Metabolomics dataset for disentangling plant and environment-mediated drivers of active rhizosphere 1 bacterial community dynamics during short-term drought
Data files
Mar 05, 2024 version files 19.36 GB
Abstract
Background: Mitigating the effects of climate stress on crops is important for global food security. The microbiome associated with plant roots, henceforth, the rhizobiome, can harbor beneficial microbes that alleviate stress impacts. However, the factors influencing the recruitment of the rhizobiome during stress are unclear. We conducted an experiment to understand bacterial rhizobiome responses to short-term drought for two crop species: switchgrass and common bean. We used 16S rRNA and 16S rRNA gene sequencing to investigate the impact of drought severity on the recruitment of active bacterial rhizobiome members. We included planted and unplanted conditions to distinguish the environment- versus plant mediated drivers of the active rhizobiome.
Results: Though each crop had a distinct rhizobiome, there were differences in the active microbiome structure between drought and watered and between planted and unplanted treatments. Despite their different community structures, the drought rhizobiome dynamics were similar across the two crops. However, the presence of a plant more strongly explained the rhizobiome variation in bean (17%) than in switchgrass (3%), with a small effect of plant mediation during drought only observed for the bean rhizobiome. The switchgrass rhizobiome was stable despite differences in the rhizosphere metabolite profiles between planted and unplanted treatments. Specifically, steroidal saponins and diterpennoids were enriched in drought, planted switchgrass soils.
Conclusions: We conclude that rhizobiome benefits to resist short-term drought are crop-specific, with the possibility of decoupling of plant exudation and rhizobiome responses, as we observed in switchgrass. We propose bacterial taxa uniquely associated with common bean plants during the short-term drought, which could be further evaluated to determine any plant benefit during drought.
README
General information
Title for the dataset: Disentangling plant- and environment-mediated drivers of active rhizosphere 1 bacterial community dynamics during short-term drought
Name/institution/address/email information for
Person responsible for collecting the data:Xingxing Li/Great Lakes Bioenergy Research Center at the Michigan State University/lixingxi@msu.edu
Contact person for questions:same as aboveDate of data collection: 9/11/2020
Information about geographic location of data collection:
Liquid Chromatography Mass Spectrometry (LC-MS) data: Mass Spectrometry and Metabolomics Core at the Michigan State UniversityKeywords used to describe the data topic: Mass spectrometry, LC-MS, untargeted metabolomics, switchgrass, drought, saponins, terpenoids, specialized metabolites, rhizosphere soil
Language information: English
Information about funding sources that supported the collection of the data:
This work was supported by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under award No. DE-SC0018409.
Data and file overview
'Switchgrass_metabolomics_DIA_feature_abundance.7z' is a zipped file for all the raw LC-MS data of untargeted metabolomics analysis. It contains 72 folders of mass spectrometry data. These folders each contain _FUNC* files and companion setup files that the software uses to build the dataset. Data can be viewed by opening the folders in Waters MassLynx. This data was directly collected from a Waters Xevo G2-XS QTof instrument, under a so-called data independent acquisition (DIA) mode. It is the source data for all the metabolite abundance information (used for making charts and statistical analysis) in the manuscript.
'Switchgrass_metabolomics_DDA_feature_identity.7z' is a zipped file for the raw LC-MS data that contains the true MS/MS spectral information needed for metabolite identification. Unzipping this file, you will see only one folder of mass spectrometry data. Same as above, it contains _FUNC* files and companion setup files that the software uses to build the dataset. It also can be viewed by opening the folders in Waters MassLynx. This data was directly collected from a Waters Xevo G2-XS QTof instrument, under a so-called data dependent acquisition (DDA) mode. It is source data for the metabolite identity prediction (see the item '5' below).
'Switchgrass_metabolomics_sample_list.SPL' is the sample list that links the sample descriptions to the 72 + 1 file folders above. You will need to copy and paste it to the SampleDB folder under your main MassLynx project folder in order to view the sample descriptions in MassLynx.
Alternatively, the 72 DIA data folders (above in the item '1') can be dropped directly into the MZmine3 open-source software for viewing.
Alternatively, 'Switchgrass_metabolomics_DDA_feature_identity_for_SIRIUS.mgf' is a file converted from the DDA data (above in the item '2'). This mgf file has a open-source data format and can be opened in SIRIUS, a java-based software framework for the analysis of LC-MS/MS data of metabolites and other "small molecules of biological interest". The machine learning based metabolite annotation (mentioned in the Method section of manuscript) was done using SIRIUS. This software can be downloaded for free from https://bio.informatik.uni-jena.de/software/sirius/.
'Switchgrass_metabolomics_sample_list.txt" is the same sample list as above in item '3' but in txt format for your convenience.
Methodological information
Description of methods for LC-MS data collection or generation: The raw data were collected using Quadrupole Time-of-Flight (Q-TOF) Mass Spectrometry coupled with a Waters (Milford, MA, USA) ACQUITY UPLC BEH C18 Column, 130Å, 1.7 µm, 2.1 mm X 100 mm, 1/pk.