Time-dependent profiling of metabolites from Snf1 mutant and wild type yeast cells

Article abstract of DOI:10.1021/ac800998j

The effect of sampling time in the context of growth conditions on a dynamic metabolic system was investigated in order to assess to what extent a single sampling time may be sufficient for general application, as well as to determine if useful kinetic information could be obtained. A wild type yeast strain (W) was compared to a snf1Δ mutant yeast strain (S) grown in high-glucose medium (R) and in low-glucose medium containing ethanol (DR). Under these growth conditions, different metabolic pathways for utilizing the different carbon sources are expected to be active. Thus, changes in metabolite levels relating to the carbon source in the growth medium were anticipated. Furthermore, the Snf1 protein kinase complex is required to adapt cellular metabolism from fermentative R conditions to oxidative DR conditions. So, differences in intracellular metabolite levels between the W and S yeast strains were also anticipated. Cell extracts were collected at four time points (0.5, 2, 4, 6 h) after shifting half of the cells from R to DR conditions, resulting in 16 sample classes (WR, WDR, SR, SDR) x (0.5, 2, 4, 6 h). The experimental design provided time course data, so temporal dependencies could be monitored in addition to carbon source and strain dependencies. Comprehensive two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC x GC-TOFMS) was used with discovery-based data mining algorithms (Anal. Chem. 2006, 78, 5068-5075 (ref 1); J. Chromatogr., A 2008, 1186, 401-411 (ref 2)) to locate regions within the 2D chromatograms (i.e., metabolites) that provided chemical selectivity between the 16 sample classes. These regions were mathematically resolved using parallel factor analysis to positively identify the metabolites and to acquire quantitative results. With these tools, 51 unique metabolites were identified and quantified. Various time course patterns emerged from these data, and principal component analysis (PCA) was utilized as a comparison tool to determine the sources of variance between these 51 metabolites. The effect of sampling time was investigated with separate PCA analyses using various subsets of the data. PCA utilizing all of the time course data, averaged time course data, and each individual time point data set independently were performed to discern the differences. For the yeast strains examined in the current study, data collection at either 4 or 6 h provided information comparable to averaged time course data, albeit with a few metabolites missing using a single sampling time point.

Full text of DOI:10.1021/ac800998j

Anal. Chem. 2008, 80, 8002–8011
Time-Dependent Profiling of Metabolites from Snf1
Mutant and Wild Type Yeast Cells
Elizabeth M. Humston,† Kenneth M. Dombek,‡ Jamin C. Hoggard,† Elton T. Young,‡ and
Robert E. Synovec*,†
Department of Chemistry, Box 351700, and Department of Biochemistry, Box 357350, University of Washington,
Seattle, Washington 98195
The effect of sampling time in the context of growth
conditions on a dynamic metabolic system was investi-
gated in order to assess to what extent a single sampling
time may be sufficient for general application, as well as
to determine if useful kinetic information could be ob-
tained. A wild type yeast strain (W) was compared to a
snf1∆ mutant yeast strain (S) grown in high-glucose
medium (R) and in low-glucose medium containing etha-
nol (DR). Under these growth conditions, different meta-
bolic pathways for utilizing the different carbon sources
are expected to be active. Thus, changes in metabolite
levels relating to the carbon source in the growth medium
were anticipated. Furthermore, the Snf1 protein kinase
complex is required to adapt cellular metabolism from
fermentative R conditions to oxidative DR conditions. So,
differences in intracellular metabolite levels between the
W and S yeast strains were also anticipated. Cell extracts
were collected at four time points (0.5, 2, 4, 6 h) after
shifting half of the cells from R to DR conditions, resulting
in 16 sample classes (WR, WDR, SR, SDR) × (0.5, 2, 4,
6 h). The experimental design provided time course data,
so temporal dependencies could be monitored in addition
to carbon source and strain dependencies. Comprehen-
sive two-dimensional (2D) gas chromatography coupled
to time-of-flight mass spectrometry (GC × GC-TOFMS)
was used with discovery-based data mining algorithms
(Anal. Chem. 2006, 78, 5068–5075 (ref 1); J. Chro-
matogr., A 2008, 1186, 401–411 (ref 2)) to locate
regions within the 2D chromatograms (i.e., metabolites)
that provided chemical selectivity between the 16 sample
classes. These regions were mathematically resolved using
parallel factor analysis to positively identify the metabo-
lites and to acquire quantitative results. With these tools,
51 unique metabolites were identified and quantified.
Various time course patterns emerged from these data,
and principal component analysis (PCA) was utilized as
a comparison tool to determine the sources of variance
between these 51 metabolites. The effect of sampling time
was investigated with separate PCA analyses using various
subsets of the data. PCA utilizing all of the time course
data, averaged time course data, and each individual time
point data set independently were performed to discern
the differences. For the yeast strains examined in the
current study, data collection at either 4 or 6 h provided
information comparable to averaged time course data,
albeit with a few metabolites missing using a single
sampling time point.
Studies of the metabolome, the complete collection of low
molecular weight molecules present in a cell of a particular state
exposed to a specific environmental condition, provide information
to better understand cellular processes.3-5 The inner workings
of a cell are controlled by a number of feedback mechanisms
involving the genome, transcriptome, proteome, and metabolome.6
Studying and integrating as many of these as possible would
provide the most complete picture of the cellular physiology.7-9
The metabolome is often thought to best describe the phenotype
because it is a collection of the end products of the various
physiological processes. Changes in gene expression can be
amplified at the metabolite level,10 and posttranslational and
transcriptional modifications may be apparent in the metabolome
that might not be seen at the genome or proteome levels.11
Metabolome studies present unique challenges because of the
magnitude of molecular species present, the large dynamic range
of concentrations, and the compositional heterogeneity of me-
tabolites. The metabolome consists of amino acids, fatty acids,
sugar phosphates, carbohydrates, vitamins, lipids, inorganic spe-
(1) Pierce, K. M.; Hoggard, J. C.; Hope, J. L.; Rainey, P. M.; Hoofnagle, A. N.;
Jack, R. M.; Wright, B. W.; Synovec, R. E. Anal. Chem. 2006, 78, 5068–
5075
(2) Mohler, R. E.; Tu, B. P.; Dombek, K. M.; Hoggard, J. C.; Young, E. T.;
Synovec, R. E. J. Chromatogr., A 2008, 1186, 401–411
(3) Goodacre, R.; Vaidyanathan, S.; Dunn, W. B.; Harrigan, G. G.; Kell, D. B.
