List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ

Parte, Aidan; Carbasse, J. Sarda; Meier‐Kolthoff, Jan P.; Reimer, L.C.; Göker, Markus

doi:10.1099/ijsem.0.004332

“…We obtained descriptions for 7,021 species, yet the total number of species validly published in the literature is 20,038 [see ref. (18)]. In total, our results suggest that databases reporting metabolic traits are far less than 1/4 complete.…”

Section: Resultsmentioning

confidence: 75%

“…We need to explore, first, sources of species descriptions other than Bergey’s Manual. Though Bergey’s Manual gave us descriptions for over 7,000 species, this represents only ~1/3 of all species validly published in the literature (18). We need to explore, second, well our methods work with rare traits.…”

Section: Discussionmentioning

confidence: 99%

Using neural networks to mine text and predict metabolic traits for thousands of microbes

Hackmann

¹

2020

Preprint

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Microbes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. Information is buried in text of books and journals, and investigators have no easy way to extract it out. Here we investigate if neural networks can extract out this information and predict metabolic traits. For proof of concept, we predicted two traits: whether microbes carry one type of metabolism (fermentation) or produce one metabolite (acetate). We collected written descriptions of 7,021 species of bacteria and archaea from Bergey's Manual. We read the descriptions and manually identified (labeled) which species were fermentative or produced acetate. We then trained neural networks to predict these labels. In total, we identified 2,364 species as fermentative, and 1,009 species as also producing acetate. Neural networks could predict which species were fermentative with 97.3% accuracy. Accuracy was even higher (98.6%) when predicting species also producing acetate. We used these predictions to draw phylogenetic trees of species with these traits. The resulting trees were close to the actual trees (drawn using labels). Previous counts of fermentative species are 4-fold lower than our own. For acetate-producing species, they are 100-fold lower. This undercounting confirms past difficulty in extracting metabolic traits from text. Our approach with neural networks can extract information efficiently and accurately. It paves the way for putting more metabolic traits into databases, providing easy access of information by investigators.

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“…proposed to merge the two genera based on the up-to-date phylogenomic evidence and similar phenotypic characteristics among the type strains [3]. As of October 2020, the genus Roseibium comprises 11 species with validly published names, which can be found in the updated LPSN website (https://lpsn.dsmz.de/genus/roseibium) [4]. The other two species, Labrenzia polysiphoniae [5, 6] and ‘ Labrenzia callyspongiae ’ [7] (the name of the latter is effectively but not yet validly published and thus is in quotation marks throughout this study), have not been emended, probably due to the proposal of these two species later than the beginning of Hördt’s study [3], and thus the taxonomic status of the two species remains to be confirmed.…”

Section: Full-textmentioning

confidence: 99%

Roseibium litorale sp. nov., isolated from a tidal flat sediment and proposal for the reclassification of Labrenzia polysiphoniae as Roseibium polysiphoniae comb. nov.

Liu

¹

,

Pei

²

,

Du

³

et al. 2021

International Journal of Systematic and Evolutionary Microbiology

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A novel Gram-stain-negative, facultatively anaerobic, rod-shaped and non-motile bacterial strain, designated as 4C16AT, was isolated from a tidal flat sediment and characterized by using a polyphasic taxonomic approach. Strain 4C16AT was found to grow at 10–40 °C (optimum, 28 °C), at pH 5.0–10.0 (optimum, pH 6.0–7.0) and in 0–6 % (w/v) NaCl (optimum, 1 %). Phylogenetic analysis based on 16S rRNA gene sequences revealed that strain 4C16AT fell into the genus Roseibium , and shared the highest identity of 98.9 % with the closest type strain Roseibium suaedae KACC 13772T and less than 98.0 % identity with other type strains of recognized species within this genus. The phylogenomic analysis indicated that strain 4C16AT formed an independent branch within this genus. The 28.6 % digital DNA–DNA hybridization estimate and 85.0 % average nucleotide identity between strains 4C16AT and R. suaedae KACC 13772T were the highest, but still far below their respective threshold for species definition, implying that strain 4C16AT should represent a novel genospecies. The predominant cellular fatty acid was summed feature 8; the polar lipids were diphosphatidylglycerol, phosphatidylcholine, phosphatidylethanolamine, phosphatidylglycerol and phosphatidylmonomethylethanolamine; the respiratory quinones were Q-9 and Q-10. The genomic DNA G+C content was 59.8mol %. Based on phylogenetic analyses and phenotypic and chemotaxonomic characteristics, strain 4C16AT is concluded to represent a novel species of the genus Roseibium , for which the name Roseibium litorale sp. nov. is proposed. The type strain of the species is 4C16AT (=GDMCC 1.1932T=KACC 22078T). We also propose the reclassification of Labrenzia polysiphoniae as Roseibium polysiphoniae comb. nov. and ‘Labrenzia callyspongiae’ as Roseibium callyspongiae sp. nov.

