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      Identification of Aspergillus westerdijkiae and its potential risk of Ochratoxin A synthesis in Cannabis inflorescences

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          Abstract

          Abstract Nowadays, fungal contamination of medical Cannabis inflorescences during postharvest has become an increasingly frequent and worrisome problem for consumers and the industry in general. This is because some of these microorganisms can produce secondary metabolites, such as mycotoxins, which can be toxic to humans. To assess the risk posed by fungal contamination and evaluate the potential for fungal isolates to produce mycotoxins, samples of medicinal Cannabis were tested for the presence of mycotoxin-forming fungi. Inflorescences were isolated on PDA agar at 23 ± 2 °C for ten days, and the microorganisms were identified. The strain with morphological characteristics compatible with the genus Aspergillus spp. was selected as the fungus with the highest risk of forming hazardous mycotoxins. This isolate was characterized conventionally and by molecular identification using primers for the internal transcribed spacer region (ITS) of ribosomal DNA and different coding genes and was identified as Aspergillus westerdijkiae. To determine mycotoxin formation, the genome of A. westerdijkiae was sequenced using the Illumina Novaseq platform in South Korea. The antiSMASH tool was used to search for gene clusters associated with producing secondary metabolites, and genes related to toxins were manually curated. Regions where the cluster of genes directly involved in OTA biosynthesis (otaA, otaB, otaC, otaR and otaD) were found, suggesting a potential risk of synthesis of this toxin.

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          Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

          The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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            antiSMASH 6.0: improving cluster detection and comparison capabilities

            Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. Since 2011, the "antibiotics and secondary metabolite analysis shell—antiSMASH" ( https://antismash.secondarymetabolites.org/ ) has supported researchers in their microbial genome mining tasks, both as a free-to-use web server and as a standalone tool under an OSI-approved open-source license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters. Graphical Abstract Here, we present version 6 of the secondary/specialized metabolite genome mining platform antiSMASH with improved detection capabilities, a new cluster compare feature and many further improvements.
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              Using SPAdes De Novo Assembler

              SPAdes-St. Petersburg genome Assembler-was originally developed for de novo assembly of genome sequencing data produced for cultivated microbial isolates and for single-cell genomic DNA sequencing. With time, the functionality of SPAdes was extended to enable assembly of IonTorrent data, as well as hybrid assembly from short and long reads (PacBio and Oxford Nanopore). In this article we present protocols for five different assembly pipelines that comprise the SPAdes package and that are used for assembly of metagenomes and transcriptomes as well as assembly of putative plasmids and biosynthetic gene clusters from whole-genome sequencing and metagenomic datasets. In addition, we present guidelines for understanding results with use cases for each pipeline, and several additional support protocols that help in using SPAdes properly. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Assembling isolate bacterial datasets Basic Protocol 2: Assembling metagenomic datasets Basic Protocol 3: Assembling sets of putative plasmids Basic Protocol 4: Assembling transcriptomes Basic Protocol 5: Assembling putative biosynthetic gene clusters Support Protocol 1: Installing SPAdes Support Protocol 2: Providing input via command line Support Protocol 3: Providing input data via YAML format Support Protocol 4: Restarting previous run Support Protocol 5: Determining strand-specificity of RNA-seq data.
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                Author and article information

                Journal
                agro
                Scientia Agropecuaria
                Scientia Agropecuaria
                Universidad Nacional de Trujillo. Facultad de Ciencias Agropecuarias (Trujillo, , Peru )
                2077-9917
                January 2024
                : 15
                : 1
                : 45-54
                Affiliations
                [3] orgnameCUBIKAN GROUP orgdiv1Quality Compliance Program Colombia
                [2] Caldas orgnameUniversidad Católica de Manizales orgdiv1Faculty of Health Science orgdiv2Research Institute of Microbiology and Agro-industrial Biotechnology Manizales (Caldas) Colombia
                [1] Caldas orgnameUniversidad de Caldas orgdiv1Faculty of Agricultural Sciences Colombia
                Article
                S2077-99172024000100004 S2077-9917(24)01500100004
                10.17268/sci.agropecu.2024.004
                bc366d23-5577-471b-ba85-f0808cf5a6d8

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 22 January 2024
                : 24 July 2023
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 62, Pages: 10
                Product

                SciELO Peru

                Categories
                Original Articles

                biosynthesis,mycotoxins,genes,fungal contamination,Cannabis sativa,risk analysis

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