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389 results for “fecal microbiota”
Data from: Wildlife fecal microbiota exhibit community stability across a semi-controlled longitudinal non-invasive sampling experiment
<p>Wildlife microbiome studies are being used to assess microbial links with animal health and habitat. The gold standard of sampling microbiomes directly from captured animals is ideal for limiting potential abiotic influences on microbiome composition, yet fails to leverage the many benefits of non-invasive sampling. Application of microbiome-based monitoring for rare, endangered, or elusive species creates a need to non-invasively collect scat samples shed into the environment. Since controlling sample age is not always possible, the potential influence of time-associated abiotic factors was assessed. To accomplish this, we analyzed partial 16S rRNA genes of fecal metagenomic DNA sampled non-invasively from Rocky Mountain elk (<em>Cervus canadensis</em>) near Yellowstone National Park. We sampled pellet piles from four different elk, then aged them in a natural forest plot for 1, 3, 7, and 14 days, with triplicate samples at each time point (i.e., a blocked, repeat measures (longitudinal) study design). We compared microbiomes of each elk through time with point estimates of diversity, bootstrapped hierarchical clustering of samples, and a version of ANOVA–simultaneous components analysis (ASCA) with PCA (LiMM-PCA) to assess the variance contributions of time, individual and sample replication. Our results showed community stability through days 0, 1, 3 and 7, with a modest but detectable change in abundance in only 2 genera (<em>Bacteroides</em> and <em>Sporobacter</em>) at day 14. The total variance explained by time in our LiMM-PCA model across the entire 2-week period was not statistically significant (p>0.195) and the overall effect size was small (<10% variance) compared to the variance explained by the individual animal (p<0.0005; 21% var.). We conclude that non-invasive sampling of elk scat collected within one week during winter/early spring provides a reliable approach to characterize microbiome composition in a 16S rDNA survey and that sampled individuals can be directly compared across unknown time points with minimal bias. Further, point estimates of microbiome diversity were not mechanistically affected by sample age. Our assessment of samples using bootstrap hierarchical clustering produced clustering by animal (branches) but not by sample age (nodes). These results support greater use of non-invasive microbiome sampling to assess ecological patterns in animal systems.</p>
16S rRNA Sequencing Data of Fecal Microbiota in an Italian Cohort of Patients with CDKL5 Deficiency Disorder
<h3>Summary of the study </h3> <p>CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by global developmental delay, early-onset seizures, intellectual disability, visual and motor impairments, distinct from Rett Syndrome (RTT) due to the absence of a clear regression period. Gastrointestinal (GI) disturbances and signs of subclinical immune dysregulation are common in CDD patients, yet the underlying causes are unknown. Recent studies hint at a possible link between neurological disorders and gut microbiota, an unexplored area in CDD. In this groundbreaking study, we examined fecal microbiota in CDD patients and their healthy relatives, revealing differences in bacterial diversity and composition. We further investigated microbiota changes based on various factors, including the severity of GI issues, seizure frequency, sleep disorders, food intake type, neuro-behavioral features (assessed through the RTT Behaviour Questionnaire – RSBQ), and ambulation capacity. </p> <p>Our findings suggest a potential connection between CDD, microbiota, and symptom severity. This study represents the first exploration of the gut-microbiota-brain axis in CDD patients, contributing to the growing body of research on the role of gut microbiota in neurodevelopmental disorders. It opens doors to potential interventions targeting intestinal microbes to enhance the well-being of individuals with CDD.