DOI: 10.5061/dryad.6t1g1jx5z
title: Metabolomics dataset for disentangling plant and environment-mediated drivers of active rhizosphere 1 bacterial community dynamics during short-term drought

description: 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.
description: ## General information 1. Title for the dataset: Disentangling plant- and
 environment-mediated drivers of active rhizosphere 1 bacterial community
 dynamics during short-term drought 2. 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 above 3.
 Date of data collection: 9/11/2020 4. Information about geographic
 location of data collection: Liquid Chromatography Mass Spectrometry
 (LC-MS) data: Mass Spectrometry and Metabolomics Core at the Michigan
 State University 5. Keywords used to describe the data topic: Mass
 spectrometry, LC-MS, untargeted metabolomics, switchgrass, drought,
 saponins, terpenoids, specialized metabolites, rhizosphere soil 6.
 Language information: English 7. 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 1.
 '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. 2.
 '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). 3.
 '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. 4. Alternatively, the 72 DIA data folders (above in the item
 '1') can be dropped directly into the MZmine3 open-source
 software for viewing. 5. 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/](https://bio.informatik.uni-jena.de/software/sirius/). 6. '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.