Plant material and experimental design

The heat-tolerant L. longiflorum and the heat-sensitive L. distichum were selected and cultivated in the tissue culture room of Yangtze Normal University under the conditions of 70% relative humidity under 25°C/18°C (day/night) and 16 h/8 h (light/dark). After 2 months of growth in aseptic condition from lily scales, the lily plantlets were comprised two groups. The control sample (CK: 0 h, 2 h, 8 h, 16 h, 24 h, 48 h, 72 h) was exposed to 25°C in 16 h/8 h (light/dark) and the heat-treated samples (HT: treatment for 0 h, 2 h, 8 h, 16 h, 24 h, 48 h, 72 h) was exposed to 42°C in the same light/dark conditions in a climate chamber respectively. At each time point, three bottles of these lily samples, each with 4 or 5 independent seedlingswere cleaned and immediately frozen in liquid nitrogen for RNA extraction and the same sampling were further performed for biochemical analysis. We abstracted cDNAs from LD_CK(0h), LD_CK(2h), LD_CK(24h), LD_0h, LD_2h, LD_24h, and LL_CK(0h), LL_CK(2h), LL_CK(24h), LL_0h, LL_2h and LL_24h, a total of 36 samples for qRT-PCR experiment with three replicates. Three independent biological replicates were performed, and a total of 42 samples for each type of lily were prepared together with the control sample, which were selected for physiological analysis of chlorophyll, electrical conductivity, carbohydrate and proline analyse.

Physiological analysis of chlorophyll, electrical conductivity, carbohydrate and proline

Total chlorophyll content was quantified according to the method described by Sims and Gamon (2002) in three replicates per treatment [26]. Pigments were extracted with 10 mL of acetone/Tris (50 mM) buffer at pH 7.8 (4:1) (v/v). After homogenization and centrifugation, supernatants were used to read absorbance at 537, 647 and 663 nm (UV-1800, www.aoe-sh.com, Shanghai, China), and the pigment content was determined.

Electrical conductivity was measured using an electrical conductivity analyser (DDS-307, mL.fzchina.com, Chengdu, China). Three replicates of 100 mg of fresh leaves were collected after high temperature exposure and immediately immersed in sterile de-ionized water. After incubation at room temperature in 24 mL under agitation at 40 rpm, the initial conductivity of each was recorded as S1. Then, the leaves were cooked in boiling water for 15 min; after cooling to room temperature, the total conductivity of each sample was recorded as S2. Relative electrical conductivity (REC) was calculated using the following equation:

Data of the total soluble sugars and the starch content were collected as previously described in Escandon et al. (2016) [24]. Briefly, 50 mg of each frozen leaf was used to extract total soluble sugars, and after centrifugation, the pellet was used to quantify starch content. Absorbance was read at 625 nm and both contents were determined against a D-glucose standard curve. Three biological replicates were performed for each time-point treatment.

Proline content was determined according to Bates et al. (1973) [27]. Three replicates of 100 mg of frozen leaves were homogenized in 4 mL of 3% sulfosalicylic acid and centrifuged. Equal volumes (2 mL) of ninhydrin, glacial acetic acids and the supernatant were incubated at 100°C for 1 min. After cooling to room temperature, 4 mL of toluene was added for the extraction of the chromophore, and the absorbance was analysed at 520 nm by spectrophotometer (UV-1800, www.aoe-sh.com, Shanghai, China). A standard curve of proline was used for calculating the proline concentration.

RNA isolation sequencing library construction and sequencing

According to the manufacturer’s instructions, total RNA was extracted from plantlets using TRIzol Reagent (Invitrogen, USA), and then contaminated DNA was removed from the extracted RNA by treated with DNase I (Promega, USA). The RNA quality and purity were verified using Nanodrop 2000 and an Agilent 2100 Bioanalyzer (Santa Clara, CA, USA) respectively. Then, high-quality RNA samples of each time point in three independent biological replicates were mixed and then used for RNA sequencing. The quality assessment should yield an OD_260/280 ratio of 1.8 to 2.0 and an OD_260/230 ratio of 2.0 to 2.2 with latter being an additional value for purity determination. A total of 6 cDNA libraries were constructed, comprising three libraries of L. longiflorum (LL_CK, LL_T2h, and LL_T24h) and three libraries of L. distichum (LD_CK, LD_ T2h, and LD_ T24h), using the BGISEQ-500 platform at the Wuhan, Hubei Province (BGI’ Tech, China). In brief, the total RNA was purified to enrich poly(A) mRNA with magnetic oligo (dT) beads. The target RNA was fragmented and then used for dscDNA library construction by random hexamer (N6) primers. The ends of the dscDNA were repaired with phosphate at the 5’ end and sticky ‘A’ at the 3’ end, after which the dscDNA strands were ligated with adapters that had a sticky ‘T’ at the 3’ end. Two specific primers were used to amplify the ligation product. Finally, the PCR product was denatured by heat, and the single-strand DNA was cyclized by splint oligo and DNA ligase. Six cDNA libraries were constructed and sequenced on a BGISEQ-500 RS platform at BGI. All raw read sequences are available in the NCBI sequence read archive under the accession number PRJNA577293 and the assembled transcript sequences were also submitted to the NCBI TSA database (GIUQ00000000).

