Original Research
07 October 2022
DOI 10.3389/fpls.2022.1019169
TYPE
PUBLISHED
OPEN ACCESS
EDITED BY
Jiehua Qiu,
China National Rice Research Institute,
(CASS), China
Natural variation in growth
and leaf ion homeostasis in
response to salinity stress in
Panicum hallii
REVIEWED BY
Yuri Shavrukov,
Flinders University, Australia
Lin Shao,
Yunnan University, China
Taslima Haque*, Govinal Badiger Bhaskara, Jun Yin,
Jason Bonnette and Thomas E. Juenger*
*CORRESPONDENCE
Taslima Haque
taslima@utexas.edu
Thomas E. Juenger
tjuenger@austin.utexas.edu
Department of Integrative Biology, University of Texas at Austin, Austin, TX, United States
SPECIALTY SECTION
Soil salinity can negatively impact plants growth, development and fitness. Natural
plant populations restricted to coastal environments may evolve in response to saline
habitats and therefore provide insights into the process of salinity adaptation. We
investigated the growth and physiological responses of coastal and inland populations
of Panicum hallii to experimental salinity treatments. Coastal genotypes demonstrated
less growth reduction and superior ion homeostasis compared to the inland
genotypes in response to saline conditions, supporting a hypothesis of local
adaptation. We identified several QTL associated with the plasticity of belowground
biomass, leaf sodium and potassium content, and their ratio which underscores the
genetic variation present in this species for salinity responses. Genome-wide
transcriptome analysis in leaf and root tissue revealed tissue specific overexpression
of genes including several cation transporters in the coastal genotype. These
transporters mediate sodium ion compartmentalization and potassium ion retention
and thus suggests that maintenance of ionic homeostasis of the coastal genotypes
might be due to the regulation of these ion transporters. These findings contribute to
our understanding of the genetics and molecular mechanisms of salinity adaptation in
natural populations, and widens the scope for genetic manipulation of these candidate
genes to design plants more resilient to climate change.
This article was submitted to
Plant Systematics and Evolution,
a section of the journal
Frontiers in Plant Science
14 August 2022
08 September 2022
PUBLISHED 07 October 2022
RECEIVED
ACCEPTED
CITATION
Haque T, Bhaskara GB, Yin J,
Bonnette J and Juenger TE (2022)
Natural variation in growth and leaf ion
homeostasis in response to salinity
stress in Panicum hallii.
Front. Plant Sci. 13:1019169.
doi: 10.3389/fpls.2022.1019169
COPYRIGHT
© 2022 Haque, Bhaskara, Yin, Bonnette
and Juenger. This is an open-access
article distributed under the terms of
the Creative Commons Attribution
License (CC BY). The use, distribution
or reproduction in other forums is
permitted, provided the original
author(s) and the copyright owner(s)
are credited and that the original
publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or
reproduction is permitted which does
not comply with these terms.
KEYWORDS
adaptation, ion transporter, natural variation, QTL, transcriptome remodeling,
ion homeostasis
1 Introduction
Plants experience a host of complex environmental stresses such as soil salinity,
drought, and high temperature in their natural habitats that can impact their growth,
physiology, and ultimately their fitness. Plant populations may over time evolve
adaptations that maintain fitness in the face of stressful local environmental conditions
Frontiers in Plant Science
01
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
populations of Mimulus guttatus exhibit tissue tolerance
(Karrenberg et al., 2006; Lowry et al., 2009). Natural
populations of Arabidopsis exhibit osmotic adjustment which
confers Na+ tolerance (Rus et al., 2006; Baxter et al., 2010). Pires
et al. (2015) found no specific salinity tolerance mechanism was
predominant among diverse rice natural populations and that
tolerance mechanisms were not mutually exclusive. In the grass
family salt tolerance evolves more frequently in C4 grass
lineages, likely due to existing predisposition of traits that
facilitate tolerance mechanisms (Bromham, 2014; Bromham
and Bennett, 2014). Several empirical studies of salt tolerant
C4 grasses reported superior ion homeostasis relative to more
sensitive counterparts (Marcum and Murdoch, 1994; Fortmeier
and Schubert, 1995; Pittaro et al., 2016). Hence, natural
populations of C4 grasses which are native to saline habitats
provide a valuable study system for adaptation to
saline conditions.
Coastal population of the C4 perennial grass, Panicum hallii,
experience higher soil salinity relative to inland populations that
are typically found in seasonally water limited xeric habitats. P.
hallii is widely distributed across the southwestern parts of the
USA and northern Mexico and demonstrates substantial
phenotypic and genetic divergence between the inland (P.
hallii var. hallii) and coastal ecotypes (P. hallii var filipes)
(Lowry et al., 2015; Lovell et al., 2018; Palacio-Mejı́a et al.,
2021). Regulatory element evolution and gene expression
divergence in response to drought has been detected in P.
hallii, and underscores the significant contribution of drought
stress as a driver of local adaptation in this species (Lovell et al.,
2016; Lovell et al., 2018). However, the potential impact of soil
salinity to adaptation in the coastal ecotype, and the likely
presence of cross tolerance of inland population adaptation to
more xeric habitats remains unexplored. In this study, we aim to
i) evaluate ecotype and genotype specific phenotypic responses
to experimental salinity treatment, ii) identify the genomic
region contributing to salinity response, and iii) detect
genotype specific transcriptome remodeling during salinity
stress and prioritize candidate genes for such response.
(Leimu and Fischer, 2008; Joe Hereford, 2009). Coastal habitats
are often exposed to elevated soil salinity due to repeated
inundation of soil by sea water, salt water spray, or saltwater
intrusion to the fresh water table. In such habitats, salinity can
act as a major driver of local adaptation (Lexer et al., 2003;
Baxter et al., 2010; Busoms et al., 2015; Busoms et al., 2018). The
underlying genetic basis and evolved molecular mechanisms for
salinity adaptation have become of great interest due to the
adverse effects of recent climate changes and risks of sea level
rise. However, these studies are mostly limited to model plants
and certain crops (Qiu et al., 2002; Ren et al., 2005; Davenport
et al., 2007; Tiwari et al., 2016; Haque et al., 2020; Atieno et al.,
2021) and are seldom conducted in natural populations
(although see Lexer et al., 2003; Rus et al., 2006; Lowry et al.,
2009; Baxter et al., 2010; Busoms et al., 2018). Studies in natural
populations can facilitate the identification of novel genetic
variation and our understanding of the underlying adaptive
molecular mechanisms for salinity stress.
Plant adaptations to salinity can be categorized into three
distinct tolerance mechanisms: osmotic stress tolerance, ion
exclusion, and the tolerance of tissue to accumulated ions.
(Munns and Tester, 2008; Roy et al., 2014). The osmotic
tolerance mechanism is triggered before leaf Na+ accumulation
by long distance signaling. In Arabidopsis, the hyperosmolaritygated calcium channel (OSCA1) was identified as a putative
hyperosmotic stress sensor that triggers cytosolic Ca2+ which
subsequently induces stomatal closure (Zhang et al., 2022). In
ion exclusion, Na+ transport processes in roots reduce the
accumulation of toxic concentrations of ions within leaves and
helps to maintain cellular ion homeostasis. Lastly, general tissue
tolerance compartmentalizes excessive sodium ions into older
leaf tissues and vacuoles. Cytosolic Na+ to K+ ratio can be a
major determinant of salinity tolerance (Dvořak et al., 1994;
Maathuis and Amtmann, 1999; Cuin et al., 2003; Colmer et al.,
2006). The optimal cytosolic ratio of Na+ to K+ can be
maintained by restricting Na+ accumulation in tissues, by
retaining K+ inside the cell, or by both processes (Shabala and
Cuin, 2008). Candidate genes responsible for ion homeostasis
have been studied in many model plants and economically
important crops. For instance, the high affinity potassium
transporter (HKT) in Arabidopsis (HKT1;1), rice (HKT1;5)
and wheat (HKT1;5) is involved in the retrieval of Na+ from
xylem (Davenport et al., 2005; Ren et al., 2005; James et al., 2006;
Davenport et al., 2007). Conversely, KT/HAK/KUP family
potassium transporters have been reported to play a role in
maintaining potassium homeostasis and help to confer salinity
tolerance in rice (Obata et al., 2007; Chen et al., 2015; Shen
et al., 2015).
