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MICROBIOLOGY
Global divergence of microbial genome sequences mediated by propagating fronts
Department of Physics and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1110 West Green Street, Urbana, IL 61801-3080
Communicated by Carl R. Woese, University of Illinois at Urbana-Champaign, Urbana, IL, April 4, 2005 (received for review January 26, 2005)
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Abstract |
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 Top Abstract ModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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evolution | horizontal gene transfer | microbial speciation | recombination
Gene transfer results when foreign DNA is taken up from the environment (transformation), delivered by a virus (transduction), or acquired through a direct cell to cell exchange (conjugation), and then permanently incorporated in the recipient genome by homologous or illegitimate recombination. Homologous recombination, mediated by dedicated cellular machinery, plays a vital error correction role in genome replication (9) but also allows a foreign DNA fragment to replace a sufficiently similar portion of the recipient genome. The probability of successful replacement in homologous recombination is proportional to the exponential of the number of sequence mismatches (10), the mechanism being organism-specific (11-13). Illegitimate recombination can be mediated by bacteriophage integrases, selfish genetic elements, or occur by chance DNA breakage and repair, and allows the acquisition of entirely novel traits from evolutionary distant organisms. Illegitimate genetic transfer, also known as horizontal gene transfer (HGT), can be inferred from the genome data through its atypical sequence composition (6) and the phylogenetic incongruences it causes (14). Although the extent of HGT is under heated debate (2), it is clear that it is much less frequent than homologous recombination. Relative rates of homologous recombination and point mutations in natural populations have been estimated by sequence diversity studies using multilocus sequence typing data in recently formed bacterial strains (15, 16). The probability that a gene changes as a result of homologous recombination can be many times higher than that for point mutations. Another manifestation of the pervasiveness of homologous recombination is that the evolution of strains within many named species cannot be represented by a phylogenetic tree (17-19). Although the importance of genetic transfer, and homologous recombination in particular, is firmly established (20), there are only a few sharp predictions about the resulting modes of microbial evolution. Relevant to our work is the observation of Lawrence (4) that HGT islands locally inhibit recombination. He concludes that global genetic isolation can be achieved through the gradual accumulation of hundreds of HGTs.
The purpose of this paper is to explore the emergent properties of the collective evolution of closely related bacterial genomes. We model the interplay of homologous recombination and point mutation in bacterial populations and show that elementary genome changes such as HGT, genome rearrangements, and insertions or deletions can trigger diversification fronts that in evolutionary short time propagate along the bacterial genomes and eventually lead to global sequence divergence of subpopulations. The diversification fronts can occur even in the absence of natural selection and demonstrate that fast neutral evolution can have nontrivial long-term evolutionary consequences. The robustness of this mechanism is sensitive to some of the details of homologous recombination, and suggests a way to classify the spectrum of evolutionary modes in bacteria based on specific details of their homologous recombination mechanisms. We establish a methodology for analyzing closely related genomes and give evidence for a large-scale step-like variation of homologous recombination rates in the Bacillus cereus group, which might be a signature of a diversification front. Finally, we discuss the biological implications of the propagation of diversification fronts, as a mechanism for speciation, a force favoring the formation of sharp genetic isolation boundaries, and a dynamical barrier for HGT and genome rearrangements.
The details of homologous recombination are by now reasonably well understood (10, 11). There are at least two common obstacles to successful integration of a DNA fragment. First, the end of the fragment must find a short region (
20 bp) of sequence identity with the target genome to initiate the process. Second, the cell's mismatch repair system can abort the recombination process if it encounters mismatches between the fragment and the portion of the genome being replaced. Both of these obstacles lead to an exponential decrease of recombination with sequence divergence. There are also potentially important variations in the mechanism. Whereas sequence identity at only one end is required in Escherichia coli, very high sequence similarity at both ends is needed in Bacillus (11, 12) and mismatch repair seems less important. In Streptococcus, the effect of mismatch repair is intermediate in strength (13) but the overall dependence of sexual isolation on sequence divergence is very close to that in Bacillus. In addition, the underlying basis for distinguishing between donor and recipient DNA can differ. Do these differences in the details translate into qualitatively different evolutionary behavior? If so, then the details of the homologous recombination mechanism could be an important criterion for classifying bacteria. The computational studies described here clarify which details are the relevant determinants of the long-term evolutionary dynamics.
