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679 results for “Gut microbiome”
Additions to AGORA2 made for Shaaban et al, "Personalized modeling of gut microbiome metabolism throughout the first year of life"
<p>This dataset contains expansions made to AGORA2 (https://www.nature.com/articles/s41587-022-01628-0) and published in Shaaban et al, "Personalized modeling of gut microbiome metabolism throughout the first year of life", in press.</p> <p>Included are:</p> <ul> <li>289 additional genome-scale reconstructions built for genomes not included in AGORA2</li> <li>250 genome-scale reconstructions from AGORA2 endapnded with human milk oligosaccharide degradation pathways</li> </ul> <p>In this version, slight updates have been made to the 289 additional genome-scale reconstructions based on additional experimental data.</p>
Comparative analysis of Parkinson's and inflammatory bowel disease gut microbiomes reveals shared butyrate-producing bacteria depletion
<p><strong>Abstract: </strong>Epidemiological studies reveal that inflammatory bowel disease (IBD) is associated with an increased risk of Parkinson’s disease (PD). Gut dysbiosis has been documented in both PD and IBD, however it is currently unknown whether gut dysbiosis underlies the epidemiological association between both diseases. To identify shared and distinct features of the PD and IBD microbiome, we recruited 54 PD, 26 IBD, and 16 healthy control individuals and performed the first joint analysis of gut metagenomes. Larger, publicly available PD and IBD metagenomic datasets were also analyzed to validate and extend our findings. Depletions in short-chain fatty acid (SCFA)-producing bacteria, including <em>Roseburia intestinalis, Faecalibacterium prausnitzii, Anaerostipes hadrus</em>, and <em>Eubacterium rectale</em>, as well depletion in SCFA-synthesis pathways were detected across PD and IBD datasets, suggesting that depletion of these microbes in IBD may influence the risk for PD development.</p> <p><strong>Zenodo contents:</strong> In this Zenodo archive we provide the post-QC and taxonomic and functional profiling "Source Data" used in all downstream analyses to generate tables and figures seen in our manuscript. We also provide the link to our GitHub repository where we have stored the code used to perform the bioinformatic processing of the shotgun metagenomic sequences and stastical analyses. Individual sample raw shotgun metagenomic sequences and metadata from our UFPF dataset are available on NCBI Sequence Read Archive (SRA) under BioProject <a href="https://www.ncbi.nlm.nih.gov/bioproject/1096686">PRJNA1096686</a>. </p>
gut_microbiome_shift_and_resistome_diversity
<p>Original Data, Code and Result files in our research.</p> <p> </p> <p>Please read the <strong>README.md</strong> in this repository or our github page: https://github.com/yhWu815/gut_microbiome_shift_and_resistome_diversity</p> <p> </p> <p> </p>
Data from: Chicken gut microbiome members limit the spread of an antimicrobial resistance plasmid in Escherichia coli
<p>Plasmid-mediated antimicrobial resistance is a major contributor to the spread of resistance genes within bacterial communities. Successful plasmid spread depends upon a balance between plasmid fitness effects on the host and rates of horizontal transmission. While these key parameters are readily quantified in vitro, the influence of interactions with other microbiome members is largely unknown. Here, we investigated the influence of three genera of lactic acid bacteria (LAB) derived from the chicken gastrointestinal microbiome on the spread of an epidemic narrow-range ESBL resistance plasmid, IncI1 carrying <em>bla<sub>CTX-M-1</sub></em>, in mixed cultures of isogenic <em>Escherichia coli </em>strains. Secreted products of LAB decreased <em>E. coli</em> growth rates in a genus-specific manner but did not affect plasmid transfer rates. Importantly, we quantified plasmid transfer rates by controlling for density-dependent mating opportunities. Parametrization of a mathematical model with our in vitro estimates illustrated that small fitness costs of plasmid carriage may tip the balance towards plasmid loss under growth conditions in the gastrointestinal tract. This work shows that microbial interactions can influence plasmid success and provides an experimental-theoretical framework for further study of plasmid transfer in a microbiome context.</p>
