Computational approaches for the analysis of gene neighbourhoods in prokaryotic genomes

Gene order in prokaryotes is conserved to a much lesser extent than protein sequences. Only some operons, primarily those that encode physically interacting proteins, are conserved in all or most of the bacterial and archaeal genomes. Nevertheless, even the limited conservation of operon organisation that is observed provides valuable evolutionary and functional clues through multiple genome comparisons. With the rapid growth in the number and diversity of sequenced prokaryotic genomes, functional inferences for uncharacterized genes located in the same conserved gene neighborhood with well-studied genes are becoming increasingly important. In this review, we discuss various computational approaches for identification of conserved gene strings and construction of local alignments of gene orders in prokaryotic genomes.     

Inference and analysis of the relative stability of bacterial chromosomes

The stability of genomes is highly variable, both in terms of gene content and gene order. Here I calibrate the loss of gene order conservation (GOC) through time by fitting a simple probabilistic model on pairwise comparisons involving 126 bacterial genomes. The model computes the probability of separation of pairs of contiguous genes per unit of time and fits the data better than previous ones while allowing a mechanistic interpretation for the loss of GOC with time. Although the information on operons is not used in the model, I observe, as expected, that most highly conserved pairs of genes are indeed within operons. However, even the other pairs are much more conserved than expected given the observed experimental rearrangement rates. After 500 Myr, about 50% of the originally contiguous orthologues remain so in the average genome. Hence, the large majority of rearrangements must be deleterious and random genome rearrangements are unlikely to provide for positively selected structural changes. I then use the deviations from the model to define an intrinsic measure of genome stability that allowed the comparison of distantly related genomes and the inference of ancestral states. This shows that clades differ in genome stability, with cyanobacteria being the least stable and gamma-proteobacteria the most stable. Without correction for phylogeny, free-living bacteria are the least stable group of genomes, followed by pathogens, and then endomutualists. However, after correction for phylogenetic inertia (or the removal of cyanobacteria from the analysis), there is no significant association between genome stability and lifestyle or genome size. Hence, although this method has allowed uncovering some of mechanisms leading to rearrangements, we still ignore the forces that differentially shape selection upon genome stability in different species.     

Identifying conserved gene clusters in the presence of homology families.

The study of conserved gene clusters is important for understanding the forces behind genome organization and evolution, as well as the function of individual genes or gene groups. In this paper, we present a new model and algorithm for identifying conserved gene clusters from pairwise genome comparison. This generalizes a recent model called "gene teams." A gene team is a set of genes that appear homologously in two or more species, possibly in a different order yet with the distance of adjacent genes in the team for each chromosome always no more than a certain threshold. We remove the constraint in the original model that each gene must have a unique occurrence in each chromosome and thus allow the analysis on complex prokaryotic or eukaryotic genomes with extensive paralogs. Our algorithm analyzes a pair of chromosomes in O(mn) time and uses O(m+n) space, where m and n are the number of genes in the respective chromosomes. We demonstrate the utility of our methods by studying two bacterial genomes, E. coli K-12 and B. subtilis. Many of the teams identified by our algorithm correlate with documented E. coli operons, while several others match predicted operons, previously suggested by computational techniques. Our implementation and data are publicly available at euler.slu.edu/ approximately goldwasser/homologyteams/.     

Optimal gene partition into operons correlates with gene functional order

Gene arrangement into operons varies between bacterial species. Genes in a given system can be on one operon in some organisms and on several operons in other organisms. Existing theories explain why genes that work together should be on the same operon, since this allows for advantageous lateral gene transfer and accurate stoichiometry. But what causes the frequent separation into multiple operons of co-regulated genes that act together in a pathway? Here we suggest that separation is due to benefits made possible by differential regulation of each operon. We present a simple mathematical model for the optimal distribution of genes into operons based on a balance of the cost of operons and the benefit of regulation that provides 'just-when-needed' temporal order. The analysis predicts that genes are arranged such that genes on the same operon do not skip functional steps in the pathway. This prediction is supported by genomic data from 137 bacterial genomes. Our work suggests that gene arrangement is not only the result of random historical drift, genome re-arrangement and gene transfer, but has elements that are solutions of an evolutionary optimization problem. Thus gene functional order may be inferred by analyzing the operon structure across different genomes.     

