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While yeast is one of the most studied organisms, its intricate biology remains to be fully mapped and understood. This is especially the case when it comes to capture rapid, in vivo fluctuations of metabolite levels. Secondary electrospray ionization-high resolution mass spectrometry SESI-HRMS is introduced here as a sensitive and noninvasive analytical technique for online monitoring of microbial metabolic activity. The power of this technique is exemplarily shown for baker’s yeast fermentation, for which the time-resolved abundance of about 300 metabolites is demonstrated. The results suggest that a large number of metabolites produced by yeast from glucose neither are reported in the literature nor are their biochemical origins deciphered. With the technique demonstrated here, researchers interested in distant disciplines such as yeast physiology and food quality will gain new insights into the biochemical capability of this simple eukaryote.
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SCientifiC RepoRTS | 7: 14236 | DOI:10.1038/s41598-017-14554-y
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Comprehensive Real-Time Analysis
of the Yeast Volatilome
Alberto Tejero Rioseras
1,2,3, Diego Garcia Gomez1,3, Birgitta E. Ebert
4, Lars M. Blank4,
Alfredo J. Ibáñez1,5 & Pablo M-L Sinues
1,6
While yeast is one of the most studied organisms, its intricate biology remains to be fully mapped and
understood. This is especially the case when it comes to capture rapid, in vivo uctuations of metabolite
levels. Secondary electrospray ionization-high resolution mass spectrometry SESI-HRMS is introduced
here as a sensitive and noninvasive analytical technique for online monitoring of microbial metabolic
activity. The power of this technique is exemplarily shown for baker’s yeast fermentation, for which the
time-resolved abundance of about 300 metabolites is demonstrated. The results suggest that a large
number of metabolites produced by yeast from glucose neither are reported in the literature nor are
their biochemical origins deciphered. With the technique demonstrated here, researchers interested in
distant disciplines such as yeast physiology and food quality will gain new insights into the biochemical
capability of this simple eukaryote.
Metabolites are small molecules that provide a direct readout of organisms’ phenotypes and cellular activity1.
During metabolic processes2, a subset of the metabolites, the so-called volatile organic compounds (VOCs) are
released to the ambient at normal pressure and temperature. VOCs are organic compounds typically C5-C203 with
molecular weights up to 500 Da4, boiling point up to 250 °C5 and high vapor pressure. Many living organisms
including humans, animals, plants, and even microorganisms, produce large varieties of VOCs6. e sum of all
VOCs produced by an organism has been termed the volatilome79. While some VOCs have dedicated pathways,
many are intermediates of anabolic pathways or are synthesized by moonlighting activities of enzymes10, hence
reect promiscuous enzymatic activities in the underlying metabolic network11,12. Although generally produced
in small concentrations (parts-per-million to parts-per-trillion range)13, VOCs reect the metabolic state of a
cell and, thus, can be used to understand many biological processes such as, e.g., oxidative stress1416. VOCs are
also at the core of the avor and fragrance manufacturing, an industry with a focus on plant-derived VOCs.
Volatile metabolites are also of importance in the food industry as taste and smell is informing the consumer
on the quality of a product. A volatile metabolite, industrially produced in large quantities, is alcohol (i.e., eth-
anol), synthesized by yeast from glucose during fermentation. Interestingly, since ethanol is neutral-avored,
the taste of fermented drinks originates from minor compounds, such as higher alcohols, aldehydes, esters, and
acids. Most of these metabolites are volatile and intensely avored. Indeed, monitoring VOCs (i.e., diacetyl,
2,3-pentanedione) abundance in beer is industrial standard, since it has a strong eect on the product quality17.
e economic impact of monitoring such compounds is thus vast18.
Gas chromatography-mass spectrometry (GC-MS)-based methods have been the workhorse to analyze VOC
proles. e main limitation of such methods is that it requires sample manipulation, resulting in laborious pro-
cedures19,20. A more recently deployed approach is ion mobility spectrometry, which provides near real-time anal-
ysis, but its poorresolution compared to mass spectrometry compromises metabolite coverage and compound
identication capabilities2124. Real-time mass spectrometric methods used for on-line quantication of a handful
of VOCs include proton transfer reaction (PTR)13 and selected ion ow tube (SIFT)25. A recent mass spectromet-
ric method based on direct ow injection, has shown to monitor metabolite dynamics in the 15–30 second range
and was used to gain insights into cellular responses to environmental changes26.
