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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the acute respiratory disease COVID-19, which has become a global concern due to its rapid spread. The common methods to monitor and quantitate SARS-CoV-2 infectivity in cell culture are so far time-consuming and labor-intensive. Using the Sleeping Beauty transposase system, we generated a robust and versatile cellular infection model that allows SARS-CoV-2 infection experiments compatible for high-throughput and live cell imaging. The model is based on lung derived A549 cells, which show a profound interferon response and convenient cell culture characteristics. ACE2 and TMPRSS2 were introduced for constitutive expression (A549-AT). Subclones with varying levels of ACE2/TMPRSS2 were screened for optimal SARS-CoV-2 susceptibility. Furthermore, extensive evaluation demonstrated that SARS-CoV-2 infected A549-AT cells were distinguishable from mock-infected cells and already showed approximately 12 h post infection a clear signal to noise ratio in terms of cell roughness, fluorescence and a profound visible cytopathic effect. Moreover, due to the high transfection efficiency and proliferation capacity, Sleeping Beauty transposase-based overexpression cell lines with a second inducible fluorescence reporter cassette (eGFP) can be generated in a very short time, enabling the investigation of host and restriction factors in a doxycycline-inducible manner. Thus, the novel model cell line allows rapid and sensitive monitoring of SARS-CoV-2 infection and the screening for host factors essential for viral replication. HIGHLIGHTS: Sleeping Beauty transposon-based cellular system was used to generate a highly susceptible cell line for monitoring SARS-CoV-2 infection; The versatile model cell line A549-AT is suitable for rapid and sensitive high-throughput assays; Additional gene specific expression cassettes allow the screening for compounds and cellular factors limiting SARS-CoV-2 replication.
High-throughput metabarcoding studies on fungi and other eukaryotic microorganisms are rapidly becoming more frequent and more complex, requiring researchers to handle ever increasing amounts of raw sequence data. Here, we provide a flexible pipeline for pruning and analyzing fungal barcode (ITS rDNA) data generated as paired-end reads on Illumina MiSeq sequencers. The pipeline presented includes specific steps fine-tuned for ITS, that are mostly missing from pipelines developed for prokaryotes. It (1) employs state of the art programs and follows best practices in fungal high-throughput metabarcoding; (2) consists of modules and scripts easily modifiable by the user to ensure maximum flexibility with regard to specific needs of a project or future methodological developments; and (3) is straightforward to use, also in classroom settings. We provide detailed descriptions and revision techniques for each step, thus giving the user maximum control over data treatment and avoiding a black-box approach. Employing this pipeline will improve and speed up the tedious and error-prone process of cleaning fungal Illumina metabarcoding data.