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Chemical composition, soil water content and 16S rRNA and ITS gene copy numbers of soil aggregates and bulk soil samples
<p>This repository contains all data to reproduce the analyses presented in "Distinct microbial communities are linked to organic matter properties in millimetre-sized soil aggregates", Simon et al 2024, <em>The ISME Journal </em>(DOI: 10.1093/ismejo/wrae156).</p>
DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea
<p><strong><em>This version is to stay up to date with the improvements and increase in 16S rRNA gene sequences (SSU) added to the GTDB release 220. Please read this post for the stats on the updates. </em></strong><strong><em>https://gtdb.ecogenomic.org/stats/r220 </em></strong><strong><em>.</em></strong><strong><em> </em></strong></p> <p><strong><em>There has been no change to the RDP-RefSeq reference database please use previous versions.</em></strong></p> <p><strong><em>If anyone has concerns with MAG extracted 16S rRNA gene contamination concerns, then I suggest that they contact the curators of GTDB themselves because it is outside of my role with these resources designed for DADA2 usage only. </em></strong></p> <p><strong><em>Another concern that was raised was the orientation of the DB sequences, to get past this problem please use the tryRC = TRUE argument in the assignTaxonomy command within DADA2, this will search your ASVs in the reverse complement as well. </em></strong></p> <p>The bacterial and archaeal 16S rRNA gene sequence databases were collated from various sources and formatted to use the "assignTaxonomy" command within the DADA2 pipeline. The data was converted to suite DADA2 format by Alishum Ali.</p> <ol> <li>Genome Taxonomy Database (GTDB): The new version of our dada2 formatted GTDB reference sequences now contains 58102 bacteria and 3672 archaea full 16S rRNA gene sequences. If you wonder why there are fewer species with 16S rRNA, that is because some metagenomics-assembled genomes (MAGs) lack the 16S gene and thus cannot be extracted. The database was downloaded from <a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release95/">https://data.ace.uq.edu.au/public/gtdb/data/releases/</a> on 24/10/2024. Please read the release notes and file descriptions. </li> </ol> <p>The formatting to DADA2 was done using simple awk bash scripts. The script takes as input a fasta file and a tab-delimited taxonomy file (slightly edited to remove special characters) and then it outputs a fasta file with all 7 taxonomy ranks separated by ";" as required for DADA2 compatibility. Additionally, we have concatenated the unique sequence GTDB ID to the species entry (but replaced the "." with an " _". We see this as an important QC step to highlight the issues/confidence associated with short-read taxonomy assignment at the finer rank levels.</p> <p>Also, this update includes two other files that you can use with the assignTaxonomy and addSpecies commands in DADA2.</p>
DADA2 formatted eHOMD 16S rRNA gene sequences databse
<p>eHOMD Refseq database (V15.22) formated to be used with dada2 <em>i.e.</em>, dada2::assignTaxonomy(seqs, "eHOMD_RefSeq_dada2_V15.22.fasta.gz" ) and dada2::addSpecies(taxa, "eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz", verbose=TRUE)</p> <p>Alternatively, you could use the metabaRpipe R package to directly update the taxonomy of a phyloseq object see: https://github.com/fconstancias/metabaRpipe#2-addingreplacing-taxonomical-table-in-a-phyloseq-object</p> <p>Example below:<br> source("https://raw.githubusercontent.com/fconstancias/metabaRpipe-source/master/Rscripts/functions.R")</p> <p>readRDS("dada2/phyloseq.RDS") %>%<br> phyloseq_dada2_tax(physeq = .,<br> threshold = 60,<br> db ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_V15.22.fasta.gz",<br> db_species ="~/metabaRpipe/databases/eHOMD_RefSeq_dada2_assign_species_V15.22.fasta.gz",<br> nthreads = 2,<br> full_return = FALSE) -> physeq_eHOMD_tax</p> <p> </p> <p> </p>
Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013
Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is jus
Inventory of High-resolution phylogenetic profiles of the planktonic microbial communities (via 16S and 18S rRNA gene amplicons) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, 2017 - ongoing
Planktonic microbial communities mediate many vital biogeochemical processes in wetland ecosystems, yet compared to other aquatic ecosystems, like oceans, lakes, rivers, or estuaries, they remain relatively underexplored. Our study site, the Florida Everglades (USA)—a vast iconic wetland consisting of a slow-moving system of shallow rivers connecting freshwater marshes with coastal mangrove forests and seagrass meadows—is a highly threatened model ecosystem for studying salinity and nutrient gradients, as well as the effects of sea level rise and saltwater intrusion. This dataset provides the first high-resolution phylogenetic profiles of planktonic bacterial and eukaryotic microbial communities (using 16S and 18S rRNA gene amplicons) from these environments. The dataset contains 16S and 18S rRNA data from 2017, and contains 16S rRNA data for monthly (2019) and quarterly water samples (2020-ongoing). The 2017 data are published in Laas et al. 2022. A detailed list of sequence data and their accession numbers in GenBank is provided and will be updated as more data are published. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA525456 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA525456) and BioProject PRJNA1018945 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1018945). This data package is associated with the following publication: Laas, P., Ugarelli, K., Travieso, R., Stumpf, S., Gaiser, E. E., Kominoski, J. S., & Stingl, U. (2022). Water column microbial communities vary along salinity gradients in the Florida Coastal Everglades wetlands. Microorganisms, 10(2), 215. https://doi.org/10.3390/microorganisms10020215 Instead of citing this package, which is an inventory, please cite the original GenBank data or journal article, as appropriate. Citation guidance for the journal article is available on the respective publisher's website.
