Skip to main content
NCBI home page
ACS AuthorChoice logoLink to ACS AuthorChoice
. 2024 Apr 18;72(17):10149–10161. doi: 10.1021/acs.jafc.4c00884

Advancing Sustainable Malting Practices: Aquaporins as Potential Breeding Targets for Improved Water Uptake during Controlled Germination of Barley (Hordeum vulgare L.)

Clare E O’Lone †,∥, Angéla Juhász †, Mitchell Nye-Wood †, David Moody §, Hugh Dunn ‡, Jean-Philippe Ral ∥, Michelle L Colgrave †,⊥,*
PMCID: PMC11066872  PMID: 38635353

Abstract

graphic file with name jf4c00884_0007.jpg

The conversion of raw barley (Hordeum vulgare L.) to malt requires a process of controlled germination, where the grain is submerged in water to raise the moisture content to >40%. The transmembrane proteins, aquaporins, influence water uptake during the initial stage of controlled germination, yet little is known of their involvement in malting. With the current focus on sustainability, understanding the mechanisms of water uptake and usage during the initial stages of malting has become vital in improving efficient malting practices. In this study, we used quantitative proteomics analysis of two malting barley genotypes demonstrating differing water-uptake phenotypes in the initial stages of malting. Our study quantified 19 transmembrane proteins from nine families, including seven distinct aquaporin isoforms, including the plasma intrinsic proteins (PIPs) PIP1;1, PIP2;1, and PIP2;4 and the tonoplast intrinsic proteins (TIPs) TIP1;1, TIP2;3, TIP3;1, and TIP3;2. Our findings suggest that the presence of TIP1;1, TIP3;1, and TIP3;2 in the mature barley grain proteome is essential for facilitating water uptake, influencing cell turgor and the formation of large central lytic vacuoles aiding storage reserve hydrolysis and endosperm modification efficiency. This study proposes that TIP3s mediate water uptake in malting barley grain, offering potential breeding targets for improving sustainable malting practices.

Keywords: aquaporins, water uptake, barley, controlled germination, malting, sustainability

Introduction

Barley (Hordeum vulgare) is a widely cultivated and economical crop used in malt production. Malt barley is produced via controlled germination or malting involving water uptake and content changes that influence endosperm modification and final malt quality.1,2 The malting process consists of three stages; the first stage, steeping, is initiated by submergence, rehydrating the grain, and raising the moisture content to around 42–47% (fresh weight).3 This involves periods of air rest and reimmersion to ensure uniform hydration, taking approximately 24 h. The increased water content also triggers the metabolism, involving cell vacuolation and the start of storage reserve mobilization for radical elongation.4,5 The second stage is germination, which proceeds for 3–5 days under controlled conditions, allowing for continued grain modification and elongation of the embryonic axis and radicle emergence.3,6,7 For the final stage, once the grain has germinated, the green malt is kilned to halt the germination process and dried to develop the desired color, flavor, and aroma of the malt.3,8,9

During the initial stage of controlled germination, the water uptake is rapid and requires about 4000 L of water to make one metric tonne (t) of malt.1 Transmembrane proteins (TMPs) embedded within membranes participate in this period, forming channels that regulate the water and solute movement in and out of cells.10 Thus, they are key targets for improving the efficiency of grain water uptake and usage, such as the group of membrane intrinsic proteins (MIPs), commonly referred to as aquaporins (AQPs). Water can traverse cellular membranes by diffusion or aided by AQPs, where AQPs are known to raise permeability coefficients for water by 10–20 times, exceeding the rate of water diffusion across membranes.11 Several AQPs have been identified in barley,12−15 including plasma intrinsic proteins (PIPs), for example, HvPIP1;1 and HvPIP2;1,16,17 and tonoplast intrinsic proteins (TIPs), such as HvTIP1;2 and HvTIP3;1,18 the two most well-known AQP subfamilies with consistently higher expression in plants.

PIPs are localized to the cell membrane and can be further divided into two subclasses, PIP1 and PIP2, based on sequence divergence, each consisting of several isoforms that are key in determining hydraulic conductivity.19,20 Analysis of barley HvPIP1 and HvPIP2 proteins revealed that HvPIP2 but not HvPIP1 channels showed robust water permeability when expressed alone; however, coexpression significantly increased water permeability, presumably through heteromerization.16 Studies also showed that PIPs allow the transfer of small neutral molecules, including hydrogen peroxide,21 oxygen,22 or carbon dioxide,17 as reviewed by Gomes et al.23 and Tan et al.24 TIPs, on the other hand, are localized in the vacuolar tonoplast and, in addition to water, facilitate the transport of glycerol,25 urea,25,26 H2O2,27 and ammonia28 and also play a pivotal role in controlling hydraulic conductivity within plant cells.19,29 TIP1 and TIP2 are present in vegetative tissue, while TIP3 is exclusively expressed in developing seeds and during early seed germination.18

Most of the processes involved in germination are inseparable from AQP-facilitated water uptake in seeds,5,30,31 also observed in our previous study.32 Due to the importance of barley in agriculture, several studies have investigated the role of AQPs in germination in field environments.16,17,33,34 However, information about their role in imbibition during controlled germination for malt barley production is limited. Therefore, this study explores the proteomic analysis of transmembrane proteins, specifically AQPs, as it relates to the steeping efficiency of two malting barley genotypes with varied water uptake phenotypes: IGB1467 (IGB; experimental), a newly developed InterGrain breeding line with demonstrated modification efficiency at reduced moisture content; and Flinders (FLN; control), a traditional InterGrain malting barley cultivar. Using sequential window acquisition of all known theoretical spectra mass spectrometry (SWATH)-MS together with targeted multiple-reaction monitoring (MRM) proteomic approaches, we carried out a quantitative proteomic analysis followed by gene ontology term enrichment analysis to understand abundance changes of water uptake-related transmembrane proteins at 0, 24, and 48 h after imbibition (HAI). Here, we report on findings demonstrating that the dynamic changes in TMPs, specifically AQPs, can be key contributors to the uptake and dispersal of water for efficient grain modification in malting barley. Previous research on malting barley found that the rate of water uptake during malting is genetically controlled.1 Therefore, we propose that AQPs contribute to a desired malting phenotype and have the genetic potential for marker-assisted breeding or genetic engineering to advance sustainable malting practices.

Materials and Methods

Plant Material, Malting Conditions, and Malt Analysis

Two InterGrain Pty Ltd. two-rowed malting barley (H. vulgare L.) genotypes, breeding line IGB1467 and its parental breeding cultivar Flinders, were grown at an InterGrain Pty Ltd. experimental field site in Brookton, Western Australia (32°18′05′ S, 117°14′32′ E). IGB1467 was selected based on its novel malting phenotype, with demonstrated efficient proteolysis at 2% lower moisture (Figure S1). Flinders was included to enable comparison to a standard malting phenotype.

Both IGB1467 and Flinders were pilot malted at Pilot Malting Australia (PMA) using the onsite 100 kg Unimalter (Heavy Duty Products, CAN), and malting regimes were adjusted to meet desirable malt quality Kolbach index (KI) specifications of 35.0–49.9%.35 Where IGB1467 required only a single steep and two water additions during the germination stages, Flinders required a double steep and four water additions (Table S2). However, the varied malting regime for IGB1467 led to other malt quality parameters not being achieved, namely, low diastatic power and high α-glucan levels (Tables S1–S3). Each genotype was sampled at approximately 0, 24, and 48 (HAI). The three sampling times corresponded to key stages along the malting time course: (1) 0 HAI, raw barley grain; (2) ∼24 HAI, after imbibition and exiting steep, at 18 HAI for IGB1467 and at 22 HAI for Flinders (Table S1); and (3) ∼48 HAI, germination. For each time point, 20 g of grain was collected, snap-frozen, and stored at −80 °C, providing a stock sample for further processing.

Measurement of Phenotypic Physiological and Biochemical Parameters

Pilot Malting Grain Moisture

Raw barley grains at 0 HAI and green malt (approximately 5 g of the 20 g stock sample) at 24 HAI, and 48 HAI from each genotype were collected during pilot malting and placed between filter papers to remove surface moisture (Whatman grade 1). The grains were then placed on a Halogen Moisture Analyzer HB43-S (Mettler Toledo, Melbourne, AUS) for 45 min at 130 °C, and grain moisture content (%) was recorded (Figure S1).

Water Uptake

Barley grains (individual whole grain, n = 12 technical replicates) from each genotype were collected at 0, 24, and 48 HAI and placed between filter paper to remove surface moisture (Whatman grade 1). The wet grain weights were recorded, followed by freeze-drying for 48 h to remove all moisture; the dry grain weights were recorded. The percentage of water uptake at 0, 24, and 48 HAI was calculated via eq 1, where w1 and w2 represents the wet weight and dry weight of the grains, respectively.

graphic file with name jf4c00884_m001.jpg 1

Germination Rate Determination via Coleoptile Growth

Embryo growth was monitored by determining the coleoptile length and used as an index of germination capacity/vigor, detecting minor changes between genotypes. Barley grains (n = 3 technical replicates) from each genotype were collected at sampling points (0, 24, and 48 HAI). A longitudinal section revealed coleoptile, embryo, endosperm, and other internal structures. Sectional images were gathered using a Leica EZ4 stereo microscope (Leica Microsystems, Macquarie Park, AUS) with an inbuilt camera (EZ4W4161) on 12.5× magnification and live capture format 16:9 (1920 × 1080p). The approximate length of the coleoptile and whole grain was measured from the captured images using the appropriate scale in mm (Figure S6). The coleoptile length as a percentage of the whole grain length at each sample time point was calculated via eq 2, where l1 and l2 represent the full grain length and coleoptile length of the sample, respectively.

graphic file with name jf4c00884_m002.jpg 2

Measurement of Oxidative Stress in the Early Stages of Malting

Oxidative stress was measured for IGB1467 and Flinders (n = 3 technical replicates) at approximately 0, 24, and 48 HAI. Wholemeal barley flour (0.1 g) from each sample was weighed, and 1 mL of 0.01 mol/L phosphate-buffered saline (pH 7.2–7.4) extract solution was added, followed by sonication in an ice bath to obtain homogenized samples. The sample was centrifuged at 4 °C and 12,000g for 15 min, and the supernatant was transferred to a fresh tube on ice. The fresh weight concentration of cellular ROS for IGB1467 and Flinders was measured via a hydrogen peroxide fluorometric assay (H2O2, kit ID ab102500, Abcam PLC, Cambridge, UK) (Supporting Information Data S8) following the established kit protocol.

