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360 results for “microbe”
Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms
<p>Dataset and script used for the publication <em>Assessing the role of soil microbes in the dynamics of P release from poorly soluble P forms, </em>doi (to be determined).</p>
Selecting for infectivity across metapopulations can increase virulence in the social microbe Bacillus thuringiensis:data set.
<p>Passage experiments that sequentially infect hosts with parasites have long been used to manipulate virulence. However, for many invertebrate pathogens passage has been applied naively without a full theoretical understanding of how best to select for increased virulence and this has led to very mixed results. Understanding the evolution of virulence is complex because selection on parasites occurs across multiple spatial scales with potentially different conflicts operating on parasites with different life-histories. For example, in social microbes, strong selection on replication rate within hosts can lead to cheating and loss of virulence, because investment in public goods virulence reduces replication rate. </p> <p>In this study<em> </em>we tested how varying mutation supply and selection for infectivity or pathogen yield (population size in hosts) affected evolution of virulence against resistant hosts in the specialist insect pathogen <em>Bacillus thuringiensis</em>, aiming to optimize methods for strain improvement against a difficult to kill insect target. We show that selection for infectivity using competition between sub-populations in a metapopulation prevents social cheating, acts to retain key virulence plasmids and facilitates increased virulence. Increased virulence was associated with reduced efficiency of sporulation, and possible loss of function in putative regulatory genes but not with altered expression of the primary virulence factors. Selection in a metapopulation provides a broadly applicable tool for improving the efficacy of biocontrol agents. Moreover, a structured host population can facilitate artificial selection on infectivity, while selection on life history traits such as faster replication or larger population sizes can reduce virulence in social microbes.</p>
Chamaecrista fasciculata Survival and Biomass in Response to Microbe Stress History and Contemporary Stress, 2018-2019
This dataset includes Chamaecrista fasciculata biomass and survival data collected as part of a greenhouse experiment that took place at Indiana University in 2018. Rhizosphere soil was collected from Chamaecrista fasciculata plants at the end of a field experiment in which plants were treated with four stress treatments: salt, herbicide, herbivory, and no stress. These field soils were used to inoculate a greenhouse experiment in which Chamaecrista fasciculata individuals from 50 manternal families were treated with these same four stress treatments in a full factorial design (4 microbe histories x 4 contemporary stress environments), plus a sterile microbial control treatment. We measured the days to first flower, noted when plants never flowered (i.e., did not survive to flower), and measured aboveground biomass.
Nitrogen addition alters plant competition directly more than indirectly through soil microbes.
Eutrophication, the excessive addition of nutrients to ecosystems, is a pervasive component of global environmental change that can alter community dynamics. Although nitrogen addition experiments have widely documented important declines in plant diversity and shifts in plant species composition, the underlying causes of these outcomes are widely debated. Nitrogen inputs may directly affect plant competition for light or soil water or may influence plant species indirectly by altering the composition of soil microbes. In a 28-year field nitrogen addition experiment, we tested whether nitrogen-induced changes to soil microbes could indirectly alter the outcome of competition between codominant foundation plant species. In the field, long-term addition of inorganic nitrogen slowed the competitive take-over of blue grama grass (Bouteloua gracilis) by black grama grass (B. eriopoda) and thereby stabilized the ecotone between two grassland ecosystems in central New Mexico, USA.
Deschampsia biomass, soil microbes and endophyte root colonization for snowmelt and microbial innoculation transplant experiment in the Green Lakes Valley, 2015-2018
As organisms shift their geographic distributions in response to climate change, biotic interactions have emerged as an important factor driving the rate and success of range expansions. Plant-microbe interactions are an understudied but potentially important factor governing plant range shifts. We studied the distribution and function of microbes present in high-elevation unvegetated soils, areas that plants are colonizing as climate warms, snow melts earlier and the summer growing season lengthens. Using a manipulative snowpack and microbial inoculation transplant experiment, we tested the hypothesis that growing season length and microbial community composition interact to control plant elevational range shifts. We predicted that a lengthening growing season combined with dispersal to patches of soils with more mutualistic microbes and fewer pathogenic microbes would facilitate plant survival and growth in previously unvegetated areas. We identified negative effects on survival of the common alpine bunchgrass Deschampsia cespitosa in both short and long growing seasons, suggesting an optimal growing season length for plant survival in this system that balances time for growth with soil moisture levels. Importantly, growing season length and microbes interacted to affect plant survival and growth, such that microbial community composition increased in importance in suboptimal growing season lengths. Further, plants grown with microbes from unvegetated soils grew as well or better than plants grown with microbes from vegetated soils. These results suggest that the rate and spatial extent of plant colonization of unvegetated soils in mountainous areas experiencing climate change could depend on both growing season length and soil microbial community composition, with microbes potentially playing more important roles as growing seasons lengthen.
