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\begin{document}

\title{Root cortical senescence enhances drought tolerance in cotton}



\author[1]{Liantao Liu}%
\author[1]{Congcong Guo}%
\author[1]{Ke Zhang}%
\author[1]{Hongchun Sun}%
\author[1]{Lingxiao Zhu}%
\author[1]{Yongjiang Zhang}%
\author[1]{Guiyan Wang}%
\author[1]{Anchang Li}%
\author[2]{Zhiying Bai}%
\author[1]{Cundong Li}%
\affil[1]{Hebei Agricultural University College of Agronomy}%
\affil[2]{Hebei Agricultural University}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
The root cortex is an important anatomical phenes, and root cortical
senescence (RCS) is closely associated with root absorptive function.
However, characteristics and responses of RCS to drought stress in
cotton have received little attention. This study subjected the
drought-tolerant cotton variety ``Guoxin 02'' and the drought-sensitive
variety ``Ji 228'' to drought stress (8\% PEF6000) and no-stress (0\%
PEG6000) treatments to determine the characteristics and responses of
cotton RCS to drought stress. The results showed that the RCS of the two
varieties was significantly promoted under drought stress. The greater
the distance from the root tip, the more severe the RCS occurrence under
drought stress compared with non-stress treatment. The occurrence of RCS
in ``Guoxin 02'' highered by 14.03\%-20.18\% compared to ``Ji 228''
under drought stress. Moreover, the RCS was significantly negatively
correlated with root respiration but significantly positively correlated
with total root length and leaf water potential ( \emph{p} \textless{}
0.05). Indole-3-acetic acid (IAA) content increased, while abscisic acid
(ABA) content decreased as the RCS increased. In summary, endogenous
hormones regulated the occurrence of RCS, which reduced the root
metabolic costs and seemingly achieved more resource redistribution to
new roots, thereby expanding the water absorption capacity of roots to
fully utilize deep water resources. Thus, the study demonstrates the
potential of RCS in improving the drought stress tolerance of cotton.%
\end{abstract}%



\sloppy


\textbf{Root corticalsenescenceenhancesdrought tolerance in cotton}

Congcong Guo\textsuperscript{1}, Ke Zhang\textsuperscript{1}, Hongchun
Sun\textsuperscript{1}, Lingxiao Zhu\textsuperscript{1}, Yongjiang
Zhang\textsuperscript{1}, Guiyan Wang\textsuperscript{1}, Anchang
Li\textsuperscript{1}, Zhiying Bai\textsuperscript{2*}, Liantao Liu
\textsuperscript{1}*, Cundong Li\textsuperscript{1}*

\textsuperscript{1} State Key Laboratory of North China Crop Improvement
and Regulation/ Key Laboratory of Crop Growth Regulation of Hebei
Province/ College of Agronomy, Hebei Agricultural University, Baoding,
Hebei 071001, China

\textsuperscript{2} State Key Laboratory of North China Crop Improvement
and Regulation, College of Life Science, Hebei Agricultural University,
Baoding 071000, China

* Correspondence:

Liantao Liu, \emph{liultday@126.com}

Cundong Li, \emph{auhlcd@163.com}

Zhiying Bai, \emph{zhiyingbai@126.com}



\textbf{Abstract}

The root cortex is an important anatomical phenes, and root cortical
senescence (RCS) is closely associated with root absorptive function.
However, characteristics and responses of RCS to drought stress in
cotton have received little attention. This study subjected the
drought-tolerant cotton variety ''Guoxin 02'' and the drought-sensitive
variety ''Ji 228'' to drought stress (8\% PEF6000) and no-stress (0\%
PEG6000) treatments to determine the characteristics and responses of
cotton RCS to drought stress. The results showed that the RCS of the two
varieties was significantly promoted under drought stress. The greater
the distance from the root tip, the more severe the RCS occurrence under
drought stress compared with non-stress treatment. The occurrence of RCS
in ''Guoxin 02'' highered by 14.03\%-20.18\% compared to ''Ji 228''
under drought stress. Moreover, the RCS was significantly negatively
correlated with root respiration but significantly positively correlated
with total root length and leaf water potential (\emph{p} \textless{}
0.05). Indole-3-acetic acid (IAA) content increased, while abscisic acid
(ABA) content decreased as the RCS increased. In summary, endogenous
hormones regulated the occurrence of RCS, which reduced the root
metabolic costs and seemingly achieved more resource redistribution to
new roots, thereby expanding the water absorption capacity of roots to
fully utilize deep water resources. Thus, the study demonstrates the
potential of RCS in improving the drought stress tolerance of cotton.

\textbf{KEYWORDS:}Root cortical senescence, Cotton, Drought tolerant,
Root metabolic costs



\textbf{1 Introduction}

Root anatomical phenes play a pivotal role in capturing soil resources,
and the root cortex is a vital anatomical phene found between the
epidermis and stele. The root cortex consists of multiple layers of
thin-walled cells differentiating from the primary meristem, occupying a
significant proportion of the root cross-section volume. The cortex is a
lateral pathway for water and solute transport from root hairs to the
central cylinder, serving as the primary site for nutrient storage,
aeration, and secretion of nutrients and growth regulators (Lynch,
2015). Root cortical senescence (RCS) is a form of programmed cell death
occurring in the cortical cells of the root system (Schneider and Lynch,
2018). During RCS, the root external appearance remains healthy and
white, but the cortical cells predominantly lose their nuclei (Bingham,
2007), a phenomenon commonly referred to as ''non-pathogenic root cortex
death'' (Deacon and Henry, 1978).

Accurately quantifying RCS is essential for comprehensively
understanding plant growth and physiological status. Histological
techniques utilizing cell viability staining have become the predominant
approach for studying RCS phenotypes. Commonly used dyes, including
neutral red (Beckel, 1956), Feulgen reagent (Holden, 1975), toluidine
blue (Brown and Hornby, 1987), acridine orange (Henry and Deacon, 1981),
and other cell viability stains, have demonstrated efficacy in RCS
assessment. While concerns were raised by Wenzel and McCully (1991)
about the effectiveness of acridine orange and other cell viability
staining dyes for RCS evaluation, research by Henry and Deacon (1981)
indicated a higher potency in RCS detection when assessed using acridine
orange staining, Feulgen staining, and single-cell pressure probe
techniques (Bingham, 2007). Similar to acridine orange staining,
technologies such as single-cell turgor pressure measurements,
TUNEL-assay and Nomarski optics revealed comparable RCS patterns and
occurrence rates across various plant species (Bingham, 2007; Henry and
Deacon, 1981; Liljeroth and Bryngelsson, 2001; Wenzel and McCully,
1991). These research findings confirm the feasibility of using acridine
orange and other cell viability staining methods to evaluate RCS.

RCS causes the damage or loss of nuclei in the cortical cells, an early
indicator of cell apoptosis and RCS manifestation. Therefore, anucleated
cortical cells are the hallmark of RCS, and the number of viable cells
gradually diminishes with increasing cortical aging (Liljeroth, 1995).
RCS significantly impacts the morphology and physiological
characteristics of the root system; for example, during RCS, the
cross-sectional area of the root cortex decreases, restricting the
radial transport pathways from the cortex to the stele (Liljeroth,
1995). The occurrence of RCS also increases the proportion of aerenchyma
in the root cortex of Gramineae crops, thus inhibiting the cortex
function and increasing nutrient and water transport resistance (Hu et
al., 2014a; Schneider et al., 2017b; Galindo-Casta\selectlanguage{ngerman}ñeda et al., 2018).
RCS has been shown to decrease the metabolic consumption of
photosynthetic products in the cortex, especially during drought stress,
with a substantial portion being utilized for new root growth (Lynch,
2015). This, in turn, promotes root elongation (Chimungu et al., 2015;
Schneider et al., 2017b) and enhances water uptake (Lynch et al., 2005).
Thus, it is evident that RCS is closely associated with the
functionality of the root system under adverse conditions.

