The prokaryotic selenoproteome

In the genetic code, the UGA codon has a dual function as it encodes selenocysteine (Sec) and serves as a stop signal. However, only the translation terminator function is used in gene annotation programs, resulting in misannotation of selenoprotein genes. Here, we applied two independent bioinformatics approaches to characterize a selenoprotein set in prokaryotic genomes. One method searched for selenoprotein genes by identifying RNA stem-loop structures, selenocysteine insertion sequence elements; the second approach identified Sec/Cys pairs in homologous sequences. These analyses identified all or almost all selenoproteins in completely sequenced bacterial and archaeal genomes and provided a view on the distribution and composition of prokaryotic selenoproteomes. In addition, lineage-specific and core selenoproteins were detected, which provided insights into the mechanisms of selenoprotein evolution. Characterization of selenoproteomes allows interpretation of other UGA codons in completed genomes of prokaryotes as terminators, addressing the UGA dual-function problem.     

In silico identification of novel selenoproteins in the Drosophila melanogaster genome

In selenoproteins, incorporation of the amino acid selenocysteine is specified by the UGA codon, usually a stop signal. The alternative decoding of UGA is conferred by an mRNA structure, the SECIS element, located in the 3′-untranslated region of the selenoprotein mRNA. Because of the non-standard use of the UGA codon, current computational gene prediction methods are unable to identify selenoproteins in the sequence of the eukaryotic genomes. Here we describe a method to predict selenoproteins in genomic sequences, which relies on the prediction of SECIS elements in coordination with the prediction of genes in which the strong codon bias characteristic of protein coding regions extends beyond a TGA codon interrupting the open reading frame. We applied the method to the Drosophila melanogaster genome, and predicted four potential selenoprotein genes. One of them belongs to a known family of selenoproteins, and we have tested experimentally two other predictions with positive results. Finally, we have characterized the expression pattern of these two novel selenoprotein genes.     

A computational approach to identify genes for functional RNAs in genomic sequences

Currently there is no successful computational approach for identification of genes encoding novel functional RNAs (fRNAs) in genomic sequences. We have developed a machine learning approach using neural networks and support vector machines to extract common features among known RNAs for prediction of new RNA genes in the unannotated regions of prokaryotic and archaeal genomes. The Escherichia coli genome was used for development, but we have applied this method to several other bacterial and archaeal genomes. Networks based on nucleotide composition were 80–90% accurate in jackknife testing experiments for bacteria and 90–99% for hyperthermophilic archaea. We also achieved a significant improvement in accuracy by combining these predictions with those obtained using a second set of parameters consisting of known RNA sequence motifs and the calculated free energy of folding. Several known fRNAs not included in the training datasets were identified as well as several hundred predicted novel RNAs. These studies indicate that there are many unidentified RNAs in simple genomes that can be predicted computationally as a precursor to experimental study. Public access to our RNA gene predictions and an interface for user predictions is available via the web.     

Reconsidering the evolution of eukaryotic selenoproteins: a novel nonmammalian family with scattered phylogenetic distribution

While the genome sequence and gene content are available for an increasing number of organisms, eukaryotic selenoproteins remain poorly characterized. The dual role of the UGA codon confounds the identification of novel selenoprotein genes. Here, we describe a comparative genomics approach that relies on the genome-wide prediction of genes with in-frame TGA codons, and the subsequent comparison of predictions from different genomes, wherein conservation in regions flanking the TGA codon suggests selenocysteine coding function. Application of this method to human and fugu genomes identified a novel selenoprotein family, named SelU, in the puffer fish. The selenocysteine-containing form also occurred in other fish, chicken, sea urchin, green algae and diatoms. In contrast, mammals, worms and land plants contained cysteine homologues. We demonstrated selenium incorporation into chicken SelU and characterized the SelU expression pattern in zebrafish embryos. Our data indicate a scattered evolutionary distribution of selenoproteins in eukaryotes, and suggest that, contrary to the picture emerging from data available so far, other taxa-specific selenoproteins probably exist.     

Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System

Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG''s regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov.     

Comparative genomics of archaea: how much have we learned in six years,and what's next?

Archaea comprise one of the three distinct domains of life (with bacteria and eukaryotes). With 16 complete archaeal genomes sequenced to date, comparative genomics has revealed a conserved core of 313 genes that are represented in all sequenced archaeal genomes, plus a variable 'shell' that is prone to lineage-specific gene loss and horizontal gene exchange. The majority of archaeal genes have not been experimentally characterized, but novel functional pathways have been predicted.     

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.     