16S rRNA V4 gut microbiome of Leptonycteris yerbabuenae in Mexico
<p>Migratory animals live in a world of constant change. Animals undergo many physiological changes preparing themselves for the migration. Although this field has been extensively studied over the last decades, we know relatively little about the seasonal changes that occur in the microbial communities that these animals carry in their guts. Here we assessed the V4 region of the 16S rRNA high-throughput sequencing data as a proxy to estimate microbiome diversity of Tequila Bats from fecal pellets and evaluate how the natural process of migration shapes the microbiome composition, and diversity. We collected samples from individual bats at two localities in the Dry Forest biome (Chamela and Coquimatlán) and one site at the end point of the migration in the Sonoran Desert (Pinacate). We found that the gut microbiome of the Tequila bats is largely dominated by Firmicutes and Proteobacteria. Our data also provide insights on how microbiome diversity shifts at the same site in consecutive years. <em>Our study has demonstrated that both locality and year-to-year variation contribute to shaping the composition, overall diversity, and the 'uniqueness' of the gut microbiome of migratory nectar-feeding female bats with localities from the dry forest biome looking more like each other when compared to the desert biome.</em> In terms of beta diversity, our data show a stratified effect in which the samples locality was the strongest factor influencing the gut microbiome, but with significant variation between consecutive years at the same locality.</p>
Twenty-five metagenome assembled genomes recovered from the gut microbiome of the domestic ferret, Mustela putorius
<p>This dataset is composed of 25 unique metagenome assembled genomes (MAGs) recovered from the gut microbiome of three domestic ferrets (<em>Mustela putorius</em>). Details on both MAG and host ferret metadata, as well as information on sample collection, DNA sequencing, and bioinformatic processing can be found in the American Society for Microbiology Resource Announcement by Amundson et al. (in prep). </p>
Differences in gut microbiome abundances and diversity by physical activity levels and BMI among patients with colorectal cancer
<p>We investigated associations of physical activity, BMI, and combinations of physical activity levels/BMI with gut microbiome diversity and differential abundances among colorectal cancer patients. Pre-surgery stool samples from 179 colorectal cancer patients were used to perform 16S rRNA gene sequencing.</p>
Habitat shapes diversity of gut microbiomes in a wild population of blue tits Cyanistes caeruleus
<p>Microbiome constitutes and important axis of individual variation that, together with genes and the environment, influences an individual's physiology and fitness. Microbiomes are dependent not only on an individual's body condition but also on external factors, such as diet or stress levels, and as such can be involved into feedbacks between the external ecological factors and internal physiology. In our study we used a wild population of blue tits (Cyanistes caeruleus) to investigate the impact of external habitat composition on the microbiome of adult birds. We hypothesized, that – through differences in plant composition, potentially affecting diet complexity – habitat type may impact the diversity and structure of the gut microbiome. Blue tits breeding in dense deciduous forests tended to have more diverse microbiomes, and significantly different in terms of microbiome composition from birds breeding in open, sparsely forested hay meadows. Distinct study plots also tended to differ in a number of parameters describing microbiome diversity. We observed no microbiome differentiation according to individual characteristics such as sex or age. The study emphasizes, that external environment is one of the important modulators of microbiome diversity and calls for more such studies in wild animal populations.</p>
