Transcriptomic evaluation of Miscanthus photosynthetic traits to salinity stress

Biomass and Bioenergy 125 (2019) 123–130

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Research paper

Transcriptomic evaluation of Miscanthus photosynthetic traits to salinity stress

T

Qian Wanga,b,1, Lifang Kanga,1, Cong Lina, Zhihong Songa,b, Chengcheng Taoa,b, Wei Liua, Tao Sanga,b,c,∗∗, Juan Yand,∗ a

Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China University of Chinese Academy of Sciences, Beijing, 100049, China c State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China d CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, Hubei, 430074, China b

ARTICLE INFO

ABSTRACT

Keywords: Miscanthus lutarioriparius Salinity stress Photosynthetic traits Gene expression Energy crop domestication

Miscanthus lutarioriparius, an endemic species of Miscanthus (Poaceae) in China, is considered to be one of the most promising second-generation energy crops that could grow in the condition of salinity stress. Studying photosynthetic traits of M. lutarioriparius under salinity stress can uncover the adaptability and foundation for selecting stress-resistant energy crops. In this study, several M. lutarioriparius populations were planted with randomized block design in the coastal saline experimental field in Dongying (∼7‰ in sanility), Shandong Province. After two growing seasons, we randomly sampled 50 individuals from five populations, correspondingly performed their photosynthetic analysis and RNA-Seq. We found the photosynthetic rate (A) of these individuals ranged from 19.19 to 42.80 μmol m−2 s−1, at an average of 32.58 μmol m−2 s−1. The mean of stomatal conductance (gs) was 0.56 mol m−2 s−1. The water use efficiency was mainly impacted by transpiration rates. The candidate genes related to photosynthetic rate mainly involved in photosynthesis, osmoregulation, abiotic stress and signal transduction. The stress-resistance genes had a significant effect on photosynthesis under long-term salinity stress, and most of the candidate genes were potentially beneficial for M. lutarioriparius to adapt to the stressful environment. Therefore, we inferred that M. lutarioriparius did not achieve adaptation by directly altering the expressions of the photosynthetic key genes, but by repairing and regulating the functions of key genes in order to maintain normal photosynthesis.

1. Introduction Saline soil is widely distributed throughout the world. The total area of saline land exceeds 1 × 109 ha, accounting for approximately 10% of the global land area, and saline land increases at an annual rate of 1 × 106 to 1.5 × 106 ha [1]. The distribution of saline land in China is also very extensive with about 99.13 million hectares, which is mainly distributed in the north, northwest, northeast of China and coastal areas [2]. Saline land is not suitable for the growth of most plants and high salinity would cause production cuts and plants death. It is important to the sustainable development of China's agriculture, forestry and ecological environment to find a wise way to improve and use the saline

land for production. At present, there are mainly two methods for the treatment, development and utilization of saline land. One is to improve the soil through irrigation, fertilization, covering and addition of chemical neutralizing agents, and the other is to select varieties that are suitable to saline environment and breed salinity-tolerant plants [3]. It is now generally believed that the latter method is the most effective. Correspondingly, the studies of plants under salinity stress mainly focused on two aspects, the hazards of salinity stress and the mechanisms of plants’ salinity tolerance. The main hazards caused by salinity stress are the inhibition of photosynthesis, the dysfunction of metabolic and the destruction of intracellular structure, resulting in disorder of ion and osmotic, oxidation-reduction imbalance and further

∗ Corresponding author. CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, Hubei, 430074, China ∗∗ Corresponding author. Key Laboratory of Plant Resources and Beijing Botanical Garden, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China E-mail addresses: [email protected] (T. Sang), [email protected] (J. Yan). 1 Qian Wang and Lifang Kang contributed equally to this study.

https://doi.org/10.1016/j.biombioe.2019.03.005 Received 28 June 2018; Received in revised form 1 March 2019; Accepted 14 March 2019 Available online 24 April 2019 0961-9534/ © 2019 Published by Elsevier Ltd.

