Genus-wide Bacillus species identification through proper artificial neural network experiments on fatty acid profiles

Chromogenicity of aerobic spore-forming bacteria of the Bacillaceae family isolated from different ecological niches and physiographic zones

To determine the distribution patterns of pigmented bacteria of the Bacilaceae family in different physiographic zones and ecological niches, we recovered 787 isolates from 185 environmental samples (including the areas with radiation pollution). Among the strains obtained, 149 pigmented representatives were detected, which synthesized intracellular and extracellular pigments of yellow, red, pink, and dark colors. In compliance with physiological, biochemical, and chemotaxonomic features, the isolates were identified as 7 species of the Bacilaceae family. We demonstrated that the ability to synthesize pigments significantly depended on the culture medium composition. According to the color of the colonies, the absorption spectra of pigment extracts, their physicochemical properties, and the implementation of several qualitative tests, the pigmented isolates were divided into ten groups. The relative number of pigmented strains in the physiographic zone was consistent with the total level of solar radiation for the year. Most pigmented members of the Bacillaceae family were recovered from deserts and semi-deserts, and fewest of them originated from mixed forests. We show that among the studied ecological niches, pigmented strains were most often isolated from the phyllosphere and aquatic environment and least often from soils. However, the isolates from soils and aquatic environments exhibited a greater diversity of pigmentation, and a lesser variety of colored strains was obtained from the phyllosphere and the gastrointestinal tract of animals. We established that the quantitative and qualitative composition of pigmented isolates from the areas with radiation contamination differed significantly from those coming from the natural radiation background.

Discrimination between the Two Closely Related Species of the Operational Group B. amyloliquefaciens Based on Whole-Cell Fatty Acid Profiling

(1) Background: Bacillus velezensis and Bacillus amyloliquefaciens are closely related members of the “operational group B. amyloliquefaciens”, a taxonomical unit above species level within the ”Bacillus subtilis species complex”. They have similar morphological, physiological, biochemical, phenotypic, and phylogenetic characteristics. Thus, separating these two taxa from each another has proven to be difficult to implement and could not be pushed easily into the line of routine analyses. (2) Methods: The aim of this study was to determine whether whole FAME profiling could be used to distinguish between these two species, using both type strains and environmental isolates. Initially, the classification was determined by partial sequences of the gyrA and rpoB genes and the classified isolates and type strains were considered as samples to develop the identification method, based on FAME profiles. (3) Results: The dissimilarities in 16:0, 17:0 iso, and 17:0 FA components have drawn a distinction between the two species and minor differences in FA 14:0, 15:0 iso, and 16:0 iso were also visible. The statistical analysis of the FA profiles confirmed that the two taxa can be distinguished into two separate groups, where the isolates are identified without misreading. (4) Conclusions: Our study proposes that the developed easy and fast-automated identification tool based on cellular FA profiles can be routinely applied to distinguish B. velezensis and B. amyloliquefaciens.

The Various Roles of Fatty Acids

Lipids comprise a large group of chemically heterogeneous compounds. The majority have fatty acids (FA) as part of their structure, making these compounds suitable tools to examine processes raging from cellular to macroscopic levels of organization. Among the multiple roles of FA, they have structural functions as constituents of phospholipids which are the "building blocks" of cell membranes; as part of neutral lipids FA serve as storage materials in cells; and FA derivatives are involved in cell signalling. Studies on FA and their metabolism are important in numerous research fields, including biology, bacteriology, ecology, human nutrition and health. Specific FA and their ratios in cellular membranes may be used as biomarkers to enable the identification of organisms, to study adaptation of bacterial cells to toxic compounds and environmental conditions and to disclose food web connections. In this review, we discuss the various roles of FA in prokaryotes and eukaryotes and highlight the application of FA analysis to elucidate ecological mechanisms. We briefly describe FA synthesis; analyse the role of FA as modulators of cell membrane properties and FA ability to store and supply energy to cells; and inspect the role of polyunsaturated FA (PUFA) and the suitability of using FA as biomarkers of organisms.

Phylogenetic diversity of the Bacillus pumilus group and the marine ecotype revealed by multilocus sequence analysis

