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1 Departments of Biochemistry, 5 Surgery, and 2 Medicine, and 3 Tulane/Veterans Affairs Environmental Astrobiology Center, Center for BioEnvironmental Research, Tulane University Health Sciences Center and 4 Veterans Affairs Medical Center, New Orleans, Louisiana 70112
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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This study utilizes Saccharomyces cerevisiae to study genetic responses to suspension culture. The suspension culture system used in this study is the high-aspect-ratio vessel, one type of the rotating wall vessel, that provides a high rate of gas exchange necessary for rapidly dividing cells. Cells were grown in the high-aspect-ratio vessel, and DNA microarray and metabolic analyses were used to determine the resulting changes in yeast gene expression. A significant number of genes were found to be up- or downregulated by at least twofold as a result of rotational growth. By using Gibbs promoter alignment, clusters of genes were examined for promoter elements mediating these genetic changes. Candidate binding motifs similar to the Rap1p binding site and the stress-responsive element were identified in the promoter regions of differentially regulated genes. This study shows that, as in higher order organisms, S. cerevisiae changes gene expression in response to rotational culture and also provides clues for investigations into the signaling pathways involved in gravitational response.
shear; gene array; Northern blot; cluster analysis
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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THERE ARE SEVERAL EXAMPLES of the transduction of mechanical forces into changes in gene expression, including changes in heat shock proteins and shear stress responses (23, 26, 36). However, the molecular mechanisms by which mechanical culture conditions in the rotating wall vessel (RWV) modulate gene expression in cultured cells remain unknown (9, 10, 14). We hypothesize that, by identifying specific regulatory motifs within the promoter regions of genes that respond to growth in the RWV, we might then be able to identify transcription factors and signaling pathways involved in this response.
National Aeronautics and Space Administration engineers developed a horizontally rotating cylindrical culture vessel, the RWV, to simulate many of the mechanical culture conditions experienced in the true microgravity of space (35). This is accomplished by using two modalities that, historically for higher order organisms, offer some components of microgravity, namely, randomized orientation to the gravity vector and falling at terminal velocity. The mechanical culture conditions in the RWV minimize induced shear while maintaining laminar flow in the suspension culture. The fluid dynamic operating principles of the RWV also provide for colocalization of cells and aggregates of different sedimentation rates, as well as three-dimensional spatial freedom for adherent cells to form aggregates. A version of the RWV used in this study, the high-aspect-ratio vessel (HARV), is designed to provide ample gas exchange, which is necessary for the growth of rapidly dividing cells.
Brewer's yeast, Saccharomyces cerevisiae, has culture and molecular characteristics making it uniquely suitable for studies of molecular mechanisms in the RWV (3, 5, 20, 32). First, the entire yeast genome has been sequenced and is available for study arrayed on affordable commercially available gene chips (3, 5, 32). Second, the yeast genome has extraordinarily few introns, and upstream promoter structures are often relatively simple compared with metazoan counterparts (3, 32). This implies that, unlike mammalian systems, once a cluster of yeast genes that changes with the same kinetics is identified, bioinformatic analysis of the upstream regions can identify common sequence motifs responsible for a specific regulatory response. Third, it is simple to manipulate these candidate regulatory motifs both by removing them and by duplicating them in the genome, to validate the role of specific sequences as mediators of changes in gene expression. Thus this study tests the hypothesis that gene expression will indeed change in response to RWV culture in a model eukaryotic system with a cell wall, S. cerevisiae, and provides the first data on the responses to the optimized suspension culture conditions of the RWV. In addition, results from this study can be used as a guide for further studies into the signaling mechanisms responsible for gravitational response.
