Background

Cells depend on the homeostatic maintenance of pH within specific cellular compartments to ensure optimal conditions for metabolic and enzymatic processes as well as for protein structure and function. Among the many molecular players, Na+(K+)/ H+ exchangers (NHXs) appear to be particularly important for the establishment and maintenance of optimal ion and pH gradients, which are essential for cell function and development [1]. In plants, NHX antiporters appeared early in evolution and belong to the CPA1 protein family, which contains many monovalent cation/ H+ antiporters that contribute to cellular pH, and Na+ and K+ homeostasis [2]. NHXs catalyze the electroneutral exchange of Na+ or K+ for H+ using the electrochemical H+ gradient to direct inward movement of Na+ or K+ in exchange for luminal H+ [3].

NHXs are ubiquitous in plants and are believed to have pivotal functions in regulating responses to salt stress [4], cold tolerance [5], drought tolerance [6], and disease resistance [7]. Recently, novel functions of NHX-type Na+/H+ antiporters were identified, including roles in cell expansion, cell volume regulation, flower development, stomatal conductance, protein processing, and vesicular trafficking [8,9,10]. However, the molecular mechanisms underlying these functions remain poorly understood. The recent advances in next-generation sequencing have enabled genome-wide scale and transcriptome-level computational analyses. RNA-seq technology is a powerful method to analyze the expression of genes at the transcriptome level and will provide a better understanding of the mechanisms underlying NHX function.

Switchgrass (Panicum virgatum L.) is a member of the Poaceae family and is a warm-season C4 perennial grass native to the U.S. It has been increasingly exploited as a dedicated bioenergy crop because of its valuable characteristics [11]. However, because it is an outcrossing and polyploid species, conventional breeding strategies to improve commercial varieties are severely restricted [12]. Fortunately, production of transgenic lines with modification of functional genes related to growth and resistance have been found to enable production of improved varieties [13,14,15,16]. For example, the successful expression of the enzyme gene PvNHX1 was shown to promote plant growth and increase resistance to salt stress in switchgrass [16].

In this study, we performed an RNA-seq analysis to compare the transcriptomes of wild-type (WT) and transgenic plants overexpressing the PvNHX1 gene in order to gain more insight into the function of NHXs in plants. This study has produced more information on the changes in the transcriptome in response to the overexpression of an NHX gene and sheds light on molecular mechanisms underlying the functions of NHXs in switchgrass. Our data provide valuable information on the potential roles of NHXs in plants.

Methods

Plant material and RNA extraction

Switchgrass (P. virgatum ‘Alamo’) was used in this study. This cultivar was originally collected in Texas (25°50′ N-36°30′ N), and we purchased seeds from Ernst Conservation Seeds (Meadville, Pennsylvania) in 2010. Switchgrass callus generated from mature seeds was transformed with a Ubi1301 binary vector (provided by the Sinogene Scientific Company) harboring a PvNHX1 overexpression cassette using the Agrobacterium-mediated transformation method described previously [16]. Wild-type (WT) and transgenic switchgrass plants were grown in plastic pots containing a mixture of soil: vermiculite: humus [1:1:1 (v/v/v)] under the same greenhouse environment (16 h/8 h light/dark cycle) in Beijing. RNA-seq analysis was carried out using three independent transgenic lines (L1, L3, L8) and three WT plants (WT1, WT2, WT3).

When the plants reached the reproductive 1 (R1) stage (the emergence of the inflorescence from the boot stage) [17], the mature leaves of internode 3 (I3) from six independent plants were pooled for RNA extraction and frozen in liquid nitrogen for later qRT-PCR analysis.

Total RNAs were extracted from switchgrass leaves using the TRIzol reagent method (Invitrogen, Carlsbad, CA, USA). A NanoPhotometer® (IMPLEN, CA, USA) and a Bioanalyzer 2100 system (Agilent Technologies, CA, USA) were used to quantify and check the quality of the RNA samples.

