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  Title	RNA-Seq Analysis Uncovered Transcriptomic Changes in Poncirus trifoliata Roots Under Long-Term Soil Drought Conditions
  ID	5008097
  External ID	7382232894
  Author	Unknown
  Published Date	Dec. 11, 2024, 10 a.m.
  Summary	College of Life Science, Leshan Normal University, Leshan 614000, China Leshan Engineering Technology Research Center for Innovative Utilization of Feature Plant Resources, Leshan 614000, China I
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  URL	https://www.mdpi.com/2311-7524/10/12/1319
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  Content ID	3017316
  Key	302c0f00-0da5-4223-b05a-170b50bdc396
  Content	College of Life Science, Leshan Normal University, Leshan 614000, China Leshan Engineering Technology Research Center for Innovative Utilization of Feature Plant Resources, Leshan 614000, China Institution of Biodiversity Conservation and Utilization in Mount Emei, Leshan Normal University, Leshan 614000, China Key Laboratory of Tree Breeding of Zhejiang Province, Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou 311400, China Author to whom correspondence should be addressed. † These authors contributed equally to this work. Horticulturae (12), 1319; https://doi.org/10.3390/horticulturae10121319 (registering DOI) Submission received: 14 November 2024 Revised: 2 December 2024 Accepted: 9 December 2024 Published: 11 December 2024 (This article belongs to the Special Issue Germplasm, Genetics and Breeding of Ornamental Plants Abstract Drought is one of the most serious abiotic stresses in citrus plantations. It is thus imperative to fully understand the drought-resistant mechanisms in these plants. Here, RNA-seq was used to analyze the transcriptomic changes in the roots of Poncirus trifoliata , a widely used rootstock in citrus plantations, under a 72-day soil drought and a 7-day recovery stage. Our results showed that the genes upregulated under drought were only enriched in the galactose metabolism and protein processing in endoplasmic reticulum pathways. In the galactose metabolism pathway, four genes related to the synthesis of raffinose family oligosaccharides, which act as osmoprotectants and ROS scavengers, were significantly upregulated under long-term drought. Several heat-shock protein (HSP) family genes were significantly upregulated under drought, leading to increased levels of HSPs to alleviate the endoplasmic reticulum-associated degradation of misfolded proteins induced by drought stress. Some other upregulated genes under drought, like late embryogenesis-abundant family genes and lipid transfer protein family genes, might also be crucial to the drought resistance of P. trifoliata roots. MSYJ196370 (heat-shock factor family gene) was the top hub gene in the protein–protein interaction analysis of upregulated genes under drought. These findings supplement the transcriptomic response of P. trifoliata root under long-term drought stress. 1. Introduction With the advent of global warming, expanding areas in the world are experiencing more severe drought stress. For instance, during 1981–2021, China witnessed significant increases in the frequencies and durations of hotspots and the areas affected by flash droughts [ ]. Increasingly frequent and severe drought events have led to severe negative impacts on the global crop industry. Therefore, how plants cope with drought stress is a research hotspot in plant cultivation. Citrus is a popular fruit crop due to its widespread cultivation and demand [ ]. It refers to a group of evergreen fruit trees demanding high levels of water for cultivation. Therefore, drought events substantially impact citrus production. These events can reduce tree vitality and yield, as well as fruit quality, and even cause the death of citrus trees, severely impacting the quality, yield, and efficient cultivation of citrus [ ]. Citrus is generally cultivated with a combination of rootstock and scion. Poncirus trifoliata , a relative of citrus, is widely used as a rootstock in orange plantations. It has a wide-ranging grafting affinity and can help increase orange production and quality [ ]. It also displays strong resistance to cold, heat, and diseases [ ]. Compared to its resistance to cold, disease, and heat, its drought resistance is relatively weak. So, improving its drought resistance is one of the main breeding directions for this plant [ ]. Wei et al. [ ] found that naturally occurring autotetraploids had stronger drought resistance than the diploid Poncirus trifoliata (L.) Raf. This finding might be attributed to the significant upregulation of genes encoding antioxidant enzymes and vacuolar invertase in the tetraploid compared with the diploid, imparting the former with stronger reactive oxygen species (ROS) scavenging and osmotic regulation abilities. These results indicate that the expression of key genes in P. trifoliata can enhance its drought