Next Article in Journal
Transcriptomic Analysis of the Influence of Methanol Assimilation on the Gene Expression in the Recombinant Pichia pastoris Producing Hirudin Variant 3
Next Article in Special Issue
Identification of Maize CC-Type Glutaredoxins That Are Associated with Response to Drought Stress
Previous Article in Journal
Integrative Analysis of Cancer Omics Data for Prognosis Modeling
Previous Article in Special Issue
Potassium Transporter LrKUP8 Is Essential for K+ Preservation in Lycium ruthenicum, A Salt-Resistant Desert Shrub
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 10
Issue 8
10.3390/genes10080605
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Transcriptome Analysis of Acid-Responsive Genes and Pathways Involved in Polyamine Regulation in Iron Walnut

by
Xiaomei Luo
* and
Juncheng Liu
College of Forestry, Sichuan Agricultural University, Huimin Road 211 in Wenjiang District, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
Genes 2019, 10(8), 605; https://doi.org/10.3390/genes10080605
Submission received: 29 June 2019 / Revised: 7 August 2019 / Accepted: 7 August 2019 / Published: 10 August 2019
(This article belongs to the Special Issue Abiotic Stress in Plants: Current Challenges and Perspectives)
Browse Figures
Versions Notes

Abstract

:
We reported changes in the co-regulated mRNA expression in iron walnut (Juglans sigillata) in response to soil pH treatments and identified mRNAs specific to acidic soil conditions. Phenotypic and physiological analyses revealed that iron walnut growth was greater for the pH 4–5 and pH 5–6 treatments than for the pH 3–4 and pH 6–7 treatments. A total of 2768 differentially expressed genes were detected and categorized into 12 clusters by Short Time-series Expression Miner (STEM). The 994 low-expression genes in cluster III and 255 high-expression genes in cluster X were classified as acid-responsive genes on the basis of the relationships between phenotype, physiology, and STEM clustering, and the two gene clusters were analyzed by a maximum likelihood (ML) evolutionary tree with the greatest log likelihood values. No prominent sub-clusters occurred in cluster III, but three occurred in cluster X. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that acid-responsive genes were related primarily to arginine biosynthesis and the arginine/proline metabolism pathway, implying that polyamine accumulation may enhance iron walnut acid stress tolerance. Overall, our results revealed 1249 potentially acid-responsive genes in iron walnut, indicating that its response to acid stress involves different pathways and activated genes.

1. Introduction

Juglans sigillata Dode or iron walnut (whose nut shells have many pits and depressions in their surface) is an important species of walnut tree known for its production of edible nuts and hard wood. This species is sometimes grown in gardens and parks as an ornamental plant. It is commonly found in Yunnan but also in Guizhou, Sichuan, and Tibet in China and grows in soils that have a pH of approximately 4–6 [1]. Due to acid rain pollution and aluminium toxicity [2], the soils in the Long River region and in southern China have gradually become acidified. Acid soil stress affects tree growth and development and endangers the agronomic yields of trees, which have also evolved various molecular and physiological strategies to counteract acid soil stress. Therefore, an in-depth understanding of the gene expression regulatory mechanisms activated in response to acid stress is needed for efficient improvement.
Studies involving acid treatments have been conducted on various species across multiple kingdoms, including Escherichia coli [3], Glycine max [4], Lactococcus lactis [5], Lactobacillus plantarum [6], Listeria monocytogenes [7], and Spinacia oleracea [8]. The ability of plants to mitigate acid rain stress and sustain productivity may be related to the scavenging of stress-induced toxic oxygen species, such as hydrogen peroxide, hydroxyl radicals and superoxide radicals [9]. Catalases and peroxidases are two major enzyme groups whose members participate in the removal of hydrogen peroxide in plants [10,11]. Moreover, polyamines are well known for their anti-senescence and anti-stress effects because of their antioxidant properties as well as their acid-neutralizing, membrane-stabilizing, and cell-wall-stabilizing abilities [12,13,14,15,16,17]. Polyamines are maintained at optimal levels by antizymes and antizyme inhibitors and perform various cellular functions [18], but at high levels, these molecules are cytotoxic [19]. The stress response (including acid stress) as a potential mechanism may inhibit the proliferation of polyamine-depleted cells [20]. However, the molecular mechanism of how polyamines act in these processes is unclear, and plant acid tolerance remains elusive.
Little research is currently available on tree responses to acid stress. Studies involving iron walnut have focused mainly on the inhibitory effects of the extracts of its green husks [21], the chemical compositions and antioxidant capabilities of oils [22], speciation and phylogenetic relationships [23], its rapid Pleistocene diversification [24], the phenylpropanoid pathway [25], and anthocyanin biosynthesis [26]. Breeding for acid tolerance is particularly challenging because of the genetic complexity of this trait. Iron walnut has a complex genome (2n = 32) [27], ~606 Mb in size [28], and it is well documented that acid tolerance results from cooperative interactions among multiple morphological, physiological, and biochemical characteristics. In this study, we used transcriptome sequencing (transcriptome-seq) to identify differentially expressed genes (DEGs) in response to acid stress; accordingly, we used the leaf tissue of acid-resistant iron walnut landrace plants grown in different soil pH treatments. This analysis serves as a reference for future studies on iron walnut response to various stresses, such as drought, cold, and salt stresses.

2. Materials and Methods

2.1. Plant Growth under Acidic Soil Conditions

An endemic iron walnut variety that is relatively acid tolerant was used for the analysis of acid tolerance-related genes. The pH of the original soil sample used for planting was measured and adjusted to generate a series of acidic soils (pH 3.25, pH 4.59, pH 5.26, pH 6.54) by adding the appropriate amount of calcium oxide (CaO). Twelve iron walnut seedlings were grown under the same conditions in a growth chamber (14 h photoperiod, 22 °C temperature, and 70% relative humidity; the soil pH was maintained at pH 3–4, pH 4–5, pH 5–6, or pH 6–7 during the acid stress treatment). One seedling was planted per pot, and three pots per soil pH treatment constituted one biological replicate. Seedlings approximately one year old were subjected to acid soil stress treatment for two months (Figure 1). Afterward, the leaf samples were first used to measure the net photosynthetic rate (Pn) on the basis of photosynthetically active radiation (PAR). The leaves were then harvested, flash frozen in liquid nitrogen and immediately stored at −80 °C for photosynthetic pigment and RNA sequencing (RNA-seq) analyses.

2.2. Contents of Photosynthetic Pigments and Pn-PAR Response Curves

To determine the effect of each pH treatment on photosynthesis, Pn-PAR response curves were generated in accordance with the non-rectangular hyperbolic photosynthetic model described by Farquhar et al. [29]. The chlorophyll and carotenoid contents were measured using the acetone-alcohol mixed extraction method described by Romero and Sanchez [30].

2.3. RNA Isolation and Iron Walnut RNA-Seq

RNA was extracted with an RNAprep Pure Plant Kit (Polysaccharides & Polyphenolics-rich) (Tiangen Biotech Co., Ltd., Beijing, China) according to the instructions provided. After extracting the total RNA and applying a DNase I treatment, we used magnetic beads with Oligo (dT) to isolate the mRNA. The mRNA molecules were fragmented at 150 bp, and cDNAs were synthesized via mRNA fragments as templates. The cDNA fragments were purified and resolved with EB buffer for end repair, single-nucleotide adenine (A) addition and adapter connections. After PCR amplification, an Agilent 2100 Bioanalyzer and an ABI Step One Plus Real-Time PCR System were used to quantify and qualify the sample library, respectively. The 150 bp library was then sequenced via an Illumina HiSeq™ 4000. Clean reads were obtained using Soap nuke software (http://www.seq500.com/ uploadfile/SOAPnuke.zip) by removing “dirty” reads containing adapter sequences, sequences with more than 10% unknown bases (“N”), and low-quality reads (reads with >50% of bases identified as low quality, where low-quality bases were defined as those whose sequencing quality was no more than 5). All of the clean reads were mapped against the genome data of walnut (ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/001/411/555/ GCF_001411555.1_wgs.5d) using HISAT2 software (version 2.1.0).