Reads processing and identification of differentially expressed genes (DEGs)

Clean reads were obtained by filtering out adaptor-only reads, trimming reads containing more than 5% unknown nucleotides, and low-quality reads with the percentage of low quality bases (base quality ≤10) using Trimmomatic and we subsquently aligned the clean high-quality reads to the SSU and LSU rRNA sequences using BWA software. After that, we assembled the clean reads from the heat-tolerant L. longiflorum after rRNA removing using de novo assembly program Trinity except K-mer value to conduct the de novo assembly [28]. Additionally, only one read copy will be kept for assembly and redundant duplication reads be eliminated for mutli-duplication’s reads, After that, overlapped nucleic acid sequence were generated to the contigs assembled using Trinity. To obtain the unigene, the paired-end reads were used for constructing scaffolds with the paired end information by realigning to contigs. Then, these contigs in one transcript were assembled by the Trinity and gained the sequence not being extended on either end defined as unigenes. To harvest as much description as possible for the assembled sequences, all unigenes were annotated based on BLAST searches using BLASTx search tool through Swiss-Prot protein databases and the National Center for Biotechnology Information non-redundant protein (Nr) with threshold E-value set as less than 1e-10, identity > 70%, query coverage ≥ 80%, and other parameters were defaulted. In general, we used BLAST alignment of transcripts (or translations of predicted ORFs from transcripts) to reference protein sets as a means of assessing coding transcript completeness. Transcripts with ≥ 80% sequence coverage (i.e. a significant alignment between a transcript sequence from our assembly and a target protein sequence, where the alignment covers at least 80% of the target protein sequence) are thus considered “full or near-full length”. In addition, the conserved ortholog content was identified using the nematod Benchmark Universal SingleCopy Orthologs (BUSCOs, v4.0.6). To further predict their functions, based on Nr and SwissPro BLAST results, the unigenes were then annotated in Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp/) database and Gene Ontology (GO, http://www.geneontology.org/) database. For the nr annotations, the BLAST2GO program was used to assign GO annotations (comprised of biological processes, molecular functions, and cellular components) of unique assembled transcripts [29]. Subsequently, WEGO (Web Gene Ontology Annotation Plot) software was used to conduct GO functional classification for understanding the distribution of gene functions at the macroscopic level [30]. After that, Bowtie2 was adopted to map the clean reads to the de novo assembly transcriptome reference sequences, and based on the mapping of RNA-seq reads to the assembled transcriptome, the developed software RSEM was performed to assess transcript abundances by quantification of the de novo assembly transcript and calculated as the FPKM (fragments per kilobase of transcript per million mapped reads); Expression data from two libraries (treatment and control) were determined by mapping to the transcriptome assembly using Bowtie2 software [31]. The fragments per kilobase of transcripts per million fragments mapped (FPKM) values were analysed further using RESM [32] and PossionDis [33] to get differentially expressed genes (DEGs) between the control and infected groups [(LL_T24h vs. LL_CK and LL_T2h vs. LL_CK for L. longiflorum) and (LD_T24h vs. LD_CK and LD_T2h vs. LD_CK for L. distichum)]. Further, to determine the threshold p-value in multiple tests, a false discovery rate (FDR) was used. Furthermore, significant enrichment was calculated when FDR was <0.05 and FPKM values showed at least a two-fold difference between the two samples reads. Furthermore, DEGs related to heat-stress responsiveness were analysed and plotted using Neighbour–Joining cluster through homemade R script. Followed by the coefficient of variation with a threshold of 1 across the different samples, these genes were selected and then transformed FPKM values by log2.

GO functional annotation and KEGG pathway analysis of common differently expressed genes (DEGs) in lilies

Common DEGs in both lily species responsive to heat stress were identified using the venny program (https://bioinfogp.cnb.csic.es/tools/venny/); subsequently, we submitted those common DEGs for GO annotation through Gene Ontology Database (http://www.geneontology.org/) and KEGG pathway enrichment analysis using KOBAS software. GO functional categories were assigned to differentially expressed genes based on Gene Ontology Database (http://mL.geneontology.org/); the false discovery rate (FDR) was calculated to correct the P-value for the GOs of all the DEGs. Moreover, KOBAS software [34] was used for pathway enrichment analysis by testing the statistical enrichment of DEGs in the KEGG pathway; the common DEGs in L. longiflorum (LL_T24h vs. LL_CK and LL_T2h vs. LL_CK) and L. distichum (LD_T24h vs. LD_CK and LD_T2h vs. LD_CK) were annotated to the KEGG database, which exhibited all significant enrichments for DEGs in the pathway. A P-value and FDR (FDR < 0.05) were presented after pathway analysis for each type. After that, their relative graphs were constructed using R script.