The RCS occurrence is influenced by multiple factors., including crop
variety, growth stage, culture medium type, position within the root
system, stress conditions, and phytohormones (Liljeroth, 1995; Schneider
et al., 2017b). For instance, wheat seedling roots exhibited the highest
rate of RCS occurrence compared to rye, barley, and oats (Henry and
Deacon, 1981). Stress conditions such as low nitrogen, low phosphorus,
and drought also induce the occurrence of RCS (Liljeroth, 1995).
Compared with the root segments without RCS, the ones with RCS decreased
by 19\% and 12\% under high N and low N conditions, respectively
(Schneider et al., 2017b). The occurrence rate of RCS is also regulated
by ethylene (Schneider et al., 2018), and exogenous ethylene
significantly increases the RCS occurrence in maize seedlings and
lateral roots (Schneider et al., 2018). However, the characteristics of
RCS occurrence and its response to drought stress in Malvidae cotton are
rarely reported.

Drought stress significantly constrains global agricultural productivity
(Lynch, 2007), and efficient acquisition of soil moisture is crucial for
enhancing plant drought tolerance. Cotton (\emph{Gossypium hirsutum} L.)
is a valuable economic crop. As a straight-rooted crop, cotton exhibits
a well-developed root system with a deep tap root and widely distributed
lateral roots. The function level and physiological metabolism of the
root cortex largely represent the functionality and metabolism of the
entire root system. Previous studies on gramineous crops found that
unfavorable conditions accelerated RCS, reduced root respiration and
decreased the transfer and allocation of photosynthetic products to
fast-growing tissues (Schneider et al., 2017b). The anatomical
structures of the root systems of dicotyledonous and monocots differ;
however, the response mechanisms of RCS to stress conditions have not
been reported in dicotyledonous crops. Therefore, there is a need to
investigate the RCS responses of dicotyledonous crops to stress
conditions. There are several unresolved questions regarding the effects
of RCS in cotton: Is the susceptibility of the aboveground parts of
cotton to drought due to premature RCS? What are the spatio-temporal
variation characteristics and patterns of RCS in cotton? Is there a
correlation between the consumption of a substantial quantity of
photosynthates by RCS and the resilience of the aboveground parts of
cotton?

We hypothesize that endogenous hormones regulate RCS in drought-tolerant
varieties under drought stress, leading to reduced root metabolic costs.
Consequently, more energy is redirected towards root growth, thereby
enhancing drought tolerance. Therefore, this study aimed to explore the
causative factors of cotton RCS and its physiological metabolism and
endogenous hormonal regulation under drought stress, clarify the
physiological role of RCS occurrence in relation to the regulation of
the whole root development, and reveal the physiological mechanism of
RCS in relation to the drought tolerance of cotton aboveground parts.
The study mainly focused on (1) evaluating the existence and occurrence
patterns of RCS in cotton and assessing the characteristics of cotton
RSC under drought stress; (2) clarifying the characteristics and
patterns of metabolic activities and endogenous hormones and their
interrelationships during the RCS in cotton; (3) exploring the mechanism
of action and effects of RCS on the root system and aboveground parts of
cotton. This study is the first to confirm the existence of RCS in a
dicotyledonous crop, cotton, and provides an in-depth understanding of
the characteristics and patterns of RCS under drought stress. The
results form the basis for using RCS to improve drought tolerance in
cotton.

\textbf{2 Materials and Methods}

\textbf{2.1 Experimental design}

The experiment was conducted in the greenhouse at Hebei Agricultural
University (38°85'N, 115°30'E) in Baoding Hebei Province, China. Two
cotton varieties, the drought-tolerant type ''Guoxin 02'' (DT type) and
the drought-sensitive type ''Ji 228'' (DS type), were selected for this
study (Guo et al., 2022). After sterilization with 75\% ethanol, the
seeds were rinsed 5-8 times repeatedly with distilled water, then soaked
in distilled water for 8 hours and placed on a towel to germinate. The
radicle was transferred to a hydroponic pot at 2 cm. Once the cotyledons
were fully expanded, the samples were transferred to an incubator
(485*355*245 mm) for continued hydroponic growth in the Hoagland
nutrient solution. Each incubator contained 24 seedlings and 50 L of
Hoagland's nutrient solution.

Cotton plants were grown to the two true leaf stages, at which point
water treatments were initiated. The treatments included no-stress (NS,
0\% PEG6000) and drought stress (DS, 8\% PEG6000) for 15 days, and each
treatment had 24 replications. The pH of the nutrient solution was
adjusted to 6.0 ± 0.1, and the solution was replaced every 3 days. The
growth parameters included an average daily temperature of approximately
(28 ± 2) / (25 ± 2) °C (day/night), a photoperiod of 14/10 hours of
light/darkness, relative humidity of 40\%-50\%, and a light intensity of
600 \selectlanguage{greek}µ\selectlanguage{english}M. Oxygen was supplied twice daily, with each session lasting 30
minutes.

\textbf{2.2 Acridine orange staining and anatomical characterization}

Root segments (approximately 0.5 cm) were sampled in triplicate at 3 cm,
6 cm, 9 cm, and 12 cm from the taproot root tip 15 days after the
drought stress treatment. The root segments were stored in 75\% ethanol
and phenotypically analyzed for the presence of RCS in the cortex using
acridine orange staining, as described by Henry and Deacon (1981).
Acridine orange staining is a viable method to assess RCS. Acridine
orange fluorescent nuclei were used for phenotyping the onset of RCS
within individual cortical layers. The disappearance of the cortex,
determined by the percentage of viable cells, was used to indicate RCS
manifestation at the root cross-sectional level. The acridine
orange-stained root segments were embedded in a gelatin capsule with
Tissue-Tek CRYO-OCT compound (Thermo Fisher Scientific, Waltham, MA,
USA) and snap-frozen in liquid nitrogen before storage at -20 \selectlanguage{ngerman}°C.
Cross-sections (8 \selectlanguage{greek}µ\selectlanguage{english}m thick) of the embedded segments were cut using a
Freeze Slicer (Leica CM1950) and imaged using a laser scanning confocal
microscope (FV-1000, Olympus, Japan).

The main measurements using the cross-sectional images included area
determination and variable counting. The following area measurements
were obtained through pixel counting: the total cross-section area and
diameter, the stele area and diameter, and the cortex area and diameter.
The average cell size was also calculated by pixel counting, and the
cortical areas of acridine orange fluorescent nuclei were measured. The
number of acridine orange fluorescent nuclei was counted in all cell
layers of the 8 \selectlanguage{greek}μ\selectlanguage{english}m thick sections. The count data included the number of
cortical cells, cortical cell files, and cortical lacunae area. The
calculation formulas were as follows (Schneider et al., 2017b; Schneider
et al., 2017a; Schneider et al., 2018; Schneider and Lynch, 2018):

\(\mathrm{Cortical\ area=Cross\ section\ area\ }\mathrm{\ }\mathrm{\text{Stele\ area}}\)(1)\(\mathrm{Cortical\ diameter=Cross\ section\ diameter\ }\mathrm{\ }\mathrm{\text{Stele\ diameter}}\)(2)\(\mathrm{Stele/whole\ ratio=Stele\ area/Cross\ section\ area\ }\)
(3)\(\mathrm{Lacunae/cort}\mathrm{\text{ex}}\mathrm{\ ratio=Total\ lacunae\ area/Cort}\mathrm{\text{ex\ }}\mathrm{\text{area}}\ \)(4)\(\mathrm{Cortex/stele\ ratio=Cross\ section\ area/Stele\ area}\) (5)

RCS-free control samples were root segments collected at 3 cm from the
root tip (Schneider et al., 2017b). The cortical senescence percentage
was calculated by comparing the cortical area (total cross-sectional
area - stele area) at 3 cm from the root tip with the corresponding area
on the sampled position (Schneider et al., 2017b).