1Department of Chemistry and Applied Biosciences, ETH Zurich, 8093, Zurich, Switzerland. 2SEADM S.L., C\ José
Lázaro Galdiano, 1, Madrid, 28036, Spain. 3Department of Analytical Chemistry, University of Cordoba, Cordoba,
Spain. 4Institute of Applied Microbiology – iAMB, Aachen Biology and Biotechnology – ABBt, RWTH Aachen
University, Worringerweg 1, Aachen, 52074, Germany. 5Instituto de Ciencias Ómicas y Biotecnología Aplicada
- Pontificia Universidad Católica del Perú (ICOBA-PUCP), Lima, Peru. 6University Children’s Hospital Basel,
Department of Biomedical Engineering, University of Basel, Basel, Switzerland. Correspondence and requests for
materials should be addressed to A.J.I. (email: aibanez@pucp.edu.pe) or P.M.-L.S. (email: pablo.mlsinues@ukbb.ch)
Received: 30 May 2017
Accepted: 12 October 2017
Published: xx xx xxxx
OPEN
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In line with such eorts to develop instrumentation capable of monitoring time-resolved metabolic informa-
tion, we show here how secondary electrospray ionization (SESI)2739 coupled to high resolution mass spectrom-
etry (HRMS) captures on-line an unprecedented wealth of volatile analytes emitted in vivo by growing baker’s
yeast (Saccharomyces cerevisiae).
Results and Discussion
Volatiles emitted by wild type Saccharomyces cerevisiae during growth in13C6-glucose.
Initially, to conrm that SESI-HRMS is indeed suitable to track growth-related metabolites in real-time, we grew
the yeast in glucose minimal medium and monitored well-known end products40 like ethanol or acetic acid. As
expected, we observed the production of ethanol despite full aeration because baker’s yeast is Crabtree positive.
Figure1a shows such an example, in which ethanol production kicked o shortly aer the start of the growth
experiment. e concentration increased exponentially to then level o, presumably concomitant with glucose
depletion. Eventually, ethanol started to decrease again. Such a diauxic growth is expected for yeast fermentation
on glucose. e depletion kinetic, however, seems too fast to be solely explained by metabolization. Evaporation
of this volatile metabolite might have been enforced by the vigorous aeration (25 gas volume ow per unit of liq-
uid volume per minute; vvm) of the bioreactor. When ethanol was almost completely depleted, we spiked glucose
into the medium (~27 h; see Methods section for experimental details). e cells immediately re-started glucose
consumption as evidenced by the instantaneous production of ethanol and acetic acid.
One of the advantages of SESI coupled to high-resolution mass spectrometry (HRMS) is that it can detect
a wide range of analytes, including species of low vapor pressure at concentrations in the low parts-per-trillion
range41. Indeed, along with the well-known volatile metabolites ethanol and acetic acid, numerous other volatile
metabolites were detected during yeast growth. us, we replicated the experiment independently; however, we
administered glucose labelled with 13C in all six carbon atoms. 13C label incorporation into metabolites syn-
thesized from glucose allowed dierentiating analytes of biological origin from environmental contaminants
or media components. Figure1b shows the resulting time proles of ethanol, acetic acid, and yeast biomass
estimated from image grayscale levels in a time-lapse video. e observed correlation between increase of etha-
nol and production of biomass conrmed that the analytical setup allowed capturing biological events soundly.
13C-label incorporation conrmed that 263 signals observed in the previous experiment originated from the
yeast’s metabolism. Inaddition, weconducted a negative control experiment whereby a bioreactor was run under
Figure 1. Real-time monitoring of volatile metabolites during yeast growth; (a) raw time proles of ethanol
(detected as the dimer) and acetic acid during growth on 12C6-glucose and re-injection of glucose aer ~27 h;
(b) normalized raw time proles of ethanol (detected as the dimer), acetic acid and the image signal captured
by a time-lapse camera during growth on 13C6-glucose; c) heatmap showing 263 time-dependent signals during
yeast growth. All the signals in the heatmap were labeled with 13C. For reference, the ethanol signal is shown on
the top.
