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4,276 results for “Transcription Factors”
Global consensus map of human transcription factor footprints
<p>Vierstra, J. <em>et al.</em> <strong>Global reference mapping of human transcription factor footprints.</strong> <em>Nature</em><strong> </strong>583, 729–736 (2020). <a href="https://doi.org/10.1038/s41586-020-2528-x">https://doi.org/10.1038/s41586-020-2528-x</a></p> <p>Preprint @ bioRxiv: <a href="https://doi.org/10.1101/2020.01.31.927798">https://doi.org/10.1101/2020.01.31.927798</a></p> <p><strong>Contact:</strong> Jeff Vierstra (<a href="mailto:jvierstra@altius.org?subject=Consensus%20DNase%20I%20footprints">jvierstra@altius.org</a>)</p> <p>Genomic DNase I footprinting enables quantitative, nucleotide-resolution delineation of sites of transcription factor occupancy within native chromatin. We combined sampling of >67 billion uniquely mapping DNase I cleavages from >240 human cell types and states to index, with unprecedented accuracy and resolution, human genomic footprints and thereby the sequence elements that encode transcription factor recognition sites.</p> <p>Please see <a href="http://vierstra.org/resources/dgf">http://vierstra.org/resources/dgf </a>for additional information and a complete set of raw DNase I data for individual datasets. Additionally, raw data can also be accessed via the ENCODE data portal (<a href="http://encodeproject.org">http://encodeproject.org</a>) using the dataset accessions found in Supplementary Table 1.</p> <p>Code for footprint analysis and tutorials on how to access and manipulate digital genomic footprint data can be found at <a href="https://footprint-tools.readthedocs.io/en/latest/">https://footprint-tools.readthedocs.io/en/latest/</a>.</p> <p>All files herein correspond to human genome build version GRCh38 (UCSC hg38).</p> <p><strong>Dataset contents:</strong></p> <ul> <li><strong>Biosample metadata</strong> – Supplementary_Table_1.xlsx</li> <li><strong>Motif clustering metadata </strong>– Supplementary_Table_2.xlsx</li> <li><strong>ChIP-seq validation metadata </strong>–<strong> </strong>Supplementary_Table_3.xlsx</li> <li><strong>Consensus footprint coordinates and assigned motif archetypes</strong><br> TSV file (BED-format) with consensus footprint (posterior probability>0.99) coordinates and overlaps with matches to motif model clusters. The legend file contains column definitions in detail. <ul> <li>consensus_footprints_and_motifs_hg38.bed.gz</li> <li>consensus_footprints_and_motifs_legend.txt</li> </ul> </li> <li><strong>Motif archetype matches overlapping consensus footprints</strong><br> TSV file (BED-format) containing the coordinates for clustered motif model matches that overlap consensus footprints <ul> <li>collapsed_motifs_overlaping_consensus_footprints.bed.gz</li> <li>collapsed_motifs_overlaping_consensus_footprints_legend.txt</li> </ul> </li> <li><strong>Footprint occupancy matrix of consensus footprints</strong><br> Rows are same order as the consensus footprint file and columns are same order as in the metadata files. <ul> <li>consensus_index_matrix_full_hg38.txt.gz (Values are –log(1-posterior))</li> <li>consensus_index_matrix_binary_hg38.txt.gz (binary occupancy matrix, where footprints with posterior footprint probability >0.99 are considered occupied)</li> </ul> </li> <li><strong>Single nucleotide variants tested for allelic imbalance </strong><br> The legend file contains column definitions in detail. <ul> <li>genotypes.vcf.gz - Genotyping and allelic read depth for each biosample (see header for more information)</li> <li>tested_snvs_padj.bed.gz - SNVs tested for imbalance (TSV, BED-format)</li> <li>tested_snvs_padj_legend.txt</li> </ul> </li> </ul>
Human T-box transcription factor T (Brachyury); A Target Enabling Package
<p>Chordoma is a rare cancer occurring along the spinal cord (OMIM: <a href="https://www.omim.org/entry/215400">215400</a>). Chordoma is derived from an embryonic tissue, the notochord, and over-expresses the embryonic transcription factor T-box transcription factor T, the homologue of mouse Brachyury. Chordomas are “genomicaly silent” cancers that do not carry an extensive mutation load. Recent studies indicate that expression of TBXT is essential for persistence and growth of chordoma cells. As TBXT is not expressed in any post-embryonic tissues, it could be an excellent target for treatment of chordoma. The long-term aim of this project is to test whether TBXT can be targeted with small molecules with sufficient affinity and specificity to be therapeutically useful. In this TEP we have determined crystal structures of the DNA-binding domain (DBD) of TBXT with and without cognate DNA oligonucleotides. The DNA-free protein crystals were used in a high-throughput fragment screen to identify 29 fragments bound in 6 clusters. The crystal structures of the bound fragments provide starting points for development of stronger binders which could be used to disrupt TBXT activity or to induce the degradation of the protein through a Proteolysis-targeting chimeric molecule (PROTAC) approach.</p>
