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. 2021 Feb 4;27(2):189–201. doi: 10.1007/s12298-020-00922-y

Transcriptome analysis of Arabidopsis reveals freezing-tolerance related genes induced by root endophytic fungus Piriformospora indica

Wei Jiang 1,#, Rui Pan 1,#, Sebastian Buitrago 1, Chu Wu 2, Mohamad E Abdelaziz 3, Ralf Oelmüller 4, Wenying Zhang 1,✉
PMCID: PMC7907345  PMID: 33707862

Abstract

Freezing stress is a serious environmental factor that obstructs plant development. The root endophytic fungus Piriformospora indica has proved to be effective to confer abiotic stress tolerance to host plants. To investigate how P. indica improves freezing tolerance, we compared the expression profiles of P. indica-colonized and uncolonized Arabidopsis seedlings either exposed to freezing stress or not. Nearly 24 million (93.5%) reads were aligned on the Arabidopsis genome. 634 genes were differentially expressed between colonized and uncolonized Arabidopsis exposed to freezing stress. Interestingly, 193 Arabidopsis genes did not respond to freezing stress but were up-regulated by P. indica under freezing stress. Freezing stress-responsive genes encoded various members of the WRKY, ERF, bHLH, HSF, MYB and NAC transcription factor families. The qRT-PCR analyses confirmed the high-throughput sequencing results for 28 genes. Functional enrichment analysis indicated that the fungus mainly controls genes for freezing-stress related proteins involved in lipid and ion transport, metabolism pathways and phytohormone signaling. Our findings identified novel target genes of P. indica in freezing-stress exposed plants and highlight the benefits of the endophyte for plants exposed to a less investigated environmental threat.

Supplementary information

The online version of this article (10.1007/s12298-020-00922-y) contains supplementary material, which is available to authorized users.

Keywords: Piriformospora indica, Endophytic fungus, Low temperature, Transcriptome analysis, Lipid and ion transport, Metabolic pathways, Phytohormone signaling

Introduction

Temperature is an important environmental factor affecting plant growth, development, and yield (Ma et al. 2018), and low temperature stress causes excessive damage to plant productivity (Li et al. 2016). Freezing changes numerous physiological processes, such as membrane fluidity, dehydration, accumulation of antifreeze proteins and osmoregulators (e.g. proline, betaine, and soluble sugar), H2O2 production, or the calcium homeostasis (Kim et al. 2013; Ju et al. 2020). Comprehensive transcriptional and metabolic adjustments are involved in the freezing-stress tolerance response (Barrero-Gil et al. 2016; Zhang et al. 2020). Freezing tolerance is mediated by the ICE (induction of CBF expression)—CBF (C-repeat binding factor)—COR (cold-responsive) pathway, which has been extensively investigated over the past two decades (Ding et al. 2018). The calcium/calmodulin-regulated receptor-like kinases CRLK1/2 (Yang et al. 2010), CIRCADIAN CLOCK ASSOCIATED1/LATE ELONGATED HYPOCOTYL (CCA1/LHY) (Dong et al. 2011) and BASIC TRANSCRIPTION FACTOR3 (BTF3) proteins (Ding et al. 2018) are considered as positive regulators of the CBF pathway, while the COLD RESPONSIVE PROTEIN KINASE1 (CRPK1) (Liu et al. 2017), PSEUDO-RESPONSE REGULATORS (PRRs) (Nakamichi et al. 2009), E3 ubiquitin-protein kinase HOS1 (Dong et al. 2006) and PHYTOCHROME-INTERACTING FACTOR4/7 (PIF4/7) proteins (Lee and Thomashow. 2012) were identified as negative regulators. Freezing stress rapidly activates the expression of CBFs, and the encoded proteins directly modulate a series of CORs to increase freezing tolerance (Ma et al. 2018). Moreover, phytohormones (e.g. abscisic acid, gibberellic acid, auxin, and salicylic acid) also play an important role in plant cold-stress responses (Lv et al. 2020; Tuang et al. 2020). In addition, inorganic ion and carbohydrate transport and metabolism, as well as the lipid metabolism respond to the cold stress, e. g. in tea plants (Zhu et al. 2019).

