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31 results for “synthetic communities”
Automated design of synthetic microbial communities
<p>In naturally occurring microbial systems, species rarely exist in isolation. There is strong ecological evidence for a positive relationship between species diversity and the functional output of communities. The pervasiveness of these communities in nature highlights that there may be advantages for engineered strains to exist in cocultures as well. Building synthetic microbial communities allows us to create distributed systems that mitigates issues often found in engineering a monoculture, especially when functional complexity is increasing. Here, we demonstrate a methodology for designing robust synthetic communities that use quorum sensing to control amensal bacteriocin interactions in a chemostat environment. We explore model spaces for two and three strain systems, using Bayesian methods to perform model selection, and identify the most robust candidates for producing stable steady state communities. Our findings highlight important interaction motifs that provide stability, and identify requirements for selecting genetic parts and tuning the community composition.</p>
Community diversity determines the evolution of synthetic bacterial communities under artificial selection
<p>These data and script are related to the article entitled "Community diversity determines the evolution of synthetic bacterial communities under artificial selection". This study reports the results of an experiment that involved bacterial communities differing for their richness level (1, 2, 4, 8, 16 species) and subjected to artificial selection or not. The property under selection was the optical density (OD). All the data needed to reproduce the figures and tables presented in the manuscript are provided.</p>
Genome of the isolates: Enhanced cultured diversity of the mouse gut microbiota enables custom-made synthetic communities
<p>The draft genome of the isolates in Mouse Intestinal Bacteria Collection (miBC)</p> <p> </p> <p>Microbiome research is hampered by the fact that many bacteria are still unknown and by the lack of publicly available isolates. Fundamental and clinical research is in need of comprehensive and well-curated repositories of cultured bacteria from the intestine of mammalian hosts. Due to host-specific features of the gut microbiota, it is sound to establish collections of isolates from single host species. Hence, this project established a collection of bacterial strains isolated from the intestine of mice.</p> <p>The original version of the collection published in 2016 (Lagkouvardos, et. al. 2016.<em> Nat. Microbiol.</em>). was doubled by the addition of 112 strains, representing a total of 141 species across 6 phyla and 35 families for the entire collection. As we aimed to create a well-curated resource, all bacterial species within miBC have been taxonomically described and are publicly available.</p> <p> </p>
Shotgun metagenomic sequencing dataset of a synthetic mock community containing 20 genomes spiked-in at even and staggered concentrations.
<p>Shotgun metagenomics (SM) sequencing is a popular method used in microbial ecology to obtain insights on microbial community structure and function potential in a given biological system without the need to cultivate microorganisms. The dataset described in this article describes technical triplicates of shotgun metagenomic sequence libraries generated from two purified and titrated mixes of 20 distinct reference bacterial genomes for which key characteristics such as genome size, sequence and spiked-in concentrations are known. In one of the genomic DNA mix, each genome is spiked-in at similar concentrations (representing an even microbial community) and in the other, genomes are spiked-in at different concentrations with some genomes highly abundant and other in low quantity, mimicking an uneven microbial community DNA extract. In order to be interpretable, SM sequencing data needs to be properly analyzed by complex analytical bioinformatic pipelines. Environments investigated with this method can range from simple to very complex. Typically, microbial communities contain microbes that are ubiquitous and some others much rarer. Analysis of rare microbes in a complex microbial community are challenging to perform as their sequencing signals get submerged by the microbial genomes that are more abundant. In this context, it is critical to have access to sequencing data of simple mock communities of mixes of well characterized genomes in order to develop and validate bioinformatic methods that aim to accurately analyze microbial communities.</p>
Dataset for Phosphite as an Engineered Niche for Pseudomonas veronii in a Synthetic Soil Bacterial Community
<p>Files containing the source data and analyses used in the manuscript "Phosphite as an Engineered Niche for <em>Pseudomonas veronii </em>in a Synthetic Soil Bacterial Community".</p> <p> </p> <p>Clara Bailey (1), Philip Gwyther (2), Senka Čaušević (2), Brandon L. Greene (1), and Jan Roelof van der Meer (2)</p> <p>1) Department of Chemistry and Biochemistry, University of California, Santa Barbara, Santa Barbara, California, United States</p> <p>2) Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland</p> <p> </p> <p>This dataset contains 16S rRNA gene amplicon sequencing data (in the form of an abundance table, "abund.csv" and combined with CFU counts in "abund_cfu.csv"), toluene quantification data, and CFU counts. All data analysis, statistical tests, and generated figures are contained in the relevant .R script. Refer to README files for data tables as well as the README section at the header of the R script. The dataset has been updated from version 1 to include manuscript revisions, and updated calculations and figures. </p> <p> </p>