S1Data: ChIP-seq Data from Ferrie et. al. "p300 Is an Obligate Integrator of Combinatorial Transcription Factors Inputs"
<p>ChIP data from Ferrie et. al. "p300 Is an Obligate Integrator of Combinatorial Transcription Factors Inputs"</p>
Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding
<p>TF binding models built by JAMS (https://github.com/csglab/JAMS), ChIP-seq peak files (from ENCODE, Najafabadi et al. 2015, Schmitges et al. 2016, and Imbeault et al. 2017; called by MACS 1.4v), ChIP-seq pulldown and control tags from said peaks, input data for JAMS, and RCADE2 motifs for C2H2 zinc finger proteins. </p>
Benchmarking tools for transcription factor prioritization
<p><strong>Abstract:</strong></p> <p>Spatiotemporal regulation of gene expression is controlled by transcription factor (TF) binding to regulatory elements, resulting in a plethora of cell types and cell states from the same genetic information. Due to the importance of regulatory elements, various sequencing methods have been developed to localise them in genomes, for example using ChIP-seq profiling of the histone mark H3K27ac that marks active regulatory regions. Moreover, multiple tools have been developed to predict TF binding to these regulatory elements based on DNA sequence. As altered gene expression is a hallmark of disease phenotypes, identifying TFs driving such gene expression programs is critical for the identification of novel drug targets.In this study, we curated 84 chromatin profiling experiments (H3K27ac ChIP-seq) where TFs were perturbed through e.g., genetic knockout or overexpression. We ran nine published tools to prioritize TFs using these real-world data sets and evaluated the performance of the methods in identifying the perturbed TFs. This allowed the nomination of three frontrunner tools, namely RcisTarget, MEIRLOP and monaLisa. Our analyses revealed opportunities and commonalities of tools that will help to guide further improvements and developments in the field.</p> <p><strong>Dataset description:</strong></p> <ul> <li>tf_tool_benchmark_atacseq_diffPeaks.tar.gz -Archive containing differential peak statistics, tool diff peak input files (fore- and background) for all currated ATAC-seq datasets. </li> <li>tf_tool_benchmark_h3K27ac_chipseq_diffPeaks.tar.gz - Archive containing differential peak statistics, tool diff peak input files (fore- and background) for all currated H3K27ac ChIP-seq datasets. </li> <li>tf_tool_benchmark_atacseq_results.tar.gz - Archive containing the raw tool results for each ATAC-seq dataset.</li> <li>tf_tool_benchmark_chipseq_results.tar.gz - Archive containing the raw tool results for each H3K27ac ChIP-seq dataset.</li> <li>tf_tool_benchmark_results.tar.gz - Archive containing tool results summary for plotting (rds files).</li> </ul> <p><strong>Contact: </strong>Sebastian Steinhauser - sebastian.steinhauser@novartis.com</p>
Evaluation of transcription factor knockout impact on paclitaxel response for Triple Negative Breast Cancer
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11238552</div> <div> </div> <div>Two experimental formats included:</div> <div>'fixed' prefix: data from terminal time point of siRNA screen applied to HCC1143, HCC1806, and MDA-MB-468 Triple Negative Breast Cancer cell lines.</div> <div>'live' prefix: data from live-cell imaging of cell cycle reporter (HDHB-mClover/NLS-mCherry) HCC1143 Triple Negative Breast Cancer cell line.</div> <div>Note: 'live' level 1 data is available upon request (heiserl@ohsu.edu, calistri@ohsu.edu).</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate whether siRNA knockdown of transcription factors elevated during paclitaxel response impact cell count, cell morphology or cycling dynamics.</div> <div> </div> <div>Methods:</div> <div>siRNA Knockdown: Cells were plated in 90ul of serum free media per well of a 96 well plate. 24 hours later, siRNA knockdown mixture was prepared using a cell-line optimized concentration of Lipofectamine RNAiMAX (cat 13778075-075, Invitrogen) and siRNA (Horizon Discovery ON-TARGETplus) following RNAiMAX recommended protocol. The final concentration of siRNA per well was 1pmol and the final volume of RNAiMAX per well was 75nL for HCC1143, and 37.5nL for HCC1806 or MDA-MB-468 in 100uL of cell containing volume. 