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the very same conditions but without inoculation with yeast (monitored for ~17 hours). As expected, neither
ethanol nor acetic acid were produced (FigureS1).
e time-resolved dataset of the yeast fermentation enabled a comprehensive overview of the volatilome
dynamics (Fig.1c). The heatmap is ordered by a cluster analysis, easing the visualization of the different
time-proles. Note that these metabolites were captured simultaneously and in real-time without any sample
manipulation. TableS1 lists the 263 signals detected in both experiments (i.e., labeled and non-labeled glucose)
as they appear in the heatmap of Fig.1c. Interestingly, homologous series of compounds tend to cluster together,
suggesting similar kinetics for closely related metabolites. For example, one cluster comprised the homologous
series C6H10, C7H12, C8H14 and C9H16. is bouquet of volatile metabolites remains to be annotated, but the
chemical formula suggests that these compounds could well be alkadienes. Several olenic compounds have
been reported for diverse organisms4246. 1,3-octadiene (C8H14), for example, is produced by several fungi and
predicted to be a degradation product of linoleic acid, but the responsible enzymes and encoding genes are yet
to be identied47. None of the potential alkadienes detected here were previously identied in the headspace of
yeast fermentations.
Volatiles emitted by wild type and mutants of Saccharomyces cerevisiae during growth in
13C1-glucose. To demonstrate that the detected series of hydrocarbons were produced by the baker’s yeast and
not by a biological contamination of the bioreactor, we repeated the measurements. Specically, we performed
a comparison of two baker’s yeast mutants (i) devoid of the oxidative pentose phosphate pathway (zwf1), which
is associated with oxidative stress response or (ii) limited in glycolysis (p1); and compared them with a wild
type (WT) strain. To discriminate in vivo pathway usage, we fed WT, zwf1, and p1 with 13C1-glucose. Figure2a
shows a zoom of the m/z range 200–215 of a typical mass spectrum acquired during such measurements for WT
during the stationary phase. It shows two distinct bell-shaped isotopic distributions. e rst one assigned to a
series of Cx(13C)yH25O2 with x ranging 11 to 7 and y 1 to 5 (i.e., molecular formula C12H25O2). Similarly, another
more abundant distribution of C15H25 (13C ranging from 2 to 9) was clearly observed. Apart from high resolution
(~ 30,000) enabling the discrimination of complete 13C isotopic envelopes, MS/MS capability is just another
advantage of our proposed technique over other common on-line technologies used to monitor volatiles in indus-
trial processes48. us, further insights in this respect were gained by identifying some of the detected com-
pounds. For example, the two compounds in Fig.2a were assigned to ethyl decanoate and farnesene based on
the MS/MS spectrum (Figs2b and S2). Ethyl esters have fruity and oral avor and constitute important aroma
compounds in wine or beer49. It is hypothesized that fatty acid ethyl esters are formed to prevent accumulation of
medium chain fatty acid under anaerobic conditions during which fatty acid synthesis is inhibited and these fatty
acids are prematurely released from the fatty acid synthase50. e sesquiterpene farnesene is synthesized from
farnesyl diphosphate, an intermediate of the mevalonate pathway.
Figure2c shows an example of some representative time proles for a set of metabolites present in the zwf1
mutant but absent in WT and p1. Notably, this set of metabolites is a homologous series of odd-numbered
carbons ranging from C7 to C15. is observation was replicated in an independent experiment (FigureS3). We
hypothesize that these compounds are methyl ketones, which can be derived from β-oxidation of fatty acids, but
their identity remains to be fully elucidated51. Likewise, some analytes were characteristic for p1 (see example
in FigureS4).
Figure2d (top) shows the time traces for ethanol. We found that the WT produced approximately twice as
much ethanol as the mutants under the same experimental conditions. e same results were obtained in a rep-
licate experiment (FigureS3). is is consistent with previous work, where similar, reduced glucose uptake and
growth rate and as a consequence, a low ethanol formation rate was found for the zwf1 mutant52. As for
Fig.1c, we subjected the time traces for all detected signals of the three strains to a cluster analysis and visualized
them in heatmaps. Figure2d (bottom) shows the results, providing an overview of the dierences in volatile
proles between the strains. While the general picture seems relatively similar for WT and p1, the zwf1 mutant
showed marked dierences for some compounds. For example, at the bottom of the heatmap one can observe
the cluster of molecules from Fig.2b. TableS2 lists all the peaks detected in this experiment. Overall, this initial
assessment illustrates the potential of SESI-HRMS to provide a comprehensive overview of VOC production by
baker’s yeast in real-time.
