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492 results for “microbial communities”

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dryad40/100

Data from: Impacts of weathered microplastic ingestion on gastrointestinal microbial communities and health endpoints in fathead minnows (Pimephales promelas)

Microplastics are a ubiquitous presence in the world's aquatic environments and their threat to aquatic biota is poorly understood, especially in freshwater ecosystems. In the environment, microbial biofilms can form on the surface of microplastics, and these plastics have the potential to adsorb harmful toxins. Because lab-based studies on microplastics are often conducted with clean polymers, in ecologically unrealistic conditions and concentrations, the impact of these weathered microplastics on aquatic organisms in ecologically realistic conditions is still unclear. To help address the need for ecologically relevant microplastic exposure data, we incubated 500 μm polyethylene microplastic beads in Muskegon Lake, Michigan, USA and used them to conduct a 28-day ingestion study with male and female fathead minnows (Pimephales promelas). We examined the effects of microplastic ingestion on the fish gut microbial community along with hepatic gene expression and health parameters. We found that microplastic ingestion had statistically significant impacts on growth in male fathead minnows. Microplastic treatment did not significantly alter the beta diversity of the gut microbial community for either males or females, but there were clear differences between sexes and over time, indicating that these factors may outweigh the impacts of microplastic ingestion on beta diversity in the gut. The expression of immune response genes was not altered in males. It did, however, cause some changes to alpha diversity metrics in both sexes and there were several differentially abundant taxa among treatments. These data suggest that microplastic ingestion has health effects, but these effects may be sex specific across certain species and they are likely not being solely driven by changes in gut microbial communities.

opencc-zeroJul 2024View details →

zenodo40/100

Interspecific social interactions shape public goods production in natural microbial communities

The R code (Hesse_etal.R) descibes step by step how metal polution affects the ecology and evolution of a community-wide public good – the production of metal-detoxifying siderophores. This code accompanies the manuscript "Interspecific interactions shape public goods production in natural microbial communities" (https://www.biorxiv.org/content/10.1101/710715v1). The R code is divided into different sections per figure (1-5 and supplementary Figure S1). Each section first starts with reading in the appropriate data files (cvs files) and continues with statistics and plotting of figures.

opencc-by-4.0Aug 2019View details →

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The mOTUs online database provides web-accessible genomic context to taxonomic profiling of microbial communities - Supplementary Tables

Supplementary Table 1: A map between each of the genomes in mOTUs-db (3’747’151), the associated study and its metagenomic sample (in case of MAGs). Columns: <code>    GENOME             → Unique mOTUs-db name of the genome</code> <code>    STUDY              → Unique mOTUs-db name of the study</code> <code>    IS_MAG             → True if genome is a MAG, otherwise False </code> <code>    METAGENOMIC_SAMPLE → Unique name of the metagenomic sample or NA in case of non-MAG genome</code> Example: <code>    GENOME                                     STUDY        IS_MAG    METAGENOMIC_SAMPLE</code> <code>    ---------------------------------------------------------------------------------------------</code> <code>    ACIN21-1_SAMN05421555_MAG_00000001         ACIN21-1     True      ACIN21-1_SAMN05421555_METAG</code> <code>    RSGB23-1_GCA-006096615-V1_GENO_10000001    RSGB23-1     False     NA</code> Supplementary Table 2: A map between all non-MAG genomes (919’090) and their source (e.g. Refseq or JGI). Columns: <code>    GENOME             → Unique mOTUs-db name of the genome</code> <code>    SOURCE_SAMPLE_LINK → Link to the original location of this genome</code> Example: <code>    #GENOME                                     SOURCE_SAMPLE_LINK</code> <code>    --------------------------------------------------------------------------------------------------------</code> <code>    JGIG23-1_GA0055041_GENO_10000001            https://gold.jgi.doe.gov/analysis_project?id=Ga0055041</code> <code>    RSGB23-1_GCA-006717865-V1_GENO_10000001     https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_006717865.1</code> Supplementary Table 3: A list of all metagenomic studies processed for the mOTUs-db, their number of samples, the number of reconstructed MAGs and the associated publication. Columns: <code>    STUDY       --> Unique mOTUs-db study identifier</code> <code>    BIOPROJECT  --> Public identifier (NCBI/JGI) of metagenomic sequencing project</code> <code>    SAMPLES     --> Number of metagenomic samples</code> <code>    MAGs        --> Number of reconstructed MAGs</code> <code>    PUBLICATION --> Link to publication</code> Example: <code>    STUDY        BIOPROJECT    SAMPLES    MAGs     PUBLICATION</code> <code>    -------------------------------------------------------------------------------------------------</code> <code>    ACIN21-1     PRJEB44456    58         1,110    https://www.nature.com/articles/s42003-021-02112-2</code> Supplementary Table 4: Mapping between mOTUs-db sample identifier, the associated biosample and the environment. Columns: <code>    SAMPLE             --> Unique mOTUS-db sample identifier</code> <code>    BIOSAMPLE          --> Public identifier (NCBI/JGI) of metagenomic sample</code> <code>    STUDY              --> Unique mOTUs-db study identifier</code> <code>    ENVIRONMENT        --> Environment of metagenomic sample</code> <code>    SOURCE_SAMPLE_LINK --> Link to the original location of this sample</code> Example: <code>    #SAMPLE                      BIOSAMPLE     STUDY    ENVIRONMENT  SOURCE_SAMPLE_LINK</code> <code>    ---------------------------------------------------------------------------------------------------------------------</code> <code>    ACIN21-1_SAMN05421555_METAG  SAMN05421555  ACIN21-1 marine       https://www.ncbi.nlm.nih.gov/biosample/SAMN05421555/</code> Supplementary Table 5: A list of environments covered in the mOTUs-db mapped to the respective NCBI taxonomy (if possible) Columns: <code>    TERM             --> Unique environment name</code> <code>    NCBI TAXONOMY ID --> Link to the NCBI taxonomy</code> Example: <code>    TERM                          NCBI TAXONOMY ID</code> <code>    ----------------------------------------------</code> <code>    activated sludge metagenome   NCBI:txid942017</code> <code>    air metagenome                NCBI:txid655179</code>