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Models |
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TopAbstractModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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d), where is a coefficient expressing the strength of the mismatch repair system and d is the pointwise sequence difference, i.e., d counts the number of mismatches between the fragment and the genome sequence it is about to replace. We will also consider model III, where rule 4 is absent. The genome strings can be thought of as representatives of different strains possessing at least partial ecological distinctiveness, so that random genetic drift is much stronger within strains than between strains. With this interpretation, we do not include random genetic drift but it can be straightforwardly added.
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Propagation of Diversification Fronts |
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TopAbstractModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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These considerations suggest that the uniform phase is metastable: even when recombination is strong enough to maintain a state of near uniformity, it will not succeed in bringing together sufficiently diverged sequences. The diverged phase, on the other hand, is stable. If there is a boundary between a stable and a metastable phase, the generic expectation is that the stable phase will grow at the expense of the metastable one, as shown in Fig. 1. This will happen because homologous recombination is inhibited not only in the diverged phase but also in a finite region flanking it within the uniform phase. Mutations will accumulate in the flanking region, and as a result the diverged phase will grow. We will refer to the boundary between the uniform and diverged phases as a diversification front. Therefore, the system has the potential to sustain the propagation of diversification fronts. Such diversification fronts can be nucleated by processes that create regions of sequence difference between genomes in the population, such as HGT, genome rearrangements, and deletions or insertions and have important biological consequences for the evolution and diversification of microbes, as will be discussed later.
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Simulations |
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TopAbstractModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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 | [1] |
where Axi denotes the letter at position x of genome i. The order parameter 
measures the average difference in the population between the sequences at genome position x normalized so that = 1 when the genomes are uncorrelated. This corresponds to the diverged phase of the system. In the opposite limit, = 0, the genomes in the system are highly correlated, giving rise to the uniform phase of the system.
For each model, we studied the time evolution of the order parameter for different values of m/r and . Typical values used for the other parameters are F = 500, M = 10, L = 10,000, N = 20, and n = 2. For each separate run, we measured as a function of position within the genome and time. By varying , we control the strength of the mismatch repair mechanism, and hence the success rate of recombination. The most important trend probed by our simulations is the behavior of the order parameter as a function of the ratio 
, the relative strength of point mutations versus recombination.
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Results for Models I and III |
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TopAbstractModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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, the equilibrium value of the order parameter varies gradually with µ = m/r, as shown in Fig. 2. The uniform and random strip initial conditions always relax to the same final state. The random strip simply dissolves, and no front propagation is observed. This situation arises when recombination is allowed almost regardless of the degree of sequence divergence.
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, the uniform and diverged phases become distinct: for small values of µ, the order parameter is 0, and the system is genetically uniform. However, for large values of µ, the order parameter is close to unity, indicating that the system is genetically diverged. This transition appears to be sharp, as shown in Fig. 3. Furthermore, there is interesting dynamical behavior as a function of µ. For µ > µu, the uniform phase becomes unstable and the sequences diverge everywhere simultaneously. For µ < µs, the uniform phase is stable, and a finite region of diverged phase shrinks as a function of time, i.e., the uniform phase invades the diverged one. For µs < µ < µu, diversification proceeds through nucleation and growth of the diverged phase; in this parameter range, front propagation occurs.
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Results for Model II |
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TopAbstractModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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= 0. Moreover, the width 
of the interval µs < µ< µu, where front propagation occurs, is very wide. Whereas we always observed w 
2 for models I and III, for model II we could not even observe the point µu, and w > 100. This results in the phase diagram qualitatively represented on Fig. 4b. The front speed can be as high as several times the fragment size per average point mutation time near the transition to the diverged phase, and is a rapidly decreasing function of the recombination rate. To summarize, there is a qualitative difference between the situation with no sequence identity requirement (model III) or sequence identity requirement at only one end (model I) and model II with sequence identity requirement at both ends. The difference is manifested in the phase diagram and the width of the front propagation region.