Although the p1 cells showed a dierent metabolic prole than WT and zwf1 cells, our study showed an
unexpected result. e p1 strain was able to undergo glycolysis (apparent from lower than expected loss of 13C)
aer an extended time of lag-phase growth. It is not entirely clear to us, how the p1 strain was able to accomplish
this. However, S. cerevisiae’s phosphofructokinase is a heterooctameric enzyme with an alpha subunit encoded
by PFK1 and a beta subunit encoded by PFK2. us, a single gene deletion mutant decient in either of these
genesmight retain phosphofructokinase activity, which may explain the observed labeling pattern53,54.
Another common approach to visualize multivariate data is principal component analysis (PCA). e score
plot of the mass spectra during the exponential phase (Fig.3a) clearly shows a separation between the three
strains, conrming the existence of a distinct volatile prole for each strain. Please note that we have excluded
ethanol and acetic acid from the analysis in order to show that even without these major end-products, the less
abundant volatile compounds suggest a very distinct prole. As expected, a similar result was obtained when we
included ethanol and acetic acid in the PCA matrix (FigureS5). To understand which metabolites contributed
mostly to the separation shown in the score plot, Fig.3b displays the loadings for the rst PC, which separate WT
from the mutants. Some other highly discriminating molecules were identied via MS/MS and by comparison
with standards (FigureS2). e esters ethyl octanoate and ethyl decanoate were major contributors, along with
farnesene and octanoic acid. TablesS3 and S4 list the major contributions for PC1 and PC2, respectively. When
ethanol was included in the analysis, it was by far the major contributor to the separation shown in the score plot
(FigureS5).
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Some of the identified compounds are not only relevant biologically, but also industrially. For example,
ethyl octanoate is a yeast-based food product with sour apple aroma55. Figure3c shows the time traces for ethyl
octanoate for the three strains investigated. It clearly shows that the levels produced by WT were about four times
as much as that of the mutants, reinforcing the potential to monitor industrial processes. Interestingly, ethyl hex-
anoate showed a very similar trend (this and additional relevant examples are shown in FigureS6). Fatty acids
such as hexanoic, octanoic, and decanoic acids are just some other examples of relevant metabolites for the food
and cosmetic industry. To demonstrate that these products can also be conveniently monitored, we switched the
polarity to negative ion mode. SESI-HRMS spectra in negative ion mode are dominated by deprotonated fatty
acids56,57. erefore, isomeric interferences such as esters could be excluded. Figure3d shows the time traces of a
complete series of 11 fatty acids detected in WT aer injecting 13C1-glucose. Acetic, octanoic, and decanoic acids
were the major products. e complete list of all 43 compounds detected in this experiment is given in supple-
mentary TableS5.
us, in total, 8 compounds were identied with a high degree of condence by real-time MS/MS, although
isomeric structures cannot be excluded. 11 fatty acids were also conrmed in negative ion mode with a high
degree of condence based on prior studies56,57. Additional hints of potential compounds were sought by que-
rying publicly available databases. Of the 263 peaks associated to glucose metabolism in positive ion mode, 111
formulae matched at least one compound present in the yeast metabolome database (TableS1)58.
By injecting 13C1-glucose, we also aimed to conrm the validity of our approach to capture well-known biolog-
ical processes. For example, while the contribution of the oxidative pentose phosphate pathway is rather small in
baker’s yeast grown on glucose, this little contribution should be detectable59. is is indeed suggested in Fig.3c,
Figure 2. Growth of WT and mutants on 13C1-glucose led to complex mass spectra that revealed a unique
in vivo metabolic response; (a) Centroided mass spectrum of the region m/z 200–215 during the stationary
phase of WT yeast growth upon injection of 13C1-glucose into the system. Two isotopic envelopes for C12H25O2
and C15H25 were clearly resolved. e incorporation of 13C into ethyl decanoate and farnesene reects the
metabolism of the yeast; (b) Fragmentation (SESI MS/MS) spectrum produced using m/z 201 as the precursor
ion from a yeast sample (top) and ethyl decanoate standard (bottom); (c) Set of odd-numbered carbon
molecules built up during growth of the zwf1 mutant but absent in WT and p1; (d) Heatmap for the 636
signals detected for the three strains. For reference, ethanol proles are shown on the top.