\textbf{2.3 The} \textbf{root metabolic enzyme activity and root
respiration}

Root segments (approximately 0.5 cm) sampled at 3 cm, 6 cm, 9 cm, and 12
cm from the taproot root tip 15 days after drought stress treatment were
used to measure root metabolic enzyme activity and root respiration. The
measurements were conducted in triplicate.

Phosphofructokinase activity levels were determined using the method
described by Baldwin et al. (2007), and changes in absorbance were
monitored continuously at 340 nm.

Malate dehydrogenase activity levels were determined using the method
described by Childress and Somero (1979), whereby the NADH oxidation was
quantified at 340 nm. Glucose-6-phosphate dehydrogenase activity induced
by the reduction of NADP+ to NADPH was measured at 340 nm (Antonietta
Ciardiello et al., 1995).

Root respiration was measured for 10 minutes using a LI-840A
CO\textsubscript{2}/H\textsubscript{2}O gas analyzer (LI-COR, Inc.,
Lincoln, NE, USA), after which the root segment was dried and weighed
(Sun et al., 2021).

\textbf{2.4 Endogenous hormone content}

Root segments were sampled as described in section 2.3. Phytohormone,
gibberellin (GA), zeatin riboside (ZR), indole-3-acetic acid (IAA),
brassinolide (BR) and abscisic acid (ABA) contents in the root segments
were determined using an Agilent 1260 High-Performance Liquid
Chromatograph with an Eclipse XDB-C18 column (250 mm \selectlanguage{ngerman}× 4.6 mm, 5 mm,
Agilent, CA, United States).

Briefly, 2 g of fresh root segments were ground in 10 mL of pre-cooled
80\% methanol in an ice bath. After centrifugation at 8,000 g for 10
minutes at 4°C, the supernatant was collected, and the residue was
resuspended in 8 mL pre-cooled 80\% methanol, followed by another round
of centrifugation and supernatant collection. The aqueous phase was then
extracted three times with 20 mL of ethyl acetate. The sample was
evaporated and dried at 40°C under pressure, and the separation of the
five endogenous phytohormones was conducted via gradient elution (Zhang
et al., 2013).

\textbf{2.5 Root system phenotyping}

Root samples were obtained in triplicate at 0, 5, 10, and 15 days after
drought stress treatment. Digital images with a resolution of 600 dots
per inch were obtained using a scanner (EPSON Expression 10000XL). The
root images were analyzed using WinRHIZO (Regent Instruments, Inc.,
Quebec City, Canada) and RootNav software (Christopher et al., 2013;
Pound et al., 2013) to obtain information on the root traits (Table S1)
(Zhang et al., 2021).

Thereafter, the fresh weight of the roots and aboveground parts was
obtained by weighing, and the biomass was obtained by drying at 105 °C
for 30 minutes and then at 75 °C for 72 hours to constant weight.

\textbf{2.6} \textbf{Morphological and physiological traitsof the
aboveground parts}

Aboveground parts were sampled as described in section 2.5. Plant height
was measured using a straightedge, and leaf area was calculated using
the length and width factor method. Stem diameter was recorded with a
vernier caliper.

The ratio of variable to maximum fluorescence (\emph{F} v/\emph{F} m)
was measured using a portable Chlorophyll Fluorescence monitoring system
(FMS-2, Hansatech, King's Lynn, UK). The relative chlorophyll content
was measured using a SPAD chlorophyll meter (SPAD-502; Konica Minolta,
Tokyo, Japan) while avoiding the leaf veins. The leaf water potential
was measured with a Model 600 plant pressure chamber (PMS, USA), while
stomatal size and density were measured using a combination of nail
polish and sticky tape. The relative water content and leaf water
saturation deficit were determined by weighing (Yang et al., 2022).
These measurements were based on the third functional leaf and were
conducted between 9:00 and 11:00.

\textbf{2.7 Statistical analysis}

Microsoft Excel 2019 (Statistical Product and Service Solutions) was
used to record and organize the data. Analysis of variance (ANOVA) was
performed using SPSS 26.0 (IBM Corp., Armonk, NY, USA), and graphs were
generated using Origin 2019b and GraphPad Prism 9.0 (GraphPad Software,
Inc., San Diego, CA, USA). Schematics and typography were created using
Adobe Illustrator 2020, and structural equation modeling was conducted
using R version 4.3.1. Phenotypic analysis of the root cross-sectional
images was performed using Image J software.

\textbf{3 RESULTS}

\textbf{3.1 Cortical senescence percentage, cortical cell files and
cortical lacunae area indicate the progress of RCS}

To investigate the spatio-temporal characteristics of cotton RCS, we
employed a laser scanning confocal microscope to scrutinize the
anatomical structures from the root tip to the root end, as depicted in
Figure 1. The lacunae area is the principal indicator of RCS. Drought
stress significantly promted the degree of lacunae area. In other words,
the lacunae area was larger in tissues far from the root tip under
drought stress compared to no-stress treatment. Both varieties exhibited
an increasing lacunae area with the increasing distance from the root
tip under no-stress conditions (Figure 1). The occurrence of RCS varied
in the two varieties. Under drought stress, RCS in ''Guoxin 02''
significantly increased compared to ''Ji 228.''.

The cortical senescence percentage, cortical cell files and cortical
lacunae area provide the basis for evaluating the progression and
patterns of RCS within the root sections (Schneider et al., 2017b). The
percentage of the senescent cortex is used to compute the disparity in
cross-sectional areas (total cross-sectional area --- stele area) at 3
cm from the root apex and at the sample position (Schneider et al.,
2017b). Essentially, this determines the difference in the
cross-sectional area between the sample and the root tip, providing a
quantitative measure of cortical senescence. Among these parameters,
cortical senescence percentage is a key indicator for evaluating RCS.
RCS was evidenced by the disappearance and absence of the root cortex,
which included cortical cell files and cortical lacunae area (Figures
2BC).

Drought stress significantly promoted the occurrence of RCS in cotton.
Compared with no-stress, the cortical senescence percentage in ''Guoxin
02'' increased significantly by 26.26\%-56.69\% (Range of all root
segments) and by 22.56\%-35.48\% in ''Ji 228'' under drought stress
(Figure 2A).

Changes of the cortical senescence percentage in different root
segments. Under drought stress, the occurrence of RCS was directly
proportional to the distance from the root tip, meaning that the
frequency of RCS significantly increased with increasing distance from
the root tip (Figure 1). The cortical senescence percentage of ''Guoxin
02'' and ''Ji 228'' at 12 cm segment from the root tip was 82.43\% and
75.64\% higher than that at the 6 cm root segment, respectively (Figure
2A).

Significant genotypic differences in RCS were also observed between
''Guoxin 02'' and ''Ji 228'' (\emph{p} \textless{} 0.01). After 15 days
of drought stress, ''Guoxin 02'' had a higher occurrence rate of RCS
than ''Ji 228''. Specifically, the cortical senescence percentage of the
''Guoxin 02'' increased significantly from 14.03\% to 20.18\% compared
to ''Ji 228'' under drought stress. (Figure 2A).

