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. 2025 Apr 22;5(5):518–534. doi: 10.1007/s43657-024-00204-7

Comparative Transcriptomic and Metabolomic Analysis of Two Related Kandelia obovata Populations in Response to Cold Wave

Jiaqi Zhang 1,2, Tianyi Fan 1, Xingxing Cai 1,2, Sheng Ouyang 1,2, Ji Yang 1,2, Zhiping Song 1,2, Wenju Zhang 1,2, Yuguo Wang 1,2, Yan Zhu 1,✉, Peng Nan 1,2,✉
PMCID: PMC13615276  PMID: 42800968

Abstract

Temperature plays a pivotal role in shaping the distribution of mangroves, as episodes of low temperature have severe impacts on their survival and geographical range. Kandelia obovata has gained recognition as an ideal species for investigating mangroves response mechanisms to cold stress. However, there is a paucity of reports focusing on its response to freezing stress and its adaptive mechanisms during recovery stages. In this study, we subjected two populations of K. obovata (southern F-J and northern Z-J) collected from the southeast coastal area of China to a simulated cold wave. We conducted phenotype analyses along with comparative transcriptomics and metabolomics investigations on these two populations, which exhibited differential freeze resistance. Our clustering analyses highlighted groups of K. obovata genes and metabolites that may contribute to the enhanced plant adaptability and survival capacity when exposed to cold wave. Notably, the freeze-resistant Z-J population exhibited thickened cuticle layer and enhanced jasmonic acid pathway during their recovery stage. This study provides novel insights into comprehending the molecular responses and survival mechanisms employed by K. obovata against cold waves, while also offering a valuable transcriptome and metabolome resource for future investigations in molecular ecology and conservation strategies for mangroves in both their native habitats and introduced environments.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1007/s43657-024-00204-7.

Keywords: Kandelia obovata, Cold wave, Transcriptome, Metabolome, Cold resistance, Jasmonic acid

Introduction

The mangrove forest is a woody community thriving in the intertidal zones of tropical and subtropical regions, renowned for its exceptional biodiversity. It stands as one of the world's most effective ecosystems in terms of carbon sequestration capacity, playing a pivotal role in mitigating wind and wave impacts, safeguarding coastlines, and preserving coastal ecological equilibrium (Su et al. 2021). While global warming offers prospects for expanding mangroves towards higher latitudes, they face substantial risks from severe weather phenomena such as cold waves (Cavanaugh et al. 2019). Kandelia obovata, a species from the Rhizophoraceae family known for its extensive geographic range and remarkable tolerance to low temperatures, is commonly employed as a key model organism in studies investigating the northward migration of mangroves. Moreover, it serves as an exceptional model species for examining the response of mangroves to extreme cold waves within the context of climate change.

Cold stress significantly impacts the growth and development of plants. Exposure to low temperatures can trigger metabolic disruptions in K. obovata, such as excessive production of reactive oxygen species (ROS), thereby hindering leaf photosynthesis through reduced pigment synthesis and antioxidant enzyme activity in young seedlings of K. obovata (Xu et al. 2024). Plants have evolved intricate and efficient regulatory mechanisms to withstand and adapt to cold stress by modulating cellular structure, regulating various osmotic regulators, enzyme activities, endogenous hormones, and the expression of cold-resistance genes. Following the publication of the inaugural reference genome for K. obovata, numerous genes associated with cold resistance in K. obovata have been identified, including the key transcription factor genes C-repeat Binding Factor 1 (KoCBF1) and KoCBF3 (Du and Li 2019; Peng et al. 2020). Recently, it has been discovered that KoCBFs play a crucial role in regulating the co-expression network involved in cold acclimation in K. obovata (He et al. 2023). Simultaneously, the transcriptional and functional levels of two vital nodes under cold stress in K. obovata, namely calcineurin B-like protein-interacting protein kinases network and mitogen-activated protein kinase (MAPK) cascade, have been investigated (Tian et al. 2023).

Notably, cold stress can be further categorized into chilling (0–15 ℃) and freezing stress (< 0 ℃). The ecological threshold for mangroves in terms of temperature is -4 °C. Extreme low temperatures (< -4 ℃) pose significant challenges for K. obovata under natural conditions (Su et al. 2019). Therefore, it becomes imperative to comprehend the adaptive mechanisms of K. obovata under extreme freezing stress. Furthermore, certain characteristics exhibited by plants subjected to cold stress, such as impaired seedling development and leaf chlorosis, may not become apparent immediately following a brief drop in temperature. Actually, the harmful effects caused by exposure to low temperatures primarily arise during the subsequent recovery period of plant growth (Zhang et al. 2016). Currently, there is limited knowledge regarding its molecular mechanism governing cold adaptation throughout each stage from pre-cold wave acclimation until it reaches extremely low temperatures and throughout its subsequent recovery period.

Despite numerous studies elucidating the transcriptional response of K. obovata to low temperatures, there have been limited reports on changes at the metabolic level or multi-omics analysis under extreme low temperatures. Metabolites, as the ultimate outcome of gene transcription and protein expression, are commonly regarded as the final plant response to environmental stress (Manickam et al. 2023). Untargeted metabolomics is a research approach that utilizes multiple techniques to screen for quantitative information on all small molecule metabolites in samples, with the aim of investigating differences in metabolic profiles between different groups (Rubert et al. 2017). Among these techniques, liquid chromatography-mass spectrometry (LC-MS) offers several advantages including high separation efficiency, rapid analysis speed, and exceptional sensitivity (Schrimpe-Rutledge et al. 2016). In this study, high-throughput sequencing and LC-MS untargeted metabolomics detection methods were employed in order to obtain a comprehensive understanding of the response mechanism of K. obovata under a simulated cold wave.

