Analysis of codon usage patterns of bacterial genomes using the self-organizing map

Genome Signatures, Self-Organizing Maps and Higher Order Phylogenies: A Parametric Analysis

Genome signatures are data vectors derived from the compositional statistics of DNA. The self-organizing map (SOM) is a neural network method for the conceptualisation of relationships within complex data, such as genome signatures. The various parameters of the SOM training phase are investigated for their effect on the accuracy of the resulting output map. It is concluded that larger SOMs, as well as taking longer to train, are less sensitive in phylogenetic classification of unknown DNA sequences. However, where a classification can be made, a larger SOM is more accurate. Increasing the number of iterations in the training phase of the SOM only slightly increases accuracy, without improving sensitivity. The optimal length of the DNA sequence k-mer from which the genome signature should be derived is 4 or 5, but shorter values are almost as effective. In general, these results indicate that small, rapidly trained SOMs are generally as good as larger, longer trained ones for the analysis of genome signatures. These results may also be more generally applicable to the use of SOMs for other complex data sets, such as microarray data.

Methods for detection of horizontal transfer of transposable elements in complete genomes

Recent advances in nucleic acid sequencing technology are creating a diverse landscape for the analysis of horizontal transfer in complete genomes. Previously limited to prokaryotes, the availability of complete genomes from close eukaryotic species presents an opportunity to validate hypotheses about the patterns of evolution and mechanisms that drive horizontal transfer. Many of those methods can be transported from methods previously used in prokaryotic genomes, as the assumptions for horizontal transfer can be interpreted as the same. Some methods, however, require a complete adaptation, while others need refinements in sensitivity and specificity to deal with the huge datasets generated from next-generation sequencing technologies. Here we list the types of methods used for horizontal transfer detection, as well as theirs strengths and weakness.

Detecting laterally transferred genes

Methods for identifying alien genes in genomes fall into two general classes. Phylogenetic methods examine the distribution of a gene's homologues among genomes to find those with relationships not consistent with vertical inheritance. These approaches include identifying orphan genes which lack homologues in closely related genomes and genes with unduly high levels of similarity to genes in otherwise unrelated genomes. Rigorous statistical tests are available to place confidence intervals for predicted alien genes. Parametric methods examine the compositional properties of genes within a genome to find those with atypical properties, likely indicating the directional mutational pressures of a donor genome. These methods may compare the properties of genes to genomic averages, properties of genes to each other, or properties of large, multigene regions of the chromosome. Here, we discuss the strengths and weaknesses of each approach.

Self-organizing maps: a tool to ascertain taxonomic relatedness based on features derived from 16S rDNA sequence

Exploitation of microbial wealth, of which almost 95% or more is still unexplored, is a growing need. The taxonomic placements of a new isolate based on phenotypic characteristics are now being supported by information preserved in the 16S rRNA gene. However, the analysis of 16S rDNA sequences retrieved from metagenome, by the available bioinformatics tools, is subject to limitations. In this study, the occurrences of nucleotide features in 16S rDNA sequences have been used to ascertain the taxonomic placement of organisms. The tetra- and penta-nucleotide features were extracted from the training data set of the 16S rDNA sequence, and was subjected to an artificial neural network (ANN) based tool known as self-organizing map (SOM), which helped in visualization of unsupervised classification. For selection of significant features, principal component analysis (PCA) or curvilinear component analysis (CCA) was applied. The SOM along with these techniques could discriminate the sample sequences with more than 90% accuracy, highlighting the relevance of features. To ascertain the confidence level in the developed classification approach, the test data set was specifically evaluated for Thiobacillus, with Acidiphilium, Paracocus and Starkeya, which are taxonomically reassigned. The evaluation proved the excellent generalization capability of the developed tool. The topology of genera in SOM supported the conventional chemo-biochemical classification reported in the Bergey manual.

Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties

Biological systems are highly organized and enormously coordinated maintaining greater complexity. The increment of secondary data generation and progress of modern mining techniques provided us an opportunity to discover hidden intra and inter relations among these non linear dataset. This will help in understanding the complex biological phenomenon with greater efficiency. In this paper we report comparative classification of Pyruvate Dehydrogenase protein sequences from bacterial sources based on 28 different physicochemical parameters (such as bulkiness, hydrophobicity, total positively and negatively charged residues, α helices, β strand etc.) and 20 type amino acid compositions. Logistic, MLP (Multi Layer Perceptron), SMO (Sequential Minimal Optimization), RBFN (Radial Basis Function Network) and SL (simple logistic) methods were compared in this study. MLP was found to be the best method with maximum average accuracy of 88.20%. Same dataset was subjected for clustering using 2*2 grid of a two dimensional SOM (Self Organizing Maps). Clustering analysis revealed the proximity of the unannotated sequences with the Mycobacterium and Synechococcus genus.

The transcriptome of the colonial marine hydroid Hydractinia echinata

An increasing amount of expressed sequence tag (EST) and genomic data, predominantly for the cnidarians Acropora, Hydra and Nematostella, reveals that cnidarians have a high genomic complexity, despite being one of the morphologically simplest multicellular animals. Considering the diversity of cnidarians, we performed an EST project on the hydroid Hydractinia echinata, to contribute towards a broader coverage of this phylum. After random sequencing of almost 9000 clones, EST characterization revealed a broad diversity in gene content. Corroborating observations in other cnidarians, Hydractinia sequences exhibited a higher sequence similarity to vertebrates than to ecdysozoan invertebrates. A significant number of sequences were hitherto undescribed in metazoans, suggesting that these may be either cnidarian innovations or ancient genes lost in the bilaterian genomes analysed so far. However, we cannot rule out some degree of contamination from commensal bacteria. The identification of unique Hydractinia sequences emphasizes that the acquired genomic information generated so far is not large enough to be representative of the highly diverse cnidarian phylum. Finally, a database was created to store all the acquired information (http://www.mchips.org/hydractinia_echinata.html).

Analyzing gene expression from relative codon usage bias in Yeast genome: a statistical significance and biological relevance

Based on the hypothesis that highly expressed genes are often characterized by strong compositional bias in terms of codon usage, there are a number of measures currently in use that quantify codon usage bias in genes, and hence provide numerical indices to predict the expression levels of genes. With the recent advent of expression measure from the score of the relative codon usage bias (RCBS), we have explicitly tested the performance of this numerical measure to predict the gene expression level and illustrate this with an analysis of Yeast genomes. In contradiction with previous other studies, we observe a weak correlations between GC content and RCBS, but a selective pressure on the codon preferences in highly expressed genes. The assertion that the expression of a given gene depends on the score of relative codon usage bias (RCBS) is supported by the data. We further observe a strong correlation between RCBS and protein length indicating natural selection in favour of shorter genes to be expressed at higher level. We also attempt a statistical analysis to assess the strength of relative codon bias in genes as a guide to their likely expression level, suggesting a decrease of the informational entropy in the highly expressed genes.

Rapid divergence of codon usage patterns within the rice genome

Background

Synonymous codon usage varies widely between genomes, and also between genes within genomes. Although there is now a large body of data on variations in codon usage, it is still not clear if the observed patterns reflect the effects of positive Darwinian selection acting at the level of translational efficiency or whether these patterns are due simply to the effects of mutational bias. In this study, we have included both intra-genomic and inter-genomic comparisons of codon usage. This allows us to distinguish more efficiently between the effects of nucleotide bias and translational selection.

Results

We show that there is an extreme degree of heterogeneity in codon usage patterns within the rice genome, and that this heterogeneity is highly correlated with differences in nucleotide content (particularly GC content) between the genes. In contrast to the situation observed within the rice genome, Arabidopsis genes show relatively little variation in both codon usage and nucleotide content. By exploiting a combination of intra-genomic and inter-genomic comparisons, we provide evidence that the differences in codon usage among the rice genes reflect a relatively rapid evolutionary increase in the GC content of some rice genes. We also noted that the degree of codon bias was negatively correlated with gene length.