Verification of the gene expression profiles of candidate DEGs by qRT-PCR in lilies

Quantitative real-time PCR was performed on QuantStudio Real-time PCR system (Applied Biosystems by Thermo Fisher Scientific) platform using SYBR Premix Ex Taq (Cat. #RR420L, TakaRa, China) following the manufacturer’s instructions. The primers employed in the qRT-PCR experiments are designed by GenScript Molecular Biology Tools online (https://mL.genscript.com/tools/real-time-pcr-tagman-primer-design-tool) (S1 Table). The SAND gene was selected as the internal control according to a previous analysis [35]. At least three independent biological replicates of RNA samples at each time point of heat treatment including CK were obtained from these lily plantlets and collected for qRT-PCR analysis. The first-stand cDNA was synthesized using PrimeScript^™ RT reagent Kit with gDNA Eraser (Cat. #RR047A, TakaRa, China). Amplification was performed using PCR as follows: a first denaturation step at 95°C for 3 min; 40 cycles of denaturation at 95°C for 15 s; followed by annealing and extension at 60°C for 1 min. Melting curves were generated to verify amplification specificity by a thermal denaturing step at 95°C for 15 s and 60°C for 1 min, and then from 60°C to 95°C slowly (0.15°C/S). To calculate the fold change in the expression levels of target genes, the relative quantitative method (2^-ΔΔCT) was selected and performed with three technical replicates for all reactions.

Statistical analysis

To evaluate differences between difference samples in L. longiflorum and L. distichum, variance analysis was carried out for the Student’s t-test using SPSS statistical software, significant differences among the relative expression levels of the genes were shown to be statistically significant (P<0.05).

Physiological changes of lily seedings under heat stress conditions

To determine the optimal time to analyse the heat response for lilies (L. longiflorum and L. distichum), uniform plantlets with 2 months of growth in aseptic conditions were subjected to high temperature (42°C) treatment and then sampled at six different times of stress (2 h, 8 h, 16 h, 24 h, 48 h and 72 h). As shown in Fig 1, the plantlets of L. longiflorum grew normally, but the leaves gradually turned white with longer durations of heat stress. After 24 h, the colour of L. longiflorum leaves started to change but no signs of wilting, and the plantlets lost their green colour during 48 h to 72 h (Fig 1A). By contrast, the leaves of L. distichum started to wilt accompanied by a colour change after 24 h; the leaves lost almost of their green colour at 48 h and finally appeared to be dying after 72 h (Fig 1B). The analysis of chlorophyll content indicated coincident results with the colour change in lilies (Fig 2). To determine leaf membrane damage, measurements of the electric conductivity showed that the levels were rapidly increased after 2 h in both lilies and reached their peak after 24 h in L. longiflorum and 8 h in L. distichum (Fig 2B). Furthermore, the levels of the measured soluble sugar and the starch increased rapidly and sharply following heat exposure of L. longiflorum leaves compared with L. distichum leaves; however, the starch level declined faster than that of the soluble sugar (Fig 2C and 2D). The general trends of the soluble sugar and proline were similar; both leveled off at 24 h, although they reached their peaks at different times, with the former reaching its peak at 16 h and the latter at 2 h (Fig 2E). The entire physiological index showed that L. longiflorum was more responsive to the high temperature condition than L. distichum, which is consistent with the heat resistance of these two lilies. Taken together, the following experiments of the two lilies were performed at 0 h (CK) and two different heat-treatment times designated as LL_CK, LL_T2h, and LL_T24h for L. longiflorum in contrast to LD_CK, LD_ T2h, and LD_ T24h for L. distichum, accounting for the early heat response and the physiological changes.

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Fig 1. Dynamic changes of growth development for different lilies (L. longiflorum and L. distichum) subjected to high temperature (42°C) treatment for six lengths of time (2 h, 8 h, 16 h, 24 h, 48 h and 72 h).

Fig 1A. Dynamic changes of growth development for L. longiflorum under heat stress for different treatment times. Fig 1B. Dynamic changes of growth development for L. distichum under heat stress for different treatment time points.

https://doi.org/10.1371/journal.pone.0239605.g001

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Fig 2. Physiological changes of chlorophyll content, soluble sugar content, relative electric conductivity, starch content and proline content for lily seedings under heat stress conditions.

Fig 2A. Dynamics changes of chlorophyll content determined in the heat-tolerance L. longiflorum and the heat-sensitive L. distichum. Fig 2B. Dynamics changes of relative electric conductivity validated by the heat-tolerance L. longiflorum and the heat-sensitive L. distichum. Fig 2C. Dynamics changes of soluble sugar content identified in the heat-tolerance L. longiflorum and the heat-sensitive L. distichum. Fig 2D. Dynamics changes of starch content determined in the heat-tolerance L. longiflorum and the heat-sensitive L. distichum. Fig 2E. Dynamics changes of proline content identified in the heat-tolerance L. longiflorum and the heat-sensitive L. distichum.

https://doi.org/10.1371/journal.pone.0239605.g002

Illumina sequencing and de novo assembly of lily transcriptome responding to heat stress