Salinity tolerance has evolved independently among many
different plant clades. For example, salt tolerant genotypes from
natural populations of rice and rye grass maintain relatively low
leaf Na+ concentration (Platten et al., 2013; Tang et al., 2013)
and the natural hybrid species Helianthus paradoxus and coastal
Frontiers in Plant Science
2 Method
2.1 Response to salinity treatment in
ecotypes of P. hallii (Experiment 1)
The first study was focused on exploring the broad pattern of
responses to salinity by comparing natural accessions of P. hallii
obtained from collections across Texas and New Mexico (Gould
et al., 2018; Lovell et al., 2018; Palacio-Mejı́a et al., 2021). Here,
seed from individual maternal lines were obtained from field
collection and seed was bulked under greenhouse conditions in
Austin Texas (Supplementary Figure S1). P. hallii is
predominantly self-fertilized and exhibits high coefficients of
02
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
(2 treatment levels x 2 genotypes x 12 biological replicates = 48
plants). These plants were established in a split-plot design. Blocks
were randomly assigned to one level of treatment and genotypes
were nested in each block. Plant growth condition and treatment
application were carried out in the same manner described for the
population study except this time we applied a different
concentration of NaCl solution progressively (100 mM, 200
mM and 300 mM). Based on our results from Experiment 1, a
moderately higher level of salinity stress was chosen to test for the
response at the genotype level. On 37th DAS aboveground fresh
biomass, belowground fresh biomass, the ratio of fresh biomass
(aboveground/belowground), leaf relative water content, leaf
water potential, leaf sodium content (Na+), leaf potassium
content (K+), and the ratio (Na+/K+) were measured. Leaf water
potential was measured by using a Scholander-type pressure
chamber (PMS Instruments Company, Albany, OR). Detailed
protocols for measuring leaf osmotic potential, leaf relative water
content, and ion contents were provided in the Supplementary
Method S1.
inbreeding. As such, these accessions exhibit very low
heterozygosity and highly inbred lines (mean inbreeding
coefficient, FIS = 0.895) (Lowry et al., 2015; Palacio-Mejı́a et al.,
2021). To test for the response to salinity between the inland and
coastal ecotypes, ten inbred genotypes from each ecotype were
chosen for study in a factorial experiment (Supplementary Table
S1). Replicates of this plant material were exposed to a realistic soil
salinity stress treatment (20 genotypes × 2 treatment levels × 5
biological replicates = 200 plants) in a split-plot design. Blocks were
randomly assigned to one level of treatment and all genotypes were
nested in each block. First, seeds were treated at 65°C for three days
to break dormancy, on the fourth day were sown on soil in
individual pots (6:1:1 homogenous mixture of Promix: Turface:
Profile) and seedlings were grown in a controlled growth chamber
at 28/25°C for 12hr/12hr day/night cycle. Irrigation was
implemented with bottom watering using a nutrient solution (see
Supplementary Method S1, method section 1.2 for detailed
methods) on alternative days. Pots were thinned on the 5th day
after sowing (DAS) and only one healthy seedling was kept in each
pot for the experiment. On that day, pots lacking a healthy seedling
were discarded and any genotype which failed to germinate for at
least 3 replicates in each genotype x treatment combination were
removed from the experiment. This filtering resulted in 9 genotypes
from the coastal group and 8 genotypes from the inland group
(Supplementary Table S1). Plants designated for the salinity stress
treatment received a concentrated NaCl solution (prepared in
nutrient solution) progressively (83 mM, 167 mM and 250 mM)
starting at 31st DAS while the control group received only nutrient
solution. This treatment was continued for six days (nutrient/
salinity solution was applied on each alternative day at 31st, 33th,
and 35th DAS) until leaf relative water content, aboveground
biomass, and belowground biomass were measured on 37th DAS
(Supplementary Method S1 for detailed methods). A salt tolerance
index was derived as the ratio of mean trait values for salinity treated
plants to control plants for a given genotype for aboveground and
belowground biomass. During the course of salinity treatment, the
soil salinity of each pot was measured at a depth of ~1 inch using a
field electrical conductivity (EC) meter (Spectrum Technologies)
after 2 h of treatment application. On the 35th DAS mean soil
salinity for treatment pots was 12.9 deciSiemens/m (dS/m) which is
equivalent to ~130 mM NaCl solution and often considered
moderate salinity stress for many species (Munns and Tester,
2008; Isayenkov and Maathuis, 2019).
2.3 Response to salinity of P. hallii RIL
population (Experiment 3)
To further study the genetic basis of salinity responses in P.
hallii, we implemented a QTL mapping study using recombinant
inbred lines (RILs) from a cross between P. hallii var. FIL2 and P.
hallii var. HAL2. This mapping population consists of 380 RILs
developed by single seed descent (Khasanova et al., 2019). This
QTL mapping experiment was based on a randomized block
design and allocated 3 replicates of each RIL to a control or
salinity treatment. Given the size and scope of the experiment,
the experiment was blocked in time by splitting the experiment
into 3 cohorts with each cohort containing a full set of the
mapping population plus 10 replicates of each parental genotype
(each cohort comprised of 380 RILs + 2 parents x 10 replicates] x
2 treatment levels = 800 plants; 3 cohorts x 800 plants = 2400
plants total). Plant growth and treatment application was carried
out as described for the population salinity response experiment
except this experiment was carried out in a controlled
greenhouse at the University of Texas at Austin in spring 2019
with natural light providing long day conditions (~14h/10 h day/
night). Each day temperature was recorded at one-minute
intervals and the mean recorded temperature was 28/25°C
during day/night. Plants were harvested at 37th DAS and
aboveground biomass (AGB), belowground biomass (BGB),
the ratio of aboveground to belowground biomass (RB), leaf
relative water content (RWC), Na+, K+, and the ratio (Na+/K+)
were measured (see Supplementary Method S1 detailed
methods). Given the cost and labor associated with obtaining
sodium and potassium ion content of our large experiment, only
the salinity treated samples of the first cohort were considered
for these two traits.
2.2 Response to salinity of two genotype
(Experiment 2)
Detailed physiological and growth response of plants to the
salinity treatment were tested on two genotypes of P. hallii [one
representative genotype of the inland hallii ecotype (HAL2) and
one representative genotype of the coastal filipes ecotype (FIL2)]
with 12 replicates in each genotype x treatment combination
Frontiers in Plant Science
03
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
2.4 Statistical analysis for phenotypes
QTLConstitutive: Ymean (mean of control and stress for each
RIL) = µ + QTL + cohort (if applicable) + QTL x cohort
(if applicable) + error
Linear mixed models were fitted using the lmer package
from R software to analyze the factorial experimental design
(Bates et al., 2015). The ecotype, treatment and the interaction of
ecotype x treatment were considered as fixed effects and the
effect of block, the effect of block nested in treatment and the
effect of genotypes nested in ecotypes were considered as
random effects for the traits measured in experiment 1. For
traits measured in experiment 2 we considered the effect of
genotype (HAL2 versus FIL2 genotypes), treatment and the
interaction as fixed effects and experimental block, and the
nested effect of block in treatment as random effects. While
analyzing traits for parental genotypes in experiment 3, a model
was fitted in which treatment, genotypes and the interaction
were incorporated as fixed effects while cohort was considered as
a random effect. The significance of fixed effects was tested by Ftests using Satterthwaite’s method to obtain p-value and the
degree of freedom.
QTLResponsive: YDiff (Difference from Stress to Control) = µ +
QTL + cohort (if applicable) + QTL x cohort (if
applicable) + error
The following QTL model was implemented for the traits
which were measure only on salinity treated plants (Na+,
K+ and Na+/K+):
QTLIonic: YTreat (Trait measure in treatment condition) = µ
+ QTL + error
To detect potential epistatic interactions the “scantwo”
function was implemented. Permutation (n=1000) for each
trait was performed with stratification by cohort to obtain the
null distribution for main and epistatic interaction effects. The
stepwise QTL function was used to complete a forwardbackward search by adding/dropping QTL effects and their
epistatic effect. The threshold value for Type I error was set as
0.1 for all the traits and QTL intervals were calculated with a
threshold of 1.5 LOD drop from the peak. Estimated QTL effects
were obtained by the “qtlStats” function from the qtlTools
package (Lovell, 2018) with the final QTL model for a given
trait. Detailed methods for QTL mapping is provided in
Supplementary Method S1, section 1.10. Gene models residing
in a given QTL confidence interval were considered as candidate
genes for that specific QTL and GO enrichment analysis was
carried out as mentioned in Supplementary Method S1, section
1.9. All gene models which were not residing in QTL confidence
intervals were considered as the genomic background for
enrichment analysis.
2.5 QTL mapping
A genetic linkage map composed of 901 markers evenly
distributed across the nine P. hallii chromosomes was used for
QTL mapping analysis. Each marker represented a window of 50
high quality SNPs called from the re-sequencing data of RIL
individuals (NCBI SRA archive Umbrella project
PRJNA701489). A detailed description of the linkage map
construction for this RIL mapping population can be found on
Dryad (https://doi.org/10.5061/dryad.73n5tb2w8). In this RIL
mapping experiment all the traits except Na+, K+ and Na+/K+
were measured in both control and salinity treatment
conditions. As such, this experimental design can be
considered a two factorial design and our aim was to identify
the main effect of inland versus coastal alleles (G) on phenotypic
variation and the interactive effect of coastal/inland allele with
treatment (GxT) on phenotypic variation. This analysis strategy
relied on a reaction norm perspective. Here, the QTL mapping
analysis was split into a portion based on the simple additive
effect of QTL averaged across the control and salinity treatment
by taking the average of replicates from the two treatment levels.
This analysis will detect QTL that are robust to the environments
and that have constitutive effects. To study the QTL that show
QTL x salinity treatment effects, the difference among the
replicates across the treatment was also studied (Lowry et al.,
2013). Prior to running QTL models the normality of each trait
was tested and transformed with a Box-Cox transformation
when required (Box and Cox, 1964). The “scanone” function
in the R/qtl (Broman et al., 2003) package was used to detect
QTL for constitutive and responsive traits by using the
following models:
Frontiers in Plant Science
2.6 TAGSeq library construction and
identification of differentially
expressed genes
To characterize the transcriptome response to salinity a
RNA-sequencing strategy was implemented. To do so, ~8
replicates of each genotype (HAL2 or FIL2) x treatment
combination from the mature plant experiment described in
section 2.1 were randomly selected for further characterization.