Drought stress significantly reduced the cortical cell files (Figure 2B)
of cotton but significantly increased the cortical lacunae areas (Figure
2C). Specifically, compared with no-stress treatment, the cortical
lacunae area of ''Guoxin 02'' and ''Ji 228'' significantly increased by
9.45 - 32.65 and 0.51 - 1.79 times, respectively, under drought stress
(Figure 2C). The cortical lacunae area significantly increased with
increasing distance from the root tip. Specifically, the cortical
lacunae area of ''Guoxin 02'' and ''Ji 228'' increased significantly by
26.13 times and 4.82 times, respectively, in the 12 cm segment compared
to the 3 cm segment from the root tip (Figure 2C).

\textbf{3.2 Association between RCS and reduced root metabolic costs}

Schneider et al.(2017b) established a negative correlation between RCS
and root metabolic costs. Therefore, to further underscore the
occurrence of RCS in cotton crops, we conducted a comprehensive series
of experiments involving measuring the changes in root metabolic enzyme
activities and root respiration across different root segments (Figure
3).

Compared to no-stress, drought stress reduced the phosphofructokinase,
malate dehydrogenase, and glucose-6-phosphate dehydrogenase activities,
and root respiration by 0\% - 21\%, 32\% - 34\%, 46\% - 51\%, and 20\% -
63\% in ''Guoxin 02'' and by 5\% - 19\%, 31\% - 39\%, 23\% - 37\%, and
9\% - 34\% in ''Ji 228'', respectively (Figures 3A-D).

Notably, the trend characteristics at different locations from the root
tip to the root end showed that RCS was inversely proportional to
phosphofructokinase, malate dehydrogenase, and glucose-6-phosphate
dehydrogenase activities, and root respiration. That is,
phosphofructokinase, malate dehydrogenase, and glucose-6-phosphate
dehydrogenase activities, and root respiration decreased significantly
with increasing distance of the root segments from the root tips
(Figures 3A-D). Specifically, compared to the 3 cm root segments,
phosphofructokinase, malate dehydrogenase, and glucose-6-phosphate
dehydrogenase activities, and root respiration significantly decreased
by 15.28\%, 10.34\%, 12.76\%, and 71.56\% in ''Guoxin 02'', (Figures
3B-D) and by 32.96, 25\%, 34.46\%, and 57.66\% in ''Ji 228'',
respectively (Figures 3A-D), in the 12 cm root segments under drought
stress.

In addition, root metabolic costs varied significantly among varieties.
Compared with ''Ji 228'', root respiration and glucose-6-phosphate
dehydrogenase activity of ''Guoxin 02'' decreased by 26.78\% and 19.39\%
(whole root segment), respectively, under drought stress (Figures 3DC).
Pearson correlation analysis showed that the cortical senescence
percentage at 6 cm, 9 cm and 12 cm from the root tip was positively
correlated with root respiration (\emph{p} \textless{}0.05, Figures
4D-F). In summary, the trends we observed in root metabolic costs
provide further scientific evidence of the presence of RCS in cotton.

\textbf{3.3 RCS affects thetissue structure of the root cortex}

The characteristics of each tissue structure in the different root
segments during RCS development were further analyzed (Figure 5A).
Drought stress increased the stole/whole ratio (Figure 5B) and
lacunae/cortex ratio (Figure 5C) but reduced the cortex/stele ratio
(Figure 5D).

Notably, the trend characteristics at different locations from the root
tip to the root end showed that RCS was directly proportional to the
stele/whole ratio, lacunae/cortex ratio, cross-section area,
cross-section diameter, stele area, stele diameter, cortical area, and
cortical diameter, but inversely proportional to the cortex/stele ratio.
That is, the stele/whole and lacunae/cortex ratio increased
significantly (Figures 5HJ), but the cortex/stele ratio decreased
significantly with increasing RCS (Figure 5I). Specifically, the
stele/whole ratio and the lacunae/cortex ratio increased by 7.14\% -
60.60\% and 50\% - 133.33\%, respectively, at the 12 cm segments
compared to 3 cm segment from the root tip (Figures 5HJ).

There were genotypic differences in the proportion variation of each
tissue structure. Among them, the stele/whole and lacunae/cortex ratios
of ''Guoxin 02'' were significantly higher (94.22\% and 117.02\%,
respectively) than those of ''Ji 228'' under drought stress (Figures
5HJ). However, the cortex/stele ratio was significantly reduced
(58.97\%) (Figure 5I).

\textbf{3.4 RCSis inversely proportional to endogenous}
\textbf{hormones}

To characterize the changes in endogenous hormone levels during RCS
development in cotton and the mechanism of their influence, we measured
the contents of five endogenous hormones in different root segments. GA,
ZR, IAA, and BR contents were decreased under drought stress compared to
the no-stress treatment (Figures 6A-D); however, ABA was increased
(Figure 6E).

Notably, the trend characteristics at different locations from the root
tip to the root end showed that RCS was inversely proportional to GA,
ZR, IAA, BR, and ABA contents. That is, GA, ZR, IAA, BR, and ABA
decreased significantly with increasing distance from the root tips
(Figures 6A-E). Specifically, compared to 3 cm root segments, GA, ZR,
IAA, BR, and ABA contents of ''Guoxin 02'' significantly decreased by
28.93\%, 68.91\%, 35.82\%, 65.15\%, and 60.01\%, respectively, in the 12
cm root segments under drought stress (Figures 6B-D). Similarly, the GA,
ZR, IAA, BR, and ABA contents of ''Ji 228'' significantly decreased by
47.12\%, 60.97\%, 56.14\%, 69.10\%, and 40.25\% in the 12 cm root
segments compared to the 3 cm root segments, respectively, under drought
stress (Figures 6B-D). The endogenous hormonal content of each segment
differed between the two varieties. Compared with ''Ji 228'', the GA and
ABA contents of ''Guoxin 02'' decreased by 24.74\% - 44.46\% (Figure 6A)
31.28\% - 54.01\% (Figure 6E), respectively, but IAA increased by
10.93\% - 69.33\% (Figure 6C).

Linear regression analysis showed a significant correlation between RCS
and the endogenous hormone content of the root system. GA and ABA
contents showed a decreasing trend with the increasing cortical
senescence percentage at 6 cm, 9 cm and 12 cm segments from the root tip
under drought stress (Figures S1 A-C and G-I). However, IAA showed an
increasing trend (Figures S1 D-F). Notably, the cortical senescence
percentage at the 12 cm segment from the root tip significantly
correlated with GA, ABA, and IAA contents (0.01 \textless{}
\emph{p}\textless{} 0.05).

\textbf{3.5 RCS is closely related to root and aboveground growth}

To clarify the physiological role of RCS in regulating the development
of the whole root system and aboveground parts, we measured several
traits, such as root architecture and shoots, at different time points
(Figures 7-8). There were genotypic differences in the response of
cotton roots to drought stress (Figure 7A). Specifically, compared to
''Ji 228'', the total root length, root dry weight, specific root
surface area, root volume, root tissue density, average length - lateral
roots, average length - all roots, and convex hull area of the ''Guoxin
02'' were increased by 45.92\%, 90.00\%, 98.70\%, 37.56\%, 84.00\%,
28.04\%, 30.63\%, 16.97\%, and 15.65\%, respectively, under drought
stress (Figures 7BCDEGIJM). However, the lateral root angle was
significantly reduced by 23.34\% (Figure 7K).

The impact of drought stress on aboveground traits of cotton plants was
the highest at 15 days after treatment (Figure S2 and Figure S3). Under
drought stress, the \emph{F} v/\emph{F} m, leaf area, leaf water
potential, plant height, relative leaf water content, stomatal length,
stomatal width, and stomatal opening of ''Guoxin 02'' increased by
80.21\%, 40.05\%, 15.56\%, 18.42 \%, 29.91\%, 15.09\%, 23.68\%, and
18.75\%, respectively, (Figures 8ABCFGIJK) compared to ''Ji 228''.
However, the leaf water saturation deficit and stomatal density showed a
significant reduction of 9.79\% and 5.91\%, respectively (Figures 8EL).

