Draft Genome Sequence of Burkholderia reimsis BE51, a Plant-Associated Bacterium Isolated from Agricultural Rhizosphere

Genome-based taxonomy of Burkholderia sensu lato: Distinguishing closely related species

The taxonomy of Burkholderia sensu lato (s.l.) has been revisited using genome-based tools, which have helped differentiate closely related species. Many species from this group are indistinguishable through phenotypic traits and 16S rRNA gene sequence analysis. Furthermore, they also exhibit whole-genome Average Nucleotide Identity (ANI) values in the twilight zone for species circumscription (95-96%), which may impair their correct classification. In this work, we provided an updated Burkholderia s.l. taxonomy focusing on closely related species and give other recommendations for those developing genome-based taxonomy studies. We showed that a combination of ANI and digital DNA-DNA hybridization (dDDH) applying the universal cutoff values of 95% and 70%, respectively, successfully discriminates Burkholderia s.l. species. Using genome metrics with this pragmatic criterion, we demonstrated that i) Paraburkholderia insulsa should be considered a later heterotypic synonym of Paraburkholderia fungorum; ii) Paraburkholderia steynii differs from P. terrae by harboring symbiotic genes; iii) some Paraburkholderia are indeed different species based on dDDH values, albeit sharing ANI values close to 95%; iv) some Burkholderia s.l. indeed represent new species from the genomic viewpoint; iv) some genome sequences should be evaluated with care due to quality concerns.

Burkholderia orbicola sp. nov., a novel species within the Burkholderia cepacia complex

Genome analysis of strains placed in the NCBI genome database as Burkholderia cenocepacia defined nine genomic species groups. The largest group (259 strains) corresponds to B. cenocepacia and the second largest group (58 strains) was identified as "Burkholderia servocepacia", a Burkholderia species classification which has not been validly published. The publication of "B. servocepacia" did not comply with Rule 27 and Recommendation 30 from the International Code of Nomenclature of Prokaryotes (ICNP) and have errors in the type strain name and the protologue describing the novel species. Here, we correct the position of this species by showing essential information that meets the criteria defined by ICNP. After additional analysis complying with taxonomic criteria, we propose that the invalid "B. servocepacia" be renamed as Burkholderia orbicola sp. nov. The original study proposing "B. servocepacia" was misleading, because this name derives from the Latin "servo" meaning "to protect/watch over", and the authors proposed this based on the beneficial biocontrol properties of several strains within the group. However, it is clear that "B. servocepacia" isolates are capable of opportunistic infection, and the proposed name Burkholderia orbicola sp. nov. takes into account these diverse phenotypic traits. The type strain is TAtl-371 ^T (= LMG 30279 ^T = CM-CNRG 715 ^T).

Identification of Microorganisms by Liquid Chromatography-Mass Spectrometry (LC-MS1) and in Silico Peptide Mass Libraries

Over the past decade, modern methods of MS (MS) have emerged that allow reliable, fast and cost-effective identification of pathogenic microorganisms. Although MALDI-TOF MS has already revolutionized the way microorganisms are identified, recent years have witnessed also substantial progress in the development of liquid chromatography (LC)-MS based proteomics for microbiological applications. For example, LC-tandem MS (LC-MS2) has been proposed for microbial characterization by means of multiple discriminative peptides that enable identification at the species, or sometimes at the strain level. However, such investigations can be laborious and time-consuming, especially if the experimental LC-MS2 data are tested against sequence databases covering a broad panel of different microbiological taxa. In this proof of concept study, we present an alternative bottom-up proteomics method for microbial identification. The proposed approach involves efficient extraction of proteins from cultivated microbial cells, digestion by trypsin and LC-MS measurements. Peptide masses are then extracted from MS1 data and systematically tested against an in silico library of all possible peptide mass data compiled in-house. The library has been computed from the UniProt Knowledgebase covering Swiss-Prot and TrEMBL databases and comprises more than 12,000 strain-specific in silico profiles, each containing tens of thousands of peptide mass entries. Identification analysis involves computation of score values derived from correlation coefficients between experimental and strain-specific in silico peptide mass profiles and compilation of score ranking lists. The taxonomic positions of the microbial samples are then determined by using the best-matching database entries. The suggested method is computationally efficient - less than 2 mins per sample - and has been successfully tested by a test set of 39 LC-MS1 peak lists obtained from 19 different microbial pathogens. The proposed method is rapid, simple and automatable and we foresee wide application potential for future microbiological applications.