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MATERIALS AND METHODS |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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Strains, Media, and Chemicals
Culture media components were obtained from Difco (Detroit, MI). Radiolabeled nucleotides were obtained from Dupont/NEN (Boston, MA). The strain used in these experiments was S. cerevisiae strain FF18984 (MATa leu2-3, -112 ura3-52 lys2-1 his7-1) (40). All cells were grown in yeast peptone media containing 2% glucose at 30°C (15, 16, 33).Growth Conditions
In preliminary experiments, we determined the cell concentration of a starter culture, which placed the yeast in the stationary growth phase. This saturated S. cerevisiae culture [optical density at 600 nM (OD600) = 7.2] was diluted to an OD600 of 0.2, separated into 55-ml aliquots, and pipetted into 10 RWVs. For this study, the type of RWV used was the HARV, which is designed to provide continuous O2-CO2 gas exchange across the membrane. Residual air was removed through the syringe port. The RWVs were then placed in a gyrorotatory shaking incubator at 30°C for 90 min to allow for recovery. Six RWVs were then transferred to rotators for the time-course experiment, and one RWV was removed before rotation as time zero. At each time point (20, 60, and 180 min), duplicate RWVs were removed from the rotators and split into aliquots for gene array analysis and Northern blot confirmation. The cells were immediately harvested by centrifugation (4,500 rpm, 10 min). Three RWVs remained in the gyrorotatory shaker incubator being agitated at 200 rpm coincidental with the gravity vector as controls and were removed at 20, 60, and 180 min, respectively.Metabolic Data
At each time point, a 1-ml aliquot was removed from each RWV. The culture was centrifuged briefly to pellet the cells. The media was removed and used for determinations of glucose concentration as well as measurements of pH, PO2, and PCO2. The pH, PO2, and PCO2 levels were obtained by using a blood-gas analyzer IL-1610 (Instrumentation Laboratory, Lexington, MA). Each sample was done in duplicate.Glucose measurements were made by using the 510-DA glucose kit from Sigma Chemical (St. Louis, MO). Briefly, a 50-µl aliquot of a 1:100 dilution of the cell-free media to water was added to 1 ml of color reagent (PGO Enzyme/ o-Dianisidine Di-HCl) prepared according to manufacturer's instructions and incubated at 30°C for 30 min. The OD at 450 nM (OD450) was read and used to calculate the amount of glucose in the media on the basis of a standard curve. Each sample was read in duplicate.
RNA Extraction
Total RNA was extracted using the hot phenol/glass beads method of Ausubel et al. (2) as described previously (15, 16). The RNA was further purified by using the QIAGEN RNeasy mini kit (Valencia, CA).Gene Array
T7-based RNA amplification. Total RNA (10 µg) was converted into double-stranded cDNA (ds-cDNA) by using SuperScript Choice System (Life Technologies, Rockville, MD) with an oligo(dT) primer containing a T7 RNA polymerase promoter (Genset, San Diego, CA). After second-strand synthesis, the reaction mixture was extracted with phenol-chloroform-isoamyl alcohol, and ds-cDNA was recovered by ethanol precipitation.
Labeling, hybridization, and scanning. In vitro transcription was performed on the above ds-cDNA by use of the Enzo (Farmingdale, NY) RNA transcript labeling kit. Biotin-labeled cRNA was purified by using an RNeasy affinity column (Qiagen) and fragmented randomly to sizes ranging from 35 to 200 bases by incubation at 94°C for 35 min. The hybridization solutions contained 100 mM MES, 1 M Na, 20 mM EDTA, and 0.01% Tween 20. The final concentration of fragmented cRNA was 0.05 mg/ml in the hybridization solution. Probes for hybridization were prepared by combining 40 ml of fragmented transcript with sonicated herring sperm DNA (0.1 mg/ml), BSA, and 5 nM control oligonucleotide in a buffer containing 1.0 M NaCl, 10 mM Tris · HCl (pH 7.6), and 0.005% Triton X-100. Target was hybridized for 16 h at 45°C to a set of oligonucleotide arrays (Affymetrix, Santa Clara, CA). Arrays were washed at 50°C with stringent solution and then at 30°C with nonstringent washes. Arrays were stained with streptavidin-phycoerythrin (Molecular Probes, Eugene, OR). DNA chips were read at a resolution of 3 µm with a Hewlett-Packard GeneArray Scanner and were analyzed with the GENECHIP software (Affymetrix). Detailed protocols for data analysis of Affymetrix (1) microarrays and extensive documentation of the sensitivity and quantitative aspects of the method have been described (22). Briefly, each gene is represented by the use of ~20 perfectly matched (PM) and mismatched (MM) control probes. The MM probes act as specificity controls that allow the direct subtraction of both background and cross-hybridization signals. The number of instances in which the PM hybridization signal is larger than the MM signal is computed along with the average of the logarithm of the PM-to-MM ratio (after background subtraction) for each probe set. These values are used to make a matrix-based decision concerning the presence or absence of an RNA molecule. Comparison analysis between control and experimental samples was made with Affymetrix software.
