Plant Science 292 (2020) 110380
Contents lists available at ScienceDirect
Plant Science
journal homepage: www.elsevier.com/locate/plantsci
Transcriptome and GWAS analyses reveal candidate gene for seminal root
length of maize seedlings under drought stress
T
Jian Guoa, Chunhui Lib,*, Xiaoqiong Zhangc, Yongxiang Lib, Dengfeng Zhangb, Yunsu Shib,
Yanchun Songb, Yu Lib, Deguang Yanga,*, Tianyu Wangb,*
a
College of Agriculture, Northeast Agricultural University, Harbin, China
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
c
College of Agriculture, Yangtze University, Jingzhou, China
b
A R T I C LE I N FO
A B S T R A C T
Keywords:
Maize
Drought stress
Seminal roots
RNA-seq
Genome-wide association study
Candidate gene
Water deficits are a major constraint on maize growth and yield, and deep roots are one of the major mechanisms
of drought tolerance. In this study, four root and shoot traits were evaluated within an association panel consisting of 209 diverse maize accessions under well-watered (WW) and water-stressed (WS) conditions. A significant positive correlation was observed between seminal root length (SRL) under WS treatment and the
drought tolerance index (DI) of maize seedlings. The transcriptome profiles of maize seminal roots were compared between four drought-tolerant lines and four drought-sensitive lines under both water conditions to
identify genes associated with the drought stress response. After drought stress, 343 and 177 common differentially expressed genes (DEGs) were identified in the drought-tolerant group and drought-sensitive group,
respectively. In parallel, a coexpression network underlying SRL was constructed on the basis of transcriptome
data, and 10 hub genes involved in two significant associated modules were identified. Additionally, a genomewide association study (GWAS) of the SRL revealed 62 loci for the two water treatments. By integrating the
results of the GWAS, the common DEGs and the coexpression network analysis, 7 promising candidate genes
were prioritized for further research. Together, our results provide a foundation for the enhanced understanding
of seminal root changes in response to drought stress in maize.
1. Introduction
Drought affects crop growth and greatly reduces crop yields globally
and is considered as one of the most severe environmental stressors
[1,2]. Maize (Zea mays L.) is the most widely grown staple food crop,
but maize production is frequently compromised as a result of the increasing frequency and intensity of drought [3]. Thus, improvement of
drought tolerance has become a priority during modern maize
breeding.
Plant roots play a crucial role in the absorption of water and nutrients from the soil and are also an important component of the
drought stress response [4–6]. Optimal root architecture is beneficial
for improving plant drought tolerance and maintaining plant productivity under drought stress [5,7,8]. The ‘steep, cheap and deep’
(SCD) ideotype root architecture, involving reduced lateral branching
density and reduced number of axial roots, has been proposed to enhance the rapid exploitation of deep soil resources in maize [9]. Reduced lateral branching density and reduced number of axial roots can
⁎
improve drought tolerance in maize by reducing intra-plant root competition, permitting greater axial root elongation, thereby improving
deep water capture [8,10]. During the development of drought, soil
moisture is lost from the surface, and deep roots have been demonstrated to serve as a strategy to access water stored deep in the soil
profile, for example, maize and wheat under moderate drought
[6,11,12]. The angle at which roots penetrate the soil may also relate to
root depth. Under moderate drought or severe drought treatments,
steeper root growth angles increase rooting depth and drought tolerance in rice [13]. In maize, seminal root angles are strongly positively
correlated with grain yield under severe drought [14].
For drought adaptation, seminal roots can improve access to residual moisture within deep soil layers [15]. For instance, previous
studies in wheat and barley have reported that relatively deep seminal
roots are beneficial for the absorption of water under drought stress
[16,17]. For the first 2–3 weeks of maize seedling growth, seminal roots
are crucial for seedling survival, prompt establishment and early development after germination [9,18,19]. Seminal roots are produced in
Corresponding authors.
E-mail addresses: lichunhui@caas.cn (C. Li), ydgl@tom.com (D. Yang), wangtianyu@caas.cn (T. Wang).
https://doi.org/10.1016/j.plantsci.2019.110380
Received 29 August 2019; Received in revised form 12 December 2019; Accepted 14 December 2019
Available online 23 December 2019
0168-9452/ © 2019 Elsevier B.V. All rights reserved.
Plant Science 292 (2020) 110380
J. Guo, et al.
2. Materials and methods
maize [20], wheat [15] and barley [16], whereas rice [21] and sorghum [22] lack this type of root. Lynch et al. [9] hypothesized that
maize seminal roots have a relatively larger diameter to improve deep
soil exploration and contribute to foraging in deeper soil horizons. In
addition, previous studies have shown that seminal root length (SRL) is
positively correlated with shoot biomass under low phosphorus and
water stress conditions [20,23]. To increase maize drought tolerance
and yield under drought stress, it is important to explore the genes
underlying SRL.
Several genes controlling the development of seminal roots have
been cloned in maize. For example, both rtcs, which encodes a LOBdomain protein [24], and rum1, which encodes an Aux/ indoleacetic
acid (IAA) protein [25] affect the initiation of seminal roots. In addition, mutation of the gene bige1, which encodes a multidrug and toxin
extrusion (MATE) transporter, results in increased number of seminal
roots [26]. By the evaluation of both biparental and natural populations, quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) have been used to elucidate the genetic basis
underlying SRL in maize. Many QTLs regulating maize SRL have been
reported under different conditions, including various phosphorus deficiencies [20], nitrogen deficiencies [27], water stress [23], and
normal conditions [28,29]. With the advent of increasing types of
economically viable sequencing technologies, GWAS has become a
popular method for identifying natural genetic variation associated
with target traits. Although previous GWAS have examined the wide
natural variation associated with SRL under normal conditions in maize
[30–32], the genetic basis of SRL under water stress, especially under
soil drought stress conditions, remains to be elucidated by GWAS.
RNA sequencing (RNA-seq) has become the favored technique for
detecting genome-wide gene expression patterns. Tai et al. [33] compared transcriptomic profiling differences between primary, seminal
and crown roots of maize, and found diverse patterns of gene activity
across all root types and highlighted the unique transcriptome of the
seminal roots. Moreover, many important differentially expressed genes
(DEGs) in primary root tissue have been identified by RNA-seq in maize
under water stress [34,35]. To identify the complex mechanism governing transcriptional regulations, Hwang et al. [36] utilized weighted
gene coexpression network analysis (WGCNA) to identify functional
connections between genes and confirmed the presence of reliable hub
genes involved in a potential subnetwork related to initial root development at different developmental stages in maize seedlings. These
studies provided general insight into root development and abiotic
stress responses, but the identification of potential key candidate genes
underlying target traits is difficult because of the large amounts of DEGs
obtained by RNA-seq. Recently, examination of candidate genes identified from RNA-seq via significant SNPs from GWAS or via candidate
gene association analysis has been employed to interpret RNA-seq results. For example, Sekhon et al. [37] showed that an integrated
genomic and transcriptomic analysis could be used to identify novel
genes and networks involved in the genetic control of senescence in
maize.
In this study, four drought-tolerant lines and four drought-sensitive
lines selected from 209 diverse inbred lines were used for RNA-seq
analysis of SRL under WW and WS conditions. The expression profiles
of genes were analyzed to identify DEGs. Using WGCNA, we detected
important modules of coexpressed genes and hub genes involved in SRL
under water stress. Using the 209 inbred lines with SRL phenotypes
under WW and WS conditions, we also conducted a GWAS to determine
genomic loci associated with SRL under drought stress. Lastly, we integrated the transcriptomic analysis and GWAS results to identify
candidate genes that respond to drought stress. The goal of this study
was to identify genomic loci and candidate genes affecting SRL under
drought conditions for improving drought tolerance breeding in maize.
