Expanded microbial genome coverage and improved protein family annotation in the COG database

Galperin, Michael Y.; Makarova, Kira S.; Wolf, Yuri I.; Koonin, Eugene V.

doi:10.1093/nar/gku1223

Cited by 1,311 publications

(1,112 citation statements)

References 74 publications

(78 reference statements)

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“…To gain an overview of its main functions, we next analyzed the A. actinomycetemcomitans essential genome for enriched Clusters of Orthologous Groups (COG) functional categories (49). As expected, VT1169 and 624 were enriched for functions known to be vital for bacterial life (50), such as lipid metabolism and cell wall biogenesis (COGs I and M) (Fig.…”

Section: Resultsmentioning

confidence: 83%

Defining Genetic Fitness Determinants and Creating Genomic Resources for an Oral Pathogen

Narayanan

Ramsey

Stacy

et al. 2017

Appl Environ Microbiol

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Periodontitis is a microbial infection that destroys the structures that support the teeth. Although it is typically a chronic condition, rapidly progressing, aggressive forms are associated with the oral pathogen Aggregatibacter actinomycetemcomitans. One of this bacterium's key virulence traits is its ability to attach to surfaces and form robust biofilms that resist killing by the host and antibiotics. Though much has been learned about A. actinomycetemcomitans since its initial discovery, we lack insight into a fundamental aspect of its basic biology, as we do not know the full set of genes that it requires for viability (the essential genome). Furthermore, research on A. actinomycetemcomitans is hampered by the field's lack of a mutant collection. To address these gaps, we used rapid transposon mutant sequencing (Tn-seq) to define the essential genomes of two strains of A. actinomycetemcomitans, revealing a core set of 319 genes. We then generated an arrayed mutant library comprising Ͼ1,500 unique insertions and used a sequencing-based approach to define each mutant's position (well and plate) in the library. To demonstrate its utility, we screened the library for mutants with weakened resistance to subinhibitory erythromycin, revealing the multidrug efflux pump AcrAB as a critical resistance factor. During the screen, we discovered that erythromycin induces A. actinomycetemcomitans to form biofilms. We therefore devised a novel Tnseq-based screen to identify specific factors that mediate this phenotype and in follow-up experiments confirmed 4 mutants. Together, these studies present new insights and resources for investigating the basic biology and disease mechanisms of a human pathogen. IMPORTANCE Millions suffer from gum disease, which often is caused by Aggregatibacter actinomycetemcomitans, a bacterium that forms antibiotic-resistant biofilms. To fully understand any organism, we should be able to answer: what genes does it require for life? Here, we address this question for A. actinomycetemcomitans by determining the genes in its genome that cannot be mutated. As for the genes that can be mutated, we archived these mutants into a library, which we used to find genes that contribute to antibiotic resistance, leading us to discover that antibiotics cause A. actinomycetemcomitans to form biofilms. We then devised an approach to find genes that mediate this process and confirmed 4 genes. These results illuminate new fundamental traits of a human pathogen.KEYWORDS Aggregatibacter, Tn-seq, antibiotics, biofilms, essential genome O ne of the longest-studied human microbiomes is the "animalcules" (bacteria), first observed by van Leeuwenhoek (1), that inhabit the oral cavity. Oral bacteria readily attach to tooth surfaces and develop into spatially organized, multispecies biofilms (2). These communities also persist beneath the gum line in the subgingival pocket, where they are kept in check by host immune cells (3). Though normally benign, subgingival communities can accumulate opportunistic oral p...

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Section: Resultsmentioning

confidence: 83%

Defining Genetic Fitness Determinants and Creating Genomic Resources for an Oral Pathogen

Narayanan

Ramsey

Stacy

et al. 2017

Appl Environ Microbiol

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“…To further test the model consistency with the empirical data, steady-state distributions were calculated for subsets of genes. The subsets were chosen based on the functional classes of genes as classified in the COG (Clusters of Orthologous Genes) database (17,18). The selection landscape and k i were optimized for the best log-likelihood fit of the distribution predicted by the model to the genomic data (SI Appendix, Table S5).…”

Section: Resultsmentioning

confidence: 99%

“…In particular, we analyzed genes that are associated with specific cellular functions as classified in the COG database (17,18) under the assumption that functionally similar genes evolve under similar selection landscapes. We obtained good fits to the model for all functional classes, indicating that the conclusion on the typical beneficial effect of gene acquisition applies to functionally diverse classes of genes.…”

Section: Discussionmentioning

confidence: 99%

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Theory of prokaryotic genome evolution

Sela

Wolf

Koonin

2016

Proc. Natl. Acad. Sci. U.S.A.