To develop a comprehensive overview of gene expression profiles of lilies (lilium sp.) responding to high temperature stress, three libraries (LL_C, LL_T2h and LL_T24h) of L. longiflorum in contrast to that (LD_C, LD_T2h and LD_T24h) of L. distichum were constructed. The RNA samples were prepared from lily fleshy leaf with differential thermotolerance and subjected to pair-end read (PE) sequencing using the BGISEQ-500 platform at Beijing Genomics Institute (BGI, Wuhan, China). PE sequencing could increase the depth of sequencing and improve de novo assembly efficiency [36]. As a result, 74.71, 74.71, and 72.22 Mb raw reads were obtained from the LL_C, LL_T2h and LL_T24h libraries, respectively, and 74.71, 74.71, and 74.71 Mb raw reads in LD_C, LD_T2h and LD_T24h libraries, respectively. After removing adaptor sequences and low-quality reads, 66.51, 62.61, and 65.36 Mb clean reads were generated in three libraries of L. longiflorum and 66.18, 66.03, and 65.16 Mb clean reads in three libraries of L. distichum (S2 Table). Subsequently, 198, 342 contigs were constructed from the raw sequence reads with an average length of 411 bp, and 130, 170 unigenes with a N50 length of 1442 bp were generated with an average length of 875 bp using paired-end reads in all the libraries. The percent of length distribution of the assembled contigs and unigenes is shown in Fig 3A, which indicated that unigenes mainly ranged from 300 to 3000 bp, and the majority were distributed from 300 to 1800 bp (over 2000 numbers) accounting for more than 69.2% of all of the unigenes. The assembly completeness has been assessed by calculating the BUSCO. The assembled transcripts were compared against the benchmarking universal single-copy orthologs (BUSCO) revealing that almost 65% of the BUSCO groups have complete gene representation, while 10% are only partially recovered, and 25% are missing (S1 Fig). It is probable that the assembly completeness was not good owing to the bigger and complex of the lily genome; there was not currently genome of the lily provided for research. To evaluate completeness of transcriptome libraries and annotations of all the assembled unigenes, the annotated sequences were corresponded to the known nucleotide sequences of plant species, with 32.96%, 27.75%, 10.52%, and 2.96% matching with Elaeis guineensis, Phoenix dactylifera, Musa acuminate subsp. malaccensis, and Nelumbo nucifera respectively, and the remaining 25.81% matching with others (except for the top matched species) (Fig 3B). All of the top three species with BLAST hits were involved in the Palmaceae family (almost 60.71% of all assembled transcripts) and the Musaceae family (10.52% of all assembled transcripts), implying that the sequences of the lily transcripts aligned more closely to these two families. Through the BLASTx alignment against the public protein databases including Nr, KEGG, COG, Swiss-Prot and InterPro proteins, the results indicated that a total of 34,301 unigenes (89.11% of all unigenes) were annotated and matched to all of the public protein databases (Fig 3C).

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Fig 3. Comprehensive overview of de novo assembly of lily transcriptome responding to heat stress in lily.

Fig 3A. length distribution of the assembled contigs and unigenes obtained from de novo assembly of lily transcriptome. Fig 3B. Species distribution of the top BLAST hits for lily assembled transcriptome. Fig 3C. Comprehensive overview of BLASTx alignment against the public protein databases including Nr, KEGG, COG, Swiss-Prot and InterPro proteins.

https://doi.org/10.1371/journal.pone.0239605.g003

Functional annotation and classification of the assembled unigenes from lily transcriptome

Based on sequence homology, the Gene Ontology (GO) assignments system was performed to classify the roles of these uncharacterized sequences. By using the BLAST2GO program, all assembled unigenes were assigned to three main categories such as “biological process” (BP), “cellular component” (CC) and “molecular function” (MP), with a total of 22,659, 24,799 and 12,212 unigenes, respectively. Then, these annotated sequences were assigned to GO-slim terms to facilitate the functional distribution of plants. All GO terms were subsequently divided into 54 functional groups such as biological processes (23 groups), cellular component (17 groups), and molecular function (14 groups). Within the biological processes, the metabolic process including 5,754 (25.4%) unigenes and cellular process including 5,445 unigenes (24.0%) were the most dominant. For cellular component, the majority of GO terms were primarily assigned to the cell and cell part including 5,103 (20.6%) and 5,055 unigenes (20.4%), respectively. With regards to molecular function, those assignments were focused on catalytic activity including 5,543 unigenes (45.4%) and binding including 5,010 unigenes (41.0%) (S2A Fig).

In addition, blast analysis from the COG database indicated that all the unigenes were assigned to 15 COG categories. Among these categories, the cluster for ‘General functions prediction only’ related to basic physiological and metabolic functions accounted for the largest group, followed by ‘Posttranslational modification, protein turnover, chaperones’, ‘Function unknown’, ‘Defense mechanisms’, ‘Cytoskeleton’, ‘Lipid transport and metabolism’, and ‘Intracellular trafficking, secretion, and vesicular transport’, while fewer unigenes were assigned to ‘Nucleotide transport and metabolism’, ‘Coenzyme transport and metabolism’ and ‘Nuclear structure’ (S2B Fig). These findings revealed that a great deal of genes with molecular functions were related to numerous biological progress under high temperature stress in lilies.

Moreover, the KEGG pathway database contributed to better understand the biological roles of genes involved in different networks [37]. Through comparing against the KEGG pathway database with KEGG Orthology (KO) numbers using BLASTx alignments with a cut-off E value of 10⁻⁵, there were altogether 51,199 unigenes belong to 21 KEGG pathways annotated and assigned. Of these categories, the most represented pathways were ‘Transport and catabolism’ (2,291, 4.47%), Signal transduction (2,445, 4.78%), Translation (4,065, 7.94%), Carbohydrate metabolism (4,427, 8.65%) and Environment adaption (1,788, 3.49%) (S2C Fig). All of the annotations provide valuable references for inquiring into these processes, pathways and functions involved in the short-term high-temperature response.

