2016
DOI: 10.1128/jvi.01622-16
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Bipartite Network Analysis of the Archaeal Virosphere: Evolutionary Connections between Viruses and Capsidless Mobile Elements

Abstract: Archaea and particularly hyperthermophilic crenarchaea are hosts to many unusual viruses with diverse virion shapes and distinct gene compositions. As is typical of viruses in general, there are no universal genes in the archaeal virosphere. Therefore, to obtain a comprehensive picture of the evolutionary relationships between viruses, network analysis methods are more productive than traditional phylogenetic approaches. Here we present a comprehensive comparative analysis of genomes and proteomes from all cur… Show more

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Cited by 90 publications
(105 citation statements)
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“…Although metabolism‐related genes appear to be more prevalent in CIE and ICE, all four classes of iMGE share a substantial fraction of genes. Accordingly, the evolutionary relationships between these iMGE are most adequately represented as a gene‐sharing network similar to those that have been previously constructed for double‐stranded DNA viruses (Jachiet et al ., ; Iranzo et al ., ,b; Bolduc et al ., ). The extensive gene sharing can be explained by three nonmutually exclusive scenarios, including (1) horizontal gene exchange, (2) independent acquisition of homologous genes from various sources and (3) evolutionary transitions between different iMGE classes.…”
Section: Discussionmentioning
confidence: 99%
“…Although metabolism‐related genes appear to be more prevalent in CIE and ICE, all four classes of iMGE share a substantial fraction of genes. Accordingly, the evolutionary relationships between these iMGE are most adequately represented as a gene‐sharing network similar to those that have been previously constructed for double‐stranded DNA viruses (Jachiet et al ., ; Iranzo et al ., ,b; Bolduc et al ., ). The extensive gene sharing can be explained by three nonmutually exclusive scenarios, including (1) horizontal gene exchange, (2) independent acquisition of homologous genes from various sources and (3) evolutionary transitions between different iMGE classes.…”
Section: Discussionmentioning
confidence: 99%
“…A combination of BLASTP analysis and a more sensitive hidden Markov model (HMM)-based HHpred (32) analysis allowed the assignment of putative functions to just one-fifth of all SPV1 ORFs (nine ORFs, 20%). Seven ORFs encoded putative proteins containing various DNA-binding domains, including zinc finger (ORF02-234, ORF07-40, and ORF15-65), (winged) helix-turn-helix (ORF16-96, ORF22-147, and ORF35-111), and ribbon-helix-helix (ORF23-115) domains, which are frequently encoded by crenarchaeal viruses (33)(34)(35). In addition, ORF29-310 and ORF32-168 encode glycosyltransferase with the GT-B fold and the S-adenosyl-L-methioninedependent methyltransferase, respectively.…”
Section: Resultsmentioning
confidence: 99%
“…The HPCs in the network satisfy the topological scale‐free criterion of the biological networks, indicating a large number of plasmid genes with highly variable abundances and often complex evolutionary histories (Albert, ). The PUs show strong modularity of the network, providing a complementary and more comprehensive account of the deep evolutionary connections within the rhizobial plasmids (Iranzo et al ., ). We believe these findings are useful for conducting higher‐level functional and evolutionary analysis of multipartite rhizobial genomes.…”
Section: Discussionmentioning
confidence: 97%