Pathogenicity and Its Implications in Taxonomy: The Brucella and Ochrobactrum Case.
Edgardo MorenoJosé María BlascoJean-Jacques LetessonJean-Pierre GorvelIgnacio MoriyónPublished in: Pathogens (Basel, Switzerland) (2022)
The intracellular pathogens of the genus Brucella are phylogenetically close to Ochrobactrum , a diverse group of free-living bacteria with a few species occasionally infecting medically compromised patients. A group of taxonomists recently included all Ochrobactrum organisms in the genus Brucella based on global genome analyses and alleged equivalences with genera such as Mycobacterium . Here, we demonstrate that such equivalencies are incorrect because they overlook the complexities of pathogenicity. By summarizing Brucella and Ochrobactrum divergences in lifestyle, structure, physiology, population, closed versus open pangenomes, genomic traits, and pathogenicity, we show that when they are adequately understood, they are highly relevant in taxonomy and not unidimensional quantitative characters. Thus, the Ochrobactrum and Brucella differences are not limited to their assignments to different "risk-groups", a biologically (and hence, taxonomically) oversimplified description that, moreover, does not support ignoring the nomen periculosum rule, as proposed. Since the epidemiology, prophylaxis, diagnosis, and treatment are thoroughly unrelated, merging free-living Ochrobactrum organisms with highly pathogenic Brucella organisms brings evident risks for veterinarians, medical doctors, and public health authorities who confront brucellosis, a significant zoonosis worldwide. Therefore, from taxonomical and practical standpoints, the Brucella and Ochrobactrum genera must be maintained apart. Consequently, we urge researchers, culture collections, and databases to keep their canonical nomenclature.
Keyphrases
- public health
- gram negative
- end stage renal disease
- cardiovascular disease
- metabolic syndrome
- newly diagnosed
- healthcare
- ejection fraction
- mycobacterium tuberculosis
- biofilm formation
- gene expression
- genome wide
- risk assessment
- dna methylation
- type diabetes
- chronic kidney disease
- physical activity
- cystic fibrosis
- mass spectrometry
- machine learning
- big data
- high resolution
- deep learning
- multidrug resistant
- peritoneal dialysis
- patient reported outcomes