Future Ocean Warming May Threaten Key Photosynthetic Microbes
<h2>Description</h2> <p>The datasets supporting the conclusions of this article, including field measurements of <em>Prochlorococcus</em> division rates, are available in this repository. </p> <p>The R code performs the following tasks:</p> <ul> <li>Loads data from various sources, including lab experiments, dilution experiments, and in-situ measurements.</li> <li>Calculates thermal norm predictions using different models (Eppley, Hinshelwood, Eppley-Norberg) to predict division rates based on temperature.</li> <li>Generates figures to visualize the results, including latitudinal and temperature effects on division rates, model predictions compared with observed data, and changes in primary production under different emission scenarios.</li> <li>Fits the Hinshelwood model to culture data and extracts best-fit parameters.</li> <li>Performs bootstrapping to estimate uncertainty in the Hinshelwood model parameters.</li> <li>Calculates confidence intervals for the bootstrapped parameters.</li> </ul> <h2>R Scripts</h2> <ul> <li><strong>Ribalet_main.R:</strong> This script contains the main analysis code, including data loading, model fitting, figure generation, and bootstrapping.</li> <li><strong>Ribalet_fitting.R:</strong> This script defines functions for fitting different growth models to the data and estimating model parameters.</li> </ul> <h2>Requirements</h2> <ul> <li>R version 4.4.2 (2024-10-31)<br>Platform: aarch64-apple-darwin20<br>Running under: macOS Sequoia 15.1.1</li> <li>Matrix products: default<br>BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib <br>LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0</li> <li>attached base packages:<br>[1] parallel stats graphics grDevices utils datasets <br>[7] methods base </li> <li>other attached packages:<br> [1] DEoptim_2.2-8 arrow_15.0.1 ggpubr_0.6.0 lubridate_1.9.3<br> [5] forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 <br> [9] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 <br>[13] tidyverse_2.0.0</li> </ul> <h2>Installation</h2> <p>Install the required R packages:</p> <div> <div>Code snippet</div> <div> <div> <pre><code>install.packages(c("tidyverse", "ggpubr", "arrow", "DEoptim")) </code></pre> </div> </div> </div> <h2>Usage</h2> <p>The scripts will generate figures and output files in the same directory.</p> <h2>Input Data</h2> <p>The code requires the following input data files:</p> <ul> <li>culture.csv</li> <li>dilution.csv</li> <li>abundance.csv</li> <li>mpm.csv</li> <li>model_results.parquet</li> <li>bootstrap_projections.csv</li> <li>modeled-thermal-traits.tsv</li> <li>sst.parquet</li> <li>culture_syn.csv</li> </ul> <p>Please ensure that these files are present in the same directory as the R script files.</p> <h2>Output Data</h2> <p>The code generates the following output files:</p> <ul> <li>Figures: Figure1.png, Figure2.png, Figure3.png, FigureS1.png, FigureS2.png, FigureS3.png, FigureS4.png, FigureS5.png, FigureS6.png, FigureS9.png, FigureS11.png, FigureS12.png, FigureS13.png, FigureS14.png, FigureS15.png</li> <li>CSV files: bootstrap_parameters.csv, cultures_thermal_reactions.csv</li> </ul> <h2>License</h2> <p>This code is licensed under the MIT License.</p>
Ruegeria pomeroyi digital microbe databases
<p>These databases consolidate a variety of datasets related to the model organism Ruegeria pomeroyi DSS-3. The data were primarily generated by members of the Moran Lab at the University of Georgia, and put together in this format using anvi'o v7.1-dev through the collaborative efforts of Zac Cooper, Sam Miller, and Iva Veseli (special thanks to Christa Smith and Lidimarie Trujillo Rodriguez for their help with gene annotations). The data includes:</p> <p>- (R_POM_DSS3-contigs.db) the complete genome and megaplasmid sequence of R. pomeroyi, along with highly-curated gene annotations established by the Moran Lab and automatically-generated annotations from NCBI COGs, KEGG KOfam/BRITE, Pfams, and anvi'o single-copy core gene sets. It also contains annotations for the Moran Lab's TnSeq mutant library (<a href="https://doi.org/10.1101/2022.09.11.507510">https://doi.org/10.1101/2022.09.11.507510</a>; <a href="https://doi.org/10.1038/s43705-023-00244-6">https://doi.org/10.1038/s43705-023-00244-6</a>).</p> <p>- (PROFILE-VER_01.db) read-mapping data from multiple transcriptome and metatranscriptome samples generated by the Moran lab to the R. pomeroyi genome. Some coverage data is stored in the AUXILIARY-DATA.db file. This data can be visualized using anvi-interactive. Publicly-available samples are labeled with their SRA accession number.