opencc-by-4.0Aug 2024View details →

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Soil microbial communities from tropical forest and oil palm

<div> <h1>Description</h1> A study examining the impact of selective logging and forest conversion to oil palm on soil microbial community composition. Soil samples were collected from old growth forest, selectively logged forest and oil palm plantations. Soil bacterial, protistan and fungal community composition were measured and summarised by calculating richness. <h1>Projects</h1> This dataset was collected as part of the following projects: <ul> <li><a href="https://safeproject.net/projects/project_view/124">https://safeproject.net/projects/project_view/124</a> </li> </ul> <h1>Funding</h1> These data were collected as part of research funded by: <ul> <li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant , NE/K016377/1 ) </li> </ul> This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. <h1>Permits</h1> These data were collected under permit from the following authorities: <ul> <li>Sabah Biodiversity Centre ( Research licence JKM/MBS.1000-2/2 JLD.5 (20))</li> <li>Sabah Biodiversity Centre ( Export licence JKM/MBS.1000-2/3 JLD.2 (70))</li> </ul> <h1>Files</h1> This dataset consists of 1 file: SAFE_Dataset_Richness.xlsx <h2>SAFE_Dataset_Richness.xlsx</h2> This file contains dataset metadata and 1 data tables: <h3>Soil_Microbial_Communities</h3> <ul> <li>Worksheet: Soil_Microbial_Communities</li> <li>Description: Summary richness statistics from bacterial 16S, protistan 18S and fungal ITS biomarker microbial sequencing from DNA extracted from soils</li> <li>Number of fields: 10</li> <li>Number of data rows: 225</li> <ul> <li>PlotName: Plot name corresponding to the GEM Carbon plot where soils were sampled (type: id)</li> <li>ForestType: Old-growth, Logged or Oil palm (type: categorical)</li> <li>ForestPlotsCode: Plot name as listed in ForestPlots database (type: id)</li> <li>Replicate: replicate identifier for which soil core taken from each subplot (type: replicate)</li> <li>location_name: Name of subplot where soils were collected (type: location)</li> <li>Bacteria_Richness: Number of observed bacterial taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>Protist_Richness: Number of observed protistan taxa from sequencing of 18S marker genes from soil samples (type: numeric)</li> <li>Fungal_Richness: Number of observed fungal taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>EcM_Fungal_Richness: Number of observed Ectomycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> <li>AMF_Fungal_Richness: Number of observed Arbuscular Mycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> </ul> </ul> <h1>Extents</h1> <ul> <li>Date range: 2014-10-01 to 2018-09-01</li> <li>Latitudinal extent: 4.64° to 4.954°</li> <li>Longitudinal extent: 116.95° to 117.796°</li> </ul> </div>

opencc-by-4.0Aug 2024View details →

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Data for "Temporal and vertical variability in phytoplankton primary production and microbial community respiration in the North Pacific Subtropical Gyre"

