Identifications of genes involved in ABA and MAPK signaling pathways positively regulating cold tolerance in rice

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This study based on two-year field observations identified two rice lines, L9 (cold stress-sensitive) and LD18 (cold stress-tolerant) showing contrasting CS response. L9 exhibited 38% reduction in photosynthetic efficiency, whereas LD18 remained unchanged, correlating with seed rates. Transcriptome analysis identified differentially expressed genes (DEGs) with LD18 showing enriched pathways (carbon fixation, starch/sucrose metabolism, glutathione metabolism). LD18 displayed dramatically enhanced expression of MAPK-related genes ( LOC4327351 , LOC4352460 , LOC4337850 ) and increased ABA signaling genes ( LOC4333690 , LOC4345611 , LOC4335640 ) compared to L9 exposed to CS. Results from qPCR confirmed the enhanced expression of the three MAPK-related genes in LD18 while dramatic reduction in L9 under CS relative to that under CK. We also observed up to 66% reduction in expression levels of the three genes related to ABA signaling pathway in L9 relative to LD18 under CS. In consistent with results of photosynthetic efficiency, metabolic analysis suggests pyruvate metabolism, TCA cycle and carbon metabolism enrichment in LD18 under CS. The study reveals reprogramming of the carbon assimilation metabolic pathways, emphasizing the critical roles of the key DEGs involved in ABA and MAPK signaling pathways in positive regulation of LD18 response to CS, offering the foundation towards cold tolerance breeding through targeted gene editing. Cold stress ABA signaling MAPK signaling Transcriptional regulation Integrative analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Temperature stands as a pivotal environmental factor shaping plant distribution in terrestrial ecosystems [ 1 ]. Globally, cold-induced crop losses pose a significant challenge. With the continuous degradation of our ecological environment, cold has emerged as a growing threat to plant life. Cold stress (CS) compromises cell membrane integrity, triggering the production of reactive oxygen species (ROS) and other detrimental compounds, ultimately impeding plant growth and yield formation [ 2 – 4 ]. Numerous studies have delved into rice’s molecular response to CS, probing its physiological and ecological characteristics [ 5 – 7 ]. Cold significantly inhibits rice's growth index and the physiological enzyme activities, a significant inhibited by prior cold treatments [ 8 ], hinting at induced cold tolerance. Under cold conditions, various genes have been observed to undergo upregulation [ 9 – 12 ]. These genes encode proteins involved in the sensing and signal transduction processes of plant cold tolerance, stimulating the synthesis of osmotic regulators [ 13 ], enhancing antioxidant enzymes activity [ 14 ] and improving cell membrane fluidity [ 15 ]. Thus enhance the plant’s resilience to CS [ 16 , 17 ]. Therefore, unraveling the intricate process and mechanisms underlying the perception, transmission of cold signals and stimulating cold tolerance in plants hold great theoretical and practical significance. It contributes to a deeper understanding of cold tolerance mechanisms in plants and expedites the molecular breeding process for cold-tolerant crops. Previous studies underscore the significance of transcriptional regulation in plant responses to cold injury [ 18 ]. The CBF/DREB-dependent signaling pathway stands out as a pivotal mechanism driving cold tolerance in both Arabidopsis thaliana and rice [ 7 ]. Notably, the activation of the calcium signaling pathway post-accumulation of cold tolerance characteristics reveals evolutionarily conserved genes crucial to plant adaptation [ 16 ]. In extensive research, showcased the significant enhancement of cold tolerance in japonica rice by overexpressing the key QTL gene, COLD1 , associated with sensing cold stress. Conversely, functional deletion mutants and antisense gene lines of COLD1 exhibited susceptibility to cold damage [ 6 ]. Additionally, genomic investigations identified three genes ( Os01g55510 , Os01g55350 , and Os01g55560 ) in chromosome 1 closely linked to cold tolerance, revealing the intricate nature of plant responses [ 19 ]. Several transcription factors, such as MYBS3 , OsWRKY71 , and OsWRKY76 , have emerged as pivotal players in rice's cold tolerance [ 20 – 22 ]. Recent studies have notably highlighted the role of calcium-dependent protein kinases in rice's cold tolerance [ 23 , 24 ]. These findings serve as crucial theoretical references, allaying the scientific basis for deeper exploration and understanding of rice's molecular mechanisms and regulatory networks governing cold tolerance. Recently advancements in omic-analysis, encompassing transcriptome, metabolism and proteomics approaches, have emerged as crucial tools for identifying potential biomarkers in both abiotic and biotic stress in higher plants [ 25 ]. Numerous studies have delved into rice transcriptome profiling using RNA-seq technology, aiming to comprehend molecular pathways and compare transcriptomes [ 26 – 31 ]. Notably, RNA-seq technology offers a significant advantage in representing the global transcriptome, overcoming the limitations of microarray technology dependent on predefined probes. Through field observations across two consecutive years, we identified two distinct rice genotypes: LD18, categorized as highly tolerant and L9 classified as highly susceptible to CS. The highly tolerant genotype LD18 exhibited survival for up to 25 days under CS of 4°C. Therefore, this investigation integrates transcriptome and non-targeted metabolism analysis of these contrasting genotypes, aiming to identify promising genes and unveil novel insights into diverse gene expressions and pathways pivotal in conferring cold tolerance in rice. 2. Materials and Methods 2.1 Plant materials and growth conditions Two distinct rice varieties were selected for this study, namely the resilient genotype Longdao18 (LD18) and the susceptible cultivar L9, focusing on cold tolerance during the vegetative stage. These varieties were obtained from the Institute of Crop Cultivation and Tillage at the Heilongjiang Academy of Agricultural Sciences. The plants were first grown in a growth chamber under a consistent temperature of 25°C and a 12-h photoperiod. Two stages (seedling stage and heading stage) were conducted for CS treatments. Forty seeds were sown into each dish for 10 days, and then exposed to CS conditions within a Conviron PGV36 walk-in cold growth chamber for 10 days at 10 o C during seeding stage. The growth chamber maintained optimal conditions at 75–85% relative humidity (RH), 800 µmol s − 1 m − 2 light intensity positioned above 60 cm from the floor and 12h photoperiod. In terms of CS treatment during heading stage, plants were raised in pots until reaching 30 days of growth. Subsequently, the potted plants were exposed to CS conditions for 10 days at 15 o C. 2.2 Photosynthetic measurements To conduct photosynthetic measurements, we employed a portable photosynthesis measurement system, the Licor 6400XT (LICOR Corp., USA). This system was utilized to evaluate photosynthetic rates (A) and stomatal conductance (gs) in accordance with established protocols [ 32 ]. The system operated at a flow rate of 400 mmol s − 1 for CO 2 , while maintaining light density at 1500 µmol m − 2 s − 1 and temperature of 27°C, with CO 2 levels set at 400 ppm. The experiment comprised four biological replicates to ensure strength and reliability of the obtained data. 2.3 Agronomic traits The CS treatment in this study was carried out during the booting stages of rice. For the treatment, five representative plants were selected to determine various agronomic traits. These included assessing the number of full grains and empty grains per panicle, as well as quantifying the number of deflated grains. 2.4 Antioxidant and oxidoreductase measurements GR activity [ 33 ], Soluble protein content [ 34 ], and SOD activity (measured by the inhibiting the photoreduction of nitro blue tetrazolium (NBT) with modifications) [ 35 ] were assessed. The reaction mixture, totaling 3 ml, was composed of 25 mM phosphate buffer (pH 7.8), 13 mM methionine, 75 mM NBT, 0.1 mM EDTA, 4 µm riboflavin, 0.25 ml distilled water and 0.05 ml enzyme extract. Initiated by riboflavin addition, the glass test tubes were exposed to fluorescent lamps (60 µmol m -2 s -1 ) for 20 min, stopped by turning off the light. With one unit of activity defined as the enzyme amount inhibiting 50% of NBT photoreduction at 560 nm. The determination of GSH levels was carried out with a modified version of an established protocol [ 36 ]. Fresh leaves and roots (0.25 g) were homogenized in 2.5 ml of ice-cold 5% (w/v) 5-sulfosalicylic acid, utilizing a chilled mortar and pestle. The homogenate was then centrifuged at 20,000 g for 20 minutes at 4ºC. CAT activity measurement relied on the reaction solution (3 ml) of 56 mm H 2 O 2 and 0.2 ml enzyme extract, with absorbance changes at 240 nm recorded every 30 sec. defining activity as µmol H 2 O 2 reduced/min/g protein [ 34 ]. POD activity, adapted from a previous protocol [ 34 ], was determined using a 3 ml solution comprising 0.1 ml enzyme extract and 2.6 ml of 0.3% guaiacol, initiated by adding 0.3 ml of 0.6% H 2 O 2 . The measurement of absorbance changes at 470 nm was performed every 30 seconds, defining activity as absorbance changes per minute and specific activity as enzyme units per gram of soluble protein. Glutamine synthetase [ 37 ], NAD-MDH and NADP-MDH [ 38 ], NAD-ME and NADP-ME [ 39 ] activities were assessed. 2.5 Transmitted electron microscopic analysis Seven-day-old rice seedlings from both L9 and LD18 were exposed to either CK or CS as mentioned above. Leaf tissues were collected and fixed for 24 h in sodium phosphate buffer at pH 7.2, containing 4% (v/v) glutaraldehyde and 3% (w/v) paraformaldehyde. Following fixation, the samples underwent three rinses, each lasting 20 min, in sodium phosphate buffer at pH 7.2. Subsequently, post-fixation was conducted for 1.5 h in sodium phosphate buffer (pH 7.2) supplemented with 1% (v/v) osmium tetroxide. Following this step, the samples underwent a dehydration process, initially using ethanol in the following sequence: 50%, 70% and 90% for 10 min each, followed by two rounds of 100% ethanol for 15 min each. Afterward, they were dehydrated twice with 100% acetone for 15 minutes each. Following dehydration, the specimens were infiltrated and embedded in Epon-812 Resin. Ultrathin sections, measuring 50–70 nm, were prepared using a Leica EM UC 6 ultra-microtome. These sections were then mounted on copper grids, stained for with 4% (w/v) uranyl acetate and lead citrate for 10 min. Subsequently, they were examined at 60–80 kV using a Zeiss EVO40 transmission electron microscope. 2.6 RNA extraction and sequencing Leaf samples from each group were obtained from three distinct replicates and individually placed in RNA stabilizing solution. To maximize the inclusion of plants in a single biological replicate, 1–2 leaves were collected from each seedling. These three biological replicates encompassed distinct sets of CS treatment. For mRNA extraction, around 80 mg of this powdered tissue was utilized for RNA extraction using the XcelGen total RNA isolation kit (Xcelris Genomics, India), followed by RNA sequencing analysis. The purity values and integrity of the isolated RNA were assessed by measuring with the Nanodrop 8000 Spectrophotometer (Thermoscientific) and RNA 6000 Nano LabChip on the Agilent Bioanalyzer 2100 (Agilent Technologies, Germany), respectively. Sequencing libraries were prepared using the Illumina TruSeq RNA Library Preparation Kit protocol (Illumina, San Diego, CA). RNA-Seq was performed using the illumina HiSeq2000 platform (illumina, San Diego, CA). 2.7 Differentially expressed gene analysis Genomic data for the reference cultivar, Nipponbare ( Oryza sativa L. subsp . japonica ) were retrieved from the publicly available repository at ftp://ftp.plantbiology.msu.edu/pub/data/EukaryoticProjects/osativa/annotationdbs/pseudomolecules/version_7.0/all.dir/ . Transcriptome libraries from all the samples were aligned to the rice Nipponbare genome using TopHat (v1.4.3) and Bowtie (v0.13.8) with default parameters. For differentially expressed genes (DEG) analysis, Cufflinks (v1.3.3) was employed, generating FPKM values through reference-guided mapping [ 40 ]. Genes expressing in notably low levels were omitted from the DEG analysis. DEGs were identified at a false discovery rate (FDR) of 0.05 and a p -value of ≤ 0.05. Heat maps illustrating differentially expressed genes were generated using the R package 'CummeRbund' [ 41 ]. 2.8 Gene ontology and KEGG analysis Gene ontology analysis of the DEGs was conducted using the agriGo toolkit and database. Statistical methods, including the Hypergeometric test, were employed to identify significant GO-terms, with a threshold of P -value < 0.05. To functionally annotate the DEGs, BLAST comparisons were performed against the Kyoto Encyclopedia of Genes and Genomes (KEGG) GENES database using the KEGG Automatic Annotation Server (KAAS). The assignment of KEGG terms utilized the Single-directional best hit (SBH) option, considering the representative gene data set for rice, specifically Oryza sativa Ref.Seq. Pathway mapping was achieved using the KEGG Orthology database accessible at http://www.genome.jp/kegg/ko.html [ 42 ]. 