Acta Physiologiae Plantarum (2020) 42:29
https://doi.org/10.1007/s11738-020-3016-5
ORIGINAL ARTICLE
Phenotyping and characterization of heat stress tolerance
at reproductive stage in rice (Oryza sativa L.)
Sourabh Karwa1,2 · Rajeev Nayan Bahuguna1,4 · Ashish K. Chaturvedi1,3 · Sadhana Maurya1 · Sunder Singh Arya2 ·
Viswanathan Chinnusamy1 · Madan Pal1
Received: 17 May 2019 / Revised: 7 January 2020 / Accepted: 22 January 2020 / Published online: 3 February 2020
© Franciszek Górski Institute of Plant Physiology, Polish Academy of Sciences, Kraków 2020
Abstract
Rice crop is known to be sensitive to heat stress particularly at the flowering stage. Breeding approaches for improving heat
tolerance in rice needs understanding of heat tolerance mechanisms and suitable heat tolerance donors. A study was planned
for screening of rice genotypes and identification of novel heat tolerant donor(s) and physiologically characterize the component traits using contrasting set of genotypes in green house environment. Genotypes were categorized as tolerant and sensitive to heat stress using heat susceptibility index and cumulative stress response index for spikelet fertility, pollen viability,
and grain yield. Among the set of genotypes screened, IET 22218 recorded high spikelet fertility (> 85%), pollen viability
(> 95%) at high temperature (39–44 °C) with relative humidity (> 60–80%). This genotype recorded higher photosynthesis,
canopy temperature depression, and accumulation of endogenous level of polyamines both under optimum and heat stress
environments. Moreover, IET 22218 genotype recorded lower H2O2 accumulation, membrane damage and higher activity
of antioxidant enzymes. Heat stress tolerance in IET 22218 was at par with heat tolerant checks, i.e., Nagina22 (N22) and
Nerica L-44 (NL-44). Interestingly, IET 22218 also maintained lower chalkiness (< 34%) and higher head rice yield (> 85%)
under heat stress. Based on above traits IET 22218 was selected as the novel donor for heat tolerance. The study concludes
that induced polyamines and antioxidant enzymes activity in IET 22218 under stress were associated with lowering oxidative
stress and maintained higher pollen viability and spikelet fertility under heat stress environment. However, more studies are
recommended to understand the role of polyamines in heat stress tolerance specifically in rice.
Keywords Antioxidant enzymes · Grain quality · Photosynthesis · Polyamines · Rice · Spikelet fertility
Introduction
Communicated by J. Huang.
Electronic supplementary material The online version of this
article (https://doi.org/10.1007/s11738-020-3016-5) contains
supplementary material, which is available to authorized users.
Rice is an important staple crop and feeds nearly 3.5 billion
people across the globe (https://ricestat.irri.org:8080/wrsv3
/entrypoint.htm, accessed on 10 April 2018). Food demand
Viswanathan Chinnusamy
viswa_iari@hotmail.com
* Madan Pal
madanpal@yahoo.com
Sourabh Karwa
sourabh86karwa@yahoo.co.in
1
Plant Physiology Division, ICAR-Indian Agricultural
Research Institute (IARI), New Delhi 12, India
Rajeev Nayan Bahuguna
r.bahuguna15@gmail.com
2
Botany Department, Maharshi Dayanand University, Rohtak,
Haryana 124 001, India
Ashish K. Chaturvedi
ashispc@gmail.com
3
Water Management (Agriculture) Division, CWRDM
Kunnamangalam, Kozhikode, Kerala 673 571, India
Sadhana Maurya
sadhanamry@gmail.com
4
Center for Advanced Studies on Climate Change,
Dr. Rajendra Prasad Central Agricultural University, Pusa,
Samastipur, Bihar 848 125, India
Sunder Singh Arya
aryasunder.hau@gmail.com
13
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Acta Physiologiae Plantarum (2020) 42:29
Page 2 of 16
would increase with rising global population (Godfray et al.
2010) and more rice would be required to feed the majority of rice consuming population living in Asia and Africa
(FAO 2014). Despite of high production demand, limiting
water resources and rising temperature are posing serious
threat to global rice production (Schleussner et al. 2018).
Conversely, frequent occurrence of heat waves reported at
regional scale in last decade had catastrophic impact on
agricultural crop production across the globe (Kadam et al.
2014). The scenario A1B for future climatic change suggested that nearly, 121 million ha of irrigated global rice
area will be vulnerable to rising temperature by 2100 (Teixeira et al. 2013). Rice is reported to be sensitive to heat stress
at flowering/ anthesis phase (Yoshida et al. 1981; Jagadish
et al. 2007, 2008; Sarsu 2018). Exposure to heat stress during reproductive stage results in impaired and poor pollen
development and their germination on stigma and high sterility of spikelet in rice (Jagadish et al. 2010; Powell et al.
2012). Moreover, heat stress could alter different physiological and molecular processes that affect several growth and
development process from germination to maturity (Bahuguna and Jagadish 2015). Increase in reactive oxygen species
(ROS) content is one of the primary events under heat stress,
which could result in peroxidation of lipids affecting the
membrane integrity. However, plants have a robust antioxidant defense mechanism to maintain ROS levels below the
harmful limits (Szymańska et al. 2017). Heat stress affects
the photosynthetic system through change in photochemical reactions in thylakoid lamellae and carbon metabolism
in the stroma of chloroplast (Wahid et al. 2007; Mathur
et al. 2011). Heat stress exposure during post anthesis stage
could affect reproductive success, seed set and grain filling
in crop plants by altering hormonal regulation and carbon
metabolism enzymes (Bahuguna and Jagadish 2015; Shi
et al. 2017).
Polyamines are aliphatic polycationic compounds with
low molecular weight and interact with various macromolecules DNA, proteins, or phospholipids (Tun et al. 2006; Pál
et al. 2015). Role of polyamines spermidine (SPD) and spermine (SPM) providing tolerance to abiotic stresses such as
heat and drought tolerance has been known in various crops
(Minocha et al. 2014; Li et al. 2018) including rice (Do et al.
2013, 2014). Mechanistically, polyamines catabolism follow
ROS pathway to activate antioxidant defense machinery in
the plants (Liu et al. 2015). However, information related to
role of polyamines and their association with heat tolerance
in rice at pre- and post-flowering stage is limited. Rice diversity is the major source of genotypes having tolerance to
various stresses including biotic and abiotic stresses. Moreover, a precise phenotyping and characterizing a diverse set
of germplasm under natural field conditions could provide
potential stress tolerant donors that can be utilized in the
breeding programs. However, there is very limited number
13
of genotypes phenotyped in detail and characterized under
heat stress. N22 and Nerica L-44 are the rice genotypes
reported as donors of heat stress tolerance and maintain
higher spikelet fertility and pollen viability (Jagadish et al.
2007, 2010; Bahuguna et al. 2015). We selected a mini set
of 36 rice genotypes and screened them under heat stress in
the field. The main objectives of the study were (i) identification of potential donors with superior agronomic traits
grain quality traits, (ii) to characterize the heat tolerant donor
for their mechanism of tolerance, growth, yield, and quality
traits.
Materials and methods
The experiment were conducted at Climatic Change Facility, Plant Physiology Division, Indian Agricultural Research
Institute; New Delhi, India (28°35 N latitude, 77°12 E longitude).The climate was semi-arid with dry hot summer
and mild winters. Seeds of the rice genotypes were collected from Indian Institute of Rice Research, located at
Hyderabad, India.
The rice plants were raised in plastic pots during two
kharif seasons of 2014–2015 and 2015–2016. In experiment
I (2014–2015), screening of 36 rice genotypes (Supplementary Table 1) was done to analyze heat stress tolerance during flowering stage. For experiment II (2015–2016), contrasting set of genotypes were selected and characterized
for physiological and biochemical traits and heat tolerance
mechanism.
