Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures

Because a natural entity “species” cannot be recognized as a group of strains that is genetically well separated from its phylogenetic neighbors, a pragmatic approach was taken to define a species by a polyphasic approach (L. G. Wayne, D. J. Brenner, R. R. Colwell, P. A. D. Grimont, O. Kandler, M. I. Krichevsky, L. H. Moore, W. E. C. Moore, R. G. E. Murray, E. Stackebrandt, M. P. Starr, and H. G. Trüper, Int. J. Syst. Bacteriol. 37:463-464, 1987), in which a DNA reassociation value of about 70% plays a dominant role. With the establishment of rapid sequence analysis of 16S rRNA and the recognition of its potential to determine the phylogenetic position of any prokaryotic organism, the role of 16S rRNA similarities in the present species definition in bacteriology needs to be clarified. Comparative studies clearly reveal the limitations of the sequence analysis of this conserved gene and gene product in the determination of relationships at the strain level for which DNA-DNA reassociation experiments still constitute the superior method. Since today the primary structure of 16S rRNA is easier to determine than hybridization between DNA strands, the strength of the sequence analysis is to recognize the level at which DNA pairing studies need to be performed, which certainly applies to similarities of 97% and higher.

BACKGROUND: For the last 25 years species delimitation in prokaryotes (Archaea and Bacteria) was to a large extent based on DNA-DNA hybridization (DDH), a tedious lab procedure designed in the early 1970s that served its purpose astonishingly well in the absence of deciphered genome sequences. With the rapid progress in genome sequencing time has come to directly use the now available and easy to generate genome sequences for delimitation of species. GBDP (Genome Blast Distance Phylogeny) infers genome-to-genome distances between pairs of entirely or partially sequenced genomes, a digital, highly reliable estimator for the relatedness of genomes. Its application as an in-silico replacement for DDH was recently introduced. The main challenge in the implementation of such an application is to produce digital DDH values that must mimic the wet-lab DDH values as close as possible to ensure consistency in the Prokaryotic species concept. RESULTS: Correlation and regression analyses were used to determine the best-performing methods and the most influential parameters. GBDP was further enriched with a set of new features such as confidence intervals for intergenomic distances obtained via resampling or via the statistical models for DDH prediction and an additional family of distance functions. As in previous analyses, GBDP obtained the highest agreement with wet-lab DDH among all tested methods, but improved models led to a further increase in the accuracy of DDH prediction. Confidence intervals yielded stable results when inferred from the statistical models, whereas those obtained via resampling showed marked differences between the underlying distance functions. CONCLUSIONS: Despite the high accuracy of GBDP-based DDH prediction, inferences from limited empirical data are always associated with a certain degree of uncertainty. It is thus crucial to enrich in-silico DDH replacements with confidence-interval estimation, enabling the user to statistically evaluate the outcomes. Such methodological advancements, easily accessible through the web service at http://ggdc.dsmz.de, are crucial steps towards a consistent and truly genome sequence-based classification of microorganisms.

Microbial taxonomy is increasingly influenced by genome-based computational methods. Yet such analyses can be complex and require expert knowledge. Here we introduce TYGS, the Type (Strain) Genome Server, a user-friendly high-throughput web server for genome-based prokaryote taxonomy, connected to a large, continuously growing database of genomic, taxonomic and nomenclatural information. It infers genome-scale phylogenies and state-of-the-art estimates for species and subspecies boundaries from user-defined and automatically determined closest type genome sequences. TYGS also provides comprehensive access to nomenclature, synonymy and associated taxonomic literature. Clinically important examples demonstrate how TYGS can yield new insights into microbial classification, such as evidence for a species-level separation of previously proposed subspecies of Salmonella enterica. TYGS is an integrated approach for the classification of microbes that unlocks novel scientific approaches to microbiologists worldwide and is particularly helpful for the rapidly expanding field of genome-based taxonomic descriptions of new genera, species or subspecies.

Microbial systematics is heavily influenced by genome-based methods and challenged by an ever increasing number of taxon names and associated sequences in public data repositories. This poses a challenge for database systems, particularly since it is obviously advantageous if such data are based on a globally recognized approach to manage names, such as the International Code of Nomenclature of Prokaryotes. The amount of data can only be handled if accurate and reliable high-throughput platforms are available that are able to both comply with this demand and to keep track of all changes in an efficient and flexible way. The List of Prokaryotic names with Standing in Nomenclature (LPSN) is an expert-curated authoritative resource for prokaryotic nomenclature and is available at https://lpsn.dsmz.de. The Type (Strain) Genome Server (TYGS) is a high-throughput platform for accurate genome-based taxonomy and is available at https://tygs.dsmz.de. We here present important updates of these two previously introduced, heavily interconnected platforms for taxonomic nomenclature and classification, including new high-level facilities providing access to bioinformatic algorithms, a considerable expansion of the database content, and new ways to easily access the data.

