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1,065 results for “gut microbiota”
Data from: "Alteration of the gut microbiota's composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters"
<p>This dataset contains all data collected and used for the publication : "Alteration of the gut microbiota’s composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters". Besides the Readme, it contains 11 files.</p> <p><br> Excel files with classification (i.e. genes according to their fold induction or repression) are provided. Data include different conditions with varying number of samples per group. Data are structured according to employed methods and then stratify the data obtained within the individual work packages.</p>
With or without you: Gut microbiota does not predict aggregation behaviour in females of the European earwig
<p>Recent studies suggest that the gut microbiota could be one of the main driving forces behind the evolution of group living. However, these studies are mainly based on our knowledge of species in which non-social individuals are rare and abnormal, calling into question the adaptive value of the reported association between group-living and gut microbiota. In this study, we addressed this issue by testing this association in females of the European earwig, an insect showing frequent, naturaland wide inter-individual variation in the expression of group living. We video-tracked 320 field-sampled females to quantify their natural variation in aggregation and then tested whether the most and least gregarious females had different gut microbiota. We also compared the general activity, boldness, body size and body condition of these females and examined the association between each of these traits and the gut microbiota. Contrary to our predictions, we found no difference in gut microbiota between the most and least gregarious females, as well as no difference between these females in terms of general activity, boldness, body size and condition. We did show that the gut microbiota of females was overall linked to their body condition, even though it was also unrelated to the other measurements. Overall, these results demonstrate that a host's gut microbiota is not necessarily a major driver of aggregation behaviour in species with inter-individual variation in group living and call for future studies to investigate the determinants and role of gut microbiota in earwigs.</p>
Kin selection explains the evolution of cooperation in the gut microbiota, by Simonet & McNally, 2020, Dataset S1 and codes for statistical analysis and figures production
<p>Dataset S1 contains all raw and processed material referred to in the published article "Kin selection explains the evolution of cooperation in the gut microbiota". R codes files provide all codes to replicate the analysis. Please refer to the README file for a description of all code files. The manifest files are those obtained by accessing the HMP portal on April 2020 under Project > HMP, Body Site > feces, Studies>WGS-PP1, File Type > WGS raw sequences set, File format > FASTQ.</p> <p>We also provide access to these data and codes at our GitHub (https://github.com/CamilleAnna/HamiltonRuleMicrobiome gitRepos.git) which can be cloned to directly re-run this analysis. </p> <p><strong>Legends for Dataset S1:</strong></p> <ul> <li>Sheet 1: Metagenomic samples used and access links.</li> <li>Sheet 2: Reference on bacterial cooperation retrieved from Web of Science search: TI¯((microb* OR bacter* OR microorganis* OR micro-organis*) AND (coop* OR social*)</li> <li>Sheet 3: Retained bacteria cooperation keywords</li> <li>Sheet 4: GOs identified by annotating all MIDAS database genomes (5944 genomes) with PANNZER2.</li> <li>Sheet 5: Full list of potential bacterial cooperation GO terms and description of manual curation decisions.</li> <li>Sheet 6: Final list of bacterial cooperation GO used for the analysis</li> <li>Sheet 7: Genomic diversity of the bacterial population within and across host. Computed from MIDAS snp_diversity.py pipeline.</li> <li>Sheet 8: final dataset for statistical analysis.</li> <li>Sheet 9: per-gene annotation of cooperation.</li> </ul>
The gut microbiota of environmentally enriched mice regulates visual cortical plasticity