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ion poisoning, oxidative damage and water shortage [4,5]. To survive, plants rapidly perceive stress through signal transduction pathways [6] and respond to salinity stress at physiological, biochemical, molecular and morphological levels [7,8]. Plants regulate through the cooperation of multiple systems and pathways to rapidly initiate the expression of genes and perform transcriptional regulation, to synthesize proteins controlling metabolite synthesis and ion transport, to regulate osmotic balance and mitigate the effects of salinity stress [9,10]. For example, the salinity-tolerance mechanism of upland cotton showed that the genes involved in the biosynthesis and signal transduction of hormone, reactive oxygen species and salinity-sensitive signal transduction had an important role in response to salinity stress [11]. Photosynthesis is the basis for plant growth and development, which provide the necessary organic matter. Salinity stress can adversely affect plant photosynthesis by leading to stomatal closure or stomatal limitation in salinity-stressed leaves [12–14]. As low leaf osmotic potential causes the decrease in stomatal conductance, the decreased stomatal conductance results in the increased resistance to CO2 uptake, thereby reducing CO2 availability in the chloroplast during photosynthesis [15]. Salinity stress also stimulates the production of reactive oxygen species (ROS) and excess ROS increases membrane lipid peroxidation and electrolyte leakage [16], impairs chloroplasts, inhibits photochemical reactions, and reduces photosynthesis [17,18]. Salinity stress also reduces the photosynthesis by destroying the plants’ photosynthetic system. According to the study of Khan, the decrease in photosynthetic rate of chickpea leaf under salinity stress was mainly caused by damage in the photosynthetic system II, not by stomatal conductance decreased [19]. In addition, salinity stress has also been found to affect the expression of important proteins in the photosynthetic system. Studies have shown that the decreased photosynthesis of rice, wheat and other crops under salinity stress conditions is caused by the differential expression of important proteins in the photosynthetic system [20,21]. In general, the effect of salinity stress on plant growth varies with the degree of stress. For example, the high concentrations of salinity will severely inhibit photosynthesis, reduce photosynthetic rate, plant height, leaf number, root length and increase root to shoot ratio [8,22]. While some studies have shown that low concentrations of salinity stress may stimulate plant growth and improve photosynthesis performance [7,23]. Here, we transplanted Miscanthus from non-salinity stress to salinity stress in wild environment. After two growing seasons, we measured their photosynthetic traits and sequenced their RNA. We tried to establish the relationship transcriptomic data and photosynthetic traits and aimed to explore the effects of long-term salinity stress on the photosynthetic characteristics and the mechanisms of long-term salinity stress adaptation of M. lutarioriparius.

readings were recorded. To ensure the accuracy, an infra-red gas analyzer (IRGA) was matched to reach equilibrium (monitor △CO2 and △H2O) every 20 min. Then, this leaf was cut and kept in liquid nitrogen for further transcriptome sequencing. 2.2. Photosynthesis gas exchange measurements and analysis The photosynthesis gas exchange parameters of M. lutarioriparius were measured with the Li-6400 portable photosynthesis system (LICOR 6400 XT system; LI-COR, USA) in DS, including photosynthetic rate (A), stomatal conductance (gs), intercellular CO2 concentration (Ci), transpiration rate (E). Instantaneous water use efficiency (WUE) was calculated as the ratio of photosynthetic rate to transpiration rate [28]. The red-blue LED light source (LI 6400-02B) was used and the light intensity was set as 1400 μmol m−2 s−1. The CO2 cylinders were used to keep the concentration of CO2 as 400 mol mol−1. Analysis of variance (ANOVA) was performed in R statistical environment release 3.0.2 to reveal the differences among populations of M. lutarioriparius for the parameters related to photosynthesis, including A, gs, Ci, E, WUE. 2.3. RNA-Seq and calculating expression data Qiagen Plant Mini Kits (Qiagen, Stanford, CA, USA) were used to extract the total RNA from leaves of 50 M. lutarioriparius individuals and RNase-free DNaseI (TaKaRa, Otsu, Shiga, Japan) was used to digest residual genomic DNA. About 5 μg purified total RNA quantified by NanoDrop ND-1000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA) was taken out to purificate the mRNA with oligod(T) beads Dynabeads® mRNA Purification Kit (Invitrogen, Carlsbad, CA, USA). After preparing the cDNA libraries with the NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA), the first strand cDNA was synthesized using random hexamer-primed and then the second-strand cDNA synthesis and adaptor ligation. Ampure XP beads (Beckman Coulter, Brea, CA, USA) was used to select the cDNA fragments of approximately 450-bp. After PCR, the library integrity and quality were estimated with Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and Qubit 2.0 fluorometer (Life Technologies, Grand Island, NY, USA), respectively. The libraries were sequenced on the Illumina HiSeq 2500 system to get 2 × 100 bp paired-end reads. Raw data were separated by the indexed nucleotides using bcl2fastq-1.8.4. The first nine bases of the 100-bp reads were unstable, so they were trimmed in all samples. Raw reads were filtered based on quality scores (Q = 20) and trimmed using FASTQC and FASTX. Based on high-quality reference transcriptome (TSA accession no. GEDE00000000) of M. luparioriparius [29], the trimmed reads of each individual were mapped to the Bowtie-build indexed reference transcriptome using TopHat v2.0.0 with default settings to measure the expression level of each gene [30,31]. Cufflinks v2.0.2 was used to calculate the expression level of each sample using FPKM standing for fragments per kilobase of exon per million fragments mapped [32].