Trends Biotechnol. 2004, 22, 245–252
(4) Weckwerth, W.; Morgenthal, K. Drug Discovery Today 2005, 10, 1551–
1558
.
.
.
.
(5) Dunn, W. B.; Bailey, N. J.; Johnson, H. E. Analyst 2005, 130, 606–625
.
(6) Hollywood, K.; Brison, D. R.; Goodacre, R. Proteomics 2006, 6, 4716–4723
.
(7) Bino, R. J.; Hall, R. D.; Fiehn, O.; Kopka, J.; Saito, K.; Draper, J.; Nikolau,
B. J.; Mendes, P.; Roessner-Tunali, U.; Beale, M. H.; Trethewey, R. N.;
Lange, B. M.; Wurtele, E. S.; Sumner, L. W. Trends Plant Sci. 2004, 9,
418–425
(8) Fiehn, O. Plant Mol. Biol. 2002, 48, 155–171
(9) Sweetlove, L. J.; Last, R. L.; Fernie, A. R. Plant Physiol. 2003, 132, 420–
425
.
.
.
(10) Raamsdonk, L. M.; Teusink, B.; Broadhurst, D.; Zhang, N.; Hayes, A.; Walsh,
M. C.; Berden, J. A.; Brindle, K. M.; Kell, D. B.; Rowland, J. J.; Westerhoff,
* To whom correspondence should be addressed. E-mail: synovec@
chem.washington.edu.
H. V.; van Dam, K.; Oliver, S. G. Nat. Biotechnol. 2001, 19, 45–50
(11) ter Kuile, B. H.; Westerhoff, H. V. Fed. Eur. Biochem. Soc. Letters 2001,
500, 169–171
.
† Department of Chemistry.
‡ Department of Biochemistry.
.
8002 Analytical Chemistry, Vol. 80, No. 21, November 1, 2008
10.1021/ac800998j CCC: $40.75 2008 American Chemical Society
Published on Web 10/01/2008

cies, and elements spanning many sizes, polarities, and volatilites.8,12
Hence, there is no single chemical analysis method currently
available to study all of the classes of metabolites simultaneously.12,13
Comprehensive two-dimensional (2D) gas chromatography
coupled to time-of-flight mass spectrometry (GC × GC-TOFMS)
is a powerful tool that is well suited for complex samples and has
been employed for numerous metabolomic studies.1,14-24 While
this is not the only approach (nuclear magnetic resonance
spectroscopy, infrared spectroscopy, MS),5,25 chromatographic
methods allow for more than a fingerprint analysis. GC methods
with exceptional resolution and well-established mass spectral
libraries have been touted as the “gold standard” even with a bias
toward smaller metabolites and the need for a chemical deriva-
tization step to improve metabolite volatility and thermal stability.8,26
GC × GC provides excellent resolving power and increased peak
capacity, generally by combining a longer nonpolar column with
a shorter polar column, i.e., a “traditional” arrangement; however,
a reverse column arrangement could also be implemented.27-33
GC × GC-TOFMS creates a cube of data for each sample
analyzed, with two dimensions of separation and a full mass
spectrum at every point in 2D separation time. This cube of data
contains numerous metabolite signals that can appear unmanage-
able when trying to determine differences between the samples.
Using a hypothesis-driven experimental design, with sample class
membership known a priori, a discovery-based analysis approach
is readily applied. Here, sample class refers to different sample
types, conditions, or both, and not to different classes of com-
pounds. One of the first steps is to find the regions of the 2D
chromatograms (i.e., analytes) that offer the most chemical
Figure 1. Metabolic pathway activity based on available carbon
source is illustrated. When glucose is present, cells ferment and follow
the dashed line. The presence of glucose represses other metabolic
pathways; thus, this condition is termed glucose repressed. When
glucose is limited, cells become derepressed and low glucose
respiratory enzymes are synthesized. This allows the cells to respire
on ethanol, following the solid lines. The Snf1 protein complex is
required to make the shift to utilize ethanol. In the absence of the
Snf1 protein complex, cells are unable to respire effectively on
ethanol.
selectivity to distinguish the sample type classes. There are a
number of algorithms that can accomplish this.23,24,34-36 Principal
component analysis (PCA), Fisher ratio analysis (F-ratio),1 and
ratio of signal (S-ratio)2 are a few of the data analysis methods
we have employed for data such as these. All of the methods locate
metabolites within the data set that have the greatest differences
between sample type classes. These metabolites, which offer the
most insight to a biological system, are targeted for further
investigation. Parallel factor analysis (PARAFAC) is a chemometric
method for mathematical resolution (deconvolution) that provides
resolved chromatographic peak profiles and mass spectra by
removing background noise and overlapping analytes (and
interferences).37-39 PARAFAC is applied to the sample class
distinguishing data locations to provide metabolite identification
and quantification. PARAFAC takes advantage of the inherent
trilinear data structure for a GC × GC-TOFMS data cube, where
an analyte signal is represented by the outer product of the peak
profile on the first column, the peak profile on the second column,
and the mass spectrum. This discovery-based approach combined
with PARAFAC is applicable to any metabolic study in which
differences between sample classes are sought.
(12) Dunn, W. B.; Ellis, D. I. Trends Anal. Chem. 2005, 24, 285–294
(13) Weckwerth, W.; Fiehn, O. Curr. Opin. Biotechnol. 2002, 13, 156–160
(14) Hope, J. L.; Prazen, B. J.; Nilsson, E. J.; Lidstrom, M. E.; Synovec, R. E.
Talanta 2005, 65, 380–388
(15) Sinha, A. E.; Prazen, B. J.; Fraga, C. G.; Synovec, R. E. J. Chromatogr., A
2003, 1019, 79–87
(16) Shellie, R.; Marriott, P.; Morrison, P. Anal. Chem. 2001, 73, 1336–1344
(17) van Deursen, M.; Beens, J.; Reijenga, J.; Lipman, P.; Cramers, C.; Blomberg,
J. J. High Resolut. Chromatogr. 2000, 23, 507–510
(18) Dalluge, J.; van Rijn, M.; Beens, J.; Vreuls, R. J.; Brinkman, U. A.Th.
J. Chromatogr., A 2002, 965, 207–217
(19) Dalluge, J.; Vreuls, R. J.; Beens, J.; Brinkman, U. A.Th. J. Sep. Sci. 2002,
25, 201–214
(20) Welthagen, W.; Shellie, R.; Spranger, J.; Ristow, M.; Zimmermann, R.; Fiehn,
O. Metabolomics 2005, 1, 65
(21) Sinha, A. E.; Hope, J. L.; Prazen, B. J.; Fraga, C. G.; Nilsson, E. J.; Synovec,
R. E. J. Chromatogr., A 2004, 1056, 145–154
(22) Mohler, R. E.; Dombek, K. M.; Hoggard, J. C.; Young, E. T.; Synovec, R. E.
