Hierarchical classification of functionally equivalent genes in prokaryotes

Functional classification of genes represents a fundamental problem to many biological studies. Most of the existing classification schemes are based on the concepts of homology and orthology, which were originally introduced to study gene evolution but might not be the most appropriate for gene function prediction, particularly at high resolution level. We have recently developed a scheme for hierarchical classification of genes (HCGs) in prokaryotes. In the HCG scheme, the functional equivalence relationships among genes are first assessed through a careful application of both sequence similarity and genomic neighborhood information; and genes are then classified into a hierarchical structure of clusters, where genes in each cluster are functionally equivalent at some resolution level, and the level of resolution goes higher as the clusters become increasingly smaller traveling down the hierarchy. The HCG scheme is validated through comparisons with the taxonomy of the prokaryotic genomes, Clusters of Orthologous Groups (COGs) of genes and the Pfam system. We have applied the HCG scheme to 224 complete prokaryotic genomes, and constructed a HCG database consisting of a forest of 5339 multi-level and 15 770 single-level trees of gene clusters covering approximately 93% of the genes of these 224 genomes. The validation results indicate that the HCG scheme not only captures the key features of the existing classification schemes but also provides a much richer organization of genes which can be used for functional prediction of genes at higher resolution and to help reveal evolutionary trace of the genes.     

The COG database: a tool for genome-scale analysis of protein functions and evolution

Rational classification of proteins encoded in sequenced genomes is critical for making the genome sequences maximally useful for functional and evolutionary studies. The database of Clusters of Orthologous Groups of proteins (COGs) is an attempt on a phylogenetic classification of the proteins encoded in 21 complete genomes of bacteria, archaea and eukaryotes (http://www. ncbi.nlm. nih.gov/COG). The COGs were constructed by applying the criterion of consistency of genome-specific best hits to the results of an exhaustive comparison of all protein sequences from these genomes. The database comprises 2091 COGs that include 56-83% of the gene products from each of the complete bacterial and archaeal genomes and approximately 35% of those from the yeast Saccharomyces cerevisiae genome. The COG database is accompanied by the COGNITOR program that is used to fit new proteins into the COGs and can be applied to functional and phylogenetic annotation of newly sequenced genomes.     

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 ~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 ~20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (~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.     

最小基因组研究进展

随着许多生物体全基因组测序的完成,兴起了最小基因组的研究,即一个能营独立生活的生物体最少需要多少个基因。已知最小细胞支原体基因组是研究最小基因组的重要内容,还通过比较多种已测序基因组COG分析最小基因组,目前通过转座子插入基因突主为和同源重组删除基因的分析,进行最小基因组研究。     

COGNAT: a web server for comparative analysis of genomic neighborhoods

Background

In prokaryotic genomes, functionally coupled genes can be organized in conserved gene clusters enabling their coordinated regulation. Such clusters could contain one or several operons, which are groups of co-transcribed genes. Those genes that evolved from a common ancestral gene by speciation (i.e. orthologs) are expected to have similar genomic neighborhoods in different organisms, whereas those copies of the gene that are responsible for dissimilar functions (i.e. paralogs) could be found in dissimilar genomic contexts. Comparative analysis of genomic neighborhoods facilitates the prediction of co-regulated genes and helps to discern different functions in large protein families.

Aim

We intended, building on the attribution of gene sequences to the clusters of orthologous groups of proteins (COGs), to provide a method for visualization and comparative analysis of genomic neighborhoods of evolutionary related genes, as well as a respective web server.

Results

Here we introduce the COmparative Gene Neighborhoods Analysis Tool (COGNAT), a web server for comparative analysis of genomic neighborhoods. The tool is based on the COG database, as well as the Pfam protein families database. As an example, we show the utility of COGNAT in identifying a new type of membrane protein complex that is formed by paralog(s) of one of the membrane subunits of the NADH:quinone oxidoreductase of type 1 (COG1009) and a cytoplasmic protein of unknown function (COG3002).

Reviewers

This article was reviewed by Drs. Igor Zhulin, Uri Gophna and Igor Rogozin.

     

Clusters of orthologous genes for 41 archaeal genomes and implications for evolutionary genomics of archaea

Background  

An evolutionary classification of genes from sequenced genomes that distinguishes between orthologs and paralogs is indispensable for genome annotation and evolutionary reconstruction. Shortly after multiple genome sequences of bacteria, archaea, and unicellular eukaryotes became available, an attempt on such a classification was implemented in Clusters of Orthologous Groups of proteins (COGs). Rapid accumulation of genome sequences creates opportunities for refining COGs but also represents a challenge because of error amplification. One of the practical strategies involves construction of refined COGs for phylogenetically compact subsets of genomes.     

