The life-cycle of operons

Operons are a major feature of all prokaryotic genomes, but how and why operon structures vary is not well understood. To elucidate the life-cycle of operons, we compared gene order between Escherichia coli K12 and its relatives and identified the recently formed and destroyed operons in E. coli. This allowed us to determine how operons form, how they become closely spaced, and how they die. Our findings suggest that operon evolution may be driven by selection on gene expression patterns. First, both operon creation and operon destruction lead to large changes in gene expression patterns. For example, the removal of lysA and ruvA from ancestral operons that contained essential genes allowed their expression to respond to lysine levels and DNA damage, respectively. Second, some operons have undergone accelerated evolution, with multiple new genes being added during a brief period. Third, although genes within operons are usually closely spaced because of a neutral bias toward deletion and because of selection against large overlaps, genes in highly expressed operons tend to be widely spaced because of regulatory fine-tuning by intervening sequences. Although operon evolution may be adaptive, it need not be optimal: new operons often comprise functionally unrelated genes that were already in proximity before the operon formed.     

Algorithm for large-scale clustering across multiple genomes

Identifying genomic regions that descended from a common ancestor helps us study the gene function and genome evolution. In distantly related genomes, clusters of homologous gene pairs are evidently used in function prediction, operon detection, etc. Currently, there are many kinds of computational methods that have been proposed defining gene clusters to identify gene families and operons. However, most of those algorithms are only available on a data set of small size. We developed an efficient gene clustering algorithm that can be applied on hundreds of genomes at the same time. This approach allows for large-scale study of evolutionary relationships of gene clusters and study of operon formation and destruction. An analysis of proposed algorithms shows that more biological insight can be obtained by analyzing gene clusters across hundreds of genomes, which can help us understand operon occurrences, gene orientations and gene rearrangements.     

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.     

Divergence and redundancy of 16S rRNA sequences in genomes with multiple rrn operons

The level of sequence heterogeneity among rrn operons within genomes determines the accuracy of diversity estimation by 16S rRNA-based methods. Furthermore, the occurrence of widespread horizontal gene transfer (HGT) between distantly related rrn operons casts doubt on reconstructions of phylogenetic relationships. For this study, patterns of distribution of rrn copy numbers, interoperonic divergence, and redundancy of 16S rRNA sequences were evaluated. Bacterial genomes display up to 15 operons and operon numbers up to 7 are commonly found, but ~40% of the organisms analyzed have either one or two operons. Among the Archaea, a single operon appears to dominate and the highest number of operons is five. About 40% of sequences among 380 operons in 76 bacterial genomes with multiple operons were identical to at least one other 16S rRNA sequence in the same genome, and in 38% of the genomes all 16S rRNAs were invariant. For Archaea, the number of identical operons was only 25%, but only five genomes with 21 operons are currently available. These considerations suggest an upper bound of roughly threefold overestimation of bacterial diversity resulting from cloning and sequencing of 16S rRNA genes from the environment; however, the inclusion of genomes with a single rrn operon may lower this correction factor to ~2.5. Divergence among operons appears to be small overall for both Bacteria and Archaea, with the vast majority of 16S rRNA sequences showing <1% nucleotide differences. Only five genomes with operons with a higher level of nucleotide divergence were detected, and Thermoanaerobacter tengcongensis exhibited the highest level of divergence (11.6%) noted to date. Overall, four of the five extreme cases of operon differences occurred among thermophilic bacteria, suggesting a much higher incidence of HGT in these bacteria than in other groups.     