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Microbe Classification |
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TopAbstractModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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The existence of class I and class II indicates that the details of homologous recombination are important beyond the fact that the probability of recombination exponentially decreases with sequence divergence. Therefore, it is necessary to elucidate further the differences between homologous recombination mechanisms in different bacteria and work out their consequences for front propagation. For example, if mismatch repair is nick-directed and not methyl-directed (13), then more mismatches will be detected near the ends of the recombining fragments. This, in turn, will make front propagation more robust, because a greater fraction of the average homogenizing capability of recombination will be inhibited by a phase boundary. Also, if nonhomologous DNA loops formed during the recombination process are not corrected efficiently, then small deletions, insertions, slippage, and inversions would not trigger diversification fronts. Because micro rearrangements are presumably frequent, the efficiency of loop repair will be an important factor in determining the rate of nucleation of fronts. Finally, it is important to know whether and how the length of the incorporated fragments is dynamically dependent on the differences between the donor and recipient.
To seek evidence for the front propagation mechanism, we now compare available completely sequenced genomes of closely related microbes. The most direct evidence for front propagation from genome data alone would be an extended step-like pattern in the sequence divergence of closely related well aligned genomes, with the diverged region centered around a region of HGT, deletion, or genome rearrangement. The front profile reflects the different times after genetic isolation of different parts of the chromosome. Under conventional uniform molecular clock assumptions, it will be approximately linear, with a slope determined by the distance the front travels during the time it takes the sequences to fully diverge once recombination is inhibited. Slowly changing components of the sequence divergence, such as nonsynonymous substitutions, lead to more extended profiles.
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Analysis of Genome Data |
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TopAbstractModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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We obtained the complete genome sequences from the NCBI database, together with the positions and orientations of the known or predicted protein coding regions, tRNAs, and rRNAs. We globally aligned all pairs using the nucmer script of the MUMMER package (21) (nucmer -b 50 -g 300 -c 65 -mum), obtaining a list of well aligned regions for each pair. Three B. cereus strains (ATCC 10987, 14579, and ZK; refs. 22 and 23), three Bacillus anthracis strains (Ames, Ames Ancestor, and Sterne), and Bacillus thuringiensis serovar konkukian str. 97-27 genomes were close, highly colinear, and analyzed further. The three anthracis strains were practically identical, and only Ames was used in the analysis.
For each pair, we mapped the well aligned regions on one of the genomes, and constructed a series of coarse-grained profiles by sliding a window of width W along the genome while excluding nonaligned regions (resulting from insertions and deletions) from the averaging, as depicted graphically in Fig. 5. The profiles have gaps where the window covers less than a threshold fraction f of fW unambiguously aligned nucleotides. We used W in the range of 40,000 to 120,000 and f between 0.5 and 0.8. We looked at the coarse-grained profiles for the DNA point differences, as well as intergene, intragene, third codon, first and second codon, synonymous, and nonsynonymous (as defined in ref. 24) differences.
B. cereus ATCC 10987 exhibits a distinct step-like pattern of sequence difference when compared to B. cereus ZK (Fig. 6), B. anthracis Ames, and B. thuringiensis serovar konkukian str. 97-27. The pattern is also present in each of the other difference components: synonymous, nonsynonymous, gene, and intergene. What is the explanation for this pattern? Does it involve homologous recombination or not? Is it a result of a front propagation during the separation of B. cereus ATCC 10987 with the common ancestor of B. cereus ZK, B. anthracis Ames, and B. thuringiensis serovar konkukian str. 97-27?
To answer these questions, we first examined the variation of the nucleotide composition along the genome. Based on the GC and AT skews, the replication terminus is located at 2.6 Mb, away from the position of the difference profile step. The GC content varies smoothly along the genome and does not exhibit a step pattern. It has a minimum near the replication terminus.
