Genome-wide identification and expression analysis under selenium stress of glutaredoxin system genes in Cardamine hupingshanensis

Abstract

Background

The glutaredoxin (GRX) system is one of the important antioxidant systems in plants, involving two key enzymes: GRX and glutathione reductase (GR). The basic bioinformatic characteristics and functions of these enzymes have been extensively studied in various animals and plants. The identification of genes/proteins related to the GRX system in Cardamine hupingshanensis and their expression under selenium stress remains unexplored.

Results

In this study, 59 GRX genes and 12 GR genes were identified in C. hupingshanensis. The ChGRX gene family was classified into three types based on phylogenetic analysis and active site characteristics: 10 CPYC-type, 8 CGFS-type, and 41 CC-type. The ChGR gene family was divided into three classes according to phylogenetic analysis: 2 Class Ⅰ, 2 Class Ⅱ, and 8 Class Ⅲ. Under selenium stress, the CC-type ChGRXs in ChGRX family exhibited the highest expression level, followed by the CPYC-type ChGRXs. In the ChGR family, Class Ⅲ genes demonstrated the highest expression level under selenium stress, while Class Ⅰ and Class Ⅱ showed nearly no significant changes. Based on the expression levels under selenium stress, subcellular localization, and intrinsic biochemical properties, we selected four CPYC- and four CC-type ChGRXs for molecular docking exploration. The results suggested that both CPYC- and CC-type ChGRXs may catalyze the deglutathionylation of macromolecular proteins and small molecule ligands, but with distinct catalytic mechanisms among different types. ChGR3-1 and ChGR3-5, which showed the highest expression under selenium stress, were selected for molecular docking. The results showed that the key residues of ChGR3-1 included H98, H395, and E394, while those of ChGR3-5 included T69, R73, and K78. These findings indicate that the GRX system in C. hupingshanensis may respond to selenium stress at varying levels.

Conclusions

This study presents a comprehensive genome-wide identification and characterization of GRX and GR gene families in C. hupingshanensis, providing a valuable foundation for future functional studies of these critical antioxidant enzymes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-026-08207-4.

Introduction

Reactive oxygen species (ROS) convert primary oxidative signals into dynamic changes in protein thiol oxidation states, thereby enabling precise regulation of critical metabolic pathways [1]. At low concentrations, ROS act as vital signaling molecules and metabolic regulators; however, excessive stress-induced buildup leads to oxidative damage, cellular dysfunction, and programmed cell death [2]. Plants utilize advanced antioxidant systems, including nonenzymatic components (like the ascorbate-glutathione (AsA-GSH) cycle), enzymatic elements (such as superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), ascorbate peroxidase (APX)), and other systems like thioredoxin (TRX) and glutaredoxin (GRX), to maintain redox balance [3]. ROS oxidize protein thiols to sulfenic (RSOH), sulfinic (RSO₂H), or sulfonic (RSO₃H) acids [4]. Oxidation to sulfonic acid is an irreversible change, while reduction of sulfinic acid to free thiols (-SH) is catalyzed by TRX or GRX systems [5]. The GRX system, which comprises GRX, GR and NADPH, plays a crucial role in redox regulation by facilitating the reduction of oxidized protein thiols. This system modulates intramolecular disulfide bonds and catalyzes the reversible glutathionylation of proteins, specifically the formation and reduction of protein-SSG mixed disulfides [6]. Functioning as a thiol switch operator, the GRX system maintains redox balance through thiol-disulfide exchange reactions, thereby fine-tuning protein function under fluctuating redox conditions.

GRXs are small redox enzymes that play crucial roles in regulating thiol-disulfide equilibrium, FeS cluster assembly, and DNA synthesis in both prokaryotes and eukaryotes [7–9]. In plants, GRXs are classified into three types based on their active site: CPYC-, CGFS-, and CC-types [10]. Among them, the CPYC-type GRXs exhibit variations in their CPYC motif. These GRXs possess oxidoreductase activity, enabling them to directly reduce disulfide bonds in specific target proteins (including ribonucleotide reductase RNR and transcription factors) or reverse the glutathionylation modifications of these proteins, thereby regulating the redox status of cysteine residues [11]. Their catalytic activity depends on GR-mediated regeneration of reduced glutathione (GSH) pools [12]. In contrast, CGFS-type GRXs feature a highly conserved CGFS motif but lack typical oxidoreductase activity. They are crucial for assembling and transferring iron-sulfur (Fe-S) clusters, functioning as scaffold proteins that collaborate with BolA family proteins to deliver Fe-S clusters to apoproteins [13]. The CC-type GRXs are unique to land plants, characterized by an active site motif of CCMC/S or CCLC/S. They have evolved specialized functions during development, such as floral organogenesis [14]. For instance, ROXY1 and ROXY2 regulate flower development and modulate maize inflorescence meristem development through redox regulation of TGA transcription factor activity [15, 16]. The CPYC- and CGFS-type GRXs are evolutionarily conserved across prokaryotes and eukaryotes yet exhibit different catalytic mechanisms [17]. The CPYC-type GRXs demonstrate vigorous enzymatic activity in vitro, whereas the CGFS-type GRXs rely on structural coordination rather than redox catalysis [18, 19]. Until now, research on the CC-type GRXs remains relatively limited.

GR belongs to the class of NADPH-dependent flavoprotein oxidoreductases and has been identified in both eukaryotes and prokaryotes [20]. In plants, GR is primarily localized in chloroplasts, mitochondria, and the cytoplasm, with a molecular weight ranging between 60 and 190 kDa, and typically exists as a homodimer [21, 22]. The active site of GR contains cysteine residues that facilitate the NADPH-dependent reduction of oxidized glutathione (GSSG) to reduced glutathione (GSH), thereby helping to maintain cellular redox homeostasis [19]. Beyond sustaining the reduced glutathione pool, GR also plays a central role in enhancing plant resistance to both abiotic and biotic stresses [23, 24].

The GRX system maintains redox balance through dithiol and monothiol catalytic pathways (Fig. 1), with the choice of mechanism determined by substrate specificity and isoform [25]. Both paths require a sustained reduction of GSH pools by GR. In the dithiol pathway, reduced glutaredoxin (GRX-(SH)₂) attacks disulfide bonds in substrate proteins, forming a unique GRX-substrate mixed disulfide intermediate. The oxidised form of GRX (GRX-S₂) is then reduced by two molecules of glutathione (GSH) (Fig. 1A). The monothiol pathway, which targets explicitly glutathionylated proteins, involves GRX catalysing deglutathionylation reactions to release the substrate proteins while forming a GRX-SSG intermediate. GSH further reduces this intermediate to generate oxidised glutathione (GSSG) (Fig. 1B). GR then reduces GSSG back to GSH using NADPH as the electron donor, thereby maintaining the intracellular GSSG/GSH balance (Fig. 1C). These coupled systems reverse ROS-induced cysteine oxidation, preventing protein inactivation under abiotic stress.

Fig. 1.

Catalytic mechanism of GRX. A Glutaredoxins reduce glutathione disulfide substrates and non-glutathione disulfide substrates through a dithiol mechanism. B Glutaredoxins utilise a monothiol mechanism to reduce glutathione disulfide substrates. C GR reduces GSSG to GSH by using the reducing power of NADPH

C. hupingshanensis is a perennial herbaceous species in the Brassicaceae family, first discovered in Yutangba, Enshi, Hubei Province, and Hupingshan, Shimen, Hunan Province, China. This species exhibits a remarkable capacity for selenium tolerance, detoxification, and accumulation, underpinned by its unique molecular mechanisms. As a selenium hyperaccumulator thriving in high-selenium environments, C. hupingshanensis serves not only as a key species for phytoremediation of selenium-contaminated soils but also as an ideal model plant for investigating the molecular mechanisms of selenium tolerance in plants [26]. While not universally recognised as an essential plant nutrient, selenium exerts profound dose-dependent effects on plant physiology [27]. Selenium enhances photosynthetic efficiency at optimal concentrations, as demonstrated in rice by increased chlorophyll content, an improved Fv/Fm ratio (the maximum quantum yield of PSII), and a 15–20% increase in yield [28]. Structurally and metabolically, as a congener of sulfur (S) within Group VIA (chalcogens), selenium shares similar chemical properties and follows the same metabolic pathway in plants [29]. A key branch point in selenium metabolism is the phosphorylation of adenosine 5’-phosphoselenate (APSe) to 3’-phosphoadenosine-5’-phosphoselenate (PAPSe), which catalyzed by adenosine phosphosulfate kinase (APK) and the key process, which channels selenium toward non-toxic compounds [30–32]. APK is known to be post-translationally regulated through S-glutathionylation [33, 34]. The GRX system plays a central role in reversing protein S-glutathionylation and maintaining cellular redox homeostasis [35–37]. Here, we integrated the genomic data of C. hupingshanensis to perform the first comprehensive identification and analysis of the ChGRX and ChGR gene families. Our investigation focused on physicochemical properties, conserved motifs, and collinearity. Furthermore, we employed RT-qPCR to screen key genes responsive to selenite stress. The selected candidate genes were subsequently subjected to molecular docking simulations to investigate their interaction mechanisms with glutathionylated substrates. In summary, we firstly identified ChGRXs and ChGRs in C. hupingshanensis to our known and explored their functions in selenite response, which lays a firm foundation for subsequent studies on the GRX system in plants.

