Tissue-specific transcriptomic analysis reveals the meolecular mechanisms responsive to cold stress in Poa crymophila, and development of EST-SSR markers linked to cold tolerance candidate genes

doi:10.21203/rs.3.rs-4601141/v1

Download PDF

Research Article

Tissue-specific transcriptomic analysis reveals the meolecular mechanisms responsive to cold stress in Poa crymophila, and development of EST-SSR markers linked to cold tolerance candidate genes

https://doi.org/10.21203/rs.3.rs-4601141/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background Poa crymophila is a perennial, cold-tolerant, native grass species, widely distributed in the Qinghai-Tibet Plateau. However, the molecular mechanism behind the cold stress tolerance and the role of key regulatory genes and pathways of P. crymophila are poorly understood as of. Therefore, in this study, based on the screening and evaluation of cold resistance of four Poa species, the cold resistance mechanism of P. crymophila’s roots, stems, and leaves and its cold resistance candidate genes were investigated through physiological and transcriptomic analyses.

Results Results of the present study suggested that the cold resistance of the four Poa species was in the following order: P. crymophila > P. botryoides > P. pratensis var. anceps > P. pratensis. Cold stress significantly changed the physiological characteristics of roots, stems, and leaves of P. crymophila in this study. In addition, the transcriptome results showed that 4434, 8793, and 14942 differentially expressed genes (DEGs) were identified in roots, stems, and leaves, respectively; however, 464 DEGs were commonly identified in these three tissues. KEGG enrichment analysis showed that these DEGs were mainly enriched in the phenylpropanoid biosynthesis pathway (roots), photosynthesis pathway (stems and leaves), circadian rhythm-plant pathway (stems and leaves), starch and sucrose metabolism pathway (roots, stems, and leaves), and galactose metabolism pathway (roots, stems, and leaves). A total of 392 candidate genes involved in Ca²⁺ signaling, ROS scavenging system, hormones, circadian clock, photosynthesis, and transcription factors (TFs) were identified in P. crymophila. Weighted gene co-expression network analysis (WGCNA) identified nine hub genes that may be involved in P. crymophila cold response. A total of 200 candidate gene-based EST-SSRs were developed and characterized. Twenty-nine polymorphic EST-SSRs primers were finally used to study genetic diversity of 40 individuals from four Poa species with different cold resistance characteristics. UPGMA cluster and STRUCTURE analysis showed that the 40 Poa individuals were clustered into three major groups, individual plant with similar cold resistance tended to group together. Notably, markers P37 (PcGA2ox3) and P148 (PcERF013) could distinguish P. crymophila from P. pratensis var. anceps, P. pratensis, and P. botryoides.

Conclusions This study provides new insights into the molecular mechanisms underlying the cold tolerance of P. crymophila, and also lays a foundation for molecular marker-assisted selection for cold tolerance improvement in Poa species.

Poa crymophila

cold stresses

physiology

transcriptome analysis

weighted gene co-expression network analysis (WGCNA)

molecular marker-assisted selection (MAS)

genetic diversity

Global climate change imposes serious threats to crop growth and yields worldwide [1]. Cold is a common abiotic stress that has become a major environmental factor affecting plant life cycle and geographic distribution as well as crop yields by influencing plant growth and development [2]. The immense impact of cold stress on global agricultural productivity is undeniable, with cold damage costing approximately $2 billion annually which includes about 13 million hectares of rice each year [3, 4]. Therefore, understanding the cold resistance mechanisms of plants to improve their cold tolerance is essential to maintain sustainable agricultural production.

Plants have evolved multiple methods to respond to cold stress during long-term environmental adaptations [5]. Cold stress signals are first sensed by the outer parts of the plant through a highly organized signaling system that further generates a series of response mechanisms in vivo [6]. These responses mainly include modification of the membrane lipid composition, synthesis of osmoprotectants, activating the ROS (Reactive oxygen species) scavenging system, signaling cascade, and changes in the expression profile of cold-responsive genes [5, 7]. ICE (Inducer of CBF Expression) is a MYC-type bHLH transcription factor, including ICE1 and ICE2, which induces and positively regulates the expression of the CBFs, which enhances cold tolerance in Arabidopsis thaliana [8, 9]. Additionally, CBFs/DREBs, which include CBF1/DREB1B, CBF2/DREB1C, and CBF3/DREB1A, are transcription factors (TFs) of the AP2/ERF family that regulate the expression of CORs (Cold-regulated genes) [10, 11]. These genes encode osmoprotectants, protein kinases, lipids, hormones, and proteins, and directly participate in responding to cold stress to enhance plant tolerance [2, 12]. Consequently, ICE, CBFs, and CORs constitute a crucial signaling pathway, the ICE-CBF-COR cascade which mitigates cold stress in plants [13].

In addition, plant responses to cold stress are regulated by other factors such as Ca²⁺ signaling, hormones, circadian rhythms, and photosynthesis [7, 12]. The Ca²⁺ signaling pathway is considered as most common low-temperature response pathway in plants, including CaM (Calmodulin), CML (Calmodulin-like protein), CBL (Calcineurin B-like protein), CIPK (CBL interacting protein kinase), CPK/CDPK (Calcium-dependent protein kinase), CAMTA (Calmodulin-binding transcription activator), which interact and regulate the CBF/DREB1 genes in response to cold stress [5, 12]. Moreover, Ca²⁺ is also associated with ROS signaling, triggering ROS and/or ABA to directly activate the MAPK cascade [2]. ROS induces the expression of TFs through the accumulation of plant hormones, and TFs alleviate the damage of ROS by regulating the expression of ROS responsive genes [2, 14]. Recent studies suggest that photosynthesis and circadian rhythms pathways are crucial pathways that play an important role in cold stress tolerance [15]. CBF genes are not only induced by cold stress but also regulated by the circadian clock and light signals [16].

Besides, in the recent years, transcriptome studies have provided a new perspective for researching the plant response to cold stress [3, 17]. A previous study reported that the plant circadian pathway was crucial for Helictotrichon virescens’s response to cold stress; the expression of DEGs encoding LHY and HY5 was strongly up-regulated during cold stress [17]. Transcriptome analysis revealed that Ca²⁺, ROS, hormone signaling, the circadian clock, and photosynthetic antenna proteins may significantly contribute to cold tolerance in Vicia sativa L. through CBF-dependent or independent transcriptional pathways [18]. Transcriptome analysis of Triticum aestivum L. at 4°C low-temperature showed that starch and sucrose metabolism, glutathione metabolism, and plant hormone signal transduction pathways were significantly enriched, and many genes belonging to the bHLH, MYB, NAC, WRKY, and ERF TF families were highly expressed [19]. Additionally, the WGCNA identified several key genes involved in seedlings development under cold stress [19]. Thus, previous studies demonstrated that transcriptome analysis could unveil the molecular mechanisms behind plant stress tolerance and explore the potential functions of crucial genes. Moreover, SSR or EST-SSR molecular markers, developed based on candidate genes, are intimately associated with key agronomic traits. These markers serve as valuable tools to enhance genetic improvement and facilitate molecular marker-assisted selection in plants [20, 21].

The Qinghai-Tibet Plateau is known as the Roof of the World, the Third Pole and the Water Tower of Asia, which is an important shield for China’s ecological security, as well as an important animal husbandry production base [22]. However, the Qinghai-Tibet Plateau has a typical plateau continental climate, with a large temperature difference between day and night, low annual average temperature, and a short frost-free period which shows the average monthly temperature is between − 20°C and − 10°C [23, 24]. The extreme climatic conditions of the Qinghai-Tibet Plateau impose selective pressures on the evolution of phenotypic and physiological characteristics. Many native species including Elymus nutans, E. sibicicus, P. pratensis and P. crymophila are widely distributed and utilized species on the Qinghai-Tibet Plateau due to their strong environmental adaptability and long-term natural domestication [23, 25, 26]. Of these native grasses, P. crymophila is widely distributed in humid grasslands, alpine grasslands, forest margins, hillsides, valleys, and tidal flats at an altitude of 2150 ~ 4800 m on the Qinghai-Tibet Plateau [23]. After more than 30 years of selection, cultivation, and domestication, P. crymophila has been cultivated as an ecological grass variety widely used in ecological restoration of degraded grassland in the alpine region. It has excellent cold tolerance and can survive at about − 40°C [23]. However, the physiological and molecular mechanisms behind the cold stress tolerance and role of key regulatory genes and pathways of P. crymophila are poorly understood yet.

Therefore, this study focused on cold-adapted P. crymophila species from the Qinghai-Tibet Plateau, the mechanism of cold resistance was explored through physiological and transcriptomic analyses, and candidate genes for cold resistance were identified. Additionally, molecular markers were developed using these candidate cold-resistant genes to conduct molecular genetic diversity analysis and cold resistance screening of different Poa species. These results provide insights into the physiological and molecular mechanisms underlying cold tolerance in P. crymophila, and also lays a foundation for molecular marker-assisted selection for cold tolerance improvement in Poa species.

Plant growing conditions and sample collection

Seeds of P. crymophila Keng cv. Qinghai, P. pratensis L. var. anceps Gaud. cv. Qinghai, P. pratensis L. cv. Qinghai, and P. botryoides were provided by the Qinghai Academy of Animal Husbandry and Veterinary Science. The seeds were vernalized at 4°C for 3 days and then sown in 0.5-L pots (five plants per pot) filled with a mixture of black soil and peat in a 2:1 (v/v) ratio. The germinated seedlings were grown in an incubator under a day/night temperature cycle of 25°C/20°C, a 16-hour light/8-hour dark photoperiod, with a light intensity of approximately 500 µmol m^− 2 s^− 1 and 70% relative humidity.

