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      Loss of a newly discovered microRNA in Chinese hamster ovary cells leads to upregulation of N‐glycolylneuraminic acid sialylation on monoclonal antibodies

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          Abstract

          Chinese hamster ovary (CHO) cells are known not to express appreciable levels of the sialic acid residue N‐glycolylneuraminic acid (NGNA) on monoclonal antibodies. However, we actually have identified a recombinant CHO cell line expressing an IgG with unusually high levels of NGNA sialylation (>30%). Comprehensive multi‐OMICs based experimental analyses unraveled the root cause of this atypical sialylation: (1) expression of the cytidine monophosphate‐N‐acetylneuraminic acid hydroxylase ( CMAH) gene was spontaneously switched on, (2) CMAH mRNA showed an anti‐correlated expression to the newly discovered Cricetulus griseus (cgr) specific microRNA cgr‐miR‐111 and exhibits two putative miR‐111 binding sites, (3) miR‐111 expression depends on the transcription of its host gene SDK1, and (4) a single point mutation within the promoter region of the sidekick cell adhesion molecule 1 ( SDK1) gene generated a binding site for the transcriptional repressor histone H4 transcription factor HINF‐P. The resulting transcriptional repression of SDK1 led to a downregulation of its co‐expressed miR‐111 and hence to a spontaneous upregulation of CMAH expression finally increasing NGNA protein sialylation.

          Abstract

          Fischer and co‐workers identified a mAb producing CHO cell line exhibiting unusually high levels of potentially immunogenic NGNA sialylation. A comprehensive multi‐OMICS based root cause analysis revealed that the CMAH gene was upregulated as a result of a point mutation identified in the promoter region of the SDK1 gene which led to the silencing of its intronic miR‐111. MiR‐111 has been discovered as a new regulatory RNA in CHO cells responsible for CMAH gene and thus sialylation regulation.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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                Author and article information

                Contributors
                simon.fischer@boehringer-ingelheim.com
                Journal
                Biotechnol Bioeng
                Biotechnol Bioeng
                10.1002/(ISSN)1097-0290
                BIT
                Biotechnology and Bioengineering
                John Wiley and Sons Inc. (Hoboken )
                0006-3592
                1097-0290
                14 January 2022
                March 2022
                : 119
                : 3 ( doiID: 10.1002/bit.v119.3 )
                : 832-844
                Affiliations
                [ 1 ] Cell Line Development, Bioprocess Development Biologicals Boehringer Ingelheim GmbH & Co. KG Biberach Germany
                [ 2 ] Early Stage Bioprocess Development, Bioprocess Development Biologicals Boehringer Ingelheim GmbH & Co. KG Biberach Germany
                [ 3 ] Institute of Applied Biotechnology University of Applied Sciences Biberach Germany
                [ 4 ] Process Science Boehringer Ingelheim Fremont Inc. Fremont California USA
                [ 5 ] Analytical Science Boehringer Ingelheim Fremont Inc. Fremont California USA
                [ 6 ] Genedata AG Basel Switzerland
                [ 7 ] Protein Science, Bioprocess & Analytical Development Boehringer Ingelheim GmbH & Co. KG Biberach Germany
                Author notes
                [*] [* ] Correspondence Simon Fischer, Cell Line Development, Boehringer Ingelheim GmbH & Co. KG, Birkendorfer Strasse 65, Biberach 88397, Germany.

                Email: simon.fischer@ 123456boehringer-ingelheim.com

                Author information
                http://orcid.org/0000-0002-5188-7620
                http://orcid.org/0000-0002-2842-8549
                Article
                BIT28015
                10.1002/bit.28015
                9306616
                34935124
                dc87c80c-1faa-4a99-a915-bbe585949407
                © 2021 Boehringer Ingelheim Pharma GmbH & Co.KG. Biotechnology and Bioengineering published by Wiley Periodicals LLC

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 10 December 2021
                : 07 June 2021
                : 11 December 2021
                Page count
                Figures: 6, Tables: 1, Pages: 13, Words: 8457
                Categories
                Article
                ARTICLES
                Animal Cell Biotechnology
                Custom metadata
                2.0
                March 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:22.07.2022

                Biotechnology
                chinese hamster ovary (cho) cells,microrna,monoclonal antibody,n‐glycosylation,sialylation

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