A treasure trove of 1034 actinomycete genomes.

Filamentous Actinobacteria, recently renamed Actinomycetia, are the most prolific source of microbial bioactive natural products. Studies on biosynthetic gene clusters benefit from or require chromosome-level assemblies. Here, we provide DNA sequences from >1000 isolates: 881 complete genomes and 153 near-complete genomes, representing 28 genera and 389 species, including 244 likely novel species. All genomes are from filamentous isolates of the class Actinomycetia from the NBC culture collection. The largest genus is Streptomyces with 886 genomes including 742 complete assemblies. We use this data to show that analysis of complete genomes can bring biological understanding not previously derived from more fragmented sequences or less systematic datasets. We document the central and structured location of core genes and distal location of specialized metabolite biosynthetic gene clusters and duplicate core genes on the linear Streptomyces chromosome, and analyze the content and length of the terminal inverted repeats which are characteristic for Streptomyces. We then analyze the diversity of trans-AT polyketide synthase biosynthetic gene clusters, which encodes the machinery of a biotechnologically highly interesting compound class. These insights have both ecological and biotechnological implications in understanding the importance of high quality genomic resources and the complex role synteny plays in Actinomycetia biology.

BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets.

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as: (i) data selection, (ii) functional annotation, (iii) phylogenetic analysis, (iv) genome mining, and (v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter Notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge from the ever-growing genomic datasets of biotechnologically relevant bacterial species.

The high mutation rate at the D614G hotspot-furin cleavage site region increases the priming efficiency of the Spike protein by furin protease: analysis of Indonesian SARS-CoV-2 G614 variants obtained during the early COVID-19 pandemic.

D614G mutation plays a significant role in the transmissibility of SARS-CoV-2. Identification of other mutations related to D614G mutation within the Spike protein is pivotal as they might contribute to the pathogenicity of SARS-CoV-2. This study aims to analyze the mutation rate of furin cleavage site (FCS) region of Indonesian origin SARS-CoV-2 and to predict the effect of mutation against Spike priming efficiency by furin. A total of 375 sequences of Indonesian isolates obtained during the early pandemic were used for mutation analysis. Mutation analysis includes mutation pattern, variability, frequency of mutation, amino acid conservation, and mutation rate. The effect of mutation against Spike priming efficiency by furin protease from eight sequences with mutation in the FCS region was analyzed by protein-protein docking. We showed that mutations related to the G614 variant were increasing through time, in contrast to the D614 variant. The FCS region at the position 675-692 contained the most variable (66.67%) as well as the highest mutation frequency (85.92%) and has been observed to be the hotspot mutations linked to the D614G mutation. The D614G hotspot-FCS region (residue 600-700) had the highest amino acid change per site (20.8%) as well as the highest mutation rate as 1.34 × 10-2 substitution per site per year (95% CI 1.79 × 10-3-2.74 × 10-2), compared with other Spike protein regions. Mutations in the FCS region were the most common mutation found after the D614G mutation. These mutations were predicted to increase the Spike priming efficiency by furin. Thus, this study elucidates the importance of D614G mutation to other mutations located in the FCS region and their significance to Spike priming efficiency by furin. Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-023-00827-w.

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis.

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC-MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.

Long-Read Metagenome-Assembled Genomes Improve Identification of Novel Complete Biosynthetic Gene Clusters in a Complex Microbial Activated Sludge Ecosystem.

Microorganisms produce a wide variety of secondary/specialized metabolites (SMs), the majority of which are yet to be discovered. These natural products play multiple roles in microbiomes and are important for microbial competition, communication, and success in the environment. SMs have been our major source of antibiotics and are used in a range of biotechnological applications. In silico mining for biosynthetic gene clusters (BGCs) encoding the production of SMs is commonly used to assess the genetic potential of organisms. However, as BGCs span tens to over 200 kb, identifying complete BGCs requires genome data that has minimal assembly gaps within the BGCs, a prerequisite that was previously only met by individually sequenced genomes. Here, we assess the performance of the currently available genome mining platform antiSMASH on 1,080 high-quality metagenome-assembled bacterial genomes (HQ MAGs) previously produced from wastewater treatment plants (WWTPs) using a combination of long-read (Oxford Nanopore) and short-read (Illumina) sequencing technologies. More than 4,200 different BGCs were identified, with 88% of these being complete. Sequence similarity clustering of the BGCs implies that the majority of this biosynthetic potential likely encodes novel compounds, and few BGCs are shared between genera. We identify BGCs in abundant and functionally relevant genera in WWTPs, suggesting a role of secondary metabolism in this ecosystem. We find that the assembly of HQ MAGs using long-read sequencing is vital to explore the genetic potential for SM production among the uncultured members of microbial communities. IMPORTANCE Cataloguing secondary metabolite (SM) potential using genome mining of metagenomic data has become the method of choice in bioprospecting for novel compounds. However, accurate biosynthetic gene cluster (BGC) detection requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until very recently with new long-read technologies. Here, we determined the biosynthetic potential of activated sludge (AS), the microbial community used in resource recovery and wastewater treatment, by mining high-quality metagenome-assembled genomes generated from long-read data. We found over 4,000 BGCs, including BGCs in abundant process-critical bacteria, with no similarity to the BGCs of characterized products. We show how long-read MAGs are required to confidently assemble complete BGCs, and we determined that the AS BGCs from different studies have very little overlap, suggesting that AS is a rich source of biosynthetic potential and new bioactive compounds.

