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Research Article
Peng Jiang, Xiaojing Wang, Rui Wang*
Improving grape fruit quality through soil
conditioner: Insights from RNA-seq analysis
of Cabernet Sauvignon roots
https://doi.org/10.1515/biol-2022-0864
received June 14, 2023; accepted March 19, 2024
Abstract: The application of fertilizers and soil quality are
crucial for grape fruit quality. However, the molecular data
linking different fertilizer (or soil conditioner [SC]) treat-
ments with grape fruit quality is still lacking. In this study,
we investigated three soil treatments, namely inorganic
fertilizer (NPK, 343.5 kg/hm
2
urea [N ≥46%]; 166.5 kg/hm
2
P
2
O
5
[P
2
O
5
≥64%]; 318 kg/hm
2
K
2
O[K
2
O≥50%]), organic
fertilizer (Org, 9 t/hm
2
[organic matter content ≥35%, N +
P
2
O
5
+K
2
O≥13%]), and SC (SC, 3 t/hm
2
[humic acid ≥38.5%;
C, 56.1%; H, 3.7%; N, 1.5%; O, 38%; S, 0.6%]), on 4-year-old
Cabernet Sauvignon grapevines. Compared with the NPK-
and Org-treated groups, the SC significantly improved the
levels of soluble solids, tannins, anthocyanins, and total phe-
nols in the grape berries, which are important biochemical
indicators that affect wine quality. Furthermore, we con-
ducted RNA-seq analysis on the grapevine roots from each
of the three treatments and used weighted gene co-expres-
sion network analysis to identify five hub genes that were
associated with the biochemical indicators of the grape ber-
ries. Furthermore, we validated the expression levels of
three hub genes (ERF,JP,andSF3B)andfive selected genes
related to anthocyanin biosynthesis (UFGT1,UFGT2,UFGT3,
GST,andAT) by using quantitative reverse transcription-
polymerase chain reaction. Compared to the NPK and Org
treatment groups, the SC treatment resulted in a significant
increase in the transcription levels of three hub genes as
well as VvUFGT1,VvUFGT3,VvGST,andVvAT. These results
suggest that the SC can improve grape fruit quality by
altering gene transcription patterns in grapevine roots and
further influence the biochemical indices of grape fruits,
particularly anthocyanin content. This study reveals that
the application of SC can serve as an important measure
for enhancing vineyard SC and elevating grape quality.
Keywords: grapes, fertilizer, soil conditioner, fruit quality,
RNA-Seq
1 Introduction
Grape (Vitis vinifera L.) is a widely cultivated and econom-
ically significant crop worldwide. Owing to its elevated
nutritional value, exceptional flavor profile, and substantial
profitability, grapes have garnered unprecedented popu-
larity [1,2]. Wine, being the most paramount product of
grape processing, exhibits certain effects in anti-aging [3],
cerebrovascular protection [4,5], anti-cancerous [6], and
so on. As the realization of wine’ssignificance grew, its
demand in China has experienced a rapid increase, propel-
ling China to the helm of the fastest-growing wine consumer
nation globally [7]. Currently, emphasis is being placed on
wine quality, thereby rendering the improvement of grape
quality a topic of great scholarly interest in the domain of
grape cultivation research.
The composition of grapes, such as soluble solids, phe-
nolic compounds, tannins, titratable acids, and sugar to
acid ratios, largely determines the quality, texture, and
aroma of wine [1]. The judicious application of fertilizers
can considerably impact the composition and flavor com-
pound of wine grape fruits. Foliar application of different
types of nitrogen can have different effects on the forma-
tion of mature fruit components, such as ammonium sul-
fate can significantly increase the content of soluble solids,
anthocyanins, and total phenols;, phenylalanine can signifi-
cantly increase the content of titratable acid and tannins in
grape fruits; and urea can increase the contents of total
anthocyanins, flavanols, and flavonol in grape skins [8].
