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54,099 results for “Expression”
Gene expression underlying parenting and being parented shows limited plasticity in response to different ambient temperatures
<p>Flexible interactions between parents and offspring are essential for buffering families against variable, unpredictable, and challenging environmental conditions. In the subsocial carrion beetle, <em>Nicrophorus orbicollis</em>, mid-summer temperatures impose steep fitness costs on parents and offspring but do not elicit behavioral plasticity in parents. Here, we ask if plasticity of gene expression underpins this behavioral stability or facilitates independent compensation by larvae. To test this, we characterized gene expression of parents and offspring before and during active parenting under benign (20°C) and stressful (24°C) temperatures to identify genes of parents and offspring associated with thermal response, parenting/being parented, and gene expression plasticity associated with behavioral stability of parental care. The main effects of thermal and social condition each shaped patterns of gene expression in females, males, and larvae. In addition, we implicated 79 genes in females as 'buffering' parental behavior across environments. The majority of these underwent significant changes in expression in actively parenting mothers at the benign temperature, but not at the stressful temperature. Our results suggest that neither genetic programs for parenting nor their effects on offspring gene expression are fundamentally different under stressful conditions, and that behavioral stability is associated primarily with the maintenance of existing genetic programs rather than replacement or supplementation. Thus, while selection for compensatory gene expression could expand the range of thermal conditions parents will tolerate, without expanding the toolkit of genes involved selection is unlikely to lead to adaptive changes of function.</p>
Variable allelic expression of imprinted genes at the Peg13, Trappc9, Ago2 cluster in single neural cells
<p>Fig 1 5' RACE - Sequence tracks and analysis of alternative Trappc9 transcriptional start sites</p> <p>Fig 2 and suppl fig 3- Brain and Kidney tissue or Neural stem cells isolated from the Hippocampus region of newborn mice generated from a C57BL/6 (female) and a Cast/EiJ (male) cross (and its reciprocal cross) was used to determine allelic bias expression. A SNP located within an exon of Peg13, Trappc9, Ago2, Chrac1 and Kcnk9 was identified and amplified via pyrosequencing PCR with the percentage of SNP identification used to determine allele expression percentages. Additionally, Pyrorun sequences of reverse transcribed RNA from Trappc9 expression in different tissues. A hybrid cross between C57BL/6 and JF1 mouse was used to generate hybrid pups that were used to determine allele specificity of Trappc9 expression in Kidney and brain tissues.</p> <p>Figs 3, 4 & 5- Single neural stem cells were isolated from the Hippocampus of newborn mice generated from a hybrid cross. Some of these cells were differentiated In vitro and either the NSC or differentiated neurons were lysed and underwent a reverse transcription. The newly formed cDNA was used as a template to amplify expressed Peg13, Trappc9 or Ago2 transcripts which was then sent for Sanger sequencing. A SNP located within the exon was used to determine whether the transcript from that cell was generated from the maternal or paternal allele.</p> <p>Fig 6- Brain-specific regulatory elements were cloned into a pGL [Luc] vector containing a Trappc9 promoter. These newly generated plasmids were then transfected into either primary neuron or fibroblast cultures alongside a Renilla vector for normalization using Lipofectamine as a transfection reagent. After 48 hours the cells were lysed and analyzed using a Glomax illuminator to determine their impact on Luciferase expression compared to that of the pGL vector containing just the Trappc9 promoter. Additionally, Plasmid vectors that were used for transfection of primary neurons to determine the impact of brain-specific regulatory elements on transcription. Plasmids can be visualized using the free software pdraw.32 downloaded from http://acaclone.com/download/install.htm</p> <p>Suppl fig 2- Single Neural stem cells and in vitro differentiated neurons isolated from Mouse Hippocampus tissue underwent reverse transcription and a qPCR reaction intended to amplify cDNA of genes associated with specific neural cell types as a method of detecting cell fate and whether this had an impact on allele-specific expression and differential methylation.</p> <p>Suppl fig 4 & 5- DNA isolated from Neural stem cells was bisulfite converted for downstream identification of methyl group presence. CpG islands located at or near the promoters of the Peg13, Trappc9, Ago2, Chrac1 and Kcnk9 genes were amplified and cloned into a TOPO vector. The cloned segments were Sanger sequenced and compared to the original non-bisulfite converted sequence using the Quantification for methylation analysis (QUMA) tool to determine which CG dinucleotides were methylated and which weren't. Pyroruns determining methylation frequency at the CpG islands of these genes can be found in a separate upload on Zenodo.</p>