24 hours after siRNA transfection cells were treated with an addition of 100uL complete media containing either DMSO vehicle control or paclitaxel. </div> <div> </div> <div>Fixed-cell assays: Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask Green in PBS (H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 50, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>HDHB reporter live-cell assays: siRNA knockdown and drug treatment was performed as described above, and then the plate was loaded on an Incucyte S3 (Sartorious) and cells imaged every 15 minutes for 72 hours post drug treatment. At each timepoint 4 fields of view were captured at 20x magnificantion in each well using the phase, red and green channels. A cytoplasmic mask was computed from the mean of normalized red/green channel (cellpose parameters: diameter = 57, chan = mean(normalized(red), normalized(green)), and a nuclear mask was computed from the red channel (cellpose parameters: diameter = 30, chan = DAPI) using custom trained Cellpose models. Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0). An additional perinuclear ring mask was computed as the 11 pixel dilation from the nuclear mask, but still bound by the cytoplasmic mask. To determine mClover localization thresholds for cell cycle assignment, 250 cell images were randomly selected and manually assigned to the G1, S/G2 or M cell cycle state based on mClover localization. The mClover intensity ratios were then used to determine thresholds for automated cell cycle phase calling which was applied to the rest of the data set (Supplemental Figure 5A). Mononuclear cells with a Perinuclear:Nuclear mean intensity ratio greater than 0.8 and Nuclear:Cytoplasmic total intensity less than 0.5 were assigned to the S/G2 phase. Mononuclear and Multinuclear cells with a Nuclear:Cytoplasmic total intensity ratio greater than 0.8 and Perinuclear:Nuclear mean intensity ratio less than 0.8 were assigned to the ‘M’ phase. The remainder of mononuclear cells were assigned ‘G1’, and the remainder of multinucleated cells were assigned ‘Multinucleated’. </div> <div> </div> <div>Included files:</div> <div>fixed_level_1-plate_#.zip : Six .zip archives containing the raw images (DAPI/CellMask/Brightfield) from fixed-cell experiments.</div> <div>plate 1: HCC1143 cells treated with plate A schema</div> <div>plate 2: HCC1143 cells treated with plate B schema</div> <div>plate 3: HCC1806 cells treated with plate A schema</div> <div>plate 4: HCC1806 cells treated with plate B schema</div> <div>plate 5: MDA-MB-468 cells treated with plate A schema</div> <div>plate 6: MDA-MB-468 cells treated with plate B schema</div> <div>fixed_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>fixed_level_3: Data from 'fixed_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>fixed_incell_to_cellpose.rmd: R markdown code for converting original incell files (fixed_level_1) to RGB images for cellpose segmentation</div> <div>fixed_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (fixed_level_1)</div> <div>fixed_cellpose_models.zip: Archive including cellpose models used for fixed experiment</div> <div>live_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>live_level_3: Data from 'live_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>live_level_4: Data from 'live_level_3.csv' collapsed to the single condition level summarizing the number, multinucleation status and phase of cells at each time point.</div> <div>live_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (live_level_1).</div> <div>l ive_incu_archive2rgb.rmd: R markdown code for converting incucyte archive formatted data into RGB images, where the blue channel is the arithmetic mean of the min-max (0-1) normalized red and green channels.</div> <div>live_cellpose_models.zip: Archive including cellpose models used for live experiment.</div> <div> </div> <div> </div>
Virtual ChIP-seq predictions of binding of 36 transcription factor in Roadmap Epigenomics Project tissues
<p>This dataset contains predictions of Virtual ChIP-seq for binding of 36 transcription factors in Roadmap Epigenomics dataset tissues with matched DNase-seq and RNA-seq data.</p> <p>Tarball contains subfolders for each of the 36 TFs where Virtual ChIP-seq median MCC in validation cell types was > 0.3.</p> <p>Each subfolder contains gzipped BED files. Each file is named as <Tissue>_<Age>_<TF>_<Accession>_Predictions.bed.gz. Columns correspond to Chromosome, Start, End, <Tissue>_<Age>_<TF>_<Accession>, Posterior probability</p> <p>You can use the posterior probabilities provided in Virchip_PosteriorCutoffs_V3.0.0.tsv. These are posterior probability cutoffs which maximized MCC in H1-hESC cell type, or are set to 0.4 if there was no ChIP-seq data of that TF in H1-hESC (0.4 is the mode of all optimal posterior probability cutoffs in H1-hESC).</p>
TF-Marker: A comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human.