Protein Extraction, Digestion, and Quantification

All grains (rootlet maintained) were thawed and inspected to exclude contamination and freeze-dried for 72 h to remove all moisture. Grains from each time point were milled using a Retsch Mixer Mill MM 400 (Metrohm, NSW, AUS) and sieved using a 300 μm sieve (Endecotts Pty Ltd., London, UK) to produce a fine-grade homogeneous wholemeal flour.

Grain proteins (20 mg ground wholemeal flour, n = 4 technical replicates) were extracted from sifted wholemeal flour from each sample as described previously.36 Briefly, 8 M urea and 2% (w/v) dithiothreitol (DTT) buffer were added to 20 mg samples at a rate of 20 μL/mg, vortexed until mixed, and sonicated (Soniclean Ultrasonic Cleaner 25HD, 650 W, 43 kHz) for 5 min. The samples were incubated on a shaker block at 400 rpm for 45 min at room temperature. The solutions were centrifuged for 15 min at 20,800g, and the protein extracts (supernatant) were used for subsequent analysis. Protein estimations were determined via a Varioscan plate reader (Thermo Scientific, AUS) using Bradford protein assay (Sigma, California, US), following the manufacturer’s protocol with dilutions and the BSA standard curve as described. Protein digestion was performed for each time point replicate as described by Colgrave et al.37 The tryptic peptides were resuspended in 90 μL of ddH2O containing 0.1% formic acid, with the addition of iRT reference peptide solution (1 pmol/μL; Biognosys, Zurich, CH) for subsequent LC–MS/MS analysis.

Discovery Proteomics (DDA)

The peptide fractions (1 μL) were separated through a high-pH reverse-phase chromatography Ekspert nanoLC415 (Eksigent, Dublin, CA, USA) column coupled to a TripleTOF 6600 MS (SCIEX, Redwood City, CA, USA). The peptides were desalted by loading them on a ChromXP C18 (12 nm, 3 μm, 120 Å, 10 × 0.3 mm) trap column at a flow rate of 10 μL/min and eluted on a ChromXP C18 (12 nm, 3 μm, 120 Å, 150 × 0.3 mm) column at a flow rate of 5 μL/min. Mobile phase A [0.1% formic acid with 5% dimethyl sulfoxide (DMSO) in 94.9% ddH2O] and mobile phase B (0.1% formic acid with 5% DMSO in 90% acetonitrile and 4.9% ddH2O) were used to establish a 68 min linear gradient, followed by re-equilibrating at 3% B for 8 min. The flow rate was 0.3 μL/min, and the elute from the HPLC was directly coupled to the DuoSpray source of the TripleTOF 6600 MS. The ion spray voltage was set to 5500 V; the curtain gas was set to 138 kPa (20 psi), and the ion source gas 1 (GS1) and gas 2 (GS2) were set to 103 and 138 kPa (15 and 20 psi). The heater interface was set to 150 °C. Peptides were then analyzed on a TripleTOF 6600 MS (SCIEX) in an IDA-data-dependent manner, automatically switching between MS and MS/MS scans using a cycle time of 3 s. The top 30 MS spectra were acquired from two separated gas-phase fractionations, with the scan ranges set from 150 to 650 m/z and from 640 to 1500 m/z and the accumulation time set to 0.25 s. Peptide fragmentation was performed using rolling collision energy and a collision energy spread (CES) of 5. The MS/MS spectra were acquired across mass ranges of 100–1800 m/z with an accumulation time of 0.05 s per spectrum and a mass tolerance of 100 ppm.

Sequential Window Acquisition of Theoretical Mass Spectra Data Acquisition (DIA)

The peptide fractions (1 μL, n = 4 technical replicates) were separated through a high-pH reverse-phase chromatography Ekspert nanoLC415 (Eksigent) column, as described for the discovery experiments. The MS source conditions were also identical. The TOF–MS survey scan was collected over the mass range of m/z 360–2000 with a 250 ms accumulation time, and the product ion mass spectra were acquired over the mass range of m/z 150–2000 with a 30 ms accumulation time using rolling collision energy and a collision energy spread (CES) of 5. A variable window of the SWATH-MS acquisition was employed, using 30 overlapping SWATH windows (including 1 Da overlap) spanning the mass range of m/z 150–2000 with SWATH windows determined using SWATH Variable Window Calculator 1.0 (SCIEX) for a total cycle time of 1.15.

Protein Identification and Quantitative Data Processing

Raw DDA LC–MS/MS files were processed with ProteinPilot version 5.0.3 software (SCIEX) using the Paragon Algorithm for peptide spectrum matching and the ProGroup protein inference algorithm. The spectral output was searched against in silico tryptic digests of the H. vulgare subset of the appended UniProt-KB database (version 2022/10; 54,629 sequences), with the Biognosys iRT pseudoprotein sequence and the common Repository of Advantageous Proteins database (cRAP).38 Peptide spectrum match settings allowed for up to one missed cleavage. As a fixed modification, carbamidomethylation of cysteine was selected and no variable modifications were allowed with a target FDR of 1%. Identifications from the cRAP database were ignored, as previously described.39

SWATH-MS files were processed in DIA-NN using deep neural networks (DNNs).40 The spectral output was searched against the developed H. vulgare database described above. DIA-NN quantitative analysis was performed using tryptic peptides of 7–30 amino acids in length, with up to one missed cleavage. As a fixed modification, carbamidomethylation of cysteine was selected, and no variable modifications were allowed. The precursor m/z range, 300–1800, was selected, and the fragment ion m/z range was 200–1800. The algorithm settings included the automatic modes for mass accuracy, MS1 accuracy, and the scan window with and without likely interferences that the software predicted. The neural network classifier was run in single-pass mode with a high-accuracy quantification strategy, and cross-run normalization was performed in a retention time-dependent manner. Peptides were identified after applying a 1% false discovery rate (FDR).

Targeted Proteomics

Aquaporin proteins were identified from ProteinPilot discovery data using peptide sequence annotated identifications, and used to determine precursor ion and product ion m/z values to define multiple reaction monitoring (MRM) transitions for each peptide (Supporting Information Data S4A,B). Pooled samples from all time points, including replicates, were separated on an Exion LC system (SCIEX) and analyzed on a 6500+ QTRAP MS instrument (SCIEX). For each protein, a minimum of two peptides and three transitions with matching retention time values and peak shape were chosen for scheduled MRM analysis. Peaks were integrated using Skyline software package version 23.1.41 The MRM peak areas from the same peptide were summed, and replicate values were subjected to statistical analysis (Supporting Information Data S6). The resulting peak area measurements were normalized, annotated with AQP subtype-specific information, and visualized using the Morpheus42 Shiny-R application.

Statistical and Bioinformatic Analysis

Physiological water uptake (individual whole grain, n = 12 technical replicates), coleoptile elongation (n = 3 technical replicates), and hydrogen peroxide concentration (n = 3 technical replicates) were subjected to one-way analysis of variance (ANOVA) multiple comparisons, followed by a t-test (Bonferroni correction) carried out and visualized using R, ver. 3.6.1,43 in RStudio ver. 2023.09.1 + 49444 (Supporting Information Data S1A–C). All data are presented as the mean ± SE, and the asterisks indicate significant differences (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001).

Quantified protein sequences were BLAST searched against the Morex genome ver.245 using CLC Main Workbench (QIAGEN; ver. 22.0.2). Protein Gene Ontology term annotations were predicted using EggNog-mapper,46 the Gene Ontology (GO) database,47 and the UniProt mapping ID database48 (all accessed 2023/03). Additional GO annotation was taken from the previously published barley genome annotation files49 and cross-referenced with the Barley Reference Transcript data set (BaRT) downloaded (2022/10) from the Barley Expression Database (EoRNA)50,51 (Supporting Information Data S2).

Unsupervised principal component analysis (PCA) was conducted to identify possible outliers resulting from technical (processing and instrumental) procedures and assess groupings (Figure S3). Supervised DESeq2 analysis52 was performed using pairwise comparisons for each time point between genotypes (IGB_0/FLN_0, IGB_24/FLN_24, and IGB_48/FLN_48 HAI) to identify differentially abundant protein groups (DAPs; log2 Fold change (log2 FC) ≥ 2, p ≤ 0.05), including GO enrichment of biological process, molecular functions, and cellular component via the iDEP.96 Shiny R platform8 (Supporting Information Datas S3 and S4). Given the inherent complexities introduced by the varied malting schedules, all subsequent analysis was carried out using pairwise comparison for consecutive time points within a genotype (IGB_0/24, IGB_24/48, FLN_0/24, and FLN_24/48 HAI).

The mean relative targeted protein abundance (n = 4 technical replicates) was subjected to one-way analysis of variance (ANOVA) multiple comparisons, followed by a t-test (Bonferroni correction) using R, ver. 3.6.1,43 in RStudio ver. 2023.09.1 + 49444 (Supporting Information Data S6). The MRM relative abundance log-fold change data between time points (Supporting Information Data S7) was visualized using the Morpheus42 Shiny-R application.

Results

In this study, we explore differences between two malting barley genotypes: (i) IGB1467 (IGB; experimental breeding line) and (ii) Flinders (FLN; control cultivar). IGB1467 demonstrated increased proteolysis under micro-malting conditions compared to Flinders; therefore, both genotypes were pilot malted to meet a KI malt quality specification of 35–49.9%35 (Table S3). The aim was to dissect the proteome and identify the key protein(s) involved in the observed differences in water uptake efficiency contributing to demonstrating malting phenotypes. To do this, we first explored the malting barley grain phenotypic traits at three stages of malting (0, 24, and 48 HAI). We then carried out proteomic abundance profile analyses (0, 24, and 48 HAI), investigating the genotype-variable protein patterns between IGB and FLN by quantitative SWATH-MS, followed by protein-of-interest MRM validation and vacuolation pathway integration analysis.

