Comparative gene expression between two yeast species
© Guan et al.; licensee BioMed Central Ltd. 2013
Received: 8 October 2012
Accepted: 3 January 2013
Published: 16 January 2013
Comparative genomics brings insight into sequence evolution, but even more may be learned by coupling sequence analyses with experimental tests of gene function and regulation. However, the reliability of such comparisons is often limited by biased sampling of expression conditions and incomplete knowledge of gene functions across species. To address these challenges, we previously systematically generated expression profiles in Saccharomyces bayanus to maximize functional coverage as compared to an existing Saccharomyces cerevisiae data repository.
In this paper, we take advantage of these two data repositories to compare patterns of ortholog expression in a wide variety of conditions. First, we developed a scalable metric for expression divergence that enabled us to detect a significant correlation between sequence and expression conservation on the global level, which previous smaller-scale expression studies failed to detect. Despite this global conservation trend, between-species gene expression neighborhoods were less well-conserved than within-species comparisons across different environmental perturbations, and approximately 4% of orthologs exhibited a significant change in co-expression partners. Furthermore, our analysis of matched perturbations collected in both species (such as diauxic shift and cell cycle synchrony) demonstrated that approximately a quarter of orthologs exhibit condition-specific expression pattern differences.
Taken together, these analyses provide a global view of gene expression patterns between two species, both in terms of the conditions and timing of a gene's expression as well as co-expression partners. Our results provide testable hypotheses that will direct future experiments to determine how these changes may be specified in the genome.
KeywordsExpression divergence Yeast Microarray Data integration Condition-specific gene expression
The Ascomycete yeasts present one of the most promising systems for comparative functional genomics. Fungi have been densely sampled by a number of sequencing projects , covering an enormous range of divergence. Genome sequence analyses of the Saccharomyces yeasts and related species have been used to establish the history of gene duplication [2–6], conservation at binding sites [7, 8], and co-evolution of binding sites with regulators . Thus, a range of evolutionary phenomena can be studied in these species based on their genomic sequence. However, sequence conservation is not always completely predictive of functional conservation. As just one example, we recently reported that only a subset of conserved promoter motifs actually drive periodic gene expression over the cell cycle in two closely related species .
Most of the experimental characterization of gene function has been performed in a small number of model fungal systems, which can provide an anchor for these broad genome sequencing surveys. These species include Saccharomyces cerevisiae, Neurospora crassa, Candida albicans, and Schizosaccharomyces pombe, along with several other emerging models such as Ashbya gossypii. Comparative studies between these species, which by some estimates cover a billion years of divergence, have been informative [11, 12]. Analysis of gene expression changes over growth , the cell cycle , and stress treatments [14, 15] highlighted both similarities and differences in ortholog expression. Unfortunately, the ability to link individual gene expression divergence with the causative molecular factors has been limited because of the vast evolutionary distances involved.
Experimental protocols developed in the model systems are often readily portable to less well-studied sister species, allowing us to choose species well-placed to identify and study functional divergence. Comparisons of gene expression across particular species with interesting characteristics can not only highlight how patterns of gene expression change over evolutionary time, but can also discover genes with particular functions. A comparison among xylose-metabolizing species of yeasts, for example, was able to couple sequence analysis with gene expression profiling to identify important genes via their presence in the genomes of interest and their induction when grown on xylose . Followup studies in S. cerevisiae confirmed these associations.
Due to their close proximity to S. cerevisiae, studies in the sensu stricto yeasts have also been particularly informative. These species cover a range of conservation, have high quality annotated reference genomes , and are becoming even more attractive as the sequences of many strains within each species are forthcoming using new high-throughput sequencing tools (e.g. ). Furthermore, their ability to form interspecific hybrids leverages the resources available in S. cerevisiae and allows tests of gene function and regulation in shared cellular environments [19–23]. Recent work on expression-based full-genome characterization is reported in [24, 25], which used S. cerevisiae microarrays to measure the gene expression consequences of heat shock stress and mating induction on three other yeast species. Their data suggest that expression divergence can occur relatively rapidly and is correlated to gene function, though relatively uncorrelated to sequence conservation . Due to the S. cerevisiae arrays used, they were unable to examine more divergent species. In order to broaden these studies to more divergent yeasts, species-specific arrays must be used, as has been done, for example, for Candida glabrata. Most importantly, due to the limited condition space of just a small number of treatments in these studies, conclusions about evolution of gene function and regulation have been difficult to generalize.
To address these challenges, we previously developed a computational framework [28, 29] to identify a set of experiments that could best characterize gene function in a naive species. Based on available expression date in the S. cerevisiae literature, we identified and carried out a set of 304 experiments over 46 conditions in the sensu stricto species S. bayanus var uvarum. By choosing only the most informative experiments from the vast S. cerevisiae literature, we were able to survey a large phenotypic space at high accuracy with a modest amount of experimentation.
To compare these expression datasets more carefully, we developed a statistical metric, Local Network Similarity (LNS), to assess correlation patterns of orthologs. This metric is general and robust – it can be used for analysis of individual matched datasets without the need to assume identical response time for the two species, or for integrated analysis of diverse compendia of experimental or genetic perturbations. Using the LNS metric to compare our large S. bayanus expression compendium with a collection of published S. cerevisiae expression data, we show that gene expression networks are largely conserved between the species, though much less than within-species comparisons constructed by comparing different conditions. Furthermore, we demonstrated strong and statistically significant evidence for correlation between the divergence of expression and open reading frame sequence, which previous studies using more limited datasets failed to detect (see review ). Despite this general conservation pattern, we observed that a quarter of orthologs exhibit condition-specific differences in expression, and 4% show strong differences in global co-expression patterns. Genes involved in the same functional groups share similar divergence patterns, indicating that pathways or processes may share characteristics. In sum, our wide-ranging survey of expression profiles and generic metric of expression divergence allowed us to identify both global and local aspects of regulatory evolution and relate these to sequence divergence.
