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ORIGINAL RESEARCH
published: 13 February 2020
doi: 10.3389/fmicb.2020.00197
Metabolic Implications of Using
BioOrthogonal Non-Canonical Amino
Acid Tagging (BONCAT) for Tracking
Protein Synthesis
Katherine F. Steward1, Brian Eilers1, Brian Tripet1, Amanda Fuchs1, Michael Dorle1,
Rachel Rawle1, Berliza Soriano1, Narayanaganesh Balasubramanian1, Valérie Copié1,2,
Brian Bothner1,2* and Roland Hatzenpichler1,2,3*
1 Department of Chemistry and Biochemistry, Montana State University, Bozeman, MT, United States, 2 Thermal Biology
Edited by: Institute, Montana State University, Bozeman, MT, United States, 3 Center for Biofilm Engineering, Montana State University,
Manuel Kleiner, Bozeman, MT, United States
North Carolina State University,
United States
Reviewed by: BioOrthogonal Non-Canonical Amino acid Tagging (BONCAT) is a powerful tool for
Ram Karan, tracking protein synthesis on the level of single cells within communities and whole
King Abdullah University of Science organisms. A basic premise of BONCAT is that the non-canonical amino acids (NCAA)
and Technology, Saudi Arabia
Dave Siak-Wei Ow, used to track translational activity do not significantly alter cellular physiology. If the
Bioprocessing Technology Institute NCAA would induce changes in the metabolic state of cells, interpretation of BONCAT
(A∗STAR), Singapore
studies could be challenging. To address this knowledge-gap, we have used a global
*Correspondence:
Brian Bothner metabolomics analyses to assess the intracellular effects of NCAA incorporation. Two
bbothner@montana.edu NCAA were tested: L-azidohomoalanine (AHA) and L-homopropargylglycine (HPG); L-
Roland Hatzenpichler methionine (MET) was used as a minimal stress baseline control. Liquid Chromatography
roland.hatzenpichler@montana.edu
Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance (NMR) were used to
Specialty section: characterize intracellular metabolite profiles of Escherichia coli cultures, with multivariate
This article was submitted to statistical analysis using XCMS and MetaboAnalyst. Results show that doping with
Microbial Physiology and Metabolism,
a section of the journal NCAA induces metabolic changes, however, the metabolic impact was not dramatic.
Frontiers in Microbiology A second set of experiments in which cultures were placed under mild stress to
Received: 07 December 2019 simulate real-world environmental conditions showed a more consistent and more
Accepted: 28 January 2020
Published: 13 February 2020 robust perturbation. Pathways that changed include amino acid and protein synthesis,
Citation: choline and betaine, and the TCA cycle. Globally, these changes were statistically
Steward KF, Eilers B, Tripet B, minor, indicating that NCAA are unlikely to exert a significant impact on cells during
Fuchs A, Dorle M, Rawle R, incorporation. Our results are consistent with previous reports of NCAA doping under
Soriano B, Balasubramanian N,
Copié V, Bothner B and replete conditions and extend these results to bacterial growth under environmentally
Hatzenpichler R (2020) Metabolic relevant conditions. Our work highlights the power of metabolomics studies in detecting
Implications of Using BioOrthogonal
Non-Canonical Amino Acid Tagging cellular response to growth conditions and the complementarity of NMR and LCMS as
(BONCAT) for Tracking Protein omics tools.
Synthesis. Front. Microbiol. 11:197.
doi: 10.3389/fmicb.2020.00197 Keywords: metabolomics, BONCAT, non-canonical amino acids, L-azidohomoalanine, L-homopropargylglycine
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INTRODUCTION This study aimed to characterize the metabolome of
Escherichia coli when grown in the presence of NCAA and
Dieterich et al. (2006) introduced a method for visualizing newly identify potentially differentiated metabolite patterns that might
synthesized proteins in mammalian cells termed BioOrthogonal inform us on the metabolic impact of NCAA on cell homeostasis
Non-Canonical Amino acid Tagging (BONCAT). BONCAT and organismal health. In order to investigate how NCAA
facilitates the tracking and localization of protein translation exposure affects intracellular metabolism, E. coli cell cultures
in single cells following a short incubation with a synthetic were grown with and without AHA or HPG. One sample
amino acid that later can be detected via azide-alkyne click- group included additional MET as a minimal perturbation to be
chemistry, a sensitive and precise biocompatible reaction (Kolb used as a control experiment for baseline stress due to media
et al., 2001). BONCAT has proven to be particularly useful for supplementation. Control cultures were also grown in media
monitoring cellular activity in complex microbial communities without amino acid amendment. “Real world” experimental and
(Hatzenpichler et al., 2014, 2016; Samo et al., 2014; Leizeaga cell culturing conditions were utilized in our analysis to best
et al., 2017; Sebastián et al., 2019), and adds a convenient evaluate the potential effects of NCAA, and to mimic current
approach to the molecular tool box available for analyzing field work in the environment that attempts to find suitable
microbial community function (Hatzenpichler et al., 2020) growth conditions for otherwise unculturable microorganisms.
because it avoids the use of radioactive substrates and is Comprehensive metabolite mapping techniques using Liquid
understood to only minimally impact protein structure and cell Chromatography Mass Spectrometry (LC-MS) and Nuclear
physiology. Currently, the two most widely used non-canonical Magnetic Resonance (NMR) spectroscopy were employed to
amino acids (NCAA) are L-azidohomoalanine (AHA) and L- assess potential metabolome differences between different E. coli
homopropargylglycine (HPG), which both replace L-methionine cell cultures and growth conditions.