</p> <p>- (DEFAULT-EVERYTHING.db) gene-level coverage data from the transcriptome and meta-transcriptomes samples stored in the profile database, as well as per-gene normalized spectral abundance counts from proteomes matched to a subset of the transcriptomes and gene mutant fitness data from <a href="https://doi.org/10.1073/pnas.2217200120">https://doi.org/10.1073/pnas.2217200120</a>. This data can also be visualized using anvi-interactive (see instructions below). The proteome data layers are labeled according to their matching transcriptome samples.</p> <p>- (R_pom_reproducible_workflow.md) a reproducible workflow describing how the databases were generated.</p> <p><em>Please note that using these databases requires the development version of anvi'o `v8-dev`, or a later version of anvi'o if available. They are not usable with anvi'o `v8` or earlier.</em></p> <p>Instructions for <strong>visualizing the genes database</strong> in the anvi'o interactive interface: Anvi'o expects genes databases to be located in a folder called `GENES`, so in order to use the specific database included in this datapack, you must move it to the expected location by running the following commands in your terminal:</p> <blockquote> <p>mkdir GENES<br>mv DEFAULT-EVERYTHING.db GENES/<br> </p> </blockquote> <p>Once that is done, you can use the following command to visualize the gene-level information:</p> <blockquote> <p>anvi-interactive -c R_POM_DSS3-contigs.db -p PROFILE-VER_01.db -C DEFAULT -b EVERYTHING --gene-mode</p> </blockquote> <p>To view <strong>only the proteomic data</strong> and its matched transcriptomes, you can add the flag `--state-autoload proteomes` to the above command.</p> <p>To view all transcriptomes and the proteomes <strong>organized by study of origin</strong>, you can add the flag `--state-autoload figure` to the above command.</p>
The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering Patterns in a Granular Basalt
<p>Data used in the Milici et al. <em>Geobiology </em>article "The Effects of Plant-Microbe-Environment Interactions on Mineral Weathering in Granular Basalt". The data result from a greenhouse experiment in which 14 genotypes of Alfalfa <em>(Medicago</em> sativa) were grown in an unweathered granular basaltic tephra, exposed to an early successional soil microbial community, and replicated across three different soil moisture treatments. This experiment seeks to identify the roles of vascular plants and soil microbes on mineral weathering. Please see the article for full project description. </p> <p>General File Descriptions:</p> <p>"AllPerformanceGeochem.csv" contains both the performance and geochemistry data associated with each plant grown in the experiment and is used for the majority of the analyses.</p> <p>"FullCensusTimeSeries.csv" contains the growth and survival data for the plants across the entire 3 month duration of the experiment and is used only to calculate survival rate and growth rate.</p> <p>"pottingsoilmass.csv" contains the data for alfalfa grown in potting soil and is used to compare how much the basalt limited plant growth relative to a potting soil control. </p> <p>These data are cleaned and formatted for analysis via the code in the github repository linked to this data repository. </p> <p> </p>
Microbes go to school - Output repository
<p>This dataset is an output repository for the final report of the Agora project "Microbes go to school" funded by SNF from 2020 to 2022. This project aims at using service-learning to bridge the gap between university and school, and disseminate knowledge in microbiology and biodiversity in the classroom by engaging students as communicators. In this repository, you'll find general content about the project (gallery, course descriptions, and the article we published), pedagogical content (protocols of the activities edited by us, original content produced by the students that was evaluated, and the feedback form that we sent to the teachers to evaluate the students), and outreach content (guide for trainers, newsletters and recipes).</p>
EOL Microbes Patch (MIP) - active: EOL Microbes Patch 2023
<p>Bacteria, viruses & microbial Eukaryotes to complement NCBI & WoRMS coverage. Hierarchy follows NCBI & Adl et al. 2019.</p> <h3>References </h3> <p>Adam, Rodney D. 2017. Diplomonadida. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–28. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_1-1">https://doi.org/10.1007/978-3-319-32669-6_1-1</a></p> <p>Adl, S. M., et al. 2019. Revisions to the classification, nomenclature, and diversity of eukaryotes. Journal of Eukaryotic Microbiology 66, 4–119. <a href="https://doi.org/10.1111/jeu.12691">https://doi.org/10.1111/jeu.12691 </a></p> <p>Azevedo, Carlos, and P. M. Hine. 2017. Haplosporidia. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–29. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_16-1">https://doi.org/10.1007/978-3-319-32669-6_16-1</a></p> <p>Baldauf, Sandra L., and Joan E. Strassmann. 2017. Dictyostelia. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–45. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_14-1">https://doi.org/10.1007/978-3-319-32669-6_14-1</a></p> <p>Beakes, Gordon W., and Marco Thines. Hyphochytriomycota and Oomycota. 2016. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–71. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_26-1">https://doi.org/10.1007/978-3-319-32669-6_26-1</a></p> <p>Bennett, R. M., D. Honda, G. W. Beakes, and M. Thines. 2017. Labyrinthulomycota. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–36. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_25-1">https://doi.org/10.1007/978-3-319-32669-6_25-1</a></p> <p>Boltovskoy, D., ed., 1999. South Atlantic zooplankton. Backhuys Publishers, Leiden. Boltovskoy, Demetrio, O. Roger Anderson, and Nancy M. Correa. 2017. Radiolaria and Phaeodaria. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–33. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_19-2">https://doi.org/10.1007/978-3-319-32669-6_19-2</a></p> <p>Brown, Matthew W., Jeffrey D. Silberman, and Frederick W. Spiegel. 2010. A Contemporary Evaluation of the Acrasids (Acrasidae, Heterolobosea, Excavata). European Journal of Protistology 48(2):103–23. <a href="https://doi.org/10.1016/j.ejop.2011.10.001">https://doi.org/10.1016/j.ejop.2011.10.001 </a></p> <p>Bulman, Simon, and Sigrid Neuhauser. 2017. Phytomyxea. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–21. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_24-1">https://doi.org/10.1007/978-3-319-32669-6_24-1</a></p> <p>Burki, F., 2014. The Eukaryotic Tree of Life from a Global Phylogenomic Perspective. Cold Spring Harb Perspect Biol 6, a016147. <a href="https://doi.org/10.1101/cshperspect.a016147">https://doi.org/10.1101/cshperspect.a016147 </a></p> <p>Burki Fabien, Kaplan Maia, Tikhonenkov Denis V., Zlatogursky Vasily, Minh Bui Quang, Radaykina Liudmila V., Smirnov Alexey, Mylnikov Alexander P., Keeling Patrick J., 2016. Untangling the early diversification of eukaryotes: a phylogenomic study of the evolutionary origins of Centrohelida, Haptophyta and Cryptista. Proceedings of the Royal Society B: Biological Sciences 283, 20152802. <a href="https://doi.org/10.1098/rspb.2015.2802">https://doi.org/10.1098/rspb.2015.2802 </a></p> <p>Burreson, Eugene M., and Susan E. Ford. 2004. A Review of Recent Information on the Haplosporidia, with Special Reference to Haplosporidium Nelsoni (MSX Disease). Aquatic Living Resources 17(4):499–517. <a href="https://doi.org/10.1051/alr:2004056">https://doi.org/10.1051/alr:2004056</a></p> <p>Cali, Ann, James J. Becnel, and Peter M. Takvorian. 2017. Microsporidia. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–60. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_27-1">https://doi.org/10.1007/978-3-319-32669-6_27-1</a></p> <p>Caragnano, A., Foetisch, A., Maneveldt, G.W., Millet, L., Liu, L.-C., Lin, S.-M., Rodondi, G., Payri, C.E., 2018. Revision of Corallinaceae (Corallinales, Rhodophyta): recognizing Dawsoniolithon gen. nov., Parvicellularium gen. nov. and Chamberlainoideae subfam. nov. containing Chamberlainium gen. nov. and Pneophyllum. Journal of Phycology 54, 391–409. <a href="https://doi.org/10.1111/jpy.12644">https://doi.org/10.1111/jpy.12644 </a></p> <p>Cavalier-Smith, T., 2016. Higher classification and phylogeny of Euglenozoa. European Journal of Protistology 56, 250–276. <a href="https://doi.org/10.1016/j.ejop.2016.09.003">https://doi.org/10.1016/j.ejop.2016.09.003 </a></p> <p>Cavalier-Smith, T., Chao, E.E., 2010. Phylogeny and Evolution of Apusomonadida (Protozoa: Apusozoa): New Genera and Species. Protist 161, 549–576. <a href="https://doi.org/10.1016/j.protis.2010.04.002">https://doi.org/10.1016/j.protis.2010.04.002 </a></p> <p>Cavalier-Smith, T., Chao, E.E., Lewis, R., 2018. Multigene phylogeny and cell evolution of chromist infrakingdom Rhizaria: contrasting cell organisation of sister phyla Cercozoa and Retaria. Protoplasma 255, 1517–1574. <a href="https://doi.org/10.1007/s00709-018-1241-1">https://doi.org/10.1007/s00709-018-1241-1 </a></p> <p>Cavalier-Smith, T., Chao, E.E.-Y., 2006. Phylogeny and megasystematics of phagotrophic heterokonts (kingdom Chromista). J. Mol. Evol. 62, 388–420. <a href="https://doi.org/10.1007/s00239-004-0353-8">https://doi.org/10.1007/s00239-004-0353-8 </a></p> <p>Čepička, Ivan, Michael F. Dolan, and Gillian H. Gile. 2016. Parabasalia. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–44. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_9-1">https://doi.org/10.1007/978-3-319-32669-6_9-1</a></p> <p>Cook, Martha E., and Linda E. Graham. 