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25,372 results for “Transcriptomics”
Single-cell and spatial transcriptomics of stricturing Crohn's disease
<p>This folder contains the spatial transcriptomics data + code. This code was generated by members of the Smillie Lab @ MGH and Harvard Medical School.</p> <ul> <li><strong>github.tar.gz: </strong>spatial analysis code and data</li> <li><strong>anndata.h5ad:</strong> anndata object (scanpy)</li> <li><strong>V*tar.gz:</strong> raw spatial transcriptomics files</li> </ul> <p>The <strong>github.tar.gz</strong> folder contains everything you need to reproduce the spatial transcriptomics figures. It is structured as follows:</p> <ul> <li><strong>1.BayesPrism:</strong> code for running BayesPrism on spatial data</li> <li><strong>2.SparCC:</strong> code for running SparCC on spatial data</li> <li><strong>3.Lasso: </strong>code for running lasso regression on spatial data</li> <li><strong>4.Analysis: </strong>code for reproducing all figures in the paper</li> <li><strong>4.Analysis/1.analysis.r</strong><strong>: </strong>script to reproduce all figures in the paper ***</li> <li><strong>code:</strong> code library containing all necessary functions</li> <li><strong>load_data.r: </strong>code to load the single-cell and spatial datasets</li> <li><strong>sco.rds:</strong> single-cell analysis object (10X Chromium) formatted as an R list</li> <li><strong>vis.rds:</strong> spatial analysis object (10X Visium) formatted as an R list</li> </ul> <p>All scripts are numbered. You need to run everything in order. For convenience, we include the output files for <strong>1.BayesPrism</strong>, <strong>2.SparCC</strong>, and <strong>3.Lasso</strong>, allowing you to skip straight to the analysis code in <strong>4.Analysis.</strong></p> <p>To reproduce all figures in the paper, you need to do the following:</p> <ol> <li>Edit your PROJECT_FOLDER in the header of <strong>load_data.r</strong></li> <li>Install the packages listed at the top of <strong>load_data.r</strong></li> <li>Go to the <strong>4.Analysis</strong> directory, start an interactive R session, and type:<br>> source('1.analysis.r')</li> </ol> <p>This will load the beginning of the <strong>1.analysis.r</strong> script (until the stop() statement on line 68). You can run the code in two different ways:</p> <ol> <li>You can step through the code line by line in your interactive R session (starting at line 68)</li> <li>Alternatively, remove the stop() statement from the script, then run the code start to finish</li> </ol> <p>If you encounter any errors, try to debug them using a combination of Google+ChatGPT. If you still have trouble, please contact the Smillie Lab.</p> <p><strong>Note: </strong>the single-cell and spatial code are also available on GitHub. However, the spatial analysis requires large files that cannot be hosted on GitHub. Therefore, it is better to download the code + files from Zenodo. The GitHub link is provided below:</p> <p><a href="https://github.com/LJ-Kong/fibrosis_scRNA_stRNA">https://github.com/LJ-Kong/fibrosis_scRNA_stRNA</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
In-depth comparative toxicogenomics of glyphosate and Roundup herbicides: Histopathology, transcriptome and epigenome signatures, and DNA damage
<p><span><span><span><span><span><span><span><span><span><span><span>Whether or not glyphosate activates cellular mechanisms involved in carcinogenesis remains controversial. We tested whether glyphosate and three glyphosate-based commercial herbicide formulations activate mechanisms known to be key characteristics of carcinogens. The mammalian stem cell-based genotoxicity ToxTracker assay showed that the representative EU formulation Roundup MON 52276 and the UK formulation Roundup MON 76473, but not glyphosate and the US Roundup formulation MON 76207, activated oxidative stress and unfolded protein responses. High-throughput molecular profiling of liver function was performed in female Sprague-Dawley rats exposed to glyphosate or MON 52276 (both at 0.5, 50, 175 mg/kg bw/day glyphosate equivalent concentration) for 90 days. Histopathology and serum biochemistry analysis showed that MON 52276 but not glyphosate treatment increased hepatic steatosis and necrosis. MON 52276 and glyphosate altered the expression of genes in liver reflecting TP53 activation by DNA damage and the regulation of circadian rhythms. The most affected genes in liver also had their expression similarly altered in kidneys. Small RNA profiling in liver showed miR-22 and miR-17 had their levels decreased by MON 52276, while mir-30 levels were decreased, whilst miR-10 levels were increased by glyphosate. DNA methylation profiling of liver revealed 5,727 and 4,496 differentially methylated CpG sites between the control and glyphosate and MON 52276 exposed groups of animals respectively. Direct DNA damage measurement by apurinic/apyrimidinic lesion formation in liver was increased with glyphosate exposure. Altogether, our results show that Roundup herbicide formulations are causing more biological changes than glyphosate alone, activating mechanisms involved in cellular carcinogenesis.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: The transcriptomics of crushing jaw convergence in cichlid fishes: comparative gene expression in recent sympatric versus older allopatric trophic adaptations