S. bayanus var uvarum (referred to as “S. bayanus” from here forward) is a sequenced but relatively unstudied species of budding yeast typically associated with fermentation environments and diverged by approximately 20 million years from S. cerevisiae. We have recently computationally chosen 46 biological treatments for gene expression analysis that would maximally cover biological processes in yeast . Using the resulting dataset of over 300 arrays in S. bayanus along with 2569 arrays collected from S. cerevisiae[30–34], we carried out both global and dataset-specific comparisons of gene expression.
A generic statistical metric to quantify expression conservation between species
To measure the divergence in gene expression between the two species, we developed a metric to compare the expression networks surrounding orthologous genes. Since gene co-expression is strongly linked to functional similarities, differences in expression neighborhoods over a reasonably comprehensive set of perturbations provide a robust proxy of functional relationships. Previously developed methods of analyzing co-expression patterns across species have relied on producing matched datasets, in which comparable timepoints were collected from multiple species exposed to the same conditions . However, exactly matched datasets are difficult to gather, and such an approach relies on the assumption that the precise timing of expression change should be conserved. We sought to develop a method that could detect large-scale patterns of co-expression in addition to those found under specifically matched conditions.
Notably, and in contrast to previous global comparison approaches, this definition does not rely on alignment of individual datasets but defines a gene’s expression pattern in the context of the genome-wide co-expression network. Therefore, the LNS concept could be extended to integrate any number of genome-scale expression datasets—or even other types of genomic data—to quantify conservation between any two species pairs.
Global expression conservation and divergence identified by LNS
In order to test whether this conservation is more or less than what would be expected at this degree of sequence divergence, we would ideally compare a similar gene expression compendium collected from genetically diverse isolates within each species or in species at different genetic distances. Although some data exist exploring differences in gene expression among S. cerevisiae strains [37–39], there are not sufficient datasets available for a full test. However, we were able to test the limit case of LNS as compared between different subsets of the S. cerevisiae literature. These experiments survey a wide diversity of conditions and have been performed in different strain backgrounds, though mostly focused on a small number of related lab strains. The LNS distribution within the subsampled S. cerevisiae datasets was significantly more positive than both the random distribution and the between species comparison (Figure 2A, Additional file 2: Table S2). This result demonstrates the robustness of LNS for identifying patterns of co-expression even across very different environmental and genetic conditions, and suggests that the level of coexpression difference observed between S. cerevisiae and S. bayanus is probably beyond what is present within a species.
Cross-species LNS revealed some dramatic changes in correlation patterns in addition to the overall pattern of conservation. We found 183 genes out of the 4701 orthologous pairs (4%) with an LNS lower than 0, suggesting these genes changed globally in their expression network (Additional file 1: Table S1). The orthologs with low LNS represent several underlying biological causes. One obvious category is genes known to carry deficiencies in laboratory strains. For example, the promoter of CTR3 is disrupted by a Ty2 insertion in many lab strains of S. cerevisiae, but is intact in S. bayanus. Most of the data in the S. cerevisiae compendium is derived from lab strains in which CTR3 expression is driven by the transposon promoter, while the native promoter is present in the S. bayanus strains used in our compendium, leading to very different expression patterns and a low LNS score of −0.07166.
Similarly, we noted a number of targets of alpha factor signaling among the lowest LNS scores, and subsequently examined a list of known transcriptional targets of signaling through Gpa1, the alpha component of the heterotrimeric G protein that activates the MAP kinase cascade in yeast . The median LNS of the GPA1 targets is 0.27, significantly lower than the set of all genes. S288c derived laboratory strains of S. cerevisiae carry a S469I GPA1 mutation that increases signaling through the MAP kinase cascade, but S. bayanus and all other members of the sensu stricto group carry the ancestral allele . Therefore, the low overall LNS scores of the GPA1 target genes may reflect the difference between wild type GPA1 activity in S. bayanus and hyperactive signaling in laboratory strains of S. cerevisiae. Background mutations in S. bayanus can be identified as well, including the alpha factor protease gene , BAR1, which is mutant in the S. bayanus strain used in nearly every experiment in our expression compendium, and scored a low 0.26 LNS.
Mis-annotated genes identified by LNS
Blast e-value to VPS21 is 1e-92. Blast to RAS1 is 11th on list.
Note similar synteny conflict with 674.45 . This gene is second best blast, e-value 1e-160.
Synteny preserved by change, new gene is best blast hit with e-value 3e-146. Note similar problem with 576.11 .
Synteny preserved by change. YOR233W is best blast hit with e-value 0.
Best hit with CLB5 (YPR120C) .