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SCientifiC RepoRTS | 7: 14236 | DOI:10.1038/s41598-017-14554-y
Figure 3. Distinct volatile metabolic proles and production kinetics for yeast WT and mutants; (a) PCA
score plot of average spectra in the exponential phase for WT and mutants suggests a clear distinction based
on the volatile metabolic prole; (b) the corresponding loading plot for score 1 shows that esters, acids, and
sesquiterpenes are major contributors to the separation between WT and mutants. Identied compounds are
highlighted in red. Note that ethanol and acetic acid were excluded from the PCA analysis; (c) Kinetic proles
of the food-relevant metabolite ethyl octanoate for WT, p1 and zwf1 illustrates the potential to monitor
industrial processes; (d) series of fatty acids detected in negative ion mode during WT growth in 13C1-glucose;
(e) isotopic distribution for farnesene obtained during the stationary phase. As expected, it shows a greater
accumulation of 13C for zwf1; (f) Time proles of 13C/12C ratios for ethanol dimer (i.e., m/z 95/93; black) and
acetic acid (red) for the three strains investigated. Note the dierent kinetic proles. e average spectra during
the stationary phase for the ethanol dimer is shown in the insets.
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SCientifiC RepoRTS | 7: 14236 | DOI:10.1038/s41598-017-14554-y
which shows the normalized isotopic distributions for farnesene for the three strains. e maximum isotope
accumulation for all three strains was found at 13C4, but clearly, the relative abundance of C5 to C9 is greater in
the culture of the zwf1 knockout strain as no 13CO2 is released during breakdown of glucose to acetyl-CoA. e
same trend was observed for acetic acid, ethanol, ethyl octanoate, ethyl decanoate, and octanoic acid. All these
observations were reproduced in a replicate experiment (FigureS7).
As metabolic systems are highly plastic, the ability to monitor metabolite dynamics is crucial for under-
standing cellular processes. Here, our time-resolved data was pivotal for understanding the metabolic path-
ways involved in the metabolism of glucose that are favored by each yeast strain. Figure3f shows the 13C/12C
ratio for ethanol and acetic acid. Note that, in the case of ethanol, we monitored the dimer, which lead to a
triplet-like isotopic distribution (i.e., m/z 93 for C2H6O-C2H6O; m/z 94 for C(13C)H6O-C2H6O and m/z 95 for
C(13C)H6O-C(13C)H6O; inset in Fig.3f). Both, the kinetic and isotopic ratios were dierent for the three strains.
Mutant zwf1 incorporated 13C into ethanol steeply to reach a plateau of 13C/12C ratio of ~1.2 aer 5 h. e label
incorporation in ethanol formed by the WT was similar but reached lower nal fractional labeling. In contrast,
no labeled ethanol was observed for p1 in the rst 4 h aer the 13C tracer injection and did not reach a steady
labeling within the 25 h of the experiment. is is explained by a slowed-down glucose metabolism and the
delayed ethanol formation in this mutant. Interestingly, the 13C in acetic acid built up immediately upon glucose
injection in all strains. Other compounds showed dierent kinetics (FigureS8). e time resolution capability
allowed to exquisitely capture ne details of kinetic proles, which is crucial to adjust industrial parameters to
tune in real-time the desired volatile prole.
In conclusion, we deployed a sensitive and selective, yet, real-time mass spectrometric technique to investigate
the production of volatile metabolites during yeast growth. e technique gently tracks biological processes at
the metabolic level in vivo with a time resolution of less than one minute. us, we benchmarked the technique
by observing well-known processes such as production of ethanol from glucose. However, despite being one of
the most widely studied organisms, the rich volatile proles (~300 metabolites combining positive and nega-
tive mode) of S. cerevisiae detected in these analyses, including non-reported analytes suggests that much work
remains to be accomplished to fully map the metabolism of yeast. Such comprehensive metabolic coverage may
also have potential to tune industrial processes where yeast fermentation is involved.
