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150 results for “host microbiome”
Dietary fibers boost gut microbiome-produced B vitamin pool and alter host immune landscape
<p>This dataset contains fcs files of lymphocytes from the colonic lamina propria, lungs, and spleens of specific-pathogen-free (SPF), gnotobiotic (14-member synthetic microbiota, 14SM) or germ-free (GF) mice fed five distinct rodent diets (Standard chow 1, SC1; Standard chow 2, SC2; Fiber-supplemented diet, FS; Inulin-supplemented diet, IN; or Fiber-free diet, FF), analysed by mass cytometry. Three million cells per organ per animal were transferred into 15 mL conical tubes. For live/dead staining, cells were incubated with 5 μM cisplatin for 5 minutes. Cells were washed, and cell surface staining mix was added containing pre-conjugated antibodies for 30 minutes at room temperature. Samples were washed twice with FACS buffer, then fixed using the FoxP3 Fix/Perm kit (eBiosciences) for 45 minutes at 4°C, followed by permeabilization wash. Samples were then incubated with the intracellular staining mix for 30 minutes at room temperature. Cells were washed with FACS buffer twice, and pellets were resuspended in Cell-ID™ Intercalator-Ir (Fluidigm) in MaxPar fixation solution (Fluidigm, catalogue no. 201192B) and refrigerated overnight, or for up to five days. Prior to acquisition, samples were washed twice with 1X PBS, and then washed twice with deionized water. Cell pellets were further resuspended in deionized water at 0.5 × 10^6 cells/mL and topped up with 10% calibration beads (EQ Four Element Calibration Beads, Fluidigm). All samples were acquired on the Helios Mass Cytometer (Fluidigm). Effector immune populations and activated T cells in the gut accumulate in a microbiota-dependent manner. Shifts in the microbiome according to dietary fiber source and content result in altered concentrations of B vitamins available to the host, which is tied to distinct alterations in innate and adaptive immune populations. </p>
Graphic Illustration of Kelly Speer's Keynote Talk: Hosts, parasites, and microbiomes: A system for studying natural complexity in a changing world
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded and synthesized the Keynote Talk by Kelly Speer at the Digital Data 2024 Conference in Lawrence, Kansas in May of 2024. We include this resource, with permission, because of its relevance to our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Effects of environmental translocation and host characteristics on skin microbiomes of sun-basking fish
<p>Variation in the composition of skin-associated microbiomes has been attributed to host species, geographic location, and habitat, but the role of intraspecific phenotypic variation among host individuals remains elusive. We explored if and how host environment and different phenotypic traits were associated with microbiome composition. We conducted repeated sampling of dorsal and ventral skin microbiomes of carp individuals (<em>Cyprinus</em> <em>carpio</em>) before and after translocation from laboratory conditions to a semi-natural environment. Both alpha and beta diversity of skin-associated microbiomes increased substantially within and among individuals following translocation, particularly on dorsal body sites. The variation in microbiome composition among hosts was significantly associated with body site, sun-basking, habitat switch, and growth, but not temperature gain while basking, sex, personality, or colour morph. We suggest that the overall increase in the alpha and beta diversity estimates among hosts were induced by individuals expressing greater variation in behaviours and thus exposure to potential colonizers in the pond environment compared to the laboratory. Our results exemplify how biological diversity at one level of organization (phenotypic variation among and within fish host individuals) together with the external environment impacts biological diversity at a higher hierarchical level of organisation (richness and composition of fish-associated microbial communities).</p>
Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine
<p>Supplementary Table Captions:</p> <p>Supplementary Table S9. Evaluation and parameter determination of host and microbiome RNA read classification using simulation datasets</p> <p>Supplementary Table S10. 40 pathways significantly upregulated in the cecum as compared to the transverse colon</p> <p>Supplementary Table S11. Host-microbiome gene co-expression network edges</p> <p>Supplementary Table S12. Host-host gene co-expression network edges</p> <p>Supplementary Table S13. Microbiome-microbiome gene co-expression network edges</p> <p>Supplementary Table S14. List of genes included in each gene module identified from the gene co-expression network</p> <p>Supplementary Table S15. Results of enrichment analysis for each gene module identified from the gene co-expression network</p> <p>Supplementary Table S16. The top 32 bacterial species in terms of expression abundance based on metatranscriptome profiles</p> <p>Supplementary Table S17. Number of microbiome RNA reads annotated by the KEGG database</p> <p>Supplementary Table S18. Results of enrichment analysis of gene modules for each parameter</p> <p>Supplementary Table S19. Evaluation of modules in each parameter of Newman algorithm</p> <p>Supplementary Table S20. Evaluation of modules in each parameter of Louvain algorithm</p> <p>Supplementary Table S21. Evaluation of modules in each parameter of Leiden algorithm</p> <p>Supplementary Table S22. Evaluation of modules in each parameter of WGCNA</p>
Data analysis pipeline for investigating drug-host-microbiome relationships in cardiometabolic disease (MetaCardis cohort).
