Atelier de BioInformatique, Université Paris VI, Paris, France
Unité REG, URA 2171, Institut Pasteur, Paris, France;
HKU-Pasteur Research Centre, Pokfulam, Hong Kong
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Abstract |
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Introduction |
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Bacterial genes transcribed at high levels have a preference for positioning into the leading strand, presumably to minimize collisions between the replication fork and the transcription bubble (McLean, Wolfe, and Devine 1998
). As a consequence, more genes are coded from the leading strand than from the lagging strand in most bacteria (in Gram-positive bacteria, this may go up to 80% of the genes in the leading strand) (Rocha et al. 2000
). Since protein-coding sequences do not contain the same abundance of G and C, this bias accumulates with the replication bias (Sueoka 1999
). Methods designed to deal with this problem revealed that replication strand bias was not due to the asymmetric distribution of genes in the bacterial chromosomes (Rocha, Danchin, and Viari 1999a
; Mackiewicz et al. 1999
).
Explanations for strand biases as a by-product of mechanisms other than replication also include transcription-coupled repair, codon usage bias, and oligonucleotide bias (Francino et al. 1996
; Mrázek and Karlin 1998
; Salzberg et al. 1998
). The former are related to the asymmetrical distribution of highly expressed genes along the two DNA strands. However, the data on codon usage bias (Moszer, Rocha, and Danchin 1999
) and on expression arrays (Tao et al. 1999
) seem to indicate that only a reduced number of genes are highly expressed at exponential growth. Indeed, differences in transcription levels have not been found to constitute a major cause of replication-linked bias (Tillier and Collins 2000a
). Finally, the contribution of signals such as the
sequence to strand bias was found to be very small due to the fraction of the genome they occupy (Tillier and Collins 2000a
).
Among the theories aimed at explaining strand bias based on the asymmetry of the replication bubble, the cytosine deamination theory enjoys the most attention (Frank and Lobry 1999
). The deamination of cytosine in DNA occurs at significant rates in vivo and leads to the formation of uracil, which is excised by the action of uracil-DNA glycosylase (Lindahl 1993
). The rate of cytosine deamination increases by a factor of 140 when the DNA is single-stranded (Beletskii and Bhagwat 1996
). Methylation of cytosine is known to increase the rate of deamination by a factor of 4. In this case, the deamination produces a T, which cannot be corrected by the glycosylase (Coulondre et al. 1978
). Since the leading strand is more exposed in the single-stranded state (in order to serve as template for the synthesis of the lagging strand) (Marians 1992
), and C
T mutations would induce the formation of GC skews, cytosine deamination has been proposed to be at the basis of strand bias (Frank and Lobry 1999
). This hypothesis has the advantage of explaining GC and TA skews (and larger GC skews in G+C-poor genomes) within a known mechanistic model and based on a well-established mutational hot spot.
Recently, a large number of studies have accounted for strand asymmetries (for reviews, see Francino and Ochman 1997
; Frank and Lobry 1999
). However, several important questions still remain unanswered: (1) Are the genomes that present strong strand bias at compositional equilibrium? (2) What are the major substitutions associated with the establishment of the bias? (3) Is there a simple specific function/mutation responsible for the bias? To tackle these questions, we benefited from the existence of the complete genome sequences of several very closely related bacteria, in particular five Chlamydia genomes (Stephens et al. 1998
; Kalman et al. 1999
; Read et al. 2000
; Shirai et al. 2000
) and two species of Bacillus (Kunst et al. 1997
; Takami et al. 2000
).
Asymmetrical changes are usually studied either by phylogenetic reconstruction of homologous sequences or by detection of deviations from the parity of bases in the genome. In the present work, we explored both types of methodologies, taking advantage of the very strong strand bias of Chlamydia and Bacillus genomes and their extensive homology.
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Materials and Methods |
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Statistical Analysis of Skews
Identification of Biased Genomes Through Linear Discriminant Analysis
Simple cumulative GC and TA skews are sensitive to different populations of genes in the two replicating strands. Therefore, we used linear discriminant analysis to identify genomes with significant strand bias (Rocha, Danchin, and Viari 1999a
). We considered a genome to contain a significant strand bias when the maximal accuracy of our method (percentage of true positives in the classification of leading- and lagging-strand genes) was better than that of the best of 10 random genomes of the same size and composition. In practice, this implies that a genome displays significant strand bias if the accuracy of the discrimination between the replicating strands is larger than 60%75% (depending on genome length). Because different bacterial strains within a species are often very similar in sequence, and in order not to bias the results, we analyzed only one representative strain for each bacterial species.