The profile of the gut microbiome in gliomas patients
<p>Through 16S rRNA sequecing of fecal samples from gliomas patients, we found the characteristics of the gut microbiome proflie.</p>
The gut microbiome of wild American marten in the Upper Peninsula of Michigan
<p>Directory Information for The gut microbiome of wild American marten in the Upper Peninsula of Michigan<br> #######</p> <p>"R Code" contains:</p> <p>--- "marten_phyobj.rds" is the phyloseq object that can be directly imported for statistical analysis if the user prefers not to go entire QIIME2 pipeline. The phyloseq object was created from QIIME2 artifacts from the "QIIMEpipe.html" pipeline: the cleaned rooted tree, the cleaned taxonomy table and the cleaned ASV table. <strong>This requires the command "readRDS()" to import. <em>The "load()" command will not work.</em></strong></p> <p><br> --- "Stat.Rmd" Markdown file for "Stat.html"</p> <p>--- "Stat.html" knitted statistical analysis file to view the studies outputs quickly</p> <p>--- "Stat.R" R code if user prefers over Rmarkdown</p> <p>#######</p> <p>"QIIME" contains:<br> --- "martendemux.qza" demultiplexed QIIME2 artifact</p> <p>--- "martendemux.qzv" visualization output of demultiplexed sequence that can be viewed at qiimeview.org</p> <p>--- "MartenMeta.tsv" metadata file for QIIME2 pipeline and statistical analysis</p> <p>--- "QIIMepipe.html" code for bioinformatic pipeline for downstream analysis</p> <p>- "Sequences" folder:<br> --- "R1_demultiplxed_pairedend_marten.fastq.gz" forward reads of demultiplexed, EMP paired end sequences (Illumina Miseq) if the user prefers to use another bioinformatic platform besides QIIME2.</p> <p>--- "R2_demultiplxed_pairedend_marten.fastq.gz" reverse reads of demultiplexed, EMP paired end sequences (Illumina Miseq) if the user prefers to use another bioinformatic platform besides QIIME2.<br> </p>
The gut microbiome variability of a butterflyfish increases on severely degraded Caribbean reefs
<p>Environmental degradation has the potential to alter key mutualisms that underlie the structure and function of ecological communities. How microbial communities associated with fishes vary across populations and in relation to habitat characteristics remains largely unknown despite their fundamental roles in host nutrition and immunity. We find significant differences in the gut microbiome composition of a facultative coral-feeding butterflyfish (Chaetodon capistratus) across Caribbean reefs that differ markedly in live coral cover (∼0–30%). Fish gut microbiomes were significantly more variable at degraded reefs, a pattern driven by changes in the relative abundance of the most common taxa potentially associated with stress. We also demonstrate that fish gut microbiomes on severely degraded reefs have a lower abundance of Endozoicomonas and a higher diversity of anaerobic fermentative bacteria, which may suggest a less coral dominated diet. The observed shifts in fish gut bacterial communities across the habitat gradient extend to a small set of potentially beneficial host associated bacteria (i.e., the core microbiome) suggesting essential fish-microbiome interactions may be vulnerable to severe coral degradation.</p>
Gut microbiome analysis of high fat diet- and control-fed on PXR-KO mice
