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      Comprehensive multiomic characterization of human papillomavirus-driven recurrent respiratory papillomatosis reveals distinct molecular subtypes

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          Abstract

          Recurrent respiratory papillomatosis (RRP) is a debilitating neoplastic disorder of the upper aerodigestive tract caused by chronic infection with low-risk human papillomavirus types 6 or 11. Patients with severe RRP can require hundreds of lifetime surgeries to control their disease and pulmonary papillomatosis can be fatal. Here we report the comprehensive genomic and transcriptomic characterization of respiratory papillomas. We discovered and characterized distinct subtypes with transcriptional resemblance to either a basal or differentiated cell state that associate with disease aggressiveness and differ in key molecular, immune and APOBEC mutagenesis profiles. Through integrated comparison with high-risk HPV-associated head and neck squamous cell carcinoma, our analysis revealed divergent molecular and immune papilloma subtypes that form independent of underlying genomic alterations. Cumulatively our results support the development of dysregulated cellular proliferation and suppressed anti-viral immunity through distinct programs of squamous cell differentiation and associated expression of low-risk HPV genes. These analyses provide insight into the pathogenesis of respiratory papillomas and provide a foundation for the development of therapeutic strategies.

          Abstract

          Cem Sievers et al. performed genomic and transcriptomic analysis in human recurrent respiratory papillomatosis (RRP). They found that RRP harbors few genomic alterations, but that distinct transcriptional subtypes correlate with HPV gene expression and frequency of clinically-indicated interventions.

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          Most cited references42

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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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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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
                clint.allen@nih.gov
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                20 December 2021
                20 December 2021
                2021
                : 4
                : 1416
                Affiliations
                [1 ]GRID grid.214431.1, ISNI 0000 0001 2226 8444, Section on Translational Tumor Immunology, , National Institute on Deafness and Other Communication Disorders, National Institutes of Health, ; Bethesda, MD USA
                [2 ]GRID grid.417768.b, ISNI 0000 0004 0483 9129, Genitourinary Malignancies Branch, National Cancer Institute, , Center for Cancer Research, National Institutes of Health, ; Bethesda, MD USA
                [3 ]Rutgers Cancer Center, New Brunswick, NJ USA
                [4 ]GRID grid.214431.1, ISNI 0000 0001 2226 8444, Tumor Biology Section, , National Institute on Deafness and Other Communication Disorders, National Institutes of Health, ; Bethesda, MD USA
                Author information
                http://orcid.org/0000-0001-6586-5804
                Article
                2942
                10.1038/s42003-021-02942-0
                8688513
                34931021
                4b4c32db-5d98-484b-8058-31e934da989a
                © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 29 June 2021
                : 2 December 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: Intramural
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2021

                antimicrobial responses,human papilloma virus
                antimicrobial responses, human papilloma virus

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