(MET) during translation (Kiick et al., 2002). These amino
acids contain either an azide (AHA) or an alkyne functional
group (HPG) which are amenable to azide-alkyne click chemistry MATERIALS AND METHODS
(Kolb et al., 2001). Experimental protocols for performing
BONCAT studies and click-labeling newly made proteins are Reagents
well established in microbiology and microbial ecology (Bagert HPLC grade solvents: water, methanol and acetonitrile were
et al., 2014; Hatzenpichler et al., 2014, 2016; Mahdavi et al., purchased from Fisher (Waltham, MA, United States). AHA and
2014; Hatzenpichler and Orphan, 2015; Babin M. B. et al., 2016; HPG were purchased from Click Chemistry Tools (Scottsdale,
Bagert et al., 2016). AZ, United States). All other chemicals were purchased from
Phenotypic markers of optical density, behavioral tests, and Millipore Sigma (St. Louis, MO, United States) and were used as
responses to visual cues have been utilized to assess the impact of provided, with no additional purification steps.
cell treatments with NCAA. Studies on HeLa cells (Bagert et al.,
2014), a range of bacterial and archaeal pure cultures (Bagert Cell Culturing
et al., 2014; Hatzenpichler et al., 2014; Hatzenpichler and Orphan, An overnight culture of E. coli K12 DH10B, which had
2015), and environmental samples (Hatzenpichler et al., 2014, been grown on M9 minimal medium (200 mg/L thiamine,
2016) have demonstrated that the addition of low concentrations 0.2% glucose), was inoculated 1:20 into 6 L of M9 medium
(nM-mM range) of AHA or HPG to a sample over short periods (200 mg/L thiamine, 0.2% glucose) to yield a fresh culture of
of time (typically 1–2 cell generations) has only minimal effects optical density, measured at a wavelength l of 600 nm, i.e.,
on the physiology, growth rate, or protein expression patterns OD600 of 0.041. 150 mL aliquots of this culture were then
of organisms. Hinz et al. studied zebrafish and investigated aliquoted into 36 Erlenmeyer flasks, which were incubated at
the potential effects of NCAA labeling in vivo, which revealed 37◦C on rotary shakers run at 200 rpm. Temperatures were
that AHA was successfully incorporated into proteins in a ratio independently checked with a thermometer at regular intervals
consistent with time and concentration, that AHA was non-toxic to validate that temperatures were consistent across incubators
and had no detrimental effect on animal behavior (Hinz et al., throughout the experiments. Immediately following inoculation,
2012). A recent proteomic study investigating the effect of AHA the following incubations were started: five flasks each for
and HPG on protein expression and the ability to incorporate (1) 50 µM MET; (2) 50 µM AHA; and (3) 50 µM HPG.
these reagents into mice showed that a small percentage (∼10%) One additional control flask without amendment was used to
of proteins change their expression patterns in response to AHA monitor growth of the cultures via optical density (OD600).
doping (Calve et al., 2016). Lastly, a recent study indicated This was done to avoid disturbing the experimental cultures
that the incorporation of AHA into a model protein only given the large number of flasks. Growth experiments with
minimally affected the protein tertiary structure (Lehner et al., 50 µM amino acid addition (MET, AHA, or HPG) were stopped
2017). The recent application of proteomics investigating the after 85 min of incubation when the control flask had reached
cell machinery have also shown that AHA and HPG have little an OD600 of 0.072, corresponding to ∼0.74 cell generations
impact on the overall fitness of the organism (Dieterich et al., (Supplementary Figure S1). The 2 × 50 mL cell cultures were
2006; Landgraf et al., 2015). However, a deeper look into the then decanted into two 50 mL tubes. Tubes were centrifuged for
metabolism of NCAA doped organisms has, to our knowledge, 5 min at 4,700 g at room temperature. Resulting supernatants
never been carried out. were decanted and the cell pellets flash-frozen in liquid N2
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and stored at −80◦C until further processing. After these Statistical Analysis of MS Data
samples had been stored at ◦−80 C, the remaining 20 flasks Extracted ion chromatograms, peak detection, peak annotation,
were processed the following way: 1 mM of (1) MET, (2) AHA, chromatogram alignment, gap filling and relative quantitation
and (3) HPG were added to 5 flasks; Five additional culture of identified features was completed using MZmine
flasks served as no-amendment control, which were used to (Pluskal et al., 2010), MetaboAnalyst (Chong and Xia, 2018),
track cell growth. The incubation was continued as described, and XCMS (Tautenhahn et al., 2012). Metabolite identifications
with a starting OD600 of control cell cultures of 0.27, and were made based on exact mass and retention time matches
stopped after 5 min of amino acid pulse labeling whereby the to authentic standards using an in house library of ∼500
control cultures had reached an OD600 of 0.31, corresponding compounds. Statistical analysis of the MZmine output was done
to ∼0.04 cell generations (Supplementary Table S1). Cells were using Microsoft Excel version 2016 and MetaboAnalyst v4.0.
pelleted, pellets flash frozen, and samples stored as described XCMS utilizes an all-inclusive processing package with a similar
above. Cultures for the heat stress experiments were conducted workflow, in which it extracts chromatograms, identifies peaks,
as described above except that cell cultures were grown and matches peaks across samples, gap fills, performs statistical
maintained at 42◦C. analyses and in silico compound identification, and graphical
visualization of the data. Identifications of unknown features
Metabolite Extraction were made using the MetLin Metabolite Database, which
Escherichia coli intracellular metabolites were extracted using provided a list of possible metabolites based on exact mass,
published protocols (Hamerly et al., 2015). Briefly, frozen species, and likelihood (Myers et al., 2017).