There were 10 pots of each species, totaling 40 pots for the experiment. To prevent nutrient deficiencies, a 1:2 Hoagland nutrient solution was applied every two days during the tillering and jointing stages. After 45 days from seedling emergence, one individual plant was selected from each pot for each of the four Poa species: P. crymophila, P. pratensis var. anceps, P. pratensis, and P. botryoides. This resulted in a total of 40 samples, which were then stored in a refrigerator at -80°C for total genomic DNA extraction. The individual plants of each species were labeled as follows: L1-L10 for P. crymophila, B1-B10 for P. pratensis var. anceps, C1-C10 for P. pratensis, and H1-H10 for P. botryoides. A mixture of 10 individual plant samples from each species were created, and these mixtures were labeled as LD, BJ, CD, and HH, respectively.

To evaluate the cold tolerance of four different Poa species, including P. crymophila, P. pratensis var. anceps, P. pratensis, and P. botryoides, the plants were subjected to cold stress at 4°C for durations of 0, 3, and 6 d, with 25°C serving as the control condition, at the 45th day after seedling emergence (Fig. S1). All treatments were conducted under a consistent 16-h light/8-h dark photoperiod. The aboveground portions (leaves) of the plants were collected to determine physiological indices for each treatment duration. Each treatment was set up with three biological replicates, with each replicate consisting of five seedlings. All samples were immediately frozen in liquid nitrogen and stored at -80°C.

To analyze the physiological and molecular mechanisms of cold tolerance in P. crymophila Keng cv. Qinghai, physiological and transcriptomic analyses were conducted on the root, stem, and leaf tissues under cold stress. Uniform 45-day-old P. crymophila seedlings were selected and randomly assigned to three treatments: (1) a control group with no cold treatment (0 d), (2) a 4°C cold treatment for 3 d, and (3) a 4°C cold treatment for 6 d. Each treatment maintained a 16-hour light/8-hour dark photoperiod. Samples of leaves (L), stems (S), and roots (R) were collected at the end of each treatment for determination physiological and transcriptomic analysis. Each treatment had three biological replicates, each consisting of five seedlings. The samples were quickly frozen in liquid nitrogen and stored at -80°C.

Determination of indicators and analysis of data

To comprehensively evaluate the cold resistance of four Poa species, the following parameters were measured: relative conductivity (REC) %, malondialdehyde (MDA), proline (Pro), soluble sugar (SS), abscisic acid (ABA), chlorophyll (Chl), fresh weight (FW), dry weight (DW), and the enzyme activities of superoxide dismutase (SOD) and peroxidase (POD). For P. crymophila, the physiological responses in different tissues (roots, stems, and leaves) were assessed by measuring MDA, Pro, and the activities of POD, catalase (CAT), and SOD. MDA content was determined using the thiobarbituric acid-reactive substances (TBARS) assay [27], while Pro and SS contents were assessed as described by Wang et al. [1]. Chl content was measured by the acetone extraction method [7], and aboveground FW and DW were determined following the method of Bai et al. [28]. ABA content and the activities of SOD, CAT, and POD were measured according to the manufacturer's instructions for the enzyme assay kits (Shanghai Enzyme-linked Biotechnology Co., Ltd., China)

In the comprehensive evaluation method of cold resistance, the process involved several steps:

(1). Initially, the raw data for each indicator were used to calculate its cold resistance coefficient (α) (Table S1), as shown in the formula:

Cold resistance coefficient (α) = treatment mean / control mean (1)

(2). Subsequently, correlation analyses (Table S2) and principal component analyses (Table S3) were per-formed on the cold resistance coefficients of each physiological indicator across the four different Poa species. These analyses were conducted using SPSS Statistics 22 in order to ascertain the contribution rate of each indicator (Table S4) to the overall evaluation.

(3). Finally, the cold resistance of the test materials was assessed using the affiliation function method of fuzzy mathematics, a method detailed by Xie et al. [29].

Statistical analysis and one-way ANOVA were performed using SPSS Statistics 22. Based on the significance of the results, Tukey’s HSD (Tukey’s Honestly Significant Difference) was used for multiple comparisons to determine the differences between means (P < 0.05). Graphs were also plotted using GraphPad Prism 8.

Total RNA extraction and sequencing

To obtain the transcriptome of leaves, stems, and roots of P. crymophila, total RNA was extracted from each tissue using a UNIQ-10 Column TRIzol Total RNA Extraction Kit (Sangon Biotech, Shanghai) as previously described [30]. The purity, concentration, and integrity of RNA samples were detected by the Nanodrop, Qubit 2.0, and Agilent 2100 methods, respectively. A total of 27 cDNA libraries were sequenced with a read length of 150 bp using the Illumina HiSeq 2000 platform with a paired-end module at Biomarker CoLtd. (BMKcloud, Beijing, China). Sequencing libraries were generated using NEBNext®Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s instructions and index codes were added to attribute sequences to each sample. Each library was sequenced on an Illumina HiSeq 2000 platform in 150-bp paired-end format according to the Illumina Paired-End Sequencing protocol. The raw reads were further processed using the bioinformatics pipeline tool, BMKCloud (www.biocloud.net) online platform.

Sequence data processing, de novo assembly, and annotation

Raw data were obtained after transcriptome sequencing of the library constructed by P. crymophila. After filtering raw data, the joint sequences and primer sequences in the raw data were removed, empty sequences and low-quality sequences were screened out, and clean data were obtained for assembly. Unigenes were obtained by de novo transcriptome assembly by Trinity-v2.5.1 (https://github.com/trinityrnaseq/trinityrnaseq/wiki).

The obtained functional gene sequences were aligned by Blastx to protein databases such as NR; Pfam; KOG/COG/eggNOG; Swiss-Prot; KEGG; GO (E-value ≤ 1e^− 5). Using KOBAS-v3.0 software (http://kobas.cbi.pku.edu.cn/kobas3) to obtain the results of KEGG Orthology of unigene in KEGG, InterProScan (https://www.ebi.ac.uk/interpro/download/) uses the database integrated by InterPro to analyze the results of GO Orthology of new genes [31]. HMMER-v3.1b2 software (http://hmmer.org/) was used for com-parison with the Pfam database [32] (E-value ≤ 1e^− 10).

Analysis of differentially expressed genes

The fragments per kilobase of gene per million mapped reads (FPKM) method was used in estimating the expression levels of each unigene. Differential expression analysis between the control/cold conditions was conducted using the DESeq R package (1.10.1) [33]. Genes were identified by DESeq with false discovery rate (FDR) < 0.01, adjusted P-value < 0.05, and |fold change (FC)| ≥2 defined as DEGs [18]. The differential expression analysis between the control/cold conditions in the P. crymophila were compared as; control root (R0) vs 4°C treatment for 3 d (R3), R0 vs 4°C treatment for 6 d (R6), R3 vs R6, control shoot (S0) vs 4°C treatment for 3 d (S3), S0 vs 4°C treatment for 6 d (S6), S3 vs S6, control leaf (L0) vs 4°C treatment for 3 d (L3), L0 vs 4°C treatment for 6 d (L6) and L3 vs L6.

Validation analysis of transcriptome data by qRT-PCR

To verify the reliability and accuracy of the RNA-seq data, a random selection of 12 genes from P. crymophila was selected for validation by qRT-PCR (Table S5). The same RNA and cDNA were used as for the RNA sequencing experiments. Total RNA was extracted from the 27 samples as described previously and followed the manufacturer’s protocol to generate cDNA (Takara, Japan). The qRT-PCR was performed using a ChamQ Universal SYBR qPCR Master Mix kit (Vazyme, Nanjing, China) on a Roche LightCycler480 quantitative PCR. The relative level of gene expression was calculated using the 2^−ΔΔct formula [34]. The qRT-PCR analysis included three independent technical repeats with three biological replicates. The beta-actin gene (PcACTIN) was used as the internal control gene [35]. Gene-specific primers were designed with DNAMAN software (Lynnon BioSoft, Vandreuil, Quebec, Canada).

Weighted gene co-Expression network analysis (WGCNA)

All the DEGs were used for the construction of weighted gene co-expression networks. Filtering the DEGs to remove low-expression genes, DEGs with FPKM > 1 in at least 27 samples were retained. A total of 7,439 DEGs were obtained for WGCNA. The WGCNA assumed that the genetic network followed scale-free networks. The similarity matrix of gene co-expression and gene network formed the adjacency function. The similarity matrix was transformed into the adjacency matrix and then, the expression correlation coefficient was calculated to construct the gene’s hierarchical clustering tree. Modules were divided according to the clustering relationship between genes, and modules with similar expression patterns were merged. Finally, the correlation between phenotypic traits of modules and samples was analyzed, and the hub genes in the network were found by the R language package [30].

Development of EST-SSR markers based on cold resistance candidate genes

Based on the transcriptome sequencing data of P. crymophila, 200 pairs of EST-SSR primers for cold resistance candidate genes were designed and synthesized according to the gene function annotations. Based on the MISA results and primer design principles, 200 pairs of EST-SSR primers for cold resistance candidate genes were designed using primer3-v2.3.4. The primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd.

DNA extraction, PCR amplification, primer validation and data analysis

Genomic DNA was extracted from fresh leaf tissues of 40 Poa individual and mixed samples of leaves of 4 Poa species by adsorption column method using the Plant Genomic DNA Extraction Kit (Tiangen Biochemical Technology Co., Ltd., Beijing, China), and the quality of DNA was determined by using the NanoDrop ND1000 Spectrophotometer, and the DNA that met the conditions was diluted to 25 ng/µL, and stored at -20°C. The DNA quality was measured by using the NanoDrop ND1000 spectrophotometer. The quality of DNA was determined using a NanoDrop ND1000 spectrophotometer. Primer screening was carried out using 10 mixed DNA samples (BJ, CD, HH, LD) of each Poa species.