Library construction and sequencing

A total of 3 μg RNA per sample was used for sequencing library construction. Libraries were generated using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s instructions. Briefly, poly (A)-containing mRNA was purified from total RNA using poly-T oligo-attached magnetic beads (Illumina, San Diego, CA, USA). Then, the mRNA was broken into short fragments by a fragmentation buffer (Ambion, Austin, TX, USA); the fragments were used as templates for cDNA synthesis. First-strand cDNA was synthesized using random hexamer-primers and M-MuLV reverse transcriptase (RNase H). Second strand cDNA synthesis was performed using DNA Polymerase I (New England Biolabs) and RNase H (Invitrogen). Poly(A) sequences were added to the 3′ ends of cDNA fragments and sequencing adaptors with hairpin loop structure were ligated to the cDNA ends. Suitable fragments (150–200 bp) were selected by agarose gel purification and enriched by PCR amplification. Finally, the PCR amplicons were purified using magnetic beads (Illumina) and dissolved in EB solution to generate the sequencing libraries. Library quality was assessed on the Agilent 2100 Bioanalyzer.

Quality control and sequence assembly

Raw reads obtained from sequencing were filtered to remove adaptor sequences, empty reads, and low quality sequences with ‘N’ percentage over 10%. We then calculated the Q20, Q30, GC-content, and sequence duplication levels of the clean data. All the downstream analyses were based on clean reads with high quality. The retained high-quality reads were mapped to the switchgrass reference genome sequence, P. virgatum v1.1 (http://www.phytozome.net/panicumvirgatum; accessed 30 November 2015) using TopHat v2.0.12 [18]. This step generates a database of splice junctions based on the gene model annotation file and gives a better mapping result than other non-splice mapping tools. An index of the reference genome was built using Bowtie v2.2.3 [19].

Differential gene expression analysis

To quantify the abundance of transcripts, all reads from samples were mapped onto the reference transcriptome by HTSeq v0.6.1 [20]. We used the FPKM (fragments per kilobase of transcript sequence per million base pairs sequenced) algorithm to normalize gene expression abundances in each library. A differential expression analysis was performed using the DESeq R package (1.18.0) using pairwise comparisons [21]. The resulting P-values were adjusted using Benjamini and Hochberg’s method for controlling false discovery rates [22]. Genes with an adjusted P-value < 0.05 were categorized as differentially expressed.

Gene ontology enrichment and pathway analysis

To identify putative functions of differentially expressed genes (DEGs), we performed functional annotation using a BLASTx search of two databases: Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). GO and KEGG enrichment analyses provide all GO terms or pathways significantly enriched in DEGs in comparison to the transcriptome background. GO enrichment was performed using GOseq R package [23], and the P-values were calculated using Benjamini and Hochberg’s method [22]. We considered a corrected P-value < 0.05 as a significantly enriched GO term.

In the KEGG enrichment analysis, the cellular metabolism, biochemical pathway, and potential biological behavior of DEGs were examined. KOBAS 2.0 (KEGG Orthology Based Annotation System, v2.0) software was used to test the statistical enrichment of DEGs in KEGG pathways [24]. We selected a corrected P-value < 0.05 as a threshold to determine significant enrichment of the gene sets.

Novel transcript prediction

The assembled transcripts were compared with the annotated genomic transcripts from the reference sequences to identify novel transcribed regions. The coding potential calculator (CPC: http://cpc.cbi.pku.edu.cn/) was used to assess the protein-coding potential [25].

qRT-PCR assays

Validation of RNA-seq results was carried out using quantitative real-time PCR (qRT-PCR) analysis. Primers specific to selected transcripts were designed using Premier 5.0 software (Premier Biosoft Int., Palo Alto, CA, USA). Switchgrass ubiquitin-1 gene (PvUBQ1) (GenBank accession number: FL899020) was used as the internal control and was amplified using primers PvUBQ1-F and PvUBQ1-R. The qRT-PCR was performed using three biological replicates and three independent technical replicates for each sample. Gene expression levels were calculated using the 2-ΔΔCt method [26]. The normalized values of relative expression and FPKM values were calculated by log2, respectively, and the values were used to analyze the correlation between qPCR and RNA-seq results. The primer sequences used for qRT-PCR are listed in Additional file 1.

Results

Morphological characterization of transgenic plants

Transgenic switchgrass overexpressing PvNHX1 showed better growth and development performance than WT plants under the same greenhouse environment (Fig. 1). The transgenic plants had longer shoots, larger stem diameters, and longer leaf blade lengths and widths [16]. Based on the measurement of fresh and dry weights, the three transgenic lines showed a significantly higher biomass than the WT plants (P < 0.01). As shown in Fig. 1c and d, root fresh weight and shoot dry weight in the transgenic lines were 1.55-fold and 1.46-fold, respectively, greater than in the corresponding tissues of WT plants. These greenhouse phenotypes may vary under field experiments; field evaluations of yield and related traits will be performed in a future study.