<p>Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/) which is committed to a comprehensive manual curation of TFs and related markers with experimental evidence in specific cell and tissue types in human. Currently, through reviewing <strong>2,091</strong> published literature, we have manually classified TFs and related markers into five types according to their functions: 1) <strong>TF</strong>: TFs, which regulate the expression of markers; 2) <strong>T Marker</strong>: markers, which are regulated by TFs (TF and T Marker pairs can identify cell types more specifically); 3) <strong>I Marker</strong>: markers, which influence the activity of TFs (I Markers can also influence the development of specific cells and tissues); 4) <strong>TFMarker</strong>: TFs, which play roles as markers (TFMarkers are cell/tissue-specific TFs used as cell markers in biology experiments); and 5) <strong>TF Pmarker</strong>: TFs, which play roles as potential markers. By curating thousands of published literature, <strong>5,905</strong> entries including <strong>1,316</strong> TFs, <strong>1,092</strong> T Markers, <strong>473</strong> I Markers, <strong>1,600</strong> TFMarkers and <strong>1,424</strong> TF Pmarkers, were annotated in <strong>383</strong> cell types and <strong>95</strong> tissue types in human. Moreover, TF-Marker divided markers into disease markers and tissue/cell-specific markers. TF-Marker is an elaborate database, which provides TFs and related markers supported by experimental evidence. We believe TF-Marker will provide strong support for research into cell/tissue-specific TFs and related markers.</p>
Analysis accompanying "Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the Drosophila larval brain"
<p>This repository documents the raw data processing and figure generation for the article “Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the <em>Drosophila </em>larval brain”, doi: 10.1261/rna.079552.122.</p>
CollecTRI Data for Investigation of SETBP1 gene expression and transcription factor activity across human tissues
<p>Here we provide the human CollecTRI prior (accessed May 2023) for inference of TF activity across 31 GTEx tissues using multivariate linear modeling method decoupleR.<br> <br> The `human_prior_tri.csv` includes 1,178 unique TFs (referred to as the source) that target 6,627 unique genes (referred to as targets) to give us 42,595 interactions in the CollecTRI prior input. Interactions are represented as a + or - 1 (mor).</p>
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>.</p> <p>The folder <em>data </em>contains<em> </em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses. </p> <p>The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong></p> <p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p> <p><strong>Results</strong></p> <p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p> <p><strong>Conclusions</strong></p> <p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p> <p> </p> <p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.</p>
Convolutional Neural Net (CNN) models for ENCODE-Roadmap DNase-seq peaks and Transcription Factor ChIP-seq peaks - Basset architecture
<p>Deep learning models trained on epigenomic landscapes from ENCODE and Roadmap Epigenomics. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4059038. The file `nn.encode-roadmap.models.basset.clf.tar.gz` contains 10 cross-validated models in Tensorflow framework files as well as details on the architecture, cross-validation scheme, and training of these models. The file `nn.encode-roadmap.models.basset.clf.np_weights.tar.gz` contains the 10 cross-validated models' weights extracted to numpy array files (.npz).</p>
Supplementary Videos: The N-Terminal Helix-Turn-Helix Motif of Transcription Factors MarA and Rob Drives DNA Recognition