The remaining genes with low LNS are from diverse biological processes and functions. These genes are enriched for orthologs whose S. cerevisiae annotations include genes involved in ascospore cell wall formation (GO:0009272 fungal cell wall-type biogenesis, Bonferroni-corrected p-value 8.97x10^-5 and related terms), and other developmental processes involved in mating and meiotic division, leading to the intriguing hypothesis that gene expression network differences may be related to speciation between these two yeasts. However, genes associated with these biological processes accounted for only 15% of the highly divergent genes. Multiple genes in this set are associated with DNA synthesis and repair, signaling, chromatin organization, metabolism, and transcription, among many other processes, emphasizing that differences are present throughout the cellular network. This list of low LNS genes in known functions should assist the prioritization of experimental work to determine the basis of evolutionary changes in expression. A full quarter of the genes with low LNS scores are of unknown function. Further experiments will be required to determine the mechanisms by which these genes diverge in their expression networks, and the degree to which these differences may contribute to phenotypic differences between the species.
Sequence conservation predicts gene expression divergence
Despite the requirement for experimental followup of individual ortholog pairs, LNS analysis on our large data collection allows us to test several hypotheses regarding the overall role of genome sequence in determining interspecies variation of gene expression. First, we considered the effect of promoter structure by grouping ortholog pairs into TATA-containing in both species, TATA-containing in one of the members, and TATA-less. Though not statistically significant (Additional file 3: Figure S1, r = −0.02, p = 0.08), TATA-containing orthologs have lower LNS, indicating higher interspecies variation, consistent with previous results  using other yeast species and measurements of expression divergence.
Secondly, changes in promoter sequence could potentially cause changes in gene expression, so we extended the evaluation of promoters to examine the relationship between upstream sequence conservation and local network similarity. Upstream sequence conservation is weakly predictive of expression conservation (Additional file 3: Figure S1, r = 0.047, p = 0.00016, n = 4701).
Thirdly, in contrast to the small effects of TATA promoter type or upstream sequence conservation, we found a stronger correlation (Figure 2B, r = −0.235, p < 2.2e-16) between protein sequence similarity and local network similarity. This relationship was observed when using several different methods of calculating sequence similarity (Additional file 3: Figure S2). This correlation between protein sequence and expression similarity is consistent with the majority of results in mammalian systems [45–47], Xenopus and Drosophila[49, 50]. This result contrasts with the conclusions of previous studies in yeast that did not detect any relationship between sequence conservation and expression conservation . Our ability to detect this relationship may result from our use of a large, diverse expression compendium and a more generic measurement of expression divergence. Indeed, if we focus solely on a pair of cell cycle datasets and align them by time points, similar to previous works [24, 25], we do not detect correlation between sequence conservation and expression conservation (not shown). As a result, although using aligned datasets could help identify orthologs responding with a similar pattern to a particular biological perturbation, calculation of expression correlation of orthologs in a single dataset cannot satisfactorily represent the expression conservation level.
Major condition-specific changes in expression between S. bayanus and S. cerevisiae
Paired datasets of S. bayanus and S. cerevisiae for condition-specific LNS calculation
Number of diverged genes (lower than average of randomization, cutoff)
Genes of different functional categories showed differential levels of expression divergence under specific conditions. In general, genes of ribonucleoprotein complex biogenesis and assembly (a term which contains primarily genes involved in ribosome structure and assembly) showed highly conserved expression patterns regardless of the nature of the expression perturbation. Other categories of genes showed more specific patterns of conservation. For example, cell cycle genes were most conserved in the cell cycle dataset and alpha factor treatment. In datasets such as rapamycin or uracil treatment, these genes did not show specific conservation in their coexpression network. This result indicates that conclusions on gene expression conservation can be generic (e.g., ribosomal-related genes) or true under only very specifically defined conditions.
Condition-specific LNS identified mis-annotated genes in S. bayanus that were overlooked by the global LNS analysis. For example, S. bayanus gene 636.13 was matched to CLB2 (YPR119W) in the initial annotation efforts by . However, it has a low cell cycle LNS (−0.03) and this lack of expression conservation is evident by its shift in peak expression during the cell cycle (Additional file 3: Figure S3). 636.13 instead corresponds to CLB5 based on both synteny and Blast alignment . This change in gene expression was observable in the condition-specific LNS analysis of cell cycle synchronized cells because this alternate phase of expression changed the correlations with other periodically regulated genes. In the global dataset that consisted of primarily asynchronous cells, these phase specific correlations were not present.
A similarly large fraction of divergent genes was observed for the paired cell cycle data. We identified 591 (in S. bayanus) and 626 (in S. cerevisiae) cell cycle-regulated genes whose one-to-one orthologs have data in the other species, making approximately the same proportion of genes cell cycle-regulated in both species. In total this represents 1106 unique genes, with 111 pairs of orthologous genes periodic in both S. bayanus and S. cerevisiae (p < 0.002, two tailed t-test). We also assessed the conservation of cell cycle specific gene expression using the 800 genes previously identified by a different dataset  as periodically expressed in synchrony with the cell cycle in S. cerevisiae, and observed that 226 of these orthologs were periodically expressed in synchronous cultures of S. bayanus (p<0.001). A large fraction of the periodically expressed genes were only cell cycle regulated in one of the species (Figure 4C-D): of the 1106 cell cycle regulated genes identified in either species, 258 had a cell cycle-specific LNS lower than 0, indicating significant change in their behavior over the cell cycle.
During the cell cycle, either phase changes or a presence of periodic expression in only one species could contribute to low LNS. We have recently correlated some of these changes in periodic gene expression with differential motif presence and nucleosome occupancy in these genes' promoters . Some of the differences in timing could result from changes in the regulation of the cell cycle, but coherent cycling of protein levels could also be achieved even when gene expression is divergent. For example, if one member of a protein complex was periodically expressed in one species, another member of the same complex could be periodically expressed in the other species. This scenario would result in divergence of expression pattern even though the protein complex was periodically regulated through the availability of the cycling component [45, 63, 64]. Indeed we observe that although most cell cycle protein complexes retain cell cycle-regulated genes in both species, the identity of dynamic members differs between species (Additional file 3: Figure S4). These observations of expression divergence are not limited to the specific examples described above: all datasets have a fraction of divergently expressed genes despite the general trend of expression conservation observed over all data, underlining the importance of a dataset-specific measurement of expression conservation.
