1 Department of Biological Sciences, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
2 Microbiology Department, Pacific Northwest National Laboratory, 902 Battelle Boulevard, PO Box 999, Mail Stop P7-50, Richland, WA 99352, USA
Correspondence
Weiwen Zhang
Weiwen.Zhang{at}pnl.gov
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ABSTRACT |
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The complete lists of all predicted highly expressed genes and their calculated CAI values for each Streptomyces genome, and the conserved PHX genes, are provided in Supplementary Tables 1, 2 and 3 with the online version of this paper.
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INTRODUCTION |
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Most of what is currently known about gene expression in Streptomyces has been obtained through investigations of individual genes in specific pathways (Hopwood, 1999). The availability of the complete genomic sequences from these organisms has made it possible for researchers to develop approaches that focus on the systemic properties of regulatory and metabolic networks, and to investigate gene expression and regulation in the context of a global cellular network. In several recent studies whole-genome DNA microarray or proteomics technologies have been applied to the study of expression patterns of genes and proteins associated with primary and secondary metabolism in S. coelicolor (Huang et al., 2001
; Hesketh et al., 2002
). Similar approaches have also been used to identify regulatory networks governing the induction of heat-shock genes in S. coelicolor (Bucca et al., 2003
).
Codon preferences vary considerably within and between organisms (Grantham et al., 1981; Sharp et al., 1988
; Karlin et al., 1998
). Across genomes, the G+C composition resulting from mutational bias has been hypothesized to determine the major trends in codon usage of high- or low-G+C organisms (Knight et al., 2001
). Within a genome, codon bias tends to be much stronger in highly expressed genes than in genes expressed at lower levels (Sharp & Li, 1986
, 1987
; Lafay et al., 2000
; dos Reis et al., 2003
). Selection for translational efficiency and accuracy has been suggested to be responsible for the stronger codon bias in the highly expressed genes of Saccharomyces cerevisiae and Escherichia coli (Ikemura, 1981
, 1982
). To dissect the patterns and causality of codon usage, many indices have been proposed to measure the degree and direction of codon bias (Sharp & Li, 1987
; Wright, 1990
). Among these, the codon adaptation index (CAI) was proposed as a measure of codon usage in a gene relative to that in a reference set of genes (Sharp & Li, 1987
). This index has been shown to correlate better with mRNA expression levels than other codon usage indices, such as the frequency of optimal codons (Ikemura, 1985
) or the effective number of codons (Wright, 1990
; Friberg et al., 2004
). Therefore, CAI has been widely applied to the prediction of highly expressed genes in various organisms (Pan et al., 1998
; Coghlan & Wolfe, 2000
; dos Reis et al., 2003
; Martin-Galiano et al., 2004
). However, it has been suggested that for the bacteria with high G+C content, codon usage may not correlate well with gene expression level because there is generally little heterogeneity in codon usage among genes in these species, and all genes feature a similar, extremely biased codon usage (Ohama et al., 1990
; Lafay et al., 2000
). With the complete genomes of two Streptomyces species available, we decided to revisit the topic with the objectives to: (1) analyse the heterogeneity of codon bias among genes in Streptomyces genomes; (2) determine the feasibility of applying the CAI to predict highly expressed genes in two Streptomyces species as an alternative to experimental approaches; and (3) comparatively analyse the genes from each genome that are predicted to be highly expressed and interpret what this implies regarding cellular metabolisms in Streptomyces.
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METHODS |
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Analysis.
Three indices of codon usage bias were calculated for all genes in two genomes. The first one was the G+C content at synonymously variable third positions of sense codons except Met and Trp codons (GC3s), which can potentially vary from 0 to 1·0. The second was the effective number of codons' (Nc) used in a gene (Wright, 1990). This is a measure of general non-uniformity of codon usage within groups of codon synonyms, which can vary from 20 (in a gene with extreme bias, where only one codon is used for each amino acid) to 61 (random codon usage). These two indices were calculated with CodonW (http://codonw.sourceforge.net//). The third was the codon adaptation index (CAI), which was calculated using CAI Calculator 2 (http://www.evolvingcode.net/codon/CalculateCAIs.php). The CAI value varies from 0 to 1·0 (Sharp & Li, 1987
), with higher CAI values indicating that the gene of interest has a codon usage pattern more similar to that in the reference genes. Statistical analysis including F-test of variance, t-test and Pearson correlation was performed as previously by Perriere & Thioulouse (2002)
.
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RESULTS AND DISCUSSION |
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A two-step approach was taken to determine if codon heterogeneity existed among genes in Streptomyces. First, the G+C content at the third positions of codons (GC3s) and the effective number of codons (Nc) for all genes in both genomes were calculated. The results from Nc versus GC3s plots, which have been suggested to be an effective means to investigate the codon usage variations among genes in the same genome (Wright, 1990), showed that the Nc values of the genes range from 22 to 60 for both Streptomyces genomes (Fig. 1
), suggesting that considerable heterogeneity is present in these GC-rich genomes. The genes encoding ribosomal proteins, which are expected to be expressed at high levels during rapid cell growth in Streptomyces (Blanco et al., 1994
), were identified and are highlighted in the Nc plots. The clustering of most of the ribosomal protein genes of the two Streptomyces genomes at low ends is similar to that in the GC3s versus Nc plot of the E. coli genome (data not shown). The significantly stronger codon bias in the ribosomal protein genes (S. coelicolor: t=3·09, one-tailed P=0·0015; S. avermitilis: t=5·94, one-tailed P=1·328x107) suggests that the codon usage in these highly expressed genes is a result of selection for translational efficiency as well.
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The top 20 PHX genes of S. coelicolor and S. avermitilis included five ribosomal protein genes and seven others: genes encoding translation elongation factor Ts; the 60 kDa chaperonin involved in protein fate; ketol-acid reductoisomerase (ilvC), involved in the isoleucine and valine biosynthetic pathway (Cordes et al., 1992); aconitate hydratase, involved in the tricarboxylic acid cycle (Fisher & Magasanik, 1984
), enolase and triosephosphate isomerase, involved in the glycolytic pathway (Leyva-Vazquez & Setlow, 1994
); and serine hydroxymethyltransferase, which catalyses the reversible interconversion of serine and tetrahydrofolate to glycine and methylenetetrahydrofolate required for cytoplasmic one-carbon metabolism (Schirch et al., 1985
). Among the top 20 PHX genes in S. coelicolor, 13 were also detected on 2D gels (Hesketh et al., 2002
). In comparison with the list of top 20 PHX genes identified from the genomes of E. coli, Vibrio cholerae, Haemophilus influenzae and Bacillus subtilis (Karlin et al., 2001
), the Streptomyces genomes shared the 60 kDa chaperonin, ketol-acid reductoisomerase and enolase, as well as five ribosomal proteins.
Comparison of the S. avermitilis and S. coelicolor genomes has previously revealed that a 6·5 Mb, highly conserved internal core region contains most of the housekeeping genes (1·07·5 Mb for S. coelicolor and 2·08·5 Mb for S. avermitilis), while most of the laterally acquired genes are present in both arms outside the core region (Bentley et al., 2002; Ikeda et al., 2003
). We have previously found that the majority of Streptomyces PPM-family protein phosphatases, whose origin involved lateral acquisition, are located outside the core conserved region (Shi & Zhang, 2004
). Analysis of the distribution of PHX genes in the linear chromosomes of these two Streptomyces species showed a preferred location in the conserved cores, while only few PHX genes were found located in the arm regions (Fig. 4
).
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Protein synthesis.
Twenty-two ribosomal protein genes were among the PHX genes in both Streptomyces genomes, with most encoding genes for the small ribosomal subunits (Fig. 1, Supplementary Table 3). Five genes encoding translation factors, including elongation factor P, G, Tu, Ts and peptide chain release factor I, were also PHX in both genomes. Among genes functioning in translation, amino-acyl tRNA synthetases are generally not identified as PHX genes in many bacteria (Mrazek et al., 2001
; Karlin et al., 2001
). However, our results showed that in Streptomyces a total of 16 genes encoding various amino-acyl tRNA synthases with different substrate specificities are predicted as PHX genes.
Amino acid biosynthesis.
Eighteen genes involved in the biosynthesis of amino acids were predicted as PHX genes, including genes in the biosynthetic pathways of tryptophan, threonine, serine and branched chain amino acids (Table 1).
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Central intermediary metabolism.
Several PHX genes in this functional class are also involved in secondary metabolism in Streptomyces (Table 2). The roles of S-adenosylmethionine synthetase (SAM-s), an intracellular factor in both cellular differentiation and antibiotic production in Streptomyces species, were previously established by showing that the overexpression of the SAM-s gene in Streptomyces lividans TK23 inhibited sporulation and aerial mycelium formation, but enhanced the production of actinorhodin (Okamoto et al., 2003
). In addition, the SAM-s gene was found to be highly expressed in the actinorhodin-overproducing S. coelicolor mutant KO-179 (Kim et al., 2003
).
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Energy metabolism.
The genes involved in energy metabolism can be divided into four groups: glycolysis, pyruvate metabolism, the pentose phosphate pathway and the TCA cycle (Table 3). The genes in glycolysis and pyruvate metabolism are predominantly PHX in most fast-growing bacteria (Karlin et al., 2001
). This holds true for S. coelicolor and S. avermitilis as well, where almost all genes involved in glycolysis and pyruvate metabolism were PHX genes in both genomes. These included the genes for 6-phosphofructokinase, fructose-1,6-bisphosphate aldolase, triosephosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, phosphoglycerate kinase, phosphoglycerate mutase, enolase, pyruvate kinase, pyruvate dehydrogenase and dihydrolipoamide dehydrogenase. Most genes in the pentose phosphate pathway are not PHX in other fast-growing bacteria (Karlin et al., 2001
). In agreement with this observation, only transketolase and transaldolase, the genes encoding the last two steps of the pentose phosphate pathway, were PHX in the Streptomyces species. In spite of the vital role the TCA cycle plays in energy metabolism, previous studies have shown that the TCA genes are generally not PHX in B. subtilis, H. influenzae and Synechocystis, and only the genes before the succinyl-CoA synthetase step are PHX in E. coli (Karlin et al., 2001
; Mrazek et al., 2001
). However, in S. coelicolor and S. avermitilis all of the genes in the TCA cycle were predicted to be PHX genes, including citrate synthase, aconitate hydratase, isocitrate dehydrogenase, 2-oxoglutarate dehydrogenase, succinyl-CoA synthetase, succinate dehydrogenase, fumarate hydratase and malate dehydrogenase (in order of their action in the TCA cycle) (Table 3
). One possible reason for the high presence of PHX genes in the TCA cycle genes of Streptomyces may be that members of this genus depend on the TCA cycle not only for ATP production, but also as a major source of carbon chain precursors to various primary and secondary metabolites, such as methylmalonyl-CoA (Karlin et al., 2001
; Zhang & Reynolds, 2001
). Several genes encoding cytochrome, electron-transfer flavoprotein genes and ATP synthase are also among the PHX in the two Streptomyces species.
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PHX genes with unknown function.
Sixty-one orthologous PHX gene pairs from the two Streptomyces genomes were annotated as hypothetical proteins or enzymes without known substrate specificity; however, one-third of them were detected in previous 2D gel experiments with S. coelicolor, suggesting that they may play important roles in cellular metabolism (Hesketh et al., 2002) (Supplementary Table 3).
PHX genes exclusive to each Streptomyces genome
A total of 356 genes were identified as PHX exclusively in S. coelicolor, and 362 genes as PHX exclusively in S. avermitilis. They were divided into two categories. The first category included genes with orthologues present in both genomes, but they were PHX genes in only one genome, not the other: 252 PHX genes in S. coelicolor and 225 PHX genes in S. avermitilis belong to this category. The second category included PHX genes without any orthologue in another genome: 103 PHX genes in S. coelicolor and 137 PHX genes in S. avermitilis belong to this category. In this paper, we will focus only on the analysis of the second category. The functionally known PHX genes exclusive to each genome are given in Table 4 and Table 5
. Several interesting observations are discussed below.
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Only one polyketide synthase gene from each Streptomyces genome was predicted as PHX (SCO6431 and SAV3649), which is consistent with the fact most secondary metabolites are synthesized after growth slows down. In addition, SAV3159, encoding a non-ribosomal peptide synthetase, was identified as PHX in S. avermitilis.
While different Streptomyces strains may share the conserved genes for certain key housekeeping functions, the regulatory systems that control the expression of these conserved genes may be different (Chater & Horinouchi, 2003). It is thus worth noting that several genes involved in regulatory function were highly expressed exclusively in each of the two Streptomyces genomes. Two regulatory genes were identified as PHX in S. coelicolor. SCO4008 encodes a putative TetR family regulatory protein and SCO5338 a putative regulatory protein with 83 % identity to regulatory protein Pra in Streptomyces ambofaciens, which has been suggested as an activator of replication, integration and excision of the site-specific integrative element pSAM2 (Sezonov et al., 1998
). Four regulatory genes were identified as PHX in S. avermitilis. SAV7267 encodes a protein with 60 % identity to a MalR repressor protein regulating regulated maltose metabolism of S. coelicolor. SAV3638 is a syrP-like gene encoding a regulatory protein that participates in a phosphorylation cascade controlling syringomycin production and virulence in Pseudomonas syringae pv. syringae (Zhang et al., 1997
); the gene is located in a non-ribosomal peptide synthetase gene cluster (nrps2 gene cluster). In addition, SAV1195 encodes a putative RNA polymerase ECF-subfamily sigma factor, and SAV1199 encodes an AraC-type transcriptional regulator.
Conclusions
Although the concept of predicting gene expression from codon usage bias was proposed decades ago (Sharp & Li, 1986, 1987
), only recently have these methods been successfully applied to the identification of highly expressed genes in various bacteria and eukaryotic organisms (Karlin & Mrazek, 2000
; Karlin et al., 2001
; Mrazek et al., 2001
; Martin-Galiano et al., 2004
). One reason for the earlier lack of success with this approach to predict gene expression levels is that it was originally proposed before whole-genome sequences were available and was based on analyses of small sets of genes. Because of this limited dataset, it has been difficult to evaluate the correlation of codon usage and expression potential in a global context. However, with recent progress in whole-genome analysis technologies, such as DNA microarray and proteomics, it is now possible to compare predictions based on codon usage data with experimental data on protein and mRNA expression in a more quantitative way (dos Reis et al., 2003
; Jansen et al., 2003
; Friberg et al., 2004
). In this study, various approaches to estimating gene expression levels based on codon usage were applied to two industrially important Streptomyces strains with the objectives of testing this alternative method of studying whole-genome gene expression. Our results demonstrated significant heterogeneity in codon usage between genes in the two Streptomyces genomes. Furthermore, the predicted gene expression level using the quantitative measure CAI was found to correlate well with the highly abundant proteins detected by a 2D gel proteomics approach (Hesketh et al., 2002
). In addition, since the expression levels measured by current DNA microarray and proteomics technologies represent the accumulated results of expression and degradation, the results from this computational approach could be used as reference data for calibrating and better interpreting experimental data. For example, observation of low levels of expression from proteomic or microarray data for a gene with a high PHX index might suggest the possible involvement of degradation in regulating expression levels of that gene. Although most of the PHX genes predicted are housekeeping genes, the study also identified a number of functionally unknown genes as PHX based on their codon profile (Supplementary Tables 1, 2 and 3). Further investigation of these genes by an integrated computational and experimental approach will enhance our knowledge of the metabolism of Streptomyces species.
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ACKNOWLEDGEMENTS |
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Received 14 December 2004;
revised 21 February 2005;
accepted 1 April 2005.
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