Microbial symbionts play an important role in plants’ tolerance to freezing or low temperature stress through improving plant nutrient acquisition, antioxidant activity (Subramanian et al. 2015), hormonal signaling and host osmotic balance (Acuña-Rodríguez et al. 2020; Askari-Khorasgani et al. 2019). The root endophytic fungus P. indica has been reported to enhance plant growth and survival under diverse biotic and abiotic stresses (Narayan et al. 2020; Jogawat et al. 2020). Many transcriptional analyses have identified differentially expressed genes in roots colonized by P. indica under different stresses, such as drought stress (Zhang et al. 2018), salt stress (Ghaffari et al. 2016; Abdelaziz et al. 2019), early blight (Panda et al. 2019) and root knot nematode infections (Atia et al. 2020). Alizadeh et al. (2016) observed that P. indica also protects green beans under chilling stress, by increasing the antioxidant enzyme activities (e.g. catalase and guaicol peroxidase). Jiang et al. (2020) demonstrated enhanced freezing tolerance as well as better post-thaw recovery in Arabidopsis when they are colonized by P. indica. The fungus influenced the antioxidant and osmolyte levels as well as chlorophyll fluorescence parameters and stimulated the performance of the freezing stress-exposed Arabidopsis seedlings. Moreover, analyses of plants exposed to low temperature for a few hours shed light on genes which are directly involved in the freezing stress tolerance, and on related genes which promote plant performance under freezing stress (Nah et al. 2016). Many genes for proteins of the freezing-stress pathways are transcriptionally activated under freezing stress conditions (Niu et al. 2020). For instance, in Arabidopsis, the CBF/DREB transcription factors control part of the cold response and produce resistance to freezing stress (Jaglo-Ottosen et al. 1998). Jiang et al. (2020) showed that the up-regulated CBF genes directly modulate a set of COR genes in Arabidopsis exposed to freezing stress. RNA-seq analysis of cucumber (Cucumis sativus L.) roots reveal crucial low temperature tolerance genes induced by AMF (arbuscular mycorrhizal fungi), which mainly participate in oxidative metabolism and ion (nitrate and iron) uptake/transport (Ma et al. 2018).

To understand how P. indica confers freezing tolerance to Arabidopsis, we performed expression profiles with colonized and uncolonized Arabidopsis seedlings which were either exposed to freezing stress or without the exposure of freezing stress.

Materials and methods

Plant materials and P. indica preparation

Seeds of Arabidopsis thaliana Columbia (Col-0) were kept at 4 °C for 2 days in dark for vernalization before transfer to soil (peat: vermiculite 2:1) in the plant growth chamber at 22/18 °C day/night cycle under long day conditions (16 h light/8 h dark) at 60% relative humidity. One week after germination, seedlings were co-cultivated with P. indica suspension under the same growth conditions for 15 days.

Piriformospora indica was propagated on PDA medium at 28 °C in the dark for one week. For liquid culture, 100 ml of ASP (Aspergillus) medium was filled in a 200 ml conical flask with 2 fungal plugs, and then incubated on a rotating shaker at 28 °C at 150 rpm for 10 days. For soil experiment, P. indica was cultured in ASP liquid medium for 10 days. One-gram fresh mycelium of P. indica was collected, washed and mixed with 1000 ml medium to prepare 0.1% P. indica suspension. Later, 200 µl of 0.1% P. indica suspension was applied to soil in an area around the seedling´s roots (Jiang et al. 2020). To confirm root colonization, roots (13 days after inoculation) of Arabidopsis were washed with ddH2O to remove medium and soil, and then cut into 1.0 cm pieces, immersed in 10% NaOH solution for 8 h and incubated in 1% HCl for 10 min. After that, roots were stained with 0.05% trypan blue overnight, and the presence of hyphae and spores were analysed using microscopy at 20× magnification (Nikon CX41-72C02) (Jiang et al. 2020).

Freezing assays and physiochemical analysis of Arabidopsis

Two weeks after P. indica colonization, Arabidopsis seedlings were exposed to freezing conditions according to Liu et al. (2017) using a freezing cabinet (Panasonic, MIR-254-PC, Japan). Briefly, growth temperature was gradually deceased by 1 °C per hour to the desired temperature (− 6 °C), then the temperature was maintained for 6 h (Novillo et al. 2004). Subsequently, seedlings were transferred to 4 °C and kept in darkness for 12 h to thaw the seedlings before they were returned to normal conditions (22/18 °C, day/night) for 7 days to recover. After recovery, the survival rate was calculated by counting the number of seedlings that still produced new leaves. Meanwhile, the water socked area was calculated by Image J.

