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      Reciprocal SOX2 regulation by SMAD1-SMAD3 is critical for anoikis resistance and metastasis in cancer

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          SUMMARY

          Growth factors in tumor environments are regulators of cell survival and metastasis. Here, we reveal the dichotomy between TGF-β superfamily growth factors BMP and TGF-β/activin and their downstream SMAD effectors. Gene expression profiling uncovers SOX2 as a key contextual signaling node regulated in an opposing manner by BMP2, -4, and -9 and TGF-β and activin A to impact anchorage-independent cell survival. We find that SOX2 is repressed by BMPs, leading to a reduction in intraperitoneal tumor burden and improved survival of tumor-bearing mice. Repression of SOX2 is driven by SMAD1-dependent histone H3K27me3 recruitment and DNA methylation at SOX2’s promoter. Conversely, TGF-β, which is elevated in patient ascites, and activin A can promote SOX2 expression and anchorage-independent survival by SMAD3-dependent his-tone H3K4me3 recruitment. Our findings identify SOX2 as a contextual and contrastingly regulated node downstream of TGF-β members controlling anchorage-independent survival and metastasis in ovarian cancers.

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          In brief

          Tumor cell survival upon loss of attachment is critical for metastasis. Shonibare et al. identify SOX2 as a contextual node regulated contrastingly by BMPs and TGF-β. Regulation occurs via distinct SMAD1- and SMAD3-dependent histone recruitment and DNA methylation mechanisms influencing anchorage-independent cell survival and intraperitoneal ovarian cancer metastasis.

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          Most cited references88

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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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            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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              Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

              We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                30 January 2023
                26 July 2022
                05 February 2023
                : 40
                : 4
                : 111066
                Affiliations
                [1 ]Department of Pathology, O’Neal Comprehensive Cancer Center, University of Alabama School of Medicine, Birmingham, AL, USA
                [2 ]Department of Chemistry and Biochemistry, University of South Carolina, Columbia, SC 29208, USA
                [3 ]Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, Columbia, SC 29208, USA
                [4 ]Department of Pathology, O’Neal Comprehensive Cancer Center, Comprehensive Neuroscience Center, University of Alabama School of Medicine, Birmingham, AL, USA
                [5 ]Department of Surgery, University of Alabama School of Medicine, Birmingham, AL, USA
                [6 ]Department of Obstetrics and Gynecology, and Microbiology and Immunology, College of Medicine, Pennsylvania State University, Hershey, PA, USA
                [7 ]Department of Medicine and Duke Cancer Institute, Duke University Medical Center, Durham, NC, USA
                [8 ]Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, NC, USA
                [9 ]Department of Gynecology Oncology, University of Alabama School of Medicine, Birmingham, AL, USA
                [10 ]Department of Chemistry and Biochemistry, Department of Pharmacology, University of Arizona, Tucson, AZ 85721, USA
                [11 ]Department of Pharmacology, and Obstetrics and Gynecology, College of Medicine, Pennsylvania State University, Hershey, PA, USA
                [12 ]Department of Medicine, Division of Hematology Oncology, University of Pittsburgh, School of Medicine, Pittsburgh, PA 15213, USA
                [13 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Z.S. contributed to study design, executed, and analyzed most of the experiments and wrote the paper. M.M. and K.O.C. conducted a subset of experiments. S.M. and R.J.-S. assisted in cell culture studies and neuroendocrine cell lines. D.A. conducted and analyzed microarray studies. A.S. and R.M. conducted and analyzed RNA-seq studies. M.D.S. and A.B.N. conducted and analyzed patient ascites ELISA studies. R.P., R.A., A.B., and R.W. provided patient ascites. N.Y.L. assisted in data interpretation. N.H. contributed to design and data interpretation and editing of the manuscript. K.M. conceived, conceptualized, and supervised the study, designed experiments, analyzed data, and wrote the manuscript.

                [* ]Correspondence: nah158@ 123456pitt.edu (N.H.), mythreye@ 123456uab.edu (K.M.)
                Article
                NIHMS1869252
                10.1016/j.celrep.2022.111066
                9899501
                35905726
                c481fa5c-14af-408b-affc-c28ebb046c59

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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