cell pellets were re-suspended with water, then sonicated
using a Biologics Ultrasonic Homogenizer model 3000 for Sample Preparation and NMR Analysis
10 pulses of 3 s each. Resulting supernatant was centrifuged Dried metabolite mixtures were re-suspended in 600 µL of
and transferred to 10 mL scintillation vials to which four NMR buffer (containing 0.25 mM 4,4-dimethyl-4-silapentane-
volumes of ice cold acetone were added, followed by storage 1-sulfonic acid (DSS) in 90%H2O/10% D2O, 25 mM sodium
of the samples at 80◦− C overnight for protein precipitation. phosphate, pH 7), and transferred into 5 mm NMR tubes. All one
Protein concentration in the samples was determined using dimensional (1D) 1H NMR spectra were recorded at 298 K using
a Bradford assay (Bradford, 1976) (Supplementary Table S2). a Bruker AVANCE III solution NMR spectrometer operating
Samples were vortexed, centrifuged, and split into two fractions at 600.13 MHz 1H Larmor frequency and equipped with a
for concurrent analysis by LC-MS and NMR: 1 mL for 5 mm liquid-helium-cooled TCI cryoprobe with Z-gradient and
LC-MS analysis and 4 mL for NMR metabolomics analysis. a SampleJetTM automatic sample loading system. 1D 1H NMR
Both fractions were dried completely using vacuum speed data were acquired using the Bruker supplied 1D excitation
concentration with no heat, and subsequently frozen at 80◦− C sculpting water suppression pulse sequence ‘zgesgp’ with 256
until further use. scans, a 1H spectral window of 9,600 Hz, 32K data points,
a dwell time interval of 52 µsec, and a recycle delay of 5 s
LCMS Instrumentation and Metabolite between scan acquisitions. The data were first processed with
Analysis the Bruker TOPSPIN 3.5 software
1 using standard parameters
for chemical shift referencing using the DSS signal and line
The dried metabolite fraction used for liquid chromatography broadening (0.3 Hz). Spectral phases were manually adjusted,
mass spectrometry (LC-MS) was re-suspended with 20 µL of and a polynomial function was applied (qfil, 0.2 ppm width)
50:50 MeOH/H2O before injection into the mass spectrometer. on the residual water peak to remove its signal. Metabolite
MS-based analysis of polar metabolites was accomplished using identification and quantification were carried out using the
an Agilent 1290 ultra-high performance liquid chromatography ChenomxTM NMR suite software (version 8.3)2 and its associated
(UPLC) system coupled to an Agilent 6538 Accurate-Mass 600 MHz small molecule reference spectral database. DSS was
quadrupole Time of Flight (TOF) mass spectrometer. A Cogent used as an internal standard for metabolite quantification,
diamond hydride HILIC chromatography column (2.2 µM, 120 while imidazole NMR signals were used to correct for small
A, 150 mm × 2.1 mm Microsolv, Leland, NC, United States) chemical shift changes arising from slight pH variations between
was used for metabolite separation. The gradient began with samples. The metabolite concentration tables (mM) generated
solvent B (0.1% formic acid in acetonitrile) for 2 min at 50%, with Chenomx were exported to a.csv file and converted to µM,
followed by a gradient ramp of 50–100% B over 14 min. This and normalized to sample protein concentration as established
step was followed by a hold at 100% solvent B for 1 min, and from Bradford protein assays.
then return to initial conditions. Mass analysis was conducted Validation of metabolite IDs, which were annotated in
in positive mode with a capillary voltage of 3500 V, dry gas Chenomx3, was accomplished using 2D 1H-1H and 2D 1H-13C
temperature of 350◦C at a flow of 8 L/min and the nebulizer total correlation spectroscopy (TOCSY) NMR or by spiking,
was set at 60 psi, injecting 2 µL sample volumes, with blanks
run intermittently between samples. Data acquisition parameters 1https://www.bruker.com/service/support-upgrades/software-downloads/nmr.
were as follows: 50–1,000 mass range at 1 Hz scan rate with a html
resolution of 18,000. Accuracy based on calibration standards was 2https://www.chenomx.com/products/
approximately 5 ppm. 3https://www.chenomx.com/products/
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when available, pure metabolite standards into the samples and analysis was done in Excel (2016) using the MZmine output, with
monitoring resulting spectral changes in the 1D 1H NMR spectra. additional statistical analysis performed using MetaboAnalyst
2D 1H-1H TOCSY spectra were acquired for representative (Chong and Xia, 2018) and XCMS (Tautenhahn et al., 2012).
samples using the Bruker-supplied ‘mlevphpr.2/mlevgpph19’ PCA was used to gather information about the variation between
pulse sequences (256 × 2048 data points, 2 s relaxation delay, sample treatments and replicates. 2D-PCA plots indicated no
32 transients per FID,1H spectral window of 6602.11 Hz, 80 ms clear separation among the different experimental groups when
TOCSY spin lock mixing period). 2D 1H-1H TOCSY spectra were all m/z features were analyzed as a single input (Figure 1A).
