* Department of Organismic and Evolutionary Biology, Harvard University
Harvard University Bauer Center for Genomics Research
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Abstract |
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Key Words: cDNA microarrays S. cerevisiae wine yeast gene expression variation population genomics regulatory evolution
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Introduction |
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Recently, the debate over the relative importance to adaptive evolution of regulatory compared with structural genetic variation has diminished, despite the discovery of examples of interspecies variation in gene expression level important to evolution (Wilson, Carlson, and White 1977; Dickinson 1987; Hammer and Wilson 1987; Dickinson 1991; Wang, Marsh, and Ayala 1996). New tools, now including those emerging from genomics, can identify regulatory polymorphism on a genomic scale, rather than gene by gene, allowing a comprehensive assessment of whether regulatory variation contributes to functional evolutionary innovations (Paigen 1989). For regulatory variation to be evolutionarily relevant, it must be present in natural populations, it must be heritable, and it must lead to differential lifetime reproductive success.
The first of these requirements has been satisfied only with regard to a number of single genes or protein products (Paigen 1979, 1986; Laurie-Ahlberg et al. 1982; Laurie-Ahlberg 1985). Many of these examples have been explored in Drosophila or Mus. Although the potential for complex tissuelevel regulatory changes to be important in adaptive evolution in these multicellular organisms is clearly very great, discerning the nature of such differences on a genome-wide scale will be difficult because few tissues are easily isolated and cellularly homogeneous. Here we compare four natural isolates of unicellular wine yeast from Tuscan vineyards, assessing the degree of variation in transcription on a genomic scale. Our microarrays contain all identified open reading frames from the sequenced genome of S. cerevisiae, eliminating any bias from choosing a single gene or pathway for study. The demonstration of variation in gene expression among individuals from a natural population is a necessary prerequisite to evaluating the larger claim that regulatory changes play an important role in organismic evolution.
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Methods |
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DNA Microarray Construction
A set of clones containing 6,188 verified open reading frames (ORFs) of the yeast genome were obtained from Research Genetics (Huntsville, Ala.) and amplified to levels required for preparation of DNA microarrays by means of the polymerase chain reaction as in Hardwick et al. (1999). Some of the longer ORFs were amplified with the Gibco BRL Elongase Amplification Kit (Life Technologies, Rockville, Md.). Each amplified product was confirmed by agarose gel electrophoresis. Ninety-eight percent of the ORF amplifications yielded bands of appropriate length. The amplified DNA was precipitated with isopropanol, washed with 70% ETOH, and resuspended in Micro Spotting Solution (Telechem, Sunnyvale, Calif.). The DNA was spotted on CMT-GAPS -aminopolysilanecoated glass slides (Corning, Corning, N.Y.) or on polylysine slides (Eisen and Brown 1999), using a microarraying robot with a 16-pin head constructed from a design by Patrick O. Brown (http://cmgm.stanford.edu/pbrown/).
Extraction of mRNA
RNA was extracted from flash frozen pellets of yeast cultures grown aerobically in 100-mL culture at 30°C in a shaker at 225 rpm to an optical density of 0.8 in YPD medium (1% yeast extract, 2% peptone, and 2% dextrose). The flash-frozen yeast pellet was resuspended and mRNA extracted with a hot acidic phenol/chloroform extraction. Nucleic acids were ethanol precipitated, washed, dried, and redissolved in TE buffer. Yield ranged from 10 to 15 mg, with a spectrophotometric ratio of absorption (260 nm/280 nm) of approximately 2.0. Pellets were stored frozen at -20°C. The mRNA was purified using the Quiagen (Valencia, Calif.) Extraction Kit. The poly-A RNA was stored at -20°C.
Reverse Transcription and Hydrolysis
Reverse transcription was performed with oligo-dT (a mixture of dT 16-mer, 17-mer, 18-mer, 19-mer, 20-mer, 21-mer, and 22-mer) and poly-dN primer (Poly-dN6). Amino-allyl-dUTP (Sigma) was incorporated into cDNA along with dNTPs using the reverse transcriptase Superscript II. After at least 2 h, 1 M NaOH and 0.5 M EDTA were added, and the mix was incubated at 65°C for 15 min. Then 1 M HEPES pH 7.5 was added.