2.4. DEG Identification and Functional Annotation

Gene expression was calculated by the fragments per kilobase of exon model per million mapped reads (FPKM) method [31]. HTSeq software (version 0.10.0) was used to calculate the gene expression levels via a union model. The genes with FPKM values >1 were subjected to subsequent analysis. The DESeq method [32] was then used to identify DEGs based on a negative quadratic term distribution model. The raw counts and FPKM values were output by HTSeq, and the raw count of each gene was input into DESeq for differential expression analysis. The q-value of the DEGs was less than 0.05 (|Log2(FC)| ≥ 1, FC = fold change). R software (version 3.5.3) was used to construct and analyze Venn diagrams, as well as to analyze expression patterns and to perform a cluster analysis with the heatmap clustering method. The DEGs were ultimately annotated by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases via Omic Share tools (http://www.omicshare.com/).

2.5. Acid-Responsive Gene Identification and Enrichment Analysis

We compared the DEG profiles of plants under acid stress via STEM clustering [33]. Significant DEG profiles (p < 0.05) were further analyzed for acid-responsive genes based on the relationship between these gene expression profiles and those of iron walnut seedlings subjected to different soil pH treatments. Molecular phylogenetic analysis of these acid-response-related genes was also performed with MEGA7 [34]; the sequences were aligned by ClustalW, and molecular phylogenetic relationships were inferred by the maximum likelihood (ML) method based on the Tamura–Nei model [35]. Initial trees for the heuristic search were obtained automatically by applying the neighbor-joining and Bio NJ algorithms to a matrix of pairwise distances that were estimated using the maximum composite likelihood approach and then selecting the topology with the superior log likelihood value. The tree is drawn to scale, and the branch lengths represent the number of substitutions per site. The acid-responsive genes related to different biological functions were classified and grouped via GO analysis. KEGG pathway analysis was also used to identify biological pathways of the acid-responsive genes.

2.6. Quantitative Real-Time PCR (qRT-PCR) Validation

To validate the results of the RNA-seq data, qRT-PCR was performed to analyze the expression of genes with SYBR Green I using the iron walnut 18S gene as a standard control [36]. cDNAs were reverse-transcribed using a True Script First Stand cDNA Synthesis Kit (Aidlab, Beijing, China). Reverse transcription was performed in a total volume of 20 μL, which consisted of 1 μL of RNA, 4 μL of 5× RT Reaction Mix, 0.8 μL of Rondam primer/oligo (dT), 0.8 μL of True Script H-RTase/RI Mix, and 13.4 μL of RNase-free dH2O. The reaction tubes were maintained at 42 °C for 40 min and then at 65 °C for 10 min. The cDNAs were then used as templates for real-time PCR using gene-specific primers. Real-time PCR was conducted in a total volume of 10 μL, which consisted of 5 μL of 2× SYBR® Green Supermix, 1 μL of primers, 1 μL of cDNA, and 3 μL of ddH2O. The reaction conditions were 95 °C for 3 min, followed by 39 cycles of 95 °C for 10 s and 57–61 °C for 30 s. The relative quantification of gene expression was determined by qRT-PCR software 3.0 using the Pfaffl method. Mean values and standard errors were calculated from three independent experiments consisting of three biological replicates of iron walnut, and the data were normalized to the relative efficiency of each primer pair.

3. Results

3.1. Determination of Pn-PAR Response Curves and Photosynthetic Pigment Contents

To investigate the effects of each pH iron walnut in such pH treatment, Pn-PAR response curves were generated (Figure 2a). The trend of the Pn-PAR response curves for the iron walnut seedlings was as follows: pH 4–5 > pH 5–6 > pH 6–7 > pH 3–4. The photosynthetic pigment contents were also measured (Figure 2b). The total contents of chlorophyll a and b were 14.29%, 11.08%, and 9.09% greater for the iron walnut seedlings at pH 4–5, pH 5–6, and pH 6–7 than at pH 3–4, respectively. Similarly, the content of chlorophyll b was 55.45%, 46.45%, and 25.98% greater for the iron walnut seedlings at pH 4–5, pH 5–6, and pH 6–7, respectively, than at pH 3–4. Moreover, the content of chlorophyll a was 6.02%, 3.97%, and 5.70% greater for the iron walnut seedlings at pH 4–5, pH 5–6, and pH 6–7 than at pH 3–4, respectively. However, the carotenoid content showed the opposite trend from the total chlorophyll content; it was 13.25% and 13.34% lower for the iron walnut seedlings at pH 4–5 and pH 5–6, respectively, but 6.89% greater in the iron walnut seedlings at pH 6–7. In total, the physiological data showed that the growth of iron walnut was greater in the pH 4–5 and pH 5–6 treatments than in the pH 3–4 and pH 6–7 treatments.

3.2. Illumina Sequencing of Different cDNA Libraries

cDNA libraries were constructed from leaves harvested two months after acid stress; three biological replicates were sequenced using the Illumina HiSeq™ 4000 platform. After cleaning and checking the read quality, we obtained nearly 32.87 million 150 bp paired-end clean reads. Among them, all of the Q30 (Phred score) values were greater than 94%, and the GC content was greater than 45% in each sample. The statistical data of the sequenced samples are shown in Table 1. The clean reads of each sample were mapped to the full gene set of walnut. The transcriptome data from 12 samples mapped to 27,316 genes, which covered 74.11% of the whole gene set. The major mapping reads indicated reliable transcriptome data.

3.3. DEG Analysis and Validation of the Iron Walnut Sequencing Data

The gene expression in each sample is presented in Table S1. After removing redundant DEGs between six comparison groups, a total of 2768 DEGs (q < 0.05) were identified based on a pairwise comparison analysis (Figure 3a). Six cross-comparisons between the different gene sets were illustrated in a Venn diagram (Figure 3b). A cross-comparison of pH 3–4 vs. pH 4–5 showed that the most-expressed genes (748) were unique to the other cross-comparisons, while pH 3–4 vs. pH 6–7 resulted in fewer uniquely expressed genes (2). The heatmap clustering showed the gene expression patterns for the 2768 DEGs to be clustered into three categories (Figure 3c). The DEGs in the pH 3–4 treatment differed from those in the other three treatments (pH 4–5, pH 5–6, pH 6–7), and the DEGs in the pH 6–7 treatment also differed from those in the other two treatments (pH 4–5, pH 5–6). Among these DEGs, ten were confirmed via qRT-PCR to be distinctly differentially expressed between treatments (Table 2, Figure 4). The results of this experiment were essentially consistent with the RNA-seq data.

3.4. GO-and KEGG-Annotated DEGs

The top ten GO term classifications of the DEGs from six cross-comparisons between the different gene sets are illustrated in Figure 5. Among these GO terms, 45% of DEGs belong to the biological process category, 47% of DEGs belong to the molecular function category, and 8% of DEGs belong to the cellular component category. In fact, 40% of the DEGs were associated with electron transport (81 DEGs) and oxidoreductase activity (80 DEGs), 6% of DEGs were associated with 3-deoxy-7-phosphoheptulonate synthase activity (3 DEGs), plastid stroma (3 DEGs), chloroplast stroma (3 DEGs), the chloroplast ribulose bisphosphate carboxylase complex (3 DEGs), the ribulose bisphosphate carboxylase complex (3 DEGs), the ribonucleoside-diphosphate reductase complex (2 DEGs), chloroplast parts (3 DEGs), and plastid parts (3 DEGs).
Thirty-eight KEGG pathways (false discovery rate (FDR) < 0.05) from six cross-comparisons between the different gene sets are listed in Table 3. Approximately 85% of the DEGs were assigned to metabolism, and the remaining 15% of the DEGs were nearly equally assigned to genetic information processing, environmental information processing, and organismal systems. In fact, 25% of the DEGs focused on metabolic pathways (229 DEGs), and 1% of the DEGs were associated with the proteasome (1 DEG), other glycan degradation (1 DEG), the mRNA surveillance pathway (1 DEG), the biosynthesis of unsaturated fatty acids (1 DEG), and isoflavonoid biosynthesis (1 DEG).

3.5. STEM Cluster Analysis of DEGs and Identification of Acid Resistance-Related Genes

STEM cluster analysis was used to estimate all of the DEGs, as shown in Figure 6 and Table S2. A total of 2768 DEGs were filtered, and 2577 DEGs ultimately formed 12 clusters. Four clusters were significantly related to pH treatment (clusters III, VI, X, XII). Furthermore, all 994 genes in cluster III were clearly expressed at low levels in the pH 4–5 and pH 5–6 treatments, while all 255 genes in cluster X were clearly expressed at high levels in the pH 4–5 and pH 5–6 treatments. In these experiments, iron walnut plants grew better in the pH 4–5 and pH 5–6 treatments than in the pH 3–4 and pH 6–7 treatments. Moreover, the physiological data showed that the growth of iron walnut was greater in the pH 4–5 and pH 5–6 treatments than in the pH 3–4 and pH 6–7 treatments. Hence, the 1249 genes in cluster III and cluster X were found to be potential acid-responsive genes.