processed using Topspin software (Bruker version 3.2)4. The 1 mM MET and both HPG groups displayed the largest
separation from the other treatment groups. While principal
Statistical Analysis of NMR Data component 1 (PC1) accounted for 44.5% of the variance, it
The NMR-based metabolite data were uploaded to the primarily separated the 1 mM HPG samples from the other
MetaboAnalyst v4.0 web server for multivariate statistical experimental conditions. The second principal component (PC2)
analysis. Metabolite concentrations were normalized by log- accounted for 14.7% of the variance but did little to differentiate
transformation and auto-scaling (mean centered divided by between the different sample groups. Overall, there was greater
the standard deviation of each variable) prior to univariate variation between sample treatment replicates than between the
and multivariate statistical analysis. Student t-test, principal different sample treatment groups.
component analysis (PCA) and partial least squares discriminant A heatmap was constructed to visualize differentiated MS
analysis (PLS-DA) were performed to identify potentially distinct features between treatment groups. Heatmaps are a powerful
metabolite patterns between the E. coli sample groups grown tool for visualizing trends and correlated changes via hierarchical
under the different conditions. Variable importance in projection clustering across all samples and all features. The control
(VIP) plots generated from the PLS-DA data were employed and baseline-MET samples intermingled, while AHA and the
to assess the importance of each variable (i.e., metabolite) in HPG sample treatments mixed on the hierarchal cluster but
the projection used in PLS-DA model building; statistics were were generally clustered apart from the MET and control
calculated for the data shown in Figure 5, using 3 components, samples (Figure 2A). The heatmap patterns indicated that not
yielding Q2 and R2 values of 0.646 and 0.913, respectively. all replicates from each treatment clustered with each other;
PLS-DA model validity was further assessed using the (B/W) however, a general grouping by type and concentration of NCAA
permutation test function of MetaboAnalyst which, using 2,000 was discernable. The relatively few “hot zones,” or regions of high
permutation steps yielded a p-value of <5 × e-04, as a measure of (dark red) or low (dark blue) abundance features on the heatmap
the significance of the PLS-DA model. For hierarchical clustering suggested that only a small number of MS features exhibited
analysis (HCA), distances were measured using a Euclidean log2fold changes > 2, indicating that generally minor metabolic
correlation and clustering by the Ward algorithm. differences existed between the different E. coli treatment groups.
Based on 2D-PCA, ANOVA (Supplementary Table S3), t-test
and HCA, we thus concluded, from the global MS data, that only
RESULTS AND DISCUSSION small metabolic changes are occurring in E. coli grown in the
presence of NCAA.
Mass Spectrometry-Based
Metabolomics of Non-canonical Amino NMR Metabolite Profiles of E. coli Grown
Acids in the Presence of Non-canonical Amino
An initial set of experiments was conducted to determine Acids
the physiological impact of NCAA additions to E. coli cell
cultures under otherwise normal growth conditions. This study Intracellular metabolite extracts from the same E. coli cell cultures1
established which NCAA concentrations are needed to evaluate were analyzed using 1D H NMR spectroscopy. As with the
changes in the metabolome of E. coli that may be relevant to MS studies, the NMR metabolomics data readily detected some
normal growth conditions (i.e., cells grown at 37◦C). Cultures changes in the metabolome of E. coli as a function of NCAA
were spiked with either 1 mM or 50 M concentrations of incorporation. However, these metabolite pattern changes wereµ
AHA, HPG, or MET. MET was added as a baseline perturbation found to be relatively small and insufficient to unambiguously
control experiment to account for the impact additional amino distinguish the different E. coli cell cultures based on 2D-PCA
acid would have on the metabolism of E. coli, as opposed to and HCA analyses (Figures 1A, 2B) of the different NMR-
the control group, which had no amendment to the minimal based metabolite profiles. 54 metabolites were annotated by
growth medium. Metabolite extracts from the six conditions analysis of the 1D
1H NMR spectra of the E. coli intracellular
and control groups were prepared and analyzed by LC-MS metabolite extracts using Chenomx (Supplementary Table S4).
using a high-resolution Q-TOF instrument. A total of 4,036 These metabolite IDs were further validated using spiking of
mass features were detected across all sample groups using the standards and 2D
1H-1H and natural abundance 1H-13C TOCSY
MZmine data reduction approach, as described above. Statistical experiments (Supplementary Table S5).Metabolite patterns between the different E. coli sample
4https://www.bruker.com/service/support-upgrades/software-downloads/nmr. groups, i.e., E. coli grown with MET, HPG and AHA at 1 mM and
html 50 µM conditions, were investigated by PCA analysis of resulting
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FIGURE 1 | 2D-PCA plots of all experimental conditions from E. coli NCAA experiment. (A) MS data and (B) NMR data are shown. (A) The PCA plot of the MS data
shows that only cultures grown with 1 mM HPG separate from the other experiment conditions. (B) The NMR data show significant overlap and a lack of
differentiation between experimental groups, the only exception being the group with 50 µM HPG.
FIGURE 2 | Heatmaps of treatment groups clustered on metabolite intensity from E. coli NCAA experiment. (A) MS data and (B) NMR data are shown. The scale of
the heat map indicates blue as lowest and red as highest in abundance as calculated across sample groups after normalization using fold change. (A) The heatmap
with HCA of 4,036 features as detected by MS is shown. The lack of clustering of the different experimental groups and no significant patterns of up- or
down-regulated features for the different groups are indicative of a lack of differentiation between sample types. (B) The NMR data (40 metabolites) also shows a
lack of group clustering, the only exception being the group with 50 µM HPG. For enlarged images with metabolite names and sample identifications see
Supplementary Figure S5.