2016. Chlorokybophyceae, Klebsormidiophyceae, Coleochaetophyceae. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–20. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_36-1">https://doi.org/10.1007/978-3-319-32669-6_36-1</a></p> <p>Díaz-Tapia, P., Pasella, M.M., Verbruggen, H., Maggs, C.A., 2019. Morphological evolution and classification of the red algal order Ceramiales inferred using plastid phylogenomics. Molecular Phylogenetics and Evolution 137, 76–85. <a href="https://doi.org/10.1016/j.ympev.2019.04.022">https://doi.org/10.1016/j.ympev.2019.04.022 </a></p> <p>Eikrem, Wenche, Linda K Medlin, Jorijntje Henderiks, Sebastian Rokitta, Björn Rost, Ian Probert, Jahn Throndsen, and Bente Edvardsen. 2017. Haptophyta. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–61. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_38-2">https://doi.org/10.1007/978-3-319-32669-6_38-2</a></p> <p>Eliáš, Marek, Raquel Amaral, Karen P. Fawley, Marvin W. Fawley, Yvonne Němcová, Jiří Neustupa, Pavel Přibyl, Lilia M. A. Santos, and Tereza Ševčíková. 2017. Eustigmatophyceae. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–39. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_39-1">https://doi.org/10.1007/978-3-319-32669-6_39-1</a></p> <p>Fang, Ling, Frederik Leliaert, Phil M. Novis, Zhenhua Zhang, Huan Zhu, Guoxiang Liu, David Penny, and Bojian Zhong. 2018. Improving Phylogenetic Inference of Core Chlorophyta Using Chloroplast Sequences with Strong Phylogenetic Signals and Heterogeneous Models. 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PeerJ 7 e6899. <a href="https://doi.org/10.7717/peerj.6899">https://doi.org/10.7717/peerj.6899</a></p> <p>Gao, F., Warren, A., Zhang, Q., Gong, J., Miao, M., Sun, P., Xu, D., Huang, J., Yi, Z., Song, W., 2016. The All-Data-Based Evolutionary Hypothesis of Ciliated Protists with a Revised Classification of the Phylum Ciliophora (Eukaryota, Alveolata). Scientific Reports 6, 24874. <a href="https://doi.org/10.1038/srep24874">https://doi.org/10.1038/srep24874</a></p> <p>Gast, Rebecca J. 2017. Centrohelida and Other Heliozoan-Like Protists. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. Simpson, Claudio H. Slamovits, Lynn Margulis, Michael Melkonian, David J. Chapman, and John O. Corliss, 1–17. Cham: Springer International Publishing. <a href="https://doi.org/10.1007/978-3-319-32669-6_28-1">https://doi.org/10.1007/978-3-319-32669-6_28-1</a></p> <p>Gibson, Wendy. 2016. Kinetoplastea. In Handbook of the Protists, edited by John M. Archibald, Alastair G.B. 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Microbe Ecomorphological Guilds
<p>Ecomorphological guild data for microbial organisms compiled from the following sources:</p> <p>Adam, R.D., 2017. Diplomonadida, in: Archibald, J.M., Simpson, A.G.B., Slamovits, C.H., Margulis, L., Melkonian, M., Chapman, D.J., Corliss, J.O. (Eds.), Handbook of the Protists. 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Journal of Eukaryotic Microbiology 66, 4–119. <a href="https://doi.org/10.1111/jeu.12691" target="_blank" rel="nofollow noopener">https://doi.org/10.1111/jeu.12691</a></p> <p>Anderson, O.R., 2013. Comparative protozoology: ecology, physiology, life history. Springer Science & Business Media.</p> <p>Badewitz, H. (2004). The genus Microcorycia Cockerell, 1911 (Testacealobosia, Rhizopoda, Protozoa). A critical monograph of the genus including a first description of a new species: Microcorycia scutella n. sp. Lauterbornia 50: 111-146.</p> <p>Baumgartner, M., Eberhardt, S., De Jonckheere, J. F., & Stetter, K. O. (2009). Tetramitus thermacidophilus n. sp., an amoeboflagellate from acidic hot springs. Journal of Eukaryotic Microbiology 56:201–206. <a href="https://doi.org/10.1111/j.1550-7408.2009.00390.x" target="_blank" rel="nofollow noopener">https://doi.org/10.1111/j.1550-7408.2009.00390.x</a></p> <p>Bell, E.M. and Laybourn‐Parry, J., 2003. Mixotrophy in the antarctic phytoflagellate Pyramimonas gelidicola (Chlorophyta: Prasinophyceae). Journal of Phycology, 39(4), pp.644-649. <a href="https://doi.org/10.1046/j.1529-8817.2003.02152.x" target="_blank" rel="nofollow noopener">https://doi.org/10.1046/j.1529-8817.2003.02152.x</a></p> <p>Bennett R.M., Honda D., Beakes G.W., Thines M. (2017) Labyrinthulomycota. In: Archibald J. et al. (eds) Handbook of the Protists. Springer, Cham. <a href="https://doi.org/10.1007/978-3-319-32669-6_25-1" target="_blank" rel="nofollow noopener">https://doi.org/10.1007/978-3-319-32669-6_25-1</a></p> <p>Bernard, Catherine, Alastair G. B. Simpson & David J. Patterson (2000) Some free-living flagellates (protista) from anoxic habitats Ophelia 52(2):113-142. <a href="https://doi.org/10.1080/00785236.1999.10409422" target="_blank" rel="nofollow noopener">https://doi.org/10.1080/00785236.1999.10409422</a></p> <p>Berney, C., Geisen, S., Van