Data Analysis
All of the data were analyzed by use of GeneSpring software version 4.0 (Silicon Genetics, Palo Alto, CA). The first part of the analysis was designed to identify genes up- or downregulated by at least twofold as a result of rotational growth. Next, methods including principal component analysis (PCA) and K-means analysis were applied to group these differentially regulated genes into sets on the basis of their expression patterns at the different time points (for reviews, see Refs. 4, 9, 30, 32). Small sets of genes with similar expression patterns were then subjected to further examination to identify common sequences within their promoter regions (Gibbs analysis) (17).Briefly, the first step in this analysis involved identifying the significant patterns of expression within a chosen group of genes by using PCA. Next, the results obtained from PCA were used to group the genes into several sets by using an additional clustering technique, K-means analysis. This form of analysis uses several mathematical correlations to define clusters, which are small sets of genes with similar expression patterns. The goal is to produce sets of genes with a high degree of similarity within the same set and a low degree of similarity between different sets. The three correlation methods used for K-means analysis are Standard, Pearson, and Spearman correlations.
Optimization and Validation of Clustering Algorithms
To determine optimal clustering, clusters are added one at a time. After each new cluster is added, the percentage of the variability in the data explained by the analysis is calculated. As clusters are added, the percent variability rises, peaks, and then falls again. This peak represents an optimal analysis. The strength of the cluster analysis is validated in three ways. First, the percent variability explained by the analysis should be high (>80%). Second, repeat analysis of the genes in each individual cluster should not allow further increase in percent variability of the overall analysis. Third, the various methods should give similar results, with similar clusters of genes observed with each form of analysis.Northern Blot Analysis
Total yeast RNA (10 µg) was electrophoresed on a 1.5% agarose-formaldehyde gel. RNA transfer was done as described previously (2, 15). RNA quality was assessed by ethidium bromide stain. Single-stranded DNA probes were produced by using asymmetric PCR as described and labeled with [
-32P]dGTP
(2). Genes for Northern analysis were selected on the basis of detectable basal expression and a large difference between control and experimental levels for ease of measurement. The primers were designed to give a 500-bp labeled probe that did not have a high
degree of homology to any other gene. The primers used for each probe
are as follows:
HEM13-LEH490 [5'-GACCCGTGGTAACGATGG-3']
HEM13-LEH491 [5'-CCGAATTGGGGTACCTCTA-3']
GRS1-LEH435 [5'-GCCGTATCATCTACTCCG-3']
GRS1-LEH286 [5'-CTGAAATGGCGCATTATCATCGGAAGAAAGGATCACAGTCATCAGCTGAAGCTTCGTACGC-3']
TIP1-LEH492 [5'-CTTGGGTCTAGAAACTGG-3']
TIP1-LEH493 [5'-TAAAGCAGCTGCACCTGC-3']
Primers were obtained from Sigma-Genosys through Fisher Scientific. The membrane was placed in prehybridization buffer [0.1% SDS, 50% formamide, 5× standard sodium citrate (SSC), 50 mM NaPO4 pH 6.8, 0.1% sodium pyrophosphate, 5× Denhardt's solution, and 50 µg/ml sheared salmon sperm DNA] for 2 h at 42°C before addition of the labeled probe. Hybridized membranes were washed once with 1) 10 ml of fresh prehybridization solution at 25°C for 30 min, 2) 10 ml of 2× SSC, 0.1× SDS at 25°C for 30 min, and twice with 3) 10 ml of 0.2× SSC, 0.1× SDS at 55°C for 45 min before exposure to X-ray film (Fuji Film, Edison, NJ).
