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83 results for “stress-response”
DNA methylation dynamics during stress-response in woodland strawberry (Fragaria vesca)
<p><strong>Genome sequence and annotation of Fragaria vesca cv. Reine des Vallées</strong></p> <p>In order to generate a reference genome for Fragaria vesca cv. Reine des Vallées, we used MinIon long-read sequencing data to substitute the <em>F. vesca</em> genome v.4.0.a2 genome. The detailed method used to obtain these results were the following:</p> <p><em>Genome sequencing and assembly NIL Fb2</em></p> <p>Genomic DNA from strawberry plants was extracted by a Hexadecyltrimethylammonium bromide (Cetrimonium bromide, CTAB) modified protocol (Healey, Furtado, Cooper, & Henry, 2014) and purified with Agencourt AMPure XP beads (cat# A63880). Long-read sequencing was performed for the genome assembly; Genomic DNA by Ligation (Oxford Nanopore, cat# SQK-LSK109) library was prepared as described by the manufacturer and sequenced on a MinION for 72 h (Oxford Nanopore).</p> <p><em>Reference genome polishing</em></p> <p>Reads obtained from nanopore were filtered with Filtlong v0.2.1 (<a href="https://github.com/rrwick/Filtlong">https://github.com/rrwick/Filtlong</a>) using --min_mean_q 80 and --min_length 200. Cleaned reads were then aligned to the most recent version of the <em>F. vesca</em> genome v4.0.a2, downloaded from the Genome Database for Rosaceae (GDR) (<a href="https://www.rosaceae.org/species/fragaria_vesca/genome_v4.0.a2">https://www.rosaceae.org/species/fragaria_vesca/genome_v4.0.a2</a>), using minimap2 v2.21 (H. Li, 2018) with parameters -aLx map-ont --MD -Y. The generated BAM file was then sorted and indexed with samtools v1.11 (H. Li et al., 2009). We used mosdepth v0.3.1 (Pedersen & Quinlan, 2018) to verify that coverage on chromosomic scaffolds was over 50 X. Sniffles v1.0.12a (Sedlazeck et al., 2018) with parameters -s 10 -r 1000 -q 20 --genotype -l 30 -d 1000 was used to detect structural variations larger than 30 bp. The VCF files obtained from Sniffles was sorted and filtered with BCFtools v1.14 (Danecek et al., 2021) to keep only structural variants (SV) with smaller than 200,00 bp (we observed that larger SV were most of the time false positive caused by misalignments in regions with gaps or Ns), supported by 10 or more reads and with allelic frequencies above 0.8 (we were interested in homozygous changes). The complete filtering command used is “bcftools view -q 0.8 -Oz -i '(SVTYPE = "DUP" || SVTYPE = "INS" || SVTYPE = "DEL" || SVTYPE = "TRA" || SVTYPE = "INV" || SVTYPE = "INVDUP") && %FILTER = "PASS" && FMT/DV>9 && SVLEN>29 && SVLEN<200000' “</p> <p>From the VCF listing all the structural variants that we detected in our <em>F. vesca </em>accession, we generated a substituted genome version based on the reference <em>F. vesca</em> genome v.4.0.a2. The reference genome was first indexed with samtools faidx v1.11(Danecek et al., 2021) and a sequence dictionary was generated with Picard CreateSequenceDictionary v2.25.6 (<a href="https://broadinstitute.github.io/picard">https://broadinstitute.github.io/picard</a>). The VCF containing the SV produced from our Nanopore sequencing was also indexed with gatk (Van der Auwera GA & O'Connor BD, 2020) IndexFeatureFile v4.2.0.0 (<a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037262651-IndexFeatureFile">https://gatk.broadinstitute.org/hc/en-us/articles/360037262651-IndexFeatureFile</a>). FastaAlternateReferenceMaker v4.2.0.0 (<a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037594571-FastaAlternateReferenceMaker">https://gatk.broadinstitute.org/hc/en-us/articles/360037594571-FastaAlternateReferenceMaker</a>) was then run with the reference genome and the VCF file to generate a substituted genome representative of our <em>Fragaria</em> accession.</p> <p>As substituting our genome with the detected structural variants changes genomic coordinates, we also corrected the public GFF genome annotation of <em>F. vesca</em> (Y, Pi, Gao, Liu, & Kang, 2019) using liftoff v1.6.1 (Shumate & Salzberg, 2021). Liftoff also detects and annotates duplications within the substituted genome.</p> <p>Transposable elements annotation was carried out using the EDTA transposable element annotation pipeline v. 1.9.6 (S. Ou et al., 2019) on the substituted genome using default parameters<em>.</em></p> <p><strong>Differentially methylated regions</strong></p> <p>The file Stress_vs_control_DMRs.zip file contains the DMRs that were called using the reads submitted to ENA (ERP135585) and obtained as follows:</p> <p>First, bedGraph files from wgbs pipeline were pre-filtered for a minimum coverage of 5 reads using awk command. These output files were then used as input for the EpiDiverse/dmr bioinformatics analysis pipeline for non-model plant species to define DMRs (Nunn <em>et al</em>., 2021) with default parameters (minimum coverage threshold 5; maximum q-value 0.05; minimum differential methylation level 10%; 10 as minimum number of Cs; Minimum distance (bp) between Cs that are not to be considered as part of the same DMR is 146 bp). The pipeline uses metilene v.0.2.6.1 (<a href="https://www.bioinf.uni-leipzig.de/Software/metilene/">https://www.bioinf.uni-leipzig.de/Software/metilene/</a>) for pairwise comparison between groups and R-packages ggplot2 v.3.3.5 and gplots v.3.1.1, for visualization results (Fig. S1). Based on our <em>F. vesca</em> genome transcript annotation and methylation data (overlapped regions with DNA methylation cytosines and DMRs), we detected the methylated genes, promoters, 3’ UTRs, 5’UTR and transposable elements in strawberry. Global DNA methylation and DMR plots were performed with R-package ggplot2. Gene analyses by methylation patterns and analysis of per-family TE DNA methylation profiles were performed with deepTools v.3.5.0 (Ramírez <em>et al</em>., 2014). DMRs comparison between treatments were done by the Venn diagram v.1.7.0 R-package.</p> <p>We produced several genome browsers tracks with DMRs that we integrated in our local instance of JBrowse available at the following url: <a href="https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub">https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub</a></p>