Fig. 1
figure 1

Phenotypes of transgenic PvNHX1 switchgrass compared with WT plants. a Phenotypes of whole plants. b Leaf lengths and leaf widths in fully expanded leaves. Statistical analysis of fresh weight c and dry weight d of shoots and roots. WT: wild type; L1, L3 and L8: different transgenic lines overexpressing PvNHX1.The data shows the mean ± S.E. of triplicate experiments. Columns with different letters indicate significant differences at P < 0.01 (Duncan’s test)

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Illumina sequencing and assembly

In total, 6.60 million raw reads were generated from control samples and 6.89 million raw reads were generated from transgenic samples. The average Q20 and Q30 levels and GC-rich contents of the six samples were 97.46%, 93.52%, and 56.66%, respectively (Table 1). We obtained approximately 40.48 million total reads, of which approximately 39.66 million passed the Illumina quality filtering threshold, yielding a quality rate of over 97.98%. This result indicated that the throughput sequencing was sufficiently accurate to allow further analysis.

Table 1 Summary statistics of Illumina transcriptome sequencing
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Reference-based transcriptome assemblies of RNA-seq data were performed using the reference switchgrass genome sequence. A total of 74.2% for WT plants and 73.9% for transgenic plants were mapped to the reference genome. Thus, there was no significant difference between transgenic and WT plants in the proportion of reads mapped to the reference genome (p < 0.01). A summary of the assembly statistics is provided in Additional file 2. These reads were then used for reference guided assembly and differential expression analysis.

Differentially expressed genes (DEGs) analysis

The aligned reads were used to measure the relative abundances of the transcripts. A total of 10,995 differentially expressed genes (DEGs) were identified from comparison of transgenic and WT transcriptomes: 5605 transcripts showed upregulation and 5390 transcripts showed downregulation (Fig. 2a). The abundance of the different DEGs is shown in Fig. 2b. Expression of 3173 DEGs showed a large change (absolute value of log2 ratio ≥ 5): 1590 of these DEGs were upregulated and 1583 were downregulated.

Fig. 2
figure 2

Identification of DEGs between transgenic and WT plants. a Volcano plot of the DEGs. b Statistics of genes from different expression levels. DEGs were filtered using adjusted P-value < 0.05 as the threshold. Red spots represent up-regulated DEGs, and green spots indicate down-regulated DEGs. Blue spots represent contigs that did not show obvious changes

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We identified many upregulated DEGs related to photosynthesis. Genes involved in chlorophyll biosynthesis (Pavir.J25629), chloroplast development (Pavir.J24058), photosynthetic electron transport (Pavir.J30556), light-signal transduction (Pavir.Eb03789), carbon dioxide fixation (Pavir.J18292), and NAD/NADP binding (Pavir.Ia04122) were significantly upregulated (Table 2). Some transcripts with significant upregulation in transgenic plants were involved in cell division and cell elongation processes (Additional file 3). Genes involved in plant responses and adaptation to stress, such as heat (Pavir.Aa00547, Pavir.J01404), salt (Pavir.Ha00186, Pavir.Ea00535), drought (Pavir.J16055), oxidative (Pavir.J40048, Pavir.Ba01869), heavy metal (Pavir.Ba00376, Pavir.Ha00320), and certain herbicides (Pavir.Ea01215, Pavir.Ia04853), were significantly upregulated in transgenic plants compared to WT plants (Additional file 4). The expression of ion transport genes (Pavir.Eb03651, Pavir.J05404, and Pavir.Aa03191), small molecule transport genes (Pavir.Fa02242, Pavir.Eb02833, and Pavir.Ia02157.), and metal transport genes (Pavir.J06378, Pavir.Ia01399, and Pavir.J38980) were also up-regulated (Additional file 5). These results suggested that PvNHX1 plays diverse roles in regulating plant development and stress tolerance.

Table 2 Significantly upregulated genes involved in photosynthesis
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Functional classification by gene ontology analysis

To evaluate the potential functions of unigenes with significant transcriptional changes between transgenic and WT plants, we performed a GO enrichment analysis. In total, 63 GO terms were functionally classified into three GO categories, namely molecular function (35 members), biological processes (23 members) and cellular components (5 members). For molecular function, the overrepresented GO terms were small molecule binding (GO:0036094), anion binding (GO:0043168), and transferase activity (GO:0016740). In the category of biological processes, two GO terms ‘macromolecule modification (GO:0043412)’ and ‘phosphorus metabolic process (GO:0006793)’ were significantly enriched. The thylakoid term (GO:0009579; 38.2%) was the largest in the cellular components category (Fig. 3).

