0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Tris(4‐chlorophenyl)methane and tris(4‐chlorophenyl)methanol disrupt pancreatic organogenesis and gene expression in zebrafish embryos

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objectives

          Tris(4‐chlorophenyl) methane (TCPM) and tris(4‐chlorophenyl)methanol (TCPMOH) are anthropogenic environmental contaminants believed to be manufacturing byproducts of the organochlorine pesticide dichlorodiphenyltrichloroethane (DDT) due to environmental co‐occurrence. TCPM and TCPMOH are persistent, bioaccumulate in the environment, and are detected in human breast milk and adipose tissues. DDT exposures have been previously shown to disrupt insulin signaling and glucoregulation, increasing risk for diabetes. We have previously shown that embryonic exposures organochlorines such as polychlorinated biphenyls disrupted pancreatic development and early embryonic glucoregulatory networks. Here, we determined the impacts of the similar compounds TCPM and TCPMOH on zebrafish pancreatic growth and gene expression following developmental exposures.

          Methods

          Zebrafish embryos were exposed to 50 nM TCPM or TCPMOH beginning at 24 hr postfertilization (hpf) and exposures were refreshed daily. At 96 hpf, pancreatic growth and islet area were directly visualized in Tg( ptf1a:: GFP) and Tg( insulin:: GFP) embryos, respectively, using microscopy. Gene expression was assessed at 100 hpf with RNA sequencing.

          Results

          Islet and total pancreas area were reduced by 20.8% and 13% in embryos exposed to 50 nM TCPMOH compared to controls. TCPM did not induce significant morphological changes to the developing pancreas, indicating TCPMOH, but not TCPM, impairs pancreatic development despite similarity in molecular responses. Transcriptomic responses to TCPM and TCPMOH were correlated ( R 2 = .903), and pathway analysis found downregulation of processes including retinol metabolism, circadian rhythm, and steroid biosynthesis.

          Conclusion

          Overall, our data suggest that TCPM and TCPMOH may be hazardous to embryonic growth and development.

          Related collections

          Most cited references84

          • Record: found
          • Abstract: found
          • Article: found
          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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/.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Cutadapt removes adapter sequences from high-throughput sequencing reads

                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Birth Defects Research
                Birth Defects Research
                Wiley
                2472-1727
                2472-1727
                March 2023
                December 05 2022
                March 2023
                : 115
                : 4
                : 458-473
                Affiliations
                [1 ] School of Public Health San Diego State University San Diego California USA
                Article
                10.1002/bdr2.2132
                0c6f1e04-b4dc-4120-9f5b-1ab2f2e5015e
                © 2023

                http://creativecommons.org/licenses/by-nc-nd/4.0/

                History

                Comments

                Comment on this article