Anal. Chem. 2006, 78, 2700–2709
(23) Sinha, A. E.; Hope, J. L.; Prazen, B. J.; Nilsson, E. J.; Jack, R. M.; Synovec,
R. E. J. Chromatogr., A 2004, 1058, 209–215
(24) Shellie, R.; Welthagen, W.; Zrostlikova, J.; Spranger, J.; Ristow, M.; Fiehn,
O.; Zimmermann, R. J. Chromatogr., A 2005, 1086, 83–90
(25) Kell, D. B. Curr. Opin. Microbiol. 2004, 7, 296–307
(26) Guttman, A.; Varoglu, M.; Khandurina, J. Drug Discovery Today 2004, 9,
136–144
(27) Murphy, R. E.; Schure, M. R.; Foley, J. P. Anal. Chem. 1998, 70, 1585–
1594
(28) Liu, Z.; Phillips, J. B. J. Chromatogr. Sci. 1991, 29, 227
(29) Kinghorn, R. M.; Marriott, P. J. J. High Resolut. Chromatogr. 1998, 21,
620–622
(30) Beens, J.; Adahchour, M.; Vreuls, R. J. J.; van Altena, K.; Brinkman, U. A.Th.
J. Chromatogr., A 2001, 919, 127–132
(31) Bruckner, C. A.; Prazen, B. J.; Synovec, R. E. Anal. Chem. 1998, 70, 2796–
2804
(32) Seeley, J. V.; Kramp, F.; Hicks, C. J. Anal. Chem. 2000, 72, 4346–4352
(33) Shellie, R.; Mondello, L.; Marriott, P.; Dugo, G. J. Chromatogr., A 2002,
970, 225–234
.
.
.
.
.
.
.
.
.
.
The yeast metabolome is dynamic, and metabolite levels can
change dramatically over time as environmental conditions
change. Notably, yeast utilize different metabolic pathways de-
pending on the available carbon source. Yeast Saccharomyces
cerevisiae extract carbon and energy from glucose, the preferred
carbon source,40 via fermentation (Figure 1). The metabolic
.
.
.
.
.
(34) Jonsson, P.; Gullberg, J.; Nordstrom, A.; Kusano, M.; Kowalczyk, M.;
.
Sjostrom, M.; Moritz, T. Anal. Chem. 2004, 76, 1738–1745.
.
(35) Jonsson, P.; Johansson, A. I.; Gullberg, J.; Trygg, J.; A, J.; Grung, B.;
Marklund, S.; Sjostrom, M.; Antti, H.; Moritz, T. Anal. Chem. 2005, 77,
.
5635–5642
(36) Sinha, A. E.; Prazen, B. J.; Synovec, R. E. Anal. Bioanal. Chem. 2004, 378,
1948–1951
(37) Bro, R. Chemom. Intell. Lab. Syst. 1997, 38, 149–171
(38) Hoggard, J. C.; Synovec, R. E. Anal. Chem. 2007, 79, 1611–1619
(39) Sinha, A. E.; Fraga, C. G.; Prazen, B. J.; Synovec, R. E. J. Chromatogr., A
2004, 1027, 269–277
(40) Carlson, M. Curr. Opin. Biotechnol. 1999, 2, 202–207
.
.
.
.
.
.
.
.
.
.
Analytical Chemistry, Vol. 80, No. 21, November 1, 2008 8003

activity of the yeast undergoes a profound reorganization when a
fermentable carbon source is no longer available in the growth
medium and nonfermentable carbon sources, such as ethanol or
glycerol, are oxidized via respiratory pathways. More than one-
quarter of the yeast transcriptome is altered during this switch
from fermentative to oxidative pathways, termed the diauxic
shift.41 These changes allow the yeast cells to feed the available
nonfermentable carbon source into the tricarboxylic acid (TCA)
cycle, the glyoxylate cycle, and gluconeogenesis, to generate
energy and essential biosynthetic intermediates. When the con-
centration of glucose in the growth medium is high, the proteins
required for these processes are not highly expressed, and the
physiological state of the cells is referred to as glucose repressed
(R). During the diauxic shift, when glucose is depleted from the
growth medium, expression of proteins required for these
processes is activated or derepressed (DR). The Snf1 protein
kinase (A capital letter followed by lowercase letters of a gene
name indicates a protein (Snf1)) is one of the important players
involved in the diauxic shift.40 The Snf1 complex is the yeast
homologue of the AMP-activated protein kinase (AMPK) found
in higher organisms.42 Snf1-dependent phosphorylation regulates
the expression and activity of several metabolic enzymes and
transcriptional factors required for the expression of many glucose-
regulated genes involved in the utilization of nonfermentable
carbon sources. This makes it essential for growth in the absence
of glucose.40 Studies at the transcriptional level have shown that
there are over 400 SNF1 (A capital letter followed by uppercase
letters, in italics, of a gene name indicates a normal or wild-type
gene (SNF1); gene names in italicized lower case letters indi-
cate a defective or deleted gene (snf1))-dependent genes, and of
the 40 most glucose-repressed genes, 29 are also SNF1-depend-
ent.41 Mutant cells lacking the Snf1 protein complex are an
important topic for metabolomic studies because mutations in the
activating subunit of the complex are implicated in cardiac disease,
and the major drug used to treat diabetes, metformin, is an
inhibitor of AMPK.43,44 Metabolomic studies might identify new
AMPK-dependent metabolic pathways and reveal some conse-
quences of reduced AMPK activity. In addition, the primary signal
that is used to sense the change in carbon source is thought to
be one or more metabolites of glucose metabolism.
implemented in order to utilize an in-house library of retention
times (from prior studies) to support the mass spectral identifica-
tion of metabolites. The effect of sampling time was investigated
using PCA as a comparison tool. Careful analysis of the results at
various time points will illustrate the dynamic nature of the yeast
metabolome, providing insight into how to discern which time
point(s) may provide the most useful information for routine
analysis.