Materials and methods

Plant Materials and Cold Wave Simulation

The original natural population of K. obovata was sourced from the Jiulong River Estuary Mangrove Provincial Nature Reserve in Zhangzhou City, Fujian Province (117°92’E, 24°46’N) and introduced to Quanzhou Bay Luoyang River Mangrove Nature Reserve in Quanzhou City, Fujian Province (24°59’N, 118°59’E, referred to as the F-J population) in 2003. Subsequently, it was also introduced to Longgang Aojiangkou Mangrove in Wenzhou City, Zhejiang Province (28°12’N,120°96’E, referred to as the Z-J population) in 2005. In May 2021, mature hypocotyls were collected from both the F-J and Z-J populations of K. obovata. Healthy and mature hypocotyls of similar size without any diseases or pests were selected for transplantation into plastic pots at the Fudan Mangrove Lingang Base in Shanghai (30°90′N, 121°97′E). After a growth period of 19 months, a total of 50 healthy and homogeneous K. obovata seedlings were individually chosen from each population in December 2022. Subsequently, these seedlings were placed in an illumination incubator (Model: GXZ-380 intelligent type) with a photoperiod set at 16-hour light/eight-hour dark, temperature set at 8 ℃, 40% humidity, and 80% light intensity. For cold wave-simulating treatment, the temperature changes and durations throughout the entire cold wave-simulating treatment are presented in Fig. 1a. The temperature changes were automatically monitored every 10 min using the precision temperature and humidity recorder GSP-8 A (https://www.e-elitech.com/). For the MeJA treatment, we sprayed the leaves with a concentration of 1 µmol/L MeJA every 12 h during the first 14 days prior to and throughout the simulating cold wave. Phenotype observation and sample collection were performed at the indicated time point.

Fig. 1.

Fig. 1

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Phenotypic response of K. obovata in F-J and Z-J populations under simulated cold wave conditions. a Experimental design employed to assess the phenotype at the end of each temperature treatment. The cold wave involved exposure to temperature of 8 ℃ for one day (T1), followed by 4 ℃ for one day (T2), then 0 ℃ for one day (T3), and finally − 4 ℃ for one day (T4). Subsequently, rewarming treatment was conducted at temperature of 0 ℃ for one day (T5), followed by 4 ℃ for one day (T6), an additional duration of two days (T7), and a further four days (T8) at 4 ℃. b Phenotypes of F-J and Z-J populations at the indicated time point in the simulated cold wave. Bar = 100 mm. c Representative leaf anatomical structure in F-J and Z-J populations at the indicated time point in the simulated cold wave. Bar = 100 μm. d Com parison of morphological traits in leaves from F-J and Z-J populations at the indicated time point. Asterisks represent significant differences between F-J and Z-J populations at the same time point (p-value < 0.05, t-test). e Evaluation of survival rate on K. obovata hypocotyls subjected to temperature pretreatment at 25 °C, 4 °C, or 0 °C for a duration of 15 days, respectively, followed by field growth over a four-month period

The cold resistance of these two K. obovata populations was further compared by subjecting their hypocotyls to different temperature pretreatments (25 ℃, 4 ℃, or 0 ℃) for a duration of 15 days prior to planting them in the experimental field in Shanghai. Survival rates were recorded after four months and visualized using the R software package ggplot2.

Leaf Phenotype Observation

The second fully developed, disease-free leaf was selected as the experimental material for measuring leaf anatomical structure. Paraffin sections were prepared using the safranin-fast green method after each time point, and ImageJ software (Schneider et al. 2012) was employed to quantify parameters including leaf thickness (LT), palisade tissue thickness (PT), and spongy tissue thickness (ST). Additionally, ratios including cell tense ratio (CTR = PT/LT), spongy ratio (SR = ST/LT), and palisade-spongy ratio (P/S = PT/ST) were also determined. Each parameter was measured for a total of 30 independent biological replicates. Data visualization was performed using GraphPad Prism 8 software.

Determination of Chlorophyll and Photosynthetic Fluorescence Parameters

The relative chlorophyll levels in leaves from the F-J and Z-J populations were assessed during each time point of cold wave-simulating treatment using Dualex Scientific+ (Shanghai Zealquest Scientific Technology Co., Ltd). Leaf chlorophyll content was quantified based on the transmittance ratio between far-red absorbed by chlorophyll and near-infrared light serving as a reference. Furthermore, the maximum photochemical efficiency of photosystem II was evaluated utilizing IMAGING-PAM, a chlorophyll fluorescence imaging system.

Transcriptome Analysis

The second mature leaves of the F-J and Z-J populations were collected at time points T1 ∼ T6. Three biological replicates were included at each time point, resulting in a total of 36 samples. These samples were sent to Shanghai Majorbio Bio-Pharm Technology Co., Ltd (www.majorbio.com) for RNA extraction and library construction, followed by transcriptome sequencing using the Illumina Novaseq 6000 sequencing platform. The raw sequencing data was filtered using the Fastp software (https://github.com/OpenGene/fastp) (Chen et al. 2018) to obtain high-quality sequencing data (clean data), ensuring subsequent analysis. The HiSat2 software (http://ccb.jhu.edu/software/hisat2/index.shtml) (Kim et al. 2015) was utilized for sequence alignment against the reference genome of K. obovata (https://ngdc.cncb.ac.cn/gwh/Assembly/990/show), facilitating the retrieval of mapped data (reads) for subsequent transcriptome assembly and expression quantification.

The RSEM software (http://deweylab.github.io/RSEM/) (Li and Dewey 2011) was utilized for quantitative analysis of transcript levels, employing transcripts per kilobase per million mapped reads (TPM) as the metric. DESeq2 software (http://bioconductor.org/packages/stats/bioc/DESeq2/) (Love et al. 2014) was utilized to perform differential expression analysis between different groups of genes. Genes meeting the screening criteria|log2FC| ≥ 1 & p-value < 0.05 were identified as differentially expressed genes (DEGs). A volcano plot illustrating multiple groups of DEGs was generated using the ggplot2 package in R (Wickham 2016). The KOBAS tool (http://kobas.cbi.pku.edu.cn/home.do) (Xie et al. 2011) was employed to perform KEGG pathway enrichment analysis using the Fisher exact test. Pathway enrichment was considered significant at a p-value < 0.05, and visualization was conducted using RStudio software. Cluster analysis of the DEGs was carried out using the ClusterGVis package in R-4.2.3 and RStudio software, utilizing the K-means clustering algorithm with 20 clusters. Marker genes associated with cold resistance were identified within each cluster.

ERF Transcription Factor (TF) Family Analysis

The FIMO algorithm in the MEME software (https://meme-suite.org/meme/tools/fimo) was employed for predicting ERF TF-binding sites and target genes. Genes exhibiting a Pearson correlation coefficient (PCC) ≥ 0.8 and p-value < 0.0001 between transcription factors and target genes were screened as potential targets of ERF TFs. The regulatory network between ERF TFs and target genes was visualized using Cytoscape software (version 3.10.2).