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137

168

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Bacteria and archaea typically possess small genomes that are tightly packed with protein-coding genes. The compactness of prokaryotic genomes is commonly perceived as evidence of adaptive genome streamlining caused by strong purifying selection in large microbial populations. In such populations, even the small cost incurred by nonfunctional DNA because of extra energy and time expenditure is thought to be sufficient for this extra genetic material to be eliminated by selection. However, contrary to the predictions of this model, there exists a consistent, positive correlation between the strength of selection at the protein sequence level, measured as the ratio of nonsynonymous to synonymous substitution rates, and microbial genome size. Here, by fitting the genome size distributions in multiple groups of prokaryotes to predictions of mathematical models of population evolution, we show that only models in which acquisition of additional genes is, on average, slightly beneficial yield a good fit to genomic data. These results suggest that the number of genes in prokaryotic genomes reflects the equilibrium between the benefit of additional genes that diminishes as the genome grows and deletion bias (i.e., the rate of deletion of genetic material being slightly greater than the rate of acquisition). Thus, new genes acquired by microbial genomes, on average, appear to be adaptive. The tight spacing of protein-coding genes likely results from a combination of the deletion bias and purifying selection that efficiently eliminates nonfunctional, noncoding sequences. T he majority of bacterial and archaeal genomes are small, at least compared with the genomes of multicellular and many unicellular eukaryotes (1, 2). Also, with the exception of deteriorating genomes of some parasitic bacteria, the prokaryotic genomes are highly compact, with densely packed protein-coding genes and a low fraction of noncoding sequences (3). The small genome size is thought to be selected for fast replication, whereas the high gene density additionally facilitates coregulation of gene expression via the operon organization (4, 5). Across the full range of cellular life forms, a significant positive correlation has been shown to exist between genome size and N e u, where N e is the effective population size, and u is the mutation rate per nucleotide (6-9). Accordingly, a simple and appealing population genetic theory has been developed, under which selection strength controls genome size and complexity (6, 9). Prokaryotes, with the exception of some parasites, have large effective population sizes on the order of 10 9 or even higher, which implies strong selection enabling prokaryotes to maintain compact genomes (10). Under this strong selection regime, even short nonfunctional sequences incur cost that is "visible" to selection, conceivably through a combination of increasing energy expenditure and reducing the replication rate, and are efficiently weeded out (11). In eukaryotes, at least the multicellular forms, the effective population ...

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“…Only 308 462 (11.70%) of the genes in the database appeared fragmented and 658 (16.65%) were missing. We also 309 obtained a high number of orthologous proteins in both the SwissProt and the X. tropicalis databases that 310 fully matched (100% alignment coverage) or nearly fully corresponded (>80% alignment coverage) to 311 unigenes in O. cruralis (Figure 3) The database of Clusters of Orthologous Groups (COGs) is another common tool for functional 447 annotation (Galperin et al 2015). In this database, orthologous genes from 722 prokaryote genomes are 448 grouped according to their biological function.…”

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confidence: 99%

First de-novo transcriptome assembly of a South American frog, Oreobates cruralis, enables population genomic studies of Neotropical amphibians

Montero‐Mendieta

Grabherr

Lantz³

et al. 2017

Preprint

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Whole genome sequencing is opening the door to novel insights into the population structure and evolutionary history of poorly known species. In organisms with large genomes, which includes most amphibians, whole-genome sequencing is excessively challenging and transcriptome sequencing (RNA-seq) represents a cost-effective tool to explore genome-wide variability. Non-model organisms do not usually have a reference genome to facilitate assembly and the transcriptome sequence must be assembled de-novo. We used RNA-seq to obtain the transcriptome profile for Oreobates cruralis, a poorly known South American direct-developing frog. In total, 550,871 transcripts were assembled, corresponding to 422,999 putative genes. Of those, we identified 23,500, 37,349, 38,120 and 45,885 genes present in the Pfam, EggNOG, KEGG and GO databases, respectively. Interestingly, our results suggested that genes related to immune system and defense mechanisms are abundant in the transcriptome of O. cruralis. We also present a workflow to assist with pre-processing, assembling, evaluating and functionally annotating a de-novo transcriptome from RNA-seq data of non-model organisms. Our workflow guides the inexperienced user in an intuitive way through all the necessary steps to build de-novo transcriptome assemblies using readily available software and is freely available at: https://github.com/biomendi/PRACTICAL-GUIDE-TO-BUILD-DE-NOVO-TRANSCRIPTOME-ASSEMBLIES-FOR-NON-MODEL-ORGANISMS/wiki

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Expanded microbial genome coverage and improved protein family annotation in the COG database

Cited by 1,311 publications

References 74 publications

Defining Genetic Fitness Determinants and Creating Genomic Resources for an Oral Pathogen

Defining Genetic Fitness Determinants and Creating Genomic Resources for an Oral Pathogen

Theory of prokaryotic genome evolution

First de-novo transcriptome assembly of a South American frog, Oreobates cruralis, enables population genomic studies of Neotropical amphibians

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