Next Article in Journal
Functional Characterization of PtoWOX1 in Regulating Leaf Morphogenesis and Photosynthesis in Populus tomentosa
Previous Article in Journal
The Dynamics of Long Terminal Repeat Retrotransposon Proliferation and Decay Drive the Evolution of Genome Size Variation in Capsicum
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 14
Issue 14
10.3390/plants14142137
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Molecular Hydrogen Improves Blueberry Main Fruit Traits via Metabolic Reprogramming

by
Longna Li
1,
Jiaxin Gong
1,
Ke Jiang
1,
Liqin Huang
2,
Lijun Gan
1,
Yan Zeng
3,
Xu Cheng
3,
Didier Pathier
3 and
Wenbiao Shen
1,*
1
Laboratory Center of Life Sciences, College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China
2
College of Sciences, Nanjing Agricultural University, Nanjing 210095, China
3
Air Liquide (China) R&D Co., Ltd., Shanghai 201108, China
*
Author to whom correspondence should be addressed.
Plants 2025, 14(14), 2137; https://doi.org/10.3390/plants14142137
Submission received: 1 June 2025 / Revised: 4 July 2025 / Accepted: 9 July 2025 / Published: 10 July 2025
(This article belongs to the Special Issue Hydrogen-Based Strategies in Plant Science: From Molecular Mechanisms to Agricultural Applications)
Browse Figures
Versions Notes

Abstract

Fruit yield and quality improvement are challenges for researchers and farmers. This study reveals that the main fruit traits of blueberry (Vaccinium ashei ‘Bluegem’) were significantly improved after hydrogen (H2)-based irrigation, assessed by the increased single fruit weight (14.59 ± 6.66%) and fruit equatorial diameter (4.19 ± 2.39%), decreased titratable acidity, increased solid–acid and sugar–acid ratios. The enhancement of fruit quality was confirmed by the increased total volatiles, vitamin C contents, and antioxidant capacity. Using weighted protein co-expression network analysis (WPCNA), proteomic interrogation revealed that serine carboxypeptidase-like proteins I/II (SCPLI/II), ADP ribosylation factor 1/2 (ARF1/2), and UDP-glucosyltransferase 85A (UGT85A) might be functionally associated with the increased fruit weight and size driven by H2. Reduced organic acid accumulation was caused by the regulation of the specific enzymes involved in sucrose metabolism (e.g., α-amylase, endoglucanase, β-glucosidase, etc.). H2 regulation of fatty acid degradation (e.g., acyl CoA oxidase 1 (ACX1), acetyl CoA acyltransferase 1 (ACAA1), etc.) and phenylpropanoid metabolism were used to explain the improved fruit aroma and anthocyanin accumulation. Meanwhile, the upregulated heat shock protein 20/70 matched with the enhanced antioxidant activity. Together, this study provides a novel approach for yield and quality improvement in horticultural crops.

1. Introduction

Blueberry (Vaccinium spp.) is rich in bioactive compounds such as anthocyanins, flavonoids, and other polyphenolic compounds and is considered to be one of the most nutritious and healthy foods [1]. By 2020, China had become the leading country globally in terms of both blueberry cultivation area and total production, encompassing fresh and processed fruit outputs [2]. As of 2024, the cultivation area expanded to 95,880 hm2, with total output reaching 780,000 t. However, the average yield per unit area and overall fruit quality remain suboptimal. Major blueberry-producing regions in China include Yunnan, Guizhou, Liaoning, Sichuan, Shandong, and Jiangsu provinces. Among various cultivars, rabbiteye blueberries (Vaccinium ashei Reade) have good resistance to damp heat and have a large planting area in the Yangtze River Basin [3]. The sustained growth in consumer demand for both fresh consumption and processing applications continues to drive agricultural producers and researchers to focus on enhancing yield and improving fruit quality.
Hydrogen (H2) is widely used as an industrial and medical gas [4,5]. Meanwhile, the production and release of H2 have been observed in algae [6], animals [7], and plants [8]. Conventionally, H2 production in animals results from gut bacterial fermentation [7]. In plants, although there have been no algal hydrogenase-like genes or proteins, H2 can be produced under normal conditions [8] or as a result of various stress conditions and phytohormone induction [9,10]. Several laboratories discovered that both the increased endogenous H2 level and exogenous H2 supplementation not only induced root formation [11] acting as a bioactive molecule, but also enhanced plant tolerance against drought [12], salt [9], cold [13], and heavy metal exposure [14]. Accordingly, its potential application in agriculture has attracted substantial attention [15]. Since H2 has diffusivity and potential flammability (range 4% and 75% v/v in air), hydrogen nanobubble water (HNW) is currently used for H2 supplementation in both laboratory and field trials [16]. When dissolved H2 concentration is <0.5 mM (0.001‰, v/v), HNW has phytological activity. It was further confirmed that it improved agronomic traits, including higher yield and quality, in field-grown rice [17], Chinese cabbage [18], strawberry [19], and tomato [20] after being irrigated with HNW during growth seasons.
Accumulating studies have indicated that serine carboxypeptidase-like (SCPL) proteins participate in various biological processes, including control of cell division [21], production of secondary metabolites [22], and mobilization of storage proteins [23]. For example, grain size 5 (GS5) encodes a serine carboxypeptidase-like (SCPL) group II protein and was found to positively regulate grain size and weight in rice [21] and wheat [24]. Previous studies observed that HNW increases the length, width, and thickness of brown/rough rice and white rice through modulating the yield-related genes, including GS3 and GS5 [17]. Moreover, ADP ribosylation factor (ARF) family proteins regulate membrane traffic and modulate organelle structure in eukaryotic cells through a regulated cycle of GTP binding and hydrolysis [25]. Studies discovered that overexpression of maize ZmARF1 and ZmARF2 in Arabidopsis could promote plant growth by increasing cell size, resulting in increased plant height, enlarged leaves, and seed size [26,27]. Mover, UDP-glucosyltransferase (UGTs) glycosylate a variety of specialized metabolites, such as flavonoids, phenols, terpenoids, anthocyanins, and plant hormones, modifying secondary metabolites and affecting plant growth and development [28]. Studies have found that seven genes in the AtUGT85A subfamily (AtUGT85A1, 2, 3, 4, 5, and 7) are highly expressed in actively dividing tissues of Arabidopsis, and silencing of AtUGT85A7 impaired auxin responsiveness, thus inhibiting root and leaf growth and development [29]. Currently, the mechanism underlying the regulation of HNW on seed or fruit development, as well as the potential targets, remains unclear.
Additionally, HNW was observed to regulate plant secondary metabolism. It reduced titrable acid content and increased sugar content in both strawberry [19] and tomato [20], leading to a significant increase in the sugar–acid ratio. However, the mechanism of H2-regulated organic acid metabolism has not been fully elucidated to date. Moreover, HNW altered the profile of strawberry volatile compounds, assessed by the stimulated gene expression related to ester and furanone derivatives synthesis, as well as linalool and nerolidol synthesis [19]. Increased anthocyanin accumulation was also observed in H2-treated radish sprouts [30]. These changes were attributed to the effects of H2 on phenylpropanoid metabolism modulation. Transcriptomic analysis showed that under UV-A radiation, H2 upregulated genes involved in the anthocyanin biosynthesis in the hypocotyls of radish seedlings, including phenylalanine ammonialyase (PAL), chalcone synthase (CHS), dihydroflavonol reductase (DFR), etc. [31]. Further metabolomics and transcriptomic analyses showed that the main active components of Ficus hirta Vahl were improved by H2 through regulating phenylpropanoid biosynthesis and metabolism [32]. Since the metabolism regulation achieved by HNW might be modulated at both transcriptional and protein levels, the related mechanism needs further elucidation, especially at the protein level.
Previous studies showed that plants of different species or different growth stages, such as irrigated conventional rice, transgenic rice, and wild rice species [33], as well as cucumber seedlings and rose flowers [34], have different responses to H2. Different from annual crops studied previously, blueberries are woody. However, studies on the effects of H2 on woody plants are limited, and there are only F. hirta roots [32] and cut rose flowers [35]. Here, we aim to investigate whether or how HNW irrigation influences blueberry yield and quality, especially using proteomic technology combined with biochemical and molecular biology approaches to provide insight into the possible molecular mechanisms from the perspective of metabolic regulation. This study might provide a reference for the improvement in yield and quality in horticultural crops and enrich the understanding of the metabolic regulatory effects of H2.

2. Results

2.1. H2-Based Irrigation Improves Blueberry Fruit Size and Weight

As expected, HNW irrigation improved the growth performance of blueberry plants and fruit size (Figure 1A). Unlike the changes in polar diameter, the fruit equatorial diameter was increased by 4.19 ± 2.39% (p < 0.001) in comparison with the control group, leading to a slight but significant decrease in the fruit shape index (p < 0.05) (Figure 1B–D). The single fruit weight of blueberries irrigated with HNW was 2.32 ± 0.78 g, also showing a significant increase (14.59 ± 6.66%; p < 0.001; Figure 1E). However, there were no obvious effects on the firmness and water content of blueberry fruits (Figure 1F,G).