2.1. Plant materials and growth conditions
A natural population including 209 diverse accessions of maize
inbred lines was used for measuring the shoot dry weight (SDW), primary root length (PRL), seminal root length (SRL), and seminal root
number (SRN) under well-watered (WW) and water-stressed (WS)
conditions. Pot experiments were arranged in a greenhouse located on
the campus of Chinese Academy of Agricultural Sciences under constant
conditions (14 h of day at 28 °C/10 h of night at 24 °C), during 20172018. The pots consisting of black polyethylene cylinder with 60 cm in
height, 13 cm in diameter, were used to facilitate root sampling. To
phenotype the root and shoot traits, the 209 diverse maize accessions
were grown in these pots, with one plant per pot and six plants per
accession (three for WW conditions and three for WS conditions). The
medium in the pot consisted of 50 % (v/v) peat-based soil and 50 % (v/
v) horticultural size 3 vermiculite (3#) [38]. Three pots with the same
accessions were arranged in a single polyvinyl chloride (PVC) tube,
which were used to make the pots stand upright and roots system grow
to downward. These PVC tubes were randomly placed on the moveable
shelves in the greenhouse. Each pot and PVC tube had three holes on
the each side (6 holes in total) for imposing a controlled stress. Nine
uniformly sized seeds of each inbred line were sown in the three pots of
each PVC tube, with the three seeds evenly distributed in each pot.
Seven days after emergence, one healthy seedling was kept in a pot, and
three similar size seedlings of each accession were kept in a PVC tube.
The PVC tubes were used for each line under WW and WS treatments.
With respect to the WW treatment, the amount of water required for
the soil was the maximum amount of water that the soil in one pot
could hold against the pull of gravity and was estimated to be 2 L. With
respect to the WS treatment, the water content of the soil was 60 % that
of the WW treatment. To maintain consistent soil moisture in each pot,
the soil and water were manually mixed together before sowing. After
the seedlings emerged, 500 mL of water was applied to the WW treatment every 10 days, whereas the WS treatment received no additional
irrigation.
2.2. Phenotypic identification, quantification and statistical analysis
Plants were harvested at 35 days after seedling emergence. To
measure the root traits, the seedlings were removed from the pots, after
which the soil was carefully washed away. For quantification of the root
length, SRL was considered as the cumulative length of all seminal roots
measured with a ruler from the coleoptilar node to the root tip, and PRL
was measured with a ruler from the coleoptilar node to the root tip. The
SRN was counted manually. The SDW was weighed after the shoot was
dried in an oven at 105 °C for 30 min and 80 °C for 12 h [39]. Drought
tolerance was evaluated via the drought tolerance index (DI) in terms of
aboveground biomass, as described by Lan [40]:
Ys
Droughttoleranceindex(DI) =
Ys× Yp
Ȳs
where Yp is the SDW of a given genotype in a non-stressed environment, Ys is the SDW of a given genotype in a drought stressed environment, and Ys is the mean SDW of all genotypes in a drought
stressed environment.
Analysis of variance (ANOVA) was conducted via the PROC GLM in
SAS (Version 9.2; SAS Institute, Cary, NC, USA) software with genotype
and replication as random effects. Repeatability was computed for each
trait as described by Xue [41]:
w2 =
σ G2
σ G2 +
σ 2e
r
where σG2 is the genotypic variance, σe2 is the error variance, and r is the
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Plant Science 292 (2020) 110380
J. Guo, et al.
Meff using a number of principal components that contribute to 99.5 %
of variation. Each chromosome was broken into regions of 133 SNPs
(default parameters of LD pruning in simpleM) in order to calculate
eigenvalues effectively.
A total of 12,151 independent SNPs were ultimately obtained, and
the threshold for significant trait-marker associations was set to 4.1 ×
10−6 on the basis of the number of independent SNPs, the family-wise
error rate was α = 0.05. All genes within LD decay centered on the
significantly associated SNPs were extracted as potential candidate
genes underlying the target trait.
number of replications.
2.3. RNA sequencing and data analysis
On the basis of results from the phenotypic evaluation of the 209
lines, four drought-tolerant lines (PHEG9, 842, PH207, CXB) and four
drought-sensitive lines (85B64, 8TA60, LH160, LH213) were chosen for
transcriptomic analysis under WW and WS conditions. The experimental conditions and treatment of the eight lines were consistent with
those of the natural population. At 10 days after seedling emergence, all
seminal root tips of each plant, encompassing the meristem and the
elongation zone, were removed 2 cm with scissors [42]. For each inbred
line, three biological replicates, each comprising six independent
plants, were sampled for both WW and WS treatments. The sampled
root material was immediately frozen in liquid nitrogen and stored at
-80 °C for further use.
RNA extraction and library preparation for each sample were performed by Novogene Corporation. All 48 libraries (8 samples per
treatment, with three biological replicates) were sequenced using the
Illumina HiSeq 2000 platform (Illumina, CA, USA), and 150 bp pairedend reads were generated. Clean reads were obtained by ng_QC software, with the following parameters -a (adapter sequence) CTCAGGTT/
AGACATCG, -N (poly_N number/read length cutoff) 0.1, -L (lowquality-base) 20, and -p (quality-cutoff) 0.5. The sequencing data were
deposited in the NCBI SRA database (BioProject ID: PRJNA562045).
The reference genome and gene model annotation files of B73
RefGen_v4 were downloaded from the website (ftp://ftp.ensemblgenomes.org/pub/plants/release-38/fasta/zea_mays/).
The
reference
genome index was constructed with Bowtie v2.2.3, and the reads were
aligned to the reference maize genome with TopHat v2.0.12. The FPKM
(expected number of Fragments Per Kilobase of transcript per Million
basepair sequences) was used to estimate gene expression levels [43].
Differential expression analysis between the two treatments was
performed via the pairwise comparison algorithm of DESeq [44]. The
DEGs were screened according to the following criteria: |log2 fold
change (FC)| ≥ 1, a false discovery rate (FDR), and an adjusted Pvalue < 0.05 [45]. Gene Ontology (GO) enrichment analysis of the
DEGs was conducted to identify the enriched biological functions
among the eight genotypes. GO enrichment and Kyoto Encyclopedia of
Genes and Genomes (KEGG) pathway analyses of the DEGs were implemented using the GOseq R software package [46] and KOBAS software [47], respectively. Transcription factors (TFs) were identified via
iTAK software [48]. Principal component analysis (PCA) was performed
using the ggord package in R software. The WGCNA package [49] in R
software was used for coexpression analysis. The coexpression network
was constructed by calculating the Person correlation of any two DEGs
in all of the genotypes. Coexpression modules were defined as clusters
of highly interconnected genes, which have high correlation coefficients within the same cluster. Here, we constructed a weighted coexpression network to identify specific genes highly associated with SRL
using WGCNA.
2.5. Quantitative real-time PCR
A quantitative real-time PCR (qRT-PCR) assay was conducted for
the DEGs obtained by RNA-seq. Total RNA from the seminal roots of the
eight genotypes under WW and WS treatments was prepared for qRTPCR analysis. Relative quantitative results were calculated by normalization to the endogenous reference GAPDH. Relative gene expression
levels were calculated according to the 2−ΔΔCt method. Three independent experiments were performed for all reactions. The genespecific primers used, which are listed in Table S1, were designed by
Primer 5.0 software (Premier Biosoft International, Palo Alto, CA).
3. Results
3.1. Evaluation of root traits and drought tolerance in diverse maize
germplasm
To systematically evaluate the relationship between root traits and
drought tolerance of maize grown in soil, a natural population comprising 209 diverse accessions was grown under WW and WS conditions, and four traits, SDW, SRL, SRN, and PRL, were phenotyped at the
seedling stage (35 days after emergence). The lines within each treatment were significantly (P < 0.001) different for all traits (Table S2).
The repeatability of the root-related traits was moderate, ranging from
0.62 (PRL) to 0.71 (SRL) under WW conditions and from 0.46 (PRL) to
0.63 (SRL) under WS conditions. The repeatability of the SDW trait was
rather high under both WW and WS conditions, 0.88 and 0.81, respectively (Table S2). The frequency distributions of the different traits
revealed a normal distribution under both WW and WS conditions
(Fig. 1). Significant differences in all traits except SRN were detected
between WW and WS conditions. Under WS conditions, plant growth
was greatly inhibited, and compared with those under WW conditions,
the values of SDW under WS conditions were reduced by 38.5 %
(Fig. 1A). The SRL and PRL under WS conditions were greatly increased
by 23.8 % and 23.0 % and the DI value varied from 0.06 to 2.22
(Fig. 1A). Under WW treatment, there were no significant correlations
between root-related traits and the DI. Under WS treatment, the DI was
significantly correlated with SRL (r = 0.32, P = 7.0 × 10−5) and SRN
(r = 0.23, P = 0.003) but was not significantly correlated with PRL
(Fig. 1B). Among the maize lines, four lines with relatively long SRL and
high DI values (the drought-tolerant lines PHEG9, 842, PH207, and
CXB) and the four maize inbred lines with relatively short SRL and low
DI values (the drought-sensitive lines 85B64, 8TA60, LH160, and
LH213) were chosen for RNA-seq (Fig. S1).