After freezing, seedlings were collected in 10 ml tubes containing 5 ml of de-ionized water. The electrical conductivity (EC) was measured immediately (S0) at 22 °C using SevenCompact S230, and again after 20 min (S1). Next, the samples were boiled at 100 °C for 15 min, and the EC was measured at 22 °C (S2). Electrolyte leakage was expressed by (S1 − S0)/(S2 − S0) (Ding et al. 2018).

RNA extraction, library construction and RNA-seq

We used three biological replicates of colonized (P+) and un-colonized (P−) seedlings either exposed to freezing treatments (− 6 °C for 1 h, F) or not (− 6 °C for 0 h, N) (Nah et al. 2016). Twelve RNA samples [P− (N01, N02, N03), P+ (N04, N05, N06), P−_F (F01, F02, F03), and P+_F (F04, F05, F06)], 1 μg RNA each, were collected correspondingly from non-frozen and frozen leaves, and then prepared to construct cDNA libraries for RNA-seq. Sequencing libraries were generated consistent with manufacturer’s instructions in NEBNext Ultra™ RNA Library Prep Kit for Illumina (NEB, San Diego, CA, USA) according to Wu et al. (2020). Index codes were added to the sequence of attributes for each sample. The clustering of the index-coded samples was conducted on a cBot Cluster Generation System by TruSeq PE Cluster Kit v4-cBot-HS (Illumia) which was consistent with the manufacturer’s recommendations. After cluster generation, the library preparations were sequenced on an Illumina NovaSeq 6000 platform (Biomarker Technologies, Beijing, China), and then paired-end reads (Table S2, 3) were generated according to Niu et al. (2020). The data of this project has been deposited at NCBI under the accessions (PRJNA658012).

Transcriptome assembly analysis of DEGs (selected different expression gene)

Raw data (reads) of fastq format were first processed by the internal perl scripts. By removing reads containing adapter, ploy-N and low-quality reads from raw data, clean reads were obtained, and then the level of Q20, Q30, GC-content and sequence duplication were determined. All the downstream analyses were based on clean reads with high quality. At the same time, hisat2 software was used to map obtained reads with the reference genome (https://www.arabidopsis.org/).

Quantification of each gene expression level was normalized by Fragments Per Kilobase of transcript per Million fragments mapped (FPKM) according to Tian et al. (2020). Different expression analysis was determined by the DESeq R package (1.10.1) (R-forge, Brussels, Belgium). The resulting p-values were adjusted using Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with a p-value < 0.01 and log2 (fold change) > 1.5 were assigned as DEGs according to Wu et al. (2020). DEGs were annotated by several public databases, including NCBI protein non-redundant database (NR), NCBI nucleic acid sequence database (NT), Swiss-Prot, the Pfam protein family database, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Clusters of Orthologous Groups of proteins (COG) according to Tian et al. (2020).

Gene ontology (GO) analysis of DEGs

Candidate DEGs were submitted to agriGO (GO analysis toolkit and database for the agricultural community, http://bioinfo.cau.edu.cn/agriGO/index.php) for gene ontology analysis. Single enrichment analysis (SEA) of Arabidopsis reference genome was used to select enrichment GO terms. Fisher’s exact test and the Bonferroni multi-test adjustment method were used to filter the overrepresented terms of biological process, cellular component, and molecular function.

qRT-PCR analysis

For isolation of total RNA, Arabidopsis seedlings were exposed to a freezing condition (− 6 °C) for 0, 1, 6 h. The total RNA of the leaf samples was extracted using TRIzol. After reverse-transcription with the Reagent kit (Takara), quantitative real-time PCR was performed by BIO-RAD CFX Connect Real-Time PCR system with SYBR Premix (ABI). The experiments for expression of different genes (three biological and three technical replicates) were conducted according to Ding et al. (2018). The expression of ACTIN2 was used as a reference control. The primers (designed by Primer Premier 5.0) were used for gene expression are listed in Table S1.