Methods

Identification and characterization of ChGRX and ChGR gene families

The genome and annotation files of C. hupingshanensis (accession number PRJCA005533) were obtained from the Genome Warehouse BIG Data Centre. AtGRX and AtGR protein sequences from Arabidopsis thaliana (TAIR; https://www.arabidopsis.org/) were used as queries to identify ChGRX and ChGR homologs using TBtools software [38]. The extracted ChGRX and ChGR protein sequences were validated using NCBI BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi). Candidate sequences were retained if they satisfied the following criteria: (A) BLASTp E-value ≤ 1 × 10^{− 5} and sequence identity ≥ 30%;(B) presence of complete conserved domains (Glutaredoxin domain for GRX; gluta_reduc domain for GR) as confirmed by CD-Search (https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi);(C) manual curation to remove fragmented or pseudogene annotations. Physicochemical properties were predicted using ExPASy ProtParam (https://web.expasy.org/protparam/), and subcellular localization was determined with WoLF PSORT (https://wolfpsort.hgc.jp/) [39].

Gene localization and phylogenetic analysis of ChGRX and ChGR gene families

The chromosomal locations of the ChGRX and ChGR genes were identified from the C. hupingshanensis genome GTF/GFF annotation files. The visualisation of gene positions was performed using the “Gene Location Visualize from GTF/GFF” function in TBtools software. To investigate the evolutionary conservation and divergence of GRX and GR genes between the selenium-hyperaccumulating dicotyledon C. hupingshanensis and other plant lineages, GRX and GR protein sequences were retrieved from 8 representative species covering key taxonomic groups. The selected species are specified as follows: (A) Dicotyledons: A. thaliana (Brassicaceae), Brassica rapa (Brassicaceae), Solanum lycopersicum (Solanaceae), Sesamum indicum (Pedaliaceae), and Phaseolus vulgaris (Fabaceae); (B) Monocotyledons: Oryza sativa (Poaceae), Hordeum vulgare (Poaceae), and Phoenix dactylifera (Arecaceae). The rationale for species inclusion is as follows: A. thaliana and B. rapa serve as reference species for Brassicaceae-specific evolution; monocotyledonous species (O. sativa, H. vulgare, P. dactylifera) enable cross-comparison between monocotyledons and dicotyledons; S. lycopersicum, P. vulgaris, and S. indicum represent diverse dicotyledon lineages to assess the overall conservation of the gene families. Protein sequences for GRX and GR were retrieved from the NCBI database (https://www.ncbi.nlm.nih.gov/) for these selected species. Multiple sequence alignment of full-length protein sequences was performed using the online CLUSTAL W tools. The alignment was visualized using ESPript 3.0 (https://espript.ibcp.fr/ESPript/cgi-bin/ESPript.cgi). A maximum likelihood (ML) phylogenetic tree was generated using the MEINVERSE website (https://www.meinverse.org/) with 1000 bootstrap replicates. The resulting tree was then edited on the iTOL7.0 website (https://itol.embl.de/).

Structural and functional characteristics analysis of ChGRX and ChGR genes

The protein sequences of ChGRX and ChGR were submitted to the MEME Suite (http://meme-suite.org/tools/meme) for conserved motif scanning using default parameters, with the number of MEME motifs specified as 10. Conserved motifs were identified using MEME suite with the following parameters: maximum number of motifs set to 10, width ranging from 6 to 50 residues, and mode specified as classic. Only motifs with an E-value < 0.05 were regarded as statistically significant [40]. Analyses were performed through CD-Search in NCBI’s Conserved Domain Database (https://www.ncbi.nlm.nih.gov/Structure/bwrpsb/bwrpsb.cgi) to identify functional domains. Gene structure visualization was achieved using the Advanced Gene Structure View module in TBtools, integrating genome annotation files with conserved domain information obtained from NCBI CD-Search.

Analysis of cis-acting elements in the ChGRX and ChGR family

To identify cis-regulatory elements in the promoters of ChGRX and ChGR genes, 2000 bp sequences upstream of the ATG start codon were extracted using TBtools (v2.154). These sequences were submitted to the PlantCARE database (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/) to predict cis-regulatory elements. The results were compiled and visualised using TBtools.

Collinearity relationship and identification of gene duplication events

Intra-genomic collinearity analysis of the C. hupingshanensis genome (GFF3 files) was conducted using MCScanX, integrated into TBtools (default parameters), to identify tandem and segmental duplications among ChGRX and ChGR genes. Cross-species synteny between C. hupingshanensis and A. thaliana was analyzed by aligning their genomes and GFF3 files via MCScanX, generating control (ctl), GFF, and collinearity files. Mitochondrial and chloroplast sequences were excluded, and results were visualised using TBtools. Coding sequences (CDS) of duplicated gene pairs were aligned to evaluate evolutionary selection pressures, and Ka/Ks ratios were calculated using TBtools’ Ka/Ks module with the Nei-Gojobori method. Gene pairs were classified under purifying (Ka/Ks < 1) or positive selection (Ka/Ks > 1).

Plant material and sample preparation

Seeds of C. hupingshanensis were collected from the Yutangba selenium-rich mine in Enshi, Hubei Province, China. This collection for non-commercial scientific research complies with the Regulations of the People’s Republic of China on Wild Plants Protection, as the species is not an endangered or protected plant in China. The seeds were cultivated under controlled conditions (22 ± 1 °C, 16-h photoperiod, 150 µmol·m^{− 2}·s^{− 1} irradiance). Forty-eight uniform seedlings (10 cm height, 4-month-old) were selected and equilibrated in Hoagland’s solution for 72 h. Treatments included sodium selenite (Na₂SeO₃) at 0 (control), 100, and 80,000 µg Se·L^{− 1} [41].The 80,000 µg Se·L^{− 1} concentration was designed to replicate the selenium pressure in the plant’s native habitat. Roots and leaves were harvested at 0, 3, 6, 9, 12, and 24 h post-treatment, immediately frozen in liquid nitrogen, and stored at -80 °C. The experiment followed a completely randomized design, comprising 16 distinct treatment-time combinations (e.g., 0 µg Se·L^{− 1} at 0, 3, 6, 9, 12, and 24 h; 100 µg Se·L^{− 1} at 3, 6, 9, 12, and 24 h; and 80,000 µg Se·L^{− 1} at 3, 6, 9, 12, and 24 h). Each combination was conducted with three independent biological replicates. A total of 48 seedlings were randomly assigned to the treatment groups to ensure that each replicate originated from a different individual plant, thereby accounting for inter-plant variability.

Gene expression analysis

Total RNA was extracted from roots and leaves using the TransZol™ Up Plus RNA Kit. RNA quality and concentration were assessed with a NanoDrop 2000 spectrophotometer, followed by integrity verification and genomic DNA contamination screening via 1% agarose gel electrophoresis. Residual genomic DNA was eliminated through RNase-free DNase treatment. Real-time PCR was performed on an ABI StepOne Plus system using Hieff™ qPCR SYBR Green Mix.