Gene expression counts from amniotic fluid cells
<p><strong>File description:</strong></p> <ol> <li> <p>Gene-level counts using the gtf file from the release 29 of GENCODE <a href="https://www.gencodegenes.org/human/release_29">https://www.gencodegenes.org/human/release_29</a></p> </li> <li> <p>Split counts spanning from one exon to another using an annotation-free algorithm, therefore capturing new splice sites</p> </li> <li> <p>Non-split counts covering exon-intron boundaries</p> </li> <li> <p>Sample annotation describing each sample from the dataset</p> </li> <li> <p>Description file with global information from the dataset</p> </li> </ol> <p><strong>Use: </strong>The count matrices are intended to help researchers that are interested in using RNA-Seq data with the purpose of diagnostics. Researchers can merge their own dataset with the downloaded ones, provided the tissue, genome build, strand, and paired end specifications match. Afterwards, the DROP pipeline can be used to compute expression and splicing outliers (<a href="https://github.com/gagneurlab/drop">https://github.com/gagneurlab/drop</a>).</p> <p><strong>Maintainer: </strong>Vicente A. Yépez, <a href="mailto:yepez@in.tum.de">yepez@in.tum.de</a></p> <p><strong>Title:</strong> Gene expression and splicing counts from amniotic fluid cells<br> <strong>Number of samples:</strong> 56<br> <strong>Tissue:</strong> Amniotic fluid cells<br> <strong>Organism:</strong> Homo sapiens<br> <strong>Genome assembly:</strong> hg19<br> <strong>Gene annotation:</strong> gencode29<br> <strong>Disease ICD-10</strong>: Q89: F2100984A and F2100985, Q23: F1901567 and Q74: F2000845<br> <strong>Strand specific</strong>: False<br> <strong>Paired end</strong>: True<br> <strong>Cite as</strong>: Cite both the resource using Zenodo's citation and the publication under References<br> <strong>Dataset contact</strong>: Brian Chung bhychung@hku.hk<br> <strong>Comments</strong>: A total of 56 RNA-seq samples were used for running pipeline. Out of those, 52 samples were included in the manuscript.</p>
Search Strategies for Gene Expression Profiling Tests
<p>The dataset includes the complete, reproducible search strategies for all literature databases searched during this project. The Endnote file contain all citations considered for inclusion in the review.</p>
Data for: Long-wavelength-sensitive (lws) opsin gene expression, foraging and visual communication in coral reef fishes
<p>Coral reef fishes are diverse in ecology and behaviour and show remarkable colour variability. Investigating the visual pigment gene (opsin) expression in these fishes makes it possible to associate their visual genotype and phenotype (spectral sensitivities) to visual tasks, such as feeding strategy or conspecific detection. By studying all major damselfish clades (Pomacentridae) and representatives from five other coral reef fish families, we show that the long-wavelength-sensitive (<em>lws</em>) opsin is highly expressed in algivorous and less or not expressed in zooplanktivorous species. <em>Lws</em> is also upregulated in species with orange/red colours (reflectance >520 nm) and expression is highest in orange/red-coloured algivores. Visual models from the perspective of a typical damselfish indicate that sensitivity to longer wavelengths does enhance the ability to detect the red to far-red component of algae and orange/red-coloured conspecifics, possibly enabling social signalling. Character state reconstructions indicate that in the early evolutionary history of damselfishes, there was no <em>lws</em> expression and no orange/red coloration. Omnivory was most often the dominant state. Although herbivory was sometimes dominant, zooplanktivory was never dominant. Sensitivity to long wavelength (increased <em>lws</em> expression) only emerged in association with algivory but never with zooplanktivory. Higher <em>lws</em> expression is also exploited by social signalling in orange/red, which emerged after the transition to algivory. Although the relative timing of traits may deviate by different reconstructions and alternative explanations are possible, our results are consistent with sensory bias whereby social signals evolve as a correlated response to natural selection on sensory system properties in other contexts.</p>