EXPERIMENTAL SECTION
Yeast Cells and Culture Conditions. A wild type yeast strain
W303-1a (MATa ade2 can1-100-his3-11,15 leu2-13,112 trp1-1
ura3-1) and a mutant yeast strain of W303-1a having the SNF1
open reading frame completely replaced with a kanMX gene
cassette (snf1∆) were analyzed in this study. Both strains were
initially grown in repressing (R) synthetic complete (SC) medium
containing 5% glucose as the sole carbon source. At time 0 h, the
cell cultures were divided in half, harvested by centrifugation at
4 °C, and washed once with cold sterile synthetic medium, SM
(SC lacking amino acids or carbon source). One aliquot of cells
from each culture was suspended in fresh R medium prewarmed
to 30 °C, while the remaining aliquot was suspended in prewarmed
derepressing (DR) medium, which contained 3% ethanol and 0.05%
glucose as carbon sources. The cell density of each culture was
monitored by measuring the optical density at 600 nm and
comparing to a calibration curve of optical density plotted against
cell number per milliliter of serial dilutions counted with a hemo-
cytometer. Glucose consumption was monitored by analyzing
aliquots of medium every hour with a PGO enzymes kit (Sigma
Aldrich, St. Louis, MO). Three cultures of both wild type and snf1∆
cells were monitored for glucose levels in R conditions and one
culture of each for DR conditions.
Extraction. At times 0.5, 2, 4, and 6 h, the metabolic activity
was quenched and small polar molecules (metabolites) were
extracted from the cells with the extraction procedure previously
described.45 At each time point, 1 mL of each cell culture was
rapidly diluted into 4 mL of -40 °C quenching buffer (10 mM
tricene, pH 7.4, in 60% methanol). These cell suspensions were
spun at 1000g in a Sorvall RC-5B Plus centrifuge at -20 °C for 3
min. Each cell pellet was washed with 1 mL of -40 °C quenching
buffer and resuspended in 1 mL of 80 °C extraction buffer (0.5
mM tricine, pH 7.4, in 75% ethanol). The suspended pellet was
held at 80 °C for ∼3 min before being cooled on ice for 5 min.
Large cellular debris was removed by spinning the suspension
twice at high speed in a microcentrifuge. A volume of each ethanol
metabolite extract corresponding to 1 × 107 cells was dried in a
SpeedVac at room temperature and stored at -80 °C under argon.
Derivatization of Metabolites. After retrieval from the -80
°C freezer, any traces of water condensate on the sample tubes
were removed by drying in a SpeedVac at room temperature for
1 h prior to derivatization. The metabolites were methoximated
by adding 30 µL of a 20 mg/mL methoxyamine solution in pyridine
and heating at 30 °C for 90 min to protect carbonyl groups. The
methoximated extracts were then trimethylsilyated by adding 70
µL of BFTSA/TMCS (99:1) reagent and heating to 60 °C for 60
min.46
In this report, GC × GC-TOFMS was used to determine
differences in metabolite levels between wild type yeast cells (W)
and snf1∆ mutant cells (S) in repressing (R) and derepressing
(DR) conditions, resulting in four strain/condition classes (WR,
WDR, SR, SDR). Time course data were generated at 0.5, 2, 4,
and 6 h to follow the dynamic, time-dependent changes after
shifting from repressing to derepressing conditions, for each
strain/growth condition resulting in 16 sample classes overall. The
previously reported data mining tool referred to as the S-ratio
method2 was used to find sample class distinguishing locations
in the 2D GC separation space, and PARAFAC was used to identify
and quantify these metabolites providing insight to the Snf1
protein complex. A nonpolar to polar column arrangement was
(41) Young, E. T.; Dombek, K. M.; Tachnibana, C.; Ideker, T. J. Biol. Chem.
2003, 278, 26146–26158
(42) Kemp, B. E.; Mitchelhill, K. I.; Stapleton, D.; Michell, B. J.; Chen, Z.-P.;
Witters, L. A. Trends Biochem. Sci. 1999, 24, 22–25
(43) Hardie, D. G. Nat. Rev. Mol. Cell Biol. 2007, 8, 774–785
(44) Hardie, D. G. Annu. Rev. Pharmacol. Toxicol. 2007, 47, 185–210
.
.
.
(45) Castrillo, J. I.; Hayes, A.; Mohammed, S.; Gaskell, S. J.; Oliver, S. G.
.
Phytochemistry 2003, 62, 929–937.
8004 Analytical Chemistry, Vol. 80, No. 21, November 1, 2008

Instrument Parameters. The yeast extracts were analyzed
via GC × GC-TOFMS instrumentation, which employed an Agilent
6890N gas chromatograph with an Agilent 7683 auto injector
(Agilent Technologies, Palo Alto, CA) in conjunction with a LECO
Pegasus III TOFMS upgraded with the commercially available
4D thermal modulator (LECO, St. Joseph, MI). The first column
was 20 m × 250 µm i.d. × 0.5 µm RTX-5MS phase coating (Restek,
Bellefonte, PA) and the second column was 2 m × 180 µm i.d. ×
0.2 µm RTX-200MS phase coating (Restek). The GC inlet and
transfer line were set to 280 °C. The first column was set at 60 °C
for 0.25 min and the second column was set at 70 °C for 0.25 min
at the commencement of the analysis. Both columns were
temperature programmed at a rate of 8 °C/min to 280 °C where
the columns were held constant for 10 min. The modulation period
between the first and second columns was 1.5 s, and the modulator
was kept 40 °C higher than the first column. A constant flow rate
of 1 mL/min hydrogen gas was maintained at the head of the
first column. The TOFMS ion source was set to 250 °C. Mass
channels 40-500 m/z were collected at 100 spectra/s after a 5-min
solvent delay. Three injections of 1 µL were made for each of the
16 sample extracts for a total of 48 chromatographic runs.
Data Analysis. The GC × GC-TOFMS data were collected with
the LECO ChromaTOF software v2.2.1 (LECO, St. Joseph, MI).
