Transcriptome profiling and phytohormone responses of Arabidopsis roots to different ambient temperatures

ABSTRACT

Ambient temperatures influence plant growth and development, however very little is known about changes in root growth in response to ambient temperature change. Here, we performed transcriptome profiling and compared the differences in gene expression at lower and higher temperatures compared with normal plant growth temperatures. Our analysis of the biological processes and molecular functions regulated by differentially expressed genes revealed that low temperature upregulated carbohydrate metabolism and transmembrane transport, and downregulated signal transduction and defense. High temperature upregulated metabolic processes, transport, and auxin biosynthesis, and downregulated catabolic processes. We found that increased temperature specifically affected the levels of Arabidopsis response regulators, ARR1 and ARR12, to decrease cytokinin signaling, altered the level of the brassinosteroid receptor BRI1 to downregulate brassinosteroid signaling, and changed the level of the gibberellin receptor DELLA to upregulate gibberellin signaling and mediate root elongation. These data contribute to our knowledge of how root growth adapts to elevated ambient temperature under climate warming.

KEYWORDS:

• Ambient temperature
• Arabidopsis root
• phytohormone
• transcriptome profiling

1. Introduction

Global warming can have significant impacts on plant growth, development, and distribution (Hedhly et al. 2009; Körner and Basler 2010; Campitelli and Simonsen 2012; Liu et al. 2012; Huang et al. 2017; Nolan et al. 2018). Many experiments on plant adaptation to global warming are based on the prediction from the Intergovernmental Panel on Climate Change (IPCC) that the soil temperature will increase by 2°C by the end of the century (Stocker et al. 2013; Fang et al. 2017). Soil temperature has a significant effect on nutrient status, leaf length and number, stem length, and root length (Walker 1969). However, little is known about how plant roots adapt to ambient temperature.

Our recent study found that higher temperatures (27°C) could promote CKRC1-dependent auxin biosynthesis by enhancement of the ethylene signaling mediated by ETR1 (ETHYLENE RESPONSE1), thus maintaining normal downward root growth at higher temperatures (Fei et al. 2017). Comparison of the regulatory mechanisms of ckrc1-1 at three temperatures (17°C, 22°C, and 27°C) revealed temperature-dependent differences in the response patterns of in vivo signals (auxin and ethylene), the interactions between signals (auxin and ethylene), and the effects of related signals (genes) (Fei et al. 2019). Related results suggest complex root adaptation mechanisms at different temperatures. In addition to auxin and ethylene, recent studies have found that another plant hormone, brassinosteroid, is also involved in Arabidopsis root elongation at high temperature (Martins et al. 2017). However, the potential contribution of other hormones to root adaptation to higher temperature remains to be determined.

In order to better understand changes in roots in response to ambient temperature changes, we compared the transcriptome of Arabidopsis roots grown at three different ambient temperatures (17°C, 22°C, and 27°C). In experiments of Arabidopsis growth, 27°C is usually selected for high temperature experiments and 17°C is selected for low temperature experiments (Edwards et al. 2006; Gould et al. 2006; Wigge 2013; Ibañez et al. 2017; Kim et al. 2017a; Gyula et al. 2018). The differentially expressed genes were identified and then analyzed based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of annotated genes, revealing differential biological responses and transcription profiles. Additionally, root growth was analyzed in mutant strains with changes in hormone-related genes. These data contribute to our understanding of how root growth can adapt to warmer soil temperature conditions that are possible with climate warming.

2. Materials and methods

2.1. Plant material and growth conditions

All Arabidopsis mutants were tested in the Col-0 background. Mutants arr1-4, arr10-5, arr12-1, and the arr1,12 double mutant were provided by Guo Guangqin (Lanzhou University); rga28 was provided by Hou Suiwen (Lanzhou University); bri1-301 was provided by Li jia (Lanzhou University).

Seeds were treated at 4°C for three days in water and then sterilized with 0.1% (w/v) HgCl₂ before placing on MS medium (Murashige and Skoog Basal Medium with Vitamins). Plants were cultivated at 16 h light/8 h dark cycle with a light intensity of 18 μmol m⁻² s⁻¹. Seedlings were grown vertically in a growth cabinet at different temperatures (27°C, 22°C, or 17°C, with temperature fluctuation ± 0.5°C). Plants were maintained at the same temperature for the duration of the experiment. For all comparisons, the mean was calculated for three separate experiments (from three ager plates, with approximately 28 seeds on each plate).