The system for PCR amplification was 20 µL, containing 10 µL of 2×Reaction Mix, 6.9 µL of ddH2O, 0.1 µL of Golden DNA polymerase (Beijing Tiangen Biotech Co., Ltd.), 0.5 µL of each of the forward primer and the reverse primer, 2 µL of genomic DNA (25 ng/µL). The PCR amplification program was as follows: pre-denaturation at 94℃ for 3 min, denaturation at 94℃ for 30 s, annealing at 60 − 55℃ for 30 s, and extension at 72℃ for 50 s, with 34 cycles; extension at 72℃ for 7 min, and storage at 4℃. The amplified fragments were separated by 6% denaturing polyacrylamide gel electrophoresis and stained with AgNO₃ solution for development. Finally, the gel was photographed and recorded for subsequent analysis. As for the verification of the authenticity of the primers and date analysis, the methods described by Zheng et al. [20] and Zhao et al. [21] were referenced for the operation, respectively.

Comprehensive evaluation of cold resistance of four Poa species

To evaluate the cold resistance of different Poa species, including P. crymophila Keng cv. Qinghai, P. pratensis L. var. anceps Gaud.cv. Qinghai, P. pratensis L.cv. Qinghai, and P. botryoides, physiological indicators were measured after subjecting the plants to cold stress at 4°C. The results showed that after cold stress treatment, indicators such as REC % (except for P. crymophila and P. botryoides), MDA content, Pro content, SS content, ABA content, and the activities of SOD and POD increased, while Chl content (except for P. crymophila and P. botryoides), FW (except for P. crymophila), and DW (except for P. crymophila) decreased (Fig. 1). Based on principal component analysis (PCA) of 10 indicators (Table S3), 7 indicators of Pro, SS, ABA, FW, Chl, and SOD and POD were finally screened to comprehensively evaluate the cold resistance using a membership function method revealed that the order of cold resistance from strong to weak was: P. crymophila > P. botryoides > P. pratensis var. anceps > P. pratensis (Table S4 and Table S6). Cluster analysis based on cold resistance evaluation indexes revealed that Poa species with similar cold resistance tended to cluster together (Fig. 2).

Physiological responses of different tissues of P. crymophila under cold stress

Forty-five days old P. crymophila seedlings were subjected to cold treatments for 3 and 6 days, respectively. Despite the 4°C treatment, no observable phenotypic differences were observed compared with control (Fig. 3A). However, cold stress resulted in an increase in proline (excluding the roots treated for 3 days) and MDA content, as well as the activity of antioxidant enzymes (SOD, CAT, and POD) in P. crymophila (Fig. 3B). The increase in proline content in each tissue was higher at 6 days (Fig. 3B). Specifically, proline content of roots increased by 29.9%, and leaves by 10.6% compared with 3 days of cold stress. As compare to control, MDA content significantly increased under cold stress and reached a maximum at 6 days of cold treatment (an increase of at least 40%). Compared with roots, the increase in MDA content in stems and leaves was higher after 6 days of cold treatment (Fig. 3B). In addition, the activities of antioxidants such as SOD, CAT, and POD in leaves were higher after 3 days of cold treatment when compared with roots (Fig. 3B). Among them, the SOD, CAT, and POD activities in leaves after 3 days of cold stress were 1.92, 2.06, and 1.92 times higher than 6 days, respectively. For the roots, the SOD, CAT, and POD activities under 3 days of cold stress were 1.08, 1.02, and 1.29 times higher than those after 6 days of cold treatment, respectively.

Transcriptome assembly and annotation

A total of 27 samples were employed to construct the cDNA library. After trimming, 193.28 Gb databases were retrieved, or approximately 5.99 Gb per sample (three biological replicates) (Table S7). Furthermore, we obtained approximately 20.03 to 29.68 million clean reads per library, mapped at a ratio of 65.45%. The number of bases ranged from 5.99 billion to 8.85 billion. The Q3 base percentage was > 92.69%. The GC content was approximately 55.25% (Table S7). These results suggested that the sequencing output and high-quality reads were adequate for further analysis.

After de novo assembly of high-quality sequences by Trinity software, a total of 1,868,509 transcripts and 53,893 unigenes were generated with average lengths of 1108.27bp and 2243.7bp respectively, and the N50 lengths of 1681bp and 2941bp respectively (Table S8). The size-distribution analysis showed that the lengths of 41,330 (76.69%) unigenes were greater than 1000 bp (Table S8). These results demonstrated the effectiveness of Illumina sequencing in rapidly capturing a large portion of the transcriptome. Further, 46,542 (86.36%) of the unigenes were successfully annotated in nine databases (Table S9).

Currently, homologous species matched results in Nr database showed that the top 8 mostly annotated plants belong to Poaceae species, and Triticum aestivum (10,398, 23.26%) was the best match for P. crymophila, followed by Brachypodium distachyon (7,328, 16.39%) and Aegilops tauschii (6,662, 14.90%) (Fig. 4A). In ad-dition, the E-value distribution and similarity statistics of the annotated unigenes showed that 89.36% of the unigenes had an E-value less than 1e^− 30 and had high homology (Fig. 4B), unigenes with a similarity greater than 60% and greater than 80% accounted for 86.93% and 58.91%, respectively, indicating that the unigenes annotation information of P. crymophila has good reliability (Fig. 4C).

Identification and analysis of DEGs

A total of 22,233 DEGs were identified in all tissues under cold treatments. The number of DEGs in leaves was three times higher than in roots and 1.5 times higher than in stems (14942, 4434, and 8793 DEGs, respectively) (Fig. 5-A). Among them, 1975, 4892, and 9894 DEGs were found to be unique to roots, stems, and leaves, respectively. These results indicated that cold-induced transcriptome responses were largely tissue-specific. Notably, 464 genes were commonly expressed in three tissues (Fig. 5A). The comparison of different treatment time points found more DEGs, 2888 and 7293 DEGs, were specifically expressed between R0 vs. R3 and L0 vs. L3, respectively (Fig. 5A). Moreover, it was found that more up-regulated DEGs were obtained in roots (3012 DEGs) and leaves (5864 DEGs) under 3 days of cold treatment than under 6 days of cold treatment (Fig. 5B). These results suggest that more transcripts responded to cold stress during 3 days than during 6 days. To obtain an overall picture of the impact of cold stress on transcriptional profiling, the expression patterns of all DEGs identified in the three tissues at the different time points were clustered together. Six expression patterns were identified in each tissue (P < 0.05) (Fig. S2). The results showed that the expression of many more DEGs was up-regulated in leaves under cold stress, followed by stems and roots.

GO and KEGG enrichment analysis of DEGs

To further determine the coordinated response mechanisms in the roots, stems, and leaves of P. crymophila seedlings under cold stress, GO category enrichment analysis was applied to determine the function of the DEGs expressed under cold stress. According to the P-value and number of DEGs associated with GO terms, the top 25 significantly enriched GO terms were selected and further analyzed. GO results showed that DEGs were most enriched in the “response to stimulus” category in roots and stems under cold stress, and most DEGs were up-regulated (Fig. 6A, B and Table S10). In addition, categories associated with water response (“water transmembrane transporter activity”, “water channel activity”, and “water transport”) and photosynthesis (“chloroplast”, “plastid”, “obsolete chloroplast part”, “obsolete plastid part”, “chloroplast stroma”, “chloroplast envelope”, “plastid stroma”) were significantly enriched in roots and stems, respectively. For leaves, inversely, large numbers of DEGs were significantly enriched in photosynthesis-related categories (“chloroplast”, “obsolete chloroplast part”, “photosystem II”, “photosynthesis”, “plastid stroma”, “chloroplast stroma”, and “plastid”) under cold stress, and the DEGs involved with these categories were predominantly down-regulate (Fig. 6C and Table S10).

The DEGs were further annotated to the reference pathways in the KEGG database to explore the key biological pathways in response to cold stress in P. crymophila, and the top 20 pathways were screened as the most enriched pathways. The DEGs of the root were significantly enriched in the “phenylpropanoid biosynthesis” both in 3 days and 6 days of cold stress, and most of them were up-regulated (Fig. 7A and Table S11). Furthermore, it was found that most of the DEGs were significantly enriched in the “MAPK signaling pathway-plant” (3 days), “plant hormone signal transduction” (6 days) pathways and were up-regulated in roots (Fig. 7A and Table S11). The DEGs involved in the “Circadian rhythm-plant” (39 DEGs) and “photosynthesis” (41 DEGs) pathways were significantly enriched in stems, and were up-regulated (Fig. 7B and Table S11). For leaves, “photosynthesis” and “starch and sucrose metabolism”, “galactose metabolism”, “circadian rhythm-plant”, “glutathione metabolism”, “arginine and proline metabolism”, “biosynthesis of amino acids” pathways were significantly enriched both at 3 and 6 days of cold stress (Fig. 7C). Most of the DEGs involved in the “photosynthesis” pathway were down-regulated in leaves while in “circadian rhythm-plant” pathway were up-regulated (Table S11). Notably, all DEGs involved in “photosynthesis-antenna proteins” (33 DEGs) were significantly down-regulated after 3 days of cold stress (Table S11). In addition, “starch and sucrose metabolism” and “galactose metabolism” pathways were significantly enriched in roots (3 days), stems (6 days), and leaves (both at 3 and 6 days) (Fig. 7).

