated whether E.coli maintains glucose and
acetate uptake capacity even after an extended starvation periods
of 24 hours. After 1 day in carbon-free M9 medium we subjected
cells harbouring the FLIP-CIT8m plasmid to a pulse of either
glucose or acetate and monitored the intracellular response at a
temporal resolution of approximately seven seconds (Figure 4). We
observed a rapid response of the citrate level which reached a
maximum already after 50 s and 90 s upon a glucose or acetate
addition, respectively. Thus, even after a long period of complete
carbon starvation E. coli is able to immediately take up and utilize
common carbon substrates. The presence of glucose activity even
in the absence of the substrate allows the cells to immediately
acquire the preferred nutrient when it becomes available, similarly
as observed in yeast .
Genetically encoded nanosensors allow minimally invasive,
timely and spatially resolved monitoring of metabolites. While
several FRET nanosensors have been developed for amino acids
and sugars (reviewed in ), sensors to monitor events further
downstream in metabolism were still lacking. In this work we
constructed a fluorescence nanosensor for citrate. To the best of
our knowledge, this is the first sensor for an organic acid. Using a
structure-based approach to increase the rigidity of the protein we
were able to enhance the total change in FRET six-fold, achieving
a total ratiometric change of 2 (55% increase). By introducing
point mutations we engineered a total of six sensor versions
spanning three logs of affinities, and additionally generated a
control sensor unable to bind citrate.
Figure 3. Response of citrate concentration in E. coli upon addition of different carbon substrates after starvation. The response was
monitored by citrate nanosensors of different affinities. Time zero indicates the addition of glucose, acetate or citrate; this interrupted the
measurement for approximately 20 s. White dots represent the buffer controls. FI: fluorescence intensity.
Nanosensors for In Vivo Measurements of Citrate
PLoS ONE | www.plosone.org4 December 2011 | Volume 6 | Issue 12 | e28245
We constructed the first sensor utilizing the periplasmic domain
of a sensor kinase. This successful example opens up a new toolbox
of potential protein scaffolds for the construction of novel sensors.
Histidine sensor kinases are part of the bacterial two component
response systems and are widely spread in the bacterial world,
sensing a great variety of substrates including different organic
acids . Similar to the typical hinge-bend movement observed
in periplasmic binding proteins, periplasmic sensor domains of
histidine kinases undergo strong conformational change to trans-
duce a signal  and are therefore ideal candidates for FRET
As first proof of principle, we demonstrate the successful
application of the sensor to monitor substrate shifts in Escherichia
coli. Using this in vivo approach we were able to show that even
after a long starvation period E. coli can take up and metabolize
glucose through glycolysis within several seconds after glucose
addition. Due to the difference in FRET efficiency between assay
and in vivo conditions, the fluorescence ratio does not so far allow
estimating absolute intracellular concentrations. However, com-
bining read-outs from sensors with different affinities and cell-
averaged concentrations obtained by by mass spectrometry could
allow reasonable estimates.
This series of citrate sensors allows non-invasively monitoring
citrate in microbes. Recently, FRET sensors have successfully been
implemented in microbes for monitoring glucose steady state levels,
the capacity of starved yeast cells to accumulate glucose, for the
identification of the nature of the transport using mutant screens
and for identifying novel sugar transporters [13,32]. Our suite of
citrate sensors could be employed to follow metabolic activity as
demonstrated in this study, to elucidate potential regulatory
functions of citrate or to facilitate metabolic engineering for citric
acid production. This work also paves the road to elucidate
compartment-specific dynamics and regulatory events in higher
cells that involve or are mediated by citrate. It was recently shown
that citrate is the precursor for histone acetylation in mammalian
cells and thus links chromatin remodelling to energy metabolism
. A citrate sensor could further shed light on mechanisms and
dynamics involved in this interesting regulatory circuit.
Materials and Methods
Construction of candidate sensors
Candidate sensors were amplified from genomic DNA of
Klebsiella pneumoniae, Salmonella typhimurium and Escherichia coli,
respectively, by polymerase chain reaction (PCR) using primers
as specified in Table 2. The PCR product was inserted between
the fluorophores in plasmid pGW1  (based on pRSET) using
restriction sites KpnI and SpeI.