This ALOHA_GOP&R.xslx data set provides measurements of biological rates conducted between April 2015 and July 2020 at different depths in the euphotic zone at or in the vicinity of Station ALOHA (22° 45' N, 158° W), the long-term sampling site of the Hawaii Ocean Time-series (HOT) program, within the North Pacific Subtropical Gyre.  The file ALOHA_GOP&R.xslx contains incubation-based measurements of gross oxygen production and community respiration that were measured in the same incubation bottles by applying the 18O-water method and tracking net changes in oxygen to argon ratios during dawn to dusk in situ incubations, following Ferrón et al. (2016). The samples were measured using membrane inlet mass spectrometry. Rates were measured at 6 depths within the euphotic zone: 5, 25, 45, 75, 100, 125 m, except in a few occasions in which there were no measurements made at 125 m. Data description <table> <tbody> <tr> <td> Variable </td> <td> Description </td> <td> Units </td> </tr> <tr> <td> Date  </td> <td> Date of sampling and start of incubation (UTC -10 hours) </td> <td>   </td> </tr> <tr> <td> Cruise ID </td> <td> Cruise identification </td> <td> # </td> </tr> <tr> <td> Latitude </td> <td> Latitude </td> <td> degrees N </td> </tr> <tr> <td> Longitude </td> <td> Longitude </td> <td> degrees E </td> </tr> <tr> <td>Stn ALOHA </td> <td>Whether the data are from Station ALOHA (yes/no)</td> <td> </td> </tr> <tr> <td>IncT</td> <td> Incubation time </td> <td> hours </td> </tr> <tr> <td> Depth </td> <td>Nominal depth of sampling and incubation </td> <td> meters </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>CR</td> <td>Estimate of community respiration </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td> Flag GOP </td> <td>Flag identification for gross oxygen production (good=1,questionable=2)</td> <td> # </td> </tr> <tr> <td> Flag CR </td> <td> Flag identification for community respiration (good=1,questionable=2) </td> <td> # </td> </tr> </tbody> </table>   The Light-dark_ALOHA_rates.xlsx file contains metabolic rates measured by the ligth-dark oxygen method between June 2005 and June 2007 at different depths in the euphotic zone at Station ALOHA (22° 45' N, 158° W). Rates of net community production, communnity respiration, and gross oxygen production were measured at 6 depths within the euphotic zone (5, 25, 45, 75, 100, 125 m)  in dawn to dawn incubations, following Williams et al. (2004).  Data description <table> <tbody> <tr> <td> Variable </td> <td> Description </td> <td> Units </td> </tr> <tr> <td> HOT </td> <td>HOT cruise number</td> <td> # </td> </tr> <tr> <td> Date </td> <td>Date of sampling and start of incubation (UTC -10)</td> <td>   </td> </tr> <tr> <td> Depth </td> <td>Nominal depth of sampling and incubation </td> <td> meters </td> </tr> <tr> <td>GOP</td> <td>Gross oxygen production, average of 8 replicates</td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>GOP SE</td> <td>Gross oxygen production standard error </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>CR</td> <td> Dark community respiration, average of 8 replicates </td> <td> mmol O2 m-3 d-1 </td> </tr> <tr> <td> CR SE </td> <td>Dark community respiration standard error</td> <td> meters </td> </tr> <tr> <td>NCP</td> <td>Net community production,average of 8 replicates </td> <td>mmol O2 m-3 d-1</td> </tr> <tr> <td>NCP SE</td> <td>Net community production standard error </td> <td>mmol O2 m-3 d-1</td> </tr> </tbody> </table>

opencc-by-4.0Oct 2024View details →

dryad40/100

Local vs. site-level effects of algae on coral microbial communities

Microbes influence ecological processes, including the dynamics and health of macro-organisms and their interactions with other species. In coral reefs, microbes mediate negative effects of algae on corals when corals are in contact with algae. However, it is unknown whether these effects extend to larger spatial scales, such as at sites with high algal densities. We investigated how local algal contact and site-level macroalgal cover influenced coral microbial communities in a field study at two islands in French Polynesia, Mo'orea and Mangareva. At 5 sites at each island, we sampled prokaryotic microbial communities (microbiomes) associated with corals, macroalgae, turf algae, and water, with coral samples taken from individuals that were isolated from or in contact with turf or macroalgae. Algal contact and macroalgal cover had antagonistic effects on coral microbiome alpha and beta diversity. Additionally, coral microbiomes shifted and became more similar to macroalgal microbiomes at sites with high macroalgal cover and with algal contact, although the microbial taxa that changed varied by island. Our results indicate that coral microbiomes can be affected by algae outside of the coral's immediate vicinity, and local- and site-level effects of algae can obscure each other's effects when both scales are not considered.