Pearson correlation analysis showed that the cortical senescence
percentage at the 6 cm, 9 cm and 12 cm segments from the root tip were
positively correlated with root total length, root dry weight, root
tissue density, leaf water potential, leaf relative water content,
and\emph{F} v/\emph{F} m (\emph{p} \textless{} 0.05, Figures 3ABCGHI).

\textbf{3.6 Structural equation model of RCS and drought tolerance}

To further investigate the direct and indirect effects of RCS on cotton
drought tolerance, we generated a structural equation model to examine
the relationship between RCS and cotton drought tolerance and determine
the path coefficients between the two (Figure 9). The structural
equation model showed a significant correlation between RCS and drought
tolerance, with a \emph{P} -value of 0.033*. This indicated that the
pathway was valid, with an effect coefficient of 0.986. Overall, the
model demonstrated a good fit (Table S2).

\textbf{4 Discussion}

\textbf{4.1 Characteristics and patterns of RSC in cotton under drought
stress}

This study reports for the first time the presence of the RCS in cotton,
a dicotyledonous crop of the mallow family. RCS has been widely explored
in monocotyledons, including wheat, triticale (\emph{Triticosecale} ),
barley (Yeates and Parker, 1986; Liljeroth, 1995), rye (\emph{Secale
cereale} ) (Deacon and Mitchell, 1985; Jupp and Newman, 1987), and oats
(\emph{Avena sativa} ) (Yeates and Parker, 1986). However, there are no
reports of RCS in dicotyledonous crops. The study also explored the
characteristics and occurrence mechanism of RSC in cotton under drought
stress.

To determine the spatio-temporal variation in the occurrence of RCS in
cotton, we examined the cortex to determine the absence of acridine
orange-stained fluorescent nuclei (Henry and Deacon, 1981). The two
cotton varieties showed a similar pattern of RCS, which started in the
outer cortical cell region and progressed inward. RCS was triggered
mainly by non-apoptotic programmed cell death of cortical cells
involving cells between the inner and outer cortex. According to laser
scanning confocal microscopy, the gap between the cortical cells
increased, and the cortical cells aged and underwent autolysis, with
their internal structures gradually disintegrating with the occurrence
of RCS. This disintegration gradually led to cortical cell contraction
and invagination. When the internal material was depleted, the residual
cell walls of the adjacent cells were superimposed, forming air spaces
on both sides. Despite the senescence and disintegration of the cortex,
the outer epidermis remained intact, and the outer epidermis, the air
space, and the stele were clearly distinguishable (Figure 1). Different
parts of the cotton root system, from the root tip to the root end,
reflect the root developmental processes, including root cell genesis,
differentiation, maturation, and death. In the root cross-section of
cotton grown under no-stress conditions, the root tip zone (3 cm from
the root tip) was almost devoid of RCS, but the root basal zone (12 cm
from the root tip) showed partial RCS. In summary, the spatial dimension
reveals that RCS shows an increasing trend in cotton with the increasing
distance from the root tip; that is, root cortical cells exhibit dynamic
changes from nucleated to non-nucleated forms from about the 3 cm region
of the root tip to the junction of the taproot (Figure 1).

Analysis of the acridine orange fluorescent nuclei in the cortical area,
cortical cell files, and cortical lacunae area also highlighted
significant changes in different locations from the root tip to the root
end. The cortical lacunae area showed a gradual increase (Figure 2C),
while cortical cell files showed an initial increase followed by a
decrease (Figure 2B), indicating that RCS mainly occurred in the
maturation zone. Notably, the stele remained viable despite the
occurrence of RCS, as demonstrated by the acridine orange fluorescent
nuclei. The proportion of the root tissues also changed dynamically with
increasing distance from the root tips (Figure 5). For example, the
stele/whole ratio and lacunae/cortex ratio increased (Figure 5BD), while
the cortex/stele ratio decreased (Figure 5C) with increasing root tip
distance.

Drought stress induced RCS in cotton. The root profile of cotton grown
under drought stress showed different spatio-temporal variations: the
root tip zone (3 cm from the root tip) was almost devoid of RCS, while
the root middle zone (6 cm from the root tip) showed partial RCS, and
the root end zone (12 cm from the root tip) showed the highest RCS rate.
However, drought stress did not change the occurrence point of RCS
relative to the root tip, but it did increase the degree of RCS
occurrence in cotton. After 15 days of drought stress treatment, the
cortical cells of cotton roots showed senescence, leaving only root tips
(Figure 1). Unlike the no-stress condition, the greater the distance
from the root tip, the more severe the RSC degree under drought stress,
which seriously affects root function. Therefore, we suggest that the
cotton aboveground resilience may exhibit reduced drought tolerance due
to premature RCS. Moreover, a previous study demonstrated that under low
P and low N conditions, the RCS of barley increased by 13\% and 11\%,
respectively (Schneider et al., 2017b), while shading decreased the RCS
of wheat and barley (Lewis and Deacon, 1982). Thus, RCS is a complex
phenomenon that depends on environmental factors, plant species, and
genetic factors (Liljeroth, 1995; Schneider et al., 2017a).

The extent to which RCS occurs varies among plant species. For example,
in 15-day-old wheat roots, 80\% - 90\% of the root cortical cells were
non-nucleated (Liljeroth, 1995), whereas only 20\% - 35\% were
non-nucleated in barley and rye (Liljeroth, 1995). This suggests that
wheat has a relatively higher degree of RCS compared to other plants
(such as wheat, barley, rye, and oats) (Deacon and Henry, 1980; Deacon
and Mitchell, 1985; Henry and Deacon, 1981; Holden, 1976; Liljeroth,
1995; Yeates and Parker, 1986). However, the comparison of RCS between
cotton and other crops has not been reported. Our study showed that only
40\% - 50\% of the root cortical cells of cotton plants grown for 31
days (from planting to harvesting) were non-nucleated (Figure 2B). Thus,
the degree of RCS in the 31-day-old cotton roots was reduced by about
20\% compared to the 15-day-old wheat. Therefore, the incidence of RCS
in cotton was lower than in wheat; however, this should also be compared
with other crops (such as triticale, barley, rye, and oats).

RCS exhibits significant genetic variability in cotton. The two cotton
varieties (with different drought tolerance) used in this study showed
significant differences in RCS. ''Guoxin 02'' had a significantly higher
rate of RSC than ''Ji 228'' under drought stress (Figure 2A), indicating
that a higher RCS rate may be a key feature of the plant's ability to
cope with drought stress. Similarly, differences in RCS have been
reported between wheat and barley varieties (Henry and Deacon, 1981;
Liljeroth, 1995). Therefore, the variation in RCS was mainly influenced
by the genotypic differences under drought stress. However, the genetic
and molecular mechanisms underlying this variation in cotton need
further exploration.

\textbf{4.2 RCS reduces root respiration}

Root respiration is closely related to root maintenance costs. Root
respiration, growth, maintenance, and ion uptake are the main components
of root metabolic costs (Lambers, 1979; Van Der Werf et al., 1988; Lynch
et al., 2005). Root metabolic costs involve changes in the internal
configuration of the organelle, where the thickness between the
cytoplasm and the vacuole membrane remains constant, such that the
increase in RCS is mainly due to increased vacuole size (Gunawardena et
al., 2001). Increased vacuole volume relative to the cytoplasm may
reduce respiration because metabolic activity is higher in the cytoplasm
than in the vacuole (Schussler and Longstreth, 1996). Our study
quantified cotton root respiration in the mature region and found that
total respiration in this region primarily involves tissue maintenance
respiration. By inducing the transition from nucleated to non-nucleated
cells, RCS alters the maintenance of respiration in the mature zone,
emphasizing the need to maintain metabolic homeostasis in the cotton
root system (Lynch et al., 2005; Lynch and Brown, 2008). Since RCS
mainly occurs in the mature zone of the cortex, it affects root
maintenance costs rather than construction costs. Therefore, we
hypothesized that under drought stress, RCS significantly affects
maintenance costs rather than construction costs and greatly impacts the
ability of plants to adapt to arid environments.