Quantitative Reverse Transcription (RT)-PCR Verification

To validate the accuracy of the RNA sequencing results, a total of nine DEGs were selected for validation using quantitative RT-PCR, with Ko18SrRNA serving as the internal reference gene. The primer sequences can be found in Table S1. The relative expression levels of selected DEGs were determined using the 2−ΔΔCT method.

Metabolome Analysis

The second mature leaves of the F-J and Z-J populations were collected at time point T1 ∼ T6. Six biological replicates were included at each time point, resulting in a total of 72 samples. For metabolite extraction, an extraction solution (methanol / water = 4: 1) containing an internal standard (L-2-chlorophenylalanine) at a concentration of 0.02 mg/mL was added. The sample solution was then ground in a cryogenic tissue grinder for six minutes (-10℃, 50 Hz), followed by low-temperature ultrasonic extraction for 30 min (5℃, 40 kHz). Subsequently, the samples were placed at -20℃ for 30 min and centrifuged at 13,000 g for 15 min at 4 ℃. The supernatant was transferred and subjected to LC-MS analysis using the UHPLC-Q Exactive system as the instrumental platform at Shanghai Majorbio Bio-Pharm Technology Co., Ltd.

Metabolome Data Processing

The original LC-MS data were imported into the metabolomics processing software Progenesis QI (Waters Corporation, Milford, USA) for analysis. Subsequently, a data matrix comprising retention time, mass-charge ratio, and peak intensity was generated. Next, the characteristic peaks were identified using the software and matched with the metabolic database to obtain MS and MS/MS mass spectrum information for metabolite identification. The primary databases utilized included PMDB (http://www.pmdb.org.cn/) and Metlin (https://metlin.scripps.edu/), along with other reputable public databases and a self-built database by Shanghai Majorbio Bio-Pharm Technology Co., Ltd.

Data pre-processing was conducted on the Majorbio cloud platform (https://cloud.majorbio.com) (Ren et al. 2022), which included filtering and filling missing values, data normalization with QC validation, and conversion steps. The 80% rule was applied to remove missing values, retaining at least one set of samples with more than 80% non-zero values, and gaps were filled using the minimum value in the original matrix. To reduce errors caused by sample preparation and instrument instability, response intensity of essential spectrum peaks was normalized using sum normalization method. Variables with RSD greater than 30% of QC samples were removed, followed by Log10 transformation to obtain final data matrix for subsequent analysis (Kind et al. 2009).

Analysis of Differentially Accumulated Metabolites (DAMs)

The pre-processed matrix files were subjected to differential analysis. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), as well as Student's t-test and multiple difference analysis, were performed using the R software package ropls (Version 1.6.2) (Thévenot et al. 2015). DAMs were screened based on the variable importance in projection (VIP) values obtained from the OPLS-DA model and the p-values obtained from Student's t-test. Metabolites with VIP > one and p-value < 0.05 were considered as DAMs.

The KOBAS software (http://kobas.cbi.pku.edu.cn/home.do) (Xie et al. 2011) was utilized for conducting KEGG pathway enrichment analysis, employing the Fisher exact test. A significance level of p-value < 0.05 was considered to indicate pathway enrichment, and visualization was performed using RStudio software.

Ascorbic Acid (AsA) Determination

AsA was determined using spectrophotometric method. Approximately 0.1 g of fresh plant leaves were cut and weighed accurately (W). After grinding with Shanghai Jingxin sample freezer mill, AsA was extracted using a reagent kit from Suzhou Comin Biological Company (http://www.cominbio.com/) by adding 1 mL of extraction solution for homogenization in an ice bath. The mixture was centrifuged at 8000 rpm for 20 min at 4℃, and the supernatant was left on ice for 25 min before measurement. The absorbance values (Ameasurement, Ablank) at 420 nm were measured using a spectrophotometer to calculate the AsA content according to the following formula: ΔA = Ameasurement - Ablank; and AsA (µmol/g) = 0.6451 × (ΔA + 0.018) / W.

Conductivity Measurement

The relative conductivity was determined using a conductivity meter. The second leaf of young K. obovata seedlings was carefully selected and gently cleansed with distilled water to remove any surface dirt. This was followed by rinses with distilled water and deionized water, respectively. The leaves were then dried using filter paper, and punchers were used to obtain equal-sized leaf samples while avoiding the veins. These samples were fully immersed in test tubes containing 15 mL of deionized water and left at room temperature for 15 h before measuring the initial conductivity (R0) using a DDS-11 A model conductivity meter. Subsequently, the samples were subjected to heating in a water bath at 100 ℃ for 25 min, cooled down to room temperature, and their conductivities (R1) were measured again using the same method. Finally, the relative electrical conductivity (%) was calculated as R0 / R1 × 100.

Results

Cold Wave-Induced Damage on Two Populations of K. Obovata

We subjected two related populations of K. obovata, namely the southern F-J (24°59’N, 118°59’E) and northern Z-J (28°12’N,120°96’E) (see plant materials), to a controlled freezing-rewarming treatment that simulated a natural cold wave (Fig. 1a). During the freezing stage (T1 ∼ T4), the leaves of K. obovata gradually experienced water loss and subsequent softening; however, both populations showed no apparent signs of freezing-induced damage. In the rewarming stage (T5 ∼ T8), leaves from the F-J population continued to undergo dehydration, resulting in browning and wilting. In contrast, leaves from the Z-J population suffered significantly less severe cold damage (Fig. 1b). The plant tannins, which are a group of antioxidant compounds, play a crucial role in protecting biological structures from oxidative stress caused by cold temperatures (Mora et al. 2022). In comparison to F-J leaves, Z-J leaves exhibited a significant increase in the accumulation of secondary metabolite tannins in the adaxial epidermal cells, especially during the later stage (T4 ∼ T8) (Fig. 1c).

The simulated cold wave caused structural disruption and made it difficult to distinguish between palisade tissue and sponge tissue (Fig. 1c). Cold-induced damage affected the cell protoplasm, resulting in reduced thickness of leaf as well as both palisade and sponge tissues. Statistical analysis revealed that Z-J leaves exhibited a smaller decrease in important leaf morphological traits like palisade tissue thickness compared to F-J leaves during the freezing-rewarming treatment. Furthermore, Z-J leaves consistently demonstrated better maintenance of anatomical traits including higher cell tense ratio and palisade/spongy tissue ratio throughout the treatment (Fig. 1d and Table S2).

