Two Strains of Lentinula edodes Differ in Their Transcriptional and Metabolic Patterns and Respond Differently to Thermostress

Temperature type is one of the key traits determining the cultivation regime of Lentinula edodes. However, the molecular and metabolic basis underling temperature type remain unclear. Here, we investigated the phenotypic, transcriptomic, and metabolic features of L. edodes with different temperature types under both control (25 °C) and high (37 °C) temperature conditions. We found that under the control condition, the high- and low-temperature types of L. edodes harbored distinct transcriptional and metabolic profiles. The high-temperature (H-)-type strain had a higher expression level of genes involved in the toxin processes and carbohydrate binding, while the low-temperature (L-)-type strain had a high expression level of oxidoreductase activity. Heat stress significantly inhibited the growth of both H- and L-type strains, while the latter had a higher growth inhibition rate. Upon exposure to heat, the H-type strain significantly up-regulated genes associated with the components of the cellular membrane, whereas the L-type strain markedly up-regulated genes involved in the extracellular region and carbohydrate binding. Metabolome data showed that thermostress altered purine and pyrimidine metabolism in the H-type strain, whereas it altered cysteine, methionine, and glycerophospholipid metabolism in the L-type strain. Transcriptome and metabolome integrative analysis was able to identify three independent thermotolerance-related gene–metabolite regulatory networks. Our results deepen the current understanding of the molecular and metabolic basis underlying temperature type and suggest, for the first time, that thermotolerance mechanisms can be temperature-type-dependent for L. edodes.


Introduction
Lentinula edodes (Berk.) Pegler is a white-rot fungus broadly distributed in the subtropical to temperate regions of the of North and South America, Asia, and Australia [1], particularly in China and Japan [2,3]. It is known as "shiitake" in Japan and "Xianggu" in China. It is the second most cultivated and the most popular edible fungus in the world [4,5]. Except for its important role as food, L. edodes possesses huge potential for therapeutic applications due to its abundant bioactive components including polysaccharides, sulfur-compounds, phenolics, flavonoids, etc. [6][7][8].
It is estimated that there are approximately 500 L. edodes cultivars present in China [9]. Based on the optimal temperature range for fruiting body formation, L. edodes can be classified into different temperature types including the high-temperature type (H-type, fruiting at 15-25 • C), medium-temperature type (M-type, fruiting at 10-20 • C), low-temperature type (L-type, fruiting at 5-15 • C) and broad-temperature type (B-type, fruiting at 5 to 25 • C) [10,11]. Temperature type is the key trait in determining the regional cultivation

Fungal Strains and Cultivation
The L. edodes strains JZB2102217 (H-type) and JZB2102031 (L-type) used in this study were supplied by the Beijing Germplasm Resource Bank for Edible Fungi. The two selected strains are among the typical H-and L-type strains widely cultivated in northern and southern China. Mycelia were punched out using a cork borer (1 cm diameter), and then, were inoculated in Petri dishes (10 cm diameter) containing 35 mL of potato dextrose agar (PDA) medium as described previously [32]. Prior to inoculation, a sterilized cellophane membrane was placed on the surface of the PDA medium for easier collection of the mycelium samples. The two strains were cultivated in a growth incubator at 25 • C and in permanent darkness. After 5 days of growth at 25 • C, the fungal cultures were divided into the control and treatment groups, where the latter were subjected to heat exposure at 37 • C for 24 h.
RNA-seq transcriptome libraries were prepared following the instructions of the TruSeq TM RNA sample preparation Kit from Illumina (San Diego, CA, USA) using 1 µg of total RNA. Briefly, mRNA, which was enriched by poly A tail selection and chemically fragmented, was used for first-strand cDNA synthesis, followed by second-strand cDNA synthesis using a SuperScript double-stranded cDNA synthesis kit (Invitrogen, Carlsbad, CA, USA) with random hexamer primers (Illumina, San Diego, CA, USA). Then, the synthesized cDNA was subjected to end-repair, phosphorylation, and 'A' base addition according to Illumina's library construction protocol. The libraries were then size-selected for cDNA target fragments of 300 bp on 2% low-range ultra-agarose followed by PCR, amplified using Phusion DNA polymerase (NEB, Ipswich, MA, USA) for 15 PCR cycles. After quantification by TBS380, the paired-end RNA-seq sequencing library was sequenced using the Illumina HiSeq X Ten/NovaSeq 6000 sequencer (2 × 150 bp read length) (Illumina, San Diego, CA, USA).