In this study, we employed a scalable measurement of expression conservation between species, Local Network Similarity, or LNS, to study the conservation of gene expression networks using large microarray data compendia from S. bayanus and S. cerevisiae. This unsupervised metric allowed us to quantify expression divergence between orthologs for datasets that are different in time-course sampling, or for species that have differential response kinetics to environmental perturbations. This distance metric scales the measurement of expression conservation between −1 and 1, with the null-hypothesis distribution centered at zero. Future research directions include extension of this metric to greater evolutionary distances and diverse data types beyond gene expression. We expect that the normalization inherent in the LNS metric will make it particularly robust for RNA-seq data in the face of the larger noise component that has been observed for genes with low expression levels [65–68].
Using the newly developed LNS metric, we found that patterns of expression-level divergence vary among different biological processes and functional groups. Certain central processes such as ribosome biogenesis are highly conserved on both the sequence and expression level. However, other functional groups involved in seemingly conserved behavior (e.g. cell cycle, diauxic shift) in fact include a significant fraction of orthologs whose expression diverges. This indicates that specific expression patterning of some genes is not critical to an organism’s response to environmental change. On the other hand, the overall conserved expression patterns between the two species might represent genes with key functional roles in responding to specific environmental changes.
One limitation of the current study is that we cannot determine to what degree sequence divergence explains overall expression network divergence between S. bayanus and S. cerevisiae. The selective forces acting on gene expression are as yet unclear and deriving a null model for gene expression evolution is a topic of active research (reviewed in [69–71]). Our observation of a bimodal distribution of LNS scores suggests that some genes could be evolving under selection for conserved expression patterns, while others may evolve more neutrally and thus show greater variance [72–74]. However, those genes that appear to be evolving neutrally could be simply not specifically perturbed in the conditions used for the available expression data. Addressing this challenge experimentally requires further collection of diverse expression datasets in genetically divergent strains within these species, as well as in other species across a range of evolutionary distances. Using such datasets, LNS can be used to delineate further how expression differences change with varying levels of sequence change.
This study focuses on the response and expression profiling of S. bayanus under different perturbations. Complementary studies of regulatory networks, such as interrogation of transcription factor occupancy  and nucleosome positioning [10, 76], will be useful to more fully characterize changes between species. Furthermore, while here we provide a prototype application of our network-based divergence measure (LNS) to gene expression, this approach should be extendable to other types of genome-wide data and can encompass diverse types of quantification of co-expression and/or network similarity. Extending our comparative approach to other groups of related species, such as Candida yeasts, Drosophila species, and mammals, could extend the observations made here. Since our experimental and analytical frameworks are agnostic to species and platform, they should be transferable to other systems. Such studies can be combined with sequence analysis to yield a more complete understanding of the mode of phenotype evolution and its relationship with sequence changes.
Pre-processing of S. cerevisiae and S. bayanusdata
We assembled 303 arrays covering 46 datasets in S. bayanus [GEO: GSE16544] and 2569 arrays covering 125 datasets in S. cerevisiae. To allow reasonable comparison between datasets, we performed the following normalization steps. For each dataset, genes that are represented in less than 50% of the arrays were removed from this dataset, and missing values were estimated using KNNimpute with K = 10, Euclidean distance . Finally, biological replicates are averaged, resulting in datasets with each gene followed by a vector representing its expression values in a series of arrays.
Calculation of pair-wise correlation
For each dataset, these z values were then normalized to Z’~N(0,1). We define the connection weights z(j,k) between any two genes j and k in a species as the average of the normalized z values over all datasets. This forms a pair-wise connection weight matrix for each species.
Calculation of local network similarity (LNS) as a measurement of expression divergence
To assess the conservation level of orthologs upon specific biological perturbation (condition-specific LNS), we manually selected 10 datasets in S. bayanus that have matched time-course data in S. cerevisiae. For each data pair, we define the connection weight as the standard normalized value of formula (2), followed by the calculation of condition-specific LNS according to formula (3) (Figure 3).
Correlation between sequence divergence and LNS
Measures of sequence divergence were used as in , including dN, dN/dS, Ka, and Ka/Ks as previously calculated [7, 79]. A normal distribution was obtained by log2 transforming the data, mean subtracting it, and normalizing by the standard deviation. Correlation was calculated using the Pearson correlation.
Clustering of condition-specific LNS and functional enrichment analysis
We determined the number of clusters in the condition-specific LNS matrix according to , resulting in 48 clusters. The enrichment of genes in each cluster was calculated through the program GOTermFinder .
Identification of cell cycle regulated expression
We acquired S. cerevisiae cell cycle data from . The following steps of identification of cell cycle regulated genes were applied to each species. For each gene in the cell cycle data, the expression values were centered so that the average over the time course equals to 0. A Fourier transformation was applied to the dataset of individual species so as to identify the period of cell cycle . In S. bayanus the top 613 genes mapping to the phase were chosen as cell cycle regulated; and 785 genes in S. cerevisiae were chosen. This corresponds to 601 and 644 genes in S. bayanus and S. cerevisiae having one-to-one orthologs in the other species respectively. Among them, 591 (S. bayanus) and 626 (S. cerevisiae) have expression data in the other species, with 111 pairs overlapping.
