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Identifying the source of unknown microcystin genes and predicting microcystin variants by comparing genes within uncultured cyanobacterial cells.

ABSTRACT: While multiple phylogenetic markers have been used in the culture-independent study of microcystin-producing cyanobacteria, in only a few instances have multiple markers been studied within individual cells, and in all cases these studies have been conducted with cultured isolates. Here, we isolate and evaluate large DNA fragments (>6 kb) encompassing two genes involved in microcystin biosynthesis (mcyA2 and mcyB1) and use them to identify the source of gene fragments found in water samples. Further investigation of these gene loci from individual cyanobacterial cells allowed for improved analysis of the genetic diversity within microcystin producers as well as a method to predict microcystin variants for individuals. These efforts have also identified the source of the novel mcyA genotype previously termed Microcystis-like that is pervasive in the Laurentian Great Lakes and they predict the microcystin variant(s) that it produces.

SUBMITTER: Allender CJ

PROVIDER: S-EPMC2687263 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Identifying the source of unknown microcystin genes and predicting microcystin variants by comparing genes within uncultured cyanobacterial cells.

Allender Christopher J CJ LeCleir Gary R GR Rinta-Kanto Johanna M JM Small Randall L RL Satchwell Michael F MF Boyer Gregory L GL Wilhelm Steven W SW

Applied and environmental microbiology 20090410 11

While multiple phylogenetic markers have been used in the culture-independent study of microcystin-producing cyanobacteria, in only a few instances have multiple markers been studied within individual cells, and in all cases these studies have been conducted with cultured isolates. Here, we isolate and evaluate large DNA fragments (>6 kb) encompassing two genes involved in microcystin biosynthesis (mcyA2 and mcyB1) and use them to identify the source of gene fragments found in water samples. Fur ...[more]

PMID: 19363074

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Project description:BACKGROUND: Cyanobacteria produce a wealth of secondary metabolites, including the group of small cyclic heptapeptide hepatotoxins that constitutes the microcystin family. The enzyme complex that directs the biosynthesis of microcystin is encoded in a single large gene cluster (mcy). mcy genes have a widespread distribution among cyanobacteria and are likely to have an ancient origin. The notable diversity within some of the Mcy modules is generated through various recombination events including horizontal gene transfer. RESULTS: A comparative analysis of the adenylation domains from the first module of McyB (McyB1) and McyC in the microcystin synthetase complex was performed on a large number of microcystin-producing strains from the Anabaena, Microcystis and Planktothrix genera. We found no decisive evidence for recombination between strains from different genera. However, we detected frequent recombination events in the mcyB and mcyC genes between strains within the same genus. Frequent interdomain recombination events were also observed between mcyB and mcyC sequences in Anabaena and Microcystis. Recombination and mutation rate ratios suggest that the diversification of mcyB and mcyC genes is driven by recombination events as well as point mutations in all three genera. Sequence analysis suggests that generally the adenylation domains of the first domain of McyB and McyC are under purifying selection. However, we found clear evidence for positive selection acting on a number of amino acid residues within these adenylation domains. These include residues important for active site selectivity of the adenylation domain, strongly suggesting selection for novel microcystin variants. CONCLUSION: We provide the first clear evidence for positive selection acting on amino acid residues involved directly in the recognition and activation of amino acids incorporated into microcystin, indicating that the microcystin complement of a given strain may influence the ability of a particular strain to interact with its environment.

Project description:BackgroundLarge-scale sequencing of entire genomes has ushered in a new age in biology. One of the next grand challenges is to dissect the cellular networks consisting of many individual functional modules. Defining co-expression networks without ambiguity based on genome-wide microarray data is difficult and current methods are not robust and consistent with different data sets. This is particularly problematic for little understood organisms since not much existing biological knowledge can be exploited for determining the threshold to differentiate true correlation from random noise. Random matrix theory (RMT), which has been widely and successfully used in physics, is a powerful approach to distinguish system-specific, non-random properties embedded in complex systems from random noise. Here, we have hypothesized that the universal predictions of RMT are also applicable to biological systems and the correlation threshold can be determined by characterizing the correlation matrix of microarray profiles using random matrix theory.ResultsApplication of random matrix theory to microarray data of S. oneidensis, E. coli, yeast, A. thaliana, Drosophila, mouse and human indicates that there is a sharp transition of nearest neighbour spacing distribution (NNSD) of correlation matrix after gradually removing certain elements insider the matrix. Testing on an in silico modular model has demonstrated that this transition can be used to determine the correlation threshold for revealing modular co-expression networks. The co-expression network derived from yeast cell cycling microarray data is supported by gene annotation. The topological properties of the resulting co-expression network agree well with the general properties of biological networks. Computational evaluations have showed that RMT approach is sensitive and robust. Furthermore, evaluation on sampled expression data of an in silico modular gene system has showed that under-sampled expressions do not affect the recovery of gene co-expression network. Moreover, the cellular roles of 215 functionally unknown genes from yeast, E. coli and S. oneidensis are predicted by the gene co-expression networks using guilt-by-association principle, many of which are supported by existing information or our experimental verification, further demonstrating the reliability of this approach for gene function prediction.ConclusionOur rigorous analysis of gene expression microarray profiles using RMT has showed that the transition of NNSD of correlation matrix of microarray profile provides a profound theoretical criterion to determine the correlation threshold for identifying gene co-expression networks.

Dataset Information

Identifying the source of unknown microcystin genes and predicting microcystin variants by comparing genes within uncultured cyanobacterial cells.

Publications

Identifying the source of unknown microcystin genes and predicting microcystin variants by comparing genes within uncultured cyanobacterial cells.

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