Data from: Loggerhead sea turtle embryos (Caretta caretta) regulate expression of stress-response and developmental genes when exposed to a biologically realistic heat stress
Oviparous reptile embryos are expected to breach their critical thermal maxima if temperatures reach those predicted under current climate change models due to the lack the maternal buffering processes and parental care. Heat shock proteins (HSPs) are integral in the molecular response to thermal stress, and their expression is heritable, but the roles of other candidate families such as the heat shock factors (HSFs) have not been determined in reptiles. Here we subject embryonic sea turtles (Caretta caretta) to a biologically realistic thermal stress and employ de novo transcriptomic profiling of brain tissue to investigate the underlying molecular response. From a reference transcriptome of 302,293 transcripts, 179 were identified as differentially expressed between treatments. As anticipated, genes enriched in the heat shock treatment were primarily associated with the Hsp families, or were genes whose products play similar protein editing and chaperone functions (e.g. bag3, MYOC and serpinh1). Unexpectedly, genes encoding the HSFs were not significantly upregulated under thermal stress, indicating their presence in unstressed cells in an inactive state. Genes that were downregulated under thermal stress were less well functionally defined but were associated with stress response, development, and cellular organization, suggesting that developmental processes may be compromised at realistically high temperatures. These results confirm that genes from the Hsp families play vital roles in the thermal tolerance of developing reptile embryos, and in addition with a number of other genes, should be targets for evaluating the capacity of oviparous reptiles to respond adaptively to the effects of climate change.
Data from: Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis thaliana
Machine learning (ML) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in large-scale data sets. We present an ML-based methodology for transcriptome analysis via comparison of gene coexpression networks, implemented as an R package called machine learning–based differential network analysis (mlDNA) and apply this method to reanalyze a set of abiotic stress expression data in Arabidopsis thaliana. The mlDNA first used a ML-based filtering process to remove nonexpressed, constitutively expressed, or non-stress-responsive "noninformative" genes prior to network construction, through learning the patterns of 32 expression characteristics of known stress-related genes. The retained "informative" genes were subsequently analyzed by ML-based network comparison to predict candidate stress-related genes showing expression and network differences between control and stress networks, based on 33 network topological characteristics. Comparative evaluation of the network-centric and gene-centric analytic methods showed that mlDNA substantially outperformed traditional statistical testing–based differential expression analysis at identifying stress-related genes, with markedly improved prediction accuracy. To experimentally validate the mlDNA predictions, we selected 89 candidates out of the 1784 predicted salt stress–related genes with available SALK T-DNA mutagenesis lines for phenotypic screening and identified two previously unreported genes, mutants of which showed salt-sensitive phenotypes.
Data from: Machine learning-based differential network analysis: a study of stress-responsive transcriptomes in Arabidopsis thaliana
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Data from: Loggerhead sea turtle embryos (Caretta caretta) regulate expression of stress-response and developmental genes when exposed to a biologically realistic heat stress
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Figure 3 from: Suleymanoglu Y, Bakalov D, Sabit Z, Vodenicharov V, Tafradjiiska-Hadjiolova R, Nocheva H (2024) The endogenous cannabinoid and the adrenergic systems in modulation of stress-response. Pharmacia 71: 1-8. https://doi.org/10.3897/pharmacia.71.e115659
Figure 3 A. Stress (and its many sensory inputs) activates different areas in the brain – the prefrontal cortex (PFC), the thalamus (Thal), the limbic system (LS, comprising the amygdala, hippocampus and hypothalamus), with subsequent adverse effects for the whole organism: fear‐related behaviour, depressive-like conditions, anxiety, helplessness and hopelessness, sleep and feeding disorders; B. Endocannabinoids modulate the activity of pyramidal glutamate neurons and prefrontal glutamatergic plasticity, and have been proved beneficial in decreasing anxiety and depressive-like symptoms, alleviation of fear-conditioned memories, improvement of sleep and feeding.