Identification of key DEGs involved in cold responses

Based on gene functional annotation and enrichment analysis, 392 candidate genes involved in low-temperature tolerance mechanisms in P. crymophila were screened (Fig. 8). They were TFs (52 DEGs), CBF pathway (70 DEGs), Ca²⁺ signaling (60 DEGs), hormone signaling (61 DEGs), ROS scavenging system (76 DEGs), photosynthesis and biological clock (49 DEGs) and plant carbohydrate metabolism pathway (24 DEGs). Further analysis of the differentially expressed gene sets for comparison revealed six C2C2-Dof and three MADS-box TFs were identified (Fig. 8A), twenty-three COR DEGs identified in the CBF pathway (Fig. 8B), five CNGC, seven GLR, nine CAMTA and five CBL DEGs identified in Ca²⁺ signaling (Fig. 8C), eleven PP2C DEGs found in hormone signaling associated with ABA (Fig. 8D), three SOD, three CAT, four POD, fifteen GST, eight MDAR DEGs found in ROS scavenging system (Fig. 8E), nine PRR, LHY, and three GI DEGs found in photosynthesis and circadian clock (Fig. 8F), and fifteen DEGs were involved in starch and sucrose metabolism and galactose metabolism pathways (Fig. 8G), which were mainly up-regulated in stems and leaves. five bHLH and ten WRKY TFs were identified (Fig. 8A), ten CRPK and six ZAT DEGs were identified in the CBF pathway (Fig. 8B), four EIN3 and four CYP and five REF DEGs were identified in hormone signaling related to ABA and ETH (Fig. 8D), and three PSI and fifteen PAP (photosynthesis-antenna protein) DEGs were identified in photosynthesis (Fig. 8F), which were mainly up-regulated in stems and leaves.

WGCNA and identification of hub genes

After screening the low-expressed genes, 7,439 DEGs were retained for the WGCNA. To ensure the network is scale-free and had more biological significance, a soft threshold power of 16 is chosen when the correlation coefficient squared is 0.9 to define the adjacency matrix (Fig. 9A). Then, based on the DynamicTreeCut algorithm, setting the minimum number of module genes as 30, and the maximum module distance as 0.25, the gene modules are generated, and the modules with high similarity are further merged (Fig. 9B, C). As shown in Fig. 9D, 17 gene modules were finally generated after merging. The results indicate a strong positive correlation (r > 0.9) between antioxidant enzyme activities (SOD, CAT, and POD) and the brown module, while a negative correlation (r < -0.6) was observed with the salmon module.

To analyse the brown and salmon modules, GO enrichment (Fig. 10A) analysis and KEGG enrichment (Fig. 10B) analysis were performed. Additionally, co-expression networks were observed between brown and salmon modules (Fig. 10C). It was found that the response to stimulus (235 DEGs) and endomembrane system (32 DEGs) were the most significantly enriched GO categories in the brown and salmon module, respectively. “Circadian rhythm-plant” and “Phagosome” were the most significantly enriched KEGG pathways in the brown and salmon modules, respectively. The first six hub genes in the brown module were identified: Unigene_600094(PcERF), Unigene_023873 (PcBLH1), Unigene_019968 (PcABCC2), Unigene_513952 (PcUGT73C1), Unigene_325532 (PcFTSH1) and Unigene_075399 (PcABC1K7). The first three hub genes in the salmon module were identified as Unigene_530024 (PcCTR1), Unigene_605651 (PcCARB) and Unigene_595662 (PcGlCAT14A), these genes were mostly up-regulated (Fig. 10C, Table S12 and Fig. S3). The nine hub genes with higher connectivity within the module correlate better with the activity of antioxidant enzymes. This provided insight into the cold-responsive genes of P. crymophila for subsequent research.

Experimental validation

To validate the RNA sequencing (RNA-seq) data of the present study, the expression profiles of a subset of 12 DEGs involved in oxidation-reduction, membrane components, and hormone signal transduction were selected for qRT-PCR assays. The variation trends and errors of these 12 DEGs at different treatment points showed a high degree of consistency with the change tendency of the transcript FPKM values (Fig. 11). The coefficients of determination obtained by linear regression analysis between the qRT-PCR and transcriptome data of the roots, stem, and leaves were R² = 0.7, R² = 0.78, and R² = 0.85, respectively, and the correlations were positive (Fig. S4). The high congruence between the RNA-seq and qRT-PCR results indicated the reliability of the gene expression values in our experiment.

Development of candidate gene-based EST-SSR markers and application in genetic diversity analysis

To facilitate molecular marker-assisted selection for cold tolerance in P. crymophila, 12,972 SSR loci were identified from 53,893 unigenes, distributed among 10,766 unigenes, accounting for 19.98% of all sequences (Table S13). Based on candidate cold-resistant genes, 200 molecular markers were developed and screened for polymorphisms. A total of 29 polymorphic molecular markers involved in the ROS scavenging system, hormones regulation, calcium regulation, photosynthesis and transcription factors (TFs) were used for genetic diversity analysis and cold resistance screening of 40 individual plants (Table S14). The polymorphic information content (PIC) of the 29 pairs of primers ranged from 0.22 to 0.42, with an average of 0.35 (Table S14).

Based on the gel electrophoresis results, the markers P37 (PcGA2ox3) and P148 (PcERF013) could distinguish P. crymophila (LD). a 150 bp band was amplified in the P. botryoides (HH), P. pratensis var. anceps (BJ), and P. pratensis (CD) genotypes under the P37 marker, whereas no band was amplified at 150 bp in the LD (Fig. S5). Under P148 marker, a band located between 150 and 200 bp was amplified in BJ and HH, as well as a band at 80 bp in BJ, CD, and HH. However, in LD, no bands were amplified at either 150 to 200 bp or 80 bp (Fig. S5). To verify the authenticity of the developed EST-SSR markers, 20 Poa sample DNAs were amplified with these two EST-SSR markers, and the amplified products were sequenced. According to the sequencing results of PCR amplification products of primer P37, three base repeat types, (CCA)2, (CCA)3, and (CCA)5, were found in different Poa species genotypes (Fig. 12A). Three base repeat types, (AGC)2, (AGC)3 and (AGC)3, were found in primer P148 for different Poa species (Fig. 12A). To verify that the variation in the number of repeats of the SSR sequences was responsible for the marker polymorphisms found in the four Poa species, the sequences of PCR products amplified by primers P37 and P148 were compared in detail. The results of the analysis showed the presence of base mutations in these sequences among the different Poa species (Fig. 12B).

The results of the UPGMA unweighted group average method, with a genetic similarity coefficient of 0.70, indicated that the 40 Poa individuals were clustered into three major groups (Fig. 13). This clustering was consistent with the results of the STRUCTURE analysis (K = 3), which demonstrated that Poa species with similar cold resistance were grouped together (CD and BJ) (Fig. 14B, C). This shows that there is cross-clustering between the BJ and CD populations. According to results of STRUCTURE analysis, we also found that the 34th individual plant (L4) of LD is closely genetically related to CD and BJ, and according to the sequence comparison of the PCR products amplified by the two primers (P37 and P148) (Fig. 12B and Fig. 14C), it was found that the sequence of the PCR product of the individual plant of L4 is indeed the same as that of the PCR products amplified by BJ and HH. When K = 5, the four populations BJ (red), CD (turquoise), HH (yellow), and LD (blue) can be clearly distinguished, whereas LD’s individual plant 34 (L4), which is pink, is the furthest away from the genetic background of the other individual plants (Fig. 14D). Therefore, EST-SSR molecular markers based on cold resistance candidate genes have the potential to distinguish Poa genotypes with different cold resistance.

Comprehensive evaluation of cold resistance of Poa species

The cold tolerance in plants is an extremely complex trait, and different Poa species may use different mechanisms to cope with low-temperature stress [36]. In this study, it was found that the fresh weight, dry weight and chlorophyll content of all Poa species generally decreased (α < 1) under low-temperature treatments, however, in terms of other indexes, the coefficient of cold hardiness showed a tendency to increase (α > 1) under low-temperature conditions, and the performances of different species were differentiated, and there were also significant differences between different physiological indexes even in the same species (Fig. 1; Table S1). In order to reveal the response mechanism of Poa species to cold stress more comprehensively and improve the accuracy of resistance evaluation, it is necessary to utilize several important physiological indexes to comprehensively evaluate their cold resistance. The affiliation function, as a comprehensive resistance evaluation method based on multiple indicators, has been used for resistance evaluation in rice, wheat and cotton [37, 38, 39]. Sun et al. [39] combined PCA to screen for key drought indicators associated with drought tolerance and screened two cotton materials for higher drought tolerance. In this study, we comprehensively evaluated the cold resistance of the four Poa species by combining correlation analysis, PCA and cluster analysis, and found that P. crymophila was the most cold-resistant (Table S6), which tended to cluster with P. botryoides, while P. pratensis var. anceps and P. pratensis, which were less cold-resistant, tended to cluster together (Fig. 2). Based on this result, we chose P. crymophila for the next physiological and transcriptomic analysis.

Physiological changes in P. crymophila responding to cold stress

When plants encounter cold stress, their physiology or cells will be disturbed, such as cell membrane permeability, membrane lipid peroxidation, plant protection enzyme activity and photosynthesis [12]. Plants can accumulate more compatible substances to mitigate damage caused by cold stress, such as proline, which acts as an osmoprotectant and allows plants to tolerate cold stress [26, 40]. Cold stress leads to overproduction of ROS in the cells that damages various biological molecules and causes membrane lipid peroxidation [15, 40]. To maintain ROS homeostasis plants have evolved defensive mechanisms composed of enzymatic and non-enzymatic components such as SOD, CAT, and POD [2, 11, 12]. In P. crymophila, the proline content, MDA content, and antioxidant enzyme activity increased in all three tissues under cold stress. Furthermore, the MDA content and antioxidant enzyme activity in stems and leaves exceeded those of roots during each stress period (Fig. 3B). Thus, it can be seen that the aboveground tissues of P. crymophila were more responsive to cold signals and better adapted to handle cold stress quickly.