Gene content and organization of an 85-kb DNA segment from the genome of the phytopathogenic mollicute Spiroplasma kunkelii

Spiroplasma kunkelii, the causative agent of corn stunt disease in maize ( Zea mays L.), is a helical, cell wall-less prokaryote assigned to the class Mollicutes. As part of a project to sequence the entire S. kunkelii genome, we analyzed an 85-kb DNA segment from the pathogenic strain CR2-3x. This genome segment contains 101 ORFs and two tRNA genes. The majority of the ORFs code for predicted proteins that can be assigned to respective clusters of orthologous groups (COGs). These COGs cover diverse functional categories including genetic information storage and processing, cellular processes, and metabolism. The most notable gene cluster in this genome segment is a super-operon capable of encoding 24 ribosomal proteins. The organization of genes in this operon reflects the unique evolutionary position of the spiroplasma. Gene duplications, domain rearrangements, and frameshift mutations in the segment are interpreted as indicators of phase variation in the spiroplasma. To our knowledge, this is the first analysis of a large genome segment from a plant pathogenic spiroplasma.Communicated by W. Goebel     

Genome engineering reveals large dispensable regions in Bacillus subtilis

Bacterial genomes contain 250 to 500 essential genes, as suggested by single gene disruptions and theoretical considerations. If this view is correct, the remaining nonessential genes of an organism, such as Bacillus subtilis, have been acquired during evolution in its perpetually changing ecological niches. Notably, approximately 47% of the approximately 4,100 genes of B. subtilis belong to paralogous gene families in which several members have overlapping functions. Thus, essential gene functions will outnumber essential genes. To answer the question to what extent the most recently acquired DNA contributes to the life of B. subtilis under standard laboratory growth conditions, we initiated a "reconstruction" of the B. subtilis genome by removing prophages and AT-rich islands. Stepwise deletion of two prophages (SPbeta, PBSX), three prophage-like regions, and the largest operon of B. subtilis (pks) resulted in a genome reduction of 7.7% and elimination of 332 genes. The resulting strain was phenotypically characterized by metabolic flux analysis, proteomics, and specific assays for protein secretion, competence development, sporulation, and cell motility. We show that genome engineering is a feasible strategy for functional analysis of large gene clusters, and that removal of dispensable genomic regions may pave the way toward an optimized Bacillus cell factory.     

Comparative genomic analysis by microbial COGs self-attraction rate

Whole genome analysis provides new perspectives to determine phylogenetic relationships among microorganisms. The availability of whole nucleotide sequences allows different levels of comparison among genomes by several approaches. In this work, self-attraction rates were considered for each cluster of orthologous groups of proteins (COGs) class in order to analyse gene aggregation levels in physical maps. Phylogenetic relationships among microorganisms were obtained by comparing self-attraction coefficients. Eighteen-dimensional vectors were computed for a set of 168 completely sequenced microbial genomes (19 archea, 149 bacteria). The components of the vector represent the aggregation rate of the genes belonging to each of 18 COGs classes. Genes involved in nonessential functions or related to environmental conditions showed the highest aggregation rates. On the contrary genes involved in basic cellular tasks showed a more uniform distribution along the genome, except for translation genes. Self-attraction clustering approach allowed classification of Proteobacteria, Bacilli and other species belonging to Firmicutes. Rearrangement and Lateral Gene Transfer events may influence divergences from classical taxonomy. Each set of COG classes’ aggregation values represents an intrinsic property of the microbial genome. This novel approach provides a new point of view for whole genome analysis and bacterial characterization.     

Tests for gene clustering.

Comparing chromosomal gene order in two or more related species is an important approach to studying the forces that guide genome organization and evolution. Linked clusters of similar genes found in related genomes are often used to support arguments of evolutionary relatedness or functional selection. However, as the gene order and the gene complement of sister genomes diverge progressively due to large scale rearrangements, horizontal gene transfer, gene duplication and gene loss, it becomes increasingly difficult to determine whether observed similarities in local genomic structure are indeed remnants of common ancestral gene order, or are merely coincidences. A rigorous comparative genomics requires principled methods for distinguishing chance commonalities, within or between genomes, from genuine historical or functional relationships. In this paper, we construct tests for significant groupings against null hypotheses of random gene order, taking incomplete clusters, multiple genomes, and gene families into account. We consider both the significance of individual clusters of prespecified genes and the overall degree of clustering in whole genomes.     

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 neighborhood network reveals insights into enediyne biosynthesis and facilitates prediction and prioritization for discovery

The enediynes are one of the most fascinating families of bacterial natural products given their unprecedented molecular architecture and extraordinary cytotoxicity. Enediynes are rare with only 11 structurally characterized members and four additional members isolated in their cycloaromatized form. Recent advances in DNA sequencing have resulted in an explosion of microbial genomes. A virtual survey of the GenBank and JGI genome databases revealed 87 enediyne biosynthetic gene clusters from 78 bacteria strains, implying that enediynes are more common than previously thought. Here we report the construction and analysis of an enediyne genome neighborhood network (GNN) as a high-throughput approach to analyze secondary metabolite gene clusters. Analysis of the enediyne GNN facilitated rapid gene cluster annotation, revealed genetic trends in enediyne biosynthetic gene clusters resulting in a simple prediction scheme to determine 9- versus 10-membered enediyne gene clusters, and supported a genomic-based strain prioritization method for enediyne discovery.     