Cluster analysis of gene expression in lilies

To investigate these key candidate genes involved in short-term heat response of lilies, we conducted the RNA-Seq experiments using tangential sections of LL_C, LL_T2h, and LL_T24h of L. longiflorum and LD_C, LD_T2h and LD_T24h of L. distichum according to physiological changes and mapped the resulting reads to the reference transcriptome. To minimize false negatives and positives, a statistical analysis was based on unigenes with a FPKM value ≥ 1 in at least one of the three stages (S3 Table). Notably, statistical significance was according to expected sampling distributions. Cluster analysis was performed by the k-means method according to the gene expression dynamics [38]. Differential expression genes were divided into 9 groups associated with their expression profiles (S3 Fig). Transcript levels of these DEGs were significantly altered during three different stages of heat stress in both lilies. Of all the DEGs, a part were downregulated from the 0 h to 2 h timepoint but were upregulated from the 2 h to 24 h timepoint, while another part indicated increased quantitative expression at 2 h but decreased expression at 24 h. To identify genes showing evidently expression changes, during different heat stress stages, these expressed tags with alteration among the three samples of the two lilies were sorted by an algorithm developed from the heat-map (S3 Fig).

Differential expression genes (DEGs) associated with heat stress in lily

To determine the differential expression genes (DEGs) among three stages of lilies in response to heat stress, we filtered the unigenes with an FDR ≤ 0.001 and |log2 (ratio)| ≥ 1 and found 26,347 DEGs in two of L. longiflorum libraries (LL_T2h vs. LL_CK and LL_T24h vs. LL_CK) and 21,221 DEGs in that of L. distichum libraries (LD_T2h vs. LD_CK and LD_T24h vs. LD_CK). Among these DEGs, 7,531 upregulated and 9,924 downregulated genes were detected in LL_T2h vs. LL_CK and 8,410 upregulated and 12,527 downregulated genes detected in LL_T24h vs. LL_CK. By contrast, 5,747 upregulated and 8,087 downregulated genes were detected in LD_T2h vs. LD_CK and 5,539 upregulated and 6,753 downregulated genes in LD_T24h vs. LD_CK (Fig 4A). The data suggested that the differentiation of genes between T24h and CK is larger than that between T2h and CK, and the number of DEGs of L. longiflorum was higher than that of L. distichum among the different heat stress stages. These findings indicate that, in L. longiflorum, transcript levels changed dramatically in contrast to that of L. distichum in response to heat stress, which was in accordance with the heat-resistance of L. longiflorum and L. distichum. These findings implied that our data was valuable for identifying heat-responsive genes and is therefore suitable for further analysis of the biological roles of these genes in lilies.

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Fig 4. Differential expression genes (DEGs) associated with heat stress in lily.

Fig 4A. Overview of up-regulated and down-regulated DEGs in L. longiflorum and L. distichum. Fig 4B. Co-modulated DEGs (common DEGs shared between L. longiflorum and L. distichum) identified in lily. Fig 4C. Specially DEGs selected with great alteration (up-regulated DEGs with significantly changed in L. longiflorum while down-regulated in L. distichum) identified in lily. Fig 4D. Specially DEGs selected with great alteration (Down-regulated DEGs with significantly changed in L. longiflorum while up-regulated in L. distichum) identified in lily.

https://doi.org/10.1371/journal.pone.0239605.g004

To further filter the DEGs between L. longiflorum and L. distichum, 1,621 common genes under high temperature were selected from LL_DEGs and LD_DEGs using an overlapping method (Fig 4B; S4 Table). Among these DEGs, we totally found 88 DEGs encoding for heat shock protein-like (HSPs) and 16 DEGs for Heat shock transcription factor (HSFs) (S5 Table). Of these HSPs, 21 and 16 DEGs were encoded for Class I heat shock protein-like and Class II heat shock protein-like, and 14, 11 and 13 DEGs encoded for heat shock 70 kDa protein, heat shock protein 83 and Small heat shock protein respectively. For the HSFs, DEGs mainly encoded for heat shock transcription factor A2, A3-like and B2c-like. Most these DEGs were upregulated in both lilies (L. longiflorum and L. distichum) under heat stress, indicating the similar behaviors of the HSPs and HSFs in other plant species when responsive to heat stress. In addition, 352 unigenes were obviously upregulated in L. longiflorum but downregulated in L. distichum during the heat-stress stages, and 133 unigenes in L. longiflorum showed decreased quantitative expression but increased transcript abundance in L. distichum (S6 Table). Furthermore, 57 upregulated DEGs with significant changes in L. longiflorum but downregulated in L. distichum are shown in Fig 4C and S7 Table, including glycine-rich cell wall structural protein-like (CL9854.Contig1_All), leucine-rich repeat receptor-like protein kinase IMK2/IMK3 (Unigene2729_All, Unigene2730_All), caffeoyl-CoA O-methyltransferase-like (CL6125.Contig8_All), pathogenesis-related protein PR10-5 (Unigene29475_All) and glutathione S-transferase U17/18/23-like (CL3902.Contig1_All, Unigene34705_All and CL4503.Contig4_All). In addition, 37 significantly downregulated genes in L. longiflorum and upregulated in L. distichum are shown in Fig 4D, such as 1-aminocyclopropane-1-carboxylate oxidase (CL428.Contig3/4/8/10_All), heat shock 70 kDa protein 8 (CL6966.Contig1_All), gibberellin-regulated protein 13-like (CL3876.Contig3_All), zinc finger protein 8-like (CL18874.Contig1_All) and signal peptide peptidase-like 2B (CL15663.Contig3_All). All the DEGs between L. longiflorum and L. distichum will be considered key candidate genes that are involved in the heat response and signalling pathways in lilies.