Results

P. indica improves Arabidopsis growth performance under freezing stress

Piriformospora indica successfully colonized the root surface of Arabidopsis plants at 14 days after inoculation (Fig. 1f). Compared to un-colonized plants, the colonized Arabidopsis seedlings displayed significantly enhanced freezing tolerance (Fig. 1a, b), and showed lower values for the water-soaked area as well as electrolyte leakage (Fig. 1c, d). The survival rates of colonized seedlings (80% at − 6 °C) were significantly higher than those of un-colonized seedlings (36% at − 6 °C) (Fig. 1e).

Fig. 1.

Fig. 1

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Freezing phenotypes (a, b showed recovery for 1 and 7 days, respectively), water-soaked area rate, ion leakage and survival rates (c, d and e) of Col Arabidopsis seedlings co-cultivated (or not) with P. indica for 2 weeks. Chlamydospores inside the root cells, bars = 100 μm (f). Data are means of three independent experiments ± SD. Asterisks indicate significant differences compared with the non-inoculated plants (*p < 0.05, ** p < 0.01, t-test). Grey (black) bars: P. indica- (un-) colonized plants

Transcriptomic sequence

To analyze the transcriptomic response to freezing stress in Arabidopsis seedlings colonized by P. indica, 12 cDNA libraries were constructed from the leaves of colonized and un-colonized seedlings which were either exposed to freezing treatments or not. An overview of the sequencing and mapping results is shown in Table S2 and Table S3. A total of 285.1 million original reads (150 bp, paired-end) were originally obtained after sequencing. We totally obtained 85.08 Gb clean data after mRNA sequencing for 12 samples with at least 6.07 Gb clean data for each sample. We observed that Q30 nucleotides percent > 93.51% of the total data for per treatment (Table S2). In addition, GC count was nearly 47%, which was consistent with that of Arabidopsis thaliana genome. Clean reads were mapped onto the Arabidopsis genome, and the mapping features for the 12 libraries are summarized in Table S3. After quality control, most reads from P− (98.03–98.34%), P+ (98.10–98.28%), P−_F (97.87–98.09%), and P+_F (97.91–98.01%) were successfully aligned to the reference genome. For the mapped reads, > 95.48% were uniquely mapped (Table S3). These results suggested the sequencing data was available to analyze gene expression.

P. indica colonization affects DEGs related to freezing stress

The expression level of the genes in P− and P+ plants were compared with those of P−_F and P+_F plants to identify DEGs involved in P. indica-mediated freezing tolerance. Our study identified 1000 (P− vs. P−_F), 380 (P− vs. P+), 707 (P+ vs. P+_F) and 634 (P−_F vs. P+_F) DEGs (Fig. 2a). Out of  1000, 547 genes were up- and 453 downregulated. Among P− versus P−_F, 165 genes were up- and 215 downregulated while among P− versus. P+, 450 genes were up- and 257 downregulated and among P+ versus P+_F, and 193 genes were up- and 441 downregulated among P−_F versus P+_F. A Venn diagram (Fig. 2b) shows the relationships between the DEGs and the treatments, and pinpoints to 551 genes which might be targets of P. indica under freezing stress. They were further analyzed.

Fig. 2.

Fig. 2

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Statistics of differentially expressed genes (a) and Venn diagram of differentially expressed genes (b) (p < 0.01, FC = 1.5)

GO annotation demonstrated that 2114 DEGs were grouped into functional subcategories for “biological process” (21), “cellular component” (17) and “molecular function” (15), respectively (Fig. 3). Since many genes were classified into more than one sub-category, they could be involved in different processes under freeze stress. The GO category for “biological process” contained a high percentage of genes involved in “cellular processes”, “metabolic processes”, “single-organism processes”, and “biological regulation”. In addition, genes belonging to the GO term “response to stimulus” were also enriched in the analyses. Other GO subcategories with many DEGs were “cellular component”, “cell”, “cell part”, “organelle” and “membrane”. The top four GO terms in “molecular function” were “binding”, “catalytic activity”, “nucleic acid binding transcription factor activity” and “transporter activity”.

Fig. 3.