2. Materials and methods 2.1. Plant materials The mature seeds of 5 populations of M. lutarioriparius were collected from its native habitat in November 2011 (The salinity of each population's habitat ranges from 0.55‰ to 1.65‰), and planted in experimental field of Dongying, Shandong Province (DS) where is near the Yellow River estuary with high salinity in April 2012 (The salinity of the experimental filed is around 7‰, almost 7-fold higher than that of native habitats; Fig. S1). They are LU5, LU7, LU10, LU14 and LU19, which corresponding to codes based on previous study [27]. In order to reduce the influences of the variety of salinity in the experimental saline soil, five populations of M. lutarioriparius were planted with randomized block design. In July 24, 2013, ten individuals were randomly selected from each of five populations in DS. For each individual, the photosynthesis parameters of the fourth leaf from the top were measured with standard chamber from 10am to 12pm. After stable, the

2.4. Identifying candidate genes related with photosynthesis As described above, the levels of gene expression for 18,503 transcripts were estimated using FPKM. To exclude the effect of 0 values on the results, the genes with 0 values in the expression were kicked. The number of genes co-expressed by these 50 individuals is 12,524. The following analysis was based on these co-expressed genes. The Pearson correlation analysis was carried out between the photosynthetic rate and the gene expression level of the 50 individuals. Pearson correlation coefficient was used to measure the linear correlation between photosynthetic rate and gene expression. The analyses were conducted using cor. test () in the R statistical environment release 3.0.2. Thus, a Pearson correlation coefficient was generated for each gene while using 124

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a test to assess its statistical significance.

significant positive correlation. The correlation coefficient between gs and E was 0.841, also showing a significant positive correlation. The stomatal conductance showed a significant negative correlation with WUE, and the correlation coefficient was −0.480. There was a significant negative correlation between Ci and WUE, and the correlation coefficients were −0.757. There was a highly significant positive correlation between Ci and E with a correlation coefficient of 0.744. Transpiration rate showed extremely high negative correlation with WUE, and the correlation coefficient is −0.742 (Table 3).

2.5. Functional analysis of candidate genes The candidate genes of M. lutarioriparius were annotated. We used BlastN (Nucleotide Basic Local Alignment Search Tool) to find the function of candidate genes from NCBI non-redundant nucleotide database (Nt) database. The expected value (E-value) as an index to evaluate the reliability of sequence alignment was only considered less than 10−10.

3.2. Identification and classification of photosynthesis-related candidate genes of M. lutarioriparius adapted to salinity stress

3. Result

Through the previous analysis of photosynthetic traits among different population of M. lutarioriparius, we found that the impact of populations can be ignored and the 50 individuals can be as a group. A total of 12,524 genes were co-expressed in these 50 individuals and the Pearson correlation analysis was performed between the FPKM value and the photosynthetic rate (A). A total of 40 candidate genes related to photosynthetic rate were found (P < 0.001), that may be involved in the adaptation of photosynthetic rate under long-term salinity stress. There were 28 candidate genes positively and 12 candidate genes negatively correlated to photosynthetic rate. The correlation coefficients of these candidate genes ranged from 0.427 to 0.562 (Table 4). In addition, the specific functions of these 40 candidate genes were annotated and classified using the BlastN function in NCBI (Table 5), including 1 gene related to photosynthesis, 3 genes related to osmotic adjustment, 19 genes related to abiotic stress, 6 genes related to signal transduction, and 11 genes for other functions.