NMR-based metabolite profiles and concentrations (Figure 1B). E. coli treatment groups, the NMR-based metabolite profile
As with the MS data, no clear clustering pattern or group differences are not sufficiently pronounced to allow for clear
separation were observable under these experimental growth treatment group separation.
conditions. The lack of separation along principal components A heatmap of all NMR features, based on HCA, did not
1 and 2 was particularly noticeable, leading us to conclude that result in group clustering by NCAA treatment type, rather
while there exist metabolome differences between the different sample types were intermingled showing no significant changes
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between groups (Figure 2B). The NMR metabolomics results utilized to assess protein content and translational activity, prior
are consistent with the MS spectral data. Only the 50 µM HPG to intracellular metabolite extraction, and indicated an average
samples separated as a unique cluster, similar to what is observed protein concentration of 2.1 mg/mL with all samples within 15%
in the heatmap of the LC-MS spectral features (Figure 2A). Taken of the average concentration (Supplementary Table S2). The
together, our results suggest that overall, the variability between range in protein concentration revealed that E. coli grown in the
biological replicates is comparable in magnitude to potential presence of AHA, HPG or MET resulted in greater intracellular
metabolic changes arising from the addition of NCAA in the amounts of proteins than the control cell cultures.
E. coli cell cultures.
This combined MS and NMR metabolomics analysis of
NCAA-treated E. coli cell cultures demonstrated that these MS Metabolomics of Cultures Grown
analytical platforms can detect subtle changes in the metabolomes With Non-canonical Amino Acids Under
of E. coli grown under different culturing conditions, but Heat Stress
that additional studies were needed to parse out how these NMR and MS analyses of the intracellular metabolomes of E. coli
small but potentially significant metabolic changes may impact cell cultures grown under heat stress were undertaken utilizing
cellular phenotypes. the same analytical approaches described for our first set of
experiments. LC-MS analysis identified 5,960 features across
all samples. To assess variation and replication trends in the
E. coli Grown With Non-canonical Amino data, PCA analysis was undertaken using the MS metabolite
Acid Under Heat Stress profile data recorded on the heat stressed E. coli cell cultures
While planning additional experiments, we evaluated how and grown in the presence of AHA, HPG, MET or the no
NCAA are typically used in field work and “real-world” addition control conditions. Resulting 2D-PCA plots did not
research applications to evaluate how an organism regulates reveal significant separations between these different groups
its translational activity in response to environmental (Samo (Figure 3A), with PC1 accounting for 77.8% of the variance
et al., 2014; Hatzenpichler et al., 2016; Leizeaga et al., 2017; between AHA, HPG or MET treated groups (red, blue, and cyan
Sebastián et al., 2019) or (co)cultivation conditions (Mahdavi circles) compared to control (green circle). Principal component
et al., 2014; Babin S. A. et al., 2016; Bagert et al., 2016). For such 2 accounted for an additional 8% of the variance, reinforcing that
purposes, cellular organisms are often grown for short periods similarities rather than differences in metabolic profiles between
of time under environmental perturbations or cellular stress. the treatment groups were most prominent. Variability between
We concluded that a more real-world evaluation of a NCAA the control and the MET-treated cell cultures was as great as
treatment would include an environmental stressor. Because of the difference between these two groups and the AHA and HPG
the extensive literature available on heat stress response in E. coli treated groups. The MET, AHA, and HPG groups clustered more
(Jozefczuk et al., 2010; Ye et al., 2012), we chose temperature tightly, as illustrated by the shaded 95% confidence intervals
increase as an appropriate stressor. Assessing metabolome of the different groups in the 2D PCA scores plot shown in
changes under BONCAT treatment during heat stress would Figure 3A, compared to that of the control group. The NCAA-
thus not only help clarify how E. coli cells are adapting to the treated samples clustered with each other, as did the control and
incorporation of NCAA, it would also recreate a stress condition MET-treated E. coli samples. This trend was present in the initial
that may best reflect “real world” research applications. This set of experiments conducted without heat stress and became
rationale thus led to a second set of metabolomics investigations, more apparent in the PCA analysis of the stressed E. coli sample
which utilized high temperature as a stress condition during groups (Figure 3A).
E. coli cell growth with or without a NCAA or MET present. An analysis of variance (ANOVA) was also undertaken for
The heat-treated experiment of E. coli consisted of four the MS-based metabolite profiles of the heat stressed and amino
groups grown at 42◦C. Based on the first set of experiments, acid treated E. coli cell cultures (Supplementary Figure S2).
we narrowed the experimental conditions to 50 µM AHA, HPG A comparison of the treatment groups to each other, resulted
or MET. A MET-supplemented culture was again used as a in an F value (St Hle and Wold, 1989) of 0.274, and an
baseline comparison for BONCAT addition, while the control F critical value of 2.61, indicating that the means of the
samples contained minimal media. Metabolomics studies and metabolite profiles, i.e., means of the intensities of the MS
resulting multivariate statistical analysis of LC-MS and NMR spectral features, for all the sample groups were not significantly
metabolite profiles were conducted on heat stressed E. coli different, and no treatment group differed significantly from
cell cultures, using the same approach described above for the the others (Supplementary Table S5). A post hoc analysis using
initial study. As with our initial NCAA addition experiments, Tukey’s honestly significant difference test (Tukey’s HSD test)
physiological data was recorded throughout the growth of the was conducted with MetaboAnalyst on individual MS spectral
E. coli to monitor phenotypic changes and to assess microbial features to identify which features accounted most significantly
health. Optical density measurements, averaged over biological for group differences between the different E. coli growth
replicates, were recorded throughout the E. coli incubation and conditions (Supplementary Table S6). The analysis resulted in
growth periods, and indicated that all of the cultures were the identification of 907 features that changed in abundance,
within 8% OD600 of each other, with an average OD600 of Supplementary Figure S2. This is less than 15% of all observed
1.3 after 210 min of cell growth. Bradford protein assays were mass features. Each significantly changed feature was subjected to
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FIGURE 3 | 2D PCA plot of MS features and NMR features of heat stressed E. coli cultures. (A) MS data and (B) NMR data are shown. (A) The variation within the
control group in the MS data completely encompasses the spread of the other sample types. (B) Experimental groups show partial separation by NMR. Data is
similar to the MS non-stressed PCA plot in that E. coli cells with HPG have the greatest separation.