Fig. 3

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GO classification results of differentially expressed genes

COG annotations showed that the freezing-stress responsive DEGs were assigned to 23 COG categories (Fig. 4). The largest categories are “carbohydrate transport and metabolism” (131) followed by “translation, ribosomal structure and biogenesis” (102), “signal transduction mechanisms” (92), “secondary metabolites biosynthesis, transport and catabolism” (73), and “posttranslational modification, protein turnover, chaperones” (72). Besides categories with only a few DEGs, the categories related to “amino acid transport and metabolism” as well as “lipid transport and metabolism” contained many DEGs.

Fig. 4.

Fig. 4

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COG diagram of differentially expressed genes

The KEGG pathway enrichment analysis (Fig. 5) identified 509 DEGs in five major groups: “cellular process”, “environmental information processing”, “genetic information processing”, and “metabolism and organismal systems”. The top four enriched biochemical pathways were “ribosome”, “carbon metabolism”, “biosynthesis of amino acids” and “plant hormone signal transduction”. DEGs belonging to “photosynthesis and starch” and “sucrose metabolism” were also found in the metabolic pathway.

Fig. 5.

Fig. 5

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KEGG classification of differentially expressed genes

Effect of P. indica on differential expression analysis

Gene annotation uncovered that 93 DEGs (50 up- and 43 downregulated) are involved in signal transduction (Table S4) (Fig. 6a), 63 (33 up- and 30 downregulated) of them encode protein kinases. Notably, 10 DEGs were found to be involved in calcium signal transduction: 4 DEGs (AT1G54450, AT1G74740, AT4G23650, AT5G66210) encode calcium-dependent proteins and 3 DEGs (AT5G57630, AT5G45820, AT3G23000) CBL-interacting protein kinases. Five DEGs (AT1G73500, AT2G30040, AT1G51660, AT1G01560, AT1G71410) encode mitogen-activated protein kinases (MAPK) and nine DEGs (AT5G01700, AT5G27930, AT4G38520, AT4G33920, AT5G02760, AT4G26080, AT2G30020, AT3G11410, AT5G66080) type 2C protein phosphatases (PP2Cs).

Fig. 6.

Fig. 6

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Cluster diagram of differentially expressed genes. a Signal transduction mechanisms. b Transcription factors. c Lipid transport and metabolism. d Inorganic ion transport and metabolism

Among the 154 DEGs, 111 up- and 38 downregulated genes are predicted to encode transcription factors (TFs) which are further subcategorized into 24 families (Table S5, cf. http://planttfdb.cbi.pku.edu.cn/) (Fig. 6b). The major TF families are WRKY, ERF, bHLH, HSF, MYB, and NAC. The ERF family contains 22 DEGs, the WRKY 15 DEGs, the bHLH 9 DEGs and the MYB family 8 DEGs. Notably, the expression of the 22 ERF family members was strongly induced by freezing stress in our experiments (Fig. 6b), followed by members of the NAC family (Fig. 6b). Moreover, 43 DEGs were involved in plant hormone signal transduction, with AT4G32280, AT3G15540, AT2G28350, AT2G28350 in auxin, AT4G26150 in cytokinin, AT4G26080, AT1G13260, AT3G02140, AT1G68840, AT1G13260, AT1G68840 in the abscisic acid (ABA), AT1G17380 and AT2G34600 in jasmonic acid (JA), AT1G19350 and AT1G73830 in brassinolide (BR), AT1G78440, AT3G10185, AT1G02400, AT1G74670, AT2G14900 in gibberellin and AT1G04310, AT2G22200, AT4G39780, AT1G53910, AT5G61600, AT3G25890, AT5G47230 in ethylene (ET) signaling pathways. Importantly, DEGs involved in the ABA and ET signal pathways showed the strong responses to freezing stress.

The annotation further uncovered that 38 DEGs belong to the category “lipid transport and metabolism pathways” (Fig. 6c, Table S6). More specifically, AT1G06290, AT1G68530, AT2G26250, AT1G01120 belong to “fatty acid metabolism”, AT4G00400, AT3G25585, AT4G01950, AT3G18850, AT1G01610 to glycerophospholipid metabolism, AT4G16690, AT1G52760, AT2G39400, AT2G45600, AT2G39420) to the “alpha/beta hydrolase family”, AT1G77230, AT1G65060, AT1G75960, AT5G23050 to “AMP-binding enzyme”, and AT2G22240 and AT4G39800) to “myo-inositol-1-phosphate synthase” groups.