Thanks to Greg Lang for GPA1 data in advance of publication; David Botstein, Ian Ehrenreich, Justin Gerke, Greg Lang, and Dannie Durand for helpful comments; and Kevin Byrne for orthology mapping data. OGT is supported by the NIH grant R01 GM071966, NSF grant IIS-0513552 and NSF CAREER award DBI-0546275. All authors were supported by the NIGMS Center of Excellence P50 GM071508, and by donations from the A. V. Davis Foundation and Princeton University for funding of QCB301, Experimental Project Laboratory. MJD is supported in part by grants from the National Center for Research Resources (5P41RR011823-17) and the National Institute of General Medical Sciences (8 P41 GM103533-17) from the National Institutes of Health. MJD is a Rita Allen Foundation Scholar. OGT is supported by the NIH grants R01 GM071966 and R01HG005998, NSF grant IIS-0513552 and NSF CAREER award DBI-0546275. AAC is supported by the Canadian Institutes for Health Research.
- Galagan JE, Henn MR, Ma LJ, Cuomo CA, Birren B: Genomics of the fungal kingdom: insights into eukaryotic biology. Genome Res. 2005, 15 (12): 1620-1631.View ArticlePubMedGoogle Scholar
- Kellis M, Birren BW, Lander ES: Proof and evolutionary analysis of ancient genome duplication in the yeast Saccharomyces cerevisiae. Nature. 2004, 428 (6983): 617-624.View ArticlePubMedGoogle Scholar
- Dietrich FS, Voegeli S, Brachat S, Lerch A, Gates K, Steiner S, Mohr C, Pohlmann R, Luedi P, Choi S, et al: The Ashbya gossypii genome as a tool for mapping the ancient Saccharomyces cerevisiae genome. Science. 2004, 304 (5668): 304-307.View ArticlePubMedGoogle Scholar
- Wapinski I, Pfeffer A, Friedman N, Regev A: Natural history and evolutionary principles of gene duplication in fungi. Nature. 2007, 449 (7158): 54-61.View ArticlePubMedGoogle Scholar
- Scannell DR, Frank AC, Conant GC, Byrne KP, Woolfit M, Wolfe KH: Independent sorting-out of thousands of duplicated gene pairs in two yeast species descended from a whole-genome duplication. Proc Natl Acad Sci USA. 2007, 104 (20): 8397-8402.PubMed CentralView ArticlePubMedGoogle Scholar
- Dujon B, Sherman D, Fischer G, Durrens P, Casaregola S, Lafontaine I, De Montigny J, Marck C, Neuveglise C, Talla E, et al: Genome evolution in yeasts. Nature. 2004, 430 (6995): 35-44.View ArticlePubMedGoogle Scholar
- Kellis M, Patterson N, Endrizzi M, Birren B, Lander ES: Sequencing and comparison of yeast species to identify genes and regulatory elements. Nature. 2003, 423 (6937): 241-254.View ArticlePubMedGoogle Scholar
- Cliften P, Sudarsanam P, Desikan A, Fulton L, Fulton B, Majors J, Waterston R, Cohen BA, Johnston M: Finding functional features in Saccharomyces genomes by phylogenetic footprinting. Science. 2003, 301 (5629): 71-76.View ArticlePubMedGoogle Scholar
- Gasch AP, Moses AM, Chiang DY, Fraser HB, Berardini M, Eisen MB: Conservation and evolution of cis-regulatory systems in ascomycete fungi. PLoS Biol. 2004, 2 (12): e398-PubMed CentralView ArticlePubMedGoogle Scholar
- Guan Y, Yao V, Tsui K, Gebbia M, Dunham MJ, Nislow C, Troyanskaya OG: Nucleosome-coupled expression differences in closely-related species. BMC Genomics. 2011, 12: 466-PubMed CentralView ArticlePubMedGoogle Scholar
- Tsong AE, Miller MG, Raisner RM, Johnson AD: Evolution of a combinatorial transcriptional circuit: a case study in yeasts. Cell. 2003, 115 (4): 389-399.View ArticlePubMedGoogle Scholar
- Tsong AE, Tuch BB, Li H, Johnson AD: Evolution of alternative transcriptional circuits with identical logic. Nature. 2006, 443 (7110): 415-420.View ArticlePubMedGoogle Scholar
- Ihmels J, Bergmann S, Berman J, Barkai N: Comparative gene expression analysis by differential clustering approach: application to the Candida albicans transcription program. PLoS Genet. 2005, 1 (3): e39-PubMed CentralView ArticlePubMedGoogle Scholar
- Gasch AP: Comparative genomics of the environmental stress response in ascomycete fungi. Yeast. 2007, 24 (11): 961-976.View ArticlePubMedGoogle Scholar
- Rustici G, van Bakel H, Lackner DH, Holstege FC, Wijmenga C, Bahler J, Brazma A: Global transcriptional responses of fission and budding yeast to changes in copper and iron levels: a comparative study. Genome Biol. 2007, 8 (5): R73-PubMed CentralView ArticlePubMedGoogle Scholar
- Wohlbach DJ, Kuo A, Sato TK, Potts KM, Salamov AA, Labutti KM, Sun H, Clum A, Pangilinan JL, Lindquist EA, et al: Comparative genomics of xylose-fermenting fungi for enhanced biofuel production. Proc Natl Acad Sci. 2011, 108 (32): 13212-13217.PubMed CentralView ArticlePubMedGoogle Scholar