<p>Nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease due to the current epidemics of obesity and diabetes. The pregnane X receptor (PXR) is a xenobiotic-sensing nuclear receptor known for trans-activating liver genes involved in drug metabolism and transport, and more recently implicated in energy metabolism. The gut microbiota can modulate the host xenobiotic biotransformation and contribute to the development of obesity. While the male sex confers a higher risk for NAFLD than women before menopause, the mechanism remains unknown. We hypothesized that the presence of PXR promotes obesity by modifying the gut-liver axis in a sex-specific manner. Male and female C57BL/6 (wild-type/WT) and PXR-knockout (PXR-KO) mice were fed control or high fat diet (HFD) for 16-weeks. Serum parameters, liver histopathology, transcriptomic profiling, 16S-rDNA sequencing, and bile acid (BA) metabolomics were performed. PXR enhanced HFD-induced weight gain, hepatic steatosis and inflammation especially in males, accompanied by PXR-dependent up-regulation in hepatic genes involved in microbial response, inflammation, oxidative stress, and cancer; PXR-dependent increase in intestinal Firmicutes/Bacteroides ratio (hallmark of obesity) and the pro-inflammatory Lactobacillus, as well as a decrease in the anti-obese Allobaculum and the anti-inflammatory Bifidobacterum, with a PXR-dependent reduction of beneficial BAs in liver. The resistance to NAFLD in females may be explained by PXR-dependent decrease in pro-inflammatory bacteria (<em>Ruminococcus gnavus and Peptococcaceae</em>). In conclusion, PXR exacerbates hepatic steatosis and inflammation accompanied by obesity- and inflammation-prone gut microbiome signature, suggesting that gut microbiome may contribute to PXR-mediated exacerbation of NAFLD.</p>
MicrobiomeHD: the human gut microbiome in health and disease
<p><strong>Overview</strong></p> <p>MicrobiomeHD is a standardized database of human gut microbiome studies in health and disease. This database includes publicly available 16S data from published case-control studies and their associated patient metadata. Raw sequencing data for each study was downloaded and processed through a standardized pipeline.</p> <p>To be included in MicrobiomeHD, datasets have:</p> <ul> <li>publicly available raw sequencing data (fastq or fasta)</li> <li>publicly available metadata with at least case and control labels for each patient</li> </ul> <p>Currently, MicrobiomeHD is focused on stool samples. Additional samples may be included in certain datasets, as indicated in the metadata.</p> <p><strong>Files</strong></p> <p>Additional information about the datasets included in this MicrobiomeHD release are in the MicrobiomeHD github repo <a href="https://github.com/cduvallet/microbiomeHD">https://github.com/cduvallet/microbiomeHD</a>, in the file <em>db/dataset_info.yaml</em>. Top-level identifiers correspond to dataset IDs labeled by disease_first-author. For the most part, sample sizes in the yaml file are those that were described in the papers, and may not exactly reflect the actual data (due to missing/extra data, samples which didn't pass quality control, etc).</p> <p>Each dataset was downloaded and processed through a standardized pipeline. The raw processing results are available in the *.tar.gz files here. Each file has the same directory structure and files, as described in the pipeline documentation: <a href="http://amplicon-sequencing-pipeline.readthedocs.io/en/latest/output.html">http://amplicon-sequencing-pipeline.readthedocs.io/en/latest/output.html</a>.</p> <p>Specific files of interest in each *.tar.gz folder include:</p> <ul> <li><strong>summary_file.txt</strong>: this file contains a summary of all parameters used to process the data</li> <li><strong>datasetID.metadata.txt</strong>: the metadata associated with the samples. Note that some samples in the metadata may not have sequencing data, and vice versa.</li> <li><strong>RDP/datasetID.otu_table.100.denovo.rdp_assigned</strong>: the 100% OTU tables with Latin taxonomic names assigned using the RDP classifier (c = 0.5).