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699 results for “association transcriptomics”
Genome- and transcriptome-wide association summary statistics for outcome from traumatic brain injury
<p>The dataset contains summary statistics for the genome- and transcriptome-wide association studies (GWAS, TWAS) of genetic effects on outcome in traumatic brain injury (TBI). The study participants attended hospital within 24 hours of TBI, and underwent head computed tomography imaging.</p> <p><strong>Study participants</strong></p> <p>European ancestry data set contains 4710 individuals; multi-ethnic cohort 5268 individuals, including Europeans (n = 4710), Africans (n = 245) and Admixed Americans (n = 313).</p> <p>The largest European population contribution was from CENTER-TBI (Collaborative European NeuroTrauma Effectiveness Research, https://www.center-tbi.eu), where each participating center (60 centers from 20 countries in Europe) recruited patients between December 2013 and December 2017. The patients recruited in CENTER-TBI were supplemented by subjects from cohorts recruited at two European centres (Cambridge, UK, and Turku, Finland).</p> <p>The majority of patients in the US cohort were recruited between 2014 and 2018 to TRACK-TBI (Transforming Research and Clinical Knowledge in TBI, https://tracktbi.ucsf.edu) by the 18 US participant sites. The subjects recruited to the US cohort from TRACK-TBI were supplemented by patients recruited to an institutional research initiative at Mass General Brigham (MGB).</p> <p><strong>Outcome definition</strong></p> <p>Outcomes were measured using the extended Glasgow Outcome Scale (GOSE), ranging from 1 (dead) to 8 (upper good recovery), measured 6 months post-TBI. TBI severity was specified using the Glasgow Coma Score (GCS), with TBI classified as mild (GCS 13-15), moderate (GCS 9-12), or severe (GCS 3-8).</p> <p>To account for the effect of injury severity on outcome, sliding dichotomization was used to categorize outcome as favourable or unfavourable. A GOSE ≤ 4 was used to define an unfavourable outcome for patients with either moderate (GCS 9-12) or severe (GCS 3-8) TBI, while the unfavourable group was extended to patients with GOSE ≤ 7 if they had mild (GCS 13-15) TBI.</p> <p><strong>Genotype data and imputation</strong></p> <p>Genotyping was completed at FIMM Technology Center for CENTER-TBI, Cambridge, Turku patients and the Broad Institute for TRACK-TBI, using the Illumina Global Screening Array (GSA-24v2-0 + Multi-Disease). The MGB cohort were genotyped using Illumina’s Multi-Ethnic Global array (MEGA) and the pre-releases forms, including MEGA and MEGA-Ex arrays at Illumina at the MGB Translational Genomics Core.</p> <p>A unified quality control procedure was applied for each study cohort and the array-based genotypes were imputed using the Haplotype Reference Consortium panel. Autosomal chromosomes were considered, post-imputation data was filtered by imputation quality (INFO > 0.4 for CENTER-TBI, Cambridge and Turku; R2 > 0.4 for TRACK-TBI and MGB) and MAF > 1%.</p> <p><strong>Genome-wide association analysis and meta-analysis</strong></p> <p>Genome-wide single-marker scans were performed using a penalized likelihood-based Firth logistic regression, and implemented in PLINK v2.0. Using favourable outcome as reference, models were fitted on the basis of imputed allelic dosages. Age, sex, major extracranial injury, pupillary reactivity, and the first 10 principal components were included as covariates. Study cohort (CENTER-TBI, Cambridge, Turku) was an additional covariate in the CENTER-TBI GWAS.</p> <p>Fixed-effects meta-analysis of the three European ancestry GWAS was performed using METAL. For trans-ethnic meta-analysis, summary statistics of five GWASs in patients of European, African and Admixed Americans were aggregated via MR-MEGA.</p> <p><strong>Transcriptome-wide association study</strong></p> <p>Genetically regulated gene expression (GREx) was imputed using a regression model fitted on a separate gene expression database. Elastic net models provided by PrediXcan for all available GTEx brain tissues and whole blood were used. For TWAS, the same sliding dichotomy model for outcome with the same set of covariates as in the GWAS, but PCA components were replaced with the top five principal components of the respective gene expression data. </p> <p><strong>Column headers - GWAS</strong></p> <p>rsID: variant rsID<br> Chrom: chromosome<br> Pos: position (build GRCh38)<br> A1: effect allele<br> A2: reference allele<br> EAF: allele frequency of effect allele<br> Effect: effect size of effect allele<br> StdErr: standard error of effect size<br> P: p value of association (with genomic correction)<br> N: sample size</p> <p>Note. 'Effect' and 'StdErr' are only available for the European ancestry meta-analysis.</p> <p><br> <strong>Column headers - TWAS</strong></p> <p>tissue: GTEx tissue type<br> id: ensembl gene id<br> coef: model coefficient<br> se: model standard error for coefficient<br> p: model-based p value<br> symbol: gene symbol<br> name: gene name written out<br> chr: chromosome<br> start: gene start position (build GRCh38)</p>
GWAS to single cell: Intersecting single-cell transcriptomics and genome wide association studies identifies crucial cell-populations and candidate genes for atherosclerosis.