2.4. Genome-wide association study
The association panel comprising 209 inbred lines was genotyped
via a MaizeSNP50 BeadChip with 56,110 SNPs. After the low-quality
SNPs were filtered and removed, 43,252 high-quality SNPs (minor allele frequency ≥ 0.05, missing rate ≤ 20 %) were remained. A GWAS
was conducted for SRL under WW and WS conditions using a mixed
linear model (MLM) in TASSEL 5.0 software [50]. PCA and linkage
disequilibrium (LD) analysis were also performed by TASSEL 5.0.
Manhattan plots were generated by the CMplot package in R software.
Owing to the nonindependence of SNPs caused by strong LD, the
number of independent SNPs was determined by the statistical program
simpleM implemented in R software [51]. The simpleM method uses
composite LD among SNPs to capture the correlation and derives the
3.2. Transcriptome sequencing analysis
The transcriptomes of the four drought-tolerant lines and four
drought-sensitive lines under WS and WW conditions were analyzed.
More than 7.21 billion reads from 48 libraries of the eight genotypes
under the two treatments were generated by 150-bp paired-end RNAseq (Fig. S2A). On average, more than 80 % of high-quality reads
mapped uniquely to the B73 RefGen_v4 reference genome (Fig. S2B).
The Pearson correlation coefficients among the different biological replicates of the same genotype varied from 0.91 to 0.99, indicating the
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J. Guo, et al.
Fig. 1. Phenotypic variation and correlation analysis of root and shoot traits. (A) Phenotypic variation of five traits under WW and WS conditions. SDW, shoot dry
weight; PRL, primary root length; SRL, seminal root length; SRN, seminal root number; DI, drought tolerance index; WW, well-watered; WS, water-stressed.
Significance: *** P < 0.001; NS, not significant. (B) Correlation analysis between the DI and root-related traits under WW and WS conditions. r: Pearson correlation
coefficient; P: P-value of the Pearson correlation test.
Fig. 2. Principal components analysis of RNA-seq data under different water treatments. (A) WW treatment. (B) WS treatment. The variance explained for the
different groups is shown in parentheses.
drought tolerant or drought sensitive (Fig. 3A). Among the DEGs, the
number of downregulated genes was greater than the number of upregulated genes in 8TA60, LH213, LH160, 85B64, CXB and PHEG9,
though the opposite results were obtained in the other two inbred lines.
GO classification of all the genotypes revealed that carbohydrate metabolic processes, responses to oxidative stress and antioxidant activity
were significantly enriched among the upregulated genes; other GO
terms, including protein phosphorylation, biological process and
transferase activity, were enriched among the downregulated genes
(Fig. 3B). TFs enriched in DEGs ranged from 7.7%–10.8% in the eight
genotypes (Fig. 3A), and these TFs belonged to 71 families. Moreover,
members of the AP2-EREBP, MYB and NAC TF families were highly
represented among the DEGs in five or more genotypes (Fig. 3C), suggesting that these three TF families may have a great impact on seminal
root development and drought tolerance.
high quality of the replicates (Fig. S3A and B). The number of transcripts identified in each sample, expressed as FPKM values, was shown
in Fig. S3C. In the 16 samples, ∼ 54 % of the expressed genes were
expressed at low levels (0 < FPKM < 1), ∼40 % were expressed at
moderate levels (1 ≤ FPKM < 60), and ∼4 % were expressed at high
levels (FPKM ≥ 60) (Fig. S3C). The FPKM dataset representing the
eight genotypes under the two treatments was subjected to PCA, and
clear separation between the drought-tolerant group and drought-sensitive group was detected under WS conditions, whereas no clear separation was found under WW conditions (Fig. 2). Different genotypes
from the drought-tolerant or drought-sensitive group clustered together
under WS conditions, suggesting that the transcriptome profile was
distinct between the two groups.
To determine which genes correlates with the drought response, we
identified DEGs between WS and WW conditions for each genotype.
The number of DEGs ranged from 1,551 in the 8TA60 line (drought
sensitive) to 14,552 in the PHEG9 line (drought tolerant), indicating
that differences were greatest in these two lines that are classified as
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J. Guo, et al.
Fig. 3. Differentially expressed genes (DEGs) in eight genotypes in response to drought stress. (A) The number of upregulated, downregulated and TF-coded DEGs of
the eight genotypes are given. (B) Enriched Gene Ontology (GO) terms (biological process) for the up- and downregulated genes in the eight genotypes. The color
scale at the bottom represents the significance (corrected P-value). (C) Distribution of DEGs within TF families as a percentage of all DEGs (only the 34 TF families
with > 15 expressed members are shown). The significantly enriched families in each genotype are indicated by blue blocks. (For interpretation of the references to
colour in this figure legend, the reader is referred to the web version of this article.)
upregulated in all the drought-tolerant lines, whereas two genes (aasr5
and aasr6) were specifically downregulated in the drought-tolerant
lines. nac1, which encodes the NaCl stress protein, was upregulated in
all the drought-sensitive lines, whereas the alcohol dehydrogenase-related gene adh2 was downregulated in the drought-tolerant group.
Among the transcription factors, the genes encoding MYB, WRKY and
AP2-EREBP were overrepresented compared with other TF genes. We
randomly chose four candidate genes involved in the auxin and ABA
pathways for expression validation by qRT-PCR, and the expression
trends were highly similar with the RNA-seq data (Fig. S4).
We also analyzed the GO terms represented by these common DEGs
in the drought-tolerant and drought-sensitive groups to obtain useful
information on plant responses to drought stress. The GO terms in the
drought-tolerant group included mainly carbohydrate binding, ATPase
activity, protein phosphorylation and peroxidase activity; the GO terms
in the drought-sensitive group included mainly regulation of the defense response, response to biotic stimulus, the alcohol catabolic process, and response to water (Fig. S5A and B). We also performed a
KEGG analysis of the common DEGs to identify the metabolic pathways
actively involved in the response to drought further. Four pathways in
3.3. DEGs between drought-tolerant and -sensitive lines
To discover common DEGs between the different genetic backgrounds, comparison assays were conducted for the drought-tolerant
and -sensitive groups. In total, 373 (115 upregulated and 258 downregulated) DEGs was sustained in the drought-tolerant group, and 177
(40 upregulated and 137 downregulated) DEGs was consistent in the
drought-sensitive group (Fig. 4). Among these common DEGs, 27 (2
upregulated and 25 downregulated) were conserved in the droughttolerant and -sensitive groups (Fig. 4). Among the common DEGs, 41
TFs and 46 genes with other functions were identified in all lines of the
drought-tolerant group, whereas 18 TFs and 16 genes with other
functions were identified in all lines of the drought-sensitive group
(Table S3). We found that many other functional genes were related to
the drought response, transport, or hormone metabolism. We also
identified several genes that were involved in auxin biosynthesis and
transport with upregulated in all the drought-tolerant lines, such as
ZmIAA15 (Zm00001d013707), iaa32 (Zm00001d018973), mads45
(Zm00001d035053) and yuc2 (Zm00001d025005). Furthermore, the
abscisic acid (ABA)-related genes abi41 and abh1 were specifically
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J. Guo, et al.