Methods
Secondary Electrospray Ionization-High Resolution Mass Spectrometry (SESI-HRMS). SESI-
HRMS experiments were carried out using a commercial ion source (SEADM S.L.)60 plugged onto a ermo-
Fisher LTQ Orbitrap mass spectrometer. e SESI solvent was 0.1% formic acid in water infused at ~100 nL/
min through a 20 μm ID silica capillary. e electrospray voltage was set to 5.4 kV in positive mode (i.e., focusing
electrode 2.59 kV and impact electrode 1.6 kV) and to 5 kV in negative mode. e sweep gas used for cleaning the
electrospray region was set to 2 a.u. All other internal Orbitrap parameters were optimized during calibration.
Orbitrap scan parameters: scan type: FTMS full MS [50–500 Da]; source fragmentation: o; resolution: 30.000;
polarity: positive and negative; Typical mass accuracies were within 2 ppm by using common chemical noise
encountered in SESI-HRMS background as lock masses (i.e. m/z 149.0233, 279.1591 and 445.1200)61,62. AGC
target: 30.000; Maximum injection time: 150 ms; micro scans: 30, which led to acquire a prole mass spectrum
every ~49 seconds to prevent the generation of intractable large les during several hours of volatiles monitoring.
e system was properly calibrated in the respective polarities prior to the analysis. MS/MS fragmentation spectra
for both, yeast and standards, were obtained under identical conditions: Same collision energies; 5 microscans;
Isolation width: 1 Da; Activation Q: 0.25 and Activation time: 30 ms.
Yeast cell cultures. e bioreactor consisted of an autoclaved 100 mL three-neck ask lled with 20 mL of
medium, stirred at 800 rpm with a Teon magnet and uniformly heated at 40 °C in a water bath. e metabolites
produced during yeast growth were dragged downstream towards the SESI source by a continuous ow of com-
pressed air at 0.5 L/min. e compressed air was ltered and humidied upstream the bioreactor. e bioreactor
was connected to the ion source through a stainless-steel tube (OD 6 mm) heated at 130 °C to minimize metabo-
lite adsorption onto the tube walls.
All the described experiments in this study were performed with Saccharomyces cerevisiae, prototrophic,
YSBN.6 (wild-type) strain. Cells were grown in minimal dened medium: BD (DIFCO) yeast nitrogen base
(#233520); and 2% glucose (as only carbon-source). For each experiment, pre-cultures were inoculated from SD
plates and grown at 30 °C while shaking with 300 rpm for 8 to 10 hours in 1 mL pre-culture tubes. en, the cells
were inoculated (starting optical density; OD ~0.1) in 500 mL Erlenmeyer asks with 50 mL of growth medium.
Cultivation was performed at 30 °C with stirrer bar at 300 rpm until an OD of 1.2. e YSBN.6 strains zwf1Δ
(Δzwf::Kan) and p1Δ (ΔPFK1::Kan), were grown in a similar minimal dened medium with the addition of
0.02% (v/v) of kanamycin and under the same time, temperature, and mixing conditions. To start the experiment,
1 mL of inoculum with an OD (between 0.8 to 1.0) was injected into the bioreactor for obtaining a starting OD
of 0.1. When the ethanol signal decayed, 0.8 mL of 50% glucose was injected to prolong the exponential growth
phase.
During the experiment, a time-lapse camera took a picture of the bioreactor every minute. Using ImageJ, an
open platform for scientic image analysis63, yeast concentrations of relative values were obtained by averaging
grayscale level in a region of interest of the image in a video. Absolute OD values were also obtained at the begin-
ning and at the end of each experiment with a ermo electron corporation GENESYS 10 UV spectrophotometer.
Figure4 shows a schematic of the experimental set-up (photograph in FigureS9).
Data analysis. e raw mass spectra were transformed into mzXML format via MSConvert (Proteowizard)64
and imported to MATLAB (R2016b). Each sample file was interpolated linearly (106 points in the range
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SCientifiC RepoRTS | 7: 14236 | DOI:10.1038/s41598-017-14554-y
50–500 Da). ese interpolated prole mass spectra were then centroided (intensity threshold of 50 a.u.), result-
ing in ~4,400 mass spectral features (number of peaks dependent of course on the experiment performed).