<p>ABSTRACT</p> <p>The complexity of brain circuits is sculpted both by innate genetic programs and environmental stimuli. Since the 1960s scientists have noticed that raising rodents in an enriched environment (EE) is able to improve all aspects of brain plasticity, from learning and memory to visual plasticity in adult and developing animals. Importantly, EE has also been shown to have beneficial effects on a variety of preclinical models of central nervous system diseases: Alzheimer’s and Parkinson’s disease, Rett syndrome, epilepsy etc, prompting intervention protocols in humans. However, the “enrichment derived key signals” through which this special environment performs its broad positive effects on brain health have not been completely elucidated yet. Here, we focused on signals coming from the body periphery and in particular on the gut microbiota. We found that the intestinal microbiota composition of EE mice is significantly different from the one of standard raised (ST) animals. Treatment of EE mice with an antibiotic cocktail completely prevented the EE-driven enhancement of OD plasticity. Strikingly, the fecal microbiota transplant from EE donors to adult ST mice was able to re-activate OD plasticity in the ST recipients. Thus, taken together our data suggest that experience-dependent changes in gut microbiota regulate brain plasticity.</p> <p>METHODS</p> <p>In the first dataset (Dataset1, files called zr2423) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J mice raised in EE or in ST from birth and collected at different time points during their lives.</p> <p>To analyze the composition of the microbiota of ST and EE mice at different ages, fresh faeces were collected longitudinally in the same subject at postnatal day (P)20 (n=6), P25 (n=6) and P90 (n=6). </p> <p>In the second dataset (Dataset2, files called zr2747) we report the raw data (.fastq) obtained from the sequencing of the fecal samples from C57BL/6J: adult donor mice living in EE (EE, n=8), adult recipient mice living in ST condition before the fecal transplantation (preFT, n=8) and 4 weeks after the fecal transplantation (postFT, n=8).</p> <p>For further details about the sample names see the “Explanation Table”.</p> <p>Bacterial DNA was extracted using a specific kit (QIAamp Powerfecal DNA kit, Qiagen) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Irvine, CA, USA). </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). The sequencing was performed with >10% PhiX spike-in.</p> <p> </p>
Raw metabolomics data of the paper: New findings in the metabolism of the saffron apocarotenoids, crocins and crocetin, by the human gut microbiota
<p>Raw dataset of the metabolomicas data of the study : New findings in the metabolism of the saffron apocarotenoids, crocins and crocetin, by the human gut microbiota.</p> <p>The csv file contain the raw data matrix exported from MS-DIAL software after total ions aligment across all study samples.</p>
Bioinformatic pipeline: Genomic diversity landscape of the honey bee gut microbiota
<p>This data-set describes the full bioinformatic pipeline used to analyze 54 metagenomic samples of the honey bee gut microbiota. Each sample was isolated from an individual honey bee, and all samples originate from two colonies of the Engel laboratory at the University of Lausanne, Switzerland. The full raw data-set is available from the sequence-read archive: SRP150166.</p> <p>A publication based on this analysis is currently under review, with the title: "Genomic diversity landscape of the honey bee gut microbiota", and an upload to Biorxiv is also underway.</p> <p>The data-set contains tar-balls for the different main workflows of the analysis. Dowload and unpack to view the contents (tar -zxvf filename.tar.gz). For each workflow, all directories contain README.txt files, describing the contents of the directory. Due to size constraints, some intermediate files have been omitted, and some workflows are demonstrated for a subset of the data. However, the full analysis can be reproduced from the raw data, using the provided scripts.