3.6. Molecular Phylogenetic Analysis of Acid Resistance-Related Genes

Evolutionary analyses of the 994 DEGs in cluster III and the 255 DEGs in cluster X were conducted with MEGA7 (Figure 7). The trees with the highest log likelihood values are shown in Figure 7a,b (−2,370,992.00 and −120,896.32). The 994 low-expression DEGs in cluster III included 1339 nucleotide sequences, as shown in Figure 7a, and the 255 high-expression DEGs in cluster X included 423 nucleotide sequences, as shown in Figure 7b. The total length of the combined sequences in cluster III is 7840 bp and that in cluster X is 5416 bp after alignment by ClustalW in Mega 7. All positions with gaps and missing data were eliminated, then we obtained 1017 bp in cluster III, and 1964 bp in cluster X. There was a total of 208 positions (Figure 7a) and 258 positions (Figure 7b) in the final dataset. No prominent sub-clusters were observed in cluster III (Figure 7a), but three notable sub-clusters were observed in cluster X (Figure 7b). Two long nucleotide sequences with nearly the same number of nucleotides were observed in the green (193 sequences) and red (194 sequences) sub-clusters. Thirty-six nucleotide sequences formed the small blue sub-cluster. Interestingly, each sub-cluster in cluster X contained sequences related to photosynthesis and chlorophyll (Figure 7b). In total, differences occurred within high-expression DEGs in cluster X but not within low-expression DEGs in cluster III.

3.7. Gene Function and Pathway Enrichment Analyses of Acid Resistance-Related Genes

The combination of the above strategies resulted in final totals of 994 and 255 DEGs. We then performed GO and KEGG enrichment analyses of these two groups of genes (Figure 8). The assigned GO functions of the genes in cluster III and cluster X broadly encompassed 45 and 37 functional categories, respectively. The 994 low-expression DEGs in cluster III prominently included the GO terms cell periphery, plasma membrane, oxidoreductase activity, transporter activity, anion transport, oxidation-reduction process, transmembrane transport, and defence response (Figure 8a). The 255 high-expression DEGs in cluster X were represented by the GO terms thylakoid, chloroplast, plastid, photosystem I, envelope, extracellular region, photosynthesis, light reaction, organic acid biosynthetic process, and carboxylic acid biosynthetic process (Figure 8b). Approximately half of the GO functional enrichment terms overlapped between clusters III and X, including response to stimulus, binding, and extracellular region. Furthermore, oligopeptide transport, peptide transport, chitin binding, oxidoreductase activity, chloroplast stroma, and cell cortex were involved in the scavenging of stress-induced toxic oxygen species and polyamine regulation.
The KEGG pathway analysis revealed that the 994 low-expression DEGs in cluster III were involved mainly in eight pathways: Limonene/pinene degradation, stilbenoid/diarylheptanoid/ gingerol biosynthesis, galactose metabolism, selenocompound metabolism, anthocyanin biosynthesis, glyoxylate/dicarboxylate metabolism, protein processing in the endoplasmic reticulum, and plant-pathogen interactions (Figure 8c). The analysis also revealed that the 255 high-expression DEGs in cluster X were involved mainly in another eight pathways: Photosynthesis, fatty acid degradation, α-linolenic acid metabolism, biosynthesis of amino acids, alanine/aspartate/glutamate metabolism, glycine/serine/threonine metabolism, arginine biosynthesis, and arginine/proline metabolism (Figure 8d). There were no overlapping KEGG pathways between clusters III and X. Among these pathways, glyoxylate/dicarboxylate metabolism, biosynthesis of amino acids, alanine/aspartate/glutamate metabolism, glycine/serine/threonine metabolism, arginine biosynthesis, and arginine/proline metabolism were involved in polyamine metabolism and regulation.
Six genes closely related to polyamine biosynthesis/metabolism (gene42780, gene28192, gene4089, gene31260, gene24987, gene9388) are listed in Table 4. They are described as spermidine hydroxycinnamoyl transferase-like, spermine synthase-like, S-adenosylmethionine synthase 1-like, arginine decarboxylase-like, serine/arginine-rich splicing factor RS40-like, and mitochondrial arginine transporter BAC2-like, which were associated with six GO terms (molecular function, catalytic activity, organic acid biosynthetic process, carboxylic acid biosynthetic process, binding, transport), and 12 KEGG pathways (biosynthesis of secondary metabolites; metabolic pathways; stilbenoid, diarylheptanoid and gingerol biosynthesis; flavonoid biosynthesis; phenylpropanoid biosynthesis; β-alanine metabolism; glutathione metabolism; cysteine and methionine metabolism; arginine and proline metabolism; biosynthesis of amino acids; spliceosome; thermogenesis).

4. Discussion

Acidic soil is prevalent in the Yangtze valley and southern area of China. Among the major trees of economic value, iron walnut is relatively tolerant to acid stress and therefore is often grown on more marginal sites. As a landrace, iron walnut has developed a strong tolerance and adaptability to acid stress. Using high-throughput RNA-seq technology, we compared in detail the transcriptional differences and overlap in response to different levels of soil pH and identified the changes in gene expression in the iron walnut seedlings during the whole acid response process.

4.1. Acid Resistance-Related Gene Identification

In this study, the mRNA of iron walnut seedlings exhibiting good acid tolerance was sequenced using an Illumina HiSeq™ 4000 platform with Sera-Mag Magnetic Oligo-dT Beads. A clear bioinformatic map of the mRNAs involved in multiple biological processes was produced. As a result, 32.87 million clean reads were collected from 12 samples of plants subjected to different soil pH treatments; this number met the requirements for further analysis. The clean Q30 reads constituted more than 94% of the total, suggesting high-quality sequencing. HISAT2 software was used to BLAST the transcriptome data against the walnut genome; 74% of the reads were mapped to the reference genome. The major mapping reads indicated reliable transcriptome data. These non-mapped tags most likely represent regions where the reference genome is incomplete [37]. Alternatively, there are genetic differences between the reference genome and the iron walnut seedlings used in this study [28]. Moreover, the RNA-seq data for reference genome annotations might not represent all major tissue types, developmental stages, or statuses during responses to abiotic and biotic stresses [38].
The global analysis of the DEGs provided a comprehensive dataset on the response to acid soil stress in the leaves of iron walnut seedlings. We identified 12 clusters for all DEGs and obtained four clusters that were significantly related to soil pH (p < 0.05). The genes in cluster III and cluster X were determined to be potential acid-responsive genes on the basis of the relationship between these gene expression profiles (STEM clustering trends) and the growth of the iron walnut seedlings subjected to the pH 4–5 and pH 5–6 treatments (the phenotype), as well as the Pn-PAR response curves and photosynthetic pigment contents (physiological data). Similarly, potential genes in both maize and humans have also been identified by gene expression profiling and phenotyping [39,40,41].