2.2. Effects of H2-Based Irrigation on Blueberry Flavor Quality

Compared to the control group, although HNW irrigation did not significantly influence soluble solids content (SSC) and total soluble sugars (TSS) in blueberries, it strikingly reduced titratable acidity (TA; −22.63 ± 9.67%; p < 0.05), consequently leading to a significant increase in SSC/TA (41.17 ± 9.63%; p < 0.01) and TSS/TA (33.93 ± 8.18%; p < 0.05) (Table 1).
A total of 25 major volatile compounds were identified in blueberry fruits, including aldehydes, alcohols, esters, ketones, terpenoids, benzenoids, and others. HNW had no effect on the types of volatile compounds, but altered their contents (Figure 2A). Except for ketones, the contents of other types, especially terpenoids (20.12 ± 8.22%), were increased by HNW to varying degrees, resulting in a significant increase in total volatile content (30.67 ± 17.13%; p < 0.05). Among these, HNW increased the contents of α,α,4-trimethyl benzenemethanol, dihydrocarvone, and cis-p-mentha-2,8-dien-1-ol (p < 0.05; Figure 2B).

2.3. Antioxidant Capacity of Blueberries Is Enhanced by H2-Based Irrigation

Although HNW had no significant effect on the content of total phenolic (TP; Figure 2C), it remarkably increased vitamin C (VC) and total anthocyanin content by 29.87 ± 17.29% and 7.92 ± 1.64%, respectively (p < 0.05; Figure 2D,E). Further gene expression analysis showed that certain anthocyanin biosynthesis-related genes, including 4-coumarate:CoA ligase (4CL), CHS, chalcone isomerase (CHI), flavanone-3-hydroxylase (F3H), anthocyanidin synthase (ANS), and tau class glutathione S-transferase (GSTU) in fruits were significantly upregulated by HNW irrigation (p < 0.01 or 0.001; Figure 2F).
It was clearly observed that HNW increased the activities of antioxidant enzymes, including superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and glutathione reductase (GR) (p < 0.05 or 0.01), with the exception of ascorbate peroxidase (APX) (Figure 3A–E). Consistently, the antioxidant capacity assessed by 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) radical (ABTS·+) scavenging ability was enhanced after HNW irrigation (p < 0.01; Figure 3F), while there was no significant change in 1,1-diphenyl-2-picryl-hydrazyl radical (DPPH·) scavenging abilities (Figure 3G).

2.4. PLS-DA Analysis of Quality Characteristics in Blueberry Fruits After H2-Based Irrigation

PLS-DA analysis was applied to differentiate the HNW group and the control group (Figure 3H). Two components explained 23% (component 1) and 19.9% (component 2) of the total variance. Component 1 clearly separated the HNW treatment from the control group. Further Variable Importance in Projection (VIP) analysis of PLS-DA identified the most important characters involved in discrimination between the HNW group and the control group (VIP score > 1.0, Figure 3I). The characters were associated with flavor quality, including TA, TSS, other volatile compounds, SSC, and esters contents, as well as characters associated with antioxidant properties, including TP, APX, SOD, GR, and CAT activities.

2.5. Protein Changes in Response to H2-Based Irrigation

Among the 4756 proteins identified (Supplementary Table S1), 59 and 83 proteins were found to be significantly upregulated and downregulated (control vs. HNW; Supplementary Table S2). The main Kyoto Encyclopedia of Genes and Genomes (KEGG) functional classifications of these differentially expressed proteins (DEPs) revealed that metabolic pathways (51 proteins), biosynthesis of secondary metabolites (32 proteins), starch and sucrose metabolism (eight proteins), glycerophospholipid metabolism (six proteins), fatty acid degradation (five proteins), and amino sugar and nucleotide sugar metabolism (five proteins) were significantly enriched (Supplementary Figure S1). These results indicated that changes in the above major metabolic pathways might contribute to HNW-induced improvement in blueberry fruit weight, size, and quality.

2.6. Identification of Proteins That Correlated Most with Main Fruit Traits

Weighted protein co-expression network analysis (WPCNA) was performed to identify candidate proteins involved in HNW-improved blueberry fruit mass and quality. A total of 41 modules were obtained, represented by different colors based on hierarchical clustering (Supplementary Figure S2). Correlation analysis further showed a significant positive correlation between the MEgreen module and single-fruit weight (r = 0.78, p = 0.022) and fruit diameter (r = 0.75, p = 0.032), respectively (Figure 4A). The similar correlation was also observed between the MEblue module and titratable acidity (r = 0.83, p = 0.011), the MEturquoise module and total volatile compounds (r = 0.89, p = 0.0031), as well as the MEsteelblue module and ABTS (r = 0.85, p = 0.0075) and DPPH radical scavenging capacity (r = 0.94, p = 0.00052), respectively, while no module was significantly correlated with anthocyanin content.
In the MEgreen module (total 251 proteins), four upregulated DEPs were observed, including serine carboxypeptidase-like protein I/II (SCPLI/II; id278 and id1792), ADP-ribosylation factor 1 (ARF1; id3005), and UDP-glucosyltransferase 85A (UGT85A; id1810) (Table 2). Among these, UGT85A was involved in the enrichment pathway for the biosynthesis of zeatin (p < 0.05; Figure 4B).
There were 60 DEPs in the MEblue module (total 458 proteins), which were mainly related to carbohydrate metabolism, included starch and sucrose metabolism (eight DEPs; p = 0.002; e.g., α-amylase, AMY; endoglucanase, EG; β-glucosidase, BGL; etc.), glycolysis/gluconeogenesis (four DEPs; p = 0.122; e.g., phosphoglucomutase, PGM; aldehyde dehydrogenase, ALDH; alcohol dehydrogenase, ADH), and amino sugar and nucleotide sugar metabolism (four DEPs; p = 0.171; phosphoglucomutase, PGM; endochitinase B, CHIB; chitinase, Chi; Figure 4B, Table 2 and Supplementary Table S3). Moreover, the top 20 proteins were selected for visualization of the interaction network in this module, and AMY, CHIB, ALDH, and ADH were listed (Figure 5A), indicating that these proteins might be closely related to the HNW decrease in blueberry organic acid.
There were 35 DEPs within the MEturquoise module (total 562 proteins). The significantly enriched pathways in this module were unsaturated fatty acid biosynthesis and α-linolenic acid metabolism (p < 0.05, Figure 4B), which included two DEPs, Acyl-CoA oxidase (ACX1), and acetyl-CoA acyltransferase 1 (ACAA) (Table 2 and Supplementary Table S3). However, among the top 20 proteins in connectivity ranking in this module, there were no known DEPs directly related to volatile compound synthesis (Figure 5A). The connectivity rankings for ACX1 and ACAA1 were 97 and 167, respectively. The above DEPs might play important roles in the accumulation of volatile compounds in blueberries stimulated by HNW irrigation.
The MEsteelblue module (total 47 proteins) had two DEPs, and they were heat shock protein 20 (HSP20) and vesicle-associated membrane protein 72 (VAMP72) (Table 2 and Supplementary Table S3). Meanwhile, there were no significantly enriched metabolic pathways. Additionally, the MEturquoise module showed significant correlation with ABTS·+ scavenging capacity (Figure 4A), where peroxidase (POD), glutathione S-transferase (GST), HSP20, and HSP70 were identified (Table 2).

2.7. Validation of Protein Profiles by qPCR

In order to validate the quality of the proteomics, 19 DEPs were selected for qPCR analysis (Figure 5B). Among these, the expression patterns of genes encoding SCPLI/II, ARF1, AMY, EG, BGL, PGM, CHIB, ACX1, ACAA1, ADH, UGT85A, UGT85K, HSP20, and HSP70 were consistent with the results in TMT-labeled proteomics data (Table 2), except for genes encoding ExgA, ALDH, and VAMP72. These results suggested that the quality of our proteomics data might be acceptable.

3. Discussion

Consistent with the previous studies on the HNW-irrigated rice [17], Chinese cabbages [18], tomatoes [20], and strawberries [19,36], this study showed that H2-based irrigation not only improved blueberry fruit quality, including the levels of flavor-related and antioxidant compounds, but also the fruit mass (Figure 1, Figure 2 and Figure 3 and Table 1). These results indicated that the HNW might have great potential in horticultural crop production. Considering the limited consumption of H2 (1–2 USD/kg H2 in the United States, Europe, and China), the cost of H2-based irrigation primarily depends on the equipment and labor expenses [37]. Therefore, the technology of H2 supplementation in horticulture emerges as both economically practicable and viable.

3.1. The Genes/Proteins for the H2-Enhanced Blueberry Fruit Size and Weight

Ample studies demonstrated that SCPLs, ARFs, and UGTs are involved in multiple plant physiological processes, including regulating plant growth and development, directly or indirectly resulting in seed size and weight improvement [21,22,23]. For instance, higher expression of extra carpels and seeds 1 (ECS1), OsGS5, and TtGS5 (all encoding SCPLII proteins), ZmARF1/2 were confirmed to control seed size or/and weight in Arabidopsis [26,27,38], rice [21], wheat [24], respectively. These SCPLs participate in cell division [21,24,38], and ARFs regulate cell size [26,27]. Moreover, seven AtUGT85A genes identified in Arabidopsis might be associated with root and leaf cell cycle regulation [29]. Previously, HNW was observed to improve rice grain size and weight, accompanied by an increase in GS5 expression. In this study, based on the proteomic data and WPCNA analysis, two SCPL proteins, an ARF1 and a UGT85A, were closely associated with the HNW-increased blueberry fruit size and weight (Table 2), indicating that the beneficial effect of HNW might be attributed to the modulation of cell division and cell size.