The first emerging leaf and the total root systems of plants were
sampled on 37th DAS around 10 AM to study global gene
expression. 3′ TAGSeq libraries were constructed as described
by Weng et al. (2019) and sequenced with 1x150 bp single-end
reads on HiSeq 2500 (Illumine, San Diego, CA, USA). Raw reads
were quality filtered, mapped to the P. hallii var. hallii (HAL2)
reference genome (v2.0), filtered for mapping quality, and
expression counts were generated using the v2.1 (https://
phytozome-next.jgi.doe.gov/info/PhalliiHAL_v2_1) gene models
of the mentioned reference genome. Raw reads can be found in
NCBI Bioproject PRJNA853054. Detailed method for library
04
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
soil sodium deposition gradient (Figure 1A). Approximately 6.6fold higher topsoil sodium concentration (p-value=0.01) was
detected in the coastal habitats compared to the inland habitats
(Figure 1B; Supplementary Method S1 section 1.1). To evaluate
the differential response of coastal and inland ecotypes, ten
genotypes from each ecotype were selected, applied a soil
salinity stress as a sodium chloride solution supplement, and
leaf relative water content, and aboveground and belowground
biomass (Method section 2.1: Experiment 1). Under a model of
local adaptation, we hypothesized that the coastal ecotype might
perform better than the inland ecotype under salinity stress. A
significant effect of ecotype-by-treatment interaction was detected
for both aboveground (p-value=0.04) and belowground biomass
(p-value=0.02), whereas only a significant effect of treatment was
detected for relative water content (Figure 1 and Supplementary
Table S2). The inland ecotype had a significantly lower salt
tolerance index (the ratio of mean trait value for salinity treated
plants to control plants) compared to the coastal ecotype for both
the biomass traits (p-value < 0.05 for one-way ANOVA). The
reduction of growth in response to salinity was 24% and 32% higher
in the inland ecotype compare to the coastal ecotype for
aboveground and belowground biomass respectively. This result
implies improved capacity of the coastal ecotype for growth
maintenance when exposed to salinity stress.
construction and count matrix generation can be found in
Supplementary Method S1, section 1.7 and sequencing statistics
are provided in Supporting Table S8. The DESeq2 package (Love
et al., 2014) in R (R Core Team, 2021) was used to test for
differentially expressed genes (DEG) at genotype (G), treatment
(T) and genotype x treatment (GxT) levels. Detailed methods for
this analysis are described in the supplementary method section. In
brief, libraries were normalized for their size and dispersion was
estimated using the fitted dispersion-mean relationship. DEG were
identified by running gene-wise likelihood ratio tests (LRT) and
compared the full model with interaction to corresponding reduced
models. The Benjamini & Hochberg method (Benjamini and
Hochberg, 1995) of False Discovery Rate (FDR) was used to
account for multiple testing and significance was determined by
an adjusted p-value <0.05. Gene Ontology (GO) enrichment was
tested for different sets of DEG using the topGo package (Adrian
and Rahnenfuhrer, 2021) against detected expressed genes for a
given tissue type as background. GO terms with adjusted p-value
<0.1 were considered significant. To evaluate the global plasticity
for transcriptomic response to salinity treatment, Discriminant
analysis of Principal Components (DAPC) was implemented on
normalized (by library size) and transformed count data with
variance stabilizing transformation (VST) using the fitted
dispersion-mean. DAPC is a multivariate analysis method
designed to identify clusters and their separation in multivariate
space. It synthesizes linear discriminatory functions in such a way
that maximizes between group variance while minimizing within
group variance. DAPC was carried out on the normalized data with
four predefined groups (genotype x treatment combinations) using
the adegenet package (Jombart et al., 2010). Subsequently, global
transcriptome plasticity in response to the salinity treatment was
measured as the Euclidean distance (of first two linear
discriminatory axes) from each biological replicate at a given
genotype in salinity treatment group to the mean of the control
group for that given genotype.
3.2 Phenotypic response to salinity
treatment in of P. hallii inland and
coastal genotypes
To evaluate the response to salinity in greater detail, one
representative genotype of each ecotype (the inland ecotype:
HAL2 and the coastal ecotype: FIL2; the genome reference
genotypes for P. hallii) were selected and a salinity stress was
applied (Detailed in Supplementary Method: Experiment 2). We
hypothesized that the coastal genotype might perform more
efficiently compared to the inland genotype at additional
physiological and growth parameters (section 3.1) related to salt
tolerance. Among the traits related to leaf water status and
osmolarity, leaf water potential demonstrated significant
genotype-by-treatment interaction (GxT) whereas osmotic
potential and relative water content exhibited significant
treatment effects (T) only (p-value < 0.05; Supplementary Table
S3 for detailed test statistics) (Figure 2). The coastal genotype
maintained a 16% less negative water potential (lower water
stress, p-value= 0.02) compared to the inland genotype, without
detectable genotype specific changes in relative water content under
salinity treatment compared to control condition. Among growth
related traits, aboveground and belowground fresh biomass showed
significant GxT, the inland genotype exhibited a 43% decrease of
belowground fresh biomass in response to salinity stress (pvalue=1.4e-05) compared to control, while the coastal genotype
demonstrated no significantly detectable reduction. Similarly, Na+
2.7 Codes availability
All code and scripts for the analyses and plotting can be
found in https://github.com/tahia/NatVariation_salinity_
adaptation_Phallii.
3 Result
3.1 Phenotypic response to salinity
treatment in P. hallii
The species range of P. hallii spans from the saline rich
coastal habitats along the Gulf Coast of Texas to the arid
Chihuahuan desert in the southwest. The edaphic conditions
vary considerably across this distribution, and exhibit a strong
Frontiers in Plant Science
05
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
A
C
B
D
E
FIGURE 1
Geographical distribution of P. hallii from natural collections and the phenotypic response to salinity of selected inland and coastal accessions.
(A) Geographical distribution of P. hallii in which filled points represent the collection location of accessions while color represents ecotypic
variation. Raster plot represents annual soil sodium deposition collected from National Atmospheric deposition program for the year 2018
(https://gaftp.epa.gov/castnet/tdep/2021_01_grids/). Points with larger radius represent the selected inland and coastal accessions that have
been tested in our salinity response experiment. (B) Bar plot of sodium concentration measured from the top soil of several coastal and inland
habitats. (C–E) Reaction norm plots at two different treatment levels (Control and Salinity stress) for leaf relative water content, aboveground
biomass, and belowground biomass respectively. The solid and dotted line represents ecotype specific and genotype specific group means
respectively.
and Na+/K+ showed significant GxT and the coastal genotype
exhibited significantly lower Na+ (40% less, p-value=0.002) and
Na+/K+ (45% less, p-value=0.001) under salinity treatment
compared to the inland genotype (Figure 2). Overall, in response
to salinity stress the coastal genotype maintained higher shoot and
root growth, less stressful water status, and better ion homeostasis
compared to the inland genotype.
parents. In congruence to our earlier findings (see section 3.2),
belowground biomass, Na+, and Na+/K+ showed significant GxT
for parents (p-value < 0.05, Supplementary Table S4: Summary
statistics). On the other hand, aboveground biomass exhibited
significant additive effect (p-value < 0.05) of genotype and
treatment but no interaction.
In this QTL analysis framework, the overall mean and
difference of genotypic values between treatment levels for a
given trait were defined as either ‘constitutive’ or ‘responsive”
respectively for our measures phenotypes (Supplementary Table
S5). The narrow-sense heritability (h2) for traits on the mean,
difference and ion homeostasis related traits (in the salinity
treatment) ranged from 0-21%, 0-5% and 24-36% respectively
(Supplementary Table S6). Very low heritability was observed
for relative water content for both constitutive (0%) and
3.3 Detection of QTL for salinity
response in RIL mapping population
To identify the genomic regions underlying the differential
response to salinity stress, quantitative trait loci (QTL) mapping was
carried out using the RIL mapping population from HAL2 and FIL2
Frontiers in Plant Science
06
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
A
B
C
D
E
F
G
H
I
FIGURE 2
Response of mature plants to salinity based on representative genotypes of coastal (FIL2) and inland (HAL2) ecotypes. Top, middle, and bottom
panels represent the reaction norm plots for leaf water status, plant growth, and leaf sodium and potassium ion homeostasis related traits,
respectively. (A–C) Water potential, Osmotic potential, and Relative water content respectively. (D–F) represents the reaction norm of
Aboveground fresh biomass, Belowground fresh Biomass, and their ratio (aboveground/belowground). (G–I) represents leaf sodium content
(Na+), Leaf potassium content (K+), and the ratio of leaf sodium to potassium content (Na+/K+).
these constitutive QTL, qAGB-5@85.8 (read as qTRAITChromosome-Position), qBGB-8@33.1, and qBGB-3@5.4
shared the three QTL cluster intervals reported for shoot
biomass, root diameter and specific root area respectively by
Khasanova et al. (2019) from the same RIL mapping population.