Metabolite Extraction
The mycelium (50 mg) was homogenized in 2 mL of polypropylene in a tube under cryogenic conditions at −10 • C (50 Hz) for 6 min using a high-throughput tissue crusher Wonbio-96c (Shanghai Wanbo Biotechnology co., LTD, Shanghai, China). For extraction, 400 µL of methanol: water (4:1, v/v) extraction solvent mixture was added to the tube containing mycelium samples and 10 µL of internal standard (2-Chloro-L-Phenylalanine, HPLC hyper grade, Merck, Darmstadt, Germany, 0.02 mg/mL). Samples were sonicated at 40 kHz in an ultrasonic bath for 30 min at 5 • C, and then, kept at −20 • C for 30 min to precipitate proteins. The solution was centrifuged at 13,000× g at 4 • C and the supernatant was recovered for further metabolomics analysis.

Non-Target Metabolomics
Metabolites were chromatographically separated using an ultra-high-performance liquid chromatography (UHPLC) system (Thermo Electron Corporation, San Jose, CA, USA). The UHPLC system was equipped with an ACQUITY BEH C18 column (100 mm × 2.1 mm i.d., 1.7 µm; Waters, Milford, MA, USA). The mobile phases consisted of 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile: isopropanol (1:1, v/v) (solvent B). The gradient elution program was set as follows to equilibrate the systems: The obtained raw data were analyzed using Progenesis QI 2.3 software (Nonlinear Dynamics, Waters, USA) to perform the peak detection, alignments, integration, isotope filtering, and peak-grouping based on peak-area correlation [39]. The preprocessed data table contains the retention time (RT), mass-to-charge ratio (m/z) value, and peak intensity. The metabolic features detected to have values of at least 80% in any set of samples were retained. After filtering, the minimum metabolite values were imputed for specific samples in which the metabolite levels fell below the lower limit of quantitation, and each metabolic feature was normalized by sum. The internal standard was used for data QC (reproducibility). Metabolic features that had a relative standard deviation (RSD) of QC > 30% were discarded. Data were logarithmically (log10) transformed prior to conducting multivariate analysis [40]. Compounds were identified via comparison of accurate mass, MS/MS fragment spectra, and isotope ratio difference against the biochemical databases the Human Metabolome Database (HMDB) (http://www.hmdb.ca/ (accessed on 10 March 2022)) and the Metlin database (https://metlin.scripps.edu/ (accessed on 10 March 2022)). The mass tolerance between the measured m/z values and the exact mass of the components of interest was ±10 ppm. Mass features without MS/MS spectra were tentatively annotated at the MS1 level using a 5.0 mDa tolerance. The metabolite abundances were quantified according to their peak areas.

Differential Metabolite Analysis
Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to determine the differential expressed metabolites between pairwise groups. Differentially expressed metabolites (VIP ≥ 1, p ≤ 0.05) between groups were mapped into biochemical pathways through metabolic enrichment and pathway analysis based on a database search (KEGG, http://www.genome.jp/kegg/ (accessed on 10 March 2022)). scipy.stats (Python packages) (https://docs.scipy.org/doc/scipy/ (accessed on 10 March 2022)) was used to identify statistically significantly enriched pathway using Fisher's exact test.

Reactive Oxygen Species Detection and Growth Rate Measurement
Reactive oxygen species (ROS) production was measured according to the method described previously in [41,42]. For fluorescence detection, sterile glass coverslips were obliquely inserted into the Petri dishes just after the inoculation of fungal blocks, which we allowed the fungal mycelium grow on later. When the hyphae grew on the coverslips, the coverslips with hyphae were then incubated in 10 µmol/L of 2 ,7 -dichlorodihydrofluorescein diacetate (DCHF-DA, Solarbio, Beijing, China) phosphate-buffered saline (PBS) solution for 25 min under dark conditions for ROS visualization. After staining, ROS production was visualized using a fluorescence microscope (Olympus, IX71, Tokyo, Japan). The mean fluorescence intensity, i.e., the mean pixel intensity over all the pixels in the region of fluorescence, was calculated using the ImageJ program (Version 1.53t, https://imagej. nih.gov/ij/index.html (accessed on 27 December 2022)) [43]. The inhibition rate was calculated as follows: growth inhibition rate = G1−G2 G2 × 100%, where G1 and G2 denote the growth rate of the fungal colony after and before heat treatment. Growth rate was calculated according to the diameter change during the period of growth before and after heat treatment (mm/d).