Identifying Origins of Replication by GC Skew
The skews for a given sequence were defined as (Lobry 1996
)
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Genes' GC Skews
We computed GC and TA skews for the gene sequences using equations (1) and (2) . To compare the differences in skews between the genes present in the different strands, we computed GC and
TA skews. These quantities are defined as the difference between the average skews of the genes in the leading strand and the ones in the lagging strand. Considering Nleading and Nlagging, the numbers of genes in the leading and lagging strands, respectively, one obtains
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Analysis of Similarity
Definition of Homologous Genes
Two genes were considered homologous if they coded for proteins similar both in sequence and in size. To identify homologous genes, we performed pairwise comparisons of all proteins of all proteome pairs, filtering potential hits with P < 10-5 in BlastP and a maximal difference of protein lengths of 20%. Subsequently, we aligned the sequences using a variant of the classical dynamic programming algorithm for global alignment, where one counts 0-weight for gaps at both ends of the largest sequence using the BLOSUM62 matrix (Erickson and Sellers 1983
). Finally, we retained pairs of proteins with more than 40% similarity.
Classification of Orthologous Genes
Two homologous genes were considered to be orthologous if they were each other's best matches in the respective genomes. We obtained 687 sets of five orthologous for the Chlamydia set and 2,123 sets of two orthologous for the Bacilli set. Using these sets, we analyzed the conservation of the gene organization between genomes by displaying a scatter-plot of the positions of orthologous genes in the different genomes. We further defined three classes of genes for Chlamydia and for Bacillus: genes present in all genomes in the same replicating strand (SS), genes present in different replicating strands (DS), and genes not present in other genomes according to our stringent orthology criteria (NS). For Chlamydia, this resulted in 49 DS and 638 SS genes, whereas for Bacillus we obtained 372 DS and 1751 SS genes. Naturally, the number of NS genes changed from species to species. For example, we obtained 1,977 NS genes for B. subtilis and 131 NS genes for C. muridarum.
Characterization of Orthologous Genes
The Chlamydia set was extremely interesting due to the small divergence between the five genomes. The similarity of the ribosomal 16S subunit was 93.5% between C. trachomatis and C. pneumoniae, 97.4% among C. trachomatis and C. muridarum genomes, and >99% among C. pneumoniae strains. This is in accordance with the phylogeny of Chlamydia that proposes a first speciation event between C. pneumoniae and the pair C. trachomatis/C. muridarum (Everett, Bush, and Andersen 1999
). The divergence between the two Bacillus 16S subunits (94.4%) was intermediate to that between different species of Chlamydia. A further advantage of the Chlamydia set was the intermediate level of synteny between the elements: within C. pneumoniae and within the pair C. trachomatis/C. muridarum, there was complete conservation of gene order, whereas between these two sets there was less conservation. As a result, the classification of orthologous genes was valid for the genomes of C. pneumoniae on one side and for C. trachomatis and C. muridarum on the other. Also, the vast majority of NS genes were present either in all C. pneumoniae strains or in both C. trachomatis and C. muridarum. Since Chlamydia and Bacillus contain strand biases, one expects a strong and significant signal in these genomes. Finally, we can neglect the effects of differences in G+C content, because they were similar within the two groups of genomes (table 1
).
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Undirected Changes
Since the switch of replicating strand took place before the divergence C. trachomatis/C. muridarum or of C. pneumoniae strains, we used a complementary approach to analyze DS genes, using pairwise alignments to count mismatches. This analysis should account for the adaptation of genes since switching strands, whereas the analysis of DS genes with multiple alignments only accounts for adaptation since speciation. To limit our analysis to well-conserved proteins, we imposed a stricter threshold of similarity (>60% in protein sequence), which resulted in a set of proteins exhibiting an average similarity of 75%. These proteins were more constrained at the amino acid level, which renders pairwise alignments more reliable. For the two bacilli, we lacked a sufficiently close outgroup to determine the direction of mutations, and the same method was used: (1) We aligned the sequences and identified the mismatches. (2) Having arbitrarily chosen one of the two species as the reference species (RS), we cataloged all mismatches in classes XRS : YnonRS (X Y).(3) We separated all DS genes into two categories: the genes that were in the leading strand in the reference species (lagging in the other genome), and those that were in the lagging strand in the reference species (leading in the other genome). (4) For each of these two categories, we computed the difference between XRS : YnonRS and YRS : XnonRS, which indicates the asymmetry in terms of mismatches. The comparison of the asymmetries between the two categories indicates the asymmetry at the basis of the adaptation to the new strand.