Compared with conventional fertilization, foliar spraying
with iron (ferrous sulfate, ferric ethylenediaminetetraacetic
Peng Jiang: College of Agronomy, Ningxia University, Yinchuan 750021,
P.R. China
Xiaojing Wang: Ningxia Research Institute of Quality Standards and
Testing Technology of Agricultural Products, Yinchuan 750001, P.R. China
* Corresponding author: Rui Wang, College of Agronomy, Ningxia
University, Yinchuan 750021, P.R. China; Ningxia Grape and Wine Research
Institute, Yinchuan 750021, P.R. China, e-mail: amwangrui@126.com
Open Life Sciences 2024; 19: 20220864
Open Access. © 2024 the author(s), published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 International License.
acid, ferric citrate, ferric gluconate, and ferric sugar alcohol)
increased berry sugar content and reduced acid content [9].
In addition, preharvest applications of phenolic acids, such
as benzoic acid, oxalic acid, and citric acid (CA), had signifi-
cant effects on the quality properties of the grapes [10].
These studies indicate that the use of fertilizers can control
the biosynthesis of compounds that affect the quality of
grape fruit.
However, the excessive and continuous application of
traditional chemical fertilizers, especially nitrogen fertili-
zers, causes soil compaction, decreases soil fertility, accel-
erates soil acidification, increases the likelihood of pest
infestations, and leads to a reduction in organic matter
load, humus load, and beneficial organisms [11,12]. Reducing
the reliance on conventional chemical fertilizers, increasing
the utilization of organic fertilizers, and improving soil
quality have become increasingly popular approaches for
grape cultivation. Increasing organic fertilizer usage in
grape management can significantly improve soil nutrient
levels and microbial community composition [13,14], as well
as increase the soluble solid content of grape berries, reduce
the pH value of grape juice, and enhance the diversity of
fungi and the relative abundance of beneficial fungi on
grape berry surfaces [15]. In addition, soil conditioners
(SC), such as vermicompost, have been shown to effectively
reduce copper phytotoxicity in young grapevines grown in
soils with high copper contents, as well as enhance the uti-
lization of certain important micronutrients [16]. Cataldo
et al. demonstrated that soil amendments can significantly
improve the efficiency of grapevines in utilizing soil nutri-
ents and water, reduce their fertilizer requirements, and,
most importantly, improve the quality of the grapes [17].
However, these previous studies have focused on the effects
of different types of fertilizers or SCs on the related bio-
chemical parameters that affect grape quality, lacking
research on the molecular mechanisms of grape under
different treatments.
In the present study, Cabernet Sauvignon, a wine
grape variety widely cultivated in the foothills of Helan
Mountain in China, was used as the plant material. Using
RNA-seq technology, we aimed to explore the effects of
different soil management measures (conventional inor-
ganic fertilizer, organic fertilizer, and SC) on grapevine
root gene expression and to clarify the relationship
between molecular effects and grape quality traits. The
findings will establish a vital molecular basis for enhan-
cing grape quality and guide future soil management
strategies in vineyards.
2 Materials and methods
2.1 Plant materials and treatments
In this experiment, 4-year-old Cabernet Sauvignon grape-
vines were cultivated in 2021 at Lilan Chateau (105°58′20ʺE,
38°16′38ʺN) located in Ningxia, China. The vineyard is situ-
ated at an elevation of approximately 1,160 m and experi-
ences an average annual temperature of 8.5°C. The vineyard
soil was classified as sandy loam. The planting direction of
the plants was north–south, with an inclined upper frame
shape. The plant row spacing was 0.6 m ×3.5 m, and drip
irrigation was used as the irrigation method.
Three treatments were set up in this experiment, including
inorganic fertilizer (NPK), organic fertilizer (Org), and SC (spe-
cific components and doses are shown in Table S1). Each treat-
ment consisted of three replicates, and each replicate included
at least 20 grapevines. Fertilization and SC were applied by
backfilling the excavated pits of 40 cm once a year prior to
plant cultivation. Other management measures, such as irriga-
tion, pruning, and pest control, were kept consistent.