</p> <p>Scripts are included within workflow directories, and are also provided as a separate tar-ball for convenience. All perl-scripts come with documentation, which can be viewed by typing: "perl script_name.pl -h". For R scripts, the usage is indicated as a comment in the top lines of each script. Note that many of the scripts require specific input-files to be present in the run-directory. Their usage is demonstrated within the workflow directories in bash-scripts (*.sh). Commands used for generating plots and some statistics are given within workflow directories in text-files "R.commands" when applicable.</p> <p>Aside from custom code, the pipeline also utilizes various open-source Software packages, which are detailed in the file "software_dependencies.txt". Note, while many of the scripts will run fast on any computer, some steps of the pipeline are computationally demanding, and will require significant computing time, as well as storage space. When scripts are known to be time-consuming, this is indicated in the script help message.</p> <p> </p> <p> </p> <p> </p>
raw data of Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice
<p>Microbiome dataset for "Gut microbiota remodeling and intestinal adaptation to lipid malabsorption after enteroendocrine cell loss in adult mice" publication</p> <p>https://doi.org/10.1016/j.jcmgh.2023.02.013</p> <p> </p>
Metagenomics uncovers dietary adaptations for chitin digestion in the gut microbiota of convergent myrmecophagous mammals
<p><strong>Metagenomics uncovers dietary adaptations for chitin digestion in the gut microbiota of convergent myrmecophagous mammals</strong></p> <p>Sophie Teullet<sup>a,#</sup>, Marie-Ka Tilak<sup>a</sup>, Amandine Magdeleine<sup>a</sup>, Roxane Schaub<sup>b,c</sup>, Nora M. Weyer<sup>d</sup>, Wendy Panaino<sup>d,e</sup>, Andrea Fuller<sup>d</sup>, William. J. Loughry<sup>f</sup>, Nico L. Avenant<sup>g</sup>, Benoit de Thoisy<sup>h,i</sup>, Guillaume Borrel<sup>j</sup> and Frédéric Delsuc<sup>a,#</sup></p> <p><sup>a</sup>Institut des Sciences de l’Evolution de Montpellier (ISEM), Univ Montpellier, CNRS, IRD, Montpellier, France</p> <p><sup>b</sup>CIC AG/Inserm 1424, Centre Hospitalier de Cayenne Andrée Rosemon, Cayenne, French Guiana</p> <p><sup>c</sup>Tropical Biome and immunopathology, Université de Guyane, Labex CEBA, DFR Santé, Cayenne, French Guiana</p> <p><sup>d</sup>Brain Function Research Group, School of Physiology, University of the Witwatersrand, Johannesburg, South Africa</p> <p><sup>e</sup>Centre for African Ecology, School of Animals, Plant, and Environmental Sciences, University of the Witwatersrand, Johannesburg, South Africa</p> <p><sup>f</sup>Department of Biology, Valdosta State University, Valdosta, GA, USA</p> <p><sup>g</sup>National Museum and Centre for Environmental Management, University of the Free State, Bloemfontein, South Africa</p> <p><sup>h</sup>Institut Pasteur de la Guyane, Cayenne, French Guiana, France</p> <p><sup>i</sup>Kwata NGO, Cayenne, French Guiana, France</p> <p><sup>j</sup>Institut Pasteur, Université Paris Cité, UMR CNRS 6047, Evolutionary Biology of the Microbial Cell, Paris, France</p> <p><sup>#</sup>Corresponding authors: sophie.teullet@umontpellier.fr; frederic.delsuc@umontpellier.fr</p> <p> </p> <p><em><strong>Abstract</strong></em></p> <p>In mammals, myrmecophagy (ant and termite consumption) represents a striking example of dietary convergence. This trait evolved independently at least five times in placentals with myrmecophagous species comprising aardvarks, anteaters, some armadillos, pangolins, and aardwolves. The gut microbiome plays an important role in dietary adaptation, and previous analyses of 16S rRNA metabarcoding data have revealed convergence in the composition of the gut microbiota among some myrmecophagous species. However, the functions performed by these gut bacterial symbionts and their potential role in the digestion of prey chitinous exoskeletons remain open questions. Using long- and short-read sequencing of fecal samples, we generated 29 gut metagenomes from nine myrmecophagous and closely related insectivorous species sampled in French Guiana, South Africa, and the USA. From these, we reconstructed 314 high-quality bacterial genome bins of which 132 carried chitinase genes, highlighting their potential role in insect prey digestion. These chitinolytic bacteria