After nearly 10 years of PCR-based analysis of prokaryotic small-subunit ribosomal RNAs for ecological studies it seems necessary to summarize reported pitfalls of this approach which will most likely lead to an erroneous description on the microbial diversity of a given habitat. The following article will cover specific aspects of sample collection, cell lysis, nucleic acid extraction, PCR amplification, separation of amplified DNA, application of nucleic probes and data analysis.

The List of Prokaryotic names with Standing in Nomenclature (LPSN) was acquired in November 2019 by the DSMZ and was relaunched using an entirely new production system in February 2020. This article describes in detail the structure of the new site, navigation, page layout, search facilities and new features.

A new hierarchic classification structure for the taxa between the taxonomic levels of genus and class is Proposed for the actinomycete line of descent as defined by analysis of small subunit (16S) rRNA and genes coding for this molecule (rDNA). While the traditional circumscription of a genus of the actinomycete subphylum is by and large in accord with the 16S rRNA/rDNA-based phylogenetic clustering of these organisms. most of the higher taxa proposed in the past do not take into account the phylogenetic clustering of genera. The rich chemical, morphological and physiological diversity of phylogenetically closely related genera makes the description of families and higher taxa so broad that they become meaningless for the description of the enclosed taxa. Here we present a classification system in which phylogenetically neighboring taxa at the genus level are clustered into families, suborders, orders, subclasses, and a class irrespective of those phenotypec characteristics on which the delineation of taxa has been based in the past. Rather than being based on a listing of a wide array of chemotaxonomic, morphological, and physiological properties, the delineation is based solely on 16S rDNA/rRNA sequence-based phylogenetic clustering and the presence of taxon-specific 16S rDNA RNA signature nucleotides.

The pragmatic species concept for Bacteria and Archaea is ultimately based on DNA-DNA hybridization (DDH). While enabling the taxonomist, in principle, to obtain an estimate of the overall similarity between the genomes of two strains, this technique is tedious and error-prone and cannot be used to incrementally build up a comparative database. Recent technological progress in the area of genome sequencing calls for bioinformatics methods to replace the wet-lab DDH by in-silico genome-to-genome comparison. Here we investigate state-of-the-art methods for inferring whole-genome distances in their ability to mimic DDH. Algorithms to efficiently determine high-scoring segment pairs or maximally unique matches perform well as a basis of inferring intergenomic distances. The examined distance functions, which are able to cope with heavily reduced genomes and repetitive sequence regions, outperform previously described ones regarding the correlation with and error ratios in emulating DDH. Simulation of incompletely sequenced genomes indicates that some distance formulas are very robust against missing fractions of genomic information. Digitally derived genome-to-genome distances show a better correlation with 16S rRNA gene sequence distances than DDH values. The future perspectives of genome-informed taxonomy are discussed, and the investigated methods are made available as a web service for genome-based species delineation.

The 16S rRNA gene sequences of 34 named and unnamed clostridial strains were determined by PCR direct sequencing and were compared with more than 80 previously determined clostridial sequences and the previously published sequences of representative species of other low- G + C-content gram-positive genera, thereby providing an almost complete picture of the genealogical interrelationships of the clostridia. The results of our phylogenetic analysis corroborate and extend previous findings in showing that the genus Clostridium is extremely heterogeneous, with many species phylogenetically intermixed with other spore-forming and non-spore-forming genera. The genus Clostridium is clearly in need of major revision, and the rRNA structures defined in this and previous studies may provide a sound basis for future taxonomic restructuring. The problems and different possibilities for restructuring are discussed in light of the phenotypic and phylogenetic data, and a possible hierarchical structure for the clostridia and their close relatives is presented. On the basis of phenotypic criteria and the results of phylogenetic analyses the following five new genera and 11 new combinations are proposed: Caloramator gen. nov., with Caloramator fervidus comb. nov.; Filifactor gen. nov., with Filifactor villosus comb. nov.; Moorella gen. nov., with Moorella thermoacetica comb. nov. and Moorella thermoautotrophica comb. nov.; Oxobacter gen. nov., with Oxobacter pfennigii comb. nov.; Oxalophagus gen. nov., with Oxalophagus oxalicus comb. nov.; Eubacterium barkeri comb. nov.; Paenibacillus durum comb. nov.; Thermoanaerobacter kivui comb. nov.; Thermoanaerobacter thermocopriae comb. nov.; and Thermoanerobacterium thermosaccharolyticum comb. nov.