2.2. RNA library construction and sequencing

Total RNA was extracted from seedling roots using Trizol reagent (Sangon) according to the manufacturer’s instructions. RNA degradation and contamination were monitored by separating samples on 1% agarose gels. RNA purity was checked using a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). RNA concentration was measured using the Qubit® RNA Assay Kit in Qubit®2.0 Flurometer (Life Technologies, CA USA). RNA integrity was assessed using the RNA Nano 6000Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA USA).

A total amount of 3 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using the NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s recommendations. The final cDNA library was enriched by PCR. Finally, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using the TruSeq PE Cluster Kit v3-cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina HiSeq platform and 125 bp/150 bp paired-end reads were generated.

2.3. Differentially expressed gene analysis

Differential expression analysis of temperature conditions was performed using the edgeR package in R. The P values were adjusted using the Benjamini Hochberg method (Klipper-Aurbach et al. 1995). A corrected P-value of 0.05 and absolute fold change of 2 were set as the thresholds for significant differential expression. Next, GO enrichment analysis of differentially expressed genes was implemented using the cluster Profiler R package, in which gene length bias was corrected. We then used the cluster Profiler R package to test the statistical enrichment of differentially expressed genes in KEGG pathways.

2.4. Quantitative real-time PCR analysis

Roots of 9-day-old Arabidopsis seedlings were removed with a scalpel, immediately frozen in liquid nitrogen, and stored at −80°C. RNA was isolated using Trizol reagent (Sangon) and reverse-transcribed into cDNA using a reverse transcription kit (DRR047A; Takara).

Quantitative RT-PCR was performed using a Bio-Rad CFX96 Real-time System (Bio-Rad) and Power SYBR green chemistry (DRR081A; Takara). There were 3 biological repetitions of the experiment, and three technical replicates of each biological sample. The primer sequences are listed in Table S1.

3. Results

3.1. Transcriptome profiling

Our previous research suggests complex mechanisms by which roots adapt to different ambient temperatures (Fei et al. 2017, 2018). To comprehensively study the transcriptome and assess differential gene expression in roots in response to ambient temperature changes, wild-type (wt) seedling roots were grown in different ambient temperatures (17°C, 22°C, and 27°C) and were sequenced. The overall distribution of genes expressing significant differences was analyzed via volcano plot, revealing 2803 up-regulated genes and 3325 down-regulated genes at 17°C vs 22°C (Figure 1(A)), and 1428 up-regulated genes and 1021 down-regulated genes at 22°C vs 27°C (|log2(FoldChange)| > 1 & q value < 0.05) (Figure 1(B)). Significant changes in the expression levels of so many genes suggest that variations in ambient temperature can confer an extensive regulatory effect on plant root growth.

Figure 1. Volcano plot. The number of differentially expressed genes of wide-type roots at 17°C vs 22°C(A) and 22°C vs 27°C (B). Red means up-regulated genes, green means down-regulated genes, and blue means no significant genes.

