Whole genome-based comparative analysis of the genus Streptomyces reveals many misclassifications.

Streptomyces species are experts in the production of bioactive secondary metabolites; however, their taxonomy has fallen victim of the tremendous interest shown by the scientific community, evident in the discovery of numerous synonymous in public repositories. Based on genomic data from NCBI Datasets and nomenclature from the LPSN database, we compiled a dataset of 600 Streptomyces species along with their annotations and metadata. To pinpoint the most suitable taxonomic classification method, we conducted a comprehensive assessment comparing multiple methodologies, including analysis of 16S rRNA, individual housekeeping genes, multilocus sequence analysis (MLSA), and Fast Average Nucleotide Identity (FastANI) on a subset of 409 species with complete data. Due to insufficient resolution of 16S rRNA and inconsistency observed in individual housekeeping genes, we performed a more in-depth analysis, comparing only FastANI and MLSA, which expanded our dataset to include 502 species. With FastANI validated as the preferred method, we conducted pairwise analysis on the entire dataset identifying 59 non-unique species among the 600, and subsequently refined the dataset to 541 unique species. Additionally, we collected data on 724 uncharacterized Streptomyces strains to investigate the uniqueness potential of the unannotated fraction of the Streptomyces genus. Utilizing FastANI, 289 strains could be successfully classified into one of the 541 Streptomyces species. KEY POINTS: • Evaluation of taxonomic classification methods for Streptomyces species. • Whole genome analysis, specifically FastANI, has been chosen as preferred method. • Various reclassifications are proposed within the Streptomyces genus.

Average Nucleotide Identity and Digital DNA-DNA Hybridization Analysis Following PromethION Nanopore-Based Whole Genome Sequencing Allows for Accurate Prokaryotic Typing.

Whole-genome sequencing (WGS) is revolutionizing clinical bacteriology. However, bacterial typing remains investigated by reference techniques with inherent limitations. This stresses the need for alternative methods providing robust and accurate sequence type (ST) classification. This study optimized and evaluated a GridION nanopore sequencing protocol, adapted for the PromethION platform. Forty-eight Escherichia coli clinical isolates with diverse STs were sequenced to assess two alternative typing methods and resistance profiling applications. Multi-locus sequence typing (MLST) was used as the reference typing method. Genomic relatedness was assessed using Average Nucleotide Identity (ANI) and digital DNA-DNA Hybridization (DDH), and cut-offs for discriminative strain resolution were evaluated. WGS-based antibiotic resistance prediction was compared to reference Minimum Inhibitory Concentration (MIC) assays. We found ANI and DDH cut-offs of 99.3% and 94.1%, respectively, which correlated well with MLST classifications and demonstrated potentially higher discriminative resolution than MLST. WGS-based antibiotic resistance prediction showed categorical agreements of ≥ 93% with MIC assays for amoxicillin, ceftazidime, amikacin, tobramycin, and trimethoprim-sulfamethoxazole. Performance was suboptimal (68.8-81.3%) for amoxicillin-clavulanic acid, cefepime, aztreonam, and ciprofloxacin. A minimal sequencing coverage of 12× was required to maintain essential genomic features and typing accuracy. Our protocol allows the integration of PromethION technology in clinical laboratories, with ANI and DDH proving to be accurate and robust alternative typing methods, potentially offering superior resolution. WGS-based antibiotic resistance prediction holds promise for specific antibiotic classes.

MariClus: Your One-Stop Platform for Information on Marine Natural Products, Their Gene Clusters and Producing Organisms.

BACKGROUND: The marine environment hosts the vast majority of living species and marine microbes that produce natural products with great potential in providing lead compounds for drug development. With over 70% of Earth's surface covered in water and the high interaction rate associated with liquid environments, this has resulted in many marine natural product discoveries. Our improved understanding of the biosynthesis of these molecules, encoded by gene clusters, along with increased genomic information will aid us in uncovering even more novel compounds. RESULTS: We introduce MariClus (https://www.mariclus.com), an online user-friendly platform for mining and visualizing marine gene clusters. The first version contains information on clusters and the predicted molecules for over 500 marine-related prokaryotes. The user-friendly interface allows scientists to easily search by species, cluster type or molecule and visualize the information in table format or graphical representation. CONCLUSIONS: This new online portal simplifies the exploration and comparison of gene clusters in marine species for scientists and assists in characterizing the bioactive molecules they produce. MariClus integrates data from public sources, like GenBank, MIBiG and PubChem, with genome mining results from antiSMASH. This allows users to access and analyze various aspects of marine natural product biosynthesis and diversity.

The CEP family in land plants: evolutionary analyses, expression studies, and role in Arabidopsis shoot development.

In Arabidopsis, more than 1000 putative small signalling peptides have been predicted, but very few have been functionally characterized. One class of small post-translationally modified signalling peptides is the C-TERMINALLY ENCODED PEPTIDE (CEP) family, of which one member has been shown to be involved in regulating root architecture. This work applied a bioinformatics approach to identify more members of the CEP family. It identified 10 additional members and revealed that this family only emerged in flowering plants and was absent from extant members of more primitive plants. The data suggest that the CEP proteins form two subgroups according to the CEP domain. This study further provides an overview of specific CEP expression patterns that offers a comprehensive framework to study the role of the CEP signalling peptides in plant development. For example, expression patterns point to a role in aboveground tissues which was corroborated by the analysis of transgenic lines with perturbed CEP levels. These results form the basis for further exploration of the mechanisms underlying this family of peptides and suggest their putative roles in distinct developmental events of higher plants.