Statistical Analysis
Orthogonal partial least squares regression discriminant analysis (OPLS-DA) was performed using SIMCA 14.1 (Umetrics, Umeå, Sweden). The robustness of the OPLS-DA models was assessed via seven-fold cross-validation [44]. The reliability of the predictive models was assessed via the analysis of variance testing of cross-validated predictive residuals (CV-ANOVA), R2 and Q2, which provide information on interpretability and predictability, respectively. The p value was estimated using a paired Student's t-test via single-dimensional statistical analysis. To test the overall associations between DEGs and DEMs, we performed Procrustes analysis in R using the 'vegan' package (version 4.2.0) [45,46]. The data were logarithmically (log10) transformed, centered, and Pareto-scaled prior to multivariate analyses [40]. For the integration and visualization of transcriptome × metabolome associations, the sparse partial least squares (sPLS) regression method [47] was implemented using the R package mixOmics [48].

H-and L-Type L. edodes Strains Differ in Their Transcriptional and Metabolic Profiles
Transcriptomics and metabolomics were used to unearth the differences between L. edodes strains of different temperature type. A total of 7959 expressed genes (Table S1, RPKM ≥ 10) and 1594 metabolites (Table S2) were identified from the examined samples. PCA was used to visualize and evaluate the overall differences in gene expression and metabolics between H-and L-type L. edodes strains. For transcriptomics data, the first two PCs explained 86.3% of the variation; the first PC, which explained 74.5% of the total variation, was able to separate H-and L-type L. edodes strains (Figure 1a). A volcano plot shows the differentially expressed genes between H-and L-type strains (Figure 1b). Compared to the L-type strain, the up-regulated genes in the H-type strain were enriched mainly in toxin processes and carbohydrate binding, whereas the L-type strain had a high expression level mainly of oxidoreductase activity (Figure 1c,d).
The PCA analysis based on the detected metabolites was also able to discriminate Hand L-type L. edodes strains (Figure 1e). Compared to the L-type strain JZB2102031, the H-type strain JZB2102217 accumulated a significantly higher abundance of compounds enriched in starch and sucrose metabolism, and arginine and proline metabolism, while accumulating a lower abundance of compounds enriched in glycerophospholipid and purine metabolism (Figure 1f,h) based on the KEGG topology analysis (padjust < 0.05, VIP ≥ 1, |log2FC| > 1). The KEGG enrichment analysis of the up-and down-regulated genes is shown in Figure S1.  The PCA analysis based on the detected metabolites was also able to discriminate and L-type L. edodes strains (Figure 1e). Compared to the L-type strain JZB2102031, the type strain JZB2102217 accumulated a significantly higher abundance of compounds e

Heat Stress Inhibited the Growth and Induced the Production of ROS in Both Temperature
Types of L. edodes Figure 2a shows the morphology of the H-type strain JZB2102217 and the L-type strain JZB2102031 under control (25 • C) and HS (37 • C) conditions. We can observe that both strains could recover growth 2 d after heat treatment. According to the growth rate, heat stress significantly suppressed the growth of both the H-and L-type strains (Figure 2a,b), while the latter grew significantly slower as a result of heat stress. After the recovery of growth, the L-type strain had a higher growth inhibition rate than the H-type strain (Figure 2c). Under optimal growth conditions, the H-and the L-type strains had comparable levels of ROS production, which was significantly induced by heat stress (Figure 2d,e).

Heat Stress Inhibited the Growth and Induced the Production of ROS in Both Temperature
Types of L. edodes Figure 2a shows the morphology of the H-type strain JZB2102217 and the L-type strain JZB2102031 under control (25 °C) and HS (37 °C) conditions. We can observe that both strains could recover growth 2 d after heat treatment. According to the growth rate, heat stress significantly suppressed the growth of both the H-and L-type strains ( Figure  2a,b), while the latter grew significantly slower as a result of heat stress. After the recovery of growth, the L-type strain had a higher growth inhibition rate than the H-type strain (Figure 2c). Under optimal growth conditions, the H-and the L-type strains had comparable levels of ROS production, which was significantly induced by heat stress (Figure  2d,e).