belonged mainly to the family Lachnospiraceae, and some were likely convergently recruited in the different myrmecophagous species as they were detected in several host orders (i.e., <em>Enterococcus faecalis</em>, <em>Blautia</em> sp), suggesting that they could be directly involved in the adaptation to myrmecophagy. Others were found to be more host-specific, possibly reflecting phylogenetic constraints and environmental influences. Overall, our results highlight the potential role of the gut microbiome in chitin digestion in myrmecophagous mammals and provide the basis for future comparative studies performed at the mammalian scale to further unravel the mechanisms underlying the convergent adaptation to myrmecophagy.</p> <p> </p> <p><em><strong>Main figures and corresponding datasets</strong></em></p> <p><strong>Figure_1_dataset.zip</strong> contains:</p> <ul> <li><strong>FIGURE 1.</strong> Phylogenetic position of the 314 high-quality selected bins reconstructed from 29 gut metagenomes of the nine focal myrmecophagous species within a reference prokaryotic phylogeny. A: Phylogeny of the 314 selected bins (red branches) with 2496 prokaryote reference genomes. Circles respectively indicate (from inner to outer circles): the bacterial phyla and kingdom to which these genome bins were assigned based on the Genome Taxonomy Database release 7 (Parks <em>et al</em>, 2021). Clades, where a subtree was defined, are highlighted in blue for the Firmicutes (Fig. 1B), green for the Bacteroidetes, and pink for the Proteobacteria (Figs. S2 A and B, respectively). B: Subtree within Fimircutes showing myrmecophagous-specific clades (blue highlights; dark blue corresponds to the three clades mentioned in the results, light blue to the other clades). The outer circle indicates the bacterial family to which these genome bins were assigned based on the Genome Taxonomy Database. Bins’ names of the myrmecophagous-specific clades are indicated at leaves of the phylogenetic tree together with the genus to which they were assigned to.</li> <li><strong>phylophlan_LR_SR_ToL_FINAL_concatenated.aln</strong>: Alignment of the concatenated markers assembled by PhyloPhlAn v3.0.58.</li> <li><strong>phylophlan_LR_SR_ToL_FINAL.tre</strong>: Phylogenetic tree reconstructed by PhyloPhlAn v3.0.58 for the 314 high quality selected genome bins and the 2496 prokaryote reference genomes.</li> </ul> <p><strong>Figure_2_dataset.zip </strong>contains:</p> <ul> <li><strong>FIGURE 2</strong>. Phylogeny of the 394 GH18 sequences identified in 132 high-quality selected bins reconstructed from 29 gut metagenomes of the nine focal myrmecophagous species and relatives. Red branches indicate the 237 sequences having an active chitinolytic site (DXXDXDXE). Circles respectively indicate (from inner to outer circles): the bacterial family and phyla of the bin the sequence was retrieved from. Colored sequence names indicate the host species. Colored circles at certain nodes indicate enzymes to which sequences are similar when blasting them against the NCBI non-redundant protein database. Sequence names are indicated at leaves of the tree and begin with the genus to which the bin they were identified in was assigned to. </li> <li><strong>GH18_sequences_from_selected_bins_alignment.fasta</strong>: Alignment of the 394 GH18 sequences identified in 132 high quality selected bins computed with MAFFT v7.450.</li> <li><strong>GH18__sequences_from_selected_bins_tree.newick</strong>: Phylogenetic tree of the 394 GH18 sequences inferred with RAxML v8.2.11 within Geneious Prime 2022.0.2.</li> </ul> <p><strong>Figure_3_dataset.zip</strong> contains:</p> <ul> <li><strong>FIGURE 3</strong>. Detection of the 314 high-quality bacterial genomes (lines) in the 29 gut metagenomes (columns) of the nine focal species. Each square indicates the detection of a genome bin in a sample as estimated by anvi’o v7 (Eren <em>et al</em>, 2021). Names of bins are indicated on the left with red indicating chitinolytic bins (Table S2). The names begin with the genus to which the bin was assigned to. Asterisks (*) indicate bins detected in at least one soil sample (detection > 0.25) (Fig. S4, Table S2, and detection table available via Zenodo). Phylogenetic relationships of host species distinguished by different color strips are represented at the bottom of the graph. Columns on the right indicate (from left to right): the number of GH18 sequences identified in each bin (from 0 to 17), the bin’s taxonomic phylum, class, order, and family. The phylogeny of the 314 selected bins inferred with PhyloPhlAn v3.0.58 (Asnicar <em>et al</em>, 2020) is also represented on the right of the graph (see Fig. S1). Silhouettes were downloaded from phylopic.org.