resistance. Previously, several genes cloned from P. trifoliata have been shown to improve drought resistance in transgenic plants, including PtADC PtrABF PtrMAPK PtrLEA7 , and PtrANR1 [ ]. Transcriptome analysis is useful in comparing the changes in gene expression in plant organs under stress and identifying the key genes participating in stress resistance. Only a few studies have investigated the Poncirus trifoliata transcriptome under drought stress. In addition to the drought response of the natural tetraploid and diploid, Yu et al. [ ] explored the whole transcriptome of trifoliate orange seedlings under polyethylene glycol-mediated osmotic stress (−1.56 Mpa). They found that abscisic acid signaling, mitogen-activated protein kinase cascade, proline biosynthesis, and polyamine synthesis were the key pathways related to the drought resistance of P. trifoliata . However, existing transcriptome-related studies exhibit two acute problems. First, as rootstock, gene expression changes in P. trifoliata roots under drought stress have not received enough attention. Most transcriptomic studies have focused on the leaves. For example, differentially expressed genes (DEGs) in leaves coding for enzymes related to antioxidant processes and sugar metabolism were found to play a vital role in enhancing the drought tolerance of P. trifoliata [ ]; however, the root system might play a more important role in this aspect. Second, most of the existing transcriptome-related studies are limited to short-term stress and pay little attention to long-term stress. Therefore, we hypothesized that the study of the P. trifoliata root transcriptome under long-term drought stress could provide some novel insights into its drought-resistance ability. Here, we used RNA-sequencing (RNA-seq) to analyze the expression of P. trifoliata root genes after long-term drought treatment (72 days) and recovery for 7 days. Our results helped identify the candidate genes valuable to the drought-resistance breeding of citrus. 2. Materials and Methods 2.1. Plants and Treatment The study was conducted in the greenhouses (29°33'34″ N, 103°44'58″ E) of Leshan Normal University, Leshan City, China. P. trifoliata , a widely used citrus rootstock, was selected as the plant material for this study. Briefly, 2-year-old diploid seedlings were transplanted into a 21 cm × 19 cm plastic pot in spring. The pots were filled with soil from a citrus orchard. The seedlings were placed in a well-ventilated transparent greenhouse and grown under natural sunlight and at a natural temperature with 60–70% relative humidity. When the seedlings had grown to a height of about 60 cm, seedlings with normal growth and no diseases or pests were selected for drought treatment. Three experimental treatment groups were devised: (1) the control (CK) group, which was provided with tap water three times a week, with the water gently added to the soil surface until it started to seep out from the holes at the bottom of pots; (2) the drought (D) group, in which irrigation was withheld from the seedlings for 72 days; (3) the drought recovery (DR) group, which was watered three times within 1 week after the end of drought treatment, with the same watering method as that of the CK group. The recovery duration was set at 7 days. The drought treatment was started in late May, and the average temperature during the study was 27 °C, the maximum temperature was 35 °C, and the minimum temperature was 19 °C. Fine roots from the CK group, D group, and DR group seedlings were collected. All the fine root samples were quickly cleaned with distilled water and DEPC-treated water. After drying with filter paper, the roots were immediately frozen in liquid nitrogen and stored at −80 °C until further use. Three biological replicates were prepared for transcriptome sequencing for each group, with each replicate containing an even mix of fine roots from two seedlings. The malonaldehyde (MDA) content of the roots was then measured using the TBA method described by Peng et al. [ ]. 2.2. RNA-Seq Transcriptome sequencing and analysis were conducted as previously described by Peng et al. [ ]. A plant RNA purification reagent (Invitrogen, Carlsbad, CA, USA) was used to extract the root RNA, and Dnase I (Takara, Otsu, Japan) was used to remove genomic DNA from the extracted sample. The RNA quality and concentration were determined using a NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). RNA integrity was detected using agarose gel electrophoresis and an Agilent 2100 Nano Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). The OD260/280 values of the RNA obtained from all the samples ranged from 1.8 to 2.2. RNA-seq libraries were constructed using the TruSeq RNA sample preparation kit (Illumina, San Diego, CA, USA) and sequenced in a HiSeq PE150 sequencer (Illumina; 2 × 150-bp read length). 