To provide further evidence for the enhanced cold resistance of the Z-J population, we subjected hypocotyls from both K. obovata populations to different temperature pretreatments and assessed their survival rates after four months of field planting. After a 15-day control treatment at 25 °C, both F-J and Z-J populations exhibited normal survival rates of 100%. However, when exposure to a chilling (4 °C) or freezing (0 °C) treatment for 15 days, their survival rates significantly declined, with the Z-J population demonstrating superior cold resistance compared to the F-J population (Fig. 1e).

The Identification and Functional Enrichment Analysis of Differentially Expressed Genes (DEGs) in Two Populations of K. Obovata Exposed to Cold Wave

We employed RNA sequencing to investigate the transcriptome response of both populations of K. obovata under the simulated cold wave, with three biological replications conducted. Given the severe phenotypes observed in the F-J population after T6 (Fig. 1b), our transcriptome analysis primarily focused on time points ranging from T1 to T6 (Fig. 2a). This resulted in a total of 36 cDNA libraries being generated (i.e., F-J_T1 ∼ T6 and Z-J_T1 ∼ T6, replicate 1 ∼ 3) (Table S3 and S4). To elucidate the molecular mechanisms underlying the superior cold wave-resistance of Z-J population compared to F-J population, we selected F-J_T1 as a control group and conducted a comprehensive analysis of DEGs in K. obovata across distinct time points and populations (Fig. 2b, Tables S5 and S6). We totally identified 3810 DEGs, and also conducted KEGG pathway enrichment analysis on the corresponding DEGs at each time point (Fig. 2c, Table S7).

Fig. 2.

Fig. 2

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Transcriptional responses of two populations of K. obovata to the simulated cold wave. a Experimental design of the sample collection at T1 ∼ T6 time points for transcriptome analysis. b Differential gene expression analysis to compare all genes in indicated sample (y-axis) with those in F-J_T1, where significantly down-regulated genes are represented by blue spots and significantly up-regulated genes are represented by red spots, respectively. The differential gene expression levels are depicted as Log2FC (x-axis), along with the number of DEGs. c KEGG pathway enrichment analysis of identified DEGs. The coloring of the p-values represents the significance of the matched gene ratio; the circle size represents the gene count

Our findings revealed significant changes in gene transcription levels between the two populations throughout the cold wave. Overall, a higher number of DEGs was observed during the rewarming stage (T5 ∼ T6) compared to the freezing stage (T2 ∼ T4), with a greater abundance of upregulated genes observed in the Z-J population than in the F-J population (Fig. 2b). Intriguingly, pathways associated with plant-pathogen interaction, MAPK signaling pathway, phenylpropanoid biosynthesis, and other pathways were consistently enriched during the cold wave in both populations; notably, these enrichments were more pronounced in the Z-J population (Fig. 2c).

The Photosynthetic Efficiency of K. Obovata was Significantly Impacted by Cold Wave

Notably, we observed a significant enrichment of DEGs in the pathways associated with photosynthesis and photosynthesis-antenna proteins (Fig. 2c). The extent of photosynthetic impairment during the cold wave was then evaluated. The ratio of variable fluorescence (Fv) to maximum fluorescence (Fm), the Fv/Fm value, serves as an indicator for evaluating the potential maximum photosynthetic capacity of plants. A higher Fv/Fm value indicates enhanced stress resilience and improved health status (Römermann et al. 2016). During the cold wave (T1 ∼ T6), the leaves of the F-J population experienced a significant decrease in Fv/Fm value, while those of the Z-J population largely maintained their initial values, resulting in substantial divergence in photosynthetic efficiency (Fig. S1a and b, Table S8).

The chlorophyll content is a crucial physiological trait that governs the photosynthetic efficiency of plants. During the freezing stage, both populations of K. obovata initially exhibited a transient increase in chlorophyll levels, followed by subsequent decreases. However, the F-J population experienced a significant decline in chlorophyll levels during the later stage, with an average reduction of 42.15% compared to the control group (T1). In contrast, the Z-J population demonstrated overall stable fluctuations in chlorophyll levels (Fig. S1c, Table S8).

Upon exposure to extreme low temperatures (T4), the F-J population exhibited a significant decrease in transcription levels of genes related to photosynthesis and photosynthesis-antenna, which remained consistently low thereafter, indicating irreversible damage to the photosynthetic system. In contrast, the Z-J population maintained high transcription levels of these genes with a temporary down-regulation at T5 followed by recovery to normal expression levels (Fig. S1d, Table S9).

Analysis of Differentially Expressed Transcription Factor Genes

Among the 3810 DEGs, we employed the Plant Transcription Factor Database (PlantTFBD v5.0, https://planttfdb.gao-lab.org/) to identify a total of 411 genes encoding various types of transcription factors (TFs) (Table S10). Notably, the Ethylene Response Factor (ERF) TF family, characterized by an ERF domain comprising 57 ∼ 70 amino acids, exhibited a predominant representation with 51 genes identified (Fig. S2a, Table S11).

The ERF TFs are an exceptional superfamily of plant-specific transcription factors that play a pivotal role in regulating plant responses to both biotic and abiotic stresses (Feng et al. 2020). Therefore, we employed RStudio software to visualize the expression levels of the 51 ERF TF genes in response to cold wave (Fig. S2b, Table S12). The results demonstrated a significant upregulation of the majority of ERF TFs in the Z-J population under cold wave conditions compared to the F-J population. The target genes of these ERF TFs were screened using Pearson correlation coefficients (|PCCs| ≥ 0.8) and a p-value threshold of < 0.0001 for screening. Subsequently, the regulatory network between transcription factors and their target genes was visualized using Cytoscape software (version 3.10.2) (Fig. S2c). The results revealed that 13 ERF TF genes exhibit positive correlations with their respective target genes (Table S13), indicating potential positive regulation by these ERF TF genes in response to cold wave.