Functional annotation of these common DEGs in lily

To further identify these candidate genes responsive to short-term heat stress, we used gene ontology (GO) analysis to assign these DEGs of L. longiflorum and L. distichum to functional categories. DEGs that encoded genes in such categories as chromatin, chromosomal part, chromosome and protein-DNA complexes were enriched for L. longiflorum and that for chloroplast and plastid were greatly enriched during the heat-stress stages. In contrast, those DEGs for L. distichum were only enriched in the earlier stage of heat stress at 2 h, and a few DEGs were lost in the heat stress stage at 24 h, including chloroplast, plastid, thylakoid and thylakoid aspects (Fig 5A; S8 Table). These results were associated with the physiological or/and biochemistry changes of L. distichum seedings at 24 h under heat stress conditions.

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Fig 5. Functional enrichment analysis among differentially expressed genes (co-modulated DEGs) in lily.

Fig 5A. Enriched GO terms of Co-modulated differentially expressed genes related to heat stress in Lily. GO terms are plotted on the ordinate, and the enrichment factor (rich factor) is plotted on the abscissa. The color of points represents the q-value, and the size of points represents the number of DEGs mapped to the reference pathway. Legends for the colour scale of q-values and size-scaling of the number of DEGs are shown to the right of the plot. Fig 5B. Pathway enrichment analysis among differentially expressed genes related to related to heat stress in Lily. Enriched KEGG pathway terms divided by different treatment time in L. longiflorum and L. distichum respectively. Red color indicates statically over represented.

https://doi.org/10.1371/journal.pone.0239605.g005

The biological pathways influenced by heat stress were further evaluated by KEGG analysis of the DEGs in L. longiflorum and L. distichum. As shown in Fig 5B, a total of 24 pathways were affected by high temperature in L. longiflorum, while 27 pathways were responsive to high temperature in L. distichum, whereas the numbers of DEGs involved in these pathways of L. longiflorum far exceeded those of L. distichum. It should be noted that DEGs were enriched mainly in these pathways of L. longiflorum, such as RNA polymerase, Pyrimidine metabolism, Purine metabolism, Protein processing in endoplasmic reticulum, Plant-pathogen interaction, Plant hormone signal transduction, Metabolic pathways, Phenylpropanoid biosynthesis, and Biosynthesis of secondary metabolites. In contrast, these pathways were not evident in L. distichum when exposed to one or two heat stress stages, which suggested that these pathways probably play a key role in regulating the short-term heat response in lilies. Specifically, 417 genes in protein processing in endoplasmic reticulum and 2,702 genes in metabolic pathways showed strongly changed expression under heat stress for 2 h in L. longiflorum in contrast to that in L. distichum (S9 Table). In addition, approximately 500 genes in each pathway of pyrimidine metabolism, purine metabolism, plant-pathogen interaction, plant hormone signal transduction, and phenylpropanoid biosynthesis and approximately 1,500 genes in biosynthesis of secondary metabolites were also affected under heat stress for 24 h in L. longiflorum but not in L. distichum (S9 Table). The collected data revealed the changes in molecular function and signalling pathways in lilies responding to heat stress as well as the differences between the heat-tolerant L. longiflorum and the heat-sensitive L. distichum.

Identification of candidate heat responsive DEGs by qRT-PCR in lily

To validate the DEGs obtained from RNA sequencing, a total of 15 DEGs showing diverse expression profiles during the heat stress stages were chosen to perform the qRT-PCR analysis. The expression levels of 9 putative DEGs, such as CL30.Contig2_All (GLP2), CL609.Contig2_All (FLXPER3), CL1329.Contig5_All (4CL), CL1607.Contig1_All (LTP), CL1931.Contig8_All (GAPDH1), CL8150.Contig3_All (14-3-3 protein), CL8339.Contig3_All (CSP3), Unigene55961_All (PR10) and Unigene59233_All (PR10-1), which were upregulated in L. longiflorum but downregulated in L. distichum at the heat-treatment stages, were further confirmed, except for the CSP3 gene, which displayed a similar expression between CK and heat treatment at 2 h or 24 h based on the qRT-PCR results (Fig 6). In addition, the qRT-PCR results showed 3 putative DEGs, CL5363.Contig7_All (Lectin), CL8508.Contig2_All (MYB39), and CL15663.Contig3_All (Sppl2b), which were downregulated in L. longiflorum and upregulated in L. distichum at the heat-treatment stages and 2 putative DEGs, CL351.Contig6_All (sHSP) and CL20106.Contig1_All (HSFA2), which were highly induced expressed in L. longiflorum and L. distichum at the heat-treatment stages, showing consistent results with the RNA-Seq expression data. Correlation between the RNA-seq and qPCR data were conducted, which indicated that both results were basically consistent for gene expression profiles (Fig 7, r = 0.7277). Althouth the expression of CL15183.Contig4_All (S-AI) was induced in L. longiflorum and repressed in L. distichum, which was a different result from that obtained using RNA-seq (Fig 6). The above results further confirmed a high reliability of the RNA-seq data obtained in our study.