In the responsive category a single QTL, qAGB-5@85.8 was
detected for belowground biomass with an interval of ~ 15 cM.
This QTL explained 1.38% of variance of the response difference
in belowground biomass and the coastal allele had a positive
effect (less reduction of belowground biomass in response
responsive categories (0.04%). In the constitutive category, six,
three, and four constitutive QTL were detected for aboveground
biomass, belowground biomass, and the ratio respectively. These
QTL explain 7.88%, 28.54% and 29.52% variance of the
respective traits based on their final multiple QTL models
(Figure 3; Table 1; Supplementary Table S7; Supplementary
Figure S2). The coastal alleles increased trait values (positive
effect) for the majority of QTL detected for aboveground and
belowground biomass, whereas for the ratio (aboveground/
below ground) inland alleles increased the ratio. Among
Frontiers in Plant Science
07
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
A
B
FIGURE 3
The position of (A) constitutive and (B) responsive category and ionic QTL for various traits on the genetic map of the P. hallii RIL population.
Vertical color filled bar indicates QTL intervals estimated by 1.5 LOD drop from the peak. The width of the bar represents the magnitude of the
LOD score for the given QTL and different colors represents different traits. The color of upward arrow indicates the genotype for alleles that
have a positive effect for a given trait.
alleles elevated the trait. Noticeably, all three QTL detected for
the ratio of leaf sodium to potassium content (qNa/K-3@76.2,
qNa/K-5@77.5, and qNa/K-9@17.2) overlap with the intervals of
detected QTL for Na+ and K+ individually resulting in three
ionic QTL clusters. Since Na+ and K+ demonstrated a weak
negative correlation (Pearson correlation coefficient = -0.29) in
this mapping population, these QTL clusters imply a plausible
association of these loci with ionic balance. Altogether, several
genetic loci were identified that were associated with the response
difference of belowground biomass and the ionic homeostasis
related traits during salinity stress. Generally, the coastal genotype
contributed alleles towards a more resilient response. However, the
detection of several QTL with inland alleles as positive contributors
for ionic homeostasis suggests that the inland genotype may also
possess genetic regulators of ionic homeostasis.
to salinity). Four QTL were detected for each leaf potassium and
leaf sodium content models. For the ratio of leaf sodium to
potassium content three QTLs were identified. Subsequently,
multiple QTL models explained 29.1%, 21.4%, and 25% of
variance for K+, Na+ and Na+/K+. Among the four QTL
detected for K+, three of these (qK-1@10, qK-3@56.7 and qK9@17.2) had positive effect from coastal alleles; however, the
fourth QTL (qK-5@77) had the largest and positive effect from
the inland allele. In addition, an epistatic interaction between
qK-1@10 and qK-9@17.2 was detected with RILs that possess
either or both coastal alleles at these loci maintaining higher leaf
potassium than RILs with both inland alleles (Supplementary
Figure S3). Out of the four QTL detected for Na+, for two QTL
(qNa-3@76.2 and qNa-9@17.2) the inland alleles increased Na+
while for the other two (qNa-3@40 and qNa-5@77.5) the coastal
Frontiers in Plant Science
08
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
TABLE 1 List of QTL for Constitutive, Responsive and Treatment Categories.
QTL Name
Category
Trait
Chromosome
Peak (cM)
qK-1@10.0
Ionic
qK-3@56.7
K+
1
10.0
5.1-14.1
4.95
FIL2
Ionic
K+
3
56.7
53.7-59.9
5.39
FIL2
qK-5@77.0
Ionic
K+
5
77.0
75.6-78.3
12.28
HAL2
qK-9@17.2
Ionic
K+
9
17.2
11.9-17.9
6.21
FIL2
qNa-3@40
Ionic
Na+
3
40.0
19.2-43.9
3.08
FIL2
qNa-3@76.2
Ionic
Na+
3
76.2
43.9-83.5
2.8
HAL2
qNa-5@77.5
Ionic
Na+
5
77.5
69.4-79.8
9.06
FIL2
qNa-9@17.2
Ionic
Na+
9
17.2
14.3-32.3
3.65
HAL2
qNa/K-3@76.2
Ionic
Na+/K+
3
76.2
21.2-100.5
2.4
HAL2
qNa/K-5@77.5
Ionic
Na+/K+
5
77.5
75.6-79.8
14.11
FIL2
qNa/K-9@17.2
Ionic
Na+/K+
9
17.2
14.3-19
4.36
HAL2
qBGB-3@15.8
Responsive
BGB
3
15.8
7.0-22.9
2.8
FIL2
qAGB-1@54.3
Constitutive
AGB
1
54.3
33.3-58.4
6.17
FIL2
qAGB-3@5.4
Constitutive
AGB
3
5.4
2.9-7
5.03
HAL2
qAGB-4@55.2
Constitutive
AGB
4
55.2
53.0-73.7
3.64
FIL2
qAGB-5@85.8
Constitutive
AGB
5
85.8
78.3-90.6
2.6
FIL2
qAGB-8@33.7
Constitutive
AGB
8
33.7
14.5-50.3
1.97
HAL2
qAGB-9@128.0
Constitutive
AGB
9
128.0
117.9-136.6
7.61
FIL2
qBGB-3@5.4
Constitutive
BGB
3
5.4
4.3-7
8.09
HAL2
qBGB-9@85.4
Constitutive
BGB
9
85.5
68.4-87.1
6.88
FIL2
qBGB-09@128.5
Constitutive
BGB
9
128.5
126.9-136.6
13.15
FIL2
qRB-1@52.1
Constitutive
RB
1
52.1
30.6-57.1
4.67
FIL2
qRB-5@13.0
Constitutive
RB
5
13.0
1.3-18.1
8.39
HAL2
qRB-8@33.2
Constitutive
RB
8
33.2
32.0-35.3
11.25
HAL2
qRB-9@70.5
Constitutive
RB
9
70.5
64.2-71.9
8.67
HAL2
3.4 Candidate genes within QTL intervals
and their functional enrichment
LOD
Positive Allele
content QTL, we noticed the presence of three a priori gene families
which have been well studied and reported to play key roles in
salinity response: the HKT gene family (Davenport et al., 2005; Ren
et al., 2005; Davenport et al., 2007), genes in the SOS pathway (Qiu
et al., 2002; Møller et al., 2009; Ji et al., 2013) and the KT/KUP/HAK
family of potassium transporter genes (Obata et al., 2007; Chen et al.,
2015; Shen et al., 2015). The frequency of these a priori gene families
(HKT, genes in SOS pathway, or KT/KUP/HAK family potassium
transporter genes) in a given QTL interval was compared to random
genomic background using a permutation test (Supplementary
Method S1, section 1.11). Four QTL intervals, qK-1@10, qK-5@
77, qNa-5@77.5 and qNa/K-5@77 were enriched with the HKT gene
family (p-value < 0.05). Overall, the ion homeostasis QTL intervals
were enriched with genes associated with oxidoreductase and
chemical homeostasis activity and significantly enriched with HKT
genes. These functional categories could be the likely candidates
conferring salinity tolerance in P. hallii.
To understand the molecular function of genes residing in
‘responsive’ or ‘ionic’ QTL intervals (average length of QTL
interval ~18 cM and ~715 candidate genes/QTL interval) during
salinity response, the enrichment of specific Gene Ontology (GO)
terms in a given QTL interval was tested (Method section 2.4). GO
terms such as response to oxidative stress, antioxidant activity and
anatomical structure development were significantly enriched
(adjusted p-value < 0.1) for qBGB-3@15.8. This result suggests
that the presence of potential candidate genes associated with
stress responses may contribute to the maintenance of
belowground growth in saline conditions. Intervals for qNa-5@
77.5 and qNa/K-5@77.5 were found to be enriched with response
to oxidative stress and antioxidant activity while intervals for qK-9@
17.2, qNa-9@17.2, and qNa/K-9@17.2 were enriched with
oxidoreductase, transferase and phosphorelay sensor kinase
activity. Since these QTL resided in two major QTL clusters it was
not surprising that these intervals would be enriched with similar
GO terms. Apart for these two treatment QTL clusters, the qNa-3@
40 interval was enriched with cell redox homeostasis and chemical
homeostasis terms. In the candidate gene lists for the majority of ion
Frontiers in Plant Science
Interval (cM)
3.5 Global patterns of gene expression in
response to salinity
To study transcriptional reprogramming during salinity
stress, the first fully expanded leaf and the total root systems
09
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
the difference of plasticity was much higher in leaf tissue. The
elevated inland plasticity in leaf tissue compared to the coastal
genotype could be due to ion exclusion that restricted
transportation of Na + from root to leaf tissue in the
coastal genotype.