2.9 Quantitative transcript measurements To validate the expression of DEGs identified in response to CS in L9 and LD18, qPCR was used following transcriptome analysis. The RNA samples used corresponded to those employed in the RNA sequencing analysis. The RNA extraction and reverse transcription to cDNA were conducted following established procedures [ 43 ]. For qPCR analysis, SYBR Green PCR Master Mix (Yuanye Bio, Shanghai, China) was utilized with a real-time PCR system (ABI StepOnePlus, Applied Biosystems, USA). Primers used for qPCR are listed in Table S1 . The running program for qPCR included 95°C for 30 s, followed by PCR with 45 cycles at 95°C for 15 s, 61°C for 20 s and 72°C for 30 s. Each assay was conducted with three biological samples. The housekeeping gene is actin1 . Relative gene expression against the housekeeping gene was calculated using the 2 −ΔΔCT method (ΔCT = CT, gene of interest − CT ) [ 44 ]. 2.10 Metabolism determinations For metabolite determinations, samples were promptly harvested upon completion of the CS treatments in both L9 and LD18 rice lines. Samples were taken from the same marked leaf sections used for gas exchange measurements. Non-targeted metabolic profiling of the leaves from LD9 and LD18 under control and CS was conducted using the LC-MS/MS (Triple Quad 6500, SCIEX) [ 43 ]. Approximately ~ 3.0 mg leaf samples from the LD9 and LD18 subjected to CS were collected in re-cooled 2 ml Eppendorf tubes and promptly stored in liquid nitrogen. The samples underwent initial extraction using a ball mill at 50 Hz for 10 min. The extracted powder was then dissolved in a 1.5 ml methanol/chloroform mixture and incubated at − 20°C for 12 h. Subsequently, the mixture was centrifuged at 2500 g and 4°C for 15 min and then filtered using 0.43 µm organic phase medium (GE Healthcare, 6789–0404). We performed metabolic analysis utilizing Metabolon software (Durham, NC, USA), and identified sample components according to retention time and mass spectra with reference metabolites. For precise identification of metabolic compounds in each sample, it is strongly advised to consult the mass spectra with entries in the NIST02 and metabolome database ( http://csbdb.mpimp-golm.mpg.de/ csbdb/gmd/gmd.html). 3. Results In this study encompassed the screening 128 rice lines sourced from northern of China, conducted over two consecutive years in field experiments to assess their response to CS. From this screening, two distinct rice lines emerged: L9, classified as a CS-sensitive rice line, and LD18 characterized as a CS-tolerant variant. To validated their performance under CS both at the seedling and heading stages, a CS alongside a control temperature of 30 o C was implemented in a growth chamber condition. Results revealed a marked distinction in phenotypic responses between L9 and LD18 (Fig. 1 A). As expected, CS induced a 30% inhibition in photosynthetic rates in L9 during heading stage, while marginal differences were observed in LD18 due to CS effects (Fig. 1 B). Moreover, stomatal conductance exhibited a more pronounced decline in L9 compared to LD18 (Fig. 1 C), indicating that the inhibitory effects of CS on these rice lines were attributed to both impaired photosynthetic machinery and stomatal limitations. Correspondingly, results from transmitted electron microscopy revealed substantial changes in the chloroplast structure of L9 under CS, whereas the chloroplast structure of LD18 exhibited less changed under the same conditions (Fig. 1 D). Regarding spike traits, conspicuous detrimental effects of CS on spike filling during heading stage were observed in both rice lines. However, these effects were notably more pronounced in L9 compared to LD18, whereas no discernible impact on spike length was noted for both lines (Figure S1 A-B). Furthermore, the percentage values of full seed were declined of 38% in L9 under CS exposure, while LD18 showed no significant difference, resulting in a sixfold increase in empty seed percentage values for L9 and a fivefold increase for LD18 (Figure S1 C-D). Interestingly, CS induced a significant increase in non-faired tillering in both rice lines (Figure S1 E). To delve deeper into the impact of CS on antioxidant and oxidoreductase activity in two rice lines, we measured the enzymatic features of eight compounds: GR (Glutathione reductase), soluble protein content, SOD (superoxide dismutase), GSH-Px (reduced glutathione), CAT (catalase), POD (peroxidase), AS (asparagine synthase) and GS (glutamine synthetase) (Figure S2A-H). Our findings revealed inhibited soluble protein content, CAT, and AS in both rice lines under CS. Specifically, CAT and AS exhibited more pronounced reduction in L9 compared to LD18 (Figure S2 E; G). While GR, SOD and POD were enhanced in both rice lines due to CS. Remarkably, there was a dramatic increase in GSH-Px and GS levels in L9, whereas no significant difference was observed in LD18 under similar CS conditions (Figure S2D, H). Considering the diminished photosynthetic activity observed in L9 under CS, we investigated the activities of enzymes associated with carbon assimilation and energy metabolism. Our findings reveal distinctive enzymatic responses. Among the enzymes analyzed, only NAD-MDH presented a notable decrease of 34% in L9, whereas in LD18, it exhibited a threefold increase exposed to CS (Figure S3A). Contrastingly, NADP-MDH demonstrated an 11.4% increase in L9 and a more pronounced 27.5% increase in LD18 (Figure S3B). Interestingly, NAD-ME levels increased by 93% in L9 but declined by 47% in LD18 (Figure S3C). Additionally, while NADP-ME exhibited a 12% increase L9, no significant variation was observed in LD18 under CS (Figure S3D). In an effort to unveil the key differentially expressed genes (DEGs) that contributing to the varying performance between the two rice lines (L9 and LD18) under CS, we conducted an integrative analysis of transcriptome and non-targeted metabolism. Our analysis based on transcriptome data revealed that GC content and Q30 in 12 samples in average were 0.52 and 94%, respectively (Table S2). Across LD18 and L9 samples, the numbers of clean reads were 21.37 million (Table S2). PCA results indicated that PC1 and PC2 contributed 68.2% and 23.4%, respective to transcriptome dataset variation (Fig. 2 A). Within L9, 1425 genes were upregulated and 1528 downregulated under CS, whereas LD18 exhibited 2495 upregulated and 2472 downregulated DEGs in CS relative to under CK (Fig. 2 B-D). There were 1588 overlapped DEGs between LD18 and L9 under CS relative to CK (Fig. 2 E). Gene Ontology (GO) analysis of DEGs revealed significant enrichment in specific biological processes for both L9 and LD18 under CS compared to CK. In L9, enriched processes included cinnamic acid biosynthetic pathways, L-phenylalanine catabolism, trehalose biosynthetic, protein folding, glutamine metabolic pathways and chloroplast-nucleus signaling (Figure S4A). Contrastingly, in LD18, enriched processes encompassed photosynthesis, regulation of jasmonic acid-mediated signaling, cation transmembrane transport, chlorophyll biosynthetic, protein refolding, stress response, and trehalose biosynthesis under CS relative to CK (Figure S4B). The KEGG analysis revealed significant enrichments in DEGs in response to CS in both L9 and LD18 rice lines compared to CK. In L9 under CS, pathways such as brassinosteroid biosynthesis, riboflavin metabolism, photosynthesis-antenna proteins, arachidonic acid metabolism, glutathione metabolism and phenylalanine metabolism showed significant enrichment among the DEGs (Fig. 3 A). Conversely, in LD18 under CS, pathways including carbon fixation in photosynthetic organisms, arachidonic acid metabolism, flavone and flavanol biosynthesis, starch and sucrose metabolism and glutathione metabolism exhibited significant enrichment within the DEGs (Fig. 3 B). To identify the promising DEGs governing the distinct responses to CS in LD18 and L9, we selected top 1% DEGs from the overlapped dataset between these lines exposed to CS. Validation of these DEGs was performed using qPCR (Fig. 4 A-H). The findings confirmed notable differences in the expression levels of the genes associated with the MAPK signaling pathway between LD18 and L9 under CS compared to their respective CK. Genes like LOC4327351 ( MPK2 ), LOC4352460 ( MAPK1 ) and LOC4337850 ( MAPK4 ) exhibited a significant increase in LD18 but a remarkable decrease in L9 under CS (Fig. 4 B, E; Figure S5A). Similarly, genes related to the ABA signaling pathway, including LOC4333690 ( GRP3 ), LOC4345611 ( RBCX1 ) and LOC4335640 ( SnRK2 ), showed a substantial reduction by up to 66% in L9 while these genes showed substantial enhancement in LD18 under CS compared to CK (Fig. 4 D; H; Figure S5B). Comparable trends were observed for other genes such as LOC4352660 ( CWM1 ), LOC4331855 ( CRP27 ), LOC4331490 ( CDPK11 ), and LOC4332786 ( STK1 ) (Fig. 4 C, F, G; Figure S5C). The non-targeted metabolism analysis through PCA revealed that for L9, PC1 and PC2 accounted for 83.5% and 8.5% of variance, respectively (Figure S6A). Correspondingly for LD18, PC1 and PC2 accounted for 91.2% and 6.1% of variance, respectively (Figure S6B). Notably, specific metabolites exhibited a dramatic upregulation in L9 under CS compared to CK, including toluene-4-sulfonate, aloesin, and (6E)-8-Oxolinalool (Figure S6C). The detailed information of extremely DAMs were listed in Figure S7A-B. Moreover, KEGG analysis based on non-targeted metabolism data revealed significant enrichments in specific metabolic pathways for both LD9 and LD18 under CS relative to CK. In LD9 under CS, pathways such as phenylpropanoid biosynthesis, tyrosine metabolism, amino acids biosynthesis, arachidonic acid metabolism, anthocyanin biosynthesis, and glyoxylate and dicarboxylate metabolism were significantly enriched (Fig. 5 A). Conversely in LD18 under CS, pathways like pyruvate metabolism, citrate cycle (TCA cycle), carbon fixation in photosynthetic organism, flavone and flavanol biosynthesis and carbon metabolism significantly enrichment among the list of DAMs (Fig. 5 B). To further identify the key DAMs responsible for the distinct responses observed in L9 and LD18 exposed to CS, we performed a comprehensive analysis of overlapped DAMs. Our findings revealed 221 DAMs in L9 and 569 DAMs in LD18 when exposed to CS compared to CK (Fig. 6 A-B). Interestingly, there were 104 DAMs that overlapped between LD18 and L9 under these CS conditions (Fig. 6 B). Utilizing log2FC values, we calculated the regulation index, uncovering significant increases in fumarate, methyl jasmonate, trehalose, tetrahydromonapterin, and methoxypenyl-hydroxypropanoyl-CoA, particularly in LD18, surpassing the increases observed in L9 exposed to CS (Fig. 6 C; Table S4). Conversely, 4-nitrophenol, indole-3-carboxylic acid, acicinomycin A, and cis-4-hydroxy-D-proline were dramatically inhibited in both rice lines, with L9 revealing a more pronounced reduction compared to LD18 (Fig. 6 C). In summary, our findings demonstrate that CS could induces significant reprogramming across various biological and metabolic pathways, notably impacting carbon assimilation, ROS scavenge via antioxidants, ABA signaling transduction, MAPK signaling pathway, and several osmoprotectant metabolites (Fig. 7 ). Particularly, our observations suggest that the ABA signaling pathway and MAPK signaling pathway might play critical roles in the response to cold stress in both LD18 and L9 rice lines. These pathways emerge as critical elements in understanding the plant’s response to cold stress. 4. Discussions 4.1 Physiology responses to cold stress Plants often encounter physiological disruptions when exposed to CS, significantly impacting their growth and developmental processes [ 1 , 45 ]. While initial exposure to low nonfreezing temperatures might trigger cold tolerance, sustained exposure becomes catastrophic. Chloroplasts, pivotal in photosynthetic processes, play a critical role in rice development. CS induces cold injury in rice leaves, hindering chlorophyll synthesis and subsequently reducing photosynthesis [ 46 – 48 ]. Photosynthesis stands as a crucial factor of rice yield and represents a prominent physiological process affected by CS [ 49 ]. Our study mirrors this impact, showcasing the contrasting responses of cold-sensitive rice (L9) and cold-tolerant rice (LD18) to low temperature (Fig. 1 B). LD18 exhibits marked resilience and adaptation to cold-induced damage compared to L9. ROS serve as important signaling molecules influencing plant growth, development and responses to diverse stresses [ 50 ]. Studies have highlighted proline’s role in protecting enzymes from denaturation, stabilizing protein synthesis machinery, regulating cytosolic acidity, enhances water-binding capacity and serving as a reservoir of carbon and nitrogen sources[ 51 ]. In this study, the levels of both POD and SOD in LD18 were significantly higher compared to those in L9. This elevation contributed to shielding rice leaves, fortifying cell membrane functionality, mitigating ROS-induced damage and enhancing cold tolerance in plants. Meanwhile, the soluble protein content exhibited dramatic reduction, notably by 10% more in the CS-sensitive line L9 than in LD18. This decline suggests that rice enhances its tolerance to CS by consuming a considerable portion of soluble protein content. Subsequently, upon stress relieve, rice rapidly recovers and synthesizes a substantial amount of soluble protein content, meeting the body’s nutritional demands. 