</li> <li><strong>datasetID.otu_seqs.100.fasta</strong>: representative sequences for each OTU in the 100% OTU table. OTU labels in the OTU table end with <code>d__denovoID</code> - these denovoIDs correspond to the sequences in this file.</li> <li><strong>README.txt</strong>: additional information about steps taken to download and process each dataset, as needed.</li> </ul> <p>The raw data was acquired as described in the supplementary materials of Duvallet et al.'s "Meta analysis of microbiome studies identifies shared and disease-specific patterns" and, when available, the respective dataset README files.</p> <p>Raw sequencing data was processed with the Alm lab's in-house 16S processing pipeline: <a href="https://github.com/thomasgurry/amplicon_sequencing_pipeline">https://github.com/thomasgurry/amplicon_sequencing_pipeline</a></p> <p>Pipeline documentation is available at: <a href="http://amplicon-sequencing-pipeline.readthedocs.io/">http://amplicon-sequencing-pipeline.readthedocs.io/</a></p> <p>Metadata was extracted from the original papers and/or data sources, and formatted manually. When possible, these steps are documented in each dataset's associated README.txt file.</p> <p><strong>Contributing</strong></p> <p>MicrobiomeHD is a resource that can be used to extract disease-specific microbiome signals in individual case-control studies. Many microbes respond non-specifically to health and disease, and the majority of bacterial associations within individual studies overlap with this non-specific response. Researchers should cross-check their results with the data presented here to ensure that their identified microbial associations are specific to their disease under study.</p> <p>We provide an updated list of non-specific microbes here, as well as the raw OTU tables for anyone who wishes to reproduce and adapt this analysis to their study question.</p> <p>If you would like to include your case-control dataset in MicrobiomeHD, please email ejalm[at]mit.edu and duvallet[at]mit.edu.</p> <p>For us to process your data through our standard pipeline, you will need to provide the following files and information about your data:</p> <ul> <li>raw sequencing data in fastq or fasta format (preferably fastq)</li> <li>information about which processing steps will be required (e.g. removing primers or barcodes, merging paired-end reads, etc)</li> <li>sample IDs associated with the sequencing data (either mapped to barcodes still in the sequences, or to each de-multiplexed sequencing file)</li> <li>case/control metadata of each sample</li> <li>other relevant metadata (e.g. sampling site, if not all samples are stool; sampling time point, if multiple samples per patient were taken; etc)</li> </ul> <p>By using MicrobiomeHD in your own analyses, you agree to contribute your dataset to this database and to make your raw sequencing data (i.e. fastq files) publicly available.</p> <p><strong>Citing MicrobiomeHD</strong></p> <p>The MicrobiomeHD database and original publications for each of these datasets are described in Duvallet et al. (2017): <a href="http://dx.doi.org/10.1038/s41467-017-01973-8">http://dx.doi.org/10.1038/s41467-017-01973-8</a></p> <p>Duvallet, C., Gibbons, S. M., Gurry, T., Irizarry, R. A., & Alm, E. J. (2017). Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. <em>Nature communications</em>, 8(1), 1784.</p> <p>If you use any of these datasets in your analysis, please cite both MicrobiomeHD (Duvallet et al. (2017)) and the original publication for each dataset that you use.</p> <p>The code used to process and analyze this data in the paper is available on github: <a href="https://github.com/cduvallet/microbiomeHD">https://github.com/cduvallet/microbiomeHD</a></p> <p><strong>Files</strong></p> <p><em>Data files</em></p> <p><strong>file-S3.nonspecific_genera.txt</strong>: Supplemental Table 3 from Duvallet et al. (2017), listing the non-specific health- and disease-associated microbes.