The peanut cultivar ‘Fuhuasheng’, utilized throughout this study, has undergone comprehensive whole-genome assembly and sequencing. Its publicly available reference genome database (Peanut Genome Resource, PGR: https://pgr.itps.ncku.edu.tw) served as the primary resource for in silico analyses. For experimental studies, ‘Fuhuasheng’ plants were cultivated in a growth chamber under a 14-h light/10-h dark (25 °C) photoperiod, with a light intensity of 350 µmol m⁻² s⁻¹ and 60-70% relative humidity. Plants were grown in a standard peat-based potting mix.

Nicotiana benthamiana and A. thaliana (Col-0) plants were maintained in a controlled-environment growth room at 22 °C with 70% relative humidity and a 14-h light/10-h dark photoperiod. A light intensity of 120 µmol m⁻² s⁻¹ was provided by cool-white fluorescent lamps. N. benthamiana plants were grown in a commercial potting soil mixture and used for transient expression assays at the 4–5 leaf stage. Arabidopsis plants grow on a 1:1 mixture of peat moss and vermiculite, and are used for Agrobacterium-mediated genetic transformation after flowering.

The complete genome sequence, annotation files, and gene structure information (introns, exons, coding sequences - CDS) for peanut were retrieved from the public PGR database. Full-length protein sequences corresponding to the 15 known Arabidopsis thaliana PERK genes (AtPERKs) served as query sequences for BLASTP searches against the peanut genome protein annotation dataset (Altschul et al., 1997). Structural characteristics of Arabidopsis PERK family are provided in Supplementary Table S1. Following removal of redundant sequences, candidate PERK homologs were validated using the SMART database to confirm the presence of characteristic PERK domain architecture prior to subsequent analyses (Letunic and Bork, 2018).

Chromosomal positions and protein lengths of identified PERK genes were extracted directly from the PGR database annotations. Key physicochemical parameters of the deduced PERK proteins, including molecular weight and theoretical isoelectric point (pI), were calculated using the ExPASy Compute pI/Mw tool (https://web.expasy.org/compute_pi/; accessed on 20 June 2025). Subcellular localization predictions were generated using the DeepLoc-2.1 server (https://services.healthtech.dtu.dk/services/DeepLoc-2.1/; accessed on 23 December 2025).

Multiple sequence alignment of the full-length amino acid sequences derived from the 23 identified AhPERKs and the 15 AtPERKs was performed using ClustalW (https://www.genome.jp/tools-bin/clustalw; accessed on 1 July 2025) (Thompson et al., 1994). A maximum-likelihood (ML) phylogenetic tree was constructed using MEGA 10.2 software (Kumar et al., 2018), with branch support assessed by 1,000 bootstrap replicates.

Conserved protein motifs within the PERK sequences were identified using the MEME suite (v5.3.3; https://meme-suite.org/meme/tools/meme; accessed on 1 July 2025) (Bailey et al., 2009) employing default search parameters. Exon-intron structural organization information for each PERK gene was extracted directly from the PGR database annotations.

To investigate potential regulatory elements, a 2.0-kb genomic region immediately upstream of the translation initiation codon (ATG) for each PERK gene was extracted utilizing TBtools software (Chen et al., 2020). Putative cis-acting elements within these promoter sequences were identified and functionally annotated using the PlantCARE online platform (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/; accessed on 5 July 2025) (Lescot et al., 2002).

Publicly available transcriptome datasets hosted within the PGR database were analyzed to determine PERK gene expression patterns across diverse tissues and under various stress conditions. Tissue samples analyzed included: Root, Root tip, Root nodule, Root and stem, Stem, Stem tip, Cotyledon, Leaf, Florescence (full flowering), Gynophore, Pericarp-I, Pericarp-II, Pericarp-III, Embryo-I, Embryo-II, Embryo-III, Embryo-IV, Testa-I, and Testa-II.

Stress treatments comprised: drought stress (normally irrigated leaves vs. drought-treated leaves), hormone treatments (ddH₂O-, salicylic acid (SA)-, paclobutrazol (PAC)-, ethephon (ETH)-, brassinolide (BL)-, or abscisic acid (ABA)-treated leaves), and temperature stress (room temperature control leaves vs. low-temperature-treated leaves).