4.2 DEGs involved in the pathway of photosynthesis Consistent with the observed photosynthetic responses in L9 and LD18 (Fig. 1 B), our analysis revealed a significant enrichment of the photosynthetic pathway in the LD18 under CS across multiple metabolic pathways, as indicated by both GO and KEGG analyses. This enrichment underscores the crucial role played by an activated photosynthetic pathway in mitigating cold sensitivity in LD18. Recognized as the basis of life on earth, photosynthesis heavily relies on Rubisco, a pivotal enzyme in the photosynthetic process. It was noticed that the lessened performance of C4 plants under cold conditions might stem from possessing 60–80% less Rubisco on a total protein basis than C3 plants [ 52 , 53 ]. Increased Rubisco content has shown promise in mitigating cold stress and hastening recovery, as seen in maize [ 54 ]. In our study, we noted a threefold increase in the transcript abundance of RBCX1 ( LOC4345611 ) in LD18 exposed to CS, whereas there was no significant change in L9. This suggests the important role of this chaperone in the facilitating Rubisco folding during the response to CS in rice. Such findings underscore the significance of proper Rubisco folding in enhancing photosynthesis during CS, potentially alleviating the cold effects. Notably, the orthologous gene of RBCX1 ( AT4G04330 ) in Arabidopsis exhibited dramatic stimulation under CS conditions, as indicated by transcriptome analysis [ 55 ]. 4.3 ABA signaling in cold stress response The involvement of ABA in signaling cascades during the cold stress is pivotal. Studies have indicated that cold stress triggers the accumulation of endogenous ABA in plants [ 56 , 57 ]. Additionally, application of exogenous ABA has shown to enhance the cold tolerance of plants [ 58 ]. Microarray data analysis in a specific cold-tolerant rice variety unraveled the complicated cross-talk between the CBF and ABA-responsive element (ABRE) regulons, emphasizing the role of ABA signaling in rice cold tolerance [ 59 ]. Within this signaling pathway, sucrose non-fermenting 1 (SNF1)-related protein kinase 2s (SnRK2s) play key roles [ 60 ]. Our study validated these findings, observing that the expression levels of two SNF1-related protein kinase genes ( LOC4335640 and LOC4339173 ) were upregulated at least twofold in LD18. In contrast, these genes remained unaltered or even exhibited decreased expression in L9 under CS, as shown by both transcriptome analysis and qPCR validation (Table S3; Fig. 4 H). 4.4 MAPK signaling in cold stress response Mitogen-activated protein kinase (MAPK) cascades have been recognized for their pivotal roles in various aspects of plant biology, particularly in responding to CS [ 61 ] (Xie et al., 2012; Wen et al., 2002). Studies in Arabidopsis have highlighted the activation of MAPK genes such as MEKK1 , MKK2 , MPK4 and MPK6 under CS [ 62 ]. Our study aligns with these observations, revealing that the expression levels of several genes associated with the MAPK signaling pathway (e.g., LOC4352460 , LOC4337850 and LOC4327351 ) were upregulated by up to 2.8-fold in CS-tolerant rice line LD18 under Cold conditions. In contrast, these genes exhibited inhibited in L9 under CS, with reductions of 13% (Fig. 4 B, E; Figure S5A; Table S3). These findings suggest that CS triggers the induction of kinase molecules, enabling rice plants to adapt and survive adverse environments. 4.5 Metabolite’s roles in cold stress response CS instigates substantial metabolic changes and impacts various physiological properties in plants, notably activating the cold-responsive signaling network in rice. Trehalose, is a non-reducing disaccharide sugar comprising two glucose molecules serve as an osmoprotectants and plays a crucial role in plant metabolism and signaling mechanisms [ 63 , 64 ]. Studies have shown that Trehalose modulates diverse osmotic substances in tomato plants under CS [ 65 ]. In line with this, our findings indicate a significant accumulation of trehalose in CS-tolerant rice line LD18 compared to L9 (Fig. 6 C; Table S4). These observations underscore the pivotal role of trehalose in regulating CS tolerance responses in rice. This emphasizes the significance of trehalose not only in plant metabolism but also in orchestrating crucial mechanisms vital for combating cold stress. Declarations Ethics approval and consent to participate Our rice collection work complies with the laws of the People’s Republic of China and has a permission letter from Heilongjiang Rice Quality Improvement and Genetic Breeding Engineering Research Center, Heilongjiang Academy of Agricultural Sciences. Voucher specimens were identified by Prof. Wei Li (Heilongjiang Academy of Agricultural Sciences) and kept at Heilongjiang Rice Quality Improvement and Genetic Breeding Engineering Research Center (No: CS001-CS128). All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Funding This work was supported by Fundamental Research Funds for the Research Institutes of Heilongjiang Province (CZKYF2023-1-C012); The Opening Project of the Collaborative Innovation Center Co-sponsored by Liaoning Provincial Government and Ministry of Education for Northeast Japonica Rice Genetic Improvement (KF2022-04); National rice industry technology system (CARS-01-57); National Natural Science Foundation of China (32170245). Competing interests The authors declare that they have no competing interests. Acknowledgements Not applicable. Author contributions Guohua Ding: Methodology, Investigation, Statistical analysis, Writing, Funding acquisition, Resources. Liangzi Cao: Methodology. Zubair Iqbal: Statistical analysis, Resources. Minghui Zhao: Funding acquisition. Zhibo Cui: Funding acquisition. Jinsong Zhou: Investigation. Lei Lei: Investigation. Yu Luo: Investigation. Liangming Bai: Investigation. Guang Yang: Investigation. Tongtong Wang: Xueyang Wang: Rongsheng Wang: Kai Liu: Supervision. Zhugang Li: Supervision. Mingnan Qu: Conceptualization, Writing. Shichen Sun: Conceptualization, Funding acquisition, Resources. Data and materials availability All data is available in the manuscript or the supplementary materials. All related sequencing data is deposited in NCBI Sequence Read Archive (SRA) database with the link of https://www.ncbi.nlm.nih.gov/sra?term=PRJNA793928. The bioProject accession is PRJNA793928. References Z. Zhang, J. Li, Y. Pan, J. Li, L. Zhou, H. Shi, Y. Zeng, H. Guo, S. Yang, W. Zheng, Natural variation in CTB4a enhances rice adaptation to cold habitats, Nat. Commun. 8 (2017) 14788. K.A. Tartoura, S.A. Youssef, Stimulation of ROS-scavenging systems in squash ( Cucurbita pepo L.) plants by compost supplementation under normal and low temperature conditions, Sci. Hortic. 130 (2011) 862-868. T. Taghavi, M. Aghajani, In Effect of chilling duration on runner production and vegetative growth of'Pajaro'strawberry, VIII International Strawberry Symposium Acta Hortic. 1156(2017), 505-508. X. Zhang, T. Liu, S. Zhu, D. Wang, S. Sun, L. Xin, Short‐term hypobaric treatment alleviates chilling injury by regulating membrane fatty acids metabolism in peach fruit, J. Food Biochem. 46 (2022) e14113. D. Mao, Y. Xin, Y. Tan, X. Hu, J. Bai, Z.-y. Liu, Y. Yu, L. Li, C. Peng, T. Fan, Natural variation in the HAN1 gene confers chilling tolerance in rice and allowed adaptation to a temperate climate, Proc. Natl. Acad. Sci. 116 (2019) 3494-3501. Y. Ma, X. Dai, Y. Xu, W. Luo, X. Zheng, D. Zeng, Y. Pan, X. Lin, H. Liu, D. Zhang, COLD1 confers chilling tolerance in rice, Cell 160 (2015) 1209-1221. Q. Ma, X. Dai, Y. Xu, J. Guo, Y. Liu, N. Chen, J. Xiao, D. Zhang, Z. Xu, X. Zhang, Enhanced tolerance to chilling stress in OsMYB3R-2 transgenic rice is mediated by alteration in cell cycle and ectopic expression of stress genes, Plant Physiol. 150 (2009) 244-256. Y.I. Kuk, J.S. Shin, N.R. Burgos, T.E. Hwang, O. Han, B.H. Cho, S. Jung, J.O. Guh, Antioxidative enzymes offer protection from chilling damage in rice plants, Crop Sci. 43 (2003) 2109-2117. J. Browse, Z. Xin, Temperature sensing and cold acclimation, Curr. Opin. Plant Biol. 4 (2001) 241-246. M.F. Thomashow, Plant cold acclimation: freezing tolerance genes and regulatory mechanisms, Annu. Rev. Plant Biol. 50 (1999) 571-599. M.A. Hughes, M.A. Dunn, The molecular biology of plant acclimation to low temperature, J. Exp. Bot. 47 (1996) 291-305. H.J. Bohnert, D.E. Nelson, R.G. Jensen, Adaptations to environmental stresses, The plant cell 7 (1995) 1099. T.H. Chen, N. Murata, Enhancement of tolerance of abiotic stress by metabolic engineering of betaines and other compatible solutes, Curr. Opin. Plant Biol. 5 (2002) 250-257. T.K. Prasad, M.D. Anderson, B.A. Martin, C.R. Stewart, Evidence for chilling-induced oxidative stress in maize seedlings and a regulatory role for hydrogen peroxide, Plant Cell 6 (1994) 65-74. A. Zhou, E. Liu, H. Li, Y. Li, S. Feng, S. Gong, J. Wang, PsCor413pm2, a plasma membrane-localized, cold-regulated protein from Phlox subulata, confers low temperature tolerance in Arabidopsis, Int. J. molecular Sci. 19 (2018) 2579. V. Chinnusamy, J. Zhu, J.-K. Zhu, Cold stress regulation of gene expression in plants, Trends Plant Sci. 12 (2007) 444-451. K. Yamaguchi-Shinozaki, K. Shinozaki, Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses, Annu. Rev. Plant Biol. 57 (2006) 781-803. K.B. Singh, R.C. Foley, L. Oñate-Sánchez, Transcription factors in plant defense and stress responses, Curr. Opin Plant Biol. 5 (2002) 430-436. M. Zhang, J. Ye, Q. Xu, Y. Feng, X. Yuan, H. Yu, Y. Wang, X. Wei, Y. Yang, Genome-wide association study of cold tolerance of Chinese indica rice varieties at the bud burst stage, Plant Cell Rep. 37 (2018) 529-539. C.-Y. Kim, K.T.X. Vo, C.D. Nguyen, D.-H. Jeong, S.-K. Lee, M. Kumar, S.-R. Kim, S.-H. Park, J.-K. Kim, J.-S. Jeon, Functional analysis of a cold-responsive rice WRKY gene, OsWRKY71, Plant Biotechnol. Rep. 10 (2016) 13-23. C.-F. Su, Y.-C. Wang, T.-H. Hsieh, C.-A. Lu, T.-H. Tseng, S.-M. Yu, A novel MYBS3-dependent pathway confers cold tolerance in rice, Plant Physiol. 153 (2010) 145-158. N. Yokotani, Y. Sato, S. Tanabe, T. Chujo, T. Shimizu, K. Okada, H. Yamane, M. Shimono, S. Sugano, H. Takatsuji, WRKY76 is a rice transcriptional repressor playing opposite roles in blast disease resistance and cold stress tolerance, J. Exp. Bot. 64 (2013) 5085-5097. Y. Liu, C. Xu, Y. Zhu, L. Zhang, T. Chen, F. Zhou, H. Chen, Y. Lin, The calcium‐dependent kinase OsCPK24 functions in cold stress responses in rice, J. Integr. Plant Biol. 60 (2018) 173-188. Y. Saijo, N. Kinoshita, K. Ishiyama, S. Hata, J. Kyozuka, T. Hayakawa, T. Nakamura, K. Shimamoto, T. Yamaya, K. Izui, A Ca2+-dependent protein kinase that endows rice plants with cold-and salt-stress tolerance functions in vascular bundles, Plant Cell Physiol. 42 (2001) 1228-1233. Z. Li, M.U. Khan, P. Letuma, Y. Xie, W. Zhan, W. Wang, Y. Jiang, W. Lin, Z. Zhang, Transcriptome Analysis of the Responses of Rice Leaves to Chilling and Subsequent Recovery, Int. J. Mol. Sci. 23 (2022) 10739. T. Lu, G. Lu, D. Fan, C. Zhu, W. Li, Q. Zhao, Q. Feng, Y. Zhao, Y. Guo, W. Li, Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq. Genome Res. 20 (2010) 1238-1249. R. Zhai, Y. Feng, H. Wang, X. Zhan, X. Shen, W. Wu, Y. Zhang, D. Chen, G. Dai, Z. Yang, Transcriptome analysis of rice root heterosis by RNA-Seq, BMC Genomics 14 (2013) 1-14. G. He, X. Zhu, A.A. Elling, L. Chen, X. Wang, L. Guo, M. Liang, H. He, H. Zhang, F. Chen, Global epigenetic and transcriptional trends among two rice subspecies and their reciprocal hybrids, Plant Cell 22 (2010) 17-33. Y. Oono, Y. Kawahara, H. Kanamori, H. Mizuno, H. Yamagata, M. Yamamoto, S. Hosokawa, H. Ikawa, I. Akahane, Z. Zhu, mRNA-Seq reveals a comprehensive transcriptome profile of rice under phosphate stress, Rice 4 (2011) 50-65. Y. Hu, L. Zhang, S. He, M. Huang, J. Tan, L. Zhao, S. Yan, H. Li, K. Zhou, Y. Liang, Cold stress selectively unsilences tandem repeats in heterochromatin associated with accumulation of H3K9ac, Plant Cell Environ. 35 (2012) 2130-2142. G. Gao, Y. Zhong, A. Guo, Q. Zhu, W. Tang, W. Zheng, X. Gu, L. Wei, J. Luo, DRTF: a database of rice transcription factors, Bioinform. 22 (2006) 1286-1287. M. Qu, G. Zheng, S. Hamdani, J. Essemine, Q. Song, H. Wang, C. Chu, X. Sirault, X.-G. Zhu, Leaf photosynthetic parameters related to biomass accumulation in a global rice diversity survey, Plant Physiol. 175 (2017) 248-258. S. Woo, D. Lee, Y. Lee, Net photosynthetic rate, ascorbate peroxidase and glutathione reductase activities of Erythrina orientalis in polluted and non-polluted areas, Photosynthetica 45 (2007) 293-295. L. Li, B.R. Singh, Role of zinc binding in type A botulinum neurotoxin light chain's toxic structure, Biochem. 39 (2000) 10581-10586. R.S. Dhindsa, P. Plumb-Dhindsa, T.A. Thorpe, Leaf senescence: correlated with increased levels of membrane permeability and lipid peroxidation, and decreased levels of superoxide dismutase and catalase, J. Exp. Bot. 32 (1981) 93-101. Y. Li, J. Hugenholtz, T. Abee, D. Molenaar, Glutathione protects Lactococcus lactis against oxidative stress, Appl. Environ. Microbiol. 69 (2003) 5739-5745. X. Yu, B. Wang, C. Zhang, W. Xu, J. He, L. Zhu, S. Wang, Effect of root restriction on nitrogen levels and glutamine synthetase activity in ‘Kyoho’grapevines, Sci. Hortic. 137 (2012) 156-163. Y.-X. Yao, Q.-L. Dong, H. Zhai, C.-X. You, Y.-J. Hao, The functions of an apple cytosolic malate dehydrogenase gene in growth and tolerance to cold and salt stresses, Plant Physiol. Biochem. 49 (2011) 257-264. C.P. Spampinato, P. Casati, C.S. Andreo, Factors affecting the oligomeric state of NADP-malic enzyme from maize and wheat tissues: a chemical crosslinking study, Biochim. Biophys. Acta, Prot. Struct. Mol. Enzymol. 1383 (1998) 245-252. C. Trapnell, A. Roberts, L. Goff, G. Pertea, D. Kim, D.R. Kelley, H. Pimentel, S.L. Salzberg, J.L. Rinn, L. Pachter, Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks, Nat. Protoc. 