Genes encoding proteins in a common pathway are often found near each other along bacterial chromosomes. Several explanations have been proposed to account for the evolution of these structures. For instance, natural selection may directly favour gene clusters through a variety of mechanisms, such as increased efficiency of coregulation. An alternative and controversial hypothesis is the selfish operon model, which asserts that clustered arrangements of genes are more easily transferred to other species, thus improving the prospects for survival of the cluster. According to another hypothesis (the persistence model), genes that are in close proximity are less likely to be disrupted by deletions. Here we develop computational models to study the conditions under which gene clusters can evolve and persist. First, we examine the selfish operon model by re-implementing the simulation and running it under a wide range of conditions. Second, we introduce and study a Moran process in which there is natural selection for gene clustering and rearrangement occurs by genome inversion events. Finally, we develop and study a model that includes selection and inversion, which tracks the occurrence and fixation of rearrangements. Surprisingly, gene clusters fail to evolve under a wide range of conditions. Factors that promote the evolution of gene clusters include a low number of genes in the pathway, a high population size, and in the case of the selfish operon model, a high horizontal transfer rate. The computational analysis here has shown that the evolution of gene clusters can occur under both direct and indirect selection as long as certain conditions hold. Under these conditions the selfish operon model is still viable as an explanation for the evolution of gene clusters.
A conspicuous feature of bacterial genomes is the grouping of genes involved in a metabolic pathway into functional units on the chromosome. Early linkage studies of Escherichia coli and Salmonella typhimurium showed that genes in the biosynthetic pathways of tryptophan and histidine occur on a contiguous region of the genome [1], [2]. Furthermore, genes are often found in their biochemical reaction order [3]. Gene clustering has since become recognized as a widespread feature of bacterial genomes. Grouped genes are sometimes transcribed together as an operon, with shared promoter and operator sequences (for example the galactose operon galETK [4], [5]). Regulatory genes have also been found close to the genes they regulate. A classic example is the lacI repressor gene, which resides near but not within the lacZYA operon in Escherichia coli. The extent of gene clustering is variable – a given set of related genes may be clustered in one species but unclustered and/or reordered in another [6], [7]. Interestingly, most clusters do not contain much intergenic DNA, and in some cases genes even overlap [8], [9]. A number of explanations for clustering have been considered over the years. The most controversial and influential hypothesis has been the selfish operon model, which offers a mechanism for the evolution of clustering without needing to invoke the action of natural selection [10], [11]. In this model, gene clusters persist because the proximity of the genes in question facilitates their collective transfer between species. It applies to genes encoding accessory functions rather than essential genes. Another model that does not require direct selection to explain clustering is the persistence model [12]. Unlike the selfish operon model, this model has been proposed to explain the clustering of essential genes – genes that are evolutionarily persistent. The hypothesis here is that by occupying less space, clustered genes are less likely to be disrupted by the deletion or insertion of DNA. In other words, an individual with clustered genes is more “resilient” to the lethal or deleterious effects of mutation. This hypothesis is similar to the idea that genes sharing regulatory sequences by residing in a single operon present a smaller target for deleterious mutation than scattered genes with individual control elements [13]. Hypotheses involving direct selection have also been examined. Here, clustering of genes confers a direct fitness advantage to the organism. For example, a scenario in which selection directly favouring the co-regulation of genes can lead to the evolution of operons has been outlined [14]. Apart from efficiently regulated transcription, a fitness advantage may arise through shorter diffusion times for proteins finding their targets when the genes encoding them are clustered. Thermodynamic models have been developed to apply this idea to enzymes and transcription factors [15], [16]. The efficiency gained from shorter diffusion times is assumed to translate into a reproductive fitness advantage [17]. Another mechanism conferring advantage to gene clustering is gene amplification [18]. In this model, gene dosage is rapidly and reversibly increased by tandem duplication of the genes in question. The closer the genes are, the greater the probability of coamplification. The increased dosage is assumed to contribute to elevated fitness. Other models for the evolution of gene clusters based on metabolic arguments have also been studied [19], [20]. Other hypotheses have been considered but rejected [10], [17, for example]. A hypothesis now called the natal model suggests that clusters arose by gene duplication and divergence such that the newly formed genes participate in a common pathway. However, the lack of sequence homology for most genes within clusters undermines this explanation [10]. Fisher' s theory of the evolution of linkage and recombination has been suggested to apply to bacteria [1], [21]. Under this theory natural selection favours increased linkage among co-adapted genes – genes whose products work well together – because recombination (chromosomal crossover during meiosis) breaks up combinations of alleles with high fitness. However, it has been pointed out that this requires high recombination rates, which are typical for eukaryotes, to work [10]. Although recombination rates are found to be high in some species [22], [23], they are not high enough relative to the cellular generation rate to support an account of clustering based on Fisher' s theory. The debate on the origins and maintenance of gene clusters continues, with recent genomic studies casting doubt on the selfish operon hypothesis. First, the prediction that non-essential genes are clustered while essential genes are not has been tested and rejected [24]. Second, if horizontal gene transfer is an important source of gene clusters, then horizontally transferred sequences should be associated with operons. Genomic data, however, do not support such an association [14]. On the other hand, they do support the possibility that genes and their regulators may have evolved close proximity via horizontal transfer [25]. Third, the selfish operon model is unable to explain the observation that genes in clusters are sometimes arranged in the order of biochemical reactions. A resolution may involve multiple mechanisms, of which horizontal transfer of selfish operons is one [12]. Here, we re-examine the theoretical basis for explaining the origins and maintenance of gene clusters. By studying a number of distinct models, we provide and discuss conditions under which clustering can evolve.