<p><strong>Background</strong></p> <p>Genome-wide association studies (GWAS) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation of susceptibility loci into biological mechanisms and targets for drug discovery remains challenging. Intersecting genetic and gene expression data has led to identification of candidate genes. However, the assayed tissues are often non-diseased and heterogeneous in cell composition confounding the candidate prioritization. We collected single-cell transcriptomics (scRNA-seq) from atherosclerotic plaques and aimed to identify cell-type-specific expression of disease-associated genes. </p> <p> </p> <p><strong>Methods and Results</strong></p> <p>To identify disease-associated candidate genes, we applied gene-based analyses using GWAS summary statistics from 46 atherosclerotic, cardiometabolic, and other traits. Next we intersected these candidates with single-cell transcriptomics (scRNA-seq) to identify those genes that are specifically expressed in individual cell (sub)populations of atherosclerotic plaques. We derive an enrichment score and show that loci that associated with coronary artery disease demonstrated a prominent substrate in plaque smooth muscle cells (<em>SKI</em>, <em>KANK2</em>, <em>SORT1</em>), endothelial cells (<em>SLC44A1</em>, <em>ATP2B1</em>), and macrophages (<em>APOE</em>, <em>HNRNPUL1</em>). Further sub clustering of SMC-subtypes revealed genes in risk loci for coronary calcification specifically enriched in a synthetic cluster of SMCs. To verify the robustness of our approach, we used liver-derived scRNAseq-data and showed enrichment of circulating lipids-associated loci in hepatocytes.</p> <p><br> <strong>Conclusion</strong></p> <p>We confirm known gene-cell pairs relevant for atherosclerotic disease, and discovered novel pairs pointing to new biological mechanisms amenable for therapy. We present an intuitive single-cell transcriptomics driven workflow rooted in human large-scale genetic studies to identify putative candidate genes and affected cells associated with cardiovascular traits.</p> <p> </p>
Processed data used in transcriptome- metabolome-wide association study
<p>Processed_RNASeq_RPKM.txt contains RPKM levels for 45484 genes quantified in 555 individuals from RNA-Seq of lymphoblastoid cell lines (LCLs).</p> <p>Processed_NMRpeaks_baseline.txt contains binned, normalized and standardised (z-scored) NMR peak intensities for 1276 bins quantified in 555 individuals from urine samples taken at baseline. NMR spectra were acquired at 300 K on a Bruker 16.4 T Avance II 700 MHz NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) using a standard 1H detection pulse sequence with water suppression. The spectra were referenced to the TSP signal and phase and baseline corrected.</p> <p>Processed_NMRpeaks_followup.txt contains binned, normalized and standardised (z-scored) NMR peak intensities for 1289 bins quantified in 315 individuals from urine samples taken during follow-up. NMR spectra were acquired with an Avance III HD 600 NMR spectrometer. Spectra were referenced to the TSP signal and phase and baseline corrected.</p> <p>More details on the data set can be found in Sönmez Flitman et al. (doi: https://doi.org/10.1101/2020.05.22.110197).</p>
Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures
<p>Processed data associated with the manuscript DOI: <a href="https://doi.org/10.1016/j.xgen.2023.100421" target="_blank" rel="noopener">10.1016/j.xgen.2023.100421 </a></p> <p>Analysis code on GitHub: <a href="../doi/10.5281/zenodo.8253917" target="_blank" rel="noopener">10.5281/zenodo.8253917</a></p> <p> </p>
Memory B Cells and Their Transcriptomic Profiles Associated with Belimumab Resistance in Systemic Lupus Erythematosus in the Maintenance Phase
<p>Normalized count of RNA sequences from systemic lupus erythematosus patients treated with belimumab.