7 (2012) 562-578. B.R. Thumma, N. Sharma, S.G. Southerton, Transcriptome sequencing of Eucalyptus camaldulensis seedlings subjected to water stress reveals functional single nucleotide polymorphisms and genes under selection, Bmc Genomics 13 (2012) 1-21. T. Kyndt, S. Denil, A. Haegeman, G. Trooskens, T. De Meyer, W. Van Criekinge, G. Gheysen, Transcriptome analysis of rice mature root tissue and root tips in early development by massive parallel sequencing, J. Exp. Bot. 63 (2012) 2141-2157. J. Li, J. Essemine, C. Shang, H. Zhang, X. Zhu, J. Yu, G. Chen, M. Qu, D. Sun, Combined proteomics and metabolism analysis unravels prominent roles of antioxidant system in the prevention of alfalfa (Medicago sativa L.) against salt stress, Int. J. Mol. Sci. 21 (2020) 909. K.J. Livak, T.D. Schmittgen, Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method, Methods 25 (2001) 402-408. J. Zhao, S. Zhang, T. Yang, Z. Zeng, Z. Huang, Q. Liu, X. Wang, J. Leach, H. Leung, B. Liu, Global transcriptional profiling of a cold‐tolerant rice variety under moderate cold stress reveals different cold stress response mechanisms, Physiol. Plant. 154 (2015) 381-394. H. Shimono, T. Hasegawa, S. Fujimura, K. Iwama, Responses of leaf photosynthesis and plant water status in rice to low water temperature at different growth stages, Field Crop. Res. 89 (2004) 71-83. T. Ariizumi, S. Kishitani, R. Inatsugi, I. Nishida, N. Murata, K. Toriyama, An increase in unsaturation of fatty acids in phosphatidylglycerol from leaves improves the rates of photosynthesis and growth at low temperatures in transgenic rice seedlings, Plant Cell Physiol. 43 (2002) 751-758. J.B. Yahmed, T.M. de Oliveira, P. Novillo, A. Quinones, M.-A. Forner, A. Salvador, Y. Froelicher, M.B. Mimoun, M. Talon, P. Ollitrault, A simple, fast and inexpensive method to assess salt stress tolerance of aerial plant part: Investigations in the mandarin group, J. Plant Physiol. 190 (2016) 36-43. S.W. Jeong, S.M. Choi, D.S. Lee, S.N. Ahn, Y. Hur, W.S. Chow, Y.-I. Park, Differential susceptibility of photosynthesis to light-chilling stress in rice (Oryza sativa L.) depends on the capacity for photochemical dissipation of light, Mol. Cells 13 (2002) 419-428. K. Zhang, G. Tian, X. Li, Z. Zhang, J. Liu, Y. Li, J. Xie, P. Wang, ROS produced via BsRBOHD plays an important role in low temperature-induced anthocyanin biosynthesis in begonia semperflorens, Russ. J. Plant Physiol. 67 (2020) 250-258. K. Apel, H. Hirt, Reactive oxygen species: metabolism, oxidative stress, and signal transduction, Annu. Rev. Plant Biol. 55 (2004) 373-399. S.P. Long, Environmental responses, C4 Plant Biol. (1999).(1999) 215-249. R.F. Sage, R.W. Pearcy, J.R. Seemann, The nitrogen use efficiency of C3 and C4 plants: III. Leaf nitrogen effects on the activity of carboxylating enzymes in Chenopodium album (L.) and Amaranthus retroflexus (L.), Plant Physiol. 85 (1987) 355-359. C.E. Salesse‐Smith, R.E. Sharwood, F.A. Busch, D.B. Stern, Increased Rubisco content in maize mitigates chilling stress and speeds recovery, Plant Biotechnol. J. 18 (2020) 1409-1420. S. Fowler, M.F. Thomashow, Arabidopsis transcriptome profiling indicates that multiple regulatory pathways are activated during cold acclimation in addition to the CBF cold response pathway, Plant Cell 14 (2002) 1675-1690. A. Chawade, A. Lindlöf, B. Olsson, O. Olsson, Global expression profiling of low temperature induced genes in the chilling tolerant japonica rice Jumli Marshi, PLoS One 8 (2013) e81729. S.K. Sah, K.R. Reddy, J. Li, Abscisic acid and abiotic stress tolerance in crop plants, Front. Plant Sci. 7 (2016) 571. W. Xing, C. Rajashekar, Glycine betaine involvement in freezing tolerance and water stress in Arabidopsis thaliana, Environ. Exp. Bot. 46 (2001) 21-28. M.A. Hossain, J.-I. Cho, M. Han, C.-H. Ahn, J.-S. Jeon, G. An, P.B. Park, The ABRE-binding bZIP transcription factor OsABF2 is a positive regulator of abiotic stress and ABA signaling in rice, J. Plant Physiol. 167 (2010) 1512-1520. S.-Y. Park, P. Fung, N. Nishimura, D.R. Jensen, H. Fujii, Y. Zhao, S. Lumba, J. Santiago, A. Rodrigues, T.-f.F. Chow, Abscisic acid inhibits type 2C protein phosphatases via the PYR/PYL family of START proteins, Science 324 (2009) 1068-1071. G. Xie, H. Kato, R. Imai, Biochemical identification of the OsMKK6–OsMPK3 signalling pathway for chilling stress tolerance in rice, Biochem J. 443 (2012) 95-102. M. Teige, E. Scheikl, T. Eulgem, R. Doczi, K. Ichimura, K. Shinozaki, J.L. Dangl, H. Hirt, The MKK2 pathway mediates cold and salt stress signaling in Arabidopsis, Mol. Cell 15 (2004) 141-152. A. Wingler, The function of trehalose biosynthesis in plants, Phytochemistry 60 (2002) 437-440. M.J. Paul, L.F. Primavesi, D. Jhurreea, Y. Zhang, Trehalose metabolism and signaling, Annu. Rev. Plant Biol. 59 (2008) 417-441. T. Liu, X. Ye, M. Li, J. Li, H. Qi, X. Hu, H2O2 and NO are involved in trehalose-regulated oxidative stress tolerance in cold-stressed tomato plants, Environ. Exp. Bot. 171 (2020) 103961. Additional Declarations No competing interests reported. Supplementary Files Supplementalfiles.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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rice lines exposed to 10-d CS at the both seedling and heading stages. \u003cstrong\u003eA\u003c/strong\u003e, Images of potted-grown plants at the seedling stage (upper panel) and heading stage (lower panels). \u003cstrong\u003eB-C\u003c/strong\u003e, Photosynthetic rates and stomatal conductance under saturated light conditions in the leaves of LD18 and L9 during heading stage. Data represented means ± S.E. (\u003cem\u003en\u003c/em\u003e=9). Percentage differences in CS compared to the CK for each rice line are shown. Symbols “*”, “**” and “***” indicate significant differences at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01 and \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, respectively, based on student \u003cem\u003et\u003c/em\u003e-test analysis.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4016805/v1/9fdb60f39602e7400d223a24.png"},{"id":52681887,"identity":"6fbf36f2-6bca-4d18-bc08-aad4d6ad357a","added_by":"auto","created_at":"2024-03-14 12:44:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":483264,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptome analysis in LD18 and L9 under CS during heading stage. \u003cstrong\u003eA\u003c/strong\u003e, Principal component analysis. \u003cstrong\u003eB\u003c/strong\u003e, Statistical analysis of the no. of DEGs in L9 and LD18 exposed to CS. \u003cstrong\u003eC-D\u003c/strong\u003e, Volcano plot representing DEGs in L9 under CS relative to CK and DEGs in LD18 under CS relative to CK. \u003cstrong\u003eE\u003c/strong\u003e, Overlapped DEGs between L9 and LD18 under CS relative to CK.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4016805/v1/b7a9063efb0d7a2129d3d4ae.png"},{"id":52681891,"identity":"6604fd43-32e9-41f7-bbb3-e198517719c6","added_by":"auto","created_at":"2024-03-14 12:44:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":885821,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG analysis performed on DEGs in the leaves of LD18 and L9 exposed to CS. \u003cstrong\u003eA\u003c/strong\u003e, KEGG analysis on the DEGs in L9 under CS compared to CK. \u003cstrong\u003eB\u003c/strong\u003e, KEGG analysis on the DEGs in LD18 under CS compared to CK.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4016805/v1/6e11274e3f4c805f4cfced34.png"},{"id":52681893,"identity":"540f60ed-5114-4db9-9b4a-7a318fdf9562","added_by":"auto","created_at":"2024-03-14 12:44:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":691130,"visible":true,"origin":"","legend":"\u003cp\u003eqPCR validation of upregulated DEGs in LD18 due to CS during heading stage. \u003cstrong\u003eA,\u003c/strong\u003e Fold change of 17 promising DEGs, representing the top 1% DEGs from the overlapped set between LD18 and L9 exposed to CS relative to CK. \u003cstrong\u003eB-H,\u003c/strong\u003e Expression profiles of individual genes: \u003cem\u003eLOC4327351 \u003c/em\u003e(\u003cem\u003eMPK2\u003c/em\u003e), \u003cem\u003eLOC4331855 \u003c/em\u003e(\u003cem\u003eCRP27\u003c/em\u003e), \u003cem\u003eLOC4333690 \u003c/em\u003e(\u003cem\u003eGRP3\u003c/em\u003e), \u003cem\u003eLOC4337850 \u003c/em\u003e(\u003cem\u003eMAPK4\u003c/em\u003e), \u003cem\u003eLOC4331490\u003c/em\u003e(\u003cem\u003eCDPK 11\u003c/em\u003e), \u003cem\u003eLOC4332786\u003c/em\u003e (\u003cem\u003eSTK1\u003c/em\u003e) and \u003cem\u003eLOC4335640\u003c/em\u003e (\u003cem\u003eSnRK2\u003c/em\u003e). Data represent as means ± S.E. (\u003cem\u003en\u003c/em\u003e=3). Percentage differences in CS against CK for each rice line. Symbols “*”, “**” and “***” indicate significant differences at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01 and \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, respectively, based on student \u003cem\u003et\u003c/em\u003e-test analysis.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4016805/v1/e62b482b9b6ac32a53df1a10.png"},{"id":52681889,"identity":"717fa897-3ee2-4607-ac36-ec6d6ae51a79","added_by":"auto","created_at":"2024-03-14 12:44:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":603141,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG analysis on DAMs in the leaves of LD18 and L9 exposed to CS based on non-targeted metabolism dataset. \u003cstrong\u003eA\u003c/strong\u003e, KEGG analysis on DAMs in L9 under CS compared to CK. \u003cstrong\u003eB\u003c/strong\u003e, KEGG analysis on DAMs in LD18 under CS compared to CK.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4016805/v1/fcb1e5c13f45bb6610f89c23.png"},{"id":52681892,"identity":"70c9a6c4-ba0b-4a12-855f-b9dfdd1bd2ce","added_by":"auto","created_at":"2024-03-14 12:44:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":728904,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of promising DAMs in L9 relative to LD18 due to CS effects. \u003cstrong\u003eA\u003c/strong\u003e, Heatmap illustrating the DAMs in L9 and LD18 under both CS and CK conditions. \u003cstrong\u003eB\u003c/strong\u003e, Overlapped DAMs between L9 and LD18 under CS relative to CK. \u003cstrong\u003eC\u003c/strong\u003e, Regulation of DAMs between L9 and LD18 under CS relative to CK. The regulation index was calculated by subtracting the log\u003csub\u003e2\u003c/sub\u003e(FC) in LD18 from that in L9.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4016805/v1/9a94e2dee7ea26b91a5c3d6d.png"},{"id":52681890,"identity":"b3949192-5959-4d23-8130-763ff0b54c82","added_by":"auto","created_at":"2024-03-14 12:44:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":427135,"visible":true,"origin":"","legend":"\u003cp\u003eSummarized model illustrating the overall changes in biological and metabolic pathways in LD18 and L9 under CS conditions. The colors are scaled to the log\u003csub\u003e2\u003c/sub\u003e(FC) ranges in L9 (left cell) and LD18 (right cell).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4016805/v1/8248e473d4bbc6b107574ccd.png"},{"id":94034309,"identity":"f753d807-c50e-4261-a9df-5f5f523455d4","added_by":"auto","created_at":"2025-10-21 15:46:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5045898,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4016805/v1/200bb3b9-fdf7-42dc-9293-94a581ad96c6.pdf"},{"id":52681894,"identity":"2d8f5f8a-4fd7-467d-99ea-adb5bb2bf2c4","added_by":"auto","created_at":"2024-03-14 12:44:45","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5779532,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-4016805/v1/e819e052b71121124c1e9cb2.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifications of genes involved in ABA and MAPK signaling pathways positively regulating cold tolerance in rice","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTemperature stands as a pivotal environmental factor shaping plant distribution in terrestrial ecosystems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, cold-induced crop losses pose a significant challenge. With the continuous degradation of our ecological environment, cold has emerged as a growing threat to plant life. Cold stress (CS) compromises cell membrane integrity, triggering the production of reactive oxygen species (ROS) and other detrimental compounds, ultimately impeding plant growth and yield formation [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Numerous studies have delved into rice\u0026rsquo;s molecular response to CS, probing its physiological and ecological characteristics [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Cold significantly inhibits rice's growth index and the physiological enzyme activities, a significant inhibited by prior cold treatments [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], hinting at induced cold tolerance. Under cold conditions, various genes have been observed to undergo upregulation [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These genes encode proteins involved in the sensing and signal transduction processes of plant cold tolerance, stimulating the synthesis of osmotic regulators [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], enhancing antioxidant enzymes activity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and improving cell membrane fluidity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Thus enhance the plant\u0026rsquo;s resilience to CS [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, unraveling the intricate process and mechanisms underlying the perception, transmission of cold signals and stimulating cold tolerance in plants hold great theoretical and practical significance. It contributes to a deeper understanding of cold tolerance mechanisms in plants and expedites the molecular breeding process for cold-tolerant crops.