Phenotypic Malting Barley Grain Traits

Moisture Content and Water Uptake of Different Malting Barley Genotypes

The grain moisture content and water uptake during steeping of IGB and FLN were recorded at 0, 24, and 48 HAI (Figures S1 and 1A, respectively). In both measurements, steeping increased the moisture and water content. IGB demonstrated an approximately 2% lower moisture content during malting than FLN (Figure S1). The water uptake of grain was significantly lower in IGB than in FLN (p = 9.95 × 10–10). However, at 48 HAI, no significant difference was observed between the genotypes (Figure 1A). The greatest difference in moisture content and water uptake was observed in the first 24 HAI (Figure 1A, Supporting Information Data S1).

Figure 1.

Figure 1

Open in a new tab

Malting barley grain trait data of IGB1467 and Flinders at 0, 24, and 48 HAI. (A) Maximal water uptake of IGB and FLN during the early stages of malting (%). (B) Coleoptile elongation (% of the whole grain length). Statistical significance was analyzed using one-way ANOVA multiple comparisons followed by a t-test (Bonferroni correction). Asterisks indicate significant differences between the time points.

Germination Rates of Different Malting Barley Genotypes

The germination rate was determined by calculating the difference in acrospire growth (coleoptile length) as a percentage of the whole grain length for IGB and FLN across the initial malting time course (Figures 1B and S6). Germination rate as a percentage of coleoptile elongation was used as an alternative index to germination capacity, providing greater detection of minor changes in germination vigor between genotypes. We found that IGB demonstrated a greater germination rate than FLN at all time points, with significant differences seen at 48 HAI (p = 6.13 × 10–4). These results indicate that both IGB and FLN had germination capacity but to varying degrees, with IGB demonstrating greater coleoptile elongation earlier than FLN (Supporting Information Data S1).

Protein Identification and Preliminary Data Exploration of Malting Barley

A total of 3433 proteins were quantified in a DIA quantitative proteomics experiment,32 covering IGB and FLN at three points after imbibition (0, 24, and 48 HAI) (Supporting Information Data S2, CSIRO Data Access Portal https://doi.org/10.25919/ypsj-4d27). Unsupervised principal component analysis (PCA) was used for preliminary data exploration to profile the samples. A clear separation was seen between samples, with PC1 having 23.2% of the explained variation due to the time point and PC2 having 16.6%, separated based on the genotype (Figure S2). The results highlight a greater proteome alteration in IGB with greater observed variance than that in FLN after water uptake.

DAP Identification and Pairwise Analysis of Malting Barley

In the three pairwise comparisons (IGB_0/FLN_0, IGB_24/FLN_24, and IGB_48/FLN_48 HAI), 302 protein groups were differentially abundant (DAPs) (Figure 2; Supporting Information Datas S3 and S4). Data set overlap revealed 32 proteins that are DAPs in all three time points and 49, 27, and 11 DAPs shared between 0/24, 24/48, and 0/48, respectively (Figure 2A, Supporting Information Data S4). In contrast, 75, 67, and 41 DAPs were unique to the time points 0, 24, and 48 HAI, respectively (Figure 2A, Supporting Information Data S4). Differential abundance analysis of both increased- and decreased-protein abundance revealed a total of 237 and 216 DAPs present across all time points in IGB and FLN, respectively. Overall, IGB had the largest number of DAPs in all IGB/FLN comparisons, with the largest (90) at 0 HAI, of which 48 were decreased-DAP and 42 were increased-DAP abundance relative to those of FLN (77) at 0 HAI, with 46 decreased-DAP and 31 increased-DAP abundances (Figure 2B, Supporting Information Data S4).

Figure 2.

Figure 2

Open in a new tab

Differentially abundant protein (DAP) groups [fold-change (FC) ≥ 2.0, FDR≤ 0.05] as observed in pairwise comparison of IGB1467 and Flinders (IGB_0/FLN_0, IGB_24/FLN_24, and IGB_48/FLN_48 HAI). (A) Venn diagram of DAP sharedness detected in IGB and FLN. (B) Bar graph representing the distribution of the 302 DAPs between genotypes at each time point and abundance changes [increased abundance (up-, red) and decreased abundance (down-, blue)].

Enrichment Analysis of the Water Uptake and Transport-Related Pathway Proteins

To illustrate the functional differences between IGB and FLN that may contribute to water uptake efficiency, we used a multilayer interface (Figure 1). We carried out gene ontology (GO) enrichment on identified proteins for each genotype and time points (0, 24, and 48 HAI) for the following three categories: biological process, cellular compartment, and molecular function (Figures 3, S4, and Supporting Information Data S2).

Figure 3.

Figure 3

Open in a new tab

GO enrichment analysis terms for DAPs found at 0 HAI. (A) IGB1467; (B) Flinders. The enrichment category is indicated to the left of the bar graph; pink is the biological function, green is the cellular component, and blue is the molecular function. The bar color indicates the enrichment significance of the legend, and the number of proteins is on the x-axis.

The largest number of enriched GO terms between IGB and FLN was found in the raw grain (0 HAI) (Figure 3). IGB had two terms related to biological processes, including the nucleotide catabolic process (GO:0009166) and oxylipin biosynthetic process (GO:0031408) (Figure 3A). FLN demonstrated no significant enrichment in the biological process. However, three terms were present in the cellular component: integral component of the plasma membrane (GO:0005886), plant-type cell wall (GO:0009505), and vacuole (GO:0005773) (Figure 3B). The largest number of molecular function terms (6) were found in the raw grain of FLN, with all enrichment related to transporter activity (GO:005215). The most significant term was inorganic molecular entity transmembrane transporter activity (GO:0015318), followed by transmembrane transporter activity (GO:0022857) and cation transmembrane transporter activity (GO:0015101). Included in these terms are water transmembrane transporter (GO:0005372), manganese ion transmembrane transporter (GO:0005384), and sucrose transmembrane transporter (GO:0008515) activities (Figure 3B).

GO enrichment at 24 and 48 HAI was limited in both genotypes and is therefore displayed in Figure S4. At 24 HAI, no significant enrichment was seen in the biological process between IGB and FLN (Figure S4A,C, respectively). The largest number of proteins for both genotypes was in the cellular component’s extracellular regions (GO:0005576). In addition, DAPs in FLN fell in the molecular function categories, including nutrient reservoir activity (GO:0045735) and sucrose transmembrane transporter activity (GO:0008515) (Figure S4C). At 48 HAI, no significant enrichment was found between IGB and FLN for all GO categories (Figure S4B,D, respectively).

The GO teams with the greatest significance were related to transmembrane proteins. Consequently, we focused on the transmembrane-related proteins and their roles in water uptake.

Transmembrane Protein Abundance Changes in Response to Controlled Germination

To explore transmembrane proteins, we included the DAPs from the transmembrane terms and all other transmembrane proteins confirmed via UniProt ID mapping classification from the 3.433 proteins quantified (Supporting Information Data S2). A total of 21 transmembrane proteins were classified into six protein families: (i) MIP/aquaporin (TC 1.A.8) family (7, 33.3%); (ii) H(+)-translocating pyrophosphatase (TC 3.A.10) family (3, 14.3%); (iii) cytochrome P450 family (1, 4.8%); (iv) solute carrier family (4, 19.0%); (v) glycoside-pentoside-hexuronide (GPH) cation symporter transporter (TC 2.A.2.4) family (3, 14.3%); and vi) vacuolar transporter family (3, 14.3%) (Figures 4 and S5). All transmembrane proteins demonstrated greater abundance in FLN than in IGB, mainly in the raw grain at 0 HAI. The largest number of DAPs and the most significant DAPs were present among the MIP/AQP family.

Figure 4.

Figure 4

Open in a new tab

SWATH-MS/MS relative abundance heatmap for IGB1467 and Flinders transmembrane DAPs. Grouped by family at 0, 24, and 48 h after imbibition. Values are log2 transformed (n = 4), showing increased protein abundance (up-, red) and decreased protein abundance (down-, blue).

Based on these findings, we focused only on the largest protein family, the AQPs, of which three represented plasma intrinsic proteins: PIP1;1 (A0A8I6XXT7), PIP1;2 (Q9AVV4), and PIP2;1 (B0I532); and four represented TIPs: TIP1;1 (D2KZ38), TIP2;3 (B8R6A6), TIP3;1 (D2KZ45), and TIP3;2 (F2EBU5) (Figure 4). The PIPs and TIPs exhibited higher abundance levels in FLN compared with those in IGB at all time points. Among the different AQPs, PIP2s and TIP3s have the highest abundance in the raw grain (0 HAI), PIP2;1 (log2 FC 0.7, p = 5.04 × 10–14) was the most abundant in both genotypes, and TIP3;1 (log2 FC 1.65, p = 1.28 × 10–44) demonstrated a larger fold-change between the genotypes (Supporting Information Data S3). At 0 HAI, PIP2s and TIP3s had similar relative abundance. However, the abundance of PIPs decreases in contrast to that of TIP3s, which increased several fold at 0 and 48 HAI (Figure 4).