This study revealed, for the first time, a significant negative
correlation between RCS and root respiration in cotton under drought
stress (\emph{p} \textless{} 0.01, Lynch, 2015). Compared with ''Ji
228'', the root respiration and glucose-6-phosphate dehydrogenase
activity (whole-root segment) of drought-resistant variety decreased by
26.78\% and 19.39\%, respectively. These results support the hypothesis
that under drought stress, varieties with lower root metabolic costs
seem to possess the ability to develop extensive and deep root systems
to fully utilize soil water resources without yield losses (Figure 9). A
previous study exploring the utility of RCS in barley under
nutrient-deficient conditions indicated that by reducing root
respiration, RCS can promote root growth and soil resource acquisition
in barley (Schneider et al., 2017b). This is because some metabolic
activities may diminish or stagnate as cells senesce, and senescent
cells require less energy and nutrients, leading to reduced energy
expenditure and metabolic costs (Postma and Lynch, 2011; Jaramillo et
al., 2013; Hu et al., 2014b; Saengwilai et al., 2014).

Based on this, we speculate that whether in barley and cotton crops or
under nutrient-deficient and drought-stress environments, the reduced
metabolic consumption of photosynthetic products caused by RCS may not
result from stress damage but rather an adaptive strategy. This complex
mechanism helps plants to cope more effectively with environmental
stresses while retaining energy for more important survival needs
(Chimungu et al., 2014a; Chimungu et al., 2014a; Lynch et al., 2014;
Saengwilai et al., 2014). Perhaps, in older root segments, RCS may
reduce radial water transport when resources in the root edge have been
depleted. Therefore, the loss of cortex means that the resource
acquisition function of young root tissues changes to support function,
axial transport, and storage of mature root tissue.

\textbf{4.3 RCS mediates resource redistribution for root growth and
improves drought tolerance}

Under abiotic stress, resource redistribution during RCS is more
important for increasing root absorption area and improving aboveground
drought tolerance of plants than is reducing root respiration (Postma
and Lynch, 2011; Jaramillo et al., 2013; Chimungu et al., 2014b) In
addition, the enlargement of cortical cells and the reduction of
cortical cell layers are important features of RCS. Research on corn has
shown that under field water stress, varieties with large cortical cells
had a root depth of 32\% to 41\%, a relative water content of 22\% to
30\% in the leaves, and a yield increase of 99\% to 145\% compared to
varieties with small cortical cells (Chimungu et al., 2014b). Meanwhile,
compared with varieties with increased cell layers in the root cortex,
corn varieties with reduced cell layers in the root cortex had a root
depth of 33\% to 40\%, a relative water content of 10\% to 35\% in the
leaves, a biomass of 35\% to 70\% in the stems and leaves, and a yield
increase of 33\% to 114\% (Chimungu et al., 2014a). This may be due to
the release of a large amount of nutrients from aging cells in the
cortex, which may significantly impact plant growth throughout the
entire growth season by supporting higher growth rates and subsequent
larger soil exploration (Lynch et al., 2014; Saengwilai et al., 2014).
Therefore, in maize, growth was promoted under stress by increased
cortical cell size and decreased number of root cortical cell layers.
However, there are no reports on the impact of resource reallocation
during RCS on the drought tolerance of cotton crops.

Based on the aboveground traits, this study is the first to explore the
impact of resource redistribution during RCS on cotton drought
tolerance. We found that RCS helps promote cotton growth, with ''Guoxin
02'' exhibiting greater root dry weight, total root length, specific
root surface area, and convex hull area, as well as higher leaf water
potential and relative leaf water content under drought stress. This is
probably due to the changes in the root structure caused by the aging of
the root cortex tissues, such as changes in internal cell walls and
morphological adjustments. The correlation analysis indicated that RCS
was significantly positively correlated with leaf water potential, leaf
relative water content, and \emph{F} v/\emph{F} m (\emph{p} \textless{}
0.01). These results support the hypothesis that RCS leads to more
extensive branching and deeper roots, thereby expanding the surface area
of the roots. This conferred the cotton plants with a stronger survival
advantage by fully utilizing deep-water resources, enabling the plants
to better adapt to arid environments and maintain normal aboveground
growth while improving drought tolerance (Figure 9, Postma and Lynch,
2011; Schneider et al., 2017a). We consider this effect as the
self-catalytic effect.

\textbf{4.4 IAA andABA regulate RCS}

The intrinsic developmental signals of plants work in synergy with
external environmental factors, inducing changes in different endogenous
hormone levels within the plant, thereby regulating the aging process in
plant tissues. Although some plant hormones, such as IAA and ABA, have
been shown to regulate leaf senescence, their role in root senescence
has not been fully elucidated (Schippers et al., 2015).

Our research found that the IAA content of ''Guoxin 02'' was
significantly higher than that of ''Ji 228'', and its content increased
with the increasing distance from the root tips (Figure 6C). This may be
because IAA promotes cell elongation and volume increase, which
increases the root growth to adapt to drought stress. Similar trends
have also been reported in rice, indicating the key role of IAA in
enhancing drought tolerance in rice seedlings (Zhang et al., 2009). Our
study also found that increased IAA content could improve the drought
tolerance of cotton, and the RCS degree of ''Guoxin 02'' is
significantly increased with increased IAA content, indicating that IAA
has a positive regulatory effect on plant RCS (Figures S1 D-F).

As a plant sesquiterpene, ABA is involved in regulating plant growth and
development processes and responding to various biotic and abiotic
stresses (Jibran et al., 2013). Our research showed that the ABA content
decreased as the distance from the root tip increased (Figure 6E). This
result is similar to that reported in barley, whereby the ABA content
was higher in the root tip compared to the root basal zone (Liu et al.,
2019). This is probably because the root tip is the growth point of the
root system, with more active and complex cellular activities, promoting
the activity of the ABA synthesis pathway. In addition, we found that
the reduction of ABA could improve the drought tolerance of cotton and
significantly increase the RCS degree of the drought-tolerance variety,
suggesting the negative regulatory effect of ABA on cotton RCS (Figures
S1 G-I).

In conclusion, this study confirms for the first time the presence of
RCS in cotton, a dicotyledonous crop, and deeply explores the
characteristics and patterns of RCS in cotton under drought stress. We
systematically studied the characteristics, patterns, and
interrelationships of root metabolic activity and endogenous hormones
during the RCS, as well as their regulatory effects on roots and
aboveground parts. We found that RCS enhances cotton drought tolerance
by reducing root metabolic costs and regulating endogenous hormone
content (Figure 10).

\textbf{ACKNOWLEDGEMENT}

This research was funded by National Natural Science Foundation of China
(No. 32172120 and No.32272220).

\textbf{CONFLICT OF INTEREST STATEMENT}

The authors declare no conflict of interest.

\textbf{DATA AVAILABILITY STATEMEN} The data that has been used is
confidential.



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\textbf{Figure legends}

\textbf{FIGURE 1} The transverse cross-sections of the taproot of
''Guoxin 02'' and ''Ji 228'' varieties sampled at different areas on the
taproot 15 days after the treatment and stained with acridine orange
fluorescent nuclei. Note: acridine orange fluorescent nuclei are not
visible in these images because they are located deeper within the
tissue, as indicated by a laser scanning confocal microscope. The red
arrow indicates the lacunae area.