H-and L-type L. edodes Strains Varied in Their Transcriptional Profiles in Response to Heat Stress
The sequencing data are summarized in Table S3. The pairwise correlation of all samples based on transcriptome data is showed in Figure S2. Transcripts with at least a 2-fold change in abundance and with a Benjamini-Hochberg-adjusted p value < 0.05 were regarded as differentially expressed genes (DEGs). A total of 2329 (1030 upregulated and 1299 downregulated) and 2834 (1206 upregulated and 1628 downregulated) DEGs were identified in H-and L-type strains upon exposure to thermostress, respectively (Table S4). The OPLS-DA model (CV-ANOVA, p < 0.05) showed clear gene expression patterns separating the H-and L-type L. edodes strains (Figure 3a). The volcano plots show that the top five down-and up-regulated genes in the H-type strain were different to those top five genes in the L-type strain, except for gene with identified as HHX47_DHR5000174, which was up-regulated in both strains under heat stress (Figure 3b,e). All the identified upand down-regulated DEGs were annotated using KEGG enrichment analysis. The results showed that the up-regulated genes in the H-type strain under heat stress were mostly enriched (padjust < 0.05) in the intrinsic component of the membrane, the cellular anatomical entity, the integral component of the membrane, and the cellular component, while the down-regulated genes were enriched in catalytic activity, oxidoreductase activity, monooxygenase activity, and DNA repair (Figure 3c,d). For the L-type strain, heat stress induced the up-regulation of genes associated with the extracellular region, cellulose, polysaccharide and carbohydrate binding, integral and intrinsic components of the membrane, etc., and down-regulated genes involved in enzyme activities, proteasome regulatory particles, base subcomplexes, and FAD binding (Figure 3f,g). The KEGG analysis of the DEGs of H-and L-type strains upon exposure to heat is shown in Figure S3.

H-and L-type L. edodes Strains Showed Different Metabolic Profiles in Response to Heat Stress
The OPLS-DA model based on the metabolomes of the H-and L-type strains showed both clear strain-derived and heat stress-derived metabolic variations (Figure 4a). The Top 30 metabolites shown in the loading plot (VIP > 1) were classified as carbohydrates, lipids, nucleic acids, and peptides using KEGG analysis (Figure 4a). For the H-type strain, the heatinduced DEMs (FDR < 0.05) were largely enriched in purine and pyrimidine metabolism (Figure 4b). Of the top 10 heat-induced metabolites in the H-type strain, 4 were peptides and 8 were up-regulated (Figure 4b). For the L-type strain under heat stress, the DEMs were mainly enriched in cysteine and methionine metabolism and in glycerophospholipid metabolism (Figure 4d). Most of the top 10 heat-induced compounds that discriminated the L-type strain under heat stress were down-regulated and unannotated based on the KEGG database (Figure 4e).
Upon exposure to heat, 394 DEMs were identified in the L-type strain, while only 24 DEMs were identified in the H-type strain when the selection criteria for DEMs were narrowed to FDR < 0.05, VIP of OPLS-DA > 1, and FC > 1.2 ( Figure S4a,b). A Venn network shows the most unique and common DEMs for the H-and L-type strains in response to thermostress ( Figure S4c).

Integrative Analysis of Transcriptome and Metabolome of L. edodes under Heat Stress
Sparse partial least squares (sPLS) regression was performed to examine the relationships between the transcriptome and metabolome data of all the samples. The results showed that the transcripts were strongly associated with metabolites ( Figure 5a). Further, interaction networks were constructed to infer the substructures of highly correlated genes and metabolites using a threshold of 0.9 (Figure 5b)   glycerophospholipid metabolism (Figure 4d). Most of the top 10 heat-induced compounds that discriminated the L-type strain under heat stress were down-regulated and unannotated based on the KEGG database (Figure 4e). Upon exposure to heat, 394 DEMs were identified in the L-type strain, while only 24 DEMs were identified in the H-type strain when the selection criteria for DEMs were narrowed to FDR < 0.05, VIP of OPLS-DA > 1, and FC > 1.2 ( Figure S4a,b). A Venn network interaction networks were constructed to infer the substructures of highly correlated genes and metabolites using a threshold of 0.9 (Figure 5b). The results showed that two clear interaction substructures could be detected without any intermediate elements. Interestingly, most of the correlations in substructure I, consisting of nine genes and 35 metabolites, were positive, while most of the correlations in substructure II, containing four genes and 16 metabolites, were negative (Figure 5b).