2.3. Transcriptomic Data Analysis The adaptor of the raw reads obtained by RNA-seq was removed using SeqPrep ( https://github.com/jstjohn/SeqPrep , accessed on 20 January 2024) [ ], and then bases with a mass value less than 20 at the 3' end of the sequence were removed using Sickle ( https://github.com/najoshi/sickle , accessed on 20 January 2024) [ ] to obtain clean reads. The clean paired-end reads were then aligned to the Citrus reticulata reference genome ( http://citrus.hzau.edu.cn/download.php , accessed on 20 January 2024) using HISAT2 software (version 2.1.0, http://ccb.jhu.edu/software/hisat2/index.shtml , accessed on 20 January 2024) [ ], and the mapped reads were assembled using StringTie (version 2.1.2, https://ccb.jhu.edu/software/stringtie/ , accessed on 20 January 2024) [ ] based on the reference genome. The transcriptomic data were analyzed on the Majorbio Cloud online platform ( www.majorbio.com , accessed on 20 March 2024) [ ]. Gene expression levels were quantified using RSEM software (version 1.3.3, http://deweylab.biostat.wisc.edu/rsem/ , accessed on 20 March 2024) [ ], and the results were expressed as fragments per kilobase of transcript per million mapped reads (FPKM). DESeq2 (version 1.24.0, http://bioconductor.org/packages/stats/bioc/DESeq2/ , accessed on 20 March 2024) was used for the differential gene expression analysis [ ], and the CK group was used as the control. A gene with an expression change ≥ 2-fold and padj < 0.05 was defined as differentially expressed. GOATOOLS (version 1.4.4, https://pypi.org/project/goatools/ , accessed on 25 March 2024) [ ] and KOBAS (version 3.0, http://bioinfo.org/kobas , accessed on 25 March 2024) [ ] were used for the enrichment analysis of the gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, respectively. Only DEGs were used in the enrichment analysis. In the Bonferroni method test, the GO terms and KEGG pathways with padj < 0.05 were defined as enriched items. The protein–protein interaction (PPI) analysis was carried out using the STRING database ( http://stringdb.org/ , accessed on 20 May 2024) [ ]. The interaction network diagram of PPI was plotted using Cytoscape (version 3.7.2) software [ ]. All the interacting proteins were analyzed using cytoHubba (version 0.1) [ ]. 2.4. Transcription Factor (TF) Prediction and Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR) The prediction of TF genes was based on the Plant Transcription Factor Database (PlantTFDB, version 5.0, http://planttfdb.gao-lab.org/ , accessed on 30 March 2024) [ ]. TF genes were screened out by blasting them against TF genes from all the plant species in the database on the Majorbio Cloud online platform [ ], with the limitation of Hmmscan E-value < 10 and Blast E-value < 10 . Seventeen TF genes significantly upregulated under drought stress were selected for RT-qPCR analysis ( Supplementary Table S1 ) to detect their expression levels and verify the accuracy of the transcriptomic data. RT-qPCR was performed using the same RNA samples used for sequencing. The reverse transcription and RT-qPCR analyses were performed as previously described by Peng et al. [ ]. Reverse transcription was performed using MonScript RTIII Super Mix with dsDNase (Monad, Wuhan, China). RT-qPCR was performed on a qTOWER 3 Real-Time PCR Thermal Cycler (Analytik Jena, Jena, Germany) using the MonAmp ChemoHS qPCR Mix (Monad). Three technical replicates per sample were used. ACTIN was used as a reference gene to normalize the expression levels of the target genes, and relative gene expression levels were measured using the 2 method [ ]. 3. Results 3.1. Overview of Sequencing and Annotation Nine cDNA libraries of P. trifoliata roots were generated and sequenced to elucidate how this citrus rootstock responded to long-term drought stress and subsequent recovery. The average numbers of clean bases and reads were 8.09 G and 54.81 M, respectively. The proportion amounts of Q20 and Q30 bases were greater than 97.48% and 92.78%, respectively. The proportion of GC bases averaged 44.29%. The average alignment rate against the reference genome was 83.16%, and an average of 80.20% of clean reads were uniquely mapped ( Supplementary Table S2 ). These results suggested that the quality of our sequencing results was robust. 3.2. MDA Content and Gene Expression After 72 days of drought stress, the MDA content in the roots in the D group was significantly higher than that in the controls ( p < 0.01, Duncan), and the average value of the D group was 1.53 times that of the CK group ( Figure 1 A), indicating that P. trifoliata roots suffered severe injury under long-term drought stress. However, after 7-day recovery, the MDA content was reduced to the control level ( p > 0.05, Duncan; Figure 1 A). The reference genome contained 28,833 genes. Among them, 22,838 genes with average FPKM values ≥ 1 in at least one of the three groups ( Supplementary Table S3 ) were recognized as expressed genes. The expressed genes were then annotated by GO, KEGG, the Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups (EggNOG), the Non-Redundant Protein Sequence Database (NR), and the Swiss-Prot and Pfam databases, and the ratios of the annotated genes were 82.58%, 41.27%, 81.52%, 99.29%, 80.51%, and 82.14%, respectively ( Figure 1 B; Supplementary Table S4 ). Principal component analysis (PCA) revealed significant variation in gene expression among the samples from the CK, D, and DR groups. The biological replicates for each treatment demonstrated consistent results, indicating that the gene expression characteristics of P. trifoliata roots were significantly different during drought treatment and recovery ( Figure 1 C). A Venn diagram was produced using genes with an FPKM value of no less than 1 in at least one treatment. In the Venn map, 359 and 393 genes were found to be expressed only in the D and DR groups, respectively ( Figure 1 D). Compared with CK, 2225 and 1110 upregulated and 4331 and 2698 downregulated DEGs were identified under drought stress and during recovery, respectively ( Figure 1 E; Supplementary Table S5 ). A higher number of downregulated genes than upregulated genes were detected in the D and DR groups. In addition, the number of DEGs detected during recovery was only 58.1% of those detected under drought stress ( Figure 1 E). 3.3. Cluster Analysis of the DEGs All 7848 DEGs in the D and DR groups were subjected to expression pattern clustering, and 99.6% of the DEGs belonged to four categories ( Figure 2 A; Supplementary Table S6 ). A total of 702 and 2044 DEGs belonged to the red and coral groups ( Figure 2 B), respectively, and their expression levels increased during recovery and drought stress. There were 309 DEGs in the blue group, and their expression decreased during recovery ( Figure 2 B). The green group contained the most DEGs (n = 4767), accounting for 60.7% of all the DEGs detected in this study. The expressions of the DEGs in this group decreased under drought stress ( Figure 2 B), indicating that drought stress inhibited several functions of the P. trifoliata root system. 3.4. Functional Enrichment Analysis of the DEGs Functional enrichment analysis was performed of the four major DEG groups in the GO and KEGG databases. The green group harbored the most significantly enriched GO terms and KEGG pathways (n = 69 and 20, respectively) ( Supplementary Tables S7 and S8 ), indicating that many of the biological functions and metabolic processes of the P. trifoliata root system were severely inhibited under long-term drought stress. Among the enriched GO terms, 34, 27, and 8 belonged to the molecular function (MF), biological process (BP), and cellular component (CC) classifications. Figure 3 A lists the 20 GO terms and KEGG pathways with the highest enrichment of DEGs downregulated in long-term drought stress. The top five of these pathways were for phenylpropanoid biosynthesis, plant–pathogen interaction, ubiquinone and other terpenoid-quinone biosynthesis, starch and sucrose metabolism, and the biosynthesis of various plant secondary metabolites. In the coral group, the DEGs were significantly enriched in 13 GO terms, with six, four, and three DEGs belonging to the BP, CC, and MF classifications, respectively ( Figure 3 B). During long-term drought stress, significantly upregulated DEGs in the P. trifoliata roots were mainly related to responses to various stimuli and stresses. In addition, heat-shock protein binding and transmembrane transport were significantly enriched. The DEGs in the coral group were significantly enriched in only two pathways: galactose metabolism (n = 18) and protein processing in the endoplasmic reticulum (n = 41) ( Figure 3 B). The top 20 DEGs in each pathway are listed in Figure 3 B. During the recovery period, the DEGs were significantly enriched in 12 GO terms, with five, two, and five belonging to the BP, CC, and MF classifications, respectively ( Figure 3 C), with most related to transmembrane transport. The DEGs in the red group were only enriched in ribosome biogenesis in the eukaryotes ( Figure 3 C), which might be related to vigorous biosynthesis during rapid growth recovery. 3.5. TF Genes and RT-qPCR A total of 169 TF genes were significantly upregulated under drought stress and/or during recovery. These DEGs belonged to 33 TF families ( Figure 4 A). In addition, 141 TF genes were significantly upregulated under drought stress. These genes belonged to 33 TF families ( Figure 4 B). Among them, the MYB family had 19 DEGs in the coral group. We observed that more than ten upregulated DEGs belonged to each of the ERF, MYB-related, and bZIP families. Several upregulated DEGs also belonged to the NAC, Dof, and HSF families. Thirty-nine TF genes belonging to 19 TF families were identified in the red group ( Figure 4 B). The top five families with the most DEGs were the ERF, bHLH, NAC, B3, and MYB-related families, with six, six, four, three, and three DEGs, respectively, and only 1–2 DEGs belonged to the rest of the families. RT-qPCR was used to analyze the relative expression levels of the 17 TF genes in the coral group ( Figure 5 ). Except for the variation in MSYJ085670 expression levels during the recovery period, the expression levels of the remaining genes altered in accordance with the transcriptomic data. In general, the correlation between the results of the qRT-PCR and transcriptome sequencing reached 0.909. 