Genomes OnLine database (GOLD) v.7: updates and new features

Mukherjee, Supratim; Stamatis, Dimitri; Bertsch, Jon; Ovchinnikova, Galina; Katta, Hema Y; Mojica, Alejandro; Chen, I-Min A.; Kyrpides, Nikos C.; Reddy, T. B. K.

doi:10.1093/nar/gky977

Cited by 187 publications

(146 citation statements)

References 29 publications

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“…If the VR coverage was below this cutoff, it was considered as significantly lower than expected, the TR-VR was colored in blue in this plot, and flagged as "low coverage" if no SNVs were detected in Fig Supplementary Table 1: List of metagenomes mined for DGRs. Metagenomes are associated with samples, ecological category, sample type, and publication based on information from IMG 31 and Gold 32 . The number of genomes in each assembly was estimated based on single-copy marker genes, while the ratio of bp in viral sequences among contigs of 10kb or more was derived from VirSorter detection of viral contigs (see Methods).…”

Section: Figure 2 Diversity and Major Types Of Dgr Targetsmentioning

confidence: 99%

“…vContact2 was run with "diamond" option to generate the PCs, clustering of VCs with cluster_one, and the reference database "ProkaryoticViralRefSeq94-Merged", all other parameters left as default. Metagenome-derived DGRs were also associated to a biome type based on the original sample classification available in the Gold database 32 ( Supplementary Table 1).…”

Section: Selection and Annotation Of Reference Genomes And Metagenomementioning

confidence: 99%

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Ecology and molecular targets of hypermutation in the global microbiome

Roux

Paul

Bagby

et al. 2020

Preprint

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Changes in the sequence of an organism's genome, i.e. mutations, are the raw material of evolution 1 . The frequency and location of mutations can be constrained by specific molecular mechanisms, such as Diversity-generating retroelements (DGRs) 2-4 . DGRs introduce mutations in specific target genes, and were characterized from several cultivated bacteria and bacteriophages 2 . Whilst a larger diversity of DGR loci has been identified in genomic data from environmental samples, i.e. metagenomes, the ecological role of these DGRs and their associated evolutionary drivers remain poorly understood 5-7 . Here we built and analyzed an extensive dataset of >30,000 metagenome-derived DGRs, and determine that DGRs have a single evolutionary origin and a universal bias towards adenine mutations. We further identified six major lineages of DGRs, each associated with a specific ecological niche defined as a genome type, i.e. whether the DGR is encoded on a viral or cellular genome, a limited set of taxa and environments, and a distinct type of target. Finally, we leverage read mapping and metagenomic time series to demonstrate that DGRs are consistently and broadly active, and responsible for >10% of all amino acid changes in some organisms at a conservative estimate. Overall, these results highlight the strong constraints under which DGRs diversify and expand, and elucidate several distinct roles these elements play in natural communities and in shaping microbial community structure and function in our environment. 2 30 35 40 45 50 55 60 65 70Here we expand the set of known DGRs ~15-fold by extracting 31,007 DGRs from public metagenomes and metatranscriptomes to obtain a holistic view of DGR diversity and their spatial and temporal dynamics. We leverage this comprehensive collection to (i) evaluate the global ecology and evolution of DGRs across viral and cellular genomes, (ii) characterize the functional diversity and molecular constraints of DGR targets, and (iii) infer temporal patterns of DGR activity across organisms and biomes. Taken together, these analyses reveal how DGRs are frequently transferred between genomes yet clearly restricted to specific ecological niches, within which they likely impact both viral and microbial dynamics by consistently driving amino acid-level diversification of their target domains.

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Section: Figure 2 Diversity and Major Types Of Dgr Targetsmentioning

confidence: 99%

Section: Selection and Annotation Of Reference Genomes And Metagenomementioning

confidence: 99%

Ecology and molecular targets of hypermutation in the global microbiome

Roux

Paul

Bagby

et al. 2020

Preprint

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“…In this study, we analyzed all protein-coding genes containing at least one pfam annotated in the genomes of 435 species, with a total of 8,209 pfams identified (see Methods). Eukaryotic species were included in our dataset if they were marked "Complete" by GOLD (Mukherjee et al 2019), and also present in TimeTree (Hedges et al 2006), a phylogenetic database which we used to assign evolutionary ages to pfams. The genes in our dataset were then assigned ages based on the oldest pfam they contained.…”

Section: Resultsmentioning

confidence: 99%

“…We compiled a dataset of whole-genome sequences with a sequencing status of 'complete' from the GOLD database (Mukherjee et al 2019, accessed August 7, 2018, resulting in a list of 1138 unique species. We then downloaded species from the Ensembl Biomart interface databases (Kinsella et al 2011;Zerbino et al 2018Zerbino et al , fungi V40, plant V40, metazoan V40, protest v40 and main V93, accessed between 07/31/2018Zerbino et al -09/12/2018.…”

Section: Data Compilationmentioning

confidence: 99%

Universal and taxon-specific trends in protein sequences as a function of age

James

Willis

Nelson

et al. 2020

Preprint

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Extant protein-coding sequences span a huge range of ages, from those that emerged only recently in particular lineages, to those present in the last universal common ancestor. Because evolution has had less time to act on young sequences, there might be "phylostratigraphy" trends in any properties that evolve slowly with age. Indeed, a long-term reduction in hydrophobicity and in hydrophobic clustering has been found in previous, taxonomically restricted studies. Here we perform integrated phylostratigraphy across 435 fully sequenced and dated eukaryotic species, using sensitive HMM methods to detect homology of protein domains (which may vary in age within the same gene), and applying a variety of quality filters. We find that the reduction in hydrophobic clustering is universal across diverse lineages, showing limited sign of saturation. But the tendency for young domains to have higher protein structural disorder, driven primarily by more hydrophilic amino acids, is found only among young animal domains, and not young plant domains, nor ancient domains predating the existence of the last eukaryotic common ancestor. Among ancient domains, trends in amino acid composition reflect the order of recruitment into the genetic code, suggesting that events during the earliest stages of life on earth continue to have an impact on the composition of ancient sequences.

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“…Additional biological information such as gene-protein-reaction associations is utilized to refine the models. Exponential growth in sequencing has resulted in an "astronomical", or better yet, "genomical", number of sequenced organisms [15]. There are now databases (e.g., KEGG [16], BioCyc [17], and BiGG [18]) that catalogue organism-specific metabolic models.…”

Section: Introductionmentioning

confidence: 99%

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

Amin¹,

Chavez²,

Nair

et al. 2019

Preprint

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1Background 2 Metabolic models are indispensable in guiding cellular engineering and in advancing our 3 understanding of systems biology. As not all enzymatic activities are fully known and/or 4 annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis 5 and leading to unexpected experimental results. We posit that one major source of unaccounted 6 metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, 7 enzymes are promiscuous -i.e., they transform substrates other than their primary substrate. 8However, there have been no systematic analyses of genome-scale metabolic models to predict 9 putative reactions and/or metabolites that arise from enzyme promiscuity. 10 Results 11Our workflow utilizes PROXIMAL -a tool that uses reactant-product transformation patterns 12 from the KEGG database -to predict putative structural modifications due to promiscuous 13 enzymes. Using iML1515 as a model system, we first utilized a computational workflow, 14 referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous 15 reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli. We 16 predict hundreds of new metabolites that can be used to augment iML1515. We then validated 17 our method by comparing predicted metabolites with the Escherichia coli Metabolome Database 18 (ECMDB). 19 Conclusions 20We utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular 21 metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of 22 reactions and metabolites that may exist in E. coli but may have not been documented in 23 3 iML1515 or other databases. We provide detailed analysis of 23 predicted reactions and 16 1 associated metabolites. Interestingly, nine of these metabolites, which are in ECMDB, have not 2 previously been documented in any other E. coli databases. Four of the predicted reactions 3 provide putative transformations parallel to those already in iML1515. We suggest adding 4 predicted metabolites and reactions to iML1515 to create an Extended Metabolic Model (EMM) 5 for E. coli. 6 7 Keywords 8 Metabolic engineering, enzyme promiscuity, extended metabolic model, systems biology, 9 enzyme activity prediction 10 11 Background 12The engineering of metabolic networks has enabled the production of high-volume commodity 13 chemicals such as biopolymers and fuels, therapeutics, and specialty products [1][2][3][4][5]. Producing 14 such compounds requires transforming microorganisms into efficient cellular factories [6][7][8][9]. 15 Biological engineering has been aided via computational tools for constructing synthesis 16 pathways, strain optimization, elementary flux mode analysis, discovery of hierarchical 17 networked modules that elucidate function and cellular organization, and many others (e.g., [10-18 14]). These design tools rely on organism-specific metabolic models that represent cellular 19 reactions and their substrates and products. Model recons...

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Genomes OnLine database (GOLD) v.7: updates and new features

Cited by 187 publications

References 29 publications

Ecology and molecular targets of hypermutation in the global microbiome

Ecology and molecular targets of hypermutation in the global microbiome

Universal and taxon-specific trends in protein sequences as a function of age

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

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