<p>*******************************************************************<br> MetaDrugs workflow<br> *******************************************************************</p> <p>Data analysis pipeline for investigating drug-host-microbiome relationships in cardiometabolic disease (MetaCardis cohort).</p> <p>For questions and requests, please contact:<br> Sofia K. Forslund (sofia.forslund@mdc-berlin.de)<br> and Till Birkner (till.birkner@mdc-berlin.de)</p> <p>*******************************************************************<br> Contents:<br> -------------------------------------------------------------------<br> Data files:<br> metadata.tar.gz - archived cohort metadata files*<br> input_features.tar.gz - archived preprocessed serum and urine metabolome and gut microbiome features<br> output_complete.tar.gz - archived example analysis output files for each of the input feature file<br> output_rerun.tar.gz - archived empty directory for generating test output files as described in this document<br> <br> *Please note: Due to conflicts with Danish Data Protection laws, metadata from the Danish subset of the cohort were removed in this repository. Please reach out for a potential case-by-case access request for access to the complete set of metadata.<br> -------------------------------------------------------------------<br> Text files:<br> archived in feature_names.tar.gz:<br> atcs_names - full names for atcs drug compounds<br> contrast_names - full names for disease comparison groups<br> file_names - brief description of the files in input_features folder<br> gmm_names - full names of GMM modules<br> kegg_names - full names of KEGG modules<br> ko_names - full names of KO modules<br> metadata_names - full names of metadata features<br> mOTU_names - species names for metagenomics data<br> taxon_names - taxon names for metagenomics data<br> -------------------------------------------------------------------<br> Scripts:<br> -------------------------------------------------------------------<br> runFrame.r - main wrapper script envoking the analysis pipeline<br> -------------------------------------------------------------------<br> runFrame_rel_comb.r - script calculating drug combination effects<br> runFrame_rel.r - script calculating dosage effects<br> testCombPresenceSeparate.r - testing of significant drug combination effects beyond single drug effects<br> testDosagePresenceSeparate.pl - testing of significant drug dosage effects beyond single drug effects<br> testDosagePresenceSeparateNegative.pl - testing of unique drug dosage effects beyond single drug effects<br> -------------------------------------------------------------------<br> prettifyResults_uncollapsed.pl - wrapper scripts to create and format a single analysis output file<br> makeTables.r - wrapper script to make excel tables with analysis results<br> -------------------------------------------------------------------<br> Example output file:<br> -------------------------------------------------------------------<br> output_all_formatted_noc_uncollapsed_complete.tsv - contains all disease-drug-host-microbiome feature analysis results in one place.<br> *******************************************************************</p>
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals (16S rRNA gene sequencing data)
<p>Microbiome data accompanying manuscript "A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals". Data is available for alpha- and beta- diversity, as well as for individual taxa both in binary and quantitative phenotypic representation. Data is available for 827 individuals that gave consent for their data to be shared outside of the Milieu intérieur consortium. </p>
A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals (Metadata)
<p>Associated demographic, lifestyle, environmental and biochemical metadata accompanying manuscript "A Comprehensive Assessment of Demographic, Environmental and Host Genetic Associations with Gut Microbiome Diversity in Healthy Individuals". Data is available for 827 individuals that gave consent for their data to be shared outside of the Milieu intérieur consortium. </p>
Resources from: Gut microbiome composition better reflects host phylogeny than diet diversity in breeding wood-warblers
<p>Understanding the factors that shape microbiomes can provide insight on the importance of host-symbiont interactions and on co-evolutionary dynamics. Unlike for mammals, previous studies have found little or no support for an influence of host evolutionary history on avian gut microbiome diversity and instead have suggested a greater influence of the environment or diet due to fast gut turnover. Because effects of different factors may be conflated by captivity and sampling design, examining natural variation using large sample sizes is important. Our goal was to overcome these limitations by sampling wild birds to compare environmental, dietary, and evolutionary influences on gut microbiome structure. We performed fecal metabarcoding to characterize both the gut microbiome and diet of fifteen wood-warbler species across a four-year period and from two geographic localities. We find host taxonomy generally explained ~10% of the variation between individuals, which is ~6-fold more variation of any other factor considered, including diet diversity. Further, gut microbiome similarity was more congruent with the host phylogeny than with host diet similarity and we found little association between diet diversity and microbiome diversity. Together, our results suggest evolutionary history is the strongest predictor of gut microbiome differentiation among wood-warblers. Although the phylogenetic signal of the warbler gut microbiome is not very strong, our data suggest that a stronger influence of diet (as measured by diet diversity) does not account for this pattern. The mechanism underlying this phylogenetic signal is not clear, but we argue host traits may filter colonization and maintenance of microbes.</p>
Cloacal microbiomes of sympatric and allopatric Sceloporus lizards vary with environment and host relatedness
<p><span>Animals and their microbiomes exert reciprocal influence; the host's environment, physiology, and phylogeny can impact the composition of the microbiome, while the microbes present can affect host behavior, health, and fitness. While some microbiomes are highly malleable, specialized microbiomes that provide important functions can be more robust to environmental perturbations. Recent evidence suggests <em>Sceloporus</em> <em>virgatus</em> has one such specialized microbiome, which functions to protect eggs from fungal pathogens during incubation. Here, we examine the cloacal microbiome of three different <em>Sceloporus</em> species (spiny lizards; Family Phrynosomatidae) – <em>Sceloporus</em> <em>virgatus</em>, <em>Sceloporus</em> <em>jarrovii</em>, and <em>Sceloporus</em> <em>occidentalis</em>. We compare two species with different reproductive modes (oviparous vs. viviparous) living in sympatry: <em>S</em>. <em>virgatus</em> and <em>S</em>. <em>jarrovii</em>. We compare sister species living in similar habitats (riparian oak-pine woodlands) but different latitudes: <em>S</em>. <em>virgatus</em> and <em>S</em>. <em>occidentalis</em>. And, we compare three populations of one species (<em>S</em>. <em>occidentalis</em>) living in different habitat types: beach, low-elevation forest, and the riparian woodland. We found differences in beta diversity metrics between all three comparisons, although those differences were more extreme between animals in different environments, even though those populations were more closely related. Similarly, alpha diversity varied among the <em>S</em>. <em>occidentalis</em> populations and between <em>S</em>. <em>occidentalis</em> and <em>S</em>. <em>virgatus</em>, but not between sympatric <em>S</em>. <em>virgatus</em> and <em>S</em>. <em>jarrovii</em>. Despite these differences, all three species and all three populations of <em>S</em>. <em>occcidentalis</em> had the same dominant taxon, <em>Enterobacteriaceae</em>. The majority of the variation between groups was in low abundance taxa and at the ASV level, and responded to habitat differences, geographic distance, and host relatedness. Understanding wild microbiomes and factors that influence their composition is important to understanding the ecology and evolution of the host animals. </span></p>
Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Visualizing phage infection
<p>We staged infections at four multiplicities of infection (MOI 0, 0.01, 0.1, and 1), and took snapshots every ten minutes over a 40-minute period. We designed FISH probes targeting the non-coding strand of the <em>gp34</em> gene, which encodes a tail fiber protein and quantified cells with 5 or more MGE spots, less than 5 spots, and no spots</p>
Data for: Intraspecific variation in dispersal probability and host quality shape nectar microbiomes
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Gut feeling: Host and habitat as drivers of the microbiome in blackbuck (Antilope cervicapra)
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Resources from: Gut microbiome composition better reflects host phylogeny than diet diversity in breeding wood-warblers
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Cloacal microbiomes of sympatric and allopatric Sceloporus lizards vary with environment and host relatedness
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Data from: A test for microbiome-mediated rescue via host phenotypic plasticity in Daphnia
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Effects of environmental translocation and host characteristics on skin microbiomes of sun-basking fish
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Data from Globally Consistent Drivers of Plant Microbiome Diversity Across Hosts and Continents
We experimentally manipulated two potential mediators of plant microbiome diversity (soil nutrient supply and herbivore density) at 23 grassland sites spanning global-scale gradients in soil nutrients, climate, and plant biomass. This work used sites that are part of the Nutrient Network Experiment (NutNet; www.nutnet.org), a globally replicated experiment manipulating elemental nutrient supplies and herbivore density in grasslands worldwide. Using amplicon sequencing, we measured relative abundances of fungal (ITS1) and prokaryotic (16S) diversity in the leaves of the most widespread grass at each of 23 grassland sites (focal hosts included 18 grass species from 15 genera).
Geography, seasonality, and host-associated population structure influence the fecal microbiome of a genetically depauparate Arctic mammal
<p>The Canadian Arctic is an extreme environment with low floral and faunal diversity characterized by major seasonal shifts in temperature, moisture and daylight. Muskoxen (<i>Ovibos moschatus</i>) are one of few large herbivores able to survive this harsh environment. Microbiome research of the gastrointestinal tract may hold clues as to how muskoxen exist in the Arctic, but also how this species may respond to rapid environmental changes. In this study, we investigated the effects of season (spring/summer/winter), year (2007-2016), and host genetic structure on population-level microbiome variation in muskoxen from the Canadian Arctic. We utilized 16S rRNA gene sequencing to characterize the fecal microbial communities of 78 male muskoxen encompassing two population genetic clusters.<a name="_Hlk534564036"> These clusters are defined by Arctic Mainland and Island populations, including; 1) two mainland sampling locations of the Northwest Territories and Nunavut; and 2) four locations of Victoria Island. </a>Between these geographic populations, we found that differences in the microbiome reflected host-associated genetic cluster with evidence of migration. Within populations, seasonality influenced bacterial diversity with no significant differences between years of sampling. We found evidence of pathogenic bacteria, with significantly higher presence in mainland samples. Our findings demonstrate the effects of seasonality and the role of host population-level structure in driving fecal microbiome differences in a large Arctic mammal.</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)
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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.