Polyphasic identification of cyanobacterial isolates from Australia.

Elvina Lee, Una M Ryan, Paul Monis, Glenn B McGregor, Andrew Bath, Cameron Gordon, Andrea Paparini
Author Information
  1. Elvina Lee: School of Veterinary and Life Sciences, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, Australia.
  2. Una M Ryan: School of Veterinary and Life Sciences, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, Australia.
  3. Paul Monis: Australian Water Quality Centre, South Australian Water Corporation, 250 Victoria Square, Adelaide 5000, Australia.
  4. Glenn B McGregor: Department of Science, Information Technology, Innovation and the Arts, GPO Box 5078, Brisbane, Queensland 4001, Australia.
  5. Andrew Bath: Drinking Water Quality Branch, Water Corporation, 629 Newcastle Street, Leederville, Western Australia 6007, Australia.
  6. Cameron Gordon: Drinking Water Quality Branch, Water Corporation, 629 Newcastle Street, Leederville, Western Australia 6007, Australia.
  7. Andrea Paparini: School of Veterinary and Life Sciences, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, Australia. Electronic address: a.paparini@murdoch.edu.au.

Abstract

Reliable identification of cyanobacterial isolates has significant socio-economic implications as many bloom-forming species affect the aesthetics and safety of drinking water, through the production of taste and odour compounds or toxic metabolites. The limitations of morphological identification have promoted the application of molecular tools, and encouraged the adoption of combined (polyphasic) approaches that include both microscopy- and DNA-based analyses. In this context, the rapid expansion of available sequence data is expected to allow increasingly reliable identification of cyanobacteria, and ultimately resolve current discrepancies between the two approaches. In the present study morphological and molecular characterisations of cyanobacterial isolates (n = 39), collected from various freshwater sites in Australia, were compared. Sequences were obtained for the small ribosomal subunit RNA gene (16S rDNA) (n = 36), the DNA-dependent RNA polymerase gene (rpoC1) (n = 22), and the phycocyanin operon, with its intergenic spacer region (cpcBA-IGS) (n = 19). Phylogenetic analyses identified three cyanobacterial orders: the Chroococcales (n = 8), Oscillatoriales (n = 6), and Nostocales (n = 25). Interestingly, multiple novel genotypes were identified, with 22% of the strains (17/77) having <95% similarity to available sequences in GenBank. Morphological and molecular data were in agreement at the species level for only 26% of the isolates obtained (10/39), while agreement at the genus level was obtained for 31% (12/39). Confident identification of the remaining 44% of the strains (17/39) beyond the order level was not possible. The present study demonstrates that, despite the taxonomic revisions, and advances in molecular-, and bioinformatics-tools, the lack of reliable morphological features, culture-induced pleomorphism, and proportion of misidentified or poorly described sequences in GenBank, still represent significant factors, impeding the confident identification of cyanobacteria species.

Keywords

MeSH Term

Australia
Bacterial Proteins
Cyanobacteria
DNA, Bacterial
Gene Expression Regulation, Bacterial
Phylogeny
RNA, Bacterial
RNA, Ribosomal, 16S
Time Factors

Chemicals

Bacterial Proteins
DNA, Bacterial
RNA, Bacterial
RNA, Ribosomal, 16S

Word Cloud

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