Systematic identification, characterization, and expression analysis of rice strubbelig-receptor family genes associated with fluroxypyr-meptyl and oxyfluorfen metabolism

Abstract

Strubbelig-receptor family (SRF) proteins are involved in various metabolic and signaling activities in plants, including abiotic stress tolerance; however, their function in rice under pesticide stress remains unclear. To address this knowledge gap, we examined the traits and roles of the rice SRF gene family under pesticide exposure. Twelve SRF genes and one differentially expressed SRF gene were identified via transcriptome analysis of rice treated with oxyfluorfen (OFF) and fluroxypyr-meptyl (FLUME). Phylogenetic analysis grouped these genes into four subfamilies: SRF-I–IV. Chromosomal mapping showed that the 12 SRF genes were unevenly distributed across 6 chromosomes, and segmental duplication contributed to gene family expansion. Five orthologous gene pairs within rice were identified using collinearity analysis, along with additional orthologs shared with other species: 1 with Arabidopsis, 11 with soybean, 27 with maize, and 39 with wheat. In silico subcellular localization predicted that all 12 encoded proteins localize to the cell membrane. Diverse SRF gene architectures, cis-acting elements, motifs, and conserved domains enabling responses to abiotic and biotic stress were found through structural analysis. Docking studies of FLUME and OFF with the 12 SRF proteins identified key amino acid residues involved in pesticide binding. Quantitative reverse transcription–polymerase chain reaction confirmed preferential expression of the 12 SRF genes under FLUME- and OFF-induced stress. The SRF’s role in FLUME and OFF metabolism was further supported by protein–protein interaction network analysis. Overall, these results improve our understanding of SRF functions and offer useful tools for investigating its roles in pesticide metabolism.

1. Introduction

Pesticides are widely used in agriculture to control insects, rodents, fungi, bacteria, and weeds, thereby enhancing crop productivity ¹. Globally, approximately 1.8 billion individuals are agricultural workers, with many relying on pesticides to protect commercial crops and food supplies ². However, excessive pesticide use has caused widespread environmental contamination; for instance, residues of fluroxypyr-meptyl (FLUME) and oxyfluorfen (OFF) are frequently detected in water and soil 2, 3, 4. Because residual herbicides reduce dry biomass, sugar content, and chlorophyll levels, they can adversely affect plant health. Even at low concentrations, herbicides alter plant morphology, disrupt photosynthesis, and interfere with the electron transport chain ⁵. Additionally, consumers risk food poisoning, cognitive impairment, and immune dysfunction due to pesticide accumulation in plant tissues. Prolonged exposure to pesticide residues has also been linked to neurodegenerative disease, skeletal abnormalities, reproductive dysfunction, and cancer, as well as cardiovascular, endocrine, respiratory, and renal disorders ².

Plant detoxification plays a crucial role in mitigating herbicide toxicity to plants and humans. Plants metabolize pesticides through enzymatic sequestration and degradation 6, 7, converting them into less harmful metabolites via a three-phase process: I) transformation, II) conjugation, and III) compartmentation. Enzymes such as cytochrome P450 and haloacid dehalogenase catalyze hydrolysis, reduction, and dehalogenation during phase I. In phase II, pesticides and phase I products are conjugated to glucosides and acetyl-CoA by enzymes including glycosyltransferases and acetyltransferases. Phase III involves transport of pesticides and metabolites for integration into macromolecules or cellular elimination through ATP-binding cassette transporters as well as multidrug and hazardous chemical extrusion transporter proteins 7, 8. Collectively, these enzymatic systems enhance pesticide metabolism and limit accumulation in plants. We previously demonstrated that metabolic enzymes, such haloacid dehalogenase, glycosyltransferase, cytochrome P450, and acetyltransferase 9, 10generate OFF and FLUME metabolites in rice. Thus, targeting a single enzymatic phase may enhance pesticide degradation. Although coordinated regulation could further increase detoxification capacity, research on this approach remains limited.

OFF, a selective diphenyl ether herbicide, is widely used for weed control in soybean, rice, and maize 11, 12. However, its low biodegradability leads to persistent residues in soil and water, with a soil half-life of 72–150 days ³. OFF accumulation causes toxic effects in various organisms and poses potential carcinogenic and developmental risks to humans 12, 13, 14, 15, 16. Similarly, FLUME, a heterocyclic herbicide widely used in rice, wheat, and corn ¹⁷, is frequently detected in aquatic and soil environments as its hydrolysate 4. FLUME and its hydrolysate inhibit plant growth and root development 18, 19. Notably, human exposure to FLUME has also been linked to an elevated risk of adverse birth outcomes ²⁰. Therefore, improving herbicide detoxification and reducing residue accumulation in rice is critical for mitigating ecological and human health risks.