3.2. H2-Reduced Organic Acids Accumulation Possibly by Regulating Sugar Metabolism

In contrast to the findings in strawberries [19] and tomatoes [20], HNW slightly increased soluble sugar content in blueberries but significantly reduced TA level (Table 1). These results indicated that H2 regulation of sugar–acid metabolism might appear to be species-dependent. In the tricarboxylic acid cycle (TCA), glyoxylic acid cycle, and other organic acid metabolism pathways, there were no DEPs observed. However, WPCNA analysis revealed that certain DEPs involved in starch and sucrose metabolism, amino sugar, nucleotide sugar metabolism, and glycolysis, including AMY, EG, BGL, GN1/2/3, PGM, CHIB, ADH, and ALDH, were significantly correlated with HNW-reduced organic acid content (Figure 4 and Table 2). Starchless pgm mutants of Arabidopsis showed lower levels of soluble sugars, malic acid, and citric acid compared to the wild type [39]. Additionally, ALDH and ADH, which facilitate the interconversion between alcohols to aldehydes, are involved in various metabolic pathways in higher plants, including glycolysis/gluconeogenesis, interconversion of pentose and glucuronate, fatty acid and amino acid degradation, thus not only affecting fruit organic acid metabolism but also volatile substance metabolism [40,41]. In glycolysis/gluconeogenesis, downregulation of ALDH and ADH may slow down the entry of carbon into the citrate cycle. Therefore, in this study, HNW-decreased TA might be attributable to the down-regulated expression (abundance) of proteins and their corresponding genes involved in sugar metabolism at the late stage of blueberry ripening.

3.3. H2-Based Irrigation Regulates Fatty Acid Metabolism to Influence Fruit Aroma Formation

In fruits, aroma compounds, including alcohols, aldehydes, esters, and ketones, are predominantly generated through the lipoxygenase (LOX) pathways and β-oxidation [42]. ALDH and ADH both participate in the LOX pathway. It was reported that SlscADH1 inhibition increased the concentrations of C5 and C6 volatile compounds in fruits [43]. ACX1 and ACAA1 are the first and last enzymes that participate in fatty acid β-oxidation, respectively [44,45]. Expression of PpACX1 and activity of ACX were positively correlated with volatile lactones in peach [46]. In this study, we noticed that HNW upregulated the expression of ACX1 and ACAA1 (Table 2). Since the stimulated ACX1 and ACAA1 promoted acetyl-CoA synthesis [47,48], we further speculated that the increased acetyl-CoA might promote terpenoids synthesis through the mevalonate (MVA) pathway [47]. This speculation was confirmed by the higher levels of terpenoids achieved by H2-based irrigation in blueberry fruits (Figure 2). Furthermore, two differentially expressed UGTs were identified, namely, UGT85A and UGT85K (Figure 4 and Table 2). They were found to be involved in triterpenoids accumulation in Cyclocarya paliurus leaves [49]. Combined, the regulation of the above protein might collectively lead to changes in the volatile profiles of HNW-treated blueberries.

3.4. The Reprogramming of Phenylpropanoid Metabolism by H2-Based Irrigation to Improve Anthocyanins Accumulation

It is well documented that reprogramming of phenylpropanoid metabolism could affect the contents of plant flavonoids, anthocyanins, and aroma, thereby affecting the quality of horticultural products [50]. For example, RNAi-mediated FaCHS silencing and simultaneous heterologous overexpression of an eugenol synthase gene stimulated the accumulation of coumaroyl-CoA-derived metabolites and redirected the carbon flux from the anthocyanin/flavonoid pathway to volatile phenol compound synthesis, thus affecting strawberry aroma [51]. Additionally, the expression of a transgenic anti-F3H gene in carnation flowers results in the emission of higher levels of methyl benzoate [52]. Consistent with the previous study on radish sprouts [30], HNW increased the anthocyanin content, along with the elevated expression of 4CL, CHS, CHI, F3H, ANS, and GSTU transcripts in blueberry fruits (Figure 2F). Proteomic analysis further showed an increase in the abundance of CHI protein that matched with its gene expression, while the abundances of CADs required for lignin synthesis, and C12RT1 and PGT1 involved in catalyzing flavone and flavonol glycosylation were decreased (Supporting Information, Table S3). The above proteomic results were consistent with the metabolomic results of our previous study, in which an increase in phenolic acids and flavonoids accumulation of blueberry fruits was achieved by HNW irrigation [53]. These results indicated that HNW might affect the composition of flavonoid compounds in blueberry fruits through reprogramming phenylpropanoid metabolism.

3.5. Stress Response Proteins Involved in HNW-Enhanced Antioxidant Ability

The enzymatic components of the plant antioxidant system, including SOD, CAT, POD, GR, and GST, collectively maintain redox homeostasis. Additionally, VAMPs and HSPs mediate defense responses against abiotic stresses (heat, drought, salinity) and biotic challenges (pathogen infection) [54,55]. Arabidopsis VAMP721/722 (VAMP72 subfamily) regulated vesicle exocytosis and contributed to plant resistance to powdery mildew [56]. Moreover, apple MsHsp16.9 (a member of the HSP20 family) was strongly induced by high temperature and overexpressing of MsHsp16.9, while in Arabidopsis, it showed increased heat tolerance [57]. Additionally, MsHsp16.9 might function alongside AtHSP70 to maintain protein homeostasis, enhance antioxidant enzyme activity, and alleviate oxidative damage.
In this study, as expected, HNW increased the levels of VC and anthocyanins in blueberries (Figure 2D,E), and the activities of SOD, CAT, POD, and GR (Figure 3A–C,E). Further proteomic analysis revealed an increase in POD, GST, VAMP72, HSP20, and HSP70 protein abundance (Table 2). These changes matched the enhanced antioxidant capacity, as assessed by ABTS·+ scavenging ability (Figure 3F) and were also supported by the results of WPCNA analysis (Figure 4 and Table 2). In our experiments, the ‘Bluegem’ blueberry matures from May to June (Shanghai, China), facing the relatively hottest environment in the greenhouse. Therefore, HNW stimulated VAMP72, HSP20, and HSP70, thus maintaining higher antioxidant enzyme activity and enhancing the fruit heat tolerance.

4. Materials and Methods

4.1. Plant Material and Experimental Design

The pot experiment was carried out in Qingpu Modern Agriculture Park, Shanghai, China (121.03° E, 30.97° N) from February to June 2022. Three-year-old blueberry plants (Vaccinium ashei ‘Bluegem’) were grown in the greenhouse with an automatic drip irrigation system. The plants were irrigated with a nutrient solution prepared with the hydrogen nanobubble water (HNW) and the ordinary water (control group). Control and HNW treatments were set up in three replicates, with 154 plants per replicate. The irrigation details were shown in Supplementary Table S4. Plants were cultivated according to current commercial practices for blueberry greenhouse cultivation in Shanghai. The fully matured fruits (both the exocarp and pedicel had turned blue with pedicel detachment initiation) were harvested in June 2022 for the analyses of main fruit traits.

4.2. The Preparation of Hydrogen Nanobubble Nutrient Solution

The hydrogen nanobubble water generation system (Liquid Air (China) R&D Co., Ltd., Shanghai, China) was integrated into the fertigation system of the blueberry plantation. The concentration of dissolved H2 in the hydrogen nanobubble nutrient solution was ~1.0 mg L−1 with >12 h residence time. The nanobubbles were ~300 nm. The composition of the nutrient solution is shown in Supplementary Table S5.

4.3. Measurement of Fruit Phenotypic Traits

Single-fruit weight, the horizontal and vertical diameters, and fruit shape index (horizontal diameter/vertical diameter) were measured. There were three replicates with 66 fruits per replicate performed for the treatments. The fruit hardness was measured using the GY-1 fruit hardness tester (Sanhe Measuring Instrument, Wenzhou, China). Control and HNW treatments were performed with three replicates, and ten fruits per replicate, respectively. The water content (%) was calculated as the difference between fresh and dry weights. There were three replicates with five fruits per replicate.