Conclusion

Our results show that mutational bias can cause a dramatic evolutionary divergence in codon usage patterns within a period of approximately two hundred million years.

The heterogeneity of codon usage patterns within the rice genome can be explained by a balance between genome-wide mutational biases and negative selection against these biased mutations. The strength of the negative selection is proportional to the length of the coding sequences. Our results indicate that the large variations in synonymous codon usage are not related to selection acting on the translational efficiency of synonymous codons.

Characterization of four novel genomic regions of uropathogenic Escherichia coli highly associated with the extraintestinal virulent phenotype: a jigsaw puzzle of genetic modules

Extraintestinal pathogenic Escherichia coli (ExPEC) are a major cause of urinary tract infections, sepsis, and neonatal meningitis. A variety of virulence factors in these strains is encoded by mobile genetic elements, such as transposons or pathogenicity islands (PAIs). Using subtractive cloning of ExPEC genomes, we recently detected short DNA fragments, which were significantly associated with the extraintestinal virulent phenotype. In this study, we identified four novel genomic DNA regions of the highly virulent uropathogenic E. coli strain JS299 carrying these previously identified DNA fragments. Characterization of the partial sequences of the genomic DNA regions revealed complex DNA arrangements with variable genetic compositions regarding the G+C contents and codon usage patterns. The prevalence of 15 previously uncharacterized genes was determined in a collection of clinical ExPECs and commensal E. coli strains by means of DNA microarray analyses. From this, 13 novel DNA sequences were demonstrated to be significantly associated with extraintestinal virulent strains, and thus may represent new virulence traits. Beside genes predicted to play a role in metabolic functions, such as sucrose utilization (scr), we identified DNA sequences shared by both ExPEC and enteropathogenic E. coli (EPEC). These sequences were significantly more prevalent among ExPECs when compared to commensal E. coli isolates. Our results support the idea of a considerable genetic variability among ExPEC strains and suggest that the novel genomic determinants described in this study may contribute to the ExPEC virulence.

A simulation test bed for hypotheses of genome evolution

MOTIVATION

Microbial genomes undergo evolutionary processes such as gene family expansion and contraction, variable rates and patterns of sequence substitution and lateral genetic transfer. Simulation tools are essential for both the generation of data under different evolutionary models and the validation of analytical methods on such data. However, meaningful investigation of phenomena such as lateral genetic transfer requires the simultaneous consideration of many underlying evolutionary processes.

RESULTS

We have developed EvolSimulator, a software package that combines non-stationary sequence and gene family evolution together with models of lateral genetic transfer, within a customizable birth-death model of speciation and extinction. Here, we examine simulated data sets generated with EvolSimulator using existing statistical techniques from the evolutionary literature, showing in detail each component of the simulation strategy.

AVAILABILITY

Source code, manual and other information are freely available at www.bioinformatics.org.au/evolsim.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

O(log2 M) self-organizing map algorithm without learning of neighborhood vectors

In this letter, a new self-organizing map (SOM) algorithm with computational cost O(log2 M) is proposed where M2 is the size of a feature map. The first SOM algorithm with O(M2) was originally proposed by Kohonen. The proposed algorithm is composed of the subdividing method and the binary search method. The proposed algorithm does not need the neighborhood functions so that it eliminates the computational cost in learning of neighborhood vectors and the labor of adjusting the parameters of neighborhood functions. The effectiveness of the proposed algorithm was examined by an analysis of codon frequencies of Escherichia coli (E. coli) K12 genes. These drastic computational reduction and accessible application that requires no adjusting of the neighborhood function will be able to contribute to many scientific areas.