<p>Supplementary Movies associated with the following work: "The N-Terminal Helix-Turn-Helix Motif of Transcription Factors MarA and Rob Drives DNA Recognition", available as a preprint on chemRxiv: https://chemrxiv.org/articles/preprint/The_N-Terminal_Helix-Turn-Helix_Motif_of_Transcription_Factors_MarA_and_Rob_Drives_DNA_Recognition/12195372 </p>
DoubleChEC program to identify transcription factor binding sites from mapped ChEC-seq data
<p>ChIP-seq (chromatin immunoprecipitation followed by sequencing) is commonly used to identify genome-wide protein-DNA interactions. However, ChIP-seq often gives a low yield, which is not ideal for quantitative outcomes. An alternative method to ChIP-seq is ChEC-seq (Chromatin endogenous cleavage with high-throughput sequencing). In this method, the endogenous TF (transcription factor) of interest is fused with MNase (micrococcal nuclease) that non-specifically cleaves DNA near binding sites. Compared to the <a href="https://www.nature.com/articles/ncomms9733" rel="nofollow">original ChEC-seq method</a>, the <a href="https://sites.northwestern.edu/bricknerlab/" rel="nofollow">modified version</a> requires far less amplification. Since <a href="https://github.com/macs3-project/MACS/tree/master#introduction">MACS3</a> failed to identify peaks in data generated from the modified ChEC-seq method, a new peak finder has been developed specifically for it.</p> <p>There are three functions in the <em><code>peak_finder/</code></em>. <code>callpeaks()</code> is used to identify peaks from BAM files. <code>goanalysis()</code> is used to make GO (Gene Ontology) term plots from peaks. <code>bedtomeme()</code> is a wrapper function to perform <a href="https://meme-suite.org/meme/tools/meme" rel="nofollow">MEME analysis</a> in R <strong>after <a href="https://meme-suite.org/meme/doc/download.html" rel="nofollow">MEME Suite</a> is installed locally</strong>.</p>
Transcription factors in moss development and defenses against abiotic and biotic stress_dataset
<p>Lists of <em>P. patens</em> genes encoding transcription factors belonging to AP2/ERF, bHLH, GRAS, MYB, NAC and WRKY families differentially expressed in transcriptomes related to response to biotic interactions, abiotic stress, and hormones.</p>
FOXO transcription factors are required for normal somatotrope function and growth
<p><strong>Supplemental Fig 1. <em>Prop1 </em>and <em>Sst</em> expression is unchanged in dKO mice. </strong>Whole pituitary glands were collected from WT and dKO mice at 6 weeks of age. RNA was isolated and cDNA generated in order to evaluate mRNA abundance for <em>Prop1 </em>in females and males. Hypothalamus was collected to evaluate expression of <em>Sst</em>.<em> </em>Expression was normalized to <em>Tfrc</em>. The data represent 7-8 animals for each genotype and sex and were analyzed using Student’s t test.</p> <p><strong>Supplemental Fig 2. Gonadotrope, thyrotrope and corticotrope cells appear normally distributed in dKO mice. </strong>Immunohistochemistry for LHB, TSHB, and ACTH was performed on pituitary gland tissue from female and male mice to determine the distribution of gonadotropes, thyrotropes, and corticotropes, respectively. No obvious difference was observed between dKO mice and WT controls. Scale bars represent 100 mm. Representative images of three animals per genotype and sex are shown.</p> <p><strong>Supplemental Fig 3. Lactotrope cells appear normally distributed in dKO mice.</strong> Immunohistochemistry for PRL was performed on pituitary gland tissue from female and male mice to determine the distribution of lactotropes. No apparent difference in lactotrope distribution was observed between dKO mice and WT controls. Scale bars represent 100 mm. Representative images of three animals per genotype and sex are shown.</p> <p><strong>Supplemental Fig 4. <em>Foxo1 </em>and <em>Foxo3</em> expression levels in liver and hypothalamus of dKO mice. </strong>Liver and hypothalamus were collected from WT and dKO mice at 6 weeks of age. RNA was isolated and cDNA generated in order to evaluate mRNA abundance for <em>Foxo1 </em>and <em>Foxo3 </em>in females and males. Expression was normalized to <em>Tfrc</em>. The data represent 5-8 animals for each genotype and sex and were analyzed using Student’s t test (*p<0.05, **p<0.01, ***p<0.001).