Community Land Model synthetic meteorology simulation model output
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Behavioral interactions between bacterivorous nematodes and predatory bacteria in a synthetic community
Theory and empirical studies in metazoans predict that apex predators should shape the behavior and ecology of mesopredators and prey at lower trophic levels. Despite the ecological importance of microbial communities, few studies of predatory microbes examine such behavioral res-ponses and the multiplicity of trophic interactions. Here, we sought to assemble a three-level microbial food chain and to test for behavioral interactions between the predatory nematode Caenorhabditis elegans and the predatory social bacterium Myxococcus xanthus when cultured together with two basal prey bacteria that both predators can eat—Escherichia coli and Flavobacterium johnsoniae. We found that >90% of C. elegans worms failed to interact with M. xanthus even when it was the only potential prey species available, whereas most worms were attracted to pure patches of E. coli and F. johnsoniae. In addition, M. xanthus altered nematode predatory behavior on basal prey, repelling C. elegans from two-species patches that would be attractive without M. xanthus, an effect similar to that of C. elegans pathogens. The nematode also influenced the behavior of the bacterial predator: M. xanthus increased its predatory swarming rate in response to C. elegans in a manner dependent both on basal-prey identity and on worm density. Our results suggest that M. xanthus is an unattractive prey for some soil nematodes and is actively avoided when other prey are available. Most broadly, we found that nematode and bacterial predators mutually influence one another's predatory behavior, with likely consequences for coevolution within complex microbial food webs.
Untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community"
<p>This dataset contains the untargeted metabolomics data for the publication Weiss et al. 2022 "In vitro interaction network of a synthetic gut bacterial community". The dataset has also been submitted to MetaboLights repository with ID "MTBLS3535". Please refer to the MetaboLights repository for the most up-to-date datasets. </p> <p>Publication abstract:</p> <p>A key challenge in microbiome research is to predict the functionality of microbial communities based on community membership and (meta)-genomic data. As central microbiota functions are determined by bacterial community networks, it is important to gain insight into the principles that govern bacteria-bacteria interactions. Here, we focused on the growth and metabolic interactions of the Oligo-Mouse-Microbiota (OMM<sup>12</sup>) synthetic bacterial community, which is increasingly used as a model system in gut microbiome research. Using a bottom-up approach, we uncovered the directionality of strain-strain interactions in mono- and pairwise co-culture experiments as well as in community batch culture. Metabolic network reconstruction in combination with metabolomics analysis of bacterial culture supernatants provided insights into the metabolic potential and activity of the individual community members. Thereby, we could show that the OMM<sup>12</sup> interaction network is shaped by both exploitative and interference competition in vitro in nutrient-rich culture media and demonstrate how community structure can be shifted by changing the nutritional environment. In particular, <em>Enterococcus faecalis</em> KB1 was identified as an important driver of community composition by affecting the abundance of several other consortium members in vitro. As a result, this study gives fundamental insight into key drivers and mechanistic basis of the OMM<sup>12</sup> interaction network in vitro, which serves as a knowledge base for future mechanistic in vivo studies.</p>
Carbohydrate complexity limits microbial growth and reduces the sensitivity of synthetic human gut communities to perturbations
<p>Illumina sequencing data for "Carbohydrate complexity limits microbial growth and reduces the sensitivity of synthetic human gut communities to perturbations". Nature Ecology and Evolution, 2022</p>
Dataset for manuscript entitled "The effects of a synthetic and biological surfactant on the community composition and metabolic activity of a freshwater biofilm"
<p>The following datasets were used for the 16s rRNA analysis in the manuscript entitled " The effects of a synthetic and biological surfactant on the community composition and metabolic activity of a freshwater biofilm". BZ2 files were obtained from next generation sequencing with the Illumina Mi-Seq. Mothur was used to analyze the BZ2 files, creating the listed excel documents.</p>
Designing a Synthetic Microbial Community through Genome Metabolic Modeling to enhance Plant-Microbe Interaction
