Viral M-dsRNAs encoding yeast killer toxins share similar genomic organization, but no overall sequence identity. The dsRNA full-length sequences of several known M-viruses either have yet to be completed, or they were shorter than estimated by agarose gel electrophoresis. High-throughput sequencing was used to analyze some M-dsRNAs previously sequenced by traditional techniques, and new dsRNAs from atypical killer strains of Saccharomyces cerevisiae and Torulaspora delbrueckii. All dsRNAs expected to be present in a given yeast strain were reliably detected and sequenced, and the previously-known sequences were confirmed. The few discrepancies between viral variants were mostly located around the central poly(A) region. A continuous sequence of the ScV-M2 genome was obtained for the first time. M1 virus was found for the first time in wine yeasts, coexisting with Mbarr-1 virus in T. delbrueckii. Extra 5′- and 3′-sequences were found in all M-genomes. The presence of repeated short sequences in the non-coding 3′-region of most M-genomes indicates that they have a common phylogenetic origin. High identity between amino acid sequences of killer toxins and some unclassified proteins of yeast, bacteria, and wine grapes suggests that killer viruses recruited some sequences from the genome of these organisms, or vice versa, during evolution.

Many computational methods are available for assembly and annotation of newly sequenced microbial genomes. However, when new genomes are reported in the literature, there is frequently very little critical analysis of choices made during the sequence assembly and gene annotation stages. These choices have a direct impact on the biologically relevant products of a genomic analysis - for instance identification of common and differentiating regions among genomes in a comparison, or identification of enriched gene functional categories in a specific strain. Here, we examine the outcomes of different assembly and analysis steps in typical workflows in a comparison among strains of Vibrio vulnificus.

Global prevalence of antibiotic residues (ABX) in rivers requires ecotoxicological impact assessment. River microbial communities serve as effective bioindicators for this purpose. We quantified the effects of eight commonly used ABXs on a freshwater river microbial community using Biolog EcoPlates™, enabling the assessment of growth and physiological profile changes. Microbial community characterization involved 16S rRNA gene sequencing. The river community structure was representative of aquatic ecosystems, with the prevalence of Cyanobacteria, Proteobacteria, Actinobacteria, and Bacteroidetes. Our findings reveal that all ABXs at 100 µg/mL reduced microbial community growth and metabolic capacity, particularly for polymers, carbohydrates, carboxylic, and ketonic acids. Chloramphenicol, erythromycin, and gentamicin exhibited the highest toxicity, with chloramphenicol notably impairing the metabolism of all studied metabolite groups. At lower concentrations (1 µg/mL), some ABXs slightly enhanced growth and the capacity to metabolize substrates, such as carbohydrates, carboxylic, and ketonic acids, and amines, except for amoxicillin, which decreased the metabolic capacity across all metabolites. We explored potential correlations between physicochemical parameters and drug mechanisms to understand drug bioavailability. Acute toxicity effects at the river-detected low concentrations (ng/L) are unlikely. However, they may disrupt microbial communities in aquatic ecosystems. The utilization of a wide array of genetically characterized microbial communities, as opposed to a single species, enables a better understanding of the impact of ABXs on complex river ecosystems.

The GPRO suite is an in-progress bioinformatic project for -omics data analysis. As part of the continued growth of this project, we introduce a client- and server-side solution for comparative transcriptomics and analysis of variants. The client-side consists of two Java applications called “RNASeq” and “VariantSeq” to manage pipelines and workflows based on the most common command line interface tools for RNA-seq and Variant-seq analysis, respectively. As such, “RNASeq” and “VariantSeq” are coupled with a Linux server infrastructure (named GPRO Server-Side) that hosts all dependencies of each application (scripts, databases, and command line interface software). Implementation of the Server-Side requires a Linux operating system, PHP, SQL, Python, bash scripting, and third-party software. The GPRO Server-Side can be installed, via a Docker container, in the user’s PC under any operating system or on remote servers, as a cloud solution. “RNASeq” and “VariantSeq” are both available as desktop (RCP compilation) and web (RAP compilation) applications. Each application has two execution modes: a step-by-step mode enables each step of the workflow to be executed independently, and a pipeline mode allows all steps to be run sequentially. “RNASeq” and “VariantSeq” also feature an experimental, online support system called GENIE that consists of a virtual (chatbot) assistant and a pipeline jobs panel coupled with an expert system. The chatbot can troubleshoot issues with the usage of each tool, the pipeline jobs panel provides information about the status of each computational job executed in the GPRO Server-Side, while the expert system provides the user with a potential recommendation to identify or fix failed analyses. Our solution is a ready-to-use topic specific platform that combines the user-friendliness, robustness, and security of desktop software, with the efficiency of cloud/web applications to manage pipelines and workflows based on command line interface software.

High‐fat diet (HFD) mouse models are widely used in research to develop medications to treat non‐alcoholic fatty liver disease (NAFLD), as they mimic the steatosis, inflammation, and hepatic fibrosis typically found in this complex human disease. The aims of this study were to identify a complete transcriptomic signature of these mouse models and to characterize the transcriptional impact exerted by different experimental anti‐steatotic treatments. For this reason, we conducted a systematic review and meta‐analysis of liver transcriptomic studies performed in HFD‐fed C57BL/6J mice, comparing them with control mice and HFD‐fed mice receiving potential anti‐steatotic treatments. Analyzing 21 studies broaching 24 different treatments, we obtained a robust HFD transcriptomic signature that included 2,670 differentially expressed genes and 2,567 modified gene ontology biological processes. Treated HFD mice generally showed a reversion of this HFD signature, although the extent varied depending on the treatment. The biological processes most frequently reversed were those related to lipid metabolism, response to stress, and immune system, whereas processes related to nitrogen compound metabolism were generally not reversed. When comparing this HFD signature with a signature of human NAFLD progression, we identified 62 genes that were common to both; 10 belonged to the group that were reversed by treatments. Altered expression of most of these 10 genes was confirmed in vitro in hepatocytes and hepatic stellate cells exposed to a lipotoxic or a profibrogenic stimulus, respectively. In conclusion, this study provides a vast amount of information about transcriptomic changes induced during the progression and regression of NAFLD and identifies some relevant targets. Our results may help in the assessment of treatment efficacy, the discovery of unmet therapeutic targets, and the search for novel biomarkers. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.