Dark matter in archaeal genomes: a rich source of novel mobile elements,defense systems and secretory complexes

Microbial genomes encompass a sizable fraction of poorly characterized, narrowly spread fast-evolving genes. Using sensitive methods for sequences comparison and protein structure prediction, we performed a detailed comparative analysis of clusters of such genes, which we denote “dark matter islands”, in archaeal genomes. The dark matter islands comprise up to 20 % of archaeal genomes and show remarkable heterogeneity and diversity. Nevertheless, three classes of entities are common in these genomic loci: (a) integrated viral genomes and other mobile elements; (b) defense systems, and (c) secretory and other membrane-associated systems. The dark matter islands in the genome of thermophiles and mesophiles show similar general trends of gene content, but thermophiles are substantially enriched in predicted membrane proteins whereas mesophiles have a greater proportion of recognizable mobile elements. Based on this analysis, we predict the existence of several novel groups of viruses and mobile elements, previously unnoticed variants of CRISPR-Cas immune systems, and new secretory systems that might be involved in stress response, intermicrobial conflicts and biogenesis of novel, uncharacterized membrane structures.     

Development of joint application strategies for two microbial gene finders

MOTIVATION: As a starting point in annotation of bacterial genomes, gene finding programs are used for the prediction of functional elements in the DNA sequence. Due to the faster pace and increasing number of genome projects currently underway, it is becoming especially important to have performant methods for this task. RESULTS: This study describes the development of joint application strategies that combine the strengths of two microbial gene finders to improve the overall gene finding performance. Critica is very specific in the detection of similarity-supported genes as it uses a comparative sequence analysis-based approach. Glimmer employs a very sophisticated model of genomic sequence properties and is sensitive also in the detection of organism-specific genes. Based on a data set of 113 microbial genome sequences, we optimized a combined application approach using different parameters with relevance to the gene finding problem. This results in a significant improvement in specificity while there is similarity in sensitivity to Glimmer. The improvement is especially pronounced for GC rich genomes. The method is currently being applied for the annotation of several microbial genomes. AVAILABILITY: The methods described have been implemented within the gene prediction component of the GenDB genome annotation system.     

Relaxation of selective constraints causes independent selenoprotein extinction in insect genomes

Background

Selenoproteins are a diverse family of proteins notable for the presence of the 21st amino acid, selenocysteine. Until very recently, all metazoan genomes investigated encoded selenoproteins, and these proteins had therefore been believed to be essential for animal life. Challenging this assumption, recent comparative analyses of insect genomes have revealed that some insect genomes appear to have lost selenoprotein genes.

Methodology/Principal Findings

In this paper we investigate in detail the fate of selenoproteins, and that of selenoprotein factors, in all available arthropod genomes. We use a variety of in silico comparative genomics approaches to look for known selenoprotein genes and factors involved in selenoprotein biosynthesis. We have found that five insect species have completely lost the ability to encode selenoproteins and that selenoprotein loss in these species, although so far confined to the Endopterygota infraclass, cannot be attributed to a single evolutionary event, but rather to multiple, independent events. Loss of selenoproteins and selenoprotein factors is usually coupled to the deletion of the entire no-longer functional genomic region, rather than to sequence degradation and consequent pseudogenisation. Such dynamics of gene extinction are consistent with the high rate of genome rearrangements observed in Drosophila. We have also found that, while many selenoprotein factors are concomitantly lost with the selenoproteins, others are present and conserved in all investigated genomes, irrespective of whether they code for selenoproteins or not, suggesting that they are involved in additional, non-selenoprotein related functions.

Conclusions/Significance

Selenoproteins have been independently lost in several insect species, possibly as a consequence of the relaxation in insects of the selective constraints acting across metazoans to maintain selenoproteins. The dispensability of selenoproteins in insects may be related to the fundamental differences in antioxidant defense between these animals and other metazoans.     

Structural and functional investigation of a putative archaeal selenocysteine synthase

Bacterial selenocysteine synthase converts seryl-tRNA(Sec) to selenocysteinyl-tRNA(Sec) for selenoprotein biosynthesis. The identity of this enzyme in archaea and eukaryotes is unknown. On the basis of sequence similarity, a conserved open reading frame has been annotated as a selenocysteine synthase gene in archaeal genomes. We have determined the crystal structure of the corresponding protein from Methanococcus jannaschii, MJ0158. The protein was found to be dimeric with a distinctive domain arrangement and an exposed active site, built from residues of the large domain of one protomer alone. The shape of the dimer is reminiscent of a substructure of the decameric Escherichia coli selenocysteine synthase seen in electron microscopic projections. However, biochemical analyses demonstrated that MJ0158 lacked affinity for E. coli seryl-tRNA(Sec) or M. jannaschii seryl-tRNA(Sec), and neither substrate was directly converted to selenocysteinyl-tRNA(Sec) by MJ0158 when supplied with selenophosphate. We then tested a hypothetical M. jannaschii O-phosphoseryl-tRNA(Sec) kinase and demonstrated that the enzyme converts seryl-tRNA(Sec) to O-phosphoseryl-tRNA(Sec) that could constitute an activated intermediate for selenocysteinyl-tRNA(Sec) production. MJ0158 also failed to convert O-phosphoseryl-tRNA(Sec) to selenocysteinyl-tRNA(Sec). In contrast, both archaeal and bacterial seryl-tRNA synthetases were able to charge both archaeal and bacterial tRNA(Sec) with serine, and E. coli selenocysteine synthase converted both types of seryl-tRNA(Sec) to selenocysteinyl-tRNA(Sec). These findings demonstrate that a number of factors from the selenoprotein biosynthesis machineries are cross-reactive between the bacterial and the archaeal systems but that MJ0158 either does not encode a selenocysteine synthase or requires additional factors for activity.     

