Evolution of cyclizing 5-aminolevulinate synthases in the biosynthesis of actinomycete secondary metabolites: outcomes for genetic screening techniques
Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection
Typ dokumentu časopisecké články
PubMed
26300877
PubMed Central
PMC4525017
DOI
10.3389/fmicb.2015.00814
Knihovny.cz E-zdroje
- Klíčová slova
- 5-aminolevulinate synthase, C5N unit, Streptomyces, gene evolution, genetic screening, horizontal gene transfer, secondary metabolites,
- Publikační typ
- časopisecké články MeSH
A combined approach, comprising PCR screening and genome mining, was used to unravel the diversity and phylogeny of genes encoding 5-aminolevulinic acid synthases (ALASs, hemA gene products) in streptomycetes-related strains. In actinomycetes, these genes were believed to be directly connected with the production of secondary metabolites carrying the C5N unit, 2-amino-3-hydroxycyclopent-2-enone, with biological activities making them attractive for future use in medicine and agriculture. Unlike "classical" primary metabolism ALAS, the C5N unit-forming cyclizing ALAS (cALAS) catalyses intramolecular cyclization of nascent 5-aminolevulinate. Specific amino acid sequence changes can be traced by comparison of "classical" ALASs against cALASs. PCR screening revealed 226 hemA gene-carrying strains from 1,500 tested, with 87% putatively encoding cALAS. Phylogenetic analysis of the hemA homologs revealed strain clustering according to putative type of metabolic product, which could be used to select producers of specific C5N compound classes. Supporting information was acquired through analysis of actinomycete genomic sequence data available in GenBank and further genetic or metabolic characterization of selected strains. Comparison of 16S rRNA taxonomic identification and BOX-PCR profiles provided evidence for numerous horizontal gene transfers of biosynthetic genes or gene clusters within actinomycete populations and even from non-actinomycete organisms. Our results underline the importance of environmental and evolutionary data in the design of efficient techniques for identification of novel producers.
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