\textbf{FIGURE 2} Characteristics and occurrence mechanisms of RCS in
different cotton varieties after 15 days of drought stress. Statistical
analysis of (A) the cortical senescence percentage, (B) cortical cell
files and (C) the cortical lacunae area. DT type, Drought-tolerant type
''Guoxin 02''; DS type, Drought-sensitive type ''Ji 228''; NS,
no-stress; DS, drought stress; C, varieties; D, drought. Error bars
represent standard errors of the means. Different lowercase letters in
the figure indicate significant differences between the same root
segments from different cotton varieties under drought stress treatments
(\emph{p} \selectlanguage{english}[?] 0.05). \emph{P} \selectlanguage{english}>0.05, no significance (ns);\emph{P} \selectlanguage{english}<0.01
significant difference (**).

\textbf{FIGURE 3} Effects of the drought treatment on the root metabolic
costs of cotton. Statistical analysis of (A) phosphofructokinase, (B)
malate dehydrogenase, (C) glucose-6-phosphate dehydrogenase, and (D)
root respiration activities at 3 cm, 6 cm, 9 cm, and 12 cm segments from
the taproot tips of the ''Guoxin 02'' and ''Ji 228''. (E) Comparative
image showing the differences in root metabolic costs between ''Guoxin
02'' and ''Ji 228'' under drought stress. Long and short arrows
represent higher and lower metabolic costs, respectively. DT type,
Drought-tolerant type ''Guoxin 02''; DS type, Drought-sensitive type
''Ji 228''; NS, no-stress; DS, drought stress; C, varieties; D, drought.
Error bars represent standard errors of the means. \emph{P} \selectlanguage{english}>0.05,
\emph{P} \selectlanguage{english}<0.05 and\emph{P} \selectlanguage{english}<0.01 indicate no significance (ns),
significant difference (*) and highly significant difference (**),
respectively. Different lowercase letters in the figure indicate
significant differences between different root segments under the same
treatment (\emph{P} \selectlanguage{english}[?] 0.05).

\textbf{FIGURE 4} Linear regression analysis showing the relationship
between RCS and root growth, root metabolic costs, and aboveground
growth under drought stress. (A) Total root length, (B) root dry weight,
(C) root tissue density, (D) root respiration at 6 cm from the root tip,
(E) root respiration at 9 cm from the root tip, (F) root respiration at
12 cm from the root tip, (G) leaf water potential, (H) leaf relative
water content, and (I) the ratio of variable to maximum fluorescence
(\emph{F} v/\emph{F} m ).

\textbf{FIGURE 5} Effects of drought stress on the ratio and size of
root cross-section in the two cotton varieties. (A) Representative image
of the root tissue size of ''Guoxin 02'' under drought stress.
Statistical analysis of (B) stele/whole ratio, (C) cortex/stele ratio,
and (D) lacunae/cortex ratio at the 3 cm, 6 cm, 9 cm, and 12 segments cm
from the root tips of ''Guoxin 02'' and ''Ji 228''. NS, no-stress; DS,
drought stress; C, varieties; D, drought. Error bars represent standard
errors of the means. \emph{P} \selectlanguage{english}>0.05, no significance (ns); \emph{P}
\selectlanguage{english}<0.01 significant difference (**). Different lowercase letters in the
figure indicate significant differences between different root segments
under the same treatment (\emph{P} \selectlanguage{english}[?] 0.05).

\textbf{FIGURE 6} Effects of the drought treatment on phytohormone
levels in the two cotton varieties. Statistical analysis of (A)
gibberellin, (B) zeatin riboside, (C) indole-3-acetic acid, (D)
brassinolide, and (E) abscisic acid contents at the 3 cm, 6 cm, 9 cm,
and 12 cm segments from the taproot tips of the ''Guoxin 02'' and ''Ji
228''. DT type, Drought-tolerant type ''Guoxin 02''; DS type,
Drought-sensitive type ''Ji 228''; NS, no-stress; DS, drought stress.
Error bars represent standard errors of the means. \emph{P} \selectlanguage{english}>0.05, no
significance (ns); \emph{P} \selectlanguage{english}<0.05, significant difference (*);\emph{P}
\selectlanguage{english}<0.01, highly significant difference (**). Different lowercase letters
in the figure indicate significant differences between different root
segments under the same treatment (\emph{P} \selectlanguage{english}[?] 0.05).

\textbf{FIGURE 7} Effects of drought stress on the root traits of
''Guoxin 02'' and ''Ji 228''. (A) Representative images of root traits
of the two varieties at 5 d, 10 d and 15 d after drought stress.
Statistical analysis of the (B) total root length, (C) root dry weight,
(D) specific root surface area, (E) root volume, (F) average diameter,
(G) root tissue density, (H) taproot length, (I) average length-lateral
roots, (J) Average length- all root, (K) lateral root angle, (L) lateral
root count, and (M) convex hull area. DT type, Drought-tolerant type
''Guoxin 02''; DS type, Drought-sensitive type ''Ji 228''. NS,
no-stress; DS, drought stress; C, varieties; D, drought. Error bars
represent standard errors of the means. \emph{P} \selectlanguage{english}<0.05, significant
difference (*); \emph{P} \selectlanguage{english}<0.01, highly significant difference (**).

\textbf{FIGURE 8} Effects of drought stress on the aboveground traits of
''Guoxin 02'' and ''Ji 228''. (A) Representative images of the growth of
''Guoxin 02'' and ''Ji 228'' at 0, 5, 10 and 15 days after drought
stress. Statistical analysis of the (B) ratio of variable to maximum
fluorescence (\emph{F} v/\emph{F} m), (C) leaf area, (D) leaf water
potential, (E) steam diameter, (F) leaf water saturation deficit, (G)
plant height, (H) relative water content, (I) SPAD value, (J) stomatal
length, (K) stomatal width, (L) stomatal opening, and (M) stomatal
density. DT type, drought-tolerant type ''Guoxin 02''; DS type,
drought-sensitive type ''Ji 228''. CK, no-stress; DS, drought stress; C,
varieties; D, drought. Error bars represent standard errors of the
means. \emph{P} \selectlanguage{english}>0.05, no significance (ns); \emph{P} \selectlanguage{english}<0.01, highly
significant difference (**). Different lowercase letters in the figure
indicate significant differences among different varieties and
treatments at the same time point (\emph{P} \selectlanguage{english}[?] 0.05).

\textbf{FIGURE 9} Structural equation modeling showing the effect of RCS
on drought tolerance of cotton under drought stress. The standardized
loading coefficients are indicated by the numbers. Positive loading
coefficients signify positive correlations between the measured
variables and the factors, while negative loading coefficients indicate
negative correlations. The red solid and dashed lines represent positive
and negative correlations, respectively.