<p>How gene expression diverges during adaptation might be strongly influenced by the geographic setting and timeframe over which species evolve. To contrast transcriptomic patterns of replicate trophic adaptations that evolved convergently during both allopatric and sympatric contexts, we conducted RNA-seq on the trophically important lower pharyngeal jaws of two sympatrically and four allopatrically diverged species pairs of cichlid fishes. We first show that all of these species pairs have convergently diverged along a crushing trophic axis and that the sympatric pairs are as phenotypically divergent as the allopatric pairs. Then, we found that distinct sets of genes were differentially expressed in the jaws of sympatrically diverging pairs as compared to jaws in older allopatric species pairs. The genes that were differentially expressed in the jaws of allopatric pairs also were more highly expressed on average than in the sympatric pairs. Finally, for genes that were differentially expressed, the magnitude of differences in expression between the jaws were greater for sympatrically diverging species pairs. The particular genes, their expression levels, and the magnitude of expression differences between sympatrically originating adaptations might all play an important role in generating and maintaining boundaries to gene flow during the rapid ecological divergence that often characterizes sympatric speciation.</p>
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.
<p>https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133</p>
Transcriptomic Analysis of Streptococcus agalactiae Periprosthetic Joint Infection
<p>Supplemental data from the study entitled "Transcriptomic Analysis of <em>Streptococcus agalactiae </em>Periprosthetic Joint Infection"</p>
Raw mass-standardized ionomic data from seven fish species and raw transcriptome sequences for mosquitofish inhabiting the Tar Creek Superfund Site in OK, USA
<p>Our understanding of the mechanisms mediating the resilience of organisms to environmental change remains lacking. Heavy metals negatively affect processes at all biological scales, yet organisms inhabiting contaminated environments must maintain homeostasis to survive. Tar Creek in Oklahoma, USA, contains high concentrations of heavy metals and an abundance of Western mosquitofish (<em>Gambusia affinis)</em>, though several fish species persist at lower frequency. To test hypotheses about the mechanisms mediating the persistence and abundance of mosquitofish in Tar Creek, we integrated ionomic data from seven resident fish species and transcriptomic data from mosquitofish to test hypotheses about the mechanisms mediating the persistence of mosquitofish in Tar Creek. We predicted that mosquitofish minimize uptake of heavy metals more than other Tar Creek fish inhabitants and induce transcriptional responses to detoxify metals that enter the body, allowing them to persist in Tar Creek at higher density than species that may lack these responses. Tar Creek populations of all seven fish species accumulated heavy metals, suggesting mosquitofish cannot block uptake more efficiently than other species. We found population-level gene expression changes between mosquitofish in Tar Creek and nearby unpolluted sites. Gene expression differences primarily occurred in the gill, where we found upregulation of genes involved with lowering transfer of metal ions from the blood into cells and mitigating free radicals. However, many differentially expressed genes were not in known metal response pathways, suggesting multifarious selective regimes and/or previously undocumented pathways could impact tolerance in mosquitofish. Our systems-level study identified well characterized and putatively new mechanisms that enable mosquitofish to inhabit heavy metal-contaminated environments.</p>
Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets
<p>This contains data for described in detail in our paper, "Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets" (Figueiredo <em>et al.</em>, 2022) which aims at revealing common and specific biological processes and mechanisms across contexts by identifying transcriptional patterns that are unique to various cell types, tissues, and cell lines, as well as patterns which are consistent across them.</p>
Annotated transcriptome data from: Transcriptome annotation reveals minimal immunogenetic diversity among Wyoming toads, Anaxyrus baxteri
<p>Briefly considered extinct in the wild, the future of the wild population of the Wyoming toad (Anaxyrus baxteri) continues to rely on captive breeding to supplement the wild population. Given its small natural geographic range and history of rapid population decline at least partly due to fungal disease, investigation of the diversity of key receptor families involved in the host immune response represents an important conservation need. Population decline may have reduced immunogenetic diversity sufficiently to increase the vulnerability of the species to infectious diseases. Here we use comparative transcriptomics to examine the diversity of toll-like receptors and major histocompatibility complex (MHC) sequences across three individual Wyoming toads. We find reduced diversity at MHC genes compared to bufonid species with a similar history of bottleneck events. Our data provide a foundation for future studies that seek to evaluate the genetic diversity of Wyoming toads, identify biomarkers for infectious disease outcomes, and guide breeding strategies to increase genomic variability and wild release successes.</p>
An overview of bioinformatics, genomics and transcriptomics resources for bryophytes. Supplemental data
<p>supplemental data file 1 - bryophyte transcriptomes</p>
Diversity, phylogeny and adaptation of bryophytes: insights from genomic and transcriptomic data
<p>Table S1. The genomic and transcriptomic data of bryophytes.</p>
Transcriptomics processed data outputs for Sofen et al 2022
<p>Processed data ouputs for transcripomtics analysis of Southern Ocean Time Series eukaryotic microbial communities. Files include: protein and HMM database used for iron stress biomarker screening, Proteins that hit to the HMM database curated to search the metatranscriptomic dataset for iron stress biomarker proteins with protein ID's, taxonomic annotation (EUKulele, MMTESP) and calculated transcripts per million (TPM) values, DNA-directed RNA Polymerase (RPB1) proteins used for taxonomic charecterization of the surface eukaryotic community with protein ID's, taxonomic annotation (EUKulele, MMTESP) and calculated transcripts per million (TPM) values.</p>
Time-series transcriptome analysis identified differentially expressed genes in broiler chicken infected with mixed Eimeria species
<p>Coccidiosis caused by the <em>Eimeria</em> species is a highly problematic disease in the chicken industry. Here, we used RNA sequencing to observe the time-dependent host responses of <em>Eimeria</em>-infected chickens to examine the genes and biological functions associated with immunity to the parasite. Transcriptome analysis was performed at three time points: 4, 7, and 21 days post-infection (dpi). Based on the changes in gene expression patterns, we defined three groups of genes that showed differential expression. This enabled us to capture evidence of endoplasmic reticulum stress at the initial stage of <em>Eimeria</em> infection. Furthermore, we found that innate immune responses against the parasite were activated at the first exposure; they then showed gradual normalization. Although the cytokine-cytokine receptor interaction pathway was significantly operative at 4 dpi, its downregulation led to an anti-inflammatory effect. Additionally, the construction of gene co-expression networks enabled identification of immunoregulation hub genes and critical pattern recognition receptors after <em>Eimeria</em> infection. Our results provide a detailed understanding of the host-pathogen interaction between chicken and <em>Eimeria</em>. The clusters of genes defined in this study can be utilized to improve chickens for coccidiosis control.</p>
Transcriptome sequencie of female and male adults of Chilo sacchariphagus
<p><strong>Table S1</strong> Primers used for qRT-PCR</p> <p><strong>Table S2</strong> list of total annotated unigenes.</p> <p><strong>Table S3</strong> list of male-biased genes.</p> <p><strong>Table S4</strong> list of female-biased genes.</p> <p><strong>Table S5</strong> list of transcription factors showed differential expression between male and female.</p> <p><strong>Table S6</strong> list of sex-determining genes identified in this work.</p>
