Summary of Study ST001489
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001006. The data can be accessed directly via it's Project DOI: 10.21228/M8NT36 This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST001489 |
| Study Title | Metabolomics by UHPLC-HRMS reveals the impact of heat stress on pathogen-elicited immunity in maize |
| Study Summary | Studies investigating crop resistance to biotic and abiotic stress have largely focused on plant responses to singular forms of stress and individual biochemical pathways that only partially represent stress responses. Thus, combined biotic and abiotic stress treatments and the global assessment of their elicited metabolic expression remains largely unexplored. In this study, we employed targeted and untargeted metabolomics to investigate the metabolic responses of maize (Zea mays) to both individual and combinatorial stress treatments using heat (abiotic) and Cochliobolus heterostrophus infection (biotic) experiments. Ultra-high-performance liquid chromatography-high-resolution mass spectrometry revealed significant metabolic responses to C. heterostrophus infection and heat stress, and comparative analyses between these individual forms of stress demonstrated differential elicitation between the two global metabolomes. In combinatorial experiments, treatment with heat stress prior to fungal inoculation negatively impacted maize disease resistance against C. heterostrophus, and distinct metabolome separation between combinatorial stressed plants and the non-heat stressed infected controls was observed. Targeted analysis revealed inducible primary and secondary metabolite responses to biotic/abiotic stress, and combinatorial experiments indicated that deficiency in the hydroxycinnamic acid, p-coumaric acid, may lead to the heat-induced susceptibility of maize to C. heterostrophus. Collectively, these findings demonstrate that abiotic stress can predispose crops to more severe disease symptoms, underlining the increasing need to investigate defense chemistry in plants under combinatorial stress. |
| Institute | Agricultural Research Service, United States Department of Agriculture |
| Department | Center of Medical, Agricultural, and Veterinary Entomology |
| Laboratory | Chemistry Research Unit |
| Last Name | Christensen |
| First Name | Shawn |
| Address | 1600 SW 23rd Drive Gainesville, FL 32608 |
| shawn.christensen@usda.gov | |
| Phone | 3523745739 |
| Submit Date | 2020-08-03 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | raw(Thermo) |
| Analysis Type Detail | LC-MS |
| Release Date | 2021-08-03 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN002466 | AN002467 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | Reversed phase | Reversed phase |
| Chromatography system | Thermo Vanquish | Thermo Vanquish |
| Column | ACE Excel 2 C18-PFP (100 x 2.1mm, 2um) | ACE Excel 2 C18-PFP (100 x 2.1mm, 2um) |
| MS Type | ESI | ESI |
| MS instrument type | Orbitrap | Orbitrap |
| MS instrument name | Thermo Q Exactive Orbitrap | Thermo Q Exactive Orbitrap |
| Ion Mode | POSITIVE | NEGATIVE |
| Units | peak intensity | peak intensity |
MS:
| MS ID: | MS002286 |
| Analysis ID: | AN002466 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | The raw acquisition data were processed using a similar workflow described in previous work (Chamberlain et al., 2019a, b), which we detail here. Raw data files were converted from .raw to .mzxml format using RawConverter (He et al., 2015). MZmine 2 was used for processing the raw data including detecting masses, building chromatograms, grouping isotopic peaks, removing duplicate peaks, and aligning features (Pluskal et al., 2010). Identification was assigned to features by m/z (≤5 ppm) and retention time (0.2 min) (level 1 – identified compounds according to Metabolomics Standards Initiative standards (Sumner et al., 2007)) matching tousing our method-specific metabolite library produced from pure standards previously analyzed using this the above-mentioned chromatographic gradient. Processed data were exported from MZmine as a feature list containing the signal intensity for each feature in each sample. A small value (half the minimum value in the dataset) was used to replace zeros (no detection). The data were filtered to remove sample features with 10% signal contribution from their corresponding features in the extraction blanks. From this point, the data were further processed, normalized, and filtered using MetaboAnalyst 4.0 (Chong et al., 2018). For whole-metabolome comparative analyses, the data were normalized to total ion signal and feature intensities were auto-scaled to facilitate statistical comparisons (van den Berg et al., 2006). Statistical significance, defined as p 0.05, was determined using the two-tailed student’s t-test, and values for significance were adjusted for the false discovery rate with the Bonferroni-Holm method (HOLM, 1979). |
| Ion Mode: | POSITIVE |
| MS ID: | MS002287 |
| Analysis ID: | AN002467 |
| Instrument Name: | Thermo Q Exactive Orbitrap |
| Instrument Type: | Orbitrap |
| MS Type: | ESI |
| MS Comments: | The raw acquisition data were processed using a similar workflow described in previous work (Chamberlain et al., 2019a, b), which we detail here. Raw data files were converted from .raw to .mzxml format using RawConverter (He et al., 2015). MZmine 2 was used for processing the raw data including detecting masses, building chromatograms, grouping isotopic peaks, removing duplicate peaks, and aligning features (Pluskal et al., 2010). Identification was assigned to features by m/z (≤5 ppm) and retention time (0.2 min) (level 1 – identified compounds according to Metabolomics Standards Initiative standards (Sumner et al., 2007)) matching tousing our method-specific metabolite library produced from pure standards previously analyzed using this the above-mentioned chromatographic gradient. Processed data were exported from MZmine as a feature list containing the signal intensity for each feature in each sample. A small value (half the minimum value in the dataset) was used to replace zeros (no detection). The data were filtered to remove sample features with 10% signal contribution from their corresponding features in the extraction blanks. From this point, the data were further processed, normalized, and filtered using MetaboAnalyst 4.0 (Chong et al., 2018). For whole-metabolome comparative analyses, the data were normalized to total ion signal and feature intensities were auto-scaled to facilitate statistical comparisons (van den Berg et al., 2006). Statistical significance, defined as p 0.05, was determined using the two-tailed student’s t-test, and values for significance were adjusted for the false discovery rate with the Bonferroni-Holm method (HOLM, 1979). |
| Ion Mode: | NEGATIVE |