Experiment I
Experiment was conducted in white color plastic pots (14"
diameter 12" height) filled with 20 kg clay-loam soil supplemented with 800 g farmyard manure mixed thoroughly
and N:P:K applied as (NH 4) 2SO 4 (0.375 g kg−1), KCl
(0.075 g kg−1), and Single Superphosphate (0.075 g kg−1),
respectively. Additional dose of N (0.125 g kg −1 soil)
was applied at 25–30 days after transplanting. Each treatment had five pots as biological replicates. Seedlings were
raised in field nursery and 21-day old seedlings were transplanted in pots and arranged randomly in the net house
(4.57 m × 4.57 m × 9.14 m) under ambient environment.
Pots were kept flooded (water 3–5 cm above soil surface)
until two weeks before the physiological maturity. No major
insect and pest events observed during the experiment.
Heat stress imposition in high temperature tunnel
Pots were transferred in high temperature tunnel (HTT) for
heat stress exposure at the heading stage of respective genotype and exposed to heat stress for at least 10 days covering
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Acta Physiologiae Plantarum (2020) 42:29
the flowering and post-flowering stage of respective genotypes. After heat stress exposure, pots were shifted to ambient environment till physiological maturity. The design,
structure, and control system of HTT was similar to one used
earlier by Sinclair et al. (1995). The real-time data on temperature and relative humidity were monitored continuously
for every 30 min time interval using MINCER obtained from
NIAES, Tsukuba, Japan (Fukuoka et al. 2012). The loggers
were installed at the center of the HTT and ambient at the
height of 1.3 m from ground level throughout the season
(Supplementary Fig. 1). However, for comparison, data represented in the figures includes the heat stress period (heading to 100% flowering) from both ambient net house and
HTT and expressed as mean of the daytime (0700–1800 h)
and nighttime (1800–0700 h) for both the experiments.
Experiment II
Out of 36 genotypes screened in experiment I, contrasting
set of genotypes ranked for heat tolerance was selected on
the basis of agronomic traits such as grain yield, spikelet fertility %, etc. using heat susceptibility indices and cumulative
stress response index (CSRI) (explained in observations).
The selected contrasting genotypes were further characterized for physiological and biochemical traits known for heat
tolerance. Similar to experiment I, the pot experiment II was
conducted using same pot size, soil type, fertilizer doses
and other agronomic practices explained above. There were
10 replications (pots) for each genotype. Heat stress treatment was given from heading to 100% flowering in HTT as
described for the experiment I. Flag leaf and spikelet samples were collected at 100% flowering from the respective
genotypes from control (ambient) and heat stress treatment.
Tissue samples were collected in liquid N2 contained falcon tubes between 0930 and 1130 h and were immediately
stored in − 80 °C until further analysis. Out of ten biological
replicates (plants/pot) for each genotype, five were kept for
yield component analysis and non-destructive physiological
observations, three for destructive biochemical samplings
and two for the reproductive sampling for microscopic analysis. Plants which were used for biochemical and microscopy sampling were discarded after sampling and not used
for any other observations.
Growth environment
In experiment I, the average day temperature was 5.9 °C
(SD ± 2.8) higher over ambient for the treatment duration,
while for experiment II, it was 4.3 °C (SD ± 2.11) higher
than ambient day temperature (Supplementary Fig. 2a–d).
The extent of heat stress was different (39.2–44 °C) for different genotypes based on the flowering period of the genotypes, however all the genotypes were exposed with same
duration of ten days. Relative humidity (RH) during this
exposure period was in range of > 60–80% in experiment 1
and 2. Vapor Pressure Deficit (VPD) in experiment 1 and 2
was in range of 1.7–2.6 kPa.
Observations
Grain yield components, spikelet fertility and in vitro pollen
viability In experiment I and II, plants were harvested at
physiological maturity. Harvested samples were separated
in different plant components. Panicles were sun dried in net
bags while straw samples were ovens dried until constant
weight was recorded. Weight of panicle and grains per plant
was measured with digital analytical balance (Sartorius
AG). Grains thrashed from panicles were mixed thoroughly
and samples were collected randomly to estimate 1000
grain weight. Spikelet fertility (SF) percentage was calculated in both the experiments following Prasad et al. (2006)
by separating filled and unfilled grain obtained from the
main tiller. In vitro pollen viability analysis was done using
a 2, 5-diphenyl tetrazolium bromide (MTT or Thiazolyl
Blue) as described by Khatun and Flowers (1995). Thirty
unopened florets were collected at the time of anthesis from
plants designated for destructive samplings for each genotype across the treatments. From these florets, anthers were
collected and pollens were squeezed using tweezers and
were collected on the slide and incubated in MTT solution
[1% MTT (w/v) + 5% sucrose (w/v)]. Pollen grains stained
were recorded under a stereomicroscope (Olympus SZX7,
Olympus Corp., Japan). The percent viability of pollen was
estimated using of viable pollen and total pollens collected
(Khatun and Flowers 1995).
Heat and cumulative stress response index Heat susceptibility index for spikelet fertility, pollen viability, gain
weight per plant and 1000 grains weight of each genotype
was calculated (Fischer and Maurer 1978). The cumulative
stress response index (CSRI) was calculated using the sum
of response of individual component treatment (Dai et al.
1994).
Grain hulling (%) and milling (%) and quality attributes Rice
grain sample were dehusked manually by palm husker and
weighted and expressed as percentage. The hulled rice was
milled using polisher (Model Pearlest Kett1-8-1, MinamiMagome, Otaku, Tokyo, Japan) and an expressed as percentage. After milling the broken rice grains were isolated
and the fraction was expressed as head rice recovery (Khush
et al. 1978).
Grain chalkiness and amylose content Milled grains were
divided into four portions. In each portion manually separating opaque or chalky grain were isolated and weighted and
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Acta Physiologiae Plantarum (2020) 42:29
Page 4 of 16
was showed as per cent of total grain (within the quarter)
following methods of Adu-Kwarteng et al. (2003). Amylose
content (AC) was analyzed in ground rice flour following
methods of Juliano (1971) and was expressed in amylose per
cent. The amylose per cent was calculated by preparing the
standard curve with amylose (Sigma-Aldrich).
Net photosynthesis rate and gas exchange In experiment II,
photosynthesis rate (μmol m−2 s−1), stomatal conductance
(mol m−2 s−1) and rate of transpiration (mmol m−2 s−1) were
recorded using Li-COR photosynthesis system (Model, LI6400XT) between 0900 and 1130 h. CO2 level in the sample
chamber was monitored by CO2 injection system (Model
6400-02B; Li-COR Inc. USA) by keeping the CO2 level of
reference air nearly to 400 µmol mol–1 under a constant flow
rate of 500 µmol s–1 and a saturating photosynthetic photon
flux density (PPFD) of 1200 µmol m−2 s−1. Chamber block
temperature was set as per ambient conditions and the RH
was kept closer to 60% (Chaturvedi et al. 2017; Bahuguna
et al. 2018).
Infra‑Red (IR) thermal Imaging of leaf and spikelets and tis‑
sue surface temperature In experiment II, the IR thermal
images of whole plant canopies were recorded on the 7th day
of heat stress exposure in between 0830 and 1130 h using
Testo 890–2 Professional Infrared Camera (Testo Solutions,
Inc, USA). The camera was placed on tripod at 1.0 m away
from canopy and height of 1.0 m from ground and for minimizing the outside interference a black cloth was used for
backing. Captured images were analyzed by IRSoft (Testo)
software (Zaman-Allah et al. 2011). The tissue temperature
depression was calculated as described by Ayeneh et al.
(2002). During image capturing mean air temperature was
38.1 °C (SD ± 1.28) and relative humidity was 68%.
Oxidative stress (H2O2) and membrane damage
(TBARS) H2O2 in flag leaf/spikelets tissue sample was analysed spectrophotometrically as described by Alexieva et al.
(2001). The content of H2O2 was expressed in μmol g−1
FW (fresh tissue weight). Thiobarbituric acid reactive substances (TBARS) in flag leaf/spikelets tissue sample were
measured spectrophotometrically following Larkindale and
Knight (2002) and calculated using extinction coefficient of
155 mM cm−1. Units of both H2O2 and TBARS in tissue has
been shown as µmol g−1 FW.
Enzymes extraction
Flag leaf/spikelets sample (500 mg) was homogenized
in pre-chilled pestle and mortar using liquid nitrogen.