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15% more divergent than protein coding regions and the gene density varies only in the 75-90% range. Therefore, the small differences in the proportions of sites with different mutation rates would have to have been somehow amplified if varying coding density were the underlying cause of the pattern. The nonaligned regions have a higher intergene fraction than aligned ones, suggesting a possible mechanism by which the density of protein coding regions can indirectly affect sequence divergence by a preferential accumulation of interstrain alignment gaps in intergene regions and a corresponding reduction of recombination rates.
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We gathered DLMEM statistics for different well aligned regions. The ratio of the standard deviation and mean is significantly above 1, as shown in Fig. 7a. Moreover, there is a positive correlation between this ratio and the length of the uninterrupted well aligned regions, a trend that agrees with the notion that nonaligned parts inhibit recombination within the adjacent aligned regions.
We then looked for evidence of different rates of homologous recombination along the chromosome by studying the changes in the DLMEM statistics in a sliding window. There is again a step-like pattern for the ratio of the standard deviation and the mean, as shown in Fig. 7b.
Deviation of the ratio of the standard deviation and the mean of a DLMEM is a sign of clustering of the differences along the chromosome. Are there reasons for clustering that do not involve homologous recombination? If different genes have very different evolution rates, then this can lead to apparent clustering. For example, different gene expression levels can lead to different synonymous mutation rates and an apparent clustering of differences within the weakly expressed genes. To control for this, we compare the DLMEM for neutral mutations with a null model with matched neutral divergence of each protein coding region separately. The pattern is present in the real data but almost completely disappears in the control. The residue is due to correlations of the divergences of adjacent proteins which are expected in the presence of homologous recombination. Because, presumably, there is no reason apart for recombination for clustering of synonymous substitutions within each gene separately, this test not only rules out genes with different evolutionary rates as an explanation but also gives confidence that the standard deviation over mean deviations from unity are predominantly due to homologous recombination.
Further evidence supporting the homologous recombination interpretation of the ratio of the standard deviation and the mean of DLMEM comes from contrasting the above observations with the results of the comparison between the completely sequenced Buchnera aphidicola strains APS, BP, and SG. Because these are intracellular parasites lacking the RecA gene, we expect no homologous recombination. Indeed, we find that there is no statistically significant deviation from unity of the standard deviation over mean and a highly uniform difference profile.
In summary, the above data indicate that there are large-scale step-like variations of the rates of homologous recombination along the analyzed microbial genomes, apparently consistent with the hypothesis that diversification proceeded by front propagation.
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Discussion |
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TopAbstractModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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A bacterium can acquire a new skill by means of HGT. This can lead to the extinction of those bacteria that do not possess the beneficial (under appropriate selection pressure) HGT fragment. Alternatively, HGT can allow the invasion or foundation of a new biochemical niche, while being disadvantageous in the former one, or lead to specialization within the old niche. [Indeed, ecological distinctiveness without spatial isolation is not unusual for microbes. Even in the simplest of environments (monoculture lab experiments) coexisting strains emerge spontaneously (26). However, the creation of coexisting genotypes by HGT cannot properly be termed speciation, because the genotypes are not genetically isolated with respect to homologous recombination, except for a small region surrounding the HGT.]
The front propagation mechanism makes local isolation unstable, because the HGT event nucleates a diversification front, leading eventually to a global isolation of the carriers of the HGT event from the rest of the population. Therefore, ecological distinctiveness accompanied by local isolation is enough to generate speciation, even when homologous recombination is not reduced by the ecological distinctiveness. Note that this outcome is different from the one proposed by Lawrence (4), who suggested that global isolation is only achieved through the accumulation of hundreds of HGTs. Our work has demonstrated that even a single HGT or genome rearrangement can lead to global sequence divergence.
It is difficult to apply the biological species concept to groups of strains that are isolated at some loci and not at others (27). Because of diversification front propagation, a community of bacteria in which pairs of bacteria are genetically isolated at some loci, but not others, is unstable and tends to partition itself into groups which are globally isolated from each other with respect to homologous recombination. This is because genetically isolated regions will suppress recombination and trigger fronts into neighboring nonisolated regions. This instability will be even stronger if the different genomes are not colinear or do not have the same set of genes. Therefore, well defined genetic isolation boundaries emerge spontaneously through the front propagation mechanism even if there is no functional barrier to gene transfer.