An ad hoc committee for the re-evaluation of the species definition in bacteriology met in Gent, Belgium, in February 2002. The committee made various recommendations regarding the species definition in the light of developments in methodologies available to systematists.

The recently described aerobic, extremely halophilic archaeobaterium, Halobacterium lacusprofundi was subjected to lipid analysis so that comparisons could be made between existing lipid data and that of the new isolate. This investigation showed that the major respiratory lipoquinones present were MK-8 and MK-8(VIII-H2), a feature found in other members of the family Halobacteriaceae. The polar lipids comprised the diether derivatives of phosphatidly glycerol, phosphatidyl glycerophosphate, phosphatidly glycerosulphate, a diglycosyl diether and its sulphate derivative. The data presented shows that Halobacterium locusprofundi is related to Hb. saccharovorum and Hb. sodomense, and is in agreement with phylogenetic data.

Taxonomy relies on three key elements: characterization, classification and nomenclature. All three elements are dynamic fields, but each step depends on the one which precedes it. Thus, the nomenclature of a group of organisms depends on the way they are classified, and the classification (among other elements) depends on the information gathered as a result of characterization. While nomenclature is governed by the Bacteriological Code, the classification and characterization of prokaryotes is an area that is not formally regulated and one in which numerous changes have taken place in the last 50 years. The purpose of the present article is to outline the key elements in the way that prokaryotes are characterized, with a view to providing an overview of some of the pitfalls commonly encountered in taxonomic papers.

The bacterial and archaeal genomes that have been sequenced to date were chosen for sequencing based mainly on their physiology, which is fine but has resulted in a distinct phylogenetic bias. An alternative approach has been taken in the Genomic Encyclopedia of Bacteria and Archaea (GEBA) project, which advocates choosing genomes based on the organism's phylogenetic position, with the aim filling in the gaps in sequencing along on bacterial and archaeal branches of the tree of life. The value of this approach has been demonstrated by a pilot study of the genome sequences of 56 culturable species selected to maximize phylogenetic coverage. Analysis of the sequences provides insights into phylogenetics, protein function and genome annotation. There are now nearly 1,000 completed bacterial and archaeal genomes available, but as most of them were chosen for sequencing on the basis of their physiology, the data are limited by a highly biased phylogenetic distribution. To explore the value added by choosing microbial genomes for sequencing on the basis of their evolutionary relationships, the genomes of 56 species of Bacteria and Archaea selected to maximize phylogenetic coverage are now sequenced and analysed. Sequencing of bacterial and archaeal genomes has revolutionized our understanding of the many roles played by microorganisms1. There are now nearly 1,000 completed bacterial and archaeal genomes available2, most of which were chosen for sequencing on the basis of their physiology. As a result, the perspective provided by the currently available genomes is limited by a highly biased phylogenetic distribution3,4,5. To explore the value added by choosing microbial genomes for sequencing on the basis of their evolutionary relationships, we have sequenced and analysed the genomes of 56 culturable species of Bacteria and Archaea selected to maximize phylogenetic coverage. Analysis of these genomes demonstrated pronounced benefits (compared to an equivalent set of genomes randomly selected from the existing database) in diverse areas including the reconstruction of phylogenetic history, the discovery of new protein families and biological properties, and the prediction of functions for known genes from other organisms. Our results strongly support the need for systematic ‘phylogenomic’ efforts to compile a phylogeny-driven ‘Genomic Encyclopedia of Bacteria and Archaea’ in order to derive maximum knowledge from existing microbial genome data as well as from genome sequences to come.

The genus Nocardiopsis was shown to be phylogenetically coherent and to represent a distinct lineage within the radiation of the order Actinomycetales. The closest relatives of the genus Nocardiopsis are members of the genera Actinomadura, Thermomonospora, Streptosporangium, and Microtetraspora. The intrageneric structure of the genus Nocardiopsis is shown to consist of a highly related species group containing Nocardiopsis dassonvillei, Nocardiopsis alborubida, and Nocardiopsis antarctica and a second group of less highly related species comprising Nocardiopsis alba subsp. alba, Nocardiopsis alba subsp. prasina, and Nocardiopsis listeri. Nocardiopsis lucentensis occupies a position intermediate between the two species groups. The results of a 16S ribosomal DNA sequence analysis are generally consistent with the available chemotaxonomic, phenotypic, and DNA-DNA hybridization data. The phylogenetic position and the morpho- and chemotaxonomic properties of Nocardiopsis species support the creation of a family for the genus Nocardiopsis, Nocardiopsaceae fam. nov.