The data were then exported to Matlab v.7.0.4 (Mathworks,
Natick, MA) for S-ratio analysis. The S-ratios were calculated using
a modification to the previously published algorithm.2 The S-ratio
method was initially designed as a data analysis tool for yeast cells
that demonstrated metabolic cycling behavior. For the previous
cycling study, the S-ratios provided information relating to the
depth of modulation (i.e., the maximum compared to the mini-
mum) over two complete cycles. A large S-ratio corresponds to a
large difference between the maximum and minimum data values
across all sample classes. In this study, cyclical behavior was not
anticipated, so one maximum and minimum was located rather
than two. Four lists were generated by calculating S-ratios for all
sample types (WR, WDR, SR, SDR) at each time point. Using the
LECO ChromaTOF software, the locations with the largest S-ratio
values were further investigated. The S-ratio list was comple-
mented with a comprehensive list of metabolites with differing
levels between R and DR conditions from previous work in our
group.22,47 Using the LECO software, the mass spectra at these
chromatographic locations were searched against the National
Institute of Standards and Technology (NIST) library along with
standard metabolite libraries created in-house for preliminary
identification. An in-house developed target-analyte PARAFAC
Graphical User Interface (GUI) implementing the N-way toolbox
was used to deconvolute overlapping chromatographic peak
profiles and mass spectra.38 The algorithm isolates the pure
component peak profile and the pure mass spectrum of an
individual component from overlapping peaks and background
noise for quantification and identification, respectively. The full
mass spectrum for one sample type at each sampled time point
was examined using PARAFAC to obtain a full mass spectrum
for a more confident identification of metabolites. Selected mass
channels were then used with PARAFAC for quantification
Figure 2. Cell growth over 6 h. The strains in repressed conditions
grew faster than the strains in derepressed conditions. By the 6-h
time point, the wild type and snf1∆ strains were entering stationary
phase in repressed conditions.
purposes. Raw PARAFAC peak volumes (i.e., signals) were
normalized to the total ion current (TIC). TIC-normalized data
are presented in some figures as indicated. For PCA analysis to
investigate the effect of time course sampling time, these TIC-
normalized PARAFAC volumes for a given metabolite were mean-
centered for ease of comparison via PCA.
RESULTS AND DISCUSSION
The experimental design used in this study allowed for the
observation of changing metabolite levels as cells moved from a
repressed metabolic state toward a derepressed state. This
approach provided an opportunity for an in-depth look at the Snf1
protein complex, time dependencies, and the impact of sampling
time. Figure 2 shows cell concentration as a function of time
during the experiment. During the first 4 h, the number of both
wild type and snf1∆ mutant cells in R medium doubled at the same
rate, every 2 h. Between 4 and 6 h, the growth of both slowed
appreciably, a sign that the cells were exiting exponential growth
and starting to enter stationary phase. As shown in Figure 3A,
the concentration of glucose in the R medium at this time was
∼80% of the initial value, indicating that the cells were still subject
to glucose repression. In DR medium, wild type cells grew
considerably faster than snf1∆ mutant cells for the first 2 h with
an estimated doubling time of 3.6 h compared to 8.8 h for the
snf1∆ mutant cells. At 2 h, wild type cells had almost exhausted
the 0.05% glucose present in the DR medium, as shown in Figure
3B, and their growth slowed to an estimated doubling time of 10 h.
During this time, the snf1∆ mutant cells were very slowly utilizing
the 0.05% glucose in the DR medium and essentially stopped
growing, having a calculated doubling time of 26 h. These
observations are consistent with the known requirement of the
Snf1 protein kinase for the utilization of limiting glucose and
ethanol as carbon sources.
Figure 4A shows a GC × GC-TOFMS chromatogram contour
plot at mass channel 73 (indicative of the trimethylsilyl group
added during derivatization) of a WR sample at 6 h. Some of the
largest reagent artifacts have been filtered out for clarity.
Representative chromatograms of the other strain/growth condi-
tions (WDR, SDR, SR) at time 6 h are provided in Supporting
Information (Figure S1A-C). A comparison of these plots shows
that by 6 h significant differences could be observed between the
(46) Fiehn, O.; Kopka, J.; Tretheway, R. N.; Willmitzer, L. Anal. Chem. 2000,
72, 3573–3580
(47) Mohler, R. E.; Dombek, K. M.; Hoggard, J. C.; Pierce, K. M.; Young, E. T.;
Synovec, R. E. Analyst 2007, 132, 756–767
.
.
Analytical Chemistry, Vol. 80, No. 21, November 1, 2008 8005

metabolites with lower S/N may not be visible at this mass
channel and threshold that may have important differences
between strain/growth conditions. One possibility for finding more
metabolites is to view selective mass channels, but more powerful
chemometric tools are better suited to fully glean the desired
information from the data.
Data reduction methods are used to identify the most interest-
ing regions of the 2D separation space that distinguish one sample
class from another and provide insight to the biological function.
S-Ratio analysis located the regions offering the greatest chemical
selectivity between classes. Initially, the data sets for the 0.5- and
4-h time points were examined more extensively. Using the S-ratio
results for the 0.5-h time point ensured that metabolites differing
early after derepression were located regardless of whether they
differed late after derepression. Conversely, using the results for
the 4-h time point ensured that metabolites differing later after
derepression were located even if they did not differ early after
derepression. The 4-h time point was selected for the late time
point because the concentration of some of the metabolites began
to drop by the 6-h time point. This may reflect the slowing of
growth and the entry into stationary phase of the cells growing
in R medium. Locations with S-ratios greater than 5.0 were
investigated. An S-ratio of 5 represents a particular metabolite that
is 5-fold more concentrated between two of the samples. This
discovery-based software approach located 51 sample class-
distinguishing metabolites as presented in Table 1. Figure 4B
shows these 51 metabolites in the 2D chromatographic space.
Match values are presented from deconvoluted PARAFAC mass
spectra, and retention times were confirmed with standards as
indicated. Investigating metabolites with an S-ratio less than 5
could be done and a few more metabolites found; however, for
the purpose of this study, we used this S-ratio threshold for brevity,
since it was providing complementary metabolites from our
previous report.2,22,47
Figure 3. Available glucose in the medium in (A) repressed
conditions and in (B) derepressed conditions. Strains in repressed
conditions have adequate glucose to remain repressed over the
course of the experiment. The small amount of glucose provided in
derepressed conditions is used by 2 h in the wild type cells and slowly
diminished over the 6 h in the snf1∆ strain.
Using the prior list of metabolites based on the locations
found with the S-ratio method (discovery-based) software and
our previous studies, metabolites were identified and quantified
using the PARAFAC target analyte GUI. The PARAFAC peak
volumes obtained were normalized to the TIC to correct for
injection and detection variation. The TIC was independent of
yeast strain or growth condition with an average value of 5.9 ×
109 and 13% RSD. TIC normalization was more precise than
use of 13C-labeled internal standards (alanine and serine). The
dynamic nature of the metabolome is again highlighted by the
presence of four different time course patterns observed for
the 51 metabolites, referred to as time decay, time increase,
peak, and unpatterned. Representative examples of these
patterns are shown in Figure 5. The following are descriptions
of these representative time course examples in relation to
potential biological implications.