As a pivotal regulator of cold stress, the C-repeat binding factor (CBF) genes belong to the ERF family. In this study, we focused on KoCBF3 (geneMaker00008995) (Peng et al. 2020), and identified a total of 23 target genes, with 19 having annotated gene information (Fig. S3a, Table S14). Importantly, these genes exhibited robust activation in response to the cold wave in the Z-J population but showed no significant response in the F-J population (Fig. S3b). Among these activated genes are several that have been reported to play crucial roles in conferring plant freezing tolerance. For instance, Trp-Arg-Lys-Tyr (WRKY) transcription factors constitute a substantial family of plant transcription factors, characterized by their conserved peptide sequence ‘Trp-Arg-Lys-Tyr-Gly-Gln-Lys’. K. obovata WRKY40 (KoWRKY40) encodes a crucial regulatory factor that primarily responds to cold stress and is associated with the regulation of biological processes, stimulus response, and small molecule metabolism. Overexpression of KoWRKY40 has been demonstrated to enhance cold resistance of K. obovata (Fei et al. 2022). Low temperature conditions significantly induce the EXPANSIN-like A2 (EXLA2) protein, and Arabidopsis mutants deficient in exla2 exhibit reduced tolerance to cold stress (Abuqamar et al. 2013). We propose that the activation of these downstream genes may contribute to the function of KoCBF3 as master TF gene in facilitating K. obovata resistance against cold stress.

Cluster Analysis of DEGs in K. Obovata Exposed to Cold Wave

Based on the observed transcriptional changes, we conducted clustering analysis on all identified DEGs (Fig. 2b) and categorized them into 20 gene groups (G1 ∼ G20) (Table S15). Among these groups, a total of 12 gene groups (G1 ∼ G12) exhibited higher transcription levels in Z-J population (Fig. 3a), while another set of eight gene groups (G13 ∼ G20) exhibited higher transcription levels in F-J population (Fig. 3b), at distinct time points in the cold wave. Each gene group contains marker genes previously reported to be involved in plant cold response or resistance. Additionally, Gene Ontology (GO) functional enrichment analysis was conducted to gain deeper insights into the function of DEGs within each gene group (Table S16).

Fig. 3.

Fig. 3

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Clustering analysis of DEGs. Gene expression levels were standardized using a standard normal distribution, resulting in relative expression levels represented as Z-scores for each gene at each time point. The color gradients indicate varying relative expression levels of distinct genes. The left panel illustrates gene expression trends for both populations, with the Z-J population depicted by a blue line and the F-J population represented by a red line, respectively. On the right panel of the heat map, each group is characterized by marker genes previously reported to be involved in plant response to cold stress. Furthermore, enriched Gene Ontology (GO) entries associated with each group are indicated on the rightmost side. a Clustering analysis of DEGs with elevated transcription levels in the Z-J population. b Clustering analysis of DEGs with elevated transcription levels in the F-J population

Among the predominant upregulated gene groups in Z-J population, as compared to F-J populations, the G4 group demonstrates the most significant disparity in average transcription levels between these two populations during the early freezing stage (T1 ∼ T3) (Fig. 3a). This particular group is specifically enriched with"hydrolase activity" and related "lyase activity". We propose that this gene group may play a crucial role in maintaining a dynamic equilibrium between biological polymers and monomers, as well as regulating cellular osmotic pressure in response to cold stress.

The genes belonging to the G1 ∼ G2 groups exhibited higher transcription levels in Z-J population in the early freezing stage (T2) and rewarming stage (T5). Within the protein products of these two gene groups, several pivotal regulators, including the crucial CBF TFs, have been identified. These gene groups also include genes encoding respiratory burst oxidase homolog protein B, which governs ROS production in stress response (Zhang et al. 2023). In consistence, we observed that the GO enrichment of "oxidoreductase activity" was predominantly identified in gene groups G1 ∼ G12 but rarely in G13 ∼ G20. This suggests that the Z-J population may possess a higher capability to address the redox state imbalance triggered by cold stress or initiate ROS signaling for cold acclimation.

The DEGs in G3 exhibited the most significant upregulation during the rewarming stage T6, while the DEGs in G7 ∼ G8 showed the highest upregulation during T5. Notably, these DEGs include multiple genes associated with secondary metabolic processes and responses to external stimuli. For instance, peroxidase has been reported to be involved in lignin synthesis (Nizam et al. 2023). Interestingly, we identified several key players implicated in the jasmonic acid (JA) pathway within these gene groups. The protein products include jasmonate ZIM-domain (JAZ) proteins involved in JA signal transduction, Lipoxygenase1-5 participating in linoleic acid synthesis, and fatty acid desaturases A involved in unsaturated fatty acid synthesis and extension (Sharma and Laxmi 2015). Additionally, gene enrichment analysis unveiled the involvement of diverse plant hormone pathways, including brassinosteroid (BR) and ethylene (ET), indicating a complex network of hormone signaling underlying the plant's response to cold wave in the Z-J population (Fig. S4).

Notably, the transcription levels of genes in group G11 ∼ G12 exhibited a significant decline during the rewarming stage in the F-J population, whereas they remained largely unchanged in the Z-J population. Representative genes encode glutathione reductase (GSR) and ascorbate peroxidase (APX), which play crucial roles in the glutathione-ascorbic acid (AsA) cycle and are essential for scavenging excess ROS in chloroplasts under cold stress conditions observed in mangroves (Liu et al. 2019) and protecting plant cells against adverse environmental conditions (Caverzan et al. 2012). The downregulation of these genes in the F-J population during the rewarming stage aligns with the observed phenotype after simulated cold wave, while concurrently, their transcription levels in the Z-J population suggest their potential contribution towards enhanced cold tolerance of K.obovata.

The transcription levels of DEGs in G13 ∼ G18 were found to be significantly higher in the F-J population compared to the Z-J population during the early stage (T1 ∼ T2). On the other hand, DEGs in G19 ∼ G20 consistently exhibited upregulation in the F-J population but not in the Z-J population throughout the late rewarming stage (T4 ∼ T6) (Fig. 3b, Table S16). Considering the phenotypic characteristics observed in the F-J population during this time frame, we cannot exclude the possibility that these transcriptional alterations in these genes may be attributed to the passive activation triggered by irreversible leaf damage.

The accuracy and reliability of transcriptome data were assessed by examining the transcription levels of multiple well-established genes (Fig. S5, Table S17), which displayed a consistent change pattern with the transcriptome analysis (Table S18). Collectively, our clustering analysis in two populations provides unprecedented insights into how DEGs from distinct groups contribute to K. obovata response to cold wave at various time points.

