Summary of Study ST003289
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 PR002041. The data can be accessed directly via it's Project DOI: 10.21228/M8SN77 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 | ST003289 |
| Study Title | Integrative LC-MS and GC-MS Metabolic Profiling Unveils Dynamic Changes during Barley Malting |
| Study Type | Untargeted LC-MS and GC-MS analysis |
| Study Summary | Malting, a crucial process for beer production, involves complex biochemical transformations affecting sensory attributes and product quality. Limited knowledge of metabolic alterations during malting hinders the ability to enhance malt quality. This study uses untargeted GC-MS and LC-MS metabolite profiling to characterize metabolic dynamics through the malting process. After data processing, a total of 4980 known metabolites were identified across six stages: dry seed, post-steeping, germination (DOG1, DOG3, DOG5), and kilned, about 82% of these showed significant changes during malting. Statistical analysis revealed stage-dependent shifts in metabolite profiles, highlighting the importance of the first 3 days of germination and kilning in determining the final metabolite content of finished malt. Dynamic changes in chemical classes and metabolic pathways provided insights into processes critical for malt quality and beer production. Additionally, metabolites associated with antimicrobial properties and stress responses were identified, underscoring the interplay between barley and microbial metabolic processes during malting. This comprehensive profiling advances our understanding of malting and suggests potential markers for process monitoring and quality control, ultimately enhancing malt quality and beer production. |
| Institute | USDA Agricultural Research Service |
| Department | Cereal Crops Research Unit |
| Laboratory | Whitcomb lab |
| Last Name | Rani |
| First Name | Heena |
| Address | 502 Walnut Street, Madison, WI 53726 |
| bansalheena10@gmail.com | |
| Phone | 7657759366 |
| Submit Date | 2024-06-17 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | cdf, mzML |
| Analysis Type Detail | GC/LC-MS |
| Release Date | 2024-10-16 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR002041 |
| Project DOI: | doi: 10.21228/M8SN77 |
| Project Title: | Malting Time-course metabolomics study |
| Project Type: | Untargeted LC-MS and GC-MS analysis |
| Project Summary: | Malting, a crucial process for beer production, involves complex biochemical transformations affecting sensory attributes and product quality. Limited knowledge of metabolic alterations during malting hinders the ability to enhance malt quality. This study uses untargeted GC-MS and LC-MS metabolite profiling to characterize metabolic dynamics through the malting process. After data processing, a total of 4980 known metabolites were identified across six stages: dry seed, post-steeping, germination (DOG1, DOG3, DOG5), and kilned, about 82% of these showed significant changes during malting. Statistical analysis revealed stage-dependent shifts in metabolite profiles, highlighting the importance of the first 3 days of germination and kilning in determining the final metabolite content of finished malt. Dynamic changes in chemical classes and metabolic pathways provided insights into processes critical for malt quality and beer production. Additionally, metabolites associated with antimicrobial properties and stress responses were identified, underscoring the interplay between barley and microbial metabolic processes during malting. This comprehensive profiling advances our understanding of malting and suggests potential markers for process monitoring and quality control, ultimately enhancing malt quality and beer production. |
| Institute: | USDA Agricultural Research Service |