TFs involved in the cold stress response

TFs play important regulators of various biological and molecular functions in plants that activate the down-stream gene transcripts to display responses against cold stresses [23, 41, 42]. For instance, OsERF096 modulates IAA accumulation and signaling in regulating cold tolerance of rice [42], the japonica version of bZIP73 interacts with bZIP71 to regulate ABA levels and ROS homeostasis for cold acclimation in japonica rice [43]. In addition, differences in the types and levels of TFs expression can significantly affect the ability of plants to adapt to cold stress. It has been shown that E. nutans specifically expresses bHLH, bZIP, C2H2, WRKY, and MYB TFs at the early stage of cold stress [25], and that the temporal specificity in the expression levels of the TFs enhances the cold tolerance of the plant [7]. In the present study, the main differentially expressed TFs in P. crymophila at 3 days of cold treatment were AP2/ERF, bHLH, WRKY, MYB, bZIP, C2H2, and HSF TFs. Additionally, MADS-box, C2C2-Dof, and E2FA TFs were up-regulated only in the stems or leaves (Fig. 8A). Thus, specific types of TFs in different tissues and different expression levels under different cold stress conditions may be the key to improving cold tolerance.

Role of ICE-CBF-COR in the cold response

The ICE-CBF-COR signaling pathway has been the best characterized cold stress signaling pathway [8, 44]. In Arabidopsis, the cold-induced AtCBF activates the expression of CORs, which in turn initiates a variety of mechanisms such as membrane stabilization and scavenging of ROS to mitigate cold damage [5, 45]. LEAs are antioxidants and stabilizers of membranes and proteins, and the expression of AtCBF3/AtDREB1A improves cold tolerance in Arabidopsis by increasing the levels of LEAs [45, 46]. CSPs were highly expressed in the cold-resistant plants, and it has also been reported that CSPs were up-regulated by the CBFs in response to cold stress in Arabidopsis [47]. In this study, the ICE-CBF-COR signaling pathway also plays an important role in the response to cold stress in P. crymophila (Fig. 8B). PcLEA1, PcLEA14-A, PcCOR413PM, PcCOR413IM2, PcCOR410, and PcBLT14 were expressed in stems and leaves, enhancing cold tolerance in P. crymophila by scavenging ROS to attenuate the cellular damage caused by cold stress (Fig. 8-B). In addition, PcICE1 and PcCSP showed higher transcriptional abundance in stems and leaves than in roots, allowing aboveground tissues to more effectively activate downstream defense reponses and further improve tolerance to cold stress.

Role of Ca²⁺ signaling, hormone signaling, and ROS scavenging system in the cold response

Ca²⁺, hormones and ROS act as signaling molecules that are involve in response to various stresses [2, 5]. In P. crymophila, many genes encoding calcium-related proteins responding to cold stress, including Ca²⁺ channel proteins CNGC and GLR, Ca²⁺ sensors CML, CBL, CPK/CDPK, CIPK, Ca²⁺-binding transcription factor CAMTA (Fig. 8C). To ensure the normal transmission of cold stress signals with the fluctuation of Ca²⁺concentration, P. crymophila has a higher expression level of the calcium channel proteins [23]. Ca²⁺ sensor protein senses temperature changes through monitoring intracellular calcium levels, interprets this signal as stress and passes it downstream, acting as a master switch to regulate various stress genes that confer cold tolerance [8, 23]. CAMTA has CaM-binding activity and contributes to the cold induction of CBF genes, when the CAMTA3 binds to the CBF2 promoter, CBF2 is activated, which increases the cold tolerance of plants [2]. Therefore, it was speculated that Ca²⁺ signaling pathway components are expressed earlier in leaves and stems than in roots, making P. crymophila more responsive to cold signals and getting ready for cold sooner.

IAA, GA and BR are growth-promoting hormones that promote cell elongation and division, ABA, ETH and JA often act as stress signaling and play an important role in regulating plant tolerance to abiotic stresses [48, 49, 50]. GH3(Gretchen Hagen 3) proteins are usually involved in regulating the synthesis and accumulation of JA and IAA [51, 52]. In rice, overexpression of OsGH3-2 inhibited the accumulation of free IAA, which leads to the enhancement of ROS scavenging capacity and ultimately promoted cold tolerance [51]. Both GA metabolism and signaling can affect the cold stress responses of plants, and studies have shown that CBF upregulates multiple genes involved in GA metabolism (GA2ox6, GA2ox9) and signaling (RGL3) under cold stress, thereby reducing bioactive GA levels [50]. The activity of DELLA is central to GA signaling, DELLA (RHT1) proteins interact with the GA receptors, alleviating DELLA repression on GA signaling [48] Moreover, BR was able to enhance the expression of AtCBF1 and AtCOR47, which ultimately improved cold tolerance in Arabidopsis [53] In response to cold, reduced expression of MwBRH1(Brassinosteroid response 1) and increased expression of MwBRI1 (brassinosteroid LRR receptor kinase BRI1) activate BR signaling in Magnolia wufengensis [54]. Therefore, it could suggest that P. crymophila reduced the level of IAA, GA and BR, while low expression of GH3 (PcEBF1and PcEBF2) reduced the decomposition of IAA, and increased the level of JA in roots and stems to make it more cold-tolerant (Fig. 8D).

Studies have shown that ABA is a core regulator of cold stress signaling and plays a crucial role in CBF-dependent pathway [50, 55]. Abscisic acid insensitive 1 (ABI1), abscisic acid insensitive 2 (ABI2) and Hypersensitive to Abscisic acid 1 (HAB1) have been shown to interact with ABA re-ceptors PYR/PYLs (Pyrabactin-resistant/PYR1-like proteins) to regulate SNF1-Related Protein Kinase 2 (SnRK2s) and Protein phosphatase 2C (PP2Cs) [48, 50]. In this study, P. crymophila may promote PP2C (PcPP2C06, PcPP2C27, PcPP2C30) and SnRK2 DEGs (PcSAPK2, PcSAPK4, PcSAPK5, PcSAPK6, PcSAPK8) expression through the downregulation of PYR/PYL DEGs (PcPYL4 and PcPYL9) in the ABA regulatory pathway, thereby improving cold tolerance (Fig. 8D). Furthermore, ethylene-insensitive 3 (EIN3) acts as an antagonist in the ETH signaling pathway, modulating cold acclimation by inhibiting CBFs [12, 48]. Under cold stress, ABA, and JA hormones can induce the expression of ERF genes, and ERFs bind to GCC box and DRE elements, conferring cold stress tolerance in A. thaliana and T. aestivum [12]. These DEGs are vital for the cold stress response in P. crymophila, and reducing EIN3 (PcEBF1 and PcEBF2) can relieve the inhibition of the CBF pathway, thereby elevating P. crymophila’s cold tolerance.

ROS can function as a cold stress marker that impacts downstream gene expression. It may be due to protein denaturation, nucleic acid mutations, and cellular damage [56, 57]. It has been found that cold stress induces SOD, POD, and CAT activities to scavenge ROS, thereby enhancing cold tolerance in plants [56, 57]. In this study, we found that low-temperature stress induced the DEGs related to the synthesis of SOD, POD, and CAT in various tissues, especially stems and leaves (Fig. 8E), resulting in a significant increase in their activities (Fig. 3B). In addition, APX, GSH and GPX, as the most important peroxidases in H2O2 detoxification, play an important role in scavenging ROS to enhance cold tolerance in plants [8, 56]. In winter wheat Dn1 [41] and Capsicum annuum L. [58], the higher activities of RBOH, DHAR and MDHAR enzymes have a significant effect on alleviating the symptoms of cold stress. In summary, these enzymes of the ROS scavenging system protect cells from damage and ensure normal life under stress, which is also the strategy of P. crymophila cold adaptation.

The MAPK cascade is a universal signal transduction module, including MAPK, MAPKK/MKK/MMK, and MAPKKK/MEKK, lies downstream of ROS, Ca²⁺ and hormones, and play vital roles in plant responses to cold stress [59, 60, 61]. The overexpression of OsMKK6 activated the OsMPK3 and enhanced the cold resistance of rice [62]. Zhao et al [59] have reported that the MAP kinase pathway (MEKK1-MKK1/2-MPK4) increased the CBFs expression and freezing tolerance by inhibiting the MKK4/5-MPK3/6 cascade. Collectively, the enhanced expression of transcripts for Ca²⁺, hormones, and ROS in P. crymophila activated a complicated signaling cascade, regulating various downstream genes that respond to cold tolerance. These DEGs could be intriguing candidates to investigate during P. crymophila seedling cold stress responses.

Role of the circadian clock and photosynthesis in the cold response

Studies have shown that core components of the circadian clock, CCA1 (Circadian clock-associated 1) and LHY (Late-elongating hypocotyl), play an active role in CBF expression, while the nocturnal component, pseudoresponse regulator (PRR), acts as a repressor of CBF expression [16, 63, 64]. A cold-induced clock component GI (GIGANTEA) can also regulate cold responses in a CBF-independent manner, positively regulates cold stress in Arabidopsis [16, 63, 64]. In P. crymophila, PcPRRs, PcLHYs, and PcGIs DEGs were enhanced under cold (Fig. 8F), which indicated the circadian clock that may be an efficient way to increase cold resistance in P. crymophila.

PSII is one of the most sensitive components of photosynthesis [65, 66]. The decline in photosynthesis under cold stress is mainly due to cold stress-induced inhibition of the activity of key enzymes associated with photosynthesis [17, 54]. The LHC (Light-harvesting complex) of photosynthetic antenna proteins (PAP) can rapidly capture light energy to promote photosynthesis in plants [14, 16]. In P. crymophila, we found that most of photosynthesis-related DEGs were down-regulated (especially in leaves) (Fig. 8F). These results confirm that P. crymophila may adapt to low temperature by reducing leaf photosynthesis and reducing energy loss.