Construction of CitA variants
Truncations of the CitA protein were achieved by PCR-
amplifying truncated forms from the original CFP-CitA-Venus
construct (using primers listed in Table 2) and inserting them into
pGW1  using restriction sites KpnI and SpeI. The sequence of
the final sensor FLIP-CIT8m can be found in Document S1. Point
mutations were introduced either with the QuickChange Kit
(Stratagene) or by mutation and fusion PCR using the primers
published in . Mutations were verified by Sanger sequencing
of both strands (Microsynth, Switzerland).
Expression and purification of proteins
Protein expression and purification protocol was modified from
ref. . E. coli BL21 harboring the nanosensor-encoding plasmid
were grownin LB medium (supplemented with 100 mg/l ampicil-
lin), at room temperature for 72 h. Cells were harvested by
centrifugation, washed twice in 20 mM Tris/HCl (pH 8) and then
resuspended in the same buffer. Cells were disrupted by sonication
and cell debris was removed by centrifugation. The supernatant
was applied to a Ni+ column for His-tag purification (GE
Healthcare, either His-Trap or Gravi-Trap) using 20 mM Tris/
HCl (pH 8) for washing and 200 mM imidazol for elution of the
proteins. To remove imidazol and extraction buffer, the protein
solution was concentrated to 50 ml in a Vivaspin500 (5 kDa
molecular cut-off, sartorius stedim biotech, Germany), washed
with 500 ml assay buffer (20 mM MOPS, 20 mM NaCl) and
finally diluted tenfold in assay buffer. Proteins were stored at 4uC
overnight before analysis to allow proper folding. SDS PAGE
indicated high purity as no additional polypeptides were detected.
In vitro assays
All in vitro assays were performed in a TECAN Infinite 200
plate reader (TECAN, Austria) in 96 well format (modified from
). Proteins, reagents and ligands were dissolved in assay buffer
(20 mM Mops, 20 mM NaCl, pH 7 unless specified otherwise).
Typically, 100 ml total volume per well were used. Protein
concentration was adjusted so that both Venus and CFP emission
were in the linear range of the detector at emission gain 80.
Twofold higher or lower protein concentrations did not affect the
outcome of the assay. Emission wavelengths 488 nm and 530 nm,
or a continuous spectrum from 470–580 nm, were monitored at
an excitation of 433 nm. A blank measurement was obtained from
a well containing only assay buffer and subtracted from samples.
Measured intensity ratios were fitted to a single site binding
E. coli in vivo experiment
In vivo experiments were adapted from  and . E. coli
BL21 cells were grown for 60 h in LB medium at room
Figure 4. Citrate concentration changes in E. coli in response to
carbon substrates after 24 hours of starvation. Addition of
substrates is indicated as time point t=0. Manual addition interrupted
the measurement for approximately 10 s. Black dots: glucose, white
dots: acetate. Time points are averaged over three independent
Nanosensors for In Vivo Measurements of Citrate
PLoS ONE | www.plosone.org5 December 2011 | Volume 6 | Issue 12 | e28245
temperature. Cultures were stored over night at 4uC and then
starved in carbon free M9 medium for 4 or 24 hours at 37uC.
190 ml cultures were transferred to 96 well plates. Fluorescence
emission at 488 and 530 nm (excitation 433 nm, bandwidth 2 nm)
were recorded in a TECAN Infinite 200 plate reader, shaking at
170 rpm between readings. 10 ml glucose, acetate, citrate (all final
concentration 2 g/l) or carbon-free M9 were added manually to
of CitA were created as described in the methods section. The
naming indicates the first and last amino acid. We determined the
FRET ratio in buffer (black bars) and the change in ratio (i.e. D
FRET) upon addition of 500 mM citrate (grey bars).
FRET ratio of CitA variants. Truncated variants
sensor FLIP CIT8m. FRET ratios were measured for 7 citrate
concentrations in duplicate and binding curves were fitted as
The effect of pH and salt concentration on the
described in the methods section. (A) citrate and proteins were
prepared in MOPS-buffer at the pH indicated in the chart, (B)
Binding curve was determined in MOPS buffer (pH 7) containing
20 mM (normal assay conditions) and 100 mM sodium chloride.
Nucleotide sequence of the sensor FLIP CitA6-
The authors kindly acknowledge Diane Chermak for technical support and
Victor Chubukov for comments on the manuscript.
Conceived and designed the experiments: JCE NZ. Performed the
experiments: JCE SR SB. Analyzed the data: JCE SR SB WBF NZ.