</p> <p>We collected blood samples from patients with systemic lupus erythematosus (n=44) before and approximately 3 and 6 months after treatment by belimumab, respectively. We also included healthy individuals (n=17). There were no missing samples in the before-treatment specimen for 44 patients; however, five samples were missing at the three-month time, and four were missing at the six-month time after treatment. Whole blood samples were stored in PAXgene tubes (QUIAGEN). RNA was extracted by PAXgene Blood RNA Kit (QUIAGEN). Library preparation was performed using TruSeq stranded Total RNA Library PrepKit with Ribo-Zero Globin Human. Sequencing was conducted by NovaSeq6000 in the 100-bp paired-end mode. Sequencing reads were trimmed using Trimmomatic ver. 0.36 (leading: 20, trailing: 20, slidingwindow: 4:15, minlen: 36) and aligned to hg38 reference genome using STAR (ver. 2.7.3a). Gene counts were generated by RSEM (ver. 1.3.1) using Homo_sapiens.GRCh38.95.gtf from the Ensembl database. Gene counts were normalized by size factor implemented in DESeq2 and converted to count per million (CPM), and log<sub>2</sub>(CPM+1) was calculated. </p> <p>Sample names starting with 'R_' indicate responders, while those ending with '_NR' indicate non-responders. '0M' represents before treatment, '3M' represents three months after treatment, and '6M' represents six months after treatment. The numeric ID, excluding the last two characters, represents individual patients.</p> <p> </p> <p>If you use this data, please cite Iwasaki et al. Front. Immunol., 04 February 2025 <a href="https://doi.org/10.3389/fimmu.2025.1506298">https://doi.org/10.3389/fimmu.2025.1506298</a></p>
Natural Killer cells demonstrate distinct eQTL and transcriptome-wide disease associations, highlighting their role in autoimmunity.
<p><strong>Abstract</strong> </p> <p>Natural Killer (NK) cells are innate lymphocytes with central roles in immunosurveillance and are implicated in autoimmune pathogenesis. The degree to which regulatory variants affect NK gene expression is poorly understood. We performed expression quantitative trait locus (eQTL) mapping of negatively selected NK cells from a population of healthy Europeans (n=245). We find a significant subset of genes demonstrate eQTL specific to NK cells and these are highly informative of human disease, in particular autoimmunity. An NK cell transcriptome-wide association study (TWAS) across five common autoimmune diseases identified further novel associations at 27 genes. In addition to these <em>cis</em> observations, we find novel master-regulatory regions impacting expression of <em>trans</em> gene networks at regions including 19q13.4, the Killer cell Immunoglobulin-like Receptor (KIR) Region, <em>GNLY</em>, <em>MC1R</em> and<em> UVSSA</em>. Our findings provide new insights into the unique biology of NK cells, demonstrating markedly different eQTL from other immune cells, with implications for disease mechanisms.</p> <p><strong>Preprint</strong></p> <p>https://www.biorxiv.org/content/10.1101/2021.05.10.443088v1</p> <p><strong>Dataset</strong></p> <p>nk_raw_for_zenodo.txt: Matrix of raw gene expression at 47,209 probes in primary human NK cells from 245 healthy individuals of European ancestry. Gene expression is quantified using the Illumina HumanHT-12 v4 BeadChip gene expression array platform. Column names represent Array Address ID for each probe, and row names represent pseudonymised sample identifiers, which can be matched to sample genotypes. Sample genotypes are available at the European Genome-Phenome Archive with accession ID EGAS00000000109).</p> <p>probes_passing_QC.txt: List of probes passing quality control; probe sequences mapping to a unique genomic locus, and probe sequences not containing common genomic variation (minor allele frequency >1%), n=29,002. Column names are Array Address ID (probeID), Ensembl ID (ensembl), Gene ID (gene), and Illumina probe ID (ilmn). </p>
Transcriptomic analysis of deceptively pollinated Arum maculatum (Araceae) reveals association between terpene synthase expression in floral trap chamber and species-specific pollinator attraction
<p>A compressed folder containing the R script and input files required to replicate the results in our manuscript entitled "Transcriptomic analysis of deceptively pollinated <em>Arum maculatum</em> (Araceae) reveals association between terpene synthase expression in floral trap chamber and species-specific pollinator attraction".</p> <p>Note: Raw Illumina sequencing files associated with this study have been uploaded to NCBI SRA, under the BioProject accession PRJNA856436.