+1 Recommend
1 collections

      Why publish your research Open Access with G3: Genes|Genomes|Genetics?

      Learn more and submit today!

      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Drosophila screen identifies a role for histone methylation in ER stress preconditioning


      Read this article at

          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.


          Stress preconditioning occurs when transient, sublethal stress events impact an organism's ability to counter future stresses. Although preconditioning effects are often noted in the literature, very little is known about the underlying mechanisms. To model preconditioning, we exposed a panel of genetically diverse Drosophila melanogaster to a sublethal heat shock and measured how well the flies survived subsequent exposure to endoplasmic reticulum (ER) stress. The impact of preconditioning varied with genetic background, ranging from dying half as fast to 4 and a half times faster with preconditioning compared to no preconditioning. Subsequent association and transcriptional analyses revealed that histone methylation, and transcriptional regulation are both candidate preconditioning modifier pathways. Strikingly, almost all subunits (7/8) in the Set1/COMPASS complex were identified as candidate modifiers of preconditioning. Functional analysis of Set1 knockdown flies demonstrated that loss of Set1 led to the transcriptional dysregulation of canonical ER stress genes during preconditioning. Based on these analyses, we propose a preconditioning model in which Set1 helps to establish an interim transcriptional “memory” of previous stress events, resulting in a preconditioned response to subsequent stress.

          Related collections

          Most cited references83

          • 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.
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            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
              • Record: found
              • Abstract: found
              • Article: not found

              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

                Author and article information

                Role: Editor
                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                February 2024
                14 December 2023
                14 December 2023
                : 14
                : 2
                : jkad265
                Department of Human Genetics, University of Utah School of Medicine , EIHG 5200, 15 North 2030 East, Salt Lake City, UT 84112, USA
                Department of Human Genetics, University of Utah School of Medicine , EIHG 5200, 15 North 2030 East, Salt Lake City, UT 84112, USA
                Author notes
                Corresponding author: Email: cchow@ 123456genetics.utah.edu

                Conflicts of interest The author(s) declare no conflicts of interest.

                Author information
                © The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 07 June 2023
                : 02 November 2023
                : 14 December 2023
                Page count
                Pages: 14
                Funded by: NIH, DOI 10.13039/100000002;
                Funded by: NIGMS, DOI 10.13039/100000057;
                Award ID: R35GM124780
                Funded by: Interdisciplinary Training Grant T32 Program in Genetics;
                Award ID: T32GM007464

                stress preconditioning,drosophila melanogaster,dgrp,er stress,heat shock,natural genetic variation


                Comment on this article