3.1. Photosynthetic traits of M. lutarioriparius The photosynthetic rate (A) ranged from 19.19 to 42.80 μmol m−2 s−1, at an average of 32.58 μmol m−2 s−1. The mean of stomatal conductance (gs) was 0.56 mol m−2 s−1, and the mean of intercellular CO2 concentration (Ci) was 228.74 μmol mol−1. The average of transpiration rate (E) was 6.2 mmol m−2 s−1, and the average of water use efficiency (WUE) was 5.4 mmol mol−1. Among them, gs had the highest degree of variation with a coefficient of variation of 0.482; followed by E with variation coefficient of 0.323. A and Ci had a small coefficient of variation at 0.143 and 0.146, respectively (Table 1). In order to compare the differences of photosynthetic traits among different populations of M. lutarioriparius, a description of photosynthetic traits were given for each population (Fig. 1). The means of A were between 29.57 and 34.90 μmol m−2 s−1, with population LU9 being the lowest and population LU7 being the highest. The means of gs were between 0.48 and 0.62 mol m−2 s−1, with population LU5 being the lowest and population LU14 being the highest. The means of Ci were between 211.7 and 241.25 μmol mol−1, with the lowest LU7 and the highest LU9. The average of E ranged from 5.88 to 6.42 mmol m−2 s−1, with the lowest LU5 and the highest LU14. The means of WUE were between 4.94 and 5.97 mmol mol−1, with LU9 being the lowest and LU7 being the highest. The population LU7 had highest mean of A and WUE, while the greatest variation in E and gs. The means of A and WUE of population LU9 were relatively low, but the variation range of photosynthetic traits were relatively large. One-way analysis of variance (ANOVA) was used to examine the effects of populations on the photosynthetic traits of M. lutarioriparius. The results showed that there were no significant differences in the photosynthetic rate, stomatal conductance, intercellular CO2 concentration, transpiration rate and water use efficiency among the populations (Table 2). Pearson correlation analysis was performed for the relation of photosynthetic gas exchange parameters of 50 M. lutarioriparius individuals in DS. The results were shown in Table 3. It can be seen that A was significantly positively correlated with gs and E (P < 0.001), and the correlation coefficients were 0.549 and 0.466, respectively. There was no significant correlation between A and Ci or WUE. The correlation coefficient between gs and Ci was 0.798, showing a highly

4. Discussion 4.1. Photosynthetic effects of M. lutarioriparius to salinity stress Photosynthesis provides abundant energy and substances for plant growth and development. It is not only an extremely complex physiological and biochemical process, but also an important aspect to analyze the effects of salinity stress on plants. Numerous studies have shown that salinity stress inhibits plant photosynthesis, but some studies found that photosynthesis was not inhibited by salinity [18] and even low salinity environments could stimulate photosynthesis performance [7,33]. The photosynthetic traits of five M. lutarioriparius populations were compared and it was found that the photosynthetic rate and water use efficiency of LU7 population were the highest (Fig. 1). It can be seen as a self-protection reaction that plants decline the photosynthesis and transpiration under salinity stress. The plant automatically closes the stomata of the leaves, reduces stomatal conductance, increases stomatal resistance to reduce water uptake by roots, which also reduces the absorption of toxic ions. The level of water use efficiency reflects the plant's adaptability to osmotic stress caused by salinity stress. This indicates that LU7 population may be a population with strong adaptability to salinity stress. It is generally believed that the reasons for the decrease of photosynthetic rate of plants under salinity stress include stomatal limitation and non-stomatal limitation. According to Farquhar and Sharkey, if stomatal limitation is the main factor, the stomatal conductance decreases and the photosynthesis of mesophyll cells becomes active [34]. At this time, the intercellular CO2 concentration decreases significantly, and the stomatal limitation value increases. Otherwise, even when the stomatal conductance is low, the intercellular CO2 concentration may still increase or remain unchanged, the stomatal limitation value decrease, and the non-stomatal limitation is the main factor for the decrease of the photosynthetic rate. There are many related studies, some of which are based on stomatal limitation [12–14], or based on nonstomatal limitation [19,22,35], and also including both ways to impactf

Table 1 Statistical dat of photosynthetic traits of M. lutarioriparius planted in DS. photosynthetic parameters

mean

Standard deviation

min

max

Coefficient of variation

A (μmol m−2 s−1) gs (mol m−2 s−1) Ci (μmol mol−1) E (mmol m−2 s−1) A/E (mmol mol−1)

32.58 0.56 228.74 6.20 5.40

4.65 0.27 33.51 1.26 0.97

19.91 0.28 160.99 4.15 2.61

42.80 1.49 325.54 9.34 7.59

0.143 0.482 0.146 0.203 0.180

Abbreviations: A photosynthetic rate; gs stomatal conductance; Ci intercellular CO2 concentration; E transpiration rate; A/E instantaneous water use efficiency. 125

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Fig. 1. Comparison of photosynthetic traits of different Miscanthus lutarioriparius populations planted in DS. Means and standard deviations (error bars) were calculated from 10 randomly sampled individuals for each population.

