The relationship between the selection affecting codon usage and selection on protein sequences of orthologous genes in diverse groups of bacteria and archaea was examined by using the Alignable Tight Genome Clusters database of prokaryote genomes. The codon usage bias is generally low, with 57.5% of the gene-specific optimal codon frequencies (Fopt) being below 0.55. This apparent weak selection on codon usage contrasts with the strong purifying selection on amino acid sequences, with 65.8% of the gene-specific dN/dS ratios being below 0.1. For most of the genomes compared, a limited but statistically significant negative correlation between Fopt and dN/dS was observed, which is indicative of a link between selection on protein sequence and selection on codon usage. The strength of the coupling between the protein level selection and codon usage bias showed a strong positive correlation with the genomic GC content. Combined with previous observations on the selection for GC-rich codons in bacteria and archaea with GC-rich genomes, these findings suggest that selection for translational fine-tuning could be an important factor in microbial evolution that drives the evolution of genome GC content away from mutational equilibrium. This type of selection is particularly pronounced in slowly evolving, “high-status” genes. A significantly stronger link between the two aspects of selection is observed in free-living bacteria than in parasitic bacteria and in genes encoding metabolic enzymes and transporters than in informational genes. These differences might reflect the special importance of translational fine-tuning for the adaptability of gene expression to environmental changes. The results of this work establish the coupling between protein level selection and selection for translational optimization as a distinct and potentially important factor in microbial evolution.
Selection affects the evolution of microbial genomes at many levels, including both the structure of proteins and the regulation of their production. Here we demonstrate the coupling between the selection on protein sequences and the optimization of codon usage in a broad range of bacteria and archaea. The strength of this coupling varies over a wide range and strongly and positively correlates with the genomic GC content. The cause(s) of the evolution of high GC content is a long-standing open question, given the universal mutational bias toward AT. We propose that optimization of codon usage could be one of the key factors that determine the evolution of GC-rich genomes. This work establishes the coupling between selection at the level of protein sequence and at the level of codon choice optimization as a distinct aspect of genome evolution.
Viruses are the most abundant biological entities on earth and encompass a vast amount of genetic diversity. The recent rapid increase in the number of sequenced viral genomes has created unprecedented opportunities for gaining new insight into the structure and evolution of the virosphere. Here, we present an update of the phage orthologous groups (POGs), a collection of 4,542 clusters of orthologous genes from bacteriophages that now also includes viruses infecting archaea and encompasses more than 1,000 distinct virus genomes. Analysis of this expanded data set shows that the number of POGs keeps growing without saturation and that a substantial majority of the POGs remain specific to viruses, lacking homologues in prokaryotic cells, outside known proviruses. Thus, the great majority of virus genes apparently remains to be discovered. A complementary observation is that numerous viral genomes remain poorly, if at all, covered by POGs. The genome coverage by POGs is expected to increase as more genomes are sequenced. Taxon-specific, single-copy signature genes that are not observed in prokaryotic genomes outside detected proviruses were identified for two-thirds of the 57 taxa (those with genomes available from at least 3 distinct viruses), with half of these present in all members of the respective taxon. These signatures can be used to specifically identify the presence and quantify the abundance of viruses from particular taxa in metagenomic samples and thus gain new insights into the ecology and evolution of viruses in relation to their hosts.
The transcriptional regulators of pluripotency, POU5F1 (OCT4), NANOG and SOX2, are highly expressed in embryonal carcinoma (EC). In contrast to OCT4 and NANOG, SOX2 has not been demonstrated in the early human germ cell lineage or carcinoma in situ (CIS), the precursor for testicular germ cell tumours (TGCTs). Here, we have analysed SOX2 expression in CIS and overt TGCTs, as well as normal second and third trimester fetal, prepubertal and adult testes by in situ hybridisation and immunohistochemistry using three different antibodies. In contrast to earlier studies, we detected SOX2 mRNA in most CIS cells. We also detected speckled nuclear SOX2 immunoreactivity in CIS cells with one primary antibody, which was not apparent with other primary antibodies. The results demonstrate SOX2 gene expression in CIS for the first time and raise the possibility of post-transcriptional regulation, most likely sumoylation as a mechanism for limiting SOX2 action in these cells.
carcinoma in situ (CIS); intratubular germ cell neoplasia (ITGCN); testicular germ cell tumour (TGCT); Sertoli cell, pluripotency
A report of the second annual Beyond the Genome conference held on the 19-22 September 2011 at The Universities at Shady Grove, Rockville, Maryland, USA, where increases in computing that may help make personal genomics a reality were a major focus.