Finally, 35 DEGs belong to the “inorganic ion transport and metabolism pathways” (Fig. 6d, Table S7). Seven DEGs (AT5G17850, AT3G53720, AT5G47560, AT3G17630, AT2G38170, AT3G06370, AT5G17860) encode sodium exchanger proteins, two DEGs (AT4G35090 and AT1G20620) catalase-related immune-responsive proteins, and two DEGs (AT5G66190 and AT1G20020) oxidoreductase NAD-binding proteins.

Validation and expression pattern analysis

To further validate the data, 28 DEGs were selected for qRT-PCR analyses. The results were consistent with the RNA-Seq data (R2 = 0.9053) (Figs. 6, 7). The analyzed genes coded for inorganic ion transporters and proteins of its metabolism (AT3G14410, AT1G62810, FSD1), desaturases (AT5G16240, AT4G29140), trehalose metabolism (AT1G78090, AT1G60140, AT2G18700) and hormone actions (e.g. ERF5, ARR15, EIL1, ABI1, ABF4, CYP85A1, SAUR78, AT3G25290) (Fig. 7). Seventeen genes were up-regulated by P. indica, 5 were downregulated and 3 did not respond to the fungus (cf. Fig. 7).

Fig. 7.

Fig. 7

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Validation of the RNA-Seq results by qRT-PCR of 28 selected genes under freezing stress. Data are means of three independent experiments ± SD

Discussion

P. indica alleviates freezing stress

It is well known that AM fungi increased tolerance to low temperature, while less attention has been paid to root endophytic fungi (Ma et al. 2018; Acuña-Rodríguez et al. 2020). Figure 1 demonstrates that P. indica has a profound effect on the survival and recovery of Arabidopsis seedlings from freezing stress. Seedlings inoculated with P. indica have lower values for ion leakage (Fig. 1d) suggesting that the fungus improves membrane stability under freezing stress. Tian et al. (2020) reported that low temperature increased electrical conductivity in Lilium davidii. Ma et al. (2019) and Caradonia et al. (2019) showed for Cucumis sativus, that AM fungi mitigated the negative effects of low temperature stress by promoting gas exchange, reducing photosystem II fluorescence and stabilizing photosynthesis-related biochemical parameters. Our results also suggest that the endophyte P. indica reduces the ion leakage in Arabidopsis seedlings under freezing stress. Electrolyte leakage and lipid degradation account for cold sensitivity in leaves of Coffea sp. plants (Campos et al. 2003). It is apparent that membrane stabilization is an important feature of root-colonizing microbes in restricting damage caused by below-zero temperatures. A transcriptome analysis may help to understand which genes and molecular processes are targeted by the microbes to achieve this stabilizing effect; also those other processes which are activated by beneficial microbes to allow better survival of plants under freezing conditions.

DEG analyses

Transcriptome analyses for Arabidopsis (Garcia-Molina et al. 2020), Camellia oleifera (Wu et al. 2020) and Populus tomentosa (Yang et al. 2019) identified genes which respond to low temperature. Overall, the identified genes induced under low temperatures are comparable in the different species and resemble those identified in this study. Guo et al. (2018) summarized the events from cold perception and signal transduction from the plasma membrane to the nucleus which involves cold sensors, calcium signals, calcium-binding proteins, mitogen-activated protein kinase cascades, and the C-repeat binding factor/dehydration-responsive element binding pathways, as well as trehalose metabolism. Again, many of the discussed proteins are also regulated at the mRNA level in our study. Using bioinformatic tools and expression profiles our goal was to identify those genes involved in freezing tolerance responses, which also respond to P. indica. Our study investigated genes which respond to P. indica under freezing stress but do not belong to the known genes and proposed signaling pathways involved in freezing-tolerance responses. A list has been provided with 634 DEGs for colonized and uncolonized Arabidopsis exposed to freezing stress. Furthermore, 193 genes did not respond to freezing stress but were up-regulated by P. indica under freezing stress. These data suggest that the fungus might target known as well as new genes involved in freezing tolerance.