from experiment 2 were sampled (section 3.2; Supplementary
Table S8) to obtain genome-wide gene expression profiles using
a 3′- TAGSeq protocol (Weng et al., 2019). Among the 33,263
annotated gene models, 17,188 and 20,903 genes were detected
in leaf and root tissue respectively (each with a mean of >4 count
across the libraries for a specific tissue) and 16,225 genes which
were shared in both tissues. A great deal of expression
divergence was observed between libraries from leaf and root
tissues (Supplementary Figure S4) and therefore differential gene
expression analyses were conducted separately for each tissue
type. To explore the global variation of gene expression within
each tissue, the normalized transcript count data from a given
tissue was used and Discriminant Analysis of Principal
Components (DAPC) was applied with predefined groups
corresponding to each genotype x treatment combination. Leaf
tissue exhibited a strong signal of genotype on the first linear
discriminatory function (Figure 4A) of the transcriptome. In
contrast, the first discriminatory function separated the two
treatment levels (Figure 4B) for root tissue. The inland
genotype exhibited stronger plastic transcriptome responses to
the salinity treatment compared to the coastal genotype in both
leaf and root tissues (p-value < 0.05 for one-way ANOVA), but
3.6 Differentially expressed genes in leaf
tissue
To investigate the contribution of genotype and salinity
treatment on gene expression profiles the expression of each
detected gene was analyzed using a generalized linear model
including Genotype (G), Treatment (T) and Genotype x
Treatment interaction (GxT). Detected differentially expressed
genes (DEGs) were categorized in four groups based on their
fixed effects; DEG that had significant i) solitary Genotype effect
(G), ii) solitary Treatment effect (T), iii) both Genotype and
Treatment additive effects (G+T) and iv) an interaction of
Genotype x Treatment (GxT). In leaf tissue 5072, 2408, 3893,
and 2448 genes were identified for G, T, G+T and GxT categories
respectively (Figure 4 and Supplementary Table S9). Our specific
A
B
C
D
FIGURE 4
Differential gene expression in leaf and root tissues of the inland (HAL2) and coastal (FIL2) genotype of P. hallii in response to salinity stress. Top
panel: (A, B) Discriminant Analysis of Principal Components (DAPC) on the normalized expression of genes from leaf (left, A) and root (right, B)
tissues. Each point represents a single TAGSeq library and axes represents the first two linear discriminant functions. Symbols representing tissue
types and genotype x treatment combinations are marked with different filled colors. Bottom pane: (C, D) Venn diagram for significant DEGs
detected for Genotype (G), Treatment (T), and the interaction of Genotype with Treatment (GxT) as fixed effect for leaf and root tissue respectively.
Frontiers in Plant Science
10
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
Compartmentalization of Na + ions into vacuoles by
tonoplast localized Na+/H+ exchanger (NHX) like antiporters
is considered as a key mechanism to avoid the toxic effects of
Na+ in the cytosol both in root and leaf tissues (Apse et al., 1999;
Apse and Blumwald, 2007). Among the three annotated NHX2
homologs (PhHAL.2G474100, PhHAL.3G288900 and
PhHAL.8G243100) in the P. hallii var HAL2 reference
genome, two were differentially expressed in this experiment.
PhHAL.3G288900 was upregulated in leaf tissue for the inland
genotype under salinity stress, whereas the coastal genotype had
higher expression in root tissue constitutively. The other NHX2
detected in this study (PhHAL.2G474100) was upregulated in
leaf tissue for both genotypes during salinity treatment. This
compartmentalization of Na+ by NHX is driven by the protonmotive force generated by the vacuolar H+-ATPase (V-H+ATPase) and the plastic response of V-H+-ATPase has been
reported during salinity stress (Lv et al., 2017). A vacuolar
ATPase (PhHAL.5G103300) was upregulated during salinity
treatment in root tissue for both genotypes. Overall, we
observed constitutive expression of vacuolar antiporter in root
tissue for the coastal genotype but the expression was higher in
leaf tissue for the inland genotype. In leaf tissue, plasma
membrane-bound Na+/H+ exchanger (SOS1) (Qiu et al., 2002;
Ji et al., 2013) and high affinity potassium transporter (HKT)
(Davenport et al., 2005; Davenport et al., 2007) play an active
role in loading Na+ into phloem recirculation and shuttle Na+
from shoot tissue to maintain ion homeostasis. SOS1
(PhHAL.3G490000) and HKT1 (PhHAL.4G025000) were
upregulated in leaf tissue for both the genotypes under salinity
stress and are perhaps involved in Na+ recirculation from leaf
tissue to phloem. Overall, NHX2 was upregulated in a genotype
and tissue specific manner whereas SOS1 and HKT1
demonstrated leaf specific upregulation irrespective of genotype.
KT/HAK/KUP family transporters help to maintain K+
homeostasis by driving K+ uptake from the soil (Wang et al.,
2022). In root tissue one KT transporter (PhHAL.2G242200)
was upregulated during salinity in the coastal genotype while a
HAK (PhHAL2G.001500) transporter was upregulated during
salinity stress irrespective of genotype. However, the
upregulation of several KT (PhHAL.2G242200) and KUP
(PhHAl.2G471600, PhHAL.5G026500) transporters in leaf
tissue in the inland genotype suggest their possible
involvement in maintaining cytoplasmic K+ levels when
external Na+ inhibits K+ uptake and cellular Na+ replaces K+
(Maathuis, 2005). Inward rectifying potassium channel (KAT)
expressed in epidermal cell can mediate K+ uptake (Obata et al.,
2007). KAT1 (PhHAL.1G101800) was upregulated in the coastal
genotype during salinity treatment and could be involved in K+
uptake in leaf tissue.
Outward rectifying K+ channels (KCO) activated by membrane
depolarization due to high Na+ influx can release K+ from the
vacuolar pool to cytosol or mediate K+ exclusion across the plasma
membrane (Voelker et al., 2006; Shabala and Cuin, 2008;
interest was in the molecular function of DEGs which were
upregulated during salinity treatment in the T, G+T or GxT
categories and tested for GO enrichments separately in each of
these groups. DEGs in these categories were enriched with
various GO terms related to transmembrane signaling receptor
activity and transmembrane transporter activity (nominal pvalue <0.05, Supplementary Table S10). DEGs which were
upregulated in G+T category were also enriched with
oxidoreductase activity (p-value <0.05). Overall, several
thousand genes were detected which demonstrated GxT
interaction for their expression in leaf tissue, including the
presence of various monovalent cation transporters and
potassium channels. These results strengthen the hypothesis
that inland and coastal expression divergence might impact
ionic homeostasis during salinity stress.
3.7 Differentially expressed genes in root
tissue
For root tissue, 3,355 genes were detected that exhibited
significant expression variation only for genotype (G category),
5,775 genes showed significant variation only for treatment (T
category), 2,818 genes displayed both genotype and treatment
specific additive effects (G+T category) and 1,105 genes
demonstrated significant genotype specific response to salinity
(GxT category) (Figure 4 and Supplementary Table S11). DEGs
which were upregulated in the T category were enriched with
transferase activity whereas upregulated DEGs in the G+T
category were enriched with GO terms such as oxidoreductive
activity and antioxidant activity (nominal p-value < 0.05,
Supplementary Table S12). On the other hand, DEGs in GxT
category were enriched with ion transport, drug transport,
response to drug, and antiporter activity along with GOs such
as oxidoreductive and antioxidant activity also found in
upregulated G+T DEGs. In general, several cation transporters
were detected in the GxT category in a fashion similar to the
detected pattern of expression in leaf.
3.8 Differentially expressed ion
transporters across leaf and root tissues
in response to salinity
The analysis of differentially expressed gene demonstrated
enrichment of transmembrane and ion transporters in T, G+T
and GxT category genes for leaf and root tissue separately.
Cation transporters play a major role in maintaining cellular
ion homeostasis under salinity stress. Therefore the expression
profile of a priori candidates for ion homeostasis with significant
effects were further examined (Figure 5, Supplementary Figure
S5). We provide a potential model of transporter activity and
divergence in Figure 6.
Frontiers in Plant Science
11
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
A
B
C
D
E
F
G
H
I
FIGURE 5
Gene expression profile of selected ion transporters (A–I panels) in leaf and root tissues with Treatment (T), both Genotype and Treatment
(G+T) and Genotype by Treatment (GxT) interaction effects. In each panel, each point and error bar represent the mean and 1 SE of normalized
gene expression of a selected gene for a given genotype, treatment and tissue level. Normalization was carried out by Variance Stabilizing
Transformation (VST). Lines represent the change of gene expression for a given tissue and genotype in response to salinity while the color of
lines represent the genotypes. Types of line represents tissue type (solid=leaf tissue; dotted=root tissue).
Kumari et al., 2021). Two KCO genes (PhHAL.9G082300 and
PhHAL.9G082500) were both upregulated under salinity stress for
the inland genotype in leaf. The presence of these two KCO family
genes in the QTL intervals for qK-9@17.2, qNa-9@17.2, and qNa/
K-9@17.2 suggests a plausible link between their activity and the
imbalance of sodium ion homeostasis. Overall, we detected
upregulation of genes involved in K+ acquisition from soil in root
tissue for the coastal genotype, genotype specific upregulation of
genes responsible for K+ influx in leaf tissue, and upregulation of
several potassium channels in the inland genotype responsible for
K+ efflux in leaf tissue during salinity treatment. Taken together the
expression profiles of various Na+ and K+ transporters in the coastal
genotype demonstrated overexpression of genes associated with
Na+ compartmentalization and K+ acquisition in root tissue.
Frontiers in Plant Science
Conversely, the inland genotype exhibited overexpression of
genes which mediates Na+ compartmentalization and both influx
and efflux of K+ in leaf tissue in the face of salinity (Figure 6).