Homogenized mixture was transferred to 5.0 ml micro
centrifuge tube (Eppendorf) containing 5.0 ml ice cold
potassium phosphate buffer (0.1 M, pH 7.0) and 0.1 mM
13
Na-ethylenediaminetetraacetic acid (Na-EDTA), (whereas
in ascorbate peroxidase analysis, where 10 mM ascorbate
was used in place of EDTA) and 1 mM phenylmethane
sulfonyl fluoride(PMSF). The homogenate was then centrifuged at 18,400g at 4 °C for 20 min and supernatant
was used as enzyme. Total soluble protein was analyzed in
extract and bovine serum albumin (BSA) standard (Bradford 1976).
Enzymes assay
SOD (Superoxide dismutase) activity was assayed by monitoring based on inhibition of photochemical reduction of
nitro blue tetrazolium (NBT). The reaction mixture was
quantified at ʎ 560 nm and used to express as SOD unit
activity mg−1 protein following Jiang and Zhang (2002).
Catalase (CAT) activity was analyzed based upon the
disappearance of H2O2 at 240 nm with extinction coefficient, ε = 39.4 mM−1 cm−1 and indicated as μmol of H2O2
consumption min−1 mg−1 protein (Jiang and Zhang 2002).
Ascorbate peroxidase (APX) activity was determined as
following Sharma and Dubey (2004) and indicated as µmol
ascorbate oxidized (APX) min−1 mg−1 protein.
Guaiacol peroxidase (GPX) activity was determined as
following de Azevedo Neto et al. (2006) and expressed as
μmol of H2O2 consumption min−1 mg−1 protein.
Estimation of endogenous free polyamines
Free polyamines viz PUT, SPD and SPM were extracted and
estimation by doing benzoylation and detection were performed in HPLC (Flores and Galston, 1982). Flag leaf/spikelet sample (200 mg) were homogenized in 1.0 ml of cold
perchloric acid (5%, v:v) and transferred in 2.0 ml microcentrifuge tubes then this homogenate were centrifuged at
18,400g and 4 °C for 30 min. The supernatant benzoylation
was done following method of Flores and Galston (1982).
For benzoylation sodium hydroxide (2 M) 1.0 ml and benzoyl chlorides 10 µl were added into 500 µl of supernatant
to another microcentrifuge tube (Eppendorf, 5 ml) and then
they were vortex and incubated for 20 min at 37 °C. To
terminate the reaction, 2.0 ml saturated solution of sodium
chloride added in benzoylation mixture. Cold diethyl ether
(2.0 ml) was added for extracting polyamines from benzyl
polyamines. 1.0 ml of the ether phase was collected in fresh
micro centrifuge tube (1.5 ml) and evaporated of ether and
re-dissolved in 100 µl HPLC grade methanol (Merck) for
determination of endogenous free polyamines (PUT, SPD,
and SPM).
HPLC analysis was performed using Agilent 5 µm particle size reverse-phase (C18) column (size of 4.6 × 250 mm)
on Agilent 1100, 20 µl of benzylated polyamines sample
were injected by autosampler, under 1.0 ml/minutes flow
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Acta Physiologiae Plantarum (2020) 42:29
rate of mobile phase [acetonitrile: water (52:48 v/v) (HPLC
Grade, Merck)]. HPLC equipped with VWD detector at
wavelength of 254 nm. Data retrieval and peak area calculation were performed using CHEM STATION for LC
system (Rev B.040.3 (16) software. The concentrations of
individual polyamines was calculated from the standard
curve plotted using HPLC grade standard PUT, SPD, and
SPM purchased from Sigma chemicals and there content
were expressed on fresh weight basis (nmol g−1 FW (fresh
weight).
Conversely, four genotypes (IET 22218, MTU1010, IET
23324 and IR64) showed 13–16% reductions in grain yield
due to heat stress over control and it was comparable to heat
stress tolerant checks (N22 and NL-44) (Table 1). G × T
interaction was observed for 1000 grain weight was also significant (P < 0.05). Maximum decline (38%) in 1000 grain
weight was recorded for PR-113, while minimum in AK
Dhan, N-22, and IET 22218 under heat stress as compared
to ambient grown plants (Table 1).
Heat and cumulative stress response index
Statistical analysis
Data obtained from both experiment were analyzed by twoway ANOVA with experiment design of completely randomized design (CRD) using SPSS 13.0 (Version 13, LEAD
Technologies Inc) and the differences between cultivars,
treatments and their interaction were compared. LSD mean
at 5% was compare using Tukey’s Post-hoc test.
Results
Experiment I
Heat stress on pollen viability, spikelet fertility and yield
attributes
In experiment I, there was a significant (P < 0.001) genotypes (G) × treatment (T) interaction for spikelet fertility.
Among thirty-six phenotyped rice genotypes, US-312, PHB71 and PR-113 recorded highest percent reduction (51–58%,
respectively) in spikelet fertility over ambient grown plants,
while MTU1010, IR64, IET 22218, and IET 23324 recorded
least (15–17%) reduction in spikelet fertility under heat
stress, as compare to ambient temperature. Heat stress tolerant checks N22 and NL-44 showed 14 and 15% reductions
in spikelet fertility due to heat stress compared to ambient
temperature (Table 1).
Pollen viability showed significant (P < 0.001) G × T
interaction. Heat stress exposure caused significant reductions (40–51%) in pollen viability of US-312, IET 23296,
PHB-71 and PR-113 under heat stress as compared to ambient temperature. Conversely, least reduction (10–11%) in
pollen viability was recorded in IET 22218 and IET 23324
under heat stress, which was comparable with heat stress
tolerant checks N22 and NL-44 exhibiting 11% reduction
in pollen viability under heat stress as compared to ambient
temperature (Table 1).
Similarly significant G × T interaction (P < 0.001) was
observed for grain yield in this experiment. Heat stress
reduced grain yield with maximum reduction in PR-113
(64%) followed by PHB-71(57%) and IET 23296 (53%).
The heat susceptibility index (HSI) indicate the rate of
change in different traits (like grain yield HSIGY, spikelet fertility HSISF, pollen viability HSIPV and for 1000 grain weight
HSITGW) for each genotype in yield between the two environments (heat stress and control) in relations to the mean values
of the traits for all genotypes. A greater heat stress tolerance
is shown by smaller values of HSI. Supplementary Fig. 3a–c
shows the most and least susceptible genotypes under heat
stress based on different indices. Based on HSIGY, HSISF
and HSIPV, least susceptible genotypes were N22, NL-44,
MTU1010, IET 23334, IET 22218, IR64 while most susceptible genotypes identified among 36 genotypes were IET
22894, US-312, IET 23296, PHB-71 and PR-113. In case
of HSITGW, there were no definite trends except in PR-113
showing maximum value of HSI (Supplementary Fig. 3d).
The CSRI was analyzed using traits like grain yield
plant−1, spikelet fertility and pollen viability and 1000 grain
weight under heat stress. On the basis of CSRI, out of 36
rice genotypes, CSRI for six genotypes (N22, NL-44, IET
22218, IET 23324, IR64 and MTU1010) ranged between
− 37 and − 53 while for five genotypes (IET 22894, US-312,
IET 23296, PHB-71 and PR-113) CSRI value ranged from
− 133 to − 210 (Fig. 1). Other genotypes were identified
in between these genotypes as moderately sensitive to heat
stress.
To understand the trait contributing maximally under heat
stress, a relationship between cumulative stress response
index (CSRI) with heat susceptibility Index (HSI) based on
grain yield (HSIGY, A), spikelet Fertility (HSISF, B) and pollen viability (HSIPV, C), thousand grain weight (HSITGW,
D) has been worked out for 36 rice genotypes (Supplementary Fig. 4). There were a significant association between
HSIGY, HSISF, HSIPV and CSRI (r2 = 0.736, r2 = 0.678 and
r2 = 0.716, respectively) while relationship between thousand grain weight (HSITGW) and CSRI was non-significant
(r2 = 0.287) (Supplementary Fig. 4d).
