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Supporting information for "HormonomicsDB: A novel workflow for the untargeted analysis of plant growth regulators and hormones" Giebelhaus, Ryland; Erland, Lauren; Murch, Susan
Description
Metabolomics allows for the simultaneous determination of all metabolites in a system. Despite significant advances in the field, compound identification remains a challenge. Prior knowledge of the compound classes of interest which are appropriate to a system can help to can help to improve metabolite identification. Hormones are a small signaling molecules, which function in coordination to direct all aspects of development, function and reproduction in living systems and which also pose challenges as environmental contaminants. By nature of their function, hormones are present at low levels in tissues, stored in many forms and mobilized rapidly in response to a stimulus making them difficult to measure, identify and quantify. Hormonomics is a new method for identification of all known and predicted hormones, their precursors, storage forms and metabolites in a biological system. We developed HormonomicsDB a tool which can be used to query an untargeted mass spectrometry (MS) dataset against a database of more than 200 known hormones, their precursors and metabolites. The protocol encompasses sample preparation, analysis, and data processing and is designed to minimize degradation of labile hormones. The plant system is used a model to illustrate the workflow and data acquisition and interpretation. Analytical conditions were standardized to a 30 min analysis time using a common solvent system to allow for easy transfer by a researcher with basic knowledge of MS. Incorporation of synthetic biotransformations algorithms allows for prediction of novel metabolites and conjugates. We performed a meta-analysis of 14 liquid chromatography-MS plant metabolomics studies using our HormonomicsDB web-tool. This protocol is suitable for use on any liquid chromatography-MS based system with compatible column and buffer system and enables the characterization of the known hormonome across a diversity of samples, as well as hypothesis generation to reveal knew insights into hormone signaling networks. Contained in this repository is the supporting information for this publication, including the summary list of all hormones archived in HormonomicsDB, and information from the meta-analysis described in this publication.
Item Metadata
| Title |
Supporting information for "HormonomicsDB: A novel workflow for the untargeted analysis of plant growth regulators and hormones"
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| Creator | |
| Contributor | |
| Date Created |
2022-06-01
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| Date Issued |
2022-07-27
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| Description |
Metabolomics allows for the simultaneous determination of all metabolites in a
system. Despite significant advances in the field, compound identification remains a
challenge. Prior knowledge of the compound classes of interest which are appropriate
to a system can help to can help to improve metabolite identification. Hormones are a
small signaling molecules, which function in coordination to direct all aspects of
development, function and reproduction in living systems and which also pose challenges as environmental contaminants. By nature of their function, hormones are
present at low levels in tissues, stored in many forms and mobilized rapidly in response
to a stimulus making them difficult to measure, identify and quantify. Hormonomics is
a new method for identification of all known and predicted hormones, their precursors,
storage forms and metabolites in a biological system. We developed HormonomicsDB a tool which can be used to query an untargeted mass spectrometry (MS) dataset against a database of more than 200 known hormones, their precursors and metabolites. The protocol encompasses sample preparation, analysis, and data processing and is designed to minimize degradation of labile hormones. The plant system is used a model to illustrate the workflow and data acquisition and interpretation. Analytical conditions were standardized to a 30 min analysis time using a common solvent system to allow for easy transfer by a researcher with basic knowledge of MS. Incorporation of synthetic biotransformations algorithms allows for prediction of novel metabolites and conjugates. We performed a meta-analysis of 14 liquid chromatography-MS plant metabolomics studies using our HormonomicsDB web-tool. This protocol is suitable for use on any liquid chromatography-MS based system with compatible column and buffer system and enables the characterization of the known hormonome across a diversity of samples, as well as hypothesis generation to reveal knew insights into hormone signaling networks.
Contained in this repository is the supporting information for this publication, including the summary list of all hormones archived in HormonomicsDB, and information from the meta-analysis described in this publication.
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| Subject | |
| Type | |
| Language |
English
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| Date Available |
2022-07-26
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| Provider |
University of British Columbia Library
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| License |
CC0 Waiver
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| DOI |
10.14288/1.0416399
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| URI | |
| Publisher DOI | |
| Rights URI | |
| Aggregated Source Repository |
Dataverse
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Item Media
Item Citations and Data
Licence
CC0 Waiver