2.2 Determination of grape fruit quality
As previously described [8], soluble solids were measured
by using a handheld sugar meter, titratable acid content
was determined by using a standard 0.1 mol L
−1
NaOH
method, soluble sugars were measured by the anthrone
reagent method, and total phenol was measured via the
Foling-Shocka method. The pH differential method was
used to test anthocyanins, while the Flynn-Dennis method
was used to evaluate tannins.
2.3 RNA extraction and sequencing
Total RNA was extracted by TRIzol reagent (Invitrogen,
Thermo Fisher Scientific Inc., USA) from each root tissue,
followed by DNase I digestion (Takara, Dalian, China) as per
the manufacturer’s instructions. Subsequently, the integrity
of RNAs was determined by checking the ratio of optical
density at 260 nm to that at 280 nm (OD260/280 =1.8–2.0)
using an ultraviolet spectrophotometer (Hoefer, MA, USA),
as well as visually assessing the integrity of 18s and 28s
ribosomal RNAs by electrophoresis in an agarose gel.
2Peng Jiang et al.
Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto,
CA, USA) and Qubit 2.0 Fluorometer (Agilent Technologies)
were used for the determination of library quality. The
Illumina Hiseq pair-end 150 platform was employed for
the sequencing.
2.4 Data processing and gene expression
analysis
Downloaded sequencing data (FASTQ files) were proceeded
using FastQC (v1.11.5; http://www.bioinformatics.babraham.
ac.uk/projects/fastqc/). Clean data were then extracted after
removing low-quality reads and adapters. The ribosome
RNA sequences with >5 mismatch bases were removed.
The mapping of clean reads to reference V. vinifera genome
was performed using TopHat2 software [18]. Cufflinks soft-
ware was used to calculate gene FPKM values (mean frag-
ments per kilobase of transcript per million mapped reads)
[19]. Novel genes (with length ≥200 bp and exon number ≥2)
were annotated against the Kyoto Encyclopedia of Genes
and Genomes (KEGG) and gene ontology (GO). Principal
component analysis (PCA) and Pearson’s correlation ana-
lysis were performed based on the FPKM value of each
gene. Differentially expressed genes (DEGs) between groups
were calculated with the criteria of false discovery rate
pvalue <0.05.
2.5 Construction of gene co-expression
network modules
As described by Langfelder and Horvath [20], a weighted
gene co-expression network analysis (WGCNA) was intro-
duced in this study to simplify genes into co-expression
modules. Specifically, the FPKM values were normalized
to create an adjacency matrix, and then the phenotype
data were inputted into the WGCNA package to compute
the correlation-based associations between gene modules
and phenotypes. Within WGCNA, the adjacency matrix was
transformed into a topology overlap matrix. Based on the
network construction, transcripts exhibiting similar expres-
sion patterns were grouped into co-expression modules, and
unique genes from these modules were identified. Genes
were exported into Cytoscape 3.7.1 using the default para-
meters from all modules [21].
2.6 Quantitative reverse transcription-
polymerase chain reaction (qRT-PCR)
validation for transcriptome data
Based on the key modules of WGCNA analysis, the gene
network was constructed and five hub genes were selected
for qRT-PCR. Primers were designed by using Genscript
Primer Design online tool (https://www.genscript.com.cn/
tools/real-time-pcr-taqman-primer-design-tool). The cDNA
was synthesized and subjected to qRT-PCR using gene-spe-
cific primers and ChamQ Universal SYBR qPCR Master Mix
(Q711-02, Vazyme, Nanjing, China). Vvactin was used as a
reference gene [22] (all the primers are listed in Supplement
data 1: Table S4). The qRT-PCR was conducted with three
biological and technical replicates. The relative expression
levels were determined by applying the 2
−ΔΔCT
method.
2.7 Data analysis
Three biological replicates were set up throughout the
study. We used SPSS for Windows (Chicago, IL, USA) for
statistical analyses. The averages (±SE) were submitted for
analysis of variance with the Tukey–Kramer test.