It is important to emphasize the differences between this and the preceding analysis. Suppose that a gene is in the leading strand of the reference species. In the analysis of the multiple alignment of SS genes, a mismatch C4,lead : T1,lead indicates a CleadTlead substitution. In the pairwise alignment of DS genes, a mismatch Tlead : Clag cannot be interpreted as a Clead
Tlead change. This mismatch may have been caused by C
T in the leading strand of the reference genome or by T
C in the lagging strand of the other genome (in the previous analysis, the probability of four Tlead
Clead changes was neglected, referring to parsimony). Therefore, C : T mismatches in DS genes indicate either a Clead
Tlead change or an Alead
Glead change. We indicate this ambiguity by C(A)
T(G).
Further Analysis of Alignments
To analyze the importance of multiple substitutions in our data set, we looked for changes in the SS multiple alignments between C. trachomatis and C. muridarum (which corresponded to a larger evolutionary distance than the one between C. pneumoniae strains). The average Ks was below one substitution per synonymous site, and the upper Ks value for the 95% confidence interval was 0.84 (for nonsynonymous sites [Ka] it was 0.07).
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Results |
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Correlation Between Discrimination and Skews
GC skews of the genomes were closely correlated with the accuracy of discrimination between strands. This was less evident for the
TA skews. In fact, the Spearman's rank correlation between maximal discrimination and
GC skew was 0.77 (P < 0.001), but it was only 0.40 between maximal discrimination and
TA skew (P < 0.1). Because the cytosine deamination theory predicts proportionality between the two skews, we built a model for the theory and tested it with the data of biased genomes.
Testing the Cytosine Deamination Theory
The composition of a hypothetical unbiased gene in terms of the four bases is NA,0, NC,0, NG,0, NT,0, taken as the mean of the average composition of the leading- and lagging-strand genes. Suppose that the gene is in the leading strand and will suffer the corresponding asymmetry. The deamination theory predicts that strand bias will induce a CT change with probability z for each C during a period t. Therefore, the composition of the gene remains unchanged for G and A, but not for C and T:
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The 21 bacterial chromosomes with strand bias showed a significant correlation between the two values of zt predicted by the two expressions in equation (6)
using third positions of codons. In fact, after excluding Buchnera sp. (see below), the Spearman's rank correlation between the two terms was 0.83 (P < 0.001), suggesting an important contribution of CT asymmetries to the establishment of the strand bias. However, a systematic underevaluation of
GC skew was also apparent (P < 0.05, Wilcoxon test). In 17 out of 20 chromosomes, the number of changes required to explain the
TA skews was larger than the one required to explain the
GC skew. The systematic underestimation of
GC skews suggests the existence of other sources of bias.
Preliminary regression analysis of the zt data indicated heteroscedasticity, with variance increasing with the values of zt. Standard procedures of regression analysis recommend a logarithmic transformation of the data in such cases (Zar 1996
). The linear regression on the transformed data (excluding Buchnera sp. and H. pylori) resulted in a fit presenting a coefficient of determination of 0.68 (P < 0.001). Although the regression line fits the data well, it does not fit the expected result (fig. 1
). This confirms the results of the Wilcoxon test, suggesting that a model based solely on C
T asymmetries underestimates GC skews.
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Characterization of Mutations in Inverted Genes
Within each of the two Chlamydia monophyletic groups the genomes were collinear. Because strand switch took place before the speciation events, the analysis of 34 DS genes was first performed using only a pair of genomes. We chose C. muridarum and C. pneumoniae AR39 for simplicity (both sequences start at the origin of replication). DS genes suffered a strand switch since speciation of these two species, and this induced opposite replication biases (Tillier and Collins 2000b
). Hence, the substitutions that took place in these genes provide clues for the establishment of the bias. We observed significant asymmetries in A(G)
C(T)C(T)
A(G), in C
G, and especially in C(A)
T(G)T(G)
C(A) (table 3
). In this analysis, the directionality of changes could not be determined (see Materials and Methods).