3.2. Low temperature-regulated transcripts

3.2.1. Up-regulated transcripts at low temperature compared to normal temperature

Differentially expressed genes were sorted according to biological process domain and the results showed that low temperature up-regulated expression of genes involved in carbohydrate metabolic processes (e.g. polysaccharide, glucan, and xyloglucan metabolic processes, glycolytic processes, and cofactor metabolic processes), transmembrane transport processes (e.g. organic acid, carboxylic acid, and anion transmembrane transport) and cell wall metabolic processes (e.g. cell wall organization or biogenesis and cell wall polysaccharide metabolic process). When classified according to molecular function, significantly up-regulated genes were involved in enzymatic activity (e.g. transferase, hydrolase, oxidoreductase, and lyase activity) and transporter activity (e.g. anion, carboxylic acid, and organic acid transmembrane transporter activity). Genes with increased expression played important roles in carbon metabolism, glycolysis/gluconeogenesis pathways, and amino acids biosynthesis pathways, according to KEGG enrichments (Table 1). Adaptation of plants to low temperature relies on many biological processes, such as utilization of energy resources and effective substrate transport systems (Mykytczuk et al. 2013). Carbohydrate metabolism, including polysaccharide, glucan, and glycolysis processes, is required to maintain cellular energy metabolism at low temperature to compensate for a low growth rate (Bakermans and Nealson 2004). The transcriptome results also showed enhanced carbohydrate metabolic processes, such as increased amounts of AT5G20280, encoding sucrose-phosphate synthase (SPS), a gene that was previously shown to be induced by cold temperature (Sun et al. 2015), AT3G44990, and AT1G65310. Transcript levels in carbohydrate metabolism increased to compensate for potential nutrient limitations at lower temperatures (Ghobakhlou et al. 2015). Transcriptional levels corresponding to transmembrane transport processes increased, such as AT2G37460 (encoding a nodulin MtN21-like transporter family protein), AT5G12860 (encoding a dicarboxylate transporter), AT2G41190, and AT1G25530 (encoding a transmembrane amino acid transporter family protein). Low temperature inhibited plant root growth (Zhu et al. 2015; Nagelmüller et al. 2017), as changes in the organization or biogenesis of cell wall compounds possibly restricted cell elongation. Xyloglucan endohydrolase 31 (XTH31), encoded by AT3G44990, showed significantly increased levels to modulate cell wall xyloglucan content (Table S2).

Table 1. GO and KEGG classification of low temperature-regulated transcripts.

	Category	Up-regulated transcripts			Down-regulated transcripts
		ID	Description	p value	ID	Description	p value
GO	Biological process	GO:0005976	Polysaccharide metabolic process	5.57E−15	GO:0009751	Response to salicylic acid	1.90E−06
		GO:0044042	Glucan metabolic process	1.39E−11	GO:0009753	Response to jasmonic acid	4.29E−06
		GO:0010411	Xyloglucan metabolic process	1.25E−10	GO:0071215	Cellular response to abscisic acid stimulus	1.09E−06
		GO:0006096	Glycolytic process	8.14E−09	GO:0009738	Abscisic acid-activated signaling pathway	1.64E−05
		GO:0051186	Cofactor metabolic process	2.79E−09	GO:0009620	Response to fungus	2.21E−07
		GO:0006757	ATP generation from ADP	8.14E−09	GO:0050832	Defense response to fungus	0.0000558
		GO:1903825	Organic acid transmembrane transport	1.98E−05	GO:0009617	Response to bacterium	0.000171
		GO:1905039	Carboxylic acid transmembrane transport	1.98E−05	GO:0009648	Photoperiodism	0.0000639
		GO:0098656	Anion transmembrane transport	4.07E−05	GO:0048573	Photoperiodism, flowering	0.0000198
		GO:0071555	Cell wall organization	3.10E−28	GO:0009408	Response to heat	2.08E−05
		GO:0071669	Plant-type cell wall organization or biogenesis	5.42E−13	GO:0008037	Cell recognition	4.86E−05
		GO:0010383	Cell wall polysaccharide metabolic process	4.80E−09	GO:0071229	Cellular response to acid chemical	4.34E−09
	Molecular function	GO:0016758	Transferase activity	1.57E−10	GO:0004620	Phospholipase activity	3.03E−07
		GO:0004553	Hydrolase activity	2.48E−08	GO:0004722	Protein serine/threonine phosphatase activity	3.30E−05
		GO:0016705	Oxidoreductase activity	2.63E−06	GO:0004871	Signal transducer activity	5.78E−06
		GO:0016829	Lyase activity	5.43E−06	GO:0004872	Receptor activity	3.92E−05
		GO:0008509	Anion transmembrane transporter activity	2.33E−05	GO:0060089	Molecular transducer activity	3.92E−05
		GO:0005342	Organic acid transmembrane transporter activity	1.92E−05	GO:0043531	ADP binding	0.0002
		GO:0046943	Carboxylic acid transmembrane transporter activity	1.92E−05	GO:0005347	ATP transmembrane transporter activity	0.000253
KEGG	Pathway	ath0120	Carbon metabolism	3.44E−10	ath04144	Endocytosis	4.07E−07
		ath00010	Glycolysis/Gluconeogenesis	5.82E−09	ath04626	Plant-pathogen interaction	2.74E−05
		ath01230	Biosynthesis of amino acids	1.84E−06	ath04016	MAPK signaling pathway	0.0004386