Figure 5.
Transcriptome-metabolome-wide association analysis of all samples. (a) Sparse partial least squares (sPLS) association analysis between all genes and metabolites from all samples; (b) integration network of highly associated genes and metabolites at threshold of 0.9; (c) sPLS association analysis between thermotolerance-related genes and all metabolites from all samples; (d) integration network of highly associated thermotolerance-related genes and metabolites at threshold of 0.9. For the metabolite ID, "metab_" in Table S2 is abbreviated to "M_" for better readability.
To better understand the regulatory network of transcripts and metabolites in response to heat stress, 18 protentional heat-tolerance (HT)-related genes were selected based on either GO ontology, or KEGG or pfam annotations. These HT-related genes were (b) integration network of highly associated genes and metabolites at threshold of 0.9; (c) sPLS association analysis between thermotolerance-related genes and all metabolites from all samples; (d) integration network of highly associated thermotolerance-related genes and metabolites at threshold of 0.9. For the metabolite ID, "metab_" in Table S2 is abbreviated to "M_" for better readability.
To better understand the regulatory network of transcripts and metabolites in response to heat stress, 18 protentional heat-tolerance (HT)-related genes were selected based on either GO ontology, or KEGG or pfam annotations. These HT-related genes were further subjected to sPLS analysis with all the detected metabolites. Strong associations were found between HT-related genes and metabolites (Figure 5c). From the correlation network, we identified three clear HT-gene-metabolite regulatory substructures (Figure 5d). In substructure I, the gene HHX47_DHR7000266 related to HSP bind-ing, the gene HHX47_DHR7000631 related to glutathione peroxidase activity, the gene HHX47_DHR6000205 related to cell redox homeostasis, the gene HHX47_DHR5000962 encoding HSP 90, and the gene HHX47_DHR8000137 encoding HSP 70 were highly correlated with 17 metabolites; in substructure II, HHX47_DHR8000005 encoding HSP70 was associated with eight compounds, and another HSP70-encoding gene, HHX47_DHR000665, and an HSP20-encoding gene, HHX47_DHR5000877, were solely associated with the monosaccharide compound M_56 (N-Acetyl-D-quinovosamine); substructure III was the largest gene-metabolite association, consisting of three HS-related genes and 50 metabolites (Figure 5d).