Self-organizing neural networks to support the discovery of DNA-binding motifs

Identification of the short DNA sequence motifs that serve as binding targets for transcription factors is an important challenge in bioinformatics. Unsupervised techniques from the statistical learning theory literature have often been applied to motif discovery, but effective solutions for large genomic datasets have yet to be found. We present here three self-organizing neural networks that have applicability to the motif-finding problem. The core system in this study is a previously described SOM-based motif-finder named SOMBRERO. The motif-finder is integrated in this work with a SOM-based method that automatically constructs generalized models for structurally related motifs and initializes SOMBRERO with relevant biological knowledge. A self-organizing tree method that displays the relationships between various motifs is also presented, and it is shown that such a method can act as an effective structural classifier of novel motifs. The performance of the three self-organizing neural networks is evaluated here using various datasets.

P-value based visualization of codon usage data

Two important and not yet solved problems in bacterial genome research are the identification of horizontally transferred genes and the prediction of gene expression levels. Both problems can be addressed by multivariate analysis of codon usage data. In particular dimensionality reduction methods for visualization of multivariate data have shown to be effective tools for codon usage analysis. We here propose a multidimensional scaling approach using a novel similarity measure for codon usage tables. Our probabilistic similarity measure is based on P-values derived from the well-known chi-square test for comparison of two distributions. Experimental results on four microbial genomes indicate that the new method is well-suited for the analysis of horizontal gene transfer and translational selection. As compared with the widely-used correspondence analysis, our method did not suffer from outlier sensitivity and showed a better clustering of putative alien genes in most cases.

Codon usage domains over bacterial chromosomes

The geography of codon bias distributions over prokaryotic genomes and its impact upon chromosomal organization are analyzed. To this aim, we introduce a clustering method based on information theory, specifically designed to cluster genes according to their codon usage and apply it to the coding sequences of Escherichia coli and Bacillus subtilis. One of the clusters identified in each of the organisms is found to be related to expression levels, as expected, but other groups feature an over-representation of genes belonging to different functional groups, namely horizontally transferred genes, motility, and intermediary metabolism. Furthermore, we show that genes with a similar bias tend to be close to each other on the chromosome and organized in coherent domains, more extended than operons, demonstrating a role of translation in structuring bacterial chromosomes. It is argued that a sizeable contribution to this effect comes from the dynamical compartimentalization induced by the recycling of tRNAs, leading to gene expression rates dependent on their genomic and expression context.

A comparative categorization of gene flux in diverse microbial species

Microbial genomes harbor genomic islands (GIs), genes presumably acquired via horizontal gene transfer (HGT). We compared GIs of hyperthermophilic, thermophilic, mesophilic, and pathogenic/nonpathogenic species and of small and large genomes. The COG database was used to characterize gene-encoded functions. Putative donors were determined to quantify gene flux between superkingdoms. In hyperthermophiles, more than 10% of the genes were on average acquired across the superkingdom border. For thermophiles and particularly mesophiles, we identified a nearly unidirectional export from bacteria to archaea. Additionally, we analyzed GI composition for Escherichia, and pairs of Listeria, Rhizobiales, Methanosarcinaceae, and Thermus thermophilus/Deinococcus radiodurans. For Escherichia and Listeria, the composition of GIs in pathogenic and nonpathogenic species did not differ significantly with respect to encoded COG classes. The analysis of related genomes showed that the composition of GIs cannot be explained with trends of gene content known to depend on genome size.

A comparative categorization of protein function encoded in bacterial or archeal genomic islands

Genomes of prokaryotes harbor genomic islands (GIs), which are frequently acquired via horizontal gene transfer (HGT). Here I present an analysis of GIs with respect to gene-encoded functions. GIs were identified by statistical analysis of codon usage and clustering. Genes classified as putatively alien (pA) or putatively native (pN) were categorized according to the COG database. Among pA and pN genes, the distribution of COG functions and classes were studied for different groupings of prokaryotes. Groups were formed according to taxonomical relation or habitats. In all groups, genes related to class L (replication, recombination, and repair) were statistically significantly overrepresented in GIs. GIs of bacteria and archaea showed a distinct pattern of preferences. In archeal GIs, genes belonging to COG class M (cell wall/membrane/envelope biogenesis) or Q (secondary metabolites biosynthesis, transport, and catabolism) were more frequent. In bacterial GIs, genes of classes U (intracellular trafficking, secretion, and vesicular transport), N (cell motility), and V (defense mechanisms) were predominant. Underrepresentation was strongest for genes belonging to class J (translation, ribosomal structure, and biogenesis). Among single COG functions overrepresented in GIs were transferases and transporters. In both superkingdoms, HGT enhances genomic content by meeting demands that are independent of the studied habitats. These findings are in agreement with the complexity theory, which predicts the preferential import of operational genes. However, only specific subsets of operational genes were enriched in GIs. Modification of the cell envelope, cell motility, secretion, and protection of cellular DNA are major issues in HGT.

