G C A T T A C G G C A T genes

Article Transcriptomic Revelation of Phenolic Compounds Involved in Aluminum Toxicity Responses in Roots of Cunninghamia lanceolata (Lamb.) Hook

Zhihui Ma 1 and Sizu Lin 2,3,*

1 Institute for Forest Resources and Environment of Guizhou, Guizhou University, Guiyang 550025, China; [email protected] 2 State Forestry Administration Engineering Research Center of Chinese Fir, Fuzhou 350002, China 3 College of Forestry, Fujian Agricultural and Forestry University, Fuzhou 350002, China * Correspondence: [email protected]



Received: 29 August 2019; Accepted: 18 October 2019; Published: 23 October 2019


Abstract: Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is one of the most important coniferous evergreen tree species in South China due to its desirable attributes of fast growth and production of strong and hardy wood. However, the yield of Chinese fir is often inhibited by aluminum (Al) toxicity in acidic soils of South China. Understanding the molecular mechanisms of Chinese fir root responses to Al toxicity might help to further increase its productivity. Here we used the Illumina Hiseq4000 platform to carry out transcriptome analysis of Chinese fir roots subjected to Al toxicity conditions. A total of 88.88 Gb of clean data was generated from 12 samples and assembled into 105,732 distinct unigenes. The average length and N50 length of these unigenes were 839 bp and 1411 bp, respectively. Among them, 58362 unigenes were annotated through searches of five public databases (Nr: NCBI non-redundant protein sequences, Swiss-Prot: A manually annotated and reviewed protein sequence database, GO: Gene Ontology, KOG/COG: Clusters of Orthologous Groups of proteins, and KEGG: the Kyoto Encyclopedia of Genes and Genomes database), which led to association of unigenes with 44 GO terms. Plus, 1615 transcription factors (TFs) were functionally classified. Then, differentially expressed genes (DEGs, |log (fold change)| 1 and FDR 0.05) were identified in comparisons 2 ≥ ≤ labelled TC1 (CK-72 h/CK-1 h) and TC2 (Al-72 h/Al-1 h). A large number of TC2 DEGs group were identified, with most being down-regulated under Al stress, while TC1 DEGs were primarily up-regulated. Combining GO, KEGG, and MapMan pathway analysis indicated that many DEGs are involved in primary metabolism, including cell wall metabolism and lipid metabolism, while other DEGs are associated with signaling pathways and secondary metabolism, including flavonoids and phenylpropanoids metabolism. Furthermore, TFs identified in TC1 and TC2 DEGs represented 21 and 40 transcription factor families, respectively. Among them, expression of bHLH, C2H2, ERF, bZIP, GRAS, and MYB TFs changed considerably under Al stress, which suggests that these TFs might play crucial roles in Chinese fir root responses to Al toxicity. These differentially expressed TFs might act in concert with flavonoid and phenylpropanoid pathway genes in fulfilling of key roles in Chinese fir roots responding to Al toxicity.

Keywords: Chinese fir; Al toxicity; Acid soil; Flavonoids pathway; Phenylpropanoids metabolism; Phenolic compounds

1. Introduction Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is an important coniferous evergreen tree species that is widely cultivated in South China due to its desirable attributes of fast growth and

Genes 2019, 10, 835; doi:10.3390/genes10110835 www.mdpi.com/journal/genes Genes 2019, 10, 835 2 of 22 production of strong and hardy wood. Based on planting area data in China’s ninth national forest inventory, Chinese fir is the second most dominant tree species in China, with cultivation occurring on approximately 11 million ha of plantations that account for about 6% of all forested land and 14.2% of total plantation area in China. This tree species, which has been planted in southeastern Asia for more than 1000 years [1], is primarily distributed in South China [2]. Generally speaking, soils in south China are largely acidic or strongly acidic, with pH values below 6.0 widespread, and in below 5.5 reported at times from strongly acidic soils. Globally, 50% of the world’s potential arable land is composed of acid soils [3], which are typically associated with deficiencies of phosphorus and other nutrients, as well as, toxic concentrations of certain metals, including iron (Fe) and Aluminum (Al) [4]. As a major constituent of mineral soils, Al is present in a wide range of primary and secondary minerals [5]. In acid soils Al solubility increases to the point where Al toxicity becomes one of the most important factors limiting plant growth [4,6,7]. Several recent reports have outlined this process in soil where, as pH falls below 5.0, Al is solubilized into phytotoxic Al3+ ions from non-toxic Al silicates and oxides [4,8,9]. The most evident symptom of Al toxicity in plants is rapid inhibition of root elongation through destruction of the root apex, which results in insufficient uptake of water and nutrients [10,11]. The root apex, particularly the transition zone, has also been identified as critical for sensing Al3+ toxicity and directing responses imparting plant tolerance to otherwise toxic levels of Al3+ [12]. In one notable study, Rengel observed that Al toxicity effects on shoots, such as growth inhibition and nutrient deficiency symptoms in aboveground tissues, manifest only after root growth is inhibited by exposure to toxic levels of Al in the rhizosphere [13]. Among plant species, tolerance to Al exposure varies widely, with micromolar concentrations of Al capable of producing tremendous damage to annual crops, while much higher concentrations of Al are required to generate obvious impacts on trees [14–16]. Even among tree species, Al can inhibit root growth (sugar maple, loblolly pine), those these impacts might be rapidly reversed when Al concentrations are reduced [17–20]. Additionally, while common crop species are known to react to Al toxicity through both Al exclusion and Al tolerance mechanisms [6,21], other means for responding to Al stress are also possible in many plant species, particularly trees [22–26]. For example, phenolic compounds such as terpenoids, tannins, flavonoids and alkaloids, which form strong complexes with Al ions, have been reported to participate in internal Al detoxification processes within certain trees and other Al accumulating species [27]. Given these results, elucidation of the roles filled by phenolic compounds in Al resistance processes promises to be a rewarding subject for researchers seeking to incorporate these mechanisms into breeding efforts [6,28]. As yet, Al stress is known to limit the growth and vigor of many tree species to the extent that it might be a factor determining the distribution of numerous tree species [29]. Although the physiological aspects of Al tolerance have been largely outlined over recent decades [6,30–33], the molecular machinery imparting Al tolerance remains mostly unexplored in most tree species, including for Chinese fir, which is confronted with the Al toxicity across wide areas of China, especially in South China. The relatively stable growth of Chinese fir in these acid soils suggests that this tree species has evolved mechanisms for adapting to Al toxicity. Future efforts to improve the production of Chinese fir stand to benefit by defining the range of Al tolerance possible in this species, while also cataloging the molecular processes underlying this tolerance. Monitoring gene expression responses to Al stress is one possible way to identify the genetic and molecular components responsible for Al toxicity and tolerance in Chinese fir and other tree species. Over the last two decades, although tremendous progress has been made in understanding the physiological mechanisms of Al toxicity and tolerance in Chinese fir, the corresponding molecular mechanisms remain poorly understood. Next-generation sequencing technologies may readily access Al toxicity responses at the molecular level, which is especially valuable for species with large genomes but lacking genome sequences, such as Chinese fir (genome size is ~12 Gb). Moreover, transcriptome sequencing represents an attractive alternative to whole genome sequencing because it only analyzes Genes 2019, 10, 835 3 of 22 transcribed portions of the genome, while avoiding non-coding and repeat sequences that can make up much of the whole genome. When observed at specific developmental stages or under controlled physiological conditions, the transcriptome can provide a complete and quantitative picture of cell transcription at a given moment. In this study, to further understand the mechanisms of Al stress tolerance in Chinese fir at the molecular level, RNA transcriptome sequencing (RNA-seq) was conducted to identify Al-associated genes in the roots of Chinese fir using the Illumina Hiseq4000 platform. Based on bioinformatics analysis of this transcriptome, pathways involved in phenolic, phenylpropanoid and flavonoid metabolism were identified as important players in Chinese fir roots subjected to Al toxicity. Furthermore, numerous transcription factors (TFs), including bHLH, C2H2, ERF, bZIP, GRAS and MYB, were identified as potential regulators of the metabolic responses through changes in transcription observed in Chinese fir roots growing under Al stress. This transcriptome sequencing of Chinese fir roots in response to Al stress complements past physiology work and may help to elucidate previously unidentified Al resistance and tolerance mechanisms, all of which might be applied in future marker-based breeding efforts geared to genetically improving tree growth in the presence of high Al3+ concentrations.

2. Materials and Methods

2.1. Plant Materials and Growth Conditions One-year-old seedlings of Chinese fir were provided by the State Forestry Administration Engineering Research Center of Chinese Fir. Firstly, the seedlings were transplanted into 0.5 mmol/L CaCl2 solution (pH = 4.0) and grown 7 days; and then the uniform seedlings were transplanted into 0.5 mmol/L CaCl2 with or without 1 mmol/L AlCl3 (pH = 4.0) solution, respectively. The hydroponics experiment was carried out in a temperature controlled room at 25 3 C, with the photoperiod set to ± ◦ 12 h light and 12 h dark cycles, constant relative humidity of 60%, 2000 lux of light intensity during the day, and continuously aerated growth media. Each treatment was observed in three biological replicates. Four cm root tips were sampled at 1 h and 72 h after initiation of Al treatments, which consisted of exposing roots to growth media (pH = 4.0) with (Al) or without (control, CK) 1 mmol/L AlCl3 added. Root samples weighing 0.1 g were individually inserted into 2.0 mL RNase/DNase-free tubes containing two sterile 5 mm steel balls, and immediately frozen in liquid nitrogen. All samples were stored at 80 C prior to extracting RNA. − ◦ 2.2. RNA Extraction, mRNA-seq Library Construction and Sequencing Total RNA was isolated from root samples using the RNApre Pure Plant Kit (TIANGEN, Beijing, China) according to the manufacturer’s instructions. The quality and concentration of extracted RNA were determined on the Agilent 2100 Bio-analyzer (Agilent, Santa Clara, CA, USA) and NanoDrop 2000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA), respectively. After total RNA was extracted, mRNA was enriched using Oligo (dT) beads, while rRNA was removed using the Ribo-ZeroTM Magnetic Kit (Epicentre, Madison, WI, USA). Enriched mRNA was then fragmented into short fragments using fragmentation buffer prior to being reverse transcribed into cDNA using random primers. Second-strand cDNA was synthesized by DNA polymerase I, RNase H, dNTP and buffer. Then, cDNA fragments were purified with 1.8x Agencourt AMPure XP Beads, end repaired, lengthened with poly(A) tails, and ligated to Illumina sequencing adapters. Ligation products were size selected in agarose gel electrophoresis, PCR amplified, and sequenced using the Illumina HiSeqTM 4000 platform as operated by Gene Denovo Biotechnology Co. (Guangzhou, China).