3 Results
3.1 Effect of different treatments on grape
berry quality
Compared to NPK and Org treatments, SC significantly
improved grape fruit quality. Here, six quality indicators
of grape fruit were investigated, including soluble solids,
titratable acid, soluble sugars, tannins, anthocyanins, and
total phenols. Specifically, the average content of soluble
solids in the NPK-, Org-, and SC-treated group was 22.4, 22.9,
and 24.9%, respectively, with the SC-treated group significantly
higher than the other two fertilizer treatments (Figure 1a).
Therewerenosignificant differences in the titratable acidity
levels among the three treatments (Figure 1b). Regarding the
soluble sugar content, the SC treatment exhibited a significant
increase of 13.4% compared to the NPK treatment, while no
significant difference was detected between the SC and NPK
treatments (Figure 1c). Moreover, the tannin and anthocyanin
levels in grape fruits treated with SC were significantly higher
Improving grape fruit quality through soil conditioner 3
compared to those treated with NPK or Org (Figure 1d and e).
The total phenolic level of fruits treated with SC was signifi-
cantly higher than that of the NPK treatment group, but no
significant difference was observed compared to the Org treat-
ment group (Figure 1f).
3.2 RNA-seq profiles
To gain a comprehensive understanding of the differences
in gene expression within grapevine roots, nine RNA-seq
libraries (NPK1, NPK2, NPK3, Org1, Org2, Org3, SC1, SC2, and
SC3) were generated. Each library yielded over 45 million
clean reads, with more than 70% of these reads mapping to
either unique or multiple genomic locations (Table S2).
Genes with an average abundance and variance of at
least five FPKM were selected to undergo PCA and hier-
archical clustering. The first and second principal compo-
nentsaccountedfor31.8and17.2%ofthetotalvariation,
respectively (Figure 2a). Pearson correlation analysis revealed
strong correlations among the samples between pairwise
treatments (Figure 2b).
To investigate the molecular mechanisms underlying
the differences between SC and the other two fertilizer
treatments, we used the DESeq2 “v1.14.1”package to iden-
tify DEGs in different treatments [23]. The selection criteria
for DEGs were based on previous study [24]. Compared to
the NPK and Org treatment groups, the SC treatment group
had 2,943 and 997 up-regulated DEGs, respectively, with 918
overlapping DEGs (Figure 2c). In addition, compared to the
NPK and Org treatment groups, the SC treatment group had
2,491 and 974 down-regulated DEGs, respectively, with 808
overlapping DEGs (Figure 2d). Cluster analysis revealed
highly similar gene expression profiles among the different
treatments, especially within the NPK or SC treatment
groups (Figure 2e).
3.3 Functional analysis of DEGs
To analyze the response of genes with different expression
levels to different soil treatments, GO was used to enrich
for DEGs. As shown in Figure 3, the top 30 GO enrichment
entries were categorized into three types: “biological pro-
cess,”“cellular component,”and “molecular function.”In
the results of comparing the SC and NPK treatments (SC vs
NPK), the biological process category of GO terms included
organonitrogen compound metabolic process (GO:1901564),
Figure 1: Effect of different treatments on grape berry quality indicators. The levels of soluble solid (a), titratable acid (b), soluble sugar (c), tannins (d),
anthocyanins (e), and total phenols (f) in grape berries were measured. The average values (±SE) followed by different letters indicate significant
difference at pvalue <0.05 using analysis of variance with the Tukey–Kramer test.
4Peng Jiang et al.
biosynthetic process (GO:0009058), organic substance bio-
synthetic process (GO:1901576), and others. The cellular com-
ponent category mainly contained entries such as cellular
anatomical entity (GO:0110165), intracellular (GO:0005622),
organelle (GO:0043226), and so on, while the molecular func-
tion category included terms such as structural molecule
activity (GO:0005198) and structural constituent of ribosome
(GO:0003735) (Figure 3a, Supplement data 2: Table S3). The
GO enrichment analysis of DEGs from comparing the SC and
Org treatment groups (SC vs Org) showed that the biological
process category includes terms such as biosynthetic process
(GO:0009058), organic substance biosynthetic process (GO:1901576),
and cellular biosynthetic process (GO:0044249). The cellular
component category includes terms such as cellular
Figure 2: DEG in V. vinifera roots after different treatments. (a) PCA analysis of RNA-Seq expression profiles in different treatment conditions. (b)
Correlation of RNA-Seq expression profiles of samples in different conditions. Venn diagram of up-regulated genes (c) and down-regulated genes (d)
identified under different conditions. (e) Cluster analysis of DEGs in different treatment conditions.