Aquaporin Abundance Analysis by Quantitative Multiple-Reaction Monitoring

Seven AQP proteins with at least two unique peptides per protein were selected for MRM validation analysis to confirm their abundance patterns and potential contribution to water uptake (Figure 5 and Supporting Information Data S5B). Our SWATH-MS analysis32 showed an observed difference between the genotypes across the time points (Figure 4). Similar patterns were observed in IGB and FLN for PIP1;1, PIP2;4, TIP2;3 TIP3;1, and TIP3;2 (Figure 5A,C,E,F,G, respectively), with an initial increase from 0 to 24 HAI followed by a decrease in abundance from 24 to 48 HAI. However, the observed difference was significantly greater in FLN. Those with different abundance patterns included PIP2;1 and TIP1;1 (Figure 5B,D). PIP2;1 decreased in both genotypes with more significant abundance changes in FLN than in IGB (Figure 5B). IGB saw a significant increase in TIP1;1 abundance from 0 to 24 h, not seen in FLN, with no significant changes in the first 24 HAI. The greatest abundance changes for IGB were seen in the TIPs from 0 to 24 HAI, with TIP1;1 (p = 7.08 × 10–3), TIP2;3 (p = 2.53 × 10–4), TIP3;1 (p = 2.38 × 10–5), and TIP3;2 (p = 3.17 × 10–4) significantly increasing in abundance (Supporting Information Data S6). In summary, seven AQP proteins showed significantly greater abundance in the raw grain of FLN than that of IGB, where relative abundance changes saw PIPs decrease (Figure 5A–C). At the same time, TIPs increased during the first 24 h of controlled germination. A greater AQP abundance increase was seen in IGB compared to FLN, especially in the TIPs (Figure 5E–G, Supporting Information Data S6).

Figure 5.

Figure 5

Open in a new tab

Scheduled MRM analysis of seven differentially abundant aquaporin proteins of IGB1467 and Flinders at 0, 24, and 48 h after imbibition (HAI). Plasma intrinsic proteins: (A) PIP1;1, A0A8I6XXT7; (B) PIP2;1, B0I532; and (C) PIP2;4, Q4LDT4. Tonoplast intrinsic proteins: (D) TIP1;1, D2KZ38; (E) TIP2;3, B8R6A6; (F) TIP3;1, D2KZ45; and (G) TIP3;2, F2EBU5. The data are presented as the mean peak area ± SD (n = 4). Statistical significance was analyzed using one-way ANOVA multiple comparisons followed by a pairwise t-test (Bonferroni correction). Asterisks indicate significant differences among the time points.

Abundance Changes in TIPs during Vacuolation

To illustrate the functional differences of AQP abundance between IGB and FLN that may contribute to the increased grain modification efficiency seen in IGB,32 we explored the fold change (log2) values between time points (0/24 and 24/48 HAI) of TIPs affecting vacuolation for storage reserve mobilization (Supporting Information Data S7). We included the changes also observed in the precursor proteins for abscisic acid (ABA) and gibberellic acid (GA) biosynthesis, given their modulation of TIPs5,53 (Figure 6, Supporting Information Datas S1, S2, and S8).

Figure 6.

Figure 6

Open in a new tab

Role of aquaporins in germination involves vacuolation. (A) TIP3s are present in small protein storage vacuoles (PSVs), negatively influencing the formation of lytic vacuoles (LVs). (C) Gibberellic acid (GA) inhibits HvTIP3 expression, which is replaced by (B) TIP1s in the process of vacuolation as PSVs are converted to LVs. (D) Abscisic acid (ABA) has the opposite effect and promotes HvTIP3 expression, negatively influencing barley grain germination.5 Red represents a >0.5 times log2-fold changeand blue represents a >−0.5 times log2-fold change between the time points 0 to 24 (0/24) and 24 to 48 (24/48) HAI (created using BioRender.com). Abbreviations: GA3ox, Gibberellin 3-oxidase; GER, Gem-Related 5; TIP, tonoplast intrinsic protein.

We found that IGB demonstrated an increase in TIP3;1 and TIP3;2 abundance from 0 to 24 HAI, followed by a decrease from 24 to 48 HAI. FLN showed a different pattern with a gradual increase over time in both TIP3;1 and TIP3;2 (Figure 6A). TIP1;1 abundance significantly increased in IGB from 0 to 24 HAI and then decreased from 24 to 48 HAI, whereas in FLN, no significant increase or decrease was observed (Figure 6B). Gibberellin 3-oxidase (GA3ox) followed a trend similar to that of TIP1;1 in IGB with an increase from 0 to 24 HAI, again not seen in FLN (Figure 6C). GEm-Related 5 protein (GER5) abundance increased at the onset of germination, with an increase seen in both genotypes from 0 to 24 HAI followed by a decrease from 24 to 48 HAI. However, the observed decrease in IGB was significantly greater from 24 to 48 HAI in contrast to FLN (Figure 6D). Overall, the greatest fold change difference of the proteins contributing to vacuolization was present in IGB (TIP3;1, TIP1;1, and GA3ox) and seen to a lesser extent in FLN (Supporting Information Datas S7 and S8).

Discussion

In this study, we explored the proteome of two malting barley genotypes with demonstrated differences in water uptake during the initial stages of controlled germination. In previous research, IGB1467 demonstrated efficient proteolysis requiring less moisture (correlating with less water uptake), a desirable malting phenotype for improving the sustainability of malting practices.32 Our results found that upon imbibition, both IGB1467 and Flinders showed significant changes in tissue water content (Figures 1 and S1). Water uptake during seed germination is known to be triphasic: phase I, rapid initial uptake (1–3 h); phase II, plateau; and phase III, further increase in water uptake during cell expansion.54 We found that the rate of water uptake in IGB1467 and Flinders followed the phase I and II trends, with the greatest water uptake seen in the first 24 HAI in both genotypes, although significantly higher in Flinders than in IGB1467 (Figure 1).

In addition, upon water uptake, IGB1467 demonstrated greater proteome alteration than Flinders (Figure S3) and greater coleoptile elongation (Figures 1B and S6). Previous studies of the barley proteome found hundreds of protein alterations during germination involved in storage reserve mobilization,55−57 reviewed by Bahmani et al.,58 Daneri-Castro et al.,59 and Fox and Watson-Fox.60 We saw earlier changes in protein abundance, and a larger number of DAPs were identified with an increased abundance (Figure 2). This observation is relevant to malting practices, contributing to the previously described malting phenotypes,32 and supports the hypothesis that IGB1467 exhibits significantly less water uptake in the first 24 HAI while demonstrating efficient modification for greater germination vigor at lower moisture content than Flinders.

Functional GO annotation of DAPs found that the increased water uptake seen in Flinders may be attributed to the significantly increased abundance of transmembrane proteins, specifically AQPs (Figure 3). Based on previous spatial expression studies in barley, members of the AQP gene family are known to allow transmembrane solute passage and are essential in water flux and cell expansion, enabling sufficient pressure for radicle protrusion.61,62 Therefore, we focused our proteomic exploration on the seven quantified AQP proteins, specifically PIPs and TIPs, demonstrating significant differentially abundant protein profiles between IGB1467 and Flinders in the early stages of malting, indicating their possible major role in water uptake during controlled germination (Figures 4 and 5).

PIPs and TIPs primarily drive this water uptake and are the most abundant AQPs in the plasma membrane and tonoplast within plant cells, respectively.10,63 Flinders et al. demonstrated the greatest aquaporin abundance in the mature malting barley grain. However, the overall abundance increase of AQPs in IGB1467 was significantly greater and occurred in four DAPs compared to one DAP in Flinders (Figure 5). This reveals how imbibition promotes the activation of metabolism for cell growth and coleoptile elongation in IGB1467 (Figure 1B). A dramatic increase in AQP abundance is a signature of the demonstrated efficient modification seen in IGB1467, as reported in our previous study32 and discussed further below.

Plasma Intrinsic Proteins in Response to Controlled Germination

PIPs such as PIP1s and PIP2s act in water transport between cytoplasmic and extracellular compartments and are needed to maintain water balance in the cytoplasm.31,64,65 We found one PIP1-isoform (PIP1;1) and two PIP2-isoforms (PIP2;1 and PIP2;4) (Figures 4, and 5). While PIP1 and PIP2 subgroups share similar amino acid sequences, their water permeability and cellular location are different.66 PIP1s are present in the plasma membrane and are considered to have low water permeability,67 whereas PIP2s have high water transport activity68,69 and are considered the primary pathway for cell-to-cell water transport.70 Although, PIP1s and PIP2s can also form heterotetramers to facilitate whole-membrane water permeability by coexpression.71−73 Yaneff et al. (2014) observed in (Fragaria × ananassa) that random heterotetramerization of FaPIP1;1 was enhanced as part of a heterotetramer with FaPIP2;1.74 Additionally, Bellati et al. (2010) found that in Beta vulgaris, coexpression of BvPIP1;1 and BvPIP2;2 enhanced membrane water permeability and reinforced the pH inhibitory response to a shutdown of the AQP following cytosolic pH acidification.71 Yaneff et al. (2015) reviewed the modulation of PIP1–PIP2 and suggests that the pair act as a functional unit and that their interaction is emerging as an important regulator of plant cell membrane water permeability.65 However, some PIP1–PIP2 pairs do not functionally interact, and the functional activity of PIP1 depends on the coexpressed PIP2 partner.16,65

In both FLN and IGB, PIP1;1 and PIP2;4 increased significantly from 0 to 24 HAI, followed by a decrease from 24 to 48 HAI (Figure 5A,B). Previous studies in maize (Zea mays) found that the ZmPIP1;1 and ZmPIP2;4 proteins were transiently induced under salt stress, promoting a decrease in cellular osmotic potential and allowing for increased cellular water uptake.75 The shared abundance patterns of PIP1;1 and PIP2;4 in both IGB and FLN suggest that heterotramer formation is likely linked to grain germination and may facilitate water uptake; however, this will require further investigations.

Of the three PIPs observed, PIP2;1 saw the most significant difference between genotypes (Figure 5B). Localized in starchy endosperm cells, PIP2s allow transmembrane water transport with nutrients from the endosperm to the embryo, providing energy for growth.76 Flinders had the greatest PIP2 abundance in the raw grain, which decreased from 0 to 24 HAI and again from 24 to 48 HAI, while IGB1467 had a significantly lower abundance in the grain and a lower decrease overall (Figure 5B). Given the known role of PIP2s in intracellular transport during germination,77 we suggest that the increased abundance of PIP2;1 in Flinders contributes to intracellular water movement, possibly occurring less in IGB1467 due to lower PIP2;1. We, therefore, hypothesize that the increased PIP2;1 seen in Flinders may contribute to the increased water content observed at 24 HAI (Figure 1), and while the water content may be lower in IGB1467, the embryo is still hydrated and driving the grain modification process.