4.4. Extraction and Analyses of Flavor Characteristics

Soluble solid content (SSC) was determined by using the LB32T handheld refractometer (SWEVY, Guangzhou, China). There were three replicates with 10 fruits per replicate performed for the control and HNW treatments. Additionally, 30 fruit samples (10 × 3) were ground into a powder using a liquid nitrogen grinder (A11, IKA, Staufen, Germany), then stored at −80 °C for the following analyses. The content of total soluble sugars (TSS) and titratable acidity (TA) were determined as described previously, using anthrone colorimetric and titration methods, respectively [19]. The absorbance of the fruit sample was measured at 620 nm, and the results were calculated using a standard curve prepared with sucrose. The titration was performed using 0.1 M NaOH to provide an endpoint at pH 8.2. The volatiles of fruits were collected and analyzed following the previous method [19], using ethyl decanoate as internal standard, the SPME fiber (50/30 μm DVB/CAR/PDMS, Supelco, Bellefonte, PA, USA), and a 320-MS gas chromatograph–mass spectrometer. The identification of the volatile compounds was performed by comparing the mass spectra of the samples with the NIST11 standard library. The relative amount of a component was calculated in reference to the internal standard (μg g−1 fresh weight). Each sample was analyzed in triplicate.

4.5. Determination of Total Phenolic, Total Anthocyanin, and Vitamin C Contents

Total phenolic content was determined by using the Folin–Ciocalteu reagent, and the absorbance was measured at 765 nm [58]. Quantification was based on a standard curve for gallic acid. The results were expressed as mg g-1 fresh weight. The total anthocyanin content was estimated using the pH differential method [58]. The fruit extract was diluted with pH 1.0 (0.025 M potassium chloride) and pH 4.5 (0.4 M sodium acetate) buffers. The absorbance was measured at both 510 nm and 700 nm. The results were obtained from the formula mentioned in the previous report [58], expressed as mg g−1 fresh weight. According to the method described by Perin et al. [59], vitamin C (VC) content was determined using High Performance Liquid Chromatography (HPLC; D-2000, Hitachi, Tokyo, Japan). The mobile phase was 0.1% oxalic acid solution at a 1.0 mL min−1 flow rate, and the detection wavelength was 254 nm. An external standard curve of L-ascorbic acid was used to quantify VC content, expressed as mg g–1 fresh weight. There were three replicates with 10 fruits per replicate performed for the control and HNW treatments.

4.6. Assay of Antioxidant Enzyme Activity, ABTS·+ and DPPH· Scavenging Activity

The activities of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), guaiacol peroxidase (POD), and glutathione reductase (GR) were measured according to the previous methods [14,60]. Protein content was determined using the method of Bradford [61]. The 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS·+) radical and 1,1-diphenyl-2-picryl-hydrazyl radical (DPPH·) scavenging assays were performed according to the previous methods [62]. There were three replicates with 10 fruits per replicate performed for the control and HNW treatments.

4.7. Protein Extraction and Proteomic Analysis

Six fruits were mixed and ground using liquid nitrogen. Four replicates for each treatment were performed for tandem mass tag (TMT)-labeled quantitative proteomic analysis. According to the previous method [63], the total protein was extracted, and the concentration was measured by the Bradford method. After digestion and estimation, the peptides were labeled based on the manufacturer’s instructions for the TMT labeling kit (Thermo Scientific, Waltham, MA, USA). The labeled peptide fragments were fractionated using high pH reverse-phase chromatography and combined into 15 fractions. The mass spectra analysis was carried out on a Q Exactive HF-X mass spectrometer coupled with an Easy-nLC 1200 system (Thermo Scientific, America). Peptides were separated through a C18 analytical column (75 μm × 25 cm × 2 μm, C18, 100 Å). The flow rate was 300 nL min−1. For data-dependent mode analysis, each scan cycle consisted of one full-scan mass spectrum (R = 60 K, AGC = 3 × 106, max IT = 20 ms, scan range = 350–1800 m/z), followed by 20 MS/MS events (R = 45 K, AGC = 1 × 105, max IT = 100 ms). HCD collision energy and isolation window for precursor selection were set to 32 eV and 1.2 Da, respectively. The former target ion exclusion was set to 35 s. The raw data were analyzed with MaxQuant v1.6.6. Protein identification was performed using Vaccinium darrowii genome assembly (GCA_020921065.1) as a reference [64]. Differentially expressed proteins (DEPs) were selected using t-test (p < 0.05, n = 4) and fold-change > 1.2 or <0.83.

4.8. Quantitative Real-Time PCR

Total RNA in fruit tissues was isolated using FastPure Plant Total RNA Isolation Kit (Polysaccharides & Polyphenolics-rich; Vazyme, Nanjing, China). The concentration and quality were estimated with a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). The cDNAs were synthesized using HiScript III RT SuperMix with gDNA wiper (Vazyme, Nanjing, China). qPCR was performed with TransStart Top Green qPCR SuperMix (TransGen Biotech, Beijing, China) and a real-time PCR system (Mastercycler ep® realplex; Eppendorf, Hamburg, Germany). Relative expression levels of genes were normalized to reference genes (Actin and GAPDH) and presented as values relative to the control group using the 2−ΔΔCT method [65]. Primers were shown in Supplementary Table S6.

4.9. Statistical Analysis

Values were presented as mean ± standard deviation (SD) from three independent experiments (except for proteomic analysis). Results were analyzed by t-test using Origin 2022. Differences were considered significant at * p < 0.05, ** p < 0.01, and *** p < 0.001 (t-test). MetaboAnalyst 5.0 (https://www.metaboanalyst.ca, accessed on 23 July 2023) was used for partial least squares discriminant analysis (PLS-DA) of fruit quality traits. The raw data were normalized by internal standard area (sample median, data transformation by cube root, and data scaling by auto scaling). Weighted protein co-expression network analysis (WPCNA) was performed using the WGCNA v1.69 package in R (v3.6.1) to identify important proteins that correlated most with blueberry main fruit traits. A soft threshold value, power of 9, was selected to transform the adjacency matrix into a scale-free network. Modules whose eigenproteins were highly correlated were merged with a mergeCutHeight of 0.25 (minimum module size = 50). Module-trait associations were evaluated by calculating the Pearson correlations. The top 20 proteins in connectivity ranking (K value) were selected in a given module, and their network was visualized using R igraph v1.2.4.2.

5. Conclusions

In summary, H2-base irrigation improves the fruit yield and quality of blueberries, including fruit weight and size, sugar–acid ratio, aroma compounds, anthocyanin content, and antioxidant capacity (Figure 6). Among these responses, SCPLI/II, UGT85A, HSP20/70, AMY, ADH, ACX1, and their encoding genes might be the candidate target proteins/genes. Further genetic work will focus on their detailed function identification. Overall, H2-based irrigation appears to be a promising approach for yield and quality improvement in horticultural products at a low carbon cost.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants14142137/s1, Figure S1: KEGG enrichment analysis of differentially expressed proteins of hydrogen nanobubble water (HNW)-irrigated blueberry fruits (p-value ranks of the 20 top); Figure S2: Co-expressed protein clustering and module construction; Table S1: Summary of protein identification; Table S2: Summary of differentially expressed proteins; Table S3: Differentially expressed proteins in blueberry fruits irrigated with/without HNW; Table S4: Timing and quantity of HNW irrigation; Table S5: Nutrient solution composition; Table S6: Primers for qPCR.

Author Contributions

Conceptualization, W.S.; methodology, L.L., J.G., and K.J.; data curation, L.L., J.G., and K.J.; visualization, L.L.; writing—original draft preparation, L.L.; writing—review and editing, L.L., L.H., L.G., Y.Z., X.C., D.P., and W.S.; supervision, W.S. and Y.Z.; funding acquisition, W.S. All authors have read and agreed to the published version of this manuscript.

Funding

This work was financially supported by Air Liquide (China) R&D Co., Ltd., and Shanghai Key Laboratory of Hydrogen Science, Center of Hydrogen Science, Shanghai Jiao Tong University, China.

Data Availability Statement

The data that support the findings of this study are available in the supplementary files. The proteomics data in this study are available in FigShare (DOI: 10.6084/m9.figshare.27216609).

Acknowledgments

We would like to thank Evan Evans from the University of Tasmania for the English editing.