Mitochondria morphology quantification datasheet of different MIGA2 expressing cells
<p>Mitochondria morphology quantification datasheet of different MIGA2 constructs expressing Hela cells: WT, MIGA2 KO, MIGA2 KO cells transfected with WT MIGA2, MIGA2 KO cells transfected with MIGA2 mutants (M1, M2, M3).</p>
Lipid droplet quantification datasheet of different MIGA2 constructs expressing cells
<p>Lipid droplet quantification datasheet of different MIGA2 constructs expressing Hela cells: WT, MIGA2 KO, MIGA2 KO cells transfected with WT MIGA2, MIGA2 KO cells transfected with MIGA2 mutants.</p>
Imbalanced expression of cation-chloride cotransporters as a potential therapeutic target in an Angelman Syndrome mouse model
<p>We provide 6 files; 1. Data for western blot analysis (WB_NKCC1_KCC2_Fig1A.xlsx), 2. Data for [Cl-]i(Intracelluar‗Cl_Fig1B.xlsx), 3. Electrophysilogical data for mIPSC and tonic current (mIPSC_Tonic current_Fig2B_C_E.xlsx), 4. Data for behavior analysis (Behavior_analysis_Fig3A_C .xlsx), 5. Data for seizure threshhold (seizure_threshold_Fig4A.xlsx), 6. Data for EEG spike number and band power (EEG_analysis_Fig4B_C.xlsx)</p>
Decoding diabetes biomarkers and related molecular mechanisms using machine learning, text mining, and gene expression analysis
<p>The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA- DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top ten DEGs. These genes were enriched in aerobic respiration, T-Cell antigen receptor pathway, Tricarboxylic acid metabolic process, vitamin D receptor pathway, Toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for ML analysis . The "Decision tree", "Extra-tree regressor" and "Random forest" algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. ML algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.</p>
Pigment-Dispersing Factor expressing neurons provide an infrastructure for conveying circadian information in the honey bee brain
<p>A movie of a 360° view of the 3D-reconstructed PDF (Pigment Dispersing Factor) network in the honey bee brain. (avi file to be opened with for example Fiji ImageJ)</p>
ROS-specific Huntingtin Interactions: Testing inducible expression of huntingtin-specific chromobodies
<p>Testing step in the generation of an inducible system for expressing YFP-tagged huntingtin-specific intrabodies in live cells.</p>
ROS-specific Huntingtin Interactions: Comparing transfection methods for inducible expression of huntingtin-specific chromobody
<p>Optimization step in the development of an inducible system expressing YFP-tagged huntingtin-specific intrabodies for stable transfection in TruHD fibroblasts.</p>
ROS-specific Huntingtin Interactions: Inducible huntingtin-specific chromobody expression by nucleofection
<p>Optimization step in the development of an inducible system expressing YFP-tagged huntingtin-specific intrabodies for stable transfection in TruHD fibroblasts.</p>
Data for paper: Transfer learning for cross-context prediction of protein expression from 5'UTR sequence
<p>This depsit contains data for the paper entitled: "<strong>Transfer learning for cross-context prediction of protein expression from 5'UTR sequence</strong>".</p> <p>The <strong>rebeca.zip</strong> file contains a snapshot of the rebeca package which can be used to train, fine tune and test the CONV-LSTM model used in this study.</p> <p>The <strong>datasets.zip</strong> file contains the compiled sequence to expression datasets from across all Flow-seq expressions considered in this study. </p> <p>The <strong>analysis.zip</strong> file contains all data files and jupyter notebooks necessary to reproduce our analysis. Each Flow-seq study has a dedicated folder (e.g., `fepB') with two sub-folders: 1. The `data\_split' folder, which contains the steps necessary to split the Flow-seq data for our ML experiments (a `readme.txt' file describes the input and output files and a jupyter notebook is available to reproduce the data split); 2. The `data\_analysis' folder, which contains a jupyter notebook and the necessary input files to reproduce the analysis of our experiments.</p>
LC-MS/MS data for WT human PANX1 with or without co-expressing Src-Y529F mutant