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Although the strand switch took place before speciation events within C. pneumoniae or between C. trachomatis and C. muridarum, one may suppose that some of the change occurred after speciation. In this case, the analysis of DS genes could be done using the multiple alignments, as for SS genes, with the advantage that the direction of the substitutions can be determined. We built the table of relative substitution frequencies for the DS genes (table 4
), which revealed three significantly different frequencies of substitution: CTT
C (11.4%), A
GG
A (8.4%). and C
GG
C (2.2%) (P < 0.01; Wilcoxon tests). The A
CC
A difference (2.4%) had the same magnitude as that of C
G-G
C, but it was not statistically significant (P > 0.1).
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Genes Evolve at Different Rates Depending on Position and Type
Amounts of Changes in Genes in the Different Replicating Strands
The lagging-strand genes presented more changes among SS genes both for Chlamydia (6.6%, P < 0.001; Wilcoxon test) and for Bacillus (6.4%, P < 0.001).
Different Evolution Rates for NS, SS, and DS Genes
One would expect SS genes to exhibit higher GC skews, but we observed the opposite. Hence, we tested to determine if genes in different strands and belonging to different types (i.e., SS, DS) evolved at similar rates. Both in Chlamydia and in Bacillus, DS genes evolved significantly faster than SS genes (P < 0.001; Wilcoxon test). Between C. trachomatis and C. muridarum, similarity scores increase as follows: NS < DS < SS (P < 0.01; Tukey-Kramer test).
Ka/Ks Ratios
We investigated the Ka/Ks ratios of orthologs of C. trachomatis and C. muridarum and of orthologs of C. muridarum and C. pneumoniae AR39 (note that we removed all pairs for which Ks > 2.0; see Materials and Methods). The median Ka/Ks value for orthologs in C. trachomatis/C. muridarum was 0.07 (0.069 in the lagging strand and 0.073 in the leading strand) and 0.12 for orthologs in C. muridarum/C. pneumoniae AR39 (0.120 in the lagging strand and 0.117 in the leading strand). These differences between leading- and lagging-strand genes are not statistically significant. The comparison of SS and DS genes among bacilli revealed similar values of Ka/Ks for both sets (respectively, 0.12 and 0.14).
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Discussion |
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One might expect that substitution frequencies in SS genes in stable genomes with strong GC skews such as C. muridarum and C. trachomatis might be at equilibrium. Instead, they were found to entail variations in the nucleotide compositions of the two strands. As a result, in C. trachomatis, there is a net increase in GC skew in SS genes (nonsignificant for C. muridarum). This means that, at least in this species, strand bias is still an ongoing process even in SS genes. Hence, it is not surprising that in both Chlamydia and Bacillus, DS genes have adapted to the composition of the new strand. It was previously reported that paralogs in different replicating strands of B. burgdorferi presented signs of adaptation to the respective strands (Lafay et al. 1999
; Rocha, Danchin, and Viari 1999a
), and a recent study of DS genes in Chlamydia demonstrated that they adapt fast to the new strand (Tillier and Collins 2000b
). Our results confirm these previous works and provide some clues for the substitutions at the basis of the adaptation.
Our analysis of SS and DS genes used closely related sequences, but not enough to assure complete avoidance of multiple substitutions. To avoid a large number of multiple substitutions and to produce faithful alignments, we restricted our study to the most conserved proteins. These proteins have evolved less due to functional constraints. We are then observing substitutions for which the weight of selection may be important, which incorporates a bias, particularly due to the codon usage bias and transcription-coupled repair of highly expressed genes. Chlamydia and Bacillus have different codon usage biases (Moszer, Rocha, and Danchin 1999
; Romero, Zavala, and Musto 2000
), but the analysis of the substitutions in the two groups is concordant. If codon usage bias was biasing the results in a very important way, this should not happen. Also, it has been found that biases associated with highly expressed genes do not significantly interfere with replication bias (Mackiewicz et al. 1999
; Tillier and Collins 2000a
), and one may expect this to not significantly change the results. The interference of some biases in the analyses of other biases is a very important question that unfortunately remains unsolved. Analyses of other close genomes with important strand bias may shed further light on this question.
Different Causes for a Simple Bias
The two major asymmetries in DS genes of C. muridarum and C. trachomatis since speciation are AGG
A and C
TT
C. This is consistent with the observations of SS genes that also indicate that these two substitutions are the most asymmetric (although the latter is not statistically significant). It is also consistent with the analysis of DS genes using pairwise alignments in order to capture the substitutions during all the processes of adaptation. Unfortunately, the latter analysis does not allow one distinguish between C
T and A
G. Both asymmetries induce increases in GC and TA skews and lead to proportionality between the increases. C
T asymmetries may be assigned to preferential cytosine deamination in single-stranded DNA, although other hypotheses based on C
T asymmetries may also be compatible with the data. G : C
A : T transitions dominate the spectra of mutations of E. coli; however, most studies have focused on C
T mutations, not on A
G (Frank and Lobry 1999
), and few data are available on A
G mutations.