Contributed reagents/materials/analysis tools: WBF. Wrote the paper:
1. Zamboni N, Sauer U (2009) Novel biological insights through metabolomics and
13C-flux analysis. Curr Opin Microbiol 12: 553–558.
2. Sreekumar A, Poisson LM, Rajendiran TM, Khan AP, Cao Q, et al. (2009)
Metabolomic profiles delineate potential role for sarcosine in prostate cancer
progression. Nature 457: 910–914.
3. Bennett BD, Kimball EH, Gao M, Osterhout R, van Dien SJ, et al. (2009)
Absolute metabolite concentrations and implied enzyme active site occupancy in
Escherichia coli. Nat Chem Biol 5: 593–599.
4. Fendt SM, Buescher JM, Rudroff F, Picotti P, Zamboni N, et al. (2010) Tradeoff
between enzyme and metabolite efficiency maintains metabolic homeostasis
upon perturbations in enzyme capacity. Mol Syst Biol 6: 356.
5. Ai HW, Hazelwood KL, Davidson MW, Campbell RE (2008) Fluorescent
protein FRET pairs for ratiometric imaging of dual biosensors. Nat Methods 5:
6. Jares-Erijman EA, Jovin TM (2006) Imaging molecular interactions in living
cells by FRET microscopy. Curr Opin Chem Biol 10: 409–416.
Table 2. Primers used for construction and engineering of sensor proteins.
geneprimer including restriction sites
forward CCCCCCGGTACCGCGCAATATTTTACGGCC AGT
truncated versions forward
amino acid 5 CCCCCCGGTACCGAGCGTCTGCATTATCAGGTCGGG
amino acid 6 CCCCCCGGTACCCGTCTGCATTATCAGGTCGGG
amino acid 7 CCCCCCGGTACCCTGCATTATCAGGTCGGGCAA
amino acid 13 CCCCCCGGTACCGGGCAACGGGCGCTGATTCAG
amino acid 51 CCCCCCGGTACCTCCGACGCCACCTACATCACC
truncated versions reverse
amino acid 126 CCCCCCACTAGTGCCTACCGACACAATGCCGATCAC
amino acid 128CCCCCCACTAGTGGTATAGCCTACCGACACAATGCC
amino acid 130CCCCCCACTAGTTTGCTCGATGGTATAGCCTACCGA
Nanosensors for In Vivo Measurements of Citrate
PLoS ONE | www.plosone.org6 December 2011 | Volume 6 | Issue 12 | e28245
7. Piston DW, Kremers GJ (2007) Fluorescent protein FRET: the good, the bad Download full-text
and the ugly. Trends Biochem Sci 32: 407–414.
8. Bermejo C, Ewald JC, Lanquar V, Jones A, Frommer WB (2011) In vivo
biochemistry – quantifying fluxes in individual cells or cultures of yeast.
Biochem J. in press.
9. Miyawaki A, Llopis J, Heim R, McCaffery JM, Adams JA, et al. (1997)
Fluorescent indicators for Ca2+ based on green fluorescent proteins and
calmodulin. Nature 388: 882–887.
10. Fehr M, Frommer WB, Lalonde S (2002) Visualization of maltose uptake in
living yeast cells by fluorescent nanosensors. Proc Natl Acad Sci U S A 99:
11. Fehr M, Lalonde S, Ehrhardt DW, Frommer WB (2004) Live imaging of glucose
homeostasis in nuclei of COS-7 cells. J Fluoresc 14: 603–609.
12. Chaudhuri B, Hormann F, Lalonde S, Brady SM, Orlando DA, et al. (2008)
Protonophore- and pH-insensitive glucose and sucrose accumulation detected by
FRET nanosensors in Arabidopsis root tips. Plant J 56: 948–962.
13. Bermejo C, Haerizadeh F, Takanaga H, Chermak D, Frommer WB (2010)
Dynamic analysis of cytosolic glucose and ATP levels in yeast using optical
sensors. Biochem J 432: 399–406.
14. Kaper T, Lager I, Looger LL, Chermak D, Frommer WB (2008) Fluorescence
resonance energy transfer sensors for quantitative monitoring of pentose and
disaccharide accumulation in bacteria. Biotechnol Biofuels 1: 11.
15. Evellin S, Mongillo M, Terrin A, Lissandron V, Zaccolo M (2004) Measuring
dynamic changes in cAMP using fluorescence resonance energy transfer.