\u003c/p\u003e \u003cp\u003ePrevious studies underscore the significance of transcriptional regulation in plant responses to cold injury [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The CBF/DREB-dependent signaling pathway stands out as a pivotal mechanism driving cold tolerance in both \u003cem\u003eArabidopsis thaliana\u003c/em\u003e and rice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Notably, the activation of the calcium signaling pathway post-accumulation of cold tolerance characteristics reveals evolutionarily conserved genes crucial to plant adaptation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In extensive research, showcased the significant enhancement of cold tolerance in \u003cem\u003ejaponica\u003c/em\u003e rice by overexpressing the key QTL gene, \u003cem\u003eCOLD1\u003c/em\u003e, associated with sensing cold stress. Conversely, functional deletion mutants and antisense gene lines of \u003cem\u003eCOLD1\u003c/em\u003e exhibited susceptibility to cold damage [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, genomic investigations identified three genes (\u003cem\u003eOs01g55510\u003c/em\u003e, \u003cem\u003eOs01g55350\u003c/em\u003e, and \u003cem\u003eOs01g55560\u003c/em\u003e) in chromosome 1 closely linked to cold tolerance, revealing the intricate nature of plant responses [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Several transcription factors, such as \u003cem\u003eMYBS3\u003c/em\u003e, \u003cem\u003eOsWRKY71\u003c/em\u003e, and \u003cem\u003eOsWRKY76\u003c/em\u003e, have emerged as pivotal players in rice's cold tolerance [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Recent studies have notably highlighted the role of calcium-dependent protein kinases in rice's cold tolerance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These findings serve as crucial theoretical references, allaying the scientific basis for deeper exploration and understanding of rice's molecular mechanisms and regulatory networks governing cold tolerance.\u003c/p\u003e \u003cp\u003eRecently advancements in omic-analysis, encompassing transcriptome, metabolism and proteomics approaches, have emerged as crucial tools for identifying potential biomarkers in both abiotic and biotic stress in higher plants [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Numerous studies have delved into rice transcriptome profiling using RNA-seq technology, aiming to comprehend molecular pathways and compare transcriptomes [\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Notably, RNA-seq technology offers a significant advantage in representing the global transcriptome, overcoming the limitations of microarray technology dependent on predefined probes. Through field observations across two consecutive years, we identified two distinct rice genotypes: LD18, categorized as highly tolerant and L9 classified as highly susceptible to CS. The highly tolerant genotype LD18 exhibited survival for up to 25 days under CS of 4\u0026deg;C. Therefore, this investigation integrates transcriptome and non-targeted metabolism analysis of these contrasting genotypes, aiming to identify promising genes and unveil novel insights into diverse gene expressions and pathways pivotal in conferring cold tolerance in rice.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant materials and growth conditions\u003c/h2\u003e \u003cp\u003eTwo distinct rice varieties were selected for this study, namely the resilient genotype Longdao18 (LD18) and the susceptible cultivar L9, focusing on cold tolerance during the vegetative stage. These varieties were obtained from the Institute of Crop Cultivation and Tillage at the Heilongjiang Academy of Agricultural Sciences. The plants were first grown in a growth chamber under a consistent temperature of 25\u0026deg;C and a 12-h photoperiod. Two stages (seedling stage and heading stage) were conducted for CS treatments. Forty seeds were sown into each dish for 10 days, and then exposed to CS conditions within a Conviron PGV36 walk-in cold growth chamber for 10 days at 10\u003csup\u003eo\u003c/sup\u003eC during seeding stage. The growth chamber maintained optimal conditions at 75\u0026ndash;85% relative humidity (RH), 800 \u0026micro;mol s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e light intensity positioned above 60 cm from the floor and 12h photoperiod. In terms of CS treatment during heading stage, plants were raised in pots until reaching 30 days of growth. Subsequently, the potted plants were exposed to CS conditions for 10 days at 15\u003csup\u003eo\u003c/sup\u003eC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Photosynthetic measurements\u003c/h2\u003e \u003cp\u003eTo conduct photosynthetic measurements, we employed a portable photosynthesis measurement system, the Licor 6400XT (LICOR Corp., USA). This system was utilized to evaluate photosynthetic rates (A) and stomatal conductance (gs) in accordance with established protocols [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The system operated at a flow rate of 400 mmol s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003efor CO\u003csub\u003e2\u003c/sub\u003e, while maintaining light density at 1500 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and temperature of 27\u0026deg;C, with CO\u003csub\u003e2\u003c/sub\u003e levels set at 400 ppm. The experiment comprised four biological replicates to ensure strength and reliability of the obtained data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Agronomic traits\u003c/h2\u003e \u003cp\u003eThe CS treatment in this study was carried out during the booting stages of rice. For the treatment, five representative plants were selected to determine various agronomic traits. These included assessing the number of full grains and empty grains per panicle, as well as quantifying the number of deflated grains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Antioxidant and oxidoreductase measurements\u003c/h2\u003e \u003cp\u003eGR activity [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], Soluble protein content [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and SOD activity (measured by the inhibiting the photoreduction of nitro blue tetrazolium (NBT) with modifications) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] were assessed. The reaction mixture, totaling 3 ml, was composed of 25 mM phosphate buffer (pH 7.8), 13 mM methionine, 75 mM NBT, 0.1 mM EDTA, 4 \u0026micro;m riboflavin, 0.25 ml distilled water and 0.05 ml enzyme extract. Initiated by riboflavin addition, the glass test tubes were exposed to fluorescent lamps (60 \u0026micro;mol m\u003csup\u003e-2\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e) for 20 min, stopped by turning off the light. With one unit of activity defined as the enzyme amount inhibiting 50% of NBT photoreduction at 560 nm. The determination of GSH levels was carried out with a modified version of an established protocol [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Fresh leaves and roots (0.25 g) were homogenized in 2.5 ml of ice-cold 5% (w/v) 5-sulfosalicylic acid, utilizing a chilled mortar and pestle. The homogenate was then centrifuged at 20,000 g for 20 minutes at 4\u0026ordm;C. CAT activity measurement relied on the reaction solution (3 ml) of 56 mm H\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003eO\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e and 0.2 ml enzyme extract, with absorbance changes at 240 nm recorded every 30 sec. defining activity as \u0026micro;mol H\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003eO\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e reduced/min/g protein [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. POD activity, adapted from a previous protocol [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], was determined using a 3 ml solution comprising 0.1 ml enzyme extract and 2.6 ml of 0.3% guaiacol, initiated by adding 0.3 ml of 0.6% H\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003eO\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e. The measurement of absorbance changes at 470 nm was performed every 30 seconds, defining activity as absorbance changes per minute and specific activity as enzyme units per gram of soluble protein. Glutamine synthetase [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], NAD-MDH and NADP-MDH [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], NAD-ME and NADP-ME [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] activities were assessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Transmitted electron microscopic analysis\u003c/h2\u003e \u003cp\u003eSeven-day-old rice seedlings from both L9 and LD18 were exposed to either CK or CS as mentioned above. Leaf tissues were collected and fixed for 24 h in sodium phosphate buffer at pH 7.2, containing 4% (v/v) glutaraldehyde and 3% (w/v) paraformaldehyde. Following fixation, the samples underwent three rinses, each lasting 20 min, in sodium phosphate buffer at pH 7.2. Subsequently, post-fixation was conducted for 1.5 h in sodium phosphate buffer (pH 7.2) supplemented with 1% (v/v) osmium tetroxide. Following this step, the samples underwent a dehydration process, initially using ethanol in the following sequence: 50%, 70% and 90% for 10 min each, followed by two rounds of 100% ethanol for 15 min each. Afterward, they were dehydrated twice with 100% acetone for 15 minutes each. Following dehydration, the specimens were infiltrated and embedded in Epon-812 Resin. Ultrathin sections, measuring 50\u0026ndash;70 nm, were prepared using a Leica EM UC 6 ultra-microtome. These sections were then mounted on copper grids, stained for with 4% (w/v) uranyl acetate and lead citrate for 10 min. Subsequently, they were examined at 60\u0026ndash;80 kV using a Zeiss EVO40 transmission electron microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 RNA extraction and sequencing\u003c/h2\u003e \u003cp\u003eLeaf samples from each group were obtained from three distinct replicates and individually placed in RNA stabilizing solution. To maximize the inclusion of plants in a single biological replicate, 1\u0026ndash;2 leaves were collected from each seedling. These three biological replicates encompassed distinct sets of CS treatment. For mRNA extraction, around 80 mg of this powdered tissue was utilized for RNA extraction using the XcelGen total RNA isolation kit (Xcelris Genomics, India), followed by RNA sequencing analysis. The purity values and integrity of the isolated RNA were assessed by measuring with the Nanodrop 8000 Spectrophotometer (Thermoscientific) and RNA 6000 Nano LabChip on the Agilent Bioanalyzer 2100 (Agilent Technologies, Germany), respectively. Sequencing libraries were prepared using the Illumina TruSeq RNA Library Preparation Kit protocol (Illumina, San Diego, CA). RNA-Seq was performed using the illumina HiSeq2000 platform (illumina, San Diego, CA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Differentially expressed gene analysis\u003c/h2\u003e \u003cp\u003eGenomic data for the reference cultivar, Nipponbare (\u003cem\u003eOryza sativa L. subsp\u003c/em\u003e. \u003cem\u003ejaponica\u003c/em\u003e) were retrieved from the publicly available repository at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.plantbiology.msu.edu/pub/data/EukaryoticProjects/osativa/annotationdbs/pseudomolecules/version_7.0/all.dir/\u003c/span\u003e\u003cspan address=\"http://ftp://ftp.plantbiology.msu.edu/pub/data/EukaryoticProjects/osativa/annotationdbs/pseudomolecules/version_7.0/all.dir/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Transcriptome libraries from all the samples were aligned to the rice Nipponbare genome using TopHat (v1.4.3) and Bowtie (v0.13.8) with default parameters. For differentially expressed genes (DEG) analysis, Cufflinks (v1.3.3) was employed, generating FPKM values through reference-guided mapping [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Genes expressing in notably low levels were omitted from the DEG analysis. DEGs were identified at a false discovery rate (FDR) of 0.05 and a \u003cem\u003ep\u003c/em\u003e-value of \u0026le;\u0026thinsp;0.05. Heat maps illustrating differentially expressed genes were generated using the R package 'CummeRbund' [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Gene ontology and KEGG analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eGene ontology\u003c/em\u003e analysis of the DEGs was conducted using the \u003cem\u003eagriGo\u003c/em\u003e toolkit and database. Statistical methods, including the Hypergeometric test, were employed to identify significant GO-terms, with a threshold of \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To functionally annotate the DEGs, BLAST comparisons were performed against the Kyoto Encyclopedia of Genes and Genomes (KEGG) GENES database using the KEGG Automatic Annotation Server (KAAS). The assignment of KEGG terms utilized the Single-directional best hit (SBH) option, considering the representative gene data set for rice, specifically \u003cem\u003eOryza sativa\u003c/em\u003e Ref.Seq.\u0026nbsp;Pathway mapping was achieved using the KEGG Orthology database accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/ko.html\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/ko.