Bacteria closely related to Bacillus pumilus cannot be distinguished from such other species as B. safensis, B. stratosphericus, B. altitudinis and B. aerophilus simply by 16S rRNA gene sequence. In this report, 76 marine strains were subjected to phylogenetic analysis based on 7 housekeeping genes to understand the phylogeny and biogeography in comparison with other origins. A phylogenetic tree based on the 7 housekeeping genes concatenated in the order of gyrB-rpoB-pycA-pyrE-mutL-aroE-trpB was constructed and compared with trees based on the single genes. All these trees exhibited a similar topology structure with small variations. Our 79 strains were divided into 6 groups from A to F; Group A was the largest and contained 49 strains close to B. altitudinis. Additional two large groups were presented by B. safensis and B. pumilus respectively. Among the housekeeping genes, gyrB and pyrE showed comparatively better resolution power and may serve as molecular markers to distinguish these closely related strains. Furthermore, a recombinant phylogenetic tree based on the gyrB gene and containing 73 terrestrial and our isolates was constructed to detect the relationship between marine and other sources. The tree clearly showed that the bacteria of marine origin were clustered together in all the large groups. In contrast, the cluster belonging to B. safensis was mainly composed of bacteria of terrestrial origin. Interestingly, nearly all the marine isolates were at the top of the tree, indicating the possibility of the recent divergence of this bacterial group in marine environments. We conclude that B. altitudinis bacteria are the most widely spread of the B. pumilus group in marine environments. In summary, this report provides the first evidence regarding the systematic evolution of this bacterial group, and knowledge of their phylogenetic diversity will help in the understanding of their ecological role and distribution in marine environments.

Conditions associated with Clostridium sporogenes growth as a surrogate for Clostridium botulinum in nonthermally processed canned butter

The objective of this study was to better understand the effect of butter composition and emulsion structure on growth and survival of Clostridium sporogenes, used as a surrogate for C. botulinum in canned butter. The lack of a thermal process step in commercially available canned butter raises questions of potential safety, because it is hermetically sealed and generally exhibits anaerobic growth conditions, which are optimal for Clostridium botulinum growth. Without thermal processing, low-acid canned foods must have inhibitory factors present to prevent C. botulinum growth. Some potential intrinsic inhibitory factors, or hurdles, within butter include: reduced water activity, acidity in cultured products, elevated salt content, and the micro-droplet nature of the aqueous phase in the butter emulsion. It was hypothesized that a normal, intact butter emulsion would have sufficient hurdles to prevent C. botulinum growth, whereas a broken butter emulsion would result in a coalesced aqueous phase that would allow for C. botulinum growth. Batch-churned butter was inoculated with C. sporogenes; butter samples with varying salt contents (0, 0.8, 1.6, and 2.4% wt/wt NaCl) were prepared and stored in coated steel cans for varying times (1 or 2 wk) and temperatures (22 or 41°C) to determine temperature and emulsion structure effects on C. sporogenes growth. Samples stored at 41°C showed a significant increase in C. sporogenes growth compared with those stored at 22°C. Furthermore, NaCl addition was found to have a significant effect on C. sporogenes growth, with 0.8% NaCl promoting more growth than 0%, but with decreases in growth observed at 1.6 and 2.4%. Uninoculated control plates were also found to have bacterial growth; this growth was attributed to other anaerobic bacteria present within the cream. It was concluded that removal of the hurdle created by the micro-droplet size of the emulsion aqueous phase could result in C. botulinum growth even at elevated salt levels and, therefore, home preparation of canned butter is not advisable. It is also possible that commercially canned butter, if heat abused, could potentially allow for C. botulinum growth and, therefore, consumption is not recommended.

Neural networks

Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement. In bioinformatics applications, neural networks have played an important role for classification, function approximation, knowledge discovery, and data visualisation. This chapter will focus on supervised neural networks and discuss their applications to bioinformatics.

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

Background

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.

Results

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.

Conclusions

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.

Towards large-scale FAME-based bacterial species identification using machine learning techniques

In the last decade, bacterial taxonomy witnessed a huge expansion. The swift pace of bacterial species (re-)definitions has a serious impact on the accuracy and completeness of first-line identification methods. Consequently, back-end identification libraries need to be synchronized with the List of Prokaryotic names with Standing in Nomenclature. In this study, we focus on bacterial fatty acid methyl ester (FAME) profiling as a broadly used first-line identification method. From the BAME@LMG database, we have selected FAME profiles of individual strains belonging to the genera Bacillus, Paenibacillus and Pseudomonas. Only those profiles resulting from standard growth conditions have been retained. The corresponding data set covers 74, 44 and 95 validly published bacterial species, respectively, represented by 961, 378 and 1673 standard FAME profiles. Through the application of machine learning techniques in a supervised strategy, different computational models have been built for genus and species identification. Three techniques have been considered: artificial neural networks, random forests and support vector machines. Nearly perfect identification has been achieved at genus level. Notwithstanding the known limited discriminative power of FAME analysis for species identification, the computational models have resulted in good species identification results for the three genera. For Bacillus, Paenibacillus and Pseudomonas, random forests have resulted in sensitivity values, respectively, 0.847, 0.901 and 0.708. The random forests models outperform those of the other machine learning techniques. Moreover, our machine learning approach also outperformed the Sherlock MIS (MIDI Inc., Newark, DE, USA). These results show that machine learning proves very useful for FAME-based bacterial species identification. Besides good bacterial identification at species level, speed and ease of taxonomic synchronization are major advantages of this computational species identification strategy.