Role of the carbohydrate metabolism under cold stress

Plant sugars serve as energy reserves, free radical scavengers, signaling molecules, and compounds that enhance plant responses to biotic and abiotic stresses. Consequently, carbohydrates are regarded as pivotal factors in plant response and adaptation to cold stress [25, 67]. In this study, DEGs involved in carbohydrate metabolism, including starch synthesis and sugars synthase-related genes, such as UDP-glucose pyrophosphorylase gene (PcUSP), beta-glucosidase gene (PcBGLU28), galactinol-sucrose galactosyltransferase gene (PcRFS) were up-regulated in roots, stems and leaves under cold. Besides, sucrose synthase genes (PcSUS4) and beta-glucosidase gene (PcBGLU32) were up-regulated both in stems and leaves under cold, beta-fructofuranosidase genes (PcCIN6, PcCIN7) and ATP-dependent 6-phosphofructokinase gene (PcPFK3) were up-regulated in roots under cold (Fig. 8G). These results confirm that P. crymophila may have adapted to cold by up-regulating genes in the carbohydrates biosynthetic pathway to increase starch accumulation and sugar accumulation.

Cold tolerance Hub genes selected by WGCNA

ABCC3, a member of the ABCC subfamily of the ABC (ATP-binding cassette) transporter family, was found to be involved in the MAPK signaling pathway to enhance freezing tolerance in alfalfa [68]. This study indicated that PcABCC2 was significantly up-regulated when exposed to cold stress (Fig. S3), potentially impacting MAPK signaling. The activity of the Protein ACTIVITY OF BC1 COMPLEX KINASE 7 (PcABC1K7) protein increased under cold stress in P. crymophila (Fig. S3). It has been suggested that ABC1K7 and ABC1K8 contribute to cross-talk between ABA and ROS signaling, indicating a potential involvement of PcABC1K7 in the regulation of ABA and ROS signaling [69]. Furthermore, this investigation uncovered significant up-regulation of PcBLH1 [70] and PcFTSH1 [71] genes associated with chlorophyll synthesis, arginine biosynthesis-related gene PcCARB [72], and regulator of the ETH signaling pathway PcCTR1 [50, 73], indicating their important role in responding to cold stress in P. crymophila (Fig. S3). In short, these genes may serve as potential targets for future research aimed at improving plant cold tolerance.

Candidate gene-based EST-SSR molecular Marker-Assisted Selection and application in genetic diversity analysis

EST-SSR molecular marker-assisted selection (MAS) of cold-resistant candidate genes can expedite the molecular breeding process of new cold-resistant varieties. A multitude of molecular markers such as SSR, AFLP, RFLP, ISSR, SRAP, SCoT, SNP, etc. have been developed. Among them, SSR markers are rich in co-occurrence, high reproducibility and high polymorphism [74]. Conventional SSR markers are developed from random genomic sequences, devoid of any certain correlation to functional genes. Unlike SSR, EST-SSR molecular markers, which are developed based on prospective genes and exhibit a definite correlation with target traits, offer extensive prospects for identifying germplasm resources and facilitating molecular marker-assisted breeding [21, 74]. Zheng et al. [20] identified identified 155 candidate genes closely related to flowering traits through transcriptome sequencing of E. sibiricus, and used 15 polymorphic EST-SSRs to analyze the genetic diversity and population structure of 20 E. sibiricus germplasm, UPGMA cluster and STRUCTURE analysis showed that the 20 E. sibiricus genotypes with similar flowering times tended to group together, which not only highlighted the great potential of EST-SSR markers in the evaluation of flowering traits in E. sibiricus, but also provided a powerful tool for further exploring the genetic characteristics of E. sibiricus. Xiao et al. [75] developed 182 EST-SSR markers for genes related to cold resistance in oil palm (Elaeis guineensis) using transcriptome data, among which three EST-SSR markers are closely linked to three cold resistance candidate genes (ICE1, CBF, and NAC), which can be directly applied in molecular breeding for cold resistance in oil palm. The P37 and P148 markers, developed based on the candidate genes PcGA2ox3 and PcERF013, can differentiate the cold-resistant P. crymophila genotype from P. pratensis var. anceps, P. pratensis, and P. botryoides (Fig. S5). Gene expression for both markers significantly improved following cold treatment, evidenced by enhanced expression in roots and leaves (Fig. 8A, D). The newly developed EST-SSR markers have the potential to distinguish different cold-resistant Poa genotypes, but the four different genotypes of Poa could not be fully distinguished due to environmental factors.

In this study, the polymorphic information content (PIC) values of the 29 primer pairs ranged from 0.22 to 0.42, with a mean value of 0.35 (Table S14). This result is similar to a previous related study on E. nutans, in which the PIC values of 14 pairs of EST-SSR primers for pendulous lancelet ranged from 0.22 to 0.37, with a mean value of 0.32 [21]. However, the PIC values in this study were low compared to those of barley (PIC values for 49 pairs of EST-SSR primers for barley ranged from 0.08 to 0.75, with a mean value of 0.46) [76]. This result may be due to the fact that the individual plant materials were used for analysis in this study, whereas other studies used population level materials and different study materials may also be one of the reasons. In the present study, it was found that P. pratensis var. anceps and P. pratensis were closely related genetically (Fig. 13 and Fig. 14C). The results of this study were partially different from the previous genetic diversity study of 31 germplasms of the Poa using ISSR [77]. This may be due to the fact that these markers are expressed sequence markers associated with cold resistance, and their detection sites also fail to represent the entire genome, which is a limitation for genetic diversity evaluation.

In this research, the cold tolerance of four Poa species was comprehensively evaluated, and the order of cold tolerance was as follows: P. crymophila > P. botryoides > P. pratensis var. anceps > P. pratensis. The key genes and molecular pathways related to cold resistance were identified, and the response mechanism of cold tolerance in P. crymophila was revealed. The Ca²⁺ signaling, ROS signaling, hormone signaling, circadian clock, and photosynthesis related DEGs are coupled to TFs and play important roles in response to cold stress through triggering a cascade of downstream CORs, CORs activations further modulates each tissue’s cellular metabolic homeostasis and enhanced tolerance (Fig. 15), and most of the DEGs were induced in the early stage of cold stress (3 d). Compared with roots, DEGs in the stems and leaves of P. crymophila were more active under cold stress. In addition, WGCNA identified nine hub genes that may be involved in P. crymophila responses to cold stress. 29 candidate gene-based EST-SSRs were used to conduct molecular genetic diversity analysis and cold resistance screening of different cold-resistant Poa species. UPGMA cluster and STRUCTURE analysis showed that the 40 Poa individuals were clustered into three major groups, with Poa species with similar cold resistance being grouped together. The markers P37 (PcGA2ox3) and P148 (PcERF013) could distinguish P. crymophila from the other three Poa species. Altogether, our results provide important insights into the future understanding of the biochemical and molecular mechanisms underlying the response of P. crymophila to cold stress, and also lays a foundation for molecular marker-assisted selection for cold tolerance improvement of Poa species. Furthermore, the functional genes involved have the potential to be used in the development of new forage grass varieties for enhanced productivity and stress resistance.

Acknowledgments

Not applicable.

Author contributions

LB. T.: Investigation, Data curation, Writing-original draft, Software. YY. Z: Software, Writing-review & editing, Discussions. HH. L.: Software, Discussions. YS. Q.: Software, Discussions. HZ. W.: Methodology, HQ. L.: Methodology. WG. X.: Investigation, Conceptualization, Funding acquisition, Writing-review & editing.

Funding

This research was funded by the Leading Scientist Project of Qinghai Province (2023-NK-147), CARS (CARS-34), Gansu Provincial Science and Technology Major Projects (22ZD6NA007), and Leading Scientist Project of Gansu Province (23ZDAK013).

Data availability

The data sets supporting the results of this article are available in the NCBI’s Sequence Read Archive (SRA) database (accession number: SRP470158 (https://www.ncbi.nlm.nih.gov/Traces/study/?acc= SRP470158&go=go).