Subsequently, we computed the time traces for each of these peaks by adding the signal intensities ± 2, 5 mDa
around the peaks. e resulting time traces (i.e., signal intensity for each m/z value as a function of time) were
subjected to an agglomerative hierarchical cluster tree (Ward method; Euclidean distance; Figs1c and 2d). To
ease visualization, the time traces were smoothed (moving mean; span = 25). e 263 features nally considered
(TableS1) were retained as they raised upon the 12C-glucose and 13C-glucose injection but did not experience
any temporal change in a blank experiment were glucose was spiked in sterile fermentation medium. e signal
to noise ratios were determined as the ratio of the variances of the signals of the experiment with yeast and the
negative control where we spiked glucose into sterile medium (FigureS1).
e generation of molecular formulae from accurate mass was performed assuming protonated ions in the
positive ion mode and deprotonated ions in negative mode. e following rules65 were taken into account: Masses
up to 500 Da; considered elements were restricted to C, H, N and O; restricted maximum number of each of these
elements, bounded H/C ratio and heteroatom/C ratio and limited number of double-bond equivalent.
Principal component analysis (PCA) was also used for dimensionality reduction and visualization of the mass
spectra (Fig.3a,b). e matrix subjected for PCA consisted of 30 rows (i.e., ten time points during the exponential
growth for each of the three strains investigated) and 636 variables (i.e., peaks listed in TableS2). e data was
mean-centered prior PCA. No further transformation of the matrix was applied.
References
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Acknowledgements
We are thankful to Prof. Renato Zenobi for hosting the ACID project at ETH Zurich. We gratefully acknowledge
Dr. Juan Zhang (Novartis AG) for the donation of the LTQ Orbitrap instrument used in this study and Martin
omas Gaugg (ETH) for his support with some of the Matlab scripts. e research leading to these results has
received funding from the European Community’s Seventh Framework Programme (FP7–2013-IAPP) within
the project “Analytical Chemistry Instrumentation Development” (609691). Lars M. Blank and Birgitta E. Ebert
acknowledge the German Federal Ministry for Education and Research (BMBF) for funding (Az. 031A301A).
is work is dedicated to Prof. John B. Fenn (1917–2010) on the occasion of the centennial celebration of his
birth.
Author Contributions
D.G.G., A.J.I. and P.M.L.S. designed the research; A.T.R., D.G.G., A.J.I. and P.M.L.S. performed the experiments;
P.M.L.S. analyzed the data; all authors interpreted the data; A.T.R., B.E.E., L.M.B., A.J.I. and P.M.L.S. wrote the
manuscript; all authors contributed to editing the manuscript.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-14554-y.
Competing Interests: Alberto Tejero Rioseras and Pablo Martinez-Lozano Sinues work in the framework of
a project (Analytical Chemistry Instrumentation Development, FP7-2013-IAPP, 609691), one of whose main
objectives is to develop the commercial ion source used in this study.
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... La DI-ESI-MS a été utilisée pour profiler les métabolites de S. cerevisiae dans le but de discriminer un certain nombre de mutants, notamment avec l'aide d'analyses multivariées pour comparer les résultats des différentes analyses [150,151]. Il existe également une technique en temps réel d'analyse de métabolites volatils injectant des molécules du mélange en phase gazeuse vers une source d'ionisation par électrospray secondaire (SESI) [152]. Ces travaux réalisés avec un analyseur de type Orbitrap ont résulté dans la détection d'approximativement 300 molécules formant un profil métabolique riche de la levure. ...