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“…At the time of writing, the genus encompasses 25 validly published species [9]. The species ‘ Glycomyces tritici ’ by Li et al [10], ‘ Glycomyces luteolus ’ by Duan et al [11] and ‘ Glycomyces xinjiangensis ’ by Guan et al [12] also have been described recently but the names are not yet validly published.…”

Section: Full-textmentioning

confidence: 99%

Glycomyces terrestris sp. nov., isolated from extremely arid soil from Yuanmou Earth Forest

Li

¹

,

Li

²

,

Lei

³

et al. 2021

International Journal of Systematic and Evolutionary Microbiology

4

0

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A Gram-stain-positive, mycelium-forming actinobacterium, YIM 121974T was isolated from an extreme arid soil sample collected at Yuanmou Earth Forest, Yunnan Province, PR China. Classification using a polyphasic approach suggested that strain YIM 121974T belonged to the genus Glycomyces and was closely related to Glycomyces dulcitolivorans SJ-25T (98.3 %), Glycomyces scopariae YIM 56256T (98 %), Glycomyces mayteni YIM 61331T (97.9 %), Glycomyces albidus NEAU-7082T (97.9 %), Glycomyces sambucus CGMCC 4.3147T (97.7 %), Glycomyces artemisiae IXS4T (97.6 %) and Glycomyces parisis CPCC 204357T (97.5 %), but could be distinguished from its closest relatives by a combination of phenotypic and phylogenetic features. Average nucleotide identity values of YIM 121974T to its closest phylogenetic neighbours were 70.7–88.9 %, which are lower than the threshold of 95 %. The digital DNA–DNA hybridization values between YIM 121974T and these relative species were 18.0–36.3 %, which are also well below the cut-off value (>70 %) for species delineation. The DNA G+C content of strain YIM 121974T was 72.3 mol% (draft genome sequence). The predominant menaquinone was MK-11. The phospholipids were composed of diphosphatidylglycerol, phosphatidylglycerol, phosphatidylinositol, phosphatidylinositol mannoside, phosphoglycolipid and glycolipid. The major fatty acid compositions were iso-C16 : 0, anteiso-C15 : 0 and anteiso-C17 : 0. The draft genome of isolate YIM 121974T was found to contain 11 secondary metabolite biosynthesis gene clusters by using the antiSMASH server. Based on the above observations, strain YIM 121974T could be distinguished from closely related species belonging to the genus Glycomyces . Thus, strain YIM 121974T represents a novel species of the genus Glycomyces , for which the name Glycomyces terrestris sp. nov. is proposed. The type strain is YIM 121974T (=KCTC 39870T=DSM 106742T).

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List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ

Cited by 1,297 publications

References 7 publications

Using neural networks to mine text and predict metabolic traits for thousands of microbes

Using neural networks to mine text and predict metabolic traits for thousands of microbes

Roseibium litorale sp. nov., isolated from a tidal flat sediment and proposal for the reclassification of Labrenzia polysiphoniae as Roseibium polysiphoniae comb. nov.

Glycomyces terrestris sp. nov., isolated from extremely arid soil from Yuanmou Earth Forest

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