In their influential paper, Lawrence and Roth describe a simulation model that produces gene clusters through a horizontal gene transfer process that is biased towards genes that are physically closer on a chromosome [10]. This is called the selfish operon model. In this model, species in which individuals carry all the genes needed for the function are called “positive” species. Each species is assumed to be monomorphically composed of genomes with a particular arrangement of genes on the chromosome, and fixation is assumed to occur instantaneously. That is, each species is associated with a single arrangement of genes. We are interested in the minimum arc distance along the chromosome that contains all genes, which is equivalently the genome length minus the longest interval between pairs of neighbouring genes. The simulation is initialised with 100 species, with each species given a random set of gene positions. Lawrence and Roth kept the number of species between 10 and 900 [10]. We have implemented this by switching off the horizontal transfer process when the number of species reaches 900 and re-instating it when the size goes under 900. We ran our simulations for 50,000 time steps. Horizontal transfer leads to a species that lacks the function (a “negative” species) acquiring the function along with the arrangement of gene positions of the donor genome. The probability of horizontal transfer decreases with distance. Although its form is not given in [10], we will assume it is exponential with a decay parameter. That is, (1) The exponential distribution is a natural choice for the size distribution of transferred DNA among bacteria, and has been empirically tested for homologous recombination [28], [29]. Some support for a skewed distribution of gene transfer fragment lengths is found in Ochman and Jones 2000 [30]. At each time step, each species or individual can undergo loss of the function with probability. Following Lawrence and Roth, we set the loss probability to 0. 001 per genome per time step and the maximum probability of horizontal transfer, occurring when the genes are located in the same minute of the chromosome, to 0. 01 per genome per time step [10]. We set by default, under which a 50 kb fragment is 6 times more likely to transfer than one of 500 kb. Because the probability of rearrangement is likely to be very low in nature [31], we set per genome per time step by default. Lawrence and Roth 1996 used a much higher value of and we investigate the effect of lowering this parameter from this high value. We studied the effect of varying and by varying parameters one at a time as well as using latin hypercube sampling [32], [33] to explore the parameter space. Under this methodology, each parameter is divided into equiprobable regions in the area of interest, and parameter sets are constructed by selecting values randomly from the resulting grid without replacement. A uniform distribution was used for each parameter. The algorithm we used for the dynamic is as follows. One problem we have noticed with this model is that given a rearrangement event, the genes in question are always affected. A more natural assumption would be that the genes in question are affected with probability, which is the proportion of the genome occupied by the genes assuming that genes are 1 kilobase in length. Thus, we have also run the simulations using this corrected translocation process, replacing step 2 (a) in the above algorithm with This correction effectively lowers the rearrangement probability by a few orders of magnitude. We have also implemented a version of the model in which rearrangement occurs by inversion instead of translocation. Here, we replace step 2 (a) in the algorithm with If (and) or (and) or () then the inversion is viable. (Recall is the tolerance to imbalance between origin and terminus.) For each gene whose location is between and, move it to its new location given by. We construct a model in which the population evolves according to a Moran process [34], [35] combined with a process of genome inversion. Here, we track a population of bacterial cells. As with the selfish operon model, we consider a pathway involving 3 or more genes. A population is initialised with all bacteria carrying the same genome with genes placed randomly uniformly on the chromosome. The population size is. Let represent the relative fitness of cells with the genes at minimum arc distance. Genomes with the genes closer together have a reproductive or survival advantage over those with the genes further apart. We use the function to describe this relationship. Because this relative fitness function is analogous to, we use the same symbol () to describe the decay in fitness with respect to distance. An alternative function was also used to ascertain the effect of using a steep sigmoidal relationship. Selection for clustering here can be due to any of the mechanisms discussed in the Introduction. The algorithm is as follows. Following the classical definition of the Moran process, a single generation is time steps. This process is very slow with high population sizes, particularly when the rearrangement probability is low. The computational demands of running these simulations precluded the possibility of systematically analysing sensitivity to parameters. This motivated us to develop a further model, which tracks the mutation and fixation process without following details at the population level. This model is described in the next subsection. Here, the population is monomorphic (except during periods of substitution of new arrangements) and so only a single genome arrangement is tracked. Again, the genes in the pathway in question can occupy positions, represents the population size and is the rearrangement probability. The assumption that the population is monomorphic implies that must not be too large. In each generation the probability of a rearrangement occurring in at least one individual is which can be approximated with since is small. The time until the next rearrangement event is distributed geometrically with parameter. We use inversion rather than translocation as the source of rearrangements. As above we specify selection through an exponential decay in fitness as a function of the minimum arc distance, so that the relative fitness of a new genome with distance is, and the selective coefficient is. A new arrangement fixes in a population with probability (2) and the time it takes to reach fixation is (3) These quantities have been derived from diffusion theory in population genetics (for details see [36]). We use in place of to apply the theory to haploids, where is the effective population size of a diploid population. The algorithm for the rearrangement substitution model is therefore as follows. was set at 50,000 generations. We investigated this model by varying one parameter at a time as well as using latin hypercube sampling to explore the parameter space.