3.6. PPI Analysis of the Coral Group DEGs In the coral group, 453 DEGs with an average RPKM value ≥ 10 and |log2 fold change| ≥ 2 under drought treatment were selected. The PPI analysis of these genes showed that a total of 145 DEGs interacted with each other. These interactions were divided into five groups: A, B, C, D, and E ( Figure 6 Supplementary Table S9 Group A exhibited the most complex interaction network and was further divided into four parts ( Figure 6 A; Supplementary Table S9 ). One part contained several heat-shock protein (HSP) genes, including seven sHSP subfamily genes ( MSYJ122060 MSYJ215040 MSYJ013000 MSYJ179550 MSYJ204750 MSYJ204760 , and MSYJ128280 ), three HSP70 subfamily genes ( MSYJ068350 MSYJ166210 , and MSYJ124800 ), HSP90 subfamily gene MSYJ233530 , and HSP100 subfamily gene MSYJ176790 . In addition, this part also contained three heat-shock factor (HSF) family TF genes ( MSYJ196370 MSYJ219220 , and MSYJ210880 ), two J-domain protein (JDP) family genes ( MSYJ032640 and MSYJ134840 ), a late embryogenesis-abundant (LEA) protein-encoding gene ( MSYJ156130 ), and a gene probably encoding the dirigent protein ( MSYJ031940 ). The second part primarily comprised genes involved in plant hormone signaling. These included the key gene involved in abscisic acid (ABA) synthesis, 9-cis-epoxycarotenoid dioxygenase (NCED, MSYJ224600 ), abscisic acid 8'-hydroxylasesabscisic-encoding gene MSYJ246380 , and three protein phosphatase 2C (PP2C)-encoding genes ( MSYJ205110, MSYJ194440 , and MSYJ247990 ). This part also contained genes encoding aminocyclopropanecarboxylate oxidase ( MSYJ028300 ) and cytokinin dehydrogenase ( MSYJ040640 ) and a gene related to gibberellin deactivation ( MSYJ187550 ). The third part formed the center of the Group A network. It primarily comprised the genes involved in the ascorbate–glutathione (AsA-GSH) cycle. It included genes encoding L-gulonolactone oxidase ( MSYJ111990 ), L-ascorbate peroxidase ( MSYJ200610 ), dehydroascorbate reductase ( MSYJ036520 ), and glutathione peroxidase ( MSYJ071990 ). It also contained two glutathione-S-transferase genes ( MSYJ062020 and MSYJ224480 ). The fourth part contained three LEA protein-encoding genes ( MSYJ031520 MSYJ115380 , and MSYJ140010 ), two lipid transfer protein (LTP) family genes ( MSYJ018090 and MSYJ018080 ), PLAT family gene MSYJ210160 , the gene-encoding oleosin protein ( MSYJ093770 ), and Ninja family gene MSYJ264790 Groups B and C primarily comprised genes involved in transcription and amino acid metabolism, respectively ( Figure 6 B,C; Supplementary Table S9 ). Group D was associated with galactose metabolism, and its central part comprised inositol 3-alpha-galactosyltransferase (GOLS) gene MSYJ066990 , two raffinose synthase genes ( MSYJ121750 and MSYJ059530 ), stachyose synthetase gene MSYJ141390 , and invertase gene MSYJ033550 . Another branch of Group D was associated with trehalose synthesis ( Figure 6 D; Supplementary Table S9 ). The TF genes in Group E, which contained several short-chain interactions, might also play important roles in drought resistance, such as MSYJ283570 (NAC family), MSYJ005540 (Dof family), MSYJ250090 (ERF family), MSYJ160550 (ZF-HD family), MSYJ051170 (ERF family), and MSYJ099600 (NAC family) ( Figure 6 E; Supplementary Table S9 The top 10 hub genes from the PPI analysis ( Figure 6 A; Supplementary Table S9 ), ranked by 12 methods, were combined with 42 genes ( Figure 7 ). Of these, 16 ranked in the top 10 after assessment via at least three methods of calculation. These 16 hub genes are annotated in Table 1 MSYJ196370 (HSF family) was ranked in the top 10 by 11 calculation methods, indicating that this TF gene might be key to the long-term drought stress response of the P. trifoliata root system. Other hub genes included six HSP genes, five genes involved in the AsA-GSH cycle, and one LEA family gene. 3.7. PPI Analysis of the Red Group DEGs PPI analysis was also performed on the DEGs from the red group; the top 13 hub genes calculated by the Matthews correlation coefficient (MCC) method are shown in Figure 8 and Table 2 . The network of DEGs upregulated in the recovery stage are mainly focused on ribosome biogenesis and RNA synthesis. The top six hub genes were related to ribosome biogenesis in the eukaryote pathway: MSYJ138420 MSYJ009110 MSYJ121060 MSYJ137320 MSYJ161780 , and MSYJ024870 MSYJ283680 MSYJ263030 , and MSYJ200690 were also associated with ribosome biogenesis or assembly. MSYJ174470 MSYJ115930 , and MSYJ000420 participated in transcription and posttranscriptional processing. 