Ethics approval and consent to participate

All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Wang LT, Jian ZL, Wang PC, Zhao LL, Chen KK. Combined physiological responses and differential expression of drought-responsive genes preliminarily explain the drought resistance mechanism of Lotus corniculatus. Funct Plant Biol. 2023;50(1):46-57.
Ding YL, Yang SH. Surviving and thriving: How plants perceive and respond to temperature stress. Dev Cell. 2022;57(8):947-958.
Dong R, Luo B, Tang L, Wang QX, Lu ZJ, Chen C, Yang F, Wang S, He J. A comparative transcriptomic analysis reveals a coordinated mechanism activated in response to cold acclimation in common vetch (Vicia sativa L.). BMC Genomics. 2022;23(1):814.
Yan T, Sun M, Su R, Wang XZ, Lu XD, Xiao YH, Deng HB, Liu X, Tang WB, Zhang GL. Transcriptomic profiling of cold stress-induced differentially expressed genes in seedling stage of Indica rice. Plants. 2023;12(14):2675.
Gusain S, Joshi S, Joshi R. Sensing, signalling, and regulatory mechanism of cold-stress tolerance in plants. Plant Physiol Biochem. 2023;197:107646.
Nouri MZ, Komatsu S. Subcellular protein overexpression to develop abiotic stress tolerant plants. Front Plant Sci. 2013;4:2.
Cheng MJ, Cui KS, Zheng MM, Yang T, Zheng JJ, Li XF, Luo X, Zhou Y, Zhang RZ, Yan DH, Yao M, Iqbal MZ, Zhou Q, He R. Physiological attributes and transcriptomics analyses reveal the mechanism response of Helictotrichon virescens to low temperature stress. BMC Genomics. 2022;23(1):280.
Zhang H, Zhu J, Gong Z, Zhu JK. Abiotic stress responses in plants. Nat Rev Genet. 2022;23(2):104-119.
Fursova OV, Pogorelko GV, Tarasov VA. Identification of ICE2, a gene involved in cold acclimation which determines freezing tolerance in Arabidopsis thaliana. Gene. 2009;429(1-2):98-103.
Shu YJ, Li W, Zhao JY, Zhang SJ, Xu HY, Liu Y, Guo CH. Transcriptome sequencing analysis of alfalfa reveals CBF genes potentially playing important roles in response to freezing stress. Genet Mol Biol. 2017;40(4):824-833.
Zhuo CL, Liang L, Zhao YQ, Guo ZF, Lu SY. A cold responsive ethylene responsive factor from Medicago falcata confers cold tolerance by up-regulation of polyamine turnover, antioxidant protection, and proline accumulation. Plant Cell Environ. 2018;41(9):2021-2032.
Ritonga FN, Chen S. Physiological and molecular mechanism involved in cold stress tolerance in plants. Plants. 2020;9(5):560.
Zhou Q, Cui Y, Dong SW, Luo D, Fang L F, Shi ZJ, Liu WX, Wang ZY, Nan ZB, Liu ZP. Integrative physiological, transcriptome, and metabolome analyses reveal the associated genes and metabolites involved in cold stress response in common vetch (Vicia sativa L.). Food and Energy Security. 2023;12 (4):0-0.
Zhu JK. Abiotic stress signaling and responses in plants. Cell. 2016;167(2):313-324.
Zhang H, Jiang CJ, Ren JY, Dong JL, Shi XL, Zhao XH, Wang XG, Wang J, Zhong C, Zhao SL. An advanced lipid metabolism system revealed by transcriptomic and lipidomic analyses plays a central role in peanut cold tolerance. Front Plant Sci. 2020;11:1110.
Qi LJ, Shi YT, Terzaghi W, Yang SH, Li JG. Integration of light and temperature signaling pathways in plants. J Integr Plant Biol. 2022;64(2):393-411.
Cheng MJ, Pan ZY, Cui KS, Zheng JJ, Luo X, Chen YJ, Yang T, Wang H, Li XF, Zhou Y, Lei X, Li Y, Zhang R, Iqbal MZ, He R. RNA sequencing and weighted gene co-expression network analysis uncover the hub genes controlling cold tolerance in Helictotrichon virescens seedlings. Front Plant Sci. 2022;13:938859.
Min XY, Liu ZP, Wang YR, Liu WX. Comparative transcriptomic analysis provides insights into the coordinated mechanisms of leaves and roots response to cold stress in common vetch. Industrial Crops and Products. 2020;158 (0):112949-112949.
Li L, Han CL, Yang JW, Tian ZQ, Jiang RY, Yang F, Jiao KM, Qi ML, Liu LL, Zhang BZ. Comprehensive transcriptome analysis of responses during cold stress in wheat (Triticum aestivum L.). Genes. 2023;14(4):844.
Zheng YY, Zhang ZY, Wan YY, Tian JY, Xie WG. Development of EST-SSR markers linked to flowering candidate genes in Elymus sibiricus L. based on RNA sequencing. Plants. 2020;9(10):1371.
Zhao YQ, Zhang JC, Zhang ZY, Xie WG. Elymus nutans genes for seed shattering and candidate gene-derived EST-SSR markers for germplasm evaluation. BMC Plant Biol. 2019;19(1):102.
Zou FL, Hu QW, Li HD, Lin J, Liu YC, Sun FL. Dynamic Disturbance Analysis of Grasslands Using Neural Networks and Spatiotemporal Indices Fusion on the Qinghai-Tibet Plateau. Front Plant Sci. 2022;12:760551.
Li XY, Wang Y, Hou XY, Chen Y, Li CX, Ma XR. Flexible response and rapid recovery strategies of the plateau forage Poa crymophila to cold and drought. Front Plant Sci. 2022;13:970496.
Hua QY, Yu Y, Dong SK, Li S, Shen H, Han YH, Zhang J, Xiao JN, Liu SL, Dong QM. Leaf spectral responses of Poa crymophila to nitrogen deposition and climate change on Qinghai-Tibetan Plateau. Agriculture, Ecosystems & Environment. 2019;284 (0):106598-106598.
Fu JJ, Miao YJ, Shao LH, Hu TM, Yang PZ. De novo transcriptome sequencing and gene expression profiling of Elymus nutans under cold stress. BMC Genomics. 2016;17(1):870.
Dong W, Ma X, Jiang H, Zhao C, Ma H. Physiological and transcriptome analysis of Poa pratensis var. anceps cv. Qinghai in response to cold stress. BMC Plant Biol. 2020;20(1):362.
Wang X, Yu C, Liu Y, Yang L, Li Y, Yao W, Cai Y, Yan X, Li S, Cai Y. GmFAD3A, a ω-3 fatty acid desaturase gene, enhances cold tolerance and seed germination rate under low temperature in rice. Int J Mol Sci. 2019;20(15):3796.
Bai WP, Li HJ, Hepworth SR, Liu HS, Liu LB, Wang GN, Ma Q, Bao AK, Wang SM. Physiological and transcriptomic analyses provide insight into thermotolerance in desert plant Zygophyllum xanthoxylum. BMC Plant Biol. 2023;23(1):7.
Xie Y, Lou HJ, Hu YX, Sun XY, Lou YH, Fu JM. Classification of genetic variation for cadmium tolerance in bermudagrass [Cynodon dactylon (L.) Pers.] using physiological traits and molecular markers. Ecotoxicology. 2014;23(6):1030-1043.
Zheng YY, Wang N, Zhang ZY, Liu WH, Xie WG. Identification of flowering regulatory networks and hub genes expressed in the leaves of Elymus sibiricus L. using comparative transcriptome analysis. Front Plant Sci. 2022;13:877908.
Jones P, Binns D, Chang HY, Fraser M, Li WZ, McAnulla C, McWilliam H, Maslen J, Mitchell A, Nuka G. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30(9):1236-1240.
Eddy SR. Profile hidden Markov models. Bioinformatics. 1998;14(9):755-763.
Anders S, Huber W. 2010. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106.
Adnan M, Morton G.Hadi S: Analysis of rpoS and bolA gene expression under various stress-induced environments in planktonic and biofilm phase using ²⁻^ΔΔ^CT method. Mol Cell Biochem. 2011;357(1-2):275-282.
Wang Y, Li XY, Li CX, He Y, Hou XY, Ma XR. The regulation of adaptation to cold and drought stresses in Poa crymophila Keng revealed by integrative transcriptomics and metabolomics analysis. Front Plant Sci. 2021;12:631117.
Xing JY, Tan JJ, Feng HQ, Zhou ZJ, Deng M, Luo HB, Deng ZP. Integrative proteome and phosphoproteome profiling of early cold response in maize seedlings. Int J Mol Sci. 2022;23(12):6493.
Kakar N, Jumaa SH, Redoña ED, Warburton ML, Reddy KR. Evaluating rice for salinity using pot-culture provides a systematic tolerance assessment at the seedling stage. Rice. 2019;12(1):57.
Huseynova IM, Rustamova SM, Suleymanov SY, Aliyeva DR, Mammadov AC, Aliyev JA. Drought-induced changes in photosynthetic apparatus and antioxidant components of wheat (Triticum durum Desf.) varieties. Photosynth Res. 2016;130(1-3):215-223.
Sun FL, Chen Q, Chen QJ, Jiang MH, Gao WW, Qu YY. Screening of key drought tolerance indices for cotton at the flowering and boll setting stage using the dimension reduction method. Front Plant Sci. 2021;12:619926.
Qi WL, Wang F, Ma L, Qi Z, Liu SQ, Chen C, Wu JY, Wang P, Yang CR, Wu Y, Sun WC. Physiological and biochemical mechanisms and cytology of cold tolerance in Brassica napus. Front Plant Sci. 2020;11:1241.
Tian Y, Peng KK, Lou GC, Ren ZP, Sun XZ, Wang ZW, Xing JP, Song CH, Cang J. Transcriptome analysis of the winter wheat Dn1 in response to cold stress. BMC Plant Biol. 2022;22(1):277.
Cai XX, Chen Y, Wang Y, Shen Y, Yang JK, Jia B W, Sun XL, Sun MZ. A comprehensive investigation of the regulatory roles of OsERF096, an AP2/ERF transcription factor, in rice cold stress response. Plant Cell Rep. 2023;42(12):2011-2022.
Liu CT, Ou SJ, Mao BG, Tang JY, Wang W, Wang HR, Cao SY, Schläppi MR, Zhao BR, Xiao GY. Early selection of bZIP73 facilitated adaptation of japonica rice to cold climates. Nat Commun. 2018;9(1):3302.
Wang DZ, Jin YN, Ding XH, Wang WJ, Zhai SS, Bai L P, Guo ZF. Gene regulation and signal transduction in the ICE-CBF-COR signaling pathway during cold stress in plants. Biochemistry. 2017;82(10):1103-1117.
Novillo F, Medina J, Salinas J. Arabidopsis CBF1 and CBF3 have a different function than CBF2 in cold acclimation and define different gene classes in the CBF regulon. Proc Natl Acad Sci U S A. 2007;104(52):21002-21007.
Adhikari L, Baral R, Paudel D, Min D, Makaju SO, Poudel HP, Acharya JP, Missaoui AM. Cold stress in plants: strategies to improve cold tolerance in forage species. Plant Stress. 2022;4 (0):100081-100081.
Shah FA, Ni J, Yao YY, Hu H, Wei RY, Wu LF. Overexpression of karrikins receptor gene Sapium sebiferum KAI2 promotes the cold stress tolerance via regulating the redox homeostasis in Arabidopsis thaliana. Front Plant Sci. 2021;12:657960.
Eremina M, Rozhon W, Poppenberger B. Hormonal control of cold stress responses in plants. Cell Mol Life Sci. 2016;73(4):797-810.
Lv XZ, Li HZ, Chen XX, Xiang X, Guo ZX, Yu JQ, Zhou YH. The role of calcium-dependent protein kinase in hydrogen peroxide, nitric oxide and ABA-dependent cold acclimation. J Exp Bot. 2018;69(16):4127-4139.
Waadt R S, Seller CA, Hsu PK, Takahashi Y, Munemasa S, Schroeder JI. Plant hormone regulation of abiotic stress responses. Nat Rev Mol Cell Biol. 2022;23(10):680-694.
Du H, Wu N, Fu J, Wang SP, Li XH, Xiao JH, Xiong LZ. A GH3 family member, OsGH3-2, modulates auxin and abscisic acid levels and differentially affects drought and cold tolerance in rice. J Exp Bot. 2012;63(18):6467-6480.
Shi Q, Zhang YY, To VT, Shi J, Zhang DB, Cai WG. Genome-wide characterization and expression analyses of the aux-in/indole-3-acetic acid (Aux/IAA) gene family in barley (Hordeum vulgare L.). Sci Rep. 2020;10(1):10242.
Kagale S, Divi UK, Krochko JE, Keller WA, Krishna P. Brassinosteroid confers tolerance in Arabidopsis thaliana and Brassica napus to a range of abiotic stresses. Planta. 2007;225(2):353-364.
Deng SX, Ma J, Zhang LL, Chen FJ, Sang ZY, Jia ZK, Ma LY. De novo transcriptome sequencing and gene expression profiling of Magnolia wufengensis in response to cold stress. BMC Plant Biol. 2019;19(1):321.
Xiang N, Hu JG, Yan SJ, Guo XB. Plant hormones and volatiles response to temperature stress in sweet corn (Zea mays L.) seedlings. J Agric Food Chem. 2021;69(24):6779-6790.
Mishra N, Jiang CK, Chen L, Paul A, Chatterjee A, Shen GX. Achieving abiotic stress tolerance in plants through antioxidative defense mechanisms. Front Plant Sci. 2023;14:1110622.
Mittler R, Zandalinas SI, Fichman Y, Van Breusegem F. Reactive oxygen species signalling in plant stress responses. Nat Rev Mol Cell Biol. 2022;23(10):663-679.
Kantakhoo J, Imahori Y. Antioxidative responses to pre-storage hot water treatment of red sweet pepper (Capsicum annuum L.) fruit during cold storage. Foods. 2021;10(12):3031.
Zhao CZ, Wang PC, Si T, Hsu CC, Wang L, Zayed O, Yu ZP, Zhu YF, Dong J, Tao WA. MAP kinase cascades regulate the cold response by modulating ICE1 protein stability. Dev Cell. 2017;43(5):618-629.e5.
Guo H, Wu TK, Li SX, He Q, Yang ZL, Zhang WH, Gan Y, Sun PY, Xiang GL, Zhang HY, Deng H. The methylation patterns and transcriptional responses to chilling stress at the seedling stage in rice. Int J Mol Sci. 2019;20(20):5089.
Cheng GM, Zhang LY, Wang HT, Lu JH, Wei HL, Yu SY. 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(14):5095.
Xie GS, Kato H, Imai R. Biochemical identification of the OsMKK6-OsMPK3 signalling pathway for chilling stress tolerance in rice. Biochem J. 2012;443(1):95-102.
Hung FY, Chen FF, Li CL, Chen C, Chen JH, Cui YH, Wu KQ. The LDL1/2-HDA6 histone modification complex interacts with TOC1 and regulates the core circadian clock components in Arabidopsis. Front Plant Sci. 2019;10:233.
Li YP, Shi YT, Li M, Fu DY, Wu SF, Li JG, Gong ZZ, Liu HT, Yang SH. The CRY2–COP1–HY5–BBX7/8 module regulates blue light-dependent cold acclimation in Arabidopsis. Plant Cell. 2021;33(11):3555-3573.
Wu H, Wu ZX, Wang YH, Ding J, Zheng YL, Tang H, Yang L. Transcriptome and metabolome analysis revealed the freezing resistance mechanism in 60-year-old overwintering Camellia sinensis. Biology (Basel). 2021;10(10):996.
Ren C, Fan PG, Li SH, Liang ZC. Advances in understanding cold tolerance in grapevine. Plant Physiol. 2023;192(3):1733-1746.
Cui P, Li YX, Cui CK, Huo YR, Lu GQ, Yan, HQ. Proteomic and metabolic profile analysis of low-temperature storage responses in Ipomoea batata Lam. tuberous roots. BMC Plant Biol. 2020;20(1):435.
Wang X, Kang WJ, Wu F, Miao JM, Shi SL. Comparative transcriptome analysis reveals new insight of alfalfa (Medicago sativa L.) cultivars in response to abrupt freezing stress. Front Plant Sci. 2022;13:798118.
Manara A, DalCorso G, Furini A. The role of the atypical kinases ABC1K7 and ABC1K8 in abscisic acid responses. Front Plant Sci. 2016;7:366.
Meng LH, Fan ZQ, Zhang QW, Gao Y, Deng YK, Zhu BZ, Zhu HL, Chen JY, Shan W, Yin XR. BEL1‐LIKE HOMEODOMAIN 11 regulates chloroplast development and chlorophyll synthesis in tomato fruit. Plant J. 2018;94(6):1126-1140.
Wu QF, Han TT, Yang L, Wang Q, Zhao YX, Jiang D, Ruan X. The essential roles of OsFtsH2 in developing the chloroplast of rice. BMC Plant Biol. 2021;21(1):445.
Ke NJ, Kumka JE, Fang MX, Weaver B, Burstyn JN, Bauer CE. RedB, a member of the CRP/FNR family, functions as a transcriptional redox brake. Microbiol Spectr. 2022;10(5):e0235322.
Huang JY, Zhao XB, Bürger M, Chory J, Wang XC. The role of ethylene in plant temperature stress response. Trends Plant Sci. 2023;28(7):808-824.
Zhang ZY, Xie WG, Zhao YQ, Zhang JC, Wang, N, Ntakirutimana F, Yan JJ, Wang, YR. EST-SSR marker development based on RNA-sequencing of E. sibiricus and its application for phylogenetic relationships analysis of seventeen Elymus species. BMC Plant Biol. 2019;19(1):235.
Xiao Y, Zhou L, Xia W, Mason AS, Yang Y, Ma Z, Peng M. Exploiting transcriptome data for the development and characterization of gene-based SSR markers related to cold tolerance in oil palm (Elaeis guineensis). BMC Plant Biol. 2014;14:384.
Zhang MA, Mao WH, Zhang GP, Wu F. Development and characterization of polymorphic EST-SSR and genomic SSR markers for Tibetan annual wild barley. PLoS One. 2014;9(4):e94881.
Zhao GQ, Liu H, Liu M. ISSR-based Genetic Diversity Analysis of Poa L. Acta Agrestia Sinica. 2011;19(05):781-786. (In Chinese)