Fig. 4. Venn diagrams of differentially expressed genes (DEGs) among drought-tolerant lines and drought-sensitive lines. Upregulated (A) and downregulated (C)
DEGs among the drought-sensitive lines. Upregulated (B) and downregulated (D) DEGs among the drought-tolerant lines. The classic maize genes and TFs are listed
for the common DEGs. Twenty-seven DEGs common to the drought-sensitive and drought-tolerant lines are indicated by orange circles.
were three zinc finger TFs, two bZIP TFs, one ABI3VP1 TF, one AP2EREBP TF, one bHLH TF, one HB TF and one MYB TF. The top five
genes with the highest connectivity encode phenylalanine ammonia
lyase, a zinc finger C3HC4-type family protein, 6-phosphogluconolactonase, a rubredoxin-like superfamily protein, and lysine-specific
histone demethylase. These top genes and TFs (in terms of their
weighted values) were shown in the network (Fig. 5D). The results of a
GO analysis indicated enrichment of most of these genes in metabolic
processes, catalytic activity and binding. Taken together, these results
provided support for the identification of key regulators involved in SRL
under water stress.
the drought-tolerant group were significantly enriched: phenylpropanoid biosynthesis, limonene and pinene degradation, tryptophan metabolism and biosynthesis of secondary metabolite pathways. In contrast, the carotenoid biosynthesis, galactose metabolism, glycerolipid
metabolism and ABC transporters were enriched in the drought-sensitive group (Fig. S5C and D).
3.4. Construction of gene coexpression networks related to SRL
To investigate the gene regulatory network of SRL under WS conditions, we applied WGCNA to identify specific genes highly associated
with SRL. A total of 45 modules, with the number of genes ranging from
60 to 1,114, were obtained under WS conditions, as labeled with different colors in Fig. 5A. Analysis of the module-trait relationships revealed that two modules showed relatively higher correlations with SRL
under WS conditions, including the yellowgreen module (r = 0.79, P =
5.0 × 10−6) and the paleturquoise module (r = 0.52, P = 9.0 × 10−3)
(Fig. 5B).
In the yellowgreen module, 104 genes were strongly correlated with
SRL under WS conditions. In this module, the top five genes with the
highest connectivity were shown in Fig. 5C. These five genes, which
encode an acetamidase/formamidase family protein, an invertase cell
wall, 3-phosphoinositide-dependent protein kinase-1, a zinc finger
protein, and a bZIP-TF were identified as hub genes for the yellowgreen
module. Another five TF genes were identified in this module. To interpret the biological significance of the gene network in this module,
GO enrichment analysis was performed, and the most significantly
enriched GO term was hydrolase activity, acting on glycosyl bonds.
In the paleturquoise module, 10 of 119 genes encode TFs: there
3.5. Genome-wide association study
To identify candidate genes controlling SRL under WW and WS
conditions, a GWAS was performed in the natural population using
MLM. The first three PCA and the kinship matrix as covariates were fit
to the MLM to control spurious associations. No SNPs significantly associated with SRL were identified at a threshold of 4.1 × 10−6. In an
attempt to identify the overlaps between GWAS and RNA-seq analysis, a
relatively loose P-value threshold of 1.0 × 10−3 (suggestive) was applied to detect association signals. In total, 62 SNPs putatively associated with SRL were ultimately identified under the two water treatments, including 27 under WS and 35 under WW (Fig. 6). Based on the
whole-genome LD decay distance (245 Kb) (Fig. S6), we identified 22
and 31 genomic regions influencing SRL under WS and WW conditions,
respectively. Among these genomic regions, only one region could be
synchronously detected under both normal conditions and drought
stress. A total of 506 unique candidate genes (B73 RefGen_v4) were
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J. Guo, et al.
Fig. 5. Gene coexpression network and significant correlation modules. (A) Hierarchical cluster tree showing 45 modules of coexpressed genes. (B) Module-SRL
correlations and corresponding P-values. Gene coexpression network of the yellowgreen module (C) and paleturquoise module (D). The candidate hub genes and TFs
are shown in red and orange, respectively. The candidate genes identified by GWAS and coexpression network analyses are indicated by squares. (For interpretation
of the references to colour in this figure legend, the reader is referred to the web version of this article.)
(Zm00001d047752), a bifunctional inhibitor/lipid-transfer protein/
seed storage 2S albumin superfamily protein (Zm00001d003757), a
putative glycerol-3-phosphate transporter (Zm00001d049554), UDPglycosyltransferase 73D1 (Zm00001d044943) and a probable acyl-activating enzyme 5 peroxisomal (Zm00001d001849). In addition, two
genes were identified by both GWAS and coexpression network analysis
and were also considered as candidate genes for SRL:
Zm00001d002266, which encodes a lysine-specific histone demethylase as a hub gene in the paleturquoise module and Zm00001d049584,
which encodes a wall-associated receptor kinase in the yellowgreen
module (Table 1).
detected at these genomic loci, some of which were known to be related
to the drought response. For example, a known ABA signaling gene,
ZmPP2C12 (Zm00001d009626), negatively regulates drought tolerance
in maize [52], and overexpression of the MYB TF OsMYB6 (a homolog
of Zm00001d001837) increases drought tolerance and salt stress in
transgenic rice [53]. Zong et al. [54,55] also reported that OsbZIP23 (a
homolog of Zm00001d044940) acts as a central regulator of both ABA
signaling and biosynthesis and drought tolerance in rice.
3.6. Identification of candidate genes by integrating RNA-seq and GWAS
The potential candidate genes were prioritized by integrating DEGs
obtained by RNA-seq and GWAS. In total, among these candidate genes
identified by GWAS, 304 of 506 (60 %) were differentially expressed in
at least one genotype under WW and WS conditions. Among these
genes, five candidate genes were simultaneously detected as common
DEGs and by GWAS (Table 1), including those encoding an LBD TF
4. Discussion
4.1. Precise root phenotyping of plants growing in soil
Drought stress is a major limiting factor for crop production
7
Plant Science 292 (2020) 110380
J. Guo, et al.
Fig. 6. Manhattan plots of the results of the association analysis of SRL in the two treatments. The dotted blue line indicates the significance threshold (P-value 1.0 ×
10−3). The significant SNPs are labeled with red dots, and the outer and inner circles represent the WS and WW treatments, respectively. (For interpretation of the
references to colour in this figure legend, the reader is referred to the web version of this article.)
4.2. Seminal roots play a positive role in improving maize drought tolerance
at seedling stage
worldwide, and plant root system is of great significance in drought
tolerance [5]. Understanding the relationship between root architecture
and drought tolerance could help in the optimization of plant root
system to improve drought tolerance. Phenotypic characterization and
quantification of the root architecture of the plants growing in the soil
are major challenges. PVC tubes and plastic pots filled with soil are
often used to phenotype maize root traits under drought stress conditions. For this purpose, Li et al. [23] used round black plastic pots (18
cm in depth) to assess seedling roots of a maize recombinant inbred line
(RIL) population under WW and WS conditions, but found that the root
length of the maize seedlings was generally longer than the pot depth.
In our study, three plastic pots were placed in each PVC tube (60 cm in
depth), and each pot was filled with manually mixed soil and water.
This method helped both to guarantee consistent soil moisture content
in each pot and to measure root traits quickly and accurately. Despite
the labor-intensive root washing involved, the root phenotypes obtained by this method better approximate the actual root architecture in
the field than do those obtained by hydroponic methods.
Changes in plant root morphology as a response to drought stress
include a reduction in the number of crown roots [10] and lateral root
branching density [8] and an increase in the root length [9]. A previous
study has suggested that plants respond to drought stress by stimulating
or maintaining root growth while reducing shoot growth [56]. Previous
studies have shown that over time, water evaporates more from the soil
surface than from deep soil strata and that elongation of the roots occurs to extract deep soil water [57–59]. In our study, SRL and PRL increased significantly (P < 0.001) under WS treatment to absorb relatively deep water in the soil. The adaptive signature of root elongation
under drought conditions may regulate the response of seedling growth
to water stress. Our results showed that SRL was significantly correlated
with the DI under WS conditions (r = 0.32, P = 7.0×10−5), but no
such correlation was detected for PRL (Fig. 1B). In general, primary
root is vital during the first weeks after germination for early vigor of
young maize seedlings [60]. For the first 2–3 weeks after germination,
seminal roots compose the bulk of the seedling root system [61]. In
maize seedlings, relatively deep seminal roots may provide more water
and nutrients for growth than previously developed primary root under
8
Plant Science 292 (2020) 110380
UDP-glycosyltransferase 73D1
LBD-transcription factor 41
Probable acyl-activating enzyme 5 peroxisomal
Lysine-specific histone demethylase 1
Wall-associated receptor kinase 3
4.3. Comparative transcriptome analysis highlights the genotypic
distinctions
128097
21732
95149
42780
25210
down
down
down
–
–
WW
WW
WS
WS
WS
Zm00001d044943
Zm00001d047752
Zm00001d001849
Zm00001d002266
Zm00001d049584
Understanding the molecular mechanisms of seminal roots development is important for improving drought tolerance of maize seedlings. In the present study, transcriptome changes within four droughttolerant lines and four drought-sensitive lines were surveyed to gain
insight into the gene expression patterns in seminal roots in response to
water deficit. First, PCA of all the expressed genes revealed similar
transcriptome profiles among the four drought-tolerant lines or four
drought-sensitive lines under drought stress. Second, the functions of
the common DEGs in the drought-tolerant group were very different
from those in the drought-sensitive group (Table S3). Eight common
DEGs were identified only in the drought-tolerant group, including four
auxin-related genes and four ABA-related genes. Previous physiological
and genetic studies have suggested that auxin genes are involved
mainly in the regulation of root development and the response to
drought stress [63]. In our data set, the four genes associated with auxin
were ZmIAA15 (Zm00001d013707), iaa32 (Zm00001d018973),
mads45 (Zm00001d035053) and yuc2 (Zm00001d025005). Ludwig
et al. [64] suggested that ZmIAA15 interacts with rum1. The Aux/IAA
gene rum1 controls seminal roots and primary lateral root initiation
[25]. The phenotypic characteristics of both rice and Arabidopsis
transgenic plants overexpressing the OsMADS26 gene (a homolog of
Zm00001d035053) are similar to those in responses to stress, (i.e.,
chlorosis, cell death, pigment accumulation, and defective root/shoot
growth) [65]. In addition, the majority of ABA biosynthesis genes in
maize roots are significantly expressed only in the elongation zone of
roots under water stress [66]. The ABA-related genes abi41
(Zm00001d023446),
abh1
(Zm00001d017762),
aasr5
(Zm00001d025401) and aasr6 (Zm00001d016760) were identified in
this study. OsASR3 (a homolog of aasr5) reportedly responds to different abiotic stress factors including drought, salt, cold and low light,
is highly expressed in rice roots [67]. Consequently, these genes may be
important candidates for drought tolerance of maize seedlings.
9
9
2
2
4
36.44
36.49
35.20
17.86
19.50
GRMZM2G403740
GRMZM2G145568
GRMZM2G105330
GRMZM2G000052
GRMZM2G072350
4
12.90
GRMZM2G104942
134906
down
WW
Zm00001d049554
Bifunctional inhibitor/lipid-transfer protein/seed
storage 2S albumin superfamily protein
Putative glycerol-3-phosphate transporter 1
Zm00001d003757
WW
up
130835
GRMZM2G136364
2
30.73
P-value a from MLM.
Minor allele frequency.
Effect is direct report of "effect" from TASSEL output.
As main regulators of gene expression, TFs play a key role in
drought stress responses, and the members of many important TF families regulate stress responses in the roots [68]. Similarly, the members of many TF families, such as ZmNAC1 and ZmWRKY33, have been
reported to be associated with root elongation and drought tolerance in
maize [69,70], however, the exact function of most of these family
members is unknown. In our study, 59 TFs (41 in the drought-tolerant
lines and 18 in the drought-sensitive lines) were identified as common
DEGs (Fig. 4). Some TFs belonged to the same families, such as the
WRKY, MYB and AP2-EREBP families, which were overrepresented
among the common DEGs (Table S3). Several WRKY TFs were involved
in root growth and abiotic stress responses in Arabidopsis. For example,
the WRKY23 TF is needed for proper root growth and development in
Arabidopsis, and its expression is controlled by the auxin pathway [71].
The Arabidopsis gene WRKY75 (AT5G13080), which is orthologous to
Zm00001d049173, has been reported to modulate the phosphate starvation response and to negatively regulate lateral root and root hair
growth [72]. Additionally, MYB TFs have been reported to play crucial
c
b
0.14
0.14
0.08
0.27
0.42
4.06E-04
1.14E-04
1.73E-04
8.64E-04
7.13E-04
8228045
138535981
1825827
8953516
34711778
0.34
7.06E-04
33283963
PUT-163a-603450842531
PZE-109007607
PZE-109097755
SYN4138
SYN6126
PZE-104028783
0.05
1.16E-04
55818510
PZE-102075415
a
Gene ID (B73_V2)
Chr
Effectc
MAFb
Physical Position
(B73_V2,bp)
P-valuea
drought stress. Similarly, Li et al. [23] reported that the correlation
between SRL and SDW (r = 0.30) was greater than the correlation
between PRL and SDW (r = 0.25) under water stress. We also found
that SRN had a significant correlation with the DI (r = 0.23, P = 0.003)
under WS conditions (Fig. 1B). As noted by Qayyum et al. [62], the
number of seminal roots may correlate with drought tolerance at the
seedling stage in maize hybrids. Taken together, these findings indicate
that seminal roots have a great positive effect on maize seedlings under
water stress.
4.4. Transcription factors mediate drought responses
SNP
Table 1
Candidate genes simultaneously detected by transcriptome analysis and GWAS.
Distance to B73_V2
gene model (bp)
Common DEGs
Regulation
Treatment
Gene ID (B73_V4)
Description
J. Guo, et al.
9
Plant Science 292 (2020) 110380
J. Guo, et al.
Comparative transcriptome analyses of seminal roots provided a comprehensive overview of the transcriptome variations between droughttolerant lines and drought-sensitive lines, and revealed the gene expression patterns of seminal root elongation under drought stress condition. The integration of transcriptome sequencing and GWAS was
helpful for mining reliable loci or candidate genes, and determined
seven promising candidate genes for further study. These findings
would provide an important foundation for revealing the molecular
mechanism of maize seminal root development in response to drought
stress.
roles in regulatory networks controlling development and responses to
biotic and abiotic stresses [73], and overexpression of ZmMYB48 in
transgenic Arabidopsis enhanced drought tolerance via ABA signaling
[74]. The expression of Zm00001d015614 (an ortholog of AtMYB44),
which increases drought tolerance in transgenic Arabidopsis [75], was
upregulated in the drought-tolerant group. Similar to that which occurs
for the other TFs, some members of the AP2/EREBP-type TF family
regulated drought stress responses in the roots. The expression of AP2/
EREBP TFs is induced under drought treatment in the roots of both
common bean [76] and rice [77]. In maize, several genes expression of
the ZmDREB1 class were upregulated in the roots in response to drought
stress [78]. In addition, two significant modules (yellowgreen and paleturquoise) were identified by WGCNA as being associated with
seminal root growth. A total of 10 hub genes were identified in these
two modules, two of which encode TFs, i.e., Zm00001d024160, which
encodes a bZIP TF, and Zm00001d001824, which encodes a zinc finger
protein. TGA1, which is orthologous to Zm00001d024160, has been
shown to respond to nitrate treatment in Arabidopsis roots and is also
important in pathways leading to root hair development in response to
nitrate [79,80]. Overall, these TFs may play important roles in regulating root development and the response to drought.
Author contributions
CL, YuL, DY and TW conceived and designed the experiment and
revised the manuscript; JG, XZ, YuS, and YaS performed the field experiment and phenotype evaluation; JG, DZ and YoL collected and
analyzed the data; JG wrote the first draft of the manuscript; CL, YuL,
DY and TW modified the manuscript. All authors have read and approved the publication of the manuscript.
Declaration of Competing Interest
The authors declare that they have no competing interests.
4.5. Integrating transcriptome sequencing and GWAS for candidate gene
prediction
Acknowledgements
GWAS has been widely used to identify candidate genes underlying
important agronomic traits in maize. Rapid LD decay within a diverse
population could increase the mapping resolution, but variation in
complex genomic structure still limits the determination of target genes
in maize. Thus, the integration of RNA-seq and GWAS is a promising
strategy for exploiting candidate genes. By using this approach, we
identified 304 candidate genes associated with seminal root development. Five of these candidate genes, Zm00001d003757,
Zm00001d049554,
Zm00001d044943,
Zm00001d047752
and
Zm00001d001849, were simultaneously detected among the common
DEGs (Table 1). These represent promising candidate genes for future
studies, as supported by other lines of evidence. Remarkably, AtG3Pp4
is an orthologous gene of Zm00001d049554, which has been demonstrated to be involved in lateral root development under phosphate
stress in Arabidopsis [81]. Zm00001d047752 encodes an LBD-TF, and
LBD proteins have crucial functions in defining lateral organ boundaries
and are involved in shoot and seminal root formation in maize [24].
