Stress levels over time in Ruditapes philippinarum: The effects of hypoxia and cold stress on hsp70 gene expression

Cardiac hypertrophy is a growth process that occurs in response to stress stimuli or injury, and leads to the induction of several pathways to alter gene expression. Under hypertrophic stimuli, sarcomeric structure is disrupted, both as a consequence of gene expression and local changes in sarcomeric proteins. Cardiac-restricted ankyrin repeat protein (CARP) is one such protein that function both in cardiac sarcomeres and at the transcriptional level. We postulate that due to this dual nature, CARP plays a key role in maintaining the cardiac sarcomere. GATA4 is another protein detected in cardiomyocytes as important in hypertrophy, as it is activated by hypertrophic stimuli, and directly binds to DNA to alter gene expression. Results of GATA4 activation over time were inconclusive; however, the role of CARP in mediating hypertrophic growth in cardiomyocytes was clearly demonstrated. In this study, Neonatal Rat Ventricular Myocytes were used as a model to detect changes over time in CARP and GATA4 under hypertrophic stimulation by phenylephrine and high serum media. Results were detected by analysis of immunoblotting. The specific role that CARP plays in mediating cellular growth under hypertrophic stimuli was studied through immunofluorescence, which demonstrated that cardiomyocyte growth with hypertrophic stimulation was significantly blunted when NRVMs were co-treated with CARP siRNA. These data suggest that CARP plays an important role in the hypertrophic response in cardiomyocytes.

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Immune-related gene expression of Apis mellifera larvae in response to cold stress and Abscisic Acid (ABA) dietary supplementation

Journal of Apicultural Research ◽

10.1080/00218839.2019.1708653 ◽

2020 ◽

Vol 59 (4) ◽

pp. 669-676 ◽

Cited By ~ 1

Author(s):

Pedro Negri ◽

Leonor Ramirez ◽

Silvina Quintana ◽

Nicolas Szawarski ◽

Matías D. Maggi ◽

...

Keyword(s):

Gene Expression ◽

Abscisic Acid ◽

Apis Mellifera ◽

Cold Stress ◽

Dietary Supplementation ◽

Related Gene ◽

Immune Related Gene

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Gene Expression Profile and Co-Expression Network of Pearl Gentian Grouper under Cold Stress by Integrating Illumina and PacBio Sequences

Animals ◽

10.3390/ani11061745 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1745

Author(s):

Ben-Ben Miao ◽

Su-Fang Niu ◽

Ren-Xie Wu ◽

Zhen-Bang Liang ◽

Bao-Gui Tang ◽

...

Keyword(s):

Gene Expression ◽

Cold Stress ◽

Differential Expression Analysis ◽

Endocrine System ◽

Control Group ◽

Regulation Mechanism ◽

Rna Seq ◽

Cold Tolerant ◽

Stress Signals ◽

Membrane Changes

Pearl gentian grouper (Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂) is a fish of high commercial value in the aquaculture industry in Asia. However, this hybrid fish is not cold-tolerant, and its molecular regulation mechanism underlying cold stress remains largely elusive. This study thus investigated the liver transcriptomic responses of pearl gentian grouper by comparing the gene expression of cold stress groups (20, 15, 12, and 12 °C for 6 h) with that of control group (25 °C) using PacBio SMRT-Seq and Illumina RNA-Seq technologies. In SMRT-Seq analysis, a total of 11,033 full-length transcripts were generated and used as reference sequences for further RNA-Seq analysis. In RNA-Seq analysis, 3271 differentially expressed genes (DEGs), two low-temperature specific modules (tan and blue modules), and two significantly expressed gene sets (profiles 0 and 19) were screened by differential expression analysis, weighted gene co-expression networks analysis (WGCNA), and short time-series expression miner (STEM), respectively. The intersection of the above analyses further revealed some key genes, such as PCK, ALDOB, FBP, G6pC, CPT1A, PPARα, SOCS3, PPP1CC, CYP2J, HMGCR, CDKN1B, and GADD45Bc. These genes were significantly enriched in carbohydrate metabolism, lipid metabolism, signal transduction, and endocrine system pathways. All these pathways were linked to biological functions relevant to cold adaptation, such as energy metabolism, stress-induced cell membrane changes, and transduction of stress signals. Taken together, our study explores an overall and complex regulation network of the functional genes in the liver of pearl gentian grouper, which could benefit the species in preventing damage caused by cold stress.