4 Discussion
In this study we sought to understand the physiological and
growth response variation of coastal and inland ecotypes of
P. hallii under salinity stress and identify the genetic basis of
traits associated with salinity adaptation. We also conducted
genome wide gene expression studies to test whether differential
transcriptional responses contribute to these adaptive
physiological responses. Growth stability and superior ion
12
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
FIGURE 6
Schematic diagram representing the localization and function of a priori ion transporters and vacuolar ATPases which plays important role in
maintaining ion homeostasis during salinity stress. Differentially expressed genes from this study are mapped in this diagram based on the
functional annotation of Panicum hallii gene models. Ion transporters are color coded by their expression patterns (Blue=Coastal genotype
specific overexpression, Red=Inland genotype specific overexpression, Yellow=Overexpression during salinity treatment irrespective of genotype
and Gray=No significant detectable overexpression).
have evolved efficient Na+ exclusion and K+ retention as a
mechanism to maintain ion homeostasis.
The detectable divergence in leaf ion homeostasis between
coastal and inland genotypes and moderate heritability in the
RIL mapping population (narrow-sense heritability, h2 ~24
-36%) implies the presence of substantial genetic variation in
this population in response to salinity. We detected several QTL
for Na+, K+, and Na+/K+. The majority of these QTL effects were
driven by the coastal alleles contributing superior ion
homeostasis (low Na+, high K+, or low Na+/K+). Moreover, we
noticed enrichment of a priori salinity tolerant candidate genes
such as the HKT gene family (Davenport et al., 2005; Ren et al.,
2005; Davenport et al., 2007) in some ionic QTL intervals.
However, these detected ionic QTL are not directly associated
with the plastic ionic response but correspond to treatment
specific response. Overall, this study provides evidence of
existing adaptive genetic variation in P. hallii for leaf
ion homeostasis.
Overexpression of various cation transporters have been
reported to confer salinity tolerance. For example,
overexpression of NHX mediates Na+ compartmentalization
while overexpression of KT/KUP/HAK gene family members
regulates K+ acquisition and certain K+ channels regulating ion
homeostasis and leads to salinity tolerance in diverse plant
species (Roy et al., 2014; Kumari et al., 2021). Several cation
homeostasis of coastal genotypes was detected during salinity
treatment and several genetic loci associated with these
responses were identified. Genome wide transcriptional
profiles revealed various ion transporters were differentially
upregulated in the coastal genotype and could be potential
candidates contributing to the maintenance of superior
ion homeostasis.
Under a model of local adaptation, we hypothesized that
coastal genotypes would outperform inland genotypes in the
presence of salinity treatment. We detected aboveground and
belowground growth and biomass stability in response to
salinity for plants collected from coastal habitats compared to
inland habitats which support our hypothesis. This is consistent
with previous report that the salt tolerant genotypes of Proso
millet, another Panicum species, had higher aboveground and
belowground biomass accumulation compared to its sensitive
counterparts (Yuan et al., 2022). Studies have reported that salt
adapted genotypes tend to maintain a lower Na+/K+ (Platten
et al., 2013; Tang et al., 2013; Assaha et al., 2017) and some
tolerant genotypes demonstrated no significant change in leaf
Na+ content while still maintaining lower Na+/K+ compare to
sensitive genotypes (Sun et al., 2015). Our physiological study
revealed lower leaf Na+ and superior maintenance of leaf ion
homeostasis (low Na+/K+) in coastal genotypes compared to
inland genotypes. This implies that the coastal population might
Frontiers in Plant Science
13
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
pathways for salinity and water stress (Zhu, 2002). Moreover, we
noticed that the confidence interval of the responsive QTL
associated with belowground biomass (qBGB-3@15.8)
overlapped (~86% of the confidence interval) with one of the
reported trans-eQTL hotspots with genotype specific drought
response by Lovel et al. (2018). This interval was enriched with
genes related to molecular function such as oxidoreductive and
antioxidant activity. It is possible that candidate genes within
this interval have a role in general stress responses associated
with scavenging reactive oxygen species (ROS) (Golldack et al.,
2014; You and Chan, 2015). Detection of genomic regions
related to both salinity and water stress supports the idea that
some convergent molecular mechanisms for adaptation to
abiotic stresses might be present in divergent locally adapted
populations of P. hallii.
In summary, our study demonstrates that coastal P. hallii
genotypes have superior performance in response to salinity
treatment compared to inland genotypes and maintained stable
growth and better ion homeostasis. We identified genetic loci
associated with growth maintenance and ion homeostasis, and
several differentially expressed candidate genes associated with
these traits are included various ion transporters. These findings
improve our understanding of molecular mechanisms underlying
local adaptation to saline habitats. Broadly, natural genetic variants
identified for ion homeostasis could provide potential resources for
functional validation of these candidates by genetic manipulation.
transporters were enriched in differentially expressed genes for
the GxT category and exhibited tissue specific expression
differences. For instance, a putative NHX (PhHAL.3G112000)
was upregulated in leaf tissue for the inland genotype under
salinity but it was constitutively overexpressed in root tissue for
the coastal genotype irrespective of treatment conditions. The
constitutive expression pattern of NHX in root tissue suggests
that the coastal genotype might be primed for grown in a saline
habitat. In addition, KT (PhHAL.2G242200) was upregulated in
the coastal genotype relative to the inland genotype during
salinity treatment in root tissue. Another K+ channel (KAT1)
(PhHAL.1G101800) was upregulated in the coastal genotype
compared to the inland genotype during salinity treatment in
leaf tissue. Conversely, two outward rectifying K+ channels
(KCO) (PhHAL.9G082300 and PhHAL.9G082500) were
upregulated during salinity treatment in the inland genotype
in leaf tissue. It is likely that the coastal population may have
evolved to strictly regulate the expression of various ion
transporters to retain potassium and regulate cytosolic ion
homeostasis. Moreover, these two KCO family genes were
detected as candidate genes in one QTL cluster (qK-9@17.2,
qNa-9@17.2, and qNa/K-9@17.2) for which the coastal allele
contributed to superior leaf ion homeostasis. Among these two
KCO family genes, PhHAL.9G082500 had elevated nonsynonymous to synonymous substitution rate (dN/dS = 1.52)
and all four nonsynonymous codon substitution were derived in
the coastal genotype (orthologs from Panicum virgatum were
used to infer the ancestral state) implying that this gene might be
under positive selection in the coastal lineage and could
contribute to efficient K + retention in these putatively
adapted genotypes.
This study aimed to investigate the response of coastal and
inland genotypes to salinity and therefore was restricted to
experiments in controlled growth conditions. However, the
effect of this response towards the relative fitness of
individuals in their native habitats can be altered by the
spatiotemporal magnitude of soil salinity and through complex
interaction with other environmental factors. Therefore, this
study cannot directly infer the role of soil salinity as a driving
force for local adaptation. However, it can provide some basic
insight into physiological functions under stress and serve as the
basis for developing hypotheses concerning local adaptation. For
instance, in an earlier study on P. hallii Lovell et al. (2018)
reported genetic clusters for drought responsive gene expression
in a field experiment and demonstrated more plastic drought
recovery responses of the inland genotype. In our genetic
mapping we detected one QTL cluster for which inland alleles
contributed to superior leaf ion homeostasis. It is possible that
the inland populations might have evolved to maintain ion
homeostasis using different sets of genetic loci compared to
coastal salt-exposed populations. However, it is also reasonable
to infer that some of the molecular mechanisms maintaining ion
homeostasis could be the result of crosstalk between shared
Frontiers in Plant Science
Data availability statement
The datasets presented in this study can be found in online
repositories or in Supplementary Material. The name of
the repository and accession numbers can be found below:
NCBI Sequence Read Archive (SRA) database under the
Bioproject PRJNA853054.
Author contributions
TH and TJ conceived the project and designed the research; TH
with the help of GB, JY, and JB performed the research. TH analyzed
the data. TH and TJ wrote the article, with input from GB. All authors
contributed to the article and approved the submitted version.
Funding
The research was supported by NSF Plant Genome Research
Program (Award: IOS-1444533) and Integrative Biology
Research Grant for graduate students, UT Austin. This
research was also supported by the US Department of Energy,
Office of Science, Office of Biological and Environmental
Research, Genomic Science Program grant DE-SC0021126 to TJ.
14
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
Acknowledgments
Publisher’s note
We thank the lab members of the Juenger Lab for helping
with phenotypic data collection and Shane Merrell for his
support while conducting experiment in UT greenhouse
facilities. We also thank Robert Heckman and Zeba I. Seraj for
their constructive comments on this manuscript.
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.
Conflict of interest
Supplementary material
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/
fpls.2022.1019169/full#supplementary-material
References
Adrian, A., and Rahnenfuhrer, J. (2021). “topGO: Enrichment analysis for gene
ontology,” in R package version 2.46.0. Available at: https://bioconductor.org/
packages/release/bioc/html/topGO.html.