3.2.2. Down-regulated transcripts at low temperature compared to normal temperature

Low temperature negatively regulated expression of genes involved in signal transduction (e.g. response to salicylic acid and jasmonic acid, and the abscisic acid-activated signaling pathway), defense processes (e.g. response to fungi and bacteria,) and rhythm processes (e.g. photoperiodism) when sorted according to biological process domain. When classified according to molecular function, enzymatic activity (e.g. phospholipase activity and phosphatase activity), and signal factor activity (e.g. signal transducer activity and receptor activity) exhibited negative correlation. KEGG enrichment analysis showed that differentially expressed genes were involved in the endocytosis pathway and the plant-pathogen interaction pathway (Table 1). Previous work showed changes in salicylic acid immunity pathway genes after exposure to low temperature and pathogen infection and jasmonic acid-dependent signaling is typically activated in response to pathogens (Pieterse et al. 2009; Martínez-Medina et al. 2017; Kim et al. 2017b). Consistent with those results, we observed significantly down-related expression of salicylic acid and jasmonic acid in response to low temperature treatment, leading to weakened defenses. For example, HR4 (AT3G50480 (Sáenzmata and Jiménezbremont 2012)) is involved in salicylic acid and jasmonic acid signaling and was down-regulated. Exogenous abscisic acid may enhance freeze tolerance (Liu et al. 2013; Vishwakarma et al. 2017). In this study, genes in the abscisic acid-activated signaling pathway (e.g. AT2G38310 and AT4G37790) were down-regulated at low temperature. Additionally, cold temperatures can disrupt circadian rhythms in many organisms (Murayama et al. 2017). Circadian 1 (CIR1) is involved in circadian regulation in Arabidopsis (Zhang et al. 2010), and our results indicate significant down-regulation of CIR1(AT5G37260) upon exposure to low temperature, so low temperature could regulate roots growth via rhythm processes (Table S2).

Overall, our results indicated that carbohydrate metabolic processes were up-regulated by low temperature to maintain cellular energy metabolism, and that salicylic acid and jasmonic acid signal transduction and defense processes were down-regulated by low temperature, resulting in weakened defenses to fungal or bacterial attack.

3.3. High temperature-regulated transcripts

3.3.1. Up-regulated transcripts by high temperature compared to normal temperature

GO analysis based on of biological process domain classification revealed that the genes that were up-regulated at high temperature were principally involved in metabolic processes (e.g. glycosinolate, glucosinolate, and glycosyl compound biosynthetic processes and secondary metabolic processes), transport processes (e.g. anion, organic acid, carboxylic acid, and malate transport), and auxin biosynthesis and auxin-activated signaling pathways. When sorted on the basis of the molecular function domain, most genes were involved in transporter activity (e.g. anion and organic acid transporter activity) and protein domain specific binding. Differentially expressed genes were significantly enriched in the photosynthesis, glucosinolate biosynthesis, and circadian rhythm pathways according to KEGG enrichments (Table 2). Glucosinolate plays roles in plant defense against bacterial and fungal pathogens (Ludwig-Müller et al. 2000). AT1G62540 and AT2G43100 were both up-regulated and are involved in the glycosinolate metabolic process. Previous studies reported that enhancement of glycosyl compound biosynthetic processes and secondary metabolic process may indicate accelerated biological processes and structure stabilization in response to high temperature (Dixon and Paiva 1995; Oh et al. 2009; Dixon et al. 2010). Cellular phospholipids may affect vesicular trafficking (Mcmahon and Gallop 2005), autophagy (Holland et al. 2016), and membrane secretion (Monteiro et al. 2005). These conclusions agreed with our results showing accelerated metabolic processes at higher temperature. In particular, transport processes regulated by genes (e.g. AT1G08430, AT1G51340, AT1G17840 and AT1G25530) were enhanced. High temperature can also promote flowering and modulate the circadian clock (Liu et al. 2015). Our results also showed that the transcription abundance of AT1G22770 was upregulated to promote flowering under long days in a circadian clock-controlled flowering pathway. The observed increase in auxin biosynthesis and the auxin-activated signaling pathway under high temperature can promote fertility to maintain good crop yields under global warming (Atsushi 2013). In roots adapting to high temperature response, these changes of auxin biosynthesis and the auxin-activated signaling pathway also included several significantly up-regulated genes (e.g. AT1G16400, AT5G12330, AT4G36260, AT5G16530, and AT1G19790) (Table S2).