16S rRNA gene sequence accessions from discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (2013-2023, ongoing)
An important component of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project is monitoring spatial and temporal patterns in the biological composition of perennially ice-covered lakes in Antarctica’s McMurdo Dry Valleys. This data package contributes to this core research area by providing a curated table linking 16S rRNA gene sequence accession numbers archived in NCBI to MCM LTER limnological sampling campaigns conducted at specific depths along the water column of Lakes Fryxell, Hoare, Bonney, and Miers. These data enable integration of microbial community data with co-collected biological, chemical, and physical measurements.
Genomes and full-length 16S reference sequences for 27 Alpha- and Gamma-Proteobacterial isolates from Red Sea Acropora corals
<p>Coral-associated bacteria contribute to the biology of their host, but the underlying molecular interactions are largely unknown. To further our functional understanding, we obtained 27 alpha- and gamma-proteobacterial isolates, many of which are Rhodobacteraceae, from three coral species of the genus <em>Acropora </em>and assembled/annotated their genomes as a resource for further functional studies. Our results reveal the immense taxonomic and genetic diversity of common alpha- and gamma-proteobacterial coral-associated bacteria. We hope these data provide a framework to study the function of specific bacteria in the coral holobiont. Isolates are available upon request.</p>
Human intestinal Bacteria Collection (HiBC): 16S rRNA gene sequences
<p>The <a href="https://hibc.rwth-aachen.de/" target="_blank" rel="noopener">Human intestinal Bacteria Collection (HiBC)</a> is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly.</p> <p>This dataset includes the sequences of the 16S rRNA gene sequences of the isolates in the FASTA nucleotide format. Sequences ending in Sanger were obtained using the Sanger dideoxy sequencing technology. Sequences ending in Genome were obtained from the genome sequence using barrnap.</p>
EAW FASTQ files for bioinformatic courses (16S rRNA genes, 2018)
<p>Selection of 6 samples, with 6 Forward (R1) and 6 Reverse (R2) files, including primers. R1 and R2 reads are ca. 300 bp long, and were obtained from Illumina MiSeq technologies, at the FEM facility sequencing platform. The files refer to the16S rRNA gene reads obtained from the analyses carried out on the samples collected and filtered (Sterivex<sup>TM</sup> 0.22 µm) in different areas and depths of Lake Garda on September, 2018 (see EAW_2018_FASTQ_16S_description.docx).</p> <p>Sampling and analyses were carried out in the framework of the project Eco-AlpsWater (ASP569), funded by the Interreg Alpine Space program.</p> <p>A bioinformatic protocol for analyzing these data using DADA2 is available in Zenodo:</p> <pre>https://doi.org/10.5281/zenodo.5232772 </pre>
16S rRNA sequencing gene datasets for CRC data
<p>Used datasets: </p> <table> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>Dataset</th> <th>16S rRNA Region</th> <th>Control (n)</th> <th>Adenoma (n)</th> <th>CRC (n)</th> <th>Available metadata</th> </tr> </thead> <tbody> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4823848/">Baxter</a></td> <td>V4</td> <td>171</td> <td>198</td> <td>120</td> <td>Gender, age, weight, height, BMI, country, race</td> </tr> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221363/">Zackular</a></td> <td>V4</td> <td>30</td> <td>30</td> <td>30</td> <td>Gender, age, weight, height, BMI, country, race, FOBT, medication</td> </tr> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299606/">Zeller</a></td> <td>V4</td> <td>50</td> <td>38</td> <td>41</td> <td>Gender, age, BMI, country, FOBT</td> </tr> <tr> <td><strong>TOTAL</strong></td> <td>V4</td> <td>251</td> <td>266</td> <td>191</td> <td><em>All of the above</em></td> </tr> </tbody> </table> </th> </tr> </thead> <tbody> <tr> <td> </td> </tr> </tbody> </table> <p>Data processing & sharing</p> <p>All datasets were processed using <a href="https://docs.qiime2.org/2021.11/">qiime2</a> pipeline with <a href="https://benjjneb.github.io/dada2/">DADA2</a> for Sequence quality control and feature table construction and <a href="https://www.arb-silva.de/">SILVA</a> database for taxonomic assignment, and then a <em>phyloseq </em>object was constructed.</p> <ul> <li>Abundance table at genus level is in file <em>genus.csv</em> (Sample counts with NO filtering).</li> <li>Clean metadata is in <em>metadata.csv</em> file (Countries: CA - Canada. USA - United States of America. FRA - France.)</li> <li>Phyloseq object is in file <em>physeq.RDS</em> (Saved as an RDS object in R)</li> </ul> <p>More information is <a href="https://hackmd.io/nbsLqCLlSNSRFc5RBX9c5Q?view">here</a>. </p> <p> </p>
16S RC-PCR control samples and mock standards
<p>16S RC-PCR datasets of control samples and mock standards.<br> Produced during the 16S RC-PCR study.</p> <p>Datasets are produced using 2x150 Illumina MiniSeq.<br> A short description of each sample is given in the METADATA_zenodo file.</p>
16S V4-V5 metabarcoding reference databases and weighted naive-bayes classifiers, dereplicated
<p>16S metabarcoding databases and naive-bayes classifiers specific to the V4-V5 region. Built from the <a href="https://www.arb-silva.de/documentation/release-138/">Silva 138.1 SSU Ref NR 99</a> database using Qiime2 (version 2023.2) and the <a href="https://github.com/BenKaehler/q2-clawback">q2-clawback plugin.</a> Includes weighted classifiers for two Earth Microbiome Project Ontology (EMPO) 3 habitat types: "sediment (saline)" and "water (saline)" , with data downloaded from <a href="https://qiita.ucsd.edu/">Qiita</a>. Sequences were dereplicated with Rescript --p-mode 'uniq' , retaining identical sequence records that have differing taxonomies.</p> <p>Primers used:</p> <p>EMP 16S 515f: GTGYCAGCMGCCGCGGTAA</p> <p>EMP 16S 926r: CCGYCAATTYMTTTRAGTTT</p> <p><strong>Stats</strong></p> <p>286,948 unique sequences</p> <p>309,567 total sequences</p> <p>46,254 unique taxa (Level 7)</p> <table> <caption>File description</caption> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>File</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>make new 16S silva V4-V5 database.md</td> <td>Markdown with code used to generate databases</td> </tr> <tr> <td>silva-138-99-seqs.qza</td> <td>Full length Silva 138.1 SSU 99 sequences</td> </tr> <tr> <td>silva-138-99-tax.qza</td> <td>Taxa for full length Silva 138.1 SSU 99 database</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-seqs.qza</td> <td>Sequences for 16S V4-V5 (primers 515f, 926r), extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible), dereplicated</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-taxa.qza</td> <td>Taxa for silva-138_1-99-515f_926r-seqs.qza database, dereplicated</td> </tr> <tr> <td>uniform-silva-138_1-99-515f_926r-uniq-classifier.qza</td> <td>Unweighted (uniform) naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-sediment-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for sediment-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-q2_2023_2-uniq-sediment-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-q2_2023_2-sediment-saline-classifier.qza</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for water-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-water-saline-classifier.qza</td> </tr> </tbody> </table> </th> <th scope="col"> </th> </tr> </thead> <tbody> </tbody> </table> <p> </p>
Soil microbial community composition (16S) data from a laboratory redox fluctuation experiment conducted with an Oxisol and Mollisol
To test the response of microbial communities to periodic oxygen limitation, we conducted a laboratory experiment where two contrasting soils (a rainforest Oxisol from Puerto Rico, and an Iowa cropland Mollisol) were incubated under headspace treatments where oxygen availability varied cyclically over time. Treatments consisted of 0, 2, 4, 8, or 12 d of anoxic conditions (dinitrogen headspace) followed by 4 d of oxic conditions (i.e., ambient oxygen concentrations), and these treatments were repeated for a total of 384 d. At 0, 48, and 384 days, DNA was extracted from replicates from each treatment for sequencing of 16S rRNA amplicons. Companion biogeochemical measurements from this experiment were published previously by Huang et al. (2021a,b). These data support the Hall et al. (2022) manuscript published in Frontiers in Microbiology.