Fig. 3
figure 3

GO classifications of DEGs between transgenic PvNHX1 lines and WT plants. GO terms were functionally classified into three GO categories: molecular function (MF), biological process (BP), and cellular component (CC). The X-axis indicates number of contigs in each category. The left Y-axis represents GO categories

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We further analyzed the overrepresented GO functions within each ontology. The enriched GO terms of molecular function were classified into two branches: binding and catalytic activity. This was consistent with the previous functional definition of PvNHX1. Classification of GO terms enriched in biological processes showed these were mainly related to protein phosphorylation and pollen-pistil interactions (Additional file 6). All GO terms identified in the cellular components were found to be related to plant photosynthesis.

Pathway enrichment analysis of DEGs

The metabolic pathways affected by PvNHX1 overexpression were evaluated by mapping the DEGs to reference canonical pathways in the KEGG database. A total of 10,995 DEGs were mapped to 121 KEGG pathways, and 11 pathways were significantly enriched (P-value < 0.05). Among the significantly enriched pathways were ‘plant-pathogen interaction (KO: sita04626)’, ‘purine metabolism (KO: sita00230)’, ‘Peroxisome (KO: sita04146)’ and ‘Porphyrin and chlorophyll metabolism (KO: sita00860)’ (Fig. 4, Additional file 7). Ninety DEGs were categorized in the ‘plant-pathogen interaction’ pathway. These included signal transduction components (Ca2+ signaling, protein kinase, and phosphatidylinositol signal molecules), defense response proteins (calcium binding protein, serine/threonine-protein kinase, and glycerol kinase), and defense proteins against fungi or bacteria (Fig. 5). We also identified many genes encoding disease resistance proteins, such as RPM1 (Pavir. J05404), RGA1 (Pavir. Gb01964), TAO1 (Pavir. Gb01964), and RPP1 (Pavir. J20461) (Table 3). These annotations provide a valuable resource for investigating the function of PvNHX1 in switchgrass pathogen defense.

Fig. 4
figure 4

KEGG enrichment of the annotated DEGs across three comparisons. The Y-axis indicates the KEGG pathway. The X-axis indicates the Rich factor. A high q value is represented by blue, and a low q value is represented by red

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Fig. 5
figure 5

DEGs in transgenic switchgrass encode proteins involved in the plant-pathogen interaction pathway (KEGG: sita04626)

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Table 3 List of disease resistance genes identified in transgenic compared to WT plants
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Transgenic switchgrass overexpressing PvNHX1 has been demonstrated to have an altered plant hormone signal transduction pathway (KO: sita04075). Auxin signaling-related genes were upregulated in the transgenic plants, including SAUR-like auxin-responsive protein gene (Pavir.Ga00493), auxin efflux carrier gene (Pavir.J25610), and auxin response factor 5 (Pavir.J32718) (Table 4). Gibberellin, salicylic acid, jasmonic acid, brassinosteroids, and abscisic acid signaling-related genes were also significantly upregulated (q-value < 0.01) in transgenic plants. Ethylene signaling was altered in transgenic plants, with upregulation of two genes (Pavir.Bb03119 and Pavir.Ea01573) and downregulation of two genes (Pavir.Fa00424 and Pavir.J10765). The cytokinin biosynthesis genes, Pavir.Ba01358 and APavir.J18777, were upregulated, while the cytokinin dehydrogenase 11 gene (Pavir.Fa00713) was downregulated in transgenic plants (Table 4).

Table 4 Genes in the plant hormone signal transduction pathway with altered expression
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Identification of transcription factors

We performed a global transcription-factor classification of differentially expressed transcripts and identified 452 transcription factors (TFs) belonging to 59 TF families. Among these TF families, FAR1 and WRKY were most abundant, followed by bHLH (26, 5.75%), MYB (24, 5.31%), AP2-EREBP (22, 4.87%), and mTERF (20, 4.42%) (Fig. 6). Notably, all members in the SBP, HB, TCP, and CCAAT families were downregulated, and members in the CAMTA and ARF families were up-regulated. In the C3H and C2H2 families, similar numbers of up- and downregulated members were present (Additional file 8).