Conflicts of Interest

Authors Yan Zeng, Xu Cheng, and Didier Pathier are employees and hold ownership interests (including patents) in Air Liquide (China) R&D Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Kalt, W.; Cassidy, A.; Howard, L.R.; Krikorian, R.; Stull, A.J.; Tremblay, F.; Zamora-Ros, R. Recent Research on the Health Benefits of Blueberries and Their Anthocyanins. Adv. Nutr. 2020, 11, 224–236. [Google Scholar] [CrossRef]
  2. Li, Y.; Liu, C.; Wei, X.; Liu, J.; Hao, J.; Chen, L.; Sun, H. Development Report of 2024 China Blueberry Industry. J. Jilin Agric. Univ. 2025, 47, 1–14. [Google Scholar]
  3. Wu, Y.; Yang, H.; Huang, Z.; Lyu, L.; Wu, W.; Li, W. Effect of Shade Strength on the Fruit Quality of Different Blueberry Cultivars. J. Soil Sci. Plant Nutr. 2023, 23, 4127–4140. [Google Scholar] [CrossRef]
  4. Muhammed, N.S.; Gbadamosi, A.O.; Epelle, E.I.; Abdulrasheed, A.A.; Haq, B.; Patil, S.; Al-Shehri, D.; Kamal, M.S. Hydrogen Production, Transportation, Utilization, and Storage: Recent Advances Towards Sustainable Energy. J. Energy Storage 2023, 73, 109207. [Google Scholar] [CrossRef]
  5. Johnsen, H.M.; Hiorth, M.; Klaveness, J. Molecular Hydrogen Therapy—A Review on Clinical Studies and Outcomes. Molecules 2023, 28, 7785. [Google Scholar] [CrossRef]
  6. Gaffron, H.; Rubin, J. Fermentative and Photochemical Production of Hydrogen in Algae. J. Gen. Physiol. 1942, 26, 219–240. [Google Scholar] [CrossRef]
  7. Czerkawski, J.W. Fate of Metabolic Hydrogen in the Rumen. Proc. Nutr. Soc. 1972, 31, 141–146. [Google Scholar] [CrossRef]
  8. Renwick, G.M.; Giumarro, C.; Siegel, S.M. Hydrogen Metabolism in Higher Plants. Plant Physiol. 1964, 39, 303–306. [Google Scholar] [CrossRef]
  9. Xie, Y.; Mao, Y.; Lai, D.; Zhang, W.; Shen, W.; Blazquez, M.A. H2 Enhances Arabidopsis Salt Tolerance by Manipulating Zat10/12-Mediated Antioxidant Defence and Controlling Sodium Exclusion. PLoS ONE 2012, 7, e49800. [Google Scholar] [CrossRef]
  10. Zeng, J.; Zhang, M.; Sun, X. Molecular Hydrogen Is Involved in Phytohormone Signaling and Stress Responses in Plants. PLoS ONE 2013, 8, e71038. [Google Scholar] [CrossRef]
  11. Zhu, Y.; Liao, W. A Positive Role for Hydrogen Gas in Adventitious Root Development. Plant Signal. Behav. 2016, 11, e1187359. [Google Scholar] [CrossRef] [PubMed]
  12. Song, R.; Zhang, X.; Feng, C.; Zhang, S.; Song, L.; Qi, J. Exogenous Hydrogen Promotes Germination and Seedling Establishment of Barley Under Drought Stress by Mediating the ASA-GSH Cycle and Sugar Metabolism. J. Plant Growth Regul. 2023, 42, 2749–2762. [Google Scholar] [CrossRef]
  13. Liu, Y.; Pan, J.; Ni, S.; Xing, B.; Cheng, K.; Peng, X. Transcriptome and Metabonomics Combined Analysis Revealed the Defense Mechanism Involved in Hydrogen-Rich Water-Regulated Cold Stress Response of Tetrastigma hemsleyanum. Front. Plant Sci. 2022, 13, 889726. [Google Scholar] [CrossRef] [PubMed]
  14. Cui, W.; Yao, P.; Pan, J.; Dai, C.; Cao, H.; Chen, Z.; Zhang, S.; Xu, S.; Shen, W. Transcriptome Analysis Reveals Insight into Molecular Hydrogen-Induced Cadmium Tolerance in Alfalfa: The Prominent Role of Sulfur and (Homo)glutathione Metabolism. BMC Plant Biol. 2020, 20, 58. [Google Scholar] [CrossRef]
  15. Wang, R.; Yang, X.; Chen, X.; Zhang, X.; Chi, Y.; Zhang, D.; Chu, S.; Zhou, P. A Critical Review for Hydrogen Application in Agriculture: Recent Advances and Perspectives. Crit. Rev. Environ. Sci. Technol. 2024, 54, 222–238. [Google Scholar] [CrossRef]
  16. Zhao, L.; Teng, M.; Zhou, L.; Li, Y.; Sun, J.; Zhang, Z.; Wu, F. Hydrogen Nanobubble Water: A Good Assistant for Improving the Water Environment and Agricultural Production. J. Agric. Food Chem. 2023, 71, 12369–12371. [Google Scholar] [CrossRef]
  17. Cheng, P.; Wang, J.; Zhao, Z.; Kong, L.; Lou, W.; Zhang, T.; Jing, D.; Yu, J.; Shu, Z.; Huang, L.; et al. Molecular Hydrogen Increases Quantitative and Qualitative Traits of Rice Grain in Field Trials. Plants 2021, 10, 2331. [Google Scholar] [CrossRef]
  18. Liu, Z.; Chen, G.; Yang, E.; Li, L.; Zeng, Y.; Cheng, X.; Pathier, D.; Xu, G.; Shen, W. Hydrogen-Based Irrigation Increases Yield and Improves Quality of Chinese Cabbage by Enhancing Nutrient Composition and Antioxidant Capabilities. Hortic. Environ. Biotechnol. 2024, 65, 593–605. [Google Scholar] [CrossRef]
  19. Li, L.; Wang, J.; Jiang, K.; Kuang, Y.; Zeng, Y.; Cheng, X.; Liu, Y.; Wang, S.; Shen, W. Preharvest Application of Hydrogen Nanobubble Water Enhances Strawberry Flavor and Consumer Preferences. Food Chem. 2022, 377, 131953. [Google Scholar] [CrossRef]
  20. Li, M.; Zhu, G.; Liu, Z.; Li, L.; Wang, S.; Liu, Y.; Lu, W.; Zeng, Y.; Cheng, X.; Shen, W. Hydrogen Fertilization with Hydrogen Nanobubble Water Improves Yield and Quality of Cherry Tomatoes Compared to the Conventional Fertilizers. Plants 2024, 13, 443. [Google Scholar] [CrossRef]
  21. Li, Y.; Fan, C.; Xing, Y.; Jiang, Y.; Luo, L.; Sun, L.; Shao, D.; Xu, C.; Li, X.; Xiao, J.; et al. Natural Variation in GS5 Plays an Important Role in Regulating Grain Size and Yield in Rice. Nat. Genet. 2011, 43, 1266–1269. [Google Scholar] [CrossRef] [PubMed]
  22. Lehfeldt, C.; Shirley, A.M.; Meyer, K.; Ruegger, M.O.; Cusumano, J.C.; Viitanen, P.V.; Strack, D.; Chapple, C. Cloning of the SNG1 Gene of Arabidopsis Reveals a Role for a Serine Carboxypeptidase-like Protein as an Acyltransferase in Secondary Metabolism. Plant Cell 2000, 12, 1295–1306. [Google Scholar] [CrossRef] [PubMed]
  23. Degan, F.D.; Rocher, A.; Cameron-Mills, V.; Wettstein, D.V. The Expression of Serine Carboxypeptidases during Maturation and Germination of the Barley Grain. Proc. Natl. Acad. Sci. USA 1994, 91, 8209–8213. [Google Scholar] [CrossRef] [PubMed]
  24. Jiang, P.; Gao, J.; Mu, J.; Duan, L.; Gu, Y.; Han, S.; Chen, L.; Li, Y.; Yan, Y.; Li, X. Interaction between Serine Carboxypeptidase-Like Protein TtGS5 and Annexin D1 in Developing Seeds of Triticum timopheevi. J. Appl. Genet. 2020, 61, 151–162. [Google Scholar] [CrossRef]
  25. Min, M.K.; Jang, M.; Lee, M.; Lee, J.; Song, K.; Lee, Y.; Choi, K.Y.; Robinson, D.G.; Hwang, I. Recruitment of Arf1-GDP to Golgi by Glo3p-Type ArfGAPs Is Crucial for Golgi Maintenance and Plant Growth. Plant Physiol. 2013, 161, 676–691. [Google Scholar] [CrossRef]
  26. Yuan, J.; Song, J.; Ma, H.; Song, X.; Wei, H.; Liu, Y. Ectopic Expression a Maize ADP-Ribosylation Factor Gene in Arabidopsis, Increase Plant Size and Growth Rate. J. Plant Biochem. Biotechnol. 2015, 24, 161–166. [Google Scholar] [CrossRef]
  27. Wang, Q.; Xue, X.; Li, Y.; Dong, Y.; Zhang, L.; Zhou, Q.; Deng, F.; Ma, Z.; Qiao, D.; Hu, C.; et al. A Maize ADP-Ribosylation Factor ZmArf2 Increases Organ and Seed Size by Promoting Cell Expansion in Arabidopsis. Physiol Plant. 2016, 156, 97–107. [Google Scholar] [CrossRef]
  28. Lu, X.; Huang, L.; Scheller, H.V.; Keasling, J.D. Medicinal Terpenoid UDP-Glycosyltransferases in Plants: Recent Advances and Research Strategies. J. Exp. Bot. 2023, 74, 1343–1357. [Google Scholar] [CrossRef]
  29. Woo, H.; Jeong, B.R.; Hirsch, A.M.; Hawes, M.C. Characterization of Arabidopsis AtUGT85A and AtGUS Gene Families and their Expression in Rapidly Dividing Tissues. Genomics 2007, 90, 143–153. [Google Scholar] [CrossRef]
  30. Hu, G.; Yue, X.; Song, J.; Xing, G.; Chen, J.; Wang, H.; Su, N.; Cui, J. Calcium Positively Mediates Blue Light-Induced Anthocyanin Accumulation in Hypocotyl of Soybean Sprouts. Front. Plant Sci. 2021, 12, 662091. [Google Scholar] [CrossRef]