<br> <strong>dataset_info.yaml</strong>: yaml file with additional dataset metadata.</p> <p><em>Datasets</em></p> <p>Note that MicrobiomeHD contains all 28 datasets from Duvallet et al. (2017), as well as additional datasets which did not meet the inclusion criteria for the meta-analysis presented in the paper. Additional information about the datasets included in this MicrobiomeHD release are in the original publications and the MicrobiomeHD github repo https://github.com/cduvallet/microbiomeHD, and in the file <em>dataset_info.yaml</em>.</p> <p>The sample sizes listed here reflect what was reported in the original publications. Some may have discrepancies between what is reported and what is in the actual data due to missing data, quality issues, barcode mismatches, etc.</p> <ul> <li><strong>asd_son_results.tar.gz</strong> (<em>asd_son</em>): NT: 44, ASD: 59 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0137725</li> </ul> </li> <li><strong>autism_kb_results.tar.gz</strong> (<em>asd_kang</em>): H: 20, ASD: 20 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0068322</li> </ul> </li> <li><strong>cdi_schubert_results.tar.gz</strong> (<em>cdi_schubert</em>): H: 155, nonCDI: 89, CDI: 94 <ul> <li>http://dx.doi.org/10.1128/mBio.01021-14</li> </ul> </li> <li><strong>cdi_vincent_v3v5_results.tar.gz</strong> (<em>cdi_vincent</em>): H: 25, CDI: 25 <ul> <li>http://dx.doi.org/10.1186/2049-2618-1-18</li> </ul> </li> <li><strong>cdi_youngster_results.tar.gz</strong> (<em>cdi_youngster</em>): H: 4, CDI: 19 <ul> <li>http://dx.doi.org/10.1093/cid/ciu135</li> </ul> </li> <li><strong>crc_baxter_results.tar.gz</strong> (<em>crc_baxter</em>): adenoma: 198, H: 172, CRC: 120 <ul> <li>http://dx.doi.org/10.1186/s13073-016-0290-3</li> </ul> </li> <li><strong>crc_xiang_results.tar.gz</strong> (<em>crc_chen</em>): H: 22, CRC: 21 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0039743</li> </ul> </li> <li><strong>crc_zackular_results.tar.gz</strong> (<em>crc_zackular</em>): adenoma: 30, H: 30, CRC: 30 <ul> <li>http://dx.doi.org/10.1158/1940-6207.CAPR-14-0129</li> </ul> </li> <li><strong>crc_zeller_results.tar.gz</strong> (<em>crc_zeller</em>): H: 75, CRC: 41 <ul> <li>http://dx.doi.org/10.15252/msb.20145645</li> </ul> </li> <li><strong>crc_zhao_results.tar.gz</strong> (<em>crc_wang</em>): H: 56, CRC: 46 <ul> <li>http://dx.doi.org/10.1038/ismej.2011.109}</li> </ul> </li> <li><strong>edd_singh_results.tar.gz</strong> (<em>edd_singh</em>): STEC: 28, CAMP: 71, SALM: 66, SHIG: 34, H: 75 <ul> <li>http://dx.doi.org/10.1186/s40168-015-0109-2</li> </ul> </li> <li><strong>hiv_dinh_results.tar.gz</strong> (<em>hiv_dinh</em>): H: 16, HIV: 21 <ul> <li>http://dx.doi.org/10.1093/infdis/jiu409</li> </ul> </li> <li><strong>hiv_lozupone_results.tar.gz</strong> (<em>hiv_lozupone</em>): H: 13, HIV: 25 <ul> <li>http://dx.doi.org/10.1016/j.chom.2013.08.006</li> </ul> </li> <li><strong>hiv_noguerajulian_results.tar.gz</strong> (<em>hiv_noguerajulian</em>): H: 34, HIV: 206 <ul> <li>https://doi.org/10.1016%2Fj.ebiom.2016.01.032</li> </ul> </li> <li><strong>ibd_alm_results.tar.gz</strong> (<em>ibd_papa</em>): IBDundef: 1, nonIBD: 24, UC: 43, CD: 23 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0039242</li> </ul> </li> <li><strong>ibd_engstrand_maxee_results.tar.gz</strong> (<em>ibd_willing</em>): CCD: 12, H: 35, ICD: 15, UC: 16, ICCD: 2 <ul> <li>http://dx.doi.org/10.1053/j.gastro.2010.08.049</li> </ul> </li> <li><strong>ibd_gevers_2014_results.tar.gz</strong> (<em>ibd_gevers</em>): H: 31, CD: 224 <ul> <li>http://dx.doi.org/10.1016/j.chom.2014.02.005</li> </ul> </li> <li><strong>ibd_huttenhower_results.tar.gz</strong> (<em>ibd_morgan</em>): H: 18, UC: 48, CD: 62 <ul> <li>http://dx.doi.org/10.1186/gb-2012-13-9-r79</li> </ul> </li> <li><strong>mhe_zhang_results.tar.gz</strong> (<em>liv_zhang</em>): CIRR: 25, H: 26, MHE: 26 <ul> <li>http://dx.doi.org/10.1038/ajg.2013.221</li> </ul> </li> <li><strong>nash_chan_results.tar.gz</strong> (<em>nash_wong</em>): H: 