the post hoc Tukey HSD test. Features that fell outside the means features, as assessed by Tukey’s HSD test, segregated into distinct
of other treatment groups is listed (Supplementary Table S6). sample groups best described by growth condition. The heatmap
A second method employed to assess the data and the contained blocks of upregulated features that were characteristic
impact of AHA and HPG on the intracellular metabolome of of each of the treatment group (Supplementary Figure S3). In
E. coli was to analyze differentially regulated mass features in this HCA analysis, the AHA and HPG-supplemented groups
the heat stressed and AHA or HPG cell cultures compared to clustered next to each other while the MET-supplemented and
the E. coli heat stressed control groups and MET-doped cell E. coli control groups were more similar. The differences between
cultures. Pairwise comparisons of AHA or HPG treated groups the AHA and HPG treated E. coli cell cultures compared to
against the MET-treated E. coli cell cultures were conducted, as the control and MET-treated groups again showed that MS
well as comparisons of control group and MET-treated cultures, metabolomics can easily distinguish between the different growth
which found that only 7% of the mass features were significantly conditions, even when the differentiated features amount to a
different (fold change > 2, p < 0.10). Using the same criteria, relatively small proportion of the intracellular metabolome mass
the AHA and HPG samples were found to contain differentially spectral features. The color changes on the heatmap indicate
expressed features at levels of 8 and 19% respectively compared fold change, and although not large does reveal that a metabolic
to the MET-doped cultures. These analyses indicate that, while adaptation takes place upon addition of the NCAA to the
E. coli adapts metabolically to the presence of NCAA in its growth medium. To complement the MS-based metabolomics
growth medium in the presence of heat stress, each NCAA analysis, NMR was utilized to expand metabolite identification
supplementation impacts the intracellular metabolomes of the and coverage, and to help with the assessment of the potential
E. coli cultures in different ways. It also appears that HPG has biological impact of those metabolic adaptations on the cellular
a greater impact on the metabolome of E. coli than AHA, based phenotypes of E. coli.
on pairwise t-tests.
HCA results were plotted on a heatmap to visualize changes
in the patterns of individual MS features identified between the NMR Metabolomics Analysis of Cultures
NCAA supplemented heat stressed E. coli cell cultures. When Grown With Non-canonical Amino Acids
taking into account all of the MS features, the AHA and HPG Under Heat Stress
supplemented E. coli samples separated to a greater extent from From analysis of 1D 1H NMR spectra and spectral profiling
the control and MET-supplemented samples, compared to the using the Chenomx software, 55 metabolites were identified and
same groups analyzed in our initial study in the absence of quantified from intracellular metabolite extracts of the heat-
heat stress (Figures 2A, 4A). Although the boundaries between stressed, NCAA-supplemented E. coli cultures (Supplementary
groups were clearer, the HCA did not separate all replicates Table S7). While the MS metabolomics data demonstrated
of a group into unique clusters, nor did the heatmap reveal the presence of a certain degree of metabolic adaptation
the presence of a large number of features with significant fold occurring in these cell cultures, the 55 metabolites annotated
changes (Figure 4A). A heatmap of the top 250 differentiated MS and validated by NMR provided some clues as to which
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FIGURE 4 | Heatmaps of heat stressed E. coli cultures. Data from MS and NMR are shown (A,B, respectively). Heat map is coded with blue as low and red as high
abundance. Fold change is indicated on the scale. (A) AHA and HPG doped stressed E. coli cultures show segregated clustering in the MS heatmap of all features
(5,960 detected features). (B) The NMR heatmap (55 identified features) shows distinct clustering of Control, HPG, and Met, the exception being AHA which had
moderate clustering with MET. For enlarged images that show metabolite names and sample identifications see Supplementary Figure S5.
metabolic pathways may be involved in these metabolic HCA, schematically represented as a heatmap of relative
adaptations. The NMR metabolomics studies of the heat stressed, NMR metabolite abundance, was employed to further evaluate
AHA, HPG, and MET supplemented E. coli cell cultures differences among the metabolite profiles of each E. coli treatment
employed the same experimental workflow used for examining group (Figure 4B). This heatmap was generated from changes in
the intracellular metabolomes of the E. coli cell cultures in metabolite concentrations observed for the 54 metabolites that
absence of heat stress. were identified by NMR. The control and MET-supplemented
Group separations were assessed using PCA. The resulting groups clustered more closely, while the NCAA treated groups
2D-PCA scores plots (Figure 3B) revealed that HPG, AHA, formed a second cluster. The one exception was a replicate from
and MET-supplemented E. coli groups could be separated the E. coli cell cultures supplemented with MET (Figure 4B).
based on their distinct NMR-based metabolome profiles Consistent with the HCA analysis of the MS spectral features,
from the control group. Furthermore, the AHA and HPG- the heatmap representation of the NMR-based metabolite profiles
treated groups also separated from each other based on suggest that although AHA or HPG supplementation does
distinct metabolite patterns (Figure 3B, red and purple 95% induce changes in intracellular metabolome of E. coli, no
confidence interval circles), while the metabolic profile of dramatic metabolic alterations appeared to have taken place
the MET supplemented group overlapped with that of the within the cells.