No competing interests reported.

Download PDF

Reviewers invited by journal
07 Jul, 2024
Editor assigned by journal
02 Jul, 2024
Submission checks completed at journal
02 Jul, 2024
First submitted to journal
18 Jun, 2024

You are reading this latest preprint version

Tissue-specific transcriptomic analysis reveals the meolecular mechanisms responsive to cold stress in Poa crymophila, and development of EST-SSR markers linked to cold tolerance candidate genes

Status:

Version 1

Abstract

Figures

Background

Materials and Methods

Plant growing conditions and sample collection

Determination of indicators and analysis of data

Total RNA extraction and sequencing

Sequence data processing, de novo assembly, and annotation

Analysis of differentially expressed genes

Validation analysis of transcriptome data by qRT-PCR

Weighted gene co-Expression network analysis (WGCNA)

Development of EST-SSR markers based on cold resistance candidate genes

DNA extraction, PCR amplification, primer validation and data analysis

Results

Transcriptome assembly and annotation

Identification and analysis of DEGs

GO and KEGG enrichment analysis of DEGs

Identification of key DEGs involved in cold responses

WGCNA and identification of hub genes

Experimental validation

Development of candidate gene-based EST-SSR markers and application in genetic diversity analysis

Discussion

TFs involved in the cold stress response

Role of ICE-CBF-COR in the cold response

Role of Ca²⁺ signaling, hormone signaling, and ROS scavenging system in the cold response

Role of the circadian clock and photosynthesis in the cold response

Role of the carbohydrate metabolism under cold stress

Cold tolerance Hub genes selected by WGCNA

Candidate gene-based EST-SSR molecular Marker-Assisted Selection and application in genetic diversity analysis

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Tissue-specific transcriptomic analysis reveals the meolecular mechanisms responsive to cold stress in Poa crymophila, and development of EST-SSR markers linked to cold tolerance candidate genes

Status:

Version 1

Abstract

Figures

Background

Materials and Methods

Plant growing conditions and sample collection

Determination of indicators and analysis of data

Total RNA extraction and sequencing

Sequence data processing, de novo assembly, and annotation

Analysis of differentially expressed genes

Validation analysis of transcriptome data by qRT-PCR

Weighted gene co-Expression network analysis (WGCNA)

Development of EST-SSR markers based on cold resistance candidate genes

DNA extraction, PCR amplification, primer validation and data analysis

Results

Transcriptome assembly and annotation

Identification and analysis of DEGs

GO and KEGG enrichment analysis of DEGs

Identification of key DEGs involved in cold responses

WGCNA and identification of hub genes

Experimental validation

Development of candidate gene-based EST-SSR markers and application in genetic diversity analysis

Discussion

TFs involved in the cold stress response

Role of ICE-CBF-COR in the cold response

Role of Ca2+ signaling, hormone signaling, and ROS scavenging system in the cold response

Role of the circadian clock and photosynthesis in the cold response

Role of the carbohydrate metabolism under cold stress

Cold tolerance Hub genes selected by WGCNA

Candidate gene-based EST-SSR molecular Marker-Assisted Selection and application in genetic diversity analysis

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1

Role of Ca²⁺ signaling, hormone signaling, and ROS scavenging system in the cold response