Wichelen, J., Nitsche, F., Vanormelingen, P., Bonkowski, M. and Bass, D., 2015. Expansion of the ‘reticulosphere’: diversity of novel branching and network-forming amoebae helps to define Variosea (Amoebozoa). Protist, 166(2):271-295. <a href="https://doi.org/10.1016/j.protis.2015.04.001" target="_blank" rel="nofollow noopener">https://doi.org/10.1016/j.protis.2015.04.001</a></p> <p>Bishop, A. (1935). Observations upon a “Trichomonas” from pond water. Parasitology 27:246–256. <a href="https://doi.org/10.1017/S0031182000015110" target="_blank" rel="nofollow noopener">https://doi.org/10.1017/S0031182000015110</a></p> <p>Boltovskoy D., Anderson O.R., Correa N.M. (2017) Radiolaria and Phaeodaria. 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Planet Microbe Functional and Taxonomic annotation of Illumina WGS Prokaryotic Fraction for Semantic Web Analysis
<p>Functional and Taxonomic annotations computed from a subset of Illumina Whole-Genome Sequencing samples from the prokaryotic fraction of the <a href="https://www.planetmicrobe.org/">Planet Microbe</a> database. Data was computed using the pipeline available from https://github.com/hurwitzlab/planet-microbe-functional-annotation/, and post processing scripts from https://github.com/hurwitzlab/planet-microbe-semantic-web-analysis. Files contain total annotation counts of Interpro, GO and NCBITaxon annotations, as well as additional sample metadata. See readme.txt file for more information.</p>
Processed data to regenerate figures in Noecker et al, "Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe Eggerthella lenta"
<p>This archive contains the processed source data for the publication by Noecker et al, "Systems biology elucidates the distinctive metabolic niche filled by the human gut microbe <em>Eggerthella lenta</em>" (2023, in review). Data tables underlying each figure panel are included, except for the following panels:</p> <ul> <li>Figure S1A: Source data is in Table S1 of the publication</li> <li>Figure 6D: Source data can be found at NCBI GEO accession GSE212420 (supplementary counts data matrix)</li> </ul> <p>Raw metabolomics data can also be found at Metabolomics Workbench accession PR001620.</p> <p>Methods used to summarize these data and generate the figures are described in the manuscript Materials and Methods and figure captions. Code to generate all figures is also available at www.github.com/turnbaughlab/2022_Noecker_ElentaMetabolism and 10.5281/zenodo.7779454.</p>
Data supporting publication: Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes
<p>Genomes, mapping databases, and data supporting the publication "Ultra-deep Sequencing of Hadza Hunter-Gatherers Recovers Vanishing Gut Microbes".</p> <p><strong>Please see the README on GitHub for descriptions of files and tutorials on how to use them: <a href="https://github.com/MrOlm/ZenodoREADME/blob/main/README.md">https://github.com/MrOlm/ZenodoREADME/blob/main/README.md</a></strong></p>
NEON Biorepository Benthic Microbe Collection (Sterivex Filters) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains benthic biofilm samples collected on 67 mm long, 1.7 cm diameter, 0.22 um Sterivex capsule filters (NEON sample class: amb_fieldParent_in.archiveID). Benthic biofilm samples are collected 3 times per year at the same time and location as periphyton (microalgae) samples and microbe samples sent for sequencing analysis, three times per year in wadeable streams during aquatic biology bout windows, roughly in spring, summer, and fall. Benthic biofilms are not collected in lakes and rivers. Samples are collected from rock and wood scrubs using field-sterile methods, and filtered through a 0.22 um Sterivex SVGP capsule filters. In wadeable streams, periphyton samples are collected in the two most dominant benthic habitat types (e.g. riffles, runs, pools, step pools). Sterivex filters are capped and flash-frozen in the field and then shipped to the Biorepository to be archived at -80 degrees Celsius. See related links below for protocols and NEON related data products.