Identification and Functional Enrichment Analysis of Differentially Accumulated Metabolites (DAMs) in Two Populations of K. Obovata Exposed to Cold Wave

Using LC-MS non-targeted metabolomics technology, a total of 3013 metabolites from K. obovata were identified in this study. The functional classification of the identified metabolites was performed utilizing the KEGG database (Fig. S6). The metabolites of two populations of K. obovata were subjected to differential analysis, with the F-J_T1 sample serving as a control. This analysis resulted in the identification of 1302 differentially accumulated metabolites (DAMs) throughout the cold wave (Table S19). Subsequently, these DAMs were further investigated by cross-referencing them with the plant metabolome database. Multivariate statistical analysis techniques were employed to screen for DAMs among various samples, consistently yielding congruent findings (Fig. S7).

The F-J population exhibited a highest number of both up- and down-regulated DAMs at T4, which represents the lowest freezing temperature, indicating an intensified metabolic response during freezing. In contrast, the Z-J population maintained relatively stable homeostasis with a higher number of DAMs throughout the rewarming stage (T4 ∼ T6) (Fig. 4a).

Fig. 4.

Fig. 4

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Changes in the metabolome of two K. obovata populations. a The identified DAMs in this study. Significantly down-regulated DAMs are shown in blue, and significantly up-regulated DAMs are shown in red, respectively. The y axis represents the number of DAMs. b KEGG pathway enrichment analysis on the identified DAMs. The coloring of the p-values represents the significance of the matched metabolite ratio; the circle size represents the metabolite count. c Clustering analysis was conducted on these identified DAMs. Accumulated metabolite levels were standardized using a standard normal distribution, resulting in relative accumulation levels represented as Z-scores for each metabolite at each time point. The color gradients indicate varying relative accumulation levels of distinct metabolite. The left panel illustrates metabolite accumulation trends for both populations, with the Z-J population depicted by a blue line and the F-J population represented by a red line, respectively

Most KEGG enrichments of identified DAMs exhibit a pattern that is strongly induced by cold stress, showing a higher upregulation in cold-stressed Z-J samples or even appearing in the control group Z-J_T1 (Fig. 4b, Table S20). Notably, many enriched pathways are specific to the Z-J population. For example, only in Z-J samples at the later stages (T4 ∼ T6), can we find KEGG pathways with metabolites enriched in "ascorbate and aldarate metabolism", "linolenic acid metabolism", and "cutin, suberine and wax biosynthesis"; this finding aligns with our transcriptome analysis results. To verify the observed KEGG changes, we utilized the spectrophotometric method to quantify the concentration of AsA. In comparison to the F-J population, the Z-J population exhibited significantly increased levels of AsA during later stages T4 and T5, which aligns with the metabolome data (Fig. S8, Table S21).

The DAMs were further subjected to clustering analysis, resulting in their classification into ten metabolic Groups (m'G1 ∼ m'G10) (Fig. 4c, Table S22). This analysis revealed distinct metabolic patterns between the gradual freezing stage and subsequent rewarming stage. Notably, a significant increase in metabolite abundance was observed for DAMs belonging to groups m'G3 and m'G8 at the T4 of F-J population. Similarly, during the rewarming stage of Z-J population, there was a significant increase in metabolite abundance of DAMs from groups m'G1, m'G4, m'G5 and m'G6. Based on these findings (Fig. 4c), it can be speculated that these specific metabolites may contribute to differences in cold wave resistance between these two K. obovata populations.

The Impact of Cold Wave on the Biosynthesis of Cutin and the Cuticle Thickness in K. Obovata

Plant long-chain fatty acids serve as precursors for the biosynthesis of wax, cutin, and suberin (Fig. 5a), which constitute the outermost layers of plant leaves. These layers play crucial roles as structural and defense components for protection (Batsale et al. 2021). During the rewarming stage, a significant upregulation in transcription levels of genes encoding enzymes involved in fatty acid elongation, wax biosynthesis, as well as cutin and suberin biosynthesis was observed. Notably, the K. obovata genome contains multiple homologous genes encoding isozymes of β-ketoacyl-CoA synthase (KCS) that catalyze the intermediate products long-chain 3-oxoacyl-CoA. Each K. obovata population activates a distinct set of KCS homologous genes during the rewarming stages (Fig. 5b), implying that these two populations may employ different metabolic machineries or pathways in response to cold wave. Additionally, our study has identified seven DAMs that act as intermediates in the biosynthesis of cutin and suberin. During the rewarming period, the Z-J population exhibited a sustained accumulation of these metabolites, which is consistent with its enhanced cold resistance (Fig. 5c, Table S23). Consistently, this study observed a gradual increase in linoleic acid and palmitic acid levels within the Z-J population as temperature decreased, while minimal changes were observed in the F-J population (Fig. S9).

Fig. 5.

Fig. 5

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Cold wave effects the elongation of fatty acid and the biosynthesis of cutin, suberin and wax. a Elongation of fatty acid and biosynthetic pathway of wax, cutin and suberin. Yellow ellipses represent key genes, and circles represent metabolites. The identified DEGs and DAMs in cold wave are both highlighted in red. b Heat maps illustrating relative expression levels represented as Z-scores for each mentioned biosynthesis gene at each time point. c The expression profile of DAMs involved in the biosynthesis of cutin and suberin. d The left arrow shows the position of the cuticle. Bar = 100 μm. The right chart represents the proportion of cuticle thickness to leaf thickness during the cold wave. Asterisks represent the significant differences between F-J population (red line) and Z-J population (blue line) at the same time point (p-value < 0.05, t-test)

Through comparative analysis of leaf anatomical structure changes, it was observed that both populations exhibited an increase in the proportion of the cuticle layer relative to the total leaf thickness during the rewarming stages (Fig. 5d). This finding provides additional evidence supporting the adaptive role played by the cuticle layer in enhancing cold resistance in K. obovata. Importantly, a rapid and significant increase specifically within the Z-J population compared to the F-J population was found during rewarming stages (T4 ∼ T5) (Fig. 5d, Table S24), indicative of its potential contribution towards improved freezing tolerance.