</p> <h3>Mehods</h3> <p>The Dataset represent the raw data (.fastq) obtained from the sequencing of the fecal samples from 17 Italian Patients with CDD, and 17 Healthy Relatives (i.e. siblings or mother), collected at a single time-point.</p> <p>Samples from Patients affected by CDD are called CDD, samples from Healthy Relatives are called HC-CDD (i.e. healthy controls of patients affected by CDD). For details about the sample names see the “Explanation Table”.</p> <p>Bacterial DNA was extracted from fecal samples using the QIAmp Powerfexal DNA Kit (Qiagen, Germany) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Germany).</p> <p><em>Targeted Library Preparation</em>: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA). </p> <p><em>Sequencing:</em> The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles). </p>
Analysis Data, "Drivers and Determinants of Strain Dynamics Following Fecal Microbiota Transplantation"
<p>This package contains datasets in `Rdata` format that underlie the analyses presented in the study, "Drivers and Determinants of Strain Dynamics Following Fecal Microbiota Transplantation" by Schmidt, Li et al.<br> </p> <p>Corresponding code, referring to these datasets, is available via `github`:</p> <p>https://github.com/grp-bork/fmt_metastudy</p> <p> </p> <p>The study is available as a preprint:</p> <p>https://www.biorxiv.org/content/10.1101/2021.09.30.462010v1</p> <p> </p> <p>The present package contains processed/derived data. Metagenome-Assembled Genomes generated for the same study are available via `Zenodo` under:</p> <p>https://zenodo.org/record/5534163#.YpoRFy8RrzA<br> doi: 10.5281/zenodo.5534163</p>
Fecal Microbiota Transplantation (FMT) of FMP30 in Relapsing-Remitting Multiple Sclerosis
ClinicalTrials.gov study NCT03594487. IPD Sharing: YES. Countries: 1. Publications: 10.
Data from: Stability of fecal microbiota during degradation in ex situ Cheetahs in the US
Open the record for dataset details and reuse information.
Fecal microbiota degradation over time in ex situ Namibian Cheetahs (Part 1 of 2)
Open the record for dataset details and reuse information.
Data from: Wildlife fecal microbiota exhibit community stability across a semi-controlled longitudinal non-invasive sampling experiment
Open the record for dataset details and reuse information.
High-resolution tracking of microbial colonization in Fecal Microbiota Transplantation experiments via metagenome-assembled genomes
<p>This project contains anvi'o profiles and contigs databases that is used and/or referenced from the Lee STM and Khan SA, <em>et al.</em> study titled "<strong>High-resolution tracking of microbial colonization in Fecal Microbiota Transplantation experiments via metagenome-assembled genomes</strong>". The pre-print of this study is available via http://dx.doi.org/10.1101/090993.</p> <p>To be able to work with the data files you will need anvi'o <strong>v2.1.0</strong> to be installed on your system. For installation instructions, or to have access to a Docker image for anvi'o, please visit this URL: http://merenlab.org/software/anvio</p> <p>Public data:</p> <ul> <li><strong>ANVIO-FMT-D-R01-R02-QUICK-VISUALIZATION.tar.gz</strong>: Data files for a quick visualization of the 97 MAGs and their distribution across the two FMT recipients. A run script in the archive explains how to use this data.<br> </li> <li><strong>ANVIO-FMT-D-R01-R02-MERGED-PROFILE.tar.gz</strong>: The merged anvi'o profile for the entire data, which also contains a collection of 97 MAGs identified in the donor. The profile database contains no hierarchical clustering of contigs, however, individual MAGs can be displayed via the following notation since the collection 'MAGs' describe the organization of contigs in each MAG referenced from the dataset `ANVIO-FMT-D-R01-R02-QUICK-VISUALIZATION`, as well as from the paper: "anvi-refine -c CONTIGS.db -p PROFILE.db -C MAGs -b <em>FMT-Donor_MAG_00054</em>". All MAG names are in the supplementary tables in our paper.<br> </li> <li><strong>ANVIO-FMT-D-R01-R02-MAGs-SUMMARY.tar.gz</strong>: A static HTML website that contains FASTA files for each MAG, and TAB-delimited matrices for coverage and detection values, and others. After unpacking, you can double-click the index.html file. </li> </ul>
Data from: Are fecal samples an appropriate proxy for amphibian intestinal microbiota?