An example of a metabolite exhibiting a “time decay” pattern
is glucose, shown in Figure 5A. Of the metabolites identified in
our analysis, it had the highest S-ratio value, 79, at the 0.5-h time
point. It was most abundant in extracts from R cells with the
largest difference from DR cells seen at the 0.5-h time point. The
DR/R ratio (i.e., concentration ratio) for glucose was very low in
both the wild type and the snf1∆ mutant strains. Glucose is
provided as the fermentable carbon source in R medium. However,
Figure 4. Location of analytes in the 2D separation space. A contour
plot of mass channel 73 shows the ion indicative of trimethylsilyl group
for a (A) wild type cells in repressed conditions at 6 h. (B) The
chromatographic location of the 51 metabolites identified in this study.
chromatograms of the various strains/growth conditions. Many
of the peaks in the wild type strains have higher intensity than
those in the snf1∆ strain. It is possible by eye to identify some
metabolites that help to distinguish sample classes. However,
8006 Analytical Chemistry, Vol. 80, No. 21, November 1, 2008

Table 1. Identified Metabolites with First Column and Apparent Second Column Retention Times and Mass Spectral
Match Valuesa
metabolite name
Kegg
MV 0.5 h
MV 2 h
MV 4 h
MV 6 h
Col. 1 tR
Col. 2 tR
lactate
c00186
c00041
870
819
820
942
904
912
938
958
958
945
858
951
923
917
895
863
861
856
811
948
885
903
946
850
949
941
833
935
835
925
828
842
788
722
963
961
727
904
737
950
881
971
876
925
771
924
922
893
876
946
955
935
850
943
784
926
765
945
917
911
934
944
954
941
851
942
712
891
932
864
865
761
956
952
814
921
720
950
900
948
462.0
508.5
0.48
0.49
0.45
0.48
0.68
0.49
0.48
1.04
0.3
0.46
0.48
0.72
0.47
0.49
0.83
0.48
0.45
0.36
0.46
0.52
0.43
0.58
0.69
0.61
0.69
0.6
*alanine
3-hydroxy butyric acid
*valine
567.0
c00183
631.5
benzoic acid
octanoic acid
*leucine
659.0
673.0
c00123
c00010
c00116
c00407
c00148
c00042
c00037
c00106
c00122
690.0
*CoA fragment
*glycerol
691.0
693.0
*isoleucine
714.0
*proline
716.0
*succinate
726.0
*glycine
727.5
*uracil
905
778
870
826
753.0
*fumarate
759.0
o-toluic acid
779.0
*serine
c00065
782.0
pyrrole-2-carboxylic acid
*threonine
748
969
907
786
913
727
954
935
963
812
962
924
900
819
893
871
951
906
814
808
964
926
945
942
889
882
736
894
947
857
713
862
808
949
796.5
c00188
c00062b
953
961
836
898
714
921
948
947
724
942
941
842
856
913
900
945
942
810.0
*arginine
867.0
homoserine
867.0
*malate
c00149
902.5
2-piperidinecarboxylic acid
*aspartic acid
*methionine
*glutamic acid
ribofuranose
*phenylalanine
*asparginine
*UDP/glucose
orotic acid
910.5
c00049
c00073
c00025
933.0
934.5
1021.0
1022.0
1030.0
1066.0
1101.0
1127.0
1146.0
1153.0
1156.0
1255.5
1266.0
1267.5
1269.0
1279.0
1279.5
1296.0
1320.0
1411.5
1420.5
1479.0
1482.0
1624.5
1639.5
1702.5
1832.0
1858.8
0.24
0.52
0.78
0.63
0.46
0.9
c00079
c00152
c00029
956
929
889
807
882
868
955
937
946
920
746
927
909
941
895
*glycerol 3-phosphate
*glucose 1-phosphate
*glutamine
c00093b
c00103
c00064
0.32
0.85
0.29
0.57
0.88
0.43
0.3
*galactose
gluconic acid
*histidine
780
871
954
916
802
918
859
864
765
938
839
913
677
829
867
934
c00135
c00047
c00031b
c00082b
c00392
931
951
951
959
940
901
883
819
880
894
886
744
852
835
958
929
939
949
946
945
876
881
725
930
942
787
718
816
*lysine
*glucose
*tyrosine
0.58
0.3
*mannitol
glucose
0.25
0.41
0.54
0.66
0.43
0.56
0.2
heptadecanoic acid
*D-ribulose 5-phosphate
tryptophan
c00199
*stearic acid
*6-phospho-D-gluconate
methanamine
glucose-6-phosphate
*fructose 1,6-bisphosphate
*trehalose
c01530
c00345
0.57
1.35
0.63
c05378
955
a Fifty-one metabolites were located using the data mining techniques. They were identified and quantified using PARAFAC. Metabolites further
confirmed by retention time are indicated with an asterisk.
simple contamination of the cells with R medium cannot fully
account for the elevated level of glucose in wild type R cells
because snf1∆ R cells have a 5-fold lower amount of detectable
glucose and contamination levels should be the same for both
cell types. This difference in glucose levels may reveal a previously
not described SNF1-dependent glucose uptake system that is not
glucose repressed. Little glucose was detected in metabolite
extracts from DR cells. This is not surprising since DR growth
medium contains only 1% the amount of glucose as the R medium.
Trehalose is an example of a metabolite exhibiting a “time
increase” pattern, shown in Figure 5B. Of the metabolites
identified, trehalose had the highest S-ratio, 148, at the 4-h (and
6-h) time point. The relative concentration in WR cells increased
over time, becoming most significantly elevated at the 4- and 6-h
time points. As for glucose, the overall DR/R ratio was low.
However, in contrast to glucose in Figure 5A, the level of trehalose
increased with time instead of decreasing. There was also a
significant difference between the wild type and the snf1∆ mutant
strain with the mutant strain having much less trehalose. In
previous studies of wild type cells, higher levels of trehalose were
observed in DR as compared to R conditions at 6 h.22 While the
cells were treated differently in this study, confirmation of a
different trend was warranted. This result was verified in three
additional biological replicates of the experiment. The peak was
further confirmed as trehalose by comigration with a trehalose
standard that had been added to the sample (standard addition
Analytical Chemistry, Vol. 80, No. 21, November 1, 2008 8007

Figure 5. Time course data for selected metabolites, normalized to TIC only. (A) Glucose is shown elevated in both wild type and mutant
strains in R conditions. (B) Trehalose is a major carbohydrate storage molecule. The relative concentrations increased over time in the WR
cells. This result was confirmed in three additional biological replicates. Additionally, a tps1∆ strain was analyzed and the absence of this peak
was observed. (C) Fumarate is a member of the TCA cycle. The relative peak volumes increase steadily in WDR but remain low in the other
classes. (D) Glucose 6-phosphate (G6P) is a member of glycolysis. The WR peak profile is elevated compared to all other classes. (E) Stearic
acid remains essentially constant between classes and time points. (F) Glutamic acid is an amino acid. Wild-type is elevated over the mutant
for both R and DR.
