1. Introduction
Maize (Zea mays L.) is the world’s most commonly grown cereal crop, with an estimated global annual production of about 1186.86 million metric tons in 2020/2021 [1]. The high dependence on maize for human, animal, and industrial consumption makes it one of the most critical food crops. However, maize growth and yield are highly dependent on sufficient environmental factors [2]. Thus, the current and expected scarcity of water sources and arable land due to the increasing world population and the recurrent extreme weather caused by global warming is projected to increase the incidence of abiotic stresses, such as drought, cold, and freezing during the planting, flowering, and grain-filling stages, in many corn-growing areas [3]. These abiotic stresses typically serve as crucial impediments to maize production and geographical distribution [4] and restrict agricultural yields worldwide.
Since maize has a tropical origin, cold stress is a significant risk factor among the several abiotic stresses in the development of maize. A previous report has shown that cold stress adversely affects maize growth from germination to harvest, resulting in significant yield losses due to low and slow germination and poor grain filling [5]. Corn production losses can surpass 20% in the most prolonged cold temperatures [6]. Therefore, the development of high-yielding cultivars tolerant to cold stress may help in augmenting maize production in vulnerable regions and act as an essential maize-breeding target.
The optimum maize growth temperatures range from 21 to 27 °C, while sub-optimal temperatures of about 10–20 °C decrease biomass production, thereby leading to growth retardation [7]. Cold stress induces multiple abnormalities in physiological, molecular, and biochemical processes, which harm plant growth and yield. Cell membranes may become disorganized, proteins may be denatured, oxidative defense and osmotic stress may be altered, photosynthesis possibly restricted, and metabolism may become dysfunctional, all of which subsequently disrupt growth and development, decrease fertility, and cause premature senescence and even plant death [8,9,10,11]. All of these cold stress-associated changes occur through accurate gene expression regulation and are therefore genetically regulated. Thus, screening cold stress-related candidate genes may help identify essential regulators and pathways as potential targets for breeding resistant varieties adaptable to environments with fluctuating temperatures.
As a result of their sessile nature, plants have developed complex cold acclimation mechanisms, which entail the interaction of multiple biochemical pathways in an organ-, genetic-, and environmental-specific manner [12]. They sense cold stress through changes in membrane fluidity and the accumulation of calcium signatures, leading to downstream activation of cold signaling pathways [13]. The enhanced cytosolic Ca2+ levels then induce C-repeat binding factors (CBFs), which act as core regulators for expressing cold-response genes [14,15]. The stress receptors, in conjunction with the cell membrane transporters, facilitate the perception of stress signals and their transmission to target genes. Multiple protein kinases, including CaMs, CMLs, CBLs, CDPKs, and MAPKs, phosphorylate other kinases and/or various TFs, resulting in activation of the cold-responsive genes [16]. Moreover, the transcriptional factor (TF) families bHLH, CAMTA, MADS, WRKY, NAC, TRAF, C3H, and AP2/ERF are critical in cold response mechanisms, while phytohormones, such as abscisic acid (ABA), regulate specific pathways that lead to cold tolerance [17,18]. Cellular redox homeostasis is protected by synthesizing defense enzymes and other antioxidant systems, while soluble sugars serve as a stabilizer of cellular components and plasma membrane [19]. Secondary metabolites, such as lignin, anthocyanin, terpenoids, chaperones, and late embryogenesis abundant (LEA), provide cold tolerance by protecting cellular components from cold-induced cellular damage [20,21]. Transporters, such as the ATP-binding cassette (ABC) transporter, play integral roles in plant growth and development, homeostasis of phytohormones, and resistance to abiotic stress [22]. Tremendous progress has been made in elucidating the mechanisms underlying cold tolerance in plants. However, the complex molecular mechanisms of cold tolerance in maize seedlings are still elusive and require comprehensive research.
Moreover, considering the genetic diversity of maize inbred lines, it will be interesting to identify the stress-responsive genes that have a consistent function in a variety of inbred lines in spite of their genetic background. Differential transcriptome analysis using RNA-sequencing (RNA-seq) approaches has recently emerged as robust, reliable, and responsive to broader levels of gene expression [23]. This effective technology makes it easier to rapidly classify stress-responsive genes and decode metabolic pathways associated with biotic and abiotic stresses [24]. Currently, little is known about the transcriptomic responses of maize seedlings to cold stress. In this study, we used RNA-seq analysis to decipher the expression profiles of the differentially expressed genes (DEGs) responsible for the contrasting cold response of 46 maize inbred lines (24 tolerant and 22 sensitive) at the seedling stage. The common cold-responsive genes were then characterized by their patterns of expression and evaluated for their functional significance. The current study provides valuable clues for the in-depth characterization of molecular responses of maize seedlings to cold stress, which could lead to effective strategies for breeding and developing cold-tolerant maize varieties.
2. Materials and Methods
2.1. Plant Materials and Treatments
The maize inbred lines were derived from the hybrid 19NL, which was provided by Heilongjiang Academy of Agricultural Sciences. The maize hybrid 19NL is a highly suitable cultivar for the spring season in northeastern China. Field experiments were conducted at open field stations during the spring maize growing season (March–June 2019) in Heilongjiang (Harbin, China). In April 2019, approximately 6000 inbred lines of 19NL were planted in rows, with 40 cm between the rows and each row containing 20 seedlings. However, in the late spring of May 2019, due to climate change, the Heilongjiang region of Harbin was affected by a cold spell of below 10 °C for more than three days. We observed that the cold spell impacted the seedlings differently, with survival rates varying from one inbred line to another. This indicated that their response to varying degrees of cold stress was different, regardless of being from the same germplasm. When seedlings had six fully expanded leaves (40 days old), all inbred lines were sampled, and 46 inbred lines with contrasting cold tolerance were selected and classified into cold-tolerant (24 lines) and cold-sensitive (22 lines) lines based on the survival rates of the seedlings, as well as a visual observation of the phenotypic changes of the leaves. The top fully expanding leaves of the 24 cold-resistant and 22 cold-sensitive inbred lines were harvested, frozen into liquid nitrogen, and later stored at −80 °C for further use.
To validate the expression of our cold-responsive DEGs, we planted B73 and CIMBL116 maize inbred lines, which have previously been reported as being cold-sensitive and -tolerant lines, respectively [25,26]. The seeds from these two maize inbred lines were provided by the Chinese Academy of Agricultural Sciences’ crop science institute). Ten seeds from each inbred line (B73 and CIMBL116) were surface sterilized with 75% (v/v) ethanol for three minutes before being rinsed three times with distilled water. Seeds were then placed between two layers of damp paper at 25 °C and left to germinate in the dark for 3 days. Uniformly germinated seeds with 2–3 cm coleoptiles were selected and sown in pots filled with peat, vermiculite, and perlite (10:1:1 by vol.). The seedlings were then grown in a growth chamber with a controlled temperature of 25/20 °C (day/night), 450 L mol m−2 s−1 light density, and a 12/12 h (light/dark) photoperiod until the third leaves were fully developed. The seedlings from the two inbred lines were divided into two groups, with the first receiving a cold stress treatment of 4 °C for 2 h followed by 25 °C for 2 days. The other group, which served as a control, was kept in the growth chamber under the same conditions as described above. The control and cold treatment samples were harvested at the same time after 2 days and immediately frozen in liquid nitrogen before being stored at −80 °C for total RNA isolation.
2.2. RNA Extraction, Library Construction, and Illumina Sequencing
TRIzol reagent (Invitrogen, CA, USA) was used to isolate total RNA from 24 tolerant and 22 sensitive leaf samples as per the manufacturer’s standard. The samples were treated with RNase-free DNaseI (Takara, Kusatsu, Japan) to remove the genomic DNA. The NanoDrop 1000 (NanoDrop Technologies, Wilmington, DE, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) were used to assess RNA concentration and integrity, respectively. The cDNA libraries were constructed and sequenced using the Illumina HiSeq™ 2500 platform to generate 150 bp paired-end reads. Moreover, the above procedure was also employed to extract RNA from B73 and CIMBL116 at control and treatment levels for the purpose of qRT-PCR.
2.3. Reads Processing, Mapping, and Gene Expression Quantification
We used FastQC (V0.11.3) to evaluate the quality of raw reads, while Trimmomatic (V0.32) was utilized to eliminate low-quality and adapter-containing reads [27]. The Phred quality scores, including Q20 (99% base call accuracy), Q30 (99.9% base call accuracy), and the GC content of the clean data, were calculated. Consequently, high-quality clean data were used in all the subsequent analyses. The maize reference genome (B73_v4) was downloaded from the maize database (
2.4. Functional Annotation of the DEGs
For functional annotation, the 2237 transcripts that qualified to be our DEGs were annotated against the maize genome (AGPv4, B73 RefGen_v4) (
2.5. Gene Ontology (GO) Enrichment and KEGG Pathway Enrichment Analyses
The GO enrichment analysis of DEGs was conducted by agriGOv2 (
2.6. Validation of Cold-Responsive DEGs by Quantitative Real-Time PCR (qRT-PCR)
To validate the reliability and repeatability of the RNA-Seq data, six DEGs were randomly selected for verification by qRT-PCR. The gene-specific primers (Table S1) were designed using Primer Premier 5.0 software (Premier Biosoft International, Palo Alto, CA, USA). The qRT-PCR was conducted in triplicate using 2× ChamQ SYBR qPCR Master Mix Kit (Low ROX Premixed) (Vazyme Biotechnology Co., Nanjing, China) on an Applied Biosystems QuantStudio® 6 Flex (Thermo Lifetech, Waltham, MA, USA). The reaction system utilized was as previously described by Zhang et al. [38]. The internal reference β-actin was utilized to normalize the expression data. The relative expression levels of the six DEGs were calculated according to the 2−ΔΔCT (cycle threshold) method [39].
3. Results
3.1. Phenotypic Analysis of Maize Population under Cold Stress Conditions
Forty-six inbred lines cultivated in Heilongjiang Province, China, were selected and evaluated for cold tolerance based on their seedling survival rate and physiological response (visual observations of the leaves). From the results, 24 inbred line seedlings displayed little phenotypic changes, maintained fully expanded green leaves, had intact plant architecture, strong vigor, and high survival rates (average of 0.8) (Table S2). These inbred lines were therefore classified as cold tolerant and were labeled with an extension of −1 (Figure 1 and Figure S1A). The remaining 22 inbred line seedlings showed visible phenotypic damage, including shriveled, curled, and yellowish spots on the leaves, and low survival rates (average of 0.3) (Table S2). These seedlings were classified as cold sensitive and were labeled with an extension of −2 (Figure 1 and Figure S1B). Collectively, the susceptible lines were more severely damaged by cold stress than the tolerant lines, as evidenced by the shriveled, curled, and yellowish patches on their leaves, as well as low seedling survival rates of about 0.3 on average.
3.2. RNA-Seq Analysis and Alignment of Unique Reads to the Maize Reference Genome
The RNA for the RNA-seq analysis was extracted from the top fully expanding leaves of six-leaf-stage maize seedlings of the 24 tolerant and 22 sensitive maize inbred lines mentioned in Section 2.1 above. The cDNA libraries developed from the RNA described above were constructed and used for Illumina Genome Analyzer (HiSeq™ 2500) deep sequencing. The resulting raw data were deposited into the Genome Sequence Archive under accession number CRA003678 and are publicly accessible at
3.3. Identification, Annotation, and Differential Analysis of DEGs
The transcription level was calculated via HTSeq-count as transcript counts, and a total of 53,037 transcripts were obtained and normalized with DESeq2. The expression patterns of these transcripts were investigated in both 24 tolerant and 22 sensitive samples. A definite expression pattern of a single transcript was established after a group comparison analysis (24 tolerant versus 22 sensitive samples), and its base mean, log2 fold change, p-value, and padj values were acquired. From this information, a gene was considered differentially expressed if the p-value was ≤0.05 and the log2 fold change value was ≥1 or ≤−1 between the 24 tolerant and the 22 sensitive samples. A total of 2237 DEGs were detected in all 46 samples. A principal component analysis (PCA) plot was generated based on the gene expression levels of the 46 samples, and the PC1 and PC2 explained 22% of the total variance (Figure 2). The PCA revealed that the correlation of the 46 samples was based on their response to cold stress. Both the tolerant and sensitive samples clustered together, implying a differential response to cold stress. A heatmap of the 2237 DEGs revealed two distinctive clusters, with 1064 DEGs possessing a positive log2 fold change enriched in the tolerant samples, and 1173 DEGs with a negative log2 fold change were enriched in the sensitive samples (Figure 3). Collectively, cold stress upregulated 1064 DEGs in the tolerant lines, while it downregulated them in the sensitive lines. Similarly, cold stress upregulated 1173 DEGs in the sensitive lines, while downregulating them in the tolerant lines.
The annotation of the 2237 DEGs with the maize reference genome B73 RefGen_v4 model resulted in 1656 (74.0%) annotated and 581 (26%) unannotated DEGs. The differential analysis of the 1656 annotated DEGs was carried out based on the two categories of cold-tolerant and cold-sensitive lines. Resultantly, 779 and 877 DEGs were significantly enhanced in the tolerant and sensitive lines, respectively. Further analysis of the 1656, 779, and 877 DEGs was carried out to establish the pathways involved in the cold stress response among the 46 samples.
For the 581 unannotated DEGs, their sequences (≥200 bp) were uploaded to the CPC website for classification as protein-coding or non-coding RNAs. A total of 271 DEGs were classified as protein-coding, while 310 were classified as long non-coding RNAs (lncRNAs) (Figure 4). Moreover, the 581 unannotated DEGs were scanned for the open reading frame (ORF). A total of 402 DEGs with an ORF greater than 120 amino acids (aa) were discarded. The remaining 179 DEGs (ORF length ≤ 120 aa) were aligned to the SWISS-PROT database for the identification of homologous proteins. A total of 56 DEGs were discarded after being homologous to known proteins (E-value ≤ 0.001), while the remaining 123 DEGs were classified as lncRNAs (Figure 4). In total, 337 putative cold-responsive lncRNAs (Figure 4) were identified from the 581 unannotated DEGs, implying the role of lncRNAs in the cold stress response. We analyzed the top-most 20 DEGs regulated by cold stress in both tolerant and sensitive lines and half were unannotated (Table 1). However, most of the annotated DEGs remain uncharacterized, which implies more research is required to unravel the molecular mechanism of cold tolerance.
3.4. Gene Ontology (GO) Analysis of DEGs
To identify the DEGs’ significantly enriched GO terms, the functions of the 1656, 779, and 877 (46 inbred lines, 24 cold-tolerant lines, and 22 cold-sensitive lines, respectively) DEGs were analyzed using AgrigoV2 software. All DEGs were classified into three main GO categories: cellular components, molecular functions, and biological processes. The GO terms related to the response to cold (GO: 0009409), homeostatic process (GO: 0042592), response to temperature stimulus (GO: 0009266), regulation of biological quality (GO: 0065008), response to abiotic stimulus (GO: 0009628), multicellular organismal process (GO: 0032501), response to stress (GO: 0006950), G-protein-coupled receptor protein signaling pathway (GO: 0007186), transcription (GO: 0006350), and cell surface receptor-linked signaling pathway (GO: 0007166) were among the most common significantly enriched terms in the biological process (BP) category of all the three groups of DEGs named above (Figure 5). Within the molecular function (MF) category, catalytic activity (GO: 0003824), protein tyrosine kinase activity (GO: 0004713), G-protein-coupled receptor activity (GO: 0004930), water binding (GO: 0050824), and transcription regulator activity (GO: 0030528) were the most significantly enriched in all the three groups of DEGs (Figure 5). In the cellular component category (CC), integral to membrane (GO: 0016021) and intrinsic to membrane (GO: 0031224) were the common significantly enriched categories (Figure 5). The GO enrichment analysis of the tolerant and sensitive lines (Figure 6) showed that the GO term of metal ion transport (GO: 0030001) was significantly enriched in the tolerant lines (Figure 6A), while the GO terms of apoptosis (GO: 0006915), proteolysis (GO: 0006508), cell death (GO: 0008219), death (GO: 0016265), and programmed cell death (GO: 0012501) were significantly enriched in the sensitive lines (Figure 6B).
3.5. KEGG Pathway Analysis of DEGs
To further explore the biological pathways of the 1656, 779, and 877 DEGs involved in maize response to cold stress, we assessed the number of DEGs in each KEGG pathway. For the 1656 DEGs, the KEGG pathways of lipid metabolism (linoleic acid and arachidonic acid), biosynthesis of other secondary metabolites (isoquinoline alkaloid, betalain, and phenylpropanoid biosynthesis), signal transduction (MAPK signaling pathway-plant), amino acid metabolism (alanine, aspartate, glutamate, glycine, serine, threonine, and phenylalanine metabolism), membrane transport (ABC transporters), terpenoids and polyketide metabolism (carotenoid biosynthesis), and metabolism of other amino acids (glutathione metabolism) were significantly enriched (Figure 7). For the 779 DEGs obtained from the cold-tolerant lines, lipid metabolism (linoleic acid, α-linolenic, ether lipid, arachidonic acid, and glycerophospholipid metabolism), replication and repair (base excision repair), biosynthesis of other secondary metabolites (monobactam and phenylpropanoid biosynthesis), membrane transport (ABC transporters), metabolism of terpenoids and polyketides (carotenoid biosynthesis), and metabolism of other amino acids (glutathione metabolism) were the most significantly enriched pathways (Figure 8A). Within the 887 DEGs from the cold-sensitive lines, the KEGG pathways of biosynthesis of other secondary metabolites (isoquinoline alkaloid biosynthesis), signal transduction (MAPK signaling pathway-plant), transport and catabolism (peroxisome), translation (ribosome), and carbon metabolism were significantly expressed (Figure 8B).
3.6. Dynamic Expression of Signaling and Transcription Factors Genes in Response to Cold Stress
Our GO analysis highlighted a significant number of GO terms related to signaling, such as G-protein-linked receptor protein signaling pathway, cell surface receptor-linked signaling pathway, and protein amino acid phosphorylation (Figure 5 and Figure 6). However, KEGG pathway analysis highlighted the MAPK signaling pathway-plant as one of the most significant pathways (Figure 8B). Stress sensing and signal transduction form crucial adaptive mechanisms in the tolerance of abiotic stresses. Cold stress causes a change in membrane fluidity and cytoskeleton rearrangement, thereby producing signals perceived in the cell membrane by either G-protein-coupled receptors (GPCRs) or osmotic sensors. In this study, 27 DEGs (11 enhanced and 16 suppressed) encoding GPCR were regulated by cold stress (Table S4). Activation of the sensors leads to the generation of reactive oxygen species (ROS), plant hormonal signaling, and cell wall integrity sensing (CWI) [40]. A substantial number of protein kinases, including 21 (11 enhanced and 10 suppressed) leucine-rich repeat protein kinase family proteins, 12 (6 enhanced and 6 suppressed) protein kinase superfamily proteins, 9 (2 enhanced and 7 suppressed) mitogen-activated protein kinases, 7 (2 enhanced and 5 suppressed) S-locus lectin protein kinase family proteins, and 5 wall-associated kinases (WAKs), were regulated by cold stress (Table S4). The high expression of protein kinase suggests that cold stress is primarily regulated at the protein level.
Transcription factors play a vital role in regulating gene expression in response to abiotic stress conditions, such as cold stress. These TFs are DNA-binding proteins that interact with cis-acting elements of genes to activate or inhibit gene transcription, hence regulating plant growth and development and response to the external environment. In the present study, we analyzed for putative TFs in the 1656 common cold-responsive genes based on the 3308 maize TFs and 56 families available in the PlantTFDB 4.0 [41]. Resultantly, 147 TFs, which fell into 32 families, were enriched from our 1656 DEGs (Table 2). ERF (18.37%), WRKY (14.29%), MYB (12.93%), NAC (8.84%), bHLH (8.84%), C2H2 (5.44%) and GRAS (2.72%) were the most abundant TF families (Table 2; Figure 9). However, the expression levels of the TF families MYB, ERF, NAC, WRKY, bHLH, and C2H2 were higher in the sensitive lines than those in the tolerant lines (Figure 9). On the contrary, the TF families MIKC_MADS, CO-like, DBB, Dof, E2F/DP, GATA, GRF, LFY, SBP, Nin-like, TALE, and ZF-HD were only induced in the tolerant lines.
Additionally, GO terms, such as regulation of transcription, transcription, and transcription factor activity, which are related to the regulation of gene expression at the transcription level, were well represented in our GO analysis (Figure 5 and Figure 6). The TF families bHLH, ERF, bZIP, and WRKY were highly regulated by cold stress in the tolerant and sensitive lines (Table S5). As observed above, DEGs encoding the TF families LFY, MIKC_MADS, GRAS, GATA, GRF, E2F/DP, TALE, and Dof were significantly induced by cold stress only in the tolerant lines (Table S5). In-depth research is required to uncover the roles of these TFs in the response of maize to cold stress at the seedling stage. Contrary, DEGs encoding the TF families AP2, B3, HSF, and RAV were all repressed by cold stress in the tolerant lines (Table S5). The tolerant lines were, to a lesser extent, affected by cold stress, which might be why fewer TFs were expressed. Otherwise, our results highlight the critical roles of TFs in the regulation of cold stress response in maize seedlings.
3.7. DEGs Related to “Response to Cold”
The GO terms of “response to cold” and “response to temperature stimulus” were well represented in our GO analysis (Figure 5 and Figure 6). These GO terms contain crucial cold-related genes, which play significant roles in the cold regulation mechanism and may contribute to the cold tolerance of maize seedlings. The expression patterns of DEGs in these GO terms (upregulated or downregulated) may shed light on the difference between the cold response of tolerant and sensitive inbred lines. Signal transduction-related proteins, such as transmembrane proteins, serine/threonine protein kinases, leucine-rich repeat receptor-like proteins, and receptor-like kinases, were regulated by cold stress (Table S6). This highlights the critical roles of the signal transduction network in the activation of various cold-responsive genes. The involvement of TFs in regulating cold stress was highlighted by the significant expression of C2H2, ERF, HD-ZIP, MYB, ERF, MYB, and NAC (Table S6). Antioxidant-related enzymes, such as glutathione peroxidase, thioredoxin, and peroxidase, were regulated by cold stress, suggesting the involvement of detoxification proteins in maize response to cold stress. Carotenoid related genes, such as β-carotene isomerase and β-carotene 3-hydroxylase, further highlighted the role of ABA as a key regulator of cold stress (Table S6). Otherwise, transporter and cell surface proteoglycan-related genes were also observed in this study (Table S6).
3.8. Expression Analysis of Genes Involved in Metabolism, Transport and Functional Impacts of Co-Expressed Gene Hubs
Our KEGG analysis highlighted significant regulation of multiple metabolism pathways, including lipid, carotenoid, ABC transport, and amino acid pathways, during cold stress conditions (Figure 7 and Figure 8). Cold stress activated the phenylpropanoid biosynthetic pathway, an essential way to accumulate various phenolic compounds during cold stress conditions. In total, 14 DEGs encoding this pathway were regulated by cold stress, and 11 of which showed high expression levels in the tolerant lines (Table S7). Moreover, there were six DEGs encoding the metabolism of alanine, aspartate, and glutamate, including β-alanine aminotransferase and glutamate decarboxylase, which are vital genes in osmotic adjustment (Table S7).
Lipid metabolism is a dynamic and complicated process involving lipid biosynthesis, transport, accumulation, turnover, and excretion, regulating the growth and tolerance of plants to different environmental stresses. In this study, 26 lipid metabolism-encoding genes were regulated by cold stress, with all of them except one being enhanced in the tolerant lines (Table S7). Among them were allene oxide synthase (AOS), an essential gene in the biosynthesis of jasmonic acid (JA), secretory phospholipase A2 (PLA2), α/β-Hydrolases, and phospholipase C (PLC), which are critical components of the signaling cascade, and 3-ketoacyl-CoA synthase (KCS) genes, which are related to the biosynthesis of cuticular wax (Table S7). Moreover, six and eight DEGs encoding ABC transporters and carotenoid biosynthesis, respectively, were regulated by cold stress. ABC transporters are associated with phytohormone homeostasis, while carotenoid genes are vital for the biosynthesis of ABA, an essential hormone in cold tolerance. Otherwise, cold stress also regulated glutathione- and peroxisome-related genes, which mediate the harmful effects of ROS. Detailed information about the metabolism genes can be found in Table S7.
WGCNA has been used to dissect the abiotic stress response in plants, thereby highlighting the power of the co-expression networks to provide deep insights into these complex processes. In this study, WGCNA identified multiple significant functional gene hubs related to the cold stress response. A total of 116 critical DEGs that were enriched by GO and KEGG into signaling, TFs, response to cold, and metabolism were defined as hub genes, highlighting the importance of their regulatory impacts on the cold stress response (Figure S2, Table S8). Thus, WGCNA elucidated the higher order relationships between genes based on their co-expression relationships and permitted a robust view of transcriptome organization in the response of cold stress. Collectively, the combination of transcriptome analysis with WGCNA represents an opportunity to achieve a higher resolution analysis that can better predict the most important functional genes that might provide a more robust bio-signature for cold tolerance in maize, thus providing more suitable biomarker candidates for future studies.
3.9. Validation of DEGs by Quantitative Real-Time PCR (qRT-PCR)
To confirm the reliability and validity of the RNA-seq results in maize seedlings, six genes were randomly selected to perform qRT-PCR. Of these, XLOC_018851, Zm00001d002748, and Zm00001d027330 all had a positive log2 fold change between tolerant and sensitive lines, suggesting that their expression was enhanced by cold stress treatment in tolerant lines and declined in sensitive lines. In contrast, XLOC_058556, Zm00001d024324, and Zm00001d024522 portrayed a negative log2 fold change between tolerant and sensitive lines, indicating that cold stress treatment increased their expression in sensitive lines but decreased their expression in tolerant lines. As a result, the fold change ratio used in this paper emphasizes the expression pattern in cold-tolerant lines, while the inverse of that ratio reflects the expression pattern in sensitive lines. We planted B73 and CIMBL116 maize inbred lines, which have previously been reported as cold-sensitive and -tolerant lines, respectively, to validate the expression pattern of these DEGs (Section 2.1 above). These two inbred lines were planted in a cold environment as well as without cold treatment (control), allowing us to compare the expression patterns of our six DEGs before (control) and after cold treatment. Resultantly, cold stress increased the expression of XLOC 018851, Zm00001d027330, and Zm00001d002748 in the tolerant line (CIMBL116), while it decreased the expression of XLOC 058556, Zm00001d024324, and Zm00001d024522 (Figure S3). A reverse expression pattern was observed in the sensitive line (B73) (Figure S3). Thus, the expression trend of our DEGs shown by our RNA-Seq results was in agreement with that shown by the qRT-PCR analyses.
Furthermore, the fold change ratio of the six DEGs between the control and the cold-treated samples was calculated and compared to the fold change obtained from RNA-Seq. As a result, the cold-sensitive line’s RNA-Seq and qRT-PCR results showed an inverse expression pattern (Figure 10A) because the fold change ratio highlighted in this study emphasizes the expression pattern in cold-tolerant lines. However, if you take the inverse of that fold change ratio, which will highlight the expression pattern in cold-sensitive lines, then the RNA-Seq and qRT-PCR of the sensitive line will have a similar expression pattern to that of the tolerant line (Figure 10B). For example, cold stress enhanced the fold change of XLOC_018851, Zm00001d002748, and Zm00001d027330 and declined that of XLOC_058556, Zm00001d024324, and Zm00001d024522 in the tolerant lines (Figure 10B). However, cold stress inversely regulated the fold change of the above-named DEGs in the sensitive lines (Figure 10A). These results validate the authenticity of the DEGs obtained in this study, as the relative fold change in qRT-PCR matched the RNA-Seq results, implying that transcript identification and abundance estimation were remarkably precise. Furthermore, the DEGs identified in this study are universal in maize seedlings of various genetic backgrounds in response to cold stress, according to the qRT-PCR results.
4. Discussion
Maize has surpassed rice and wheat as the world’s most significant cereal crop. However, cold stress affects maize at any stage of development, including the germination, vegetative, and reproductive stages. Screening for cold-tolerant maize cultivars, such as rice, is difficult due to the lack of linkage between cold resistance and developmental periods [42]. Furthermore, cold tolerance is a quantitative trait influenced by the interactions of numerous genes as well as the environment. Nevertheless, RNA-seq research, which evaluates the main genes and regulatory pathways at the transcriptome level, has been widely used to investigate the molecular basis of maize response to abiotic stress [43,44]. To gain a deeper understanding of maize’s cold stress tolerance mechanisms and to develop cold-tolerant maize cultivars, we conducted a comparative transcriptome analysis of 24 cold-tolerant and 22 cold-sensitive maize inbred lines to uncover the common cold-responsive DEGs and pathways. To the best of our knowledge, this is among the first studies to profile a broad set of maize inbred lines at the seedling stage with varying levels of resistance to cold stress in the field. Previous research has usually profiled two samples with differing tolerances to specific environmental stress [45]. As a result, our study provides valuable insight into the in-depth characterization of the molecular responses of maize seedlings to cold stress, because, to date, there remains a scarcity of data on the expression patterns of critical genes and pathways across a gradient of various genotypes with varied responses to specific stress conditions.
Plants perceive abiotic stress via cell wall receptors, which activate internal signaling components through several mechanisms. The cyclic nucleotide–gated channel (CNGC) and glutamate receptors (GLRs) are the primary cell membrane cold receptors reported in plants [46]. These receptors mediate membrane Ca2+ fluxes and produce several endogenous signals responsible for cold tolerance [46]. Multiple G-protein-coupled receptors were shown to be strongly regulated by cold stress in our study (Table S4). The regulation of these receptors may have caused Ca2+ fluxes across membranes and generated multiple signals important for maize’s cold stress response. A G-protein subunit γ gene (Zm00001d032072) and a trihelix GT-2 (Zm00001d027335) were both upregulated in the tolerant lines (Table S4), indicating their role in cold tolerance. In a previous study, transgenic cucumber plants overexpressing CsGG3.2 had increased CBF gene expression and were more tolerant to chilling stress [47]. COLD1 provided chilling tolerance in rice by encoding a signal regulator for guanine nucleotide-binding proteins (G-proteins) on the plasma membrane [48]. A GT2 family (AtGT2L) gene in Arabidopsis has been reported to interact with calcium/calmodulin, allowing plants to withstand cold and salt stress [49]. Two soybean GT2 genes provided abiotic stress resistance to transgenic Arabidopsis plants [50]. Thus, in this study, increased GPCR protein expression may have increased cytosolic calcium levels in the tolerant lines, activating a quick and diverse signaling mechanism responsible for cold acclimation.
Cold stress perceived by membrane sensors triggers an influx of Ca2+ into the cytoplasm, which then generates Ca2+ signatures that induce the activation of downstream genes, such as CBF/COR genes, in the cold signaling pathway [15]. Proteins with an EF-hand domain, such as CaMs, CMLs, CBLs, and CDPKs, act as Ca2+ sensors in response to cold stress [51,52,53]. Following the binding of Ca2+, these proteins interact with other proteins, thereby regulating downstream activities of multiple genes, which provides cold tolerance. In this study, CaMs, CMLs, CBLs, CDPKs, 21 leucine-rich repeat receptor-like kinase proteins (LRR-RLKs), 8 lectin receptor-like kinases (LecRLKs), 12 protein kinases (PKs), and 5 WAKs were regulated by cold stress (Table S4). In Camellia japonica, protein phosphorylation by CDPKs and CIPKs improve cold tolerance [54], whereas in plants, WAKs play an important function in abiotic stress tolerance [55]. Interestingly, TMK1 (Zm00001d033777, Zm00001d007313), CBL10 (Zm00001d023353, Zm00001d010459), RKL1 (Zm00001d048054), NIK3 (Zm00001d018635), and PSKR (Zm00001d018635) displayed a high expression pattern in the tolerant lines. In a previous study on Arabidopsis, CBL10 mediated salt tolerance [56], while RKL1 regulated cold and salicylic acid stresses [57]. Recent research has highlighted the key roles of CBL10 in plant abiotic stress tolerance through the regulation of Na+ and Ca2+ homeostasis [58], whereas under cold stress, TMK1 significantly regulated plant development [59]. MAPKs, including MAPKK and MAPKKK, are key players in cold tolerance [48], where they phosphorylate other kinases and/or various TFs. In this study, 14 DEGs encoding MAPKs were regulated by cold stress (Table S4). Multiple MAPKs have been implicated in improving cold tolerance in rice and Chinese jujube, according to previous research [60,61]. In this study, RBOH (Zm00001d009248), MAP3 (Zm00001d001978), and MKK3 (Zm00001d013510 and Zm00001d028026) had higher expression levels in the tolerant lines (Table S4). In a previous study, two strawberry RBOHs were reported to enhance cold stress tolerance and defense responses [62]. MKK3 was substantially expressed in tolerant lines during a comparative transcriptome investigation of two cotton cultivars with differing responses to cold [63]. The transgenic tobacco over-expressing MAP3K gene demonstrated improved tolerance to a variety of environmental stresses, including cold stress [64]. PP2C adversely controls stress-induced MAPK and SnRK2 protein kinases [4,65]. A previous study on maize has shown that stress-induced proline accumulation and tolerance to hyperosmotic stress were negatively controlled by maize PP2C [66]. This might explain why five PP2C-encoding genes were suppressed by cold stress in the tolerant lines (Table S4). Heptahelical protein 2 (HHP2) (Zm00001d046852), a FASCICLIN-like arabinogalactan (FLA) (Zm00001d016059, Zm00001d052819, and Zm00001d006009), and a membrane-associated kinase (Zm00001d044176) were enhanced in tolerant lines (Table S6). A previous study on Arabidopsis reported that the HHP2-MYB module is involved in integrating cold and abscisic acid signaling to activate the CBF–COR pathway [67]. Cold stress activated a membrane-associated kinase in rice, and FLAs were found to improve banana resistance to low temperatures by activating a cold signal pathway [68,69]. Collectively, the increased expression of Ca2+ signaling protein transcripts in maize tolerant lines activated a complex signaling cascade that regulated various downstream cold tolerance responsive genes. These genes will have significant implications for future research into maize cold tolerance at the seedling stage.
Transcription factors are key regulators of cold stress as they control multiple downstream stress-responsive genes [70]. In this study, 147 TFs belonging to 32 TF families, including AP2/ERF (27), MYB (19), bHLH (13), WRKY (21), C2H2 (8), and NAC (13), were largely regulated by cold stress (Table 2, Figure 5). Similar to our findings, in previous studies, comparative transcriptome analysis of rice, Chinese jujube, and peanut under cold stress conditions identified the above TF families to be the most regulated gene members [60,61,71,72]. These TF genes in their respective families are divided into diverse subgroups based on their specific motif structures, showing that they may perform their specific biological activities under cold stress. Moreover, individual TFs from these families have previously been reported to play a crucial function in controlling plant cold tolerance. In this study, four ERFs, namely ERF38 (Zm00001d002748), ERF022 (Zm00001d048991), DREB26 (Zm00001d018191), and ERF (Zm00001d029669), had higher expression in the tolerant lines (Table S5). They all encode for DREB elements, which enhance cold tolerance by activating CORs. ERF38, ERF022, and DREB26 effectively regulate COR genes and sugar and proline accumulation in Arabidopsis, resulting in abiotic stress tolerance [73,74]. Thus, these genes may have impacted maize cold tolerance by modulating COR genes and osmotic regulators. WRKYs are yet another important class of plant TFs with diverse roles in plant response to cold stress. In this study, 18 WRKY genes were regulated by cold stress (Table S5), with WRKY70 (Zm00001d023332) and WRKY53 (Zm00001d023336) genes showing higher expression in the tolerant lines. In previous studies, the expression of WRKY70 and TcWRKY53 was induced by cold stress in wheat and Thlaspi caerulescens, respectively [75,76]. Moreover, peanut cold stress tolerance was regulated by WRKY70 and WRKY53 via the plant–pathogen interaction pathway [72]. Moreover, MYB4 (Zm00001d041853), bHLH57 (Zm00001d027419), PIF4 (Zm00001d013130), PTF1 (Zm00001d045046), AGL22 (Zm00001d018142), GRF6 (Zm00001d000238), ATHB4 (Zm00001d002754), Scl7 (Zm00001d033834), BTB/POZ (Zm00001d023313, Zm00001d030864), and C2H2 (Zm00001d024883) were all enhanced in the tolerant lines in this study (Tables S5 and S6). SIPIF4 and ZmPTF1 have been attributed to cold and drought stress tolerance in tomatoes and maize, respectively, via the modulation of ABA synthesis and signaling pathways [77,78]. In Arabidopsis thaliana, rice MYB4 was induced by cold stress, which in return transactivated the expression of COR genes, such as RD29A, COR15a, and PAL2 [79]. The over-expression of finger millet bHLH57 caused salinity and drought stress in tobacco [80], while SlGRF6 was significantly regulated by cold stress in Solanum Lycopersicum [81]. C2H2 zinc finger proteins targeted C-repeat/DRE-binding factor genes (CBFs) to provide cold resistance in plants [82], while BTB/POZ significantly accumulated in resistant cotton cultivars during chilling stress conditions [83]. Thus, all these differentially expressed TFs identified in tolerant lines in response to cold stress could represent a useful genetic resource for breeding cold-tolerant crops. Nevertheless, many cold stress-regulating TFs are yet to be identified along with known TFs whose functions are not yet known (Table S5). Understanding the role of the above-mentioned cold-regulating TFs at the molecular level will be pivotal in improving maize performance under cold stress conditions.
Abscisic acid is an essential plant hormone that regulates cold stress via interactions between ABA-dependent and ABA-independent pathways [84]. Moreover, exogenous ABA treatment at normal temperature improves freezing tolerance [85]. In this study, carotenoid biosynthesis genes, such as ZEP (Zm00001d025968), NCED (Zm00001d042076 and Zm00001d018819), β-carotene isomerase (Zm00001d007549 and Zm00001d007560), β-carotene 3-hydroxylase (Zm00001d048469), and ABA 8′-hydroxylase (Zm00001d051554 and Zm00001d050021) were all enhanced by cold stress in the tolerant lines except NCED (Tables S6 and S7). In Arabidopsis, ABA regulates cold tolerance by improving the levels of ABA 8′-hydroxylase [86]. However, Alfalfa-related ZEP is regulated in response to drought, cold, and heat [87]. Moreover, β-carotene hydroxylase regulates the biosynthesis of a carotenoid precursor of abscisic acid called zeaxanthin. A previous report highlighted that a β-carotene hydroxylase gene caused drought and oxidative stress in rice by elevating the synthesis of ABA and xanthophylls [88]. Higher ABA levels induced cold tolerance in herbaceous plants [89]. The elevated expression of ABA-related genes was correlated to cold adaptation in a comparative transcriptome investigation of tea and tobacco plants [90,91]. Therefore, elevated carotenoid biosynthesis genes triggered ABA accumulation, and the transcriptional regulation of ABA-related gene expression is one factor that contributed to cold stress tolerance in maize. Otherwise, the suppression of NCED genes in the tolerant lines reflects the complexity of cold tolerance in plants.
Cold stress triggers rapid and intermittent ROS production that can damage plant cellular components and structures, but ROS also act as signaling molecules for abiotic stress tolerance [92]. Nevertheless, plants deploy a cascade of antioxidant machinery consisting of enzymatic and non-enzymatic defense systems to diminish the deleterious effects of ROS on plant cells [93]. The antioxidant enzymes include SOD, CAT, APX, GPX, GST, and GPX, which can trap and scavenge free radicals [94]. In this study, antioxidant genes, such as SOD (Zm00001d014632), GST (Zm00001d029699, Zm00001d043787, and Zm00001d018809), GPX (Zm00001d029089), PRX (Zm00001d008266, Zm00001d028348, Zm00001d031635, Zm00001d032406 and Zm00001d041827), and APX (Zm00001d024253), were all enhanced by cold stress in the tolerant lines (Table S7). The activities of SOD and GST were reported to reduce cold injury in cold acclimatized wheat [95], while the over-expression of GST in transgenic rice enhanced growth and development at a low temperature [96]. In cassava, chilling and oxidative stress was correlated with increased levels of SOD and APX genes [97]. PRXs play a role in phytoalexin-mediated plant defense and ROS metabolism [98]. Thioredoxin (TRX), however, functions as a redox transmitter [99]. Thus, the enhanced expression of TRX (Zm00001d011352 and Zm00001d007800) genes in the tolerant lines might be crucial in cold acclimation through redox regulation. A previous study reported that soybean TRX genes (Sb03g004670 and Sb06g029490) were significantly regulated in the cold acclimation of different accessions [100]. These findings confirm that ROS-mediated signaling could activate antioxidant enzymes, which might be responsible for imparting cold stress tolerance in maize seedlings.
The phenylpropanoid pathway and its branches of secondary metabolites are activated under cold stress, leading to the accumulation of various phenolic compounds for protection and mechanical support [101]. In this study, 14 phenylpropanoid genes, including CCR (Zm00001d019669, Zm00001d008435), PAL (Zm00001d033286), trans-cinnamate 4-monooxygenase (Zm00001d016471 and Zm00001d032468), and β-glucosidase (Zm00001d028199), and 5 PRXs were significantly enhanced in the tolerant lines (Table S7). Elevated PAL expression stimulates the biosynthesis of phenolic compounds, such as suberin and lignin, which reinforce the cell wall and prevent cell collapse during cold stress. Similarly, enhanced expression of the CCR gene has previously been correlated with lignin biosynthesis under abiotic stress [102]. Trans-cinnamate 4-monooxygenase is positioned at the turning point of phenylalanine, lignin biosynthesis, and flavonoid metabolism, making it one of the key enzymes in the synthesis of lignin and flavonoids [103]. Moreover, PRXs in the presence of H2O2 catalyze the oxidative polymerization of phenols, such as lignin precursors, which improve cell wall rigidity by boosting the cross-linking of cell wall components [104]. This increased suberin or lignin biosynthesis increases the thickness of the cell wall, preventing chilling injury and cell collapse during cold stress [105,106]. The magnitude of lignification in plants is significantly associated with their potential for cold tolerance. Previous studies have shown that β-glucosidase activates several processes, including lignin precursors [107], the release of phytohormones from inactive glycosides, and the activation of several defense compounds essential for abiotic stress tolerance [108]. However, Nicotiana benthamiana plants over-expressing CsBGlu12 displayed abiotic stress tolerance via the accumulation of antioxidant flavanols that played a crucial role in scavenging ROS [109]. Collectively, the upregulation of various transcripts encoding phenylpropanoid pathway genes in the present study indicates enhanced lignification-mediated cold acclimatization in maize seedlings.
A high accumulation of osmoprotectants, such as amino acids, polyamines, quaternary ammonium compounds, and sugars, mediates diverse functions in plant defense mechanisms under varying environmental conditions [110]. Herein, genes encoding β-alanine aminotransferase (Zm00001d038453 and Zm00001d038460) and glutamate decarboxylase (GAD) (Zm00001d031749) were enhanced by cold stress in the tolerant lines (Table S7). The enzyme β-alanine aminotransferase catalyzes the biosynthesis of pyruvate and β-alanine, with the latter product being converted to an essential osmoprotective compound (β-alanine betaine) involved in plant abiotic stress tolerance [111,112]. However, GAD catalyzes the decarboxylation of L-glutamate to form γ-aminobutyric acid (GABA), which accumulates at high concentrations under abiotic stress [113]. Glutamate decarboxylation and GABA metabolism have been reported to play a crucial role in the cold acclimation of wheat and barley [114]. Thus, GABA played a vital role in the cold acclimation of the tolerant lines. Simultaneously, the enhanced expression of β-alanine aminotransferase facilitated a β-alanine-based osmoprotectant in maize during cold stress.
During cold stress, plants adjust their lipid content to retain membrane stability and integrity. Cold tolerance in peanuts was previously found to be associated with changes in membrane modifications, such as lipid metabolism and lipid signaling [115]. In the present study, 26 lipid metabolism genes were regulated by cold stress (Table S7). Among them, PLC (Zm00001d040205), PLA2s (Zm00001d013461, Zm00001d029136), SAD (Zm00001d024273), AOS (Zm00001d028282), α/β-hydrolase (Zm00001d010840 and Zm00001d012147), KCS (Zm00001d046444 and Zm00001d032728), nsLTP (Zm00001d027332), and seven GDSL-like lipases were all enhanced by cold stress in the tolerant lines (Tables S6 and S7). AOS is a critical gene in the synthesis of jasmonic acid (JA), which affects the expression of cold-responsive genes and governs plant defense responses to various abiotic stressors [116]. In Arabidopsis, JA was found to provide cold acclimation [117]. PLC participates in signaling pathways that lead to the activation of the cold response through the CBF pathway [118]. The cold acclimation of spinach (Spinacia oleracea) leaves was found to be associated with the positive roles of PLA2 [119]. Higher expression of a SAD gene is linked to the total amount of unsaturated fatty acids (UFAs), which has been correlated to cold tolerance in tobacco plants [120]. On component change and permeability, the KCS gene catalyzes the biosynthesis of cuticular wax, which acts as a protective barrier against abiotic stresses [121]. GDSL lipases regulate plants’ development and stress response. A previous study by Kong et al. [122] highlighted an essential role of a pepper GDSL lipase gene in regulating abiotic stress tolerance. During cold stress, nsLTPs reduce lipid fluidity and membrane solute permeability, thereby reducing solute diffusion rates across the membrane and preventing osmotic membrane rupture upon thawing [123]. A previous maize study revealed that ZmLTPs have a role in response to cold stress [124]. Overall, our findings highlight the putative association of multiple lipid metabolic components and nsLTP proteins in maize cold tolerance.
Membrane transport systems help maintain cellular homeostasis in environmental stressful situations by redistributing different molecules, such as phytohormones, carbohydrates, and amino acids [125]. These unique roles of plant membrane transport systems may be leveraged to enhance productivity under unfavorable stress conditions as their impact on total plant physiology [126]. The increased expression of numerous transporters and channel protein genes has been reported in the Arabidopsis thaliana response to various abiotic stresses [127] and rice under water stress [128]. In the present study, ABCB1 (Zm00001d024600, Zm00001d025703, Zm00001d026041, Zm00001d045279, and Zm00001d049565), MATE efflux (Zm00001d031730 and Zm00001d032971), and polyol transporter (Zm00001d048774, Zm00001d029645) genes were enhanced by cold stress in the tolerant lines (Tables S6 and S7). Plant ABCB transporters transport molecules, such as plant hormones, lipids, metabolites, contaminants, and defense molecules, which play key roles in abiotic stress tolerance. Various environmental stresses were reported to enhance the expression of distinct ABCB transporters in maize [129]. Polyols play a crucial function in the symplastic and apoplastic transfer of carbon and energy in plants’ response to salt and drought [130]. Transgenic Arabidopsis overexpressing the cotton MATE gene enhanced antioxidant enzyme production and abscisic acid translocation in response to cold, drought, and salt stress [131]. Therefore, the upregulation of various transporters might be associated with cold stress tolerance, the transport of plant secondary metabolites, hormones, and general growth and development in maize.
Network analysis reveals the regulatory impacts of a group of genes on target genes, revealing unique regulatory linkages that add to our understanding of abiotic stress response. The network analysis in this study had 116 nodes and 1907 connections, with 724 activation (positive) and 1183 repression (negative) connections (Figure S2, Table S8). Some of the highly connected positive regulators were found among the 116 nodes, including TFs (bHLH, MYB4, MYB8, GATA4, TALE, and WRKY53) and signaling (respiratory burst oxidase, GPCR, BAM2, RKL1, NIK3, SRF7, SRF8, and PK), antioxidant (peroxiredoxin 6, peroxidase, thioredoxin), and metabolism/biosynthesis regulators (Table S8). The cold induction of TFs regulates a set of other downstream genes. The upregulation of MYB4 (Zm00001d041853) in the cold-tolerant line upregulated 11 DEGs related to signaling, amino acid phosphorylation, TFs, and metabolism (Table S8). In a previous study on Arabidopsis thaliana, the over-expression of OsMYB4 increased cold and chilling tolerance by increasing the expression of COR genes, such as RD29A, COR15a, and PAL2 [79]. In this study, the upregulation of the WRKY53 (Zm00001d023336) gene in the tolerant line increased the expression of 10 additional DEGs involved in signaling, amino acid phosphorylation, and transcription factors (Table S8). In a previous study, WRKY53 was highly increased in Arabidopsis thaliana under cold stress, where it interacted with hub genes, such as mitogen-activated protein kinase 3 (MPK3), WRKY33, and WRKY40, all of which are implicated in plant defense [72]. As a result, WRKY53 could have influenced maize cold tolerance via the plant–pathogen interaction route. Differential expression levels of the 116 DEGs that make up the nodes in our cold studies show that various genes respond to cold stress in different ways and have varied biological functions. These DEGs could be intriguing candidates to investigate during maize seedling cold stress responses. Further research in this regard can look into the molecular specifics of any potential role of these DEGs in the adaptation of maize seedlings to cold stress.
Non-coding RNAs (ncRNAs), such as lncRNAs, have been discovered to regulate plant response to abiotic and biotic stress by controlling the expression of functional genes [132]. A previous study on cassava reported 318 lncRNAs responsive to cold and drought stress [133], while the expression of 2088 lncRNAs in grapevine (Vitis vinifera L.) was induced by cold stress [134]. In this study, the expression of 337 putative lncRNAs was regulated by cold stress (Figure 4). Thus, these lncRNAs might have modulated multiple biological processes involved in cold acclimation in maize by influencing gene expression at the transcriptional, post-transcriptional, and epigenetic levels. Otherwise, there is considerable interest in lncRNAs among molecular biologists, plant breeders, and geneticists, and our research may have identified crucial candidates that can aid in the development of cold-tolerant cultivars. However, more research is needed to fully understand the link between these lncRNAs and cold stress.
We developed a molecular model for cold stress tolerance in maize seedlings, as shown in Figure 11, based on our main findings of the critical cold-responsive DEGs and their associated pathways, as well as the numerous published citations in the present study.
5. Conclusions
In this study, we comprehensively compared the leaf transcriptome and phenotypic response of the maize population (24 cold-tolerant and 22 cold-sensitive lines) in response to cold stress at the seedling stage. Resultantly, the tolerant lines maintained a strong vigor with higher survival rates, while the majority of the sensitive line seedlings died and had yellow spots on the leaves. Using the RNA-seq-based approach, 2237 (1656 annotated and 581 unannotated) DEGs were identified between the tolerant and sensitive samples. Moreover, cold stress significantly enhanced 779 and 887 DEGs in the tolerant and sensitive lines, respectively. Functional annotation was carried out on the three categories (1656, 779, and 887) of DEGs. In the tolerant lines, genes associated with GPCR, Ca2+ signaling, protein kinases, and ROS may have played a significant role in rapid sensing and signaling, whereas genes associated with hormones, such as ABA and JA, may have played a role in signaling and cross-talk between diverse stimuli. The activation of TFs and their binding to promoter sites of certain genes results in activation of stress-responsive genes. The upregulation of several antioxidants, transport, and osmoprotectants suggested protection of the cellular machinery, whereas genes associated with the phenylpropanoid biosynthesis pathway might be involved in providing mechanical support and protection against cold stress. Moreover, genes involved in lipid metabolism may play a critical role in cold stress resistance via membrane modification. Thus, the networks involved in the function of the genes and regulators of the above-named pathways are critical in the cold acclimation of maize at the seedling stage. Moreover, genes related to ribosome, proteolysis, peroxisome, and carbon metabolism were significantly enriched in the sensitive lines. The unannotated DEGs were more inclined in the functions of long non-coding RNAs. Our findings indicate the involvement of plant signaling, transcription factors, and protective mechanisms in the molecular mechanisms underlying cold acclimation in maize at the seedling stage. Otherwise, the essential genes and metabolic pathways identified in this study may serve as valuable genetic resources or selection targets for the genetic engineering of cold-tolerant maize cultivars.
Conceptualization, J.L., H.W. and J.K.W.; experiments Y.S. (Ying Sun), Y.S. (Yingly Sun), C.L. and J.K.W. Analysis of the data and writing of the original draft J.K.W.; revising and editing the manuscript H.W., J.L., C.Z., Y.S. (Ying Sun), Y.S. (Yingly Sun), Q.C., C.L. and J.K.W. All authors have read and agreed to the published version of the manuscript.
This research was funded by the National Key Research and Development Program of China, grant numbers 2018YFD1000702/2018YFD1000700 and 2016YFD0101202; National Natural Science Foundation of China, grant number 31900452; key research and development program of Xinjiang province, China, grant number 2018B01006-4; and the Agricultural Science and Technology Innovation Program of CAAS.
Not applicable.
Not applicable.
This manuscript includes the essential data either as figures or as
We thank Junjie Fu (Institute of Crop Science, Chinese Academy of Agricultural Sciences) for assisting us with B73 and CIMBL116 seeds.
The authors declare no conflict of interest.
| bHLH | Basic helix-loop-helix |
| DEGs | Differentially expressed genes |
| MYB | Myeloblastosis |
| KEGG | Kyoto Encyclopedia of Genes and Genomes |
| GO | Gene Ontology |
| PCA | principal component analysis |
| GPCR | G-protein coupled receptor |
| JA | jasmonic acid |
| PPs | protein phosphatase |
| WAK | wall-associated kinase |
| PAL | phenylalanine ammonia lyase |
| KCS | ketoacyl-CoA synthase |
| GAD | glutamate decarboxylase |
| SOD | Superoxide dismutase |
| APX | Ascorbate peroxidase |
| GLR | Glutamate receptor |
| ROS | Reactive oxygen species |
| ABA | Abscisic acid |
| PK | Protein kinase |
| PRX | Peroxidases |
| CDPK | Calcium dependent protein kinase |
| CaMs | Calmodulin |
| CBLs | Calcineurin B-like |
| CMLs | CaM-related proteins |
| MAPK | Mitogen-activated protein kinase |
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Perbesar gambar ini.Figure 1. Maize seedling performance under cold stress. The performances were based on seedling survival rates and visual observations of the seedling leaves. The white and black dotted lines represent the cold-tolerant and cold-sensitive lines, respectively. The majority of the tolerant lines maintained a strong vigor with higher survival rates, while most of the sensitive line died, and their leaves had yellowish spots.
Perbesar gambar ini.Figure 2. Principal component analysis (PCA) of pairwise genetic distance. The grouping of the 46 maize inbred line populations is indicated using blue (tolerant) and orange (sensitive). The proportion of variance captured is given as a percentage for both the first and second principal components (PC1 and PC2).
Perbesar gambar ini.Figure 3. Heatmap showing the clustering analysis of 2237 common cold-responsive genes. The x-axis represents different maize samples. The purple color denotes the cold-tolerant lines while the pink color represents the cold-sensitive lines. The red and blue color scale represents high and low expressions, respectively.
Perbesar gambar ini.Figure 4. Analysis of 581 unannotated cold-responsive DEGs. CPC generated 271 coding and 310 long non-coding RNAs. The ORF method generated 123 long non-coding RNAs. In total, 337 putative long non-coding RNAs were generated by the two methods.
Perbesar gambar ini.Figure 5. Gene Ontology (GO) enrichment analysis of the 1656 common cold-responsive genes. The GO terms shown here are the top-most biological process (BP), molecular function (MF), and cellular component (CC) categories.
Perbesar gambar ini.Figure 6. Gene Ontology (GO) enrichment analysis. (A) The 779 DEGs highly enriched in the tolerant lines. (B) The 877 DEGs highly enriched in the sensitive lines. The GO terms shown here are the top-most biological process (BP), molecular function (MF), and cellular component (CC) categories.
Perbesar gambar ini.Figure 7. KEGG pathway enrichment analysis of the 1656 common cold-responsive genes. The experimental comparisons were based on the hypergeometric test, while the significance of the enrichment of the KEGG pathway was based on q value, q < 0.05. The color gradient represents the size of the q value; the color ranges from green to red, and the closer to green, the smaller the q value and the higher the significant degree of enrichment of the corresponding KEGG pathway. The “rich factor” represents the percentage of DEGs to total genes in a given pathway.
Perbesar gambar ini.Figure 8. KEGG pathway enrichment analysis. (A) The 779 DEGs highly enriched in the tolerant lines. (B) The 877 DEGs highly enriched in the sensitive lines. The experimental comparisons were based on the hypergeometric test, while the significance of the enrichment of the KEGG pathway was based on q value, q < 0.05. The color gradient represents the size of the q value; the color ranges from green to red, and the closer to green, the smaller the q value and the higher the significant degree of enrichment of the corresponding KEGG pathway. The “rich factor” represents the percentage of DEGs to total genes in a given pathway.
Perbesar gambar ini.Figure 9. Area map of the various transcription factor families regulated by cold stress. The x-axis represents the TF families, while the number of genes per family is represented by the y-axis. The blue and orange colors indicate the TFs regulated in the tolerant and sensitive lines, respectively.
Perbesar gambar ini.Figure 10. Validation of RNA-Seq results by qRT-PCR. Each log2 fold change calculated from qRT-PCR was compared with the log2 fold change of the RNA-Seq data. (A) The inverse expression patterns of the RNA-Seq and qRT-PCR results from the cold-sensitive line (B73) is because the fold change ratio highlighted in this study emphasizes the expression pattern in cold-tolerant lines. However, if you take the inverse of that fold change ratio, which will highlight the expression pattern in cold-sensitive lines, then the RNA-Seq and qRT-PCR expression trends would be identical. (B) The cold-tolerant line’s (CIMBL116) RNA-Seq and qRT-PCR results show a similar expression trend. Orange and blue bars represent RNA-Seq and qRT-PCR data, respectively.
Perbesar gambar ini.Figure 11. The schematic molecular model describing the signaling pathways involved in the acquisition of cold tolerance in maize seedlings. The model was constructed based on the main cold response components identified in this report, as well as plant abiotic stress pathway schemes previously described. The downward pointing arrows represent the sequence of events in cold tolerance in maize, from stress signal perception to acclimation mechanisms. Abbreviation key: GPCR, G-protein-coupled receptor; GLR, glutamate receptor; ROS, reactive oxygen species; ABA, abscisic acid; JA, jasmonic acid; CDPKs, calcium-dependent protein kinases; MAPK, mitogen-activated protein kinase; PPs, protein phosphatase; WAKs, wall-associated kinases; PKs, protein kinases; PRX, peroxidases; PAL, phenylalanine ammonia lyase; KCS, ketoacyl-CoA synthase; GAD, glutamate decarboxylase; SOD, superoxide dismutase; APX, ascorbate peroxidase; TRX, thioredoxin; GST, glutathione transferase; GPX, glutathione peroxidase; ZEP, zeaxanthin epoxidase.
Transcription factor gene families identified from 2237 DEGs in maize under cold stress.
| TF Family | DEGs in the Tolerant Line | DEGs in the Sensitive Line | Total |
|---|---|---|---|
| B3 | 0 | 2 | 2 |
| GRF | 1 | 0 | 1 |
| ERF | 3 | 25 | 28 |
| DBB | 1 | 0 | 1 |
| Dof | 1 | 0 | 1 |
| HSF | 0 | 3 | 3 |
| LBD | 2 | 1 | 3 |
| LFY | 1 | 0 | 1 |
| MYB | 7 | 12 | 19 |
| NAC | 2 | 11 | 13 |
| RAV | 0 | 1 | 1 |
| SBP | 1 | 0 | 1 |
| WOX | 0 | 1 | 1 |
| TCP | 0 | 1 | 1 |
| E2F/DP | 1 | 0 | 1 |
| GATA | 1 | 0 | 1 |
| GRAS | 2 | 2 | 4 |
| bZIP | 1 | 2 | 3 |
| C2H2 | 1 | 7 | 8 |
| bHLH | 8 | 5 | 13 |
| Nin-like | 1 | 0 | 1 |
| NF-YC | 0 | 1 | 1 |
| ZF-HD | 1 | 0 | 1 |
| G2-like | 1 | 1 | 2 |
| CO-like | 1 | 0 | 1 |
| HD-ZIP | 2 | 2 | 4 |
| WRKY | 2 | 19 | 21 |
| TALE | 1 | 0 | 1 |
| Trihelix | 1 | 1 | 2 |
| MIKC_MADS | 3 | 0 | 3 |
| MYB_related | 1 | 3 | 4 |
| Total | 47 | 100 | 147 |
List of top 20 most regulated DEGs by cold stress.
| Locus ID | Gene ID | log2 Fold Change (T/S) | p_Value | Chr | Start | End | Annotation |
|---|---|---|---|---|---|---|---|
| XLOC_000983 | Zm00001d027606 | 23.73473512 | 6.81 × 10−31 | Chr1 | 8,915,719 | 8,918,057 | transmembrane protein |
| XLOC_056725 | - | −23.7343301 | 9.38 × 10−29 | Chr7 | 170,162,017 | 170,164,579 | - |
| XLOC_046285 | Zm00001d017622 | −2.670016539 | 9.74 × 10−25 | Chr5 | 201,997,628 | 201,998,691 | OSJNBa0088A01 |
| XLOC_018249 | - | 24.01063195 | 1.05 × 10−24 | Chr2 | 137,103,173 | 137,104,433 | - |
| XLOC_058213 | Zm00001d021006 | −1.554984072 | 3.70 × 10−22 | Chr7 | 140,181,294 | 140,183,018 | MTD1 |
| XLOC_034912 | - | 23.0239833 | 5.49 × 10−21 | Chr4 | 176,582,912 | 176,584,805 | - |
| XLOC_056365 | Zm00001d021394 | 22.79156651 | 7.11 × 10−21 | Chr7 | 150,891,109 | 150,895,395 | hypothetical protein |
| XLOC_067464 | - | 20.05873411 | 6.49 × 10−20 | Chr9 | 20,669,455 | 20,671,879 | - |
| XLOC_049980 | - | 24.72008389 | 2.60 × 10−17 | Chr6 | 114,599,197 | 114,601,220 | - |
| XLOC_018351 | - | 24.4739764 | 5.35 × 10−17 | Chr2 | 150,920,460 | 150,923,282 | - |
| XLOC_055724 | - | 22.96632545 | 9.06 × 10−17 | Chr7 | 95,897,276 | 95,897,694 | - |
| XLOC_018159 | Zm00001d004620 | 22.6740887 | 1.68 × 10−16 | Chr2 | 122,442,335 | 122,447,585 | uncharacterized protein |
| XLOC_004771 | - | −23.94951263 | 2.29 × 10−16 | Chr1 | 1,487,983 | 1,491,882 | - |
| XLOC_007853 | Zm00001d033411 | −23.88106934 | 2.77 × 10−16 | Chr1 | 262,279,726 | 262,300,105 | hypothetical protein |
| XLOC_005508 | Zm00001d028673 | 23.87942745 | 2.96 × 10−16 | Chr1 | 42,581,898 | 42,586,482 | small nuclear protein G |
| XLOC_036989 | - | −23.84216727 | 3.11 × 10−16 | Chr4 | 108,230,941 | 108,236,238 | - |
| XLOC_014865 | Zm00001d025968 | 23.6089741 | 6.37 × 10−16 | Chr1 | 134,994,735 | 134,998,645 | hypothetical protein |
| XLOC_018011 | Zm00001d004338 | 23.58304546 | 6.85 × 10−16 | Chr2 | 104,459,685 | 104,460,441 | hypothetical protein |
| XLOC_046112 | Zm00001d017287 | −4.317481518 | 2.92 × 10−13 | Chr5 | 191,750,113 | 191,750,625 | uncharacterized protein |
| XLOC_027574 | Zm00001d043525 | −1.350259753 | 4.09 × 10−13 | Chr3 | 202,741,852 | 202,743,157 | oxidative stress 3 |
Comparison of DEGs between tolerant (T) and sensitive (S) inbred lines after cold stress. The p-value is less than 0.05.
Supplementary Materials
The following are available online at
References
1. United States Department of Agriculture (USDA). World Agricultural Supply and Demand Estimates. 2020; Available online: https://downloads.usda.library.cornell.edu/usda-esmis/files/5q47rn72z/z890sd52n/hd76sk58s/production.pdf (accessed on 12 May 2020).
2. Gong, F.L.; Yang, F.; Tai, X.; Wang, W. “Omics” of maize stress response for sustainable food production: Opportunities and challenges. Omics A J. Integr. Biol.; 2014; 18, pp. 714-732. [DOI: https://dx.doi.org/10.1089/omi.2014.0125]
3. Stocker, T.F.; Qin, D.; Plattner, G.K.; Tignor, M.M.; Allen, S.K.; Boschung, J.; Nauels, A.; Xia, Y.; Bex, V.; Midgley, P.M. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of IPCC the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014.
4. Krasensky, J.; Jonak, C. Drought, salt, and temperature stress-induced metabolic rearrangements and regulatory networks. J. Exp. Bot.; 2012; 63, pp. 1593-1608. [DOI: https://dx.doi.org/10.1093/jxb/err460] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22291134]
5. Peleg, Z.; Blumwald, E. Hormone balance and abiotic stress tolerance in crop plants. Curr. Opin. Plant Biol.; 2011; 14, pp. 290-295. [DOI: https://dx.doi.org/10.1016/j.pbi.2011.02.001]
6. Ma, S.Q.; Xi, Z.X.; Wang, Q. Risk evaluation of cold damage to corn in Northeast China. J. Nat. Disasters; 2003; 12, pp. 137-141.
7. Greaves, J.A. Improving suboptimal temperature tolerance in maize-the search for variation. J. Exp. Bot.; 1996; 47, pp. 307-323. [DOI: https://dx.doi.org/10.1093/jxb/47.3.307]
8. Marocco, A.; Lorenzoni, C.; Fracheboud, Y. Chilling stress in maize. Maydica; 2005; 50, pp. 571-580.
9. Sobkowiak, A.; Jończyk, M.; Adamczyk, J.; Szczepanik, J.; Solecka, D.; Kuciara, I.; Hetmańczyk, K.; Trzcinska-Danielewicz, T.; Grzybowski, M.; Skoneczny, M. Molecular foundations of chilling-tolerance of modern maize. BMC Genom.; 2016; 17, 125. [DOI: https://dx.doi.org/10.1186/s12864-016-2453-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26897027]
10. Leipner, J.; Stamp, P. Chilling Stress in Maize Seedlings. Handbook of Maize: Its Biology; Springer: New York, NY, USA, 2009; pp. 291-310.
11. Sowiński, P.; Rudzińska-Langwald, A.; Dalbiak, A.; Sowińska, A. Assimilate export from leaves of chilling-treated seedlings of maize. The path to vein. Plant Physiol. Biochem.; 2001; 39, pp. 881-889. [DOI: https://dx.doi.org/10.1016/S0981-9428(01)01302-X]
12. Zhang, B.; Yang, L.; Li, Y. Comparison of physiological and biochemical characteristics related to cold resistance in sugarcane under field conditions. Acta Agron. Sin.; 2011; 37, pp. 496-505. [DOI: https://dx.doi.org/10.3724/SP.J.1006.2011.00496]
13. Virdi, A.S.; Singh, S.; Singh, P. Abiotic stress responses in plants: Roles of calmodulin-regulated proteins. Front. Plant Sci.; 2015; 6, 809. [DOI: https://dx.doi.org/10.3389/fpls.2015.00809]
14. Chinnusamy, V.; Zhu, J.; Zhu, J.K. Cold stress regulation of gene expression in plants. Trends Plant Sci.; 2007; 12, pp. 444-451. [DOI: https://dx.doi.org/10.1016/j.tplants.2007.07.002] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17855156]
15. Chinnusamy, V.; Zhu, J.K.; Sunkar, R. Gene Regulation During Cold Stress Acclimation in Plants. Plant Stress Tolerance; Springer: Totowa, NJ, USA, 2010; Volume 639, pp. 39-55.
16. Zhao, C.; Wang, P.; Si, T.; Hsu, C.C.; Wang, L.; Zayed, O.; Yu, Z.; Zhu, Y.; Dong, J.; Tao, W.A. MAP kinase cascades regulate the cold response by modulating ICE1 protein stability. Dev. Cell; 2017; 43, pp. 618-629. [DOI: https://dx.doi.org/10.1016/j.devcel.2017.09.024] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29056551]
17. Shi, Y.; Ding, Y.; Yang, S. Cold signal transduction and its interplay with phytohormones during cold acclimation. Plant Cell Physiol.; 2015; 56, pp. 7-15. [DOI: https://dx.doi.org/10.1093/pcp/pcu115]
18. Sah, S.K.; Reddy, K.R.; Li, J. Abscisic acid and abiotic stress tolerance in crop plants. Front. Plant Sci.; 2016; 7, 571. [DOI: https://dx.doi.org/10.3389/fpls.2016.00571]
19. Tarkowski, Ł.P.; Van den Ende, W. Cold tolerance triggered by soluble sugars: A multifaceted countermeasure. Front. Plant Sci.; 2015; 6, 203. [DOI: https://dx.doi.org/10.3389/fpls.2015.00203] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25926837]
20. Arnholdt-Schmitt, B.; Costa, J.H.; de Melo, D.F. AOX—A functional marker for efficient cell reprogramming under stress?. Trends Plant Sci.; 2006; 11, pp. 281-287. [DOI: https://dx.doi.org/10.1016/j.tplants.2006.05.001] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16713324]
21. Al-Whaibi, M.H. Plant heat-shock proteins: A mini review. J. King Saud Univ. Sci.; 2011; 23, pp. 139-150. [DOI: https://dx.doi.org/10.1016/j.jksus.2010.06.022]
22. Alejandro, S.; Lee, Y.; Tohge, T.; Sudre, D.; Osorio, S.; Park, J.; Bovet, L.; Lee, Y.; Geldner, N.; Fernie, A.R. AtABCG29 is a monolignol transporter involved in lignin biosynthesis. Curr. Biol.; 2012; 22, pp. 1207-1212. [DOI: https://dx.doi.org/10.1016/j.cub.2012.04.064]
23. Wang, B.; Guo, G.; Wang, C.; Lin, Y.; Wang, X.; Zhao, M.; Guo, Y.; He, M.; Zhang, Y.; Pan, L. Survey of the transcriptome of Aspergillus oryzae via massively parallel mRNA sequencing. Nucleic Acids Res.; 2010; 38, pp. 5075-5087. [DOI: https://dx.doi.org/10.1093/nar/gkq256] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20392818]
24. Zhang, Z.; Huang, R. Enhanced tolerance to freezing in tobacco and tomato overexpressing transcription factor TERF2/LeERF2 is modulated by ethylene biosynthesis. Plant Mol. Biol.; 2010; 73, pp. 241-249. [DOI: https://dx.doi.org/10.1007/s11103-010-9609-4]
25. Jończyk, M.; Sobkowiak, A.; Trzcinska-Danielewicz, J.; Sowiński, P. Chromatin-Level Differences Elucidate Potential Determinants of Contrasting Levels of Cold Sensitivity in Maize Lines. Plant Mol. Biol. Report.; 2021; 39, pp. 335-350. [DOI: https://dx.doi.org/10.1007/s11105-020-01254-7]
26. Grzybowski, M.; Adamczyk, J.; Jończyk, M.; Sobkowiak, A.; Szczepanik, J.; Frankiewicz, K.; Sowiński, P. Increased photosensitivity at early growth as a possible mechanism of maize adaptation to cold springs. J. Exp. Bot.; 2019; 70, pp. 2887-2904. [DOI: https://dx.doi.org/10.1093/jxb/erz096] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30825373]
27. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics; 2014; 30, pp. 2114-2120. [DOI: https://dx.doi.org/10.1093/bioinformatics/btu170]
28. Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods; 2015; 12, pp. 357-360. [DOI: https://dx.doi.org/10.1038/nmeth.3317] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25751142]
29. Roberts, A.; Pimentel, H.; Trapnell, C.; Pachter, L. Identification of novel transcripts in annotated genomes using RNA-Seq. Bioinformatics; 2011; 27, pp. 2325-2329. [DOI: https://dx.doi.org/10.1093/bioinformatics/btr355]
30. Anders, S.; Pyl, P.T.; Huber, W. HTSeq—A Python framework to work with high-throughput sequencing data. Bioinformatics; 2015; 31, pp. 166-169. [DOI: https://dx.doi.org/10.1093/bioinformatics/btu638] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25260700]
31. Anders, S.; Huber, W. Differential expression analysis for sequence count data. Nat. Preced.; 2010; [DOI: https://dx.doi.org/10.1038/npre.2010.4282.2]
32. Li, L.; Eichten, S.R.; Shimizu, R.; Petsch, K.; Yeh, C.T.; Wu, W.; Chettoor, A.M.; Givan, S.A.; Cole, R.A.; Fowler, J.E. Genome-wide discovery and characterization of maize long non-coding RNAs. Genome Biol.; 2014; 15, R40. [DOI: https://dx.doi.org/10.1186/gb-2014-15-2-r40] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24576388]
33. Kong, L.; Zhang, Y.; Ye, Z.Q.; Liu, X.Q.; Zhao, S.Q.; Wei, L.; Gao, G. CPC: Assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res.; 2007; 35, pp. W345-W349. [DOI: https://dx.doi.org/10.1093/nar/gkm391]
34. Tian, T.; Liu, Y.; Yan, H.; You, Q.; Yi, X.; Du, Z.; Xu, W.; Su, Z. agriGO v2. 0: A GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res.; 2017; 45, pp. W122-W129. [DOI: https://dx.doi.org/10.1093/nar/gkx382]
35. Supek, F.; Bošnjak, M.; Škunca, N.; Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE; 2011; 6, e21800. [DOI: https://dx.doi.org/10.1371/journal.pone.0021800] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21789182]
36. Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res.; 2000; 28, pp. 27-30. [DOI: https://dx.doi.org/10.1093/nar/28.1.27] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10592173]
37. Xie, C.; Mao, X.; Huang, J.; Ding, Y.; Wu, J.; Dong, S.; Kong, L.; Gao, G.; Li, C.Y.; Wei, L. KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res.; 2011; 39, pp. W316-W322. [DOI: https://dx.doi.org/10.1093/nar/gkr483] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21715386]
38. Zhang, Y.; Zhu, L.; Xue, J.; Yang, J.; Hu, H.; Cui, J.; Xu, J. Selection and Verification of Appropriate Reference Genes for Expression Normalization in Cryptomeria fortunei Under Abiotic Stress and Hormone Treatments. Genes; 2021; 12, 791. [DOI: https://dx.doi.org/10.3390/genes12060791] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34064253]
39. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods; 2001; 25, pp. 402-408. [DOI: https://dx.doi.org/10.1006/meth.2001.1262] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11846609]
40. Novaković, L.; Guo, T.; Bacic, A.; Sampathkumar, A.; Johnson, K.L. Hitting the wall—Sensing and signaling pathways involved in plant cell wall remodeling in response to abiotic stress. Plants; 2018; 7, 89. [DOI: https://dx.doi.org/10.3390/plants7040089] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30360552]
41. Jin, J.; Tian, F.; Yang, D.C.; Meng, Y.Q.; Kong, L.; Luo, J.; Gao, G. PlantTFDB 4.0: Toward a central hub for transcription factors and regulatory interactions in plants. Nucleic Acids Res.; 2016; 45, gkw982. [DOI: https://dx.doi.org/10.1093/nar/gkw982] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27924042]
42. Cruz, R.P.D.; Sperotto, R.A.; Cargnelutti, D.; Adamski, J.M.; de FreitasTerra, T.; Fett, J.P. Avoiding damage and achieving cold tolerance in rice plants. Food Energy Secur.; 2013; 2, pp. 96-119. [DOI: https://dx.doi.org/10.1002/fes3.25]
43. Li, Y.; Wang, X.; Li, Y.; Zhang, Y.; Gou, Z.; Qi, X.; Zhang, J. Transcriptomic Analysis Revealed the Common and Divergent Responses of Maize Seedling Leaves to Cold and Heat Stresses. Genes; 2020; 11, 881. [DOI: https://dx.doi.org/10.3390/genes11080881]
44. Ji, C.Y.; Kim, H.S.; Lee, C.J.; Kim, S.E.; Lee, H.U.; Nam, S.S.; Kwak, S.S. Comparative transcriptome profiling of tuberous roots of two sweet potato lines with contrasting low temperature tolerance during storage. Gene; 2020; 727, 144244. [DOI: https://dx.doi.org/10.1016/j.gene.2019.144244]
45. Yu, T.; Zhang, J.; Cao, J.; Cai, Q.; Li, X.; Sun, Y.; Duan, Y. Leaf transcriptomic response mediated by cold stress in two maize inbred lines with contrasting tolerance levels. Genomics; 2021; 113, pp. 782-794. [DOI: https://dx.doi.org/10.1016/j.ygeno.2021.01.018] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33516847]
46. Zhu, J.K. Abiotic stress signaling and responses in plants. Cell; 2016; 167, pp. 313-324. [DOI: https://dx.doi.org/10.1016/j.cell.2016.08.029] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27716505]
47. Bai, L.; Liu, Y.; Mu, Y.; Anwar, A.; He, C.; Yan, Y.; Li, Y.; Yu, X. Heterotrimeric G-protein γ subunit CsGG3. 2 positively regulates the expression of CBF genes and chilling tolerance in cucumber. Front. Plant Sci.; 2018; 9, 488. [DOI: https://dx.doi.org/10.3389/fpls.2018.00488]
48. Guo, X.; Liu, D.; Chong, K. Cold signaling in plants: Insights into mechanisms and regulation. J. Integr. Plant Biol.; 2018; 60, pp. 745-756. [DOI: https://dx.doi.org/10.1111/jipb.12706] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30094919]
49. Xi, J.; Qiu, Y.; Du, L.; Poovaiah, B.W. Plant-specific trihelix transcription factor AtGT2L interacts with calcium/calmodulin and responds to cold and salt stresses. Plant Sci.; 2012; 185, pp. 274-280. [DOI: https://dx.doi.org/10.1016/j.plantsci.2011.11.013]
50. Xie, Z.M.; Zou, H.F.; Lei, G.; Wei, W.; Zhou, Q.Y.; Niu, C.F.; Chen, S.Y. Soybean Trihelix transcription factors GmGT-2A and GmGT-2B improve plant tolerance to abiotic stresses in transgenic Arabidopsis. PLoS ONE; 2009; 4, e6898. [DOI: https://dx.doi.org/10.1371/journal.pone.0006898]
51. Sanders, D.; Pelloux, J.; Brownlee, C.; Harper, J.F. Calcium at the crossroads of signaling. Plant Cell; 2002; 14, pp. S401-S417. [DOI: https://dx.doi.org/10.1105/tpc.002899] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12045291]
52. Luan, S.; Kudla, J.; Rodriguez-Concepcion, M.; Yalovsky, S.; Gruissem, W. Calmodulins and calcineurin B–like proteins: Calcium sensors for specific signal response coupling in plants. Plant Cell; 2002; 14, pp. S389-S400. [DOI: https://dx.doi.org/10.1105/tpc.001115]
53. Pareek, A.; Khurana, A.; Sharma, K.; Kumar, R. An overview of signaling regulons during cold stress tolerance in plants. Curr. Genom.; 2017; 18, pp. 498-511. [DOI: https://dx.doi.org/10.2174/1389202918666170228141345]
54. Li, Q.; Lei, S.; Du, K.; Li, L.; Pang, X.; Wang, Z.; Xu, L. RNA-seq based transcriptomic analysis uncovers α-linolenic acid and jasmonic acid biosynthesis pathways respond to cold acclimation in Camellia japonica. Sci. Rep.; 2016; 6, 36463. [DOI: https://dx.doi.org/10.1038/srep36463]
55. Wu, X.; Bacic, A.; Johnson, K.L.; Humphries, J. The role of brachypodium distachyon wall-associated kinases (WAKs) in cell expansion and stress responses. Cells; 2020; 9, 2478. [DOI: https://dx.doi.org/10.3390/cells9112478] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33202612]
56. Kim, B.G.; Waadt, R.; Cheong, Y.H.; Pandey, G.K.; Dominguez-Solis, J.R.; Schültke, S.; Lee, S.C.; Kudla, J.; Luan, S. The calcium sensor CBL10 mediates salt tolerance by regulating ion homeostasis in Arabidopsis. Plant J.; 2007; 52, pp. 473-484. [DOI: https://dx.doi.org/10.1111/j.1365-313X.2007.03249.x]
57. Lee, B.H.; Henderson, D.A.; Zhu, J.K. The Arabidopsis cold-responsive transcriptome and its regulation by ICE1. Plant Cell; 2005; 17, pp. 3155-3175. [DOI: https://dx.doi.org/10.1105/tpc.105.035568] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16214899]
58. Martínez, F.A.P.; Estrada, Y.; Flores, F.B.; Ortíz-Atienza, A.; Lozano, R.; Egea, I. The Ca2+ Sensor Calcineurin B-like Protein 10 in Plants: Emerging New Crucial Roles for Plant Abiotic Stress Tolerance. Front. Plant Sci.; 2020; 11, 2155.
59. Kamal, M.M.; Ishikawa, S.; Takahashi, F.; Suzuki, K.; Kamo, M.; Umezawa, T.; Uemura, M. Large-Scale Phosphoproteomic Study of Arabidopsis Membrane Proteins Reveals Early Signaling Events in Response to Cold. Int. J. Mol. Sci.; 2020; 21, 8631. [DOI: https://dx.doi.org/10.3390/ijms21228631]
60. Kumar, M.; Gho, Y.S.; Jung, K.H.; Kim, S.R. Genome-wide identification and analysis of genes, conserved between japonica and indica rice cultivars that respond to low-temperature stress at the vegetative growth stage. Front. Plant Sci.; 2017; 8, 1120. [DOI: https://dx.doi.org/10.3389/fpls.2017.01120]
61. Zhou, H.; He, Y.; Zhu, Y.; Li, M.; Song, S.; Bo, W.; Pang, X. Comparative transcriptome profiling reveals cold stress responsiveness in two contrasting Chinese jujube cultivars. BMC Plant Biol.; 2020; 20, 240. [DOI: https://dx.doi.org/10.1186/s12870-020-02450-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32460709]
62. Zhang, Y.; Li, Y.; He, Y.; Hu, W.; Zhang, Y.; Wang, X.; Tang, H. Identification of NADPH oxidase family members associated with cold stress in strawberry. FEBS Open Bio; 2018; 8, pp. 593-605. [DOI: https://dx.doi.org/10.1002/2211-5463.12393] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29632812]
63. Cheng, G.; Zhang, L.; Wang, H.; Lu, J.; Wei, H.; Yu, S. Transcriptomic Profiling of Young Cotyledons Response to Chilling Stress in Two Contrasting Cotton (Gossypium hirsutum L.) Genotypes at the Seedling Stage. Int. J. Mol. Sci.; 2020; 21, 5095. [DOI: https://dx.doi.org/10.3390/ijms21145095] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32707667]
64. Kovtun, Y.; Chiu, W.L.; Tena, G.; Sheen, J. Functional analysis of oxidative stress-activated mitogen-activated protein kinase cascade in plants. Proc. Natl. Acad. Sci. USA; 2000; 97, pp. 2940-2945. [DOI: https://dx.doi.org/10.1073/pnas.97.6.2940]
65. Schweighofer, A.; Hirt, H.; Meskiene, I. Plant PP2C phosphatases: Emerging functions in stress signaling. Trends Plant Sci.; 2004; 9, pp. 236-243. [DOI: https://dx.doi.org/10.1016/j.tplants.2004.03.007] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15130549]
66. Liu, L.; Hu, X.; Song, J.; Zong, X.; Li, D.; Li, D. Over-expression of a Zea mays L. protein phosphatase 2C gene (ZmPP2C) in Arabidopsis thaliana decreases tolerance to salt and drought. J. Plant Physiol.; 2009; 166, pp. 531-542. [DOI: https://dx.doi.org/10.1016/j.jplph.2008.07.008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18930563]
67. Lee, H.G.; Seo, P.J. The MYB 96–HHP module integrates cold and abscisic acid signaling to activate the CBF–COR pathway in Arabidopsis. Plant J.; 2015; 82, pp. 962-977. [DOI: https://dx.doi.org/10.1111/tpj.12866] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25912720]
68. Meng, J.; Hu, B.; Yi, G.; Li, X.; Chen, H.; Wang, Y.; Xu, C. Genome-wide analyses of banana fasciclin-like AGP genes and their differential expression under low-temperature stress in chilling sensitive and tolerant cultivars. Plant Cell Rep.; 2020; 39, pp. 693-708. [DOI: https://dx.doi.org/10.1007/s00299-020-02524-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32128627]
69. Martín, M.L.; Busconi, L. A rice membrane-bound calcium-dependent protein kinase is activated in response to low temperature. Plant Physiol.; 2001; 125, pp. 1442-1449. [DOI: https://dx.doi.org/10.1104/pp.125.3.1442] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11244123]
70. Agarwal, M.; Hao, Y.; Kapoor, A.; Dong, C.H.; Fujii, H.; Zheng, X.; Zhu, J.K. A R2R3 type MYB transcription factor is involved in the cold regulation of CBF genes and in acquired freezing tolerance. J. Biol. Chem.; 2006; 281, pp. 37636-37645. [DOI: https://dx.doi.org/10.1074/jbc.M605895200] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17015446]
71. Pradhan, S.K.; Pandit, E.; Nayak, D.K.; Behera, L.; Mohapatra, T. Genes, pathways and transcription factors involved in seedling stage chilling stress tolerance in indica rice through RNA-Seq analysis. BMC Plant Biol.; 2019; 19, 352. [DOI: https://dx.doi.org/10.1186/s12870-019-1922-8]
72. Jiang, C.; Zhang, H.; Ren, J.; Dong, J.; Zhao, X.; Wang, X.; Yu, H. Comparative Transcriptome-Based Mining and Expression Profiling of Transcription Factors Related to Cold Tolerance in Peanut. Int. J. Mol. Sci.; 2020; 21, 1921. [DOI: https://dx.doi.org/10.3390/ijms21061921] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32168930]
73. Övernäs, E.; Sundås Larsson, A.; Söderman, E. Two AP2 Transcription Factors, AtERF38 and AtERF39, Have Similar Function but are Differently Regulated in ABA and Stress Responses; Uppsala University Publications: Upsala, Sweden, 2010.
74. Krishnaswamy, S.; Verma, S.; Rahman, M.H.; Kav, N.N. Functional characterization of four APETALA2-family genes (RAP2. 6, RAP2. 6L, DREB19 and DREB26) in Arabidopsis. Plant Mol. Biol.; 2011; 75, pp. 107-127. [DOI: https://dx.doi.org/10.1007/s11103-010-9711-7]
75. Wang, J.; Tao, F.; An, F.; Zou, Y.; Tian, W.; Chen, X.; Xu, X.; Hu, X. Wheat transcription factor TaWRKY70 is positively involved in high-temperature seedling plant resistance to Puccinia striiformis f. sp. tritici. Mol. Plant Pathol.; 2017; 18, pp. 649-661. [DOI: https://dx.doi.org/10.1111/mpp.12425]
76. Wei, W.; Zhang, Y.; Han, L.; Guan, Z.; Chai, T. A novel WRKY transcriptional factor from Thlaspi caerulescens negatively regulates the osmotic stress tolerance of transgenic tobacco. Plant Cell Rep.; 2008; 27, pp. 795-803. [DOI: https://dx.doi.org/10.1007/s00299-007-0499-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18183400]
77. Wang, F.; Chen, X.; Dong, S.; Jiang, X.; Wang, L.; Yu, J.; Zhou, Y. Crosstalk of PIF4 and DELLA modulates CBF transcript and hormone homeostasis in cold response in tomato. Plant Biotechnol. J.; 2020; 18, pp. 1041-1055. [DOI: https://dx.doi.org/10.1111/pbi.13272]
78. Li, Z.; Liu, C.; Zhang, Y.; Wang, B.; Ran, Q.; Zhang, J. The bHLH family member ZmPTF1 regulates drought tolerance in maize by promoting root development and abscisic acid synthesis. J. Exp. Bot.; 2019; 70, pp. 5471-5486. [DOI: https://dx.doi.org/10.1093/jxb/erz307] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31267122]
79. Vannini, C.; Locatelli, F.; Bracale, M.; Magnani, E.; Marsoni, M.; Osnato, M.; Mattana, M.; Baldoni, E.; Coraggio, I. Overexpression of the rice Osmyb4 gene increases chilling and freezing tolerance of Arabidopsis thaliana plants. Plant J.; 2004; 37, pp. 115-127. [DOI: https://dx.doi.org/10.1046/j.1365-313X.2003.01938.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/14675437]
80. Babitha, K.; Vemanna, R.S.; Nataraja, K.N.; Udayakumar, M. Overexpression of EcbHLH57 transcription factor from Eleusine coracana L. in tobacco confers tolerance to salt, oxidative and drought stress. PLoS ONE; 2015; 10, e0137098. [DOI: https://dx.doi.org/10.1371/journal.pone.0137098] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26366726]
81. Khatun, K.; Robin, A.H.K.; Park, J.I.; Nath, U.K.; Kim, C.K.; Lim, K.B.; Nou, I.S.; Chung, M.Y. Molecular characterization and expression profiling of tomato GRF transcription factor family genes in response to abiotic stresses and phytohormones. Int. J. Mol. Sci.; 2017; 18, 1056. [DOI: https://dx.doi.org/10.3390/ijms18051056]
82. Vogel, J.T.; Zarka, D.G.; Van Buskirk, H.A.; Fowler, S.G.; Thomashow, M.F. Roles of the CBF2 and ZAT12 transcription factors in configuring the low temperature transcriptome of Arabidopsis. Plant J.; 2005; 41, pp. 195-211. [DOI: https://dx.doi.org/10.1111/j.1365-313X.2004.02288.x]
83. Tang, S.; Xian, Y.; Wang, F.; Luo, C.; Song, W.; Xie, S.; Liu, H. Comparative transcriptome analysis of leaves during early stages of chilling stress in two different chilling-tolerant brown-fiber cotton cultivars. PLoS ONE; 2021; 16, e0246801. [DOI: https://dx.doi.org/10.1371/journal.pone.0246801]
84. Agarwal, P.K.; Jha, B. Transcription factors in plants and ABA dependent and independent abiotic stress signaling. Biol. Plant; 2010; 54, pp. 201-212. [DOI: https://dx.doi.org/10.1007/s10535-010-0038-7]
85. Chen, J.; Tian, Q.; Pang, T.; Jiang, L.; Wu, R.; Xia, X.; Yin, W. Deep-sequencing transcriptome analysis of low temperature perception in a desert tree, Populus euphratica. BMC Genom.; 2014; 15, 326. [DOI: https://dx.doi.org/10.1186/1471-2164-15-326] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24884892]
86. Baron, K.N.; Schroeder, D.F.; Stasolla, C. Transcriptional response of abscisic acid (ABA) metabolism and transport to cold and heat stress applied at the reproductive stage of development in Arabidopsis thaliana. Plant Sci.; 2012; 188, pp. 48-59. [DOI: https://dx.doi.org/10.1016/j.plantsci.2012.03.001] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22525244]
87. Zhang, Z.; Wang, Y.; Chang, L.; Zhang, T.; An, J.; Liu, Y.; Cao, Y.; Zhao, X.; Sha, X.; Hu, T. MsZEP, a novel zeaxanthin epoxidase gene from alfalfa (Medicago sativa), confers drought and salt tolerance in transgenic tobacco. Plant Cell Rep.; 2016; 35, pp. 439-453. [DOI: https://dx.doi.org/10.1007/s00299-015-1895-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26573680]
88. Du, H.; Wang, N.; Cui, F.; Li, X.; Xiao, J.; Xiong, L. Characterization of the β-carotene hydroxylase gene DSM2 conferring drought and oxidative stress resistance by increasing xanthophylls and abscisic acid synthesis in rice. Plant Physiol.; 2010; 154, pp. 1304-1318. [DOI: https://dx.doi.org/10.1104/pp.110.163741]
89. Nayyar, H.; Chander, S. Protective effects of polyamines against oxidative stress induced by water and cold stress in chickpea. J. Agron. Crop Sci.; 2004; 190, pp. 355-365. [DOI: https://dx.doi.org/10.1111/j.1439-037X.2004.00106.x]
90. Li, Y.; Wang, X.; Ban, Q.; Zhu, X.; Jiang, C.; Wei, C.; Bennetzen, J.L. Comparative transcriptomic analysis reveals gene expression associated with cold adaptation in the tea plant Camellia sinensis. BMC Genom.; 2019; 20, 624. [DOI: https://dx.doi.org/10.1186/s12864-019-5988-3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31366321]
91. Liu, T.; Li, C.X.; Zhong, J.; Shu, D.; Luo, D.; Li, Z.M.; Ma, X.R. Exogenous 1′, 4′-trans-Diol-ABA Induces Stress Tolerance by Affecting the Level of Gene Expression in Tobacco (Nicotiana tabacum L.). Int. J. Mol. Sci.; 2021; 22, 2555. [DOI: https://dx.doi.org/10.3390/ijms22052555] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33806336]
92. Filiz, E.; Ozyigit, I.I.; Saracoglu, I.A.; Uras, M.E.; Sen, U.; Yalcin, B. Abiotic stress-induced regulation of antioxidant genes in different Arabidopsis ecotypes: Microarray data evaluation. Biotechnol. Biotechnol. Equip.; 2019; 33, pp. 128-143. [DOI: https://dx.doi.org/10.1080/13102818.2018.1556120]
93. Baek, K.H.; Skinner, D.Z. Alteration of antioxidant enzyme gene expression during cold acclimation of near-isogenic wheat lines. Plant Sci.; 2003; 165, pp. 1221-1227. [DOI: https://dx.doi.org/10.1016/S0168-9452(03)00329-7]
94. Rezaie, R.; Mandoulakani, B.A.; Fattahi, M. Cold stress changes antioxidant defense system, phenylpropanoid contents and expression of genes involved in their biosynthesis in Ocimum basilicum L. Sci. Rep.; 2020; 10, pp. 1-10. [DOI: https://dx.doi.org/10.1038/s41598-020-62090-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32210286]
95. Wang, R.; Ma, J.; Zhang, Q.; Wu, C.; Zhao, H.; Wu, Y.; He, G. Genome-wide identification and expression profiling of glutathione transferase gene family under multiple stresses and hormone treatments in wheat (Triticum aestivum L.). BMC Genom.; 2019; 20, 986. [DOI: https://dx.doi.org/10.1186/s12864-019-6374-x]
96. Takesawa, T.; Ito, M.; Kanzaki, H.; Kameya, N.; Nakamura, I. Overexpression of ζ glutathione S-transferase in transgenic rice enhances germination and growth at low temperature. Mol. Breed.; 2002; 9, pp. 93-101. [DOI: https://dx.doi.org/10.1023/A:1026718308155]
97. Xu, J.; Yang, J.; Duan, X.; Jiang, Y.; Zhang, P. Increased expression of native cytosolic Cu/Zn superoxide dismutase and ascorbate peroxidase improves tolerance to oxidative and chilling stresses in cassava (Manihot esculenta Crantz). BMC Plant Biol.; 2014; 14, 208. [DOI: https://dx.doi.org/10.1186/s12870-014-0208-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25091029]
98. Almagro, L.; Gómez Ros, L.V.; Belchi-Navarro, S.; Bru, R.; Ros Barceló, A.; Pedreno, M.A. Class III peroxidases in plant defence reactions. J. Exp. Bot.; 2009; 60, pp. 377-390. [DOI: https://dx.doi.org/10.1093/jxb/ern277] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19073963]
99. Meyer, Y.; Siala, W.; Bashandy, T.; Riondet, C.; Vignols, F.; Reichheld, J.P. Glutaredoxins and thioredoxins in plants. Biochim. Biophys. Acta Mol. Cell Res.; 2008; 1783, pp. 589-600. [DOI: https://dx.doi.org/10.1016/j.bbamcr.2007.10.017]
100. Ortiz, D.; Hu, J.; Salas Fernandez, M.G. Genetic architecture of photosynthesis in Sorghum bicolor under non-stress and cold stress conditions. J. Exp. Bot.; 2017; 68, pp. 4545-4557. [DOI: https://dx.doi.org/10.1093/jxb/erx276] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28981780]
101. Khaledian, Y.; Maali-Amiri, R.; Talei, A. Phenylpropanoid and antioxidant changes in chickpea plants during cold stress. Russ. J. Plant Physiol.; 2015; 62, pp. 772-778. [DOI: https://dx.doi.org/10.1134/S1021443715060102]
102. Srivastava, S.; Vishwakarma, R.K.; Arafat, Y.A.; Gupta, S.K.; Khan, B.M. Abiotic stress induces change in Cinnamoyl CoA Reductase (CCR) protein abundance and lignin deposition in developing seedlings of Leucaena leucocephala. Physiol. Mol. Biol. Plants; 2015; 21, pp. 197-205. [DOI: https://dx.doi.org/10.1007/s12298-015-0289-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25931776]
103. Chun-yan, F. Research progress on 4-coumarate: Coenzyme A ligase (4CL) of plants. Mod. Agric. Sci. Technol.; 2010; 8, pp. 39-40.
104. Pandey, V.; Awasthi, M.; Singh, S.; Tiwari, S.; Dwivedi, U. A comprehensive review on function and application of plant peroxidases. Biochem. Anal. Biochem.; 2017; 6, 308. [DOI: https://dx.doi.org/10.4172/2161-1009.1000308]
105. Naikoo, M.I.; Dar, M.I.; Raghib, M.F.; Jaleel, H.; Ahmad, B.; Raina, A.; Khan, F.A.; Naushin, F. Role and regulation of plants phenolics in abiotic stress tolerance: An overview. Plant Signal. Mol.; 2019; pp. 157-168. [DOI: https://dx.doi.org/10.1016/B978-0-12-816451-8.00009-5]
106. Vogt, T. Phenylpropanoid biosynthesis. Mol. Plant; 2010; 3, pp. 2-20. [DOI: https://dx.doi.org/10.1093/mp/ssp106]
107. Dharmawardhana, D.P.; Ellis, B.E.; Carlson, J.E. A [β]-Glucosidase from lodge pole pine xylem specific for the lignin precursor coniferin. Plant Physiol.; 1995; 107, pp. 331-339. [DOI: https://dx.doi.org/10.1104/pp.107.2.331] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/7724669]
108. Kleczkowski, K.; Schell, J.; Bandur, R. Phytohormones conjugates: Nature and function. Crit. Rev. Plant. Sci.; 1995; 14, pp. 283-298. [DOI: https://dx.doi.org/10.1080/07352689509382361]
109. Baba, S.A.; Vishwakarma, R.A.; Ashraf, N. Functional characterization of CsBGlu12, a β-glucosidase from Crocus sativus, provides insights into its role in abiotic stress through accumulation of antioxidant flavonols. J. Biol. Chem.; 2017; 292, pp. 4700-4713. [DOI: https://dx.doi.org/10.1074/jbc.M116.762161] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28154174]
110. Slama, I.; Abdelly, C.; Bouchereau, A.; Flowers, T.; Savoure, A. Diversity, distribution and roles of osmoprotective compounds accumulated in halophytes under abiotic stress. Ann. Bot.; 2015; 115, pp. 433-447. [DOI: https://dx.doi.org/10.1093/aob/mcu239] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25564467]
111. Hanson, A.D.; Rathinasabapathi, B.; Chamberlin, B.; Gage, D.A. Comparative physiological evidence that β-alanine betaine and choline-O-sulfate act as compatible osmolytes in halophytic Limonium species. Plant Physiol.; 1991; 97, pp. 1199-1205. [DOI: https://dx.doi.org/10.1104/pp.97.3.1199] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16668509]
112. Rocha, M.; Licausi, F.; Araujo, W.L.; Nunes-Nesi, A.; Sodek, L.; Fernie, A.R.; van Dongen, J.T. Glycolysis and the tricarboxylic acid cycle are linked by alanine aminotransferase during hypoxia induced by waterlogging of Lotus japonicus. Plant Physiol.; 2010; 152, pp. 1501-1513. [DOI: https://dx.doi.org/10.1104/pp.109.150045] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20089769]
113. Renault, H.; Roussel, V.; El Amrani, A.; Arzel, M.; Renault, D.; Bouchereau, A.; Deleu, C. The Arabidopsis pop2-1 mutant reveals the involvement of GABA transaminase in salt stress tolerance. BMC Plant Biol.; 2010; 10, 20. [DOI: https://dx.doi.org/10.1186/1471-2229-10-20]
114. Mazzucotelli, E.; Tartari, A.; Cattivelli, L.; Forlani, G. Metabolism of γ-aminobutyric acid during cold acclimation and freezing and its relationship to frost tolerance in barley and wheat. J. Exp. Bot.; 2006; 57, pp. 3755-3766. [DOI: https://dx.doi.org/10.1093/jxb/erl141] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16997899]
115. Zhang, H.; Dong, J.I.; Wang, J. Research progress in membrane lipid metabolism and molecular mechanism in peanut cold tolerance. Front. Plant Sci.; 2019; 10, 838. [DOI: https://dx.doi.org/10.3389/fpls.2019.00838]
116. Hu, Y.; Jiang, Y.; Han, X.; Wang, H.; Pan, J.; Yu, D. Jasmonate regulates leaf senescence and tolerance to cold stress: Crosstalk with other phytohormones. J. Exp. Bot.; 2017; 68, pp. 1361-1369. [DOI: https://dx.doi.org/10.1093/jxb/erx004] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28201612]
117. Hu, Y.; Jiang, L.; Wang, F.; Yu, D. Jasmonate regulates the inducer of CBF expression–c-repeat binding factor/DRE binding factor1 cascade and freezing tolerance in Arabidopsis. Plant Cell; 2013; 25, pp. 2907-2924. [DOI: https://dx.doi.org/10.1105/tpc.113.112631]
118. Vergnolle, C.; Vaultier, M.N.; Taconnat, L.; Renou, J.P.; Kader, J.C.; Zachowski, A.; Ruelland, E. The cold-induced early activation of phospholipase C and D pathways determines the response of two distinct clusters of genes in Arabidopsis cell suspensions. Plant Physiol.; 2005; 139, pp. 1217-1233. [DOI: https://dx.doi.org/10.1104/pp.105.068171]
119. Gustavsson, M.H.; Sommarin, M. Characterisation of a plasma membrane-associated phospholipase A2 activity increased in response to cold acclimation. J. Plant Physiol.; 2002; 159, pp. 1219-1227. [DOI: https://dx.doi.org/10.1078/0176-1617-00724]
120. Craig, W.; Lenzi, P.; Scotti, N.; De Palma, M.; Saggese, P.; Carbone, V.; Curran, N.M.; Magee, A.M.; Medgyesy, P.; Kavanagh, T.A. Transplastomic tobacco plants expressing a fatty acid desaturase gene exhibit altered fatty acid profiles and improved cold tolerance. Transgenic Res.; 2008; 17, pp. 769-782. [DOI: https://dx.doi.org/10.1007/s11248-008-9164-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18214708]
121. Bernard, A.; Joubès, J. Arabidopsis Cuticular waxes: Advances in synthesis, export and regulation. Prog. Lipid Res.; 2013; 52, pp. 110-129. [DOI: https://dx.doi.org/10.1016/j.plipres.2012.10.002]
122. Hong, J.K.; Choi, H.W.; Hwang, I.S.; Kim, D.S.; Kim, N.H.; Choi, D.S.; Kim, Y.J.; Hwang, B.K. Function of a novel GDSL-type pepper lipase gene, CaGLIP1, in disease susceptibility and abiotic stress tolerance. Planta; 2008; 227, pp. 539-558. [DOI: https://dx.doi.org/10.1007/s00425-007-0637-5]
123. Hincha, D.K.; Neukamm, B.; Sror, H.A.; Sieg, F.; Weckwarth, W.; Rückels, M.; Schmitt, J.M. Cabbage cryoprotectin is a member of the nonspecific plant lipid transfer protein gene family. Plant Physiol.; 2001; 125, pp. 835-846. [DOI: https://dx.doi.org/10.1104/pp.125.2.835] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11161041]
124. Wei, K.; Zhong, X. Non-specific lipid transfer proteins in maize. BMC Plant Biol.; 2014; 14, pp. 1-18. [DOI: https://dx.doi.org/10.1186/s12870-014-0281-8]
125. Osakabe, Y.; Yamaguchi-Shinozaki, K.; Shinozaki, K.; Tran, L.S.P. ABA control of plant macroelement membrane transport systems in response to water deficit and high salinity. New Phytol.; 2014; 202, pp. 35-49. [DOI: https://dx.doi.org/10.1111/nph.12613]
126. Schroeder, J.I.; Delhaize, E.; Frommer, W.B.; Guerinot, M.L.; Harrison, M.J.; Herrera-Estrella, L.; Sanders, D. Using membrane transporters to improve crops for sustainable food production. Nature; 2013; 497, pp. 60-66. [DOI: https://dx.doi.org/10.1038/nature11909]
127. Seki, M.; Narusaka, M.; Ishida, J.; Nanjo, T.; Fujita, M.; Oono, Y.; Shinozaki, K. Monitoring the expression profiles of 7000 Arabidopsis genes under drought, cold and high-salinity stresses using a full-length cDNA microarray. Plant J.; 2002; 31, pp. 279-292. [DOI: https://dx.doi.org/10.1046/j.1365-313X.2002.01359.x]
128. Chai, C.; Subudhi, P.K. Comprehensive analysis and expression profiling of the OsLAX and OsABCB auxin transporter gene families in rice (Oryza sativa) under phytohormone stimuli and abiotic stresses. Front. Plant Sci.; 2016; 7, 593. [DOI: https://dx.doi.org/10.3389/fpls.2016.00593]
129. Yue, R.; Tie, S.; Sun, T.; Zhang, L.; Yang, Y.; Qi, J.; Yan, S.; Han, X.; Wang, H.; Shen, C. Genome-wide identification and expression profiling analysis of ZmPIN, ZmPILS, ZmLAX and ZmABCB auxin transporter gene families in maize (Zea mays L.) under various abiotic stresses. PLoS ONE; 2015; 10, e0118751. [DOI: https://dx.doi.org/10.1371/journal.pone.0118751] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25742625]
130. Noiraud, N.; Maurousset, L.; Lemoine, R. Transport of polyols in higher plants. Plant Physiol. Biochem.; 2001; 39, pp. 717-728. [DOI: https://dx.doi.org/10.1016/S0981-9428(01)01292-X]
131. Lu, P.; Magwanga, R.O.; Kirungu, J.N.; Hu, Y.; Dong, Q.; Cai, X.; Liu, F. Overexpression of cotton a DTX/MATE gene enhances drought, salt, and cold stress tolerance in transgenic Arabidopsis. Front. Plant Sci.; 2019; 10, 299. [DOI: https://dx.doi.org/10.3389/fpls.2019.00299] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30930923]
132. Di, C.; Yuan, J.; Wu, Y.; Li, J.; Lin, H.; Hu, L.; Zhang, T.; Qi, Y.; Gerstein, M.B.; Guo, Y. Characterization of stress-responsive lncRNAs in Arabidopsis thaliana by integrating expression, epigenetic and structural features. Plant J.; 2014; 80, pp. 848-861. [DOI: https://dx.doi.org/10.1111/tpj.12679] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25256571]
133. Li, S.; Yu, X.; Lei, N.; Cheng, Z.; Zhao, P.; He, Y.; Wang, W.; Peng, M. Genome-wide identification and functional prediction of cold and/or drought-responsive lncRNAs in cassava. Sci. Rep.; 2017; 7, 45981. [DOI: https://dx.doi.org/10.1038/srep45981]
134. Wang, P.; Dai, L.; Ai, J.; Wang, Y.; Ren, F. Identification and functional prediction of cold-related long non-coding RNA (lncRNA) in grapevine. Sci. Rep.; 2019; 9, 6638. [DOI: https://dx.doi.org/10.1038/s41598-019-43269-5]
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; Cai, Quan 2 ; Sun, Ying 1 ; Sun, Yinglu 3 ; Li, Congcong 1 ; Zhang, Chunyi 1 ; Liu, Jun 3
; Wang, Huan 4
1 Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
2 Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China;
3 National Key Facility for Crop Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
4 Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China;