<p>The intestinal microbiota, an invisible organ supporting a host's survival, has essential roles in metabolism, immunity, growth, and development. Since intestinal microbiota influences a host's biology, application of such data to wildlife conservation has gained interest. There are standard protocols for studying the human intestinal microbiota, but no equivalent for wildlife. A major challenge is sampling the intestinal microbiota in an effective, unbiased way. Fecal samples are a popular proxy for intestinal microbiota because collection is non-invasive, convenient, and allows for longitudinal sampling. Yet, it is unclear whether the fecal microbiota is representative of the intestinal microbiota. In amphibians, research on sampling methodology is limited. In this study, we characterize and compare microbiota (small intestine, large intestine, feces) of two Hong Kong stream-dwelling frog species: Lesser Spiny Frog (<em>Quasipaa exilispinosa</em>), and Hong Kong Cascade Frog (<em>Amolops hongkongensis</em>). We found that both species have similar dominant phyla and families, but diverge in terms of the dominant genera. Next, we assess the performance of fecal microbiota in representing the intestinal microbiota in these two species. We found that (1) microbiota of small and large intestine differs significantly, (2) feces are not an appropriate proxy of both intestinal sections, and (3) a set of microbial taxa significantly differs between sample types. Our cautions equating fecal and intestinal microbiota. Sampling feces can avoid sacrifice of an animal, but researchers should avoid over-extrapolation and interpret results carefully.</p>
Immune-mediated hematological disease in dogs is associated with alterations of the fecal microbiota: a pilot study
<div> <h3>Background</h3> <p>The dog is the most popular companion animal and is a valuable large animal model for several human diseases. Canine immune-mediated hematological diseases, including immune-mediated hemolytic anemia (IMHA) and immune thrombocytopenia (ITP), share many features in common with autoimmune hematological diseases of humans. The gut microbiome has been linked to systemic illness, but few studies have evaluated its association with immune-mediated hematological disease. To address this knowledge gap, 16S rRNA gene sequencing was used to profile the fecal microbiota of dogs with spontaneous IMHA and ITP at presentation and following successful treatment. In total, 21 affected and 13 healthy control dogs were included in the study.</p> <h3>Results</h3> <p>IMHA/ITP is associated with remodeling of fecal microbiota, marked by decreased relative abundance of the spirochete <em>Treponema</em> spp., increased relative abundance of the pathobionts <em>Clostridium septicum</em> and <em>Escherichia coli</em>, and increased overall microbial diversity. Logistic regression analysis demonstrated that <em>Treponema</em> spp. were associated with decreased risk of IMHA/ITP (odds ratio [OR] 0.24–0.34), while Ruminococcaceae UCG-009 and Christensenellaceae R-7 group were associated with increased risk of disease (OR =&thinsp;6.84 [95% CI 2–32.74] and 8.36 [95% CI 1.85–71.88] respectively).</p> <h3>Conclusions</h3> <p>This study demonstrates an association of immune-mediated hematological diseases in dogs with fecal dysbiosis, and points to specific bacterial genera as biomarkers of disease. Microbes identified as positive or negative risk factors for IMHA/ITP represent an area for future research as potential targets for new diagnostic assays and/or therapeutic applications.</p> </div>
Data supporting the article: "Comparative analysis of fecal microbiota between adolescents with early-onset psychosis and adults with schizophrenia"
<p>This dataset supports the article titled <em>"Comparative analysis of fecal microbiota between adolescents with early-onset psychosis and adults with schizophrenia.", </em>available at<em> <a href="https://doi.org/10.3390/microorganisms12102071">https://doi.org/10.3390/microorganisms12102071</a></em><em>.</em></p> <p>The dataset includes fecal microbiota sequencing data from adolescent patients with early-onset psychosis, adult patients with schizophrenia, and non-psychotic controls. The data were generated using 16S rRNA gene sequencing and analyzed with QIIME2 and PICRUSt2 to assess microbial diversity and functional pathways. Variables such as age, diagnosis, and medication use are included.</p> <p>The dataset contains:</p> <ul> <li><strong>Processed results</strong> from fecal microbiota analysis (OTUs and taxonomic classifications)</li> <li><strong>Metadata</strong> associated with each sample (age, diagnosis, medication)</li> <li><strong>Results from diversity analysis</strong> (alpha and beta diversity metrics)</li> <li><strong>Functional analysis</strong> of microbial pathways (PICRUSt2)</li> </ul> <p>These data are essential for reproducing the findings discussed in the article. Note that the raw sequencing data are available upon request.</p>
