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J. Biol. Chem., Vol. 277, Issue 40, 37001-37008, October 4, 2002
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,§,
,**
From the Kluyver Laboratory of Biotechnology,
Technical University of Delft, Julianalaan 26, Delft 2628BC, The
Netherlands and the ¶ Center for Process Biotechnology,
BioCentrum-DTU, Bldg. 223, and the Center for Biological
Sequence Analysis, BioCentrum-DTU, Bldg. 208, Technical University of
Denmark, Kongens Lyngby DK-2800, Denmark
Received for publication, May 8, 2002, and in revised form, June 7, 2002
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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Assessment of reproducibility of DNA-microarray
analysis from published data sets is complicated by the use of
different microbial strains, cultivation techniques, and analytical
procedures. Because intra- and interlaboratory reproducibility is
highly relevant for application of DNA-microarray analysis in
functional genomics and metabolic engineering, we designed a set of
experiments to specifically address this issue. Saccharomyces
cerevisiae CEN.PK113-7D was grown under defined conditions in
glucose-limited chemostats, followed by transcriptome analysis with
Affymetrix GeneChip arrays. In each of the laboratories, three
independent replicate cultures were grown aerobically as well as
anaerobically. Although variations introduced by in vitro
handling steps were small and unbiased, greater variation from
replicate cultures underscored that, to obtain reliable information,
experimental replication is essential. Under aerobic conditions, 86%
of the most highly expressed yeast genes showed an average
intralaboratory coefficient of variation of 0.23. This is
significantly lower than previously reported for shake-flask-culture
transcriptome analyses and probably reflects the strict control of
growth conditions in chemostats. Using the triplicate data sets and
appropriate statistical analysis, the change calls from anaerobic
versus aerobic comparisons yielded an over 95% agreement
between the laboratories for transcripts that changed by over 2-fold,
leaving only a small fraction of genes that exhibited laboratory bias.
Together with other system-wide analytical techniques, genome-wide
transcriptional analysis with oligonucleotide microarrays is rapidly
transforming modern biological science (1). This transformation is
reflected in the growing quantity of research and review articles
published in the last few years that deal specifically with this topic.
Initially, the majority of microarray experiments focused on the yeast
Saccharomyces cerevisiae as the experimental system, because
the quality of its sequence information is extremely high and therefore
appropriate for whole genome-coverage array design (2, 3). Following
several papers that were focused solely on the use of microarrays in
profiling global gene expression, more recent articles have reflected
their use as an integrated tool for research of cellular physiology.
This includes identification of target genes for functional analysis
(4) as well as in the developing field of integrative whole-cell
analyses (5).
In practical terms, DNA microarray experiments are expensive and
generate vast amounts of data from which relevant changes in transcript
levels need to be identified. To optimize experimental efficiency, it
is of critical importance to minimize experimental noise, standardize
handling protocols, and perform appropriate experimental replication
and statistical analyses. The Affymetrix oligonucleotide GeneChip
arrays (6) have been designed to overcome some concerns of
reproducibility and hybridization specificity by including multiple
(~16) pairs of oligonucleotides per transcript to measure both
specific and nonspecific hybridization. Furthermore, all procedures
associated with probe preparation, hybridization, and washing have been
standardized (7). In the literature, reports concerning the
quantitative accuracy of microarray results (8, 9) and the quality of
the associated protocols (10, 11) have set commercial oligonucleotide
array experiments in a favorable light. On the question of data
interpretation, however, many research articles still apply arbitrary
rules to decide whether transcript levels differ (change calls) between
arrays of an experiment. Furthermore, there is uncertainty that arises
from the exchange of data among different laboratories, for example via
freely accessible databases, because no direct interlaboratory
comparisons have been performed on the reproducibility of DNA-array data.