Improving grape fruit quality through soil conditioner 5
anatomical entity (GO:0110165), intracellular (GO:0005622),
and organelle (GO:0043226). In addition, the molecular func-
tion category mainly includes entries such as structural
molecule activity (GO:0005198), structural constituent of
ribosome (GO:0003735), and transcription regulator activity
(GO:0140110) (Figure 3b, Supplement data 2: Table S5). We
further divided the GO enrichment analysis of DEGs from SC
vs NPK and SC vs Org treatment groups into up- and down-
regulated expression (Supplement data 2: Table S4, Table
S6). The results showed that the majority of the DEGs
obtained from the two compared groups were concentrated
in up-regulated expression regulation.
DEGs were also employed to identify KEGG pathways.
The comparison between the SC-treated group and the
NPK-treated group revealed that up-regulated DEGs were
primarily enriched in KEGG pathways such as ribosome
(vvi03010), protein processing in endoplasmic reticulum
(vvi04141), glutathione metabolism (vvi00480), oxidative
phosphorylation (vvi00190), spliceosome (vvi03040), protein
export (vvi03060), endocytosis (vvi04144), SNARE interac-
tions in vesicular transport (vvi04130), and galactose meta-
bolism (vvi00052) (Figure 4a, Supplement data 1: Table S3).
In contrast, down-regulated DEGs were mainly enriched in
pathways like fatty acid metabolism (vvi01212), fatty acid
biosynthesis (vvi00061), and starch and sucrose metabolism
(vvi00500) (Figure 4b, Supplement data 1: Table S3). The
comparison between the SC-treated group and the Org-
treated group revealed that up-regulated DEGs were mainly
concentrated in KEGG pathways such as ribosome (vvi03010),
spliceosome (vvi03040), oxidative phosphorylation (vvi00190),
endocytosis (vvi04144), galactose metabolism (vvi00052), sulfur
relay system (vvi04122), protein export (vvi03060), and protein
processing in endoplasmic reticulum (vvi04141) (Figure 4c, Sup-
plement data 1: Table S3). On the other hand, down-regulated
DEGs were primarily enriched in pathways like carotenoid
biosynthesis (vvi00906), circadian rhythm (vvi04712), and por-
phyrin and chlorophyll metabolism (vvi00860) (Figure 4d, Sup-
plement data 1: Table S3).
3.4 Co-expression network
In order to explore the association between the regulatory
network of grapevine root genes and berry phenotypes
(soluble solid, titratable acid, soluble sugar, tannin, antho-
cyanin, and total phenols), WGCNA analysis was performed
to globally identify highly correlated hub genes within
highly connected gene networks based on the complete set
of transcripts. Thirty-one modules were identified by
applying the DynamicTreeCut function, and each module
was visually distinguished by a series of color schemes
(Figure 5a). By conducting Pearson correlation coefficient
analysis, co-expression modules were identified for each
grape fruit quality trait. The analysis results showed that
the purple module, which contained 599 genes, had the
highest correlation with soluble solids, soluble sugars, tan-
nins, and anthocyanins (Figure 5b). Furthermore, we inves-
tigated key genes in the purple module. Through gene
network construction, we obtained five hub genes, encoding
Josephin-like protein (JP), 60S ribosomal protein L18a
(60S), uncharacterized protein LOC100262900, and two
Figure 3: GO enrichment analysis of DEGs: (a) SC vs NPK and (b) SC vs Org.
6Peng Jiang et al.
transcription factors, namely, ethylene-responsive tran-
scription factor (ERF) and splicing factor 3B subunit 6
(SF3B) (Figure 5c).
