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29,880 results for “gene expression”
Depletion of cap-binding protein eIF4E dysregulates amino acid metabolic gene expression
<p><span>Protein synthesis is </span><span>metabolically costly </span><span>and</span><span> must be tightly coordinated with </span><span>changing </span><span>cellular needs and nutrient availability. T</span><span>he cap-binding protein eIF4E</span><span> makes </span><span>the earliest contact between mRNAs and the translation machinery</span><span>, offering a key regulatory nexus</span><span>. </span><span>W</span><span>e acute</span><span>ly</span><span> deplet</span><span>ed</span> <span>this essential protein </span><span>and </span><span>found </span><span>s</span><span>urprisingly modest effects on cell growth and </span><span>recovery of </span><span>protein synthesis.</span><span> Paradoxically, </span><span>impaired protein biosynthesis upregulated </span><span>genes involved in catabolism of aromatic amino acids</span><span>simultaneously with the </span><span>induction of the </span><span>amino acid</span><span> biosynthetic regulon</span> <span>driven</span> <span>by </span><span>the integrated stress response factor</span> <span>GCN4</span><span>. </span><span>W</span><span>e</span><span> further</span><span> identified translation</span><span>al </span><span>control</span><span> of </span><span>PCL5</span><span>, </span><span>a negative regulator of Gcn4, that provides a consistent protein-to-mRNA ratio under varied translation environments. </span><span>This</span> <span>regulation </span><span>depende</span><span>d in part</span><span> on a uniquely long poly-(A) tract in the </span><span>PCL5</span><span> 5´ UTR and poly-(A) binding protein. Collectively, these results highlight</span> <span>how eIF4E connects</span> <span>protein synthesis </span><span>to</span> <span>metabolic gene regulation</span><span>,</span><span>uncover</span><span>ing</span><span> new mechanisms control</span><span>ling</span> <span>translation</span><span> during environmental challenges.</span></p>
GEO gene expression dataset recompute for selected tumor samples
<div> <p>We aligned and quantified RNA-Seq data present in GEO with a standardized pipeline to homogenize data preprocessing for downstream applications.</p> <p>All uploaded files are UTF-8, <code>.csv</code>-formatted matrices. The <code>*_expected_count.csv.gz</code> files are unlogged, raw expression counts as reported by <code>rsem-quantify-expression</code> (see details below). The associated <code>*_metadata.csv.gz</code> files contain metadata pertinent to each column of the corresponding expression matrix.<br>Some metadata files may have more rows than the associated number of columns. This is for series that were only partially RNA-Seq based (e.g. combinated RNA-Seq plus miRNA-Seq samples in the same GEO accession ID).</p> <p>Metadata columns are derived from GEO series files, and follow their definitions. See each GEO entry directly to determine metadata meaning.</p> <p>Each recompute has at least the <code>gene_id</code> column holding Ensembl Gene IDs. The remaining columns are ENA run accession IDs of the specific recomputed samples.<br>Each associated metadata has at least the following columns:</p> <ul> <li><code>geo_accession</code>: The GEO sample ID of the sample.</li> <li><code>ena_sample</code>: The ENA sample ID of the sample.</li> <li><code>ena_run</code>: The ENA run accession ID of the sample, to be cross-referenced with the expression matrices.</li> </ul> <p>The remaining columns are derived from GEO metadata files and other ENA-provided data. Please refer to the <code>x.FASTQ</code> package for more information.</p> <h3>Pipeline Details</h3> <p>The alignment and quantification was made with the <code>x.FASTQ</code> tool available <a href="https://github.com/TCP-Lab/x.FASTQ">on Github</a> installed locally on an Arch Linux machine on commit <code>3a93dd77a70df59c74f7b15216c26f12cd918e81</code> running the Linux <code>6.7.8-zen1-1-zen</code> kernel with a <code>11th Gen Intel i7-1185G7 (8)</code> CPU and a <code>Intel TigerLake-LP GT2 [Iris Xe Graphics]</code> GPU. Please note that no sample filtering or omissions were done based on sample quality or sequencing depth. However, sensible trimming (e.g. low-quality bases and common adapters) was performed on all the samples.</p> <p>Reference genome was downloaded from Ensembl, version <code>hg38</code>. STAR was used to create the index genome with overhang set to <code>149</code>.</p> </div>
R code for differential gene expression and enrichment analyses
<p>The information about the magnitude of differences in thermal plasticity both between and within populations, as well as identification of the underlying molecular mechanisms are key to understanding the evolution of thermal plasticity. In particular, genes underlying variation in the physiological response to temperature can provide raw material for selection acting on plastic traits. Using RNAseq, we investigate the transcriptional response to temperature in males and females from bulb mite populations selected for the increased frequency of one of two discrete male morphs (fighter- and scrambler-selected populations) that differ in relative fitness depending on temperature. We show that different mechanisms underlie the divergence in thermal response between fighter- and scrambler-selected populations at decreased vs. increased temperatures. Temperature decrease to 18°C was associated with higher transcriptomic plasticity of males with more elaborate armaments, as indicated by a significant selection-by-temperature interaction effect on the expression of 40 genes, 38 of which were upregulated in fighter-selected populations in response to temperature decrease. In response to 28°C, no selection-by-temperature interaction in gene expression was detected. Hence, differences in phenotypic response to temperature increase likely depended on genes associated with their distinct morph-specific thermal tolerance. Selection on males also drove gene expression patterns in females. These patterns could be associated with temperature-dependent fitness differences between females from fighter- vs. scrambler-selected populations reported in previous studies. Our study shows that selection for divergent male sexually selected morphologies and behaviors has the potential to drive divergence in metabolic pathways underlying plastic response to temperature in both sexes.</p>