Buffer Cleanup and Cyanine Dye Coupling
The reverse transcription reaction product was diluted 10-fold, then concentrated 20-fold with Microcon-30 microconcentrators. Dilution 20-fold and concentration 20-fold was performed another two times. To this purified concentrate, 1 M NaHCO3 pH 9 was added, with an appropriate NHS-cyanine dye aliquot. This coupling reaction was incubated in the dark at 25°C for 75 min and then stored in the dark at 4°C for less than 24 h.
Probe Purification
The labeled probe was purified with a Quiaquick column. This elution of purified cyanine-labeled cDNA was stored at 4°C for less than 24 h.
Hybridization
The labeled cDNA was concentrated in Microcon-30 microconcentrators, combining appropriate cyanine-3labeled and cyanine-5labeled paired samples. Poly-dA 12-mer to 18-mer, SSC, and HEPES pH 7.0 were added. The mix was filtered with a Millipore 0.45 µm filter. 10% SDS was added, and the mix boiled for 2 min. It was then cooled at 27°C for 10 min. A microarray slide was set in a hybridization chamber, using drops of 3 x SSC on the underside adsorbing to the slide corners and the chamber bottom. Then 3 x SSC was added to the hybridization chamber wells. A Lifterslip coverslip was cleaned with ethanol, then placed over the printed microarray. The labeled cDNA mix was injected at the corners of the Lifterslip, and the chamber was sealed and then placed level in a 60°C water bath to be incubated at 60°C to 63°C for 12 to 15 h.
Array Wash
Hybridized microarray slides were washed in a solution of 387 mL purified water, 12 mL 20 x SSC, and 1 mL 10% SDS and rinsed in a solution of 399 mL purified water and 1 mL 20 x SSC. The array was stored, if needed, in the dark, for less than 2 h and then scanned.
Data Acquisition and Analysis
Fluorescent DNA bound to the microarray was detected with a GenePix 4000 microarray scanner (Axon Instruments, Foster City, Calif.), using the GenePix 4000 software package to locate spots in the microarray. Fluorescence intensity values were adjusted by subtracting background from foreground. To eliminate signals that are most prone to estimation error, any spot was excluded from analysis if both the cyanine-3 and cyanine-5 fluorescence signals were within three standard deviations of the distribution of intensities of the background pixels for that spot. This procedure avoids artificially inflated measurements of expression due to near-zero values in the denominator. Expression values were normalized by linear scaling of the cyanine-5 values so that the mean cyanine-5 and cyanine-3 background-corrected intensity values of nonexcluded spots were equal. Because the hybridizations were of uniformly high quality, this straightforward method yielded linear log-log cyanine-3cyanine-5 intensity scatterplots for all hybridizations and no further manipulation of the data was necessary.
We chose to study four homothallic, diploid natural isolates from Montalcino, Italy. These isolates were previously characterized using RAPD, genetic analysis, and a subtelomeric probe (Cavalieri et al. 1998). Results here are derived from comparisons of global gene expression of those four isolates in an eight-microarray, dye-swap circle (fig. 1), so that there would be equivalent information on each isolate. Both cross-comparisons were performed once to provide a direct comparison between nonneighboring strains in the experimental design.
