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      FOXC1 Binds Enhancers and Promotes Cisplatin Resistance in Bladder Cancer

      , , , , , , , ,
      Cancers
      MDPI AG

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          Abstract

          Chemotherapy resistance is traditionally attributed to DNA mutations that confer a survival advantage under drug selection pressure. However, in bladder cancer and other malignancies, we and others have previously reported that cancer cells can convert spontaneously to an aggressive drug-resistant phenotype without prior drug selection or mutational events. In the current work, we explored possible epigenetic mechanisms behind this phenotypic plasticity. Using Hoechst dye exclusion and flow cytometry, we isolated the aggressive drug-resistant cells and analyzed their chromatin accessibility at regulatory elements. Compared to the rest of the cancer cell population, the aggressive drug-resistant cells exhibited enhancer accessibility changes. In particular, we found that differentially accessible enhancers were enriched for the FOXC1 transcription factor motif, and that FOXC1 was the most significantly overexpressed gene in aggressive drug-resistant cells. ChIP-seq analysis revealed that differentially accessible enhancers in aggressive drug-resistant cells had a higher FOXC1 binding, which regulated the expression of adjacent cancer-relevant genes like ABCB1 and ID3. Accordingly, cisplatin treatment of bladder cancer cells led to an increased FOXC1 expression, which mediated cell survival and conversion to a drug-resistant phenotype. Collectively, these findings suggest that FOXC1 contributes to phenotypic plasticity by binding enhancers and promoting a mutation-independent shift towards cisplatin resistance in bladder cancer.

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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
                (View ORCID Profile)
                Journal
                CANCCT
                Cancers
                Cancers
                MDPI AG
                2072-6694
                April 2022
                March 28 2022
                : 14
                : 7
                : 1717
                Article
                10.3390/cancers14071717
                4fe72e4d-64cd-4293-b2d5-50de0d4f121c
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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