Table 2. GO and KEGG classification of high temperature-regulated transcripts.

	Category	Up-regulated transcripts			Down-regulated transcripts
		ID	Description	p value	ID	Description	p value
GO	Biological process	GO:0019758	Glycosinolate biosynthetic process	1.67E−07	GO:0048511	Rhythmic process	1.42E−08
		GO:0019761	Glucosinolate biosynthetic process	1.67E−07	GO:0042787	Protein ubiquitination	8.80E−08
		GO:1901659	Glycosyl compound biosynthetic process	8.49E−05	GO:0010498	Proteasomal protein catabolic process	2.29E−07
		GO:0019748	Secondary metabolic process	9.60E−05	GO:000762	Circadian rhythm	5.40E−07
		GO:0006820	Anion transport	9.77E−06	GO:0016054	Organic acid catabolic process	6.93E−07
		GO:0015849	Organic acid transport	1.69E−05	GO:0046395	Carboxylic acid catabolic process	6.93E−07
		GO:0046942	Carboxylic acid transport	1.69E−05	GO:0006914	Autophagy	2.86E−06
		GO:0006091	Generation of precursor metabolites and energy	1.39E−06	GO:1901565	Organonitrogen compound catabolic process	1.18E−06
		GO:0009851	Auxin biosynthetic process	0.0002684	GO:0090693	Plant organ senescence	4.26E−06
		GO:0009734	Auxin-activated signaling pathway	0.000454	GO:0006979	Response to oxidative stress	4.97E−06
		GO:0015743	Malate transport	0.000749	GO:0012501	Programmed cell death	5.70E−06
	Molecular function	GO:0019904	Protein domain specific binding	5.96E−13	GO:0061630	Ubiquitin protein ligase activity	3.39E−05
		GO:0005342	Organic acid transmembrane transporter activity	4.10E−05	GO:0061659	Ubiquitin-like protein ligase activity	3.39E−05
		GO:0008509	Anion transmembrane transporter activity	0.000139	GO:0005543	Phospholipid binding	0.000151
KEGG	Pathway	ath00195	Photosynthesis	4.02E−10	ath00280	Valine, leucine and isoleucine degradation	9.64E−09
		ath00966	Glucosinolate biosynthesis	9.57E−05	ath04141	Protein processing in endoplasmic reticulum	2.31E−05
		ath04712	Circadian rhythm	0.032353	ath04075	Plant hormone signal transduction	0.003093

3.3.2. Down-regulated transcripts by high temperature compared to normal temperature

Classification on the basis of biological process domain revealed that genes that were downregulated in response to exposure to high temperature were involved principally in catabolic processes (e.g. protein ubiquitination, proteasomal protein) and rhythmic processes. When sorted on the basis of molecular function domain, most genes were related to enzyme activity (e.g. ubiquin/ubiquitin-like protein ligase activity) and phospholipid binding. Differentially expressed genes were significantly enriched in valine, leucine, and isoleucine degradation and protein processing in endoplasmic reticulum pathways according to KEGG enrichments (Table 2). The plant ubiquitination pathway is involved in the regulation of morphogenesis, the circadian clock, and response to hormone or pathogen signal molecules (Nelson et al. 2000; Osterlund et al. 2000; Stone et al. 2006; Rosebrock et al. 2007; Takahashi et al. 2009). Ubiquitylation and proteasome-mediated ubiquitin-dependent protein catabolic processes are required for protein ubiquitination and degradation (Callis and Vierstra 2000). Previous work found that U-box E3 ubiquitin ligases AtCHIP and PLANT U-BOXs (PUBs) are involved in temperature stress tolerance and the AtCHIP transcript was upregulated by high temperature (Yan et al. 2003; Cho et al. 2006; Min et al. 2016; Byun et al. 2017). We found that elevated ambient temperature had the same effect on AtCHIP (AT3G07370) expression, and that genes encoding RING/U-box superfamily proteins involved in protein ubiquitination (e.g. AT3G18710 (PUB29) AT5G37490 (PUB21), AT3G11840 (PUB24), AT1G60190 (PUB19), AT2G35930 (PUB23), AT5G55970, AT1G14200, AT1G05880, and AT1G55530) were dominantly downregulated in response to high temperature treatment. Previous studies showed increased valine, leucine, and isoleucine degradation in extended darkness to compensate for limits in respiration (Däschner et al. 2001; Binder 2010; Schertl et al. 2017). The downregulated genes involved in the valine, leucine, and isoleucine degradation pathway (e.g. AT3G06850, AT1G03090, AT1G21400, and AT1G10070) may indicate that the availability of carbohydrates for respiration was less limited at higher temperature. Expression of BT2 (AT3G48360), a regulator of circadian rhythm, was also downregulated and may influence multiple biological processes (Table S2). BT2 also participates in hormone signaling transduction, nutrient status, and stress responses (Mandadi et al. 2009). Therefore, high temperature also could regulate roots growth via rhythm processes.