What happens when a HGT or a rearrangement brings some advantage, but without enabling the recipient to adopt an entirely distinct ecological role? Achieving complete ecological distinctiveness might be a gradual process. In this case, the new genotype will be successful initially but not necessarily in the long run because it will be competing with other beneficial mutations at other loci that emerge throughout the population. Beneficial mutations trigger selective sweeps that can be either global, purging the diversity throughout some ecological niche or, because of homologous recombination, local, purging the diversity only around the locus of the beneficial mutation. In a population in which relative sequence uniformity is maintained by homologous recombination, local selective sweeps will be the norm. However, diversification fronts nucleated in the carriers of a HGT or a rearrangement will propagate by accumulation of neutral mutations and potentially lead to global genetic isolation of the carriers long before they have a chance to achieve a full ecological distinctiveness.
New strains are easily formed by readily absorbing foreign genetic material, rearranging the genomes, etc. However, they are typically short-lived entities, because they are excluded from the communal evolution following a diversification front propagation. Front propagation implies that the evolutionary rate of HGT accumulation is less than the rate suggested by looking at strains; this can be, in principle, tested against the data. This mechanism can also explain why gene order is highly conserved in some bacterial groups: there exists a dynamical barrier to the survival of rearranged genomes.
These considerations also have implications for the applicability of molecular phylogenetics and the ongoing debate about the nature of the impact of HGT on the tree of life. Front propagation limits the impact of HGT, reinforcing in a complementary way Woese's concept of a complexity barrier to HGT (1). Our argument is complementary because it does not rely on the nature of the interactions between the genes: there is a barrier to HGT arising from the population dynamics alone.
Our work leaves open a number of interesting issues related to the effect of highly conserved regions on front propagation. A large immutable region can present an impassable obstacle to front propagation. Candidates for such obstacles are rRNA operons, tRNA genes, and overlapping genes. Such regions lack the flexibility arising from the degeneracy of the genetic code. HGT islands inserted near front obstacles will lead to the diversification of a smaller fraction of the recipient genome, and have a greater chance to avoid extinction. Is there a correlation between evolutionary persistent HGTs and RNA gene positions? If a genome region is already diversified there is no penalty for the incorporation of another useful HGT island. Is there clustering of HGT islands? How is front propagation modified for clonal bacteria (19)? Finally, is front propagation beneficial? If front propagation obstacles are allowed to evolve or at least reposition themselves, what configuration of obstacles would result?
On the basis of computer simulations, we have suggested that the interplay between homologous recombination and point mutations can lead to propagating fronts, in whose wake a population of microbes becomes genetically diverse in evolutionary short time. Thus, even in the absence of selection pressure and ecological barriers to genetic exchange, gene-exchange boundaries can emerge as a statistical consequence of the detailed dynamics of recombination. We have presented a preliminary analysis of available genome data for the B. cereus group that is consistent with the presence of front propagation. These findings prompt speculations about the implications for the evolution and the classification of microbes.
Our model can be extended in a number of directions, including explicit accounting for the role of space, the existence of a nontrivial network of gene exchange connectivity, and the effects of sharing of beneficial mutations.
A promising approach to looking for diversification fronts is metagenomics data. Such data can give us a consensus genome for an ensemble of closely related organisms, inhabiting the same environment, and an estimate for the sequence diversity along the consensus genome (28). This diversity can be directly related to the order parameter (x). A step-like variation in (x) might be an indication of a diversification front.
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Acknowledgements |
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Footnotes |
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Abbreviations: HGT, horizontal gene transfer; DLMEM, distribution of lengths of maximal exact matches.
* To whom correspondence should be addressed. E-mail: nigel{at}uiuc.edu.
© 2005 by The National Academy of Sciences of the USA
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TopAbstractModelsPropagation of Diversification...SimulationsResults for Models I...Results for Model IIMicrobe ClassificationAnalysis of Genome DataDiscussionReferences |
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