Inventory of soil prokaryotic microbiome (via 16S based on rRNA gene amplicons) in freshwater and brackish water marshes following saltwater intrusion along Shark River Slough boundary, Everglades National Park (FCE LTER), Florida, USA, September 2018
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased episodic or chronic saltwater exposure, leading to shifts in microbial communities and related ecological services. Soil prokaryotes play a fundamental role in regulating important biogeochemical processes in coastal wetland ecosystem. Yet, it is still difficult to predict how soil prokaryotic communities respond to the saltwater exposure because of poorly understood prokaryotic sensitivity within complex wetland soil microbial communities, as well as the high heterogeneity of wetland soils and saltwater exposure. To address this, a four-year experimental simulation of saltwater intrusion in a pristine freshwater site and a previously saltwater-impacted site was conducted. The saltwater addition started in October 2014 on a monthly basis and continued through October 2018. The dataset contains amplicon sequencing date of 16S rRNA gene obtained from saltwater-exposed soils and unmanipulated native soils in both sites (collected in September 2018). The 2018 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804545 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804545). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-Habbena, W. (2023). Year-around survey and manipulation experiments reveal differential sensitivities of soil prokaryotic and fungal communities to saltwater intrusion in Florida Everglades wetlands. Science of The Total Environment, 858, 159865. https://doi.org/10.1016/j.scitotenv.2022.159865 Instead of citing this package, which is an
Inventory of soil prokaryotic and fungal microbiome (via 16S rRNA gene amplicons and ITS sequencing) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, February 2019 - October 2020
Global sea-level rise is transforming coastal ecosystems, especially freshwater wetlands, in part due to increased saltwater exposure, leading to change in soil microbial communities and many important biogeochemical processes. Given the high spatial and temporal heterogeneity in coastal wetlands, especially in tropical or subtropical climates characterized by seasonal temperature, precipitation, and tidal fluctuations, it remains unclear which environmental factors influence the compositions of soil microbial communities in wetlands affected by varying degrees of sea-water intrusion. To address this, a two-year survey was conducted on microbial community structure in submerged surface soils from 14 wetland sites across the Florida Everglades, representing three major ecosystem types, i.e. freshwater marshes, mangrove forests, and seagrass meadows. Bulk surface soil samples of each site were collected from February 2019 to October 2020 to cover dry and wet seasons. In addition to bulk soil samples, soil cores were collected from each site in August 2020 to assess vertical gradients of microbial communities. The dataset contains amplicon sequencing data of 16S rRNA gene (both bulk soil and soil cores) and ITS gene (only the bulk soil). The 2019 to 2020 data are published in Zhao et al. 2023. A detailed list of sequence data and their accession numbers in GenBank is provided, and data collection is complete. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA804243 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804243), PRJNA804246 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804246), and PRJNA804228 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA804228). This data package is associated with the following publication: Zhao, J., Chakrabarti, S., Chambers, R., Weisenhorn, P., Travieso, R., Stumpf, S., Standen, E., Briceno, H., Troxler, T., Gaiser, E., Kominoski, J., Dhillon, B., & Martens-H
Fig. 2. Minimum spanning network depicting relationships among 16S in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France
Fig. 2. Minimum spanning network depicting relationships among 16S haplotypes of Fejervarya cancrivora (Gravenhorst, 1829). The size of each circle is proportional to the haplotype frequency and the lengths of the connecting lines are proportional to the number of mutations. Colors refer to distinct regions (Indonesia: Java, Sumatra, Bali, Kalimantan, Bangka; Malaysia; Taiwan) and commercialized frogs of unknown origin are in black.
Appendix. List of the 28S and 16S rRNA sequences recovered from GenBank. 28S = 28S rRNA GenBank accession number; 16S = 16S rRNA GenBank accession number. in Genetic and morphological evidence for cryptic species in Macrobrachium australe and resurrection of M. ustulatum (Crustacea, Palaemonidae)
Appendix. List of the 28S and 16S rRNA sequences recovered from GenBank. 28S = 28S rRNA GenBank accession number; 16S = 16S rRNA GenBank accession number.
Neighbor-joining phylogenetic tree based on 16S rRNA sequences.
<p><strong>Supplementary Figure (S1):</strong> Bayesian 50% majority rule phylogram of 16S ribosomal RNA region showing the phylogenetic relationships among the bacterial isolates in our study. The newly generated sequences are preceded by red circle. The GenBank sequences are preceded by blue squares. The GenBank accession number appears after the species name. Numbers above the branches represent Bayesian posterior probabilities (≥ 0.90), and the maximum parsimony bootstrap support values are given below the branches (≥70%). The out group used for tree construction preceded by empty circle.</p>
16S gene and ASV sequences of bacteria isolated from soil and the phyllosphere of Arabidopsis thaliana
<p>Data from "Induction of antibiotic specialized metabolism by co-culturing in a collection of phyllosphere bacteria" by Qi et al. </p> <p>- 16S gene sequences of bacteria isolated from soil and the phyllosphere of Arabidopsis thaliana in FASTA format. </p> <p>- Filtered OTU (ASV) table across all samples </p> <p>- ASV sequences</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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.