The other two candidate genes (Zm00001d002266 and
Zm00001d012330) were identified by the combination of GWAS and
WGCNA. Zm00001d002266 is an important hub gene in the coexpression network, and its orthologous gene AtPAO2 in Arabidopsis has
been reported to regulate responses to ABA treatment and enhance
primary and lateral root growth in media supplemented with NO3−or
NH4+ [82]. Members of the Arabidopsis PAO gene family exhibit distinct tissue-specific and expression patterns during seedling growth
[83]. AtWAK1 (a homolog of Zm00001d049584) interacts with
AtGRP3, and overexpression of AtGRP3 in Arabidopsis plants results in
shorter roots and increased aluminum tolerance [84]. Therefore, the
combined use of GWAS and differentially expressed genes will be
beneficial for identifying important candidate genes associated with
seminal root growth under drought stress. These findings may improve
our understanding of the mechanisms underlying seminal root adaptation to water scarcity and will accelerate future efforts aimed at improving maize drought tolerance.
This research was supported by the Programs of MOST and MOA of
China (2016YFD0101201), the National Natural Science Foundation of
China (31971891), Young Elite Scientists Sponsorship Program by
CAST (2016QNRC001), and the CAAS Innovation Program.
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.plantsci.2019.110380.
References
[1] J.S. Boyer, Plant productivity and environment, Science 218 (1982) 443–448.
[2] H.H. Hu, L.Z. Xiong, Genetic engineering and breeding of drought-resistant crops,
Annu. Rev. Plant Biol. 65 (2014) 715–741.
[3] D.B. Lobell, M.J. Roberts, W. Schlenker, N. Braun, B.B. Little, R.M. Rejesus,
G.L. Hammer, Greater sensitivity to drought accompanies maize yield increase in
the U.S. Midwest, Science 344 (2014) 516–519.
[4] Y. Coudert, C. Perin, B. Courtois, N.G. Khong, P. Gantet, Genetic control of root
development in rice, the model cereal, Trends Plant Sci. 15 (2010) 219–226.
[5] L.H. Comas, S.R. Becker, V.V. Cruz, P.F. Byrne, D.A. Dierig, Root traits contributing
to plant productivity under drought, Front. Plant Sci. 4 (2013).
[6] J.P. Lynch, T. Wojciechowski, Opportunities and challenges in the subsoil: pathways to deeper rooted crops, J. Exp. Bot. 66 (2015) 2199–2210.
[7] M. Rostamza, R.A. Richards, M. Watt, Response of millet and sorghum to a varying
water supply around the primary and nodal roots, Ann. Bot. 112 (2013) 439–446.
[8] A. Zhan, H. Schneider, J.P. Lynch, Reduced lateral root branching density improves
drought tolerance in maize, Plant Physiol. 168 (2015) 1603–1615.
[9] J.P. Lynch, Steep, cheap and deep: an ideotype to optimize water and N acquisition
by maize root systems, Ann. Bot. 112 (2013) 347–357.
[10] Y.Z. Gao, J.P. Lynch, Reduced crown root number improves water acquisition under
water deficit stress in maize (Zea mays L.), J. Exp. Bot. 67 (2016) 4545–4557.
[11] A.P. Wasson, R.A. Richards, R. Chatrath, S.C. Misra, S.V. Prasad, G.J. Rebetzke,
J.A. Kirkegaard, J. Christopher, M. Watt, Traits and selection strategies to improve
root systems and water uptake in water-limited wheat crops, J. Exp. Bot. 63 (2012)
3485–3498.
[12] J.P. Lynch, J.G. Chimungu, K.M. Brown, Root anatomical phenes associated with
water acquisition from drying soil: targets for crop improvement, J. Exp. Bot. 65
(2014) 6155–6166.
[13] Y. Uga, K. Sugimoto, S. Ogawa, J. Rane, M. Ishitani, N. Hara, Y. Kitomi, Y. Inukai,
K. Ono, N. Kanno, H. Inoue, H. Takehisa, R. Motoyama, Y. Nagamura, J. Wu,
T. Matsumoto, T. Takai, K. Okuno, M. Yano, Control of root system architecture by
DEEPER ROOTING 1 increases rice yield under drought conditions, Nat. Genet. 45
(2013) 1097–1102.
[14] M.L. Ali, J. Luetchens, J. Nascimento, T.M. Shaver, G.R. Kruger, A.J. Lorenz,
Genetic variation in seminal and nodal root angle and their association with grain
yield of maize under water-stressed field conditions, Plant Soil 397 (2015)
213–225.
5. Conclusions
In the present study, we accurately phenotyed the root architecture
and drought tolerance index of a diverse maize association panel at
seedling stage, and found that the seminal root length was more conducive to improving drought tolerance than the primary root length.
10
Plant Science 292 (2020) 110380
J. Guo, et al.
[43] C. Trapnell, B.A. Williams, G. Pertea, A. Mortazavi, G. Kwan, M.J. van Baren,
S.L. Salzberg, B.J. Wold, L. Pachter, Transcript assembly and quantification by RNAseq reveals unannotated transcripts and isoform switching during cell differentiation, Nat. Biotechnol. 28 (2010) 511–515.
[44] S. Anders, D.J. McCarthy, Y.S. Chen, M. Okoniewski, G.K. Smyth, W. Huber,
M.D. Robinson, Count-based differential expression analysis of RNA sequencing
data using R and Bioconductor, Nat. Protoc. 8 (2013) 1765–1786.
[45] R. Bi, P. Liu, Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments, BMC Bioinform. 17
(2016).
[46] M.D. Young, M.J. Wakefield, G.K. Smyth, A. Oshlack, Gene ontology analysis for
RNA-seq: accounting for selection bias, Genome Biol. 11 (2010).
[47] C. Xie, X.Z. Mao, J.J. Huang, Y. Ding, J.M. Wu, S. Dong, L. Kong, G. Gao, C.Y. Li,
L.P. Wei, KOBAS 2.0: a web server for annotation and identification of enriched
pathways and diseases, Nucleic Acids Res. 39 (2011) W316–W322.
[48] P. Perez-Rodriguez, D.M. Riano-Pachon, L.G. Correa, S.A. Rensing, B. Kersten,
B. Mueller-Roeber, PlnTFDB: updated content and new features of the plant transcription factor database, Nucleic Acids Res. 38 (2010) D822–7.
[49] P. Langfelder, S. Horvath, WGCNA: an R package for weighted correlation network
analysis, BMC Bioinform. 9 (2008).
[50] P.J. Bradbury, Z. Zhang, D.E. Kroon, T.M. Casstevens, Y. Ramdoss, E.S. Buckler,
TASSEL: software for association mapping of complex traits in diverse samples,
Bioinformatics 23 (2007) 2633–2635.
[51] X. Gao, J. Starmer, E.R. Martin, A multiple testing correction method for genetic
association studies using correlated single nucleotide polymorphisms, Genet.
Epidemiol. 32 (2008) 361–369.
[52] Y.G. Wang, F.L. Fu, H.Q. Yu, T. Hu, Y.Y. Zhang, Y. Tao, J.K. Zhu, Y. Zhao, W.C. Li,
Interaction network of core ABA signaling components in maize, Plant Mol. Biol. 96
(2018) 245–263.
[53] Y.H. Tang, X.X. Bao, Y.L. Zhi, Q. Wu, Y.R. Guo, X.H. Yin, L.Q. Zeng, J. Li, J. Zhang,
W.L. He, W.H. Liu, Q.W. Wang, C.K. Jia, Z.K. Li, K. Liu, Overexpression of a MYB
Family Gene, OsMYB6, Increases drought and salinity stress tolerance in transgenic
rice, Front. Plant Sci. 10 (2019).
[54] W. Zong, N. Tang, J. Yang, L. Peng, S.Q. Ma, Y. Xu, G.L. Li, L.Z. Xiong, Feedback
Regulation of ABA signaling and biosynthesis by a bZIP transcription factor targets
Drought-Resistance-Related Genes, Plant Physiol. 171 (2016) 2810–2825.