AHA-treated group (Figure 3B, red and cyan 95% confidence
intervals). Analysis of loading factors (Supplementary Table S8)
contributing to PC1 and PC2 of the 2D PCA-score plots revealed Combined Pathway Analysis of Heat
that betaine, xanthosine, N-carbamoyl-aspartate, glucose, 4- Stressed E. coli Cultures
aminobutyrate, adenosine contributed significantly to PC1, The NMR metabolomics data provided important information
which accounted for 50.5% of the variance, while PC2 accounted about potential changes in metabolic pathway usage based
for an additional 10.7%. on a metabolic pathway impact analysis that was conducted
Although the samples separated from each other primarily using MetaboAnalyst. Partial least squares discriminate analysis
along the PC1 axis, as with the MS metabolomics findings, (PLS-DA) (Figure 5A), with resulting variable importance in
the variation between sample replicates was rather large and projection (VIP) scores for metabolites that have the highest
resulted in minimal separation by treatment type. In other words, discriminatory power among the treatment groups was used
although metabolic adaptations occur within the cell in the (Cho et al., 2008). Metabolites that contributed most to the
presence of NCAA under heat stress, those metabolic responses separation of the different sample groups are listed in the
appear to be rather limited and do not seem to suggest that VIP scores plot and revealed interesting trends between
a significant overhaul of the metabolic machinery of E. coli the different cell culture treatment groups (Figure 5B).
is taking place. This analysis indicated that NCAA addition impacted
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FIGURE 5 | 3D-PLSDA of metabolites as identified by NMR from the heat stressed non-canonical amino acid doped E. coli cultures and corresponding VIP scores
table. (A) The PLSDA shows distinct separation between the doping groups (B) VIP shows the top 12 metabolites (out of 55 total metabolites) that contributed the
most to the variation between sample types.
amino acid, protein, and lipid metabolism. Intermediates degradation were altered, as leucine, MET, and tyrosine
in central carbon metabolism via lipid and amino acid were present at higher concentrations in the AHA and
synthesis and TCA cycle related metabolites, including HPG doped samples than in the control and the MET
aspartate, glycine, fumarate, glucose, pyruvate, malate, and doped samples (Supplementary Figure S4). These results
4-aminobutyrate were altered as a result of AHA or HPG indicate that amino acid metabolism in E. coli is altered
supplementation in the growth medium, resulting in high upon addition of a NCAA suggesting that leucine, MET, and
differentiation between sample treatments for these molecules tyrosine catabolism may be suppressed in the AHA and HPG-
(Supplementary Tables S9, S10). Several metabolites related supplemented cell cultures, or that other metabolites serve
to pyruvate metabolism demonstrated a statistically significant as metabolic precursors for energy production under these
differentiation between the NCAA-treated E. coli groups, conditions, sparing the utilization of leucine, MET, and tyrosine
supporting the idea that TCA cycle activity was altered for such purpose.
in the AHA and HPG supplemented E. coli cell cultures. In the heat exposed, AHA or HPG doped growth conditions,
Intracellular levels of pyruvate, succinate, formate, and acetate intracellular levels of amino acids were found to be higher than in
were found to be higher in the E. coli cell cultures grown control or MET-treated groups (Supplementary Tables S9, S10),
under heat stress and supplemented with NCAA. Metabolites including higher abundance of acetylated amino acids like
associated with purine and amino acid metabolism, like N-actylglycine, N-acetylgultamate, and N-acetylaspartate.
xanthosine, dTTP, glycine and adenosine, also pointed to Acetylated amino acids could represent breakdown products
metabolic networks related to energy production as being of proteins that have been acetylated (Arnesen, 2011) and
altered in the NCAA treated cells. In addition to amino acid have been reported to be used for metabolic adaptations of
biosynthesis, glycerophospholipid metabolism was dysregulated microorganisms. Protein acetylation is a common post- and
as a result of NCAA incorporation with O-phosphocholine, co-translational modification process for metabolic enzymes
and sn-glycero-3-phosphocholine are present at higher involved in central metabolism (Christensen et al., 2019). This
concentrations in the HPG and AHA supplemented cultures. modification is usually found on the side chains of amino
Metabolites associated with lipid, amino acid and purine acids, not on protein backbone residues, and could explain why
metabolism were consistently higher in abundance in the free acetylated amino acids were detected in high abundances
NCAA-treated E. coli groups then the control and methionine in the AHA and HPG-treated E. coli cell cultures. Amino
treated E. coli. acid acetylation could also be indicative of a higher rate of
While NCAA addition impacted TCA cycle activity within post translational modifications in the NCAA doped samples
the cell and potentially energy production via amino acid, purine (Elf and Ehrenberg, 2005), which would suggest changes in
and lipid metabolism, the implications of such metabolic the accuracy of protein translation, protein signaling, and
changes remain unclear. Amino acid biosynthesis and protein-protein interactions. There are differing hypotheses on
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the implications of acetylation on protein degradation. One profiles, group clustering, and metabolic change at the individual
school of thought indicates that it is protective (Carabetta and metabolite level. HPG seemed to perturb E. coli to a larger
Cristea, 2017), while more recent studies have reported the extent than AHA based on paired t-tests which had 19 and
opposite (Arnesen, 2011). Additional studies are needed to 8% of metabolites changing, respectively. This was not expected
fully elucidate the impact of AHA or HPG supplementation on because the differential impact of AHA and HPG on E. coli had
protein acetylation. not been reported previously.