In addition to analytical procedures, the techniques used for
cultivation and sampling may contribute to experimental variation. Many
of the available microarray data for S. cerevisiae have been obtained with shake-flask cultures (for a selection of literature see
Refs. 12-22) where it is known that important cultivation conditions (dissolved oxygen concentration, metabolite concentrations, pH, etc.)
change over time. These problems are augmented when complex media, such
as the commonly used yeast extract-peptone-based media for yeast
cultivation (23) are used. In such media, the sequential use of
available substrates (in particular nitrogen sources) is likely to
increase experimental variation between replicate cultures. This is
combined with the fact that in shake flasks growth can only be studied
at the maximum specific growth rate (µmax,
h Chemostat cultivation is a laboratory technique that has been
especially developed to grow microorganisms and cell cultures of higher
eukaryotes under constant, carefully controlled conditions. Automated
adjustments made in response to on-line monitoring enable strict
control of culture pH, dissolved oxygen concentration, and temperature,
all of which are known to affect transcription (13, 18, 25).
Furthermore, by controlling the rate at which a single growth-limiting
nutrient is supplied, the specific growth rate (µ, h The aim of the present study was to critically evaluate the
reproducibility of DNA-microarray transcriptome analyses in
nutrient-limited chemostat cultures of S. cerevisiae, using
commercially available oligonucleotide arrays. We based this study on a
previous experiment (25) by comparing multiply replicated aerobic and
anaerobic chemostat cultures. This earlier work involved comparison of
only single arrays from each condition and revealed 359 genes
that differed by more than 3-fold between aerobic and anaerobic growth. By expanding this analysis to include two different laboratories that
ran independent chemostat cultures and ran independent microarray analyses, we also investigated the interlaboratory reproducibility of
this microarray experiment. The primary goal of this study was to study
the methodology and reproducibility of microarray analyses and not to
biologically interpret the different transcript profiles in anaerobic
and aerobic cultures. The data sets used for this study are available
at www.cbs.dtu.dk/yeast/.
Strain and Maintenance--
This study was performed with the
prototrophic laboratory strain S. cerevisiae CEN.PK113-7D
(MATa) (26). The two laboratories involved in
this study independently obtained this strain from the EUROSCARF strain
collection (Frankfurt, Germany) courtesy of Dr. P. Kötter. Upon
arrival, the yeast was grown in shake-flask cultures and stored frozen
with glycerol in small aliquots as described previously (27). These
frozen stock cultures were used to inoculate precultures for chemostat cultivation.
Chemostat Cultivation--
Steady-state chemostat cultures were
grown in Applikon laboratory fermentors of 1-liter working volume as
described in detail elsewhere (28). In brief, the cultures were fed
with a defined mineral medium containing glucose as the growth-limiting
nutrient (29). The dilution rate (which equals the specific growth
rate) in the steady-state cultures was 0.10 h DNA Microarrays--
For a detailed description of the
Affymetrix GeneChip, see Ref. 6. Briefly, genes are
represented on the arrays as a panel of spots, with each spot
containing a different 25-mer oligonucleotide sequence that is
complementary to part of a transcript (perfect match oligonucleotide).
In addition, each perfect match sequence is accompanied by a
neighboring spot that contains an oligonucleotide with a single
nucleotide different from its partner (mismatch oligonucleotide). The
difference between the signals from these "probe pairs" is combined
for the whole "probe set" to give a value called the "average
difference." This is a specific measure of transcript abundance in
the sample. Most genes are represented by 16 probe pairs, but if unique
sequences are limited for a gene, an incomplete probe set is used. The
Affymetrix S98 yeast microarrays contain probe sets representing 9335 distinct transcription features, of which 6383 were nominated yeast
genes due to assignment of either a standard yeast open reading
frame abbreviation (e.g. YAL001c) or a
known function (e.g. SUC4 encoding invertase)
(31).
Sampling and RNA Isolation--
Samples from the chemostat
cultures were taken as rapidly as possible to limit any potential
changes in transcript profiles during the procedure. 40-60 ml of
culture broth was sampled directly from the chemostat into a beaker
containing 200 ml of liquid nitrogen. With vigorous stirring, the
sample froze instantly. The frozen sample was then broken into small
fragments and transferred to a 50-ml centrifuge tube. The sample was
then thawed at room temperature, ensuring that it remained as close to
zero as possible. Cells were pelleted (5000 rpm at 0 °C for 4 min),
resuspended in 2 ml of ice-cold AE buffer (50 mM sodium
acetate, 10 mM EDTA, pH 5.0) and aliquoted into 5 Eppendorf
tubes. This corresponded to ~20 mg of dry weight per tube. For
each array, total RNA was extracted from a single tube using the
hot-phenol method (32) or the FastRNA kit, Red (BIO 101, Inc., Vista, CA).