Files for Manuscript "A pulmonologist's guide to perform and analyse cross-species single-lung-cell transcriptomics"
<p>Input Files for Manuscript "A pulmonologist’s guide to perform and analyse cross-species single-lung-cell transcriptomic"</p> <p>See https://github.com/GenStatLeipzig/pulmonologists_interspecies_scRNA for details.</p> <p>Manuscript authored by:</p> <p>Peter Pennitz1,2*, Holger Kirsten3*, Vincent D. Friedrich3,4, Emanuel Wyler5, Cengiz Goekeri1,2,6, Benedikt Obermayer7, Gitta A. Heinz8, Mir-Farzin Mashreghi8,9, Maren Büttner10,11 Jakob Trimpert12, Markus Landthaler5,13, Norbert Suttorp2, Andreas C. Hocke1,2, Stefan Hippenstiel2, Mario Tönnies14, Markus Scholz3, Wolfgang M. Kuebler15,16, Martin Witzenrath1,2,16, Katja Hoenzke1,2 and Geraldine Nouailles1,2,# </p> <p> </p> <p>1 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Division of Pulmonary Inflammation, Berlin, Germany. </p> <p>2 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany. </p> <p>3 University of Leipzig, Institute for Medical Informatics, Statistics, and Epidemiology, Leipzig, Germany. </p> <p>4 Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany. </p> <p>5 Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany. </p> <p>6 Cyprus International University, Faculty of Medicine, Nicosia, Cyprus. </p> <p>7 Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Core Unit Bioinformatics, Berlin, Germany. </p> <p>8 Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), A Leibniz Institute, Therapeutic Gene Regulation, Berlin, Germany. </p> <p>9 Berlin Institute of Health at Charité – Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Berlin, Germany. </p> <p>10 University of Bonn, Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, Bonn, Germany. </p> <p>11 Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Systems Medicine, Bonn, Germany. </p> <p>12 Freie Universität Berlin, Institute of Virology, Berlin, Germany. </p> <p>13 Humboldt-Universität zu Berlin, Institute for Biology, IRI Life Sciences, Berlin, Germany. </p> <p>14 HELIOS Clinic Emil von Behring, Department of Pneumology and Department of Thoracic Surgery, Chest Hospital Heckeshorn, Berlin, Germany. </p> <p>15 Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Physiology, Berlin, Germany. </p> <ol> <li> <p>German Center for Lung Research (DZL), Berlin, Germany. </p> </li> </ol> <p> </p> <p>* Authors contributed equally to this work </p> <p> </p> <p>References for original datasets :</p> <p>Human Charité: Hocke A, Hönzke K, Obermayer B, Baumgardt M, Wyler E, Hippenstiel S, Mache C. Charité Berlin /Berlin Institute of Health. GEO accessions GSM5958267, GSM5958272, GSM5958283, GSM5958285</p> <p>Human Travaglini et al.: published at <a href="https://www.synapse.org/">https://www.synapse.org</a> by Travaglini et al. (<a href="https://doi.org/10.1038/s41586-020-2922-4">https://doi.org/10.1038/s41586-020-2922-4</a>)</p> <p>Monkey: published at <a href="https://www.ncbi.nlm.nih.gov/geo">https://www.ncbi.nlm.nih.gov/geo</a> by Speranza et al. (<a href="https://doi.org/10.1126/scitranslmed.abe8146">https://doi.org/10.1126/scitranslmed.abe8146</a>)</p> <p>Hamster: Charité Berlin, see: <a href="https://doi.org/10.1038/s41467-021-25030-7">https://doi.org/10.1038/s41467-021-25030-7</a></p> <p>Mouse: Pennitz P, Witzenrath, M, Nouailles G, Berlin Charité.</p> <p>Rat and Pig: published at <a href="https://www.ncbi.nlm.nih.gov/geo">https://www.ncbi.nlm.nih.gov/geo</a> by Raredon et al. (<a href="https://doi.org/10.1126/sciadv.aaw3851">https://doi.org/10.1126/sciadv.aaw3851</a>) .</p> <p>Annotation: Ensembl BioMart</p>
Data from: Metabolomic and transcriptomic responses of ticks during recovery from cold shock reveal mechanisms of survival
<p>Ticks are blood-feeding ectoparasites but spend most of their life off-host where they may have to tolerate low winter temperatures. Rapid cold-hardening (RCH) is a process commonly used by arthropods, including ticks, to improve survival of acute low temperature exposure. However, little is known about the underlying mechanisms in ticks associated with RCH, cold shock, and recovery from these stresses. In the present study, we investigated the extent to which RCH influences gene expression and metabolism during recovery from cold stress in Dermacentor variabilis, the American dog tick, using a combined transcriptomics and metabolomics approach. Following recovery from RCH, 1,860 genes were differentially expressed in ticks, whereas only 99 genes responded during recovery to direct cold shock. Recovery from RCH resulted in an upregulation of various pathways associated with ion binding, transport, metabolism, and cellular structures seen in the response of other arthropods to cold. The accumulation of various metabolites, including several amino acids and betaine, corresponded to transcriptional shifts in the pathways associated with these molecules, suggesting congruent metabolome and transcriptome changes. Ticks receiving exogenous betaine and valine demonstrated enhanced cold tolerance, suggesting cryoprotective effects of these metabolites. Overall, many of the responses during recovery from cold shock in ticks were similar to those observed in other arthropods, but several adjustments may be distinct from other currently examined taxa.</p>