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Fig 6. The quantitative real-time polymerase chain reaction (qRT-PCR) analysis of expression levels of 15 selected genes under different heat stress treatment time in L. longiflorum and L. distichum.

Three independent biological repeats were performed, and all data points were the means of three biological replicates ± standard error (SE). A student’s t-test was used to calculate the significance of differences (*: P < 0.05, **: P < 0.01).

https://doi.org/10.1371/journal.pone.0239605.g006

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Fig 7. Correlations of transcript levels of heat response genes in L. longiflorum and L. distichum between RNA-seq and qPCR data.

https://doi.org/10.1371/journal.pone.0239605.g007

HSF-HSP signalling pathway in lily heat response

It was well-documented that the expression of heat shock transcription factor HSFs and heat shock protein HSPs were obviously induced when plants such as Tomato and wheat etc. are exposed to elevated temperature [22, 23, 48–50]. HSFs function as central components in plant response to heat stress by regulating the expression of HSPs and other chaperones [12]. In the present data, a total of 16 DEGs encoding for HSFs and 87 DEGs for HSPs were significantly upregulated in both lilies (L. longiflorum and L. distichum), taking the 0.9870% and 5.3671% in total of these DEGs. The findings further identified our sequencing data of lilies exposure to heat stress. These DEGs such as heat shock transcription factor A2 (Unigene14710_All, Unigene14708_All, CL209.Contig1_All and CL20106.Contig1,3,4,6_All), A3 (CL5951.Contig2,5_All), small heat shock protein (Unigene22366_All, Unigene22367_All and CL351.Contig6_All), heat shock protein 83 (CL20541.Contig9_All), class II heat shock protein-like (CL7583.Contig1_All, CL163.Contig4_All and CL16968.Contig1_All), and Hsp70-Hsp90 organizing protein-like (CL2253.Contig2,3,4,6,10,11,12_All), as well as Hsp70-binding protein 1-like (CL1733.Contig3,5,6,7_All), was in agreement with previous studies and further confirmed the conserved mechanism of heat response in Lilium and other plants [3, 23, 51–53]. Moreover, heat shock transcription factor HSFA2, HSFA3 from L. longiflorum has been indicated an important role in the heat signaling pathway by reverse genetic transformation in Arabidopsis [14, 16].

Plant hormone signal transduction and lily heat response

Auxin, abscisic acid (ABA), and salicylic acid (SA) have been shown to be important in the heat stress response of various plants and may possibly regulate heat stress signalling pathways [3]. In our data, the candidate genes in auxin response signalling pathway were obviously decreased by heat stress in heat-sensitive L. distichum, including TIR1, Aux/IAA, ARF and GH3 genes, which was consistent with previous studies [54, 55]. However, these genes corresponding to TIR1, Aux/IAA and ARF remained obscure in the heat-tolerant L. longiflorum, specifically, the GH3 genes (Unigene12571_All, CL6902.Contig2_All, CL6902.Contig1_All) were upregulated in the heat-tolerant genotype, suggesting that GH3 was possibly involved in heat tolerance. Previous studies indicated that ABA signalling is upregulated by heat stress in pea [56] and Agrostis stolonifera [57], and the Pyrabactin resistance (PYR)/PYR1-like (PYL) genes were upregulated after heat stress in the heat-susceptible pepper [42]. However, in the present study, the PYR/PYL genes of ABA receptor (CL14565.Contig1-5_All, CL20275.Contig1_All, CL8078.Contig1_All, CL20508.Contig6_All, Unigene14869_All, CL762.Contig8_All, CL8513.Contig1_All) were repressed in heat-sensitive L. distichum. In addition, ABA responses negatively regulated by the type 2C protein phosphatases (PP2C) the and the candidate genes of PP2C were upregulated in heat-sensitive L. distichum, indicating that ABA signalling might possibly be repressed and that ABA signalling is critical in the response of lilies to high temperature. SA can improve antioxidant capacity and enhance heat resistance in rice [58] and Arabidopsis [59]; we also found target genes of NPR1 and TGA involved in SA metabolic pathway were obviously upregulated in heat-tolerant L. longiflorum after heat stress (2 h and 24 h) compared with being downregulated in heat-sensitive L. distichum, which suggested that NPR1 (CL9114.Contig1-4_All, and Unigene589_All) and TGA (CL925.Contig1-5_All, CL14874.Contig1-2_All, CL910.Contig1_All, Unigene19378_All, Unigene22208_All, CL11558.Contig5-6_All, CL11449.Contig2_All) might be promising targets for the improvement of lily heat resistance and confirmed the critical role of SA response to heat stress.