Figure 2 from: Suleymanoglu Y, Bakalov D, Sabit Z, Vodenicharov V, Tafradjiiska-Hadjiolova R, Nocheva H (2024) The endogenous cannabinoid and the adrenergic systems in modulation of stress-response. Pharmacia 71: 1-8. https://doi.org/10.3897/pharmacia.71.e115659
Figure 2 Effects on r-SIA (1 h RS) estimated by PP-test after A. Clonidine (Clo); B. Desipramine (Des); and C. Yohimbine (Yoh) administrations alone or in combination with anandamide (AEA) / AM251 (AM). Mean values ± S.E.M. are presented in arbitrary units (AU) on the 10th, 20th, 30th, and 40th min after substances administration. A–C ***p < 0.001 vs. controls; +++p < 0.001vs. 1 h RS; xxxp < 0.001vs. A. 1 h RS+Clo; B. 1 h RS+Des; C. 1 h RS+Yoh; xp < 0.05 vs. B. 1 h RS+Des; $$$p < 0.001vs. A. 1 h RS+Clo+AEA; B. 1 h RS+Des+AEA; C. 1 h RS+Yoh+AEA; $p < 0.05 vs. C. 1 h RS+Yoh+AEA.
Figure 1 from: Suleymanoglu Y, Bakalov D, Sabit Z, Vodenicharov V, Tafradjiiska-Hadjiolova R, Nocheva H (2024) The endogenous cannabinoid and the adrenergic systems in modulation of stress-response. Pharmacia 71: 1-8. https://doi.org/10.3897/pharmacia.71.e115659
Figure 1 Effects of AEA on r-SIA estimated by PP-test after one hour of restraint (1 h RS) in rats. Mean values ± S.E.M. are presented in arbitrary units (AU) on the 10th, 20th, 30th, and 40th min after substances administration. ***p < 0.001 vs. controls; +++p < 0.001vs. RS.
Dynamic induction of drug resistance through a stress-responsive enhancer in acute myeloid leukemia [RNA-seq]
GEO Series GSE131823. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
The DEAD-box helicase Hlc regulates basal transcription and chromatin opening of stress-responsive genes
GEO Series GSE205804. Drosophila melanogaster. 6 samples. Type: Expression profiling by high throughput sequencing.
eIF1-eIF4G1 inhibitors uncover alternative translation activation of stress-response genes via enhanced ribosome loading and 5’UTR translation [Ribo-Seq and Ti-Seq]
GEO Series GSE166742. Homo sapiens. 12 samples. Type: Other.
Interplay between bacterial RNA 5'-termini deNADding protein NudC and DEAD-box RNA helicase CsdA in stress-responses
GEO Series GSE224608. Escherichia coli. 21 samples. Type: Expression profiling by high throughput sequencing.
Identification and characterization of wilt and salt stress-responsive microRNAs from chickpea by high-throughput sequencing.
GEO Series GSE57857. Cicer arietinum. 3 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Data-driven identification of inherent features of eukaryotic stress-responsive genes
GEO Series GSE171427. Saccharomyces cerevisiae. 18 samples. Type: Expression profiling by high throughput sequencing.
Transcriptomic analysis of Sirt3 knock-out mouse embryonic fibroblasts reveals major differences in metabolism and stress-response between sexes
GEO Series GSE246699. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Dynamic regulation of stress-responsive non-genomic CTCF complexes [ChIP-seq]
GEO Series GSE139882. Homo sapiens. 5 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Identification of copper (Cu) stress-responsive grapevine microRNAs and their target genes by high-throughput sequencing
GEO Series GSE78133. Vitis labrusca x Vitis vinifera. 2 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Transcriptome-wide marker gene expression analysis of stress-responsive Oleidesulfovibrio alaskensis
GEO Series GSE240798. Oleidesulfovibrio alaskensis. 9 samples. Type: Expression profiling by high throughput sequencing.
Identification and integrated analysis of glyphosate stress-responsive microRNAs, lncRNAs, and mRNAs in rice using genome-wide high-throughput sequencing
GEO Series GSE142323. Oryza sativa. 6 samples. Type: Expression profiling by high throughput sequencing.
FRA1 (FOSL1) Suppresses Neoplastic Transformation and Modulates Radiation Responses via Transcriptional Control of Mitogenic and Stress-Responsive Networks
GEO Series GSE307991. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing.
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