Fig. 6
figure 6

Distribution of transcription factors families in transgenic switchgrass

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Validation of gene expression profiles using qRT-qPCR

To confirm the accuracy and reproducibility of the Illumina RNA-seq results, 24 DEGs were selected for qPCR assays, including growth-related, resistance-related and transporters-related genes. The qPCR results for selected DEGs showed good agreement with the transcript-abundance changes determined by RNA-seq. For example, Pavir.Ia04853, glutathione S-transferase F11 (GSTF11) was upregulated 8.85-fold, and the FPKM value in RNA-seq was 9.32 (Additional file 9). A highly significant correlation was found between the qPCR and RNA-seq (R2 = 0.8591, P < 0.01), indicating the reproducibility and reliability of the RNA-seq data (Fig. 7).

Fig. 7
figure 7

Correlations of expression levels analyzed by log2RNA-Seq platform with data obtained using log2qPCR. X-axis: log2RNA-Seq;Y-axis: log2qPCR

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Discussion

It was previously shown that transgenic switchgrass overexpressing PvNHX1 exhibited significantly better growth performance than WT plants (P < 0.05) and that the enhanced growth phenotype was associated with the expression level of the transgene in different lines [16]. Recent studies have suggested that NHXs play an important role in orchestrating plant growth by influencing the rates of photosynthetic activity [27, 28] and cell expansion [29, 30]. The results of the transcriptome analysis here are consistent with this suggestion. All GO terms identified in cellular components were related to plant photosynthesis (Fig. 3). The level of transcription of related genes involved in photosynthesis and cell expansion were significantly upregulated in transgenic lines (Table 2; Additional file 3). Other studies have shown that NHXs are abundantly expressed in flower organs of transgenic rice [31], Arabidopsis [32], mungbean [4], and cowpea [33]. Thus, NHXs would be expected to be involved in the regulation of plant flower development. Transgenic switchgrass overexpressing PvNHX1 was found to display two distinct expression patterns in key flowering-time regulators, suggesting that the role of NHXs in flowering time was complex and did not simply act through promotion or inhibition [16]. In this study, our GO analysis indicated that DEGs in biological processes were associated with flower development, such as pollen-pistil interaction, and recognition of pollen and pollination, suggesting that NHXs might have specific roles in pollen development (Fig. 3; Additional file 6). This speculation is supported by findings from Arabidopsis, in which the filaments in plants with the double knockout nhx1 nhx2 did not elongate sufficiently to position anthers at the height of the stigma. Additionally, the anthers lacked the ability to dehisce and release pollen, leading to a failure of flower set and silique formation [34]. Overall, our data point to an important role in which PvNHX1 regulates K+ homeostasis, cell expansion, and pollen development in stamens, and that this homeostasis enables filament elongation and anther dehiscence to occur. Our analysis showed that the potassium channel gene KCO2 (Pavir. J05404), a highly selective inward-rectifying potassium channel [35], and three HAK (high-affinity potassium transporter) genes, that exhibit potassium ion transmembrane transporter activity [36], were significantly upregulated in transgenic plants (Additional file 5). Moreover, MSP1 (Pavir.Ha01736), which is involved in cell specification during anther development and initiation of anther wall formation [37] (Additional file 3), and LecRK42 (Pavir.J33874), which is required for pollen development in Arabidopsis [38],

Were also significantly upregulated in transgenic plants. These results provide information on the molecular mechanisms in which NHXs participate in flower development.