Evidences of lateral gene transfer between archaea and pathogenic bacteria

Acquisition of new genetic material through horizontal gene transfer has been shown to be an important feature in the evolution of many pathogenic bacteria. Changes in the genetic repertoire, occurring through gene acquisition and deletion, are the major events underlying the emergence and evolution of bacterial pathogens. However, horizontal gene transfer across the domains i.e. archaea and bacteria is not so common. In this context, we explore events of horizontal gene transfer between archaea and bacteria. In order to determine whether the acquisition of archaeal genes by lateral gene transfer is an important feature in the evolutionary history of the pathogenic bacteria, we have developed a scheme of stepwise eliminations that identifies archaeal-like genes in various bacterial genomes. We report the presence of 9 genes of archaeal origin in the genomes of various bacteria, a subset of which is also unique to the pathogenic members and are not found in respective non-pathogenic counterparts. We believe that these genes, having been retained in the respective genomes through selective advantage, have key functions in the organism’s biology and may play a role in pathogenesis.     

Phylogenomics of prokaryotic ribosomal proteins

Archaeal and bacterial ribosomes contain more than 50 proteins, including 34 that are universally conserved in the three domains of cellular life (bacteria, archaea, and eukaryotes). Despite the high sequence conservation, annotation of ribosomal (r-) protein genes is often difficult because of their short lengths and biased sequence composition. We developed an automated computational pipeline for identification of r-protein genes and applied it to 995 completely sequenced bacterial and 87 archaeal genomes available in the RefSeq database. The pipeline employs curated seed alignments of r-proteins to run position-specific scoring matrix (PSSM)-based BLAST searches against six-frame genome translations, mitigating possible gene annotation errors. As a result of this analysis, we performed a census of prokaryotic r-protein complements, enumerated missing and paralogous r-proteins, and analyzed the distributions of ribosomal protein genes among chromosomal partitions. Phyletic patterns of bacterial and archaeal r-protein genes were mapped to phylogenetic trees reconstructed from concatenated alignments of r-proteins to reveal the history of likely multiple independent gains and losses. These alignments, available for download, can be used as search profiles to improve genome annotation of r-proteins and for further comparative genomics studies.     

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.     

Genome majority vote improves gene predictions

Recent studies have noted extensive inconsistencies in gene start sites among orthologous genes in related microbial genomes. Here we provide the first documented evidence that imposing gene start consistency improves the accuracy of gene start-site prediction. We applied an algorithm using a genome majority vote (GMV) scheme to increase the consistency of gene starts among orthologs. We used a set of validated Escherichia coli genes as a standard to quantify accuracy. Results showed that the GMV algorithm can correct hundreds of gene prediction errors in sets of five or ten genomes while introducing few errors. Using a conservative calculation, we project that GMV would resolve many inconsistencies and errors in publicly available microbial gene maps. Our simple and logical solution provides a notable advance toward accurate gene maps.     

Gene recognition from questionable ORFs in bacterial and archaeal genomes

The ORFs of microbial genomes in annotation files are usually classified into two groups: the first corresponds to known genes; whereas the second includes 'putative', 'probable', 'conserved hypothetical', 'hypothetical', 'unknown' and 'predicted' ORFs etc. Since the annotation is not 100% accurate, it is essential to confirm which ORF of the latter group is coding and which is not. Starting from known genes in the former, this paper describes an improved Z curve method to recognize genes in the latter. Ten-fold cross-validation tests show that the average accuracy of the algorithm is greater than 99% for recognizing the known genes in 57 bacterial and archaeal genomes. The method is then applied to recognize genes of the latter group. The likely non-coding ORFs in each of the 57 bacterial or archaeal genomes studied here are recognized and listed at the website http://tubic.tju.edu.cn/ZCURVE_C_html/noncoding.html. The working mechanism of the algorithm has been discussed in details. A computer program, called ZCURVE_C, was written to calculate a coding score called Z-curve score for ORFs in the above 57 bacterial and archaeal genomes. Coding/non-coding is simply determined by the criterion of Z-curve score > 0/ Z-curve score < 0. A website has been set up to provide the service to calculate the Z-curve score. A user may submit the DNA sequence of an ORF to the server at http://tubic.tju.edu.cn/ZCURVE_C/Default.cgi, and the Z-curve score of the ORF is calculated and returned to the user immediately.