</li> <li><strong>detection_bins_across_gut_metagenomes.txt</strong>: Detection table as tab-delimited file containing the detection values inferred by anvi'o v7 for the 314 high quality selected bins across the 29 gut metagenomes from the nine focal myrmecophagous species. </li> </ul> <p><strong>Figure_4_dataset.zip</strong> contains:</p> <ul> <li><strong>FIGURE 4</strong>. Distribution of chitinolytic selected bins (red links) among the nine focal myrmecophagous species and relatives. Phylogenies of the 314 high-quality selected bins (Fig. S1) and of the nine host species (downloaded from timetree.org) are represented respectively on the left and the right of the graph. Links illustrate, for each bin, in which host species the bin was detected (detection threshold > 0.25). Red links indicate bins in which at least one GH18 sequence with an active chitinolytic site (DXXDXDXE) was found (chitinolytic bins). The size of the circles at the tips of the host phylogeny is proportional to the number of samples (n = 1 for <em>D. kap</em>; n = 2 for <em>D. nov</em>, <em>C. uni</em> and <em>M. tri</em>; n = 3 for <em>T. tet </em>and <em>O. af</em>e; n = 4 for <em>D. sp. nov </em>FG; n = 6 for <em>P. cri </em>and <em>S. tem</em>). Bins’ names are indicated at the tip of the bins’ phylogeny and main bacterial phyla are indicated by colored vertical bars. This graph was done with the cophylo R package within the phytools suite (Revell, 2012). Silhouettes were downloaded from phylopic.org.</li> <li><strong>presence_absence_MAGs_in_metagenomes.txt</strong>: Presence/absence matrix of the 314 selected genome bins across the 29 gut metagenomes.</li> <li><strong>host_species_phylo_reduced_fig4.newick</strong>: Host phylogenetic timetree.</li> </ul> <p><strong>Table_1_sample_infos.xls: </strong>Detailed sample information for the 33 fecal samples collected. <em>N.B</em>.: Diet was determined based on field observations (i.e., dissections) and the literature.</p> <p><strong> </strong></p> <p><em><strong>Supplementary results</strong></em></p> <p><strong>Supplementary_results_Teullet_etal_2023.zip </strong>includes a comparison of genome statistics of the selected bins reconstructed from the long-read vs the short-read datasets, a phylogeny of the set of selected bins before dereplication (n = 407) and a comparison of the distribution of shared and specific genome bins carrying GH18 among host orders.</p> <p> </p> <p><em><strong>Supplementary material</strong></em></p> <p><strong>Supplementary_material_Teullet_etal_2023.zip </strong>contains</p> <ul> <li>Supplementary figures (S1-S4) and tables (S1-S4).</li> <li><strong>phylophlan_314_bins_phylogeny_FINAL_concatenated.aln and phylophlan_314_bins_phylogeny_FINAL.tre</strong>: Alignment of the concatenated markers and the final tree (respectively) reconstructed by PhyloPhlAn v3.0.58 for the 314 high-quality selected and dereplicated genome bins.</li> <li><strong>phylophlan_407_selected_bins_nodRep_concatenated.aln and phylophlan_407_selected_bins_phylogeny_FINAL.tre</strong>: Alignment of the concatenated markers and the final tree (respectively) reconstructed by PhyloPhlAn v3.0.58 for the 407 high-quality selected genome bins before dereplication.</li> <li><strong>abundance_bins_across_gut_metagenomes.txt</strong>: A tab-delimited file corresponding to the absolute abundance values inferred by anvi'o v7 for the 314 high-quality selected bins across the 29 gut metagenomes from the nine focal myrmecophagous species. </li> <li><strong>detection_bins_across_soil_samples.txt</strong>: A tab-delimited file corresponding to the detection values inferred by anvi'o v7 for the 140 high-quality selected bins reconstructed from the aardvark, ground pangolin and southern aardwolf gut metagenomes across the eight soil samples collected on sample sites in South Africa.