Summary of Study ST001854
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 PR001169. The data can be accessed directly via it's Project DOI: 10.21228/M8M40V This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
| Study ID | ST001854 |
| Study Title | Metabolic profiling of Rafflesia-infected Tetrastigma and applications for propagation |
| Study Summary | Endemic to the forests of Southeast Asia, Rafflesia (Rafflesiaceae) is a genus of holoparasitic plants producing the largest flowers in the world, yet completely dependent on its host, the tropical grape vine, Tetrastigma. Rafflesia species are threatened with extinction, making them an iconic symbol of plant conservation. Thus far, propagation has proved challenging, greatly decreasing efficacy of conservation efforts. This study compared the metabolites in the shoots of Rafflesia-infected and non-infected Tetrastigma loheri to examine how Rafflesia infection affects host metabolomics and elucidate the Rafflesia infection process. Results from LC-MS-based untargeted metabolomics analysis showed benzylisoquinoline alkaloids were significantly elevated in non-infected shoots and are here reported for the first time in the genus Tetrastigma, and in the grape family, Vitaceae. These metabolites have been implicated in plant defense mechanisms and may prevent a Rafflesia infection. In Rafflesia-infected shoots, oxygenated fatty acids, or oxylipins, and a flavonoid, previously shown involved in plant immune response, were abundant. This study provides a preliminary assessment of metabolites that differ between Rafflesia-infected and non-infected Tetrastigma hosts and may have applications in Rafflesia propagation to meet conservation goals. |
| Institute | Long Island University |
| Last Name | Molina |
| First Name | Jeanmaire |
| Address | 1 University Plaza |
| jeanmaire.molina@liu.edu | |
| Phone | - |
| Submit Date | 2021-06-26 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | d |
| Analysis Type Detail | LC-MS |
| Release Date | 2021-07-03 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Combined analysis:
| Analysis ID | AN003005 |
|---|---|
| Analysis type | MS |
| Chromatography type | Reversed phase |
| Chromatography system | Thermo Scientific Ultimate-3000 UHPLC system |
| Column | Agilent Acclaim 120 C18-column (2.1 mm x 100 mm, 5 µm) |
| MS Type | ESI |
| MS instrument type | QTOF |
| MS instrument name | Bruker Daltonics maXis-II UHR-ESI-QqTOF |
| Ion Mode | POSITIVE |
| Units | ion intensity |
MS:
| MS ID: | MS002794 |
| Analysis ID: | AN003005 |
| Instrument Name: | Bruker Daltonics maXis-II UHR-ESI-QqTOF |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | Raw data were analyzed by using the online version of XCMS metabolomics software (version 1.10.9; Tautenhahn et al. 2012). To analyze the data in XCMS, we applied a pairwise comparison between infected and non-infected samples with default parameters for Bruker Q-TOF. After XCMS analysis, the difference reports were filtered. The features from XCMS with p-value < 0.05, intensities above 50000, and fold difference of at least 5, were analyzed further in Bruker Compass Data Analysis v4.3 and Metfrag Web (Ruttkies et al. 2016; https://msbi.ipb-halle.de/MetFragBeta/) to identify metabolites of interest. The neutral molecular formula of the precursor ions (desired features) and their MS/MS fragmentation spectra were then obtained in Bruker Compass Data Analysis and given as input in the MS/MS peak list in Metfrag. All other settings were kept at default values. Candidate metabolites were then retrieved with the highest scoring candidates subjected to additional analysis in CFM-ID (Allen et al. 2014; http://cfmid.wishartlab.com/) to confirm Metfrag candidates. Metfrag and CFM-ID are silico fragmentation tools that utilize known compounds from structure databases to calculate fragments that are matched to experimentally obtained spectra (Blaženović et al. 2018). In addition to these automated approaches, we have also performed a manual dereplication approach to verify the metabolites of interest, as described in previous publications (Gödecke et al. 2009; Nikolić et al. 2012; Nikolić et al. 2015; Nikolic et al. 2017). |
| Ion Mode: | POSITIVE |
