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      Hedgehog regulation of epithelial cell state and morphogenesis in the larynx

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          Abstract

          The larynx enables speech while regulating swallowing and respiration. Larynx function hinges on the laryngeal epithelium which originates as part of the anterior foregut and undergoes extensive remodeling to separate from the esophagus and form vocal folds that interface with the adjacent trachea. Here we find that sonic hedgehog (SHH) is essential for epithelial integrity in the mouse larynx as well as the anterior foregut. During larynx-esophageal separation, low Shh expression marks specific domains of actively remodeling epithelium that undergo an epithelial-to-mesenchymal transition (EMT) characterized by the induction of N-Cadherin and movement of cells out of the epithelial layer. Consistent with a role for SHH signaling in regulating this process, Shh mutants undergo an abnormal EMT throughout the anterior foregut and larynx, marked by a cadherin switch, movement out of the epithelial layer and cell death. Unexpectedly, Shh mutant epithelial cells are replaced by a new population of FOXA2-negative cells that likely derive from adjacent pouch tissues and form a rudimentary epithelium. These findings have important implications for interpreting the etiology of HH-dependent birth defects within the foregut. We propose that SHH signaling has a default role in maintaining epithelial identity throughout the anterior foregut and that regionalized reductions in SHH trigger epithelial remodeling.

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          Epithelial-mesenchymal transitions in development and disease.

          The epithelial to mesenchymal transition (EMT) plays crucial roles in the formation of the body plan and in the differentiation of multiple tissues and organs. EMT also contributes to tissue repair, but it can adversely cause organ fibrosis and promote carcinoma progression through a variety of mechanisms. EMT endows cells with migratory and invasive properties, induces stem cell properties, prevents apoptosis and senescence, and contributes to immunosuppression. Thus, the mesenchymal state is associated with the capacity of cells to migrate to distant organs and maintain stemness, allowing their subsequent differentiation into multiple cell types during development and the initiation of metastasis.
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            StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

            Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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              Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

              High-throughput sequencing of messenger RNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate, and flexible software to reduce the raw read data to comprehensible results. HISAT, StringTie, and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample, and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol’s execution time depends on the computing resources, but typically takes under 45 minutes of computer time. Pertea et al. describe a protocol to analyze RNA-seq data using HISAT, StringTie, and Ballgown (the “new Tuxedo” package). The protocol can be used for assembly of transcripts, quantification of gene expression levels and differential expression analysis.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                18 November 2022
                2022
                : 11
                : e77055
                Affiliations
                [1 ] Department of Molecular Biosciences, The University of Texas at Austin ( https://ror.org/00hj54h04) Austin United States
                [2 ] Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health Baltimore United States
                [3 ] Center for Stem Cell and Organoid Medicine (CuSTOM), Division of Developmental Biology, and Perinatal Institute, Cincinnati Children’s Hospital Medical Center ( https://ror.org/01hcyya48) Cincinnati United States
                [4 ] Department of Pediatrics, University of Cincinnati College of Medicine ( https://ror.org/01e3m7079) Cincinnati United States
                California Institute of Technology ( https://ror.org/05dxps055) United States
                California Institute of Technology ( https://ror.org/05dxps055) United States
                California Institute of Technology ( https://ror.org/05dxps055) United States
                California Institute of Technology ( https://ror.org/05dxps055) United States
                University of Georgia ( https://ror.org/02bjhwk41) United States
                Author information
                https://orcid.org/0000-0003-1231-1749
                https://orcid.org/0000-0002-2473-5402
                https://orcid.org/0000-0003-3217-3590
                https://orcid.org/0000-0002-1724-0102
                Article
                77055
                10.7554/eLife.77055
                9718526
                36398878
                c9eb97f0-974f-45ea-b93e-0c08ddf845d2
                © 2022, Ramachandran et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 14 January 2022
                : 18 November 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: RO1 HD090163
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: RO1 HD093363
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: F30 HL142201
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008562, University of Texas at Austin;
                Award ID: Continuing Graduate Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008562, University of Texas at Austin;
                Award ID: Provost's Graduate Excellence Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008562, University of Texas at Austin;
                Award ID: TIDES Summer Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008562, University of Texas at Austin;
                Award ID: Experiential Learning Summer Scholarship
                Award Recipient :
                The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Developmental Biology
                Genetics and Genomics
                Custom metadata
                HH signaling prevents precocious EMT and cell death to maintain epithelial cell states during early stages of larynx development.

                Life sciences
                larynx development,foregut,epithelial-to-mesenchymal transition,sonic hedgehog,vocal fold morophogenesis,mouse

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