NEON Biorepository Soil Microbe Collection (Bulk Subsamples) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains samples collected during periodic soil sampling and frozen at ultra-low temperatures in order to provide material for microbial sequencing or other microbial analyses (NEON sample classes: sls_soilCoreCollection_in.geneticArchiveSample1ID, sls_soilCoreCollection_in.geneticArchiveSample2ID, sls_soilCoreCollection_in.geneticArchiveSample3ID, sls_soilCoreCollection_in.geneticArchiveSample4ID, sls_soilCoreCollection_in.geneticArchiveSample5ID,sls_metagenomicsPooling_in.compositeSampleID). Archive samples are collected during each soil sampling bout and are promptly frozen. Three unique locations are sampled per plot, with ten plots per site. Bouts occur three times per year in order to capture the prevailing conditions at the site during different seasons, except in Alaska where only 1 bout is possible. Soil sampling is conducted to a maximum depth of 30 ± 1 cm, and when organic (O) and mineral (M) horizons are present within a single profile, they are separated prior to analysis and archiving. However, other sub-horizons are not separated. During the majority of bouts, only the top horizon (O if present, else M) is collected and archived. Soils are homogenized and non-soil material is removed by hand in the field, then subsamples are immediately frozen on dry ice. They are maintained in ultra-low temperature freezers until shipment to the Biorepository. See links below for NEON data products that provide physical, chemical, and biological measurements for these same soils (soil pH and moisture are always measured; chemical properties as well as microbial community composition and biomass are determined only for a subset of collection bouts). In addition, a more detailed characterization of the dominant soil types at each site, including taxonomy, texture, bulk density, and geochemical properties, occurred during the construction period of NEON through two projects. These data are available in NEON data products Soil physical and chemical
NEON Biorepository Soil Microbe Collection (DNA Extracts ) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains genetic extracts from soil microbes collected during periodic soil sampling at NEON terrestrial sites (NEON sample class: mic_dnaExtraction_in.soilDnaSampleID). Three unique locations are sampled per plot with ten soil plots per site. Bouts occur three times per year in order to capture the prevailing conditions at the site during different seasons, except in Alaska where there only 1 bout is possible. However, the frequency of genetic analysis and thus DNA archiving varies by site type. Soil sampling is conducted to a maximum depth of 30 ± 1 cm, and when organic (O) and mineral (M) horizons are present within a single profile, they are separated prior to analysis and archiving. However, other sub-horizons are not separated. During the majority of bouts, only the top horizon (O if present, else M) is analyzed for genetic content. Soils are homogenized and non-soil material is removed by hand in the field, then subsamples are immediately frozen on dry ice. They are maintained in ultra-low temperature freezers until they are shipped to an analytical facility for DNA extraction, sample preparation and sequencing. During peak greenness bouts, subsamples from each of the 3 sampling locations per plot are combined to form a plot-level composite that is used for metagenomics analysis. Laboratory metadata are delivered to NEON for QC testing and acceptance, and then formatted for upload to public sequence repositories. Genetic extracts are shipped from the analytical facility in 96-well plates to the NEON Biorepository to be archived at -80 degrees Celsius. See links below for NEON data products that provide physical, chemical, and biological measurements for these same soils (soil pH, moisture, and microbial properties are always measured; chemical properties are determined only for a subset of collection bouts). The metabarcoding protocol used by Battelle Applied Genomics is available in the NEON document library (https://data.neonscience.org/docu
NEON Biorepository Surface Water Microbe Collection (Sterivex Filters) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains surface water microbe samples collected on 67 mm long, 1.7 cm diameter, 0.22 um Sterivex capsule filters (NEON sample class: amc_fieldCellCounts_in.archiveID). Surface water microbe samples are collected at the same time and location as surface water cell count samples and surface water chemistry samples once per month in wadeable streams (12 times per year) and every-other month in lakes and rivers (6 times per year). Details on sampling locations and timing are provided in the NEON document titled Surface Water Chemistry Sampling in Aquatic Habitats (https://data.neonscience.org/documents). In wadeable streams, surface water microbe samples are collected near the downstream S2 sensor location. In lakes, microbial samples are collected near the the 'buoy', 'littoral 1', and 'littoral 2' sensors, and sampling depth(s) is dependent on lake stratification. In rivers, microbial samples are collected near the buoy sensor. Water samples are filtered on 0.22 um Sterivex capsule filters, capped and flash-frozen in the field. Sterivex filters are archived at the NEON Biorepository at -80 degrees Celsius. See related links below for protocol.