Hormone signaling in freezing stress response

In general, plant cells sense freezing tolerance at the plasma membrane (Ma et al. 2018). After signal reception, diverse downstream signaling pathways are activated, which also includes hormone signaling (Li et al. 2016). Several ABA-responsive genes respond to abiotic stress, suggesting that ABA may also play a role in freezing responses (Gusta et al. 2005). We identified several DEGs related to ABA signaling including AT1G67080 (for ABA4) which provides additional evidence for an involvement of this hormone in freezing stress responses (Welling and Palva 2006). The aba4 mutant in Arabidopsis synthesizes reduced levels of ABA on dehydration (North et al. 2007) suggesting that maintenance of the water homeostasis could be important under freezing conditions. However, our data also show that many other hormones are involved in the plant’s response to freezing temperatures. We identified genes for the gibberellic acid methyltransferase 2 (AT5G56300), SAUR (AT1G72430) and the auxin-responsive protein AT3G25290, the brassinosteroid-6-oxidase (AT5G38970), the cytokinin-responsive GATA factor AT4G26150, as well as genes involved in jasmonic acid (AT2G34600) and ethylene (AT4G39780) functions, indicating a complex contribution of hormones and hormone signaling to the cold response and possible induction of antifreeze proteins (Yu et al. 2001). Yang et al. (2019) have also observed  that plant hormones play important roles in freezing stress signaling in Arabidopsis. It remains to be determined that how far downstream of freezing perception does the altered hormone homeostasis occur in the signaling pathway, and whether the regulatory effect of P. indica on these genes is an important contribution to the freezing tolerance. Our established conditions allow now to test this with the corresponding mutants.

Ca2+-mediated freezing stress signaling

Ca2+-mediated signal transduction is one of the most focused signaling pathways in plants and involved in biotic interactions and abiotic stress responses, including cold stress (Yuan et al. 2018). Low temperature shock causes a transient rise in cytosolic calcium levels, which probably results (at least in part) from low temperature induced opening of plasma membrane calcium channels (Li et al. 2016). This trend is confirmed by our RNA-seq data that identified genes involved in Ca2+-mediated signal transduction and inorganic ion transport and metabolism. We identified genes for the calmodulin binding protein AT1G73805, the calcium-dependent phosphotriesterase AT3G59530, the calmodulin like 37 (AT5G42380), the calcium-dependent cation/H+ exchanger 19, the ATPase E1-E2 protein AT3G22910, cupredoxin (AT1G72230), and genes for potential calcium transporter (e.g. AT1G62810 and AT3G06130). Since calcium signaling is tightly connected to many hormone functions, in particular ABA signaling (Atif et al. 2019), and a central player in beneficial plant–microbe interaction (Yuan et al. 2017), the identified genes might be useful starting points to unravel processes involved in microbe-induced freezing stress tolerance.

Reactive Oxygen Species (ROS) signaling in freezing stress response

ROS such as superoxide, hydrogen peroxide, and hydroxyl radicals accumulate under freezing conditions (Liu et al. 2018). To prevent lethal damage caused by ROS, plants have evolved complex antioxidant systems, including ROS scavenging enzymes such as catalases (CATs), superoxide dismutases (SODs), and glutathione transferases (GSTs) and peroxidases (PODs). For instance, low temperature in Lilium davidii increased the proline content as well as the SOD and POD activities while the CAT activity was decreased (Tian et al. 2020). In our RNA-seq data, we identified genes for an Fe-superoxide dismutase (AT4G25100), the catalase (AT1G20620) and the phosphoadenosine phosphosulfate reductase (AT1G62180). The stabilizing effects of these enzymes on the ROS homeostasis under freezing conditions and the role of P. indica on these enzymes need to be investigated in more detail.