Accurate inference of orthologous genes is a pre-requisite for most comparative genomics studies, and is also important for functional annotation of new genomes. Identification of orthologous gene sets typically involves phylogenetic tree analysis, heuristic algorithms based on sequence conservation, synteny analysis, or some combination of these approaches. The most direct tree-based methods typically rely on the comparison of an individual gene tree with a species tree. Once the two trees are accurately constructed, orthologs are straightforwardly identified by the definition of orthology as those homologs that are related by speciation, rather than gene duplication, at their most recent point of origin. Although ideal for the purpose of orthology identification in principle, phylogenetic trees are computationally expensive to construct for large numbers of genes and genomes, and they often contain errors, especially at large evolutionary distances. Moreover, in many organisms, in particular prokaryotes and viruses, evolution does not appear to have followed a simple ‘tree-like’ mode, which makes conventional tree reconciliation inapplicable. Other, heuristic methods identify probable orthologs as the closest homologous pairs or groups of genes in a set of organisms. These approaches are faster and easier to automate than tree-based methods, with efficient implementations provided by graph-theoretical algorithms enabling comparisons of thousands of genomes. Comparisons of these two approaches show that, despite conceptual differences, they produce similar sets of orthologs, especially at short evolutionary distances. Synteny also can aid in identification of orthologs. Often, tree-based, sequence similarity- and synteny-based approaches can be combined into flexible hybrid methods.
homolog; ortholog; paralog; xenolog; orthologous groups; tree reconciliation; comparative genomics
Metagenomic analysis of viruses suggests novel patterns of evolution, changes the existing ideas of the composition of the virus world and reveals novel groups of viruses and virus-like agents. The gene composition of the marine DNA virome is dramatically different from that of known bacteriophages. The virome is dominated by rare genes, many of which might be contained within virus-like entities such as gene transfer agents. Analysis of marine metagenomes thought to consist mostly of bacterial genes revealed a variety of sequences homologous to conserved genes of eukaryotic nucleocytoplasmic large DNA viruses, resulting in the discovery of diverse members of previously undersampled groups and suggesting the existence of new classes of virus-like agents. Unexpectedly, metagenomics of marine RNA viruses showed that representatives of only one superfamily of eukaryotic viruses, the picorna-like viruses, dominate the RNA virome.
We have identified conserved orthologs in completely sequenced genomes of double-strand DNA phages and arranged them into evolutionary families (phage orthologous groups [POGs]). Using this resource to analyze the collection of known phage genomes, we find that most orthologs are unique in their genomes (having no diverged duplicates [paralogs]), and while many proteins contain multiple domains, the evolutionary recombination of these domains does not appear to be a major factor in evolution of these orthologous families. The number of POGs has been rapidly increasing over the past decade, the percentage of genes in phage genomes that have orthologs in other phages has also been increasing, and the percentage of unknown “ORFans” is decreasing as more proteins find homologs and establish a family. Other properties of phage genomes have remained relatively stable over time, most notably the high fraction of genes that are never or only rarely observed in their cellular hosts. This suggests that despite the renowned ability of phages to transduce cellular genes, these cellular “hitchhiker” genes do not dominate the phage genomic landscape, and a large fraction of the genes in phage genomes maintain an evolutionary trajectory that is distinct from that of the host genes.
Prostaglandins (PGs) play key roles in development and maintenance of homeostasis of the adult body. Despite these important roles, it remains unclear whether the PG pathway is a target for endocrine disruption. However, several known endocrine-disrupting compounds (EDCs) share a high degree of structural similarity with mild analgesics.
Objectives and Methods
Using cell-based transfection and transduction experiments, mass spectrometry, and organotypic assays together with molecular modeling, we investigated whether inhibition of the PG pathway by known EDCs could be a novel point of endocrine disruption.
