The concept of taxonomic space and integral estimates of shift in the structure of microbial community based on analysis of 16S rRNA gene libraries

The problem of taxonomic structure and dynamics of soil, plant animal and human microbiomes is one of the most intriguing in modern microbiology. High Performance Technologies sequencing of the 16S rRNA gene allows to get much more metagenomic data, but their correct analysis and biological interpretation are still complicated, in particular with regard to the effect of selective amplification with universal primers and proper attribution of the samples. To resolve the problems, we created a special operating environment, the taxonomic space (TS), in which the sequences of 16S rRNA gene are represented by dots, geometric distance between which corresponds to the genetic distance between the sequences. Mapping the 16S-rRNA gene biodiversity data in this TS and evaluation of the microbial community as overorganism, with its integral parameters, have a number of advantages if compared to the traditional approaches. Thus, in the TS where each sequence of the 16S rRNA gene gets its own identifier of the 42 coordinates, the unattributed amplicons in any PCR-library can be analyzed. Although the described TS is not strictly a multi-dimensional mathematical space, in particular, its axes are interdependent, an extremely high correlation coefficients, obtained for genetic distances between sequences and their geometric counterparts, unconditionally, testify in favor of the validity of the use of TS in practice. The development of TS concept is of great importance not only in the analysis of the structure of microbial communities, but also in imvestigation of 16S rRNA genes evolution. Since the model allows to give a description for any variant, both realized and not yet realized in evolution, the issues related to the origin and divergent evolution of prokaryotes may be investigated, for example, the hypothetical center of origin can be determine, and then the TS will become an evolutionary space. As a model, we used different soil microbiomes in which the changes were induced by environmental conditions (salinity), both natural and simulated. However, the application of this approach can be extended to other complex microbiomes, particularly the microbiota in animals. Moreover, the proposed mathematical method is universal and can be used to study not only biodiversity in prokaryotes, but also the communities of eukaryotic organisms, including animals and plants, with the 18S rRNA gene as a taxonomic marker.