Experiment II
Based on the HSI for component traits (HSI GY, HSI SF,
HSIPV, HSITGW) and CSRI as a cumulative stress response
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Page 6 of 16
Table 1 Agronomic and yield components of 36 rice genotypes under heat stress exposure at flowering during 2014
Genotypes
N 22
NL-44
IET 23324
MTU1010
IET 22218
IET 21411
IET 23299
IET 23275
IET 21404
AK Dhyan
PA-6129
IR64
Shanti
Sasyasree
PA-6444
IET 22116
IET 23297
IET 21577
PS-5
Swarna
IET 22308
PUSA-1121
DRRH-3
US-382
IET 22905
IET 23300
IET 23279
Lalat
IET 22894
IET 21515
Nagarjuna
PR-113
IET 23296
PHB-71
US-312
Sampada
Lsd P < 0.05
G
T
G×T
Spikelet fertility (%)
Grain yield plant−1 (g)
1000 grain weight (g)
Pollen viability %
AT
HT
AT
HT
AT
HT
AT
HT
96.3 ± 0.3
94.1 ± 1.2
94.1 ± 0.8
95.2 ± 0.3
91.9 ± 0.6
93.7 ± 0.5
95.9 ± 0.4
96.2 ± 0.7
88.5 ± 0.5
86.1 ± 1.4
92.5 ± 0.2
78.3 ± 2.5
86.1 ± 1.2
87.5 ± 0.8
85.5 ± 2.8
78.4 ± 0.4
90.5 ± 1.1
83.6 ± 4.6
83.9 ± 0.5
85.2 ± 1.6
88.3 ± 1.1
91.6 ± 0.7
84.4 ± 1.3
88.1 ± 1.1
87.2 ± 1.6
82.6 ± 1.1
85.1 ± 1.7
81.6 ± 0.7
76.4 ± 3.2
66.9 ± 4.2
66.4 ± 0.9
91.5 ± 0.4
72.0 ± 2.5
75.1 ± 0.9
73.1 ± 1.4
60.4 ± 2.3
83.2 ± 0.2
80.3 ± 1.2
79.3 ± 1.1
78.2 ± 0.5
76.3 ± 1.3
75.3 ± 0.6
71.0 ± 2.1
70.8 ± 3.0
68.9 ± 0.3
67.7 ± 0.7
67.6 ± 5.7
64.7 ± 3.1
64.5 ± 2.0
63.8 ± 1.6
63.6 ± 0.7
61.8 ± 1.1
60.4 ± 0.7
60.3 ± 2.6
58.3 ± 2.9
57.8 ± 2.5
57.6 ± 1.6
57.4 ± 2.3
55.9 ± 2.5
55.3 ± 2.7
54.7 ± 3.0
54.6 ± 1.7
52.8 ± 0.4
52.6 ± 2.4
43.9 ± 1.4
41.7 ± 4.7
40.2 ± 1.0
38.8 ± 0.8
36.7 ± 2.1
36.2 ± 0.9
35.9 ± 1.4
35.9 ± 2.3
3.93***
0.92***
5.56***
13.1 ± 0.5
39.2 ± 1.6
27.3 ± 1.5
38.0 ± 1.5
25.1 ± 03
39.4 ± 27
34.9 ± 2.2
41.3 ± 2.6
15.2 ± 1.1
27.7 ± 1.7
34.6 ± 1.3
28.8 ± 1.9
24.2 ± 1.1
25.0 ± 0.5
28.8 ± 1.3
7.2 ± .3.2
31.8 ± 1.5
50.0 ± 1.3
19.8 ± 0.5
33.6 ± 0.8
25.1 ± 2.7
30.6 ± 1.5
44.7 ± 1.5
26.6 ± 1.2
32.5 ± 1.0
28.8 ± 2.0
25.9 ± 0.5
19.4 ± 0.9
16.1 ± 1.0
33.1 ± 1.3
27.3 ± 1.2
38.7 ± 2.1
37.8 ± 2.7
26.2 ± 1.5
20.8 ± 1.0
25.2 ± 1.6
11.8 ± 0.4
34.8 ± 2.2
23.2 ± 1.1
32.9 ± 1.7
21.0 ± 0.4
27.2 ± 1.7
25.1 ± 2.1
29.7 ± 2.2
12.1 ± 0.8
22.1 ± 1.2
25.4 ± 1.5
24.1 ± 2.3
17.4 ± 1.0
17.4 ± 0.6
19.6 ± 2.6
5.6 ± 0.3
20.4 ± 1.4
35.5 ± 3.0
13.3 ± 1.0
22.0 ± 1.3
13.6 ± 0.8
18.1 ± 0.8
29.3 ± 1.8
17.1 ± 1.0
20.1 ± 1.9
22.2 ± 3.5
16.8 ± 1.1
11.5 ± 0.8
8.0 ± 0.7
17.6 ± 2.2
15.9 ± 2.0
13.7 ± 0.7
17.9 ± 0.7
11.3 ± 0.3
10.2 ± 0.7
15.4 ± 1.6
3.14***
0.74***
4.45***
17.1 ± 0.6
22.9 ± 1.4
17.4 ± 1.2
20.8 ± 0.6
18.2 ± 0.8
18.9 ± 0.8
22.5 ± 1.1
22.2 ± 1.6
19.3 ± 0.3
22.4 ± 1.2
21.3 ± 0.5
21.9 ± 1.0
19.7 ± 0.4
24.0 ± 1.3
19.3 ± 0.8
17.7 ± 1.0
21.4 ± 0.9
17.6 ± 0.5
24.4 ± 1.7
18.6 ± 1.3
21.5 ± 0.6
25.3 ± 1.5
17.5 ± 0.5
20.4 ± 1.2
20.1 ± 1.0
18.9 ± 0.8
21.0 ± 1.6
19.9 ± 0.3
18.5 ± 0.7
25.0 ± 1.3
18.4 ± 0.4
26.8 ± 0.7
17.8 ± 0.7
19.2 ± 1.1
17.2 ± 0.4
15.8 ± 0.3
16.7 ± 0.8
21.3 ± 0.3
16.7 ± 0.9
19.5 ± 1.9
17.9 ± 1.6
17.0 ± 1.0
21.9 ± 0.8
21.0 ± 0.8
18.5 ± 0.6
22.4 ± 1.0
20.1 ± 0.8
21.3 ± 1.3
18.4 ± 0.6
22.2 ± 1.3
18.8 ± 0.5
16.4 ± 0.9
17.6 ± 1.2
16.4 ± 1.0
20.6 ± 0.9
17.4 ± 1.8
20.5 ± 0.4
22.7 ± 1.0
14.9 ± 0.8
17.1 ± 1.2
19.7 ± 1.0
17.8 ± 0.6
19.0 ± 1.1
19.1 ± 1.0
17.9 ± 0.6
22.1 ± 0.4
16.6 ± 1.5
16.7 ± 1.3
16.7 ± 1.0
18.3 ± 0.4
15.6 ± 0.5
14.3 ± 0.5
1.95***
0.46***
2.76*
100 ± 0.0
100 ± 0.0
97.3 ± 1.9
100 ± 0.0
100 ± 0.0
95.3 ± 2.7
100 ± 0.0
100 ± 0.0
98.1 ± 3.2
99.8 ± 0.9
90.6 ± 1.6
93.9 ± 0.6
95.5 ± 3.7
97.6 ± 1.6
90.8 ± 2.5
95.4 ± 0.8
100 ± 0.0
100 ± 0.0
87.9 ± 1.0
94.5 ± 2.4
96.3 ± 1.6
86.2 ± 2.2
99.5 ± 1.2
100 ± 0.0
97.7 ± 3.5
100 ± 0.0
100 ± 0.0
98.1 ± 1.7
92.8 ± 3.2
94.3 ± 1.9
93.0 ± 1.4
100 ± 0.0
90.5 ± 2.5
96.4 ± 3.1
91.4 ± 0.9
91.2 ± 2.2
87.6 ± 1.1
88.7 ± 0.5
86.3 ± 2.0
84.2 ± 1.9
90.0 ± 0.7
72.1 ± 2.9
78.0 ± 4.2
74.6 ± 1.7
75.6 ± 4.8
80.0 ± 0.8
68.5 ± 0.5
79.9 ± 1.1
73.7 ± 3.9
72.3 ± 2.4
70.5 ± 1.7
74.7 ± 0.7
73.8 ± 2.4
75.7 ± 1.2
59.1 ± 3.3
66.1 ± 3.4
66.8 ± 2.8
59.0 ± 2.7
74.4 ± 2.0
70.5 ± 2.8
63.3 ± 4.0
73.8 ± 1.7
70.8 ± 1.5
66.7 ± 2.4
57.3 ± 1.6
65.1 ± 2.1
66.5 ± 1.2
49.1 ± 1.2
48.4 ± 2.2
50.0 ± 2.5
54.6 ± 2.1
59.7 ± 2.6
4.55***
1.07***
6.43***
Data represent mean of five replications ± SE
ns denotes non significant
Indicate the significant difference at *P < 0.05, **P < 0.01, ***P < 0.001
index in experiment I, six genotypes were identified and
characterized based on physiological, biochemical, agronomical and grain quality attributes to work out the component mechanism associated with heat tolerance at reproductive stage.