Analysis of differentially expressed genes under high temperature suggested that glycosyl compound biosynthetic processes and secondary metabolic processes were enhanced by high temperature and accelerated biological processes and promoted structure stabilization to strengthen defense. Additionally, the protein ubiquitination pathway in roots was downregulated by high temperature.

3.4. Identification of hormone-related genes regulated at elevated ambient temperature

Interactions of plant hormones constitute a complex regulation network to respond to ambient temperature. Previous studies have shown that auxin, ethylene, cytokinin, and brassinosteroid hormones are involved in ambient temperature-mediated root growth (Zhu et al. 2015; Fei et al. 2017; Martins et al. 2017). And phenotypes of ga3ox1/2 and ga20ox1/2 mutants producing reduced gibberellin levels and signaling have been shown to contribute to plant tolerance exposure to abiotic stresses, while ga2ox mutant producing high gibberellin levels can’t survive under same condition (Colebrook et al. 2014). To confirm that GA signaling also engages in ambient temperature regulation, transcript abundance of gibberellin biosynthetic and metabolic genes was compared. The KEGG analysis and functional annotation results indicated higher levels of plant hormone signal transduction at 27°C compared to 22°C (Table 2). To analyze hormone-signaling response to elevated ambient temperature in roots, we compared the relative expression levels of hormone-related genes (Table 3). When transcript abundance of seedling roots was analyzed at 27°C and compared to that at 22°C, gibberellin biosynthesis genes (GA3OX2, GA2OX1, and GA2OX4) were upregulated and genes encoding DELLA proteins (RGL3 and RGA) were downregulated. Cytokinin biosynthesis genes (LOG1, LOG7, and CYP735A2) were downregulated, degradation genes (CKX4 and CKX7) were upregulated, and signaling genes (AHK2, ARR1, ARR2, ARR4, ARR7, and ARR12) were downregulated. Brassinosteroid signaling genes (BRI1, BSK3) were also downregulated. Overall, the results suggest that DELLA-mediated gibberellin signaling may enhance response to elevated ambient temperature, and that ARR1 and ARR12-mediated cytokinin and BRI1-mdiated brassinosteroid signaling may reduce this response.

Table 3. Identification of hormone-related genes elevated ambient temperature.

ID	Gene name	27vs22	Category
AT2G28305	LOG1	↓	Cytokinin riboside 5′-monophosphate phosphoribohydrolase
AT5G06300	LOG7	↓	Cytokinin riboside 5′-monophosphate phosphoribohydrolase LOG7
AT1G67110	CYP735A2	↓	Cytokinin hydroxylase
AT4G29740	CKX4	↑	Cytokinin dehydrogenase 4
AT5G21482	CKX7	↑	Cytokinin dehydrogenase 7
AT5G35750	AHK2	↓	Histidine kinase 2
AT1G19050	ARR7	↓	response regulator 7
AT4G16110	ARR2	↓	response regulator 2
AT3G16857	ARR1	↓	Two-component response regulator ARR1
AT1G10470	ARR4	↓	Two-component response regulator ARR4
AT2G25180	ARR12	↓	Two-component response regulator ARR12
AT1G80340	GA3OX2	↑	Gibberellin 3-beta-dioxygenase 2
AT1G78440	GA2OX1	↑	Gibberellin 2-beta-dioxygenase 1
AT1G47990	GA2OX4	↑	Gibberellin 2-beta-dioxygenase 4
AT5G17490	RGL3	↓	DELLA protein RGL3
AT2G01570	RGA	↓	DELLA protein RGA
AT4G39400	BRI1	↓	Protein BRASSINOSTEROID INSENSITIVE 1
AT4G00710	BSK3	↓	BR-signaling kinase 3