3.5 qRT-PCR analysis of anthocyanin
synthesis genes
Considering that the application of SC resulted in higher
anthocyanin content in grape berries compared to the two
fertilization treatments, and that WGCNA analysis revealed
a module of genes highly correlated with anthocyanin
levels, we selected and validated the expression levels
of five genes related to anthocyanin synthesis (three
UDP-glucuronosyltransferases, glutathione-S-transferase, and
anthocyanin acyltransferase) based on previous study [2]. In
addition, we selected three genes for validation of their root
transcription levels among the five hub genes identified in the
gene network.
The qRT-PCR results showed that the expression levels
of the three hub genes, VvERF,VvJP, and VvSF3B, were
significantly higher in the SC-treated group than in the
NPK- and Org-treated groups (Figure 6a–c). In addition,
among the five genes related to anthocyanin synthesis, the
transcript levels of VvUFGT1 and VvUFGT3 in the roots of
grapevines treated with SC were significantly higher than
those in the other two fertilizer-treated groups (Figure 6e
and f). Moreover, the expression level of VvGST in the roots
of the SC treatment group was significantly higher than
that in the NPK treatment group (Figure 6g). The expres-
sion level of VvAT in the SC-treated group was signifi-
cantly higher than that in the Org-treated group (Figure 6h).
The transcript level of another UDP-glucuronosyltransferase
gene, VvUFGT2, did not show significant differences between
the SC-treated group and the other two treatment groups
(Figure 6e).
4 Discussion
With increasing planting years, soil quality in wine grape
vineyards deteriorates, particularly in terms of physical
Figure 4: KEGG enrichment analysis of DEGs: (a) and (b) SC vs NPK; (c) and (d) SC vs Org.
Improving grape fruit quality through soil conditioner 7
structure, chemical composition, and microbial diversity
[25,26]. Failure to address these issues promptly reduces
production capacity and lowers wine grape quality [27].
Therefore, rational soil management measures are urgently
needed to improve soil quality and provide a favorable
growth environment for grapevines. This study investigates
the effects of three soil management measures on grapevine
root system and fruit quality, with a focus on the advantages
of SC compared to other two conventional fertilizers.
Soluble solids, titratable acidity, soluble sugars, tan-
nins, anthocyanins, and total phenolics are considered
important indicators of grape and wine quality [28], and
fertilizers can have a significant impact on these quality
indicators [2,8]. In this study, a SC was applied [29], which
was formed by simple processing of lignite and contained
approximately 38.5% humic acid. Grapes grown in soil
treated with the SC had a higher proportion of soluble
solids and soluble sugars compared to those grown in
soil treated with inorganic and organic fertilizers, and
their content of tannins, anthocyanins, and total phenolics
also significantly increased (Figure 1). SCs can improve the
physical and chemical properties of soil, and the humic
acid they contain is known to stimulate plant growth and
enhance nutrient uptake from the soil [30–32]. This can
benefit root nutrient absorption and root system develop-
ment, ultimately affecting fruit nutrient accumulation [33].
Under drought conditions, the application of humic acid at a
concentration of 100–200 mg/L significantly enhanced the
yield, dry weight, and root–shoot ratio of foxtail millet,
while increasing the concentration of essential mineral ele-
ments such as P, Fe, Cu, Zn, and Mg in grains [34]. Humic
acid has cytokinin, auxin, gibberellin-like activities, and con-
tains 3-indoleacetic acid, which promote root growth and
increase nutrient and water absorption surface area [35–37].
Thus, the use of humic acid as a foliar and soil application has
been found to improve the growth, yield, and quality of coffee
[37]. Moreover, humic acid can regulate soil microbial com-
munity, physical and chemical properties, and secondary
metabolites in the bayberry rhizosphere, providing a novel
strategy for managing bayberry decline disease [38]. Similarly,
Figure 5: WGCNA identified gene networks. (a) Hierarchical clusters represent 31 distinct modules with co-expressed genes. Each leaflet in the tree
represents an individual gene. (b) Pearson correlation-based module trait relationship. The color grids from green to red represent the r
2
value (−1to
1). (c) Gene network of purple module. Hub genes are highlighted by triangles, and transcription factors are shown in green.