</p> <p> </p> <p><strong>Materials and Methods</strong></p> <p><em>Animals and genotyping</em></p> <p> To obtain <em>Foxo1<sup>Δpit</sup></em> mice <em>Foxo1<sup>+/-</sup></em> mice (15) were mated to <em>Foxg1<sup>+/cre</sup></em> mice (10) to produce <em>Foxo1<sup>+/-</sup>;Foxg1<sup>+/cre</sup></em> mice. These were then mated to <em>Foxo1<sup>fl/fl</sup></em> mice (13) to obtain <em>Foxo1<sup>fl/-</sup>;Foxg1<sup>+/cre</sup></em> (<em>Foxo1<sup>Δpit</sup></em>) mice. Experimental <em>Foxo1<sup>fl/fl</sup>;Foxo3<sup>fl/fl</sup>;Foxg1<sup>+/cre</sup></em> (dKO) animals were generated by crossing <em>Foxo1<sup>fl/fl</sup>;Foxo3<sup>fl/fl</sup></em> females with <em>Foxo1<sup>+/fl</sup>;Foxo3<sup>fl/fl</sup>;Foxg1<sup>+/cre</sup></em> males. <em>Foxg1<sup>+/cre</sup></em> mice were purchased from Jackson Laboratories, Bar Harbor, ME, USA (stock no. 004337) and were maintained on a 129SvJ (stock no. 000691) background (10,11). <em>Foxo1<sup>fl/fl</sup></em> mice (Jackson Laboratories, stock no. 024756) which have <em>loxP</em> sites flanking exon two of the <em>Foxo1</em> gene (15) were a generous gift from Drs. Accili and Pajvani, with permission from Dr. DePinho. <em>Foxo1<sup>+/-</sup></em> mice (15) were provided by Drs. Accili and Pajvani.<em> Foxo3<sup>fl/fl</sup></em> mice were purchased from Jackson Laboratories (stock no. 024668) (16). Genotyping was performed using specific primers for <em>Foxo1-null </em>(<em>LacZ</em> fwd and rev),<em> Foxo1-flox </em>(FK1ckA-C),<em> Foxg1-cre </em>(<em>cre </em>fwd and rev), and <em>Foxo3-flox </em>(ofk2ck1-3). A list of primers used can be found in Table S1.</p> <p> All mice were housed in a 12-hour light/dark cycle with feed (Formulab Diet 5008; Purina Mills, Gray Summit, MO, USA) and water <em>ad libitum</em>. Animals were weighed once per week starting at postnatal day seven. Mice were euthanized using CO<sub>2</sub> inhalation. Mouse length was measured post-euthanization by measuring from nose to rump. All procedures were conducted in accordance with the principles and procedures outlined in the National Institutes of Health Guidelines for the Care and Use of Experimental Animals and in accordance with Southern Illinois University Carbondale policies.</p> <p><em>Immunohistochemistry</em></p> <p> Pituitary gland tissue was collected post-euthanization and fixed in 10% formalin in PBS then dehydrated in graded ethanol solutions (50% then 80%). Tissue was then embedded in paraffin blocks and cut into 5 μm sections and mounted on positively charged slides. All immunohistochemistry (IHC) was begun by deparaffinization and rehydration of tissue sections using xylene (twice for 5 minutes each), 100% ethanol (twice for 3 minutes each), 95% ethanol (twice for 3 minutes each), then PBS. For immunofluorescent detection where antibody signal was amplified (Supplemental Table S2), slides were then incubated in 1.5% H<sub>2</sub>O<sub>2</sub> for 20 minutes. All tissue sections were blocked for 60 minutes using the Tyramide Signal Amplification (TSA) Kit Blocking Solution (TSB; Perkin Elmer, Waltham, MA, USA), which was also used as the diluent for all antibody solutions. Primary antibodies were incubated overnight at 4°C but all other steps were performed at room temperature (RT). After primary antibody incubation, tissue sections were washed three times for 3 minutes each in PBS-TWEEN 20 (PBS-T, 0.05%). Fluorophore-conjugated secondary antibody was then incubated on tissue sections for 60 minutes. Nuclei were stained using 4’,6’-diamidino-2-phenylindole (DAPI). Sections were then mounted using immunofluorescent mount (0.5 mM polyvinyl alcohol, 0.12 M Tris pH 8.0, 0.3% w/v glycerol, 2.5% w/v 1,4-diazabicyclo[2.2.2]octane) and glass coverslips.