放线菌基因组大片段克隆载体及异源表达宿主改造的研究进展

大规模基因组测序发现放线菌基因组内包含有极丰富的天然产物合成基因, 是非常有价值的资源。放线菌基因组中负责天然产物合成的基因通常成簇存在。要想完整地克隆这些较大的基因簇并且进行功能研究, 或者通过异源表达激活原本沉默的天然产物合成基因簇, 需要大容量的载体系统和合适的异源宿主。本文重点介绍了放线菌中常用于基因组大片段克隆的载体及异源表达宿主改造的研究进展。     

Selective maintenance of Drosophila tandemly arranged duplicated genes during evolution

Background

The physical organization and chromosomal localization of genes within genomes is known to play an important role in their function. Most genes arise by duplication and move along the genome by random shuffling of DNA segments. Higher order structuring of the genome occurs in eukaryotes, where groups of physically linked genes are co-expressed. However, the contribution of gene duplication to gene order has not been analyzed in detail, as it is believed that co-expression due to recent duplicates would obscure other domains of co-expression.

Results

We have catalogued ordered duplicated genes in Drosophila melanogaster, and found that one in five of all genes is organized as tandem arrays. Furthermore, among arrays that have been spatially conserved over longer periods than would be expected on the basis of random shuffling, a disproportionate number contain genes encoding developmental regulators. Using in situ gene expression data for more than half of the Drosophila genome, we find that genes in these conserved clusters are co-expressed to a much higher extent than other duplicated genes.

Conclusions

These results reveal the existence of functional constraints in insects that retain copies of genes encoding developmental and regulatory proteins as neighbors, allowing their co-expression. This co-expression may be the result of shared cis-regulatory elements or a shared need for a specific chromatin structure. Our results highlight the association between genome architecture and the gene regulatory networks involved in the construction of the body plan.     

Microsatellite motifs with moderate GC content are clustered around genes on Arabidopsis thaliana chromosome 2

Microsatellites, arrays of 1-6 bp sequences, are abundant in almost all the eukaryotic genomes. Their distribution in the genome is widely accepted to be differential and non random along the axis of the chromosomes. Arabidopsis thaliana genome is dominated by mononucleotide repeats, (A)n being the most abundant motif. In total, 39 microsatellite motifs extended to more than 100 bp in length. Of these, 8 loci are devoid of any gene in their proximity. (AG)n is the most abundant motif among longer repeats. The non-random distribution of microsatellite in the genome is reflected as occurrence of microsatellite clusters in the genome. In total, 3400 microsatellite clusters have been identified in the Arabidopsis genome. Chromosome 2, which is 19.7 Mb long, harbors 550 clusters accommodating 29% of all the microsatellites present on this chromosome. Further, 409 of the 6239 genes on chromosome 2 are associated with 323 microsatellite clusters. Motifs like (AGG)n and (ACT)n, show preferential accommodation in clusters that overlap with genes. Among all the microsatellite clusters that show an overlap with genes, 80% of the clusters show an overlap in such a way that the cluster ends beyond the 3'-end of the gene or starts before the 5'-end of a gene. Genes with diverse functions show association with the clusters. However, not all members of a gene family show similar associations.     

Singular value decomposition-based regression identifies activation of endogenous signaling pathways in vivo

Background

The physical organization and chromosomal localization of genes within genomes is known to play an important role in their function. Most genes arise by duplication and move along the genome by random shuffling of DNA segments. Higher order structuring of the genome occurs in eukaryotes, where groups of physically linked genes are co-expressed. However, the contribution of gene duplication to gene order has not been analyzed in detail, as it is believed that co-expression due to recent duplicates would obscure other domains of co-expression.

Results

We have catalogued ordered duplicated genes in Drosophila melanogaster, and found that one in five of all genes is organized as tandem arrays. Furthermore, among arrays that have been spatially conserved over longer periods than would be expected on the basis of random shuffling, a disproportionate number contain genes encoding developmental regulators. Using in situ gene expression data for more than half of the Drosophila genome, we find that genes in these conserved clusters are co-expressed to a much higher extent than other duplicated genes.

Conclusions

These results reveal the existence of functional constraints in insects that retain copies of genes encoding developmental and regulatory proteins as neighbors, allowing their co-expression. This co-expression may be the result of shared cis-regulatory elements or a shared need for a specific chromatin structure. Our results highlight the association between genome architecture and the gene regulatory networks involved in the construction of the body plan.