Distinct Types of rRNA Operons Exist in the Genome of the Actinomycete Thermomonospora chromogena and Evidence for Horizontal Transfer of an Entire rRNA Operon

We describe here the presence of two distinct types of rRNA operons in the genome of a thermophilic actinomycete Thermomonospora chromogena. The genome of T. chromogena contains six rRNA operons (rrn), of which four complete and two incomplete ones were cloned and sequenced. Comparative analysis revealed that the operon rrnB exhibits high levels of sequence variations to the other five nearly identical ones throughout the entire length of the operon. The coding sequences for the 16S and 23S rRNA genes differ by approximately 6 and 10%, respectively, between the two types of operons. Normal functionality of rrnB is concluded on the basis of the nonrandom distribution of nucleotide substitutions, the presence of compensating nucleotide covariations, the preservation of secondary and tertiary rRNA structures, and the detection of correctly processed rRNAs in the cell. Comparative sequence analysis also revealed a close evolutionary relationship between rrnB operon of T. chromogena and rrnA operon of another thermophilic actinomycete Thermobispora bispora. We propose that T. chromogena acquired rrnB operon from T. bispora or a related organism via horizontal gene transfer.     

Nebulon: a system for the inference of functional relationships of gene products from the rearrangement of predicted operons

Since operons are unstable across Prokaryotes, it has been suggested that perhaps they re-combine in a conservative manner. Thus, genes belonging to a given operon in one genome might re-associate in other genomes revealing functional relationships among gene products. We developed a system to build networks of functional relationships of gene products based on their organization into operons in any available genome. The operon predictions are based on inter-genic distances. Our system can use different kinds of thresholds to accept a functional relationship, either related to the prediction of operons, or to the number of non-redundant genomes that support the associations. We also work by shells, meaning that we decide on the number of linking iterations to allow for the complementation of related gene sets. The method shows high reliability benchmarked against knowledge-bases of functional interactions. We also illustrate the use of Nebulon in finding new members of regulons, and of other functional groups of genes. Operon rearrangements produce thousands of high-quality new interactions per prokaryotic genome, and thousands of confirmations per genome to other predictions, making it another important tool for the inference of functional interactions from genomic context.     

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.     

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.     

A global analysis of adaptive evolution of operons in cyanobacteria

Operons are an important feature of prokaryotic genomes. Evolution of operons is hypothesized to be adaptive and has contributed significantly towards coordinated optimization of functions. Two conflicting theories, based on (i) in situ formation to achieve co-regulation and (ii) horizontal gene transfer of functionally linked gene clusters, are generally considered to explain why and how operons have evolved. Furthermore, effects of operon evolution on genomic traits such as intergenic spacing, operon size and co-regulation are relatively less explored. Based on the conservation level in a set of diverse prokaryotes, we categorize the operonic gene pair associations and in turn the operons as ancient and recently formed. This allowed us to perform a detailed analysis of operonic structure in cyanobacteria, a morphologically and physiologically diverse group of photoautotrophs. Clustering based on operon conservation showed significant similarity with the 16S rRNA-based phylogeny, which groups the cyanobacterial strains into three clades. Clade C, dominated by strains that are believed to have undergone genome reduction, shows a larger fraction of operonic genes that are tightly packed in larger sized operons. Ancient operons are in general larger, more tightly packed, better optimized for co-regulation and part of key cellular processes. A sub-clade within Clade B, which includes Synechocystis sp. PCC 6803, shows a reverse trend in intergenic spacing. Our results suggest that while in situ formation and vertical descent may be a dominant mechanism of operon evolution in cyanobacteria, optimization of intergenic spacing and co-regulation are part of an ongoing process in the life-cycle of operons.     

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.     

A hybrid gene team model and its application to genome analysis

It is well-known that functionally related genes occur in a physically clustered form, especially operons in bacteria. By leveraging on this fact, there has recently been an interesting problem formulation known as gene team model, which searches for a set of genes that co-occur in a pair of closely related genomes. However, many gene teams, even experimentally verified operons, frequently scatter within other genomes. Thus, the gene team model should be refined to reflect this observation. In this paper, we generalized the gene team model, that looks for gene clusters in a physically clustered form, to multiple genome cases with relaxed constraints. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with and/or without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. We also compared the result from our hybrid model to those from the traditional gene team model. We also show that predicted gene teams can be used for various genome analysis: operon prediction, phylogenetic analysis of organisms, contextual sequence analysis and genome annotation. Our program is fast enough to provide a service on the web at http://platcom.informatics.indiana.edu/platcom/. Users can select any combination of 97 genomes to predict gene teams.     