</p> <p><strong>ABSTRACT</strong></p> <p>Deceptive pollination often involves volatile organic compound (VOC) emissions that mislead insects into performing non-rewarding pollination. Among deceptively pollinated plants, <em>Arum maculatum</em> is particularly well-known for its potent dung-like VOC emissions and specialized floral chamber, which traps pollinators – mainly <em>Psychoda phalaenoides</em>and <em>P. grisescens</em> – overnight. However, little is known about the genes underlying the production of many <em>A. maculatum</em>VOCs, and their influence on variation in pollinator attraction rates. Therefore, we performed <em>de novo</em> transcriptome sequencing of <em>A. maculatum</em> appendix and male floret tissue collected during- and post-anthesis, from ten natural populations across Europe. These RNA-seq data were paired with GC-MS analyses of floral scent composition and pollinator data collected from the same inflorescences. Differential expression analyses revealed candidate transcripts in appendix tissue linked to malodourous VOCs including indole, <em>p</em>-cresol, and 2-heptanone. Additionally, we found that terpene synthase expression in male floret tissue during anthesis significantly covaried with sex- and species-specific attraction of <em>Psychoda phalaenoides</em> and <em>P.</em> <em>grisescens</em>. Taken together, our results provide the first insights into molecular mechanisms underlying pollinator attraction patterns in <em>A. maculatum</em>, and highlight floral chamber sesquiterpene (<em>e.g.</em>bicyclogermacrene) synthases as interesting candidate genes for further study.</p>
Fig. 2 in Subtle transcriptomic response of Eurasian perch (Perca fluviatilis) associated with Triaenophorus nodulosus plerocercoid infection
Fig. 2. MA and volcano plots comparing infected and uninfected spleen samples (a & b) and liver samples (c & d). In the MA plots (a & c), the log counts and the log fold change are represented on the x- and y-axis, respectively. For each volcano plot (b & d), log fold change is represented on the x-axis and the –log10 p-value on the y-axis, respectively. Positive fold change corresponds to upregulated genes in infected individuals.
Fig. 1. Differentially expressed genes between infected and uninfected P in Subtle transcriptomic response of Eurasian perch (Perca fluviatilis) associated with Triaenophorus nodulosus plerocercoid infection
Fig. 1. Differentially expressed genes between infected and uninfected P. fluviatilis in a) spleen and b) liver tissues. Filled-in and empty boxes on the top of each plot represent infected and uninfected individuals, respectively. N/A indicates unknown protein.
ATACSeq fastq files associated with the manuscript entitled 'Interspecies transcriptome analyses identify genes that control the development and evolution of limb skeletal proportion'
<p>This next-generation sequencing dataset is associated with the research manuscript entitled ‘<em>Interspecies transcriptome analyses identify genes that control the development and evolution of limb skeletal proportion</em>’ (https://www.biorxiv.org/content/10.1101/754002v2).</p> <p>The zipped folder ‘<strong>Zenodo_Saxena_etal_2021_ATACSeq_FastqFiles</strong>’ contains raw/unprocessed ATACSeq Fatsq read files for postnatal day 5 (P5) mouse (Mus) and jerboa (Jac) cartilage samples (Metatarsal = MT; Radius/Ulna = RU).</p> <p>> The <strong>Jac_P5</strong> subfolder contains paired-end reads (R1 and R2) for three jerboa metatarsals (MT1-3) and radius/ulna (RU1-3) biological replicates.</p> <p>> The <strong>Mus_P5</strong> subfolder contains paired-end reads (R1 and R2) for two mouse metatarsals (MT1-2) and radius/ulna (RU1-2) biological replicates.</p>
Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations
<p>This Zenodo file collection contains transcriptome prediction models built for PrediXcan, as well as concatenated raw results from S-PrediXcan. Files belong to the research paper "Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations" published at HGG Advances (2023, doi: 10.1016/j.xhgg.2023.100216). Please refer to the README file for a more detailed description.</p>
Genome-wide association results from: Transcriptomic stratification of late-onset Alzheimer's cases reveals novel genetic modifiers of disease pathology