The reconstruction of bacterial and archaeal genomes from shotgun metagenomes has enabled insights into the ecology and evolution of environmental and host-associated microbiomes. Here we applied this approach to >10,000 metagenomes collected from diverse habitats covering all of Earth's continents and oceans, including metagenomes from human and animal hosts, engineered environments, and natural and agricultural soils, to capture extant microbial, metabolic and functional potential. This comprehensive catalog includes 52,515 metagenome-assembled genomes representing 12,556 novel candidate species-level operational taxonomic units spanning 135 phyla. The catalog expands the known phylogenetic diversity of bacteria and archaea by 44% and is broadly available for streamlined comparative analyses, interactive exploration, metabolic modeling and bulk download. We demonstrate the utility of this collection for understanding secondary-metabolite biosynthetic potential and for resolving thousands of new host linkages to uncultivated viruses. This resource underscores the value of genome-centric approaches for revealing genomic properties of uncultivated microorganisms that affect ecosystem processes.

The Critical Assessment of Metagenome Interpretation (CAMI) community initiative presents results from its first challenge, a rigorous benchmarking of software for metagenome assembly, binning and taxonomic profiling. Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.

Abstract Bacterial menaquinones were separated isocratically on a reverse phase Li Chrosorb RP18 5 μm and a silver loaded ion exchanger. On octyldecylsilica support the separation of the menaquinones depends on their lipophilic character, on the silver column mainly on the number of double bonds in the isoprenyl chain. Comparing the runs of both columns the menaquinones were easily differentiated.

A Gram-positive, motile, short-rod-shaped strain, designated YIM 004(T), was isolated from a forest-soil sample collected from Lijiang, Yunnan Province, China, and was investigated using a polyphasic taxonomic approach. The isolate contained chemotaxonomic markers that corresponded to those of its phylogenetic neighbour, Georgenia muralis, i.e. it possessed peptidoglycan type A4 alpha with lysine as the diagnostic cell-wall diamino acid, the predominant menaquinone was MK-8(H(4)) and the major fatty acid was ai-C(15 : 0). The G+C content of the genomic DNA was 72.9 mol%. Strain YIM 004(T) exhibited a 16S rRNA gene sequence similarity of 97.3 % and a DNA-DNA relatedness value of 18 % with respect to G. muralis DSM 14418(T). On the basis of the phenotypic and genotypic differences between the isolate and G. muralis, strain YIM 004(T) represents a novel species of the genus Georgenia, for which the name Georgenia ruanii sp. nov. is proposed. The type strain is YIM 004(T) (=CCTCC AB 204065(T)=DSM 17458(T)=KCTC 19029(T)). In addition, an emended description of the genus Georgenia is presented.

Abstract Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine 1,2 . Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes 3 . However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation 4,5 . Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

The BRENDA enzyme database (https://www.brenda-enzymes.org), established in 1987, has evolved into the main collection of functional enzyme and metabolism data. In 2018, BRENDA was selected as an ELIXIR Core Data Resource. BRENDA provides reliable data, continuous curation and updates of classified enzymes, and the integration of newly discovered enzymes. The main part contains >5 million data for ∼90 000 enzymes from ∼13 000 organisms, manually extracted from ∼157 000 primary literature references, combined with information of text and data mining, data integration, and prediction algorithms. Supplements comprise disease-related data, protein sequences, 3D structures, genome annotations, ligand information, taxonomic, bibliographic, and kinetic data. BRENDA offers an easy access to enzyme information from quick to advanced searches, text- and structured-based queries for enzyme-ligand interactions, word maps, and visualization of enzyme data. The BRENDA Pathway Maps are completely revised and updated for an enhanced interactive and intuitive usability. The new design of the Enzyme Summary Page provides an improved access to each individual enzyme. A new protein structure 3D viewer was integrated. The prediction of the intracellular localization of eukaryotic enzymes has been implemented. The new EnzymeDetector combines BRENDA enzyme annotations with protein and genome databases for the detection of eukaryotic and prokaryotic enzymes.