Thesis
Cette thèse s’intitule « Identification et cartographie des métabolites secondaires de nouvelles souches de levures tropicales ». L’objectif principal est le développement d’une méthodologie de déréplication pour l’exploration des métabolites secondaires extraits de cultures de champignons unicellulaires. La méthode développée fait intervenir la chromatographie liquide à ultra-haute performance couplée à la spectrométrie de masse avec l’outil des réseaux moléculaires en complémentarité de la spectrométrie de masse à ultra-haute résolution. La méthodologie permet d’annoter les métabolites détectés par le procédé analytique, c’est-à-dire formuler des hypothèses sur l’identité et la structure de nombreuses molécules produites par les nouvelles espèces de levures sur lesquelles elle est appliquée. De plus, des cartographies et profils métaboliques sont dressés grâce aux diagrammes et réseaux moléculaires construits. L’application de la méthodologie à de nouvelles souches de levures a permis l’annotation de plusieurs centaines de métabolites, parmi lesquels une large proportion d’alcaloïdes mais aussi des acides aminés, des terpénoïdes et des stérols.
... The volatilome constitutes the set of volatile organic compounds (VOCs) produced by an organism (Amann et al., 2014;Filipiak et al., 2016). These are low-mass molecular substances characterised by low boiling points and high vapour pressures (Ebert et al., 2016;Tejero Rioseras et al., 2017), which can therefore be monitored by non-invasive methods and easier samplings compared to other metabolites (Sinha et al., 2017;Singh et al., 2018). On the other hand, faeces are a complex biological material, interesting for their easy and accessible collection as well as the information they provide about physiology (Rodríguez-Hernández et al., 2020). ...
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Online breath analysis is an attractive approach to track exhaled compounds without sample preparation. Current commercially available real-time breath analysis platforms require the purchase of a full mass spectrometer. Here we present an ion source compatible with virtually any preexisting atmospheric pressure ionization mass spectrometer that allows real-time analysis of breath. We illustrate the capabilities of such technological development by upgrading an orbitrap mass spectrometer. As a result, we detected compounds in exhaled breath between 70 and 900 Da, with a mass accuracy of typically <1 ppm; resolutions between m /Δ m 22 000 and 70 000 and fragmentation capabilities. The setup was tested in a pilot study, comparing the breath of smokers ( n = 9) and non-smokers ( n = 10). Exogenous compounds associated to smoking, as well as endogenous metabolites suggesting increased oxidative stress in smokers, were detected and in some cases identified unambiguously. Most of these compounds correlated significantly with smoking frequency and allowed accurate discrimination of smokers and non-smokers.
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Rationale: Direct mass spectrometry (MS)-based methods make it possible to monitor the molecular compositions of hundreds of volatile organic compounds (VOCs) in exhaled human breath in real time. Mass resolution and mass accuracy play important roles for direct MS analysis, especially for the low-concentration isobaric compounds in non-target research. Methods: Direct detection of VOCs in exhaled breath of four healthy subjects (3 males and 1 female aged between 25 to 35 years old) has been performed by using secondary nano-electrospray ionization mass spectrometry (Sec-nanoESI-UHRMS) at resolutions (R) of 15,000, 30,000, 60,000 and 120,000. Results: For some low-intensity isobaric ions, they could be distinguished only when R ≥ 60,000, e.g., signals at m/z 96.9591 (sulfate/sulfuric acid), m/z 96.9687 (phosphate/phosphoric acid) and m/z 96.9756 ([C4 H2 O7 S]- ), m/z 234.1161 ([C10 H20 O3 NS]+ ) and m/z 234.1338 ([C10 H20 O5 N]+ ), m/z 119.0686 (isotope of indole) and m/z 119.0705 (an interfering signal), respectively. At R 120,000, the mass errors were obtained from a set of reference ions, and the values were ≤0.6 mDa for ions detected in positive detection mode and in the range of -1.0-1.1 mDa for the negative mode. These mass errors were used to exclusively identify unknown compounds detected in the breath samples. By utilizing the present setup, besides the normal VOCs reported previously, we detected non-volatile species (sulfate/sulfuric acid, silicate/silicic acid, phosphate/phosphoric acid and nitrate/nitric acid), dichlorobenzene and an ammonium adduct ([(C2 H6 SiO)6 + NH4 ]+ ), which were ascribed to exhaled particles, indoor air pollution and an endogenous source, respectively. Conclusions: For direct breath analysis, high mass resolution of ≥60,000 and mass errors of 1.0 mDa (absolute value) covering the mass range of interests (e.g., m/z 50-500) are necessary for the exploration and accurate identification of low-intensity unknown isobaric compounds in non-target research. Copyright © 2017 John Wiley & Sons, Ltd.
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