When three genes are placed randomly around a chromosome with a uniform distribution, the average minimum arc distance between them is around 1900 kb. When the rearrangement probability is or, the selfish operon model [10] produces an initial wave of gene clustering down to around 600–800kb ( (Figure 1A), also reflected in the rise of the proportion of genomes that are clustered under a threshold (Figure 1B). The maximum population size of 900 is reached quickly (Figure 1C) and the dynamics of clustering undergo a change as a new population dynamic regime sets in. When the rearrangement probability is high, clusters break up until the average minimum arc distance settles on high values (Figure 1A). In these cases, the selfish operon model fails to maintain tight clustering in the long term. In particular, gene clusters do not evolve under the parameter values used by Lawrence and Roth [10]. To determine if there are conditions under which the selfish operon model does produce clustering, we re-examined this model by exploring its parameter space. Figure 2 reveals the effect of varying the parameters in this model on the average minimum arc distance. It shows that under the original model clustering is only produced when the rearrangement probability is low, the number of genes is small, and the maximum transfer probability is sufficiently high. Under the corrected translocation process, the effective rearrangement probability is lowered by a factor and the probability itself has no apparent effect on clustering. The decay in transfer probability (see Equation 1) must take intermediate values of around for clustering to evolve. If is too low, selection is too weak to promote clustering while if it is too high, the probability of transfer is depressed for most minimum arc distances, preventing selection from acting effectively. Very similar results are observed when translocation is replaced by inversion, as shown by varying one parameter at a time as well as by latin hypercube sampling analysis (Figure 3). The major difference is that a high probability of inversion does not prevent the evolution of clusters to the same extent as observed in the uncorrected translocation process of Figure 2A. We further explored the evolution of clustering using the Moran model with selection for gene clusters. By holding the population size constant this model also allows us to disentagle the effects of population dynamics from those of rearrangement and selection. Figure 4 shows simulation runs of the process for progressively lower values of: . It was not computationally feasible to run the simulation under even lower, and more realistic, values. The general pattern emerging from these sample trajectories is that the minimum arc distance is reduced through a series of selective sweeps. The time taken until the appearance of a rearranged genome that reaches fixation is long and depends on the rearrangement probability and the population size. The reduction of minimum arc distance is a slow process made even slower by lowering. Using a steep sigmoidal function for selection instead of exponential decay (Figure 4D) gave qualitatively similar results. The rearrangement substitution model, which “compresses” time by tracking fixation events, is amenable to sensitivity analysis. Figure 5 demonstrates that a low rearrangement probability of is able to produce clustering in 50,000 generations. Even lower probabilities lead to weak or no clustering because successful rearrangements that reduce the distance between genes are too rare. Increasing the population size improves the efficiency of selection and leads to clustering. Similarly, increasing the decay in fitness improves clustering. Gene clusters are also more readily formed for pathways with a smaller number of genes. Similar results are produced when the parameter space is explored using latin hypercube sampling (Figure 6). Minimum arc distance decreases with and and increases with. Distance also decreases with, though this effect is subtle. For (panel B) and (panel C) the correlation with distance is statistically detectable using a non-parametric method (Kendall' s tau), with -values of and 0. 0148 respectively. The corresponding -values for (panel A) and (panel D) were both less than. Note that each factor on its own does not explain much of the variation in distance.
Genes involved in a common pathway or function are frequently found near each other on bacterial chromosomes. A number of hypotheses have been previously presented to explain this observation. A particularly influential theory is the selfish operon model, which posits that horizontal transfer could promote gene clustering by favouring transfer of arrangements of genes that are close together. Subsequent theoretical development and analysis of genomic data have contributed to the debate about the plausibility of this model. Here, by re-examining the evolutionary dynamics of gene clusters, we provide and discuss conditions under which gene clusters can evolve. We find that first, some form of bias for clustering is required for clusters to evolve. This bias can be in the form of bias in horizontal transfer towards genes that are close together, or direct natural selection for gene proximity. Our computational work does not present a theoretical obstacle to the selfish operon model as a possible explanation for the evolution of gene clusters.
computational biology/evolutionary modeling
microbiology/microbial evolution and genomics
Conditions for the Evolution of Gene Clusters in Bacterial Genomes