13
Effect of heat stress on grain yield and quality
A significant (P < 0.05–0.001) interaction among genotype,
treatment, as well as genotype × treatment was observed
for grain weight (g hill−1) (Table 2). Heat stress exposure
Acta Physiologiae Plantarum (2020) 42:29
Fig. 1 Cumulative stress response index (CSRI) on the basis of yield,
spikelet fertility and pollen viability of 36 rice genotypes was calculated as described by Koti et al. (2007). CSRI = ([(Trait1 in treatmentTrait1 in control)/Trait1 in control] + [(Trait2 in treatment-Trait2 in
control)/Trait2 in control] + ….) × 100. Each represented data point
was mean of 5 replicates with vertical bars indicate ± SEM
decreased significantly (P < 0.001) grain weight hill−1 (45
and 52%) in rice genotypes namely IET 23296 and PHB71. Conversely, IET 22218, and IET 23324 had shown a
decline of 15–19% which was at par to NL-44 (23%) and
N22 (3%) grain weight hill−1 under heat stress. Significant
interaction for G (P < 0.001), T (P < 0.001) as well as G × T
(P < 0.001) interaction effect was also recorded for spikelet
fertility (Table 2). It was reduced significantly under heat
stress (10–54%) in all the rice genotypes. However, the
reduction in spikelet fertility was more prominent (> 50%)
in rice genotype PHB-71. Conversely, IET 22218, and IET
23324 showed lesser reduction (18%) in Spikelet fertility
which was comparable to N22 (10%) and NL-44 (17%) (heat
tolerant checks) under heat stress. Results for pollen viability
followed the similar trend showing higher reduction (47%)
in pollen viability of PHB-71 and least reduction in pollen
viability of IET 22218 (16%), and IET 23324 (14%) which
was comparable to N22 (3%) and NL-44 (11%) (heat tolerant
checks) under heat stress (Table 2).
Significant difference (P < 0.05–0.001) in hulling, percentage of milling and recovery of head rice was recorded
for genotypes × treatment (Table 2). Heat stress decreased
hulling and milling percentage significantly by 10–23% for
hulling and 16–33% for milling. IET 22218 (10, 16%) and
Page 7 of 16 29
IET 23324 (12, 17%) recorded lesser reduction in above
attributes and followed NL-44 and N22 (10, 16%) under
heat stress. Conversely, maximum percent reduction in these
attributes was recorded for PHB-71 (23, 33%) (Table 2).
Head rice recovery was recorded minimum (45%) in IET
23296 while, IET 22218 recorded head rice recovery of 79%
which was followed IET 23324, NL-44 and N22 (60–63%)
under heat stress.
Among the grain quality parameters, grain chalkiness
percent increased significantly (P < 0.001) under heat stress
(30–80%) as compared to ambient growth conditions.
Chalky percent was higher among all the tested genotypes
except IET 22218 (39%), IET 23324 (45%) following by
NL-44 (30%) (Table 2). Maximum grain chalkiness of (80
and 71%) was recorded for IET 23,296 and PHB-71 across
the genotypes and treatment (Table 2).
AC varied significantly (P < 0.001) for genotype and
treatment, however, for G × T interaction it was non significant (Table 2). Heat stress treatment decreased significantly (P < 0.001) AC in the genotypes. IET 22218 recording
significantly lesser reduction of 9% which followed NL-44
(11%) compared to control. There was significant genotypic
(P < 0.001), and treatment (P < 0.001) effect for 1000 grain
weight (Table 2). Heat stress showed a significant reduction
(24 and 22%) in 1000 grain weight of IET 23296 and PHB71. Conversely, there was a lesser reduction in 1000 grain
weight of IET 22218 and IET 23324 (14%) which was at par
to comparable to N22 (11%) and NL-44 (13%).
Characterization of component traits for heat stress
tolerance
Net photosynthesis rate and gaseous exchange A significant G, T, and G × T (P < 0.001) effect was recorded for net
photosynthesis rate (PN) (Fig. 2a). PN showed significantly
reduction (P < 0.001) under heat stress across the genotypes.
Under heat stress treatment, two rice genotypes (IET 23296
and PHB- 71) recorded significant (P < 0.001) reductions of
42% in PN while in IET 22218 and IET 23324 minimum
decline of (8 and 11%) was recorded (Fig. 2a), which was at
par to heat tolerant checks NL-44 (10%) under heat stress.
A significant genotypic (P < 0.001) effect was recorded for
gs (Fig. 2b). Moreover, N22 recorded highest reductions
(13%) in gs under heat stress as compared with ambient
conditions (Fig. 2b). There was significant effect due to
heat stress exposure on gs was noted for IET 22218, N22,
IET 23324, and NL-44 (Fig. 2b). A significant genotypic
(P < 0.001), treatment (P < 0.001) and genotype × treatment
(P < 0.001) effect was recorded for transpiration rate (E). E
was recorded highest in IET 23324 (16.1 mmol m−2 s−1) in
the genotypes and stress treatments. However, under heat
stress it was significantly increased in IET 22218 (56%),
which was comparable with heat tolerant checks N22 (65%)
13
29
Page 8 of 16
13
Table 2 Grain yield, seed-set and grain quality attributes of contrasting rice genotypes exposed to heat stress during 2015
Genotypes
IET 22218
IET 23324
N-22
NL-44
IET 23296
PHB-71
Treatments
Ambient
HT
Ambient
HT
Ambient
HT
Ambient
HT
Ambient
HT
Ambient
HT
Genotype (G)
Treatment (T)
G×T
Spikelet fertility (%)
92.6 ± 1.8
75.4 ± 5.2
88.7 ± 6.2
72.3 ± 1.7
95.6 ± 1.7
86.3 ± 3.2
74.8 ± 4.1
62.3 ± 1.3
81.9 ± 2.0
45.9 ± 3.8
77.9 ± 1.7
35.7 ± 0.6
6.55***
3.78***
9.26***
Grain yield
plant−1 (g)
Pollen viability (%)
32.1 ± 2.2
25.8 ± 2.5
31.0 ± 1.1
25.9 ± 0.8
26.2 ± 1.5
25.3 ± 1.8
33.3 ± 2.3
25.6 ± 1.5
32.2 ± 1.4
17.5 ± 1.4
33.8 ± 1.4
16.1 ± 1.2
3.45*
1.99***
4.88***
95.6 ± 0.9
80.4 ± 0.7
89.5 ± 0.5
76.8 ± 1.0
91.7 ± 0.8
82.0 ± 2.7
85.3 ± 2.1
82.9 ± 0.8
86.4 ± 0.3
59.8 ± 1.9
89.4 ± 0.7
47.1 ± 0.3
2.63***
1.52***
3.73***
ns denotes non significant
Indicate the significant difference at *P < 0.05, **P < 0.01, ***P < 0.001
Hulled rice (%)
Milled rice (%)
Head rice
Chalky grain (%)
recovery (%)
Amylose
1000 Grain
weight (g)
72.6 ± 1.2
65.2 ± 1.7
82.6 ± 1.1
72.9 ± 1.9
86.1 ± 0.8
77.3 ± 2.5
80.0 ± 0.5
71.7 ± 1.9
75.7 ± 0.7
62.7 ± 2.1
79.9 ± 0.9
61.4 ± 2.6
3.39***
1.95***
4.8*
63.8 ± 1.7
53.1 ± 2.3
79.7 ± 1.2
65.7 ± 0.7
78.1 ± 1.3
65.5 ± 1.2
74.6 ± 0.6
62.2 ± 0.5
74.2 ± 0.7
52.5 ± 3.6
75.4 ± 1.1
50.2 ± 3.1
3.66***
2.11***
5.17***
86.2 ± 0.8
79.5 ± 0.7
70.3 ± 1.8
62.8 ± 0.8
69.8 ± 1.8
60.1 ± 1.6
65.6 ± 0.8
60.5 ± 5.3
60.6 ± 0.9
45.0 ± 5.4
71.4 ± 1.3
56.4 ± 3.5
5.39***
3.11***
7.62*
18.7 ± 1.3
17.0 ± 1.0
12.5 ± 2.3
10.1 ± 1.2
18.7 ± 0.2
16.1 ± 1.2
13.8 ± 0.8
12.3 ± 1.8
18.3 ± 0.9
10.1 ± 2.2
18.7 ± 0.3
10.7 ± 2.0
2.96***
1.71***
ns
18.2 ± 0.4
15.7 ± 0.4
19.3 ± 0.8
16.5 ± 1.0
19.4 ± 0.3
17.3 ± 0.3
22.8 ± 0.7
19.9 ± 0.6
19.7 ± 0.8
14.6 ± 0.5
20.5 ± 0.7
15.9 ± 0.4
1.32***
0.76***
ns
33.9 ± 1.3
38.7 ± 0.8
37.0 ± 0.4
45.7 ± 2.2
50.9 ± 0.6
70.6 ± 1.2
25.9 ± 1.5
30.1 ± 1.3
46.5 ± 2.4
79.6 ± 1.1
43.7 ± 1.9
71.0 ± 0.8
2.95***
1.70***
4.17***
Acta Physiologiae Plantarum (2020) 42:29
Data represent mean of five replications ± SE
Grain quality
Acta Physiologiae Plantarum (2020) 42:29
Page 9 of 16 29
and NL-44 (60%). While in case of PHB-71 and IET 23296
there were no significant differences in the values for E was
recorded (Fig. 2c).