Mate choice in the brain: Species differ in how male traits 'turn on' gene expression in female brains
<p>Mate choice plays a fundamental role in speciation, yet we know little about the molecular mechanisms that underpin this crucial decision-making process. Stickleback fish differentially adapted to limnetic and benthic habitats are reproductively isolated and females of each species use different male traits to evaluate prospective partners and reject heterospecific males. Here, we integrate behavioral data from a mate choice experiment with gene expression profiles from the brains of females actively deciding whether to mate. We find substantial gene expression variation between limnetic and benthic females, regardless of behavioral context, suggesting general divergence in constitutive gene expression patterns, corresponding to their genetic differentiation. Intriguingly, female gene co-expression modules covary with male display traits but in opposing directions for sympatric populations of the two species, suggesting male displays elicit a dynamic genomic response that reflects known differences in female preferences. Furthermore, we confirm the role of numerous candidate genes previously implicated in female mate choice in other species, suggesting that evolutionary tinkering with these conserved molecular processes underlies divergent mate preferences and sexual isolation. Taken together, our study adds important new insights to our understanding of the molecular processes underlying female decision-making critical for generating sexual isolation and speciation.</p>
Source Data for main text and supplemental figures for manuscript "Community assessment of methods to deconvolve cellular composition from bulk gene expression"
<p>Source Data for main text and supplemental figures for manuscript "Community assessment of methods to deconvolve cellular composition from bulk gene expression"</p>
Figure 4 in Perinatal exposure to a high-fat diet alters proopiomelanocortin, neuropeptide Y and dopaminergic receptors gene expression and the food preference in offspring adult rats
Figure 4. Body weight on the 120th offspring from mothers submitted the control diet or the high-fat diet. Values are presented as mean ± SEM using two-way ANOVA followed by the Bonferroni multiple-comparison test. *p<0,05; **p<0,005.
Figure 3 in Perinatal exposure to a high-fat diet alters proopiomelanocortin, neuropeptide Y and dopaminergic receptors gene expression and the food preference in offspring adult rats
Figure 3. Body weight on the second day life's of offspring from mothers submitted the control diet (C) or the high-fat diet (H). Values are presented as mean + SEM using Student t-test. C: n = 23; H: n = 23.
Figure 2 in Perinatal exposure to a high-fat diet alters proopiomelanocortin, neuropeptide Y and dopaminergic receptors gene expression and the food preference in offspring adult rats
Figure 2. Pomc (A) and npy (B) gene expression in the hypothalamus of offspring exposed or not to a control diet or high-fat diet during perinatal and/or postnatal period. Values are presented as mean ± SEM using two-way ANOVA followed by the Bonferroni multiplecomparison test. Level of significance: *p<0,05; "a": compared to CC, "b": compared to CH; "c": compared to HC; "d": compared to HH.
Figure 1. Drd1 in Perinatal exposure to a high-fat diet alters proopiomelanocortin, neuropeptide Y and dopaminergic receptors gene expression and the food preference in offspring adult rats
Figure 1. Drd1 (A) and drd2 (B) gene expression in the nucleus accumbens of offspring exposed or not to a control diet or high-fat diet during perinatal and/or postnatal period. Values are presented as mean ± SEM using two-way ANOVA followed by the Bonferroni multiplecomparison test. Level of significance: *p<0,05; "a": compared to CC, "b": compared to CH; "c": compared to HC; "d": compared to HH.
Gene expression QTL mapping in stimulated iPSC-derived macrophages provides insights into common complex diseases.
<p>Many disease-associated variants are thought to be regulatory but are not present in existing catalogues of expression quantitative trait loci (eQTL). We hypothesise that these variants may regulate expression in specific biological contexts, such as stimulated immune cells. Here, we used human iPSC-derived macrophages to map eQTLs across 24 cellular conditions. We found that 76% of eQTLs detected in at least one stimulated condition were also found in naive cells. The percentage of response eQTLs (reQTLs) varied widely across conditions (3.7% - 28.4%), with reQTLs specific to a single condition being rare (1.11%). Despite their relative rarity, reQTLs were overrepresented (p=0.05, Fisher's exact test) among disease-colocalizing eQTLs. We nominated an additional 21.7% of disease effector genes at GWAS loci via colocalization of reQTLs, with 38.6% of these not found in the Genotype–Tissue Expression (GTEx) catalogue. Our study highlights the diversity of genetic effects on expression and demonstrates how condition-specific regulatory variation can enhance our understanding of common disease risk alleles.</p>
Fig 1 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 1: Relative gene expression in tambaqui juveniles farmed in two Brazilian regions: Northern (Balbina; BA) and Southeast (Brumado; BRU). Different letters represent statistical differences between populations. The graphs show expression of A) hif-1α (p = 0.137), B) hsp-70 (p = 0.465), C) mstn (p = 0.907), D) ube3a (p = 0.205), E) ras (p = 0.041), F) cry-1 (p = 0.001), G) per-1 (p = 0.001), H) ogt (p = 0.001) and I) acly (p = 0.025).