Tonoplast Intrinsic Proteins in Response to Controlled Germination

TIPs are the most abundant AQPs in the vacuolar membrane,10 though their roles have been partially explored in the plant kingdom.66 We found four TIPs (TIP1;1, TIP2;3, TIP3;1, and TIP3;2) that demonstrate varied significant changes in abundance between genotypes and may confer differential water uptake during controlled germination (Figure 5).

TIP1;1 abundance in IGB1467 increased signicantly over the first 48 HAI, compared to an observed decrease in Flinders (Figure 5D). Whereas TIP3;1 and TIP3;2 increase in abundance in both IGB1467 and Flinders in the first 24 HAI. followed by a non-signifcant plateaued between 24 and 48 HAI (Figure 5F,G). Previous studies have found that the protein storage vacuole-specific TIP1;1, TIP3;1, and TIP3;2 isoforms are coexpressed and colocalized to the same tonoplast in the germinating grain after imbibition.78 However, TIP3;1 and TIP3;2 are the only embryo- and endosperm-specific TIP isoforms found in Arabidopsis in the late stages of seed maturation and early-stage germination (phases I and II).78−80 During the early-stage of barley germination, Peirats-Llobet et al. (2023) found the TIP1;1 isoform was the most abundantly expressed member of the TIP family.61 We found TIP1;1 abundance to be greater in Flinders than in IGB1467. TIP1;1 is known to be a housekeeping APQ involved in the establishment of basal tonoplast hydraulic conductance.61,81 Therefore, TIP1;1 abundance may contribute to the initially increased water uptake seen in Flinders, although we saw a greater increase in the level of TIP1;1 in IGB1467 from 0 to 24 HAI (Figure 5D) suggesting an additonal role discussed further below. These findings are also supported by our discovery and quantitative proteomics results presented in our previous study,32 where TIP1;1, TIP3;1, and TIP3;2 are in the raw grain of IGB1467 and Flinders, with greater abundance in Flinders. However, only IGB1467 saw an increase in TIP1;1, whereas both genotypes saw a significant increase in TIP3;1 and TIP3;2, which are known to influence initial water uptake during periods of seed development.18 Flinders demonstrated a considerably greater abundance of TIP3s than IGB1467 at all three time points. Therefore, we hypothesize that TIP3;1 and TIP3;2 protein abundance being present in the raw grain is a major factor in water uptake in malting barley during controlled germination.

AQPs’ Role during Controlled Germination

Water uptake in seeds during germination is influenced by the balance in the sensitivity between ABA and GA, in which AQPs play a role. The ABA/GA balance regulates dormancy induction and relief via AQPs, shifting the water potential threshold for growth and radicle emergence.79,82 Lee et al. (2015)83 found that in germinating barley grain, ABA-induced HvTIP3;1 expression delays the fusion of the protein storage vacuoles (PSVs), while GA promotes the vacuolation process by inhibiting HvTIP3 expression.83 The aleurone cells of mature barley grain contain small protein storage vacuoles (PSVs) that store proteins, carbohydrates, lipids, and minerals (Bewley and Black, 1994).6,54 During germination, vacuolation occurs where the PSVs are converted from a storage organelle into large acidic lytic vacuole (LV) compartments, rapidly hydrolyzing primary metabolites such as storage proteins that serve as a source of nutrients and a reserve of amino acids that aid in protein synthesis and increased energy production.5,84,85 Therefore, the TIP3 isoforms have an antagonistic relationship during germination, changing sensitivity to ABA, and although TIP3;1 aids in the generation of turgor to drive radicle protrusion,31 TIP3;1 can be seen as a negative regulator in the initiation of the germination process until dormancy is removed.78

We found a significantly greater abundance of TIP3;1 and TIP3;2 in Flinders, possibly contributing to increased water uptake. However, we saw a greater increase (log2 fold change) in abundance in IGB1467 from 0 to 24 HAI (Figure 6A and Supporting Information Data S7). The increased abundance of TIP3;1 and 3;2 may be attributed to the increasing abundance of the ABA stress response and the GEm-Related 5 proteins (GER) found in both genotypes from 0 to 24 HAI (Figure 6D and Supporting Information Data S8). However, IGB1467 abundance decreased from 24 to 48 HAI, suggesting a switch in the balance between ABA and GA (Figure 6). This was supported by an increased abundance of the GA precursor gibberellin 3-oxidase (GA3ox) in IGB1467 from 0 to 24 HAI, not seen in Flinders (Figure 6C and Supporting Information Data S8). This switch in the ABA/GA balance in IGB1467 may positively regulate the increase in TIP1;1 from 0 to 24 HAI (Figures 5D and 6B). Therefore, we suggest that a greater abundance of GA3ox positively influenced the increased abundance of TIP1;1 in the first 24 HAI in IGB1467 and may further facilitate vacuolization, aiding in the mobilization of storage reserves for enhanced germination vigor (Figure 1B) aided by increased protein synthesis, as previously reported.32Figure 6 illustrates the complex integration of TIP abundance and the ABA/GA balance seen in the proteome of malting barley reported here; however, further studies are required to determine endogenous ABA and GA hormone levels.

AQP Response to Increased Reactive Oxygen Species during Controlled Germination

The effects of H2O2 and other ROS species depend on their concentration, where high levels of ROS inhibit aquaporins,86,87 while low levels of H2O2 increase their activity.88 Upon imbibition on the first day of malting, barley seeds experience a 60% increase in hydrogen peroxide (H2O2).89 We found a significant increase of H2O2 in both IGB1467 and Flinders from 0 to 24 HAI, p = 8.5 × 10–4 and p = 7.3 × 10–6, respectively (Figure S2 and Supporting Information Data S1C). It is proposed that the significantly higher H2O2 formation, especially during the first 24 HAI, is due to the reassumption of the metabolism and the impacts of low-O2 stress during germination.90 H2O2 also functions as an important secondary messenger in plants and regulates downstream cellular signaling and events by interacting with functional proteins and antioxidants.91 AQPs are essential for the transmission of H2O2 from metabolic sources like NADPH oxidases89 to downstream targets, including Ca2+ channels, protein carbonylation, and detoxifying antioxidants.91 Several AQP isoforms transport both H2O2 and water, such as AtTIP1;1 in Arabidopsis and the seed-specific TIP3s that influences the longevity of Arabidopsis seeds.92 Importantly, the overexpression of Thellungiella salsuginea TIP1 (TsTIP1;2) enhanced stress tolerance, including drought, salt, and oxidative stress.93 Our results found that although TIP1;1 decreased in abundance, TIP2;3, TIP3;1, and TIP3;2 increased in abundance in both IGB1467 and Flinders (Figure 5D–G). This increase may aid in channeling stress-induced H2O2 into vacuoles, lowering cytosolic ROS and alleviating damage caused by stress, as previously found in T. salsuginea.93 ROS generated during controlled germination can lead to the oxidation of protein and lipids to the point where it affects final malt quality.89 Therefore, as previously suggested,32 the greater abundance of AQPs in Flinders aided in detoxification, while efficient modification of IGB1467 at reduced moisture may lead to the overaccumulation of ROS. However, further research is needed to explore the impacts of ROS production, specific effects of antioxidant enzyme activities, and their influence on malt quality.

Influence of AQPs on the Malting Phenotype for Improved Malting Practices

The present study found that the rate of water uptake during controlled germination was significantly lower in IGB1467 than in Flinders in the first 24 HAI. GO analysis highlighted that this may be attributed to differences in transmembrane proteins, particularly AQPs. We identified seven AQPs in the malting barley proteome and found key water uptake proteins contributing to the varied malting phenotypes of IGB1467 and Flinders. We found that PIPs were involved in intracellular water transport and were not found to be involved in water uptake. We also saw a significantly lower abundance of TIP3;1 and TIP3;2 in IGB1467 involved in cellular turgor; therefore, we suggest that TIP3;1 and TIP3;2 may be the only aquaporins mediating malting barley grain water uptake. In addition, IGB1467 demonstrated significantly greater germination vigor, which we suggest may be attributed to an increase in TIP1;1, an AQP known to facilitate vacuolization, to aid the mobilization of storage reserves and drive grain modification. We suggest that the TIP1;1, TIP3;1, and TIP3;2 present in the malting barley proteome are essential for water uptake since they mainly influence the formation of large central lytic vacuoles for hydrolyzing storage reserves for radicle elongation and also the buildup of cell turgor pressure.

Although the role of transmembrane protein activity is complicated and diverse, these results provide more direct evidence of AQP-regulated water uptake. These findings provide a foundation for a better understanding of the specific functions of malting barley transmembrane proteins, particularly AQPs. They can be investigated as potential molecular targets in generating malting barley with enhanced water use efficiency phenotypes for improved malting practices. In addition, further studies can be focused on the effect of AQPs on stress tolerance, detoxification, and related mechanisms for improved malt quality.

Acknowledgments

The authors thank the editor and reviewers for their comments. We want to thank Jon Luff from Pilot Malting Australia for his advice and expertise in pilot malting. The authors would like to acknowledge InterGrain Pty Ltd. for providing the breeding lines and facilitating this research collaboration while understanding the need for unbiased scientific exploration.

Data Availability Statement

The data sets presented in this study can be found in online repositories: CSIRO Data Access Portal Accession id: https://doi.org/10.25919/ypsj-4d27.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jafc.4c00884.

  • Grain moisture content, grain hydrogen peroxide concentration, unsupervised principal component analysis of data variance in FLN vs IGB, GO enrichment analysis for DAPs, abundance of malting barley grain transmembrane proteins during controlled germination, stereo microscope images of dissected barley grain for coleoptile measurements, Pilot Malting Australia batch details, Pilot Malting Australia treatment details, and malt quality analyses (PDF)

  • Statistical analysis of water uptake, bioinformatic identification and annotation of quantitative data, differentially abundant protein groups, UpSet analysis, aquaporin peptide quantitation analysis, statistical analysis of aquaporin sMRM quantitative data, pairwise comparative analysis of MRM data, pairwise comparative analysis of SWATH-MS, and hydrogen peroxide assays (XLSX)

The research is financially supported by an Industry Ph.D. scholarship, sponsored by Edith Cowan University (ECU), InterGrain Pty Ltd., the Commonwealth Scientific and Industrial Research Organization (CSIRO), and Agriculture and Food (G1004654) (C.E.O.). The research was also supported by the Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science (CE200100012) (M.N.-W. and A.J.).

The authors declare the following competing financial interest(s): The author DM was employed by the company InterGrain Pty. Ltd.

This paper originally published ASAP on April 18, 2024. The scale in Figure 6 was updated, and a new version reposted on April 22, 2024.

Supplementary Material

jf4c00884_si_001.pdf (1.1MB, pdf)
jf4c00884_si_002.xlsx (1.2MB, xlsx)

References

  1. Cu S.; Collins H. M.; Betts N. S.; March T. J.; Janusz A.; Stewart D. C.; Skadhauge B.; Eglinton J.; Kyriacou B.; Little A.; Burton R. A.; Fincher G. B. Water uptake in barley grain: Physiology; genetics and industrial applications. Plant Sci. 2016, 242, 260–269. 10.1016/j.plantsci.2015.08.009. [DOI] [PubMed] [Google Scholar]
  2. Fox G. P.; Bettenhausen H. M. Variation in quality of grains used in malting and brewing. Front. Plant Sci. 2023, 14, 1172028. 10.3389/fpls.2023.1172028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. MacLeod L., Evans E.. Barley: Malting. Encyclopedia of Food Grains; 2nd ed; Elsevier, 2016; pp 423–433 10.1016/B978-0-12-394437-5.00153-4 [DOI] [Google Scholar]
  4. Bønsager B. C.; Finnie C.; Roepstorff P.; Svensson B. Spatio-temporal changes in germination and radical elongation of barley seeds tracked by proteome analysis of dissected embryo, aleurone layer, and endosperm tissues. Proteomics 2007, 7 (24), 4528–4540. 10.1002/pmic.200700766. [DOI] [PubMed] [Google Scholar]
  5. Wang Y.; Zhao Z.; Liu F.; Sun L.; Hao F. Versatile roles of aquaporins in plant growth and development. Int. J. Mol. Sci. 2020, 21 (24), 9485. 10.3390/ijms21249485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Finch-Savage B. Seeds: Physiology of development, germination and dormancy (3rd edition) - J.D. Bewley, K.J. Bradford, H.W.M. Hilhorst H. Nonogaki. 392 pp.Springer, New York – Heidelberg – Dordrecht – London2013978-1-4614-4692-7. Seed Sci. Res. 2013, 23, 289. 10.1017/S0960258513000287. [DOI] [Google Scholar]
  7. Rajjou L.; Duval M.; Gallardo K.; Catusse J.; Bally J.; Job C.; Job D. Seed germination and vigor. Annu. Rev. Plant Biol. 2012, 63, 507–533. 10.1146/annurev-arplant-042811-105550. [DOI] [PubMed] [Google Scholar]
  8. Bamforth C. Current perspectives on the role of enzymes in brewing. J. Cereal Sci. 2009, 50 (3), 353–357. 10.1016/j.jcs.2009.03.001. [DOI] [Google Scholar]
  9. Briggs D. E.; Boulton C.; Brookes P.; Stevens R.. Brewing: Science and Practice; Woodhead Publishing, 2004; Vol. 108. 10.1201/9780203024195. [DOI] [Google Scholar]
  10. Maurel C.; Boursiac Y.; Luu D.-T.; Santoni V.; Shahzad Z.; Verdoucq L. Aquaporins in Plants. Physiol. Rev. 2015, 95 (4), 1321–1358. 10.1152/physrev.00008.2015. [DOI] [PubMed] [Google Scholar]
  11. Obroucheva N.; Sin’Kevich I. Aquaporins and cell growth. Russ. J. Plant Physiol. 2010, 57, 153–165. 10.1134/S1021443710020019. [DOI] [Google Scholar]
  12. Hove R. M.; Ziemann M.; Bhave M. Identification and Expression Analysis of the Barley (Hordeum vulgare L.) Aquaporin Gene Family. PLoS One 2015, 10 (6), e0128025 10.1371/journal.pone.0128025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Katsuhara M.; Akiyama Y.; Koshio K.; Shibasaka M.; Kasamo K. Functional analysis of water channels in barley roots. Plant Cell Physiol. 2002, 43 (8), 885–893. 10.1093/pcp/pcf102. [DOI] [PubMed] [Google Scholar]
  14. Knipfer T.; Besse M.; Verdeil J.-L.; Fricke W. Aquaporin-facilitated water uptake in barley (Hordeum vulgare L.) roots. J. Exp. Bot. 2011, 62 (12), 4115–4126. 10.1093/jxb/err075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Kurowska M. M.; Wiecha K.; Gajek K.; Szarejko I. Drought stress and re-watering affect the abundance of TIP aquaporin transcripts in barley. PLoS One 2019, 14 (12), e0226423 10.1371/journal.pone.0226423. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Horie T.; Kaneko T.; Sugimoto G.; Sasano S.; Panda S. K.; Shibasaka M.; Katsuhara M. Mechanisms of Water Transport Mediated by PIP Aquaporins and Their Regulation Via Phosphorylation Events Under Salinity Stress in Barley Roots. Plant Cell Physiol. 2011, 52 (4), 663–675. 10.1093/pcp/pcr027. [DOI] [PubMed] [Google Scholar]
  17. Mori I. C.; Rhee J.; Shibasaka M.; Sasano S.; Kaneko T.; Horie T.; Katsuhara M. CO2 transport by PIP2 aquaporins of barley. Plant Cell Physiol. 2014, 55 (2), 251–257. 10.1093/pcp/pcu003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Utsugi S.; Shibasaka M.; Maekawa M.; Katsuhara M. Control of the Water Transport Activity of Barley HvTIP3;1 Specifically Expressed in Seeds. Plant Cell Physiol. 2015, 56 (9), 1831–1840. 10.1093/pcp/pcv104. [DOI] [PubMed] [Google Scholar]
  19. Banerjee A.; Roychoudhury A.. Chapter 25 - The role of aquaporins during plant abiotic stress responses. In Plant Life Under Changing Environment; Tripathi D. K., Pratap Singh V., Chauhan D. K., Sharma S., Prasad S. M., Dubey N. K., Ramawat N., Eds.; Academic Press, 2020; pp 643–661. [Google Scholar]
  20. Postaire O.; Tournaire-Roux C.; Grondin A.; Boursiac Y.; Morillon R.; Schäffner A. R.; Maurel C. A PIP1 aquaporin contributes to hydrostatic pressure-induced water transport in both the root and rosette of Arabidopsis. Plant Physiol. 2010, 152 (3), 1418–1430. 10.1104/pp.109.145326. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Jang J. Y.; Rhee J. Y.; Chung G. C.; Kang H. Aquaporin as a membrane transporter of hydrogen peroxide in plant response to stresses. Plant Signaling Behav. 2012, 7 (9), 1180–1181. 10.4161/psb.21178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Zwiazek J. J.; Xu H.; Tan X.; Navarro-Ródenas A.; Morte A. Significance of oxygen transport through aquaporins. Sci. Rep. 2017, 7 (1), 40411. 10.1038/srep40411. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gomes D.; Agasse A.; Thiébaud P.; Delrot S.; Gerós H.; Chaumont F. Aquaporins are multifunctional water and solute transporters highly divergent in living organisms. Biochim. Biophys. Acta, Biomembr. 2009, 1788 (6), 1213–1228. 10.1016/j.bbamem.2009.03.009. [DOI] [PubMed] [Google Scholar]
  24. Tan X.; Xu H.; Khan S.; Equiza M. A.; Lee S. H.; Vaziriyeganeh M.; Zwiazek J. J. Plant water transport and aquaporins in oxygen-deprived environments. J. Plant Physiol. 2018, 227, 20–30. 10.1016/j.jplph.2018.05.003. [DOI] [PubMed] [Google Scholar]
  25. Gerbeau P.; Güçlü J.; Ripoche P.; Maurel C. Aquaporin Nt-TIPa can account for the high permeability of tobacco cell vacuolar membrane to small neutral solutes. Plant J. 1999, 18 (6), 577–587. 10.1046/j.1365-313x.1999.00481.x. [DOI] [PubMed] [Google Scholar]
  26. Liu L.-H.; Ludewig U.; Gassert B.; Frommer W. B.; von Wirén N. Urea transport by nitrogen-regulated tonoplast intrinsic proteins in Arabidopsis. Plant Physiol. 2003, 133 (3), 1220–1228. 10.1104/pp.103.027409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Bienert G. P.; Møller A. L.; Kristiansen K. A.; Schulz A.; Møller I. M.; Schjoerring J. K.; Jahn T. P. Specific aquaporins facilitate the diffusion of hydrogen peroxide across membranes. J. Biol. Chem. 2007, 282 (2), 1183–1192. 10.1074/jbc.M603761200. [DOI] [PubMed] [Google Scholar]
  28. Jahn T. P.; Møller A. L.; Zeuthen T.; Holm L. M.; Klærke D. A.; Mohsin B.; Kühlbrandt W.; Schjoerring J. K. Aquaporin homologues in plants and mammals transport ammonia. FEBS Lett. 2004, 574 (1–3), 31–36. 10.1016/j.febslet.2004.08.004. [DOI] [PubMed] [Google Scholar]
  29. Chaumont F.; Tyerman S. D.. Plant Aquaporins; New York, NY: Springer, 2017; Vol. 10, pp 978–983. [Google Scholar]
  30. Hoai P. T. T.; Tyerman S. D.; Schnell N.; Tucker M.; McGaughey S. A.; Qiu J.; Groszmann M.; Byrt C. S. Deciphering aquaporin regulation and roles in seed biology. J. Exp. Bot. 2020, 71 (6), 1763–1773. 10.1093/jxb/erz555. [DOI] [PubMed] [Google Scholar]
  31. Obroucheva N.; Sinkevich I.; Lityagina S.; Novikova G. Water relations in germinating seeds. Russ. J. Plant Physiol. 2017, 64, 625–633. 10.1134/S102144371703013X. [DOI] [Google Scholar]
  32. O’Lone C. E.; Juhász A.; Nye-Wood M.; Dunn H.; Moody D.; Ral J.-P.; Colgrave M. L. Proteomic exploration reveals a metabolic rerouting due to low oxygen during controlled germination of malting barley (Hordeum vulgare L.). Front. Plant Sci. 2023, 14, 1305381. 10.3389/fpls.2023.1305381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Katsuhara M.; Hanba Y. T. Barley plasma membrane intrinsic proteins (PIP aquaporins) as water and CO 2 transporters. Eur. J. Physiol. 2008, 456, 687–691. 10.1007/s00424-007-0434-9. [DOI] [PubMed] [Google Scholar]
  34. Katsuhara M.; Shibasaka M. Barley root hydraulic conductivity and aquaporins expression in relation to salt tolerance. Soil Sci. Plant Nutr. 2007, 53 (4), 466–470. 10.1111/j.1747-0765.2007.00154.x. [DOI] [Google Scholar]
  35. Fox G. P.; Panozzo J. F.; Li C. D.; Lance R. C. M.; Inkerman P. A.; Henry R. J. Molecular basis of barley quality. Aust. J. Agric. Res. 2003, 54 (12), 1081–1101. 10.1071/ar02237. [DOI] [Google Scholar]
  36. Bose U.; Broadbent J. A.; Juhász A.; Karnaneedi S.; Johnston E. B.; Stockwell S.; Byrne K.; Limviphuvadh V.; Maurer-Stroh S.; Lopata A. L.; Colgrave M. L. Protein extraction protocols for optimal proteome measurement and arginine kinase quantitation from cricket Acheta domesticus for food safety assessment. Food Chem. 2021, 348, 129110. 10.1016/j.foodchem.2021.129110. [DOI] [PubMed] [Google Scholar]
  37. Colgrave M. L.; Byrne K.; Blundell M.; Heidelberger S.; Lane C. S.; Tanner G. J.; Howitt C. A. Comparing multiple reaction monitoring and sequential window acquisition of all theoretical mass spectra for the relative quantification of barley gluten in selectively bred barley lines. Anal. Chem. 2016, 88 (18), 9127–9135. 10.1021/acs.analchem.6b02108. [DOI] [PubMed] [Google Scholar]
  38. Frankenfield A. M.; Ni J.; Ahmed M.; Hao L. Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics. J. Proteome Res. 2022, 21 (9), 2104–2113. 10.1021/acs.jproteome.2c00145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Bose U.; Broadbent J. A.; Byrne K.; Blundell M. J.; Howitt C. A.; Colgrave M. L. Proteome Analysis of Hordein-Null Barley Lines Reveals Storage Protein Synthesis and Compensation Mechanisms. J. Agric. Food Chem. 2020, 68 (20), 5763–5775. 10.1021/acs.jafc.0c01410. [DOI] [PubMed] [Google Scholar]
  40. Demichev V.; Messner C. B.; Vernardis S. I.; Lilley K. S.; Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 2020, 17 (1), 41–44. 10.1038/s41592-019-0638-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Pino L. K.; Searle B. C.; Bollinger J. G.; Nunn B.; MacLean B.; MacCoss M. J. The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. Mass Spectrom. Rev. 2020, 39 (3), 229–244. 10.1002/mas.21540. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. BroadInstitute Morpheus: Versatile matrix visualization and analysis software. https://software.broadinstitute.org/morpheus/.
  43. R Core Team . R: A language and environment for statistical computing; R Foundation for Statistical Computing. https://www.R-project.org/.
  44. RStudio RStudio: Integrated Development for R. https://www.rstudio.com/.
  45. Mascher M.Pseudomolecules and Annotation of the Second Version of the Reference Genome Sequence Assembly of Barley Cv. Morex [Morex V2]; e!DAL—Plant Genomics and Phenomics Research Data Repository (PGP), 2019.
  46. Cantalapiedra C. P.; Hernández-Plaza A.; Letunic I.; Bork P.; Huerta-Cepas J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol. Biol. Evol. 2021, 38 (12), 5825–5829. 10.1093/molbev/msab293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Carbon S.; Douglass E.; Good B. M.; Unni D. R.; Harris N. L.; Mungall C. J.; Basu S.; Chisholm R. L.; Dodson R. J.; Hartline E.; et al. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 2021, 49 (D1), D325–d334. 10.1093/nar/gkaa1113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Bateman A.; Martin M. J.; Orchard S.; Magrane M.; Ahmad S.; Alpi E.; Bowler-Barnett E. H.; Britto R.; Bye-A-Jee H.; Cukura A.; et al. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2022, 51 (D1), D523–D531. 10.1093/nar/gkac1052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Mascher M.; Gundlach H.; Himmelbach A.; Beier S.; Twardziok S. O.; Wicker T.; Radchuk V.; Dockter C.; Hedley P. E.; Russell J.; Bayer M.; Ramsay L.; Liu H.; Haberer G.; Zhang X.-Q.; Zhang Q.; Barrero R. A.; Li L.; Taudien S.; Groth M.; Felder M.; Hastie A.; Šimková H.; Staňková H.; Vrána J.; Chan S.; Muñoz-Amatriaín M.; Ounit R.; Wanamaker S.; Bolser D.; Colmsee C.; Schmutzer T.; Aliyeva-Schnorr L.; Grasso S.; Tanskanen J.; Chailyan A.; Sampath D.; Heavens D.; Clissold L.; Cao S.; Chapman B.; Dai F.; Han Y.; Li H.; Li X.; Lin C.; McCooke J. K.; Tan C.; Wang P.; Wang S.; Yin S.; Zhou G.; Poland J. A.; Bellgard M. I.; Borisjuk L.; Houben A.; Doležel J.; Ayling S.; Lonardi S.; Kersey P.; Langridge P.; Muehlbauer G. J.; Clark M. D.; Caccamo M.; Schulman A. H.; Mayer K. F. X.; Platzer M.; Close T. J.; Scholz U.; Hansson M.; Zhang G.; Braumann I.; Spannagl M.; Li C.; Waugh R.; Stein N. A chromosome conformation capture ordered sequence of the barley genome. Nature 2017, 544 (7651), 427–433. 10.1038/nature22043. [DOI] [PubMed] [Google Scholar]
  50. Rapazote-Flores P.; Bayer M.; Milne L.; Mayer C.-D.; Fuller J.; Guo W.; Hedley P. E.; Morris J.; Halpin C.; Kam J.; McKim S. M.; Zwirek M.; Casao M. C.; Barakate A.; Schreiber M.; Stephen G.; Zhang R.; Brown J. W. S.; Waugh R.; Simpson C. G. BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq. BMC Genomics 2019, 20 (1), 968. 10.1186/s12864-019-6243-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Milne L.; Bayer M.; Rapazote-Flores P.; Mayer C.-D.; Waugh R.; Simpson C. G. EORNA, a barley gene and transcript abundance database. Sci. Data 2021, 8 (1), 90. 10.1038/s41597-021-00872-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Love M. I.; Huber W.; Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15 (12), 550. 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Lee S.-e.; Yoon I. s.; Hwang Y.-s. Aquaporin activity of barley tonoplast intrinsic proteins is involved in the delay of the coalescence of protein storage vacuoles in aleurone cells. J. Plant Physiol. 2020, 251, 153186. 10.1016/j.jplph.2020.153186. [DOI] [PubMed] [Google Scholar]
  54. Bewley J. D. Seed germination and dormancy. Plant Cell 1997, 9 (7), 1055–1066. 10.1105/tpc.9.7.1055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Daneri-Castro S. N.; Chandrasekar B.; Grosse-Holz F. M.; van der Hoorn R. A.; Roberts T. H. Activity-based protein profiling of hydrolytic enzymes induced by gibberellic acid in isolated aleurone layers of malting barley. FEBS Lett. 2016, 590 (17), 2956–2962. 10.1002/1873-3468.12320. [DOI] [PubMed] [Google Scholar]
  56. Finnie C.; Andersen B.; Shahpiri A.; Svensson B. Proteomes of the barley aleurone layer: A model system for plant signalling and protein secretion. Proteomics 2011, 11 (9), 1595–1605. 10.1002/pmic.201000656. [DOI] [PubMed] [Google Scholar]
  57. Osama S. K.; Kerr E. D.; Yousif A. M.; Phung T. K.; Kelly A. M.; Fox G. P.; Schulz B. L. Proteomics reveals commitment to germination in barley seeds is marked by loss of stress response proteins and mobilisation of nutrient reservoirs. J. Proteomics 2021, 242, 104221. 10.1016/j.jprot.2021.104221. [DOI] [PubMed] [Google Scholar]
  58. Bahmani M.; O’Lone C. E.; Juhász A.; Nye-Wood M.; Dunn H.; Edwards I. B.; Colgrave M. L. Application of Mass Spectrometry-Based Proteomics to Barley Research. J. Agric. Food Chem. 2021, 69 (31), 8591–8609. 10.1021/acs.jafc.1c01871. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Daneri-Castro S. N.; Svensson B.; Roberts T. H. Barley germination: Spatio-temporal considerations for designing and interpreting ‘omics’ experiments. J. Cereal Sci. 2016, 70, 29–37. 10.1016/j.jcs.2016.05.012. [DOI] [Google Scholar]
  60. Fox G. P.; Watson-Fox L.. Barley: Current Understanding of ‘Omics Data on Quality; Elsevier, 2021. [Google Scholar]
  61. Peirats-Llobet M.; Yi C.; Liew L. C.; Berkowitz O.; Narsai R.; Lewsey M. G.; Whelan J. Spatially resolved transcriptomic analysis of the germinating barley grain. Nucleic Acids Res. 2023, 51 (15), 7798–7819. 10.1093/nar/gkad521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Steinbrecher T.; Leubner-Metzger G. The biomechanics of seed germination. J. Exp. Bot. 2017, 68 (4), 765–783. 10.1093/jxb/erw428. [DOI] [PubMed] [Google Scholar]