</li> </ul> <p> </p> <p><strong><em>Assemblies</em></strong></p> <p><strong>Long-read_metagenomic_assemblies_polished.zip</strong> contains the 31 long-read metagenomes assembled with metaFlye strain v2.9 and polished with short reads using Pilon v1.4, which were used for binning.</p> <p><strong>Long-read_metagenomic_assemblies_not_polished.zip</strong> contains the 33 long-read metagenomes assembled with metaFlye strain v2.9 before polishing.</p> <p><strong>Short-read_metagenomic_assemblies.zip</strong> contains the 31 short-read metagenomes assembled with metaSPAdes and MEGAHIT.</p> <p><em>N.B</em>:</p> <ol> <li>Two samples (DASY M1746 and DASY VLD168) were not sequenced using Illumina short reads. Only long reads were generated and assembled for these two samples and are made available here. As these assemblies could not be polished, these samples were not included in downstream analyses.</li> <li>Two samples (CAB M3141 and MYR M5293) were highly contaminated by host reads and not used in downstream analyses. As they were still assembled with the other samples, the corresponding metagenomes are made available here.</li> </ol> <p> </p> <p><strong><em>Binning: genome bins and dereplication results</em></strong></p> <p><strong>High-quality_selected_bins_dereplicated.zip</strong> contains the 314 high quality selected bins (>90% completion, <5% redundancy) reconstructed from long- and short-read metagenomes with metaBAT2 and dereplicated with dRep at 98% ANI.</p> <p><strong>metaBAT2_short-read_assemblies_bins.zip </strong>contains all bins reconstructed from the short-read assemblies with metaBAT2 (i.e., output of metaBAT2).</p> <p><strong>metaBAT2_long-read_assemblies_bins.zip</strong> contains all bins reconstructed from the long-read polished assemblies with metaBAT2 (i.e., output of metaBAT2).</p> <p><strong>Output_dRep_98ANI_407_bins_long-short-reads.zip</strong> contains the output of the dereplication analysis done on the set of 407 high-quality selected genome bins reconstructed from long- (n = 201) and short-read (n = 206; labeled "spad") metagenomes. It was performed with dRep using default parameters. After this step, the final dataset included 314 high-quality non-redundant genome bins. This folder includes:</p> <ul> <li><strong>LR_SR_407_bins_dRep_98ANI_Primary_clustering_dendrogram.pdf</strong>: The primary clustering of selected genome bins using the Mash algorithm with an ANI threshold of 90%.</li> <li><strong>LR_SR_407_bins_dRep_98ANI_Secondary_clustering_dendrograms.pdf</strong>: The secondary clustering of selected genome bins using the fastANI algorithm with an ANI threshold of 98%.</li> <li><strong>LR_SR_407_bins_dRep_98ANI_Cluster_scoring.pdf</strong>: The clustering score attributed to each genome bin during dereplication. Asteriks (*) indicate genomes chosen to be the representative genomes of their cluster.</li> </ul> <ul> </ul>
Double-negative B cells and DNASE1L3 colocalise with microbiota in gut-associated lymphoid tissue - IMC+RNAScope images
<p><span>Intestinal homeostasis is maintained by the response of gut-associated lymphoid tissue to bacteria transported across the follicle associated epithelium into the subepithelial dome. The initial response to antigens and how bacteria are handled is incompletely understood. By iterative application of spatial transcriptomics and multiplexed single-cell technologies, we identify that the double negative 2 subset of B cells, previously associated with autoimmune diseases, is present in the subepithelial dome in health. We show that in this location double negative 2 B cells interact with dendritic cells co-expressing the lupus autoantigens DNASE1L3 and C1q and microbicides.<span> </span>We observe that in humans, but not in mice, dendritic cells expressing DNASE1L3 are associated with sampled bacteria but not DNA derived from apoptotic cells. We propose that fundamental features of autoimmune diseases are microbiota-associated, interacting components of normal intestinal immunity.</span></p>
The gut microbiota affects the social network of honey bees
<p>This dataset contains input files needed to reproduce the automated behavioral tracking data analyses of the research article "The gut microbiota affects the social network of honey bees”. Codes using these data and additional datasets are available at: https://github.com/JoanitoLiberti/The-gut-microbiota-affects-the-social-network-of-honey-bees/</p> <p> </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: infrequent increases in tolerance at high antibiotic concentrations and frequent growth enhancements at low antibiotic concentrations. 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>
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: infrequent increases in tolerance at high antibiotic concentrations and frequent growth enhancements at low antibiotic concentrations. 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>
Genome of the isolates: Enhanced cultured diversity of the mouse gut microbiota enables custom-made synthetic communities
<p>The draft genome of the isolates in Mouse Intestinal Bacteria Collection (miBC)</p> <p> </p> <p>Microbiome research is hampered by the fact that many bacteria are still unknown and by the lack of publicly available isolates. Fundamental and clinical research is in need of comprehensive and well-curated repositories of cultured bacteria from the intestine of mammalian hosts. Due to host-specific features of the gut microbiota, it is sound to establish collections of isolates from single host species. Hence, this project established a collection of bacterial strains isolated from the intestine of mice.</p> <p>The original version of the collection published in 2016 (Lagkouvardos, et. al. 2016.<em> Nat. Microbiol.</em>). was doubled by the addition of 112 strains, representing a total of 141 species across 6 phyla and 35 families for the entire collection. As we aimed to create a well-curated resource, all bacterial species within miBC have been taxonomically described and are publicly available.</p> <p> </p>
Illumina Sequencing Data for "Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes"
<p>Illumina Sequencing Data for Sulaiman et al., "Elucidating human gut microbiota interactions that robustly inhibit diverse Clostridioides difficile strains across different nutrient landscapes".</p>
Data and code: Gut microbiota structure differs between honey bees in winter and summer
<p>This dataset contains data and code underlying the qPCR, amplicon sequencing, and statistical analysis of the research article "Gut microbiota structure differs between honey bees in winter and summer”. Short read datasets are available under NCBI Bioproject accession PRJNA578869.</p>
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>
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>
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>
Data from: Seasonal restructuring facilitates compositional convergence of gut microbiota in free-ranging rodents
<p>Gut microbes provide essential services to their host and shifts in their composition can impact host fitness. However, despite advances in our understanding of how microbes are assembled in the gut, we understand little about the stability of these communities within individuals, nor what factors influence its composition over the life of an animal. For this reason, we conducted a longitudinal survey of the gut microbial communities of individual free-ranging woodrats (<em>Neotoma</em> spp.) across a hybrid zone in the Mojave Desert, USA, using amplicon sequencing approaches to characterize gut microbial profiles and diet. We found that gut microbial communities were individualized and experienced compositional restructuring as a result of seasonal transitions and changes in diet. Turnover of gut microbiota was highest amongst bacterial subspecies and was much lower at the rank of Family, suggesting there may be selection for conservation of core microbial functions in the woodrat gut. Lastly, we identified an abundant core gut bacterial community that may aid woodrats in metabolizing a diet of plants and their specialized metabolites. These results demonstrate that the gut microbial communities of woodrats are highly dynamic and experience seasonal restructuring which may facilitate adaptive plasticity in response to changes in diet. </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.