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2,708 results for “Microbiota”
Figure 1 in A first assessment of the microbiota of Taurida Cave
Figure 1. Taurida cave, location of sampling sites. The plan was drawn up by G.V. Samokhin according to the expeditions of the Crimean Federal University, 2018.
Fig. 2 in First line of defence: Skin microbiota may protect anurans from infective larval lungworms
Fig. 2. The consequences of inoculation of cane toads with larval lungworms, as a function of whether the toads had an undisturbed skin microbiota (group 1) or a disturbed (partially. removed) skin microbiota (group 2). Establishment success of the lungworms was measured by the mean percentage ± SE of the larva that established themselves as adults in the lungs after 18 days.
Fig. 3 in First line of defence: Skin microbiota may protect anurans from infective larval lungworms
Fig. 3. Histological photomicrographs of toad skin. Skin sections from the lateral body of toad A are depicted in images 1 (sham wiping with a gloved hand) and 2 (microbiota removal technique using sterile cotton gauze). Skin sections from the dorsal body of toad B are depicted in images 3 (sham) and 4 (microbiota removal technique). Notations identifying major skin structures are shown in image 1: E, epidermis, D, dermis, G, large granular gland and stratum corneum (arrowhead). Haematoxylin and eosin stain. Bar = 100 μm in all images.
Fig. 1 in First line of defence: Skin microbiota may protect anurans from infective larval lungworms
Fig. 1. The steps involved in the two experimental treatments. The undisturbed skin of group 1 toads was swabbed for microbes 48 h after being exposed to lungworm larvae so that the swabbing process did not disturb the microbiota before exposure. The skin of group 2 toads was swabbed before and after being wiped with sterile gauze so that the efficacy of this disturbance or "cleaning" action could be evaluated. Group 2 toads were exposed to lungworm larvae after the second swab.
Fig. 4 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling
Fig. 4. | Faecal bacterial community profile of captive chimpanzees infected with Giardia duodenalis detected by rapid antigen test. (A) Relative abundance of colour coded bacterial phyla separated based on presence (+) or absence (‒) of Giardia using rapid antigen test (RAT). The sample identity is located at the bottom of the graph with two labels (C20, C3) shaded indicating samples that were found as Giardia positive by real-time PCR. (B) Alpha diversity based on observed OTU and Shannon's index plotted as box plot and evaluated using t-tests. (C) Principal coordinates analysis (PCoA) 2D plot using first two principal components from Bray-Curtis dissimilarity matrix at the genus taxonomic levels. The clustering between Giardia positive (RAT+) and negative (RAT-) samples was tested using ANOSIM. (D) Linear discriminant analysis effect size (LEfSe) used plot of significant factors discriminating G. duodenalis positive from negative sample. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling
Fig. 3. | Faecal bacterial community profile of captive chimpanzees infected with Giardia duodenalis as detected by rapid antigen test and real-time PCR combined. (A) Relative abundance of colour coded bacterial phyla separated based on presence (+) or absence (‒) of Giardia. The sample identity is located at the bottom of the graph. (B) Alpha diversity based on observed OTU and Shannon's index plotted as box plot and evaluated using t-tests. (C) Principal coordinates analysis (PCoA) 2D plot using first two principal components from Bray-Curtis dissimilarity matrix at the genus taxonomic levels. The clustering between Giardia positive (+) and negative (‒) samples was tested using ANOSIM. (D) Linear discriminant analysis effect size (LEfSe) used plot of significant factors discriminating G. duodenalis positive from negative sample. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling
Fig. 2. | Results of Giardia duodenalis rapid antigen test applied on faecal samples from chimpanzees. A positive result for the Giardia duodenalis rapid antigen test (RAT, Anigen Rapid Giardia AG Test Kit) is represented by the line in the 'T' position in the window along with the positive control line in the 'C' position.
Fig. 1 in Giardia duodenalis in a clinically healthy population of captive zoo chimpanzees: Rapid antigen testing, diagnostic real-time PCR and faecal microbiota profiling
Fig. 1. Captive chimpanzees and their enclosure in Sydney, Australia. (A) Main chimpanzee open air exhibit with multiple climbing structures. (B) View from the other direction showing entry to the indoor area at the end of the exhibit. (C) smaller exhibit with mesh covering and more climbing and sleeping structures. (D) Members of the chimpanzee troop at the Taronga Zoo.