CO2 was not the main factor for restricting the photosynthesis of M. lutarioriparius in DS. Plants ingest CO2 through stomata for photosynthesis and lose moisture. Stomata acts as a common pathway for CO2 and water to move in and out of the leaves, simultaneously impacts the physiological processes such as photosynthesis and transpiration [37]. Therefore, the stomata controls the photosynthetic rate and transpiration rate to some extent. Studies have shown that photosynthetic efficiency, stomatal conductance and transpiration of rice under salinity stress were all significantly reduced, but salinity-tolerant rice had a faster stomatal response, and its stomata tended to close more quickly and partly restored after a short period of adaptation [14]. In this study, the stomatal conductance was significantly positively correlated with the photosynthetic rate and transpiration rate, indicating that under long-term salinity stress conditions, M. lutarioriparius can adjust the balance of photosynthesis and transpiration by adjusting the opening and closing degree of stomata. Stomata responds to a variety of environmental and physiological signals, adjusting pore size to optimize plant water use efficiency, maximizing CO2 uptake while minimizing water loss [38]. In DS, the correlation coefficient of water use efficiency and photosynthetic rate was 0.229, showing no significant correlation, while the correlation coefficients with stomatal conductance, intercellular CO2 concentration and transpiration rate were −0.48, −0.757, and - 0.742, showed a significant negative correlation (Table 3). The effect of decreased stomatal conductance on photosynthesis was less than that of transpiration, while transpiration rate had a strong dependence on stomata and partial closure of stomata was beneficial to increase water use efficiency of the leaf [39]. The results of our study indicated that the changes in water use efficiency were mainly caused by changes in the transpiration rate, and partial closure of the stomata reduced the transpiration rate more than decreased the photosynthetic rate. In addition, factors that affect photosynthesis in plants were divided into external factors and internal factors, including the types of plants, photosynthetic pathways, growth and development stages, structural

Table 2 One-way ANOVA of photosynthetic traits of M. lutarioriparius in DS. photosynthetic traits

effect

d.f

Sum Sq

Mean Sq

F

P

A

Population Residuals Population Residuals Population Residuals Population Residuals Population Residuals

4 45 4 45 4 45 4 45 4 45

166.2 892.5 0.103 3.364 5251.5 49,774 1.8737 76.273 5.81 40.12

41.55 19.83 0.026 0.075 1312.9 1106.1 0.4684 1.6950 1.4527 0.8915

2.095

0.0972

0.344

0.8465

1.187

0.3295

0.2764

0.8917

1.6294

0.1834

gs Ci E WUE

Table 3 Correlation analysis among photosynthetic traits of M. lutarioriparius individuals planted in DS.

A gs Ci E A/E

A

gs

Ci

E

A/E

1 0.549*** 0.077 0.466*** 0.229

1 0.798*** 0.841*** −0.480***

1 0.744*** −0.757***

1 −0.742***

1

***P < 0.001, ** 0.001 < P < 0.01, * 0.01 < P < 0.05.

the photosynthetic rates [36]. It can be seen that, with the change of the concentration of stress and the length of time, the stomatal factors that restrict photosynthesis and the non-stomatal factors are not mutually independent, but are in dynamic changes. According to the correlation analysis of photosynthetic traits in DS (Table 3), the correlation coefficient between photosynthetic rate and intercellular CO2 concentration was 0.077. There was no significant correlation, indicating that the supply of photosynthesis raw material 126

