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108 results for “human microbiota”
Humanization of wildlife gut microbiota in urban environments
<p>Urbanization is rapidly altering Earth's environments, demanding investigations of the impacts on resident wildlife. Here, we show that urban populations of coyotes (Canis latrans) and crested anole lizards (Anolis cristatellus) acquire gut microbiota constituents found in humans, including the gut bacterial lineages most significantly associated with urbanization in humans (e.g., Bacteroides). Comparisons of urban and rural wildlife and human populations revealed significant convergence of the gut microbiota among urban host populations. Remarkably, all microbial lineages found in humans that were overrepresented in urban wildlife relative to rural wildlife were also overrepresented in urban humans relative to rural humans. These results indicate parallel effects of urbanization on human and wildlife gut microbiota and suggest spillover of bacteria from humans into wildlife in cities</p>
Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota
<div> <p><span><span>Background</span></span><span><span>: Gut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. </span><span>This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases</span><span>, with each sample sequenced using both 16S and shotgun methods</span><span>. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models.</span></span><span> </span></p> </div> <div> <p><span><span>Results</span></span><span><span>: </span><span>As expected, the results showed that </span></span><span><span>16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The </span></span><span><span>16S </span><span>abundance data was sparser and </span><span>exhibited</span><span> lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, </span><span>partially</span><span> due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their </span><span>PCoAs</span><span>. </span><span>Regarding</span><span> the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not </span><span>demonstrate</span><span> a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques reveal</span><span>ed</span><span> taxa previously associated with CRC development, e.g., </span></span><span><span>Parvimonas</span><span> micra</span></span><span><span>.</span></span><span> </span></p> </div> <div> <p><span><span>Conclusions</span></span><span><span>: </span></span><span><span>Shotgun and 16S sequencing provide two different lenses to examine microbial communities.</span><span> While we have </span><span>demonstrated</span><span> that they can unravel common patterns (including microbial signatures), </span><span>shotgun often gives</span><span> a more detailed snapshot than 16S, both in depth and breadth. </span><span>Instead</span><span>,</span> <span>16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample.</span> <span>Therefore, w</span><span>e recommend choosing one or another sequencing technique before launching a study.</span><span> Specifically, </span><span>s</span><span>hotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is </span><span>more </span><span>suitable for tissue samples</span><span> and</span><span> studies with </span><span>targeted</span> <span>aims</span><span>.</span></span><span> </span></p> </div>
Species interactions drive the spread of ampicillin resistance in human-associated gut microbiota
<p><span><span><span><span><span><span><span><span><span><span><span><b>Background and objectives</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Slowing the spread of antimicrobial resistance is urgent if we are to continue treating infectious diseases successfully. There is increasing evidence microbial interactions between and within species are significant drivers of resistance. On one hand, cross-protection by resistant genotypes can shelter susceptible microbes from the adverse effects of antibiotics, reducing the advantage of resistance. On the other hand, antibiotic-mediated killing of susceptible genotypes can alleviate competition and allow resistant strains to thrive (competitive release). Here, by observing interactions both within and between species in microbial communities sampled from humans, we investigate the potential role for cross-protection and competitive release in driving the spread of ampicillin resistance in the ubiquitous gut commensal and opportunistic pathogen <i>Escherichia coli</i>. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methodology</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Using anaerobic gut microcosms comprising <i>E. coli</i> embedded within gut microbiota sampled from humans, we tested for cross-protection and competitive release both within and between species in response to the clinically important beta-lactam antibiotic ampicillin. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>While cross-protection gave an advantage to antibiotic-susceptible <i>E. coli</i> in standard laboratory conditions (well-mixed LB medium), competitive release instead drove the spread of antibiotic-resistant <i>E. coli</i> in gut microcosms (ampicillin boosted growth of resistant bacteria in the presence of susceptible strains). </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions and implications</b></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>Competition between resistant strains and other members of the gut microbiota can restrict the spread of ampicillin resistance. If antibiotic therapy alleviates competition with resident microbes by killing susceptible strains, as here, microbiota-based interventions that restore competition could be key for slowing the spread of resistance. </span></span></span></span></span></span></span></span></span></span></span></p>
Ceftriaxone and Cefotaxime Have Similar Effects on the Intestinal Microbiota in Human Volunteers
<p><strong>Pour ce TP, nous allons déterminer l’impact d’un traitement à la ceftriaxone sur la flore intestinale (<a href="https://aac.asm.org/content/63/6/e02244-18.abstract">https://aac.asm.org/content/63/6/e02244-18.abstract</a>). 22 volontaires sains ont reçu par intraveineuse de la ceftriaxone (1g / 24 h) ou de la cefotaxime ( 1g / 8h) pendant 3 jours. Le consortium CEREMI a collecté des échantillons de selles de ces volontaires et réalisé une étude de métagénomique ciblée sur le gène 16S rRNA. En raison des limitations de vos machines, nous allons analyser les échantillons du groupe B uniquement à 2 temps: Jm1 (un jour avant le traitement) et J4</strong></p>
Low Grade Inflammation, Gut Microbiota and Barrier Function in Elderly Humans
ClinicalTrials.gov study NCT01218165. IPD Sharing: Not stated. Countries: 1. Publications: 24.
Establishment of the Human Intestinal and Salivary Microbiota Biobank - Kidney Diseases
ClinicalTrials.gov study NCT04689074. IPD Sharing: NO. Countries: 1. Publications: 5.
Establishment of the Human Intestinal and Salivary Microbiota Biobank - Gastrointestinal Diseases
ClinicalTrials.gov study NCT04698148. IPD Sharing: NO. Countries: 1. Publications: 5.
What's Hopping? Cricket Protein and Human Gut Microbiota
ClinicalTrials.gov study NCT03383341. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Human-derived Human Milk Fortifiers (H2MF), Gut Microbiota and Oxidative Stress in Premature Infants
ClinicalTrials.gov study NCT03214822. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
Effects of a Novel Food Product Containing Microbiota Accessible Carbohydrates on the Human Microbiome
ClinicalTrials.gov study NCT03058575. IPD Sharing: NO. Countries: 1. Publications: 8.
Human Microbiota and Liver Transplant
ClinicalTrials.gov study NCT02223468. IPD Sharing: Not stated. Countries: 1. Publications: 18.
Human Genes and Microbiota in Early Life
ClinicalTrials.gov study NCT06926166. IPD Sharing: NO. Countries: 1. Publications: 5.
Establishment of the Human Intestinal and Salivary Microbiota Biobank - Oncologic Diseases
ClinicalTrials.gov study NCT04698161. IPD Sharing: NO. Countries: 1. Publications: 5.
Establishment of the Human Intestinal and Salivary Microbiota Biobank- Diabetes
ClinicalTrials.gov study NCT04698122. IPD Sharing: NO. Countries: 1. Publications: 4.
Effects of Inulin and Arabinoxylan on Satiety, Energy/Food Intake and Changes in the Human Gut Microbiota
ClinicalTrials.gov study NCT02846454. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Adaptation of Human Gut Microbiota to Energetic Restriction
ClinicalTrials.gov study NCT01454232. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The Effect of Under-nutrition on the Human Microbiota
ClinicalTrials.gov study NCT02021799. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Lactobacillus Rhamnosus GG: Interaction With Human Microbiota and Immunity
ClinicalTrials.gov study NCT01148667. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Role of Human Milk in Development of Breast Fed Child's Intestinal Microbiota
ClinicalTrials.gov study NCT01548313. IPD Sharing: NO. Countries: 1. Publications: 7.
Transplantation of Anaerobic Cultured Human Intestinal Microbiota in Irritable Bowel Syndrome
ClinicalTrials.gov study NCT02857257. IPD Sharing: NO. Countries: 1. Publications: 4.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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
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.