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Dataset results
12 results for “synthetic bacterial communities”
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>
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>
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>
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: Construction and characterization of synthetic bacterial community for experimental ecology and evolution
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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.
RNA-seq of Arabidopsis thaliana seedlings growing in the presence of a bacterial synthetic community under full and low phosphate conditions [PBI_preTreat]
GEO Series GSE87337. Arabidopsis thaliana. 144 samples. Type: Expression profiling by high throughput sequencing.
RNA-seq of Arabidopsis thaliana seedlings growing axenically, or in the presence of different bacterial Synthetic Community treatments that induced different root morphology
GEO Series GSE131156. Arabidopsis thaliana. 15 samples. Type: Expression profiling by high throughput sequencing.
RNA-seq of Arabidopsis thaliana seedlings growing in the presence of a bacterial synthetic community under full and low phosphate conditions [PBI]
GEO Series GSE87338. Arabidopsis thaliana. 104 samples. Type: Expression profiling by high throughput sequencing.
Long read genome assembly of species from the synthetic HAMBI bacterial community
<p>We present complete genome sequences of 30 bacterial species that can be used to construct defined synthetic communities that stably form in the laboratory under controlled conditions.</p><p>This repository contains the final genome assemblies, logs, reports/summaries, scripts, and associated computational methods. All genomes were circularized (or in the case of <i>Agrobacterium tumefaciens</i> properly linearized) with the recovery of some small plasmids.</p><p>30 Species from the HAMBI community were sent for Oxford Nanopore or PacBio HiFi sequencing at <a href="https://www.seqcenter.com/">SeqCenter</a> early November 2022. I assembled multiple subsets of the read data for each genome and generated consensus assemblies using <a href="https://github.com/rrwick/Trycycler">Trycycler</a>.</p><p>All sequence data is available under BioProject <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1047486">PRJNA1047486</a></p><p>This is a mirror of the repository here: <a href="https://gitlab.utu.fi/slhogl/hambiLongRead">https://gitlab.utu.fi/slhogl/hambiLongRead</a></p>
RNA-seq of Arabidopsis thaliana seedlings growing in the presence of a 185-members bacterial synthetic community under full and low phosphate conditions
GEO Series GSE129395. Arabidopsis thaliana. 24 samples. Type: Expression profiling by high throughput sequencing.
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