Each 10 µL reaction contained the following components: 5 µL of Hieff™ qPCR SYBR Green Master Mix, 1 µL of cDNA template, 0.2 µL of forward primer (10 µmol/L), 0.2 µL of reverse primer (10 µmol/L), and 3.6 µL of RNase-free ddH₂O. The cDNA added to each reaction corresponded to 100 ng of total RNA. The thermal cycling program was set as follows: initial denaturation at 95℃ for 5 min; followed by 40 cycles of denaturation at 95℃ for 10 s, annealing at 57–59℃ (gene-specific), and extension at 72℃ for 20 s. Melt curve analysis was performed to verify amplification specificity under the following conditions: 95℃ for 15 s, 60℃ for 1 min, and then a continuous increase from 60℃ to 95℃ at a rate of 0.5℃/s with constant fluorescence monitoring.

The ChActin gene was used as the reference gene for normalization. The cycle threshold (C_T) values of the target genes were normalized to ChActin, and the relative expression levels were calculated using the 2^−ΔΔCT method [42]. The primers for ChActin were as follows: Forward: 5’-GGTACTGAGGGAAGCCAAGA-3’, Reverse: 5’-GGAATCGCCGACAGAATG-3’. All PCR reactions were performed with three technical replicates, and the average value was used for subsequent analysis. Relative expression data were normalized to the 0 h time point as the control.

Statistical analysis was performed using GraphPad Prism software. Given the multifactorial design involving three selenium concentrations, two tissue types (roots and leaves), and six time points, we employed a ‘Multiple t-tests - one per row’ approach to specifically compare the relative expression of each gene at different time points/tissues/treatments against its corresponding 0 h control. To control the false positive rate in multiple comparisons, P-values were adjusted using the False Discovery Rate (FDR) correction method [43]. Significance was determined based on the FDR-corrected P-values as follows: P > 0.05 (not significant, ns), 0.01 < P ≤ 0.05 (significant, *), and P ≤ 0.01 (highly significant, **). Heatmaps for visualizing gene expression data were generated using the Heatmap tool in TBtools software. The primer sequences used for qRT-PCR analysis of ChGRXs and ChGRs genes are listed in Supplementary Table S1.

Homology modeling and ligand preparation

Homology modeling of target proteins was performed using SWISS-MODEL (https://swissmodel.expasy.org/). The quality of all generated models was validated using SWISS-MODEL’s standard metrics: all showed high sequence identity and sufficient coverage of functional domains, with Global Model Quality Estimate (GMQE) scores > 0.7, confirming good to excellent model reliability. ChAPK glutathionylation was subsequently conducted using Schrödinger Maestro. The 2D structure of GSSG was retrieved from the ChemSpider database and converted to a 3D conformation using OpenBabel (v3.1.1). Protein active sites for ChGR were predicted using the PrankWeb server [44].

Molecular docking

Protein-protein molecular docking between ChGRX and glutathionylated ChAPK was conducted using the HADDOCK2.4 web server (https://rascar.science.uu.nl/haddock2.4/) [45]. The glutathionylation site and the active site of ChGRX were designated as reactive sites, and the best conformations were chosen based on parameters including Z-score and HADDOCK score. For docking of ChGRX/ChGR proteins with GSSG, AutoDock v4.2 and AutoDock Vina v1.1.2 were used [46]. Protein and ligand structures were preprocessed in AutoDock v4.2 by adding polar hydrogens, merging nonpolar hydrogens, and assigning Gasteiger charges. The docking simulations were then performed using AutoDock Vina (v1.1.2). The search space was defined by a grid box centered on the active site of the target protein with dimensions of 40 × 40 × 40 Å and a grid spacing of 0.375 Å. To ensure sufficient sampling of the conformational space, the exhaustiveness parameter was set to 10. The resulting docking poses were first ranked by predicted binding affinity. Subsequently, cluster analysis was performed using a root-mean-square deviation (RMSD) cutoff of 2.0 Å to group similar conformations. The conformation with the lowest binding energy within the largest cluster was selected as the optimal binding mode for further analysis. Interactions were analyzed and visualised with PyMol, and heatmaps showing binding energy distributions were created using TBtools [47, 48].

Results

Identification and characterization of ChGRX and ChGR gene families

Using Arabidopsis thaliana as the reference species, 59 ChGRX genes and 12 ChGR genes were identified in C. hupingshanensis through genome-wide BLASTp searches (Genome Warehouse accession number: PRJCA005533). The key features of these gene, including molecular weight (MW), amino acid length (aa), isoelectric point (pI), subcellular localization (Loc), Grand Average of Hydropathicity (GRAVY), instability index, and aliphatic index, were systematically analyzed (Tables 1 and 2). The coding sequences (CDS) and protein sequences are provided in Table S2. ChGRX family members contained 89–489 amino acid residues, with molecular weights ranging from 9.84 to 18.75 kDa and pI values spanning 4.96–9.67. Subcellular localization predictions indicated that they were distributed in the cytoplasm, plastids, nucleus, vacuole, and extracellular space. All CGFS-type GRXs exhibited negative GRAVY values, and 70.0% of CPYC-type GRXs showed negative GRAVY values, indicating their hydrophilic nature. In contrast, 87.8% of CC-type GRXs possessed positive GRAVY values, demonstrating hydrophobic characteristics. The instability index (indicating in vitro stability of proteins) with values exceeding 40 manifest most ChGRX proteins were unstable. The pI values of ChGRX spanned acidic, neutral, and alkaline ranges, suggesting the diverse biochemical properties of their encoded proteins. The ChGR family had more extended sequences (1,491–1,839 residues), higher molecular weights (120.05–150.70 kDa), and consistently acidic pI values (4.96–5.05), with localization limited to the cytoplasm and plastids. Most ChGR proteins had negative GRAVY values, indicating their hydrophilicity and ChGR proteins were stable (instability index values below 40). The aliphatic index, which is related to the thermal stability of proteins, varied among family members and higher values indicate selenium stress greater thermal stability. Proteins of the CC-type GRXs exhibited stronger thermal stability than those in the CPYC- and CGFS-type GRXs and the ChGR family. Overall, our analysis successfully identified members of the ChGRX and ChGR gene families in C. hupingshanensis and provided a comprehensive characterization of their biochemical and predicted cellular properties.

Table 1.