Inflammatory responses induced by the monophasic variant of Salmonella typhimurium in pigs play a role in the high shedder phenotype and fecal microbiota composition
<p>Pigs infected with <em><span>Salmonella</span></em> may excrete large amounts of <em><span>Salmonella</span></em>, increasing the risk of spread of this pathogen in the food chain. Identifying <em><span>Salmonella</span></em> high shedder pigs is therefore required to mitigate this risk. We analyzed immune-associated markers and composition of the gut microbiota in specific-pathogen-free pigs presenting different shedding levels after an oral infection with <span>Salmonella</span>. Immune response was studied through total blood cell counts, production of anti-<em><span>Salmonella</span></em> antibodies and cytokines, and gene expression quantification. Total <em><span>Salmonella</span></em> shedding for each pig was estimated and hierarchical clustering was used to cluster pigs into high, intermediate, and low shedders. Gut microbiota compositions were assessed using 16S rRNA microbial community profiling. Comparisons were made between control and inoculated pigs, then between high and low shedders pigs. Prior to infection, high shedders had similar immunological profiles compared to low shedders. As soon as 1 day postinoculation (dpi), significant differences in the cytokine production level and on the expression level of several host genes related to a proinflammatory response were observed between high and low shedders. Infection with <em><span>Salmonella</span></em> induced an early and profound remodeling of the immune response in all pigs, but the intensity of the response was stronger in high shedders. In contrast, low shedders seroconverted earlier than high shedders. Just after induction of the proinflammatory response (at 2 dpi), some taxa of the fecal microbiota were specific to the shedding phenotypes. This was related to the enrichment of several functional pathways related to anaerobic respiration in high shedders. In conclusion, our data show that the immune response to <em><span>Salmonella</span></em> modifies the fecal microbiota and subsequently could be responsible for shedding phenotypes. Influencing the gut microbiota and reducing intestinal inflammation could be a strategy for preventing <em><span>Salmonella</span></em> high shedding in livestock.</p>
Fecal Microbiota Transplantation for Pancreatitis
ClinicalTrials.gov study NCT02318134. IPD Sharing: Not stated. Countries: 1. Publications: 12.
Fecal Microbiota Transplant (FMT) Plus Fidaxomicin for Severe or Fulminant Clostridium Difficile Infection
ClinicalTrials.gov study NCT03760484. IPD Sharing: NO. Countries: 1. Publications: 1.
Autologous Fecal Microbiota Transplantation to Prevent Antibiotic Resistant Bacteria Colonization
ClinicalTrials.gov study NCT03061097. IPD Sharing: NO. Countries: 1. Publications: 1.
Pilot Feasibility Study of Fecal Microbiota Transplant for the Treatment of Small Intestinal Bacterial Overgrowth
ClinicalTrials.gov study NCT05967871. IPD Sharing: YES. Countries: 1. Publications: 25.
A Longitudinal Study to Identify IBS Phenotypes Using Fecal Microbiota and Hydrogen Breath Testing
ClinicalTrials.gov study NCT03219528. IPD Sharing: NO. Countries: 1. Publications: 0.
Fecal Microbiota Transplant for Improvement of Metabolism
ClinicalTrials.gov study NCT02530385. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Dietary Fructose on Gut Microbiota and Fecal Metabolites in Obese Men and Postmenopausal Women: A Pilot Study
ClinicalTrials.gov study NCT03339245. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Fecal Microbiota Transplant (FMT) in Melanoma Patients
ClinicalTrials.gov study NCT03341143. IPD Sharing: NO. Countries: 1. Publications: 2.
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Allen Brain Atlas
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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.