Probe Preparation and Hybridization to Arrays--
mRNA
extraction, cDNA synthesis, cRNA synthesis and labeling, as well as
array hybridization were performed as described in the Affymetrix
users' manual (7). Briefly, poly(A)+ RNA was enriched from
total RNA in a single round using the Qiagen Oligotex kit.
Double-stranded cDNA synthesis was carried out incorporating the T7
RNA-polymerase promoter in the first round. This cDNA was then used
as template for in vitro transcription (ENZO BioArray High
Yield IVT kit), which amplifies the RNA pool and incorporates biotinylated ribonucleotides required for the staining procedures after
hybridization. 15 µg of fragmented, biotinylated cRNA was hybridized
to Affymetrix yeast S98 arrays at 45 °C for 16 h as described
in the Affymetrix users' manual (7). Washing and staining of arrays
were performed using the GeneChip Fluidics Station 400 and scanning
with the Affymetrix GeneArray Scanner.
Data Acquisition and Primary Analysis--
Acquisition and
quantification of array images as well as primary data analysis were
performed using the Affymetrix software packages: Microarray Suite
version 4.0.1, MicroDB version 2.0, and Data Mining Tool version 2.0. Microsoft Excel was used for further statistical analyses.
All arrays were globally scaled to a target value of 150 using the
average signal from all gene features using Microarray Suite version
4.0.1. When pairwise comparisons were performed (using Microarray Suite
version 4.0.1), a transcript was considered "changed" when a call
of Increase or Decrease was made, the -fold change was at least 2, and
the higher of the two average difference values was called present.
Statistical Comparison of Data from Replicate
Experiments--
The Significance Analysis of Microarrays (SAM version
1.12)1 add-in to Microsoft
Excel was used for comparisons of replicate array experiments (33). SAM
assesses the difference between two mean values when taking into
account the standard errors of those means. The significance of that
difference is estimated by comparing it against the probability of its
occurrence by chance alone. The model of chance occurrence is generated
by permutation of the input data, rather than a predetermined model
(e.g. a normal distribution), as is used by the t test.
The SAM algorithm was used instead of the standard t test,
because it showed a better ability to scale down to small numbers of
replicates. This was determined by comparing the significant change
calls made by SAM and the t test for triplicate arrays against the change calls made by the t test using the
sextuplicate arrays (p < 0.005; the lowered
p threshold was used to reduce the expected number of false
positives, which increases linearly with the number of t
tests performed; see for review, Ref. 34). This conclusion concerning
SAM was also reached in a recent study by Lönnstedt and Speed
(35) using a simulated data set.
Non-biological Sources of Experimental Variation--
Many
in vitro handling steps are required for transcriptome
analyses with Affymetrix GeneChips. Each of these processing steps has
the potential to generate non-biological variability in the data. To
quantify the potential sources of error, we carried out a set of
replicate preparations on a single sample. To ensure any variability
measured was of non-biological origin, our two laboratories performed
the same set of preparations on cells from different growth conditions.
Cells used by laboratory A were grown aerobically while those used by
laboratory B were grown anaerobically.
To test the combined reproducibility of hybridization, washing,
staining, amplification, and microarray scanning, a single pool of
fragmented cRNA was used for hybridization to two arrays within each
laboratory (Fig. 1; arrays 1 and 1a, and 11 and 11a). This yielded
36 and 10 difference calls (based on the criteria outlined for pairwise
comparisons under "Materials and Methods") in laboratory A and
laboratory B, respectively. By visual inspection of the scanned images
for each of these changes, ~10% of these calls were shown to be due
to microscopic faults on the arrays (e.g. scratches, bright
spots, or darkened areas).
The variation introduced by cell breakage, mRNA preparation,
cDNA synthesis, and in vitro transcription was assessed
by hybridizing two arrays with different cRNA preparations from a
single chemostat sample (Fig. 1, arrays 2 and 2a,
and 12 and 12a). Once again, this comparison was
performed with aerobically grown cells in laboratory A and
anaerobically grown cells in laboratory B. These comparisons yielded 80 and 106 difference calls, respectively. In all comparisons, less than
20% of the changes exceeded 3-fold (this category included all changes
that originated from array faults).