Discussion
Generally, based on the temperature for fruiting, L. edodes, including both the wild and cultivated strains, could be divided into the groups H (high-temperature)-type, M (medium temperature)-type, L (low-temperature)-type, and B (broad-temperature)-type [11,12]. The temperature type of L. edodes is usually obtained by observing the fruiting temperature during cultivations under natural climatic conditions [11,49]. The temperature type of L. edodes could, moreover, be characterized by other strain-typing approaches such as intersimple sequence repeat (ISSR) analysis [12] and amplified fragment length polymorphism (AFLP) analysis [10]. These phenotypic divergence-and molecular marker-based results suggest strong genetic divergences underlying the temperature type of L. edodes.
Except for characterization from the aspect of the phenotype, the genetic basis underlying the temperature type of L. edodes is largely unknown. Here, using comparative transcriptomics and metabolomics, we show, for the first time, that the H-and L-type L. edodes strains possess distinct transcriptional and metabolic patterns under optimum temperature conditions, which underlie the genetic divergence of the temperature type. Similarly, a recent population genomics study shows that temperature is the key environmental factor involved in the genetic divergence and phenotypic differentiation of L. edodes [14]. Our data show that the H-type strain had a higher expression level in the genes involved in toxin processes and cellulose, polysaccharide, and carbohydrate binding, while it had a lower level in the genes involved in oxidoreductase activity, protein-disulfide reductase activity, etc. Oxidoreductase activity has been found to be involved in lightinduced brown-film formation in L. edodes [50][51][52]. From the aspects of metabolism, our data show that the H-type L. edodes strains accumulated more metabolites in the pathways of starch and sucrose metabolism and arginine and proline metabolism, while the L-type strain accumulated more metabolites in glycerophospholipid and purine metabolism. Through we have found some interesting differences in gene expression and metabolism between different temperature types of the L. edodes strain, we are still a long way from building an explicit connection between genes and temperature type. Nevertheless, the current paper represents pioneering work that deepens our understanding of the genetic basis and metabolic processes regarding temperature type.
The optimum growth temperature for L. edodes mycelium ranges from 24 to 27 • C and the lethal temperature could be 38 • C or above, depending on the strain [26]. Heat stress appears to be the major abiotic constraint inhibiting mycelium growth, disease resistance, and fruiting body development, thereby seriously reducing fruiting body productivity and the quality of L. edodes [24,[53][54][55]. Therefore, it is of great significance to decipher the mechanisms by which L. edodes addresses thermostress. For L. edodes, the optimum temperature required for fruiting body induction is different from that for vegetative growth. However, whether the temperature type is associated with the optimum mycelium growth temperature for L. edodes remains unknown. In this study, we compared the physiological and phenotypic responses of H-and L-type L. edodes strains under exposure to heat stress. We found that the growth rate of the L-type strain decreased more and had a higher growth inhibition rate after recovery than that of the H-type strain in response to heat stress, suggesting that the L-type strain might be more sensitive to heat stress. Though the temperature-type is defined by the fruiting temperature rather than the temperature at which mycelia grow [12], we found distinct responses of the H-and L-type L. edodes strains in their gene expression and metabolism under heat stress. Moreover, Wang et al. reported that the high-temperature type L. edodes strain could form a fruiting body at a higher temperature and had a higher fruiting body yield than the low-temperature type of L. edodes [56,57]. Taken together, it is interesting that the temperature types of L. edodes could be associated with the thermotolerance of mycelium. Thus, we suggest a large-scale evaluation on the thermotolerance of L. edodes strains with different temperature types.
A considerable number of studies have accumulated over the years addressing the topic of heat resistance in the mycelium of L. edodes [15,26,55]. Previous studies have documented that heat shock proteins (HSPs), indoleacetic acid (IAA), catalase, and trehalose play crucial roles in the thermotolerance of L. edodes [15,29,30]. HSPs are a family of conserved proteins that are up-regulated at high temperatures and play a crucial role in protein renaturation, enzyme and membrane stability, and cell homeostasis [58,59]. In this paper, we found that 13 Hsp20 genes were significantly up-regulated in response to heat stress in both the H-and L-type L. edodes strains, highlighting the importance of HSPs for L. edodes to cope with heat stress. More recently, it was reported that overexpression of the Agaricus bisporus Hsp20 gene enhanced the mycelial thermotolerance of L. edodes [55]. Moreover, the HSP40 protein LeDnaJ07 was also proven to be integral to conferring heat resistance on L. edodes [15,29,30].
The central outcome of our study is that the H-and L-type L. edodes strains respond differently upon exposure to heat stress. To guarantee a more representative assessment, we selected two typical H-and L-type strains which are widely cultivated in northern and southern China, respectively. However, we still suggest more comprehensive investigations to dig deeper in their temperature-type-dependent thermotolerance mechanisms.
Though the explicit mechanisms by which L. edodes copes with heat remain to be elucidated, our data suggest, for the first time, that the molecular strategy for addressing heat can be temperature-type-dependent and provide informative clues for further relevant studies.
Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/jof9020179/s1, Figure S1: KEGG enrichment analysis of DEGs between H-type strain JZB2102217 and L-type strain JZB2102031; Figure S2: Correlation of different samples based one RNA-seq data; Figure S3: KEGG enrichment of DEGs of H-type strain JZB2102217 and L-type strain JZB2102031 in response to thermostress; Figure S4: Volcano plot, Venn network, and KEGG enrichment for DEMS of H-type strain JZB2102217 and L-type strain JZB2102031 in response to thermostress; Table S1: Expression level and annotation of all expressed genes (RPKM > 10); Table  S2: Abundance and annotations of all detected metabolites; Table S3: Overview of the RNA-seq data; Table S4: Number of DEGs in H-type strain JZB2102217 and L-type strain JZB2102031 in response to thermostress.