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2,708 results for “Microbiota”
Effects of crown gall disease on natural microbiota of Vitis vinifera - genome annotations
<p>Young grapevines (Vitis vinifera) frequently die due to the crown gall (CG) disease induced by the plant pathogen Allorhizobium vitis (Rhizobiaceae). Virulent members of A. vitis harbour a tumor-inducing (Ti) plasmid and cause formation of CGs due to genes encoded on the T-DNA. Expression of the oncogenes by transformed host cells induce cell proliferation, metabolic and physiological changes. The CG produces opines uncommon to plants, which provide an important nutrient source for A. vitis harbouring opine catabolism enzymes. CGs host a defined bacterial community and the mechanisms establishing a CG-specific bacterial community are currently unknown. Thus, we were interested in whether genes homologous to those of the Ti-plasmid coexist in the genomes of the microbial species coexisting in CGs. We isolated eight bacterial strains from grapevine CGs, sequenced their genomes and tested their virulence and opine utilization ability in bioassays. In addition, the eight genome sequences were aligned to the sequences of a Ti-plasmid and seven published bacterial genomes, including closely related plant associated bacteria but not from CGs. Homologous genes for virulence and opine anabolism were only present in the virulent Rhizobiaceae. By contrast, homologs of the opine catabolism genes were present in all strains including the non-virulent members of the Rhizobiaceae and non-Rhizobiaceae, indicating horizontal gene transfer of the opine degradation cluster from virulent to non-virulent strains. These results along with those of the opine utilization assay support the important role of opine utilization for co-colonization of virulent and non-virulent bacteria in CGs, thereby shaping the CG community.</p> <p>This dataset contains the prokka annotations of the genomes as used in "Opportunistic bacteria of grapevine crown galls are equipped with the genomic repertoire for opine utilization"</p>
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>
Cysteine dependence of Lactobacillus iners is a potential therapeutic target for vaginal microbiota modulation
<p>Compressed directories containing code and data files sufficient to reproduce analysis from Bloom et al paper on <em>Lactobacillus iners</em> (<em>Nature Microbiology</em>). An earlier, non-peer-reviewed manuscript version containing largely the same analysis was posted as a pre-print in <em>bioRxiv</em> at (https://doi.org/10.1101/2021.06.12.448098). Three compressed directory for analyses of:</p> <ol> <li>Vaginal <em>Lactobacillus </em>genome catalog characterization and gene content analysis.</li> <li>Analysis of relationship between cervicovaginal microbiota composition and cysteine concentrations in vaginal fluid from a South African cohort</li> <li>Analysis of results of <em>in vitro </em>mixed culture competition assays including: <ol> <li>Pairwise competition between <em>L. iners</em> and <em>Lactobacillus crispatus</em> in <em>Lactobacillus</em> MRS broth containing L-cysteine +/- S-methyl-L-cysteine (SMC)</li> <li>Defined bacterial-vaginosis (BV)-like communities including <em>L. iners</em>, <em>L. crispatus</em>, and BV-associated species <em>Gardnerella vaginalis</em>, <em>Prevotella bivia</em>, and <em>Atopobium (Fannyhessea) vaginae</em> cultured in S-broth with or without SMC and/or metronidazole.</li> </ol> </li> </ol>
Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"
<p>Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing.</p> <p>The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab.</p> <p>This work is available in medRxiv and has been submitted</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>
Data associated with "Microbiota-derived metabolites inhibit Salmonella virulent subpopulation development by acting on single-cell behaviors"
<p>Data used for the publication Microbiota-derived metabolites inihibit Salmonella virulent subpopulation development by acting on single-cell behaviors. </p> <p> </p> <p>all_hi_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the presence of SCFAs.</p> <p>all_no_2307202.csv Single-cell quantifications of Salmonella SPI-1 reporter cells grown in the absence of SCFAs.</p> <p>odmeasurements.csv OD measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p> <p>gfpmeasurements.csv GFP measurements of plate-reader assays of Salmonella SPI-1 reporter cells and controls grown in a range of SCFA conditions. </p>
Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer - pre-processed data
<p>A multicentre, prospective observational study was conducted of colorectal cancer (CRC) patients undergoing primary surgical resection in the United Kingdom and Czech Republic. Analysis was performed using metataxonomics (microbiome) and ultra-performance liquid chromatography mass spectrometry (UPLC-MS, metabolomics). Both datasets were pre-processed as described in the methods section of the main article. The data here were used as the input to the data analysis workflows available from <a href="https://github.com/jmp111/CRC">Github</a>.</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>
The diversity and function of the peanut pods-associated microbiota and their effects on aflatoxin contamination in China