NHXs were previously shown to have a key role in plant responses to abiotic stresses [4,5,6, 16]. Our observations here were consistent with those reports as we found that transgenic switchgrass had higher levels of transcription of related genes, for instance, Pavir.J31898 (general defense protein), Pavir.Ha00186 (response to salt stress), Pavir.Aa00547 (response to hyperosmotic and heat shock), and Pavir.J16055 (response to drought and freezing stress) (Additional file 4). We also found significant upregulation of genes related to oxidative stress (Pavir.J40048; Pavir.Ba04000; Pavir.Ba01869) and heavy metal stress (Pavir.Ba00376; Pavir.Ha00320), suggesting stress responses are activated in transgenic PvNHX1 lines. To date, little is known about the function of NHXs in biotic stresses, such as weed stress and pathogen attack. In the present study, we found that the glutathione S-transferase genes (Pavir.Ea01215; Pavir.Ia04853), which have a significant detoxification activity against some herbicides [39], were significantly upregulated (Additional file 4). Our results also showed significant enrichment of DEGs related to metabolic pathways for plant-pathogen interactions (Fig. 4, Additional file 7). These results suggest a close correlation between NHXs and plant disease resistance. This speculation is supported by a report from tobacco (Nicotiana benthamiana L.), in which NbNHX1 silencing resulted in increased sensitivity to Phytophthora parasitica var. nicotianae (Ppn) sensitivity, whereas ectopic expression of NHX1 from Salicornia europaea or Arabidopsis enhanced Ppn resistance in tobacco [40]. Here, we sought to obtain greater insights into the molecular events underlying NHX activities in plant disease resistance by analyzing the level of transcription of DEGs involved in plant-pathogen interaction pathways. Our results showed that many DEGs were involved in pathways for biosynthesis of secondary signaling compounds, such as Ca2+ signaling, protein kinase, and phosphatidylinositol signaling (Fig. 5). Protein kinases represent an important mechanism in defense signal transduction, and have been implicated in a wide variety of plant biotic and abiotic stress responses [41]. These findings suggest that overexpression of PvNHX1 activated a complex signal transduction network and enhanced disease resistance. In addition, the expression levels of many genes encoding disease resistance proteins were significantly upregulated in PvNHX1-overexpressing plants, for instance, RPM1 [42], RGAs [43], TAO1 [44], RPP1 [45], RPP13 [46], and GDPDL2 [47]. We also identified many potential disease resistance genes, such as RXW24L (Pavir. Cb00342), RPPL1 (Pavir.Gb00467), RPP13L3 (Pavir.J08640), RPP13L4 (Pavir.Fb00106), At1g58400 (Pavir.Ha01139), At1g59780 (Pavir.J11190), and At1g50180 (Pavir.J17143) (Table 3). These disease resistance genes may be a valuable resource for future molecular breeding to develop plants with greater protection against multiple diseases.

Plant hormones have vital regulatory roles in plant growth, development, and defense response. These hormones can be functionally divided into growth hormones (auxins, cytokinins, gibberellins, and brassenosteroids), and stress hormones (abscisic acid, jasmonic acid, and salicylic acid) [48]. In the present study, transgenic plants overexpressing PvNHX1 showed upregulation of five auxin signaling-related genes, such as SAUR71 [49], PIN1B [50], and ARF5 [51] (Table 4). PvNHX1 also influenced gibberellin signaling, which is involved in the regulation of plant growth and flowering. Most genes involved in cytokinin and brassenosteroid signaling pathways were upregulated, except CKX11 (Pavir.Fa00713), which catalyzes the oxidation of cytokinins [52], that was downregulated. Ethylene plays a crucial role in plant growth and development, and also functions in regulation of responses to various biotic and abiotic stresses. Overexpression of PvNHX1 changed the transcription levels of many genes involved in ethylene biosynthesis and signal transduction (Table 4). In addition, many genes involved in abscisic acid, jasmonic acid and salicylic acid signaling pathways were altered, suggesting a close correlation between PvNHX1-induced plant defense responses and these hormone-regulated pathways. In general, TFs control differential gene expression in most major biological processes. In the present study, many TFs, such as WRKY, MYB, FAR1, and bHLH, were found to be either upregulated or downregulated in the transgenic plants (Fig. 6; Additional file 8). These results indicate that the critical roles of PvNHX1 in plant growth, development, and defense are mediated via transcriptional regulation of related genes and/or TFs.

Conclusions

This study provides a comprehensive overview of the regulation of transcription in transgenic switchgrass overexpressing PvNHX1. We identified a number of DEGs and annotated these using the GO and KEGG databases. This study demonstrated that PvNHX1 had an important role in plant growth and development through its regulation of photosynthetic activity, cell expansion, K+ homeostasis, and pollen development. Focusing on the regulatory mechanisms of stress response, we found that PvNHX1 overexpression activated a complex signal transduction network to enhance disease resistance. Many known and potential disease resistance genes were also identified and will be of use in future molecular approaches to switchgrass breeding. Our analyses also showed that overexpression of PvNHX1 altered the transcription of hormones and TFs that have roles in the regulation of plant growth, development, and defense mechanisms. To our knowledge, this is the first report on gene expression profiling of transgenic switchgrass overexpressing PvNHX1 using RNA-seq technology. These data will contribute to our understanding of the molecular mechanisms underlying the action of NHXs in plants, and provide important clues for further study of genes and networks that contribute to growth, development and defense responses in switchgrass.