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salinity stress. Therefore, we paid attention to the photosynthesis-related genes under salinity stress in natural environment, which provided an idea for the study of trait-related candidate genes under stress. There were 12,524 genes co-expressed in 50 individuals in DS. A total of 40 related candidate genes were found (P < 0.001) by Pearson correlation analysis of FPKM and the photosynthetic rate. The functions of candidate genes involved in photosynthesis, osmosis adjustment, abiotic stress and signal transduction, respectively (Fig. 2). These candidate genes participated in various important physiological processes and were thought to affect photosynthetic rate in long-term salinity adaptation. First, a transcript MluLR17421 was found in the photosynthesisrelated functional class coding protein psb29. Psb29 is homologous in all cyanbacteria and vascular plants with a prominent feature of a long alpha helix at the C-terminal extension of the globular protein domain. It has a variety of important functions, such as participating in the assembly of photosynthetic system II, regulating the opening of the pores and water potential. Its destruction will cause damage to PSII under high light intensity. Photosystem II is a membrane protein complex which is one of the most important part of photosynthetic system. All oxygenated photosynthetic organisms from cyanobacteria to vascular plants rely on the activity of PSII pigment protein complexes to transfer electrons from water to the plastoquinone, driven by solar energy [41]. The irreversible inactivation of PSII occurs at all light intensities [42,43], but the activity of PSII can be restored by replacing the damaged protein subunits with the new protein (mainly D1 response center subunits) [44,45]. Net loss of PSII activities only occurs when the repair fails to match the damaged lost. There was a positive correlation between the expression level of transcript MluLR17421 and the photosynthetic rate (Table 4) that showed the increased expression of PSB29 can improve the efficiency of PSII repaired cycle, help to reduce the damage of the PSII and maintain the photosynthetic rate under salinity stress. Second, there were 3 transcripts in the osmoregulatory-related functional class. Osmosis adjustment is an important pathway for plants to respond to salinity stress and plants normally accumulate large amounts of proline as osmoregulatory substances under salinity stress conditions. Transcripts MluLR8414 encodes an arginine deiminase that belongs to the hydrolase family, acts on carbon-nitrogen bonds and peptide bonds and participates in the metabolism of arginine and valine. The positive correlation between its expression and photosynthetic rate indicated that the osmotic adjustment of proline played an important role in maintaining photosynthetic rate under salinity stress. Transcript MluLR10335 encodes a BLH1 protein. The studies have shown that the blh1 gene mutant was more resistant to salinity stress than the wild type in the seed germination and early development of Arabidopsis. In contrast, the BLH1 protein overexpression strain was more sensitive to salinity conditions [46]. In this study, the expression level of MluLR10335 was significantly negatively correlated with the photosynthetic rate, indicating that the plants not sensitive to salinity stress are more conducive to surviving and obtain higher photosynthetic rates. The transcript MluLR7667 encodes a vacuolar sorting protein that played an important role in ion-osmotic regulation of salinity stress. Third, a total of 19 transcripts were associated with the abiotic stress, including ubiquitin ligase, immunophilin, and so on, accounting for the largest proportion of candidate genes. Ubiquitin ligase is thought to positively regulate ABA signaling pathway when plants faced the abiotic stresses [47]. ABA is responsible for the defense of plants against abiotic stresses and a large number of studies have shown that environmental conditions such as drought, salinity, cold, high temperature stress and injury, contribute to elevated levels of ABA [48,49]. In Arabidopsis, the CER9 gene encodesd an ubiquitin ligase that inhibits transpiration through promoting the formation of stratum corneum and contributes to water retention in plants [50]. Therefore, ubiquitin ligase plays an important role in stomatal and photosynthetic

Table 4 Candidate genes of the photosynthetic rate identified at the 0.001 level by Pearson correlation coefficient. No.

Transcripts

Correlation Coefficients

P value

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

MluLR6839 MluLR3837 MluLR11164 MluLR18016 MluLR7320 MluLR377 MluLR8414 MluLR8277 MluLR15733 MluLR6170 MluLR3735 MluLR13322 MluLR8967 MluLR7169 MluLR7667 MluLR14172 MluLR13278 MluLR1145 MluLR17421 MluLR7150 MluLR9486 MluLR2961 MluLR9635 MluLR9459 MluLR13702 MluLR2228 MluLR12219 MluLR18086 MluLR5605 MluLR5880 MluLR14045 MluLR14174 MluLR4529 MluLR11110 MluLR14370 MluLR10335 MluLR15328 MluLR14466 MluLR16462 MluLR11710

0.552 0.517 0.505 0.491 0.488 0.486 0.480 0.476 0.473 0.470 0.470 0.468 0.463 0.461 0.460 0.460 0.459 0.454 0.452 0.447 0.442 0.442 0.440 0.434 0.433 0.432 0.432 0.427 −0.427 −0.432 −0.435 −0.438 −0.447 −0.463 −0.473 −0.479 −0.496 −0.499 −0.499 −0.562

