Analysis of the unexplored features of rrs (16S rDNA) of the Genus Clostridium

Background: Bacterial taxonomy and phylogeny based on rrs (16S rDNA) sequencing is being vigorously pursued. In fact, it has been stated that novel biological findings are driven by comparison and integration of massive data sets. In spite of a large reservoir of rrs sequencing data of 1,237,963 entries, this analysis invariably needs supplementation with other genes. The need is to divide the genetic variability within a taxa or genus at their rrs phylogenetic boundaries and to discover those fundamental features, which will enable the bacteria to naturally fall within them. Within the large bacterial community, Clostridium represents a large genus of around 110 species of significant biotechnological and medical importance. Certain Clostridium strains produce some of the deadliest toxins, which cause heavy economic losses. We have targeted this genus because of its high genetic diversity, which does not allow accurate typing with the available molecular methods. Results: Seven hundred sixty five rrs sequences (> 1200 nucleotides, nts) belonging to 110 Clostridium species were analyzed. On the basis of 404 rrs sequences belonging to 15 Clostridium species, we have developed species specific: (i) phylogenetic framework, (ii) signatures (30 nts) and (iii) in silico restriction enzyme (14 Type II REs) digestion patterns. These tools allowed: (i) species level identification of 95 Clostridium sp. which are presently classified up to genus level, (ii) identification of 84 novel Clostridium spp. and (iii) potential reduction in the number of Clostridium species represented by small populations. Conclusions: This integrated approach is quite sensitive and can be easily extended as a molecular tool for diagnostic and taxonomic identification of any microbe of importance to food industries and health services. Since rapid and correct identification allows quicker diagnosis and consequently treatment as well, it is likely to lead to reduction in economic losses and mortality rates.