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A numerical classification of the genus Bacillus.

F G Priest¹, M Goodfellow, C Todd

¹Department of Brewing and Biological Sciences, Heriot-Watt University, Edinburgh, UK.

Journal of General Microbiology

|July 1, 1988

Summary

This study used numerical phenetics to analyze 368 Bacillus strains, revealing species heterogeneity and confirming the status of several taxa. New species names are proposed based on phenotypic and genetic data.

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Area of Science:

Microbiology
Bacteriology
Numerical Taxonomy

Background:

The genus Bacillus comprises numerous aerobic, endospore-forming bacteria with diverse ecological roles.
Accurate taxonomic classification is crucial for understanding bacterial diversity and relationships.
Previous classifications have indicated heterogeneity within several recognized Bacillus species.

Purpose of the Study:

To perform a numerical phenetic analysis of a large collection of Bacillus strains.
To assess the taxonomic status of various Bacillus species and identify potential new taxa.
To propose revised nomenclature for Bacillus species based on integrated phenotypic and genotypic data.

Main Methods:

Studied 368 strains including type cultures and environmental isolates of Bacillus.
Determined overall similarities using 118 unit characters with Simple Matching (SSM), Simple Jaccard (SJ), and Dice (DP) coefficients.
Clustering was achieved using the Unweighted Pair Group with Arithmetic Mean (UPGMA) algorithm.

Main Results:

Six major cluster-groups were defined at 70% SSM similarity.
Bacillus strains formed 31 major, 18 minor, and 30 single-member clusters at 83% SSM.
The analysis indicated heterogeneity in species like Bacillus brevis and confirmed the status of several uncertain taxa.

Conclusions:

Numerical phenetics effectively delineates Bacillus species, with most clusters representing distinct taxospecies.
The study proposes the recognition of five new/revised Bacillus species names: B. flexus, B. fusiformis, B. kaustophilus, B. psychrosaccharolyticus, and B. simplex.
The findings highlight the utility of integrating numerical phenetic data with molecular genetic information for robust bacterial taxonomy.

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