Effect of foot disturbance to cyanobacteria-dominated biocrusts on microbes and nitrogen in Chihuhuahan grassland and shrubland
Interactions between plants and soil microbes influence plant nutrient transformations, including nitrogen (N) fixation, nutrient mineralization, and resource exchanges through fungal networks. Physical disturbances to soils can disrupt soil microbes and associated processes that support plant and microbial productivity. In low resource drylands, biological soil crusts ("biocrusts") occupy surface soils and house key autotrophic and diazotrophic bacteria, non-vascular plants, or lichens. Interactions among biocrusts, plants, and fungal networks between them are hypothesized to drive carbon and nutrient dynamics; however, comparisons across ecosystems are needed to generalize how soil disturbances alter microbial communities and their contributions to N pools and transformations. To evaluate linkages among plants, fungi, and biocrusts, we disturbed all unvegetated surfaces with human foot trampling twice yearly in dry conditions from 2013-2018 in cyanobacteria-dominated biocrusts in Chihuahuan Desert grassland and shrubland ecosystems. Our study included microbial communities and N pools sampled at different time points in the disturbance treatments at one or both sites. We began our sampling after observations in April 2018 that the chlorophyll a content was at least double in control than disturbed plots in both ecosystems (Chung et al. 2019). Stomping occurred in May, and we collected soil and plant samples in June 2018 for N pools and soil and root fungal abundance. We collected additional soil samples in September 2018 and conducted the 15N tracer experiment to observe rates of N transfer from biocrust to plants before the fall stomp treatment in October. We collected chlorophyll a samples and soils for sequencing bacteria in September of 2019, also before the fall stomp treatment.
Is there a latitudinal diversity gradient for symbiotic microbes? A case study with sensitive partridge peas
<p><span>Mutualism is thought to be more prevalent in the tropics than temperate zones and may therefore play an important role in generating and maintaining high species richness found at lower latitudes. However, results on the impact of mutualism on latitudinal diversity gradients are mixed, and few empirical studies sample both temperate and tropical regions. We investigated whether a latitudinal diversity gradient exists in the symbiotic microbial community associated with the legume <em>Chamaecrista</em> <em>nictitans</em>. We sampled bacteria DNA from nodules and the surrounding soil of plant roots across a latitudinal gradient (38.64 °N to 8.68 °N). Using 16S rRNA sequence data, we identified many non-rhizobial species within <em>C. nictitans </em>nodules that cannot form nodules or fix nitrogen. Species richness increased towards lower latitudes in the non-rhizobial portion of the nodule community but not in the rhizobial community. The microbe community in the soil did not effectively predict the non-rhizobia community inside nodules, indicating that host selection is important for structuring non-rhizobia communities in nodules. We next factorially manipulated the presence of three non-rhizobia strains in greenhouse experiments and found that co-inoculations of non-rhizobia strains with rhizobia had a marginal effect on nodule number and no effect on plant growth. Our results suggest that these non-rhizobia bacteria are likely commensals – species that benefit from associating with a host but are neutral for host fitness. Overall, our study suggests that temperate <em>C. nictitans</em> plants are more selective in their associations with the non-rhizobia community, potentially due to differences in soil nitrogen across latitude.</span></p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.