opencc-zeroAug 2021View details →

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Market forces determine the distribution of a leaky function in a simple microbial community

Many biological functions are leaky, and organisms that perform them contribute some of their products to a community "marketplace" where non-performing individuals may compete for them. Leaky functions are partitioned unequally in microbial communities, and the evolutionary forces determining which species perform them and which become beneficiaries are poorly understood. Here we demonstrate that the market principle of comparative advantage determines the distribution of a leaky antibiotic resistance gene in an environment occupied by two "species" - strains of Escherichia coli growing on mutually exclusive resources and thus occupying separate niches. Communities comprised of antibiotic-resistant cells were rapidly invaded by sensitive cells of both types. While the two phenotypes coexisted stably for 500 generations, in 15/18 replicates, antibiotic sensitivity became fixed in one species. Fixation always occurred in the same species, despite both species being genetically identical except for their niche-defining mutation. In the absence of antibiotic, the fitness cost of resistance was identical in both species. However, the intrinsic resistance of the species that ultimately became the sole helper was significantly lower, and thus its reward for expressing the resistance gene was higher. Opportunity cost of resistance, not absolute cost or efficiency of antibiotic removal, determined which species became helper, consistent with the economic theory of comparative advantage. We present a model that suggests that this market-like dynamic is a general property of Black Queen systems, and in communities dependent on multiple leaky functions, could lead to the spontaneous development of an equitable and efficient division of labor.

opencc-zeroSep 2021View details →

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Concentrations of dissolved dimethyl sulfide (DMS), methanethiol and other trace gases in context of microbial communities from the temperate Atlantic to the Arctic Ocean

This archive contains bioinformatic code and data files to reproduce bacterial analyses from Gros et al. (Biogeosciences; https://doi.org/10.5194/bg-2022-150). In this study, seawater samples collected along an Atlantic-Arctic transect (55-80°N) were evaluated for microbial community composition and trace gas concentrations. Microbes were sampled underway using "AUTOFIM" automatic filtration; and bacterial communities amplicon-sequenced using 16S rRNA gene primers. Raw sequence data is available at ENA under BioProject PRJEB50492. The archive contains:   - Rmarkdown for primer-clipping, ASV generation and taxonomy assignment: 1_DADA.Rmd   - Rscript for ASV and metadata processing; alpha-diversity calculations: 2_DataLoad.R   - Rscript for generation of manuscript figures: 3_Results.R   - Rscript for generation of supplementary figures: 4_Supplement.R   - Metadata and environmental parameters as .txt files

opencc-by-4.0Feb 2023View details →

dryad40/100

Data from: Enhanced climate tolerance for trees derived from microbial communities

Changing climates are pushing species outside of their evolved tolerances; populations must acclimate or adapt to the new conditions or migrate to avoid extinction. However, because plants associate with diverse microbial communities that shape their phenotype, shifts in microbial associations may provide an alternative source of novel climate tolerance. Here we show that tree seedlings inoculated with microbial communities sourced from drier, warmer, or colder sites displayed higher survival when facing drought, heat, or cold stress, respectively. Microbially mediated drought tolerance was associated with increased diversity of arbuscular mycorrhizal fungi, while cold tolerance was related to reduced diversity of non-adapted taxa. Understanding microbially mediated climate tolerance may enhance our ability to predict and manage the adaptability of forest ecosystems to changing climates.

opencc-zeroMar 2023View details →

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Microplastics effects on marine microbial communities and their functioning

Microplastics pervade ocean ecosystems. Despite their effects on individuals or populations are well documented, the consequences of microplastics on ecosystem functioning, especially regarding lower trophic levels, are still largely unknown. Here we show how microplastics alter the structure and functioning of pelagic microbial ecosystems. Using experimental pelagic mesocosms, we found that microplastics indirectly affect marine productivity by changing the bacterial and phytoplankton assemblages. Specifically, the addition of microplastics increased phytoplankton biomass and shifted bacterial assemblages' composition. Such changes altered the interactions between heterotrophic and autotrophic microbes and the cycling of ammonia in the water column, which ultimately benefited photosynthetic efficiency. The effects of microplastics on marine productivity were consistent for different microplastic types. This study demonstrates that microplastics affect bacteria and phytoplankton communities and influence marine productivity, which ultimately alters the functioning of the whole ocean ecosystem.