\textbf{FIGURE 10} The regulatory model and function of RCS in the root
growth and development of different cotton varieties. Comparative
analysis of the regulatory mechanism of RCS on drought tolerance of the
different cotton under drought stress. In drought-tolerance varieties,
endogenous hormones (showing increased indole-3-acetic acid and reduced
abscisic acid contents) are involved in the regulation of RCS. Increased
lacunae/cortex ratio, reduced cortical cell files and cortex/stele
ratio, further reduced root respiration and metabolic enzyme activity
(phosphofructokinase, malate dehydrogenase and glucose-6-phosphate
dehydrogenase). This might be due to the redistribution of more
resources to root growth (reflected by increased root dry weight, total
root length, and convex hull area), ultimately increasing the
aboveground drought tolerance (reflected by increased leaf water
potential, relative leaf water content, and stomatal opening under
drought stress). The regulatory mechanism of RCS varies based on plant
species, drought severity and specific growth stages. ''Ji 228''
exhibited a lower incidence of RCS than ''Guoxin 02''. Pink arrows
denote positive regulation, while green arrows indicate negative
regulation. Long and short arrows represent higher and lower metabolic
costs, respectively.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{FIGURE 1} The transverse cross-sections of the taproot of
''Guoxin 02'' and ''Ji 228'' varieties sampled at different areas on the
taproot 15 days after the treatment and stained with acridine orange
fluorescent nuclei. Note: acridine orange fluorescent nuclei are not
visible in these images because they are located deeper within the
tissue, as indicated by a laser scanning confocal microscope. The red
arrow indicates the lacunae area.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{FIGURE 2} Characteristics and occurrence mechanisms of RCS in
different cotton varieties after 15 days of drought stress. Statistical
analysis of (A) the cortical senescence percentage, (B) cortical cell
files and (C) the cortical lacunae area. DT type, Drought-tolerant type
''Guoxin 02''; DS type, Drought-sensitive type ''Ji 228''; NS,
no-stress; DS, drought stress; C, varieties; D, drought. Error bars
represent standard errors of the means. Different lowercase letters in
the figure indicate significant differences between the same root
segments from different cotton varieties under drought stress treatments
(\emph{p} [?] 0.05).\emph{P} >0.05, no significance (ns); \emph{P} <0.01
significant difference (**).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{FIGURE 3} Effects of the drought treatment on the root metabolic
costs of cotton. Statistical analysis of (A) phosphofructokinase, (B)
malate dehydrogenase, (C) glucose-6-phosphate dehydrogenase, and (D)
root respiration activities at 3 cm, 6 cm, 9 cm, and 12 cm segments from
the taproot tips of the ''Guoxin 02'' and ''Ji 228''. DT type,
Drought-tolerant type ''Guoxin 02''; DS type, Drought-sensitive type
''Ji 228''; NS, no-stress; DS, drought stress; C, varieties; D, drought.
Error bars represent standard errors of the means. \emph{P}
>0.05,\emph{P} <0.05 and \emph{P} <0.01 indicate no significance
(ns), significant difference (*) and highly significant difference (**),
respectively. Different lowercase letters in the figure indicate
significant differences between different root segments under the same
treatment (\emph{P} [?] 0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{FIGURE 4} Linear regression analysis showing the relationship
between RCS and root growth, root metabolic costs, and aboveground
growth under drought stress. (A) Total root length, (B) root dry weight,
(C) root tissue density, (D) root respiration at 6 cm from the root tip,
(E) root respiration at 9 cm from the root tip, (F) root respiration at
12 cm from the root tip, (G) leaf water potential, (H) leaf relative
water content, and (I) the ratio of variable to maximum fluorescence
(\emph{F} v/\emph{F} m ).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{FIGURE 5} Effects of drought stress on the ratio and size of
root cross-section in the two cotton varieties. (A) Representative image
of the root tissue size of ''Guoxin 02'' under drought stress.
Statistical analysis of (B) stele/whole ratio, (C) cortex/stele ratio,
and (D) lacunae/cortex ratio at the 3 cm, 6 cm, 9 cm, and 12 segments cm
from the root tips of ''Guoxin 02'' and ''Ji 228''. NS, no-stress; DS,
drought stress; C, varieties; D, drought. Error bars represent standard
errors of the means. \emph{P} >0.05, no significance (ns); \emph{P}
<0.01 significant difference (**). Different lowercase letters in the
figure indicate significant differences between different root segments
under the same treatment (\emph{P} [?] 0.05)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{FIGURE 6} Effects of the drought treatment on phytohormone
levels in the two cotton varieties. Statistical analysis of (A)
gibberellin, (B) zeatin riboside, (C) indole-3-acetic acid, (D)
brassinolide, and (E) abscisic acid contents at the 3 cm, 6 cm, 9 cm,
and 12 cm segments from the taproot tips of the ''Guoxin 02'' and ''Ji
228''. DT type, Drought-tolerant type ''Guoxin 02''; DS type,
Drought-sensitive type ''Ji 228''; NS, no-stress; DS, drought stress.
Error bars represent standard errors of the means. \emph{P} >0.05, no
significance (ns); \emph{P} <0.05, significant difference (*);\emph{P}
<0.01, highly significant difference (**). Different lowercase letters
in the figure indicate significant differences between different root
segments under the same treatment (\emph{P} [?] 0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{FIGURE 7} Effects of drought stress on the root traits of
''Guoxin 02'' and ''Ji 228''. (A) Representative images of root traits
of the two varieties at 5d, 10d and 15d after drought stress.
Statistical analysis of the (B) total root length, (C) root dry weight,
(D) specific root surface area, (E) root volume, (F) average diameter,
(G) root tissue density, (H) taproot length, (I) average length-lateral
roots, (J) Average length- all root, (K) lateral root angle, (L) lateral
root count, and (M) convex hull area. DT type, Drought-tolerant type
''Guoxin 02''; DS type, Drought-sensitive type ''Ji 228''. NS,
no-stress; DS, drought stress; C, varieties; D, drought. Error bars
represent standard errors of the means. \emph{P} <0.05, significant
difference (*);\emph{P} <0.01, highly significant difference (**).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{FIGURE 8} Effects of drought stress on the aboveground traits of
''Guoxin 02'' and ''Ji 228''. (A) Representative images of the growth of
''Guoxin 02'' and ''Ji 228'' at 0d, 5d, 10d, and 15d after drought
stress. Statistical analysis of the (B) ratio of variable to maximum
fluorescence (\emph{F} v/\emph{F} m), (C) leaf area, (D) leaf water
potential, (E) steam diameter, (F) leaf water saturation deficit, (G)
plant height, (H) relative water content, (I) SPAD value, (J) stomatal
length, (K) stomatal width, (L) stomatal opening, and (M) stomatal
density. DT type, drought-tolerant type ''Guoxin 02''; DS type,
drought-sensitive type ''Ji 228''. CK, no-stress; DS, drought stress; C,
varieties; D, drought. Error bars represent standard errors of the
means. \emph{P} >0.05, no significance (ns); \emph{P} <0.01, highly
significant difference (**). Different lowercase letters in the figure
indicate significant differences among different varieties and
treatments at the same time point (\emph{P} [?] 0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{FIGURE 9} Structural equation modeling showing the effect of
root cortical senescence (RCS) on drought tolerance of cotton under
drought stress. The standardized loading coefficients are indicated by
the numbers. Positive loading coefficients signify positive correlations
between the measured variables and the factors, while negative loading
coefficients indicate negative correlations. The red solid and dashed
lines represent positive and negative correlations, respectively.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{FIGURE 10} The regulatory model and function of root cortical
senescence (RCS) in the root growth and development of different cotton
varieties. Comparative analysis of the regulatory mechanism of RCS on
drought tolerance of the different cotton under drought stress. In
drought-tolerance varieties, endogenous hormones (showing increased
indole-3-acetic acid and reduced abscisic acid contents) are involved in
the regulation of RCS. Increased lacunae/cortex ratio, reduced cortical
cell files and cortex/stele ratio, further reduced root respiration and
metabolic enzyme activity (phosphofructokinase, malate dehydrogenase and
glucose-6-phosphate dehydrogenase). This might be due to the
redistribution of more resources to root growth (reflected by increased
root dry weight, total root length, and convex hull area), ultimately
increasing the aboveground drought tolerance (reflected by increased
leaf water potential, relative leaf water content, and stomatal opening
under drought stress). The regulatory mechanism of RCS varies based on
plant species, drought severity and specific growth stages. ''Ji 228''
exhibited a lower incidence of RCS than ''Guoxin 02''. Pink arrows
denote positive regulation, while green arrows indicate negative
regulation. Long and short arrows represent higher and lower metabolic
costs, respectively.

\selectlanguage{english}
\FloatBarrier
\end{document}