Colmer, T. D., Flowers, T. J., and Munns, R. (2006). Use of wild relatives to
improve salt tolerance in wheat. J. Exp. Bot. 57 (5), 1059–1078. doi: 10.1093/jxb/
erj124
Apse, M. P., and Blumwald, E. (2007). Na+ transport in plants. FEBS Lett. 581
(12), 2247–2254. doi: 10.1016/j.febslet.2007.04.014
Cuin, T. A., Miller, A. J., Laurie, S. A., and Leigh, R. A. (2003). Potassium
activities in cell compartments of salt-grown barley leaves. J. Exp. Bot. 54 (383),
657–661. doi: 10.1093/jxb/erg072
Apse, M. P., Aharon, G. S., Snedden, W. A., and Blumwald, E. (1999). Salt
tolerance conferred by overexpression of a vacuolar Na+/H+ antiport in
arabidopsis. Science 285 (5431), 1256–1258. doi: 10.1126/science.285.5431.1256
Davenport, R., James, R. A., Zakrisson-Plogander, A., Tester, M., and Munns, R.
(2005). Control Sodium Transport Durum Wheat. Plant Physiol. 137 (3), 807–818.
doi: 10.1104/pp.104.057307
Assaha, D. V. M., Ueda, A., Saneoka, H., Al-Yahyai, R., and Yaish, M. W. (2017).
The role of na+ and k+ transporters in salt stress adaptation in glycophytes. Front.
Physiol. 8. doi: 10.3389/fphys.2017.00509
Davenport, R. J., Alicia, M.-M., Jha, D., Essah, P. A., Rus, A., and Tester, M.
(2007). The Na+ transporter AtHKT1;1 controls retrieval of na+ from the xylem in
arabidopsis. Plant Cell Environ. 30 (4), 497–507. doi: 10.1111/j.13653040.2007.01637.x
Atieno, J., Colmer, T. D., Taylor, J., Li, Y., Quealy, J., Kotula, L., et al. (2021).
Novel salinity tolerance loci in chickpea identified in glasshouse and field
environments. Front. Plant Sci. 12. doi: 10.3389/fpls.2021.667910
Dvořak, J., Noaman, M. M., Goyal, S., and Gorham, J. (1994). Enhancement of
the salt tolerance of triticum turgidum l. by the Kna1 locus transferred from the
triticum aestivum l. chromosome 4D by homoeologous recombination. Theor.
Appl. Genet. 87 (7), 872–877. doi: 10.1007/BF00221141
Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015). Fitting linear mixedeffects models using lme4. J. Stat. Software 67 (1), 1–48. doi: 10.18637/jss.v067.i01
Baxter, I., Brazelton, J. N., Yu, D., Huang, Y. S., Lahner, B., Yakubova, E., et al.
(2010). A coastal Cline in sodium accumulation in arabidopsis thaliana is driven by
natural variation of the sodium transporter AtHKT1;1. PloS Genet. 6 (11),
e1001193. doi: 10.1371/journal.pgen.1001193
Fortmeier, R., and Schubert, S. (1995). Salt tolerance of maize (Zea mays l.): The
role of sodium exclusion. Plant Cell Environ. 18 (9), 1041–1047. doi: 10.1111/
j.1365-3040.1995.tb00615.x
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: A
practical and powerful approach to multiple testing. J. R. Stat. Society: Ser. B
(Methodological) 57 (1), 289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x
Golldack, D., Li, C., Mohan, H., and Probst, N. (2014). Tolerance to drought and
salt stress in plants: Unraveling the signaling networks. Front. Plant Sci. 5. doi:
10.3389/fpls.2014.00151
Box, G. E. P., and Cox, D. R. (1964). An analysis of transformations. J. R. Stat.
Society Ser. B (Methodological) 26 (2), 211–252. doi: 10.1111/j.25176161.1964.tb00553.x
Broman, K. W., Wu, H., Sen, Ś ., and Churchill, G. A. (2003). R/qtl: QTL
mapping in experimental crosses. Bioinformatics 19 (7), 889–890. doi: 10.1093/
bioinformatics/btg112
Gould, B. A., Palacio-Mejia, J. D., Jenkins, J., Mamidi, S., Barry, K., Schmutz, J.,
et al. (2018). Population genomics and climate adaptation of a C4 perennial grass,
panicum hallii (Poaceae). BMC Genomics 19 (1), 792. doi: 10.1186/s12864-0185179-7
Bromham, L. (2014). Macroevolutionary patterns of salt tolerance in
angiosperms. Ann. Bot. 115 (3), 333–341. doi: 10.1093/aob/mcu229
Haque, T., Elias, S. M., Razzaque, S., Biswas, S., Khan, S. F., Azad Jewel, G. M.N.,
et al. (2020). Natural variation in growth and physiology under salt stress in rice:
QTL mapping in a horkuch × IR29 mapping population at seedling and
reproductive stages. bioRxiv 2020.2003.2001.971895. doi: 10.1101/
2020.03.01.971895
Bromham, L., and Bennett, T. H. (2014). Salt tolerance evolves more frequently
in C4 grass lineages. J. Evolutionary Biol. 27 (3), 653–659. doi: 10.1111/jeb.12320
Isayenkov, S. V., and Maathuis, F. J. M. (2019). Plant salinity stress: Many
unanswered questions remain. Front. Plant Sci. 10. doi: 10.3389/fpls.2019.00080
Busoms, S., Teres, J., Huang, X.-Y., Bomblies, K., Danku, J., Douglas, A., et al.
(2015). Salinity is an agent of divergent selection driving local adaptation of
arabidopsis to coastal habitats plant physiology. Plant Physiology 168 (3), 915–929.
doi: 10.1104/pp.15.00427
James, R. A., Davenport, R. J., and Munns, R. (2006). Physiological
characterization of two genes for na+ exclusion in durum wheat, Nax1 and
Nax2. Plant Physiol. 142 (4), 1537–1547. doi: 10.1104/pp.106.086538
Ji, H., Pardo, J. M., Batelli, G., Van Oosten, M. J., Bressan, R. A., and Li, X.
(2013). The salt overly sensitive (SOS) pathway: Established and emerging roles.
Mol. Plant 6 (2), 275–286. doi: 10.1093/mp/sst017
Busoms, S., Paajanen, P., Marburger, S., Bray, S., Huang, X.-Y., Poschenrieder,
C., et al. (2018). Fluctuating selection on migrant adaptive sodium transporter
alleles in coastal arabidopsis thaliana. Proc. Natl. Acad. Sci. 115 (52), E12443–
E12452. doi: 10.1073/pnas.1816964115
Joe Hereford, A. (2009). Quantitative survey of local adaptation and fitness
trade-offs. Am. Nat. 173 (5), 579–588. doi: 10.1086/597611
Chen, G., Hu, Q., Luo, L., Yang, T., Zhang, S., Hu, Y., et al. (2015). Rice
potassium transporter OsHAK1 is essential for maintaining potassium-mediated
growth and functions in salt tolerance over low and high potassium concentration
ranges. Plant Cell Environ. 38 (12), 2747–2765. doi: 10.1111/pce.12585
Frontiers in Plant Science
Jombart, T., Devillard, S., and Balloux, F. (2010). Discriminant analysis of
principal components: a new method for the analysis of genetically structured
populations. BMC Genet. 11 (1), 94. doi: 10.1186/1471-2156-11-94
15
frontiersin.org
Haque et al.
10.3389/fpls.2022.1019169
Pires, I. S., Negrão, S., Oliveira, M. M., and Purugganan, M. D. (2015).
Comprehensive phenotypic analysis of rice (Oryza sativa) response to salinity
stress. Physiologia Plantarum 155 (1), 43–54. doi: 10.1111/ppl.12356
Karrenberg, S., Edelist, C., Lexer, C., and Rieseberg, L. (2006). Response to
salinity in the homoploid hybrid species helianthus paradoxus and its progenitors
h. annuus H. petiolaris New Phytol. 170 (3), 615–629. doi: 10.1111/j.14698137.2006.01687.x
Pittaro, G., Cá ceres, L., Bruno, C., Tomá s, A., Bustos, D., Monteoliva, M., et al.
(2016). Salt tolerance variability among stress-selected panicum coloratum cv.
Klein plants. Grass Forage Sci. 71 (4), 683–698. doi: 10.1111/gfs.12206
Khasanova, A., Lovell, J. T., Bonnette, J., Weng, X., Jenkins, J., Yoshinaga, Y.,
et al. (2019). The genetic architecture of shoot and root trait divergence between
mesic and xeric ecotypes of a perennial grass. Front. Plant Sci. 10. doi: 10.3389/
fpls.2019.00366
Platten, J. D., Egdane, J. A., and Ismail, A. M. (2013). Salinity tolerance, na+
exclusion and allele mining of HKT1;5 in oryza sativa and o. glaberrima: many
sources, many genes, one mechanism? BMC Plant Biol. 13 (1), 32. doi: 10.1186/
1471-2229-13-32
Kumari, S., Chhillar, H., Chopra, P., Khanna, R. R., and Khan, M. I. R.. (2021).
Potassium: A track to develop salinity tolerant plants. Plant Physiol. Biochem. 167,
1011–1023. doi: 10.1016/j.plaphy.2021.09.031
Qiu, Q.-S., Guo, Y., Dietrich, M. A., Schumaker, K. S., and Zhu, J.-K. (2002).