Organisation of the S10, spc and alpha ribosomal protein gene clusters in prokaryotic genomes

Although it is well known that there is no long range colinearity in gene order in bacterial genomes, it is thought that there are several regions that are under strong structural constraints during evolution, in which gene order is extremely conserved. One such region is the str locus, containing the S10-spc-alpha operons. These operons contain genes coding for ribosomal proteins and for a number of housekeeping genes. We compared the organisation of these gene clusters in 111 sequenced prokaryotic genomes (99 bacterial and 12 archaeal genomes). We also compared the organisation to the phylogeny based on 16S ribosomal RNA gene sequences and the sequences of the ribosomal proteins L22, L16 and S14. Our data indicate that there is much variation in gene order and content in these gene clusters, both in bacterial as well as in archaeal genomes. Our data indicate that differential gene loss has occurred on multiple occasions during evolution. We also noted several discrepancies between phylogenetic trees based on 16S rRNA gene sequences and sequences of ribosomal proteins L16, L22 and S14, suggesting that horizontal gene transfer did play a significant role in the evolution of the S10-spc-alpha gene clusters.     

Evolution of paralogous genes: Reconstruction of genome rearrangements through comparison of multiple genomes within Staphylococcus aureus

Analysis of evolution of paralogous genes in a genome is central to our understanding of genome evolution. Comparison of closely related bacterial genomes, which has provided clues as to how genome sequences evolve under natural conditions, would help in such an analysis. With species Staphylococcus aureus, whole-genome sequences have been decoded for seven strains. We compared their DNA sequences to detect large genome polymorphisms and to deduce mechanisms of genome rearrangements that have formed each of them. We first compared strains N315 and Mu50, which make one of the most closely related strain pairs, at the single-nucleotide resolution to catalogue all the middle-sized (more than 10 bp) to large genome polymorphisms such as indels and substitutions. These polymorphisms include two paralogous gene sets, one in a tandem paralogue gene cluster for toxins in a genomic island and the other in a ribosomal RNA operon. We also focused on two other tandem paralogue gene clusters and type I restriction-modification (RM) genes on the genomic islands. Then we reconstructed rearrangement events responsible for these polymorphisms, in the paralogous genes and the others, with reference to the other five genomes. For the tandem paralogue gene clusters, we were able to infer sequences for homologous recombination generating the change in the repeat number. These sequences were conserved among the repeated paralogous units likely because of their functional importance. The sequence specificity (S) subunit of type I RM systems showed recombination, likely at the homology of a conserved region, between the two variable regions for sequence specificity. We also noticed novel alleles in the ribosomal RNA operons and suggested a role for illegitimate recombination in their formation. These results revealed importance of recombination involving long conserved sequence in the evolution of paralogous genes in the genome.     

Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome

We present a computational method for operon prediction based on a comparative genomics approach. A group of consecutive genes is considered as a candidate operon if both their gene sequences and functions are conserved across several phylogenetically related genomes. In addition, various supporting data for operons are also collected through the application of public domain computer programs, and used in our prediction method. These include the prediction of conserved gene functions, promoter motifs and terminators. An apparent advantage of our approach over other operon prediction methods is that it does not require many experimental data (such as gene expression data and pathway data) as input. This feature makes it applicable to many newly sequenced genomes that do not have extensive experimental information. In order to validate our prediction, we have tested the method on Escherichia coli K12, in which operon structures have been extensively studied, through a comparative analysis against Haemophilus influenzae Rd and Salmonella typhimurium LT2. Our method successfully predicted most of the 237 known operons. After this initial validation, we then applied the method to a newly sequenced and annotated microbial genome, Synechococcus sp. WH8102, through a comparative genome analysis with two other cyanobacterial genomes, Prochlorococcus marinus sp. MED4 and P.marinus sp. MIT9313. Our results are consistent with previously reported results and statistics on operons in the literature.     

Gene context conservation of a higher order than operons

Operons, co-transcribed and co-regulated contiguous sets of genes, are poorly conserved over short periods of evolutionary time. The gene order, gene content and regulatory mechanisms of operons can be very different, even in closely related species. Here, we present several lines of evidence which suggest that, although an operon and its individual genes and regulatory structures are rearranged when comparing the genomes of different species, this rearrangement is a conservative process. Genomic rearrangements invariably maintain individual genes in very specific functional and regulatory contexts. We call this conserved context an uber-operon.