method). Additionally, a strain that is unable to synthesize
trehalose because it lacks the TPS1 gene, which is required for
the conversion of G6P and UDPG into trehalose 6-phosphate, a
precursor of trehalose, was analyzed and shown to lack the
putative trehalose peak. Trehalose is a nonreducing disaccharide
that can serve as a carbohydrate energy storage molecule and a
protectant from heat, starvation, osmotic stress, and other
stresses.48 Accumulation of trehalose in the WR cells at the late
time points is consistent with the observed slowing of growth and
entry into the stationary growth phase, as shown in Figure 2, in
the presence of glucose. A surprising observation from these
results is that Snf1 seems to be required for the accumulation of
trehalose in medium with abundant glucose, conditions where it
is thought to be inactive. There is evidence in the literature that
Snf1 may play a role in response to stress even when glucose is
not growth-limiting.49
for wild type cells at the 6-h time point and a ratio of close to 1 for
the snf1∆ mutant cells. Fumarate is an intermediate of the TCA cycle,
which is most active when cells are growing on nonfermentable
carbon sources in the absence of glucose. The Snf1 protein kinase
is required for maximal expression of a number of genes encoding
TCA cycle proteins under these growth conditions.41 Our observa-
tions are consistent with the prediction that snf1∆ mutant cells should
have lower amounts of TCA cycle intermediates. The other detected
members of the TCA cycle also displayed a time-dependent increase
that was absent in snf1∆ mutant cells. Taken together, these results
suggest that carbon flow through the TCA cycle is severely reduced
in the absence of the Snf1 protein kinase.
An example of the “time peak” pattern is provided by glucose
6-phosphate (G6P) shown in Figure 5D. The amount of G6P in
wild type R cells was observed to increase from the early time
points until it reached its highest level by 4 h, 2.5-fold higher than
the level at 0.5 h. It then decreased almost 2-fold by the 6-h time
point. The decrease in G6P in R wild type cells observed at the
As for trehalose, fumarate demonstrated a “time increase” pattern
as shown in Figure 5C. The amount of fumarate detected in wild
type cells showed a large increase with time that was not observed
in the snf1∆ mutant cells. This is reflected in the high DR/R ratio
(48) Voit, E. O. J. Theor. Biol. 2003, 223, 55–78
(49) Hong, S.-P.; Carlson, M. J. Biol. Chem. 2007, 282, 16838–16845
.
.
8008 Analytical Chemistry, Vol. 80, No. 21, November 1, 2008

Figure 6. Averages of time course data shown for the same metabolites as in Figure 5: (A) glucose, (B) trehalose, (C) fumarate, (D) glucose
6-phosphate (G6P), (E) stearic acid, and (F) glutamic acid.
later time points is consistent with a decreasing flow of carbon
through glycolysis that accompanies entry into stationary phase.
In wild type DR cells, G6P was detected at early time points.
However, after 2 h, when the glucose of the DR medium was fully
exhausted, the level of G6P became undetectable. G6P in R snf1∆
mutant cells did not increase as in the wild type cells, remaining
relatively constant during the course of the experiment, and was
undetectable in DR snf1∆ mutant cells. G6P is the first cellular
metabolite formed when glucose in taken up by yeast cells. It is
located at the beginning of the glycolytic and the pentose
phosphate pathways and can be used to synthesize trehalose in
response to environmental stresses.48 Cellular levels of G6P appear
to be carefully regulated to avoid excess intracellular G6P that
seems to be toxic to cells.50 The peak in G6P corresponds to the
beginning of the trehalose accumulation in wild type R cells at
the 4-h time point. This is nicely predicted by the hypothesis that
trehalose synthesis can be used by yeast to sequester G6P so
that it does not accumulate in excess and cause a toxic metabolic
imbalance.
Examples of “unpatterned” metabolites are stearic acid and
glutamic acid. Stearic acid is an 18-carbon saturated fatty acid
whose amount remained essentially constant between sample
classes and time points as shown in Figure 5E. The DR/R ratio
was ∼1; the wild type and mutant levels were also approximately
equal at all time points. Similar results were obtained for the amino
acid, glutamic acid, as shown in Figure 5F. However, unlike for
stearic acid, the level of glutamic acid in the DR wild type strain
was elevated compared to the level in the DR snf1∆ mutant strain.
Glutamic acid is directly synthesized from oxoglutarate, an
intermediate of the TCA cycle. Results presented earlier show
that the levels of TCA cycle intermediates are significantly reduced
in DR snf1∆ mutant cells. This may account for the observed lower
level of glutamic acid.
The time course patterns offer interesting insight to the
dynamic changes in metabolite levels and perhaps how levels
between metabolites are related to each other (i.e., G6P and
trehalose). However, the time course patterns can overshadow
the overriding strain/growth condition trends, which are also of
interest. In order to comprehensively identify metabolite concen-
tration differences between yeast strain and growth conditions
over the course of the experiment, TIC-normalized time point data
were averaged together for each of the four strain and growth
condition combinations. The same six metabolites from Figure 5
are shown in Figure 6 after time course averaging. Approaching
the data in this way, the overall relationship between the various
metabolite levels and the condition and strain was clarified.
Since it was impractical to show all of the metabolites found
to change significantly per growth condition and yeasts strain,
PCA was employed as a data comparison tool to determine
similarities between metabolites and to investigate time depend-
encies. This is a different approach to PCA than is typically used
because the metabolites are not expected to form tight clusters
or groups. Instead the relative position in the PCA plot of the
metabolites to each other can be used to discern trends and
patterns. This provides a qualitative platform to discuss both
similarities between metabolites and the impact of sampling time
on metabolite classifications and the conclusions drawn from the
data. Each point in a given PCA plot represents one metabolite.
The metabolites’ coordinates are provided in Supporting Informa-
tion (Table S1). Using these coordinates, other metabolites of
interest can be compared to the patterns for the six metabolites
selected as representatives in Figures 5 and 6.
(50) Vanderpool, C. K. Curr. Opin. Microbiol. 2007, 10, 146–151
.
Analytical Chemistry, Vol. 80, No. 21, November 1, 2008 8009

expect clustering; so in this regard, it is a comparison tool and
not a classification tool. Using the same six metabolites shown in
Figure 5, indicated in the PCA plot, it was determined that PC1
captured the various time patterns creating a continuum between
one extreme and another. Trehalose (B) and fumarate (C), which
both exhibit a time growth pattern, have negative scores on PC1,
while glucose (A) with a time decay pattern has the most positive
score on PC1. Stearic acid (E) and glutamic acid (F), which do
not show a distinct time course pattern, fall near the PC1 center
axis, and G6P (D) that shows the time peak pattern has a positive
score on PC1. The time patterns of the other metabolites can be
discerned based on their relative position in the scores plot to
the six representative metabolites.
Removing the time dependency from the data by time point
averaging clarified the relationship between the growth conditions
and the strains, as demonstrated by the selected metabolites in
Figure 6. The PCA scores plot in Figure 7A was also clarified by
prior time point averaging, as shown in Figure 7B. This data
analysis approach would be analogous to pooling samples prior
to analysis. Mapping the six metabolites onto the scores plot allows
one to discern the source of variance. With the time patterns
averaged out, PC1 captured 67% of the variance and primarily
captured the differences between DR and R conditions. Fumarate
(C) with a high DR/R ratio has a negative score on PC1. Stearic
acid (E) and glutamic acid (F) with DR/R ratios approximately
equal to 1 are close to the PC1 axis. Additionally, glucose (A),
trehalose (B), and G6P (D), which have low DR/R ratios, all have
positive scores on PC1. PC2 captured an additional 22% of the
variance that can be attributed to differences between the snf1∆
mutant strain and the wild type strain. The metabolites with
positive scores on PC2 (B, C, D, F) all had lower levels in the
mutant strain than in the wild type strain. Glucose (A) and stearic
acid (E), which have comparable levels in the mutant and wild
type cells, have negative scores on PC2. This plot does not directly
show any time course information, but instead it contains a
summary of all the time course data. This is an important benefit
over probing only one time point because a metabolite data point
is not lost from the plot as it might be if it is not present at a
given time point.
Many experiments in the literature do not collect time course
data or pool samples but instead only investigate a single time
point. In order to determine if a single time point would provide
as comprehensive a data set as time point averaged data, PCA
plots for each individual time point were examined. The PCA
results from time 4 h are shown in Figure 7C. The results are
most similar to those from the time course averages (Figure 7B).
PC1 again captured the differences between R and DR resulting
in 62% of the variance. Additionally, PC2 captured the differences
between the mutant and the wild type strains (another 19% of the
variance). The six metabolites map onto the scores plot in the
same way as in the summed averages plot.
Results from time 6 and 2 h are also similar and are provided in
Supporting Information (Figure S2A and B). For example, at time
2 h, 48 and 30% variance are captured by the first 2 PCs, respectively.
The differences observed at 2 h between the strain/growth conditions
are less distinct than they become at later time points. At time 6 h,
the first two PCs capture 59 and 22%, respectively. However, some
of the metabolites with the “time peak” pattern have started to drop
Figure 7. PCA scores plots of PARAFAC signal volume patterns
for each metabolite, normalized as explained in the text. The same
six metabolites shown in Figures 5 (and 6) are indicated on these
plots with letters corresponding to Figures 5 (and 6). (A) PCA results
with time course information when only injection replicates are
averaged. There are 16 classes (WR, WDR, SR, SDR) × (0.5, 2, 4,
6 h). (B) PCA results when all injections and all time points are
averaged for a given growth condition and yeast strain. There are
four classes (WR, WDR, SR, SDR). (C) PCA results for a single time,
4 h. There are four classes (WR, WDR, SR, SDR).
For all of the PCA plots, the data values were mean centered
to eliminate the undue influence of data intensity and focus instead
on data patterns. Figure 7A shows the results of the first analysis
approach of using all of the time course data without time course
averaging. PC1 captured only 30% of the variance. Note that, in
this figure and other PCA scores plots presented, PCA is used to
discern trends in the metabolite patterns and not necessarily to
8010 Analytical Chemistry, Vol. 80, No. 21, November 1, 2008

in concentration by 6 h, so the scores plot at time 4 h is more similar
to the average plot. While there are some differences between these
time points, the PCs still capture variance based on the same trends.
This is not the case for the scores plot at time 0.5 h provided in
Supporting Information (Figure S2C). This plot is quite different from
all the other scores plots. The variance captured on PC1 was primarily
related to the number of different classes in which the metabolite
was present. Metabolites with negative scores on PC1 have informa-
tion in all four classes; as the scores on PC1 become more positive
there are fewer classes that contain information. Trehalose (B) has
the most positive score on PC1 and can only be detected in WR
conditions at 0.5 h, while stearic acid (E) has one of the more negative
scores on PC1 and can be detected in all four conditions/strains.
Because PCA focuses on this trend, rather than strain, growth
condition, or time pattern, conclusions made from only the 0.5-h time
point would be misleading in regard to strain and condition patterns.
These results suggest that PCA can be used to observe metabolite
patterns. There are a number of ways to approach a rich time course
data set such as this. PCA performed on the time course information
is most useful to see time patterns and less useful to see strain/
growth condition patterns. PCA on a time course averaged data set
loses the time course pattern information but is better to identify
trends between strains and conditions. Averaging time course data
yields more effective data comparisons than using a single time point.
And, as stated earlier, one could also pool samples taken over a time
course prior to analysis to obtain the averaging effect while using
less instrument time. Further yet, it is possible to substitute a single
time point for averaged time course data with the penalty of losing
a small subset of metabolites that are not detected at that time in
the analysis. Additionally, because the metabolome is dynamic, not
all time point data sets will provide the same information. Initial
investigation should be done to determine the best time point for a
given study.
For the yeast strains and growth conditions tested in this study,
if a single time point were to be substituted, data collection at
either 4 or 6 h provided the most information. With all of these
results, it was observed that carbon flow through the TCA cycle
in DR conditions is severely reduced in the absence of the Snf1
protein complex as expected. A surprising observation is that the
absence of the Snf1 protein complex altered metabolite levels in
R conditions as well where it was not thought to play a role.
ACKNOWLEDGMENT
This work was supported by grants from the National Institute
of Health (GM26079 and NIDDK67276 to E.T.Y.) and also by the
LECO Corporation (to R.E.S.).
SUPPORTING INFORMATION AVAILABLE
Additional information as noted in the text. This information
is available free of charge via the Internet at http://pubs.acs.org.
Received for review May 15, 2008. Accepted August 14,
2008.
AC800998J
Analytical Chemistry, Vol. 80, No. 21, November 1, 2008 8011