8Peng Jiang et al.
applying soil amendment in continuously cropped soil could
lead to changes in the gene expression patterns in strawberry
plant roots, such as an overall increase in the expression of
nutrient transport genes and a decrease in the expression of
defense response genes [39–41]. In this study, compared to the
two fertilizer treatments, SC exerted distinct effects on the
transcription levels of certain genes in grapevine roots
(Figure 2). Among the up- and down-regulated DEGs, the
most enriched GO terms showed that many up-regulated
DEGs were associated with the biosynthesis of substances
(Figure 3). KEGG pathway analysis revealed that SC pro-
moted protein synthesis (Figure 4).
Anthocyanins are water-soluble flavonoids that contri-
bute to the colors of plants. Key enzymes in the antho-
cyanin biosynthetic pathway include AT, UFGT, and GST.
Potassium fertilizer application significantly increases the tran-
script levels of these enzymes in grape berries [2,42,43]. Simi-
larly, in this study, treatment with SC significantly increased
the transcript levels of UFGT1, UFGT3, GST, and AT in grape-
vine roots, potentially promoting potassium uptake and
enhancing anthocyanin accumulation in grape berries [2].
Gene network analysis identified hub genes, including ERF,
JP, and SF3B, which were significantly up-regulated in the SC
treatment [2]. These genes play roles in regulating plant stress
resistance and mRNA splicing [44–50]. Soil amendments can
alleviate soil environmental stress, allowing plants to redis-
tribute defense resources to development [39]. The utilization
of SC may have impacted the microbial communities in
the soil, leading to the activation of plant root genes
responsible for stress resistance [51]. Further investiga-
tion is needed to understand the underlying molecular
mechanisms [2,39,44–51].
5 Conclusion
In this study, we compared the effects of three soil manage-
ment practices on grapevines, demonstrating that SC sig-
nificantly improved grape fruit quality and altered the
Figure 6: Expression levels of five genes involved in anthocyanin biosynthesis and three hub genes were validated by qRT-PCR. Three biological
replicates are used for qRT-PCR validation. The averages (±SE) following different letters indicate significant difference at pvalue <0.05 using analysis
of variance with the Tukey–Kramer test.
Improving grape fruit quality through soil conditioner 9
gene expression patterns in the plants compared to inor-
ganic and organic fertilizers. Specifically, the application of
SC significantly increased the soluble solids, soluble sugars,
tannins, anthocyanins, and total phenols in grape fruits.
Through WGCNA analysis of the transcriptomic data from
the root of Cabernet Sauvignon, a module highly correlated
with features such as anthocyanin was identified, with five
hub genes identified within. We further validated three hub
genes and specifically investigated five genes related to
anthocyanin synthesis. The results showed that the expres-
sion levels of the three hub genes and VvUFGT1 and VvUFGT3
were significantly increased in the roots treated with SC.
These findings suggest that improvements in soil quality,
particularly with the application of SC, can effectively
enhance grape quality.
Acknowledgements: The authors thank their colleagues
for their comments regarding this article and the journal’s
editors and anonymous reviewers for their critical reviews
and comments regarding this manuscript.
Funding information: This work was supported by the
Leading Talents Fund in Science and Technology Innovation
in Ningxia Province (2022GKLRLX09), the Ningxia Natural
Science Foundation (grant number: 2023AAC03432), the Key
Research and Development Program of Ningxia (grant number:
2020BCF01003), and Ningxia Agricultural Science and
Technology Innovation Project (grant number: NKYZZ-J-
19-04).
Author contributions: R.W. and P.J. conceived and designed
the experiments; P.J. and X.W. performed most of the experi-
ments; P.J., R.W., and X.W. analyzed the RNA-seq data; R.W.
organized the data and wrote the manuscript.
Conflict of interest: Authors state no conflict of interest.
Data availability statement: The datasets generated during
and/or analyzed during the current study are available from
the corresponding author on reasonable request.
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