JA Biosynthesis and Signal Transduction in K. Obovata was Induced During the Rewarming Stage of Cold Wave

In this study, we observed the differential expression of many genes implicated in JA biosynthesis and JA signal transduction in both populations (Fig. S4 and S10a, Table S25). JA is synthesized in plants via the α-linolenic acid pathway, with Jasmonate Resistant 1 (JAR1) enzyme facilitating conjugation between JA and leucine/isoleucine to form bioactive JA-isoleucine (JA-Ile). This process promotes ubiquitin-mediated degradation of JAZ proteins, which are transcriptional repressors crucial for regulating JA signaling in plants, leading to release of pivotal transcription factor MYC2 that initiates transcriptional activation of early JA response genes conferring resistance against low temperature stress (Guan et al. 2023; Wang et al. 2023a).

Notably, the transcription levels of key enzymes involved in JA synthesis were rapidly upregulated at T5 following freezing stage in the Z-J population. Moreover, genes encoding JAZ proteins exhibited significant up-regulation during this period. In contrast to these findings in the Z-J population, JA biosynthesis and signal transduction genes remained at low levels in the F-J population (Fig. S4 and S10b). Consistent with the transcriptomic data findings, there was a sustained increase observed in a total of 14 DAMs associated with JA synthesis in the Z-J population, especially during the rewarming stage (Fig. S10c, Table S26). These results imply a strong correlation between resistance to cold stress and enhanced generation of JA signaling molecules in Z-J population; conversely, no significant changes were observed in the F-J population.

To provide additional evidence for the contribution of enhanced JA pathway to the higher cold resistance of K. obovata, we subjected seedlings from the cold-sensitive F-J population to exogenous MeJA treatment. The application of MeJA significantly bolstered the cold tolerance of F-J population and slowed down the rate at which plant leaves were damaged by cold waves (Fig. 6a). Intriguingly, no accumulated tannins were observed in the adaxial epidermal cells (Fig. 6b), suggesting that this metabolite may not be directly regulated by the JA pathway.

Fig. 6.

Fig. 6

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Exogenous MeJA treatment can significantly improve the cold-resistance of F-J population to the cold wave. a Phenotypes of F-J population with exogenous MeJA treatment at the indicated time point during the cold wave. Bar = 100 mm. b Representative leaf anatomical structure at the indicated time points. Bar = 100 μm. c The relative electrical conductivity (%) of Z-J seedlings, F-J seedlings, and F-J seedlings with MeJA treatment. Asterisks represent the significant differences at each time point (p-value < 0.05, t-test)

When plants are exposed to cold stress, the plasma membrane becomes the primary target for damage, resulting in an increase in cell membrane permeability and electrolyte leakage, leading to an enhanced relative electrical conductivity. Prolonged exposure to cold waves resulted in a significantly greater increase in overall relative electrical conductivity in the F-J population compared to the Z-J population, with peak value observed at T6. Interestingly, MeJA treatment significantly alleviated the severity of electrolyte leakage in the F-J population (Fig. 6c, Table S27), indicating a strong correlation between cold resistance and JA signaling among different populations of K. obovata.

Discussion

The Z-J Population Exhibited Different Changes in Leaf Morphological Structure upon Cold Wave

The adaptability of K. obovata to cold environments is contingent upon the degree of cold domestication within their population. Leaf internal morphological structure serves as a direct indicator for plants to acclimate to environmental changes, while leaf functional traits facilitate adaptation strategies in response to changing conditions (Pavanetto et al. 2024). By simulating a cold wave event, we discovered that K. obovata can adjust to low temperatures by reducing leaf thickness and increasing the ratio of palisade tissue/spongy tissue, thereby enhancing structural tension and promoting light penetration from the leaf epidermis to mesophyll layers. The adjustment enhances their capacity to capture and convert light energy. During the rewarming stages, the F-J population exhibited significant damage resulting from cold stress, characterized by a propensity for cellular structure fragmentation or breakage. In contrast, the Z-J population maintained relatively intact cellular structures throughout the freezing stage, with both palisade tissue/spongy tissue ratios and structural tension progressively increasing. This suggests that the Z-J population employs an adaptive strategy to mitigate cold stress.

The F-J and Z-J Populations Exhibited Distinct Responses to cold Waves During Their Adaptation to the Environment

When plants are exposed to cold stress, multiple aspects of photosynthesis are impacted, leading to not only direct damage to the photosynthetic structure but also affecting photophosphorylation, photosynthetic electron transfer, and photosynthetic enzyme systems. Therefore, investigating the effects of low temperature on photosynthesis is a pivotal research focus in plant science (Burnett and Kromdijk 2022).

In this study, we observed distinct photosynthetic response strategies between the cold-sensitive F-J population and the cold-tolerant Z-J population during a cold wave event. The reduction in the Fv/Fm ratio provided evidence of photoinhibition of photosystem II (PSII). Our findings revealed that when exposed to freezing stress, the F-J population exhibited a more pronounced decrease in both effective quantum yield (Fv/Fm) and chlorophyll content of PSII compared to the Z-J population, indicating a more severe degradation of the photoreaction center in the F-J population (Fig. S1). At an extreme low temperature of -4℃, significant alterations in gene expression associated with photosynthesis were observed in the F-J population, encompassing light capture, photorespiration, and carbon fixation processes within photosynthetic organisms (Table S9). Consequently, the capacity for electron transfer in photosynthesis was attenuated, leading to a substantial decline in photosynthetic activity. As a result, the photoinhibition phenomenon became more pronounced and caused irreversible damage to the photosynthetic system in K. obovata.

In contrast, the Z-J population exhibited a recovery to basal levels after a temporary downregulation of DEGs associated with photosystem activity and electron transport at time point T5, effectively protecting the photosystem against cold-induced damage in K. obovata. Variations in gene expression related to photosynthesis and light inhibition likely play significant roles in determining the divergence of cold tolerance between these two populations.

Transcriptional and Metabolic Responses of K. Obovata upon Cold Wave

In the transcriptome analysis, we identified 315 upregulated genes and 227 downregulated genes in the Z-J population compared to the F-J population at time point T1. These DEGs reflect the inherent transcriptomic differences between the two populations, which are accompanied by limited enrichments in KEGG analysis (Fig. 2c). Notably, the Z-J population displays several exclusive enrichments, such as plant hormone signal transduction and diterpenoid biosynthesis. Moreover, specific enrichments are exclusively observed in the Z-J population following simulated cold wave exposure, including linoleic acid metabolism, biosynthesis of various plant secondary metabolites, and glycerolipid metabolism. Diterpenoid biosynthesis and linoleic acid metabolism play pivotal roles in the synthesis of photosynthetic pigments and fat-soluble hormones (e.g., JA) in plants (Wang et al. 2023b). These disparities likely reflect distinct transcriptional adaptive mechanisms between these two populations that significantly contribute to the enhanced cold resistance of the Z-J population while potentially facilitating their adaptation to cold stress; this notion is further supported by our subsequent metabolome analysis.