Image Analysis
Northern blots were scanned by use of a Hewlett-Packard ScanJet 5P. Image analysis was performed with the Kodak Digital Science 1D Image Analysis software version 1.6 for Macintosh (Eastman Kodak, Rochester, NY).Statistics
Statistics analysis was performed by use of InStat 3.0 for Macintosh (GraphPad Software, San Diego, CA). One-way ANOVA and post hoc Bonferroni tests were applied to obtain the mean, standard error of the mean, and P values for replicate samples. Statistical significance was taken as P < 0.05 before Bonferroni adjustment.| |
RESULTS |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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Growth Conditions in the RWVs
Yeast cultures were grown in the RWVs (Fig. 1) placed either on a rotator or a gyrorotatory shaker to assess the effect of the mechanical culture environment (rotator) compared with conventional growth environment (gyrorotatory shaker) on gene expression. Cells were inoculated into the RWVs and removed from culture at 0, 20, 60, and 180 min postinoculation.
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To ensure that any changes in gene expression were not caused by
differing growth rates in the rotator, the optical density of the
cultures was measured at each time point. As seen in Fig. 2, the cells exhibit comparable growth
rates up until 180 min, at which time the cells grown under conditions
of rotational growth enter logarithmic phase before the cells grown in
the shaker.
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In addition to growth rates, the conditions inside the RWVs in both the shaker and the rotator were also monitored to determine whether there were any differences in the growth conditions. At each time point, an aliquot was removed from the RWVs, and glucose, pH, PO2, and PCO2 levels were measured. As seen in Fig. 2, the pH and PO2 of the two cultures are almost identical throughout the time course, whereas glucose and PCO2 levels deviate from each other significantly (P < 0.05, n = 4) at 180 min. Thus it is possible to conclude that gene expression changes seen before the 180-min time point are likely due to the effects of the low-shear environment produced by the rotator and not to differences in growth rate or metabolic parameters.
Gene Expression Changes Caused by Rotational Growth
Total RNA was extracted from each sample described above and used to prepare cDNA for hybridization to chips containing the S. cerevisiae genome. Cells grown under conditions of rotational growth demonstrated definite changes in gene expression as seen in Fig. 3A. To obtain a general idea of the effects of rotation on gene expression, the entire data set of 6,400 genes was submitted to PCA and K-means analysis. PCA first identified the significant patterns of expression within this group of genes as illustrated in Fig. 3B. Next, the results obtained from PCA were used to group the genes into several sets. In this case, PCA identified eight significant patterns of expression; therefore, K-means analysis was used to separate the same group of genes into eight sets (Fig. 3C). Each set consists of a unique group of genes whose expression patterns all match one particular pattern identified by PCA. For example, if PCA identified a pattern that consisted of upregulation at 20 min followed by a decrease at 60 and 180 min, then K-means analysis would group together all genes whose expression profile fit this pattern. These same clustering techniques were then repeated with the control data to illustrate the differences in expression caused by rotational growth. Changes in expression profiles can be seen first in PCA as the gene expression data from the rotators do not contain the same patterns seen in the control data (Fig. 3B). Differences in relative intensity of the control and rotator data can also be seen in several of the clusters from K-means analysis (Fig. 3C). For example, in the second cluster on the right (shown in red) a difference can be seen in the relative intensity when comparing the 20-min control and experimental groups. This difference is caused by a change in expression values for these particular genes as a result of rotational growth.
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Identification and Functional Analysis of Rotation-Responsive Genes
Once it was determined that yeast gene expression was affected by rotational growth, bioinformatic analyses were performed on the data to identify specific genes whose expression pattern changed. Expression values from time points taken in duplicate (20, 60, and 180 min) were averaged and considered as a single value for the remainder of the analysis. The agreement between duplication was excellent with highly significant correlation of the linear regression lines (P < 0.01 for each comparison). The rotator and shaker values at 20, 60, and 180 min, respectively, were then compared to identify genes whose expression patterns were different at each time point by at least twofold (Fig. 3A). At 20, 60, and 180 min, 353, 52, and 41 genes were upregulated and 96, 41, and 145 were downregulated, respectively. This figure illustrates that, despite the presence of a cell wall, RWV culture conditions induce diverse changes in yeast gene expression. (Note: A list of differentially expressed genes can be provided on CD-ROM on request.)Functional analysis. Genes upregulated and downregulated at each time point were then compared with functional assignment lists to determine the functional breakdown of each group. This was done by using the Munich Information Center for Protein Sequences. The list of genes from each cluster was compared with these functional assignment lists to determine the functional category of each gene upregulated or downregulated at each time point (Fig. 3D). The majority of the genes changing as a result of rotational growth are involved in cellular organization, a finding consistent with earlier observations in diverse cell types exposed to rotation and true microgravity conditions (6, 13, 18, 19).