To verify the accuracy and repeatability of the RNA-seq results, 18 genes related to hormones were selected for qRT-PCR analysis. The results indicated that the relative variation of genes treated at different temperatures was highly consistent with the variation observed by sequencing (Figure 2(A,B)). Therefore, the transcriptome profiling data can be used to investigate gene expression patterns and compare differentially expressed genes.

Figure 2. (A, B) Relative expression of 18 genes presented by qRT-PCR (left side) and RNA-seq (right side).

3.5. Phenotypes of mutant response to elevated ambient temperatures

To further characterize hormone response to elevated ambient temperature, we dissected the phenotypes of cytokinin, gibberellin, and brassinosteroid-related mutants at 22°C and 27°C. A previous report showed that BRI1-dependent brassinosteroid signaling is involved in root response to prolonged elevated ambient temperature (Martins et al. 2017). The root growth of arr1-4, arr10-5, arr12-1, arr1,12, bri1-301 and rga28 mutants were analyzed at both 22°C and 27°C (Figure 3(A,B)). Root growth in bri1-301 and rga28 was more sensitive to elevated ambient temperatures than that of wild-type plants, and arr1,12 was less sensitive to elevated ambient temperatures than that of wild-type plants, as revealed by the ratio of the root lengths of plants grown at 27°C and 22°C (Figure 3(B)). This is consistent with our transcriptome data that cytokinin, gibberellin, and brassinosteroid hormones have important effects on root response to elevated ambient temperature.

Figure 3. Phenotypes of mutant response to different ambient temperatures. (A) Seedlings were grown on vertical MS plates at 22°C, and 27°C for 7 days. Bar = 5 mm. (B) Relative ratios of root lengths in mutants grown under the same conditions. (n = 25–50; P < 0.001, based on t-test).

Besides, previous reports showed that arr1-3 arr12-1 was less sensitive to low temperature than wild-type seedlings (Zhu et al. 2015). To analyze how bri1-301 and rga28 respond to low temperatures, we also observed the phenotype of these mutants at low temperatures (17°C). The result revealed that the root length of bri1-301 was less sensitive to low temperatures than that of wild-type plants, and that rga28 was more sensitive to low temperatures than that of wild-type plants, as revealed by the ratio of the root lengths of corresponding lines grown at 17°C and 22°C (Figure S1(A,B)). Therefore, we speculate that different hormones respond differently to different ambient temperatures.

4. Discussion

Overall, roots show differential response to low and high temperature. Low temperature enhanced carbohydrate metabolism to maintain cellular energy metabolism and compensate for nutrient limitations (Bakermans and Nealson 2004; Ghobakhlou et al. 2015) and inhibited root growth (Zhu and Geisler 2015; Nagelmüller et al. 2017). In contrast, high temperature increased glycosyl compound biosynthetic processes and secondary metabolic processes to facilitate stabilization (Dixon and Paiva 1995; Oh et al. 2009; Dixon et al. 2010). Therefore, enhanced defense against bacterial and fungal pathogens at high temperature was required for plant roots, and low temperature decreased defenses with reduction of salicylic acid and jasmonic acid signals. The root response patterns to low and high temperature also revealed similar regulation of biological processes, with changes in expression of genes involved in transport processes (anion, organic acid, and carboxylic acid transport) and circadian rhythm. Lower and higher temperature conditions both alter gene expression to influence circadian rhythm in roots. It was shown previously that auxin and ethylene are involved in root growth regulated by temperature (Fei et al. 2017, 2018), and that increased temperature downregulated brassinosteroid signaling mediated by BRI1 to regulate root growth (Martins et al. 2017). Our data are consistent with brassinosteroid signaling reducing the response to elevated ambient temperature. ARR1 and ARR12-mediated cytokinin signaling was reduced under continuous high temperature treatment, the root growth of arr1,12 was inhibited at 27°C compared to 22°C, gibberellin signaling was enhanced by elevated ambient temperature, and the root growth of rga28 was promoted at 27°C compared to 22°C. Overall, our results show extensive regulation of hormones at elevated ambient temperature during root growth.