A problem in multivariate analysis of codon usage data and a possible solution

Multivariate analyses are often used to identify major trends of variation in synonymous codon usage among genes. These analyses need to be performed on properly normalized codon usage data to avoid biases masking this synonymous variation, i.e., gene length, amino acid usage, and codon degeneracy; however, previous studies have failed to do so. In this paper, we demonstrate that the use of alternative normalized data (called 'relative adaptiveness' in the literature) can avoid all these biases and furthermore, can identify more trends of variation among genes, including GC-ending codon usage, GT-ending codon usage, and gene expression level.

Comparison of codon usage measures and their applicability in prediction of microbial gene expressivity

Background

There are a number of methods (also called: measures) currently in use that quantify codon usage in genes. These measures are often influenced by other sequence properties, such as length. This can introduce strong methodological bias into measurements; therefore we attempted to develop a method free from such dependencies. One of the common applications of codon usage analyses is to quantitatively predict gene expressivity.

Results

We compared the performance of several commonly used measures and a novel method we introduce in this paper – Measure Independent of Length and Composition (MILC). Large, randomly generated sequence sets were used to test for dependence on (i) sequence length, (ii) overall amount of codon bias and (iii) codon bias discrepancy in the sequences. A derivative of the method, named MELP (MILC-based Expression Level Predictor) can be used to quantitatively predict gene expression levels from genomic data. It was compared to other similar predictors by examining their correlation with actual, experimentally obtained mRNA or protein abundances.

Conclusion

We have established that MILC is a generally applicable measure, being resistant to changes in gene length and overall nucleotide composition, and introducing little noise into measurements. Other methods, however, may also be appropriate in certain applications. Our efforts to quantitatively predict gene expression levels in several prokaryotes and unicellular eukaryotes met with varying levels of success, depending on the experimental dataset and predictor used. Out of all methods, MELP and Rainer Merkl's GCB method had the most consistent behaviour. A 'reference set' containing known ribosomal protein genes appears to be a valid starting point for a codon usage-based expressivity prediction.

Computational characterization of behavioral response of medaka (Oryzias latipes) treated with diazinon

The behavior of indicator specimens in response to sub-lethal doses of toxic substances has been used to detect contamination in aquatic ecosystems. Changes in the movement behaviors of medaka (Oryzias latipes) were analyzed after being treated with diazinon at a concentration of 0.1 mg/l. The movement tracks of medaka were continuously recorded in two-dimension by a digital image processing system both before and after the treatments. Subsequently, two computational methods--two-dimensional fast Fourier transform (2D FFT) and self-organizing map (SOM), were implemented to extract information from the movement data. The differences in the shapes of the movement tracks before and after the treatments were clearly manifested through 2D FFT. The short-distance, irregular turnings in the movement tracks observed after the treatments in the time domain were characteristically transformed to circular or ellipsoidal patterns in the frequency domain. The amplitudes of 2D FFT were efficiently classified by SOM, demonstrating the effects of the different treatments. To evaluate the feasibility of information extraction by 2D FFT, SOM was similarly carried out on the parameters (speed, meander, stop duration, etc.) conventionally used for characterizing the movement tracks. 2D FFT was more efficient in information extraction from the movement data than the parameters. The 2D FFT and SOM were useful as computational methods for automatically detecting response behaviors of indicator specimens exposed to toxic chemicals in aquatic ecosystems.