Clustering and Promoter Analysis
Identification of rotation responsive genes was the first step in isolating specific sequences in the upstream promoter region of these genes that could be targeted as a result of rotational growth. To identify these potential regulatory sequences, it was first necessary to divide the group of up- and downregulated genes into smaller sets on the basis of their expression patterns. The data were analyzed in two ways; the first was designed to look at kinetic changes in gene expression, and the second focused on a specific time point. After specific genes corresponding to these two categories were isolated, PCA and K-means were performed on these particular groups of genes to separate each into small clusters of genes with similar expression profiles. The promoter regions of genes within these clusters were then examined for common sequence motifs using Gibbs promoter analysis.Time-dependent gene expression. The six lists of genes upregulated and downregulated at each time point were condensed to three groups that summarized those genes that changed at each time point. For example, the list of 353 genes upregulated at 20 min was added to the list of 96 genes downregulated at the same time to create a new grouping of 449 genes that differed from the control values at 20 min. Next, by using only the data obtained from cells grown under conditions of rotational growth, groupings were made of genes whose expression level changed over time, i.e., from 0 to 20 min, 0 to 60 min, and 0 to 180 min. A comparison of these two filter restrictions yielded a final group of 127 genes whose expression pattern differed from the control and were either upregulated or downregulated compared with the zero time point.
These genes were then subjected to PCA and K-means analysis. Because PCA identified five significant patterns of expression, K-means was used to separate these genes into five clusters. Genes within these five clusters were then examined by using Gibbs analysis to determine whether they contained similar upstream regulatory motifs. As seen in Table 1, two candidate motifs were identified in one cluster. The AGGGGT motif was identified in 10 of the 27 genes in cluster 2; two genes had TTCAGGGG in their promoter regions, whereas four genes contained both motifs. Interestingly, the two motifs have the same core sequence, AGGGG, which is a known positive transcriptional control element, stress-responsive element (STRE). STREs are bound by the transcription factors Msn2p and Msn4p, which activate stress response genes as a result of many different environmental and physiological conditions (31, 31a, 38). The expression levels of MSN2 and MSN4 did not change significantly as a result of rotational growth.
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Expression changes at 20 min. Given the significant number of genes upregulated at the 20-min time point, these 353 genes were chosen for further analysis. With the use of PCA, it was determined that this group of genes contained four significant patterns of expression. For example, one of these patterns consisted of an increase from 0 to 20 min, followed by a decrease from 20 to 60 min and no change from 60 to 180 min. K-means analysis was used to cluster these 353 into four sets. To verify these results, two additional K-means methods, Pearsons and Spearman correlation, were used to cluster the genes. These additional correlation methods yielded comparable results (data not shown).
Promoter analysis of these four clusters revealed three motifs in 13 of 147 genes grouped together by K-means analysis. These 13 genes share functional similarity because they all encode ribosomal proteins, and, as seen in Fig. 4, they show a similar change in their pattern of expression. The identified motifs have the same core sequence differing only by one nucleotide on either side. The motifs as well as the genes involved are listed in Table 1, top. The Saccharomyces cerevisiae Promoter Database (SCPD) identified these motifs as being similar to the sequence bound by the Rap1 transcription factor. Rap1p can act as either an activator or repressor of transcription, depending on its binding context. Rap1p is also associated with telomeric silencing (19). The SCPD was used to examine the promoter regions of the 13 genes that were upregulated to determine whether the motif identified by GeneSpring is indeed contained within the previously identified Rap1 binding site. As seen in Table 1, top, 5 of the 13 upregulated genes did contain the motif identified in this study within its Rap1 binding site. The transcription level of Rap1 itself was not affected by rotational growth. The identification of the Rap1-like motif within the promoter regions of a subset of the upregulated genes opens the possibility for Rap1-dependent transcriptional regulation in response to rotational growth.