Fig 3 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 3: IBR analyses of relative gene expression in Balbina (BA) and Brumado (BRU) populations. The IBR values are 42.7 (Balbina) and 6.79 (Brumado).
Fig 2 in Colossoma macropomum (Characiformes: Serrasalmidae) adapted to new climate regime: differential gene expression from farmed tambaqui juveniles raised in subtropical and tropical regions
Fig 2: Heatmap of relative expression in Balbina (BA) and Brumado (BRU) populations. The colour scale ranges from blue (low transcript levels) to red (high transcript levels).
Figure 3 in Effect of Vicia sativa L. on Motility, Mortality and Expression Levels of hsp Genes in J2 Stage of Meloidogyne hapla
Figure 3: Influence of Vicia sativa cv. Ina diffusate treatment on Hsp gene expression in Meloidogyne Hapla J2 stage. Each value represents the mean ± s.d. of three biological replicates. The expression levels are indicated as the fold-change normalized to the control (untreated diffusate), normalized to the value of 1 (dashed line). Values were expressed as the mean fold difference, and statistically significant differences between treated and control samples are shown; *p≤0.01 based on t-Student test.
Figure 2 in Effect of Vicia sativa L. on Motility, Mortality and Expression Levels of hsp Genes in J2 Stage of Meloidogyne hapla
Figure 2: Distribution of 36 combinations of temperatures, cultivars, and variants in the space of the first two canonical variables.
Figure 1 in Effect of Vicia sativa L. on Motility, Mortality and Expression Levels of hsp Genes in J2 Stage of Meloidogyne hapla
Figure 1: Dendrogram of the nearest neighbour cluster grouping of combinations of temperature, cultivars, and variants on the basis of four traits.
Data for: Effects of testosterone on gene expression are concordant between sexes but divergent across species of Sceloporus lizards
<p>Hormones mediate sexual dimorphism by regulating sex-specific patterns of gene expression, but it is unclear how much of this regulation involves sex-specific hormone levels versus sex-specific transcriptomic responses to the same hormonal signal. Moreover, transcriptomic responses to hormones can evolve, but the extent to which hormonal pleiotropy in gene regulation is conserved across closely related species is not well understood. We addressed these issues by elevating testosterone levels in juvenile females and males of three <em>Sceloporus </em>lizard species prior to sexual divergence in circulating testosterone<em>, </em>then characterizing transcriptomic responses in the liver. In each species, more genes were responsive to testosterone in males than in females, suggesting that early developmental processes prime sex-specific transcriptomic responses to testosterone later in life. However, overall transcriptomic responses to testosterone were concordant between sexes, with no genes exhibiting sex-by-treatment interactions. By contrast, hundreds of genes exhibited species-by-treatment interactions, particularly when comparing distantly related species with different patterns of sexual dimorphism, suggesting evolutionary lability in gene regulation by testosterone. Collectively, our results indicate that early organizational effects may lead to sex-specific differences in the magnitude, but not the direction, of transcriptomic responses to testosterone, and that the hormone-genome interface accrues regulatory changes over evolutionary time.</p>
Figure 4 in Differential gene expression pattern and plasma sex steroids during testicular development in Genyatremus luteus (Perciforme: Haemulidae) (Bloch, 1790)
Figure 4. Principal component analysis (PCA) used to classify the influence of lhr and er gene expression, plasma steroids (11-KT, 17- OHP and E2), ichthyological parameters and GSI on male G. luteus individuals. Legend: LHR = LH receptor; ER = estrogen receptor; KT = 11-ketotestosterone; E2 = 17β-estradiol; OHP = 17-α-hydroxyprogesterone; TW = total weight; TL = total length; GW = gonad weight; GSI = gonadosomatic index.
Figure 1 in Differential gene expression pattern and plasma sex steroids during testicular development in Genyatremus luteus (Perciforme: Haemulidae) (Bloch, 1790)
Figure 1. Photomicrographs of germ cell and testes development stages of Genyatremus luteus. Stages were determined as (A) Immature, (B) Maturing, (C) Mature. Abbreviations are as follows: SPG, spermatogonia; SPC, spermatocyte; SPZ, spermatozoa. All panels were at 60x magnification.
Figure 2 in Differential gene expression pattern and plasma sex steroids during testicular development in Genyatremus luteus (Perciforme: Haemulidae) (Bloch, 1790)
Figure 2. Steroid concentrations in the blood plasma of male Genyatremus luteus individuals during their reproductive cycle. (A) 11-ketotestosterone. (B) 17 α-hidroxy progesterone. (C) 17β-estradiol. Data are represented as mean ± SEM. abc: indicates statistically significant difference (p<0.05).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.