Transcription factor binding site identification using the self-organizing map

MOTIVATION

The automatic identification of over-represented motifs present in a collection of sequences continues to be a challenging problem in computational biology. In this paper, we propose a self-organizing map of position weight matrices as an alternative method for motif discovery. The advantage of this approach is that it can be used to simultaneously characterize every feature present in the dataset, thus lessening the chance that weaker signals will be missed. Features identified are ranked in terms of over-representation relative to a background model.

RESULTS

We present an implementation of this approach, named SOMBRERO (self-organizing map for biological regulatory element recognition and ordering), which is capable of discovering multiple distinct motifs present in a single dataset. Demonstrated here are the advantages of our approach on various datasets and SOMBRERO's improved performance over two popular motif-finding programs, MEME and AlignACE.

AVAILABILITY

SOMBRERO is available free of charge from http://bioinf.nuigalway.ie/sombrero

SUPPLEMENTARY INFORMATION

http://bioinf.nuigalway.ie/sombrero/additional.

The 'weighted sum of relative entropy': a new index for synonymous codon usage bias

Shannon entropy from information theory has been applied to estimate the degree of deviation from equal usage of synonymous codons; however, previous attempts have failed to take into account all three aspects of amino acid usage, i.e. (i) the number of distinct amino acids, (ii) their relative frequencies, and (iii) their degree of codon degeneracy. A new index taking into account all of these aspects is proposed. The index, designated as the 'weighted sum of relative entropy' (E(w)), is defined as the sum of the relative entropy of each amino acid weighted by its relative frequency in the sequence. In this paper, we demonstrate that E(w) allows us to avoid some amino acid usage biases and can yield results contradictory to those obtained by previous methods.

Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models

Background

Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation.

Results

This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential.

Conclusions

While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

Informatics for unveiling hidden genome signatures

With the increasing amount of available genome sequences, novel tools are needed for comprehensive analysis of species-specific sequence characteristics for a wide variety of genomes. We used an unsupervised neural network algorithm, a self-organizing map (SOM), to analyze di-, tri-, and tetranucleotide frequencies in a wide variety of prokaryotic and eukaryotic genomes. The SOM, which can cluster complex data efficiently, was shown to be an excellent tool for analyzing global characteristics of genome sequences and for revealing key combinations of oligonucleotides representing individual genomes. From analysis of 1- and 10-kb genomic sequences derived from 65 bacteria (a total of 170 Mb) and from 6 eukaryotes (460 Mb), clear species-specific separations of major portions of the sequences were obtained with the di-, tri-, and tetranucleotide SOMs. The unsupervised algorithm could recognize, in most 10-kb sequences, the species-specific characteristics (key combinations of oligonucleotide frequencies) that are signature features of each genome. We were able to classify DNA sequences within one and between many species into subgroups that corresponded generally to biological categories. Because the classification power is very high, the SOM is an efficient and fundamental bioinformatic strategy for extracting a wide range of genomic information from a vast amount of sequences.

Analysis of the antimalarial drug resistance protein Pfcrt expressed in yeast

Mutations in the novel membrane protein Pfcrt were recently found to be essential for chloroquine resistance (CQR) in Plasmodium falciparum, the parasite responsible for most lethal human malaria (Fidock, D. A., Nomura, T., Talley, A. K., Cooper, R. A., Dzekunov, S. M., Ferdig, M. T., Ursos, L. M., Sidhu, A. B., Naude, B., Deitsch, K. W., Su, X. Z., Wootton, J. C., Roepe, P. D., and Wellems, T. E. (2000) Mol. Cell 6, 861-871). Pfcrt is localized to the digestive vacuolar membrane of the intraerythrocytic parasite and may function as a transporter. Study of this putative transport function would be greatly assisted by overexpression in yeast followed by characterization of membrane vesicles. Unfortunately, the very high AT content of malarial genes precludes efficient heterologous expression. Thus, we back-translated Pfcrt to design idealized genes with preferred yeast codons, no long poly(A) sequences, and minimal stem-loop structure. We synthesized a designed gene with a two-step PCR method, fused this to N- and C-terminal sequences to aid membrane insertion and purification, and now report efficient expression of wild type and mutant Pfcrt proteins in the plasma membrane of Saccharomyces cerevisiae and Pichia pastoris yeast. To our knowledge, this is the first successful expression of a full-length malarial parasite integral membrane protein in yeast. Purified membranes and inside-out plasma membrane vesicle preparations were used to analyze wild type versus CQR-conferring mutant Pfcrt function, which may include effects on H(+) transport (Dzekunov, S., Ursos, L. M. B., and Roepe, P. D. (2000) Mol. Biochem. Parasitol. 110, 107-124), and to perfect a rapid purification of biotinylated Pfcrt. These data expand on the role of Pfcrt in conferring CQR and define a productive route for analysis of important P. falciparum transport proteins and membrane associated vaccine candidates.