Evolutionary dynamics of SARS-CoV-2 circulating in Yogyakarta and Central Java, Indonesia: sequence analysis covering furin cleavage site (FCS) region of the spike protein / Dinámica evolutiva del SARS-CoV-2 que circula en Yogyakarta y Java Central, Indonesia: análisis de secuencia que cubre la región del sitio de escisión de furina (FCS) de la proteína espiga

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new virus responsible for the COVID-19 pandemic. The emergence of the new SARS-CoV-2 has been attributed to the possibility of evolutionary dynamics in the furin cleavage site (FCS) region. This study aimed to analyze the sequence of the FCS region in the spike protein of SARS-CoV-2 isolates that circulated in the Special Region of Yogyakarta and Central Java provinces in Indonesia. The RNA solution extracted from nasopharyngeal swab samples of confirmed COVID-19 patients were used and subjected to cDNA synthesis, PCR amplification, sequencing, and analysis of the FCS region. The sequence data from GISAID were also retrieved for further genome analysis. This study included 52 FCS region sequences. Several mutations were identified in the FCS region, i.e., D614G, Q675H, Q677H, S680P, and silent mutation in 235.57 C > T. The most important mutation in the FCS region is D614G. This finding indicated the G614 variant was circulating from May 2020 in those two provinces. Eventually, the G614 variant totally replaced the D614 variant from September 2020. All Indonesian SARS-CoV-2 isolates during this study and those deposited in GISAID showed the formation of five clade clusters from the FCS region, in which the D614 variant is in one specific cluster, and the G614 variant is dispersed into four clusters. The data indicated there is evolutionary advantage of the D614G mutation in the FCS region of the spike protein of SARS-CoV-2 circulating in the Special Region of Yogyakarta and Central Java provinces in Indonesia.(AU)

Evolutionary dynamics of SARS-CoV-2 circulating in Yogyakarta and Central Java, Indonesia: sequence analysis covering furin cleavage site (FCS) region of the spike protein.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new virus responsible for the COVID-19 pandemic. The emergence of the new SARS-CoV-2 has been attributed to the possibility of evolutionary dynamics in the furin cleavage site (FCS) region. This study aimed to analyze the sequence of the FCS region in the spike protein of SARS-CoV-2 isolates that circulated in the Special Region of Yogyakarta and Central Java provinces in Indonesia. The RNA solution extracted from nasopharyngeal swab samples of confirmed COVID-19 patients were used and subjected to cDNA synthesis, PCR amplification, sequencing, and analysis of the FCS region. The sequence data from GISAID were also retrieved for further genome analysis. This study included 52 FCS region sequences. Several mutations were identified in the FCS region, i.e., D614G, Q675H, Q677H, S680P, and silent mutation in 235.57 C > T. The most important mutation in the FCS region is D614G. This finding indicated the G614 variant was circulating from May 2020 in those two provinces. Eventually, the G614 variant totally replaced the D614 variant from September 2020. All Indonesian SARS-CoV-2 isolates during this study and those deposited in GISAID showed the formation of five clade clusters from the FCS region, in which the D614 variant is in one specific cluster, and the G614 variant is dispersed into four clusters. The data indicated there is evolutionary advantage of the D614G mutation in the FCS region of the spike protein of SARS-CoV-2 circulating in the Special Region of Yogyakarta and Central Java provinces in Indonesia.

Detection of SARS-CoV-2 spike protein D614G mutation by qPCR-HRM analysis.

OBJECTIVES: Monitoring the spread of the G614 in specific locations is critical as this variant is highly transmissible and can trigger the emergence of other mutations. Therefore, a rapid and accurate method that can reliably detect the D614G mutation will be beneficial. This study aims to analyze the potential use of the two-step Reverse Transcriptase quantitative polymerase chain reaction - high resolution melting analysis (RT-qPCR-HRM) to detect a specific mutation in the SARS-CoV-2 genome. METHODS: Six SARS-CoV-2 RNA samples were synthesized into cDNA and analyzed with the qPCR-HRM method in order to detect the D614G mutation in Spike protein of SARS-CoV-2. The primers are designed to target the specific Spike region containing the D614G mutation. The qPCR-HRM analysis was conducted simultaneously, and the identification of the SARS-CoV-2 variant was confirmed by conventional PCR and Sanger sequencing methods. RESULTS: The results showed that the melting temperature (Tm) of the D614 variant was 79.39 ± 0.03 °C, which was slightly lower than the Tm of the G614 variant (79.62 ± 0.015 °C). The results of the HRM analysis, visualized by the normalized melting curve and the difference curve were able to discriminate the D614 and G614 variant samples. All samples were identified as G614 variants by qPCR-HRM assay, which was subsequently confirmed by Sanger sequencing. CONCLUSIONS: This study demonstrated a sensitive method that can identify the D614G mutation by a simple two-step RT-qPCR-HRM assay procedure analysis, which can be useful for active surveillance of the transmission of a specific mutation.

The toughest recorded spider egg case silks are woven into composites with tear-resistant architectures.

In this communication, we report important preliminary evidence for possibly the toughest egg case silk threads recorded to date spun by the hermit spider, Nephilengys cruentata (G¯=193MJm(-3)). We further elucidate that the egg case itself is woven with a specialised repeat cross-weave that when subjected to tension, drives perpendicular-to-force threads to pile. This piling of threads constrains damage to small areas and retains the architectural integrity of the surrounding egg case material. We deduce that by having ultra-tough threads coupled to a tear resistant architecture, N. cruentata is able to protect its eggs from predators with a considerable level of effectiveness.