<p>Supplementary data 1 - <strong>Reconstructed genome-scale metabolic networks from MAGs and Hosts</strong></p> <p>Supplementary data 2 - P<strong>lant growth-promoting traits among members of the minimal community</strong></p> <p> </p> <p>Manipulating the rhizosphere microbial community through beneficial microorganism inoculation has gained interest in improving crop productivity and stress resistance. Synthetic microbial communities, known as SynCom, mimic natural microbial compositions while reducing the number of components. However, achieving this goal requires a comprehensive understanding of natural microbial communities and a careful selection of compatible microorganisms with colonization traits, which still pose challenges. In this study, we employed an <em>in-silico</em> approach using genome metabolic modeling to design a synthetic microbial community aimed at improving the yield of important crop plants. We used a targeted approach to select a minimal community (MinCom) encompassing essential compounds for microbial metabolism and compounds relevant to plant interactions. This resulted in a reduction of the initial community size by approximately 4.5-fold. Notably, the MinCom retained crucial genes associated with essential plant growth-promoting traits, such as iron acquisition, EPS production, potassium solubilization, nitrogen fixation, GABA production, and IAA-related tryptophan metabolism. Furthermore, our selection process for the SymCom, based on a comprehensive understanding of microbe-microbe-plant interactions, yielded a set of six hub species that displayed notable taxonomic novelty, including members of the Eremiobacterota and Verrucomicrobiota phyla. Our study contributes to the growing body of research on synthetic microbial communities and their potential to enhance agricultural practices. The insights gained from our in-silico approach and the selection of hub species pave the way for further investigations into the development of tailored microbial communities that can optimize crop productivity and improve stress resilience in agricultural systems.</p>
Behavioral interactions between bacterivorous nematodes and predatory bacteria in a synthetic community
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Code and Data Repository for "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments"
<p>The repository includes data and R code necessary to reproduce all figures and analyses presented in the study titled "Identifying microbiota community patterns important for plant protection using machine learning in synthetic community experiments".</p>
A Passaged 3-species Synthetic Microbial Community
<p>We constructed <em>de novo</em> a community of three model bacterial species, <em>Vibrio cholerae, Staphylococcus aureus, </em>and <em>Escherichia coli</em>, which all constitutively produce a distinct fluorescent protein. This fluorescence permitted hourly fluorescence tracking over three daily passages.</p> <p>Cells were quantified using a 10μm hemocytometer, imaged in a Nikon ELCIPSE Ti microscope, using a combination of brightfield and fluorescence microscopy. The filters used were GFP for green, YFP for yellow, and dsRed for red fluorescent proteins. Counts from four hemocytometer squares were averaged to quantify each replicate.</p>
Data from: Classifying interactions in a synthetic bacterial community is hindered by inhibitory growth medium
<p>Predicting the fate of a microbial community and its member species relies on understanding the nature of their interactions. However, designing simple assays that distinguish between interaction types can be challenging. Here, we performed spent media assays based on the predictions of a mathematical model to decipher the interactions between four bacterial species: <em>Agrobacterium</em> <em>tumefaciens</em> (<em>At</em>), <em>Comamonas</em> <em>testosteroni</em> (<em>Ct</em>), <em>Microbacterium</em> <em>saperdae</em> (<em>Ms</em>) and <em>Ochrobactrum</em> <em>anthropi</em> (<em>Oa</em>). While most experimental results matched model predictions, the behavior of <em>Ct</em> did not: its lag phase was reduced in the pure spent media of <em>At</em> and <em>Ms</em>, but prolonged again when we replenished with our growth medium. Further experiments showed that the growth medium actually delayed the growth of <em>Ct</em>, leading us to suspect that <em>At</em> and <em>Ms</em> could alleviate this inhibitory effect. There was, however, no evidence supporting such "cross-detoxification" and instead, we identified metabolites secreted by <em>At</em> and <em>Ms</em> that were then consumed or "cross-fed" by <em>Ct</em>, shortening its lag phase. Our results highlight that even simple, defined growth media can have inhibitory effects on some species and that such negative effects need to be included in our models. Based on this, we present new guidelines to correctly distinguish between different interaction types, such as cross-detoxification and cross-feeding.</p>
Data from: Classifying interactions in a synthetic bacterial community is hindered by inhibitory growth medium
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Data from: Increases and fluctuations in nutrient availability do not promote dominance of alien plants in synthetic communities of common natives
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Data from: Construction and characterization of synthetic bacterial community for experimental ecology and evolution
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Data from: Integrated culturing, modeling and transcriptomics uncovers complex interactions and emergent behavior in a three-species synthetic gut community
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RNA-seq of Arabidopsis thaliana seedlings growing in the presence of a bacterial synthetic community under full and low phosphate conditions
GEO Series GSE102248. Arabidopsis thaliana. 429 samples. Type: Expression profiling by high throughput sequencing.
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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.