This review is devoted to the marine bacterial producers of tetrodotoxin (TTX), a potent non-protein neuroparalytic toxin. In addition to the issues of the ecology and distribution of TTX-producing bacteria, this review examines issues relating to toxin migration from bacteria to TTX-bearing animals. It is shown that the mechanism of TTX extraction from toxin-producing bacteria to the environment occur through cell death, passive/active toxin excretion, or spore germination of spore-forming bacteria. Data on TTX microdistribution in toxic organs of TTX-bearing animals indicate toxin migration from the digestive system to target organs through the transport system of the organism. The role of symbiotic microflora in animal toxicity is also discussed: despite low toxin production by bacterial strains in laboratory conditions, even minimal amounts of TTX produced by intestinal microflora of an animal can contribute to its toxicity. Special attention is paid to methods of TTX detection applicable to bacteria. Due to the complexity of toxin detection in TTX-producing bacteria, it is necessary to use several methods based on different methodological approaches. Issues crucial for further progress in detecting natural sources of TTX investigation are also considered.

BackgroundIt has been suggested that statistical parsimony network analysis could be used to get an indication of species represented in a set of nucleotide data, and the approach has been used to discuss species boundaries in some taxa.Methodology/Principal FindingsBased on 635 base pairs of the mitochondrial protein-coding gene cytochrome c oxidase I (COI), we analyzed 152 nemertean specimens using statistical parsimony network analysis with the connection probability set to 95%. The analysis revealed 15 distinct networks together with seven singletons. Statistical parsimony yielded three networks supporting the species status of Cephalothrix rufifrons, C. major and C. spiralis as they currently have been delineated by morphological characters and geographical location. Many other networks contained haplotypes from nearby geographical locations. Cladistic structure by maximum likelihood analysis overall supported the network analysis, but indicated a false positive result where subnetworks should have been connected into one network/species. This probably is caused by undersampling of the intraspecific haplotype diversity.Conclusions/SignificanceStatistical parsimony network analysis provides a rapid and useful tool for detecting possible undescribed/cryptic species among cephalotrichid nemerteans based on COI gene. It should be combined with phylogenetic analysis to get indications of false positive results, i.e., subnetworks that would have been connected with more extensive haplotype sampling.

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