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Results |
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Discussion |
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Whereas DNA microarray technology has enabled the analysis of global patterns of gene expression and revealed diverse networks of coordinated function, the genetic differences examined have been primarily differences between growth conditions or between mutant strains containing the limited genetic background of laboratory yeast (Mortimer and Johnston 1986). Although these studies explore the impact of environment and developmental state upon gene expression, their generality may be limited by having focused on the restricted and somewhat artificial range of genotypes available among laboratory strains. There is great potential for understanding molecular population genetics and evolution by the study of gene expression in yeast strains isolated from natural environments. In contrast, because dramatic differences in transcriptional profile may be observed between the progeny of a single natural isolate, it is unlikely that the continuous phenotype of gene expression level will be of much utility in phylogenetic reconstruction. This inference is consistent with the results of Dickinson (1987) and Thorpe and Dickinson (1988) who showed that constructions of the phylogeny of the Hawaiian picture-winged Drosophila, based on regulatory characters for the most part, do not reconstruct the known phylogeny. This, in turn, reinforces the conception that the introduction of regulatory variation is frequent.
Here we have shown variation in gene expression level in genes associated with amino acid metabolism, protein degradation, metal ion transport, growth phenotype, and transposable element activity. What can we take from this? Cavalieri, Townsend, and Hartl (2000) demonstrated extensive differences in expression profile of the progeny of M2-8. These differences, largely among genes of amino acid biosynthesis pathways, may represent, in part, segregating differences in how high levels of sulfite are dealt with. It is known from extensive enology experiments attempting to reduce hydrogen sulfide production in wine fermentation that supplementation or increased production of amino acids gives a nitrogenous substrate for processing of excess sulfite. The implication is that whatever difference is segregating in the M2-8 strain, the characteristics that make the two metabolic phenotypes distinct in offspring are both present in the parent strain, although to a considerably modulated degree and manifested in slightly different form. The segregating molecular phenotype appears to consist partly of two different ways of compensating for a homozygous characteristic of M2-8: the most dramatically differentially expressed gene among the four isolates, SSU1.
Transposable elements vary in expression among these four isolates and may generally, inadvertently, play an important role in introducing regulatory variation (Paigen 1986). Transposable elements have been implicated in extensive genetic rearrangement (Rachidi, Barre, and Blondin 1999). Moreover, the insertion of a transposable element near a gene frequently leads to changes in the level or developmental timing of expression of that gene (McClintock 1984).
Paigen (1986) reviews evidence that there is also regulatory polymorphism in translation and degradation rates. Here we demonstrate that the variation present in mRNA abundance is considerable, quite apart from downstream variation in translation and degradation. Furthermore, variation in downstream regulatory systems is implicit in the observed variation among strains in expression levels of genes in the ubiquitin pathway and the 20S proteasome (tables 2 and 3). These results encourage further exploration of variation at the proteomic level, as well as continued development of technological and statistical methodologies allowing inference of differences in mRNA and protein concentration of a factor below 2. The remarkable stoichiometric consistency of estimated expression levels among genes whose products make up the 20S proteasome demonstrates the potential to detect differences of low magnitude reliably using properly analyzed replication.
Such differences in expression of small magnitude may be of considerable importance as a kind of variation that may be selected upon during adaptive change. How much does the average gene differ, from isolate to isolate, in gene expression? Figure 13 shows the relative frequency at which various ratios in gene expression were observed among statistically significant differences in gene expression, based on all 12 pairwise comparisons of these four isolates. This histogram indicates the frequency of differences in gene expression as a function of the magnitude of difference. Note that the vast majority of differences in gene expression among natural isolates are below the twofold level. The fact that small differences in gene expression are more difficult to detect implies that many more genes are differentially expressed at these very low levels but were not detected. This reinforces the point that most of the variation in gene expression levels within a natural population is slight. Note also that much of that variation in gene expression may be composed of the coordinated cellular effects of a few genetic changes (Wilton et al. 1982).
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With regard to the role that regulatory variation can play in providing the raw material for adaptive evolution, Brem et al. (2002) demonstrate clearly the heritability of transcription, and Ferea et al. (1999) demonstrate rapid change in gene expression level in response to selection. These results, combined with the evidently considerable variation in gene expression level in natural populations disclosed here, argue for renewed attention to the role that regulatory variation plays during adaptive evolution.
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Supplementary Material |
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Acknowledgements |
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Footnotes |
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E-mail: townsend{at}nature.berkeley.edu.
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Literature Cited |
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