The transcriptomic difference between inoculated and uninoculated during freezing treatment

Our RNA-seq data identified more DEGs during freezing treatment compared to those without freezing treatment. This outcome suggests that the different molecular bases between the inoculated and uninoculated plants during freezing treatments are important for their different freezing stress responses. Our recent work on Arabidopsis shows that CBF genes significantly increased in colonized seedlings compared to un-colonized seedlings under freezing stress (Jiang et al. 2020). Several well-known genes (e.g. AT3G18773, AT4G29780, and AT2G35230) responding to cold or freezing are stronger regulated in the presence of P. indica. We have also identified genes (e.g. AT5G57020, AT3G28300, and AT4G02720) which are regulated by P. indica during freezing stress, but have not described yet to be involved in the freezing response. The proportion between upregulated and downregulated genes was modified by presence of P. indica, indicating that values of upregulated and downregulated was 54% and 45%; 43% and 56%; 63% and 36%; 30% and 69% for P− versus P−_F, P− versus P+, P+  versus P+_F and P−_F versus P+_F, respectively. While, Lilium divadii exposed to low temperature resulted 54% and 45% of up-regulated and downregulated DEGs, respectively (Tian et al. 2020); that match our results with P− versus P−_F treatments. Obviously, GO outcome related to metabolic process and protein phosphorylation were augmented with inoculated plants. On the other hand, with uninoculated plants, GO was more connected to transcription, G-protein signaling pathway, and nitrogenous compound metabolic process. Thus, these biological processes may play major roles in the different freezing responses between inoculated and uninoculated plants.

Conclusions

The successful establishment of P. indica as fungal endophytes in Arabidopsis plants provides a strategy for freezing tolerance. Among the DEGs identified in this study are genes which respond strongly to freezing stress when the roots are colonized by P. indica, and genes, which are only up-regulated under freezing stress in the presence of P. indica. GO and KEGG pathway analyses indicate that many of them code for TF of the AP2/ERF, bHLH, WRKY, and MYB families, participate in signal transduction, are associated with hormone functions, inorganic ion transport and lipid metabolism. Our findings provide a source for elucidating the molecular mechanisms which mediate P. indica-induced freezing tolerance in host plants.

Electronic supplementary material

Below is the link to the electronic supplementary material.

12298_2020_922_MOESM1_ESM.docx (16.6KB, docx)

Table S1 List of primer sequences used in this study, Table S2 Sequencing data statistics, Table S3 Alignment results of each sample.

Supplementary material 1 (DOCX 17 kb)

12298_2020_922_MOESM2_ESM.xlsx (60.7KB, xlsx)

Table S4 DEGs involved in signal transduction.

Supplementary material 1 (XLXS 61 kb)

12298_2020_922_MOESM3_ESM.xlsx (94.7KB, xlsx)

Table S5 Information of the 154 predicted TFs.

Supplementary material 1 (XLXS 95 kb)

12298_2020_922_MOESM4_ESM.xlsx (33.8KB, xlsx)

Table S6 DEGs involved in lipid transport and metabolism.

Supplementary material 1 (XLXS 34 kb)

12298_2020_922_MOESM5_ESM.xlsx (32.4KB, xlsx)

Table S7 Information of the 35 predicted inorganic ion transport and metabolism.

Supplementary material 1 (XLXS 33 kb)

Acknowledgements

We acknowledge Rong Zeng, Guang Chen, Banda Milca Medison, and Hongyu Xia for their discussion on the original draft, and two anonymous reviewers for their critical reading and invaluable comments and suggestions on the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 31870378).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Wei Jiang and Rui Pan have contribute equally to this work.

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Associated Data

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

Supplementary Materials

12298_2020_922_MOESM1_ESM.docx (16.6KB, docx)

Table S1 List of primer sequences used in this study, Table S2 Sequencing data statistics, Table S3 Alignment results of each sample.

Supplementary material 1 (DOCX 17 kb)

12298_2020_922_MOESM2_ESM.xlsx (60.7KB, xlsx)

Table S4 DEGs involved in signal transduction.

Supplementary material 1 (XLXS 61 kb)

12298_2020_922_MOESM3_ESM.xlsx (94.7KB, xlsx)

Table S5 Information of the 154 predicted TFs.

Supplementary material 1 (XLXS 95 kb)

12298_2020_922_MOESM4_ESM.xlsx (33.8KB, xlsx)

Table S6 DEGs involved in lipid transport and metabolism.

Supplementary material 1 (XLXS 34 kb)

12298_2020_922_MOESM5_ESM.xlsx (32.4KB, xlsx)

Table S7 Information of the 35 predicted inorganic ion transport and metabolism.

Supplementary material 1 (XLXS 33 kb)

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