| Department: | Cereal Crops Research Unit |
| Laboratory: | Whitcomb lab |
| Last Name: | Rani |
| First Name: | Heena |
| Address: | 502 Walnut Street, Madison, WI 53726 |
| Email: | bansalheena10@gmail.com |
| Phone: | 7657759366 |
| Funding Source: | USDA-ARS |
| Publications: | https://doi.org/10.1016/j.foodchem.2024.141480 |
Subject:
| Subject ID: | SU003409 |
| Subject Type: | Plant |
| Subject Species: | Hordeum vulgare subsp. vulgare |
| Taxonomy ID: | 112509 |
Factors:
Subject type: Plant; Subject species: Hordeum vulgare subsp. vulgare (Factor headings shown in green)
| mb_sample_id | local_sample_id | Sample source | Genotype | Treatment | Platform |
|---|---|---|---|---|---|
| SA356372 | 22-1-blank-blank-Blank | Seed | Conrad | BLANK | LC-MS |
| SA356373 | 39-1-blank-blank-Blank | Seed | Conrad | BLANK | LC-MS |
| SA356374 | 55-1-blank-blank-Blank | Seed | Conrad | BLANK | LC-MS |
| SA356375 | 230327dEGsa22_1 | Seed | Conrad | DOG0 | GC-MS |
| SA356376 | 230327dEGsa01_1 | Seed | Conrad | DOG0 | GC-MS |
| SA356377 | 230327dEGsa37_1 | Seed | Conrad | DOG0 | GC-MS |
| SA356378 | 230327dEGsa41_1 | Seed | Conrad | DOG0 | GC-MS |
| SA356379 | 230327dEGsa17_1 | Seed | Conrad | DOG0 | GC-MS |
| SA356380 | 230327dEGsa18_1 | Seed | Conrad | DOG0 | GC-MS |
| SA356381 | 230327dEGsa15_1 | Seed | Conrad | DOG0 | GC-MS |
| SA356382 | 230327dEGsa38_1 | Seed | Conrad | DOG0 | GC-MS |
| SA356383 | 230327dEGsa08_1 | Seed | Conrad | DOG0 | GC-MS |
| SA356384 | 63-1-2-dog0-11 | Seed | Conrad | DOG0 | LC-MS |
| SA356385 | 16-1-2-dog0-10 | Seed | Conrad | DOG0 | LC-MS |
| SA356386 | 18-1-2-dog0-17 | Seed | Conrad | DOG0 | LC-MS |
| SA356387 | 48-1-2-dog0-16 | Seed | Conrad | DOG0 | LC-MS |
| SA356388 | 64-1-2-dog0-13 | Seed | Conrad | DOG0 | LC-MS |
| SA356389 | 13-1-2-dog0-14 | Seed | Conrad | DOG0 | LC-MS |
| SA356390 | 27-1-2-dog0-15 | Seed | Conrad | DOG0 | LC-MS |
| SA356391 | 56-1-2-dog0-12 | Seed | Conrad | DOG0 | LC-MS |
| SA356392 | 41-1-2-dog0-18 | Seed | Conrad | DOG0 | LC-MS |
| SA356393 | 230327dEGsa12_1 | Seed | Conrad | DOG1 | GC-MS |
| SA356394 | 230328dEGsa03_1 | Seed | Conrad | DOG1 | GC-MS |
| SA356395 | 230327dEGsa47_1 | Seed | Conrad | DOG1 | GC-MS |
| SA356396 | 230327dEGsa04_1 | Seed | Conrad | DOG1 | GC-MS |
| SA356397 | 230327dEGsa25_1 | Seed | Conrad | DOG1 | GC-MS |
| SA356398 | 230327dEGsa06_1 | Seed | Conrad | DOG1 | GC-MS |
| SA356399 | 230327dEGsa23_1 | Seed | Conrad | DOG1 | GC-MS |
| SA356400 | 230328dEGsa01_1 | Seed | Conrad | DOG1 | GC-MS |
| SA356401 | 230327dEGsa29_1 | Seed | Conrad | DOG1 | GC-MS |
| SA356402 | 14-1-3-dog1-22 | Seed | Conrad | DOG1 | LC-MS |
| SA356403 | 4-1-3-dog1-20 | Seed | Conrad | DOG1 | LC-MS |
| SA356404 | 20-1-3-dog1-26 | Seed | Conrad | DOG1 | LC-MS |
| SA356405 | 61-1-3-dog1-19 | Seed | Conrad | DOG1 | LC-MS |
| SA356406 | 53-1-3-dog1-25 | Seed | Conrad | DOG1 | LC-MS |
| SA356407 | 51-1-3-dog1-27 | Seed | Conrad | DOG1 | LC-MS |
| SA356408 | 29-1-3-dog1-24 | Seed | Conrad | DOG1 | LC-MS |
| SA356409 | 28-1-3-dog1-21 | Seed | Conrad | DOG1 | LC-MS |
| SA356410 | 21-1-3-dog1-23 | Seed | Conrad | DOG1 | LC-MS |
| SA356411 | 230327dEGsa35_1 | Seed | Conrad | DOG3 | GC-MS |
| SA356412 | 230327dEGsa19_1 | Seed | Conrad | DOG3 | GC-MS |
| SA356413 | 230327dEGsa32_1 | Seed | Conrad | DOG3 | GC-MS |
| SA356414 | 230327dEGsa24_1 | Seed | Conrad | DOG3 | GC-MS |
| SA356415 | 230327dEGsa09_1 | Seed | Conrad | DOG3 | GC-MS |
| SA356416 | 230327dEGsa02_1 | Seed | Conrad | DOG3 | GC-MS |
| SA356417 | 230327dEGsa07_1 | Seed | Conrad | DOG3 | GC-MS |
| SA356418 | 230327dEGsa34_1 | Seed | Conrad | DOG3 | GC-MS |
| SA356419 | 230327dEGsa43_1 | Seed | Conrad | DOG3 | GC-MS |
| SA356420 | 17-1-4-dog3-30 | Seed | Conrad | DOG3 | LC-MS |
| SA356421 | 2-1-4-dog3-29 | Seed | Conrad | DOG3 | LC-MS |
| SA356422 | 36-1-4-dog3-33 | Seed | Conrad | DOG3 | LC-MS |
| SA356423 | 42-1-4-dog3-28 | Seed | Conrad | DOG3 | LC-MS |
| SA356424 | 57-1-4-dog3-34 | Seed | Conrad | DOG3 | LC-MS |
| SA356425 | 65-1-4-dog3-35 | Seed | Conrad | DOG3 | LC-MS |
| SA356426 | 10-1-4-dog3-36 | Seed | Conrad | DOG3 | LC-MS |
| SA356427 | 25-1-4-dog3-31 | Seed | Conrad | DOG3 | LC-MS |
| SA356428 | 5-1-4-dog3-32 | Seed | Conrad | DOG3 | LC-MS |
| SA356429 | 230327dEGsa28_1 | Seed | Conrad | DOG5 | GC-MS |
| SA356430 | 230327dEGsa39_1 | Seed | Conrad | DOG5 | GC-MS |
| SA356431 | 230327dEGsa05_1 | Seed | Conrad | DOG5 | GC-MS |
| SA356432 | 230327dEGsa36_1 | Seed | Conrad | DOG5 | GC-MS |
| SA356433 | 230327dEGsa46_1 | Seed | Conrad | DOG5 | GC-MS |
| SA356434 | 230327dEGsa03_1 | Seed | Conrad | DOG5 | GC-MS |
| SA356435 | 230327dEGsa33_1 | Seed | Conrad | DOG5 | GC-MS |
| SA356436 | 230327dEGsa10_1 | Seed | Conrad | DOG5 | GC-MS |
| SA356437 | 230327dEGsa31_1 | Seed | Conrad | DOG5 | GC-MS |
| SA356438 | 47-1-5-dog5-43 | Seed | Conrad | DOG5 | LC-MS |
| SA356439 | 24-1-5-dog5-37 | Seed | Conrad | DOG5 | LC-MS |
| SA356440 | 38-1-5-dog5-40 | Seed | Conrad | DOG5 | LC-MS |
| SA356441 | 26-1-5-dog5-38 | Seed | Conrad | DOG5 | LC-MS |
| SA356442 | 62-1-5-dog5-41 | Seed | Conrad | DOG5 | LC-MS |
| SA356443 | 40-1-5-dog5-44 | Seed | Conrad | DOG5 | LC-MS |
| SA356444 | 19-1-5-dog5-45 | Seed | Conrad | DOG5 | LC-MS |
| SA356445 | 66-1-5-dog5-39 | Seed | Conrad | DOG5 | LC-MS |
| SA356446 | 11-1-5-dog5-42 | Seed | Conrad | DOG5 | LC-MS |
| SA356447 | 230327dEGsa30_1 | Seed | Conrad | DRY | GC-MS |
| SA356448 | 230327dEGsa27_1 | Seed | Conrad | DRY | GC-MS |
| SA356449 | 230327dEGsa14_1 | Seed | Conrad | DRY | GC-MS |
| SA356450 | 230327dEGsa42_1 | Seed | Conrad | DRY | GC-MS |
| SA356451 | 230327dEGsa21_1 | Seed | Conrad | DRY | GC-MS |
| SA356452 | 230327dEGsa16_2 | Seed | Conrad | DRY | GC-MS |
| SA356453 | 230327dEGsa26_1 | Seed | Conrad | DRY | GC-MS |
| SA356454 | 230327dEGsa48_1 | Seed | Conrad | DRY | GC-MS |
| SA356455 | 230327dEGsa13_1 | Seed | Conrad | DRY | GC-MS |
| SA356456 | 230327dEGsa40_1 | Seed | Conrad | DRY | GC-MS |
| SA356457 | 54-1-1-dry-3 | Seed | Conrad | DRY | LC-MS |
| SA356458 | 50-1-1-dry-7 | Seed | Conrad | DRY | LC-MS |
| SA356459 | 49-1-1-dry-1 | Seed | Conrad | DRY | LC-MS |
| SA356460 | 43-1-1-dry-2 | Seed | Conrad | DRY | LC-MS |
| SA356461 | 46-1-1-dry-9 | Seed | Conrad | DRY | LC-MS |
| SA356462 | 35-1-1-dry-4 | Seed | Conrad | DRY | LC-MS |
| SA356463 | 44-1-1-dry-8 | Seed | Conrad | DRY | LC-MS |
| SA356464 | 7-1-1-dry-5 | Seed | Conrad | DRY | LC-MS |
| SA356465 | 58-1-1-dry-6 | Seed | Conrad | DRY | LC-MS |
| SA356466 | 230327dEGsa11_1 | Seed | Conrad | KILNED | GC-MS |
| SA356467 | 230327dEGsa50_1 | Seed | Conrad | KILNED | GC-MS |
| SA356468 | 230328dEGsa02_1 | Seed | Conrad | KILNED | GC-MS |
| SA356469 | 230327dEGsa45_1 | Seed | Conrad | KILNED | GC-MS |
| SA356470 | 230327dEGsa49_1 | Seed | Conrad | KILNED | GC-MS |
| SA356471 | 230327dEGsa20_1 | Seed | Conrad | KILNED | GC-MS |
Collection:
| Collection ID: | CO003402 |
| Collection Summary: | 9 samples were collected (three per micro-malting replicate) at 6 different stages: unmalted mature grain (DRY), out of steep/post-step (DOG0), on alternating days of germination (DOG1, DOG3, and DOG5), and at the end of kilning (KILNED). Samples were collected in Eppendorf tubes, immediately flash frozen in liquid nitrogen and subsequently stored at −80°C until grinding. |
| Sample Type: | Barley seeds |
Treatment:
| Treatment ID: | TR003418 |
| Treatment Summary: | 9 samples were collected (three per micro-malting replicate) at 6 different stages: unmalted mature grain (DRY), out of steep/post-step (DOG0), on alternating days of germination (DOG1, DOG3, and DOG5), and at the end of kilning (KILNED) |
Sample Preparation:
| Sampleprep ID: | SP003416 |
| Sampleprep Summary: | For grinding, the seeds were lyophilized, placed in 5 ml polycarbonate vials along with three stainless steel balls (6.3 mm), and cooled in liquid nitrogen. The frozen tissue was ground to a fine powder in a 2010 Geno/Grinder (Spex SamplePrep) with intermittent rests, rapidly re-cooled in liquid nitrogen, and transferred to their final storage vials. The ground samples were stored at −80°C until further analysis Preparation of samples for GC-MS analysis Finely ground samples (4 mg) were extracted using 1 ml of 3:3:2 isopropanol (IPA)/acetonitrile (ACN)/water (v/v/v) for 6 min at 4°C. After centrifugation, the supernatant was aliquoted into two equal portions and dried. One aliquot was derivatized with methoxamine, a mixture of fatty acid methyl esters (FAMEs) ranging from C08 to C30 was added to each sample, and finally derivatized with N-methyl-N-trimethylsilyltrifluoroacetamide (MSTFA). The prepared samples were then transferred to vials, sealed, and loaded into the GC-MS instrument. Quality control (QC) samples were prepared by equally pooling ground powder from all biological samples and were processed as described above. Method blanks were prepared using the same 3:3:2 IPA/ACN/water mixture without the addition of biological sample and processed using the same extraction and processing procedures as the other samples. Preparation of samples for LC-MS analysis Finely ground samples (100-130mg) were extracted with 800 μl of 80% IPA in water for 10 minutes at room temperature. After centrifugation, 700 μl of the supernatant was transferred to a fresh vial. This process was repeated with another 700 µl of 80% IPA, and the resulting 1.4 ml of combined supernatant was discarded. The pellet was re-extracted twice more using 700 µl of 80% methanol. The 1.4 ml of combined methanol extract was dried under nitrogen and then reconstituted in 100 µl of methanol. The reconstituted extracts were then transferred to autosampler vials for UPLC-MS analysis. QCs were prepared by equally pooling ground powder from all samples and were processed as described above. Method blanks were prepared and processed in the same manner without the addition of biological sample. |
Combined analysis:
| Analysis ID | AN005386 | AN005387 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | Reversed phase | GC |
| Chromatography system | Waters Acquity | Agilent 7890A |
| Column | Waters Acquity UPLC CSH Phenyl Hexyl column (1.7 μM, 1.0 x 100 mm) | Agilent DB5-MS (30m x 0.25mm, 0.25um) |
| MS Type | ESI | EI |
| MS instrument type | QTOF | GC-TOF |
| MS instrument name | Waters Xevo-G2-XS | Leco Pegasus IV TOF |
| Ion Mode | POSITIVE | POSITIVE |
| Units | Peak Area | Peak Height |
Chromatography:
| Chromatography ID: | CH004083 |
| Instrument Name: | Waters Acquity |
| Column Name: | Waters Acquity UPLC CSH Phenyl Hexyl column (1.7 μM, 1.0 x 100 mm) |
| Column Temperature: | 65 °C |
| Flow Gradient: | 99% A, held at 99% A for 1 min, ramped to 98% B over 12 minutes, held at 98% B for 3 minutes, and then returned to starting conditions over 0.05 minutes and allowed to re-equilibrate for 3.95 minutes |
| Flow Rate: | 200 μL/min |
| Solvent A: | 100% water; 0.1% ammonium formate |
| Solvent B: | 100% acetonitrile; 0.1% formic acid |
| Chromatography Type: | Reversed phase |
| Chromatography ID: | CH004084 |
| Chromatography Summary: | Injection temperature: 50°C ramped to 250°C by 12°C s-1. Oven temperature program: 50°C for 1 min, then ramped at 20°C min-1 to 330°C, held constant for 5 min. |
| Instrument Name: | Agilent 7890A |
| Column Name: | Agilent DB5-MS (30m x 0.25mm, 0.25um) |
| Column Temperature: | 50-330°C |
| Flow Gradient: | N/A |
| Flow Rate: | 1 ml/min |
| Solvent A: | Mobile phase: Helium |
| Solvent B: | N/A |
| Chromatography Type: | GC |
MS:
| MS ID: | MS005113 |
| Analysis ID: | AN005386 |
| Instrument Name: | Waters Xevo-G2-XS |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | Following data collection, raw data files were preprocessed using Leco ChromaTOF software v2.32 with baseline subtraction just above the noise level and automatic mass spectral deconvolution and peak detection at a signal/noise ratio of 5:1. Apex masses were reported for use in BinBase algorithm. Result files were exported to a data server with absolute spectra intensities and further processed by a filtering algorithm implemented in the metabolomics BinBase database. Spectra were cut to 5% base peak abundance and matched to database entries from most to least abundant spectra using the following matching filters: retention index window ±2,000 units (equivalent to about ±2 s retention time), validation of unique ions and apex masses (unique ion must be included in apexing masses and present at >3% of base peak abundance), mass spectrum similarity must fit criteria dependent on peak purity and signal/noise ratios and a final isomer filter. Raw data peak heights were normalized to the sum of peak heights of all identified metabolites in each sample. One of the replicates from the dry seeds failed and was removed from further GC-MS analysis. |
| Ion Mode: | POSITIVE |
| MS ID: | MS005114 |
| Analysis ID: | AN005387 |
| Instrument Name: | Leco Pegasus IV TOF |
| Instrument Type: | GC-TOF |
| MS Type: | EI |
| MS Comments: | R package XCMS v3.20.0 was used to process raw data. XCMS steps included: peak detection (CentWave), peak grouping (PeakDensity), retention time correction (PeakGroups), peak regrouping (PeakDensity), and missing peak filling (FillChromPeaks). R package RAMClustR v1.2.4 (Broeckling et al., 2014) was used to normalize, filter, cluster features into spectra, and infer molecular weights. Missing values were replaced with small values simulating noise: for each feature, the replacement value was equal to the absolute value of 0.5 times the minimum detected value of that feature. Features were normalized by linearly regressing run order versus QC feature intensities to account for instrument signal intensity drift. Only features showing significant regression (p < 0.05, r-squared > 0.1) were corrected. Features failing to demonstrate at least a 2-fold greater signal intensity in QC samples compared to blanks were removed. Subsequent filtering based on QC sample coefficient of variation (CV) values retained only features with CV values ≤ 0.5 in MS or MSMS data sets. Features were clustered using the ramclustR algorithm (Broeckling et al., 2014). MSFinder v 3.52 (Tsugawa et al., 2016) was used for spectral matching, formula inference, and tentative structure assignment. MSFinder results were imported into the RAMClustR object where a total score was computed based on the product scores from the findmain function and the MSFinder formula and structure scores. Spectra matches took precedence over computational inference-based annotations. CHEBI and COCONUT were set as priority databases, and matches to these databases were given a priority factor value of 1. Matches to databases other than the priority databases were assigned a priority factor of 0.9. A custom database of compounds found in barley was generated based on associations from FooDB and PubChem. The list of 8858 InChIKey structures in this custom barley database was also used for annotation prioritization. Annotations with InChIKey(s) that didn’t match those in the custom barley database were assigned an InChIKey priority factor of 0.9 to decrease scores for these annotations). The annotation with the highest total score was selected for each compound. |
| Ion Mode: | POSITIVE |