1.62E-05 5.97E-05 9.27E-05 0.0001 0.0002 0.0002 0.0002 0.0002 0.0003 0.0003 0.0003 0.0003 0.0004 0.0004 0.0004 0.0004 0.0004 0.0005 0.0005 0.0006 0.0006 0.0007 0.0007 0.0008 0.0008 0.0009 0.0009 0.0010 0.0010 0.0009 0.0008 0.0007 0.0006 0.0004 0.0003 0.0002 0.0001 0.0001 0.0001 1.08E-05

characteristics, and enzyme system components. External environmental factors mainly include light, temperature, moisture, and air concentration. Plants regulated internal factors and affected the values of various photosynthetic traits through constant adaptation to the external environment. One-way ANOVA analysis showed that there was no significant difference in photosynthetic traits among the different populations of M. lutarioriparius in DS (Table 2). This indicated that even if there was a difference in the genetic basis among populations, the phenotypes of surviving individuals might be consistent through the strong selection of the environment and the adaptation of M. lutarioriparius to the environment. 4.2. Responses of candidate genes related to photosynthetic traits for salinity stress Responding for salinity stress of plants, many of studies have focused on the performance of plants under short-term salinity stress in laboratory conditions. Although artificial control can guarantee the consistency of stress conditions, the plants often perform differently in natural environment from in the actual environment and laboratory conditions. Song et al. (2017) used a comparative transcriptome analysis of M. lutarioriparius in the native habits and high salinity domestication experiment field in DS, and found 59 genes that responded to salinity stress [40]. However, the study did not focus on specific traits. Generally, the photosynthetic traits of plants are very sensitive to salinity stress, which is directly related to the survival of plant under 127

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Table 5 Functional annotation and categorization of candidate genes in DS. Functional categorization

Transcripts

BlastN

E-value

Photosynthesis Osmotic Adjustment

MluLR17421 MluLR8414 MluLR7667 MluLR10335 MluLR6170 MluLR3735 MluLR14172 MluLR377 MluLR8967 MluLR11710 MluLR2961 MluLR8277 MluLR1145 MluLR3837 MluLR7150 MluLR4529 MluLR16462 MluLR9486 MluLR14466 MluLR12219 MluLR9635 MluLR14174 MluLR14045 MluLR15328 MluLR18016 MluLR7320 MluLR7169 MluLR13278 MluLR5605 MluLR15733 MluLR13702 MluLR18086 MluLR5880 MluLR14370 MluLR2228 MluLR6839 MluLR11164 MluLR13322 MluLR11110 MluLR9459

Photosystem II subunit 29 (PSB29) [Sorghum bicolor] Arginine deiminase [Sorghum bicolor] Vacuolar-sorting receptor 1 (VSR1) [Sorghum bicolor] BEL1-like homeodomain protein 7 (BLH-7) [Sorghum bicolor] Ubiquitin-conjugating enzyme E2 27 (UBE2) [Sorghum bicolor] Ubiquitin-conjugating enzyme E2 (UBE2) [Sorghum bicolor] Methylthioribose-1-phosphate isomerase (MRI1) [Sorghum bicolor] Homeobox-leucine zipper protein HOX16-like [Sorghum bicolor] Putative plastid-lipid-associated protein 13 [Zea mays] 70 kDa peptidyl-prolyl isomerase [Sorghum bicolor] Peptidyl-prolyl cis-trans isomerase CYP65 (CYP65) [Sorghum bicolor] Heparan-alpha-glucosaminide N-acetyltransferase [Sorghum bicolor] Mitochondrial import inner membrane translocase subunit Tim 9 (TIMM9) [Sorghum bicolor] Cinnamyl-alcohol dehydrogenase [Sorghum bicolor] Formin-like protein 16 (FMNL16)[Sorghum bicolor] Pyruvate dehydrogenase (acetyl-transferring) kinase [Sorghum bicolor] Cinnamyl-alcohol dehydrogenase [Sorghum bicolor] Arabinanase/levansucrase/invertase [Zea mays] Heat stress transcription factor B-2b-like [Sorghum bicolor] Phosphoglucan phosphatase DSP4 [Sorghum bicolor] Sorbitol dehydrogenase homolog 1 (SDH1) [Zea mays] Protein activity of BC1 complex kinase 1 [Sorghum bicolor] Cytosolic NADP-isocitrate dehydrogenase [Sorghum bicolor] Cysteine-rich receptor-like protein kinase 20 (Cysteine-rich RLK 20) [Sorghum bicolor] Zinc finger protein CONSTANS-LIKE 16-like (COL16) [Zea mays] Transcription factor bHLH157 [Sorghum bicolor] Zinc finger protein CONSTANS-LIKE16 (COL16) [Zea mays] tRNA-dihydrouridine (20/20a) synthase [Sorghum bicolor] Cytokinin riboside 5′-monophosphate phosphoribohydrolase LOGL1 [Sorghum bicolor] ABC transporter A family member 7 (ABCA7) [Sorghum bicolor] Nuclear transport factor 2 family protein (NTF2) [Zea mays] Nuclear transport factor 2 (NTF2) [Sorghum bicolor] Cinnamoyl-CoA reductase 1 protein CROWDED NUCLEI 1 (CRWN) [Sorghum bicolor] Unknow [Zea mays] Hypothetical protein [Zea mays] Unknow [Sorghum bicolor] Unknow [Sorghum bicolor] Unknow [Sorghum bicolor] Hypothetical protein [Zea mays]

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6E-150 0 0 0 0 0 3E-169 0 0 0 0 1E-101 0 0 0 0

Abiotic Stress

Signal Transduction

Others

Fig. 2. The functional categorization of BLAST matches of 40 photosynthesis-related candidate genes. A detailed list of the function for each gene can be found in Table 5.

rate regulation of M. lutarioriparius under salinity stress. The MluLR2961 transcript encoded a peptidyl-prolyl cis/trans isomerase (PPIase), a major class of protein folding enzymes that catalyzed the peptide bond isomerization around Pro residues. Some of the immunophilins localized in cytoplasm and nuclei can affect plant growth

and development in a variety of ways. For example, CYP18-3 can regulate brassinosteroid signal transduction, affecting plant growth and development, and its activity is affected by the phytochrome-mediated light signal pathway [51]. The MluLR377 transcript encodes a leucine zipper protein that has been found to be associated with adversity 128

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adaptation and involved in light signal response. It was found to be associated with drought stress in rice [52]. Final, candidate genes associated with signal transduction always ensured the response correctly to salinity stress by encoding related proteins. Six transcripts related to signal transduction are found in this study: transcripts MluLR15328, MluLR18016, MluLR7320, MluLR7169, MluLR13278, and MluLR5605. In plants, the cysteine-rich receptor-like receptor (CRK) is a subfamily of receptor-like protein kinases that contain the DUF26 motif in their extracellular domain. Many of these cysteine-enriched RLK genes are induced by pathogen infection and may be suggestive in plant defense responses. The transcript MluLR15328 encoded CRK20. CRK20 was transcriptionally induced by pathogens, salicylic acid and ozone, involved in protein phosphorylation and controlled water or nutrient transport in Arabidopsis [53]. There was a negative correlation between CRK20 gene expression and photosynthetic rate, suggesting that high expression of CRK20 might predict plants more severely responding to abiotic stress. Transcripts MluLR18016 and MluLR7169 encoded COL protein that has DNA binding transcription factor activity and may participate in the regulation of transcription and the photoperiod of plant flowering [54].

[3] [4] [5] [6] [7] [8] [9] [10] [11]

[12]

5. Conclusions

[13]

Here, we focused on the photosynthetic and transcriptomic traits of M. lutarioriparius under long-term salinity stress in the natural environment. Water use efficiency was mainly impacted by transpiration rates under long-term salinity stress. The stress-resistance genes had a significant effect on photosynthesis, and the photosynthesis-related candidate genes mainly related to the photosynthesis, osmotic adjustment, abiotic stress and signal transduction. Exploring the photosynthetic adaptive basis of long-term salinity stress in M. lutarioriparius, we found that M. lutarioriparius did not achieve adaptation by directly changing the expressions of key genes in the photosynthetic pathway, but rather by repairing and regulating the key genes’ functions under the long-term salinity stress.

[14]

Acknowledgements

[19]

The work was supported by the Key Research Program of the Chinese Academy of Sciences (No. KFZD-SW-112-01-08), the Science and Technology Service Network Initiative of the Chinese Academy of Sciences (KFJ-EW-STS-061), the Youth Promotion Association of the Chinese Academy of Sciences (Y829281G02) and the Project for Autonomous Deployment of the Wuhan Botanical Garden (55Y755271G02).

[20]

[15] [16]

[17]

[18]

[21]

[22]

Appendix A. Supplementary data

[23]

Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.biombioe.2019.03.005.

[27] [28]

List of abbreviations A Ci DS E FPKM gs WUE

[29]

photosynthetic rate intercellular CO2 concentration Dongying in Shandong Province transpiration rate Expected number of fragments per kilobase of transcript sequence per millions base pairs sequenced stomatal conductance water use efficiency

[30] [31] [32]

[33]

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