  31. Zhang, X.; Su, N.; Jia, L.; Tian, J.; Li, H.; Huang, L.; Shen, Z.; Cui, J. Transcriptome Analysis of Radish Sprouts Hypocotyls Reveals the Regulatory Role of Hydrogen-Rich Water in Anthocyanin Biosynthesis Under UV-A. BMC Plant Biol. 2018, 18, 227. [Google Scholar] [CrossRef] [PubMed]
  32. Zeng, J.; Yu, H. Integrated Metabolomic and Transcriptomic Analyses to Understand the Effects of Hydrogen Water on the Roots of Ficus Hirta Vahl. Plants 2022, 11, 602. [Google Scholar] [CrossRef] [PubMed]
  33. Liu, F.; Jiang, W.; Han, W.; Li, J.; Liu, Y. Effects of Hydrogen-Rich Water on Fitness Parameters of Rice Plants. Agron. J. 2017, 109, 2033–2039. [Google Scholar] [CrossRef]
  34. Hancock, J.T.; LeBaron, T.W.; May, J.; Thomas, A.; Russell, G. Molecular Hydrogen: Is This a Viable New Treatment for Plants in the UK? Plants 2021, 10, 2270. [Google Scholar] [CrossRef]
  35. Wang, C.; Fang, H.; Gong, T.; Zhang, J.; Niu, L.; Huang, D.; Huo, J.; Liao, W. Hydrogen Gas Alleviates Postharvest Senescence of Cut Rose ‘Movie Star’ by Antagonizing Ethylene. Plant Mol. Biol. 2020, 102, 271–285. [Google Scholar] [CrossRef]
  36. Li, L.; Huang, H.; Jin, Z.; Jiang, K.; Zeng, Y.; Pathier, D.; Cheng, X.; Shen, W. Strawberry Yield Improvement by Hydrogen-Based Irrigation is Functionally Linked to Altered Rhizosphere Microbial Communities. Plants 2024, 13, 1723. [Google Scholar] [CrossRef]
  37. Alwazeer, D.; Hancock, J.T.; Russell, G.; Stratakos, A.C.; Li, L.; Çiğdem, A.; Engin, T.; LeBaron, T.W. Molecular Hydrogen: A Sustainable Strategy for Agricultural and Food Production Challenges. Front. Food Sci. Technol. 2024, 4, 1448148. [Google Scholar] [CrossRef]
  38. Wen, J.; Li, J.; Walker, J.C. Overexpression of a Serine Carboxypeptidase Increases Carpel Number and Seed Production in Arabidopsis thaliana. Food Energy Secur. 2012, 1, 61–69. [Google Scholar] [CrossRef]
  39. Lehmann, M.M.; Ghiasi, S.; George, G.M.; Cormier, M.; Gessler, A.; Saurer, M.; Werner, R.A. Influence of Starch Deficiency on Photosynthetic and Post-Photosynthetic Carbon Isotope Fractionations. J. Exp. Bot. 2019, 70, 1829–1841. [Google Scholar] [CrossRef]
  40. Brocker, C.; Vasiliou, M.; Carpenter, S.; Carpenter, C.; Zhang, Y.; Wang, X.; Kotchoni, S.O.; Wood, A.J.; Kirch, H.; Kopečný, D.; et al. Aldehyde Dehydrogenase (ALDH) Superfamily in Plants: Gene Nomenclature and Comparative Genomics. Planta 2013, 237, 189–210. [Google Scholar] [CrossRef]
  41. Strommer, J. The Plant ADH Gene Family. Plant J. 2011, 66, 128–142. [Google Scholar] [CrossRef] [PubMed]
  42. El Hadi, M.A.; Zhang, F.J.; Wu, F.F.; Zhou, C.H.; Tao, J. Advances in Fruit Aroma Volatile Research. Molecules 2013, 18, 8200–8229. [Google Scholar] [CrossRef] [PubMed]
  43. Moummou, H.; Tonfack, L.B.; Chervin, C.; Benichou, M.; Youmbi, E.; Ginies, C.; Latche, A.; Pech, J.; van der Rest, B. Functional Characterization of SlscADH1, A Fruit-Ripening-Associated Short-Chain Alcohol Dehydrogenase of Tomato. J. Plant Physiol. 2012, 169, 1435–1444. [Google Scholar] [CrossRef] [PubMed]
  44. Hooks, M.A.; Kellas, F.; Graham, I.A. Long-Chain Acyl-CoA Oxidases of Arabidopsis. Plant J. 1999, 20, 1–13. [Google Scholar] [CrossRef]
  45. Kunau, W.H.; Dommes, V.; Schulz, H. β-Oxidation of Fatty Acids in Mitochondria, Peroxisomes, and Bacteria: A Century of Continued Progress. Prog. Lipid Res. 1995, 34, 267–342. [Google Scholar] [CrossRef]
  46. Xi, W.P.; Zhang, B.; Liang, L.; Shen, J.Y.; Wei, W.W.; Xu, C.J.; Allan, A.C.; Ferguson, I.B.; Chen, K.S. Postharvest Temperature Influences Volatile Lactone Production via Regulation of Acyl-CoA Oxidases in Peach Fruit. Plant Cell Environ. 2012, 35, 534–545. [Google Scholar] [CrossRef]
  47. Ferrao, L.F.V.; Johnson, T.S.; Benevenuto, J.; Edger, P.P.; Colquhoun, T.A.; Munoz, P.R. Genome-Wide Association of Volatiles Reveals Candidate Loci for Blueberry Flavor. New Phytol. 2020, 226, 1725–1737. [Google Scholar] [CrossRef]
  48. Graham, I.A.; Eastmond, P.J. Pathways of Straight and Branched Chain Fatty Acid Catabolism in Higher Plants. Prog. Lipid Res. 2002, 41, 156–181. [Google Scholar] [CrossRef]
  49. Sun, C.; Fang, S.; Shang, X. Triterpenoids Biosynthesis Regulation for Leaf Coloring of Wheel Wingnut (Cyclocarya paliurus). Forests 2021, 12, 1733. [Google Scholar] [CrossRef]
  50. Dong, N.; Lin, H. Contribution of Phenylpropanoid Metabolism to Plant Development and Plant-Environment Interactions. J. Integr. Plant Biol. 2021, 63, 180–209. [Google Scholar] [CrossRef]
  51. Hoffmann, T.; Kurtzer, R.; Skowranek, K.; Kiessling, P.; Fridman, E.; Pichersky, E.; Schwab, W. Metabolic Engineering in Strawberry Fruit Uncovers a Dormant Biosynthetic Pathway. Metab. Eng. 2011, 13, 527–531. [Google Scholar] [CrossRef] [PubMed]
  52. Zuker, A.; Tzfira, T.; Ben-Meir, H.; Ovadis, M.; Shklarman, E.; Itzhaki, H.; Forkmann, G.; Martens, S.; Neta-Sharir, I.; Weiss, D.; et al. Modification of Flower Color and Fragrance by Antisense Suppression of the Flavanone 3-Hydroxylase Gene. Mol. Breed. 2002, 9, 33–41. [Google Scholar] [CrossRef]
  53. Jin, Z.; Huang, H.; Huang, H.; Li, L.; Zeng, Y.; Cheng, X.; Pathier, D.; Gan, L.; Shen, W. The Delayed Senescence in Harvested Blueberry by Hydrogen-Based Irrigation is Functionally Linked to Metabolic Reprogramming and Antioxidant Machinery. Food Chem. 2024, 453, 139563. [Google Scholar] [CrossRef] [PubMed]
  54. Levine, A.; Belenghi, B.; Damari-Weisler, H.; Granot, D. Vesicle-Associated Membrane Protein of Arabidopsis Suppresses Bax-Induced Apoptosis in Yeast Downstream of Oxidative Burst. J. Biol. Chem. 2001, 276, 46284–46289. [Google Scholar] [CrossRef]
  55. Rajput, V.D.; Harish; Singh, R.K.; Verma, K.K.; Sharma, L.; Quiroz-Figueroa, F.R.; Meena, M.; Gour, V.S.; Minkina, T.; Sushkova, S.; et al. Recent Developments in Enzymatic Antioxidant Defence Mechanism in Plants with Special Reference to Abiotic Stress. Biology 2021, 10, 267. [Google Scholar] [CrossRef]
  56. Kwon, C.; Neu, C.; Pajonk, S.; Yun, H.S.; Lipka, U.; Humphry, M.; Bau, S.; Straus, M.; Kwaaitaal, M.; Rampelt, H.; et al. Co-Option of a Default Secretory Pathway for Plant Immune Responses. Nature 2008, 451, 810–835. [Google Scholar] [CrossRef]
  57. Yang, M.; Zhang, Y.; Zhang, H.; Wang, H.; Wei, T.; Che, S.; Zhang, L.; Hu, B.; Long, H.; Song, W.; et al. Identification of MsHsp20 Gene Family in Malus sieversii and Functional Characterization of MsHsp 16.9 in Heat Tolerance. Front. Plant Sci. 2017, 8, 1761. [Google Scholar] [CrossRef]
  58. de Souza, V.R.; Pereira, P.A.P.; Silva, T.L.T.D.; de Oliveira Lima, L.C.; Pio, R.; Queiroz, F. Determination of the Bioactive Compounds, Antioxidant Activity and Chemical Composition of Brazilian Blackberry, Red Raspberry, Strawberry, Blueberry and Sweet Cherry Fruits. Food Chem. 2014, 156, 362–368. [Google Scholar] [CrossRef]
  59. Perin, E.C.; Messias, R.D.S.; Borowski, J.M.; Crizel, R.L.; Schott, I.B.; Carvalho, I.R.; Rombaldi, C.V.; Galli, V. ABA-Dependent Salt and Drought Stress Improve Strawberry Fruit Quality. Food Chem. 2019, 271, 516–526. [Google Scholar] [CrossRef]
  60. Cakmak, I.; Marschner, H. Magnesium Deficiency and High Light Intensity Enhance Activities of Superoxide-Dismutase, Ascorbate Peroxidase, and Glutathione-Reductase in Bean-Leaves. Plant Physiol. 1992, 98, 1222–1227. [Google Scholar] [CrossRef]