22, NASH: 16 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0062885</li> </ul> </li> <li><strong>nash_ob_baker_results.tar.gz</strong> (<em>nash_ob_zhu</em>): H: 16, NASH: 22, OB: 25 <ul> <li>http://dx.doi.org/10.1002/hep.26093</li> </ul> </li> <li><strong>ob_escobar_results.tar.gz</strong> (<em>ob_escobar</em>): OW: 10, H: 10, OB: 10 <ul> <li>https://doi.org/10.1186/s12866-014-0311-6</li> </ul> </li> <li><strong>ob_goodrich_results.tar.gz</strong> (<em>ob_goodrich</em>): OW: 322, H: 433, OB: 183 <ul> <li>http://dx.doi.org/10.1016/j.cell.2014.09.053</li> </ul> </li> <li><strong>ob_gordon_2008_v2_results.tar.gz</strong> (<em>ob_turnbaugh</em>): H: 61, OB: 219 <ul> <li>http://dx.doi.org/10.1038/nature07540</li> </ul> </li> <li><strong>ob_jumpertz_results.tar.gz</strong> (<em>ob_jumpertz</em>): H: 12, OB: 9 <ul> <li>http://ajcn.nutrition.org/content/early/2011/05/03/ajcn.110.010132</li> </ul> </li> <li><strong>ob_ross_results.tar.gz</strong> (<em>ob_ross</em>): H: 26, OB: 37 <ul> <li>http://dx.doi.org/10.1186/s40168-015-0072-y</li> </ul> </li> <li><strong>ob_wu_results.tar.gz</strong> (<em>ob_wu</em>): bmi_data: 101 <ul> <li>http://dx.doi.org/10.1126/science.1208344</li> </ul> </li> <li><strong>ob_zeevi_results.tar.gz</strong> (<em>ob_zeevi</em>): bmi_data: 870 <ul> <li>http://dx.doi.org/10.1016/j.cell.2015.11.001</li> </ul> </li> <li><strong>ob_zupancic_results.tar.gz</strong> (<em>ob_zupancic</em>): H: 167, OB: 117 <ul> <li>http://dx.doi.org/10.1371/journal.pone.0043052</li> </ul> </li> <li><strong>par_scheperjans_results.tar.gz</strong> (<em>par_scheperjans</em>): H: 72, PAR: 72 <ul> <li>http://dx.doi.org/10.1002/mds.26069</li> </ul> </li> <li><strong>ra_littman_results.tar.gz</strong> (<em>art_scher</em>): H: 28, NORA: 44, CRA: 26, PSA: 16 <ul> <li>http://dx.doi.org/10.7554/eLife.01202</li> </ul> </li> <li><strong>t1d_alkanani_results.tar.gz</strong> (<em>t1d_alkanani</em>): T1D: 21, H: 55, T1D_new-onset: 35 <ul> <li>http://dx.doi.org/10.2337/db14-1847</li> </ul> </li> <li><strong>t1d_mejialeon_results.tar.gz</strong> (<em>t1d_mejialeon</em>): T1D: 21, H: 8 <ul> <li>http://dx.doi.org/10.1038/srep03814</li> </ul> </li> </ul> <p><strong>Version changes</strong></p> <p>Version 3</p> <ul> <li>added missing ob_escobar metadata</li> <li>added ob_jumpertz, ob_zeevi, and ob_wu</li> <li>added README.txt files to all folders, with info about data downloading and processing steps</li> <li>removed deprecated quality_control folders from all dataset results</li> <li>changed Supplemental File S3 to the most updated version of non-specific genera (as published in Duvallet et al 2017)</li> </ul> <p>Version 2</p> <ul> <li>added crc_zhu and ob_escobar datasets</li> <li>added list of core genera and dataset_info.yaml</li> </ul>
Data from: Oral exposure to benzalkonium chlorides in male and female mice reveals sex-dependent alteration of the gut microbiome and bile acid profile
<p>Benzalkonium chlorides (BACs) are commonly used disinfectants in a variety of consumer and food-processing settings, and the COVID-19 pandemic has led to increased usage of BACs. The prevalence of BACs raises the concern that BAC exposure could disrupt the gastrointestinal microbiota, thus interfering with the beneficial functions of the microbes. We hypothesize that BAC exposure can alter the gut microbiome diversity and composition, which will disrupt bile acid homeostasis along the gut-liver axis. In this study, male and female mice were exposed orally to d<sub>7</sub>-C12- and d<sub>7</sub>-C16-BACs at 120 µg/g/day for one week. UPLC-MS/MS analysis of liver, blood, and fecal samples of BAC-treated mice demonstrated the absorption and metabolism of BACs. Both parent BACs and their metabolites were detected in all exposed samples. Additionally, 16S rRNA sequencing was carried out on the bacterial DNA isolated from the cecum intestinal content. For female mice, and to a lesser extent