We found that many known EDCs inhibit the PG pathway in a mouse Sertoli cell line and in human primary mast cells. The EDCs also reduced PG synthesis in ex vivo rat testis, and this reduction was correlated with a reduced testosterone production. The inhibition of PG synthesis occurred without involvement of canonical PG receptors or the peroxisome proliferator–activated receptors (PPARs), which have previously been described as targets of EDCs. Instead, our results suggest that the compounds may bind directly into the active site of the cyclooxygenase (COX) enzymes, thereby obstructing the conversion of arachidonic acid to PG precursors without interfering with the expression of the COX enzymes. A common feature of the PG inhibitory EDCs is the presence of aromatic groups that may stabilize binding in the hydrophobic active site of the COX enzymes.
Our findings suggest a hitherto unknown mode of action by EDCs through inhibition of the PG pathway and suggest new avenues to investigate effects of EDCs on reproductive and immunological disorders that have become increasingly common in recent decades.
antiandrogens; benzophenones; cyclooxygenase; endocrine disruptors; parabens; phthalates; PPARs; prostaglandins
Motivation: Identifying orthologous genes in multiple genomes is a fundamental task in comparative genomics. Construction of intergenomic symmetrical best matches (SymBets) and joining them into clusters is a popular method of ortholog definition, embodied in several software programs. Despite their wide use, the computational complexity of these programs has not been thoroughly examined.
Results: In this work, we show that in the standard approach of iteration through all triangles of SymBets, the memory scales with at least the number of these triangles, O(g3) (where g = number of genomes), and construction time scales with the iteration through each pair, i.e. O(g6). We propose the EdgeSearch algorithm that iterates over edges in the SymBet graph rather than triangles of SymBets, and as a result has a worst-case complexity of only O(g3log g). Several optimizations reduce the run-time even further in realistically sparse graphs. In two real-world datasets of genomes from bacteriophages (POGs) and Mollicutes (MOGs), an implementation of the EdgeSearch algorithm runs about an order of magnitude faster than the original algorithm and scales much better with increasing number of genomes, with only minor differences in the final results, and up to 60 times faster than the popular OrthoMCL program with a 90% overlap between the identified groups of orthologs.
Availability and implementation: C++ source code freely available for download at ftp.ncbi.nih.gov/pub/wolf/COGs/COGsoft/
Supplementary information: Supplementary materials are available at Bioinformatics online.
A phyletic vector, also known as a phyletic (or phylogenetic) pattern, is a binary representation of the presences and absences of orthologous genes in different genomes. Joint occurrence of two or more genes in many genomes results in closely similar binary vectors representing these genes, and this similarity between gene vectors may be used as a measure of functional association between genes. Better understanding of quantitative properties of gene co-occurrences is needed for systematic studies of gene function and evolution. We used the probabilistic iterative algorithm Psi-square to find groups of similar phyletic vectors. An extended Psi-square algorithm, in which pseudocounts are implemented, shows better sensitivity in identifying proteins with known functional links than our earlier hierarchical clustering approach. At the same time, the specificity of inferring functional associations between genes in prokaryotic genomes is strongly dependent on the pathway: phyletic vectors of the genes involved in energy metabolism and in de novo biosynthesis of the essential precursors tend to be lumped together, whereas cellular modules involved in secretion, motility, assembly of cell surfaces, biosynthesis of some coenzymes, and utilization of secondary carbon sources tend to be identified with much greater specificity. It appears that the network of gene coinheritance in prokaryotes contains a giant connected component that encompasses most biosynthetic subsystems, along with a series of more independent modules involved in cell interaction with the environment.
Summary:The Evolutionary Trace Annotation (ETA) Server predicts enzymatic activity. ETA starts with a structure of unknown function, such as those from structural genomics, and with no prior knowledge of its mechanism uses the phylogenetic Evolutionary Trace (ET) method to extract key functional residues and propose a function-associated 3D motif, called a 3D template. ETA then searches previously annotated structures for geometric template matches that suggest molecular and thus functional mimicry. In order to maximize the predictive value of these matches, ETA next applies distinctive specificity filters—evolutionary similarity, function plurality and match reciprocity. In large scale controls on enzymes, prediction coverage is 43% but the positive predictive value rises to 92%, thus minimizing false annotations. Users may modify any search parameter, including the template. ETA thus expands the ET suite for protein structure annotation, and can contribute to the annotation efforts of metaservers.
Availability:The ETA Server is a web application available at http://mammoth.bcm.tmc.edu/eta/.
Structural genomics projects such as the Protein Structure Initiative (PSI) yield many new structures, but often these have no known molecular functions. One approach to recover this information is to use 3D templates – structure-function motifs that consist of a few functionally critical amino acids and may suggest functional similarity when geometrically matched to other structures. Since experimentally determined functional sites are not common enough to define 3D templates on a large scale, this work tests a computational strategy to select relevant residues for 3D templates.
Based on evolutionary information and heuristics, an Evolutionary Trace Annotation (ETA) pipeline built templates for 98 enzymes, half taken from the PSI, and sought matches in a non-redundant structure database. On average each template matched 2.7 distinct proteins, of which 2.0 share the first three Enzyme Commission digits as the template's enzyme of origin. In many cases (61%) a single most likely function could be predicted as the annotation with the most matches, and in these cases such a plurality vote identified the correct function with 87% accuracy. ETA was also found to be complementary to sequence homology-based annotations. When matches are required to both geometrically match the 3D template and to be sequence homologs found by BLAST or PSI-BLAST, the annotation accuracy is greater than either method alone, especially in the region of lower sequence identity where homology-based annotations are least reliable.
These data suggest that knowledge of evolutionarily important residues improves functional annotation among distant enzyme homologs. Since, unlike other 3D template approaches, the ETA method bypasses the need for experimental knowledge of the catalytic mechanism, it should prove a useful, large scale, and general adjunct to combine with other methods to decipher protein function in the structural proteome.
Major changes in climate have been observed in the Arctic and climate models predict further amplification of the enhanced greenhouse effect at high-latitudes leading to increased warming. We propose that warming in the Arctic may affect the annual growth conditions of the cold adapted Arctic charr and that such effects can already be detected retrospectrally using otolith data.
Inter-annual growth of the circumpolar Arctic charr (Salvelinus alpinus, L.) was analysed in relation to climatic changes observed in the Arctic during the last two decades. Arctic charr were sampled from six locations at Qeqertarsuaq in West Greenland, where climate data have been recorded since 1990. Two fish populations met the criteria of homogeny and, consequently, only these were used in further analyses. The results demonstrate a complex coupling between annual growth rates and fluctuations in annual mean temperatures and precipitation. Significant changes in temporal patterns of growth were observed between cohorts of 1990 and 2004.
Differences in pattern of growth appear to be a consequence of climatic changes over the last two decades and we thereby conclude that climatic affects short term and inter-annual growth as well as influencing long term shifts in age-specific growth patterns in population of Arctic charr.
Function prediction frequently relies on comparing genes or gene products to search for relevant similarities. Because the number of protein structures with unknown function is mushrooming, however, we asked here whether such comparisons could be improved by focusing narrowly on the key functional features of protein structures, as defined by the Evolutionary Trace (ET). Therefore a series of algorithms was built to (a) extract local motifs (3D templates) from protein structures based on ET ranking of residue importance; (b) to assess their geometric and evolutionary similarity to other structures; and (c) to transfer enzyme annotation whenever a plurality was reached across matches. Whereas a prototype had only been 80% accurate and was not scalable, here a speedy new matching algorithm enabled large-scale searches for reciprocal matches and thus raised annotation specificity to 100% in both positive and negative controls of 49 enzymes and 50 non-enzymes, respectively—in one case even identifying an annotation error—while maintaining sensitivity (∼60%). Critically, this Evolutionary Trace Annotation (ETA) pipeline requires no prior knowledge of functional mechanisms. It could thus be applied in a large-scale retrospective study of 1218 structural genomics enzymes and reached 92% accuracy. Likewise, it was applied to all 2935 unannotated structural genomics proteins and predicted enzymatic functions in 320 cases: 258 on first pass and 62 more on second pass. Controls and initial analyses suggest that these predictions are reliable. Thus the large-scale evolutionary integration of sequence-structure-function data, here through reciprocal identification of local, functionally important structural features, may contribute significantly to de-orphaning the structural proteome.