Microbiota Study Crohn's Disease AG Chang
<p>We have investigated a Crohn’s disease (CD) cohort by our multi-parameter microbiota flow cytometry approach to characterise the microbiota on single-cell level for attributes of the disease. The microbiota is isolated from stool samples and stained according to the published protocol for (a) host immunoglobulins IgA1, IgA2, IgM, IgG and (b) agglutinin binding to mannose, galactose or N-Acetyl-glucosamine surface sugar moieties. For all samples we also determined the microbiome composition by 16S rRNA (V3-V4) sequencing on the illumina MiSeq platform. We provide the raw .fcs and FASTQ files of 64 CD patients distributed into two cohorts (55) individuals in cohort 1, 19 individuals in cohort 2). Cohort 2 has two samples for two timepoints.</p> <p>For comparison we additionally analysed 55 healthy donors.</p> <p>All .fcs files were generated on <span>BD Influx®.</span></p> <p> </p> <p>The metadata is collected in the provided meta.csv.</p> <p><span>The staining parameters are summarized in provided panel.csv. </span></p>
Linked collectors and determiners for: Root-associated microbiota of decline-affected and asymptomatic Pinus sylvestris trees.
Natural history specimen data linked to collectors and determiners held within, "Root-associated microbiota of decline-affected and asymptomatic Pinus sylvestris trees". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1826d3c2-9e33-43e8-afaa-450b7d6e2b9b">https://bionomia.net/dataset/1826d3c2-9e33-43e8-afaa-450b7d6e2b9b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1826d3c2-9e33-43e8-afaa-450b7d6e2b9b">https://gbif.org/dataset/1826d3c2-9e33-43e8-afaa-450b7d6e2b9b</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Bacterial members of the Pinus pinaster rhizosphere microbiota in a forest subjected to drought conditions.
Natural history specimen data linked to collectors and determiners held within, "Bacterial members of the Pinus pinaster rhizosphere microbiota in a forest subjected to drought conditions". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/ffae417e-b2d8-476c-afe4-8c1093b67071">https://bionomia.net/dataset/ffae417e-b2d8-476c-afe4-8c1093b67071</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/ffae417e-b2d8-476c-afe4-8c1093b67071">https://gbif.org/dataset/ffae417e-b2d8-476c-afe4-8c1093b67071</a>. Formatted as a Frictionless Data package.
Gut microbiota of wild arboreal and ground-feeding tropical primates
<p>16S rRNA gene and ITS sequences from fecal samples of 12 groups of Udzungwa (Tanzania) red colobus Procolobus gordonorum (total number of samples: 89), and five groups of yellow baboon Papio cynocephalus (total number of samples: 69).</p> <p>The dataset consists of the following files:</p> <ul> <li><strong>16S_raw.tar</strong>: raw 16S rRNA gene sequences;</li> <li><strong>16S_filtered.fasta.gz</strong>: merged, adapter trimmed and quality filtered 16S rRNA gene sequences;</li> <li><strong>16S_ID.txt</strong>: 16S file ID to sample ID mapping;</li> <li><strong>ITS_raw.tar</strong>: raw ITS sequences;</li> <li><strong>ITS_filtered.fasta.gz</strong>: merged, adapter trimmed and quality filtered ITS sequences;</li> <li><strong>ITS_ID.txt</strong>: ITS file ID to sample ID mapping;</li> <li><strong>Metadata.tsv</strong>: sample metadata;</li> <li><strong>Accessions.tsv</strong>: ENA project and sample accessions;</li> <li><strong>Helminths.tsv</strong>: helminths abundances table.</li> </ul> <p> </p>
Diversity and compositional changes in the gut microbiota of wild and captive vertebrates: a meta-analysis
<p>Bioinformatic code, data files, raw figures and data accessibility table associated to the manuscript "Diversity and compositional changes in the gut microbiota of wild and captive vertebrates: a meta-analysis".</p>
The impact of cefuroxime prophylaxis on human intestinal microbiota in surgical oncological patients - Dataset (FASTQ FILES)
<p>Dataset containing FASTQ files of the sequenced samples, generated by the Illumina MiSeq platform. </p> <p><span>This data is freely available under a CC-BY license; if you use it in your work, please cite our paper, "The impact of cefuroxime prophylaxis on human intestinal microbiota in surgical oncological patients" (DOI 10.3389/frmbi.2022.1092771).</span></p>
Individual variation in the avian gut microbiota: The influence of host state and environmental heterogeneity
<div class="abstract-group metis-abstract"> <div class="article-section__content en main"> <p>The gut microbiota have important consequences for host biological processes and there is some evidence that they also affect fitness. However, the complex, interactive nature of ecological factors that influence the gut microbiota has scarcely been investigated in natural populations. We sampled the gut microbiota of wild great tits (<em>Parus major</em>) at different life stages allowing us to evaluate how microbiota varied with respect to a diverse range of key ecological factors of two broad types: (1) host state, namely age and sex, and the life history variables, timing of breeding, fecundity and reproductive success; and (2) the environment, including habitat type, the distance of the nest to the woodland edge, and the general nest and woodland site environments. The gut microbiota varied with life history and the environment in many ways that were largely dependent on age. Nestlings were far more sensitive to environmental variation than adults, pointing to a high degree of flexibility at an important time in development. As nestlings developed their microbiota from one to two weeks of life, they retained consistent (i.e., repeatable) among-individual differences. However these apparent individual differences were driven entirely by the effect of sharing the same nest. Our findings point to important early windows during development in which the gut microbiota are most sensitive to a variety of environmental drivers at multiple scales, and suggest reproductive timing, and hence potentially parental quality or food availability, are linked with the microbiota. Identifying and explicating the various ecological sources that shape an individual's gut bacteria is of vital importance for understanding the gut microbiota's role in animal fitness.</p> </div> </div> <div class="pb-dropzone"> </div>
MAG Collection - Rühlemann et al.: Comparative metagenomics reveals host-specific functional adaptation of intestinal microbiota across hominids
<p>This tar-Archives hold the complete collection of n=7,506 metagenome-assembled genomes presented in the preprint "Comparative metagenomics reveals host-specific functional adaptation of intestinal microbiota across hominids" by Rühlemann <em>et al., <a href="https://www.biorxiv.org/content/10.1101/2023.03.01.530589v1">bioRxiv</a>, </em>2023.</p> <p>Article Summary</p> <p>Characterizing trajectories of the composition and function of hominid gut microbiota across diverse environments and host species can help reveal specific properties of the human microbiota, with possible implications for host evolution and health. Using shotgun metagenomic sequencing, we investigated taxonomic and functional diversity in the gut microbiota of wild-living great apes, including two gorilla subspecies (<em>Gorilla gorilla gorilla, Gorilla beringei beringei</em>), three chimpanzee subspecies (<em>Pan troglodytes verus, P.t. troglodytes, P.t. schweinfurthii</em>), and bonobos (<em>Pan paniscus</em>), together with human samples from Africa and Europe. We identified microbial taxonomic and functional adaptations convergent with host phylogeny at both the community and microbial genomic levels. We could show that repeated horizontal gene transfer and gene loss are processes involved in these adaptations. We hypothesize, that these adaptation processes and changes in the microbiome predispose the host to chronic inflammatory disorders, such as type 2 diabetes via altered histidine metabolism and inflammatory bowel disease indicated by adaptation of microbes to aerobic conditions. Additionally, we find multiple lines of evidence suggesting a widespread loss of microbial diversity and evolutionary conserved clades in the human microbiota, especially in the European population. Lastly, we observed patterns consistent with codivergence of hosts and microbes, particularly for the bacterial family <em>Dialisteraceae</em>, though we find that overall, co-phylogeny patterns are frequently disrupted in humans.</p>
Gut microbiota inter-species interactions shape the response of Clostridioides difficile to clinically relevant antibiotics
<p>In the human gut, the growth of <em>Clostridioides difficile </em>is impacted by a complex web of inter-species interactions with members of human gut microbiota. We investigate the contribution of inter-species interactions on the antibiotic response of <em>C. difficile </em>to clinically relevant antibiotics using bottom-up assembly of human gut communities. We discover two classes of microbial interactions that alter <em>C. </em>difficile’s antibiotic susceptibility: interactions resulting in increased <em>C. diffiicle </em>tolerance at high antibiotic concentrations (rare) and interactions resulting in <em>C. difficile </em>growth enhancement at low antibiotic concentrations (common). Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species <em>Desulfovibrio piger </em>increases metronidazole tolerance of <em>C. difficile</em>. Competition with species that display higher sensitivity to the antibiotic than <em>C. difficile </em>leads to enhanced growth of <em>C. difficile </em>at low antibiotic concentrations. A dynamic computational model identifies the ecological design principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping <em>C. difficile</em>’s response to antibiotics, which could inform therapeutic interventions.In the human gut, the growth of <em>Clostridioides difficile </em>is impacted by a complex web of inter-species interactions with members of human gut microbiota. We investigate the contribution of inter-species interactions on the antibiotic response of <em>C. difficile </em>to clinically relevant antibiotics using bottom-up assembly of human gut communities. We discover two classes of microbial interactions that alter <em>C. </em>difficile’s antibiotic susceptibility: interactions resulting in increased <em>C. diffiicle </em>tolerance at high antibiotic concentrations (rare) and interactions resulting in <em>C. difficile </em>growth enhancement at low antibiotic concentrations (common). Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species <em>Desulfovibrio piger </em>increases metronidazole tolerance of <em>C. difficile</em>. Competition with species that display higher sensitivity to the antibiotic than <em>C. difficile </em>leads to enhanced growth of <em>C. difficile </em>at low antibiotic concentrations. A dynamic computational model identifies the ecological design principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping <em>C. difficile</em>’s response to antibiotics, which could inform therapeutic interventions.</p>
Diet-induced changes in the jejunal microbiota of developing broilers reduce the abundance of Enterococcus hirae and Enterococcus faecium
<p>This ready to load <strong>phyloseq </strong>R S4 object contains the OTU table, taxonomy table and sample metadata. This data was build using kraken v2.1.2, kraken-biom v1.0.1 and phyloseq version 1.4. Sequencing data is deposited at NCBI-SRA under BioProject: PRJNA952340. </p><p> </p><p><strong>Study abstract</strong></p><p>Paul B. Stege, Dirkjan Schokker, Frank Harders, Soumya K. Kar, Norbert Stockhofe, Vera Perricone, Johanna M. J. Rebel, Ingrid C. de Jong, Alex Bossers </p><p> </p><p>Modern broiler breeds allow for high feed efficiency and rapid growth, which come at a cost of increased susceptibility to pathogens and disease. Broiler growth rate, feed efficiency, and health are affected by the composition of the gut microbiota, which in turn is influenced by diet. In this study, we therefore assessed how diet composition can affect the broiler jejunal gut microbiota. A total of 96 broiler chickens were divided into four diet groups: control, coated butyrate supplementation, medium-chain fatty acid supplementation, or a high-fibre low-protein content. Diet groups were sub-divided into age groups (4, 12 and 33 days of age) resulting in groups of 8 broilers per diet per age. The jejunum content was used for metagenomic shotgun sequencing to determine the microbiota taxonomic composition at species level. The composed diets resulted in a total of 104 differentially abundant bacterial species. Most notably were the butyrate-induced changes in the jejunal microbiota of broilers 4 days post-hatch, resulting in the reduced relative abundance of mainly <i>Enterococcus</i> <i>faecium</i> and the opportunistic pathogen <i>Enterococcus</i> <i>hirae</i>, when compared to the control diet. This effect takes place during early broiler development, which is critical for broiler health, thus exemplifying the importance of how diet can influence the microbiota composition and its relation to broiler health. Future studies should therefore elucidate how diet can be used to promote a beneficial microbiota in the early stages of broiler development.</p>
Symbiotic microbiota vary with breeding group membership in a highly social joint-nesting bird
<p>Symbiotic microbes affect the health, fitness, and behavior of their animal hosts, and can even affect the behavior of non-hosts. Living in groups presents numerous benefits and challenges to social animals, including exposure to symbiotic microbes, which can mediate both cooperation and competition. In social mammals, individuals from the same social group tend to share more similar microbes, and this social microbiome, the microbial community of all hosts in the same social group, can shape the benefits and costs of group living. In contrast, little is known about the social microbiome of group-living birds. We tested the predictions that communally breeding smooth-billed anis (<em>Crotophaga</em> <em>ani</em>) belonging to the same breeding group share more similar microbes and that microbial community composition differs between body regions. To test this, we used 16S rRNA gene sequencing to characterize the preen gland and body feather microbiota of adult birds from 16 breeding groups at a long-term study site in southwestern Puerto Rico. As predicted, individuals from the same breeding group shared more similar microbiota than non-group members and preen gland and body feathers harboured distinct microbial communities. Future research will evaluate whether this social microbiome affects the behavior of group living birds.</p>
Interaction of genital microbiota in infertile couples
<p>Bacteria colonise most of the human body and the genital tract is not an exception. While it has been known for decades that a vaginal microbiota exists, other genital sites have traditionally been viewed as sterile environments, with bacterial presence associated only with pathological conditions. However, recent studies identified specific patterns of bacterial colonisation in most genital sites. Shifts in the bacterial colonisation of the female genital tract have been linked to impairment of reproduction and adverse pregnancy outcomes, such as preterm birth.</p> <p>The goal of this project is to understand the association between the genital microbiota of couples seeking assisted procreation aid and the outcome of this treatment. Male and female partners will be studied as a unit (“couple microbiota”) and the interaction between their microbiota will be evaluated.</p> <p>We have characterized microbial samples coming from vaginal and penile swabs, as well as follicular fluid and semen, using next generation sequencing (16S rRNA profiling). The results were linked to clinical data of the patients included in the study and particularly to the results of the fertility treatment process. With this project, we aim to gain a better understanding of how the male genital microbiota could influence the lower (vagina) and upper (follicular fluid) female genital tracts.</p> <p>Github repository link: <a href="https://github.com/dfmemicrobiota/infertile_couples">https://github.com/dfmemicrobiota/infertile_couples</a></p>
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)
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.