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Confirmation of Microarray Result by Northern Blot Analysis
To directly verify the results from the microarray analysis, the mRNA levels of three randomly selected genes were determined by Northern blot analysis. As shown in Fig. 5 and confirmed by quantification of the RNA signals (data not shown), the expression of genes HEM13 and TIP1 increases over time in the RWVs placed on the rotators (compare lanes 2, 4, and 6). These same levels of expression are not seen in yeast cells grown in a shaking incubator (compare lanes 3, 5, and 7). The Northern blot analysis shows a pattern of expression similar to that seen in the microarray data. HEM13 and TIP1 have increased expression over the shaker starting at 60 min, whereas the mRNA levels for GRS1 show no differences in the pattern of expression between cells grown in the rotator or shaking incubator.
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
MATERIALS AND METHODS
RESULTS
DISCUSSION
REFERENCES
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The present study provides the first data on the responses of yeast, or indeed any organism with a cell wall, to the optimized suspension culture conditions of the RWV. Growth of yeast in the RWV was characterized by changes in growth rate, metabolic profiling, and gene expression. The mechanical culture conditions in the RWV induce diverse changes in yeast despite the necessity for signal transduction across a cell wall. These results can be used to give clues as to the signaling mechanisms responsible for the observed changes in gene expression and to help guide further studies.
This study generated a genomewide data set documenting the kinetic changes in gene expression in S. cerevisiae during RWV suspension culture. The duplicates were highly reproducible, and sufficient time points were included to allow initial attempts at kinetic clustering analysis of the gene expression patterns observed. The entire set of 6,400 genes was grouped in several ways to produce small clusters, which could then be subjected to further analysis. Each cluster underwent Gibbs promoter alignment analysis to determine whether the genes contained within that set had a similar regulatory sequence in the promoter region. The identification of two independent clusters containing such sequences not only validates this type of study but also provides clues as to how the cell responded to rotational growth.
We found that the shift from normal gyrorotatory growth to rotational growth triggers the cell's stress response pathway, specifically through binding of STRE sites in several types of genes. This does appear to be a targeted response because the differentially regulated genes containing a STRE site are functionally related. They can be divided into two groups: those involved in the stress response and those involved in metabolism/glucose utilization. The first group contains the genes HLJ1 and SSA4, which are involved in stress responsiveness. HLJ1 encodes a protein homologous to the Escherichia coli DnaJ protein that is a member of the 40-kDa family of heat shock proteins (HSP40), and SSA4 encodes one of the S. cerevisiae HSP70 proteins. The second group of genes, PGM2, GPM2, and COX5B, are all involved in aspects of the cells metabolic cycle. PGM2 encodes phosphoglucomutase, the enzyme responsible for converting glucose-1-phosphate to glucose-6-phosphate, the last step in the conversion of glycogen to glucose. The protein product of GPM2, phosphoglycerate mutase, is involved in the last stage of glycolysis. Finally, COX5B encodes cytochrome-c oxidase chain Vb, and cytochrome oxidase catalyzes the final step in this chain, which is responsible for generating the ATP needed in metabolic processes. As with HSP that responds to a variety of environmental stimuli, these proteins may be responding to the new environment. This points to the inadequacy of the nomenclature, because these proteins are not just stress proteins but environmental sensors for the cell.
In addition to the identification of the STRE site in one group of differentially regulated genes, putative Rap1-like binding motifs were identified in a separate group of 13 genes upregulated after 20 min of rotational growth. Because only five of these genes were previously identified as potential targets for Rap1 binding, this opens the possibility that Rap1 may target different ribosomal protein genes in response to rotational growth. Activation of Rap1 binding sites is often associated with increased transcription in response to changes in the growth rate. This change can be mediated by several factors, including a shift in the availability of carbon sources (19, 41). Therefore, increased glucose utilization in the rotator could provide a model for increased Rap1-mediated transcription. Indeed, several genes involved in glycolysis are also upregulated after 20 min of growth in the rotator. One of these encodes phosphofructokinase, which catalyzes one of the irreversible reactions, making it a key enzyme in the regulation of the glycolytic pathway. Together these results indicate that Rap1p may be involved in the signaling pathway induced by the cell's response to rotational growth.
Beyond the identification of specific regulatory sites, the information obtained from the functional analysis (Fig. 3D) can be used to hypothesize about other adaptations induced by rotational growth. Looking at Fig. 3D, it appears that one of the largest groups of genes affected at all three time points comprises those involved in cellular organization. In particular, the up- and downregulation of certain genes in this category at the 20-min time point suggests that the cells are immediately responding to the change in environment. The upregulation of several ribosomal protein genes at this time also indicates that the upregulation of transcription, determined through analysis of the microarray data, is likely followed by an increase in the synthesis of these proteins.