<p>In essence, these results are instructive for developing novel aflatoxin-control technology, selecting for better peanut species, and predicting aflatoxin contamination, therefore are of great interest for improving peanut quality and safety. </p>
Data from: Linking pollen foraging of megachilid bees to their nest bacterial microbiota
<p>Solitary bees build their nests by modifying the interior of natural cavities and they provision them with food by importing collected pollen. As a result, the microbiota of the solitary bee nests may be highly dependent on introduced materials. In order to investigate how the collected pollen is associated with the nest microbiota, we used metabarcoding of the ITS2 rDNA and the 16S rDNA to simultaneously characterize the pollen composition and the bacterial communities of 100 solitary bee nest chambers belonging to seven megachilid species. We found a weak correlation between bacterial and pollen alpha-diversity and significant associations between the composition of pollen and that of the nest microbiota, contributing to the understanding of the link between foraging and bacteria acquisition for solitary bees. Since solitary bees cannot establish bacterial transmission routes through eusociality, this link could be essential for obtaining bacterial symbionts for this group of valuable pollinators.</p>
MMGC: gene catalogues of the mouse and human microbiota
<p>We produced protein cluster catalogues in order to establish the gene-level taxonomic overlap between the human and mouse microbiome. </p> <p>For gene-clustering, we concatenated 76,937,350 pre-clustered human predicted proteins for non-redundant, near-complete genomes of the UHGG with 45,598,646 mouse predicted protein-coding sequences from non-redundant, near-complete species of the MMGC, and performed protein clustering using the ‘linclust’ function from MMseqs285 v10-6d92c (-c 0.8 --cov-mode 1 --cluster-mode 2 --kmer-per-seq 80). Proteins were clustered at 100%, 90%, 80% and 50% sequence identity; clusters were considered shared if they contained genes from both human and mouse commensals.</p> <p>This archive includes two files for each sequence identity threshold: a representative protein sequence file (.fa) and a sequence membership file (.tsv). </p> <p> </p> <p> </p>
Metagenome assemblies and metagenome-assembled genomes from the Daphnia magna microbiota
<p>Metagenome assemblies generated from raw reads not mapping to the Daphnia magna genome for six samples assembled individually (G4, G14, S1-S4) and a coassembly of all six samples (a_assembly) using metaSPAdes in SPAdes v3.14. Assemblies can be found in metagenome_assemblies.zip.</p> <p>Metagenome-assembled genomes generated using VAMB v3.0.2 (vamb_bins.zip) and ProxiMeta (proximeta_bins.zip). These MAGs were taxonomically identified using GTDB-Tk v1.3 and quality checked using CheckM v1.1. Outputs from GTDB-Tk and CheckM can be found in the .tsv and .tab files, respectively.</p>
Phyloseq R object accompanying the paper Temporal Dynamics Cloacal Microbiota 16S metataxonomics
<p>This ready to load <strong>phyloseq</strong> R S4 object contains the ASV table, taxonomy table and sample metadata. This data was build using the DaDa2 (version 1.6.0) and phyloseq (version 1.223) R packages using our raw MiSeq PE300 sequencing data deposited at NCBI-SRA under BioProject: PRJNA673103.</p> <p>The accompanying (peer-reviewed) scientific article can be found here: </p> <ul> <li>https://www.frontiersin.org/articles/10.3389/fmicb.2020.626713/abstract </li> <li>DOI: 10.3389/fmicb.2020.626713</li> </ul> <p> </p> <p><strong>Study/paper</strong> </p> <p>J. Schreuder, F.C. Velkers, A. Bossers, R.J. Bouwstra, W.F. de Boer, P. van Hooft, J.A. Stegeman, S.D. Jurburg.</p> <p>Associations between animal health and performance, and the host’s microbiota have been recently established. In poultry, changes in the intestinal microbiota have been linked to housing conditions and host development, but how the intestinal microbiota respond to environmental changes under farm conditions is less well understood. To gain insight into the microbial responses following a change in the host’s immediate environment, we monitored four indoor flocks of adult laying chickens three times over 16 weeks, during which two flocks were given access to an outdoor range, and two were kept indoors. To assess changes in the chickens’ microbiota over time, we collected cloacal swabs of 10 hens per flock and performed 16S rRNA gene amplicon sequencing.<br> The poultry house (i.e., the stable in which flocks were housed) and sampling time explained 9.2 % and 4.4 % of the variation in the microbial community composition of the flocks, respectively. Remarkably, access to an outdoor range had no detectable effect on microbial community composition, the variability of microbiota among chickens of the same flock, or microbiota richness, but the microbiota of outdoor flocks became more even over time. Fluctuations in the composition of the microbiota over time within each poultry house were mainly driven by turnover in rare, rather than dominant, taxa and were unique for each flock. We identified 16 amplicon sequence variants that were differentially abundant over time between indoor and outdoor housed chickens, however none were consistently higher or lower across all chickens of one housing type over time. Our study shows that cloacal microbiota community composition in adult layers is stable following a sudden change in environment, and that temporal fluctuations are unique to each flock. By exploring microbiota of adult poultry flocks within commercial settings, our study sheds light on how the chickens’ immediate environment affects the microbiota composition.</p>