Prokaryotic genomes: the emerging paradigm of genome-based microbiology

Comparative analysis of the complete sequences of seven bacterial and three archaeal genomes leads to the first generalizations of emerging genome-based microbiology. Protein sequences are, generally, highly conserved, with ∼70% of the gene products in bacteria and archaea containing ancient conserved regions. In contrast, there is little conservation of genome organization, except for a few essential operons. The most striking conclusions derived by comparison of multiple genomes from phylogenetically distant species are that the number of universally conserved gene families is very small and that multiple events of horizontal gene transfer and genome fusion are major forces in evolution.     

Incorporation of a horizontally transferred gene into an operon during cnidarian evolution

Genome sequencing has revealed examples of horizontally transferred genes, but we still know little about how such genes are incorporated into their host genomes. We have previously reported the identification of a gene (flp) that appears to have entered the Hydra genome through horizontal transfer. Here we provide additional evidence in support of our original hypothesis that the transfer was from a unicellular organism, and we show that the transfer occurred in an ancestor of two medusozoan cnidarian species. In addition we show that the gene is part of a bicistronic operon in the Hydra genome. These findings identify a new animal phylum in which trans-spliced leader addition has led to the formation of operons, and define the requirements for evolution of an operon in Hydra. The identification of operons in Hydra also provides a tool that can be exploited in the construction of transgenic Hydra strains.     

Operon prediction using both genome-specific and general genomic information

We have carried out a systematic analysis of the contribution of a set of selected features that include three new features to the accuracy of operon prediction. Our analyses have led to a number of new insights about operon prediction, including that (i) different features have different levels of discerning power when used on adjacent gene pairs with different ranges of intergenic distance, (ii) certain features are universally useful for operon prediction while others are more genome-specific and (iii) the prediction reliability of operons is dependent on intergenic distances. Based on these new insights, our newly developed operon-prediction program achieves more accurate operon prediction than the previous ones, and it uses features that are most readily available from genomic sequences. Our prediction results indicate that our (non-linear) decision tree-based classifier can predict operons in a prokaryotic genome very accurately when a substantial number of operons in the genome are already known. For example, the prediction accuracy of our program can reach 90.2 and 93.7% on Bacillus subtilis and Escherichia coli genomes, respectively. When no such information is available, our (linear) logistic function-based classifier can reach the prediction accuracy at 84.6 and 83.3% for E.coli and B.subtilis, respectively.     

基于迭代自学习的操纵子结构预测

原核生物操纵子结构的准确注释对基因功能和基因调控网络的研究具有重要意义,通过生物信息学方法计算预测是当前基因组操纵子结构注释的最主要来源.当前的预测算法大都需要实验确认的操纵子作为训练集,但实验确认的操纵子数据的缺乏一直成为发展算法的瓶颈.基于对操纵子结构的认识,从基因间距离、转录翻译相关的调控信号以及COG功能注释等特征出发,建立了描述操纵子复杂结构的概率模型,并提出了不依赖于特定物种操纵子数据作为训练集的迭代自学习算法.通过对实验验证的操纵子数据集的测试比较,结果表明算法对于预测操纵子结构非常有效.在不依赖于任何已知操纵子信息的情况下,算法在总体预测水平上超过了目前最好的操纵子预测方法,而且这种自学习的预测算法要优于依赖特定物种进行训练的算法.这些特点使得该算法能够适用于新测序的物种,有别于当前常用的操纵子预测方法.对细菌和古细菌的基因组进行大规模比较分析,进一步提高了对基因组操纵子结构的普遍特征和物种特异性的认识.