Changes in leaf and spikelets tissue temperature Based
on IR thermal imaging temperature difference between
the plants grown heat stress and ambient conditions was
calculated for leaf and spikelets, and termed as canopy
temperature difference (CTD) in six rice genotypes. Heat
stress treatment showed significant difference in flag leaf
and spikelet temperature of rice genotypes (Fig. 3a–l).
Maximum CTD in leaf was noted in heat tolerant checks
NL-44 (5.53 °C) and N22 (5.46 °C) which was followed
by IET 22218 (5.48 °C) and IET 23324 (5.12 °C). While
lower CTD of 2.2 and 3.9 °C was recorded in PHB-71and
IET 23296 (Fig. 3 m). Similar trend was recorded in spikelets maximum cooling was presented in N22 (4.62 °C) followed by IET 23324, NL-44, and IET 22218 (4.37, 3.40,
and 2.82 °C), respectively. On the other hand, the CTD
for PHB-71 and IET 23296 varied from 1.4 and 1.56 °C
(Fig. 3n).
Change in oxidative stress and membrane stability
Fig. 2 Effect of heat stress on gas exchange parameters (a–c) of contrasting rice genotypes under heat stress during flowering stage. Five
replicated was used to represent the mean in vertical column. Bars
indicate ± SEM. Tukey HSD was used to compare the means and was
presented. Different letters indicate significant at 5%. PN photosynthetic rate, gS stomatal conductance, E transpiration rate, LSD least
significant difference, G genotypes, T treatment, AT ambient temperature, HT heat stress. Significance level: *P < 0.05, ***P < 0.001, ns
non significant
Effect of heat stress on membrane lipid peroxidation
(TBARS) and oxidative stress (H2O2 accumulation) in flag
leaf and spikelet tissues are given in Table 3. There was significant variation for H2O2 and TBARS across G (P < 0.001)
and T (P < 0.001) and G × T (P < 0.01 to 0.001) was
recorded. Highest H2O2 content was recorded in flag leaf
(4.99 µmol g−1 FW) of N22 and in spikelets (1.78 µmol g−1
FW) of IET 23296 while lowest in flag leaf (1.34 µmol g−1
FW), and in spikelets (1.12 µmol g−1 FW) of NL-44 across
the genotypes and treatment (Table 3). IET 22218 maintained lower content of H2O2 (1.78 µmol g−1 FW) in flag
leaf which was followed NL-44 and N22 under heat stress.
Maximum TBARS accumulation was noted in spikelets of IET 23296 (9.65 µmol g−1 FW) whereas lowest in
IET 22218 (2.3 µmol g−1 FW) across the genotypes and
treatment while in flag leaf it was highest in IET 23296
(14.37 µmol g−1 FW) and lowest in N22 and IET 22218
(6.36 µmol g−1 FW) across the treatment (Table 3). In general, heat stress significantly (P < 0.001) increased the H2O2
and TBARS content of flag leaf as well as spikelet tissues of
rice genotypes. Under heat stress exposure, IET 22218, IET
23324, N22, and NL-44 recorded lower H2O2 (4–13%) and
TBARS (5–20%) accumulation in flag leaf and in spikelet
(3–7% for H2O2 and 3–22% for TBARS) than IET 23296 and
PHB-71 showing higher accumulation of H2O2 (58 and 57%)
and TBARS (88 and 123%) in flag leaf and in spikelet (43
and 25% for H2O2 and 105 and 41% for TBARS) compared
to their respective controls (Table 3).
13
29
Page 10 of 16
Acta Physiologiae Plantarum (2020) 42:29
Fig. 3 Surface temperature of flag leaf (m) and spikelets (n) of contrasting rice genotypes exposed to heat stress using Testo 890-2Professional Infrared Camera (Testo Solutions, Inc, USA) distance
of 1 m from canopy. Tissue surface temperature depression was
calculated, air temperature at the time of measurement was 38.1ºC
(SD ± 1.28) recorded using MINCER data logger. Five replicated was
used to represent the mean vertical bar. Bars indicate ± SEM. Each
filled circles shows difference between air temperature and tissue surface temperature. Bars indicate ± SE. Thermal images under ambient
and heat stress conditions a, g IET 22218; b, h IET 23324; c, i N22;
d, j NL-44; e, k IET 23,296; f, l PHB-71. AT ambient temperature,
HT heat stress, CTD canopy temperature depression
Change in antioxidant enzymes under heat stress There
was significant genotypic G difference (P < 0.05–0.001)
for SOD, APX, catalase CAT, and GPX in the flag leaf and
spikelets tissues (Fig. 4 and Supplementary Table 2). In the
leaf tissue, the maximum activity of SOD was recorded in
N22, CAT in IET 23296, APX in IET 22218 and NL-44,
and GPX in IET 23324 while least activity of SOD, CAT,
and APX in PHB-71 and GPX in IET 23296 across the genotypes and treatment (Fig. 4a). In case of spikelet tissues,
the maximum SOD activity was recorded in NL-44, CAT
in IET 23324, APX in IET 22218 and NL-44, and GPX in
IET 23324 across the genotypes and treatment while least
activity of enzyme viz. SOD, CAT, APX and GPX in IET
23296 across the genotypes and treatment (Fig. 4b). Heat
stress caused significant increase in flag leaf and spikelet
enzyme activity of SOD (30–50% and 15–32%), APX (44–
62% and 30–49%) and GPX (20–60% and 24–64%) in of
IET 22218, IET 23324, NL-44 and N22 (Fig. 4a, b). Conversely, PHB-71 and IET 23296 showed lesser enhancement
in the antioxidant enzyme activity in flag leaf and spikelet
tissues (Fig. 4a, b).