2.3. De Novo Assembly and Functional Annotation After removing adapters and low-quality reads, and filtering contaminant rRNA reads, clean reads were de novo assembled in the short read assembling program Trinity [34]. Expression of unigenes was calculated and normalized to RPKM (Reads Per kb per Million reads) values [35]. To annotate these Genes 2019, 10, 835 4 of 22 unigenes, sequences were subjected to BLASTx (http://www.ncbi.nlm.nih.gov/BLAST/) searching of four databases, including the NCBI non-redundant protein (Nr) database (http://www.ncbi.nlm.nih.gov), the Swiss-Prot protein database (http://www.expasy.ch/sprot), the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg), and the COG/KOG database (http: //www.ncbi.nlm.nih.gov/COG). All BLASTx searches were conducted with an E-value threshold 5 of 10− .

2.4. Differentially Expressed Gene (DEG) Analysis To identify differentially expressed genes across samples or groups, the edgeR package (http: //www.r-project.org/) was used, and raw counts were inputted in edgeR. Transcripts responding to Al stress with a fold change 2 and a false discovery rate (FDR) 0.05 were considered differentially ≥ ≤ expressed. All of the DEGs identified in groups TC1 (control roots at 72 h relative to control roots at 1 h) and TC2 (Al treated roots at 72 relative to Al treated roots at 1 h) were further annotated and analyzed using GO functional annotation, KEGG pathway analysis and pathway visualization in MapMan (https://mapman.gabipd.org/mapman-download).

2.5. Transcription Factor Analysis The predicted plant protein coding sequences of assembled unigenes were aligned by BLASTp to Plant TFdb (http://planttfdb.cbi.pku.edu.cn/) sequences with potential TFs filtered to those matching 5 database sequences with an E-value threshold of 10− .

2.6. Quantitative Real-Time PCR (qRT-PCR) Twelve RNA samples were reverse transcribed into cDNA using the reverse transcript reagent TransScript® All-in-One First-Strand cDNA Synthesis SuperMix for qPCR (One-Step gDNA Removal) (TransGen Biotech, Beijing, China) following the manufacturer’s protocol. The primers used in qRT-PCR for validation of DEGs are listed in (Table S1, Fasta File S1, SRA accession code: PRJNA577561). qRT-PCR was carried out on the LightCycler® Nano Real-Time PCR System (Roche Diagnostics GmbH, Mannheim, Germany) under the following conditions: 94 ◦C for 30 s, and 40 cycles of 95 ◦C for 5 s, 58 ◦C for 15 s, and 72 ◦C for 10 s, followed by melting curve generation (60 ◦C to 95 ◦C), and a final step of 94 ◦C for 30 s. All treatments included three biological replicates, and the analysis of gene - Cq expression was calculated using 2 44 values.

3. Results

3.1. RNA Sequencing, Assembly and Functional Annotation To understand potential Al tolerance mechanisms active in Chinese fir roots, 12 root samples from control or Al stress treatments and collected 1 h or 72 h after transferring to treatment media were sequenced on the Illumina HiSeqTM 4000 platform. After removing adapters, low-quality reads and contaminant rRNA reads, the remaining high-quality RNA-seq datasets contained 5.3–10.4 Gb of clean data from each root sample (Table S2). The Q20, Q30 and GC content values were 98.55–98.83%, 95.53–96.45% and 44.00–45.95%, respectively (Table S2). Since no reference genome is available for Chinese fir, transcriptome de novo assembly was carried out in the short read assembly program Trinity [34]. This yielded a total of 105,732 unigenes with an average length of 839 bp and an N50 length of 1411 bp. Running BLASTx searches with an E-value threshold of 1E-5 led to annotation of 58,362 unigenes, including 56,577 with matches in the NCBI non-redundant (Nr) protein database, 46,225 matching Swiss-Prot protein database entries, 39,868 with matches in the eukaryotic Orthologous Groups (KOG) database, and 26,914 unigenes aligning with sequences in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (Figure S1). About 22.35% (23,632/105,732) of the assembled unigenes could be assigned to homologs in all four databases (Figure1A). Based on the Nr database, Genes 2019, 10, x FOR PEER REVIEW 5 of 23

Running BLASTx searches with an E-value threshold of 1E-5 led to annotation of 58,362 unigenes, including 56,577 with matches in the NCBI non-redundant (Nr) protein database, 46,225 matching Swiss-Prot protein database entries, 39,868 with matches in the eukaryotic Orthologous Groups (KOG) database, and 26,914 unigenes aligning with sequences in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (Figure S1). About 22.35% (23,632/105,732) of the assembled unigenes could be assigned to homologs in all four databases (Figure 1A). Based on the Nr database, 28.41% of the assembled unigenes exhibited homology (10−20 < E-value <= 10−5) to plant proteins, while 47.40% showed strong homology (10−100 < E-value <= 10−20) and the remaining 24.19% displayed very strong homology (E-value <10−100) to available plant sequences (Figure S1B). Determination of species represented in matches with assembled unigenes (Figure S1C , Table S3) in the Nr database revealed that a high percentage of Chinese fir transcripts closely matched sequences of Anthurium amnicola (16.79%), Amborella trichopoda (6.33%), Nelumbo nucifera (4.37%), Nannochloropsis gaditana (2.76%), Picea sitchensis (2.21%), Klebsormidium flaccidum (2.04%), Genes 2019Ectocarpus, 10, 835 siliculosus (1.94%), Elaeis guineensis (1.69%), Vitis vinifera (1.58%), and Phoenix 5 of 22 dactylifera (1.42%). GO annotations were assigned to unigenes in the Blast2GO [36] application using the Nr 20 5 28.41% ofannotation the assembled results fromunigenes the BLASTx exhibited alignments homology described (10− above,< E-value and functional<= 10− classification) to plant proteins, of while unigenes was performed using WEGO100 [37] software. A total20 of 10,837 Unigenes were therefore 47.40% showed strong homology (10− < E-value <= 10− ) and the remaining 24.19% displayed very categorized into 44 main groups100 distributed among GO biological process, molecular function, and strong homologycellular component (E-value categories<10− ) (Figure to available 1). As shown plant sequences in Figure 1, (Figure the top S1B). five most Determination represented of species representedmetabolic in matches process subcategories with assembled in the unigenes Chinese fir (Figure transcriptome S1C, were Table metabolic S3) in the process, Nr database cellular revealed that a highprocess, percentage single-organism of Chinese process, fir transcripts response to closely stimulus, matched and localization. sequences The of top Anthurium five most amnicola (16.79%),represented Amborella cellular trichopoda component (6.33%), subcategories Nelumbo were nucifera cell, cell (4.37%), part, Nannochloropsis organelle, macromolecular gaditana (2.76%), complex, and membrane, and the top five most represented molecular function subcategories were Picea sitchensis (2.21%), Klebsormidium flaccidum (2.04%), Ectocarpus siliculosus (1.94%), Elaeis catalytic activity, binding, transporter activity, structural molecule activity, and nucleic acid binding guineensistranscription (1.69%), factor Vitis activity. vinifera (1.58%), and Phoenix dactylifera (1.42%).

Figure 1.FigureGene 1.Ontology Gene Ontology (GO) ( classificationGO) classification of all of allunigenes unigenes identified identified from from transcriptomes of of Chinese fir rootChinese tips exposed fir root to tips Al exposed stress to conditions. Al stress conditions. The results The results are summarized are summarized in in three three main main categories, biologicalcategories, process, biological cellular process, component cellular andcomponent molecular and molecular function. function.

To place the transcripts identified in this study in a metabolic context, BLASTx hits from the Nr GO annotations were assigned to unigenes in the Blast2GO [36] application using the Nr annotation database obtained for the Chinese fir root tip transcriptome were also used to assign unigenes to results from the BLASTx alignments described above, and functional classification of unigenes was performedGenes using 2019, 10,WEGO x; doi: FOR [PEER37] software.REVIEW A total of 10,837 Unigenes werewww.mdpi.com/journal/ therefore categorizedgenes into 44 main groups distributed among GO biological process, molecular function, and cellular component categories (Figure1). As shown in Figure1, the top five most represented metabolic process subcategories in the Chinese fir transcriptome were metabolic process, cellular process, single-organism process, response to stimulus, and localization. The top five most represented cellular component subcategories were cell, cell part, organelle, macromolecular complex, and membrane, and the top five most represented molecular function subcategories were catalytic activity, binding, transporter activity, structural molecule activity, and nucleic acid binding transcription factor activity. To place the transcripts identified in this study in a metabolic context, BLASTx hits from the Nr database obtained for the Chinese fir root tip transcriptome were also used to assign unigenes to KEGG pathways. In this analysis, 15,542 of the 26,914 identified unigenes mapped to 137 KEGG pathways (Supplemental Table S4). The top four KEGG pathway categories based on the number of assigned unigenes were Metabolic pathways (ko01100, 38.12% of unigenes), Biosynthesis of secondary metabolites (ko01110, 21.12%), Ribosome (ko03010, 11.85) and Biosynthesis of antibiotics (ko01130, 10.58%). The 56,577 unigenes with hits in the Nr database were also queried for homology with sequences in the KOG database to further assess the validity and integrity of the transcriptome sequences, as well as, and to further classify potential functions in Chinese fir. In this analysis, a total of 39,868 unigenes were assigned to 25 KOG classifications (Figure2, Table S5). Among them, the top five KOG classifications were R (General function prediction only, 13,154 unigenes), O (Posttranslational modification, protein turnover, chaperones, 8795 unigenes), T (Signal transduction mechanisms, 8591 Genes 2019, 10, x FOR PEER REVIEW 6 of 23

KEGG pathways. In this analysis, 15,542 of the 26,914 identified unigenes mapped to 137 KEGG pathways (Supplemental Table S4). The top four KEGG pathway categories based on the number of assigned unigenes were Metabolic pathways (ko01100, 38.12% of unigenes), Biosynthesis of secondary metabolites (ko01110, 21.12%), Ribosome (ko03010, 11.85) and Biosynthesis of antibiotics (ko01130, 10.58%). The 56,577 unigenes with hits in the Nr database were also queried for homology with sequences in the KOG database to further assess the validity and integrity of the transcriptome sequences, as well as, and to further classify potential functions in Chinese fir. In this analysis, a total Genesof 392019,868, 10 ,unigenes 835 were assigned to 25 KOG classifications (Figure 2, Table S5). Among them,6 of 22the top five KOG classifications were R (General function prediction only, 13,154 unigenes), O (Posttranslational modification, protein turnover, chaperones, 8795 unigenes), T (Signal unigenes),transduction J (Translation, mechanisms, ribosomal 8591 unigenes), structure J and (Translation, biogenesis, ribosomal 4493 unigenes) structure and and A (RNA biogenesis, processing 4493 andunigenes) modification, and A 4202(RNA unigenes). processing and modification, 4202 unigenes).

FigureFigure 2. 2.Eukaryotic Eukaryotic ortholog ortholog group group (KOG) (KOG) function function classification classification of of Chinese Chinese fir fir root root tip tip transcripts transcripts identifiedidentified in in an an Al Al stress stress experiment. experiment. The The X-axis X-axis lists lists KOG KOG clusters, clusters, and and the the Y-axis Y-axis is is the the number number of of unigenesunigenes assigned assigned to to each each KOG KOG classification. classification.

3.2. Analysis of Differential Expressed Genes (DEGs) 3.2. Analysis of Differential Expressed Genes (DEGs) The edgeR package (http://www.r-project.org/) was used to identify differentially expressed genes The edgeR package (http://www.r-project.org/) was used to identify differentially expressed (DEGs) between control and Al treated roots. Transcripts with at least a 2-fold change in RPKM genes (DEGs) between control and Al treated roots. Transcripts with at least a 2-fold change in expression values and a false discovery rate (FDR 0.05) corrected t-test p-value corr in pairwise RPKM expression values and a false discovery rate≤ (FDR ≤ 0.05) corrected t-test p-value corr in comparisons were considered significant DEGs. These DEGs were then subjected to enrichment pairwise comparisons were considered significant DEGs. These DEGs were then subjected to analysis of GO categories and KEGG pathways. Only 3 up-regulated DEGs were identified in the T1 enrichment analysis of GO categories and KEGG pathways. Only 3 up-regulated DEGs were groupidentified (Al-1 in h/ theCK-1 T1 h), group while (Al 140-1 up-regulatedh/ CK-1 h), while DEGs 140 and up 6244-regulated down-regulated DEGs and DEGs 6244 weredown identified-regulated in the T2 group (Al-72 h/ CK-72 h) (Figure3). In time course comparisons, there were 3918 up-regulated DEGs were identified in the T2 group (Al-72 h/ CK-72 h) (Figure 3). In time course comparisons, DEGsthere andwere 396 3918 down-regulated up-regulated DEGs DEGs and in the 396 TC1 down group-regulated (CK-72 DEGs h/ CK-1 in the h), TC1 while group 3130 (CK up-regulated-72 h/ CK-1 DEGs and 20138 down-regulated DEGs were identified in the TC2 group (Al-72 h/Al-1 h) (Table S6). h), while 3130 up-regulated DEGs and 20138 down-regulated DEGs were identified in the TC2 Thegroup number (Al-72 of DEGs h/Al-1 was h) (Table notably S high6). The in the number TC2 group, of DEGs especially was notably for down-regulated high in the TC2 DEGs. group, To better understand the potential mechanisms of Chinese fir root responses to Al, the TC1 and TC2 groupedGenes 2019 DEGs, 10, x; doi: were FOR subjected PEER REVIEW to further analysis. www.mdpi.com/journal/genes Genes 2019, 10, x FOR PEER REVIEW 7 of 23

especially for down-regulated DEGs. To better understand the potential mechanisms of Chinese fir root responses to Al, the TC1 and TC2 grouped DEGs were subjected to further analysis. Interestingly, 785 DEGs were identified in both TC1 and TC2 comparisons (Figure 3B, Table S7), the majority displayed consistent expression patterns between comparisons. However, eight DEGs did display altered expression patterns between TC1 and TC2. These eight unigenes with altered expression patterns between TC1 and TC2 were Unigene0048758 (no annotation), Unigene0073541 (fatty acid synthase), Unigene0071412 (Sulfite reductase), Unigene0057949 (Inositol-3-phosphate synthase), Unigene0002857 (acetyl-coenzyme a synthetase), Unigene0063838 (chaperone, heat shock protein 70), Unigene0042239 (glutathione S-transferase theta-1), and Unigene0060101 (uroporphyrin-III C-m). The majority of DEGs with consistent expression patterns observed between GenesTC12019 and, 10, TC2 835 might not be affected by Al, but rather could have expression altered as part of normal7 of 22 growth and development processes in Chinese fir roots.

FigureFigure 3. Summary3. Summary of of di differentiallyfferentially expressedexpressed genes genes (D (DEGs)EGs) in inChinese Chinese fir firroots roots under under Al stressed. Al stressed. (A)( TheA) The X-axis X-axis lists listscomparison comparison groups, groups, T1: indicated T1: indicated the DEGs the DEGs number number in the in group the group of Al-1 of h Al compared-1 h withcompared CK-1 h, Al-1 with h CK/CK-1-1 h, h; Al T2:-1 h/CK indicated-1 h; T2: the indicated DEGs number the DEGs in the number group in of the Al-72 group h compared of Al-72 h with CK-72compared h, Al -72 with h/ CK-72CK-72 h, h; Al TC1: -72 h/CK CK-72-72 h h;/ CK-1 TC1: CK h, TC2:-72 h/ Al-72CK-1 h, h /TC2:Al-1 Al h.-72 The h /Al Y-axis-1 h. isThe the Y- numberaxis is of up-regulatedthe number or of down-regulated up-regulated or DEGsdown- inregulated each comparison DEGs in each group. comparison (B) Venn group. diagram (B) ofVenn DEGs diagram identified in threeof DEGs diff erentialidentified comparison in three differential groups. comparison groups.

3.3.Interestingly, GO Analysis 785of DEGs DEGs were identified in both TC1 and TC2 comparisons (Figure3B, Table S7), the majorityTo det displayedermine the consistent main biological expression functions patterns filled by between TC1 and comparisons. TC2 DEGs, these However, transcripts eight were DEGs didmapped display to altered GO terms expression in BLAST2GO patterns analysis. between TC1 and TC2. These eight unigenes with altered expressionIn the patterns TC1 group, between a total TC1 of and 4314 TC2 DEGs, were including Unigene0048758 3918 up (no-regulated annotation), and 396 Unigene0073541 down-regulated (fatty acidDEGs, synthase), were Unigene0071412 categorized to 35 (Sulfite GO term reductase),s (Figure 4, Unigene0057949 Table S8). Among (Inositol-3-phosphate them, the top 3 biological synthase), Unigene0002857process GO terms (acetyl-coenzyme were metabolic a synthetase), process (536 Unigene0063838 up-regulated DEGs, (chaperone, 18 down heat-regulated shock proteinDEGs), 70), Unigene0042239cellular process (glutathione (352 up-regulated S-transferase DEGs, theta-1),13 down and-regulated Unigene0060101 DEGs), and (uroporphyrin-III single-organism process C-m). The (295 up-regulated DEGs, 17 down-regulated DEGs). The top 4 cellular component GO terms were majority of DEGs with consistent expression patterns observed between TC1 and TC2 might not be cell (480 up-regulated DEGs, 8 down-regulated DEGs), cell part (480 up-regulated DEGs, 8 affected by Al, but rather could have expression altered as part of normal growth and development down-regulated DEGs), organelle (282 up-regulated DEGs, 7 down-regulated DEGs), and processesmacromo inlecular Chinese complex fir roots. (256 up-regulated DEGs). The top 2 molecular function GO terms were catalytic activity (416 up-regulated DEGs, 24 down-regulated DEGs), and binding (289 up-regulated 3.3. GO Analysis of DEGs DEGs, 16 down-regulated DEGs). To determineIn the TC2 the group, main biological a total of functions 23,268 DEGs, filled by including TC1 and 3130 TC2 DEGs, up-regulated these transcripts and 20,138 were mappeddown to-regulated GO terms DEGs, in BLAST2GO were categorized analysis. to 41 GO terms. Among them, the top 3 biological process GOIn theterms TC1 were group, metabolic a total process of 4314 (287 DEGs, up-regulated including DEGs, 3918 1296 up-regulated down-regulated and 396 DEGs), down-regulated cellular DEGs, were categorized to 35 GO terms (Figure4, Table S8). Among them, the top 3 biological process GOGenes terms 2019 were, 10, x; metabolic doi: FOR PEER process REVIEW (536 up-regulated DEGs, 18 down-regulatedwww.mdpi.com/journal/ DEGs), cellulargenes process (352 up-regulated DEGs, 13 down-regulated DEGs), and single-organism process (295 up-regulated DEGs, 17 down-regulated DEGs). The top 4 cellular component GO terms were cell (480 up-regulated DEGs, 8 down-regulated DEGs), cell part (480 up-regulated DEGs, 8 down-regulated DEGs), organelle (282 up-regulated DEGs, 7 down-regulated DEGs), and macromolecular complex (256 up-regulated DEGs). The top 2 molecular function GO terms were catalytic activity (416 up-regulated DEGs, 24 down-regulated DEGs), and binding (289 up-regulated DEGs, 16 down-regulated DEGs). Genes 2019, 10, x FOR PEER REVIEW 8 of 23

process (197 up-regulated DEGs, 1097 down-regulated DEGs), and single-organism process (163 up-regulated DEGs, 912 down-regulated DEGs). The top 4 cellular component GO terms were cell (307 up-regulated DEGs, 948 down-regulated DEGs), cell part (307 up-regulated DEGs, 948 down-regulated DEGs), organelle (221 up-regulated DEGs, 613 down-regulated DEGs), and macromolecular complex (175 up-regulated DEGs, 322 down-regulated DEGs). The top 2 molecular function GO terms were catalytic activity (198 up-regulated DEGs, 1351 down-regulated DEGs), and Genes 2019, 10, 835 8 of 22 binding (172 up-regulated DEGs, 833 down-regulated DEGs) (Table S8).

FigureFigure 4. 4.GO GO classifications classifications of of DEGs DEGs identified identified in in Chinese Chinese fir fir roots roots subjected subjected to to Al Al stress stress treatments. treatments. TC1: CK-72 h/ CK-1 h. TC2: Al-72 h /Al-1 h. DEGs were annotated in 3 GO categories: biological TC1: CK-72 h/ CK-1 h. TC2: Al-72 h /Al-1 h. DEGs were annotated in 3 GO categories: biological process, molecular function, and cellular component. Red represents up-regulated DEGs. Green process, molecular function, and cellular component. Red represents up-regulated DEGs. Green represents down-regulated DEGs. The number besides each bar represents the number of DEGs represents down-regulated DEGs. The number besides each bar represents the number of DEGs associated with the corresponding GO term. associated with the corresponding GO term. In the TC2 group, a total of 23,268 DEGs, including 3130 up-regulated and 20,138 down-regulated 3.4. KEGG Pathway Analysis of DEGs DEGs, were categorized to 41 GO terms. Among them, the top 3 biological process GO terms were metabolicTo better understand process (287 the up-regulated metabolic functions DEGs, of 1296 DEGs down-regulated identified in Chinese DEGs), fir cellular roots under process Al (197stress, up-regulated TC1 and TC2 DEGs, group 1097 DEGs down-regulated were queried DEGs), against and the single-organism KEGG database. process (163 up-regulated DEGs,TC1 912 down-regulatedDEGs mapped into DEGs). 127 KEGG The top pathways 4 cellular (Figure component 5). The GO top terms five K wereEGG cellpathways (307 up-regulated based on the DEGs,number 948 of down-regulatedassigned DEGs were DEGs), Global cell and part overview (307 up-regulated maps (54 down DEGs,-regulated 948 down-regulated DEGs, 526 up-regulated DEGs), organelleDEGs), Translation (221 up-regulated (1 down DEGs,-regulated 613 DEGs, down-regulated 422 up-regulated DEGs), DEGs), and macromolecular Carbohydrate metabolism complex (175(13 down up-regulated-regulated DEGs, DEGs, 322 182 down-regulated up-regulated DEGs), DEGs). folding The,top sorting 2 molecular and degradation function (4 GO down terms-regulated were catalytic activity (198 up-regulated DEGs, 1351 down-regulated DEGs), and binding (172 up-regulated DEGs,Genes 2019 833, 10 down-regulated, x; doi: FOR PEER DEGs)REVIEW (Table S8). www.mdpi.com/journal/genes

3.4. KEGG Pathway Analysis of DEGs To better understand the metabolic functions of DEGs identified in Chinese fir roots under Al stress, TC1 and TC2 group DEGs were queried against the KEGG database. Genes 2019, 10, x FOR PEER REVIEW 9 of 23 Genes 2019, 10, 835 9 of 22 DEGs, 187 up-regulated DEGs), and amino acid metabolism (13 down-regulated DEGs, 164 TC1up-regulated DEGs mapped DEGs). into 127 KEGG pathways (Figure5). The top five KEGG pathways based on theTC2 number DEGs, of most assigned of which DEGs were were down Global-regulated, and overview mapped maps into (54 134 down-regulated KEGG pathways DEGs, (Figure 526 5, up-regulatedTable S9). The DEGs), KEGG Translation pathway (1 Environmental down-regulated adaptation DEGs, 422 is up-regulated particularly DEGs), notable Carbohydrate for including metabolism83 down-regulated (13 down-regulated and 18 up DEGs,-regulated 182 up-regulated TC2 DEGs, DEGs), as well folding, as sorting 3 down and-regulated degradation and (415 down-regulated up-regulated TC1 DEGs, DEGs. 187 up-regulated These changes DEGs), in Environmental and amino acid adaptation metabolism pathways (13 down-regulated may include DEGs,important 164 up-regulated actors filling DEGs).roles in Chinese fir root responses to Al stress.

Figure 5. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations of DEGs Figure 5. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations of DEGs identified in Chinese fir roots subjected to Al stress. TC1_up: up-regulated DEGs in the comparison of CK-72identified h with in CK-1Chinese h; TC1_down:fir roots subjected down-regulated to Al stress. DEGs TC1_up: in the up comparison-regulated ofDEGs CK-72 in hthe with comparison CK-1 h; TC2_up:of CK-72 up-regulated h with CK-1 DEGsh; TC1_down: in the comparison down-regulated of Al-72 DEGs h with in the Al-1 comparison h; TC2_down: of CK down-regulated-72 h with CK-1 DEGsh; TC2_up: in the comparison up-regulated of Al-72 DEGs h with in th Al-1e comparison h. Categories of in Alblack-72 font h with on the Al Y-axis-1 h; are TC2_down: KEGG B classes,down- whileregulated categories DEGs in in other the comparison colored fonts of on Al the-72 Y-axis h with are Al specific-1 h. Categories pathways classified in black under font on each the KEGGY-axis B are class. KEGG The B X-axis classes, represents while categories the number in other of DEGs colored annotated fonts on to the each Y-axis pathway. are specific pathways classified under each KEGG B class. The X-axis represents the number of DEGs annotated to each TC2pathway. DEGs, most of which were down-regulated, mapped into 134 KEGG pathways (Figure5, Table S9). The KEGG pathway Environmental adaptation is particularly notable for including 83 down-regulatedEnrichment and analysis 18 up-regulated of KEGG TC2 terms DEGs, associated as well as 3 withdown-regulated DEGs was and conducted 15 up-regulated to further TC1 DEGs.understand These potential changes metabolic in Environmental roles played adaptation by these pathways gene products may include in Chinese important fir roots actors subjected filling to rolesAl stress in Chinese treatments fir root for responses 1 h and 72 to h. Al TC1 stress. DEGs were significantly enriched in Citrate cycle, Lysine biosynthesis,Enrichment Oxidative analysis of phosphorylation, KEGG terms associated and specific with DEGs amino was acid conducted biosynthesis to further and understand metabolism potentialpathway, metabolic while TC2 roles DEGs played were bysignificantly these gene en productsriched in indifferent Chinese pathways, fir roots subjectedincluding toFlavone Al stress and treatmentsflavonol biosynthesis, for 1 h and 72 Terpenoidh. TC1 DEGs backbone were significantly biosynthesis, enriched Steroid in Citrate biosynthesis, cycle, Lysine Phenylpropanoid biosynthesis, Oxidativebiosynthesis, phosphorylation, and Linoleic acid and specificmetabolism amino (Figure acid biosynthesis 6, Table S9). and These metabolism differences pathway, in KEGG while pathway TC2 DEGsenrichment were significantly between TC1 enriched and TC2 in di mightfferent mark pathways, pathways including that Flavone are important and flavonol in Chinese biosynthesis, fir root Terpenoidresponses backboneto Al stress. biosynthesis, Steroid biosynthesis, Phenylpropanoid biosynthesis, and Linoleic acid metabolism (Figure6, Table S9). These di fferences in KEGG pathway enrichment between TC1 and TC2 might mark pathways that are important in Chinese fir root responses to Al stress.

Genes 2019, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/genes Genes 2019, 10, 835 10 of 22 Genes 2019, 10, x FOR PEER REVIEW 10 of 23

FigureFigure 6. 6.KEGG KEGG pathway pathway analysis analysis ofof TC1TC1 andand TC2TC2 grouped DEGs. (A (A) )Top Top 20 20 enriched enriched pathways pathways associatedassociated with with TC1 TC1 DEGs DEGs (CK-72 (CK- h72/ CK-1h/ CK h);-1 (h);B) top(B) top 20 enriched 20 enriched pathways pathways associated associated with with TC2 TC2 DEGs (Al-72DEGs h (Al/Al-1-72 h).h /Al The-1 h). Rich The Factor Rich Factor refers refers to the to ratio the ratio of the of the number number of DEGsof DEGs to to the the total total number number of genesof genes in the in pathway.the pathway. The The larger larger the the rich rich factor, factor, the the higher higher the the RichRich Factor. The The size size of of the the bub bubblesbles indicatesindicates the the number number of of DEGs, DEGs, with with larger larger bubbles bubbles indicatingindicating the the more more pathway pathway DEGs. DEGs. Bubble Bubble color color indicatesindicates the the level level of of significance significance (q-value) (q-value) inin FDRFDR corrected t t tests, tests, with with deeper deeper red red color color indicating indicating higherhigher significance significance of of di differentialfferential expression. expression.

TheThe software software MapMan MapMan was was used used toto displaydisplay the global overview overview of of DEGs DEGs in in Chinese Chinese fir fir roots roots respondingresponding to Al to stress. Al stress. This This visualization visualization helps helps to understand to understand the contrasting the contrasting roles between roles between primarily up-regulatedprimarily up TC1-regulated DEGs (Figure TC1 DEGs7A) and(Figure predominantly 7A) and predominantly down-regulated down in-regulated TC2 DEGs in TC (Figure2 DEGs7B). Notably,(Figure contrasting 7B). Notably, DEG contrasting responses DEG between responses TC1 between and TC2 TC1 comparisons and TC2 comparisons were evident were in evident primary metabolismin primary pathways, metabolism including pathways, those including associated those associated with trehalose, with trehalose, callose, phospholipid callose, phospholipid synthesis andsynthesis steroids and (sphingolipids), steroids (sphingolipids), FA synthesis FA and synthesis desaturation, and desaturation, glycolysis, glycolysis, the tricarboxylic the tricarboxylic acid (TCA) cycle,acid starch, (TCA) sucrose, cycle, starch, ascorbate sucrose, and glutathioneascorbate and metabolism glutathione (Figure metabolism7). Among (Figure the 7). analyzed Among DEGs, the sixanalyzed encoding DEGs, enzymes six putatively encoding involved enzymes in putatively trehalose biosynthesis involved in were trehalose up-regulated biosynthesis over timewere in up-regulated over time in control roots group (Figure 7A), while 10 such DEGs were control roots group (Figure7A), while 10 such DEGs were down-regulated over time in Al treated down-regulated over time in Al treated roots (Figure 8B). Six other DEGs encoding GSL10, a roots (Figure8B). Six other DEGs encoding GSL10, a member of the Glucan Synthase-Like (GSL) family member of the Glucan Synthase-Like (GSL) family believed to be involved in the synthesis of the cell believed to be involved in the synthesis of the cell wall component callose were up-regulated from 1 h wall component callose were up-regulated from 1 h to 72 h in control roots (TC1), while 4 glycosyl to 72 h in control roots (TC1), while 4 glycosyl transferase DEGs, possibly acting in callose synthase or transferase DEGs, possibly acting in callose synthase or glucan synthase-like 7 (GSL7) reactions, glucan synthase-like 7 (GSL7) reactions, were down-regulated from 1 h to 72 h in Al stress-treated were down-regulated from 1 h to 72 h in Al stress-treated roots (TC2). Furthermore, 18 DEGs rootsinvolved (TC2). in Furthermore, cellulose biosynthesis, 18 DEGs 7 involved DEGs of inFASCICLIN cellulose biosynthesis,-like arabinogalactan 7 DEGs proteins of FASCICLIN-like putatively arabinogalactanlocalized in plasma proteins membranes, putatively and localized 1 Alpha in-1,4 plasma-glucan membranes,-protein synthase and 1DEG Alpha-1,4-glucan-protein possibly involved in synthaseplant cell DEG wall possibly synthesis involved were all in down plant-regu celllated wall in synthesis TC2 comparisons, were all down-regulated none of which in were TC2 comparisons,significant DEGs none in of TC1 which comparisons. were significant Moreover, DEGs two of in the TC1 three comparisons. DEGs encoding Moreover, a putative two sorbitol of the threedehydrogenase DEGs encoding that a can putative be thiolated sorbitol in dehydrogenase vitro, and three that of canthe seven be thiolated DEGs inannotated vitro, and as threeraffinose of the sevenfamily DEGs proteins annotated possibly as raactingffinose as familygalacturonosyltransferases proteins possibly acting were asdown galacturonosyltransferases-regulated in TC2, but were were down-regulatedunaffected in TC1 in TC2, comparisons. but were unaFinally,ffected except in TC1 for comparisons. down-regulation Finally, of three except chloroplast for down-regulation localized ofDEGs three chloroplast annotated as localized phosphofructokinase DEGs annotated 3 (PFK3), as phosphofructokinase the remaining 27 DEGs 3 (PFK3), assoc theiated remaining with the 27 DEGsenergy associated payoff phase with theof the energy glycolytic payoff pathwayphase of were the glycolytic all upregulated pathway in TC1 were roots, all upregulated including DEGs in TC1 roots,encoding including triosephosphate DEGs encoding isomerase, triosephosphate phosphoenolpyruvate isomerase, phosphoenolpyruvate carboxylase-related kinase carboxylase-related 1 (PEPKR1), kinaseenolase, 1 (PEPKR1), and pyruvate enolase, kinase and pyruvatewere all up kinase-regulated wereall in up-regulatedTC1 comparisons. in TC1 In comparisons. contrast, 63 of In contrast,the 66 63similarly of the 66 annotated similarly annotatedDEGs in TC2 DEGs were in down TC2 were-regulated. down-regulated.

Genes 2019, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/genes Genes 2019, 10, 835 11 of 22 Genes 2019, 10, x FOR PEER REVIEW 11 of 23

FigureFigure 7. 7.Metabolic Metabolic overviewoverview ofof DEG DEG identified identified in in Chinese Chinese fir fir roots roots responding responding to to Al Al stress. stress. This This MapManMapMan generated generated global global overview overview of DEGs of in DEGs a metabolic in a pathway metabolic context pathway showing context transcriptional showing changestranscriptional observed changes in Chinese observed fir roots in Chinese subjected fir toroots control subjected or Al to treatments control or for Al1 treatments h and 72 h. for (A 1) h TC1 and DEGs (log (CK-72 h/CK-1 h)). (B) TC2 DEGs (log (Al-72 h/Al-1 h). Deeper blue indicates stronger 72 h. (A)2 TC1 DEGs (log2(CK-72 h/CK-1 h)). (B2 ) TC2 DEGs (log2(Al-72 h/Al-1 h). Deeper blue down-regulation,indicates stronger and down deeper-regulation, red indicates and deeper more red up-regulation. indicates more Individual up-regulation. DEGs Individual are represented DEGs by small squares. The scale from 4.5 to +4.5 represents the normalized log of fold change in each are represented by small squares.− The scale from −4.5 to +4.5 represents the2 normalized log2 of fold comparedchange in group. each compared group.

This contrast of DEG response patterns was also observed between TC1 and TC2 comparisons in analysis of DEGS associated with secondary metabolism, including DEGs annotated to

Genes 2019, 10, x; doi: FOR PEER REVIEW www.mdpi.com/journal/genes Genes 2019, 10, 835 12 of 22

Genes 2019, 10, x FOR PEER REVIEW 13 of 23

Figure 8. FigureVisualization 8. Visualization of DEGs of DEGs associated associated with with phenylpropanoid phenylpropanoid and and flavonoid flavonoid metabolism. metabolism. The The TC1 TC1 and TC2 DEGs included here are associated with the biosynthesis of shikimate, and TC2 DEGs included here are associated with the biosynthesis of shikimate, phenlypropanoids, phenlypropanoids, simple phenols, lignin and lignans, and flavonoids. (A) TC1 DEGs (log2(CK-72 simple phenols,h/CK-1 h)). lignin (B) TC2 and DEGs lignans, (log2(Al and-72 h/Al flavonoids.-1 h). Individual (A) TC1 DEGs DEGs are represented (log2(CK-72 by small h/CK-1 squares. h)). (B) TC2 DEGs (logDeeper2(Al-72 blue h/ indicatesAl-1 h). stronger Individual down DEGs-regulation, are represented and deeper red by indicates small squares.more up-regulation. Deeper blue The indicates strongerscale down-regulation, from −1 to +1 represents and deeper normalized red log indicates2 of fold change more in up-regulation. each comparison group. The scale from 1 to +1 − represents normalized log2 of fold change in each comparison group.

This contrast of DEG response patterns was also observed between TC1 and TC2 comparisons in analysis of DEGS associated with secondary metabolism, including DEGs annotated to Phenylpropanoid and Phenolic biosynthesis, Flavonoid biosynthesis, Amino acid synthesis and degradation, Ammonia metabolism and Sulfur metabolism, and Nucleotide metabolism (Figure7). Among TC1 DEGs, only eight of the 47 that were annotated as secondary metabolism pathways were down-regulated, while the remaining 39 DEGs were up-regulated. In contrast, only 53 of 342 TC2 DEGs were up-regulated, while the remaining 289 DEGs were down-regulated. Among all DEGs associated with secondary metabolism, 19 exhibited similar patterns between the TC1 and TC2 comparison groups (8 were consistently down-regulated over time and 11 were up-regulated). This indicates that these DEGs may be constitutively expressed genes in Chinese fir roots regardless of Al stress. Based on the KEGG pathway enrichment results for TC1 and TC2 DEGs, TC2 DEGs were found to be significantly enriched in different pathways than TC1 DEGs, with TC2 DEGs being enrichedGenes in flavone 2019, 10, x; and doi: FOR flavonol PEER REVIEW biosynthesis, terpenoid backbone biosynthesis,www.mdpi.com/journal/ steroidgenes biosynthesis, phenylpropanoid biosynthesis, and linoleic acid metabolism (Figure6, Table S9). Therefore, further analysis was conducted only for TC1 and TC2 DEGs involved in phenylpropanoid and phenolic biosynthesis and flavonoid biosynthesis, which are highlighted in light blue in Figure7, and are presented in more detail in Figures8 and9. Genes 2019, 10, 835 13 of 22

Genes 2019, 10, x FOR PEER REVIEW 14 of 23

1 Figure 9. Visualization of TC1 and TC2 DEGs involved in phenylpropanoid and flavonoid biosynthesis. Red boxes outline squares representing TC1 DEGs (log2(CK-72 h/CK-1 h)), and squares lacking a red outline box represent TC2 DEGs (log2(Al-72 h/Al-1 h). Within squares, deeper blue indicates stronger down-regulation, and deeper red indicatesGenes 2019 more, 10, x; up-regulation. doi: FOR PEER REVIEW Flavonoid biosynthesis is highlightedwww.mdpi.com/journal/ by a lightgenes red background, and phenylpropanoid biosynthesis is highlighted by a light blue background. PAL: phenylalanine ammonia-lyase; 4CL: 4-coumarate-CoA ligase; C4H: cinnamic acid 4-hydroxylase (CYP73A: trans-cinnamate 4-monooxygenase, (EC:1.14.13.11)); CCR: cinnamoyl-CoA reductase; CAD: cinnamyl alcohol dehydrogenase, (EC:1.1.1.195); C3H: 4-coumarate 3-hydroxylase

(C30H: coumaroylquinate (coumaroylshikimate) 30-monooxygenase, (EC:1.14.13.36)); CSE: caffeoyl shikimate esterase; COMT: caffeic acid 3-O-methyltransferase; HCT (HST): shikimate O-hydroxycinnamoyltransferase, (EC:2.3.1.133); CCOAOMT: caffeoyl-CoA O-methyltransferase, (EC:2.1.1.104); CHS: chalcone synthase

(Naringenin-chalcone synthase), (EC:2.3.1.74); FLS: flavonol synthase, (EC:1.14.11.23); F3’5’H (CYP75A): flavonoid 30,50-hydroxylase, (EC:1.14.13.88); F3’H: flavonoid 30-monooxygenase, (EC:1.14.13.21); DFR: dihydroflavonol-4-reductase, (EC:1.1.1.219); F3H: naringenin 3-dioxygenase, (EC:1.14.11.9); LAR: leucoanthocyanidin reductase; ANR: anthocyanidin reductase, (EC:1.3.1.77); ANS: Anthocyanidin synthase; CHI: chalcone isomerase, (EC:5.5.1.6); REF1: coniferyl-aldehyde dehydrogenase; UGT (UFGT): flavonol 7-O-glucosyltransferase, (EC:2.4.1.237) or Anthocyanidin 3-O-glucosyltransferase (EC:2.4.1.115) (Flavonol 3-O-glucosyltransferase) (UDP-glucose flavonoid 3-O-glucosyltransferase) (Anthocyanin rhamnosyl transferase). Genes 2019, 10, 835 14 of 22

Phenylpropanoid biosynthesis and flavonoid biosynthesis are both known to play important roles in plant abiotic stress and biotic stress responses. In this study, several DEGs were annotated as involved in phenylpropanoid biosynthesis and metabolism, including phenylalanine ammonia-lyase (PAL), 4-coumarate-CoA ligase (4CL), cinnamic acid 4-hydroxylase (C4H), cinnamoyl-CoA reductase (CCR), cinnamyl alcohol dehydrogenase (CAD), 4-coumarate 3-hydroxylase (C3H), and caffeic acid 3-O-methyltransferase (COMT). Similarly, several DEGs possibly involved in flavonoid biosynthesis were also observed, including DEGs annotated as chalcone synthase (Naringenin-chalcone synthase) (CHS), flavonol synthase (FLS), dihydroflavonol-4-reductase (DFR), anthocyanidin reductase (ANR), leucoanthocyanidin reductase (LAR), and caffeoyl-CoA O-methyltransferase (CCOAOMT). Among them, 4CL, CHS, FLS, ANS, and CAD were all up-regulated in the TC1 comparison, while CCR was down-regulated (Figure9). Meanwhile, expression responses for these phenylpropanoid and flavonoid associated genes were in the opposite direction in TC2 DEGs compared to TC1 DEGs, with the exception that CCR responded similarly in both comparison groups. As a result, it is reasonable to conclude that DEGs involved in phenylpropanoid and flavonoid metabolism may play important roles in Chinese fir roots in response to Al stress.

3.5. Transcription Factor Analysis MapMan was also used to visualize TC1 and TC2 DEGs associated with regulatory terms, such as transcription factor, protein modification, protein degradation, hormone metabolism, signaling pathway, and redox pathway. This visualization of differentially expressed regulatory proteins is shown for TC1 and TC2 comparisons in Figure 10. In this study, we focused on TF analysis of TC1 and TC2 DEGs putatively involved in regulation pathways. A total of 1615 unigenes hit matches from 58 families in the plant transcription factor database PlantTFdb (Table S8). The top 11 TF families associated with TC1 and TC2 DEGs were C2H2 (10.77%), ERF (10.28%), bHLH (8.79%), MYB-related (7.12%), MYB (6.75%), bZIP (5.20%), C3H (5.14%), GRAS (3.84%), Trihelix (3.34%), WRKY (2.97%) and NAC (2.85%) (Figure 11). These TF types are known to regulate the expression of many genes, including those involved in secondary metabolism, regulation of gene expression and response to biotic and abiotic stress. The 1615 TFs identified above were then clustered into 20 profiles based on expression across treatments (Figure 12). Of these profiles, profile 9 with 305 TFs, profile 4 with 265 TFs, profile 14 with 146 TFs and profile 15 with 119 TFs were each significantly enriched compared to an expected equal distribution of observed profiles. To further determine which TFs may play roles in Chinese fir root responses to Al stress, the TFs of TC1 and TC2 DEGs were predicted using the plant transcription factor database (PlantTFdb) (Table S10 and Table S11). In this analysis, 64 TFs from 21 TF families were identified in TC1 DEGs, and 495 TFs from 40 TF families were identified in TC2 DEGs (Figure 13). Interestingly, expression of bHLH (basic helix-loop-helix), C2H2 (Cys2-His2), ERF (ethylene response factor), bZIP, GRAS, and MYB (Myeloblastosis) TF family members all changed considerably in TC2 DEGs, indicating that these TFs might be involved in Chinese fir root responses to Al stress (Figure 13, Table S12). Genes 2019, 10, 835 15 of 22 Genes 2019, 10, x FOR PEER REVIEW 2 of 23

Figure 10. MapMan overview of regulation pathways containing TC1 or TC2 DEGs. (A) TC1

FigureDEGs 10. (log MapMan2(CK-72 overview h/CK-1 of h)). regulation (B) TC2 pathways DEGs (log containing2(Al-72 h /TC1Al-1 or h). TC2 Deeper DEGs. blue (A) TC1 indicates DEGs stronger (logdown-regulation,2(CK-72 h/CK-1 h)). and (B deeper) TC2 red DEGs indicates (log2(Al more-72 h/Al up-regulation.-1 h). Deeper Individual blue indicates DEGs stronger are represented downby- smallregulation, squares. and deeper The scale red indicates from 3 more to +3 up represents-regulation. the Individual normalized DEGs log are ofrepresented fold change by in each − 2 smallcomparison squares. group. The scale IAA: from Indole-3-acetic −3 to +3 represents acid; ABA: the abscisic normaliz acid;ed BA: log2 brassinosteroid; of fold change SA: in salicyliceach acid; comparisonGA: gibberellins. group. IAA: Indole-3-acetic acid; ABA: abscisic acid; BA: brassinosteroid; SA: salicylic acid; GA: gibberellins.

Genes 2019, 10, x FOR PEER REVIEW 3 of 23

200 174 180 Genes 2019, 10, 835 166 16 of 22 Genes 2019, 10, x FOR PEER REVIEW 3 of 23 160 142 200140 115 120 174 109 180 166 160100 142 84 83 14080 62

Number of of Unigenes Number 115 54 12060 109 48 46

10040 84 83 8020 62

Number of of Unigenes Number 0 54 60 48 46 40

20

0 Figure 11. Top 11 differentially regulated TF families in Chinese fir roots responding to Al stress. The X-axis lists the top 11 TF families. The Y-axis indicates the number of Unigenes assigned to each TF family.

TheFigure 1615 11. TopTFs 11identified differentially above regulated were then TF families clustered in Chinese into 20 fir profiles roots responding based on to expression Al stress. The across Figure 11. Top 11 differentially regulated TF families in Chinese fir roots responding to Al stress. The treatmentsX-axis lists(Figure the top12). 11OfTF these families. profiles, The prof Y-axisile 9 indicates with 305 the TFs, number profile of 4 Unigenes with 265 assigned TFs, profile to each 14 with X-axis lists the top 11 TF families. The Y-axis indicates the number of Unigenes assigned to each TF 146 TFsTF family.and profile 15 with 119 TFs were each significantly enriched compared to an expected equal family. distribution of observed profiles. The 1615 TFs identified above were then clustered into 20 profiles based on expression across treatments (Figure 12). Of these profiles, profile 9 with 305 TFs, profile 4 with 265 TFs, profile 14 with 146 TFs and profile 15 with 119 TFs were each significantly enriched compared to an expected equal distribution of observed profiles.

Figure 12. Cluster analysis of 1615 TFs differentially expressed across Al treatments. Unigenes identified Figure 12. Cluster analysis of 1615 TFs differentially expressed across Al treatments. Unigenes as TFs were classified based on similarity of expression patterns in Al and CK treated Chinese fir roots identified as TFs were classified based on similarity of expression patterns in Al and CK treated sampled after 1 h and 72 h exposure to Al stress. Profiles are ordered based on the number of TFs. Chinese fir roots sampled after 1 h and 72 h exposure to Al stress. Profiles are ordered based on the Profiles with colored backgrounds were significantly enriched compared to an expectation of equal number of TFs. Profiles with colored backgrounds were significantly enriched compared to an distribution of TFs among profiles. Profiles with a white background were not significantly enriched. expectation of equal distribution of TFs among profiles. Profiles with a white background were not Figure 12. Cluster analysis of 1615 TFs differentially expressed across Al treatments. Unigenes significantly enriched. identified as TFs were classified based on similarity of expression patterns in Al and CK treated Chinese fir roots sampled after 1 h and 72 h exposure to Al stress. Profiles are ordered based on the number of TFs. Profiles with colored backgrounds were significantly enriched compared to an expectation of equal distribution of TFs among profiles. Profiles with a white background were not significantly enriched.

Genes 2019, 10, x FOR PEER REVIEW 4 of 23

To further determine which TFs may play roles in Chinese fir root responses to Al stress, the TFs of TC1 and TC2 DEGs were predicted using the plant transcription factor database (PlantTFdb) (Table S10 and Table S11). In this analysis, 64 TFs from 21 TF families were identified in TC1 DEGs, and 495 TFs from 40 TF families were identified in TC2 DEGs (Figure 13). Interestingly, expression of bHLH (basic helix-loop-helix), C2H2 (Cys2-His2), ERF (ethylene response factor), bZIP, GRAS, and MYB (Myeloblastosis) TF family members all changed considerably in TC2 DEGs, indicating that these TFsGenes might2019, 10 ,be 835 involved in Chinese fir root responses to Al stress (Figure 13, Table S1172 of). 22

Figure 13. Expression patterns of DEGs identified as TFs in TC1 and TC2 DEGs. The circle legend Figure 13. Expression patterns of DEGs identified as TFs in TC1 and TC2 DEGs. The circle legend shows the TF type, TC1_TF: TC1 DEGs identified as TFs; TC2_TF: TC2 DEGs identified as TFs. The shows the TF type, TC1_TF: TC1 DEGs identified as TFs; TC2_TF: TC2 DEGs identified as TFs. The scale 0 to 7, represents log2(TF number). Red color marks more abundant TFs, and green color marks scale 0 lessto 7, prevalent represents TFs. log2(TF number). Red color marks more abundant TFs, and green color marks less prevalent TFs. 3.6. Validation of RNA-Seq Results by qRT-PCR 3.6. ValidationTo validateof RNA the-Seq accuracy Results ofby RNA-Seq, qRT-PCR qRT-PCR was conducted to measure the expression of the 21 selected DEGs, including one Al-activated malate transporter 10-like protein (Unigene0062724), Totwo validate auxin/ Althe responsive-like accuracy of RNA proteins-Seq, (Unigene0067300 qRT-PCR was and conducted Unigene0067301), to measure two the peroxidase-like expression of the 21 selectedproteins DEGs, (Unigene0007466 including one and Al Unigene0075814),-activated malate five transporter phosphate 10 transporters-like protein (Unigene0067425, (Unigene0062724), two auxin/AlUnigene0007397, responsive Unigene0074352,-like proteins Unigene0074941,(Unigene0067300 Unigene0012553), and Unigene0067301), and 10 DEGs two involved peroxidase in -like proteinsflavonoid (Unigene0007466 and phenylpropanoid and Unigene0075814), pathways (Unigene0094771, five phosphate Unigene0070938, transporters Unigene0076509, (Unigene0067425, Unigene0007397,Unigene0071623, Unigene0074352, Unigene0070437, Unigene0074941, Unigene0063962, Unigene0012553), Unigene0072773, and 10 Unigene0022972, DEGs involved in Unigene0069446, Unigene0068571) (Figure 14). The results indicate that the expression patterns of flavonoid and phenylpropanoid pathways (Unigene0094771, Unigene0070938, Unigene0076509, these selected DEGs were all similar between RNA-Seq and qRT-PCR observations. Unigene0071623, Unigene0070437, Unigene0063962, Unigene0072773, Unigene0022972, Unigene0069446, Unigene0068571) (Figure 14). The results indicate that the expression patterns of these selected DEGs were all similar between RNA-Seq and qRT-PCR observations.

Genes 2019, 10, 835 18 of 22 Genes 2019, 10, x FOR PEER REVIEW 5 of 23

Figure 14. qRT-PCR validation of 21 selected DEGs. RNA-Seq and qRT-PCR data are displayed as Figure 14. qRT-PCR validation of 21 selected DEGs. RNA-Seq and qRT-PCR data are displayed as log2(fold changes). A: RNA-Seq results for 21 selected DEGs. B: qRT-PCR results for 21 selected DEGs. log2(fold changes). A: RNA-Seq results for 21 selected DEGs. B: qRT-PCR results for 21 selected Al1h: log2(Al-1 h/CK-1 h); Al72h: log2(Al-72 h/CK-72 h). DEGs. Al1h: log2(Al-1 h/CK-1 h); Al72h: log2(Al-72 h/CK-72 h). 4. Discussion 4. Discussion In this study, transcriptome analysis was employed to investigate mechanisms of Al tolerance in ChineseIn this fir. study, As such, transcriptome this study constitutes analysis was thefirst employed investigation to investigate of transcriptomic mechanisms analysis of Al of tolerance Chinese infir Chineseroot responses fir. Asto such, Al toxicity. this study After constitutes subjecting the Chinese first investigation fir roots to 1 mM of transcriptomic Al and control analysis treatments of Chinesefor 1 h or fir 72 root h, RNA-seq responses analysis to Al toxicity. returned After a total subjecting of 105,732 Chinese unigenes, fir roots with anto 1 average mM Al length and control of 839 treatmentsbp and an N50for 1 length h or 72 of h, 1411 RNA bp.-seq Compared analysis withreturned previous a total studies of 105 of,732 transcriptomes unigenes, with obtained an average from lengthChinese of fir 839 vascular bp and cambium an N50 length [38] or of immature 1411 bp. xylemCompared [2] tissue with sampledprevious at studies different of tra developmentalnscriptomes obtainedphases, the from sequencing Chinese andfir vascular assembly cambium conducted [38] herein or immature returned xylem much longer[2] tissue N50 sampled (1411 bp) atand different mean developmental(839 bp) lengths. phases, This demonstrated the sequencing that and the assembly final assembly conducted quality herein in our returned study was much satisfactory, longer N50 and (1411further bp) suggests and mean that (839 transcriptome bp) lengths. data This can demonstrated provide accurate that the sequence final assembly data for quality future in cloning our study and wasfunctional satisfactory, analysis and eff ortsfurther aimed suggests to improve that transcriptome Al resistance anddata tolerance can provide in cultivated accurate Chinesesequence fir. data for futureQueries cloning of four and sequence functional databases analysis with efforts the 105732 aimed assembled to improve unigenes Al resistance returned and annotations tolerance forin cultivated58,362 of them, Chinese of which fir. 56,577 were homologous with sequences in the Nr database. To maximize the possibleQueries functional of four predictions sequence databases of transcripts with isolated the 105732 from assembled Chinese firunigenes roots responding returned annotations to Al stress, forfurther 58,362 analysis of them, was limited of which to these 56,577 56,577 were unigenes homologous aligning with to Nr sequences annotated in sequences. the Nr database. Furthermore, To maximizeDEG analysis the was possible also limited functional to this predictions set of 56,577 of unigenes. transcripts Upon isolated exposure from to 1 Chinese mM Al for fir 1 roots h or responding72 h, Chinese to fir Al roots stress, yielded further only analysis 3 DEGs was in the limited T1 comparison to these group,56,577 butunigenes 6384, 4314 aligning and 23,268 to Nr annotatedDEGs in T2, sequences. TC1 and TC2 Furthermore, comparison DEG groups, analysis respectively was also (Figure limited3A). to The th factis set that ofonly 56,577 3 DEGs unigenes. were Uponfound exposurein the T1 comparison, to 1 mM Al to for some 1 h extent, or 72 further h, Chinese illustrates fir roots that Chinese yielded fir only is a 3 relatively DEGs in resistant the T1 comparisonto Al, and short group, time but Al treatment6384, 4314 could and not 23 induce,268 DEGs large in changes T2, TC1 in gene and expression. TC2 comparison However, groups, with respectivelythe extension (Figure of Al treatment3A). The fact time, that the only number 3 DEGs of were down-regulated found in the DEGs T1 comparison, increased significantly,to some extent, as furtherevident illustrates in TC2 comparison. that Chinese This fir issuggests a relatively that resistant rapid responses to Al, and toshort Al toxicitytime Al treatment in Chinese co firuld roots not inducemight belarge dominated changes by in down-regulation gene expression. of However, many genes. with Further the extension analysis ofof TC1Al treatment and TC2 DEGstime, alsothe numbrevealeder ofthat down TC2- DEGsregulated were DEGssignificantly increased enriched significantly, in different as pathways evident than in TC2 TC1 comparison. DEGs. Significant This suggests that rapid responses to Al toxicity in Chinese fir roots might be dominated by down-regulation of many genes. Further analysis of TC1 and TC2 DEGs also revealed that TC2

Genes 2019, 10, 835 19 of 22 pathways of interest associated with TC2 DEGs include flavone and flavonol biosynthesis, as well as phenylpropanoid biosynthesis (Figure7), which suggests that these two pathways might participate in Chinese fir root responses to Al stress. Coincidently, Chakraborty et al. reported that phenolic and flavonoid contents in root exudates of Azolla increased significantly under exposure to 100, 250, and 500 µM Al stress treatments, but declined in the highest exposure treatment of 750 µM Al [39]. These research findings appear to support our conclusion that DEGs involved in phenylpropanoid and flavonoid pathways are largely down-regulated in Chinese fir roots under exposure to 1 mM Al stress. Moreover, Kidd et al. also reported that phenolics and flavonoids can chelate with Al in an Al-tolerant maize cultivar [40]. Thus, our results confirm existing evidence that phenolics and flavonoids play important roles in Chinese fir root responses to Al toxicity. However, the specific secondary metabolites involved in responses to Al stress, and the mechanisms through which they act need to be studied in more detail in future research. In this study, we also identified hundreds of Al responsive transcription factors belonging to diverse families, including bHLH, C2H2, ERF, bZIP, and MYB members identified among TC2 DEGs, along with other, largely distinct sets of TFs identified among TC1 DEGs. These results indicate that the TFs found among TC2 DEGs might be involved in Chinese fir root responses to Al toxicity (Figure 14). All of the TF families illuminated in this study are known to participate in plant abiotic and biotic stress responses [41]. In a recent study, the C2H2-type TF STOP1 was demonstrated as a crucial player in Arabidopsis resistance responses to Al stress through its role in inducing the expression of a set of Al-resistance related genes, including AtALMT1 [42]. In addition, several previous studies have also reported that MYB and bHLH TFs can modulate flavonoid biosynthesis in plants experiencing Al stress. Yet, in this study, none of the identified TFs are known to be involved in phenylpropanoid or flavonoid pathways. Whether any of these identified TFs interacts with the expression of genes participating in either of these two pathways should also be worthwhile subjects of future research projects. Several groups have reported that ERF Tfs regulate many key functions, including developmental processes and responses to abiotic and biotic stresses [43,44]. In this study, the TF family containing the largest number of TC2 DEGs was the ERF family (79), which may induce the expression of genes acting in the synthesis of phenols in secondary metabolic pathways. These findings suggested that ERF TFs might play important roles in Chinese fir root responses to Al toxicity. Deciphering the mechanisms through which these TFs act might prove to be a key component of future research meant to elucidate and improve Chinese fir root responses to Al toxicity.

5. Conclusions Little is known about the molecular mechanisms underlying Chinese fir responses to Al stress. This study is the first attempt to investigate changes of Chinese fir roots in response to Al toxicity at the transcriptional level, which also provided indications of which biochemical mechanisms underlie Al tolerance in Chinese fir. Several DEGs associated with phenylpropanoid and flavonoid metabolism were identified in this research as strongly down-regulated in Chinese fir roots responding to Al toxicity, suggesting that phenylpropanoid and flavonoid metabolism could participate in regulating responses to Al toxicity in Chinese fir. In addition, bHLH, C2H2, ERF, bZIP, MYB and the other TFs were differentially expressed among Al treatments over time, and, therefore, may play important roles in Chinese fir root responses to Al stress. Overall, this research represents a first step toward understanding the molecular mechanisms behind Chinese fir response to Al stress. The transcriptome data in this study will help to further develop novel Al-resistance strategies in Chinese fir and other tree species. Genes 2019, 10, 835 20 of 22

Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4425/10/11/835/s1, Fasta File S1: Sequence of DEGs used for qRT-PCR validation; Table S1 Primers of candidate DEGs used for qRT-PCR, Table S2. Summary of Illumina sequencing results obtained from Chinese fir root tips grown in Al or control (CK) treatments for 1 or 72 hours; Table S3: Number of Unigenes matching the top 20 species in the Nr database, Table S4: KEGG pathway annotation of Unigenes in Chinese fir roots under Al stress; Table S5: KOG Classifications of unigenes assembled from RNA-seq reads obtained from Chinese fir roots under Al stress; Table S6: Statistics for TC1 and TC2 DEGs obtained from Chinese fir roots; Table S7: The common DEGs of Chinese fir roots in TC1 group and TC2 group; Table S8: GO classifications of TC1 and TC2 DEGs; Table S9: KEGG pathways of TC1 and TC2 DEGs; Table S10: TF classification of DEGs identified as transcription factors from Chinese fir roots exposed to Al stress treatments; Table S11: TF analysis of TC1 DEGs obtained from Chinese fir roots subjected to Al stress treatments; Table S12: TF analysis of TC2 DEGs obtained from Chinese fir roots subjected to Al stress treatments; Figure S1: Characteristics of Chinese fir unigene homologues in four protein sequence databases. Author Contributions: Conceptualization, Z.M. and S.L.; Formal analysis, Z.M.; Funding acquisition, S.L.; Investigation, Z.M.; Methodology, Z.M.; Resources, Z.M.; Software, Z.M.; Validation, Z.M.; Visualization, Z.M.; Writing—original draft, Z.M.; Writing—review & editing, S.L. Funding: This work was funded by the State Forestry Administration Engineering Research Center of Chinese fir, grant number ptjh13002, and Key laboratory of physiological ecology and molecular biology of forest stress in Fujian province, grant number ptjh17009 and KJG17016A. Acknowledgments: We are grateful to Hong Liao and Zilong Guo for the valuable suggestions on the revision. We also thank Thomas Walk for the English grammar editing and providing valuable comments on the revision. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study.

References

1. Dong, T.; Duan, B.; Zhang, S.; Korpelainen, H.; Niinemets, Ü.; Li, C. Growth, biomass allocation and photosynthetic responses are related to intensity of root severance and soil moisture conditions in the plantation tree Cunninghamia lanceolata. Tree Physiol. 2016, 36, 807–817. [CrossRef][PubMed] 2. Zhang, Y.; Han, X.; Sang, J.; He, X.; Liu, M.; Qiao, G.; Zhuo, R.; Hu, J. Transcriptome analysis of immature xylem in the Chinese fir at different developmental phases. PeerJ 2016, 4, e2097. [CrossRef][PubMed] 3. Von Uexkull, H.R.; Mutert, E.; Uexkull, H.R. Global extent, development and economic impact of acid soils. Plant Soil 1995, 171, 1–15. [CrossRef] 4. Bojórquez-Quintal, E.; Escalante-Magaña, C.; Echevarría-Machado, I.; Martínez-Estévez, M. Aluminum, a Friend or Foe of Higher Plants in Acid Soils. Front. Plant Sci. 2017, 8, 1767. [CrossRef][PubMed] 5. Zahra, T.; Amin, B.; Shekofeh, E. The effects of aluminum and phosphorous on Brassica napus. Am. Eurasian J. Agric. Environ. Sci. 2009, 6, 137–142. 6. Kochian, L.V.; Piñeros, M.A.; Liu, J.; Magalhaes, J.V. Plant Adaptation to Acid Soils: The Molecular Basis for Crop Aluminum Resistance. Annu. Rev. Plant Boil. 2015, 66, 571–598. [CrossRef] 7. Cárcamo, M.P.; Reyes-Díaz, M.; Rengel, Z.; Alberdi, M.; Omena-Garcia, R.P.; Nunes-Nesi, A.; Inostroza-Blancheteau, C. Aluminum stress differentially affects physiological performance and metabolic compounds in cultivars of highbush blueberry. Sci. Rep. 2019, 9, 11275. 8. Yang, L.T.; Qi, Y.P.; Jiang, H.X.; Chen, L.S. Roles of organic acid anion secretion in aluminium tolerance of higher plants. BioMed Res. Int. 2013, 2013, 173682. [CrossRef] 9. Kinraide, T.B. Identity of the rhizotoxic aluminium species. Plant Soil 1991, 134, 167–178. [CrossRef] 10. Kopittke, P.M.; Moore, K.L.; Lombi, E.; Gianoncelli, A.; Ferguson, B.J.; Blamey, F.P.C.; Menzies, N.W.; Nicholson, T.M.; McKenna, B.A.; Wang, P.; et al. Identification of the primary lesion of toxic aluminum in plant roots. Plant Physiol. 2015, 167, 1402–1411. [CrossRef] 11. Zhao, C.R.; Ikka, T.; Sawaki, Y.; Kobayashi, Y.; Suzuki, Y.; Hibino, T.; Sato, S.; Sakurai, N.; Shibata, D.; Koyama, H. Comparative transcriptomic characterization of aluminum, sodium chloride, cadmium and copper rhizotoxicities in Arabidopsis thaliana. BMC Plant Biol. 2009, 9, 32. [CrossRef][PubMed] 12. Chen, L.; Wang, T.; Zhao, M.; Tian, Q.; Zhang, W.H. Identification of aluminum-responsive microRNAs in Medicago truncatula by genome-wide high-throughput sequencing. Planta 2012, 235, 375–386. [CrossRef] [PubMed] 13. Rengel, Z. Uptake of aluminium by plant cells. New Phytol. 2010, 134, 389–406. [CrossRef] 14. Schaedle, M.; Thornton, F.C.; Raynal, D.J.; Tepper, H.B. Response of tree seedlings to aluminum. Tree Physiol. 1989, 5, 337–356. [CrossRef][PubMed] Genes 2019, 10, 835 21 of 22

15. Silva, I.R.; Novais, R.F.; Jham, G.N.; Barros, N.F.; Gebrim, F.O.; Nunes, F.N.; Neves, J.C.L.; Leite, F.P. Responses of eucalypt species to aluminum: The possible involvement of low molecular weight organic acids in the Al tolerance mechanism. Tree Physiol. 2004, 24, 1267–1277. [CrossRef][PubMed] 16. Tomioka, R.; Takenaka, C. Difference in response to aluminum among Japanese coniferous species. Soil Sci. Plant Nutr. 2004, 50, 755–761. [CrossRef] 17. Nowak, J.; Friend, A.L. Loblolly pine and slash pine responses to acute aluminum and acid exposures. Tree Physiol. 2006, 26, 1207–1215. [CrossRef] 18. Nowak, J.; Friend, A.L. Aluminum fractions in root tips of slash pine and loblolly pine families differing in Al resistance. Tree Physiol. 2005, 25, 245–250. [CrossRef] 19. Kelly, J.M.; Schaedle, M.; Thornton, F.C.; Joslin, J.D. Sensitivity of tree seedlings to aluminum. II. Red oak, sugar maple, and European beech. J. Environ. Qual. 1990, 19, 172–179. [CrossRef] 20. Thornton, F.C.; Schaedle, M.; Raynal, D.J. Effect of aluminum on the growth of sugar maple in solution culture. Can. J. For. Res. 1986, 16, 892–896. [CrossRef] 21. Chen, Z.C.; Liao, H. Organic acid anions: An effective defensive weapon for plants against aluminum toxicity and phosphorus deficiency in acidic soils. J. Genet. Genom. 2016, 43, 631–638. [CrossRef] 22. Osawa, H.; Endo, I.; Hara, Y.; Matsushima, Y.; Tange, T. Transient proliferation of proanthocyanidin-accumulating cells on the epidermal apex contributes to highly aluminum-resistant root elongation in camphor tree. Plant Physiol. 2011, 155, 433–446. [CrossRef][PubMed] 23. Grisel, N.; Zoller, S.; Künzli-Gontarczyk, M.; Lampart, T.; Münsterkötter, M.; Brunner, I.; Bovet, L.; Métraux, J.-P.; Sperisen, C. Transcriptome responses to aluminum stress in roots of aspen (Populus tremula). BMC Plant Boil. 2010, 10, 185. [CrossRef][PubMed] 24. Naik, D.; Smith, E.; Cumming, J.R. Rhizosphere carbon deposition, oxidative stress and nutritional changes in two poplar species exposed to aluminum. Tree Physiol. 2009, 29, 423–436. [CrossRef][PubMed] 25. Tahara, K.; Hashida, K.; Otsuka, Y.; Ohara, S.; Kojima, K.; Shinohara, K. Identification of a hydrolyzable tannin, oenothein B, as an aluminum-detoxifying ligand in a highly aluminum-resistant tree, Eucalyptus camaldulensis. Plant Physiol. 2014, 164, 683–693. [CrossRef][PubMed] 26. Maejima, E.; Osaki, M.; Wagatsuma, T.; Watanabe, T. Contribution of constitutive characteristics of lipids and phenolics in roots of tree species in Myrtales to aluminum tolerance. Physiol. Plant. 2017, 160, 11–20. [CrossRef] 27. Ofei-Manu, P.; Wagatsuma, T.; Ishikawa, S.; Tawaraya, K. The plasma membrane strength of the root-tip cells and root phenolic compounds are correiated with Al tolerance in several common woody plants. Soil Sci. Plant Nutr. 2001, 47, 359–375. [CrossRef] 28. Moghadam, N.D.; Poozesh, V.; Rezaei, A. Biochemical Responses of Two Soybean (Glycine max) Varieties to Aluminum Stress in Nutrient Solution. J. Chem. Health Risks 2016, 6, 237–247. 29. Zhang, X.; Long, Y.; Huang, J.; Xia, J. Molecular Mechanisms for Coping with Al Toxicity in Plants. Int. J. Mol. Sci. 2019, 20, 1551. [CrossRef] 30. Ma, J.F.; Chen, Z.C.; Shen, R.F. Molecular mechanisms of Al tolerance in gramineous plants. Plant Soil 2014, 381, 1–12. [CrossRef] 31. Hirano, Y.; Pannatier, E.G.; Zimmermann, S.; Brunner, I. Induction of callose in roots of Norway spruce seedlings after short-term exposure to aluminum. Tree Physiol. 2004, 24, 1279–1283. [CrossRef][PubMed] 32. Umemura, T.; Usami, Y.; Aizawa, S.-I.; Tsunoda, K.-I.; Satake, K.-I. Seasonal change in the level and the chemical forms of aluminum in soil solution under a Japanese cedar forest. Sci. Total Environ. 2003, 317, 149–157. [CrossRef] 33. Halman, J.M.; Schaberg, P.G.; Hawley, G.J.; Pardo, L.H.; Fahey, T.J. Calcium and aluminum impacts on sugar maple physiology in a northern hardwood forest. Tree Physiol. 2013, 33, 1242–1251. [CrossRef][PubMed] 34. Grabherr, M.G.; Haas, B.J.; Yassour, M.; Levin, J.Z.; Thompson, D.A.; Amit, I.; Adiconis, X.; Fan, L.; Raychowdhury, R.; Zeng, Q.; et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 2011, 29, 644. [CrossRef][PubMed] 35. Mortazavi, A.; Williams, B.A.; McCue, K.; Schaeffer, L.; Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 2008, 5, 621–628. [CrossRef][PubMed] 36. Conesa, A.; Götz, S.; García-Gómez, J.M.; Terol, J.; Talón, M.; Robles, M. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 2005, 21, 3674–3676. [CrossRef] Genes 2019, 10, 835 22 of 22

37. Ye, J.; Fang, L.; Zheng, H.; Zhang, Y.; Chen, J.; Zhang, Z.; Wang, J.; Li, S.; Li, R.; Bolund, L.; et al. WEGO: A web tool for plotting GO annotations. Nucleic Acids Res. 2006, 34, W293–W297. [CrossRef] 38. Xu, H.; Cao, D.; Feng, J.; Wu, H.; Lin, J.; Wang, Y. Transcriptional regulation of vascular cambium activity during the transition from juvenile to mature stages in Cunninghamia lanceolata. J. Plant Physiol. 2016, 200, 7–17. [CrossRef] 39. Chakraborty, S.; Mishra, A.; Verma, E.; Tiwari, B.; Mishra, A.K.; Singh, S.S. Physiological mechanisms of aluminum (Al) toxicity tolerance in nitrogen-fixing aquatic macrophyte Azolla microphylla Kaulf: Phytoremediation, metabolic rearrangements, and antioxidative enzyme responses. Environ. Sci. Pollut. Res. 2019, 26, 9041–9054. [CrossRef] 40. Kidd, P.; Poschenrieder, C.; Llugany, M.; Gunse, B.; Barceló, J. The role of root exudates in aluminium resistance and silicon-induced amelioration of aluminium toxicity in three varieties of maize (Zea mays L.). J. Exp. Bot. 2001, 52, 1339–1352. 41. Kumari, M.; Taylor, G.J.; Deyholos, M.K. Transcriptomic responses to aluminum stress in roots of Arabidopsis thaliana. Mol. Genet. Genom. 2008, 279, 339. [CrossRef][PubMed] 42. Zhang, Y.; Zhang, J.; Guo, J.; Zhou, F.; Singh, S.; Xu, X.; Xie, Q.; Yang, Z.; Huang, C.-F. F-box protein RAE1 regulates the stability of the aluminum-resistance transcription factor STOP1 in Arabidopsis. Proc. Natl. Acad. Sci. USA 2019, 116, 319–327. [CrossRef][PubMed] 43. An, J.-P.; Yao, J.-F.; Ren, Y.-R.; You, C.-X.; Wang, X.-N.; Hao, Y.-J. Apple MdMYC2 reduces aluminum stress tolerance by directly regulating MdERF3 gene. Plant Soil 2017, 418, 255–266. [CrossRef] 44. Yao, Y.; He, R.J.; Xie, Q.L.; Zhao, X.H.; Deng, X.M.; He, J.B.; Song, L.; He, J.; Marchant, A.; Chen, X.-Y.; et al. ETHYLENE RESPONSE FACTOR 74 (ERF74) plays an essential role in controlling a respiratory burst oxidase homolog D (RbohD)-dependent mechanism in response to different stresses in Arabidopsis. New Phytol. 2017, 213, 1667–1681. [CrossRef][PubMed]

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