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      Preassembled Cas9 Ribonucleoprotein-Mediated Gene Deletion Identifies the Carbon Catabolite Repressor and Its Target Genes in Coprinopsis cinerea

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          ABSTRACT

          Cre1 is an important transcription factor that regulates carbon catabolite repression (CCR) and is widely conserved across fungi. The cre1 gene has been extensively studied in several Ascomycota species, whereas its role in gene expression regulation in the Basidiomycota species remains poorly understood. Here, we identified and investigated the role of cre1 in Coprinopsis cinerea, a basidiomycete model mushroom that can efficiently degrade lignocellulosic plant wastes. We used a rapid and efficient gene deletion approach based on PCR-amplified split-marker DNA cassettes together with in vitro assembled Cas9-guide RNA ribonucleoproteins (Cas9 RNPs) to generate C. cinerea cre1 gene deletion strains. Gene expression profiling of two independent C. cinerea cre1 mutants showed significant deregulation of carbohydrate metabolism, plant cell wall degrading enzymes (PCWDEs), plasma membrane transporter-related and several transcription factor-encoding genes, among others. Our results support the notion that, like reports in the ascomycetes, Cre1 of C. cinerea orchestrates CCR through a combined regulation of diverse genes, including PCWDEs, transcription factors that positively regulate PCWDEs, and membrane transporters which could import simple sugars that can induce the expression of PWCDEs. Somewhat paradoxically, though in accordance with other Agaricomycetes, genes related to lignin degradation were mostly downregulated in cre1 mutants, indicating they fall under different regulation than other PCWDEs. The gene deletion approach and the data presented here will expand our knowledge of CCR in the Basidiomycota and provide functional hypotheses on genes related to plant biomass degradation.

          IMPORTANCE Mushroom-forming fungi include some of the most efficient lignocellulosic plant biomass degraders. They degrade dead plant materials by a battery of lignin-, cellulose-, hemicellulose-, and pectin-degrading enzymes, the encoding genes of which are under tight transcriptional control. One of the highest-level regulations of these metabolic enzymes is known as carbon catabolite repression, which is orchestrated by the transcription factor Cre1, and ensures that costly lignocellulose-degrading enzyme genes are expressed only when simple carbon sources (e.g., glucose) are not available. Here, we identified the Cre1 ortholog in a litter decomposer Agaricomycete, Coprinopsis cinerea, knocked it out, and characterized transcriptional changes in the mutants. We identified several dozen lignocellulolytic enzyme genes as well as membrane transporters and other transcription factors as putative target genes of C. cinerea cre1. These results extend knowledge on carbon catabolite repression to litter decomposer Basidiomycota.

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Role: Editor
                Journal
                Appl Environ Microbiol
                Appl Environ Microbiol
                AEM
                Applied and Environmental Microbiology
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                0099-2240
                1098-5336
                14 November 2022
                December 2022
                14 November 2022
                : 88
                : 23
                : e00940-22
                Affiliations
                [a ] Institute of Biochemistry, Biological Research Centre, Szeged, Hungary
                Royal Botanic Gardens
                Author notes

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-4102-8566
                Article
                00940-22 aem.00940-22
                10.1128/aem.00940-22
                9746306
                36374019
                4a063e0b-d09d-4c56-a48b-8dd980e7b55d
                Copyright © 2022 Pareek et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 6 June 2022
                : 11 October 2022
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 112, Pages: 17, Words: 12550
                Funding
                Funded by: Hungarian Academy of Sciences, FundRef https://doi.org/10.13039/100007626;
                Award ID: LP2019-13/2019
                Award Recipient :
                Funded by: EC | European Research Council (ERC), FundRef https://doi.org/10.13039/501100000781;
                Award ID: 758161
                Award Recipient :
                Categories
                Genetics and Molecular Biology
                spotlight-selection, Spotlight Selection
                applied-and-industrial-microbiology, Applied and Industrial Microbiology
                Custom metadata
                December 2022

                Microbiology & Virology
                carbon catabolite repression,genome editing,mushroom-forming fungi,cazymes,transcription factors (tfs),split-marker,transporters

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