The physicochemical properties of ChGRX proteins

Class	Gene ID	Gene name	Length(aa)	pI	MW (Da)	Subcellular localization	GRAVY	Instability index	Aliphatic index	Active site
CPYC	Chu012405	ChGRX1	125	7.67	13576.64	chlo	-0.194	42.96	83.36	CPYC
	Chu029817	ChGRX2-1	111	6.71	11691.43	chlo	0.068	32.72	91.44	CPYC
	Chu020352	ChGRX2-2	111	8.50	11831.66	chlo	-0.016	31.58	90.54	CPYC
	Chu018129	ChGRX3-1	489	8.71	53494.26	chlo	-0.067	47.77	91.47	CSYS
	Chu049855	ChGRX3-2	184	7.64	19590.32	chlo	-0.028	40.12	90.00	CSYS
	Chu020869	ChGRX3-3	195	8.61	21411.69	chlo	-0.073	34.67	92.41	CSYS
	Chu043681	ChGRX4-1	89	5.25	9842.01	mito	-0.462	27.43	86.40	YC
	Chu037218	ChGRX4-2	134	6.29	14740.96	vacu	0.003	36.90	96.64	CPYC
	Chu004446	ChGRX5-1	130	5.64	14331.48	vacu	0.145	38.22	113.85	CPYC
	Chu032293	ChGRX5-2	130	6.09	14401.62	chlo	0.145	42.96	116.15	CPYC
CGFS	Chu007883	ChGRX6-1	291	7.06	32102.57	chlo	-0.243	45.06	87.01	CGFS
	Chu034579	ChGRX6-2	291	8.27	31989.60	chlo	-0.138	40.77	91.37	CGFS
	Chu022588	ChGRX7-1	171	7.77	18756.26	mito	-0.409	51.39	79.77	CGFS
	Chu049249	ChGRX7-2	153	7.70	17436.09	chlo	-0.118	68.01	89.22	CGFS
	Chu046544	ChGRX8-1	181	8.35	19750.58	chlo	-0.123	49.84	77.51	CGFS
	Chu025639	ChGRX8-2	181	9.25	19875.97	chlo	-0.057	45.80	82.93	CGFS
	Chu010410	ChGRX9-1	488	5.27	53167.56	chlo	-0.252	30.26	84.28	CGFS
	Chu039313	ChGRX9-2	488	5.13	53117.38	chlo	-0.278	30.40	83.11	CGFS
CC	Chu042735	ChGRX10-1	140	5.75	14960.59	nucl	0.361	59.45	100.14	CCMC
	Chu017745	ChGRX10-2	148	4.97	16006.56	cyto	0.361	56.58	113.85	CCMC
	Chu027219	ChGRX10-3	187	4.99	20997.05	cyto	-0.107	61.57	99.47	CCMC
	Chu014680	ChGRX11-1	121	5.08	13143.31	nucl	0.153	57.62	117.44	CCMC
	Chu016854	ChGRX11-2	157	9.00	17104.73	mito	-0.111	67.29	94.97	CCMG
	Chu000266	ChGRX11-3	157	8.86	17107.75	mito	-0.096	71.44	94.33	CCMG
	Chu050507	ChGRX12-1	123	6.88	12662.02	extr	0.664	42.57	110.16	CCMC
	Chu047557	ChGRX12-2	135	7.71	14138.59	chlo	0.449	38.59	101.11	CCMC
	Chu042919	ChGRX13-1	137	8.80	14710.26	chlo	0.255	49.65	93.21	CCMC
	Chu036635	ChGRX13-2	104	7.65	10733.91	chlo	0.859	54.13	114.42	CCMC
	Chu048789	ChGRX14-1	102	8.54	11082.09	chlo	0.419	48.54	105.10	CCMS
	Chu022056	ChGRX14-2	102	8.54	11040.01	chlo	0.394	48.54	103.24	CCMS
	Chu047386	ChGRX15-1	103	8.84	11327.20	mito	-0.020	38.39	89.13	CCMC
	Chu026343	ChGRX15-2	103	8.49	11255.13	cyto	0.026	39.85	92.91	CCMC
	Chu035477	ChGRX16-1	103	7.69	11374.19	cyto	-0.091	35.96	86.21	CCMC
	Chu006923	ChGRX16-2	103	8.98	11346.35	cyto	0.106	33.25	91.75	CCMC
	Chu000549	ChGRX17-1	99	8.30	10841.92	chlo	0.244	49.51	102.22	CCLS
	Chu016579	ChGRX17-2	99	7.62	10827.85	chlo	0.248	49.51	102.22	CCLS
	Chu035479	ChGRX18-1	102	7.63	11275.32	nucl	0.285	51.16	107.84	CCLC
	Chu026344	ChGRX18-2	102	7.63	11259.24	chlo	0.245	47.00	104.02	CCLC
	Chu047387	ChGRX18-3	102	6.70	11216.21	chlo	0.326	47.00	107.84	CCLC
	Chu033867	ChGRX19-1	102	6.80	11138.22	cyto	0.320	54.60	100.20	CCMS
	Chu008666	ChGRX19-2	102	8.32	11125.26	cyto	0.274	52.66	97.35	CCMS
	Chu008669	ChGRX19-3	102	6.80	11163.22	chlo	0.251	53.51	101.18	CCMS
	Chu043386	ChGRX20-1	102	6.07	11097.17	cyto	0.378	55.02	105.10	CCMS
	Chu037047	ChGRX20-2	102	6.07	11112.20	cyto	0.351	46.93	102.25	CCMS
	Chu047384	ChGRX21-1	102	9.22	11072.07	chlo	0.268	41.15	100.29	CCMS
	Chu026341	ChGRX21-2	102	8.55	11058.98	chlo	0.271	50.07	100.29	CCMS
	Chu000193	ChGRX22-1	102	5.72	10976.86	chlo	0.274	52.17	109.90	CCMS
	Chu016922	ChGRX22-2	102	6.55	11037.86	chlo	0.194	53.28	106.08	CCMS
	Chu019307	ChGRX23-1	102	9.67	11385.79	cyto	0.318	52.17	115.59	CCMS
	Chu028889	ChGRX23-2	102	8.93	11255.42	chlo	0.314	51.70	111.76	CCMS
	Chu028890	ChGRX24-1	102	7.73	11302.37	cyto	0.204	50.57	109.80	CCMS
	Chu019308	ChGRX24-2	102	8.54	11328.50	cyto	0.283	51.70	116.47	CCMS
	Chu028888	ChGRX25-1	102	8.52	11257.38	chlo	0.275	54.25	108.82	CCMS
	Chu019306	ChGRX25-2	102	7.75	11273.33	chlo	0.254	54.25	107.84	CCMS
	Chu028886	ChGRX26	102	7.74	11206.20	cyto	0.244	49.85	105.98	CCMS
	Chu019304	ChGRX27	102	7.74	11262.27	cyto	0.243	57.71	107.94	CCMS
	Chu028887	ChGRX28-1	102	7.74	11221.22	chlo	0.267	58.77	106.96	CCMS
	Chu028884	ChGRX28-2	102	6.71	11249.23	cyto	0.240	58.77	106.96	CCMS
	Chu019305	ChGRX28-3	102	7.74	11227.18	chlo	0.249	60.94	105.98	CCMS

Table 2.

The physicochemical properties of ChGR proteins

Gene ID	Gene name	Length(aa)	pI	MW (Da)	Subcellular localization	GRAVY	Instability index	Aliphatic index
Chu019249	ChGR1-1	1695	4.99	139385.79	chlo	0.001	27.16	96.03
Chu028827	ChGR1-2	1791	4.98	147284.30	chlo	0.041	28.33	97.57
Chu022465	ChGR2-1	1839	4.99	150703.26	chlo	0.041	21.15	97.50
Chu049375	ChGR2-2	1803	4.99	148274.91	cyto	-0.014	25.51	97.52
Chu003321	ChGR3-1	1524	5.01	124764.08	chlo	-0.049	33.38	93.06
Chu013692	ChGR3-2	1524	5.01	124912.30	mito	-0.038	33.59	94.02
Chu022440	ChGR3-3	1524	5.00	124983.22	mito	-0.039	34.88	92.66
Chu049399	ChGR3-4	1530	4.99	125670.36	chlo	-0.025	34.81	92.87
Chu021775	ChGR3-5	1491	5.05	120094.97	chlo	-0.124	22.58	89.68
Chu048563	ChGR3-6	1491	5.05	120046.66	cyto	-0.114	23.88	89.48
Chu025614	ChGR3-7	1695	4.97	140926.60	chlo	-0.162	38.56	83.16
Chu046569	ChGR3-8	1704	4.96	141888.73	chlo	-0.161	37.89	83.07

Chromosomal localization and structure analysis of ChGRX and ChGR gene families

Genome distribution analysis showed that ChGRX genes were randomly located across all the C. hupingshanensis chromosomes (Fig. 2). Chromosome 6 and 16 possessed the highest number of ChGRX genes, each containing eight members. ChGRXs exhibited a closely related distribution across chromosomes 1, 2, 6, 8, 9, 11, 13, and 16. Type-specific distribution patterns were evident. The CPYC- and CGFS-type GRXs are predominantly localized at both ends of the chromosomes, while CC-type GRXs members exhibited no preference. ChGR genes were mapped to chromosomes 1, 2, 5, 6, 8, 9, 13, and 16, with chromosome 2 containing the highest number of ChGR genes. This uneven chromosomal distribution indicates potential evolutionary mechanisms that contribute to functional diversification, such as tandem duplications or segmental rearrangements.

Fig. 2.