Plant-pathogen interaction and phenylpropanoid/lignin biosynthesis in lily heat response

Specifically, we found pathogenesis-related protein 10 (PR10) genes, such as PR10-1 (Unigene59233_All, CL523.Contig6_All), PR10-2 (Unigene7708_All), PR10-3 (CL20508.Contig3_All, CL20508.Contig7_All, CL20508.Contig9-11_All, Unigene19673_All, Unigene29327_All, CL20508.Contig2_All), PR10-4 (Unigene52606_All, CL20508.Contig6_All, CL20508.Contig8_All), PR10-5 (Unigene29475_All), PR10-6 (Unigene52945_All), PR10-7 (CL523.Contig1_All), PR10-8 (CL20508.Contig5_All) and PR10 (CL4502.Contig2-3_All), glutathione S-transferase genes (Unigene34705_All, CL3902.Contig1,2,4,6,7_All, CL651.Contig3,7_All, CL4503.Contig4_All, CL3718.Contig3_All, CL863.Contig7,8_All, CL15152.Contig3_All, CL1001.Contig4_All), 14-3-3 protein gene (CL8150.Contig3_All) and germin-like protein GLP1,2 (CL3274.Contig2_All, CL30.Contig2_All) as well as WRKY-related gene (CL170.Contig3_All, CL3728.Contig1,8_All), involved in Plant-pathogen interaction were surprisingly upregulated in heat-tolerant L. longiflorum but repressed in heat-sensitive L. distichum after heat stress for 2 h and 24 h [60–62]. These findings are very interesting and are similar to the finding that immune response genes were more expressed in the heat-tolerant Chinese cabbage ‘XK’ after 12 h of heat treatment [21]. In addition, genes such has 4CL (CL1329.Contig5_All), CCoAOMT (CL6125.Contig1, 2,4,5,8_All), ferulate-5-hydroxylase (F5H, CL12000.Contig1_All), and peroxidase (CL609.Contig1, 4, 6_All, CL10016.Contig2_All, CL9430.Contig1,2_All), which are involved in the Phenylpropanoid/ monolignol biosynthesis pathway, were also upregulated in L. longiflorum but downregulated in L. distichum. This finding first indicates that lignin biosynthesis is related to heat stress and might play an important role in lily plant response to high temperature.

Kinase signalling genes in lily heat response

Kinases are critical components involved in signal transduction pathway in plant response to various environmental stimuli. Of these, redox-sensitive transcription factors are activated by the MAPK signalling pathway, including the Zat family, the WRKY family, MBF1c and Rboh [3], and has been involved in a heat shock response of alfalfa cells [63, 64]. Similarly, in our results, the candidate genes MPK4 (CL15793.Contig2_All) and NDPK2 (CL7963.Contig2, 3_All) in heat-tolerant L. longiflorum were upregulated, while they (CL15793.Contig1, 4_All and CL15981.Contig1, 2_All) were repressed in heat-sensitive L. distichum after 24 h of heat stress. In addition, MKK3 (CL6477.Contig3, 4_All) was induced by heat stress in L. longiflorum, although it showed no response in L. distichum. Other kinases, such as leucine-rich repeat receptor-like protein kinase (IMK2, Unigene2729_All; IMK3, Unigene2730_All, CL5334.Contig5_All), proline-rich receptor-like protein kinase PERK1 (CL5771.Contig2_All), CBL-interacting serine/threonine-protein kinase 23-like (Unigene45_All, CL20344.Contig3_All), guanylate kinase GK-1 (CL6633.Contig5_All), ATP-dependent 6-phosphofructokinase 2-like (CL11999.Contig4_All), mitogen-activated protein kinase NPK1-like (CL3282.Contig2_All), and ribose-phosphate pyrophosphokinase 1-like (CL5046.Contig1_All), were also upregulated in L. longiflorum but downregulated in L. distichum, which suggested that they could play important roles in lily heat-response pathways. These data further provide novel insights into the idea that various kinases are involved in the heat response signalling pathway of lilies and might be used as key targets for genetic modification of lilies.

Calcium and phospholipid pathways in lily heat response

Previous studies have indicated that calcium signalling plays dynamic and key roles in the regulation of heat-responsive genes [65, 66]. Calcium-binding protein CML19 (CL5289.Contig1_All) was induced during heat stress in the two genotypes, and calmodulin (CALM, CL1722.Contig4_All) was upregulated after 24 h of heat stress in L. longiflorum and downregulated in L. distichum. The phospholipid-based signalling is involved in response to various abiotic and biotic stresses, including heat stress [67]. Under heat stress, the G protein transduces the heat-initiated signal required for PIP2 and PA accumulation involved in Phospholipid pathway [68]. In addition, phosphatidylinositol phospholipase C (PLC) further catalysed the hydrolysis of PIP2 to produce DAG and IP3, which is rapidly converted to IP6 for the release of Ca²⁺ [69]. In the present study, candidate genes encoding for G1 protein-like (CL5934.Contig2, 4_All, CL8626.Contig3_All, and CL11614.Contig5_All) were significantly upregulated in heat-tolerant L. longiflorum and downregulated in heat-sensitive L. distichum. In addition, PLC (CL8354.Contig2_All, CL8354.Contig3_All, CL14032.Contig2_All) was also induced in the heat-tolerant genotype, which indicated that the two genes might be responsible for the heat-stress resistance.