4. Discussion This study analyzed the transcriptome of the P. trifoliata root system after 72 days of drought treatment and 7 days of recovery to understand the response of the P. trifoliata root system to long-term drought stress. Long-term drought stress led to a significant downregulation of several genes in the P. trifoliata roots. These genes were enriched in 69 GO terms and 20 KEGG pathways ( Figure 3 A), indicating a severe inhibition of the normal function of the P. trifoliata root system. Interestingly, protein phosphorylation and cell wall organization were upregulated in the roots of Citrus sunki (Sunki Tropical) under 7-day drought treatment as response strategies to avoid or tolerate drought conditions [ ]. Genes associated with flavonoid biosynthesis were also upregulated in the roots of Citrus aurantium (Sour Orange) under 14-day drought treatment as a protective strategy for short-term drought [ ]. However, protein phosphorylation, cell wall organization, and flavonoid biosynthesis were all downregulated under long-term drought treatment in the present study ( Figure 3 A) and were distinct from when under short-term drought conditions. In contrast to the downregulated genes, the upregulated genes under long-term drought conditions were only significantly enriched in two pathways: galactose metabolism and protein processing in the endoplasmic reticulum ( Figure 3 B). This finding showed that these pathways were crucial to combating drought stress. The subsequent PPI analysis also showed that an interaction circle in Group A primarily comprised the HSP gene involved in protein processing in the endoplasmic reticulum ( Figure 6 A; Supplementary Table S9 ). Group D, on the other hand, was mainly composed of genes related to the synthesis of raffinose family oligosaccharides (RFOs) in the galactose metabolism pathway ( Figure 6 D; Supplementary Table S9 ). A number of HSP genes and genes related to sugar metabolism were also upregulated in the leaves of P. trifoliata under 28-day drought stress [ ]. Based on existing reports, several GO terms enriched by upregulating genes under long-term drought in the present study might be root-specific, such as intracellular membrane-bound organelles, membrane-bound organelles, intracellular organelles, transmembrane transporter activity, and transporter activity ( Figure 3 B). During protein processing in the endoplasmic reticulum, HSPs are mainly involved in the endoplasmic reticulum-associated degradation (ERAD) of misfolded proteins. The HSP40 and HSP70 families are also involved in the formation of the ubiquitin ligase complex in the cytoplasm [ ]. Stress usually causes protein misfolding, forming protein aggregates and leading to cell death. HSPs help maintain proteins in their functional conformations, preventing the aggregation of non-native proteins. This process is crucial to cell survival under stress [ ]. We also found that two JDPs closely interacted with HSP70s ( Figure 6 A; Supplementary Table S9 ). JDPs play a crucial role in mediating the ATP hydrolysis-dependent locking of substrates into the binding cavity of HSP70 proteins, thus ensuring their normal function [ ]. Reports about the HSPs participating in drought stress signaling are scarce. CsHsp70 CsHsp70.1 , and CsHsp90 showed expression within 6 h under heat and drought stress in the Brazilian Sour Orange, a citrus rootstock tolerant to water deficits, but CsHsp70 in the Savage Citrange, a sensitive variety, only exhibited slight expression [ ]. In many other species, HSP genes and their encoded proteins were found to be upregulated during drought stress; these included sorghum [ ], maize [ ], and pumpkin [ ]. The overexpression and gene silencing of HSP90 HSP70 , and sHSP genes in rice, tomatoes, cotton, and tobacco also revealed their role in enhancing plant drought tolerance, mainly by enhancing antioxidant and osmotic regulation and regulating the ABA and IAA signaling pathways [ ]. In the current study, the HSP genes upregulated during long-term drought stress, especially the six HSP genes in Table 1 , might play an important role in promoting the drought resistance of P. trifoliata , warranting further study. As a core component of the largest PPI group, the AsA-GSH cycle was linked to three interaction circles, including HSPs and plant hormone signaling ( Figure 6 A; Supplementary Table S9 ). Our results indicated that HSP, LEA, and LTP genes and genes related to hormone signaling might enhance the antioxidant capacity of cells by promoting the expression of genes related to the AsA-GSH cycle, thereby enhancing the resistance to stress. Many plant hormones can regulate the expression of genes related to the AsA-GSH cycle to improve plant stress resistance, such as ABA [ ], brassinolide [ ], salicylic acid [ ], jasmonate [ ], and cytokinin [ ]. In Group A of the PPI network, the AsA-GSH cycle was directly connected to a cytokinin dehydrogenase gene and then indirectly connected to NCED , a key gene in ABA synthesis, and PP2C , which regulates the ABA response. PP2C is a negative regulator of ABA signal transduction and plays a central role in the crosstalk among ABA signals in response to drought stress [ ]. Although PP2C can negatively regulate ABA signal transduction and alleviate the stomatal closure effect of ABA, it can also be induced by the ABA signal and drought stress, forming a negative feedback loop to maintain cell homeostasis under high ABA conditions [ ]. Another interaction circle in Group A included LEA, LTP, PLAT, and oleosin ( Figure 6 A; Supplementary Table S9 ), which might enhance the drought resistance of P. trifoliata by increasing protein stability and promoting protein transmembrane transport. The LEA family comprises seven groups of LEA genes. These are late-induced genes that respond slowly and sustainably to stress [ ]. Most LEA proteins are small proteins exhibiting a random uncoiled structure in the solution state. However, under severe drought stress, these proteins exhibit an α-helical structure, forming hairpin-like structures [ ]. Water loss under drought stress leads to the inactivation of enzymes or other proteins. LEA proteins in a highly dehydrated state might help maintain normal protein activities by reducing aggregate formation. Moreover, HSPs prevent aggregate formation by denatured proteins [ ], ensuring the normal activity of several proteins, such as ROS-scavenging enzyme catalase and ascorbate peroxidase [ ]. For instance, in P. trifoliata PtrLEA7 overexpression lines exhibited enhanced dehydration and drought tolerance compared to their wild-type counterparts, along with significantly increased catalase activity [ ]. In sweet orange, the expression of CsLEA55 and CsLEA60 in roots were also found to be induced by drought stress [ ]. Furthermore, the LTP protein family is also a vast family. Its members contain an N-terminal signal peptide that localizes the protein on the outer side of the plasma membrane [ ]. Previously, LTP-overexpressed plants have been shown to exhibit improved drought resistance via enhanced cuticle formation and ROS-scavenging ability [ ]. RFOs are a family of soluble carbohydrates containing α-1,6-galactosyl extensions derived from sucrose [ ], such as galactinol, raffinose, and stachyose. RFOs have been implicated in different abiotic stress responses in plants by maintaining membrane stability, acting as osmoprotectants and ROS scavengers, protecting thylakoids, and stabilizing photosystem II [ ]. The present study also reported the first two functions of RFOs. GOLS, raffinose synthase, and stachyose synthase in Group D of the PPI network are all crucial to RFO synthesis ( Figure 6 D; Supplementary Table S9 ). Previously, an investigation into the ZmRAFS overexpression/loss-of-function complementation lines of maize showed that ZmRAFS enhances plant drought tolerance through either raffinose synthesis or galactinol hydrolysis, depending on the sucrose availability in plant cells [ ]. By increasing the amount of raffinose synthase protein, the leaves of ZmRAFS -overexpressed maize can increase their raffinose content and then decrease their water loss by reducing transpiration, thus enhancing the drought stress tolerance of the maize [ ]. Zhuo et al. [ ] found that MfGolS1 transcription in Medicago sativa could be induced by dehydration stress but not by ABA. They also found that MfGolS1 overexpression in tobacco resulted in elevated tolerance to multiple stresses, including drought, by elevating the levels of RFOs. Another study on the CsGolS4 overexpression/RNAi lines of cucumber also reported that GOLS promoted RFO accumulation, osmotic adjustment, and the ROS-scavenging ability [ ]. In the citrus cultivar Citrus junos Siebold cv. ‘Ziyang', two GOLS members and two stachyose synthase members were also found to respond to dehydration positively [ ]. Besides functional proteins, regulatory proteins are also involved in the drought response via the regulation of signal transduction and transcription [ ], such as PP2C ( Figure 6 A; Supplementary Table S9 ). Many TF families harbor the regulatory proteins involved in the drought stress response, such as bZIP, Dof, HSF, MYB, NAC, TCP, and WRKY [ ]. PtrbHLH66 , a TF gene from the bHLH family in P. trifoliata , was proved to be able to positively regulate drought tolerance by mediating root growth and ROS scavenging [ ]. In the current study, several related TF genes were upregulated under drought stress ( Figure 4 ). Among them, MSYJ196370 (HSF family) ranked among the top 10 hub genes via 11 methods in the cytoHubba analysis ( Table 1 ). HSFs mediate their function by binding to the heat-stress promoter element of eukaryotic HSF-inducible genes and play a crucial role in the regulation of stress-responsive genes [ ]. Some HSF genes cloned from plants, such as corn and rice, have been shown to either enhance or reduce the drought resistance of the transformed plants [ ]. Studies have shown that HSF genes impact plant drought resistance via three mechanisms: by enhancing the ROS-scavenging ability by modulating flavonoid and antioxidant enzyme synthesis, by enhancing the ABA sensitivity of the plant, and by degrading ubiquitinated protein aggregates under drought stress. As the top hub gene in the present study, whether MSYJ196370 can enhance or reduce the drought resistance of plants needs to be elucidated through subsequent functional studies using CRISPR. A number of DEGs were also identified in the post-drought recovery period ( Figure 8 Table 2 ) that might influence recovery in P. trifoliata after long-term drought. MSYJ009110 and MSYJ024870 are two genes that encode tryptophan and aspartate repeat (WDR) proteins. AtWDR26 and BnSWD1 , WDR genes in Arabidopsis and Brassica napus , respectively, were also found to be activated at transcript level by abiotic stress, as well as by hormone signaling [ ]. The AtWDR26 -overexpressed Arabidopsis line upregulated a large number of genes related to defense cellular events, including the abiotic stress response [ ]. The expressions of MSYJ263030 and MSYJ000420 , which encode small defense-associated protein 1 (SDA1) and DEAD-box ATP-dependent RNA helicase 27, respectively, were also upregulated in the recovery stage. SDA1 (in Arabidopsis ) and OsBIRH1 (a member of the DEAD-box RNA helicase family in Oryza sativa ) were reported to play a role in modulating tolerance to oxidative stress [ ]. Stress-responsive DEAD-box helicases are regarded as a pathway to enhance plant stress tolerance, and a few other members of the DEAD-box RNA helicase family, like AtRH17 (in Arabidopsis ) and TaDEAD-box57 (in wheat), were reported to play a role in improving plant tolerance to various abiotic stresses [ ]. Thus, the above four hub genes in the recovery stage also deserve further exploration in relation to their functions. 5. Conclusions We performed in-depth transcriptomic analyses to elucidate the molecular mechanisms underlying the drought-resistance mechanisms in P. trifoliata roots. A series of HSP family genes were found to be upregulated under long-term drought conditions. The HSPs encoded by these genes could promote the degradation of misfolded proteins under drought stress via ERAD. The genes encoding GOLS, raffinose synthase, and stachyose synthase were also upregulated under soil drought conditions. They play a vital role in the biosynthesis and accumulation of RFOs, facilitating osmotic adjustment. The ROS-scavenging ability was also enhanced in P. trifoliata roots, as HSPs and LEA family proteins, which were upregulated under drought, could maintain or enhance the activities of antioxidant enzymes by preventing protein aggregation. Sixteen hub genes were acquired for PPI analysis, including one HSF family gene, six HSP family genes, one LEA family gene, and five genes involved in the AsA-GSH cycle. The top hub gene was MSYJ196370 (HSF family gene), and its impact, either positive or negative, on drought resistance warrants further study. Supplementary Materials The following supporting information can be download at: https://www.mdpi.com/article/10.3390/horticulturae10121319/s1 , Table S1: Primers used in qRT-PCR analysis; Table S2: Qualities of RNA-seq libraries; Table S3: Gene expression data; Table S4: Gene annotation; Table S5: DEG analysis; Table S6: DEG list in green, red, coral and blue groups; Table S7: GO term enrichment of DEGs in green, red and coral groups; Table S8: KEGG pathway enrichment of DEGs in green, red and coral groups; Table S9: Information of genes in PPI analysis. Author Contributions Formal analysis, C.S.; writing—original draft preparation, X.Z.; investigation, L.Z. (Lin Zheng) and L.Z. (Lingshan Zhong); validation, Y.Z. and H.Y.; visualization, Q.H.; writing—review and editing, Y.P. All authors have read and agreed to the published version of the manuscript. Funding This research was funded by the Research Project of Sichuan Province Key Lab for Bamboo Pest Control and Resource Development, grant numbers ZL2019006 and ZLKF20-08, Sichuan Science and Technology Program, grant number 2023NSFSC1164, and Leshan Normal University Science and Technology Program, grant number RC202005 and KYCXTD2024-2. Data Availability Statement RNA-seq raw data have been deposited in the NCBI Short Read Archive (SRA) database, and the accession number is PRJNA1003064. Conflicts of Interest The authors declare no conflicts of interest. References Zhang, S.; Li, M.; Ma, Z.; Jian, D.; Lv, M.; Yang, Q.; Duan, Y.; Amin, D. The intensification of flash droughts across China from 1981 to 2021. Clim. Dyn. , 1233–1247. [ Google Scholar ] [ CrossRef ] Wani, S.A.; Kour, J.; Haq, R.-U.; Elshikh, M.S. Fruits and Their Roles in Nutraceuticals and Functional Foods ; CRC Press: Boca Raton, FL, USA, 2023. [ Google Scholar ] Liu, Q. 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