Methods Mol Biol 284: 259–270.
16. Palmer AE, Jin C, Reed JC, Tsien RY (2004) Bcl-2-mediated alterations in
endoplasmic reticulum Ca2+ analyzed with an improved genetically encoded
fluorescent sensor. Proc Natl Acad Sci U S A 101: 17404–17409.
17. Imamura H, Nhat KP, Togawa H, Saito K, Iino R, et al. (2009) Visualization of
ATP levels inside single living cells with fluorescence resonance energy transfer-
based genetically encoded indicators. Proc Natl Acad Sci U S A 106:
18. Dulla C, Tani H, Okumoto S, Frommer WB, Reimer RJ, et al. (2008) Imaging
of glutamate in brain slices using FRET sensors. J Neurosci Methods 168:
19. Geelen MJ, Schmitz MG (1993) The role of citrate in the regulation of hepatic
fatty acid synthesis by insulin and glucagon. Horm Metab Res 25: 525–527.
20. Gnoni GV, Priore P, Geelen MJ, Siculella L (2009) The mitochondrial citrate
carrier: metabolic role and regulation of its activity and expression. IUBMB Life
21. Newsholme EA, Sugden PH, Williams T (1977) Effect of citrate on the activities
of 6-phosphofructokinase from nervous and muscle tissues from different animals
and its relationships to the regulation of glycolysis. Biochem J 166: 123–129.
22. Nagai T, Ibata K, Park ES, Kubota M, Mikoshiba K, et al. (2002) A variant of
yellow fluorescent protein with fast and efficient maturation for cell-biological
applications. Nat Biotechnol 20: 87–90.
23. Okumoto S, Takanaga H, Frommer WB (2008) Quantitative imaging for
discovery and assembly of the metabo-regulome. New Phytol 180: 271–295.
24. Reinelt S, Hofmann E, Gerharz T, Bott M, Madden DR (2003) The structure of
the periplasmic ligand-binding domain of the sensor kinase CitA reveals the first
extracellular PAS domain. J Biol Chem 278: 39189–39196.
25. Gerharz T, Reinelt S, Kaspar S, Scapozza L, Bott M (2003) Identification of
basic amino acid residues important for citrate binding by the periplasmic
receptor domain of the sensor kinase CitA. Biochemistry 42: 5917–5924.
26. Kaspar S, Perozzo R, Reinelt S, Meyer M, Pfister K, et al. (1999) The
periplasmic domain of the histidine autokinase CitA functions as a highly specific
citrate receptor. Mol Microbiol 33: 858–872.
27. Demaurex N (2002) pH Homeostasis of cellular organelles. News Physiol Sci 17:
28. Pos KM, Dimroth P, Bott M (1998) The Escherichia coli citrate carrier CitT: a
member of a novel eubacterial transporter family related to the 2-oxoglutarate/
malate translocator from spinach chloroplasts. J Bacteriol 180: 4160–4165.
29. Niittylae T, Chaudhuri B, Sauer U, Frommer WB (2009) Comparison of
quantitative metabolite imaging tools and carbon-13 techniques for fluxomics.
Methods Mol Biol 553: 355–372.
30. West AH, Stock AM (2001) Histidine kinases and response regulator proteins in
two-component signaling systems. Trends Biochem Sci 26: 369–376.
31. Sevvana M, Vijayan V, Zweckstetter M, Reinelt S, Madden DR, et al. (2008) A
ligand-induced switch in the periplasmic domain of sensor histidine kinase CitA.
J Mol Biol 377: 512–523.
32. Chen LQ, Hou BH, Lalonde S, Takanaga H, Hartung ML, et al. (2010) Sugar
transporters for intercellular exchange and nutrition of pathogens. Nature 468:
33. Wellen KE, Hatzivassiliou G, Sachdeva UM, Bui TV, Cross JR, et al. (2009)
ATP-citrate lyase links cellular metabolism to histone acetylation. Science 324:
34. Bermejo C, Haerizadeh F, Takanaga H, Chermak D, Frommer WB (2011)
Optical sensors for measuring dynamic changes of cytosolic metabolite levels in
yeast. Nature Protocols;in press.
Nanosensors for In Vivo Measurements of Citrate
PLoS ONE | www.plosone.org7 December 2011 | Volume 6 | Issue 12 | e28245