in males, we found that treatment with either d<sub>7</sub>-C12- or d<sub>7</sub>-C16-BAC led to decreased alpha diversity and differential composition of gut bacteria with notably decreased actinobacteria phylum. Lastly, through a targeted bile acid quantitation analysis, we observed decreases in secondary bile acids in BAC-treated mice, which was more pronounced in the female mice. This finding is supported by decreases in bacteria known to metabolize primary bile acids into secondary bile acids, such as the families of Ruminococcaceae and Lachnospiraceae. Together, these data signify the potential impact of BACs on human health through disturbance of the gut microbiome and gut-liver interactions.</p>
Gut microbiome in the Graves' disease: comparison before and after anti-thyroid drug treatment
<p>Several studies have explored the link between the gut microbiome and Graves's disease (GD) but lacked data on microbiome changes following anti-thyroid drug (ATD) treatment. Stool samples from 29 newly diagnosed GD patients were analyzed before and after 6 months of ATD treatment. Results showed a significant decrease in microbial diversity in GD patients, which increased after treatment, resembling healthy levels. GD patients had lower levels of Firmicutes and higher levels of Bacteroidota, which normalized post-treatment. Specific microbial changes were associated with hyperthyroid symptoms. The study suggests the gut microbiome's involvement in GD pathogenesis and potential restoration with treatment.</p>
Data supporting publication "Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome"
<p>Data supporting publication "Metagenomic Immunoglobulin Sequencing (MIG-Seq) Exposes Patterns of IgA Antibody Binding in the Healthy Human Gut Microbiome"</p>
SHIP gut microbiome dataset for GP2 analysis
<p>The Study of Health in Pomerania (SHIP) is a population-based study. SHIP consists of the two independent cohorts: SHIP-START and SHIP-TREND. The aim of the study is the investigation of common risk factors, subclinical disorders and manifest diseases in the population of Northeast Germany. Determination of gut microbiota profiles using 16S rRNA gene sequencing was performed as described before in detail [1]. In brief, DNA was extracted from fecal samples stored in a DNA-stabilizing EDTA-buffer using the PSP Spin Stool DNA Kit (Stratec Biomedical AG, Birkenfeld, Germany) following the manufacturer's instructions and isolates were stored at -20 °C degrees until sequencing of the V1/V2 region of the bacterial 16S rRNA gene using the 27F and the 338R primers on a MiSeq platform (Illumina, San Diego, USA).</p> <p>[1] Frost, F., Kacprowski, T., Rühlemann, M., Bülow, R., Kühn, J.-P., Franke, A., Heinsen, F.-A., Pietzner, M., Nauck, M., Völker, U., Völzke, H., Aghdassi, A. A., Sendler, M., Mayerle, J., Weiss, F. U., Homuth, G., & Lerch, M. M. (2019). Impaired Exocrine Pancreatic Function Associates With Changes in Intestinal Microbiota Composition and Diversity. Gastroenterology, 156(4), 1010–1015. https://doi.org/10.1053/j.gastro.2018.10.047</p> <p> </p>
Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Predicts Gut Metabolites from Gut Microbiome: Supplemental Data
<p>This upload contains the supplemental data to the manuscript titled, "Deep in the Bowel: Highly Interpretable Neural Encoder-Decoder Predicts Gut Metabolites from Gut Microbiome". It contains the transformed data used to train the model (*-grouped-clr.csv), the latent feature space (latent_z.txt), and the differential abundance results (*DA*.csv).</p>
A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals (GWAS)
<p>GWAS summary statistics accompanying manuscript "A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals".</p>
HiPR-FISH Mouse Gut Microbiome Experiments 1
<p>This dataset contains images of a mouse gut microbiome.</p>
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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.