Chromosomal distribution of ChGRX and ChGR genes. The chromosome numbers are shown on the left side of each strip. Chromosome colors represent gene abundance

Phylogenetic and protein sequence analysis of ChGRX and ChGR gene families

Based on active site characteristics, the ChGRX family was classified into three types (CPYC, CC, and CGFS), encompassing 186 GRX genes across different species (Fig. 3A). The CC clade contained the highest number of members in C. hupingshanensis (41 genes), followed by the CPYC group (10 genes). The CGFS group owned the smallest gene number. C. hupingshanensis contained 59 ChGRX genes clustered closely with those of (A) thaliana and (B) rapa, suggesting a conserved evolutionary linkage. ChGRX genes were distantly related to those of monocot species (e.g., H. vulgare), indicating functional divergence. For ChGR family, the phylogenetic tree revealed three distinct groups (Group I–III) based on topological structure (Fig. 3B). C .hupingshanensis exhibited a significantly larger number of ChGR genes than other species, such as A. thaliana and P. dactylifera. These results suggest that the ChGR gene family may have undergone extensive diversification in C. hupingshanensis. This expansion is consistent with the enhanced demand for antioxidant capacity in a selenium hyperaccumulator, although the direct role of selenium as an evolutionary driver requires further comparative genomic investigation.

Fig. 3.

Phylogenetic tree of GRX and GR genes. The phylogenetic tree from Brassica rapa (Br), Hordeum vulgare (Hv), Oryza sativa (Os), Phaseolus vulgaris (Pv), Sesamum indicum (Si), Solanum lycopersicum (Sl), Phoenix dactylifera (Pd), A. thaliana (At), C. hupingshanensis (Ch). A The phylogenetic tree of GRX genes. B The phylogenetic tree of GR genes

Analysis of the ChGRX protein sequence alignment revealed that the three types of GRXs possess distinct active site motifs, but high conservation was observed among members within the same type (Fig. 4A–C). The CPYC-, CGFS-, and CC-type GRXs contain conserved active site sequences characterized as C[P/S/G] Y[C/S], CGFS, and CC[M/L] [C/S/G], respectively. ChGRX4-1 and ChGRX23-1 lack the first N-terminal cysteine residue within the active site. Additionally, most CC-type GRXs carry a highly conserved ALWL motif at the C-terminus, and only nine members lack this sequence. We identified several amino acid residues involved in GSH binding in both CPYC- and CC-type GRXs. The CGFS-type GRXs contain a conserved five-amino-acid loop located between a highly conserved lysine residue and the active site cysteine [49, 50]. This structural feature is known to interfere with their redox enzyme activity [51]. Alignment of the ChGR protein sequences indicated that all members contain a conserved active site with the motif GGTC [V/L] [N/I/L] [R/V] GC [V/I] P [S/K] K [A/I] L [L/V], which includes two reactive cysteine residues (Fig. 4D).

Fig. 4.

Multiple alignment of partial sequences of the ChGRX and ChGR proteins. The black, blue, and purple boxes indicate the active site, the conserved five-residue loop between the lysine residue and the active-site cysteine in CGFS-type GRXs, and the C-terminal ALWL motif of CC-type GRXs, respectively. Triangles indicate amino acids that have been shown to contact GSH in at least one solved structure. The figure was adapted from Begas et al. 2017 and Liedgens et al. 2020

Analysis of the conserved motif and gene structure of ChGRXs and ChGRs

The ChGRX gene family comprises 10 conserved motifs, exhibiting distinct patterns across its types (Fig. 5). In CPYC-type GRXs, all members contain motif 1, motif 2, motif 3, and motif 8, except ChGRX4-1. In the CPYC-type GRXs, most members contained motif 1, motif 2, motif 3, motif 6, and motif 8, except ChGRX4-1. Notably, motif 4 was uniquely present in ChGRX1 and ChGRX2. The CC-type GRXs universally shared six motifs (motif 1–6), but motif 5 was absent in ChGRX10-1 to ChGRX13-2 and ChGRX16-2, while motif 4 was missing in ChGRX10-1 and ChGRX17. Additionally, ChGRX12 and ChGRX13-1 uniquely acquired motif 8. The CGFS-type GRXs was distinguished by motif 7, motif 9, and motif 10. Motif 7 (containing the CGFS active site) and motif 3 (housing the CPYC/CC active site) are functionally critical. All ChGRX retained the Glutaredoxin domain, but structural variations were evident. In the CC-type GRXs, most genes lacked untranslated regions (UTRs), except ChGRX20-2 and ChGRX11-3, and only four genes contained introns. The CPYC members uniformly possessed both introns and coding sequences (CDS). The CGFS-type GRXs showed structural divergence, with ChGRX8 retaining only the CDS while ChGRX7 lacked UTRs. All members of the ChGR gene family contained motif 1–10, demonstrating remarkable motif conservation. Except for ChGR3-8, all genes harbored the gluta_reduc_1 domain, while gluta_reduc_2 was absent in ChGR3-1, ChGR3-2, ChGR3-3, and ChGR3-4. Structural analysis revealed that UTRs and CDS were ubiquitous, except in ChGR2-2. These findings highlight the evolutionary conservation and functional diversification of redox-related genes in C. hupingshanensis, providing a foundation for further exploration of their roles under selenium stress adaptation.

Fig. 5.

Phylogenetic trees, motif, domain, and gene structure of the ChGRX and ChGR genes. A The phylogenetic tree; (B, C) Conserved motifs and domains of the proteins, different colors represent different motifs or domains; (D) Exon-intron structures; yellow boxes indicate exons, and lines indicate introns

Analysis of cis-acting elements in the ChGRX and ChGR gene families

In order to explore the transcriptional regulation mechanism of GRX system genes in C. hupingshanensis, we applied cis-acting elements analysis in the promoters of ChGRXs and ChGRs. There exist 11 distinct regulatory elements in ChGRX promoters and 8 in ChGR promoters, which were categorized into four functional groups: plant hormone response, light response, growth and development, and stress response (Fig. 6). In ChGRX promoters, light-responsive elements represented the highest proportion at 59.2%, followed by hormone-responsive elements, which accounted for 25.9%, with abscisic acid-responsive motifs being the predominant type. Elements governing growth and development, such as those involved in meristem expression and palisade mesophyll differentiation, accounted for 6.2%. In contrast, stress-responsive elements, exclusively drought-inducible MYB binding sites, were minimal. Similarly, ChGR promoters exhibited parallel patterns, with light-responsive elements being most abundant at 52.9%, hormone-responsive elements comprising 33.1%, development-related elements representing 8.8%, and stress-responsive elements being the least frequent at 5.2%.

Fig. 6.

Prediction of cis-acting elements in the promoter region of ChGRX and ChGR gene families

Both gene families share comparable cis-element distribution, though ChGRX promoters exhibit greater regulatory diversity. The enrichment of light-responsive elements exceeding 50% in both families, along with the significant abundance of ABA-responsive motifs, which coincides with highlights the crucial role of the GRX system in integrating environmental signals and coordinating metabolism within the redox cycle.

Collinearity relationship and identification of gene duplication events

Within the ChGRX gene family, 34 syntenic gene pairs were identified, with all pairs distributed across distinct chromosomes (Fig. 7A). Ten ChGRX genes participated in three syntenic pairs. In comparison, genes formed two syntenic pairs. Frequent intra-type synteny was observed, exemplified by the ChGRX7-1/ChGRX7-2 and ChGRX2-1/ChGRX2-2 pairs, suggesting their critical roles in expanding the ChGRX gene family. Parallel analysis of the ChGR gene family identified 12 syntenic gene pairs, with four genes involved in three syntenic pairs, two genes forming two syntenic pairs, and the remaining genes each maintaining a single syntenic pair. Comparative synteny analysis between C. hupingshanensis and A. thaliana identified 68 homologous GRX gene pairs and 16 homologous GR gene pairs (Fig. 7B, C), indicating high evolutionary conservation between these species. To assess selective pressures on duplicated ChGRX and ChGR genes, Ka/Ks ratios were calculated. All Ka/Ks values were significantly less than 1 (Table S3), indicating that the ChGRX and ChGR gene families have undergone strong purifying selection. Although the Ka/Ks ratios suggest strong purifying selection in both ChGRX and ChGR families, it should be noted that these analyses alone do not provide direct evidence for selenium-driven adaptive evolution. Further evolutionary tests and comparative genomic approaches.

Fig. 7.

Collinearity Analysis in ChGRX and ChGR Gene families. A Intragenomic collinearity map of ChGRX and ChGR gene families. From the inner to outer of the Circos plot: Collinear analysis of ChGRX and ChGR gene family. The gray lines in the background represent collinear blocks of the genome, while red lines emphasize collinear ChGRX gene pairs and black lines emphasize collinear ChGR gene pairs; point plot for N-ratio distribution; line plot for GC skew; heatmap for gene density profile overlapped with Line plot for GC ratio variation; tag labels for a gene family. B, C Synteny analysis of GRX and GR genes between C. hupingshanensis and A. thaliana. Gray lines in the background represent collinear blocks of C. hupingshanensis and A. thaliana genomes, and the red lines represent paralog ChGRX and ChGR pairs

Expression analysis of ChGRX and ChGR gene families in different tissues under se stress

To explore the molecular functions of ChGRX and ChGR genes under different selenium concentrations and time points, this study employed RT-qPCR to analyze gene expression changes in leaves and roots following treatment with selenite at concentrations of 0 µg Se·L^− 1, 100 µg Se·L^− 1, and 80,000 µg Se·L^− 1. Significance analysis was subsequently performed on the expression data (Table S4). In the CPYC-type GRXs, most genes exhibited higher expression levels in roots than in leaves (Fig. 8). Under selenium stress, only a few genes exhibited significant changes in expression, while the others remained relatively stable. In leaves, ChGRX3-1 expression was upregulated 3.0-fold after 6 h of treatment with 100 µg Se·L^− 1 and 3.9-fold under 80,000 µg Se·L^− 1, whereas ChGRX2-2 exhibited a significant 4.0-fold increase following 9 h exposure to 80,000 µg Se·L^− 1. In roots, ChGRX1 exhibited progressive upregulation from 9 to 24 h under 80,000 µg Se·L^− 1, peaking at 17.8-fold at 24 h, significantly higher than control levels. ChGRX2-1 remained continuously upregulated from 3 to 24 h of high-Se exposure, reaching 11.7-fold at 24 h. ChGRX5-2 exhibited a transient 14.2-fold upregulation only at 12 h under 80,000 µg Se·L^− 1, with little change at other time points.

Fig. 8.

Expression profiles of CPYC-type ChGRX genes in root and leaf tissues under selenium stress. The heatmap depicts the relative expression levels of each gene, which were calculated using the 2^−ΔΔCT method. Gene expression was normalized against ChActin as the reference gene, with the expression level at 0 h set to 1 as the calibrator. The radial axis represents individual ChGRX genes, which are ordered by hierarchical clustering based on Euclidean distance and average linkage. The concentric circles represent different treatment time points and selenium concentrations. The color scale (right) indicates expression levels from low (blue) to high (red)

Within the CGFS-type GRXs, similar to the CPYC-type GRXs, most genes exhibited higher basal expression levels in roots than leaves (Fig. 9). Under selenium stresses, some genes responded significantly, with changes over time and concentration, while others remained relatively stable. ChGRX8-1 exhibited concentration-independent activation in leaves, with significant upregulation (> 7-fold) at 100 µg Se·L^− 1 and 80,000 µg Se·L^− 1 treatments at 24 h. In contrast, ChGRX7-1 exhibited different responses to selenium concentration: it exhibited a quick, transient activation at 100 µg Se·L^− 1, peaking with a 5.5-fold upregulation by 3 h, while at 80,000 µg Se·L^− 1, its response was delayed, reaching a significant 6.9-fold increase only at 24 h. In roots, the expression changes of ChGRX6-1 and ChGRX7-1 at 24 h were especially notable. ChGRX6-1 exhibited an apparent dose-dependent effect, with the level of upregulation gradually increasing with selenium concentration, reaching a maximum of 11.3-fold at 80,000 µg Se·L^− 1. The expression level of ChGRX7-1 in the CK group at 24 h was 8.2-fold higher than at 0 h. Under 100 µg Se·L^− 1 treatment, its expression was upregulated 4.6-fold compared to 0 h but remained significantly lower than in the CK group at 24 h. Conversely, under 80,000 µg Se·L^− 1 treatment, its expression was significantly upregulated by 9.7-fold compared to the 0 h and CK groups at 24 h. This pattern indicates that 80,000 µg Se·L^− 1 selenium specifically upregulated ChGRX7-1 expression at 24 h, while 100 µg Se·L^− 1 selenium treatment had a suppressive effect compared to the control.

Fig. 9.

Expression profiles of CGFS-type ChGRX genes in root and leaf tissues under selenium stress. The heatmap depicts the relative expression levels of each gene, which were calculated using the 2^−ΔΔCT method. Gene expression was normalized against ChActin as the reference gene, with the expression level at 0 h set to 1 as the calibrator. The radial axis represents individual ChGRX genes, which are ordered by hierarchical clustering based on Euclidean distance and average linkage. The concentric circles represent different treatment time points and selenium concentrations. The color scale (right) indicates expression levels from low (blue) to high (red)

Within the CC-type GRXs, genes exhibited significantly higher expression induction under selenium stress compared to the CPYC- and CGFS-type GRXs (Fig. 10). A subset of genes exhibited high expression in both leaves and roots, while others exhibited little change, suggesting that this type forms a crucial functional module in the Se stress response. In leaves, the expression of ChGRX18-2 in CK-24 h was upregulated 10.1-fold relative to 0 h. Following 24 h of treatment with 100 µg Se·L^− 1 and 80,000 µg Se·L^− 1, its expression was upregulated further to 23.5-fold and 37.9-fold, respectively, demonstrating an apparent concentration-dependent increase. Similarly, ChGRX15-1 in CK was upregulated 4.4-fold at 6 h compared to 0 h. After 6 h of treatment with 100 µg Se·L^− 1 and 80,000 µg Se·L^− 1, its expression surged to 34.3-fold and 41.4-fold, respectively. Notably, under 80,000 µg Se·L^− 1 treatment, ChGRX15-1 was significantly upregulated as early as 3 h (33.4-fold relative to 0 h), indicating an early, rapid response to high selenium concentration, with expression levels increasing with Se concentration within the 6 h timeframe. In roots, ChGRX19-3 expression increased significantly under 100 µg Se·L^− 1, peaking at 47.8-fold at 6 h relative to 0 h. Under 80,000 µg Se·L^− 1, its peak expression was lower (32.8-fold relative to 0 h) and occurred earlier at 3 h. For ChGRX11-3, expression in the CK at 24 h was upregulated 16.5-fold relative to 0 h. Under 100 µg Se·L^− 1 treatment, its expression was upregulated 11.7-fold compared to 0 h but remained significantly lower than the CK at 24 h. In contrast, 80,000 µg Se·L^− 1 treatment caused a substantial 30.7-fold upregulation relative to 0 h, exceeding expression levels at both 0 h and in the CK at 24 h.

Fig. 10.

Expression profiles of CC-type ChGRX genes in root and leaf tissues under selenium stress. The heatmap depicts the relative expression levels of each gene, which were calculated using the 2^−ΔΔCT method. Gene expression was normalized against ChActin as the reference gene, with the expression level at 0 h set to 1 as the calibrator. The radial axis represents individual ChGRX genes, which are ordered by hierarchical clustering based on Euclidean distance and average linkage. The concentric circles represent different treatment time points and selenium concentrations. The color scale (right) indicates expression levels from low (blue) to high (red)

Within the ChGR family, most genes exhibited minimal changes in expression under selenium stress (Fig. 11). However, a subset was significantly upregulated, with overall expression levels higher under 80,000 µg Se·L^− 1 selenium stress compared to 100 µg Se·L^− 1 selenium stress. In leaves, the ChGR3-1 gene shows a significant increase under 80,000 µg Se·L^− 1, with upregulation after 6 h and a continued rise, reaching 30.73-fold at 24 h. In roots, ChGR3-5 and ChGR3-2 showed dynamic expression patterns over time and selenium levels. ChGR3-5 showed no significant response to 100 µg Se·L^− 1 treatment at 12 h, but was markedly upregulated by 11.2-fold under 80,000 µg Se·L^− 1 exposure. At 24 h, it was induced by 12.2-fold and 27.7-fold following treatment with 100 µg Se·L^− 1 and 80,000 µg Se·L^− 1, respectively. These results suggest that ChGR3-5 exhibits high-Se-specific activation at 12 h and dual-concentration induction at 24 h. For ChGR1-2, the CK group exhibited a 10.2-fold upregulation at 24 h, while 100 µg Se·L^− 1 and 80,000 µg Se·L^− 1 induced 9.3-fold and 15.2-fold upregulations, respectively, emphasizing the specific activation caused by high selenium stress. Subcellular localization predictions showed that of the 11 highly expressed ChGRX genes, eight are located in chloroplasts and three in mitochondria, while among the three highly expressed ChGR genes, two are targeted to chloroplasts and one to mitochondria. This indicates organelle-specific functional diversification of these genes under selenium stress adaptation.

Fig. 11.

Expression profiles of ChGR genes in root and leaf tissues under selenium stress. The heatmap depicts the relative expression levels of each gene, which were calculated using the 2^−ΔΔCT method. Gene expression was normalized against ChActin as the reference gene, with the expression level at 0 h set to 1 as the calibrator. The radial axis represents individual ChGRX genes, which are ordered by hierarchical clustering based on Euclidean distance and average linkage. The concentric circles represent different treatment time points and selenium concentrations. The color scale (right) indicates expression levels from low (blue) to high (red)

Secondary and tertiary structures prediction of ChGRX and ChGR enzymes

A total of 11 ChGRX genes and three ChGR genes were screened via RT-qPCR, and the secondary structures were predicted with the SOPMA online tool [52]. The results indicated that the protein sequences of these two gene families mainly consist of four secondary structure elements: alpha helices, beta sheets, extended strands, and random coils. Among these, alpha helices and random coils are the most prevalent, while beta sheets comprise a smaller proportion (Table S5). Regarding tertiary structure modeling, most ChGRX and ChGR members produced high-quality models, with sequence identities to their templates ranging from 70.0% to 94.4% and coverage above 0.66, demonstrating high structural conservation with their homologous templates. The modeling approach for ChAPK was consistent with previous studies, ensuring comparability of the results [53]. Further analysis of structural features showed that proteins from the CPYC- and CC-type GRXs typically adopt a conformation with four alpha helices surrounding three beta sheets, aligning with published research and supporting the reliability of the predictions. The CGFS-type GRXs has a distinctive extended terminal tail region that might play a role in its functional differences. (Figure S1). For ChGRX11-3, while certain global metrics approached the lower acceptable bound, its modeled structure fully encompassed the key binding sites, ensuring its suitability for subsequent analyses.

Molecular docking

Based on the active cysteine (C82) and chloroplast localization characteristics of ChAPK, a glutathionylated ChAPK substrate model was constructed through site-directed glutathionylation modification (Figure S2). Utilizing intrinsic biochemical properties of different GRX types [51], five ChGRX proteins highly expressed under selenium stress and localized in chloroplasts were selected, and protein docking was performed on the HADDOCK website with ChAPK-C82 and the active site of ChGRX as docking sites. By monitoring the GS⁻ removal efficiency and the S-S distances (C82-GS⁻, C82-active cysteine of ChGRX), the molecular driving mechanism of deglutathionylation was analyzed, and the length of the S-S distance is a necessary condition for judging the subsequent reaction direction. Molecular docking simulations predicted that all five ChGRXs might be capable of mediating the deglutathionylation of ChAPK (Fig. 12), but there were significant subtype-specificities. The S-S distance between C82 of ChAPK and GS⁻ was 4.6–6.3 Å. Among them, ChGRX18-2/19-2 had the shortest S-S distance, suggesting a potential for weak catalytic activity. Conversely, ChGRX2 exhibited the longest S-S distance, a configuration that could be compatible with substrate binding and subsequent catalysis. The S-S distance between C82 of ChAPK and each cysteine residue in the active site of ChGRX was 3.3–10.3 Å. Analysis of the docking poses suggested a potential difference in interaction mode between the two GRX types. For CPYC-type GRXs, the modeled S-S distance was shorter between the Cys¹ of GRX and C82 of ChAPK. In contrast, for CC-type GRXs, the shorter distance was observed for Cys². This spatial distinction in the in silico models lead us to hypothesize that CPYC- and CC-type GRXs might employ different catalytic cysteine residues to initiate the deglutathionylation reaction.

Fig. 12.

Predicted interactions between ChGRX and ChAPK-SG from molecular docking. The left panel is the overall view, and the right is the focused view. The ChAPK protein is colored in blue. ChGRX proteins are color-coded by type: CPYC-type GRXs in cyan and CC-type GRXs in salmon. A-D are the docking results of ChGRX1, ChGRX2, ChGRX3-1, ChGRX18-2/19-2 with ChAPK-SG protein, respectively. The yellow dashed line represents the S-S distance. All numbers in Panels A-F are distances in angstroms

The results showed that the binding energy between ChGRX and GSSG ranged from -3.6 to -4.5 kcal·mol^{− 1} (Fig. 13), with ChGRX1 exhibiting the highest binding energy and ChGRX7-1 showing the lowest.ChGRX2 featured a CPYC active site, whereas ChGRX1 and ChGRX3-1 possessed CGYC and CSYC active sites, respectively, indicating that their active site sequences conform to CSYC or CGYC motifs, consistent with previous studies [54]. The S-S distance between GSSG and the cysteine residue in the active site of ChGRX was measured to be 4.2–11.6 Å. Consistent with the docking results against ChAPK-SG, the models with GSSG also supported the hypothesis that Cys¹ might be the primary interacting residue in CPYC-type GRXs, while Cys² could play that role in CC-type GRXs. These docking simulations suggest that ChGRX may be capable of reducing both glutathionylated proteins and small molecules like GSSG. Furthermore, the predicted spatial arrangements indicate potential differences in how the active-site cysteines of different GRX types engage with their substrates.

Fig. 13.

Predicted interactions between ChGRX proteins and GSSG ligand from molecular docking. The left panel is the overall view, and the right is the focused view. ChGRX proteins are color-coded by type: CPYC-type GRXs in cyan and CC-type GRXs in salmon. A-F are the molecular docking diagrams of ChGRX1, ChGRX2, ChGRX3-1, ChGRX11-3, ChGRX16-1, ChGRX18-2/19-2 with GSSG, respectively. G shows the binding energy between ChGRX and GSSG (unit: kcal·mol^− 1). The yellow dashed lines show the S-S distances labeled with numbers in Å

This study investigated ChGR3-1 and ChGR3-5, which are highly expressed under selenium stress. Due to the identical structural modelling of ChGR3-1 and ChGR3-2, we collectively refer to them as ChGR3-1 henceforth. The prediction revealed that the two enzymes had 16 and 7 ligand binding sites, respectively (Figure S3). The top 7 binding sites with higher scores were selected for molecular docking and subsequently imported into PILP (Protein-Ligand Interaction Analyzer) for visualization [55]. The docking results showed that sites 2 and 3 of ChGR3-1 exhibited no binding activity, while the binding energies of the remaining sites ranged from -6.7 to -4.7 kcal·mol^{− 1}, indicating binding activity. The docking results indicated that all seven binding sites exhibited negative binding energies, ranging from -4.6 to -8.1 kcal·mol⁻¹, with site1 showing the strongest binding affinity (Fig. 13C). In the docking model of ChGR3-1 (Fig. 14A), GSSG formed multiple hydrogen bonds and salt bridges with conserved residues such as H98 (A/B) and H395 (B) in the active pocket. Among them, the imidazole ring of H98 (A) anchored the core structure of GSSG through salt bridges, and the side chain groups of H395 (B) and E394 (B) stabilized the ligand conformation through polar interaction hydrogen bonds, thus constructing a specific binding microenvironment. In the docking model of ChGR3-5 (Fig. 14B), the interaction is mediated by synergistic effects involving hydrophobic interactions, hydrogen bonds, and salt bridges. Specifically, T69 and I211 confine the ligand conformation within a hydrophobic pocket, residues including E52, R295, and G33 form a hydrogen-bonding network that stabilizes the complex, while the side chains of R73 and K78 further enhance binding through salt bridge formation.

Fig. 14.

Predicted interactions between ChGR proteins and GSSG ligand from molecular docking. The left panel is the overall view, and the right is the focused view. The amino acid residues at the binding site are gray-blue, and the ligand is dark yellow. The gray dotted line represents hydrophobic interactions, the solid blue line represents hydrogen bonds, and the dashed yellow line represents salt bridges. A Interaction between ChGR3-1 and GSSG. B Interaction between ChGR3-5 and GSSG. C Heatmap showing the binding energies of different ChGR proteins to each binding site of GSSG (unit: kcal·mol^− 1)

Discussion

This study presents a genome-wide systematic analysis of the GRX system in the selenium hyperaccumulator C. hupingshanensis. 59 ChGRX and 12 ChGR genes were identified, indicating significant gene family expansion compared to the model plant A. thaliana (which contains 31 AtGRX and 2 AtGR genes). Subcellular localization predictions revealed that most ChGRXs are targeted to chloroplasts and mitochondria. ChGRs were predicted to localize to chloroplasts and mitochondria. However, this differs from the typical cytoplasmic and chloroplastic distribution observed in most plants, with only limited mitochondrial localization documented in other species [56]. This distinctive subcellular localization may contribute to the exceptional selenium tolerance of C. hupingshanensis. Multiple protein sequence alignment showed that the active sites of ChGRXs are consistent with previously characterized types without mutations [57]. Although variations were observed in the active sites of ChGRs compared to those in A. thaliana, Avena sativa, and Populus trichocarpa, the two conserved cysteine residues were retained across all members [58–60]. Phylogenetic analysis further indicated that ChGRs include conventional Class Ⅰ and Class II isoforms and form a distinct clade designated as Class III. This divergence could be associated with expansion of key antioxidant enzyme gene families potentially in response to elevated ROS levels under selenium stress, though this hypothesis requires further validation.GR is a core component of the GRX system and participates in the AsA-GSH cycle [61, 62].

This study employed RT-qPCR to analyze the expression patterns of ChGRX and ChGR genes in the leaves and roots of C. hupingshanensis at different time points under varying selenium treatments, gaining deeper insights into the molecular functions of the GRX system under selenium stress. In CPYC- and CGFS-type GRXs, expression levels in roots were higher than in leaves under selenium stress, with elevated expression observed under high selenium concentrations compared to low concentrations. Temporal analysis revealed peak expression at 12 h and 24 h for these types. Among CC-type GRXs, although the highest expression under selenium stress was detected in ChGRX19-3, overall expression was greater in leaves than in roots. In leaves, expression was higher under high selenium treatment, while the opposite trend was observed in roots. Temporal expression patterns of CC-type GRXs showed high expression at 6 h, 9 h, and 24 h under low selenium treatment, and at 3 h, 6 h, and 24 h under high selenium treatment. The expression response of CC-type GRXs to selenium stress was significantly more pronounced than that of CPYC- and CGFS-type GRXs, consistent with their notable family expansion. Among GRs, ChGR3-1 showed the highest expression in leaves under high selenium treatment, while ChGR3-5 was most highly expressed in roots. Both genes exhibited peak expression at 24 h under high and low selenium treatments. Subcellular localization predictions indicated that highly responsive ChGRX and ChGR genes primarily target chloroplasts and mitochondria—key organelles serving as redox signaling hubs under selenium stress. This organelle-specific localization aligns with the disruption of photosynthetic electron transport and mitochondrial respiration caused by selenium-induced ROS overproduction. In this context, localized antioxidant systems are critical for counteracting excessive ROS accumulation [63, 64].

GRXs specifically catalyze the reversible protein S-glutathionylation, playing a pivotal role in redox signal transduction, as well as in stress response and developmental processes. Existing studies have confirmed the deglutathionylation enzymatic activities of both CPYC- and CC-type GRXs [51]. The redox enzyme activity of CPYC-type GRXs has been well established, while recent advances have been made in understanding CC-type GRXs. Mrozek et al. obtained milligram quantities of recombinant Arabidopsis CC-type GRX ROXY9 using a baculovirus-insect cell expression system, and demonstrated its weak reductase activity toward glutathionylated glyceraldehyde-3-phosphate dehydrogenase (GAPDH-SG). Moreover, active-site cysteine glutathionylation was only triggered under highly oxidizing conditions. Based on these findings, among the 11 GRXs and three GRs highly expressed under selenium stress, four CPYC-type GRXs, four CC-type GRXs, and three GRs were selected for molecular docking. To investigate the differences in deglutathionylation between CPYC- and CC-type GRXs, interaction models of ChGRX with both the macromolecular target ChAPK-SG and the small molecule GSSG were constructed. Based on our in silico docking models, the binding behavior of ChGRX toward ChAPK-SG and GSSG appeared to be consistent. The simulations suggested that CPYC-type GRXs might possess a stronger predicted deglutathionylation capacity than CC-type GRXs, implying potential differences in their catalytic mechanisms: CPYC-type GRXs primarily utilized CYS¹ to approach ChAPK-SG/GSSG, whereas CC-type GRXs relied on CYS². Molecular docking results revealed that both ChGR3-1 and ChGR3-5 proteins exhibited negative binding energies with oxidized glutathione (GSSG), and facilitated GSSG reduction through the formation of stable hydrogen bonds and salt bridges mediated by conserved residues, such as H98 and R73. These molecular docking results suggest that the GRX system may influence selenium metabolism by modulating the activity of the key enzyme APK.

While the CPYC- and CGFS-type GRXs have been extensively characterized in mammals [65, 66], their functional roles in plants remain underexplored. Similarly, although well-studied in plant development, the CC-type GRXs have been less investigated in redox regulation [67]. This study provides important insights into the functions of ChGRX and ChGR genes in selenium hyperaccumulator plants and their responsive mechanisms under selenium stress. It also offers valuable perspectives for investigating the flux and regulation of selenium metabolic pathways.

It should be noted that the functional interpretation of ChGRX and ChGR genes in this study is primarily based on results from genome-wide identification, bioinformatic predictions, gene expression profiling, and molecular docking simulations. While these in silico and expression-based analyses provide valuable insights into the potential mechanisms of GRX system-mediated selenium stress response, the proposed roles and interactions await direct biochemical and physiological validation.

Conclusion

This study comprehensively analyzes the ChGRX and ChGR gene families in C. hupingshanensis. 59 ChGRX genes and 12 ChGR genes were identified, and their phylogenetic relationships with monocots, dicots, legumes, and model plants were elucidated. Analysis of gene structures, motif compositions, and sequence alignments revealed that proteins within the same subtype share conserved active sites. Under various selenium stress conditions, the expression patterns of ChGRX and ChGR genes were examined across different tissues, identifying 11 ChGRX and 3 ChGR genes exhibiting strong responsiveness to selenium. Among these, eight ChGRX proteins were predicted to localize to chloroplasts and three to mitochondria. At the same time, ChGR genes were also targeted to both organelles, suggesting chloroplasts and mitochondria as primary sites of GRX system-mediated selenium stress response. Furthermore, molecular docking analyzes demonstrated that ChGRX proteins exhibit type-specific affinities toward glutathionylated substrates, suggesting potential functional divergence in deglutathionylation activity. This study elucidates the functions of GRX system genes in C. hupingshanensis and lays a solid foundation for further functional analysis of these genes.

Authors’ contributions

Conceptualization: Huanqiu Xue, Yao Li, Jing Xiao, Yifeng Zhou and Qiaoyu Tang; Methodology: Huanqiu Xue, Yao Li, Yifeng Zhou and Qiaoyu Tang; Software: Huanqiu Xue, Yao Li; Validation: Yifeng Zhou, Yanke Lu and Zhi Hou; Formal analysis: Yanke Lu, Zhi Hou and Zhixin Xiang; Investigation: Huanqiu Xue and Yifeng Zhou; Resources: Huanqiu Xue, Yao Li; Data curation: Huanqiu Xue; Writing—original draft preparation: Huanqiu Xue; Visualization: Huanqiu Xue, Yao Li; Supervision: Yifeng Zhou; Funding acquisition: Yifeng Zhou and Qiaoyu Tang. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (32260070) and the Innovation Project for Graduate Education of Hubei Minzu University (MYK2025056).

Data availability

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable. The plant material collection was conducted in accordance with the Regulations of the People’s Republic of China on Wild Plants Protection. The studied species is not endangered or protected.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.