  61. Bradford, M.M. A Rapid and Sensitive Method for the Quantitation of Microgram Quantities of Protein Utilizing the Principle of Protein-Dye Binding. Anal. Biochem. 1976, 72, 248–254. [Google Scholar] [CrossRef] [PubMed]
  62. Liu, L.; Sun, Y.; Laura, T.; Liang, X.; Ye, H.; Zeng, X. Determination of Polyphenolic Content and Antioxidant Activity of Kudingcha Made from Ilex kudingcha C.J. Tseng. Food Chem. 2009, 112, 35–41. [Google Scholar] [CrossRef]
  63. Wang, F.; Ge, S.; Xu, X.; Xing, Y.; Du, X.; Zhang, X.; Lv, M.; Liu, J.; Zhu, Z.; Jiang, Y. Multiomics Analysis Reveals New Insights into the Apple Fruit Quality Decline Under High Nitrogen Conditions. J. Agric. Food Chem. 2021, 69, 5559–5572. [Google Scholar] [CrossRef] [PubMed]
  64. Yu, J.; Hulse-Kemp, A.M.; Babiker, E.; Staton, M. High-Quality Reference Genome and Annotation Aids Understanding of Berry Development for Evergreen Blueberry (Vaccinium darrowii). Hortic. Res. 2021, 8, 228. [Google Scholar] [CrossRef]
  65. Livak, K.J.; Schmittgen, T.D. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef]
Plants 14 02137 g001
Figure 1. Growth and fruit of blueberry after hydrogen nanobubble water (HNW) irrigation. Photos of blueberry plants, flowers, and fruits were taken on 7 May 2022 and 15 June 2022, respectively (A). Changes in fruit equatorial diameter (B), polar diameter (C), fruit shape index (D), single-fruit weight (E), firmness (F), and fruit water content (G) in response to HNW. The number of samples in (B–E), (F), and (G) is 198 fruits (66 × 3), 30 fruits (10 × 3), and 15 fruits (5 × 3), respectively. * and *** indicate p < 0.05 and 0.001, respectively (t-test).
Figure 1. Growth and fruit of blueberry after hydrogen nanobubble water (HNW) irrigation. Photos of blueberry plants, flowers, and fruits were taken on 7 May 2022 and 15 June 2022, respectively (A). Changes in fruit equatorial diameter (B), polar diameter (C), fruit shape index (D), single-fruit weight (E), firmness (F), and fruit water content (G) in response to HNW. The number of samples in (B–E), (F), and (G) is 198 fruits (66 × 3), 30 fruits (10 × 3), and 15 fruits (5 × 3), respectively. * and *** indicate p < 0.05 and 0.001, respectively (t-test).
Plants 14 02137 g001
Plants 14 02137 g002
Figure 2. Effects of hydrogen nanobubble water (HNW) on contents of total volatiles (A), representative volatiles (B), total phenols (C), vitamin C (D), total anthocyanins (E), and anthocyanin biosynthesis-related genes expression profiles (F) in blueberry fruits. Volatile concentration is expressed as μg g−1 of fresh weight, equivalent to ethyl decanoate. Values are expressed as mean ± SD (three replicates and ten fruits per each). * p < 0.05, ** p < 0.01, and *** p < 0.001 (t-test). 4CL: 4-coumarate:CoA ligase; ANS: anthocyanidin synthase; C4H: cinnamic acid-4-hydroxylase; CHI: chalcone isomerase; CHS: chalcone synthase; DFR: dihydroflavonol 4-reductase; F3H: flavanone-3-hydroxylase; F3′H: flavonoid 3′-hydroxylase; F3′5′H: flavonoid 3′,5′-hydroxylase; GSTU: tau class glutathione S-transferase; PAL: phenylalanine ammonia lyase; UFGT: UDP-glycose flavonoid glycosyltransferase.
Figure 2. Effects of hydrogen nanobubble water (HNW) on contents of total volatiles (A), representative volatiles (B), total phenols (C), vitamin C (D), total anthocyanins (E), and anthocyanin biosynthesis-related genes expression profiles (F) in blueberry fruits. Volatile concentration is expressed as μg g−1 of fresh weight, equivalent to ethyl decanoate. Values are expressed as mean ± SD (three replicates and ten fruits per each). * p < 0.05, ** p < 0.01, and *** p < 0.001 (t-test). 4CL: 4-coumarate:CoA ligase; ANS: anthocyanidin synthase; C4H: cinnamic acid-4-hydroxylase; CHI: chalcone isomerase; CHS: chalcone synthase; DFR: dihydroflavonol 4-reductase; F3H: flavanone-3-hydroxylase; F3′H: flavonoid 3′-hydroxylase; F3′5′H: flavonoid 3′,5′-hydroxylase; GSTU: tau class glutathione S-transferase; PAL: phenylalanine ammonia lyase; UFGT: UDP-glycose flavonoid glycosyltransferase.
Plants 14 02137 g002
Plants 14 02137 g003
Figure 3. Effects of hydrogen nanobubble water (HNW) on antioxidant capacity and multivariate analysis of quality-related characters in blueberry fruits. The activities of superoxide dismutase (SOD; (A)), peroxidase (POD; (B)), catalase (CAT; (C)), ascorbate peroxidase (APX; (D)), and glutathione reductase (GR; (E)). The scavenging abilities of 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) radical (ABTS·+; (F)) and 1,1-diphenyl-2-picryl-hydrazyl radical (DPPH·; (G)). Values are expressed as mean ± SD (three replicates and ten fruits per each). * p < 0.05 and ** p < 0.01 (t-test). Score plot of partial least squares-discriminant analysis (PLS-DA; (H)). Variable importance in projection (VIP; (I)) scores listed importance of quality-related characters. The colored boxes on the right indicate the relative abundances of the corresponding characters.
Figure 3. Effects of hydrogen nanobubble water (HNW) on antioxidant capacity and multivariate analysis of quality-related characters in blueberry fruits. The activities of superoxide dismutase (SOD; (A)), peroxidase (POD; (B)), catalase (CAT; (C)), ascorbate peroxidase (APX; (D)), and glutathione reductase (GR; (E)). The scavenging abilities of 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) radical (ABTS·+; (F)) and 1,1-diphenyl-2-picryl-hydrazyl radical (DPPH·; (G)). Values are expressed as mean ± SD (three replicates and ten fruits per each). * p < 0.05 and ** p < 0.01 (t-test). Score plot of partial least squares-discriminant analysis (PLS-DA; (H)). Variable importance in projection (VIP; (I)) scores listed importance of quality-related characters. The colored boxes on the right indicate the relative abundances of the corresponding characters.
Plants 14 02137 g003
Plants 14 02137 g004
Figure 4. The proteomic response of blueberry fruits after hydrogen nanobubble water (HNW) irrigation. Correlation between modules and blueberry yield and quality traits (A). Each row of the left heatmap represents a module, and each column represents a character of blueberry yield and quality. The value in each cell represents the Pearson correlation coefficient between the module and the trait, and the value in parentheses in each cell represents the p-value. Red color represents the positive correlation between module and trait, and blue color represents the negative correlation. ABTS: ABTS·+ scavenging ability; DPPH: DPPH· scavenging ability; SFW: single-fruit weight; ED: equatorial diameter; TA: titratable acidity; TV: total volatile content. KEGG enrichment analysis of proteins in the corresponding modules that were highly positively correlated with traits (B); p-value ranks of the 20 top.
Figure 4. The proteomic response of blueberry fruits after hydrogen nanobubble water (HNW) irrigation. Correlation between modules and blueberry yield and quality traits (A). Each row of the left heatmap represents a module, and each column represents a character of blueberry yield and quality. The value in each cell represents the Pearson correlation coefficient between the module and the trait, and the value in parentheses in each cell represents the p-value. Red color represents the positive correlation between module and trait, and blue color represents the negative correlation. ABTS: ABTS·+ scavenging ability; DPPH: DPPH· scavenging ability; SFW: single-fruit weight; ED: equatorial diameter; TA: titratable acidity; TV: total volatile content. KEGG enrichment analysis of proteins in the corresponding modules that were highly positively correlated with traits (B); p-value ranks of the 20 top.