  63. Afzal Z.; Howton T. C.; Sun Y.; Mukhtar M. S. The Roles of Aquaporins in Plant Stress Responses. J. Dev. Biol. 2016, 4 (1), 9. 10.3390/jdb4010009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Obroucheva N. V. Aquaporins in seeds. Seed Sci. Res. 2013, 23 (4), 213–216. 10.1017/S0960258513000275. [DOI] [Google Scholar]
  65. Yaneff A.; Vitali V.; Amodeo G. PIP1 aquaporins: Intrinsic water channels or PIP2 aquaporin modulators?. FEBS Lett. 2015, 589 (23), 3508–3515. 10.1016/j.febslet.2015.10.018. [DOI] [PubMed] [Google Scholar]
  66. Kapilan R.; Vaziri M.; Zwiazek J. J. Regulation of aquaporins in plants under stress. Biol. Res. 2018, 51 (1), 4. 10.1186/s40659-018-0152-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Chaumont F.; Barrieu F.; Jung R.; Chrispeels M. J. Plasma membrane intrinsic proteins from maize cluster in two sequence subgroups with differential aquaporin activity. Plant Physiol. 2000, 122 (4), 1025–1034. 10.1104/pp.122.4.1025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Sakurai J.; Ahamed A.; Murai M.; Maeshima M.; Uemura M. Tissue and Cell-Specific Localization of Rice Aquaporins and Their Water Transport Activities. Plant Cell Physiol. 2008, 49 (1), 30–39. 10.1093/pcp/pcm162. [DOI] [PubMed] [Google Scholar]
  69. Sakurai J.; Ishikawa F.; Yamaguchi T.; Uemura M.; Maeshima M. Identification of 33 rice aquaporin genes and analysis of their expression and function. Plant Cell Physiol. 2005, 46 (9), 1568–1577. 10.1093/pcp/pci172. [DOI] [PubMed] [Google Scholar]
  70. Kaldenhoff R.; Fischer M. Aquaporins in plants. Acta Physiol. 2006, 187 (1–2), 169–176. 10.1111/j.1748-1716.2006.01563.x. [DOI] [PubMed] [Google Scholar]
  71. Bellati J.; Alleva K.; Soto G.; Vitali V.; Jozefkowicz C.; Amodeo G. Intracellular pH sensing is altered by plasma membrane PIP aquaporin co-expression. Plant Mol. Biol. 2010, 74, 105–118. 10.1007/s11103-010-9658-8. [DOI] [PubMed] [Google Scholar]
  72. Jozefkowicz C.; Rosi P.; Sigaut L.; Soto G.; Pietrasanta L. I.; Amodeo G.; Alleva K. Loop A is critical for the functional interaction of two Beta vulgaris PIP aquaporins. PLoS One 2013, 8 (3), e57993 10.1371/journal.pone.0057993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Schuurmans J. A.; van Dongen J. T.; Rutjens B. P.; Boonman A.; Pieterse C. M.; Borstlap A. C. Members of the aquaporin family in the developing pea seed coat include representatives of the PIP, TIP, and NIP subfamilies. Plant Mol. Biol. 2003, 53, 655–667. 10.1023/B:PLAN.0000019070.60954.77. [DOI] [PubMed] [Google Scholar]
  74. Yaneff A.; Sigaut L.; Marquez M.; Alleva K.; Pietrasanta L. I.; Amodeo G. Heteromerization of PIP aquaporins affects their intrinsic permeability. Proc. Natl. Acad. Sci. U.S.A. 2014, 111 (1), 231–236. 10.1073/pnas.1316537111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Zhu C.; Schraut D.; Hartung W.; Schäffner A. R. Differential responses of maize MIP genes to salt stress and ABA. J. Exp. Bot. 2005, 56 (421), 2971–2981. 10.1093/jxb/eri294. [DOI] [PubMed] [Google Scholar]
  76. Matsunami M.; Hayashi H.; Murai-Hatano M.; Ishikawa-Sakurai J. Effect of hydropriming on germination and aquaporin gene expression in rice. Plant Growth Regul. 2022, 97 (2), 263–270. 10.1007/s10725-021-00725-5. [DOI] [Google Scholar]
  77. Liu H.-Y.; Yu X.; Cui D.-Y.; Sun M.-H.; Sun W.-N.; Tang Z.-C.; Kwak S.-S.; Su W.-A. The role of water channel proteins and nitric oxide signaling in rice seed germination. Cell Research 2007, 17 (7), 638–649. 10.1038/cr.2007.34. [DOI] [PubMed] [Google Scholar]
  78. Footitt S.; Clewes R.; Feeney M.; Finch-Savage W. E.; Frigerio L. Aquaporins influence seed dormancy and germination in response to stress. Plant, Cell Environ. 2019, 42 (8), 2325–2339. 10.1111/pce.13561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Gattolin S.; Sorieul M.; Frigerio L. Mapping of tonoplast intrinsic proteins in maturing and germinating Arabidopsis seeds reveals dual localization of embryonic TIPs to the tonoplast and plasma membrane. Mol. Plant 2011, 4 (1), 180–189. 10.1093/mp/ssq051. [DOI] [PubMed] [Google Scholar]
  80. Vander Willigen C.; Postaire O.; Tournaire-Roux C.; Boursiac Y.; Maurel C. Expression and inhibition of aquaporins in germinating Arabidopsis seeds. Plant Cell Physiol. 2006, 47 (9), 1241–1250. 10.1093/pcp/pcj094. [DOI] [PubMed] [Google Scholar]
  81. Regon P.; Panda P.; Kshetrimayum E.; Panda S. K. Genome-wide comparative analysis of tonoplast intrinsic protein (TIP) genes in plants. Funct. Integr. Genomics 2014, 14, 617–629. 10.1007/s10142-014-0389-9. [DOI] [PubMed] [Google Scholar]
  82. Finch-Savage W. E.; Leubner-Metzger G. Seed dormancy and the control of germination. New Phytol. 2006, 171 (3), 501–523. 10.1111/j.1469-8137.2006.01787.x. [DOI] [PubMed] [Google Scholar]
  83. Lee S.-e.; Yim H.-k.; Lim M.-n.; Yoon I. s.; Kim J. h.; Hwang Y.-s. Abscisic acid prevents the coalescence of protein storage vacuoles by upregulating expression of a tonoplast intrinsic protein gene in barley aleurone. J. Exp. Bot. 2015, 66 (5), 1191–1203. 10.1093/jxb/eru467. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Bai B.; van der Horst S.; Cordewener J. H. G.; America T.; Hanson J.; Bentsink L. Seed-Stored mRNAs that Are Specifically Associated to Monosomes Are Translationally Regulated during Germination. Plant Physiol. 2020, 182 (1), 378–392. 10.1104/pp.19.00644. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Fincher G.Biochemistry, physiology, and genetics of endosperm mobilization in germinated barley grain. In Barley: production, improvement, and uses; Chichester: Wiley-Blackwell Publishing Ltd, 2010; pp 449–477. 10.1002/9780470958636.ch14. [DOI] [Google Scholar]
  86. Cordeiro R. M. Molecular dynamics simulations of the transport of reactive oxygen species by mammalian and plant aquaporins. Biochim. Biophys. Acta, Gen. Subj. 2015, 1850 (9), 1786–1794. 10.1016/j.bbagen.2015.05.007. [DOI] [PubMed] [Google Scholar]
  87. Kim Y. X.; Steudle E. Gating of aquaporins by light and reactive oxygen species in leaf parenchyma cells of the midrib of Zea mays. J. Exp. Bot. 2009, 60 (2), 547–556. 10.1093/jxb/ern299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Kudoyarova G.; Veselov D.; Yemelyanov V.; Shishova M. The Role of Aquaporins in Plant Growth under Conditions of Oxygen Deficiency. Int. J. Mol. Sci. 2022, 23 (17), 10159. 10.3390/ijms231710159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Mahalingam R.; Graham D.; Walling J. G. The Barley (Hordeum vulgare ssp. vulgare) Respiratory Burst Oxidase Homolog (HvRBOH) Gene Family and Their Plausible Role on Malting Quality. Front. Plant Sci. 2021, 12, 608541. 10.3389/fpls.2021.608541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Jayawardhane J.; Wijesinghe M.; Bykova N. V.; Igamberdiev A. U. Metabolic Changes in Seed Embryos of Hypoxia-Tolerant Rice and Hypoxia-Sensitive Barley at the Onset of Germination. Plants 2021, 10 (11), 2456. 10.3390/plants10112456. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Steinhorst L.; Kudla J. Calcium and reactive oxygen species rule the waves of signaling. Plant Physiol. 2013, 163 (2), 471–485. 10.1104/pp.113.222950. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Mao Z.; Sun W. Arabidopsis seed-specific vacuolar aquaporins are involved in maintaining seed longevity under the control of ABSCISIC ACID INSENSITIVE 3. J. Exp. Bot. 2015, 66 (15), 4781–4794. 10.1093/jxb/erv244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Wang L.-L.; Chen A.-P.; Zhong N.-Q.; Liu N.; Wu X.-M.; Wang F.; Yang C.-L.; Romero M. F.; Xia G.-X. The Thellungiella salsuginea tonoplast aquaporin TsTIP1; 2 functions in protection against multiple abiotic stresses. Plant Cell Physiol. 2014, 55 (1), 148–161. 10.1093/pcp/pct166. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

jf4c00884_si_001.pdf (1.1MB, pdf)
jf4c00884_si_002.xlsx (1.2MB, xlsx)

Data Availability Statement

The data sets presented in this study can be found in online repositories: CSIRO Data Access Portal Accession id: https://doi.org/10.25919/ypsj-4d27.

Articles from Journal of Agricultural and Food Chemistry are provided here courtesy of American Chemical Society

RESOURCES