Additionally, certain enrichments ectopically in the unstressed samples of the Z-J population but not in those of the F-J population, such as phenylpropanoid biosynthesis, were also observed in both populations after exposure to a cold wave, with higher gene numbers and greater enrichment significance specifically in the Z-J population. Recent investigations have reported that exposure to cold stress induces the upregulation of key structural genes involved in the phenylpropanoid pathway. Phenylpropanoid biosynthesis is a crucial metabolic pathway that facilitates plant survival under low-temperature conditions by producing various secondary metabolites, such as lignin and flavonoids (Dong and Lin 2021). We propose that these enhancements may functionally contribute to K. obovata’s response to cold waves, which have been ectopically activated in the Z-J population even without cold stress.

The ICE-CBF pathway plays a pivotal role in plant cold response, and JA has been reported to positively regulate the ICE-CBF pathway to enhance freezing tolerance in many model plants (Hu et al. 2013). In this study, we identified a total of 23 target genes of KoCBF3, which exhibited robust activation in response to the cold wave in the Z-J population but showed no significant response in the F-J population. Moreover, the combined analysis of transcriptomes and metabolomes revealed a significant increase in JA level, as well as the transcription levels of JA biosynthesis and response genes, specifically observed in the cold-tolerant Z-J population, but not in F-J population. Notably, during the rewarming stage at T5, several genes associated with JA biosynthesis pathway were rapidly up-regulated. In consistence, there was a sharp rise in DAMs associated with JA content and JA synthesis intermediates in the rewarming stage. It is noteworthy that JAZ transcription factor genes occupy a central position within the JA signal transduction pathway and determine the downstream branch (ET- or ABA-branch) pathways activation depending on distinct protein partners (Stroud et al. 2022). We identified eight DEGs encoding JAZ proteins in Z-J population, which may synergistically contribute to cold adaptation regulation of K. obovata.

Conclusion

Collectively, through simulating the temperature changes in cold wave, we performed phenotypic, transcriptomics and metabolomics analysis on the cold-sensitive F-J population and cold-resistant Z-J population. Our findings revealed distinct transcriptional and metabolic response patterns between these two populations upon cold wave. Furthermore, our study identified a more pronounced induction of transcriptional and metabolic activity during the rewarming stage following freezing stress, leading to the identification of potential genes and metabolites that may contribute significantly to enhancing K. obovata's adaptation and recovery ability under freezing stress. The underlying mechanisms may be associated with the differential regulatory pathways in genetic cis-element and/or epigenetic modulation which require comprehensive elucidation through future omics analyses. A profound comprehension in this field will greatly contribute to enhancing plant freezing resistance through genetic engineering in the future.

Electronic Supplementary Material

Supplementary Material 1 (32.7MB, xlsx)
Supplementary Material 2 (4.3MB, docx)

Acknowledgements

The present manuscript commemorates professor Yang Zhong's enduring vision and exceptional contributions to the mangroves in China. This work was supported by the Public Welfare Founds from Morgan Stanley Securities (China) Co., Ltd. and Shanghai Genshi Medical Technology Development Co., Ltd. and the National Natural Science Foundation of China (32270629 and 32070201).

Abbreviations

ROS

Reactive oxygen species

CBF

C-repeat binding factor

MAPK

Mitogen-activated protein kinase

LC-MS

liquid chromatography-mass spectrometry

LT

Leaf thickness

PT

Palisade tissue thickness

ST

Spongy tissue thickness

CTR

Cell tense ratio

SR

Spongy ratio

P/S

Palisade-spongy ratio

TPM

Transcripts per kilobase per million mapped reads

DEG

Differentially expressed gene

PCC

Pearson correlation coefficient

RT

Reverse transcription

PCA

Principal component analysis

OPLS-DA

Orthogonal partial least squares discriminant analysis

DAM

Differentially accumulated metabolite

VIP

Variable importance in projection

AsA

Ascorbic acid

Fv

Variable fluorescence

Fm

Maximum fluorescence

ERF

Ethylene response factor

TF

Transcription factor

EXLA2

EXPANSIN-like A2

GO

Gene ontology

JA

Jasmonic acid

JAZ

Jasmonate ZIM-domain

BR

Brassinosteroid

ET

Ethylene

ABA

Abscisic acid

GSR

Glutathione reductase

APX

Ascorbate peroxidase

KCS

β-ketoacyl-CoA synthase

JAR1

Jasmonate Resistant 1

JA-Ile

Jasmonic acid-isoleucine

PSII

Jasmonic acid-isoleucine

Authors' Contributions

PN and YZ planned and designed the research; JZ, TF, XC and SO performed experiments and analyzed data; JY, ZS, WZ, YW participated in the research design, discussed the data and revised the manuscript; JZ, PN and YZ wrote the manuscript.

Data Availability

The data presented in the study are deposited in the NCBI repository, accession number PRJNA1093421 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1093421). All untargeted metabolomic data used in this publication have been deposited to the EMBL-EBI MetaboLights database with the identifier MTBLS9845. The complete data set can be accessed at https://www.ebi.ac.uk/metabolights/MTBLS9845.

Declarations

Ethical Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Conflict of Interest

There is no conflict of interest regarding the publication of this paper.

Contributor Information

Yan Zhu, Email: zhu_yan@fudan.edu.cn.

Peng Nan, Email: nanpeng@fudan.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer, Cham. 10.1007/978-3-319-24277-4 [DOI]

Supplementary Materials

Supplementary Material 1 (32.7MB, xlsx)
Supplementary Material 2 (4.3MB, docx)

Data Availability Statement

The data presented in the study are deposited in the NCBI repository, accession number PRJNA1093421 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1093421). All untargeted metabolomic data used in this publication have been deposited to the EMBL-EBI MetaboLights database with the identifier MTBLS9845. The complete data set can be accessed at https://www.ebi.ac.uk/metabolights/MTBLS9845.

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