[55] W. Zong, J. Yang, J. Fu, L. Xiong, Synergistic regulation of drought-responsive
genes by transcription factor OsbZIP23 and histone modification in rice, J. Integr.
Plant Biol. (2019).
[56] A.J. Bloom, F.S. Chapin, H.A. Mooney, Resource limitation in plants-an economic
analogy, Annu. Rev. Ecol. Syst. 16 (1985) 363–392.
[57] J.P. Lynch, Rightsizing root phenotypes for drought resistance, J. Exp. Bot. 69
(2018) 3279–3292.
[58] M.C. Sanguineti, S. Li, M. Maccaferri, S. Corneti, F. Rotondo, T. Chiari, R. Tuberosa,
Genetic dissection of seminal root architecture in elite durum wheat germplasm,
Ann. Appl. Biol. 151 (2007) 291–305.
[59] J.L. Araus, G.A. Slafer, C. Royo, M.D. Serret, Breeding for yield potential and stress
adaptation in cereals, CRC. Crit. Rev. Plant Sci. 27 (2008) 377–412.
[60] R. Peter, T.W. Eschholz, P. Stamp, M. Liedgens, Early growth of flint maize landraces under cool conditions, Crop Sci. 49 (2009) 169–178.
[61] L. Feldman, The maize root, in: M. Freeling, V. Walbot (Eds.), The Maize Handbook,
Springer, Berlin Heidelberg New York, 1994, pp. 29–37.
[62] A. Qayyum, S. Ahmad, S. Liaqat, W. Malik, E. Noor, H.M. Saeed, M. Hanif,
Screening for drought tolerance in maize (Zea mays L.) hybrids at an early seedling
stage, Afr. J. Agric. Res. 7 (2012) 3594–3604.
[63] J. Lavenus, T. Goh, I. Roberts, S. Guyomarc’h, M. Lucas, I. De Smet, H. Fukaki,
T. Beeckman, M. Bennett, L. Laplaze, Lateral root development in Arabidopsis: fifty
shades of auxin, Trends Plant Sci. 18 (2013) 455–463.
[64] Y. Ludwig, K.W. Berendzen, C.Z. Xu, H.P. Piepho, F. Hochholdinger, Diversity of
stability, localization, interaction and control of downstream gene activity in the
maize Aux/IAA protein family, PLoS One 9 (2014).
[65] S. Lee, Y.M. Woo, S.I. Ryu, Y.D. Shin, W.T. Kim, K.Y. Park, I.J. Lee, G. An, Further
characterization of a rice AGL12 group MADS-box gene, OsMADS26, Plant Physiol.
147 (2008) 156–168.
[66] L. Ernst, J.Q.D. Goodger, S. Alvarez, E.L. Marsh, B. Berla, E. Lockhart, J. Jung,
P.H. Li, H.J. Bohnert, D.P. Schachtman, Sulphate as a xylem-borne chemical signal
precedes the expression of ABA biosynthetic genes in maize roots, J. Exp. Bot. 61
(2010) 3395–3405.
[67] J. Joo, Y.H. Lee, Y.K. Kim, B.H. Nahm, S.I. Song, Abiotic stress responsive rice ASR1
and ASR3 exhibit different tissue-dependent sugar and hormone-sensitivities, Mol.
Cells 35 (2013) 421–435.
[68] A. Janiak, M. Kwasniewski, I. Szarejko, Gene expression regulation in roots under
drought, J. Exp. Bot. 67 (2016) 1003–1014.
[69] J. Li, G.H. Guo, W.W. Guo, G.G. Guo, D. Tong, Z.F. Ni, Q.X. Sun, Y.Y. Yao,
miRNA164-directed cleavage of ZmNAC1 confers lateral root development in maize
(Zea mays L.), BMC Plant Biol. 12 (2012).
[70] H. Li, Y. Gao, H. Xu, Y. Dai, D.Q. Deng, J.M. Chen, ZmWRKY33, a WRKY maize
transcription factor conferring enhanced salt stress tolerances in Arabidopsis, Plant
Growth Regul. 70 (2013) 207–216.
[71] W. Grunewald, I. De Smet, D.R. Lewis, C. Lofke, L. Jansen, G. Goeminne,
R.V. Bossche, M. Karimi, B. De Rybel, B. Vanholme, T. Teichmann, W. Boerjan,
M.C.E. Van Montagu, G. Gheysen, G.K. Muday, J. Friml, T. Beeckman, Transcription
factor WRKY23 assists auxin distribution patterns during Arabidopsis root development through local control on flavonol biosynthesis, Proc. Natl. Acad. Sci. U. S. A.
109 (2012) 1554–1559.
[72] B.N. Devaiah, A.S. Karthikeyan, K.G. Raghothama, WRKY75 transcription factor is a
[15] C.A. Richard, L.T. Hickey, S. Fletcher, R. Jennings, K. Chenu, J.T. Christopher,
High-throughput phenotyping of seminal root traits in wheat, Plant Methods 11
(2015) 13.
[16] A. Hamada, M. Nitta, S. Nasuda, K. Kato, M. Fujita, H. Matsunaka, Y. Okumoto,
Novel QTLs for growth angle of seminal roots in wheat (Triticum aestivum L.), Plant
Soil 354 (2012) 395–405.
[17] H. Robinson, L. Hickey, C. Richard, E. Mace, A. Kelly, A. Borrell, J. Franckowiak,
G. Fox, Genomic regions influencing seminal root traits in barley, Plant Genome 9
(2016).
[18] F. Hochholdinger, W.J. Park, M. Sauer, K. Woll, From weeds to crops: genetic
analysis of root development in cereals, Trends Plant Sci. 9 (2004) 42–48.
[19] F. Hochholdinger, R. Zimmermann, Conserved and diverse mechanisms in root
development, Curr. Opin. Plant Biol. 11 (2008) 70–74.
[20] J.M. Zhu, S.M. Mickelson, S.M. Kaeppler, J.P. Lynch, Detection of quantitative trait
loci for seminal root traits in maize (Zea mays L.) seedlings grown under differential
phosphorus levels, Theor. Appl. Genet. 113 (2006) 1–10.
[21] S. Morita, J. Abe Modeling root system morphology in rice Davis TD, Haissig BE
Biology of adventitious root formation New York Springer 191–202.
[22] V. Singh, E.J. van Oosterom, D.R. Jordan, C.D. Messina, M. Cooper, G.L. Hammer,
Morphological and architectural development of root systems in sorghum and
maize, Plant Soil 333 (2010) 287–299.
[23] P. Li, Y. Zhang, S. Yin, P. Zhu, T. Pan, Y. Xu, J. Wang, D. Hao, H. Fang, C. Xu,
Z. Yang, QTL-by-environment interaction in the response of maize root and shoot
traits to different water regimes, Front. Plant Sci. 9 (2018) 229.
[24] G. Taramino, M. Sauer, J.L. Stauffer, D. Multani, X.M. Niu, H. Sakai,
F. Hochholdinger, The maize (Zea mays L.) RTCS gene encodes a LOB domain
protein that is a key regulator of embryonic seminal and post-embryonic shootborne root initiation, Plant J. 50 (2007) 649–659.
[25] I. von Behrens, M. Komatsu, Y.X. Zhang, K.W. Berendzen, X.M. Niu, H. Sakai,
G. Taramino, F. Hochholdinger, Rootless with undetectable meristem 1 encodes a
monocot-specific AUX/IAA protein that controls embryonic seminal and post-embryonic lateral root initiation in maize, Plant J. 66 (2011) 341–353.
[26] M. Suzuki, Y. Sato, S. Wu, B.H. Kang, D.R. McCarty, Conserved functions of the
MATE transporter BIG EMBRYO1 in regulation of lateral organ size and initiation
rate, Plant Cell 27 (2015) 2288–2300.
[27] P.C. Li, F.J. Chen, H.G. Cai, J.C. Liu, Q.C. Pan, Z.G. Liu, R.L. Gu, G.H. Mi,
F.S. Zhang, L.X. Yuan, A genetic relationship between nitrogen use efficiency and
seedling root traits in maize as revealed by QTL analysis, J. Exp. Bot. 66 (2015)
3175–3188.
[28] W.B. Song, B.B. Wang, A.L. Hauck, X.M. Dong, J.P. Li, J.S. Lai, Genetic dissection of
maize seedling root system architecture traits using an ultra-high density bin-map
and a recombinant inbred line population, J. Integr. Plant Biol. 58 (2016) 266–279.