<p>Late-Onset Alzheimer's disease (LOAD) is a common, complex genetic disorder well-known for its heterogeneous pathology. The genetic heterogeneity underlying common, complex diseases poses a major challenge for targeted therapies and the identification of novel disease-associated variants. Case-control approaches are often limited to examining a specific outcome in a group of heterogenous patients with different clinical characteristics. Here, we developed a novel approach to define relevant transcriptomic endophenotypes and stratify decedents based on molecular profiles in three independent human LOAD cohorts. By integrating post-mortem brain gene co-expression data from 2114 human samples with LOAD, we developed a novel quantitative, composite phenotype that can better account for the heterogeneity in genetic architecture underlying the disease. We used iterative weighted gene co-expression network analysis (WGCNA) to reduce data dimensionality and to isolate gene sets that are highly co-expressed within disease subtypes and represent specific molecular pathways. We then performed single variant association testing using whole genome-sequencing data for the novel composite phenotype in order to identify genetic loci that contribute to disease heterogeneity. Distinct LOAD subtypes were identified for all three study cohorts (two in ROSMAP, three in Mayo Clinic, and two in Mount Sinai Brain Bank). Single variant association analysis identified a genome-wide significant variant in <i>TMEM106B</i> (p-value < 5´10<sup>-8</sup>, rs1990620<sup><span><span>G</span></span></sup>) in the ROSMAP cohort that confers protection from the inflammatory LOAD subtype. Taken together,<b> </b>our novel approach can be used to stratify LOAD into distinct molecular subtypes based on affected disease pathways.</p>
Supplementary information associated with a "Whole-Organism Integrated DNA Methylation and Transcriptomics Analysis of Butterfly Metamorphosis".
<p>Supplementary information, annotation and code related to the manuscript studying <em>Bicyclus anynana</em> development entitled "Whole-Organism Integrated DNA Methylation and Transcriptomics Analysis of Butterfly Metamorphosis".</p>
Regulatory T cell therapy is associated with distinct immune regulatory lymphocytic infiltrates in kidney transplants: Spatial transcriptomic dataset and images
<p>The outputs of the NanoString GeoMx DSP platform were concatenated into three xlsx files, each illustrating a separate experiment along with their sample annotations. This technique analyzes protein or RNA abundance within regions of interest (ROIs) or specific cell segments selected based on histological features and immunofluorescence. In this repository, the concatenated GeoMx output files are presented, along with PowerPoint presentations for each biopsy that show immunofluorescence images of the selected ROIs and/or cell segments.</p> <ul> <li><strong>Protein_Full ROI:</strong> This experiment measured the abundance of 41 proteins in discrete regions of interest (ROIs) within transplant kidney biopsies.</li> <li><strong>Protein_Rare cell:</strong> This experiment measured the abundance of 40 proteins in specific cell segments, such as CD4+FoxP3- cells vs. CD4+FoxP3+ cells, within transplant kidney biopsies.</li> <li><strong>RNA:</strong> This experiment measured the abundance of 90 genes in discrete ROIs within transplant kidney biopsies.</li> </ul>
Liver and Blood Expression Prediction Generated for Opportunities and challenges for transcriptome-wide association studies
<p>db files are in sqlite format and contain weights to calculate genetic predictors of gene expression</p> <p>Blood, Liver indicate the tissue where the RNA was extracted from</p> <p>Crohns and LDL indicate that prediction models were generated using SNPs available in the GWAS summary results used in the paper (Wainberg et al <a href="https://doi.org/10.1038/s41588-019-0385-z">https://doi.org/10.1038/s41588-019-0385-z</a>)</p> <p>*txt.gz filex contain covariances between SNPs needed to run the summary version of PrediXcan or other TWAS methods</p> <p>Information about the RNAseq data can be found here https://pubmed.ncbi.nlm.nih.gov/27540175/</p> <p>Sample size n=522</p>
Experiences of Discrimination are Associated with Microbiome and Transcriptome Alterations in the Gut
<p>Experiences of Discrimination are Associated with Microbiome and Transcriptome Alterations in the Gut. Human stool processed microbiome and transcriptomic data. </p>
Dataset associated with A. Hallou, R. He, et al. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894
<p>Dataset associated with:</p> <p>Adrien Hallou, Ruiyang He, Benjamin David Simons and Bianca Dumitrascu. A computational pipeline for spatial mechano-transcriptomics. bioRxiv 2023.08.03.551894; doi: <a href="https://doi.org/10.1101/2023.08.03.551894">https://doi.org/10.1101/2023.08.03.551894</a></p> <p>Licence</p> <p>This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial 4.0 International License</a>.</p>
RNASeq fastq files associated with the manuscript entitled 'Interspecies transcriptome analyses identify genes that control the development and evolution of limb skeletal proportion'