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Temporal changes in DNA methylation and RNA expression in a small song bird: within- and between-tissue comparisons

BMC Genomics ◽

10.1186/s12864-020-07329-9 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Melanie Lindner ◽

Irene Verhagen ◽

Heidi M. Viitaniemi ◽

Veronika N. Laine ◽

Marcel E. Visser ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Environmental Conditions ◽

Brain Regions ◽

Temporal Changes ◽

Egg Laying ◽

Gene Body ◽

Cpg Site ◽

Repeated Sampling ◽

Over Time

Abstract Background DNA methylation is likely a key mechanism regulating changes in gene transcription in traits that show temporal fluctuations in response to environmental conditions. To understand the transcriptional role of DNA methylation we need simultaneous within-individual assessment of methylation changes and gene expression changes over time. Within-individual repeated sampling of tissues, which are essential for trait expression is, however, unfeasible (e.g. specific brain regions, liver and ovary for reproductive timing). Here, we explore to what extend between-individual changes in DNA methylation in a tissue accessible for repeated sampling (red blood cells (RBCs)) reflect such patterns in a tissue unavailable for repeated sampling (liver) and how these DNA methylation patterns are associated with gene expression in such inaccessible tissues (hypothalamus, ovary and liver). For this, 18 great tit (Parus major) females were sacrificed at three time points (n = 6 per time point) throughout the pre-laying and egg-laying period and their blood, hypothalamus, ovary and liver were sampled. Results We simultaneously assessed DNA methylation changes (via reduced representation bisulfite sequencing) and changes in gene expression (via RNA-seq and qPCR) over time. In general, we found a positive correlation between changes in CpG site methylation in RBCs and liver across timepoints. For CpG sites in close proximity to the transcription start site, an increase in RBC methylation over time was associated with a decrease in the expression of the associated gene in the ovary. In contrast, no such association with gene expression was found for CpG site methylation within the gene body or the 10 kb up- and downstream regions adjacent to the gene body. Conclusion Temporal changes in DNA methylation are largely tissue-general, indicating that changes in RBC methylation can reflect changes in DNA methylation in other, often less accessible, tissues such as the liver in our case. However, associations between temporal changes in DNA methylation with changes in gene expression are mostly tissue- and genomic location-dependent. The observation that temporal changes in DNA methylation within RBCs can relate to changes in gene expression in less accessible tissues is important for a better understanding of how environmental conditions shape traits that temporally change in expression in wild populations.

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Jonckheere–Terpstra–Kendall-based non-parametric analysis of temporal differential gene expression

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab021 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Hitoshi Iuchi ◽

Michiaki Hamada

Keyword(s):

Gene Expression ◽

Time Series ◽

Time Course ◽

Time Series Data ◽

Expression Patterns ◽

Detection Methods ◽

Series Data ◽

Expression Levels ◽

Over Time ◽

Non Parametric

Abstract Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks modeling of linearly increasing genes with higher-order functions, or fitting of cyclic gene expression with linear functions, thereby leading to false positives/negatives. Here, we present a Jonckheere–Terpstra–Kendall (JTK)-based non-parametric algorithm for TEG detection. Benchmarks, using simulation data, show that the JTK-based approach outperforms existing methods, especially in long time-series experiments. Additionally, application of JTK in the analysis of time-series RNA-seq data from seven tissue types, across developmental stages in mouse and rat, suggested that the wave pattern contributes to the TEG identification of JTK, not the difference in expression levels. This result suggests that JTK is a suitable algorithm when focusing on expression patterns over time rather than expression levels, such as comparisons between different species. These results show that JTK is an excellent candidate for TEG detection.

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