<p>Protein phosphorylation is one of the major molecular mechanisms regulating protein activity and function throughout the cell. Pannexin 1 (PANX1) is a large-pore channel permeable to ATP and other cellular metabolites. Its tyrosine phosphorylation and subsequent activation have been found to play critical roles in diverse cellular conditions, including neuronal cell death, acute inflammation, and smooth muscle contraction. Specifically, the non-receptor kinase Src has been reported to phosphorylate Tyr198 and Tyr308 of mouse PANX1 (equivalent to Tyr199 and Tyr309 of human PANX1), resulting in channel opening and ATP release. Although the Src-dependent PANX1 activation mechanism has been widely discussed in the literature, independent validation of the tyrosine phosphorylation of PANX1 has been lacking. Here, we show that commercially available antibodies against the two phosphorylation sites mentioned above—which were used to identify endogenous PANX1 phosphorylation at these two sites—are nonspecific and should not be used to interpret results related to PANX1 phosphorylation. We further provide evidence that neither tyrosine residue is a major phosphorylation site for Src kinase in heterologous expression systems. We call on the field to re-examine the existing paradigm of tyrosine phosphorylation-dependent activation of the PANX1 channel.</p>
The expressivity of rare coding variants for blood lipids in over a million individuals
<p>The supplemental data and codes for Koyama et al. "The expressivity of rare coding variants for blood lipids in over a million individuals"</p>
CRISPRi-based circuits to control gene expression in plants
<p>The plasmid data supporting the publication: CRISPRi-based circuits to control gene expression in plants</p>
Gene expression of PFAS exposed human liver spheroids and quality control
<p>Per- and polyfluoroalkyl substances (PFAS) are a wide range of chemicals that are used in a variety of consumer and industrial products leading to direct human exposure. Many PFAS are chemically non-reactive and persistent in the environment, resulting in additional exposure from water, soil, and dietary intake. While some PFAS have documented negative health effects, data on simultaneous exposures to multiple PFAS (PFAS mixtures) are inadequate for making informed decisions for risk assessment. The current study leverages data from previous work in our group using Templated Oligo-Sequencing (TempO-Seq™) for high-throughput transcriptomic analysis of PFAS-exposed primary human liver cell spheroids; herein, we determine the transcriptomic potency of PFAS in mixtures. Gene expression data from single PFAS and mixture exposures of liver cell spheroids were subject to benchmark concentration (BMC) analysis. We used the 25<sup>th</sup> lowest gene BMC as the point of departure to compare the potencies of single PFAS to PFAS mixtures of varying complexity and composure. Specifically, the empirical potency of eight PFAS mixtures were compared to predicted mixture potencies calculated using the principal of concentration addition (i.e., dose addition) in which mixture component potencies are summed by proportion to predict mixture potency. In this study, for most mixtures, empirical mixture potencies were comparable to potencies calculated through concentration addition. This work supports that the effects of PFAS mixtures on gene expression largely follow the concentration addition predicted response and suggests that effects of these individual PFAS in mixtures are not strongly synergistic or antagonistic.</p>
The expression of glucocorticoid and mineralocorticoid receptors in corticotroph pituitary tumors
<p>Data include the basic clinical parameters of patients with corticotroph pituitary tumors and the results of the measurements of relative expression level of NR3C1 and NR3C2 genes as well as the results of the evaluation of the expression of glucocorticoid and mineralocorticoid receptor. Gene expression was measured with qRT-PCR, protein expression was assessed with immunohistochemistry.</p>
Oncogenic calreticulin induces TGF-β expression and Treg expansion in the bone marrow microenvironment as a mechanism of immune escape
<p>This repository contains all necessary scRNA-seq inputs to reproduce the results described in "Oncogenic calreticulin induces TGF-β expression and Treg expansion in the bone marrow microenvironment as a mechanism of immune escape" by Schmidt et al. (Cancer Research 2024). </p> <p>Content:</p> <ol> <li>"MPN_calreticulin_bm.R" --> R script containing all code</li> <li>"cells_table.RDS" --> cells table containing, cell_id, UMAP coordinates, complexity, cell type annotation and metadata</li> <li>"normalized_matrix.RDS" --> quality control filtered, log2-normalized and centered expression matrix</li> <li>"reference_signatures.RDS" --> all external signatures used for this study</li> <li>"EV2_*", "EV5_*", "MPN2_*", "MPN5_*", --> cellranger outputs</li> </ol>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.