From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.</p> <p>Results</p> <p>In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model.</p> <p>Conclusions</p> <p>FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.</p

The Family Streptomycetaceae

The family Streptomycetaceae comprises the genera Streptomyces, Kitasatospora, and Streptacidiphilus that are very difficult to differentiate both with genotypic and phenotypic characteristics. A separate generic status for Kitasatospora and Streptacidiphilus is questionable. Members of the family can be characterized as non-acid-alcohol-fast actinomycetes that generate most often an extensively branched substrate mycelium that rarely fragments. At maturity, the aerial mycelium forms chains of few to many spores. A large variety of pigments is produced, responsible for the color of the substrate and aerial mycelium. The organisms are chemoorganotrophic with an oxidative type of metabolism and grow within different pH ranges. Streptomyces are notable for their complex developmental cycle and production of bioactive secondary metabolites, producing more than a third of commercially available antibiotics. Antibacterial, antifungal, antiparasitic, and immunosuppressant compounds have been identified as products of Streptomyces secondary metabolism. Streptomyces can be distinguished from other filamentous actinomycetes on the basis of morphological characteristics, in particular by vegetative mycelium, aerial mycelium, and arthrospores. The genus comprises at the time of writing more than 600 species with validated names. 16S rRNA gene sequence-based analysis for species delineation within the Streptomycetaceae is of limited value. The variations within the 16S rRNA genes—even in the variable regions—are too small to resolve problems of species differentiation and to establish a taxonomic structure within the genus. Comprehensive comparative studies including protein-coding gene sequences with higher phylogenetic resolution and genome-based studies are needed to clarify the species delineation within the Streptomycetaceae

Prokaryotic Hydrocarbon Degraders

Hydrocarbons have been part of the biosphere for millions of years, and a diverse group of prokaryotes has evolved to use them as a source of carbon and energy. To date, the vast majority of formally defined genera are eubacterial, in 7 of the 24 major phyla currently formally recognized by taxonomists (Tree of Life, http://tolweb.org/Eubacteria. Accessed 1 Sept 2017, 2017); principally in the Actinobacteria, the Bacteroidetes, the Firmicutes, and the Proteobacteria. Some Cyanobacteria have been shown to degrade hydrocarbons on a limited scale, but whether this is of any ecological significance remains to be seen – it is likely that all aerobic organisms show some basal metabolism of hydrocarbons by nonspecific oxygenases, and similar “universal” metabolism may occur in anaerobes. This chapter focuses on the now quite large number of named microbial genera where there is reasonably convincing evidence for hydrocarbon metabolism. We have found more than 320 genera of Eubacteria, and 12 genera of Archaea. Molecular methods are revealing a vastly greater diversity of currently uncultured organisms – Hug et al. (Nat Microbiol 1:16048, 2016) claim 92 named bacterial phyla, many with almost totally unknown physiology – and it seems reasonable to believe that the catalog of genera reported here will be substantially expanded in the future