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Quantitative transcript measurements\u003c/h2\u003e \u003cp\u003eTo validate the expression of DEGs identified in response to CS in L9 and LD18, qPCR was used following transcriptome analysis. The RNA samples used corresponded to those employed in the RNA sequencing analysis. The RNA extraction and reverse transcription to cDNA were conducted following established procedures [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. For qPCR analysis, SYBR Green PCR Master Mix (Yuanye Bio, Shanghai, China) was utilized with a real-time PCR system (ABI StepOnePlus, Applied Biosystems, USA). Primers used for qPCR are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The running program for qPCR included 95\u0026deg;C for 30 s, followed by PCR with 45 cycles at 95\u0026deg;C for 15 s, 61\u0026deg;C for 20 s and 72\u0026deg;C for 30 s. Each assay was conducted with three biological samples. The housekeeping gene is \u003cem\u003eactin1\u003c/em\u003e. Relative gene expression against the housekeeping gene was calculated using the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e method (ΔCT\u0026thinsp;=\u0026thinsp;CT, gene of interest\u003csup\u003e\u0026minus;\u0026thinsp;CT\u003c/sup\u003e) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Metabolism determinations\u003c/h2\u003e \u003cp\u003eFor metabolite determinations, samples were promptly harvested upon completion of the CS treatments in both L9 and LD18 rice lines. Samples were taken from the same marked leaf sections used for gas exchange measurements. Non-targeted metabolic profiling of the leaves from LD9 and LD18 under control and CS was conducted using the LC-MS/MS (Triple Quad 6500, SCIEX) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Approximately\u0026thinsp;~\u0026thinsp;3.0 mg leaf samples from the LD9 and LD18 subjected to CS were collected in re-cooled 2 ml Eppendorf tubes and promptly stored in liquid nitrogen. The samples underwent initial extraction using a ball mill at 50 Hz for 10 min. The extracted powder was then dissolved in a 1.5 ml methanol/chloroform mixture and incubated at \u0026minus;\u0026thinsp;20\u0026deg;C for 12 h. Subsequently, the mixture was centrifuged at 2500 g and 4\u0026deg;C for 15 min and then filtered using 0.43 \u0026micro;m organic phase medium (GE Healthcare, 6789\u0026ndash;0404).\u003c/p\u003e \u003cp\u003eWe performed metabolic analysis utilizing Metabolon software (Durham, NC, USA), and identified sample components according to retention time and mass spectra with reference metabolites. For precise identification of metabolic compounds in each sample, it is strongly advised to consult the mass spectra with entries in the NIST02 and metabolome database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://csbdb.mpimp-golm.mpg.de/\u003c/span\u003e\u003cspan address=\"http://csbdb.mpimp-golm.mpg.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e csbdb/gmd/gmd.html).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eIn this study encompassed the screening 128 rice lines sourced from northern of China, conducted over two consecutive years in field experiments to assess their response to CS. From this screening, two distinct rice lines emerged: L9, classified as a CS-sensitive rice line, and LD18 characterized as a CS-tolerant variant. To validated their performance under CS both at the seedling and heading stages, a CS alongside a control temperature of 30\u003csup\u003eo\u003c/sup\u003eC was implemented in a growth chamber condition. Results revealed a marked distinction in phenotypic responses between L9 and LD18 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). As expected, CS induced a 30% inhibition in photosynthetic rates in L9 during heading stage, while marginal differences were observed in LD18 due to CS effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Moreover, stomatal conductance exhibited a more pronounced decline in L9 compared to LD18 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), indicating that the inhibitory effects of CS on these rice lines were attributed to both impaired photosynthetic machinery and stomatal limitations. Correspondingly, results from transmitted electron microscopy revealed substantial changes in the chloroplast structure of L9 under CS, whereas the chloroplast structure of LD18 exhibited less changed under the same conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding spike traits, conspicuous detrimental effects of CS on spike filling during heading stage were observed in both rice lines. However, these effects were notably more pronounced in L9 compared to LD18, whereas no discernible impact on spike length was noted for both lines (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). Furthermore, the percentage values of full seed were declined of 38% in L9 under CS exposure, while LD18 showed no significant difference, resulting in a sixfold increase in empty seed percentage values for L9 and a fivefold increase for LD18 (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC-D). Interestingly, CS induced a significant increase in non-faired tillering in both rice lines (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eTo delve deeper into the impact of CS on antioxidant and oxidoreductase activity in two rice lines, we measured the enzymatic features of eight compounds: GR (Glutathione reductase), soluble protein content, SOD (superoxide dismutase), GSH-Px (reduced glutathione), CAT (catalase), POD (peroxidase), AS (asparagine synthase) and GS (glutamine synthetase) (Figure S2A-H). Our findings revealed inhibited soluble protein content, CAT, and AS in both rice lines under CS. Specifically, CAT and AS exhibited more pronounced reduction in L9 compared to LD18 (Figure S2 E; G). While GR, SOD and POD were enhanced in both rice lines due to CS. Remarkably, there was a dramatic increase in GSH-Px and GS levels in L9, whereas no significant difference was observed in LD18 under similar CS conditions (Figure S2D, H).\u003c/p\u003e \u003cp\u003eConsidering the diminished photosynthetic activity observed in L9 under CS, we investigated the activities of enzymes associated with carbon assimilation and energy metabolism. Our findings reveal distinctive enzymatic responses. Among the enzymes analyzed, only NAD-MDH presented a notable decrease of 34% in L9, whereas in LD18, it exhibited a threefold increase exposed to CS (Figure S3A). Contrastingly, NADP-MDH demonstrated an 11.4% increase in L9 and a more pronounced 27.5% increase in LD18 (Figure S3B). Interestingly, NAD-ME levels increased by 93% in L9 but declined by 47% in LD18 (Figure S3C). Additionally, while NADP-ME exhibited a 12% increase L9, no significant variation was observed in LD18 under CS (Figure S3D).\u003c/p\u003e \u003cp\u003eIn an effort to unveil the key differentially expressed genes (DEGs) that contributing to the varying performance between the two rice lines (L9 and LD18) under CS, we conducted an integrative analysis of transcriptome and non-targeted metabolism. Our analysis based on transcriptome data revealed that GC content and Q30 in 12 samples in average were 0.52 and 94%, respectively (Table S2). Across LD18 and L9 samples, the numbers of clean reads were 21.37\u0026nbsp;million (Table S2). PCA results indicated that PC1 and PC2 contributed 68.2% and 23.4%, respective to transcriptome dataset variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Within L9, 1425 genes were upregulated and 1528 downregulated under CS, whereas LD18 exhibited 2495 upregulated and 2472 downregulated DEGs in CS relative to under CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-D). There were 1588 overlapped DEGs between LD18 and L9 under CS relative to CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGene Ontology (GO) analysis of DEGs revealed significant enrichment in specific biological processes for both L9 and LD18 under CS compared to CK. In L9, enriched processes included cinnamic acid biosynthetic pathways, L-phenylalanine catabolism, trehalose biosynthetic, protein folding, glutamine metabolic pathways and chloroplast-nucleus signaling (Figure S4A). Contrastingly, in LD18, enriched processes encompassed photosynthesis, regulation of jasmonic acid-mediated signaling, cation transmembrane transport, chlorophyll biosynthetic, protein refolding, stress response, and trehalose biosynthesis under CS relative to CK (Figure S4B).\u003c/p\u003e \u003cp\u003eThe KEGG analysis revealed significant enrichments in DEGs in response to CS in both L9 and LD18 rice lines compared to CK. In L9 under CS, pathways such as brassinosteroid biosynthesis, riboflavin metabolism, photosynthesis-antenna proteins, arachidonic acid metabolism, glutathione metabolism and phenylalanine metabolism showed significant enrichment among the DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Conversely, in LD18 under CS, pathways including carbon fixation in photosynthetic organisms, arachidonic acid metabolism, flavone and flavanol biosynthesis, starch and sucrose metabolism and glutathione metabolism exhibited significant enrichment within the DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify the promising DEGs governing the distinct responses to CS in LD18 and L9, we selected top 1% DEGs from the overlapped dataset between these lines exposed to CS. Validation of these DEGs was performed using qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-H). The findings confirmed notable differences in the expression levels of the genes associated with the MAPK signaling pathway between LD18 and L9 under CS compared to their respective CK. Genes like \u003cem\u003eLOC4327351\u003c/em\u003e (\u003cem\u003eMPK2\u003c/em\u003e), \u003cem\u003eLOC4352460\u003c/em\u003e (\u003cem\u003eMAPK1\u003c/em\u003e) and \u003cem\u003eLOC4337850\u003c/em\u003e (\u003cem\u003eMAPK4\u003c/em\u003e) exhibited a significant increase in LD18 but a remarkable decrease in L9 under CS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, E; Figure S5A). Similarly, genes related to the ABA signaling pathway, including \u003cem\u003eLOC4333690\u003c/em\u003e (\u003cem\u003eGRP3\u003c/em\u003e), \u003cem\u003eLOC4345611\u003c/em\u003e (\u003cem\u003eRBCX1\u003c/em\u003e) and \u003cem\u003eLOC4335640\u003c/em\u003e (\u003cem\u003eSnRK2\u003c/em\u003e), showed a substantial reduction by up to 66% in L9 while these genes showed substantial enhancement in LD18 under CS compared to CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD; H; Figure S5B). Comparable trends were observed for other genes such as \u003cem\u003eLOC4352660\u003c/em\u003e (\u003cem\u003eCWM1\u003c/em\u003e), \u003cem\u003eLOC4331855\u003c/em\u003e (\u003cem\u003eCRP27\u003c/em\u003e), \u003cem\u003eLOC4331490\u003c/em\u003e (\u003cem\u003eCDPK11\u003c/em\u003e), and \u003cem\u003eLOC4332786\u003c/em\u003e (\u003cem\u003eSTK1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, F, G; Figure S5C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe non-targeted metabolism analysis through PCA revealed that for L9, PC1 and PC2 accounted for 83.5% and 8.5% of variance, respectively (Figure S6A). Correspondingly for LD18, PC1 and PC2 accounted for 91.2% and 6.1% of variance, respectively (Figure S6B). Notably, specific metabolites exhibited a dramatic upregulation in L9 under CS compared to CK, including toluene-4-sulfonate, aloesin, and (6E)-8-Oxolinalool (Figure S6C). The detailed information of extremely DAMs were listed in Figure S7A-B. Moreover, KEGG analysis based on non-targeted metabolism data revealed significant enrichments in specific metabolic pathways for both LD9 and LD18 under CS relative to CK. In LD9 under CS, pathways such as phenylpropanoid biosynthesis, tyrosine metabolism, amino acids biosynthesis, arachidonic acid metabolism, anthocyanin biosynthesis, and glyoxylate and dicarboxylate metabolism were significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Conversely in LD18 under CS, pathways like pyruvate metabolism, citrate cycle (TCA cycle), carbon fixation in photosynthetic organism, flavone and flavanol biosynthesis and carbon metabolism significantly enrichment among the list of DAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further identify the key DAMs responsible for the distinct responses observed in L9 and LD18 exposed to CS, we performed a comprehensive analysis of overlapped DAMs. Our findings revealed 221 DAMs in L9 and 569 DAMs in LD18 when exposed to CS compared to CK (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Interestingly, there were 104 DAMs that overlapped between LD18 and L9 under these CS conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Utilizing log2FC values, we calculated the regulation index, uncovering significant increases in fumarate, methyl jasmonate, trehalose, tetrahydromonapterin, and methoxypenyl-hydroxypropanoyl-CoA, particularly in LD18, surpassing the increases observed in L9 exposed to CS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC; Table S4). Conversely, 4-nitrophenol, indole-3-carboxylic acid, acicinomycin A, and cis-4-hydroxy-D-proline were dramatically inhibited in both rice lines, with L9 revealing a more pronounced reduction compared to LD18 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, our findings demonstrate that CS could induces significant reprogramming across various biological and metabolic pathways, notably impacting carbon assimilation, ROS scavenge via antioxidants, ABA signaling transduction, MAPK signaling pathway, and several osmoprotectant metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Particularly, our observations suggest that the ABA signaling pathway and MAPK signaling pathway might play critical roles in the response to cold stress in both LD18 and L9 rice lines. These pathways emerge as critical elements in understanding the plant\u0026rsquo;s response to cold stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussions","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Physiology responses to cold stress\u003c/h2\u003e \u003cp\u003ePlants often encounter physiological disruptions when exposed to CS, significantly impacting their growth and developmental processes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. While initial exposure to low nonfreezing temperatures might trigger cold tolerance, sustained exposure becomes catastrophic. Chloroplasts, pivotal in photosynthetic processes, play a critical role in rice development. CS induces cold injury in rice leaves, hindering chlorophyll synthesis and subsequently reducing photosynthesis [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Photosynthesis stands as a crucial factor of rice yield and represents a prominent physiological process affected by CS [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Our study mirrors this impact, showcasing the contrasting responses of cold-sensitive rice (L9) and cold-tolerant rice (LD18) to low temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). LD18 exhibits marked resilience and adaptation to cold-induced damage compared to L9.\u003c/p\u003e \u003cp\u003eROS serve as important signaling molecules influencing plant growth, development and responses to diverse stresses [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Studies have highlighted proline\u0026rsquo;s role in protecting enzymes from denaturation, stabilizing protein synthesis machinery, regulating cytosolic acidity, enhances water-binding capacity and serving as a reservoir of carbon and nitrogen sources[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In this study, the levels of both POD and SOD in LD18 were significantly higher compared to those in L9. This elevation contributed to shielding rice leaves, fortifying cell membrane functionality, mitigating ROS-induced damage and enhancing cold tolerance in plants. Meanwhile, the soluble protein content exhibited dramatic reduction, notably by 10% more in the CS-sensitive line L9 than in LD18. This decline suggests that rice enhances its tolerance to CS by consuming a considerable portion of soluble protein content. Subsequently, upon stress relieve, rice rapidly recovers and synthesizes a substantial amount of soluble protein content, meeting the body\u0026rsquo;s nutritional demands.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 DEGs involved in the pathway of photosynthesis\u003c/h2\u003e \u003cp\u003eConsistent with the observed photosynthetic responses in L9 and LD18 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), our analysis revealed a significant enrichment of the photosynthetic pathway in the LD18 under CS across multiple metabolic pathways, as indicated by both GO and KEGG analyses. This enrichment underscores the crucial role played by an activated photosynthetic pathway in mitigating cold sensitivity in LD18. Recognized as the basis of life on earth, photosynthesis heavily relies on Rubisco, a pivotal enzyme in the photosynthetic process. It was noticed that the lessened performance of C4 plants under cold conditions might stem from possessing 60\u0026ndash;80% less Rubisco on a total protein basis than C3 plants [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Increased Rubisco content has shown promise in mitigating cold stress and hastening recovery, as seen in maize [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In our study, we noted a threefold increase in the transcript abundance of \u003cem\u003eRBCX1\u003c/em\u003e (\u003cem\u003eLOC4345611\u003c/em\u003e) in LD18 exposed to CS, whereas there was no significant change in L9. This suggests the important role of this chaperone in the facilitating Rubisco folding during the response to CS in rice. Such findings underscore the significance of proper Rubisco folding in enhancing photosynthesis during CS, potentially alleviating the cold effects. Notably, the orthologous gene of \u003cem\u003eRBCX1\u003c/em\u003e (\u003cem\u003eAT4G04330\u003c/em\u003e) in Arabidopsis exhibited dramatic stimulation under CS conditions, as indicated by transcriptome analysis [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 ABA signaling in cold stress response\u003c/h2\u003e \u003cp\u003eThe involvement of ABA in signaling cascades during the cold stress is pivotal. Studies have indicated that cold stress triggers the accumulation of endogenous ABA in plants [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Additionally, application of exogenous ABA has shown to enhance the cold tolerance of plants [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Microarray data analysis in a specific cold-tolerant rice variety unraveled the complicated cross-talk between the CBF and ABA-responsive element (ABRE) regulons, emphasizing the role of ABA signaling in rice cold tolerance [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Within this signaling pathway, sucrose non-fermenting 1 (SNF1)-related protein kinase 2s (SnRK2s) play key roles [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Our study validated these findings, observing that the expression levels of two SNF1-related protein kinase genes (\u003cem\u003eLOC4335640\u003c/em\u003e and \u003cem\u003eLOC4339173\u003c/em\u003e) were upregulated at least twofold in LD18. In contrast, these genes remained unaltered or even exhibited decreased expression in L9 under CS, as shown by both transcriptome analysis and qPCR validation (Table S3; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 MAPK signaling in cold stress response\u003c/h2\u003e \u003cp\u003eMitogen-activated protein kinase (MAPK) cascades have been recognized for their pivotal roles in various aspects of plant biology, particularly in responding to CS [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] (Xie et al., 2012; Wen et al., 2002). Studies in Arabidopsis have highlighted the activation of MAPK genes such as \u003cem\u003eMEKK1\u003c/em\u003e, \u003cem\u003eMKK2\u003c/em\u003e, \u003cem\u003eMPK4\u003c/em\u003e and \u003cem\u003eMPK6\u003c/em\u003e under CS [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Our study aligns with these observations, revealing that the expression levels of several genes associated with the MAPK signaling pathway (e.g., \u003cem\u003eLOC4352460\u003c/em\u003e, \u003cem\u003eLOC4337850\u003c/em\u003e and \u003cem\u003eLOC4327351\u003c/em\u003e) were upregulated by up to 2.8-fold in CS-tolerant rice line LD18 under Cold conditions. In contrast, these genes exhibited inhibited in L9 under CS, with reductions of 13% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, E; Figure S5A; Table S3). These findings suggest that CS triggers the induction of kinase molecules, enabling rice plants to adapt and survive adverse environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Metabolite\u0026rsquo;s roles in cold stress response\u003c/h2\u003e \u003cp\u003eCS instigates substantial metabolic changes and impacts various physiological properties in plants, notably activating the cold-responsive signaling network in rice. Trehalose, is a non-reducing disaccharide sugar comprising two glucose molecules serve as an osmoprotectants and plays a crucial role in plant metabolism and signaling mechanisms [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Studies have shown that Trehalose modulates diverse osmotic substances in tomato plants under CS [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In line with this, our findings indicate a significant accumulation of trehalose in CS-tolerant rice line LD18 compared to L9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC; Table S4). These observations underscore the pivotal role of trehalose in regulating CS tolerance responses in rice. This emphasizes the significance of trehalose not only in plant metabolism but also in orchestrating crucial mechanisms vital for combating cold stress.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur rice collection work complies with the laws of the People\u0026rsquo;s Republic of China and has a permission letter from Heilongjiang Rice Quality Improvement and Genetic Breeding Engineering Research Center, Heilongjiang Academy of Agricultural Sciences. Voucher specimens were identified by Prof. Wei Li (Heilongjiang Academy of Agricultural Sciences) and kept at Heilongjiang Rice Quality Improvement and Genetic Breeding Engineering Research Center (No: CS001-CS128). All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Fundamental Research Funds for the Research Institutes of Heilongjiang Province (CZKYF2023-1-C012);\u0026nbsp;The Opening Project of the Collaborative Innovation Center Co-sponsored by Liaoning Provincial Government and Ministry of Education for Northeast Japonica Rice Genetic Improvement (KF2022-04); National rice industry technology system (CARS-01-57); National Natural Science Foundation of China (32170245).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuohua Ding: Methodology, Investigation, Statistical analysis, Writing, Funding acquisition, Resources. Liangzi Cao: Methodology. Zubair Iqbal: Statistical analysis, Resources. Minghui Zhao: Funding acquisition. Zhibo Cui: Funding acquisition. Jinsong Zhou: Investigation. Lei Lei: Investigation. Yu Luo: Investigation. Liangming Bai: Investigation. Guang Yang: Investigation. Tongtong Wang: Xueyang Wang: Rongsheng Wang: Kai Liu: Supervision. Zhugang Li: Supervision. Mingnan Qu: Conceptualization, Writing. Shichen Sun: Conceptualization, Funding acquisition, Resources.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and materials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data is available in the manuscript or the supplementary materials. All related sequencing data is deposited in NCBI Sequence Read Archive (SRA) database with the link of https://www.ncbi.nlm.nih.gov/sra?term=PRJNA793928. The bioProject accession is PRJNA793928.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZ. Zhang, J. Li, Y. Pan, J. Li, L. Zhou, H. Shi, Y. Zeng, H. Guo, S. Yang, W. Zheng, Natural variation in CTB4a enhances rice adaptation to cold habitats, Nat. Commun. 8 (2017) 14788.\u003c/li\u003e\n\u003cli\u003eK.A. Tartoura, S.A. Youssef, Stimulation of ROS-scavenging systems in squash (\u003cem\u003eCucurbita pepo\u003c/em\u003e L.) plants by compost supplementation under normal and low temperature conditions, Sci. Hortic. 130 (2011) 862-868.\u003c/li\u003e\n\u003cli\u003eT. Taghavi, M. Aghajani, In Effect of chilling duration on runner production and vegetative growth of\u0026apos;Pajaro\u0026apos;strawberry, VIII International Strawberry Symposium Acta Hortic. 1156(2017), 505-508.\u003c/li\u003e\n\u003cli\u003eX. Zhang, T. Liu, S. Zhu, D. Wang, S. Sun, L. Xin, Short‐term hypobaric treatment alleviates chilling injury by regulating membrane fatty acids metabolism in peach fruit, J. Food Biochem. 46 (2022) e14113.\u003c/li\u003e\n\u003cli\u003eD. Mao, Y. Xin, Y. Tan, X. Hu, J. Bai, Z.-y. Liu, Y. Yu, L. Li, C. Peng, T. Fan, Natural variation in the HAN1 gene confers chilling tolerance in rice and allowed adaptation to a temperate climate, Proc. Natl. Acad. Sci. 116 (2019) 3494-3501.\u003c/li\u003e\n\u003cli\u003eY. Ma, X. Dai, Y. Xu, W. Luo, X. Zheng, D. Zeng, Y. Pan, X. Lin, H. Liu, D. Zhang, COLD1 confers chilling tolerance in rice, Cell 160 (2015) 1209-1221.\u003c/li\u003e\n\u003cli\u003eQ. Ma, X. Dai, Y. Xu, J. Guo, Y. Liu, N. Chen, J. Xiao, D. Zhang, Z. Xu, X. Zhang, Enhanced tolerance to chilling stress in OsMYB3R-2 transgenic rice is mediated by alteration in cell cycle and ectopic expression of stress genes, Plant Physiol. 150 (2009) 244-256.\u003c/li\u003e\n\u003cli\u003eY.I. Kuk, J.S. Shin, N.R. Burgos, T.E. Hwang, O. Han, B.H. Cho, S. Jung, J.O. Guh, Antioxidative enzymes offer protection from chilling damage in rice plants, Crop Sci. 43 (2003) 2109-2117.\u003c/li\u003e\n\u003cli\u003eJ. Browse, Z. Xin, Temperature sensing and cold acclimation, Curr. Opin. Plant Biol. 4 (2001) 241-246.\u003c/li\u003e\n\u003cli\u003eM.F. Thomashow, Plant cold acclimation: freezing tolerance genes and regulatory mechanisms, Annu. Rev. Plant Biol. 50 (1999) 571-599.\u003c/li\u003e\n\u003cli\u003eM.A. Hughes, M.A. Dunn, The molecular biology of plant acclimation to low temperature, J. Exp. Bot. 47 (1996) 291-305.\u003c/li\u003e\n\u003cli\u003eH.J. Bohnert, D.E. Nelson, R.G. Jensen, Adaptations to environmental stresses, The plant cell 7 (1995) 1099.\u003c/li\u003e\n\u003cli\u003eT.H. Chen, N. Murata, Enhancement of tolerance of abiotic stress by metabolic engineering of betaines and other compatible solutes, Curr. Opin. Plant Biol. 5 (2002) 250-257.\u003c/li\u003e\n\u003cli\u003eT.K. Prasad, M.D. Anderson, B.A. Martin, C.R. Stewart, Evidence for chilling-induced oxidative stress in maize seedlings and a regulatory role for hydrogen peroxide, Plant Cell 6 (1994) 65-74.\u003c/li\u003e\n\u003cli\u003eA. Zhou, E. Liu, H. Li, Y. Li, S. Feng, S. Gong, J. Wang, PsCor413pm2, a plasma membrane-localized, cold-regulated protein from Phlox subulata, confers low temperature tolerance in Arabidopsis, Int. J. molecular Sci. 19 (2018) 2579.\u003c/li\u003e\n\u003cli\u003eV. Chinnusamy, J. Zhu, J.-K. Zhu, Cold stress regulation of gene expression in plants, Trends Plant Sci. 12 (2007) 444-451.\u003c/li\u003e\n\u003cli\u003eK. Yamaguchi-Shinozaki, K. Shinozaki, Transcriptional regulatory networks in cellular responses and tolerance to dehydration and cold stresses, Annu. Rev. Plant Biol. 57 (2006) 781-803.\u003c/li\u003e\n\u003cli\u003eK.B. Singh, R.C. Foley, L. O\u0026ntilde;ate-S\u0026aacute;nchez, Transcription factors in plant defense and stress responses, Curr. Opin Plant Biol. 5 (2002) 430-436.\u003c/li\u003e\n\u003cli\u003eM. Zhang, J. Ye, Q. Xu, Y. Feng, X. Yuan, H. Yu, Y. Wang, X. Wei, Y. Yang, Genome-wide association study of cold tolerance of Chinese indica rice varieties at the bud burst stage, Plant Cell Rep. 37 (2018) 529-539.\u003c/li\u003e\n\u003cli\u003eC.-Y. Kim, K.T.X. Vo, C.D. Nguyen, D.-H. Jeong, S.-K. Lee, M. Kumar, S.-R. Kim, S.-H. Park, J.-K. Kim, J.-S. Jeon, Functional analysis of a cold-responsive rice WRKY gene, OsWRKY71, Plant Biotechnol. Rep. 10 (2016) 13-23.\u003c/li\u003e\n\u003cli\u003eC.-F. Su, Y.-C. Wang, T.-H. Hsieh, C.-A. Lu, T.-H. Tseng, S.-M. Yu, A novel MYBS3-dependent pathway confers cold tolerance in rice, Plant Physiol. 153 (2010) 145-158.\u003c/li\u003e\n\u003cli\u003eN. Yokotani, Y. Sato, S. Tanabe, T. Chujo, T. Shimizu, K. Okada, H. Yamane, M. Shimono, S. Sugano, H. Takatsuji, WRKY76 is a rice transcriptional repressor playing opposite roles in blast disease resistance and cold stress tolerance, J. Exp. Bot. 64 (2013) 5085-5097.\u003c/li\u003e\n\u003cli\u003eY. Liu, C. Xu, Y. Zhu, L. Zhang, T. Chen, F. Zhou, H. Chen, Y. Lin, The calcium‐dependent kinase OsCPK24 functions in cold stress responses in rice, J. Integr. Plant Biol. 60 (2018) 173-188.\u003c/li\u003e\n\u003cli\u003eY. Saijo, N. Kinoshita, K. Ishiyama, S. Hata, J. Kyozuka, T. Hayakawa, T. Nakamura, K. Shimamoto, T. Yamaya, K. Izui, A Ca2+-dependent protein kinase that endows rice plants with cold-and salt-stress tolerance functions in vascular bundles, Plant Cell Physiol. 42 (2001) 1228-1233.\u003c/li\u003e\n\u003cli\u003eZ. Li, M.U. Khan, P. Letuma, Y. Xie, W. Zhan, W. Wang, Y. Jiang, W. Lin, Z. Zhang, Transcriptome Analysis of the Responses of Rice Leaves to Chilling and Subsequent Recovery, Int. J. Mol. Sci. 23 (2022) 10739.\u003c/li\u003e\n\u003cli\u003eT. Lu, G. Lu, D. Fan, C. Zhu, W. Li, Q. Zhao, Q. Feng, Y. Zhao, Y. Guo, W. Li, Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq. Genome Res. 20 (2010) 1238-1249.\u003c/li\u003e\n\u003cli\u003eR. Zhai, Y. Feng, H. Wang, X. Zhan, X. Shen, W. Wu, Y. Zhang, D. Chen, G. Dai, Z. Yang, Transcriptome analysis of rice root heterosis by RNA-Seq, BMC Genomics 14 (2013) 1-14.\u003c/li\u003e\n\u003cli\u003eG. He, X. Zhu, A.A. Elling, L. Chen, X. Wang, L. Guo, M. Liang, H. He, H. Zhang, F. Chen, Global epigenetic and transcriptional trends among two rice subspecies and their reciprocal hybrids, Plant Cell 22 (2010) 17-33.\u003c/li\u003e\n\u003cli\u003eY. Oono, Y. Kawahara, H. Kanamori, H. Mizuno, H. Yamagata, M. Yamamoto, S. Hosokawa, H. Ikawa, I. Akahane, Z. Zhu, mRNA-Seq reveals a comprehensive transcriptome profile of rice under phosphate stress, Rice 4 (2011) 50-65.\u003c/li\u003e\n\u003cli\u003eY. Hu, L. Zhang, S. He, M. Huang, J. Tan, L. Zhao, S. Yan, H. Li, K. Zhou, Y. Liang, Cold stress selectively unsilences tandem repeats in heterochromatin associated with accumulation of H3K9ac, Plant Cell Environ. 35 (2012) 2130-2142.\u003c/li\u003e\n\u003cli\u003eG. Gao, Y. Zhong, A. Guo, Q. Zhu, W. Tang, W. Zheng, X. Gu, L. Wei, J. Luo, DRTF: a database of rice transcription factors, Bioinform. 22 (2006) 1286-1287.\u003c/li\u003e\n\u003cli\u003eM. Qu, G. Zheng, S. Hamdani, J. Essemine, Q. Song, H. Wang, C. Chu, X. Sirault, X.-G. Zhu, Leaf photosynthetic parameters related to biomass accumulation in a global rice diversity survey, Plant Physiol. 175 (2017) 248-258.\u003c/li\u003e\n\u003cli\u003eS. Woo, D. Lee, Y. Lee, Net photosynthetic rate, ascorbate peroxidase and glutathione reductase activities of Erythrina orientalis in polluted and non-polluted areas, Photosynthetica 45 (2007) 293-295.\u003c/li\u003e\n\u003cli\u003eL. Li, B.R. Singh, Role of zinc binding in type A botulinum neurotoxin light chain\u0026apos;s toxic structure, Biochem. 39 (2000) 10581-10586.\u003c/li\u003e\n\u003cli\u003eR.S. Dhindsa, P. Plumb-Dhindsa, T.A. Thorpe, Leaf senescence: correlated with increased levels of membrane permeability and lipid peroxidation, and decreased levels of superoxide dismutase and catalase, J. Exp. Bot. 32 (1981) 93-101.\u003c/li\u003e\n\u003cli\u003eY. Li, J. Hugenholtz, T. Abee, D. Molenaar, Glutathione protects Lactococcus lactis against oxidative stress, Appl. Environ. Microbiol. 69 (2003) 5739-5745.\u003c/li\u003e\n\u003cli\u003eX. Yu, B. Wang, C. Zhang, W. Xu, J. He, L. Zhu, S. Wang, Effect of root restriction on nitrogen levels and glutamine synthetase activity in \u0026lsquo;Kyoho\u0026rsquo;grapevines, Sci. Hortic. 137 (2012) 156-163.\u003c/li\u003e\n\u003cli\u003eY.-X. Yao, Q.-L. Dong, H. Zhai, C.-X. You, Y.-J. Hao, The functions of an apple cytosolic malate dehydrogenase gene in growth and tolerance to cold and salt stresses, Plant Physiol. Biochem. 49 (2011) 257-264.\u003c/li\u003e\n\u003cli\u003eC.P. Spampinato, P. Casati, C.S. Andreo, Factors affecting the oligomeric state of NADP-malic enzyme from maize and wheat tissues: a chemical crosslinking study, Biochim. Biophys. Acta, Prot. Struct. Mol. Enzymol. 1383 (1998) 245-252.\u003c/li\u003e\n\u003cli\u003eC. Trapnell, A. Roberts, L. Goff, G. Pertea, D. Kim, D.R. Kelley, H. Pimentel, S.L. Salzberg, J.L. Rinn, L. Pachter, Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks, Nat. Protoc. 7 (2012) 562-578.\u003c/li\u003e\n\u003cli\u003eB.R. Thumma, N. Sharma, S.G. Southerton, Transcriptome sequencing of Eucalyptus camaldulensis seedlings subjected to water stress reveals functional single nucleotide polymorphisms and genes under selection, Bmc Genomics 13 (2012) 1-21.\u003c/li\u003e\n\u003cli\u003eT. Kyndt, S. Denil, A. Haegeman, G. Trooskens, T. De Meyer, W. Van Criekinge, G. Gheysen, Transcriptome analysis of rice mature root tissue and root tips in early development by massive parallel sequencing, J. Exp. Bot. 63 (2012) 2141-2157.\u003c/li\u003e\n\u003cli\u003eJ. Li, J. Essemine, C. Shang, H. Zhang, X. Zhu, J. Yu, G. Chen, M. Qu, D. Sun, Combined proteomics and metabolism analysis unravels prominent roles of antioxidant system in the prevention of alfalfa (Medicago sativa L.) against salt stress, Int. J. Mol. Sci. 21 (2020) 909.\u003c/li\u003e\n\u003cli\u003eK.J. Livak, T.D. Schmittgen, Analysis of relative gene expression data using real-time quantitative PCR and the 2\u0026minus; \u0026Delta;\u0026Delta;CT method, Methods 25 (2001) 402-408.\u003c/li\u003e\n\u003cli\u003eJ. Zhao, S. Zhang, T. Yang, Z. Zeng, Z. Huang, Q. Liu, X. Wang, J. Leach, H. Leung, B. Liu, Global transcriptional profiling of a cold‐tolerant rice variety under moderate cold stress reveals different cold stress response mechanisms, Physiol. Plant. 154 (2015) 381-394.\u003c/li\u003e\n\u003cli\u003eH. Shimono, T. Hasegawa, S. Fujimura, K. Iwama, Responses of leaf photosynthesis and plant water status in rice to low water temperature at different growth stages, Field Crop. Res. 89 (2004) 71-83.\u003c/li\u003e\n\u003cli\u003eT. Ariizumi, S. Kishitani, R. Inatsugi, I. Nishida, N. Murata, K. Toriyama, An increase in unsaturation of fatty acids in phosphatidylglycerol from leaves improves the rates of photosynthesis and growth at low temperatures in transgenic rice seedlings, Plant Cell Physiol. 43 (2002) 751-758.\u003c/li\u003e\n\u003cli\u003eJ.B. Yahmed, T.M. de Oliveira, P. Novillo, A. Quinones, M.-A. Forner, A. Salvador, Y. Froelicher, M.B. Mimoun, M. Talon, P. Ollitrault, A simple, fast and inexpensive method to assess salt stress tolerance of aerial plant part: Investigations in the mandarin group, J. Plant Physiol. 190 (2016) 36-43.\u003c/li\u003e\n\u003cli\u003eS.W. Jeong, S.M. Choi, D.S. Lee, S.N. Ahn, Y. Hur, W.S. Chow, Y.-I. Park, Differential susceptibility of photosynthesis to light-chilling stress in rice (Oryza sativa L.) depends on the capacity for photochemical dissipation of light, Mol. Cells 13 (2002) 419-428.\u003c/li\u003e\n\u003cli\u003eK. Zhang, G. Tian, X. Li, Z. Zhang, J. Liu, Y. Li, J. Xie, P. Wang, ROS produced via BsRBOHD plays an important role in low temperature-induced anthocyanin biosynthesis in begonia semperflorens, Russ. J. Plant Physiol. 67 (2020) 250-258.\u003c/li\u003e\n\u003cli\u003eK. Apel, H. Hirt, Reactive oxygen species: metabolism, oxidative stress, and signal transduction, Annu. Rev. Plant Biol. 55 (2004) 373-399.\u003c/li\u003e\n\u003cli\u003eS.P. Long, Environmental responses, C4 Plant Biol. (1999).(1999) 215-249.\u003c/li\u003e\n\u003cli\u003eR.F. Sage, R.W. Pearcy, J.R. Seemann, The nitrogen use efficiency of C3 and C4 plants: III. Leaf nitrogen effects on the activity of carboxylating enzymes in Chenopodium album (L.) and Amaranthus retroflexus (L.), Plant Physiol. 85 (1987) 355-359.\u003c/li\u003e\n\u003cli\u003eC.E. Salesse‐Smith, R.E. Sharwood, F.A. Busch, D.B. Stern, Increased Rubisco content in maize mitigates chilling stress and speeds recovery, Plant Biotechnol. J. 18 (2020) 1409-1420.\u003c/li\u003e\n\u003cli\u003eS. Fowler, M.F. Thomashow, Arabidopsis transcriptome profiling indicates that multiple regulatory pathways are activated during cold acclimation in addition to the CBF cold response pathway, Plant Cell 14 (2002) 1675-1690.\u003c/li\u003e\n\u003cli\u003eA. Chawade, A. Lindl\u0026ouml;f, B. Olsson, O. Olsson, Global expression profiling of low temperature induced genes in the chilling tolerant japonica rice Jumli Marshi, PLoS One 8 (2013) e81729.\u003c/li\u003e\n\u003cli\u003eS.K. Sah, K.R. Reddy, J. Li, Abscisic acid and abiotic stress tolerance in crop plants, Front. Plant Sci. 7 (2016) 571.\u003c/li\u003e\n\u003cli\u003eW. Xing, C. Rajashekar, Glycine betaine involvement in freezing tolerance and water stress in Arabidopsis thaliana, Environ. Exp. Bot. 46 (2001) 21-28.\u003c/li\u003e\n\u003cli\u003eM.A. Hossain, J.-I. Cho, M. Han, C.-H. Ahn, J.-S. Jeon, G. An, P.B. Park, The ABRE-binding bZIP transcription factor OsABF2 is a positive regulator of abiotic stress and ABA signaling in rice, J. Plant Physiol. 167 (2010) 1512-1520.\u003c/li\u003e\n\u003cli\u003eS.-Y. Park, P. Fung, N. Nishimura, D.R. Jensen, H. Fujii, Y. Zhao, S. Lumba, J. Santiago, A. Rodrigues, T.-f.F. Chow, Abscisic acid inhibits type 2C protein phosphatases via the PYR/PYL family of START proteins, Science 324 (2009) 1068-1071.\u003c/li\u003e\n\u003cli\u003eG. Xie, H. Kato, R. Imai, Biochemical identification of the OsMKK6\u0026ndash;OsMPK3 signalling pathway for chilling stress tolerance in rice, Biochem J. 443 (2012) 95-102.\u003c/li\u003e\n\u003cli\u003eM. Teige, E. Scheikl, T. Eulgem, R. Doczi, K. Ichimura, K. Shinozaki, J.L. Dangl, H. Hirt, The MKK2 pathway mediates cold and salt stress signaling in Arabidopsis, Mol. Cell 15 (2004) 141-152.\u003c/li\u003e\n\u003cli\u003eA. Wingler, The function of trehalose biosynthesis in plants, Phytochemistry 60 (2002) 437-440.\u003c/li\u003e\n\u003cli\u003eM.J. Paul, L.F. Primavesi, D. Jhurreea, Y. Zhang, Trehalose metabolism and signaling, Annu. Rev. Plant Biol. 59 (2008) 417-441.\u003c/li\u003e\n\u003cli\u003eT. Liu, X. Ye, M. Li, J. Li, H. Qi, X. Hu, H2O2 and NO are involved in trehalose-regulated oxidative stress tolerance in cold-stressed tomato plants, Environ. Exp. Bot. 171 (2020) 103961.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cold stress, ABA signaling, MAPK signaling, Transcriptional regulation, Integrative analysis","lastPublishedDoi":"10.21203/rs.3.rs-4016805/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4016805/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCold stress (CS) significantly impacts rice growth during seedling and heading stages. This study based on two-year field observations identified two rice lines, L9 (cold stress-sensitive) and LD18 (cold stress-tolerant) showing contrasting CS response. L9 exhibited 38% reduction in photosynthetic efficiency, whereas LD18 remained unchanged, correlating with seed rates. Transcriptome analysis identified differentially expressed genes (DEGs) with LD18 showing enriched pathways (carbon fixation, starch/sucrose metabolism, glutathione metabolism). LD18 displayed dramatically enhanced expression of MAPK-related genes (\u003cem\u003eLOC4327351\u003c/em\u003e, \u003cem\u003eLOC4352460\u003c/em\u003e, \u003cem\u003eLOC4337850\u003c/em\u003e) and increased ABA signaling genes (\u003cem\u003eLOC4333690\u003c/em\u003e, \u003cem\u003eLOC4345611\u003c/em\u003e, \u003cem\u003eLOC4335640\u003c/em\u003e) compared to L9 exposed to CS. Results from qPCR confirmed the enhanced expression of the three MAPK-related genes in LD18 while dramatic reduction in L9 under CS relative to that under CK. We also observed up to 66% reduction in expression levels of the three genes related to ABA signaling pathway in L9 relative to LD18 under CS. In consistent with results of photosynthetic efficiency, metabolic analysis suggests pyruvate metabolism, TCA cycle and carbon metabolism enrichment in LD18 under CS. The study reveals reprogramming of the carbon assimilation metabolic pathways, emphasizing the critical roles of the key DEGs involved in ABA and MAPK signaling pathways in positive regulation of LD18 response to CS, offering the foundation towards cold tolerance breeding through targeted gene editing.\u003c/p\u003e","manuscriptTitle":"Identifications of genes involved in ABA and MAPK signaling pathways positively regulating cold tolerance in rice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-14 12:44:40","doi":"10.21203/rs.3.rs-4016805/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c632f50-d1a2-4b5a-97ee-a08f6f43c4be","owner":[],"postedDate":"March 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T15:38:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-14 12:44:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4016805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4016805","identity":"rs-4016805","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}