<p>This next-generation sequencing dataset is associated with the research manuscript entitled ‘<em>Interspecies transcriptome analyses identify genes that control the development and evolution of limb skeletal proportion</em>’ (https://www.biorxiv.org/content/10.1101/754002v2).</p> <p>The folder ‘<strong>Zenodo_Saxena_etal_2021_RNASeq_FastqFiles</strong>’ contains raw/unprocessed RNASeq Fatsq read files for postnatal day 5 (P5) mouse (Mus) and jerboa (Jac) cartilage samples (Metatarsal = MT; Radius/Ulna = RU).</p> <p>> The <strong>Jac_P5</strong> subfolder contains single-end reads (R1) for five jerboa metatarsals (MT1-5) and radius/ulna (RU1-5) biological replicates. Jac_MT1-3 and Jac_RU1-3 were used in the primary differential expression analysis (n=3). Jac_MT4-5 and Jac_RU4-5 were used for independent validation (n=2) of the the primary analysis. </p> <p>> The <strong>Mus_P5</strong> subfolder contains single-end reads (R1) for five mouse metatarsals (MT1-5) and radius/ulna (RU1-5) biological replicates. Mus_MT1-3 and Mus_RU1,3 & 4 were used in the primary differential expression analysis (n=3). Mus_MT4-5 and Mus_RU4 & 5 were used for independent validation (n=2) of the the primary analysis. </p>
Neonatal exposure to BPA, BDE-99, and PCB produces persistent changes in hepatic transcriptome associated with gut dysbiosis in adult mouse livers
<p class="western"><span><span><span><b>Background</b>. Recent evidence suggests that multigenic and complex environmentally modulated diseases result from early life exposure to toxicants at least partly via gut microbial influences. Environmental toxicants, polybrominated diphenyl ethers (PBDEs), and polychlorinated biphenyls (PCBs) are breast milk-enriched persistent organic pollutants (POPs) and thus remain a continuing threat to human health despite being banned from production. Recent findings focused on the liver developmental reprogramming capabilities from neonatal BPA exposure; however, little is known on how PBDEs and PCBs regulate the liver transcriptome with respect to the gut microbiome. </span></span></span></p> <p class="western"><span><span><span><b>Objectives</b>. We investigated whether the gut microbiome can be persistently reprogrammed with the liver following neonatal exposure to POPs, and whether microbial biomarkers associated with disease-prone changes in the hepatic epigenetic and transcriptomic landscape in adulthood. </span></span></span></p> <p class="western"><span><span><span><b>Methods. </b>C57BL/6 male and female mouse pups were orally administered vehicle, bisphenol A (BPA), BDE-99 (a breast milk-enriched PBDE congener), or the Fox River PCB mixture (an environmentally relevant PCB mixture), between postnatal day (PND) 2 to 4, once daily for three consecutive days. Tissues were collected at PND5 and PND60 for 16S rDNA sequencing and targeted metabolomics.</span></span></span></p> <p class="western"><span><span><span><b>Results</b>. Neonatal exposure to BDE-99, followed by BPA and PCB, produced the greatest persistent changes in the adult hepatic transcriptome, including an inflammation and cancer-prone transcriptomic signature. BDE-99 exposure resulted in a persistent increase in <i>Akkermansia muciniphila</i> throughout the intestinal sections and feces. We observed persistent increases in acetate and succinate, metabolites <i>A. muciniphila</i><span> is able to produce.</span> Correspondingly, liver H3K4me1 and H3K27 acetylation were enriched around the loci encoding liver cancer-related genes following neonatal BDE-99 exposure. </span></span></span></p> <p class="western"><span><span><span><b>Conclusion. </b><span>Similar to BPA, early life exposure to BDE-99 also produced a cancer-prone hepatic transcriptomic signature corresponding to an increase in permissive epigenetic signatures around cancer-related genes in adulthood. This positively associates with BDE-99 mediated increase in </span><i><span>A. muciniphila</span></i><span> and its metabolites which are established epigenetic modifiers. </span></span></span></span></p>
Dataset for the transcriptome analysis of hippocampal subfields identifies gene expression profiles associated with long-term active place avoidance memory
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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)
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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.