opencc-zeroMay 2023View details →

dryad40/100

Microbial community composition of earthworm-invaded and earthworm-free soils of the Canadian boreal forest

Earthworm invasion in North American forests has the potential to greatly impact soil microbiomes by altering soil physicochemical properties. We characterized and compared microbial communities of earthworm-invaded and non-invaded soils in previously described sites across three major soil types found in the Canadian boreal forest using phospholipid fatty acid (PLFA) analysis and metabarcoding of the 16S rRNA gene (bacteria and archaea) and ITS2 region (fungi).

opencc-zeroAug 2023View details →

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Anaerobic Methane Oxidation is Quantitatively Important in Deeper Peat Layers of Boreal Peatlands: Evidence from in situ Stable Isotopes Depth Profiles, Anaerobic Incubations, and Microbial Communities

Dataset contains complete result of laboratory anaerobic incubations with peat samples from 3 West Siberian peatlands. Before the incubation, the peat samples were thoroughly mixed to ensure homogeneity. Aliquots (140 ± 1 g) were mixed with distilled water at a ratio of 1:2 by weight, and then placed into sterile 500 ml glass bottles, flushed with pure argon (99.9999%, Voessen, Russia) for 5 min to remove any oxygen and sealed with butyl rubber septa to maintain anaerobic conditions. The bottles were kept at +5°C for 1 day to allow the equilibration between the peat and the headspace. Then bottles were again thoroughly flushed with argon for 5 min, sealed, and an additional 5 ml of argon was added to prevent air diffusion into the bottle headspace. The incubation was performed in two different ways: i) unamended control, and ii) amended with 10 ml of CH3F to inhibit acetotrophic methanogenesis. Peat samples from the depths of 15-20 and 40-50 cm were incubated at 15° and 10°C, respectively, for a period of 60 days, whereas deeper peat samples were incubated at 5°С, representing the temperature of the deeper peat layers of Mukhrino bog during the snow-free period for 150 days. Gas (1 mL for Н2, CH4 and CO2 concentration, 1 mL for stable isotope compositions) and liquid (1 mL for organic acids) samples were taken for analysis every two weeks after manually shaking the bottles for approximately 5 s to equilibrate the gaseous and aqueous phases. At the end of the incubation, methane headspace concentrations ranged from 1 to 3 % for deeper samples. The incubations with and without CH3F addition were carried out in three replicates for samples from Mukhrino bog and in two replicates for Chistoe and Lempino bogs. Net methane and CO2 production were calculated from the gas concentrations, the volume of the gas space, and the water volume using the ideal gas law. Gas solubility was calculated using Henry’s law. The reported net methane and CO2 production are the averages of 2-3 replicates. See further details in a paper with the same title and the first author.

opencc-by-4.0Aug 2023View details →

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Dataset for the article "Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities"

The following are data and code used for statistical analysis and figure plotting in the manuscript Gorka et al. (2023) "Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities", Soil Biology and Biochemistry It contains the following files: 1. Pure lipid standard data <ul> <li>Total ion chromatogram (TIC) area data (area.csv)</li> <li>Assignment of lipids that the measured fatty acids originate from (LipidClass.csv)</li> <li>An R script reproducing the calculations and plotting for Fig. 2 and Fig. S1 (pure_lipids.R)</li> </ul> 2. Microbial pure culture fatty acid data data <ul> <li>TIC area data of the PLFA, NLFA, and GLFA data from pure culture extracts (area.csv)</li> <li>Files needed for calculating the data and assigning taxonomic groups in the R code (weights.csv, C_atoms.csv, species_list.csv)</li> <li>An R script reproducing the calculations and plotting for Fig. 3, Fig. 4, Fig. S2, and Fig. S3 (pure_cultures.R)</li> </ul> 3. Soil fatty acid data <ul> <li>Absolute abundance data in nmol C g-1 dry weight of the PLFA, NLFA, and GLFA data from soil extracts (nmolC.csv)</li> <li>Taxonomic group assignments of fatty acids needed to run the R code (phylum.csv)</li> <li>An R script reproducing the calculations and plotting for Fig. 5, and Fig. S4 (soil.R)</li> </ul>

opencc-by-4.0Oct 2023View details →

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Data and analytical codes for: Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure

Data and analytical codes for: Ishizawa et al. (2023) Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure, bioRxiv, 2023.07.04.546222  https://www.biorxiv.org/content/10.1101/2023.07.04.546222v1

opencc-by-4.0Aug 2023View details →

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