Regulation of SOS1, a plasma membrane Na+/H+ exchanger in arabidopsis
thaliana, by SOS2 and SOS3. Proc. Natl. Acad. Sci. 99 (12), 8436–8441. doi:
10.1073/pnas.122224699
Leimu, R., and Fischer, M. (2008). A meta-analysis of local adaptation in plants.
PloS One 3 (12), e4010. doi: 10.1371/journal.pone.0004010
Lexer, C., Welch, M. E., Durphy, J. L., and Rieseberg, L. H. (2003). Natural
selection for salt tolerance quantitative trait loci (QTLs) in wild sunflower hybrids:
Implications for the origin of helianthus paradoxus, a diploid hybrid species. Mol.
Ecol. 12 (5), 1225–1235. doi: 10.1046/j.1365-294X.2003.01803.x
R Core Team (2021). “A language and environment for statistical computing,” in
R foundation for statistical computing.
Ren, Z.-H., Gao, J.-P., Li, L.-G., Cai, X.-L., Huang, W., Chao, D.-Y., et al. (2005).
A rice quantitative trait locus for salt tolerance encodes a sodium transporter. Nat.
Genet. 37 (10), 1141–1146. doi: 10.1038/ng1643
Love, M. I., Huber, W., and Anders, S. (2014). Moderated estimation of fold
change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15 (12), 550.
doi: 10.1186/s13059-014-0550-8
Roy, S. J., Negrão, S., and Tester, M. (2014). Salt resistant crop plants. Curr.
Opin. Biotechnol. 26, 115–124. doi: 10.1016/j.copbio.2013.12.004
Lovell, J. T. (2018). qtlTools.
Lovell, J. T., Schwartz, S., Lowry, D. B., Shakirov, E. V., Bonnette, J. E., Weng, X.,
et al. (2016). Drought responsive gene expression regulatory divergence between
upland and lowland ecotypes of a perennial C4 grass. Genome Res. doi: 10.1101/
gr.198135.115
Rus, A., Baxter, I., Muthukumar, B., Gustin, J., Lahner, B., Yakubova, E., et al.
(2006). Natural variants of AtHKT1 enhance na+ accumulation in two wild
populations of arabidopsis. PloS Genet. 2 (12), e210. doi: 10.1371/
journal.pgen.0020210
Lovell, J. T., Jenkins, J., Lowry, D. B., Mamidi, S., Sreedasyam, A., Weng, X., et al.
(2018). The genomic landscape of molecular responses to natural drought stress in
panicum hallii. Nat. Commun. 9 (1), 5213. doi: 10.1038/s41467-018-07669-x
Shabala, S., and Cuin, T. A. (2008). Potassium transport and plant salt tolerance.
Physiologia Plantarum 133 (4), 651–669. doi: 10.1111/j.1399-3054.2007.01008.x
Shen, Y., Shen, L., Shen, Z., Jing, W., Ge, H., Zhao, J., et al. (2015). The potassium
transporter OsHAK21 functions in the maintenance of ion homeostasis and tolerance to
salt stress in rice. Plant Cell Environ. 38 (12), 2766–2779. doi: 10.1111/pce.12586
Lowry, D. B., Hall, M. C., Salt, D. E., and Willis, J. H.. (2009). Genetic and
physiological basis of adaptive salt tolerance divergence between coastal and inland
mimulus guttatus. New Phytol. 183 (3), 776–788. doi: 10.1111/j.14698137.2009.02901.x
Sun, Y., Kong, X., Li, C., Liu, Y., and Ding, Z.. (2015). Potassium retention under
salt stress is associated with natural variation in salinity tolerance among
arabidopsis accessions. PLoS One 10 (5), e0124032. doi: 10.1371/
journal.pone.0124032
Lowry, D. B., Logan, T. L., Santuari, L., Hardtke, C. S., Richards, J. H., DeRoseWilson, L. J., et al. (2013). Expression quantitative trait locus mapping across water
availability environments reveals contrasting associations with genomic features in
arabidopsis. The Plant Cell 25, 9, 3266–3279. doi: 10.1105/tpc.113.115352
Tang, J., Yu, X., Luo, N., Xiao, F., Camberato, J. J., and Jiang, Y. (2013). Natural
variation of salinity response, population structure and candidate genes associated
with salinity tolerance in perennial ryegrass accessions. Plant Cell Environ. 36 (11),
2021–2033. doi: 10.1111/pce.12112
Lowry, D. B., Hernandez, K., Taylor, S. H., Meyer, E., Logan, T. L., Barry, K. W.,
et al. (2015). The genetics of divergence and reproductive isolation between
ecotypes of panicum hallii. New Phytol. 205 (1), 402–414. doi: 10.1111/nph.13027
Tiwari, S., Sl, K., Kumar, V., Singh, B., Rao, A. R., Mithra Sv, A., et al. (2016).
Mapping QTLs for salt tolerance in rice (Oryza sativa l.) by bulked segregant
analysis of recombinant inbred lines using 50K SNP chip. PLoS One 11 (4),
e0153610. doi: 10.1371/journal.pone.0153610
Lv, S., Jiang, P., Tai, F., Wang, D., Feng, J., Fan, P., et al. (2017). The V-ATPase
subunit a is essential for salt tolerance through participating in vacuolar na+
compartmentalization in salicornia europaea. Planta 246 (6), 1177–1187.
doi: 10.1007/s00425-017-2762-0
Voelker, C., Schmidt, D., Mueller-Roeber, B., and Czempinski, K. (2006).
Members of the arabidopsis AtTPK/KCO family form homomeric vacuolar
channels in planta. Plant J. 48 (2), 296–306. doi: 10.1111/j.1365-313X.2006.02868.x
Møller, I. S., Gilliham, M., Jha, D., Mayo, G. M., Roy, S. J., Coates, J. C., et al.
(2009). Shoot na+ exclusion and increased salinity tolerance engineered by cell
type–specific alteration of na+ transport in arabidopsis. The Plant Cell 21, 7, 2163–
2178. doi: 10.1105/tpc.108.064568
Wang, Y., Zeng, F.-R., Wang, Y., Xu, S., and Chen, Z.-H.. (2022). “Chapter 4 potassium transporters and their evolution in plants under salt stress,” in Cation
transporters in plants. Ed. S. K. Upadhyay (Academic Press), 63–83. Available at:
https://www.sciencedirect.com/science/article/pii/B9780323857901000221.
Maathuis, F. J. M. (2005). The role of monovalent cation transporters in plant
responses to salinity. J. Exp. Bot. 57 (5), 1137–1147. doi: 10.1093/jxb/erj001
Maathuis, F. J. M., and Amtmann, A. (1999). K + nutrition and Na + toxicity:
The basis of cellular K + /Na + ratios. Ann. Bot. 84 (2), 123–133. doi: 10.1006/
anbo.1999.0912
Weng, X., Lovell, J. T., Schwartz, S. L., Cheng, C., Haque, T., Zhang, L., et al.
(2019). Complex interactions between day length and diurnal patterns of gene
expression drive photoperiodic responses in a perennial C4 grass. Plant Cell
Environ. 42 (7), 2165–2182. doi: 10.1111/pce.13546
Marcum, K. B., and Murdoch, C. L. (1994). Salinity tolerance mechanisms of six C4
turfgrasses. J. Am. Soc. Hortic. Sci. jashs 119 (4), 779–784. doi: 10.21273/JASHS.119.4.779
You, J., and Chan, Z. (2015). ROS regulation during abiotic stress responses in
crop plants. Front. Plant Sci. 6. doi: 10.3389/fpls.2015.01092
Munns, R., and Tester, M. (2008). Mechanisms of salinity tolerance. Annu. Rev.
Plant Biol. 59 (1), 651–681. doi: 10.1146/annurev.arplant.59.032607.092911
Yuan, Y., Wu, C., Liu, L., Ma, Q., Yang, Q., and Feng, B. (2022). Unravelling the
distinctive growth mechanism of proso millet (Panicum miliaceum l.) under salt
stress: From root-to-leaf adaptations to molecular response. GCB Bioenergy 14 (2),
192–214. doi: 10.1111/gcbb.12910
Obata, T., Kitamoto, H. K., Nakamura, A., Fukuda, A., and Tanaka, Y. (2007).
Rice shaker potassium channel OsKAT1 confers tolerance to salinity stress on yeast
and rice cells. Plant Physiol. 144 (4), 1978–1985. doi: 10.1104/pp.107.101154
Zhang, H., Zhu, J., Gong, Z., and Zhu, J.-K. (2022). Abiotic stress responses in
plants. Nat. Rev. Genet. 23 (2), 104–119. doi: 10.1038/s41576-021-00413-0
Palacio-Mejı́a, J. D., Grabowski, P. P., Ortiz, E. M., Silva-Arias, G. A., Haque, T.,
Des Marais, D. L., et al. (2021). Geographic patterns of genomic diversity and
structure in the C4 grass panicum hallii across its natural distribution. AoB Plants
13 (2). doi: 10.1093/aobpla/plab002
Frontiers in Plant Science
Zhu, J.-K. (2002). Salt and drought stress signal transduction in plants. Annu.
Rev. Plant Biol. 53 (1), 247–273. doi: 10.1146/annurev.arplant.53.091401.143329
16
frontiersin.org