Plants 14 02137 g004
Plants 14 02137 g005
Figure 5. Co-expression networks of proteins in MEblue and MEturquoise modules (A) and the expression levels of genes coding differentially expressed proteins (B). Nodes represent the top 20 proteins for connectivity (K value) in each module, labeled by NCBI accession number, and lines represent the linear relationship. ACAA1: acetyl-CoA acyltransferase 1; ACX1: acyl-CoA oxidase; ADH: alcohol dehydrogenase; ALDH: alcohol dehydrogenase; AMY: α-amylase; ARF1: ADP-ribosylation factor 1; BGL: β-glucosidase; CHIB: endochitinase B; EG: endoglucanase; ExgA: glucan 1,3-β-glucosidase; HSP20/70: heat shock 20/70kDa protein; PGM: phosphoglucomutase; SCPLI/II: serine carboxypeptidase-like protein I/II; UGT85A/85K: UDP-glucosyltransferase 85A/85K; VAMP72: vesicle-associated membrane protein 72. * p < 0.05, ** p < 0.01 and *** p < 0.001 (t-test; n = 3).
Figure 5. Co-expression networks of proteins in MEblue and MEturquoise modules (A) and the expression levels of genes coding differentially expressed proteins (B). Nodes represent the top 20 proteins for connectivity (K value) in each module, labeled by NCBI accession number, and lines represent the linear relationship. ACAA1: acetyl-CoA acyltransferase 1; ACX1: acyl-CoA oxidase; ADH: alcohol dehydrogenase; ALDH: alcohol dehydrogenase; AMY: α-amylase; ARF1: ADP-ribosylation factor 1; BGL: β-glucosidase; CHIB: endochitinase B; EG: endoglucanase; ExgA: glucan 1,3-β-glucosidase; HSP20/70: heat shock 20/70kDa protein; PGM: phosphoglucomutase; SCPLI/II: serine carboxypeptidase-like protein I/II; UGT85A/85K: UDP-glucosyltransferase 85A/85K; VAMP72: vesicle-associated membrane protein 72. * p < 0.05, ** p < 0.01 and *** p < 0.001 (t-test; n = 3).
Plants 14 02137 g005
Plants 14 02137 g006
Figure 6. Schematic model describing hydrogen nanobubble water (HNW)-improved yield and quality of blueberries. 4CL: 4 coumarate CoA ligase; ACAA1: acetyl-CoA acyltransferase 1; ACX1: acyl-CoA oxidase; ADH: alcohol dehydrogenase; ALDH: alcohol dehydrogenase; AMY: α-amylase; ANS: anthocyanidin synthase; ARF1: ADP-ribosylation factor 1; BGL: β-glucosidase; CAD: cinnamyl alcohol dehydrogenase; CHI: chalcone isomerase; CHIB: endochitinase B; CHS: chalcone synthase; C12RT1: flavanone 7-O-glucoside 2′-O-β-L-rhamnosyltransferase; EG: endoglucanase; ExgA: glucan 1,3-β-glucosidase; F3H: flavanone 3-hydroxylase; GST: glutathione S-transferase; GSTU: tau class glutathione S-transferase; HSP20/70: heat shock 20/70kDa protein; PGM: phosphoglucomutase; PGT1: phlorizin synthase; POD: peroxidase; SCPLI/II: serine carboxypeptidase-like protein I/II; UGT85A/85K: UDP-glucosyltransferase 85A/85K; VAMP72: vesicle-associated membrane protein 72. The red and blue arrows indicate the upregulation and downregulation of gene exression, respectvely.
Figure 6. Schematic model describing hydrogen nanobubble water (HNW)-improved yield and quality of blueberries. 4CL: 4 coumarate CoA ligase; ACAA1: acetyl-CoA acyltransferase 1; ACX1: acyl-CoA oxidase; ADH: alcohol dehydrogenase; ALDH: alcohol dehydrogenase; AMY: α-amylase; ANS: anthocyanidin synthase; ARF1: ADP-ribosylation factor 1; BGL: β-glucosidase; CAD: cinnamyl alcohol dehydrogenase; CHI: chalcone isomerase; CHIB: endochitinase B; CHS: chalcone synthase; C12RT1: flavanone 7-O-glucoside 2′-O-β-L-rhamnosyltransferase; EG: endoglucanase; ExgA: glucan 1,3-β-glucosidase; F3H: flavanone 3-hydroxylase; GST: glutathione S-transferase; GSTU: tau class glutathione S-transferase; HSP20/70: heat shock 20/70kDa protein; PGM: phosphoglucomutase; PGT1: phlorizin synthase; POD: peroxidase; SCPLI/II: serine carboxypeptidase-like protein I/II; UGT85A/85K: UDP-glucosyltransferase 85A/85K; VAMP72: vesicle-associated membrane protein 72. The red and blue arrows indicate the upregulation and downregulation of gene exression, respectvely.
Plants 14 02137 g006
Table 1. Effects of hydrogen nanobubble water (HNW) on soluble solids (SSC) and total soluble sugar (TSS) contents, titratable acidity (TA), SSC/TA, and TSS/TA in blueberry fruits.
Table 1. Effects of hydrogen nanobubble water (HNW) on soluble solids (SSC) and total soluble sugar (TSS) contents, titratable acidity (TA), SSC/TA, and TSS/TA in blueberry fruits.
TreatmentSSC (%)TSS (%)TA (%)SSC/TATSS/TA
SW11.57 ± 0.7210.82 ± 0.710.48 ± 0.0924.62 ± 2.9523.06 ± 3.31
HNW12.54 ± 0.2111.2 ± 1.470.36 ± 0.02 *34.57 ± 1.78 **30.75 ± 3.14 *
Values are expressed as mean ± SD (three replicates and ten fruits per each). * p < 0.05 and ** p < 0.01 (t-test).
Table 2. Functional annotation of candidate proteins in different modules.
Table 2. Functional annotation of candidate proteins in different modules.
ModuleIDNCBI Accession No.RegulatedAnnotation
MEgreen278KAH7834522.1upSerine carboxypeptidase-like clade I
1792KAH7845398.1upSerine carboxypeptidase-like clade II
1810KAH7845536.1upUDP-glucosyltransferase 85A
3005KAH7854533.1upADP-ribosylation factor 1
MEblue2315KAH7849624.1downα-Amylase
4438KAH7864589.1downEndoglucanase
1216KAH7840938.1downEndoglucanase
694KAH7837295.1downβ-glucosidase
1073KAH7839787.1downGlucan endo-1,3-β-glucosidase1/2/3
3786KAH7860109.1downGlucan endo-1,3-β-glucosidase1/2/3
2412KAH7850303.1downGlucan 1,3-β-glucosidase
303KAH7834718.1downPhosphoglucomutase
4573KAH7865523.1downAldehyde dehydrogenase (NAD+)
4840KAH7867194.1downAlcohol dehydrogenase (NADP+)
101KAH7833386.1downAlcohol dehydrogenase class-P
4170KAH7862891.1downEndochitinase B
2116KAH7848277.1downEndochitinase B
4333KAH7863910.1downChitinase
MEturquoise543KAH7836374.1upAcetyl-CoA acyltransferase 1
3870KAH7860773.1upAcyl-CoA oxidase
3896KAH7860965.1upCyanohydrin UDP-glucosyltransferase
719KAH7837441.1upHSP20 family protein
3481KAH7857874.1upHeat shock 70kDa protein
4457KAH7864708.1upPeroxidase
3694KAH7859366.1upGlutathione S-transferase
MEsteelblue39KAH7832952.1upHSP20 family protein
4124KAH7862607.1upVesicle-associated membrane protein 72
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Li, L.; Gong, J.; Jiang, K.; Huang, L.; Gan, L.; Zeng, Y.; Cheng, X.; Pathier, D.; Shen, W. Molecular Hydrogen Improves Blueberry Main Fruit Traits via Metabolic Reprogramming. Plants 2025, 14, 2137. https://doi.org/10.3390/plants14142137

AMA Style

Li L, Gong J, Jiang K, Huang L, Gan L, Zeng Y, Cheng X, Pathier D, Shen W. Molecular Hydrogen Improves Blueberry Main Fruit Traits via Metabolic Reprogramming. Plants. 2025; 14(14):2137. https://doi.org/10.3390/plants14142137

Chicago/Turabian Style

Li, Longna, Jiaxin Gong, Ke Jiang, Liqin Huang, Lijun Gan, Yan Zeng, Xu Cheng, Didier Pathier, and Wenbiao Shen. 2025. "Molecular Hydrogen Improves Blueberry Main Fruit Traits via Metabolic Reprogramming" Plants 14, no. 14: 2137. https://doi.org/10.3390/plants14142137

APA Style

Li, L., Gong, J., Jiang, K., Huang, L., Gan, L., Zeng, Y., Cheng, X., Pathier, D., & Shen, W. (2025). Molecular Hydrogen Improves Blueberry Main Fruit Traits via Metabolic Reprogramming. Plants, 14(14), 2137. https://doi.org/10.3390/plants14142137

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop