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Yuri I Wolf

Publications and source records attributed to Yuri I Wolf.

At least 19 recordsLinked to original sources

The COG database: an updated version includes eukaryotes.

BACKGROUND: The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. RESULTS: We describe here a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or approximately 54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of approximately 20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (approximately 1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes. CONCLUSION: The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies.

Animals↗

Remarkable interkingdom conservation of intron positions and massive, lineage-specific intron loss and gain in eukaryotic evolution.

Sequencing of eukaryotic genomes allows one to address major evolutionary problems, such as the evolution of gene structure. We compared the intron positions in 684 orthologous gene sets from 8 complete genomes of animals, plants, fungi, and protists and constructed parsimonious scenarios of evolution of the exon-intron structure for the respective genes. Approximately one-third of the introns in the malaria parasite Plasmodium falciparum are shared with at least one crown group eukaryote; this number indicates that these introns have been conserved through >1.5 billion years of evolution that separate Plasmodium from the crown group. Paradoxically, humans share many more introns with the plant Arabidopsis thaliana than with the fly or nematode. The inferred evolutionary scenario holds that the common ancestor of Plasmodium and the crown group and, especially, the common ancestor of animals, plants, and fungi had numerous introns. Most of these ancestral introns, which are retained in the genomes of vertebrates and plants, have been lost in fungi, nematodes, arthropods, and probably Plasmodium. In addition, numerous introns have been inserted into vertebrate and plant genes, whereas, in other lineages, intron gain was much less prominent.

Amino Acid Sequence↗

Evolution of mosaic operons by horizontal gene transfer and gene displacement in situ.

BACKGROUND: Shuffling and disruption of operons and horizontal gene transfer are major contributions to the new, dynamic view of prokaryotic evolution. Under the 'selfish operon' hypothesis, operons are viewed as mobile genetic entities that are constantly disseminated via horizontal gene transfer, although their retention could be favored by the advantage of coregulation of functionally linked genes. Here we apply comparative genomics and phylogenetic analysis to examine horizontal transfer of entire operons versus displacement of individual genes within operons by horizontally acquired orthologs and independent assembly of the same or similar operons from genes with different phylogenetic affinities. RESULTS: Since a substantial number of operons have been identified experimentally in only a few model bacteria, evolutionarily conserved gene strings were analyzed as surrogates of operons. The phylogenetic affinities within these predicted operons were assessed first by sequence similarity analysis and then by phylogenetic analysis, including statistical tests of tree topology. Numerous cases of apparent horizontal transfer of entire operons were detected. However, it was shown that apparent horizontal transfer of individual genes or arrays of genes within operons is not uncommon either and results in xenologous gene displacement in situ, that is, displacement of an ancestral gene by a horizontally transferred ortholog from a taxonomically distant organism without change of the local gene organization. On rarer occasions, operons might have evolved via independent assembly, in part from horizontally acquired genes. CONCLUSIONS: The discovery of in situ gene displacement shows that combination of rampant horizontal gene transfer with selection for preservation of operon structure provides for events in prokaryotic evolution that, a priori, seem improbable. These findings also emphasize that not all aspects of operon evolution are selfish, with operon integrity maintained by purifying selection at the organism level.

Alkyl and Aryl Transferases↗

Genome sequence of the cyanobacterium Prochlorococcus marinus SS120, a nearly minimal oxyphototrophic genome.

Prochlorococcus marinus, the dominant photosynthetic organism in the ocean, is found in two main ecological forms: high-light-adapted genotypes in the upper part of the water column and low-light-adapted genotypes at the bottom of the illuminated layer. P. marinus SS120, the complete genome sequence reported here, is an extremely low-light-adapted form. The genome of P. marinus SS120 is composed of a single circular chromosome of 1,751,080 bp with an average G+C content of 36.4%. It contains 1,884 predicted protein-coding genes with an average size of 825 bp, a single rRNA operon, and 40 tRNA genes. Together with the 1.66-Mbp genome of P. marinus MED4, the genome of P. marinus SS120 is one of the two smallest genomes of a photosynthetic organism known to date. It lacks many genes that are involved in photosynthesis, DNA repair, solute uptake, intermediary metabolism, motility, phototaxis, and other functions that are conserved among other cyanobacteria. Systems of signal transduction and environmental stress response show a particularly drastic reduction in the number of components, even taking into account the small size of the SS120 genome. In contrast, housekeeping genes, which encode enzymes of amino acid, nucleotide, cofactor, and cell wall biosynthesis, are all present. Because of its remarkable compactness, the genome of P. marinus SS120 might approximate the minimal gene complement of a photosynthetic organism.

Adaptation, Physiological↗

No simple dependence between protein evolution rate and the number of protein-protein interactions: only the most prolific interactors tend to evolve slowly.

BACKGROUND: It has been suggested that rates of protein evolution are influenced, to a great extent, by the proportion of amino acid residues that are directly involved in protein function. In agreement with this hypothesis, recent work has shown a negative correlation between evolutionary rates and the number of protein-protein interactions. However, the extent to which the number of protein-protein interactions influences evolutionary rates remains unclear. Here, we address this question at several different levels of evolutionary relatedness. RESULTS: Manually curated data on the number of protein-protein interactions among Saccharomyces cerevisiae proteins was examined for possible correlation with evolutionary rates between S. cerevisiae and Schizosaccharomyces pombe orthologs. Only a very weak negative correlation between the number of interactions and evolutionary rate of a protein was observed. Furthermore, no relationship was found between a more general measure of the evolutionary conservation of S. cerevisiae proteins, based on the taxonomic distribution of their homologs, and the number of protein-protein interactions. However, when the proteins from yeast were assorted into discrete bins according to the number of interactions, it turned out that 6.5% of the proteins with the greatest number of interactions evolved, on average, significantly slower than the rest of the proteins. Comparisons were also performed using protein-protein interaction data obtained with high-throughput analysis of Helicobacter pylori proteins. No convincing relationship between the number of protein-protein interactions and evolutionary rates was detected, either for comparisons of orthologs from two completely sequenced H. pylori strains or for comparisons of H. pylori and Campylobacter jejuni orthologs, even when the proteins were classified into bins by the number of interactions. CONCLUSION: The currently available comparative-genomic data do not support the hypothesis that the evolutionary rates of the majority of proteins substantially depend on the number of protein-protein interactions they are involved in. However, a small fraction of yeast proteins with the largest number of interactions (the hubs of the interaction network) tend to evolve slower than the bulk of the proteins.

Bacterial Proteins↗

Potential genomic determinants of hyperthermophily.

We searched for genes that could be important for hyperthermophily using a flexible approach to phyletic pattern analysis. We identified 290 clusters of orthologous groups of proteins (COGs) that are preferentially present in archaeal and bacterial hyperthermophiles. Of these, 58 COGs include proteins from at least one bacterium and two archaea, and these were considered to be the best candidates for a specific association with the hyperthermophilic phenotype. Detailed sequence and genome-context analysis of these COGs led to functional predictions for several previously uncharacterized protein families, including a novel group of putative molecular chaperones and a unique transcriptional regulator.

Archaea↗

The structure of the protein universe and genome evolution.

Despite the practically unlimited number of possible protein sequences, the number of basic shapes in which proteins fold seems not only to be finite, but also to be relatively small, with probably no more than 10,000 folds in existence. Moreover, the distribution of proteins among these folds is highly non-homogeneous -- some folds and superfamilies are extremely abundant, but most are rare. Protein folds and families encoded in diverse genomes show similar size distributions with notable mathematical properties, which also extend to the number of connections between domains in multidomain proteins. All these distributions follow asymptotic power laws, such as have been identified in a wide variety of biological and physical systems, and which are typically associated with scale-free networks. These findings suggest that genome evolution is driven by extremely general mechanisms based on the preferential attachment principle.

Databases, Protein↗

Birth and death of protein domains: a simple model of evolution explains power law behavior.

BACKGROUND: Power distributions appear in numerous biological, physical and other contexts, which appear to be fundamentally different. In biology, power laws have been claimed to describe the distributions of the connections of enzymes and metabolites in metabolic networks, the number of interactions partners of a given protein, the number of members in paralogous families, and other quantities. In network analysis, power laws imply evolution of the network with preferential attachment, i.e. a greater likelihood of nodes being added to pre-existing hubs. Exploration of different types of evolutionary models in an attempt to determine which of them lead to power law distributions has the potential of revealing non-trivial aspects of genome evolution. RESULTS: A simple model of evolution of the domain composition of proteomes was developed, with the following elementary processes: i) domain birth (duplication with divergence), ii) death (inactivation and/or deletion), and iii) innovation (emergence from non-coding or non-globular sequences or acquisition via horizontal gene transfer). This formalism can be described as a birth, death and innovation model (BDIM). The formulas for equilibrium frequencies of domain families of different size and the total number of families at equilibrium are derived for a general BDIM. All asymptotics of equilibrium frequencies of domain families possible for the given type of models are found and their appearance depending on model parameters is investigated. It is proved that the power law asymptotics appears if, and only if, the model is balanced, i.e. domain duplication and deletion rates are asymptotically equal up to the second order. It is further proved that any power asymptotic with the degree not equal to -1 can appear only if the hypothesis of independence of the duplication/deletion rates on the size of a domain family is rejected. Specific cases of BDIMs, namely simple, linear, polynomial and rational models, are considered in details and the distributions of the equilibrium frequencies of domain families of different size are determined for each case. We apply the BDIM formalism to the analysis of the domain family size distributions in prokaryotic and eukaryotic proteomes and show an excellent fit between these empirical data and a particular form of the model, the second-order balanced linear BDIM. Calculation of the parameters of these models suggests surprisingly high innovation rates, comparable to the total domain birth (duplication) and elimination rates, particularly for prokaryotic genomes. CONCLUSIONS: We show that a straightforward model of genome evolution, which does not explicitly include selection, is sufficient to explain the observed distributions of domain family sizes, in which power laws appear as asymptotic. However, for the model to be compatible with the data, there has to be a precise balance between domain birth, death and innovation rates, and this is likely to be maintained by selection. The developed approach is oriented at a mathematical description of evolution of domain composition of proteomes, but a simple reformulation could be applied to models of other evolving networks with preferential attachment.

Animals↗

Congruent evolution of different classes of non-coding DNA in prokaryotic genomes.

Prokaryotic genomes are considered to be 'wall-to-wall' genomes, which consist largely of genes for proteins and structural RNAs, with only a small fraction of the genomic DNA allotted to intergenic regions, which are thought to typically contain regulatory signals. The majority of bacterial and archaeal genomes contain 6-14% non-coding DNA. Significant positive correlations were detected between the fraction of non-coding DNA and inter- and intra-operonic distances, suggesting that different classes of non-coding DNA evolve congruently. In contrast, no correlation was found between any of these characteristics of non-coding sequences and the number of genes or genome size. Thus, the non-coding regions and the gene sets in prokaryotes seem to evolve in different regimes. The evolution of non-coding regions appears to be determined primarily by the selective pressure to minimize the amount of non-functional DNA, while maintaining essential regulatory signals, because of which the content of non-coding DNA in different genomes is relatively uniform and intra- and inter-operonic non-coding regions evolve congruently. In contrast, the gene set is optimized for the particular environmental niche of the given microbe, which results in the lack of correlation between the gene number and the characteristics of non-coding regions.

DNA, Intergenic↗

Connected gene neighborhoods in prokaryotic genomes.

A computational method was developed for delineating connected gene neighborhoods in bacterial and archaeal genomes. These gene neighborhoods are not typically present, in their entirety, in any single genome, but are held together by overlapping, partially conserved gene arrays. The procedure was applied to comparing the orders of orthologous genes, which were extracted from the database of Clusters of Orthologous Groups of proteins (COGs), in 31 prokaryotic genomes and resulted in the identification of 188 clusters of gene arrays, which included 1001 of 2890 COGs. These clusters were projected onto actual genomes to produce extended neighborhoods including additional genes, which are adjacent to the genes from the clusters and are transcribed in the same direction, which resulted in a total of 2387 COGs being included in the neighborhoods. Most of the neighborhoods consist predominantly of genes united by a coherent functional theme, but also include a minority of genes without an obvious functional connection to the main theme. We hypothesize that although some of the latter genes might have unsuspected roles, others are maintained within gene arrays because of the advantage of expression at a level that is typical of the given neighborhood. We designate this phenomenon 'genomic hitchhiking'. The largest neighborhood includes 79 genes (COGs) and consists of overlapping, rearranged ribosomal protein superoperons; apparent genome hitchhiking is particularly typical of this neighborhood and other neighborhoods that consist of genes coding for translation machinery components. Several neighborhoods involve previously undetected connections between genes, allowing new functional predictions. Gene neighborhoods appear to evolve via complex rearrangement, with different combinations of genes from a neighborhood fixed in different lineages.

Algorithms↗

Evolution of gene fusions: horizontal transfer versus independent events.

BACKGROUND: Gene fusions can be used as tools for functional prediction and also as evolutionary markers. Fused genes often show a scattered phyletic distribution, which suggests a role for processes other than vertical inheritance in their evolution. RESULTS: The evolutionary history of gene fusions was studied by phylogenetic analysis of the domains in the fused proteins and the orthologous domains that form stand-alone proteins. Clustering of fusion components from phylogenetically distant species was construed as evidence of dissemination of the fused genes by horizontal transfer. Of the 51 examined gene fusions that are represented in at least two of the three primary kingdoms (Bacteria, Archaea and Eukaryota), 31 were most probably disseminated by cross-kingdom horizontal gene transfer, whereas 14 appeared to have evolved independently in different kingdoms and two were probably inherited from the common ancestor of modern life forms. On many occasions, the evolutionary scenario also involves one or more secondary fissions of the fusion gene. For approximately half of the fusions, stand-alone forms of the fusion components are encoded by juxtaposed genes, which are known or predicted to belong to the same operon in some of the prokaryotic genomes. This indicates that evolution of gene fusions often, if not always, involves an intermediate stage, during which the future fusion components exist as juxtaposed and co-regulated, but still distinct, genes within operons. CONCLUSION: These findings suggest a major role for horizontal transfer of gene fusions in the evolution of protein-domain architectures, but also indicate that independent fusions of the same pair of domains in distant species is not uncommon, which suggests positive selection for the multidomain architectures.

DNA, Archaeal↗

The complete genome of hyperthermophile Methanopyrus kandleri AV19 and monophyly of archaeal methanogens.

We have determined the complete 1,694,969-nt sequence of the GC-rich genome of Methanopyrus kandleri by using a whole direct genome sequencing approach. This approach is based on unlinking of genomic DNA with the ThermoFidelase version of M. kandleri topoisomerase V and cycle sequencing directed by 2'-modified oligonucleotides (Fimers). Sequencing redundancy (3.3x) was sufficient to assemble the genome with less than one error per 40 kb. Using a combination of sequence database searches and coding potential prediction, 1,692 protein-coding genes and 39 genes for structural RNAs were identified. M. kandleri proteins show an unusually high content of negatively charged amino acids, which might be an adaptation to the high intracellular salinity. Previous phylogenetic analysis of 16S RNA suggested that M. kandleri belonged to a very deep branch, close to the root of the archaeal tree. However, genome comparisons indicate that, in both trees constructed using concatenated alignments of ribosomal proteins and trees based on gene content, M. kandleri consistently groups with other archaeal methanogens. M. kandleri shares the set of genes implicated in methanogenesis and, in part, its operon organization with Methanococcus jannaschii and Methanothermobacter thermoautotrophicum. These findings indicate that archaeal methanogens are monophyletic. A distinctive feature of M. kandleri is the paucity of proteins involved in signaling and regulation of gene expression. Also, M. kandleri appears to have fewer genes acquired via lateral transfer than other archaea. These features might reflect the extreme habitat of this organism.

Base Sequence↗

Classification and evolution of P-loop GTPases and related ATPases.

Sequences and available structures were compared for all the widely distributed representatives of the P-loop GTPases and GTPase-related proteins with the aim of constructing an evolutionary classification for this superclass of proteins and reconstructing the principal events in their evolution. The GTPase superclass can be divided into two large classes, each of which has a unique set of sequence and structural signatures (synapomorphies). The first class, designated TRAFAC (after translation factors) includes enzymes involved in translation (initiation, elongation, and release factors), signal transduction (in particular, the extended Ras-like family), cell motility, and intracellular transport. The second class, designated SIMIBI (after signal recognition particle, MinD, and BioD), consists of signal recognition particle (SRP) GTPases, the assemblage of MinD-like ATPases, which are involved in protein localization, chromosome partitioning, and membrane transport, and a group of metabolic enzymes with kinase or related phosphate transferase activity. These two classes together contain over 20 distinct families that are further subdivided into 57 subfamilies (ancient lineages) on the basis of conserved sequence motifs, shared structural features, and domain architectures. Ten subfamilies show a universal phyletic distribution compatible with presence in the last universal common ancestor of the extant life forms (LUCA). These include four translation factors, two OBG-like GTPases, the YawG/YlqF-like GTPases (these two subfamilies also consist of predicted translation factors), the two signal-recognition-associated GTPases, and the MRP subfamily of MinD-like ATPases. The distribution of nucleotide specificity among the proteins of the GTPase superclass indicates that the common ancestor of the entire superclass was a GTPase and that a secondary switch to ATPase activity has occurred on several independent occasions during evolution. The functions of most GTPases that are traceable to LUCA are associated with translation. However, in contrast to other superclasses of P-loop NTPases (RecA-F1/F0, AAA+, helicases, ABC), GTPases do not participate in NTP-dependent nucleic acid unwinding and reorganizing activities. Hence, we hypothesize that the ancestral GTPase was an enzyme with a generic regulatory role in translation, with subsequent diversification resulting in acquisition of diverse functions in transport, protein trafficking, and signaling. In addition to the classification of previously known families of GTPases and related ATPases, we introduce several previously undetected families and describe new functional predictions.

Adenosine Triphosphatases↗

Selection in the evolution of gene duplications.

BACKGROUND: Gene duplications have a major role in the evolution of new biological functions. Theoretical studies often assume that a duplication per se is selectively neutral and that, following a duplication, one of the gene copies is freed from purifying (stabilizing) selection, which creates the potential for evolution of a new function. RESULTS: In search of systematic evidence of accelerated evolution after duplication, we used data from 26 bacterial, six archaeal, and seven eukaryotic genomes to compare the mode and strength of selection acting on recently duplicated genes (paralogs) and on similarly diverged, unduplicated orthologous genes in different species. We find that the ratio of nonsynonymous to synonymous substitutions (Kn/Ks) in most paralogous pairs is <<1 and that paralogs typically evolve at similar rates, without significant asymmetry, indicating that both paralogs produced by a duplication are subject to purifying selection. This selection is, however, substantially weaker than the purifying selection affecting unduplicated orthologs that have diverged to the same extent as the analyzed paralogs. Most of the recently duplicated genes appear to be involved in various forms of environmental response; in particular, many of them encode membrane and secreted proteins. CONCLUSIONS: The results of this analysis indicate that recently duplicated paralogs evolve faster than orthologs with the same level of divergence and similar functions, but apparently do not experience a phase of neutral evolution. We hypothesize that gene duplications that persist in an evolving lineage are beneficial from the time of their origin, due primarily to a protein dosage effect in response to variable environmental conditions; duplications are likely to give rise to new functions at a later phase of their evolution once a higher level of divergence is reached.

Animals↗

Scale-free networks in biology: new insights into the fundamentals of evolution?

Scale-free network models describe many natural and social phenomena. In particular, networks of interacting components of a living cell were shown to possess scale-free properties. A recent study((1)) compares the system-level properties of metabolic and information networks in 43 archaeal, bacterial and eukaryal species and claims that the scale-free organization of these networks is more conserved during evolution than their content.

Archaea↗

Microevolutionary genomics of bacteria.

The availability of multiple complete genome sequences from the same species can facilitate attempts to systematically address basic questions in genome evolution. We refer to such efforts as "microevolutionary genomics". We report the results of comparative analyses of complete intraspecific genome (and proteome) sequences from four bacterial species--Chlamydophila pneumoniae, Escherichia coli, Helicobacter pylori and Neisseria meningitidis. Comparisons of average synonymous (K(s)) and nonsynonymous (K(a)) substitution rates were used to assess the influence of various biological factors on the rate of protein evolution. For example, E. coli experiences the most intense purifying selection of the species analyzed, and this may be due to the relatively larger population size of this species. In addition, essential genes were shown to be more evolutionarily conserved than nonessential genes in E. coli and duplicated genes have higher rates of evolution than unique genes for all species studied except C. pneumoniae. Different functional categories of genes were shown to evolve at significantly different rates emphasizing the role of category-specific functional constraints in determining evolutionary rates. Finally, functionally characterized genes tend to be conserved between strains, while uncharacterized genes are over-represented among the unique, strain-specific genes. This suggests the possibility that nonessential genes are responsible for driving the evolutionary diversification between strains.

Bacteria↗

Purifying and directional selection in overlapping prokaryotic genes.

In overlapping genes, the same DNA sequence codes for two proteins using different reading frames. Analysis of overlapping genes can help in understanding the mode of evolution of a coding region from noncoding DNA. We identified 71 pairs of convergent genes, with overlapping 3' ends longer than 15 nucleotides, that are conserved in at least two prokaryotic genomes. Among the overlap regions, we observed a statistically significant bias towards the 123:132 phase (i.e. the second codon base in one gene facing the degenerate third position in the second gene). This phase ensures the least mutual constraint on nonconservative amino acid replacements in both overlapping coding sequences. The excess of this phase is compatible with directional (positive) selection acting on the overlapping coding regions. This could be a general evolutionary mode for genes emerging from noncoding sequences, in which the protein sequence has not been subject to selection.

Amino Acid Sequence↗

Genome trees and the tree of life.

Genome comparisons indicate that horizontal gene transfer and differential gene loss are major evolutionary phenomena that, at least in prokaryotes, involve a large fraction, if not the majority, of genes. The extent of these events casts doubt on the feasibility of constructing a 'Tree of Life', because the trees for different genes often tell different stories. However, alternative approaches to tree construction that attempt to determine tree topology on the basis of comparisons of complete gene sets seem to reveal a phylogenetic signal that supports the three-domain evolutionary scenario and suggests the possibility of delineation of previously undetected major clades of prokaryotes. If the validity of these whole-genome approaches to tree building is confirmed by analyses of numerous new genomes, which are currently being sequenced at an increasing rate, it would seem that the concept of a universal 'species' tree is still appropriate. However, this tree should be reinterpreted as a prevailing trend in the evolution of genome-scale gene sets rather than as a complete picture of evolution.

Animals↗