- Scannell DR, Zill OA, Rokas A, Payen C, Dunham MJ, Eisen MB, Rine J, Johnston M, Hittinger CT: The Awesome Power of Yeast Evolutionary Genetics: New Genome Sequences and Strain Resources for the Saccharomyces sensu stricto Genus. G3 (Bethesda, Md). 2011, 1 (1): 11-25.View ArticleGoogle Scholar
- Liti G, Carter DM, Moses AM, Warringer J, Parts L, James SA, Davey RP, Roberts IN, Burt A, Koufopanou V, et al: Population genomics of domestic and wild yeasts. Nature. 2009, 458 (7236): 337-341.PubMed CentralView ArticlePubMedGoogle Scholar
- Bullard JH, Mostovoy Y, Dudoit S, Brem RB: Polygenic and directional regulatory evolution across pathways in Saccharomyces. Proc Natl Acad Sci. 2010, 107 (11): 5058-5063.PubMed CentralView ArticlePubMedGoogle Scholar
- Horinouchi T, Yoshikawa K, Kawaide R: Genome-wide expression analysis of Saccharomyces pastorianus orthologous genes using oligonucleotide microarrays. J Biosci Bioeng. 2010, 110 (5): 602-607.View ArticlePubMedGoogle Scholar
- Martin OC, DeSevo CG, Guo BZ, Koshland DE, Dunham MJ, Zheng Y: Telomere behavior in a hybrid yeast. Cell Res. 2009, 19 (7): 910-View ArticlePubMedGoogle Scholar
- Tirosh I, Reikhav S, Levy AA, Barkai N: A yeast hybrid provides insight into the evolution of gene expression regulation. Science (New York, NY). 2009, 324 (5927): 659-662.View ArticleGoogle Scholar
- Zill OA, Scannell D, Teytelman L, Rine J: Co-evolution of transcriptional silencing proteins and the DNA elements specifying their assembly. PLoS Biol. 2010, 8 (11): e1000550-PubMed CentralView ArticlePubMedGoogle Scholar
- Tirosh I, Weinberger A, Carmi M, Barkai N: A genetic signature of interspecies variations in gene expression. Nat Genet. 2006, 38 (7): 830-834.View ArticlePubMedGoogle Scholar
- Tirosh I, Weinberger A, Bezalel D, Kaganovich M, Barkai N: On the relation between promoter divergence and gene expression evolution. Mol Syst Biol. 2008, 4: 159-PubMed CentralView ArticlePubMedGoogle Scholar
- Tirosh I, Barkai N: Evolution of gene sequence and gene expression are not correlated in yeast. Trends Genet. 2008, 24 (3): 109-113.View ArticlePubMedGoogle Scholar
- Lelandais G, Tanty V, Geneix C, Etchebest C, Jacq C, Devaux F: Genome adaptation to chemical stress: clues from comparative transcriptomics in Saccharomyces cerevisiae and Candida glabrata. Genome Biol. 2008, 9 (11): R164-PubMed CentralView ArticlePubMedGoogle Scholar
- Guan Y, Dunham M, Caudy A, Troyanskaya O: Systematic planning of genome-scale experiments in poorly studied species. PLoS Comput Biol. 2010, 6 (3): e1000698-PubMed CentralView ArticlePubMedGoogle Scholar
- Flintoft L: Learning to prioritize. Nat Rev Genet. 2010, 11 (5): 315-View ArticlePubMedGoogle Scholar
- Parkinson H, Kapushesky M, Shojatalab M, Abeygunawardena N, Coulson R, Farne A, Holloway E, Kolesnykov N, Lilja P, Lukk M, et al: ArrayExpress--a public database of microarray experiments and gene expression profiles. Nucleic Acids Res. 2007, 35 (Database issue): D747-D750.PubMed CentralView ArticlePubMedGoogle Scholar
- Ball CA, Jin H, Sherlock G, Weng S, Matese JC, Andrada R, Binkley G, Dolinski K, Dwight SS, Harris MA, et al: Saccharomyces Genome Database provides tools to survey gene expression and functional analysis data. Nucleic Acids Res. 2001, 29 (1): 80-81.PubMed CentralView ArticlePubMedGoogle Scholar
- Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30 (1): 207-210.PubMed CentralView ArticlePubMedGoogle Scholar
- Le Crom S, Devaux F, Jacq C, Marc P: yMGV: helping biologists with yeast microarray data mining. Nucleic Acids Res. 2002, 30 (1): 76-79.PubMed CentralView ArticlePubMedGoogle Scholar
- Marinelli RJ, Montgomery K, Liu CL, Shah NH, Prapong W, Nitzberg M, Zachariah ZK, Sherlock GJ, Natkunam Y, West RB, et al: The Stanford Tissue Microarray Database. Nucleic Acids Res. 2008, 36 (Database issue): D871-D877.PubMed CentralPubMedGoogle Scholar
- Dutilh BE, Huynen MA, Snel B: A global definition of expression context is conserved between orthologs, but does not correlate with sequence conservation. BMC Genomics. 2006, 7: 10-PubMed CentralView ArticlePubMedGoogle Scholar
- Tirosh I, Barkai N: Comparative analysis indicates regulatory neofunctionalization of yeast duplicates. Genome Biol. 2007, 8 (4): R50-PubMed CentralView ArticlePubMedGoogle Scholar
- Brem RB, Yvert G, Clinton R, Kruglyak L: Genetic dissection of transcriptional regulation in budding yeast. Science (New York, NY). 2002, 296 (5568): 752-755.View ArticleGoogle Scholar
- Fay JC, McCullough HL, Sniegowski PD, Eisen MB: Population genetic variation in gene expression is associated with phenotypic variation in Saccharomyces cerevisiae. Genome Biol. 2004, 5 (4): R26-PubMed CentralView ArticlePubMedGoogle Scholar
- Smith EN, Kruglyak L: Gene-environment interaction in yeast gene expression. PLoS Biol. 2008, 6 (4): e83-PubMed CentralView ArticlePubMedGoogle Scholar
- Knight SA, Labbe S, Kwon LF, Kosman DJ, Thiele DJ: A widespread transposable element masks expression of a yeast copper transport gene. Genes Dev. 1996, 10 (15): 1917-1929.View ArticlePubMedGoogle Scholar
- Lang GI, Murray AW, Botstein D: The cost of gene expression underlies a fitness trade-off in yeast. Proc Natl Acad Sci USA. 2009, 106 (14): 5755-5760.PubMed CentralView ArticlePubMedGoogle Scholar
- Yvert G, Brem RB, Whittle J, Akey JM, Foss E, Smith EN, Mackelprang R, Kruglyak L: Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat Genet. 2003, 35 (1): 57-64.View ArticlePubMedGoogle Scholar
- Zill OA, Rine J: Interspecies variation reveals a conserved repressor of alpha-specific genes in Saccharomyces yeasts. Genes Dev. 2008, 22 (12): 1704-1716.PubMed CentralView ArticlePubMedGoogle Scholar
- Gordon JL, Byrne KP, Wolfe KH: Additions, losses, and rearrangements on the evolutionary route from a reconstructed ancestor to the modern Saccharomyces cerevisiae genome. PLoS Genet. 2009, 5 (5): e1000485-PubMed CentralView ArticlePubMedGoogle Scholar
- Jordan IK, Marino-Ramirez L, Koonin EV: Evolutionary significance of gene expression divergence. Gene. 2005, 345 (1): 119-126.PubMed CentralView ArticlePubMedGoogle Scholar
- Khaitovich P, Hellmann I, Enard W, Nowick K, Leinweber M, Franz H, Weiss G, Lachmann M, Paabo S: Parallel patterns of evolution in the genomes and transcriptomes of humans and chimpanzees. Science. 2005, 309 (5742): 1850-1854.View ArticlePubMedGoogle Scholar
- Liao BY, Zhang J: Evolutionary conservation of expression profiles between human and mouse orthologous genes. Mol Biol Evol. 2006, 23 (3): 530-540.View ArticlePubMedGoogle Scholar
- Sartor MA, Zorn AM, Schwanekamp JA, Halbleib D, Karyala S, Howell ML, Dean GE, Medvedovic M, Tomlinson CR: A new method to remove hybridization bias for interspecies comparison of global gene expression profiles uncovers an association between mRNA sequence divergence and differential gene expression in Xenopus. Nucleic Acids Res. 2006, 34 (1): 185-200.PubMed CentralView ArticlePubMedGoogle Scholar
- Nuzhdin SV, Wayne ML, Harmon KL, McIntyre LM: Common pattern of evolution of gene expression level and protein sequence in Drosophila. Mol Biol Evol. 2004, 21 (7): 1308-1317.View ArticlePubMedGoogle Scholar
- Lemos B, Bettencourt BR, Meiklejohn CD, Hartl DL: Evolution of proteins and gene expression levels are coupled in Drosophila and are independently associated with mRNA abundance, protein length, and number of protein-protein interactions. Mol Biol Evol. 2005, 22 (5): 1345-1354.View ArticlePubMedGoogle Scholar
- Shalem O, Dahan O, Levo M, Martinez MR, Furman I, Segal E, Pilpel Y: Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation. Mol Syst Biol. 2008, 4: 223-PubMed CentralView ArticlePubMedGoogle Scholar
- Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO: Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell. 2000, 11 (12): 4241-4257.PubMed CentralView ArticlePubMedGoogle Scholar
- Saldanha AJ, Brauer MJ, Botstein D: Nutritional homeostasis in batch and steady-state culture of yeast. Mol Biol Cell. 2004, 15 (9): 4089-4104.PubMed CentralView ArticlePubMedGoogle Scholar
- Urban J, Soulard A, Huber A, Lippman S, Mukhopadhyay D, Deloche O, Wanke V, Anrather D, Ammerer G, Riezman H, et al: Sch9 is a major target of TORC1 in Saccharomyces cerevisiae. Mol Cell. 2007, 26 (5): 663-674.View ArticlePubMedGoogle Scholar
- Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. 1998, 9 (12): 3273-3297.PubMed CentralView ArticlePubMedGoogle Scholar
- Primig M, Williams RM, Winzeler EA, Tevzadze GG, Conway AR, Hwang SY, Davis RW, Esposito RE: The core meiotic transcriptome in budding yeasts. Nat Genet. 2000, 26 (4): 415-423.View ArticlePubMedGoogle Scholar
- Pramila T, Wu W, Miles S, Noble WS, Breeden LL: The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle. Genes Dev. 2006, 20 (16): 2266-2278.PubMed CentralView ArticlePubMedGoogle Scholar
- Brauer MJ, Saldanha AJ, Dolinski K, Botstein D: Homeostatic adjustment and metabolic remodeling in glucose-limited yeast cultures. Mol Biol Cell. 2005, 16 (5): 2503-2517.PubMed CentralView ArticlePubMedGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95 (25): 14863-14868.PubMed CentralView ArticlePubMedGoogle Scholar
- Arroyo-López FN, Salvadó Z, Tronchoni J, Guillamón JM, Barrio E, Querol A: Susceptibility and resistance to ethanol in Saccharomyces strains isolated from wild and fermentative environments. Yeast (Chichester, England). 2010, 27 (12): 1005-1015.View ArticleGoogle Scholar
- Masneuf-Pomarède I, Bely M, Marullo P, Lonvaud-Funel A, Dubourdieu D: Reassessment of phenotypic traits for Saccharomyces bayanus var. uvarum wine yeast strains. Int J Food Microbiol. 2010, 139 (1–2): 79-86.View ArticlePubMedGoogle Scholar
- Salvadó Z, Arroyo-López FN, Guillamón JM, Salazar G, Querol A, Barrio E: Temperature adaptation markedly determines evolution within the genus Saccharomyces. Appl Environ Microbiol. 2011, 77 (7): 2292-2302.PubMed CentralView ArticlePubMedGoogle Scholar
- de Lichtenberg U, Jensen LJ, Brunak S, Bork P: Dynamic complex formation during the yeast cell cycle. Science. 2005, 307 (5710): 724-727.View ArticlePubMedGoogle Scholar
- Jensen LJ, Jensen TS, de Lichtenberg U, Brunak S, Bork P: Co-evolution of transcriptional and post-translational cell-cycle regulation. Nature. 2006, 443 (7111): 594-597.PubMedGoogle Scholar
- Tarazona S, Garcia-Alcalde F, Dopazo J, Ferrer A, Conesa A: Differential expression in RNA-seq: A matter of depth. Genome Res. 2011, 21 (12): 2213-2223.PubMed CentralView ArticlePubMedGoogle Scholar
- McIntyre LM, Lopiano KK, Morse AM, Amin V, Oberg AL, Young LJ, Nuzhdin SV: RNA-seq: technical variability and sampling. BMC Genomics. 2011, 12 (1): 293-PubMed CentralView ArticlePubMedGoogle Scholar
- Liu S, Lin L, Jiang P, Wang D, Xing Y: A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. Nucleic Acids Res. 2011, 39 (2): 578-588.PubMed CentralView ArticlePubMedGoogle Scholar
- Bloom JS, Khan Z, Kruglyak L, Singh M, Caudy AA: Measuring differential gene expression by short read sequencing: quantitative comparison to 2-channel gene expression microarrays. BMC Genomics. 2009, 10: 221-PubMed CentralView ArticlePubMedGoogle Scholar
- Gilad Y, Oshlack A, Rifkin SA: Natural selection on gene expression. Trends Genet. 2006, 22 (8): 456-461.View ArticlePubMedGoogle Scholar
- Zheng W, Gianoulis TA, Karczewski KJ, Zhao H, Snyder M: Regulatory variation within and between species. Annu Rev Genomics Hum Genet. 2011, 12: 327-346.View ArticlePubMedGoogle Scholar
- Romero IG, Ruvinsky I, Gilad Y: Comparative studies of gene expression and the evolution of gene regulation. Nat Rev Genet. 2012, 13 (7): 505-516.PubMed CentralView ArticlePubMedGoogle Scholar
- Whitehead A, Crawford DL: Neutral and adaptive variation in gene expression. Proc Natl Acad Sci USA. 2006, 103 (14): 5425-5430.PubMed CentralView ArticlePubMedGoogle Scholar
- Rifkin SA, Kim J, White KP: Evolution of gene expression in the Drosophila melanogaster subgroup. Nat Genet. 2003, 33 (2): 138-144.View ArticlePubMedGoogle Scholar
- Khaitovich P, Pääbo S, Weiss G: Toward a neutral evolutionary model of gene expression. Genetics. 2005, 170 (2): 929-939.PubMed CentralView ArticlePubMedGoogle Scholar
- Borneman AR, Gianoulis TA, Zhang ZD, Yu H, Rozowsky J, Seringhaus MR, Wang LY, Gerstein M, Snyder M: Divergence of transcription factor binding sites across related yeast species. Science. 2007, 317 (5839): 815-819.View ArticlePubMedGoogle Scholar
- Tsankov AM, Thompson DA, Socha A, Regev A, Rando OJ: The role of nucleosome positioning in the evolution of gene regulation. PLoS Biol. 2010, 8 (7): e1000414-PubMed CentralView ArticlePubMedGoogle Scholar
- Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB: Missing value estimation methods for DNA microarrays. Bioinformatics (Oxford, England). 2001, 17 (6): 520-525.View ArticleGoogle Scholar
- Fisher RA: Frequency distribution of the values of the correlation coefficients in samples from an indefinitely large population. Biometrika. 1915, 10 (4): 507-521.Google Scholar
- Wall DP, Hirsh AE, Fraser HB, Kumm J, Giaever G, Eisen MB, Feldman MW: Functional genomic analysis of the rates of protein evolution. Proc Natl Acad Sci USA. 2005, 102 (15): 5483-5488.PubMed CentralView ArticlePubMedGoogle Scholar
- Mardia KV, Kent T, Bibby JM: Multivariate Analysis. 1997, London, New York, Toronto, Sydney, San Francisco: Academic Press, sixthGoogle Scholar
- Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G: GO:TermFinder–open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes. Bioinformatics. 2004, 20 (18): 3710-3715.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.