4.2. Analysis of Acid Resistance-Related Genes

The acid resistance-related genes in clusters III and X were further analyzed by the ML method. No prominent sub-clusters were observed in cluster III, but three notable sub-clusters were observed in cluster X, indicating a large difference between the low-expression genes in cluster III and the high-expression genes in cluster X. Two nearly identical large sub-clusters and a small sub-cluster in cluster X all contained sequences related to photosynthesis and chlorophyll, indicating that differences in nucleotide sequences occurred within the high-expression genes in cluster X, although these nucleotide sequences have similar biological functions. A similar phenomenon was also found in Arabidopsis thaliana [42], Capsicum annuum [43], Lactobacillus kefiranofaciens [44], Rana arvalis [45], and Zea mays [46].
The GO functional terms, including plasma membrane, oxidoreductase activity, anion transport, oxidation-reduction process, transmembrane transport, and defence response, were the most affected among the terms associated with the low-expression genes in cluster III, indicating that the genes in these different functional categories were predicted to be involved at low expression levels in iron walnut seedlings. GO terms including chloroplast, photosystem I, photosynthesis, and light reaction were associated with the most strongly affected, high enough genes in cluster X, indicating that the genes associated with these different functional categories can be predicted to be involved at high expression levels in iron walnut seedlings. GO terms such as photosystem I and photosynthesis were reported by Shikanai et al. [47], Guan et al. [48], and Otani et al. [49]. Approximately half of the GO functional enrichment terms overlapped between clusters III and X, including response to stimulus, binding, and extracellular region, suggesting that these associated genes can be predicted to be involved in essential plant biological processes [17,50,51]. Several similar GO terms, such as oligopeptide transport, peptide transport, chitin binding, oxidoreductase activity, chloroplast stroma, and cell cortex, have been identified as being associated with anti-senescence and anti-acid stress effects in A. thaliana, C. annuum, Nicotiana tabacum, Triticum aestivum, and Z. mays [11,12,16,40,46,52], implying that these GO terms are involved in the scavenging of stress-induced toxic oxygen species and polyamine regulation.
KEGG pathway analysis in this study revealed sixteen pathways that were significantly affected by acid soil stress. The low-expression genes associated with eight pathways, glyoxylate/ dicarboxylate metabolism, limonene/pinene degradation, plant-pathogen interaction, etc., in cluster III significantly differed throughout the whole acid soil stress period, implying that these genes are involved in functions of defence, antioxidation, free radical scavenging, sugar, lipids as an energy source, protein processing, and polyamine metabolism and regulation. The expression of high-expression genes associated with another eight pathways (alanine/aspartate/glutamate metabolism, arginine biosynthesis, arginine/ proline metabolism, etc.) in cluster X significantly varied throughout the whole acid soil stress period. These results indicated that polyamine accumulation enhanced iron walnut acid stress tolerance. Similar studies by Gupta et al. [11] reported that the accumulation of polyamines occurs under many types of abiotic stress, including acid stress, and modulation of their biosynthetic pathway results in tolerance to stress. Hussain et al. [9] also reported a strong correlation between the presence of polyamines and improved tolerance to environmental stresses (acid drought, salinity, low temperature, oxidative stress and metal toxicity). Taie et al. [16] reported that polyamines promote growth and antioxidant activity and protect genomic DNA in heavy metal-stressed wheat plants. Similarly, Liu et al. [53] noted that polyamines provided environmental stress tolerance to plants. Majumdar et al. [46] demonstrated that polyamines contributed to the resistance of maize against Aspergillus flavus infection and to aflatoxin production. Amrani et al. [15] revealed that polyamines improved sucrose-induced tolerance to atrazine-mediated chemical stress in A. thaliana. Furthermore, Velikova et al. [4] proposed that exogenous polyamines could be attributed to protecting acid rain-treated bean plants via acid-neutralizing and antioxidant effects, as well as by their ability to stabilize membranes by associating with negatively charged phospholipids.

4.3. Hypothetical Regulatory Mechanisms of Acid Resistance

We further analyzed six genes related to arginine biosynthesis and the metabolic pathways of acid stress responses, which included genes related to arginine, ornithine, agmatine, methionine, putrescine, spermidine, and spermine. Gene42780, “spermidine hydroxycinnamoyl transferase-like”, plays a role in producing tricoumaroyl-, tricaffeoyl-, and triferuloyl-spermidine [54]. Gene28192, “spermine synthase-like”, plays a role in producing spermine from spermidine [11]. Gene4089, “S-adenosylmethionine synthase 1-like”, plays a role in producing spermine and spermidine from putrescine [9]. Gene31260, “arginine decarboxylase-like”, plays a role in producing agmatine from L-arginine [55,56]. Gene24987, “serine/arginine-rich splicing factor RS40-like”, and Gene9388, “mitochondrial arginine transporter BAC2-like”, participate in producing ornithine/agmatine from arginine [57,58].
The hypothetical regulatory mechanisms of enhanced acid stress tolerance via polyamines are illustrated in Figure 9. These varied hypothetical mechanisms were proposed by Liu et al. [53], Hussain et al. [9], Gupta et al. [11], Taie et al. [16], and Zarza et al. [17]. The biosynthesis of polyamines in plants has been well demonstrated [59,60,61,62,63,64,65]. Putrescine is produced either directly from ornithine or indirectly from arginine. Putrescine is converted into spermidine, and spermidine is converted into spermine, both of which require methionine. Polyamines include putrescine (diamine), spermidine (triamine), and spermine (tetramine). The hypothetical roles of polyamines include antioxidant responses, programmed cell death, acting as a compatible solute in higher plants, acting as a signaling molecule during stress responses, regulating ion channels and Ca2+ homeostasis, and other unknown pathways. Previous works [4,9,11,15,16,17,18,20,43,46,52,53,62] have indicated that accumulated polyamines may play vital roles in counteracting stress (1) as antioxidants, free radical scavengers, and membrane stabilizers that can interact freely with DNA/RNA/proteins/membrane lipids to prevent the membrane system from denaturing under acid stress conditions; (2) through the cationic nature of spermine+4 > spermidine+3 > putrescine+2 at physiological pH and/or improvement of the K+/Ca2+ ion balance; (3) as cellular signals in intrinsic cross-talk with abscisic acid (ABA), hydrogen peroxide, and nitrous oxide during acid stress responses by a complex network; and (4) by increasing cell proliferation, decreasing apoptosis and increasing the expression of genes associated with tumor invasion and metastasis, thus making their metabolism a target for cancer treatment and prevention.

5. Conclusions

In summary, this study provides a comprehensive analysis of the acid-responsive genes and transcriptome expression profiles of iron walnut leaves via a combination of RNA-seq, STEM profiling, the ML method, and physiological approaches. Our results revealed 1249 potentially acid-responsive genes in iron walnut, which we annotated by using the GO and KEGG databases, showing that polyamine accumulation enhanced iron walnut acid stress tolerance. These results fill the knowledge gap in the available literature concerning acid resistance-related genes in iron walnut. Next, we seek to understand how environmental changes (such as acid stress) are communicated via cellular signal transduction to induce a coordinated metabolic response and how the function of polyamines is influenced by transcriptional and post-translational modifications.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4425/10/8/605/s1, Table S1: Gene expression for each sample. The first column in the table presents the gene ID, and the first row in the table presents the treatment and gene descriptions. (XLSX 3.32 MB); Table S2 Short Time-series Expression Miner (STEM) cluster analysis. The first column in the table presents the gene ID, and the first row in the table presents the treatment and gene descriptions (XLSX 311 kB).

Author Contributions

Conceptualization, X.L.; methodology, J.L.; software, J.L.; validation, X.L.; formal analysis, X.L.; investigation, J.L.; resources, X.L.; data curation, X.L.; writing—original draft, X.L.; writing—review and editing, X.L.; visualization, J.L.; supervision, X.L.; project administration, X.L.; funding acquisition, X.L. All authors consented to this submission.

Funding

This research was funded by the Natural Science Foundation of China (grant number 31500993).

Acknowledgments

The authors thank Qianwen Xiao and Guanglan Pu for their assistance in obtaining plant materials and support.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

  1. Surhone, L.M.; Tennoe, M.T.; Henssonow, S.F. Juglans Sigillata; Betascript Publishing: Beau Bassin, Mauritius, 2010; pp. 1–68. ISBN 13 978-613-3-34400-6. [Google Scholar]
  2. Kochian, L.V. Cellular mechanisms of aluminum toxicity and resistance in plants. Annu. Rev. Plant Physiol. Plant Mol. Biol. 1995, 46, 237–260. [Google Scholar] [CrossRef]
  3. Oberto, J.; Nabti, S.; Jooste, V.; Mignot, H.; Rouviere-Yaniv, J. The HU regulon is composed of genes responding to anaerobiosis, acid stress, high osmolarity and SOS induction. PLoS ONE 2009, 4, e4367. [Google Scholar] [CrossRef] [PubMed]
  4. Velikova, V.; Yordanov, I.; Edreva, A. Oxidative stress and some antioxidant systems in acid rain-treated bean plants: Protective role of exogenous polyamines. Plant Sci. 2000, 151, 59–66. [Google Scholar] [CrossRef]
  5. Garcíaquintáns, N.; Magni, C.; Mendoza, D.D.; López, P. The citrate transport system of Lococcuslactis subsp. Lactisbio var. diacetylactis is induced by acid stress. Appl. Environ. Microbiol. 1998, 64, 850–857. [Google Scholar]
  6. Duary, R.K.; Batish, V.K.; Grover, S. Expression of the atpD gene in probiotic Lactobacillus plantarum strains under in vitro acidic conditions using RT-qPCR. Res. Microbiol. 2010, 161, 399–405. [Google Scholar] [CrossRef] [PubMed]
  7. Wemekamp Kamphuis, H.H.; Wouters, J.A.; de Leeuw, P.P.; Hain, T.; Chakraborty, T.; Abee, T. Identification of sigma factor sigma b-controlled genes and their impact on acid stress, high hydrostatic pressure, and freeze survival in Listeria monocytogenes egd-e. Appl. Environ. Microbiol. 2004, 70, 3457–3466. [Google Scholar] [CrossRef] [PubMed]
  8. Yang, J.L.; Zheng, S.J.; He, Y.F.; Matsumoto, H. Aluminium resistance requires resistance to acid stress: A case study with spinach that exudes oxalate rapidly when exposed to Al stress. J. Exp. Bot. 2005, 56, 1197–1203. [Google Scholar] [CrossRef]
  9. Hussain, S.S.; Ali, M.; Ahmad, M.; Siddique, K.H. Polyamines: Natural and engineered abiotic and biotic stress tolerance in plants. Biotechnol. Adv. 2011, 29, 300–311. [Google Scholar] [CrossRef]
  10. Willekens, H.; Inze, D.; van Montagu, M.; van Camp, W. Catalases in plants. Mol. Breed. 1995, 1, 207–228. [Google Scholar] [CrossRef]
  11. Gupta, K.; Dey, A.; Gupta, B. Plant polyamines in abiotic stress responses. Acta Physiol. Plant 2013, 35, 2015–2036. [Google Scholar] [CrossRef]
  12. Kaur-Sawhney, R.; Galston, A.W. Physiological and biochemical studies on the anti-senescence properties of polyamines in plants. In Biochemistry and Physiology of Polyamines in Plants; Slocum, R.D., Floress, H.E., Eds.; CRC Press: Boca Raton, FL, USA, 1991; pp. 201–211. [Google Scholar]
  13. Berta, G.; Altamura, M.M.; Fusconi, A.; Cerruti, F.; Capitani, F.; Bagni, N. The plant cell wall is altered by inhibition of polyamine biosynthesis. New Phytol. 1997, 137, 569–577. [Google Scholar] [CrossRef]
  14. Borrell, A.; Carbonell, L.; Farra’s, R.; Puig-Parellada, P.; Tiburcio, A.F. Polyamines inhibit lipid peroxidation in senescing oat leaves. Physiol. Plant. 1997, 99, 385–390. [Google Scholar] [CrossRef]
  15. Amrani, A.E.; Couée, I.; Berthomé, R.; Ramel, F.; Gouesbet, G.; Sulmon, C. Involvement of polyamines in sucrose-induced tolerance to atrazine-mediated chemical stress in Arabidopsis thaliana. J. Plant Physiol. 2019, 238, 1–11. [Google Scholar] [CrossRef]
  16. Taie, H.A.A.; Seif El-Yazal, M.A.; Ahmed, S.M.A.; Rady, M.M. Polyamines modulate growth, antioxidant activity, and genomic DNA in heavy metal-stressed wheat plant. Environ. Sci. Pollut. Res. Int. 2019, 1, 1–13. [Google Scholar] [CrossRef]
  17. Zarza, X.; Shabala, L.; Fujita, M.; Shabala, S.; Haring, M.A.; Tiburcio, A.F.; Munnik, T. Extracellular Spermine Triggers a rapid intracellular phosphatidic acid response in Arabidopsis, involving PLD activation and stimulating ion flux. Front. Plant Sci. 2019, 10, 601. [Google Scholar] [CrossRef]
  18. Kahana, C. The antizyme family for regulating polyamines. J. Biol. Chem. 2018, 293, 18730–18735. [Google Scholar] [CrossRef] [Green Version]
  19. Porat, Z.; Wender, N.; Erez, O.; Kahana, C. Mechanism of polyamine tolerance in yeast: Novel regulators and insights. Cell. Mol. Life Sci. 2006, 62, 3106–3116. [Google Scholar] [CrossRef]
  20. Landau, G.; Ran, A.; Bercovich, Z.; Feldmesser, E.; Horn-Saban, S.; Korkotian, E.; Jacob-Hirsh, E.; Rechavi, G.; Ron, D.; Kahana, C. Expression profiling and biochemical analysis suggest stress response as a potential mechanism inhibiting proliferation of polyamine-depleted cells. J. Biol. Chem. 2012, 287, 35825–35837. [Google Scholar] [CrossRef]
  21. Li, C.; Zhang, Z.; Zhang, S.; Yan, W.; Si, C.; Lee, M.H.; Li, Z. Inhibitory effects of the extracts of Juglans sigillata green husks on the proliferation, migration and survival of KYSE150 and EC9706 human esophageal cancer cell lines. Nutr. Cancer 2019, 71, 149–158. [Google Scholar] [CrossRef]
  22. Gao, P.; Liu, R.; Jin, Q.; Wang, X. Comparative study of chemical compositions and antioxidant capacities of oils obtained from two species of walnut: Juglans regia and Juglans sigillata. Food Chem. 2019, 279, 279–287. [Google Scholar] [CrossRef]
  23. Zhao, P.; Zhou, H.J.; Potter, D.; Hu, Y.H.; Feng, X.J.; Dang, M.; Feng, L.; Zulfiqar, S.; Liu, W.Z.; Zhao, G.F.; et al. Population genetics, phylogenomics and hybrid speciation of Juglans in China determined from whole chloroplast genomes, transcriptomes, and genotyping-by-sequencing (GBS). Mol. Phylogenet. Evol. 2018, 126, 250–265. [Google Scholar] [CrossRef]
  24. Bai, W.N.; Yan, P.C.; Zhang, B.W.; Woeste, K.E.; Lin, K.; Zhang, D.Y. Demographically idiosyncratic responses to climate change and rapid Pleistocene diversification of the walnut genus Juglans (Juglandaceae) revealed by whole-genome sequences. New Phytol. 2018, 217, 1726–1736. [Google Scholar] [CrossRef]
  25. Persic, M.; Mikulic-Petkovsek, M.; Halbwirth, H.; Solar, A.; Veberic, R.; Slatnar, A. Red walnut: Characterization of the phenolic profiles, activities and gene expression of selected enzymes related to the phenylpropanoid pathway in pellicle during walnut development. J. Agricul. Food Chem. 2018, 66, 2742–2748. [Google Scholar] [CrossRef]
  26. Li, Y.; Luo, X.; Wu, C.; Cao, S.; Zhou, Y.; Jie, B.; Cao, Y.; Meng, H.; Wu, G. Comparative transcriptome analysis of genes involved in anthocyanin biosynthesis in red and green walnut (Juglans regia L.). Molecules 2017, 23, 25. [Google Scholar] [CrossRef]
  27. Woodworth, R.H. Meiosis of microsporogenesis in the Juglandaceae. Am. J. Bot. 1930, 17, 863–869. [Google Scholar] [CrossRef]
  28. Martínez-García, P.J.; Crepeau, M.W.; Puiu, D.; Gonzalez-Ibeas, D.; Whalen, J.; Stevens, K.A.; Paul, R.; Butterfield, T.S.; Britton, M.T.; Reagan, R.L.; et al. The walnut (Juglans regia) genome sequence reveals diversity in genes coding for the biosynthesis of non-structural polyphenols. Plant J. 2016, 87, 507–532. [Google Scholar] [CrossRef]
  29. Farquhar, G.D.; von Caemmerer, S.; Berry, J.A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 1980, 149, 78–90. [Google Scholar] [CrossRef]
  30. Romero, J.; Sanchez, M.A. Extraction of chlorophyll and carotenoids of whole protein concentrate obtained from green sweet potato by-products of harvest (Ipomoea batatas). Available online: http://aims.fao.org/serials/c_debd36ae (accessed on 9 August 2019).
  31. Mortazavi, A.; Williams, B.A.; McCue, K.; Schaeffer, L.; Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 2008, 5, 621–628. [Google Scholar] [CrossRef]
  32. Anders, S.; Huber, W. Differential expression analysis for sequence count data. Genome Biol. 2010, 11, R106. [Google Scholar] [CrossRef]
  33. Ernst, J.; Barjoseph, Z. STEM: A tool for the analysis of short time series gene expression data. BMC Bioinform. 2006, 7, 191. [Google Scholar] [CrossRef]
  34. Kumar, S.; Stecher, G.; Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 2016, 33, 1870–1874. [Google Scholar] [CrossRef]
  35. Tamura, K.; Nei, M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 1993, 10, 512–526. [Google Scholar]
  36. Li, X.; Pan, X.J.; Zhang, W.E.; Zhang, R.; Chen, J. Stability evaluation of reference genes for quantitative real-time PCR analysis in walnut (Juglans spp.). Plant Physiol. J. 2017, 53, 1795–1802. [Google Scholar]
  37. Wu, J.; Gu, Y.Q.; Hu, Y.; You, F.M.; Dandekar, A.M.; Leslie, C.A.; Aradhya, M.; Dvorak, J.; Luo, M.C. Characterizing the walnut genome through analyses of BAC end sequences. Plant Mol. Biol. 2012, 78, 95–107. [Google Scholar] [CrossRef]
  38. Xu, X.; Pan, S. Genome sequence and analysis of the tuber crop potato. In Proceedings of the 12th National Congress on Plant Genomics, San Diego, CA, USA, 4–6 December 2011; pp. 189–195. [Google Scholar]
  39. Hu, G.; Wei, B.; Wang, L.; Wang, L.; Kong, D.; Jin, Y.; Sun, Z. Analysis of gene expression profiles associated with glioma progression. Mol. Med. Rep. 2015, 12, 1884. [Google Scholar] [CrossRef]
  40. Forestan, C.; Cigliano, R.A.; Farinati, S.; Lunardon, A.; Sanseverino, W.; Varotto, S. Stress-induced and epigenetic-mediated maize transcriptome regulation study by means of transcriptome reannotation and differential expression analysis. Sci. Rep. 2016, 6, 30446. [Google Scholar] [CrossRef] [Green Version]
  41. Zheng, Y.; Li, X.; Huang, Y.; Jia, L.; Li, W. Time series clustering of mRNA and LncRNA expression during osteogenic differentiation of periodontal ligament stem cells. PeerJ 2018, 6, e5214. [Google Scholar] [CrossRef]
  42. Uyttewaal, M.; Burian, A.; Alim, K.; Landrein, B.; Borowska-Wykret, D.; Dedieu, A.; Peaucelle, A.; Ludynia, M.; Traas, J.; Boudaoud, A. Mechanical stress acts via katanin to amplify differences in growth rate between adjacent cells in Arabidopsis. Cell 2012, 149, 439–451. [Google Scholar] [CrossRef]
  43. Piñero, M.C.; Porras, M.E.; López-Marín, J.; Sánchez-Guerrero, M.C.; Medrano, E.; Lorenzo, P.; del Amor, F.M. Differential nitrogen nutrition modifies polyamines and the amino-acid profile of sweet pepper under salinity stress. Front. Plant Sci. 2019, 10, 301. [Google Scholar] [CrossRef]
  44. Chen, M.J.; Tang, H.Y.; Chiang, M.L. Effects of heat, cold, acid and bile salt adaptations on the stress tolerance and protein expression of kefir-isolated probiotic Lactobacillus kefiranofaciens M1. Food Microbiol. 2017, 66, 20–27. [Google Scholar] [CrossRef]
  45. Räsänen, K.; Laurila, A.; Merilä, J. Geographic variation in acid stress tolerance of the moor frog, Rana arvalis. II. Adaptive maternal effects. Evolution 2003, 57, 352–362. [Google Scholar] [CrossRef]
  46. Majumdar, R.; Minocha, R.; Lebar, M.D.; Rajasekaran, K.; Long, S.; Carter-Wientjes, C.; Minocha, S.; Cary, J.W. Contribution of maize polyamine and amino acid metabolism toward resistance against Aspergillus flavus infection and aflatoxin production. Front. Plant Sci. 2019, 10, 692. [Google Scholar] [CrossRef]
  47. Shikanai, T. Central role of cyclic electron transport around photosystem I in the regulation of photosynthesis. Curr. Opin. Biotechnol. 2014, 26, 25–30. [Google Scholar] [CrossRef]
  48. Guan, N.; Du, B.; Li, J.; Shin, H.D.; Chen, R.R.; Du, G.; Chen, J.; Liu, L. Comparative genomics and transcriptomics analysis-guided metabolic engineering of Propionibacterium acidipropionici for improved propionic acid production. Biotechnol Bioeng. 2018, 115, 483–494. [Google Scholar] [CrossRef]
  49. Otani, T.; Kato, Y.; Shikanai, T. Specific substitutions of light-harvesting complex I proteins associated with photosystem I are required for super complex formation with chloroplast NADH dehydrogenase-like complex. Plant J. 2018, 94, 122–130. [Google Scholar] [CrossRef]
  50. Benes, K.; Krekule, J. Plant Growth Substances from the Viewpoint of Regulation of Biological Processes; Biologické Listy: Prague, Czech, 1979; ISSN 0366–0486. [Google Scholar]
  51. Yang, M.; Cao, X.; Wu, R.; Liu, B.; Ye, W.; Yue, X.; Wu, J. Comparative proteomic exploration of whey proteins in human and bovine colostrum and mature milk using iTRAQ-coupled LC-MS/MS. Int. J. Food Sci. Nutr. 2017, 68, 671–681. [Google Scholar] [CrossRef]
  52. Yin, X.M.; Yu, S.Q.; Jiang, H.; Liu, M.H. Effects of acid rain stress on Eleocarpus glabripetalus seedlings leaf chlorophyll fluorescence characteristics and growth. Chinese. J. Appl. Ecol. 2010, 21, 1374–1380. [Google Scholar]
  53. Liu, J.; Kitashiba, H.; Wang, J.; Ban, Y.; Moriguchi, T. Polyamines and their ability to provide environmental stress tolerance to plants. Plant Biotechnol. 2007, 24, 117–126. [Google Scholar] [CrossRef] [Green Version]
  54. Grienenberger, E.; Besseau, S.; GeoVroy, P.; Debayle, D.; Heintz, D.; Lapierre, C.; Pollet, B.; Heitz, T.; Legrand, M. A BAHD acyltransferase is expressed in the tapetum of Arabidopsis anthers and is involved in the synthesis of hydroxycinnamoyl spermidines. Plant J. 2009, 58, 246–259. [Google Scholar] [CrossRef]
  55. Watson, M.B.; Malmberg, R.L. Regulation of Arabidopsis thaliana (L.) Heynh arginine decarboxilase by potassium deficiency stress. Plant Physiol. 1996, 111, 1077–1083. [Google Scholar] [CrossRef]
  56. Nam, K.H.; Lee, S.H.; Lee, J. Purification and characterization of arginine decarboxylase from soybean (Glycine max) hypocotyls. Plant Cell Physiol. 1997, 38, 1150–1155. [Google Scholar] [CrossRef]
  57. Wang, J.; Gao, Q.S.; Wang, Y.; Lafyatis, R.; Stamm, S.; Andreadis, A. Tau exon 10, whose missplicing causes frontotemporal dementia, is regulated by an intricate interplay of cis elements and trans factors. J. Neurochem. 2004, 88, 1078–1090. [Google Scholar] [CrossRef] [Green Version]
  58. Camacho, J.A.; Rioseco-Camacho, N. The human and mouse SLC25A29 mitochondrial transporters rescue the deficient ornithine metabolism in fibroblasts of patients with the hyperornithinemia-hyperammonemia-homocitrullinuria (HHH) syndrome. Pediatr. Res. 2009, 66, 35–41. [Google Scholar] [CrossRef]
  59. Bitrián, M.; Zarza, X.; Altabella, T.; Tiburcio, A.F.; Alcázar, R. Polyamines under abiotic stress: Metabolic crossroads and hormonal cross talks in plants. Metabolites 2012, 2, 516–528. [Google Scholar] [CrossRef]
  60. Fuell, C.; Elliott, K.A.; Hanfrey, C.C.; Franceschetti, M.; Michael, A.J. Polyamine biosynthetic diversity in plants and algae. Plant Physiol. Biochem. 2010, 48, 513–520. [Google Scholar] [CrossRef]
  61. Gandre, S.; Bercovich, Z.; Kahana, C. Ornithine decarboxylase-antizyme is rapidly degraded through a mechanism that requires functional ubiquitin-dependent proteolytic activity. Eur. J. Biochem. 2002, 269, 1316–1322. [Google Scholar] [CrossRef] [Green Version]
  62. Keren-Paz, A.; Bercovich, Z.; Kahana, C. Antizyme inhibitor: A defective ornithine decarboxylase or a physiological regulator of polyamine biosynthesis and cellular proliferation. Biochem. Soc. Trans. 2007, 35, 311–313. [Google Scholar] [CrossRef]
  63. Minguet, E.G.; Vera-Sirera, F.; Marina, A.; Carbonell, J.; Blázquez, M.A. Evolutionary diversification in polyamine biosynthesis. Mol. Biol. Evol. 2008, 25, 2119–2128. [Google Scholar] [CrossRef]
  64. Tabor, C.W.; Tabor, H. Polyamines. Annu. Rev. Biochem. 1984, 53, 749–790. [Google Scholar] [CrossRef]
  65. Takahashi, T.; Kakehi, J.I. Polyamines: Ubiquitous polycations with unique roles in growth and stress responses. Ann. Bot. 2010, 105, 1–6. [Google Scholar] [CrossRef]
Genes 10 00605 g001 550
Figure 1. Iron walnut seedlings subjected to a series of soils of differing acidity.
Figure 1. Iron walnut seedlings subjected to a series of soils of differing acidity.
Genes 10 00605 g001
Genes 10 00605 g002 550
Figure 2. Pn-PAR response curves (a) and contents of chlorophyll and carotenoids (b) of J. sigillata seedlings subjected to soils with different pH values. The percentage in the top bar in (b) shows the percentage increase for each treatment compared with the pH 3–4 treatment. The four colors indicate different pH values: pH 3–4 (purple), pH 4–5 (green), pH 5–6 (orange), and pH 6–7 (red). Chla—chlorophyll a, Chlb—chlorophyll b, Caro—carotenoid, Pn—net photosynthetic rate, PAR—photosynthetically active radiation.
Figure 2. Pn-PAR response curves (a) and contents of chlorophyll and carotenoids (b) of J. sigillata seedlings subjected to soils with different pH values. The percentage in the top bar in (b) shows the percentage increase for each treatment compared with the pH 3–4 treatment. The four colors indicate different pH values: pH 3–4 (purple), pH 4–5 (green), pH 5–6 (orange), and pH 6–7 (red). Chla—chlorophyll a, Chlb—chlorophyll b, Caro—carotenoid, Pn—net photosynthetic rate, PAR—photosynthetically active radiation.
Genes 10 00605 g002
Genes 10 00605 g003 550
Figure 3. Comparison of differentially expressed genes (DEGs) in seedlings subjected to different acid stresses. (a) The x-axis represents each comparison treatment, and the y-axis presents the gene number. The red color indicates upregulated genes, and the purple color indicates downregulated genes. (b) A Venn diagram shows the overlap between DEGs that are responsive to acid stress treatments. (c) the gene expression patterns and cluster analysis results of twelve samples are shown. The red/blue color on the far right indicates the up/down regulated DEGs of each sample.
Figure 3. Comparison of differentially expressed genes (DEGs) in seedlings subjected to different acid stresses. (a) The x-axis represents each comparison treatment, and the y-axis presents the gene number. The red color indicates upregulated genes, and the purple color indicates downregulated genes. (b) A Venn diagram shows the overlap between DEGs that are responsive to acid stress treatments. (c) the gene expression patterns and cluster analysis results of twelve samples are shown. The red/blue color on the far right indicates the up/down regulated DEGs of each sample.
Genes 10 00605 g003
Genes 10 00605 g004 550
Figure 4. Expression of 10 DEGs (a–j) in response to acid stress treatments. The vertical bar charts with simple error bars (left y-axis) represent the quantitation of 10 gene transcripts among twelve samples via qRT-PCR. The values are the means ± SDs (n = 3). The line and scatter plot (right y-axis) represent the transcript abundance (FPKM) of twelve samples of each gene, as revealed by RNA-seq. The three replicate samples per treatment were treated with soils of pH 3–4, 4–5, 5–6, and 6–7.
Figure 4. Expression of 10 DEGs (a–j) in response to acid stress treatments. The vertical bar charts with simple error bars (left y-axis) represent the quantitation of 10 gene transcripts among twelve samples via qRT-PCR. The values are the means ± SDs (n = 3). The line and scatter plot (right y-axis) represent the transcript abundance (FPKM) of twelve samples of each gene, as revealed by RNA-seq. The three replicate samples per treatment were treated with soils of pH 3–4, 4–5, 5–6, and 6–7.
Genes 10 00605 g004
Genes 10 00605 g005 550
Figure 5. Top ten Gene Ontology (GO) term classifications of the DEGs from six cross-comparisons between the different gene sets. BP—biological process; MF—molecular function; CC—cellular component. The blue color on the left indicates downregulated DEGs, while the red color on the right indicates upregulated DEGs.
Figure 5. Top ten Gene Ontology (GO) term classifications of the DEGs from six cross-comparisons between the different gene sets. BP—biological process; MF—molecular function; CC—cellular component. The blue color on the left indicates downregulated DEGs, while the red color on the right indicates upregulated DEGs.
Genes 10 00605 g005
Genes 10 00605 g006 550
Figure 6. Comparison of DEG profiles in response to acid stress as determined by STEM clustering. For each profile, the top-left number indicates the p-value, the bottom-left number indicates the profile ID, and the top-right number indicates the genes assigned to this profile. The color profile indicates p < 0.05.
Figure 6. Comparison of DEG profiles in response to acid stress as determined by STEM clustering. For each profile, the top-left number indicates the p-value, the bottom-left number indicates the profile ID, and the top-right number indicates the genes assigned to this profile. The color profile indicates p < 0.05.
Genes 10 00605 g006
Genes 10 00605 g007 550
Figure 7. Evolutionary analyses of cluster III (a) and cluster X (b) conducted with MEGA7. The evolutionary history was inferred using the maximum likelihood (ML) method based on the Tamura–Nei model. Initial tree(s) for the heuristic search were obtained automatically by applying the neighbor-joining and Bio NJ algorithms to a matrix of pairwise distances that were estimated using the maximum composite likelihood approach and then selecting the topology with the superior log likelihood value. The tree is drawn to scale, and the branch lengths represent the number of substitutions per site. All positions containing gaps and missing data were eliminated. The red color represents the cluster including 194 sequences, while the green color represents another cluster including 193 sequences, the blue color represents the rest cluster including 36 sequences.
Figure 7. Evolutionary analyses of cluster III (a) and cluster X (b) conducted with MEGA7. The evolutionary history was inferred using the maximum likelihood (ML) method based on the Tamura–Nei model. Initial tree(s) for the heuristic search were obtained automatically by applying the neighbor-joining and Bio NJ algorithms to a matrix of pairwise distances that were estimated using the maximum composite likelihood approach and then selecting the topology with the superior log likelihood value. The tree is drawn to scale, and the branch lengths represent the number of substitutions per site. All positions containing gaps and missing data were eliminated. The red color represents the cluster including 194 sequences, while the green color represents another cluster including 193 sequences, the blue color represents the rest cluster including 36 sequences.
Genes 10 00605 g007
Genes 10 00605 g008 550
Figure 8. GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway distributions in cluster III (a,c) and cluster X (b,d). In (a,b), x-axis indicates each GO term, and the y-axis indicates the number of DEGs in (c) and (d) indicates the number of DEGs, and the y-axis indicates each pathway. The three colors in (a,b) indicate the GO terms belonging to the biological process (red), molecular function (purple), and cellular component (light green) categories. The three colors in the pathways in (c) and (d) represent metabolism (red), genetic information processing (purple), and organismal systems (light green). The number above/to the right of each bar indicates the number of DEGs assigned to that GO term or pathway.
Figure 8. GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway distributions in cluster III (a,c) and cluster X (b,d). In (a,b), x-axis indicates each GO term, and the y-axis indicates the number of DEGs in (c) and (d) indicates the number of DEGs, and the y-axis indicates each pathway. The three colors in (a,b) indicate the GO terms belonging to the biological process (red), molecular function (purple), and cellular component (light green) categories. The three colors in the pathways in (c) and (d) represent metabolism (red), genetic information processing (purple), and organismal systems (light green). The number above/to the right of each bar indicates the number of DEGs assigned to that GO term or pathway.
Genes 10 00605 g008
Genes 10 00605 g009 550
Figure 9. Overview of hypothetical regulatory mechanisms of enhanced acid stress tolerance via polyamines. Acid stress induces the accumulation of polyamines, which then function in antioxidant responses, in programmed cell death, as compatible solutes in higher plants, as signaling molecules during stress responses, in the regulation of ion channels and Ca2+ homeostasis, and in other unknown pathways, by scavenging excess free radicals or stabilizing membranes by binding to negatively charged groups, leading to enhanced acid stress tolerance.
Figure 9. Overview of hypothetical regulatory mechanisms of enhanced acid stress tolerance via polyamines. Acid stress induces the accumulation of polyamines, which then function in antioxidant responses, in programmed cell death, as compatible solutes in higher plants, as signaling molecules during stress responses, in the regulation of ion channels and Ca2+ homeostasis, and in other unknown pathways, by scavenging excess free radicals or stabilizing membranes by binding to negatively charged groups, leading to enhanced acid stress tolerance.
Genes 10 00605 g009
Table
Table 1. Summary of the RNA-seq data obtained from different samples.
Table 1. Summary of the RNA-seq data obtained from different samples.
SampleClean ReadsQ30GCMapped GenomeUnique Mapped GenomeMapped Genes Expressed Genes
pH 3–4_128,020,62294.93%45.98%87.60%68.87%75.69%26,186
pH 3–4_226,738,56096.36%46.37%82.25%63.00%69.82%28,134
pH 3–4_327,011,23696.32%46.85%85.60%66.22%73.12%28,922
pH 4–5_127,605,12696.02%46.39%85.75%68.97%74.31%27,392
pH 4–5_227,634,85496.44%47.05%88.56%70.73%75.93%27,670
pH 4–5_327,109,75096.50%46.59%87.53%69.72%75.85%27,582
pH 5–6_126,864,46496.47%46.51%87.59%71.06%73.90%26,644
pH 5–6_227,448,38496.36%46.37%84.86%67.01%73.6%27,525
pH 5–6_327,338,82696.40%46.60%87.13%68.69%75.66%26,942
pH 6–7_127,698,73494.98%46.42%88.08%68.34%76.53%26,864
pH 6–7_227,894,57696.50%46.45%84.43%66.27%72.39%27,221
pH 6–7_327,288,78296.36%46.73%86.17%69.15%72.39%26,706
Table
Table 2. Primers for qRT-PCR assays used for the twelve RNA-seq libraries in this study.
Table 2. Primers for qRT-PCR assays used for the twelve RNA-seq libraries in this study.
Gene IDForward Primer (5′ to 3′)Reverse Primer (5′ to 3′)Amplicon Size (bp)
gene17916GAAGGCGAAGAAGAAGAAGACTGGCGGTAACTGTAACTC96
gene22045GGCGTGAAGGAGTTGATTACAGTGTTAAGGTCGTATCG97
gene25150AAGACGATGTTGATGATTCCTTCCAGTATTAGCGGTAAGA100
gene25184TGAGGCTGAAGAGTATGCCGTAGATGGTTGGATGGT82
gene29464CTGTGTTGTGGTAGAGGATCTTCATCGGCTGTGTAA81
gene29846GAGAAGGCTATCACAAGAAGCCAGTATGACAAGGAGTAATC113
gene31088GATGCTGTGTTGCTGTTCCCGCCATTATCTGCTTGA131
gene31799GCTATAACTACGCTATATTCGTTACTTCTGATTCTCCTATGT111
gene35192GCTGGAAGTCATAGTAAGGATGGCTGCTAATCACAAG167
gene39645GAGTGGGAATGAAGGAAGAATTGGCAGAGGAATTGGA72
Table
Table 3. Pathways from six cross-comparisons between the different gene sets.
Table 3. Pathways from six cross-comparisons between the different gene sets.
Pathway IDPathwayFDR < 0.05No. DEGs
ko01200Carbon metabolism5.70 × 10−750
ko01100Metabolic pathways5.70 × 10−7229
ko01110Biosynthesis of secondary metabolites1.65 × 10−6146
ko00710Carbon fixation in photosynthetic organisms1.83 × 10−628
ko00630Glyoxylate and dicarboxylate metabolism8.98 × 10−617
ko00908Zeatin biosynthesis2.25 × 10−514
ko00195Photosynthesis1.20 × 10−412
ko00010Glycolysis/gluconeogenesis1.04 × 10−329
ko03430Mismatch repair1.81 × 10−32
ko03050Proteasome1.81 × 10−31
ko00220Arginine biosynthesis2.13 × 10−310
ko00903Limonene and pinene degradation4.64 × 10−314
ko00910Nitrogen metabolism4.64 × 10−310
ko00030Pentose phosphate pathway4.64 × 10−320
ko01230Biosynthesis of amino acids5.00 × 10−335
ko00071Fatty acid degradation5.88 × 10−36
ko00511Other glycan degradation6.71 × 10−31
ko00500Starch and sucrose metabolism7.97 × 10−340
ko00670One carbon pool by folate8.93 × 10−37
ko04075Plant hormone signal transduction1.07 × 10−243
ko00260Glycine, serine and threonine metabolism1.22 × 10−213
ko00250Alanine, aspartate and glutamate metabolism1.42 × 10−29
Ko00330Arginine and proline metabolism1.42 × 10−27
ko00051Fructose and mannose metabolism1.42 × 10−217
ko03015mRNA surveillance pathway1.60 × 10−21
ko03040Spliceosome1.60 × 10−22
ko01040Biosynthesis of unsaturated fatty acids2.06 × 10−21
ko00945Stilbenoid, diarylheptanoid and gingerol biosynthesis2.06 × 10−24
ko00450Selenocompound metabolism2.15 × 10−27
ko00740Riboflavin metabolism2.25 × 10−24
ko04626Plant-pathogen interaction2.72 × 10−251
ko00052Galactose metabolism2.90 × 10−212
ko00943Isoflavonoid biosynthesis2.90 × 10−21
ko00240Pyrimidine metabolism2.90 × 10−22
ko00592α-linolenic acid metabolism3.33 × 10−26
ko00196Photosynthesis-antenna proteins4.07 × 10−24
ko00942Anthocyanin biosynthesis4.44 × 10−26
ko04141Protein processing in endoplasmic reticulum4.44 × 10−240
Table
Table 4. Six genes closely related to polyamine biosynthesis/metabolism.
Table 4. Six genes closely related to polyamine biosynthesis/metabolism.
Gene IDDescriptions
GO TermsKEGG Pathways
gene42780spermidine hydroxycinnamoyl transferase-like
----ko01110, Biosynthesis of secondary metabolites
ko01100, Metabolic pathways
ko00945, Stilbenoid, diarylheptanoid and gingerol biosynthesis
ko00941, Flavonoid biosynthesis
ko00940, Phenylpropanoid biosynthesis
gene28192spermine synthase-like
GO:0003674, molecular function GO:0003824, catalytic activityko00410, β-Alanine metabolism
ko00480, Glutathione metabolism
ko00270, Cysteine and methionine metabolism
ko00330, Arginine and proline metabolism
ko01100, Metabolic pathways
gene4089S-adenosylmethionine synthase 1-like
----ko01230, Biosynthesis of amino acids
gene31260arginine decarboxylase-like
----ko00330, Arginine and proline metabolism
gene24987serine/arginine-rich splicing factor RS40-like
----ko03040, Spliceosome
gene9388mitochondrial arginine transporter BAC2-like
GO:0016053, organic acid biosynthetic processko04714, Thermogenesis
GO:0046394, carboxylic acid biosynthetic process
GO:0005488, binding
GO:0006810, transport

Share and Cite

MDPI and ACS Style

Luo, X.; Liu, J. Transcriptome Analysis of Acid-Responsive Genes and Pathways Involved in Polyamine Regulation in Iron Walnut. Genes 2019, 10, 605. https://doi.org/10.3390/genes10080605

AMA Style

Luo X, Liu J. Transcriptome Analysis of Acid-Responsive Genes and Pathways Involved in Polyamine Regulation in Iron Walnut. Genes. 2019; 10(8):605. https://doi.org/10.3390/genes10080605

Chicago/Turabian Style

Luo, Xiaomei, and Juncheng Liu. 2019. "Transcriptome Analysis of Acid-Responsive Genes and Pathways Involved in Polyamine Regulation in Iron Walnut" Genes 10, no. 8: 605. https://doi.org/10.3390/genes10080605

APA Style

Luo, X., & Liu, J. (2019). Transcriptome Analysis of Acid-Responsive Genes and Pathways Involved in Polyamine Regulation in Iron Walnut. Genes, 10(8), 605. https://doi.org/10.3390/genes10080605

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop