The Sequence of a 1.8-Mb Bacterial Linear Plasmid Reveals a Rich Evolutionary Reservoir of Secondary Metabolic Pathways

Abstract

Plasmids are mobile genetic elements that play a key role in the evolution of bacteria by mediating genome plasticity and lateral transfer of useful genetic information. Although originally considered to be exclusively circular, linear plasmids have also been identified in certain bacterial phyla, notably the actinomycetes. In some cases, linear plasmids engage with chromosomes in an intricate evolutionary interplay, facilitating the emergence of new genome configurations by transfer and recombination or plasmid integration. Genome sequencing of Streptomyces clavuligerus ATCC 27064, a Gram-positive soil bacterium known for its production of a diverse array of biotechnologically important secondary metabolites, revealed a giant linear plasmid of 1.8 Mb in length. This megaplasmid (pSCL4) is one of the largest plasmids ever identified and the largest linear plasmid to be sequenced. It contains more than 20% of the putative protein-coding genes of the species, but none of these is predicted to be essential for primary metabolism. Instead, the plasmid is densely packed with an exceptionally large number of gene clusters for the potential production of secondary metabolites, including a large number of putative antibiotics, such as staurosporine, moenomycin, β-lactams, and enediynes. Interestingly, cross-regulation occurs between chromosomal and plasmid-encoded genes. Several factors suggest that the megaplasmid came into existence through recombination of a smaller plasmid with the arms of the main chromosome. Phylogenetic analysis indicates that heavy traffic of genetic information between Streptomyces plasmids and chromosomes may facilitate the rapid evolution of secondary metabolite repertoires in these bacteria.

Characterization of the tunicamycin gene cluster unveiling unique steps involved in its biosynthesis

“…It seems to present us with the potential hopes for the identification of the tunicamycin gene cluster; however, efforts with bioinformatic and genetic analysis of the genome data were unsuccessful because of the paucity of the knowledge on tunicamycin biosynthesis (Price and Tsvetanova, 2007;Medema et al, 2010).…”

Giant linear plasmids in Streptomyces: a treasure trove of antibiotic biosynthetic clusters

“…34,35 pSCL4 of S. clavuligerus is composed of a 7-kb pSCL1 sequence flanked by the two chromosomal arms. 67 In this connection, I discussed on horizontal transfer of the extremely condensed biosynthetic clusters on pSLA2-L. 3 We could not detect any transposition-related genes or sequences surrounding the clusters, except for several truncated genes. 43 This is true for the methylenomycin cluster on SCP1.…”

“…Nucleotide sequencing of pSLC1 and pSLC2 neither revealed any genes for cephamycin or clavulanic acid biosynthesis. 65,66 Recently, Madema et al 67 isolated an extremely large linear plasmid, pSCL4, from S. clavuligerus ATCC 27064 and determined its 1.8-Mb nucleotide sequence. Surprisingly, this plasmid was densely packed with an exceptionally large number of gene clusters for the potential production of antibiotics, such as staurosporine, moenomycin, b-lactams and enediynes.…”

Design-based re-engineering of biosynthetic gene clusters: plug-and-play in practice

“…Ever since the introduction of next-generation sequencing, the amount of genome information for natural product-producing organisms has been increasing rapidly, leading to the identification of an unexpectedly large number of biosynthetic gene clusters: typically up to a few dozen per genome, tens of thousands in total. It has become clear that most of the products of these gene clusters are not produced at detectable levels under laboratory conditions [1][2][3]; the gene clusters are 'silent', 'sleeping' or 'cryptic'. Sequence-guided genome mining and heterologous expression of biosynthesis gene clusters have been successful in awakening some cryptic clusters through knocking out or overexpressing regulatory genes [4,5,6 ], but no high-throughput methodology has been fully developed yet.…”