[29] Z.G. Liu, K. Gao, S. Shan, R.C. Gu, Z.K. Wang, E.J. Craft, G.H. Mi, L.X. Yuan,
F.J. Chen, Comparative analysis of root traits and the associated QTLs for maize
seedlings grown in paper poll, hydroponics and vermiculite culture system, Front.
Plant Sci. 8 (2017).
[30] J. Pace, C. Gardner, C. Romay, B. Ganapathysubramanian, T. Lubberstedt, Genomewide association analysis of seedling root development in maize (Zea mays L.), BMC
Genom. 16 (2015).
[31] P.H. Zaidi, K. Seetharam, G. Krishna, L. Krishnamurthy, S. Gajanan, R. Babu,
M. Zerka, M.T. Vinayan, B.S. Vivek, Genomic regions associated with root traits
under drought stress in tropical maize (Zea mays L.), PLoS One 11 (2016).
[32] D.L. Sanchez, S.S. Liu, R. Ibrahim, M. Blanco, T. Lubberstedt, Genome-wide association studies of doubled haploid exotic introgression lines for root system architecture traits in maize (Zea mays L.), Plant Sci. 268 (2018) 30–38.
[33] H.H. Tai, X. Lu, N. Opitz, C. Marcon, A. Paschold, A. Lithio, D. Nettleton,
F. Hochholdinger, Transcriptomic and anatomical complexity of primary, seminal,
and crown roots highlight root type-specific functional diversity in maize (Zea mays
L.), J. Exp. Bot. 67 (2016) 1123–1135.
[34] N. Opitz, A. Paschold, C. Marcon, W.A. Malik, C. Lanz, H.P. Piepho,
F. Hochholdinger, Transcriptomic complexity in young maize primary roots in response to low water potentials, BMC Genom. 15 (2014).
[35] N. Opitz, C. Marcon, A. Paschold, W.A. Malik, A. Lithio, R. Brandt, H.P. Piepho,
D. Nettleton, F. Hochholdinger, Extensive tissue-specific transcriptomic plasticity in
maize primary roots upon water deficit, J. Exp. Bot. 67 (2016) 1095–1107.
[36] S.G. Hwang, K.H. Kim, B.M. Lee, J.C. Moon, Transcriptome analysis for identifying
possible gene regulations during maize root emergence and formation at the initial
growth stage, Genes Genom. 40 (2018) 755–766.
[37] R.S. Sekhon, C. Saski, R. Kumar, B. Flinn, F. Luo, T.M. Beissinger, A.J. Ackerman,
M.W. Breitzman, W.C. Bridges, N. de Leon, S.M. Kaeppler, Integrated genome-scale
analysis identifies novel genes and networks underlying senescence in maize, Plant
Cell (2019).
[38] G.P. Zhang, N. Xu, H.L. Chen, G.X. Wang, J.L. Huang, OsMADS25 regulates root
system development via auxin signalling in rice, Plant J. 95 (2018) 1004–1022.
[39] J.Y. Chen, L. Xu, Y.L. Cai, J. Xu, QTL mapping of phosphorus efficiency and relative
biologic characteristics in maize (Zea mays L.) at two sites, Plant Soil 313 (2008)
251–266.
[40] J. Lan, Comparison of evaluating methods for agronomic drought tolerance in
crops, Acta Agric. Boreali-Occidentalis Sin. 7 (1998) 85–87.
[41] Y. Xue, M.L. Warburton, M. Sawkins, X. Zhang, T. Setter, Y. Xu, P. Grudloyma,
J. Gethi, J.M. Ribaut, W. Li, X. Zhang, Y. Zheng, J. Yan, Genome-wide association
analysis for nine agronomic traits in maize under well-watered and water-stressed
conditions, Theor. Appl. Genet. 126 (2013) 2587–2596.
[42] T. Kyndt, S. Denil, A. Haegeman, G. Trooskens, T. De Meyer, W. Van Criekinge,
G. Gheysen, Transcriptome analysis of rice mature root tissue and root tips in early
development by massive parallel sequencing, J. Exp. Bot. 63 (2012) 2141–2157.
11
Plant Science 292 (2020) 110380
J. Guo, et al.
[73]
[74]
[75]
[76]
[77]
[78]
[79] J.M. Alvarez, E. Riveras, E.A. Vidal, D.E. Gras, O. Contreras-Lopez, K.P. Tamayo,
F. Aceituno, I. Gomez, S. Ruffel, L. Lejay, X. Jordana, R.A. Gutierrez, Systems approach identifies TGA1 and TGA4 transcription factors as important regulatory
components of the nitrate response of Arabidopsis thaliana roots, Plant J. 80 (2014)
1–13.
[80] J. Canales, O. Contreras-Lopez, J.M. Alvarez, R.A. Gutierrez, Nitrate induction of
root hair density is mediated by TGA1/TGA4 and CPC transcription factors in
Arabidopsis thaliana, Plant J. 92 (2017) 305–316.
[81] M. Ramaiah, A. Jain, J.C. Baldwin, A.S. Karthikeyan, K.G. Raghothama,
Characterization of the phosphate starvation-induced glycerol-3-phosphate permease gene family in Arabidopsis, Plant Physiol. 157 (2011) 279–291.
[82] R. Wimalasekera, F. Schaarschmidt, R. Angelini, A. Cona, P. Tavladoraki,
G.F.E. Scherer, POLYAMINE OXIDASE2 of Arabidopsis contributes to ABA mediated
plant developmental processes, Plant Physiol. Biochem. 96 (2015) 231–240.
[83] P. Fincato, P.N. Moschou, A. Ahou, R. Angelini, K.A. Roubelakis-Angelakis,
R. Federico, P. Tavladoraki, The members of Arabidopsis thaliana PAO gene family
exhibit distinct tissue- and organ-specific expression pattern during seedling growth
and flower development, Amino Acids 42 (2012) 831–841.
[84] A. Mangeon, A.D. Menezes-Salgueiro, G. Sachetto-Martins, Start me up: revision of
evidences that AtGRP3 acts as a potential switch for AtWAK1, Plant Signal. Behav.
12 (2017).
modulator of phosphate acquisition and root development in arabidopsis, Plant
Physiol. 143 (2007) 1789–1801.
C. Dubos, R. Stracke, E. Grotewold, B. Weisshaar, C. Martin, L. Lepiniec, MYB
transcription factors in Arabidopsis, Trends Plant Sci. 15 (2010) 573–581.
Y. Wang, Q.Q. Wang, M.L. Liu, C. Bo, X. Wang, Q. Ma, B.J. Cheng, R.H. Cai,
Overexpression of a maize MYB48 gene confers drought tolerance in transgenic
arabidopsis plants, J. Plant Biol. 60 (2017) 612–621.
C. Jung, J.S. Seo, S.W. Han, Y.J. Koo, C.H. Kim, S.I. Song, B.H. Nahm, Y. Do Choi,
J.J. Cheong, Overexpression of AtMYB44 enhances stomatal closure to confer
abiotic stress tolerance in transgenic Arabidopsis, Plant Physiol. 146 (2008)
623–635.
G.H. Recchia, D.G.G. Caldas, A.L.A. Beraldo, M.J. da Silva, S.M. Tsai,
Transcriptional Analysis of drought-induced genes in the roots of a tolerant genotype of the common bean (Phaseolus vulgaris L.), Int. J. Mol. Sci. 14 (2013)
7155–7179.
A.R. Rabello, C.M. Guimaraes, P.H.N. Rangel, F.R. da Silva, D. Seixas, E. de Souza,
A.C.M. Brasileiro, C.R. Spehar, M.E. Ferreira, A. Mehta, Identification of droughtresponsive genes in roots of upland rice (Oryza sativa L), BMC Genom. 9 (2008).
S.X. Liu, X.L. Wang, H.W. Wang, H.B. Xin, X.H. Yang, J.B. Yan, J.S. Li, L.S.P. Tran,
K. Shinozaki, K. Yamaguchi-Shinozaki, F. Qin, Genome-wide analysis of ZmDREB
genes and their association with natural variation in drought tolerance at seedling
stage of Zea mays L, PLoS Genet. 9 (2013).
12