On a genomic scale (6383 designated yeast genes and open reading frames
were included in the comparisons), the numbers for variability
introduced by the sample preparation steps were satisfyingly low,
indicating that they were highly reproducible. When the identities of
these differentials were compared for the two laboratories, we found
the variation introduced to be unbiased because only five genes were
found to be common to any two comparisons. Two of these
(YAL005c and YNL140c) could be explained
by incomplete probe sets on the arrays.
Intralaboratory Reproducibility--
To assess the experimental
variation that results from replicate cultivation, each laboratory grew
three independent steady-state chemostat cultures under aerobic as well
as under anaerobic conditions (Fig. 1). To quantify the variation
introduced by replicate cultivation, all possible pairwise comparisons
from within growth conditions (15 each) were performed. On average,
this resulted in 402 ± 170 (range, 66-869) difference calls
(using a change threshold of 2.0) for independent replicate cultures
grown under the same experimental conditions.
These experiments illustrate that, even after extensive precautions to
standardize cultivation conditions, any comparison of two conditions
that is based on a single-array from each condition could result in
extremely high numbers of difference calls. Such potential high
false-discovery rates are not acceptable in most biological experiments
and demonstrate that replication of experiments is a prerequisite for
meaningful application of microarray analysis.
To visualize the variability for each individual S. cerevisiae transcript within triplicate experiments performed in a
single laboratory, the coefficient of variation (standard deviation
divided by the mean) for each transcript was plotted as a function of its average transcript level (Fig. 2).
This representation revealed several important features concerning
intralaboratory variation. First, for the majority of transcripts, the
variability measured in each laboratory was very low; second, at low
signal intensities, the signal-to-noise ratio decreased; third, some
genes with high average-signal intensities still exhibited large
variability; and fourth, the signals from anaerobically cultivated
cells were subject to slightly higher variability than those from
aerobically cultivated cells.
Based on these observations, we deemed the 900 transcripts with the
lowest expression to be poorly reproducible (illustrated by an upturn
in the trend line representing the coefficient of variation; Fig. 2).
These genes gave a signal intensity on the array of less than 6% of
the average intensity for a yeast gene. When the average coefficient of
variation was calculated for the remaining 5483 (86%) "measurable"
yeast genes, the values were 0.23 and 0.20 for aerobic cultures grown
in laboratory A and laboratory B, respectively, whereas the
corresponding values for anaerobic cultures were 0.27 and 0.29.
The closeness of these values indicates that chemostat cultivation and
microarray analysis were carried out with a similar level of
reproducibility in the two laboratories. This, however, did not yet
exclude the possibility that some, or even many, transcripts were
present at reproducibly different levels in the two laboratories.
Interlaboratory Reproducibility--
One of the key goals of
transcriptome analysis is to identify biologically meaningful
differences in gene expression under varying experimental conditions or
in different microbial strains. The simplest way of assessing these
differences is using change calls. This requires an appropriate
statistical tool to decide on the significance of gene expression
changes between growth conditions. To investigate whether the change
calls between the aerobic and anaerobic cultures found in laboratories
A and B were consistent, we used the statistical analysis software
package Significance Analysis of Microarrays (SAM (33)). Specifically, SAM was used to evaluate the interlaboratory agreement of observed changes between the aerobic and anaerobic cultures, by investigating the anaerobic/aerobic comparison from each laboratory separately.
A graphical representation of the -fold changes found in the two
laboratories already indicated generally good agreement (Fig. 3). Not surprisingly, the agreement
worsened at low -fold change values, with a substantial number of genes
changed in opposite directions (found in the upper left and
lower right quadrants of the graph). This observation was
quantitatively supported by the statistical analysis. The consistency
of the change calls in the two laboratories was over 95% for genes
with a -fold change above 2.0, but strongly decreased at lower -fold
changes (Table I). Above 3.0-fold change,
only less than 3% of the change calls (representing a total number of
14 transcripts) was laboratory-specific.
In a further analysis, randomly picked combinations of two aerobic and
two anaerobic array data sets were considered for genes that exhibited
a 1.5- to 2-fold change in transcript level. Statistical analysis by
SAM revealed that, at these low -fold changes, pairs of data sets
originating from one laboratory only agreed on average in 42 and 47%
of cases for laboratory A and laboratory B, respectively. When
"mixed" pairs of data sets were considered, poorer consistency was
observed because agreement dropped to 20% on average. This indicates
that, at low -fold changes, some laboratory bias occurred. In a similar
interlaboratory comparison of the triplicate data sets for each
cultivation condition using SAM, 246 differentials were observed
between laboratories for aerobic conditions and 90 differentials for
anaerobic conditions. In both cases, at least two-thirds of these
differences fell between 1.5- to 2-fold change. Because these
interlaboratory differences increase with decreasing -fold change
values, they highlight the caution with which such small changes should
be treated.
Despite the problems outlined above, many genes with relatively low
-fold changes were in good agreement between the two laboratories due
to similar absolute transcript abundance. This is illustrated in Fig.
4, which compares data from the two
laboratories for the transcripts encoding enzymes of the
tricarboxylic acid cycle and branched-chain amino acid biosynthesis.
Intralaboratory Reproducibility of Transcriptome
Analysis--
Using the procedures for RNA extraction and microarray
analysis recommended by the manufacturer of the equipment used in this study (7), the sum of the variations introduced by all handling steps
was very low. More importantly, our results indicated that the
direction and magnitude of these variations were not reproducible, implying that they can be eliminated by replication of experiments. These findings extend the conclusion of Baugh et al. (10)
that a single round of amplification and labeling by the in
vitro transcription step was the source of low, unbiased variation.
In contrast to sample handling, independent culture replication was
found to introduce significant experimental variability. The present
study involved the use of a standardized commercial system for
DNA-microarray analysis, standardized protocols for chemostat
cultivation, and a single S. cerevisiae strain. From triplicate cultures under these optimized conditions, 14% of the genome was expressed below the reliable detection limit of this technique, and analysis of the remaining 5483 genes showed an average
experimental variation of 0.20-0.23 and 0.27-0.29 for aerobic and
anaerobic chemostat cultures, respectively (Fig. 2).
Because a total of 12 independent cultivations, run in two different
laboratories, was taken into account in this study, the reported
experimental variations can be taken as reliable indicators for the
reproducibility of transcriptome analysis using chemostat cultures. The
higher experimental variation that was observed in the anaerobic
cultures as compared with the aerobic cultures may reflect the
technical difficulties in maintaining "true" anaerobic conditions
in laboratory fermenters (30). Because the biosynthetic requirements of
yeasts for oxygen can be extremely small (30, 36), minute leakages of
oxygen into the cultures might already have a significant impact on
gene expression.
The experimental variation for each transcript, derived from
independent replicate experiments, enables the application of statistical algorithms to evaluate whether transcript levels have changed as a function of cultivation conditions or genetic
interventions. This issue lies at the center of DNA microarray studies
for metabolic engineering and functional analysis. We found our data
from triplicate arrays to be most informative when using the
statistical algorithm SAM in combination with a lower -fold change
threshold of 2.0. For data sets with higher average experimental
variation than found in the present study, fewer genes would meet the
criterion established here when using SAM and the -fold change cutoff
of 2.0 would be too low for reliably assigning biologically meaningful change calls.
The experimental variability of independent replicate cultures would
lead to the identification of many false positives if culture
replication were not performed. Previous data published from one of our
laboratories also addressed the question of genome-wide transcriptional
differences between anaerobically and aerobically grown cells (25). In
this study, only two arrays were used, one for the aerobic condition
and one for the anaerobic condition. This data set was compared with
that from the full comparison in the present study (encompassing six
aerobic cultures and six anaerobic cultures), in both cases applying an
arbitrary -fold change threshold of 2.0. If it is assumed that the
six-by-six comparison provides the "true" transcriptional response
of S. cerevisiae, 257 of the 818 change calls reported in
the previous publication were "false positives." When the threshold
for change calls was raised to 3.0, 65 of 259 change calls were
identified as "false." As expected, an increase in the -fold change
threshold is accompanied by an increase in the accuracy of the change
calls, but at the cost of ignoring many genes that should have been
called changed. These data graphically illustrate the need for
experimental replication in DNA-microarray studies.
Interlaboratory Reproducibility of Transcriptome Analysis--
At
a time when formulation of hypotheses and experimental design are
increasingly based on transcriptome data from other laboratories compiled in electronic databases (37), interlaboratory reproducibility of DNA-microarray data is a key issue. Both in terms of absolute transcript levels and in terms of change calls in the aerobic/anaerobic comparison, a very good interlaboratory consistency of the results was
found in our two laboratories.
Although considerable effort was invested in standardizing the yeast
strain, cultivation conditions, and analytical procedures, the presence
of a small subset of genes that exhibited
laboratory-dependent transcript levels indicated that some
parameters were not perfectly reproduced. The (mostly unknown) roles of
the proteins encoded by these transcripts did not enable us to identify
a plausible explanation for these differences. Although our two
laboratories obtained CEN.PK113-7D strain from the same culture
collection, we cannot exclude the possibility that minor mutations
occurred during propagation and storage. Other factors that might, at
least in theory, contribute to these differences include non-obvious differences in medium composition, for example as a result of obtaining
chemicals with marginally different purities from different suppliers
or differences in the equipment for chemostat cultivation.
A caveat that arises from our experiments is that, even after rigorous
standardization of experimental conditions, transcriptome analysis in
different laboratories may lead to a low but significant number of
laboratory-specific results. The occurrence of (apparent) laboratory
bias in the change calls became more prominent at low -fold changes
between the aerobic and anaerobic transcript levels. However, by
employing SAM in combination with a lower threshold of 2.0 for change
calls, a value that is often employed intuitively in microarray
analyses (38-40), resulted in an over 95% consistency in the change
calls between the aerobic and anaerobic cultures.
To further explore the consistency of data obtained in different
laboratories, our results were compared with two other array studies
performed via similar (but not identical) experimental approaches. To
this end, the ten most strongly induced genes during aerobic growth
(Table II) and anaerobic growth (Table
III) are listed, accompanied by data
found in the literature for genome-wide aerobic/anaerobic comparisons.
ter Linde et al. (25) used the same yeast strain as used in
the present study and identical chemostat cultivation conditions.
However, they used a different version of the GeneChip microarrays,
applied only one array per cultivation condition, and used a different
sampling protocol. Kwast et al. (41) compared aerobic and
anaerobic transcript profiles on gene filters, with a different yeast
strain grown on galactose in batch cultures.
The results obtained in our two laboratories exhibit similar rankings
when transcripts were ordered by magnitude of their -fold change.
Furthermore, the rankings are relatively well conserved in the study of
ter Linde et al. (25) and for the genes with higher
transcript abundance under anaerobic conditions in the study of Kwast
et al. (41). However, in the comparison of the genes that
are higher during aerobic growth, there is a large divergence between
the data reported here and that of Kwast et al. (41). This
is most likely to be due to a combination of differences in strain
background and media composition. This observation indicates that
physiological interpretation of transcriptome data should always take
into account the context of experimental design.
Chemostat Cultivation and Transcriptome Analysis--
To compare
the reproducibility of transcriptome analysis in chemostat cultures
with that in shake-flask cultures, we analyzed data from two
laboratories who used the same commercial set-up for DNA-microarray
analysis and made extensive datasets on independent replicate
shake-flask cultures available on the World Wide Web (43-45).
Following the same procedure as illustrated in Fig. 2, again
eliminating the 900 transcripts with the lowest expression level, the
experimental variations for these shake-flask-based datasets were
calculated at 0.45 and 0.32, respectively (calculations not shown).
These values are higher than those found in the chemostat studies, thus
supporting the notion that chemostat cultivation offers a more rigorous
control of cultivation conditions than shake-flask cultivation. This
enhanced reproducibility, in combination with the possibility to study
the impact of genetic modifications and environmental conditions at a
fixed specific growth rate, makes chemostat cultivation a useful tool
for quantitative transcriptome analysis in functional genomics and
metabolic engineering. The comprehensive dataset for aerobic and
anaerobic chemostat cultures that has been compiled in the present
study and is available via the World Wide Web may serve as a useful set
of reference data for other yeast researchers who intend to work with
chemostat cultures of the CEN.PK113-7D strain.
INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
1) for the given set of environmental conditions. This
limits the applicability of transcriptome analysis, because, in
industrial applications of microorganisms as well as in their natural
environments, growth is nearly always limited by nutrient availability.
Moreover, because specific growth rate is known to have a drastic
impact on gene expression in S. cerevisiae (24), changes in
the environmental conditions or introduction of targeted genetic
modifications that affect specific growth rate will indirectly impact
on genome-wide expression levels. These factors complicate data interpretation.
1)
can be maintained at a chosen, constant value. This allows for transcriptome comparisons between different environmental conditions or
between different strains at an identical, submaximal specific growth rate.
MATERIALS AND METHODS
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
1, the
temperature was 30 °C, and the culture pH was 5.0. Aerobic conditions were maintained by sparging the cultures with air (0.5 liter·min1). The dissolved oxygen concentration, which
was continuously monitored with an Ingold model 34-100-3002 probe,
remained above 80% of air saturation. For anaerobic cultivation, the
reservoir medium was supplemented with Tween 80 and ergosterol as
described previously (29). Anaerobic conditions were maintained by
sparging the medium reservoir and the fermentor with pure nitrogen gas (0.5 liter·min1). Furthermore, Norprene tubing and
butyl rubber septa were used to minimize oxygen diffusion into the
anaerobic cultures (30).
RESULTS
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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Fig. 1.
Experimental design for assessing
reproducibility of oligonucleotide-microarray analysis on chemostat
cultures. Both laboratories performed biological and
non-biological replicate microarray experiments covering both aerobic
and anaerobic cultivation conditions. Two pairs of microarrays were run
to assess the non-biological variability from the in vitro
handling steps. These were replicate microarrays from a single source
of cRNA (arrays 1 and 1a in laboratory A and
arrays 11 and 11a in laboratory B) and replicate
microarrays from a single source of cells (arrays 2 and
2a in laboratory A and arrays 12 and
12a in laboratory B). Furthermore, to assess the
reproducibility of independent replicate cultures, three aerobic and
three anaerobic replicates were done in each laboratory.
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Fig. 2.
Variation of transcript levels from
independent replicate cultures. Independent triplicate chemostat
cultures (aerobic and anaerobic) were run in laboratories A and B. The
coefficient of variation (standard deviation divided by the mean) was
calculated for each transcript and plotted as a function of increasing
average transcript abundance. A trend line (of the coefficient of
variation) was generated using the average from a moving window of 50 transcripts and overlaid on each scatter plot. The gene numbers (from
1 to 6383) on the x-axes were
generated by ranking the genes by increasing average transcript level.
A list of the 900 transcripts with the lowest average expression in all
conditions can be found at www.cbs.dtu.dk/yeast/.
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Fig. 3.
Interlaboratory comparison of the -fold
change for each transcript between aerobic and anaerobic
conditions. Average log2-fold change values for each yeast
transcript, obtained from the aerobic versus anaerobic
comparison in laboratory A, were plotted against those from laboratory
B.
Description of the consistency of change calls for each laboratory by
intervals of -fold change
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Fig. 4.
Interlaboratory comparison of measured
transcript levels for structural genes encoding enzymes of the
tricarboxylic acid cycle and branched-chain amino acid
biosynthesis. Data from each laboratory represent the average ± S.D. of independent triplicate chemostat cultivations for each
growth condition.
DISCUSSION
TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
List of genes whose expression was increased most under aerobic
conditions
List of genes whose expression was increased most under anaerobic
conditions
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ACKNOWLEDGEMENTS |
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We thank Lene Christiansen and Marijke Luttik for technical assistance as well as the Danish Biotechnology Instrument Center, the Danish Technical Research Council (STVF), and the Board of the Delft University of Technology (Beloning Excellent Onderezoek program) for financial support.
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FOOTNOTES |
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* This work was supported in part by the Delft University of Technology (Beloning Excellent Onderzoek program).The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
§ Supported by a European Union Marie Curie Grant.
** To whom correspondence should be addressed. Tel.: 31-15-278-2410; Fax: 31-15-278-2355; E-mail: j.t.pronk@tnw.tudelft.nl.
Published, JBC Papers in Press, July 16, 2002, DOI 10.1074/jbc.M204490200
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ABBREVIATIONS |
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The abbreviations used are: SAM, Significance Analysis of Microarray; ORF, open reading frame.
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REFERENCES |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
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