13
Endogenous polyamine induction under heat stress
Endogenous free polyamines (PUT, SPD and SPM) showed
significant interaction (P < 0.05 to 0.001) G, T and G × T
Acta Physiologiae Plantarum (2020) 42:29
Table 3 H2O2 and TBARS
content in rice genotypes
under heat stress and ambient
temperature conditions in flag
leaf and spikelets in experiment
II
Genotypes
IET 22218
IET 23324
N-22
NL-44
IET 23296
PHB-71
Page 11 of 16
Treatment
Ambient
HT
Ambient
HT
Ambient
HT
Ambient
HT
Ambient
HT
Ambient
HT
Genotype (G)
Treatment (T)
G×T
29
H2O2 (µmol g−1 FW)
TBARS (µmol g−1 FW)
Flag leaf
Spikelets
Flag leaf
Spikelets
1.66 ± 0.18
1.78 ± 0.11
2.92 ± 0.05
3.06 ± 0.05
4.99 ± 0.02
5.33 ± 0.13
1.35 ± 0.13
1.52 ± 0.03
3.14 ± 0.03
4.99 ± 0.16
2.49 ± 0.02
3.92 ± 0.03
0.20***
0.11***
0.28***
1.17 ± 0.02
1.22 ± 0.03
1.35 ± 0.01
1.40 ± 0.01
1.22 ± 0.01
1.26 ± 0.01
1.12 ± 0.01
1.21 ± 0.01
1.24 ± 0.03
1.78 ± 0.02
1.17 ± 0.01
1.46 ± 0.03
1.45***
0.84***
2.06***
6.03 ± 0.65
6.36 ± 0.29
5.86 ± 0.30
6.96 ± 0.28
5.94 ± 0.73
6.36 ± 0.33
6.13 ± 0.31
7.38 ± 0.72
7.6 ± 0.85
14.37 ± 0.90
7.52 ± 0.92
16.83 ± 1.29
0.04***
0.02***
0.05***
2.30 ± 0.32
2.37 ± 0.32
4.07 ± 0.09
4.54 ± 1.26
3.08 ± 0.27
3.72 ± 0.07
4.07 ± 0.11
4.17 ± 0.57
4.70 ± 0.52
9.65 ± 0.40
5.97 ± 1.08
8.45 ± 0.53
1.20***
0.69***
1.70**
Both the H2O2 and TBARS are expressed in µmol g−1 FW
Each point represents mean of three replicates ± SE
ns denotes non significant, respectively, HT heat stress, TBARS thiobarbituric acid reactive substance (lipid
peroxidation), H2O2 hydrogen peroxide
Least significant difference indicate at *P < 0.05, **P < 0.01, ***P < 0.001;
effect for spikelets and flag leaf (Fig. 5a–f). In case of leaf
tissues, maximum PUT concentration (866.3 nmoles g−1 FW)
was recorded in NL-44 while minimum (43.9 nmoles g−1 FW)
in PHB-71 across the genotypes and treatment (Fig. 5a). IET
22218 (702.1 nmoles g−1 FW) recorded significantly accumulation of PUT in follow to N22 and NL-44 under heat stress.
While in spikelet, maximum PUT concentration (591.3 nmoles
g−1 FW) was recorded in IET 22218 while minimum (37.5
nmoles g−1 FW) in PHB-71 across the genotypes and treatment (Fig. 5d). Similarly in leaf tissue SPD and SPM concentration was highest in NL-44 (114.9 and 1639.7 nmoles
g−1 FW) and lowest in PHB-71 (48.7 and 56.3 nmoles g−1
FW) across the genotypes and treatment (Fig. 5b, c). In spikelet, IET 22218 recorded highest SPD and SPM concentration
(466 and 951.5 nmoles g−1 FW) and lowest in PHB-71 (45.6
and 55.7 nmoles g−1 FW) across the genotypes and treatment
(Fig. 5e, f). In general, under heat stress, accumulation of
PUT, SPD and SPM was increased in genotypes IET 22218,
IET 23324, N22 and NL-44 in flag leaf as well as spikelet
tissues compared to respective control. While genotypes IET
23296 and PHB-71 have shown either no change or significant
decline in PUT, SPD and SPM concentration of leaf as well as
spikelet tissues due to heat stress.
Discussion
Heat stress exposure in rice at flowering stage is known to
decreased grain yield in rice (Jagadish et al. 2015). Despite
of increase in global mean temperature, heat spikes at
regional level coinciding with critical growth stages
could be detrimental to rice yield and quality attributes
(Krishnan et al. 2011; Lyman et al. 2013). Breeding efforts
for heat tolerance in rice are hampered majorly due to
limited understanding of heat stress tolerance mechanism
and unavailability of adequate number of donor genotypes
(Challinor et al. 2014). N22 is well known check for heat
stress tolerance (Jagadish et al. 2007). However, undesired
poor agronomic traits of N22 make it poor choice for the
breeding programs (Bahuguna et al. 2015). Nerica L-44
has been shown as an excellent source for heat stress tolerance at vegetative as well as reproductive stage, having
excellent agronomic traits (Bahuguna et al. 2015; Chaturvedi et al. 2017). There is need to find out genetic diversity
for heat tolerance in rice and identification of donors is
warranted to support breeding programs for heat stress tolerance. This study was planned to phenotype/ screen rice
genotypes and characterize them for heat stress tolerance.
Screening of rice genotypes under heat stress
A mini set of rice genotypes was screened to find out potential heat tolerance donor(s). Heat susceptibility index and
13
29
Page 12 of 16
Fig. 4 Change in activity of antioxidant enzymes viz. SOD, CAT,
APX and GPX in the flag leaf (a) and spikelet (b) tissue of rice genotypes. All activity of enzymatic across all the tissue was analyzed and
represent in unit for SOD, µmol H2O2 for CAT and GPX) and µmol
ascorbate oxidized (APX) min−1 mg−1 protein. Each data point represents the relative change between heat stress and ambient temperature. Horizontal bar represents ± SEM. LSD value at 5% for evaluation are given in Supplementary Table 2
cumulative stress response index revealed some genotypes
that could maintain high spikelet fertility (> 75%), pollen viability (> 85%) and grain yield under heat exposure
(39.2–44 °C) at the flowering and early grain filling stage
(Table 1; Supplementary Fig. 3). In this study, we have identified two potential donors as IET 22218 and IET 23324,
which recorded spikelet fertility (> 75%) and pollen viability
(> 85%) at par with known checks N22, Nerica L-44 and
MTU1010 (Fig. 1; Supplementary Fig. 3). Two top performing genotypes (IET 22218, IET 23324) were selected for the
further characterization under heat stress at flowering stage
along with heat tolerant checks and two least performing
genotypes.
Yield components and grain quality under heat
stress
Exposure to heat stress at flowering stage caused significant
reduction in spikelet fertility (10–54%) grain yield (3–52%),
13
Acta Physiologiae Plantarum (2020) 42:29
and 1000 grain weight (13–25%) in sensitive cultivars across
the experiment I and II (Tables 1, 2; Supplementary Fig. 3).
Spikelet fertility is considered as most prominent trait that
gets affected under heat stress environment. Pollen viability
and their germination on stigma, are the major factors affecting in spikelet fertility when exposed to heat stress during
anthesis (Jagadish et al. 2010; Bahuguna et al. 2015; Zhang
et al. 2016). High percent of spikelet fertility (> 75%) even
under heat exposure for 7 consecutive days confirms reproductive resilience of new donor genotypes to long-term heat
stress (40 °C). These results were in line with high percent of
pollen viability (85–90%) in the genotypes showing higher
spikelet fertility (Tables 1 and 2). Higher spikelet sterility is
major cause of yield reduction in rice. Genotypes showing
high spikelet fertility recorded higher grain yield and 1000
grain weight across the experiments and treatments. Both
IET 22218 and IET 23324 recorded higher grain yield and
1000 grain weight at par with high yielding variety (IR64)
across the treatments. Besides grain yield, rice grain quality
is crucial factor to determine the rice market prize (Lyman
et al. 2013). Heat stress (day/night) can affect rice grain
quality by affecting starch accumulation in grain resulting in lower brown and milled rice percent, high proportion of chalky grains resulting in poor head rice recovery
and lower amylose content (Counce et al. 2005; Dong et al.