Transcriptomic analysis reveals potential candidate pathways and genes involved in toxin biosynthesis in true toads
<p>Synthesized chemical defenses have broadly evolved across countless taxa and are important in 30 shaping evolutionary and ecological interactions within ecosystems. However, the underlying 31 genomic mechanisms by which these organisms synthesize and utilize their toxins are relatively 32 unknown. Herein, we use comparative transcriptomics to uncover potential toxin synthesizing 33 genes and pathways, as well as interspecific patterns of toxin synthesizing genes across ten 34 species of North American true toads (Bufonidae). Upon assembly and annotation of the ten 35 transcriptomes, we explored patterns of relative gene expression and possible protein-protein 36 interactions across the species to determine what genes and/or pathways may be responsible for 37 toxin synthesis. We also tested our transcriptome dataset for signatures of positive selection to 38 reveal how selection may be acting upon potential toxin producing genes. We assembled high 39 quality transcriptomes of the bufonid parotoid gland, a tissue not often investigated in other 40 bufonid related RNAseq studies. We found several genes involved in metabolic and biosynthetic 41 pathways (e.g. steroid biosynthesis, terpenoid backbone biosynthesis, isoquinoline biosynthesis, 42 glucosinolate biosynthesis) that were functionally enriched and/or relatively expressed across the 43 ten focal species that may be involved in the synthesis of alkaloid and steroid toxins, as well as 44 other small metabolic compounds that cause distastefulness in bufonids. We hope that our study 45 lays a foundation for future studies to explore the genomic underpinnings and specific pathways 46 of toxin synthesis in toads, as well as at the macroevolutionary scale across numerous taxa that 47 produce their own defensive toxins.</p>
Processed data for "SpotClean adjusts for spot swapping in spatial transcriptomics data"
<p>This repo contains processed data to reproduce results in the paper "SpotClean adjusts for spot swapping in spatial transcriptomics data".</p>
Co-transcriptomic analysis of the maize-western corn rootworm interaction
<p>We used a maize genotype, Mp708, that is resistant to a large guild of herbivore pests to study the underlying plant defense signaling network between below and aboveground tissues. We also evaluated WCR compensatory transcriptome responses. Using RNA-seq, we profiled the transcriptome of roots and leaves that interacted with WCR infestation up to 5 days post infestation (dpi). Our results suggest that several maize defense genes were induced in roots, whereas WCR defense genes were downregulated at 5 dpi. These findings indicate a dynamic transcriptomic dialog between WCR and WCR-infested maize plants.</p>
Saccharisymbium: Proteome and transcriptome information
<p>Supplementary information to proteome and transcriptome expression analysis of <em>Candidatus</em> Saccharisymbium, an alphaproteobacterial symbiont of gutless oligochaetes.</p>
Transcriptome data of maize leaves inoculated with water and Colletotrichum graminicola
<p>The experiment was conducted in the greenhouse of Plant Protection Institute, Chinese Academy of Agricultural Sciences in 2019. The transcriptome sequencing was performed on a typical maize variety B73. For biological stress treatment, the maize leaves were inoculated with water and <em>Colletotrichum graminicola</em><em>. </em>The first sampling of maize leaves was performed at 0 h after treatment of water. Subsequent samples of maize leaves were taken at 24h, 40h, 60h and 96h after inoculation with <em>C. graminicola</em>. Three biological replicates of leaf samples were collected at each time point. Total RNA was extracted from leaf samples using Trizol method. Then, the RNA was analyzed by agrose gel electrophoresis. RNA-seq libraries was finally constructed with the Illumina standard mRNASeq Prep Kit (TruSeq RNA and DNA Sample Preparation Kits).</p>
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