At the 60 min time point, genes such as ICY1, CBF1, SDS23, and ASE1 are upregulated. These genes are involved in a variety of processes including mitotic segregation and spindle elongation (24, 26). In addition, genes encoding subunits of both DNA and RNA polymerase, additional glycolytic components, protein translocators, and mitochondrial ribosomal subunits are also upregulated. Thus it would appear that the cells are still responding to the change in conditions by upregulating genes that should lead to an increased rate of growth, if the corresponding proteins are translated at a similar level. In agreement with this, the OD600 of the rotating cells begins to increase significantly over those in the shaker after 60 min, and these cells also begin to utilize more glucose (Fig. 2). This upregulation of genes involved in mitosis may start to explain the increased growth rate after the 60-min time point as seen in Fig. 2. The cells are still responding to the rotator conditions at this time through the upregulation of genes involved in cell wall maintenance. After the 60-min time point, it is difficult to judge whether the gene expression changes observed are due to the continued adjustment to the environment or to the increased number of cells.
This yeast study confirms and extends observations in other systems on the mechanisms by which mechanical culture conditions modulate the phenotype of cultured cells. Physical forces can induce changes in cell metabolism and function in diverse culture systems. Heat shock proteins are a well-documented example of how small changes in environmental parameters induce a cascade of genetic and protein changes (35). More recently, there is abundant evidence that shear stress due to flow across vascular endothelium is critical to maintain many attributes of normal cell structure and function, in studies that delineate some candidate promoter motifs (23, 25).
The physics of the RWV determine that the shear stress is fixed by
three factors: gravity, the size of cell aggregates, and the difference
in density between the culture medium and cell aggregates (12,
42). A plethora of signaling pathways has been implicated by
different laboratories as mediators of the cellular responses to
mechanical conditions. For instance, vibration resulted in increased
c-fos expression in osteoblasts (37), whereas
suspension culture resulted in protein kinase C activation anomalies (7, 35). The conundrum has been to tie
together diverse mediators, which range from cytoskeletal changes
(23, 36) through nuclear translocation of transcription
factors such as the vitamin D receptor, c-jun, and nuclear
factor-
B (10, 11). By using yeast as a simple model
system, these data give the first insight into the molecular mechanisms
at the transcriptional level.
These experiments have begun to define the molecular mechanisms of the genomic response to changes in mechanical culture conditions in the RWV and should facilitate further optimization of suspension culture systems for both commercial and academic applications. This study shows that, like higher order organisms, the yeast S. cerevisiae, a model eukaryotic organism with a cell wall, changes gene expression in response to RWV culture, including participation of STRE sites and Rap1p-like binding motifs.
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ACKNOWLEDGEMENTS |
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We thank Dr. J. Wilson for critical reading of the manuscript.
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FOOTNOTES |
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This work was supported by National Aeronautics and Space Administration (NASA) Nasa Research Announcement NAG-8-1362, NASA Cooperative agreement NCC 2-1177, and Department of Defense Defense Threat Reduction Agency to the Tulane/Xavier Center for Bioenvironmental Research. Affymetrix system was purchased by the Georgia Research Alliance.
Address for reprint requests and other correspondence: L. E. Hyman, Dept. of Biochemistry SL-43, Tulane Univ. Medical Center, 1430 Tulane Ave., New Orleans, LA 70112 (E-mail: lhyman{at}tulane.edu).
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.
August 16, 2002;10.1152/japplphysiol.01087.2001
Received 30 October 2001; accepted in final form 30 July 2002.
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TOP
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MATERIALS AND METHODS
RESULTS
DISCUSSION
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, gyrorotatory shaker control values; E,
, experimental rotator data. Samples were done in
duplicate for every vessel. Error bars represent SE (n = 4, * P < 0.05, C vs. E). The absence of error
bars indicates SEs too small to be illustrated. The major differences
in glucose consumption, OD600, and
PCO2 content between the 2 cultures occur at
the 180-min time point. After 60 min of growth, OD600 and
PCO2 content rose with time in the rotator,
whereas the glucose level decreased.