5. Conclusion

Plant roots efficiently uptake water and mineral nutrients from soil (Augstein and Carlsbecker 2018). Ambient temperature regulates physiological and biochemical processes in plant roots by influencing the soil microenvironment. Through transcriptome sequencing analysis, we found changes in regulatory mechanisms at different ambient temperatures.

Understanding the molecular mechanisms of plant root function and adaption provide insight into how plants adapt to environmental temperature change. Global warming between the Last Glacial Maximum (LGM) and the early Holocene (10,000 years before the present) was on the order of 4–7°C, and warming is predicted to continue (Annan and Hargreaves 2013; Massondelmotte and Schulz 2013; Stocker et al. 2013; Nolan et al. 2018). Thus, it is important to learn how plant roots respond to changes in ambient temperatures. Our results indicate that temperature variations of only a few degrees have significant effects on gene expression in plant roots, indicating complex responses through multiple mechanisms. Comparison of the changes in biological processes and hormonal functions at different temperatures should provide insight into how plants adapt to global temperature changes. It is becoming increasingly important to see what the future of agriculture, phenology, and ecology might look like by understanding the effects of slightly higher ambient temperatures on plant physiology.

Author contributions

X.F.L. designed the experiments; Q.H.F. performed most of the experiments, interpreted data, generated figures, and wrote the manuscript; others performed some of the experiments.

Acknowledgments

This work was supported by grants from the Chinese National Science Foundation (31201062), Fund for Fostering Talents in Basic Science of the National Natural Science Foundation of China (J1210077, J1210033, and J110350), the Fundamental Research Funds for the Central Universities (lzujbky-2018-109 and lzujbky-2019-it14), and the foundation of the Ministry of Education Key Laboratory of Cell Activities and Stress Adaptations (lzujbky-2018-kb05). The funding sources had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Thanks to the Core Facility of the School of Life Sciences, Lanzhou University. Prof Guangqin Guo, Li jia, and Suiwen Hou provided necessary materials.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Xiaofeng Li http://orcid.org/0000-0003-3017-0855

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities [grant number lzujbky-2018-109]; the foundation of the Ministry of Education Key Laboratory of Cell Activities and Stress Adaptations [grant number lzujbky-2018-kb05]; Fund for Fostering Talents in Basic Science of the National Natural Science Foundation of China [grant numbers J1210077, J1210033, J110350]; the Chinese National Science Foundation [grant number 31201062].

Notes on contributors

Qionghui Fei

Qionghui Fei is graduate student of Lanzhou University. Fei is a member of Xiaofeng Li’s team.

Liyuan Liang

Liyuan Liang is graduate student of Lanzhou University. Liang is a member of Xiaofeng Li’s team.

Fan Li

Fan Li is graduate student of Lanzhou University. Li is a member of Xiaofeng Li’s team.

Li Zhang

Li Zhang is graduate student of Lanzhou University. Zhang is a member of Xiaofeng Li’s team.

Youxia Li

Youxia Li is graduate student of Lanzhou University. Li is a member of Xiaofeng Li’s team.

Yuman Guo

Yuman Guo is graduate student of Lanzhou University. Guo is a member of Xiaofeng Li’s team.

Lei Wu

Lei Wu is graduate student of Lanzhou University. Wu is a member of Xiaofeng Li’s team.

Xueqin Meng

Xueqin Meng is graduate student of Lanzhou University. Meng is a member of Xiaofeng Li’s team.

Huanhuan Gao

Huanhuan Gao is graduate student of Lanzhou University. Gao is a member of Xiaofeng Li’s team.

Xiaofeng Li

Xiaofeng Li is Assistant Professor at Lanzhou University in the Department of cell biology. His research examines the interaction between plant endogenous hormones and environmental ambient temperature. His research aims to reveal how ambient temperature changes regulate plant growth and development.