Full-length coding sequences of selected PERK genes were amplified via PCR and subsequently cloned into the pCAMBIA1300-35S-HA-RBS expression vector for Agrobacterium tumefaciens (strain GV3101)-mediated transient expression in mature leaves of 5- to 6-week-old N. benthamiana plants. Forty-eight hours post-infiltration, leaf discs (8 mm diameter) were excised using a sterile cork borer (Sigma-Aldrich) and equilibrated overnight in 200 μL of ultrapure water within 96-well plates. Discs were subsequently challenged with 200 μL of reaction buffer containing 20 μM L-012 (Wako Chemicals), 10 μg/mL horseradish peroxidase (HRP; Sigma-Aldrich), and 1 μM flg22 peptide. Luminescent signals generated by ROS production were quantified at 30-second intervals for a duration of 15 minutes using a Tecan F200 microplate reader. Three independent biological replicates were analyzed per construct, with statistical significance determined by one-way ANOVA followed by Tukey’s post hoc test (p < 0.05).

In the Pst DC3000 infection experiments, Arabidopsis plants cultivated in soil for 4–5 weeks were used. Bacterial suspensions at a concentration of 5 × 10⁸ CFU/mL were sprayed onto the leaves. Bacterial titers were assessed 3 days after inoculation.

Total RNA was extracted from Arabidopsis tissues employing the Plant Total RNA Kit (ZomanBio, Beijing, China). Subsequently, first-strand cDNA was reverse-transcribed using PrimeScript RT Master Mix (TaKaRa, Dalian, China). Quantitative real-time PCR (qRT-PCR) was carried out with SYBR^® Green Premix Pro Taq HS qPCR Kit (Accurate, Changsha, China) on an ABI QuantStudio 6 Flex instrument. All primer sequences utilized in this study are provided in Supplementary Table S2.

Systematic interrogation of the peanut genome annotation file using the BLASTP algorithm led to the identification of 23 PERK family members. These were designated PERK1 to PERK23 based on their sequential chromosomal locations (Table 1). Comprehensive characterization of these 23 PERK genes was performed, encompassing amino acid sequence length, predicted protein molecular weight (MW), theoretical isoelectric point (pI), and subcellular localization (Table 1). Results revealed moderate diversity in protein length, ranging from 503 amino acids (PERK14) to 761 amino acids (PERK19). Corresponding molecular masses varied from 55.18 kDa (PERK14) to 79.24 kDa (PERK16). Theoretical pI values spanned a broad range from 5.46 (PERK21) to 9.34 (PERK5), suggesting functional diversity. Subcellular localization predictions unanimously indicated cell membrane and/or lysosome/vacuole distribution for all PERK proteins. Detailed characteristics of the 23 PERK gene family members are compiled in Table 1.

Chromosomal positional information for the 23 PERK genes was retrieved from the PGR database (Table 1). Localization analysis demonstrated that these genes are widely, yet unevenly, distributed across the peanut genome. They are located on 12 specific chromosomes: Chr03, Chr04, Chr05, Chr07, Chr09, Chr10, Chr13, Chr14, Chr15, Chr17, Chr19, and Chr20. The distribution density among chromosomes was highly asymmetric. Chromosome 5 harbored the highest number of PERK genes (5 genes), indicative of a region of elevated density. Chromosomes 14 and 15 each contained 4 PERK genes. Chromosome 10 possessed 2 PERK genes. The remaining chromosomes (Chr03, Chr04, Chr07, Chr09, Chr13, Chr17, Chr19, Chr20) each contained only a single PERK gene, signifying regions of sparse PERK gene distribution (Figure 1).

To elucidate the evolutionary relationships between peanut and Arabidopsis PERK genes, a maximum likelihood (ML) phylogenetic tree was constructed based on multiple sequence alignments of the full-length amino acid sequences of the 23 peanut PERK proteins and 15 Arabidopsis PERK proteins. Based on sequence similarity and phylogenetic topology, the peanut PERK genes were resolved into three primary clades, designated Group I, Group II, and Group III. As depicted in Figure 2, Group I comprised 9 peanut PERK proteins and 8 Arabidopsis PERK proteins. Group II contained 6 peanut PERK proteins and 1 Arabidopsis PERK protein. Group III included 8 peanut PERK proteins and 6 Arabidopsis PERK proteins (Figure 2).

To gain insights into the evolutionary dynamics of the PERK gene family in peanut, the exon-intron structures were analyzed in conjunction with a phylogenetic tree constructed from the full-length PERK protein sequences. As shown in Figure 3A, the 23 peanut PERK genes were phylogenetically classified into three distinct subgroups. Gene structure analysis confirmed that all PERK gene family members in peanut contain introns (Figure 3B). The function and structure of PERK proteins are critically dependent on conserved motifs, which play essential roles in protein activity. In-depth motif analysis using the MEME suite identified 10 conserved motifs within the peanut PERK family (Figure 3C). All members possess Motifs 1, 3, 4, 5, 9, and 10. All members except PERK10 contain one instance of Motif 2. Motifs 6 and 7 are absent in PERK2, PERK13, and PERK14 but are present once in all other members. Within the peanut PERK family, all members except PERK14 contain at least one instance of Motif 8. Notably, the copy number of Motif 8 exhibited the greatest variation across family members, suggesting its potential contribution to functional diversification within the family (Figure 3C). The detailed sequences of the six putative motifs are shown in Supplementary Table S3.

To investigate the potential molecular mechanisms governing peanut PERK gene responses to abiotic stress and phytohormone signaling, a 2.0 kb genomic region upstream of the initiation codon (ATG) for each PERK gene was extracted from the PGR database. These promoter sequences were analyzed using the PlantCARE platform for the identification of stress-associated cis-acting regulatory elements. Analysis identified 12 distinct types of responsive cis-acting elements within the promoters of the 23 peanut PERK genes (Figure 4), encompassing elements associated with ABA response, anaerobic response, mixed stress response, zein response, light response, low-temperature response, MeJA response, salicylic acid (SA) response, auxin response, gibberellin (GA) response, elicitor response, and anoxic response.

As illustrated in Figure 4, results demonstrate that all 23 peanut PERK genes harbor at least 2 (PERK2) stress-responsive cis-acting elements, strongly suggesting their functional involvement in stress response. Association with light-responsive elements varied considerably among PERK members, ranging from a minimum of 6 elements (PERK21) to a maximum of 29 elements (PERK9). Critically, all PERK genes possess light-responsive cis-acting elements, representing the most abundant category identified. Furthermore, within the PERK family, only PERK5, PERK12, and PERK18 contain anoxic response elements, which constitute the least frequent stress response element type. Similarly, only four genes (PERK1, PERK9, PERK12, and PERK19) each harbor a single elicitor response-related cis-acting element (Figure 4).

Within the PERK gene family, 18 members contain ABA-responsive cis-acting elements, and 16 members contain anaerobic response-related elements. These elements are likely instrumental in regulating PERK gene expression during ABA signaling and anaerobic responses. Cis-acting elements associated with auxin response, zein metabolism response, low temperature response, SA response, MeJA response, and mixed stress response were identified in 6, 9, 7, 10, 18, and 6 PERK genes, respectively. Nine PERK genes contain one or more GA-responsive cis-acting elements (Figure 4). Collectively, the cis-element analysis strongly suggests potential involvement of all 23 PERK genes in mediating responses to diverse environmental stresses and hormonal cues.

Genes typically exhibit distinct expression profiles across tissues or organs, reflecting their roles in regulating specific physiological processes. To elucidate the tissue-specific expression patterns of the peanut PERK gene family, transcript abundance (FPKM values) was analyzed across diverse tissues: Root, Root tip, Root nodule, Root and stem, Stem, Stem tip, Cotyledon, Leaf, Florescence, Gynophore, Pericarp-I, Pericarp-II, Pericarp-III, Embryo-I, Embryo-II, Embryo-III, Embryo-IV, Testa-I, and Testa-II. Transcript abundance data for the 23 peanut PERK genes across these tissues are presented in Figure 5.

All 23 PERK genes were expressed in at least one examined tissue. Analysis revealed that 11 PERK genes exhibited ubiquitous expression across all tissues analyzed. No family member demonstrated universally high expression (FPKM values > 3). Genes such as PERK6 and PERK20 displayed high expression levels (FPKM values > 3) in the majority of tissues. PERK2, PERK13, PERK14, and PERK18 exhibited highly tissue-specific expression profiles: PERK2 expression was restricted to Root, Root nodule, and Florescence; PERK13 and PERK14 were exclusively expressed in Root and Root nodule; PERK18 was solely detected in Leaf and Florescence (Figure 5). This pronounced tissue specificity suggests potential specialized roles for these genes in regulating functions unique to these organs.

To elucidate the functional roles of peanut PERK genes under phytohormone application and abiotic stress conditions, the transcriptional responses of the 23 PERK genes were systematically analyzed following treatments with SA, ethephon, BR, ABA, paclobutrazol, as well as exposure to drought, ddH₂O control, and low temperature.

Transcript abundance data for the 23 PERK genes are presented in Figure 6. Results showed that SA treatment significantly altered the expression of 2 genes (PERK8 and PERK19), defined as a fold change ≥ 2. Paclobutrazol treatment significantly altered the expression of 3 genes (PERK1, PERK10, and PERK23). Ethephon treatment induced significant changes in PERK3, PERK4, PERK6, PERK7, PERK17, PERK19, and PERK20 expression. BR treatment significantly altered the expression of 5 genes (PERK6, PERK8, PERK11, PERK12, and PERK20). Under ABA treatment, 6 genes (PERK1, PERK3, PERK4, PERK8, PERK11, and PERK23) exhibited significant expression level fluctuations. Drought stress significantly altered the expression of 9 genes (PERK4, PERK8, PERK9, PERK11, PERK15, PERK16, PERK17, PERK22, and PERK23), highlighting their important roles in drought response. In contrast, low temperature treatment significantly altered the expression of 12 genes: PERK1, PERK6, PERK7, PERK8, PERK9, PERK10, PERK11, PERK12, PERK19, PERK20, PERK22, and PERK23 (Figure 6).

To further investigate the functional roles of PERK genes in plant immunity, 12 PERK family members were successfully cloned. Their regulatory activity in flg22-induced ROS production was assessed using a transient expression assay in N. benthamiana. We observed that the expression of 3 genes (PERK4, PERK12, PERK15) strongly suppressed flg22-induced ROS accumulation, while the expression of 1 gene (PERK8) strongly enhanced flg22-induced ROS burst (Figure 7). This functional divergence suggests distinct roles for these PERK members in either suppressing or enhancing plant basal immune responses during pathogen perception.

To further investigate the roles of PERK4, PERK12, PERK15, and PERK8 in plant immunity, we introduced PERK4-FLAG, PERK12-FLAG, PERK15-FLAG, and PERK8-FLAG into Arabidopsis via Agrobacterium-mediated stable transformation and obtained two independent transgenic lines for each construct: PERK4-FLAG-L1, PERK4-FLAG-L2, PERK12-FLAG-L1, PERK12-FLAG-L2, PERK15-FLAG-L1, PERK15-FLAG-L2, PERK8-FLAG-L1, and PERK8-FLAG-L2.

We subsequently examined PAMP-induced ROS production, PR gene expression, and resistance to Pst DC3000 in these transgenic lines. Our results showed that flg22-induced ROS burst was significantly damaged in the PERK4-, PERK12-, and PERK15-related transgenic plants compared to Col-0, whereas it was markedly enhanced in PERK8 transgenic lines. This indicates that stable expression of PERK4, PERK12, and PERK15 proteins significantly suppresses flg22-induced ROS burst, while stable expression of PERK8 enhances it (Figure 8A). We also detected chitin-induced ROS production in these transgenic lines, and the results were consistent with the trends observed for flg22-induced ROS burst (Figure 8B).

Consistent with these findings, we further observed that flg22-induced expression of FRK1, a commonly used defense marker gene, was significantly reduced in the PERK4-, PERK12-, and PERK15-related transgenic plants. In contrast, flg22-induced FRK1 expression was slightly enhanced in the PERK8-related transgenic lines (Figure 8C).

To assess the roles of PERKs in plant disease resistance, we challenged the transgenic plants with Pst DC3000. Compared with Col-0 plants, the PERK4-, PERK12-, and PERK15-related transgenic lines exhibited significantly enhanced susceptibility to Pst DC3000. Conversely, the PERK8-related transgenic plants showed markedly enhanced resistance to Pst DC3000 (Figure 8D).

Taken together, these results indicate that PERK4, PERK12, and PERK15 negatively regulate plant basal immune responses, whereas PERK8 acts as a positive regulator, revealing their bidirectional immune regulatory functions.