</p> <p> Imaging was performed using a Retiga 2000R digital camera attached to a Leica DM 5000B fluorescent microscope (Leica Biosystems, St. Louis, MO, USA). Individual captures of FITC and DAPI channels were merged using Adobe Photoshop CS3. Some images were brightened for illustrative purposes; however, the exact alterations were duplicated in both control and experimental images to maintain the ability to compare results. Three animals per genotype were analyzed for these studies.</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>ID</strong></p> </td> <td> <p><strong>Antigen</strong></p> </td> <td> <p><strong>Citation</strong></p> </td> <td> <p><strong>Host</strong></p> </td> <td> <p><strong>Company</strong></p> </td> <td> <p><strong>Cat. No.</strong></p> </td> <td> <p><strong>Dilution</strong></p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Rabbit anti-mouse PRL antibody</p> </td> <td> <p><a href="http://antibodyregistry.org/AB_2721133">AB_2721133</a></p> </td> <td> <p>mouse PRL</p> </td> <td> <p>(A.F. Parlow National Hormone and Peptide Program Cat# AFP107120402, RRID:AB_2721133)</p> </td> <td> <p>rabbit</p> </td> <td> <p>A.F. Parlow National Hormone and Peptide Program</p> </td> <td> <p>AFP10712402</p> </td> <td> <p>IF 1:10000</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Rabbit anti-Rat TSHβ antibody</p> </td> <td> <p><a href="http://antibodyregistry.org/AB_2665563">AB_2665563</a></p> </td> <td> <p>rat TSHB</p> </td> <td> <p>(A.F. Parlow National Hormone and Peptide Program Cat# rTSHb, RRID:AB_2665563)</p> </td> <td> <p>rabbit</p> </td> <td> <p>A.F. Parlow National Hormone and Peptide Program</p> </td> <td> <p>rTSHb also AFP-1274789</p> </td> <td> <p>IF 1:2000</p> </td> </tr> <tr> <td> <p>ACTH (adrenocorticotropic hormone) antibody</p> </td> <td> <p><a href="http://antibodyregistry.org/AB_2313902">AB_2313902</a></p> </td> <td> <p>ACTH</p> </td> <td> <p>(National Hormone & Peptide Program, Torrance, CA Cat# AFP-156102789, RRID:AB_2313902)</p> </td> <td> <p>rabbit</p> </td> <td> <p>A.F. Parlow National Hormone and Peptide Program</p> </td> <td> <p>AFP-156102789</p> </td> <td> <p>IF 1:500</p> </td> </tr> </tbody> </table> <p><em>RTqPCR</em></p> <p> Tissue collected for mRNA analysis was stored in RNA Later (AM7021, Invitrogen, Carlsbad, CA, USA) until use. Whole pituitary glands were lysed, and RNA was extracted and purified using the RNAqueous Micro Kit (AM1931) according to the kit protocol. The resultant mRNA was reversed transcribed to cDNA using the Promega M-MLV kit according to included instructions (M5313, Promega, Madison, WI, USA). Ten ng of cDNA were used for mRNA analysis. All samples were run in duplicate and a sample processed with no reverse transcriptase enzyme was included as a negative control. Results were calculated using the ΔΔCt method by first normalizing to RNA-polymerase subunit II b (<em>Polr2b</em>) as an internal control then calculated relative to transferrin receptor (<em>Tfrc</em>) to compare between groups. Both genes are expressed at consistent levels between genotypes. At least five mice per genotype were used in these studies.</p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Sequence (5’ to 3’)</strong></p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>LacZ fwd</p> </td> <td> <p>TTCACTGGCCGTCGTTTTACAAGCTCGTGA</p> </td> </tr> <tr> <td> <p>LacZ rev</p> </td> <td> <p>ATGTGAGCGAGTAACAACCCGTCGGATTCT</p> </td> </tr> <tr> <td> <p>FK1ckA</p> </td> <td> <p>GCTTAGAGCAGAGATGTTCTCACATT</p> </td> </tr> <tr> <td> <p>FK1ckB</p> </td> <td> <p>CCAGAGTCTTTGTATCAGGCAAATAA</p> </td> </tr> <tr> <td> <p>FK1ckC</p> </td> <td> <p>CAAGTCCATTAATTCAGCACATTGA</p> </td> </tr> <tr> <td> <p><em>cre </em>fwd</p> </td> <td> <p>GCGGTCTGGCAGTAAAAACTATC</p> </td> </tr> <tr> <td> <p><em>cre </em>rev</p> </td> <td> <p>GTGAAACAGCATTGCTGTCACTT</p> </td> </tr> <tr> <td> <p>ofk2ck3</p> </td> <td> <p>CATGCAGTCCGAGAGATTTG</p> </td> </tr> <tr> <td> <p>ofk2ck2</p> </td> <td> <p>AGTGTCTGATACCGAAGAGC</p> </td> </tr> <tr> <td> <p>ofk2ck1</p> </td> <td> <p>AACAACCTCACACATGTGCC</p> </td> </tr> <tr> <td> <p><em>mPolr2b </em>RTqPCR<em> </em>fwd</p> </td> <td> <p>AGATGTATGACGCCGACGAG</p> </td> </tr> <tr> <td> <p><em>mPolr2b </em>RTqPCR<em> </em>rev</p> </td> <td> <p>GTAAGAACTGATCACGATCCAGCA</p> </td> </tr> <tr> <td> <p><em>mTfrc </em>RTqPCR<em> </em>fwd</p> </td> <td> <p>GCAAGATGTAAAGCATCCAGTTGATGG</p> </td> </tr> <tr> <td> <p><em>mTfrc </em>RTqPCR<em> </em>rev</p> </td> <td> <p>GCATATTCTGGAATCCCAGCAG</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><em>mFoxo1 </em>RTqPCR<em> </em>fwd</p> </td> <td> <p>AGGATAAGGGCGACAGCAAC</p> </td> </tr> <tr> <td> <p><em>mFoxo1 </em>RTqPCR<em> </em>rev</p> </td> <td> <p>CCGCTCTTGCCTCCCTC</p> </td> </tr> <tr> <td> <p><em>mFoxo3 </em>RTqPCR<em> </em>fwd</p> </td> <td> <p>GGGCGACAGCAACAGCT</p> </td> </tr> <tr> <td> <p><em>mFoxo3 </em>RTqPCR<em> </em>rev</p> </td> <td> <p>CCCGCTCTTTCCCCCATC</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p><em>mProp1 </em>RTqPCR fwd</p> </td> <td> <p>GCCTCTGGGACTCTGATCTCC</p> </td> </tr> <tr> <td> <p><em>mProp1</em> RTqPCR rev</p> </td> <td> <p>CAGGATACTGGTTCCTCCCAA</p> </td> </tr> <tr> <td> <p><em>mSst </em>RTqPCR fwd</p> </td> <td> <p>TCTGCATCGTCCTGGCTTTG</p> </td> </tr> <tr> <td> <p><em>mSst </em>RTqPCR rev</p> </td> <td> <p>GACAGCAGCTCTGCCAAGAA</p> </td> </tr> </tbody> </table> <p> </p> <p><em>Statistical analysis</em></p> <p> All data were analyzed using Student’s t test unless otherwise noted where (*) indicates p < 0.05, (**) indicates p < 0.01, (***) indicates p < 0.001, and (****) indicates p < 0.0001. Error bars indicate standard error of the mean (SEM).</p>
Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet: transcription factor preferences
<p>The main output of our analysis, as a raw dataset. This data was used to create the plots depicting transcription factor preferences across our paper, including for our treemaps.</p>
Data set for Sicoli et al. - Conformational tuning of a DNA-bound transcription factor
<p>The data set contains NMR, EPR and MD data. The NMR folder contains 1H-15N correlation NMR data of DNA-bound MAX with a paramagnetic MTSL spin label at position 5, with a chemically reduced, diamagnetic spin label, respectively. The EPR folder contains DEER data of MAX for three difference labeling positions R5C, G35C and R55C, with and without bound DNA. The MD folder contains MD trajecrories at three different temperatures, 310 K, 320 K and 330 K. Further details can be found in the readme.txt files in the respective folders.</p>
Processed data from "Chromatin information content landscapes inform transcription factor and DNA interactions"
<p><strong>Chromatin information content landscapes inform transcription factor and DNA interactions</strong></p> <p>Authors: Ricardo D’Oliveira Albanus, Yasuhiro Kyono, John Hensley, Arushi Varshney, Peter Orchard, Jacob O. Kitzman, Stephen C. J. Parker</p> <p><a href="https://doi.org/10.1101/777532">https://doi.org/10.1101/777532</a></p> <p> </p> <p>This record contains the processed data used in our manuscript. For instructions on how to use or regenerate this data, please refer to <a href="https://github.com/ParkerLab/chromatin_information">https://github.com/ParkerLab/chromatin_information</a>.</p>
Fig 2 in Docosahexaenoic Acid (DHA) Reduces LPSInduced Inflammatory Response Via ATF3 Transcription Factor and Stimulates Src/ Syk Signaling-Dependent Phagocytosis in Microglia
<p>Proteome profiler arrays (A and B) and expression of ATF3 gene (C) in microglia. Representative array membranes (A) and the relative levels of cytokines and chemokines (B) in microglia preincubated with 20 μM DHA and next treated with 10 ng/ml LPS.</p>
ScienceDex guides
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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.
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.