Two other different substitution frequencies are asymmetric in the adaptation process of DS genes: CGG
C and A
CC
A. Both were also observed in the analyses of pairwise alignments, but in this case A
CC
A was indistinguishable from G
T (although G
T was not significant in the multiple-alignment approaches). Interestingly, C
G and G
C are among the most rare mutations observed in E. coli (Hutchinson 1996
). However, the asymmetry is not necessarily correlated with the absolute number of substitutions. Indeed, also in our data set, C
G and G
C are systematically the most rare substitutions (tables 2 and 4
). Nevertheless, it is puzzling to observe that the pairwise alignments indicate a stronger role for C
GG
C in the adaptation to the strand than do the multiple alignments (7% and 2.2%, respectively). One may consider two explanations for this observation: (1) C
G asymmetries are more important in earlier phases of the adaptation, or (2) multiple substitutions in the pairwise alignments are biasing our results concerning these rare mutations (pairwise alignments represent a longer period of evolution). Although the A
CC
A asymmetry plays no direct role in the establishment of the bias (it just converts TA skew in GC skew), one may suppose that part of the C
G substitutions in fact correspond to C
A
G multiple substitutions. Independent of its origin, the C
G asymmetry results in an increase in GC skew without an increase in TA skew. This may explain the systematic underevaluation of
GC skews by the model based only on C
T asymmetry: the contribution of C
G asymmetry would "correct" this effect (note that A
T is not asymmetric).
We have suggested elsewhere that genome shuffling would disturb strand bias (Rocha, Danchin, and Viari 1999b
). However, one might also suppose mutation rates and repair efficiency to play an important role in the tuning of the process. As for mutation, using the example of cytosine deamination, the methylation of cytosine increases C
T mutations. In bacteria, such methylation is provided by restriction modification systems, which are constantly being acquired and lost by horizontal transfer (Jeltsch and Pingoud 1996
). Therefore, mutation could be a cause of the change in bias with time. As for repair, several reports indicate that the mismatch repair system compensates for C
T substitution asymmetries, even when the cytosine is methylated (Jones, Wagner, and Radman 1987
). A small change in the repair machinery could originate an increased capacity for repair and a consequent reduction in the asymmetry. The efficiency of mismatch repair does change along the evolutionary history of bacteria (Taddei et al. 1997
; Sniegowski et al. 2000
). It has also been shown that replicating strands exhibit different replication accuracy rates (Izuta, Roberts, and Kunkel 1995
; Iwaki et al. 1996
; Fijalkowska et al. 1998
) and that repair might be involved in it (Radman 1998
).
Since the analyses of DS genes were performed between genomes that diverged some time ago, we cannot assume that the substitutions we observe are devoid of selective constraints. Nevertheless, we observed that switched genes adapted fast to the new strand and that this adaptation resulted in a small Ka/Ks ratio. This suggests that amino acid bias is not at the origin of such biases, even if the adaptation process involves changing the amino acid content of the coded proteins (Perrière, Lobry, and Thioulouse 1996
; Lafay et al. 1999
). Effects of amino acid selection on strand bias have also been discarded by other analyses (Mackiewicz et al. 1999
; Tillier and Collins 2000b
). However, selective effects at the DNA level cannot be ruled out through this approach. In particular, one cannot exclude the possibility that strand bias is the result of selection for more efficient replication by the asymmetric replication bubble.
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Conclusions |
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Acknowledgements |
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Footnotes |
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1 Abbreviations: DS, orthologous genes present in different replicating strands (i.e., leading versus lagging); Ka, nonsynonymous substitution rate; Ks, synonymous substitution rate; NS, genes without orthologs in the other species; SS, orthologous genes present in the same replicating strand.
2 Keywords: replication
strand bias
mutation
genome analysis
sequence evolution
3 Address for correspondence and reprints: Eduardo P. C. Rocha, Atelier de BioInformatique, Université Paris VI, 12 Rue Cuvier, 75005 Paris, France. erocha{at}abi.snv.jussieu.fr
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