Synonymous codon usage is subject to selection in thermophilic bacteria

The patterns of synonymous codon usage, both within and among genomes, have been extensively studied over the past two decades. Despite the accumulating evidence that natural selection can shape codon usage, it has not been possible to link a particular pattern of codon usage to a specific external selective force. Here, we have analyzed the patterns of synonymous codon usage in 40 completely sequenced prokaryotic genomes. By combining the genes from several genomes (more than 80 000 genes in all) into a single dataset for this analysis, we were able to investigate variations in codon usage, both within and between genomes. The results show that synonymous codon usage is affected by two major factors: (i) the overall G+C content of the genome and (ii) growth at high temperature. This study focused on the relationship between synonymous codon usage and the ability to grow at high temperature. We have been able to eliminate both phylogenetic history and lateral gene transfer as possible explanations for the characteristic pattern of codon usage among the thermophiles. Thus, these results demonstrate a clear link between a particular pattern of codon usage and an external selective force.

Horizontal gene transfer in microbial genome evolution

Horizontal gene transfer is the collective name for processes that permit the exchange of DNA among organisms of different species. Only recently has it been recognized as a significant contribution to inter-organismal gene exchange. Traditionally, it was thought that microorganisms evolved clonally, passing genes from mother to daughter cells with little or no exchange of DNA among diverse species. Studies of microbial genomes, however, have shown that genomes contain genes that are closely related to a number of different prokaryotes, sometimes to phylogenetically very distantly related ones. (Doolittle et al., 1990, J. Mol. Evol. 31, 383-388; Karlin et al., 1997, J. Bacteriol. 179, 3899-3913; Karlin et al., 1998, Annu. Rev. Genet. 32, 185-225; Lawrence and Ochman, 1998, Proc. Natl. Acad. Sci. USA 95, 9413-9417; Rivera et al., 1998, Proc. Natl. Acad. Sci. USA 95, 6239-6244; Campbell, 2000, Theor. Popul. Biol. 57 71-77; Doolittle, 2000, Sci. Am. 282, 90-95; Ochman and Jones, 2000, Embo. J. 19, 6637-6643; Boucher et al. 2001, Curr. Opin., Microbiol. 4, 285-289; Wang et al., 2001, Mol. Biol. Evol. 18, 792-800). Whereas prokaryotic and eukaryotic evolution was once reconstructed from a single 16S ribosomal RNA (rRNA) gene, the analysis of complete genomes is beginning to yield a different picture of microbial evolution, one that is wrought with the lateral movement of genes across vast phylogenetic distances. (Lane et al., 1988, Methods Enzymol. 167, 138-144; Lake and Rivera, 1996, Proc. Natl. Acad. Sci. USA 91, 2880-2881; Lake et al., 1999, Science 283, 2027-2028).

Detection of lateral gene transfer among microbial genomes

An increasingly comprehensive assessment is being developed of the extent and potential significance of lateral gene transfer among microbial genomes. Genomic sequences can be identified as being of putatively lateral origin by their unexpected phyletic distribution, atypical sequence composition, differential presence or absence in closely related genomes, or incongruent phylogenetic trees. These complementary approaches sometimes yield inconsistent results. Not only more data but also quantitative models and simulations are needed urgently.