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      Activating Transcription Factor 3 Stimulates Follicle-Stimulating Hormone-β Expression In Vitro But Is Dispensable for Follicle-Stimulating Hormone Production in Murine Gonadotropes In Vivo

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          Abstract

          Follicle-stimulating hormone (FSH), a dimeric glycoprotein produced by pituitary gonadotrope cells, regulates spermatogenesis in males and ovarian follicle growth in females. Hypothalamic gonadotropin-releasing hormone (GnRH) stimulates FSHβ subunit gene (Fshb) transcription, though the underlying mechanisms are poorly understood. To address this gap in knowledge, we examined changes in pituitary gene expression in GnRH-deficient mice (hpg) treated with a regimen of exogenous GnRH that increases pituitary Fshb but not luteinizing hormone β (Lhb) messenger RNA levels. Activating transcription factor 3 (Atf3) was among the most upregulated genes. Activating transcription factor 3 (ATF3) can heterodimerize with members of the activator protein 1 family to regulate gene transcription. Co-expression of ATF3 with JunB stimulated murine Fshb, but not Lhb, promoter-reporter activity in homologous LβT2b cells. ATF3 also synergized with a constitutively active activin type I receptor to increase endogenous Fshb expression in these cells. Nevertheless, FSH production was intact in gonadotrope-specific Atf3 knockout [conditional knockout (cKO)] mice. Ovarian follicle development, ovulation, and litter sizes were equivalent between cKOs and controls. Testis weights and sperm counts did not differ between genotypes. Following gonadectomy, increases in LH secretion were enhanced in cKO animals. Though FSH levels did not differ between genotypes, post-gonadectomy increases in pituitary Fshb and gonadotropin α subunit expression were more pronounced in cKO than control mice. These data indicate that ATF3 can selectively stimulate Fshb expression in vitro but is not required for FSH production in vivo.

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          Is Open Access

          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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              Near-optimal probabilistic RNA-seq quantification.

              We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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                Author and article information

                Contributors
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                Journal
                Endocrinology
                The Endocrine Society
                1945-7170
                May 01 2023
                March 13 2023
                May 01 2023
                March 13 2023
                March 23 2023
                : 164
                : 5
                Article
                10.1210/endocr/bqad050
                3c804b1d-371b-4204-a947-bb4dace1f0da
                © 2023

                https://academic.oup.com/pages/standard-publication-reuse-rights

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