The NMR data lent power to our analysis in the form
Screening for Potential Degradation of metabolite annotation and validation. Changes in specific
Products of AHA and HPG metabolite levels indicated that pyruvate metabolism andintermediates of the TCA cycle were affected. Changes in central
A question remaining to be address on the use of BONCAT carbon metabolism is a common stress response in E. coli, so
relates to whether protein synthesis indeed serves as the the perturbations we observed as a result of NCAA additions
only sink for the incorporation of NCAA or whether some are consistent with this archetypical stress response (Jozefczuk
organisms could be capable of metabolizing NCAA for their et al., 2010). Along with TCA metabolites, glycerophospholipids,
energetic needs. In an attempt to provide answers to this issue, amino acids and acetylated amino acids were detected at
our LC-MS data was searched for potential breakdown and higher concentrations in the AHA and HPG supplemented
conversion products of AHA and HPG as predicted from KEGG E. coli samples.
pathways and assuming that AHA or HPG could serve as In-depth NMR and MS metabolomic analyses show that
substrates for enzymatic conversions. Potential compounds of supplementing E. coli cultures with NCAA has an impact
interest included N-formyl-AHA and N-formyl-HPG as well on the concentration of specific metabolites leading to a
as AHA/HPG versions of 4-(methylsulfanyl)-2-oxobutanoate. metabolic adjustment. This should serve as a cautionary note
Other degradation products were ruled out because they all to scientists about how and when NCAA can be used. Our
required activation of the MET-sulfur functional group, which data implies that the common practices of using optical density
is absent from both HPG and AHA. No features that matched for cells in culture or behavioral analyses for multicellular
these suspected products were detected. This implies that species to assess the impact of NCAA supplementation are
breakdown of AHA and HPG is not a major metabolic activity not telling a complete story. Metabolic profiles do change,
of E. coli, and that the main sink for AHA and HPG is, indeed, but our overall assessment is that under normal or even
protein synthesis. moderately stressful growth conditions, NCAA doping causes
minor perturbations to the overall metabolic homeostasis of
Summary microbial cells.
Utilizing NMR and LC-MS approaches, we were able to establish
that NCAA addition can cause metabolic perturbation and
adaptation in E. coli, especially when the bacteria are subjected DATA AVAILABILITY STATEMENT
to heat stress. MS analyses indicated that the presence of
NCAA altered the concentration of approximately 15% of The datasets generated for this study can be found
the global mass features identified based on ANOVA. To in the Metabolomics Work Bench, https://www.
put this into perspective, the addition of MET altered the metabolomicsworkbench.org/data/MWTABMetadata4.php?
abundance of 7% of the all the mass spectral features detected F=kfsteward_20191206_111451_mwtab_analysis_1.txt&Mode=
in the E. coli cells. This mild perturbation is consistent with Study&DataMode=AllData&StudyType=MS#DataTabs.
previous studies that have investigated the impact of AHA
or HPG replacement of MET (Dieterich et al., 2006; Bagert
et al., 2014; Hatzenpichler et al., 2014, 2016; Hatzenpichler AUTHOR CONTRIBUTIONS
and Orphan, 2015; Landgraf et al., 2015; Calve et al., 2016;
Lehner et al., 2017). Although the observed metabolic changes RH, BB, VC, and KS conceptualized and designed the study.
were mild, the heatmaps and 2D-PCA score plots highlighted RH, BS, MD, BE, and NB worked on the experimental setup
trends between the different E. coli treatment groups. HCA and manipulations. All authors analyzed and interpreted the
also showed that while AHA and HPG addition impacts the data, critically revised the manuscript for important intellectual
global metabolism of E. coli to some extent, the lack of group content. KS, BB, RH, and VC drafted the manuscript.
separation based on distinct metabolite profiles suggests that
these metabolic changes are minimal under regular growth
conditions and become more pronounced when cells are FUNDING
subjected to heat stress.
The global NMR and MS data are consistent in revealing This research was supported in part by funding from the
the absence of significant group separation between the different Keck Foundation and the National Science Foundation (MCB-
E. coli cell cultures. The largest difference between groups was 1817428). Undergraduate participation in this research was
observed for the HPG-treated cells. Along this same trend, the made possible through a National Science Foundation Research
AHA- and MET-doped cultures were more similar in metabolite Experiences for Undergraduates Grant (REU-1461218). Funding
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Steward et al. Metabolic Implications of BONCAT
for Proteomics, Metabolomics and Mass Spectrometry Facility ACKNOWLEDGMENTS
used in this publication was made possible in part by the
MJ Murdock Charitable Trust and the National Institute The authors thank Jesse Thomas for technical assistance with
of General Medical Sciences of the National Institutes of mass spectrometry.
Health under Award Number P20GM103474. Funding for
the NMR facility was provided in part by the NIH SIG
program (1S10RR13878 and 1S10RR026659), the National SUPPLEMENTARY MATERIAL
Science Foundation (NSF-MRI:DBI-1532078), the Murdock
Charitable Trust Foundation (2015066:MNL), and support from The Supplementary Material for this article can be found
the Office of the Vice President for Research and Economic online at: https://www.frontiersin.org/articles/10.3389/fmicb.
Development at MSU. 2020.00197/full#supplementary-material
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