Data from: Wildlife fecal microbiota exhibit community stability across a semi-controlled longitudinal non-invasive sampling experiment
<p>Wildlife microbiome studies are being used to assess microbial links with animal health and habitat. The gold standard of sampling microbiomes directly from captured animals is ideal for limiting potential abiotic influences on microbiome composition, yet fails to leverage the many benefits of non-invasive sampling. Application of microbiome-based monitoring for rare, endangered, or elusive species creates a need to non-invasively collect scat samples shed into the environment. Since controlling sample age is not always possible, the potential influence of time-associated abiotic factors was assessed. To accomplish this, we analyzed partial 16S rRNA genes of fecal metagenomic DNA sampled non-invasively from Rocky Mountain elk (<em>Cervus canadensis</em>) near Yellowstone National Park. We sampled pellet piles from four different elk, then aged them in a natural forest plot for 1, 3, 7, and 14 days, with triplicate samples at each time point (i.e., a blocked, repeat measures (longitudinal) study design). We compared microbiomes of each elk through time with point estimates of diversity, bootstrapped hierarchical clustering of samples, and a version of ANOVA–simultaneous components analysis (ASCA) with PCA (LiMM-PCA) to assess the variance contributions of time, individual and sample replication. Our results showed community stability through days 0, 1, 3 and 7, with a modest but detectable change in abundance in only 2 genera (<em>Bacteroides</em> and <em>Sporobacter</em>) at day 14. The total variance explained by time in our LiMM-PCA model across the entire 2-week period was not statistically significant (p>0.195) and the overall effect size was small (<10% variance) compared to the variance explained by the individual animal (p<0.0005; 21% var.). We conclude that non-invasive sampling of elk scat collected within one week during winter/early spring provides a reliable approach to characterize microbiome composition in a 16S rDNA survey and that sampled individuals can be directly compared across unknown time points with minimal bias. Further, point estimates of microbiome diversity were not mechanistically affected by sample age. Our assessment of samples using bootstrap hierarchical clustering produced clustering by animal (branches) but not by sample age (nodes). These results support greater use of non-invasive microbiome sampling to assess ecological patterns in animal systems.</p>
Cross-reactive CD8+ T cell responses to tumor-associated antigens (TAAs) and homologous microbiota-derived antigens (MoAs)
<p><strong><span>Background: </span></strong><span>We have recently shown extensive sequence and conformational homology between tumor-associated antigens (TAAs) and antigens derived from microorganisms (MoAs). The present study aimed to assess the breadth of T-cell recognition specific to MoAs and the corresponding TAAs in healthy subjects (HS) and patients with cancer (CP).</span></p> <p><strong><span>Method: </span></strong><span>A library of >100 peptide-MHC (pMHC) combinations was used to generate DNA-barcode labelled multimers. Homologous peptides were selected from the Cancer Antigenic Peptide Database, as well as Bacteroidetes/Firmicutes-derived peptides. They were incubated with CD8+ T cells from the peripheral blood of HLA-A*02:01 healthy individuals (n=10) and cancer patients (n=16). T cell recognition was identified using tetramer-staining analysis. Cytotoxicity assay was performed using as target cells TAP-deficient T2 cells loaded with MoA or the paired TuA.</span></p> <p><strong><span>Results: </span></strong><span>A total of 66 unique pMHC recognized by CD8+ T cells across all groups were identified. Of these, 21 epitopes from microbiota were identified as novel immunological targets. Reactivity against selected TAAs was observed for both HS and CP. pMHC tetramer staining confirmed CD8+ T cell populations cross-reacting with CTA SSX2 and paired microbiota epitopes. Moreover, PBMCs activated with the MoA where shown to release IFNγ as well as to exert cytotoxic activity against cells presenting the paired TuA.</span></p> <p><strong><span>Conclusions: </span></strong><span>Several predicted microbiota-derived MoAs are recognized by T cells in HS and CP. Reactivity against TAAs was observed also in HS, primed by the homologous bacterial antigens. CD8+ T cells cross-reacting with MAGE-A1 and paired microbiota epitopes were identified in three subjects. Therefore, the microbiota can elicit an extensive repertoire of natural memory T cells to TAAs, possibly able to control tumor growth (“natural anti-cancer vaccination”). In addition, non-self MoAs can be included in preventive/therapeutic off-the-shelf cancer vaccines with more potent anti-tumor efficacy than those based on TAAs.</span></p>
ScienceDex guides
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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.