2014; Bahuguna et al. 2015, 2017). Genotypes IET 22218
and IET 23324 recorded higher milling outcome showing
with higher percent of hulled rice, milled rice, and head rice
recovery. Moreover, IET 22218 showed significantly lower
% chalkiness as compared to sensitive cultivars and at par to
heat tolerant checks (Table 2).
Net photosynthesis rate and gas exchange traits
under heat stress
Photosynthesis is the most important process, which gets
affected under heat stress. Sensitivity of photosystem II and
membrane damage by high ROS generation are the most
prominent routes that reduce photosynthesis (Szymańska
et al. 2017). In this study, photosynthesis reduced significantly due to heat stress exposure particularly in the sensitive genotypes (Fig. 2a). However, stomatal conductance and
transpiration rate showed contrasting response as compare to
photosynthetic rate across the genotypes. Transpiration rate
was significantly higher in IET 22218 and IET 23324 including heat tolerant checks N22 and Nerica L-44 (Fig. 2c). It
has been reported that, under adequate water supply, plants
avoid rise in tissue temperature by increased evapotranspiration resulting in significant lower tissue temperature as
compared to ambient air temperature. We could observe a
significant difference in thermal cooling capacity between
sensitive and tolerant genotypes (Fig. 3). Tolerant genotypes
IET 22218 recorded higher CTD values as compared to
Acta Physiologiae Plantarum (2020) 42:29
Page 13 of 16
29
Fig. 5 Changes in endogenous content of polyamines in flag leaf
(a–c) and spikelet (d–f) tissues of rice genotypes under heat stress.
Five replicated was used to represent the mean vertical column.
Bars indicate ± SEM. Tukey HSD was used to compare the means
and was presented. Different letters indicates significant at 5%. PUT
putrescine, SPD spermidine, SPM spermine. LSD least significant
difference, G genotypes, T treatment. Significance level: *P < 0.05,
**P < 0.01, ***P < 0.001, ns non significant
sensitive ones. CTD is an important parameter to determine
the level of thermal avoidance in a plant (Zhang et al. 2016;
Ayeneh et al. 2002). IET 22218 and IET 23324 showed
significantly higher degree of thermal avoidance in flag leaf
and spikelets as compared to sensitive cultivars and at par
with heat tolerant checks N22 and Nerica L-44 (Fig. 3m, n).
13
29
Acta Physiologiae Plantarum (2020) 42:29
Page 14 of 16
Change in oxidative stress and antioxidative
enzymes under heat stress
Reduction in rate of photosynthesis due to heat stress can be
attributed to heat stress-induced damage to photosynthetic
machinery by higher ROS generation. We have observed
higher accumulation of H2O2, which is a prominent ROS
species and signature molecule for oxidative stress. Sensitive
cultivars recorded many fold increase in tissue H2O2 level.
Consequently, higher TBARS content in the corresponding
plants could relate heat stress-induced damage in membranes
and other cellular components. Interestingly, IET 22218 and
IET 23324 recorded significantly lower levels of H2O2 and
TBARS content in flag leaf and spikelets under heat stress as
compared to sensitive genotypes (Table 3). Lower accumulation of H2O2 and TBARS in IET 22218 and IET 23324 and
heat tolerant checks N22 and Nerica L-44 could be attributed
to induced higher activity of antioxidant enzymes. Tolerant
checks and newly identified genotypes IET 22218 and IET
23324 recorded higher activity of ascorbate peroxidase (APX)
and guiacol peroxidase (GPX) compared to sensitive genotypes across the flag leaf and spikelets (Fig. 4). Both APX
and GPX are involved in scavenging H2O2 from the system
(Das and Roychoudhury 2014) and directly related to the heat
tolerance in several crops (Wahid et al. 2007). Some of the
transgenic plants overexpressing APX and GPX genes has
been shown to have heat tolerance when compared with wild
type plants (Milla et al. 2003; Shi et al. 2001). Conversely,
lower catalase activity recorded in IET 22218, IET 23324 and
heat tolerant checks plausibly favored salicylic acid induced
pathway for heat tolerance, which operates by down regulation
of salicylic acid binding proteins (catalase) to maintain desired
H2O2 levels to activate systemic defense signaling throughout
the plants and activating antioxidant defense machinery (Conrath et al. 1995; Dat et al.1998).
Induced polyamines accumulation under heat stress
Polyamines with low molecular weight aliphatic nitrogenous bases contain two or more amino groups (Pál et al.
2015). Polyamines are central to the defense signaling
involved under various types of abiotic stress including heat
stress in several crop species (Minocha et al. 2014; Pál et al.
2015). Accumulation of putrescine, spermine, and spermidine has been shown to activate defense pathway via H2O2
signaling when spermidine and spermine breaks down by
enzymes PAO (polyamine oxidase) under stress condition.
Initial accumulation of putrescine required reaching a threshold level before activating the accumulation of spermidine
and spermine. Earlier studies have demonstrated that accumulation of putrescine alone did not correlate well with the
tolerance level of the plant. However, higher accumulation
of both spermidine and spermine has been associated with
13
better tolerance of plants to heat stress or any other abiotic
stress conditions (Liu et al. 2004; Do et al. 2014; Ikbal et al.
2014). Interestingly, we have observed that IET 22218, IET
23324 and heat tolerant checks recorded significantly higher
accumulation of putrescine, spermidine, and spermine under
heat stress across the flag leaf and spikelet tissues as compared to the plants grown under ambient temperature. In
contrast, sensitive genotypes recorded lower levels of all
three polyamine molecules across the treatments (Fig. 5).
Accumulation of polyamines under heat stress has been
associated with higher photosynthesis, augmented antioxidant system and osmotic adjustment capability (Tian et al.
2012; Tang et al. 2018). Ability of polyamines to activate
antioxidant enzymes could help in scavenging accumulated
ROS in the tissues and prevent membrane lipid peroxidation
and help to stabilize the membrane structure under stress
environment (Singh 2015; Ouyang et al. 2017; Chen et al.
2019).
Conclusion
This study led to identification of two novel rice donors for
heat stress tolerance on the basis higher spikelet fertility, pollen viability as well grain yield and quality. One of the donor
(IET 22218) performed better than reported heat tolerant
checks, i.e., N22 and NL-44 for above traits under heat stress
environment. The study suggests that higher photosynthesis,
canopy temperature depression, antioxidant enzymes activity and accumulation of spermidine and spermine were the
component traits that could be explored to dissect the tolerance mechanism in the identified donors. These donors can be
used in future rice breeding programs by focusing on components traits along with acceptable agronomic traits and yield
potential under optimum and stress environments. High grain
chalkiness score under heat stress reduces head rice yield and
other quality traits. Low chalkiness score under heat stress was
found in above tolerant rice cultivars in this study and could
be used as source for improving grain yield and quality of elite
cultivars. This study also highlights the possible involvement
of polyamines in heat tolerance in rice and suggests further
investigation to explore the mechanism of polyamine mediated pathway contributing to heat stress tolerance.
Author contribution statement The authors have made the
following declaration about their contributions. Conceived
and designed the experiments: AKC, RNB and MP. Performed the experiments: SK, SM, AKC, and RNB. Analyzed
and interpreted the data: SK, AKC and RNB. Performed
statistical analysis: AKC, RNB, and SK. Drafted the manuscript: AKC, RNB, SK SS, VC and MP. Edited and finalized
the manuscript: RNB, AKC, and MP.
Acta Physiologiae Plantarum (2020) 42:29
Acknowledgement Authors thank to S.R.Voleti and P.R.Rao, IIRR,
Rajendranagar, Hyderabad Telangana, 500030, India for providing seed
material under ACRIP project,
Funding Authors acknowledge the financial grant received from Indian
Council of Agricultural Research (ICAR), New Delhi, India through
National Innovations on Climate Resilient Agriculture (NICRA-IARI)
Project Grant No.12–115. RNB acknowledges the financial support
from DST Fast Track Young Scientist Grant (YSS/2015/000523)
2015–2019.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict of
interests related to publication of this paper.
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