Receptor-like kinases (RLKs) are transmembrane proteins abundant on plant cell surfaces ²¹, and leucine-rich repeat receptor-like kinases (LRR-RLKs) represent the largest class. Based on extracellular LRR count and kinase conservation, LRR-RLKs are divided into 15 branches (LRR I–XV), comprising over 1,100 members in rice ²². LRR-RLKs activate intracellular kinase activity upon binding extracellular signaling molecules, transmitting signals through phosphorylation cascades that regulate downstream gene expression and essential biological processes, including plant growth, development, and responses to biotic and abiotic stresses 23, 24, 25, 26, 27, 28.

The strubbelig-receptor family (SRF), a member of the LRR-V subfamily of LRR-RLKs, also regulates plant growth, development, and stress resistance 21, 29. SRF gene expression increases under environmental stresses, including drought, high salinity, and low temperature, enhancing plant tolerance to these conditions ⁸. For example, Ahmad et al. reported that high temperature and drought stress induce expression of SRF genes (GhSRF2, GhSRF3, GhSRF4, GhSRF10, and GhSRF22) in cotton ²¹. Additionally, the protein kinase RLK-Pelle_DLSV has been shown to enhance wheat tolerance to methyl disulfiram and reduces its residues by mediating signal transduction and promoting expression of multiphase detoxifying enzymes, including cytochrome P450 and glutathione S-transferase ³⁰. Based on these findings, we postulate that SRF proteins regulate multiphase detoxifying enzymes to facilitate pesticide detoxification and metabolism in plants. Currently, detailed evidence that SRF genes enhance plant tolerance by increasing pesticide degradation is lacking, and their functions in rice pesticide metabolism and detoxification remain unclear.

To explore potential mechanistic and physiological roles in pesticide detoxification and metabolism, we analyzed the transcripts of SRF protein genes responsive to FLUME and OFF across the rice genome. Their chromosomal locations, conserved domains, gene structures, motif compositions, cis-acting elements, predicted subcellular localizations, and collinearity were examined. SRF gene expression during FLUME and OFF exposure was verified using quantitative reverse transcription–polymerase chain reaction (qRT-PCR). Furthermore, the binding affinity of 12 SRF proteins to FLUME and OFF substrates was assessed. Our findings support development of a practical screening strategy to identify rice SRF genes responsive to FLUME and OFF, potentially improving toxicological resilience and herbicide resistance in crops and ecosystems.

The present study was designed to investigate the expression and functional characteristics of SRF genes in Oryza sativa under FLUME and OFF stress, aiming to identify core OsSRF genes mediating rice's response to herbicide stress. We analyzed the transcripts of SRF protein genes responsive to FLUME and OFF across the rice genome, and examined their chromosomal locations, conserved domains, gene structures, motif compositions, cis-acting elements, predicted subcellular localizations and collinearity. SRF gene expression during FLUME and OFF exposure was verified using qRT-PCR, and the binding affinity of 12 SRF proteins to FLUME and OFF substrates was further assessed. This study preliminarily revealed the potential regulatory roles of OsSRF genes in rice metabolism and detoxification of OFF and FLUME. The findings will support developing a practical screening strategy for FLUME- or OFF-responsive OsSRF genes, advance the understanding of rice's herbicide stress adaptation mechanisms, and may improve herbicide resistance in crops and ecosystems to underpin sustainable agriculture.

2. Materials and methods

2.1. Plant growth conditions and treatment

Rice seeds (Oryza sativa ssp. japonica cv. Nipponbare) were sterilized for 10 min with 3% hydrogen peroxide solution and rinsed three times with sterile distilled water. After 2 days of dark germination at 30°C, seedlings were transferred to hydroponic pots containing half-strength Hoagland medium (pH 5.6 ± 0.2). They were cultured in a growth chamber for 10 days at 30℃/25℃ (day/night) under a 14/10-h (light/dark) photoperiod (200 μmol m⁻² s⁻¹ light intensity) with 75% relative humidity. Uniform seedlings (three replicates) were then cultured for 6 days in fresh growth medium containing 0–0.14 mg/L FLUME or 0–0.25 mg/L OFF under the same conditions. The medium was replaced every 2 days. Posttreatment, samples were collected for qRT-PCR analysis, with three replicates per treatment.

2.2. Transcriptome library construction and high-throughput sequencing

Ten-day-old rice seedlings were exposed to 0.08 mg/L FLUME or 0.15 mg/L OFF for 2, 4, and 6 days. Shoots and roots from each time point were pooled for total RNA extraction using TRIzol (Invitrogen, Carlsbad, CA), and combined root and shoot samples from each condition were subjected to RNA sequencing. A total of 24 sequencing libraries were generated, representing three biological replicates for each of the following eight treatment groups: Shoot − FLUME/OFF (control), Shoot + FLUME/OFF (treated), Root + FLUME/OFF (treated), and Root − FLUME/OFF (control). Three biological replicates (totaling 24 samples) were sequenced on the Illumina HiSeqTM 2,500 platform. After removing low-quality reads, clean reads were mapped to the rice genome (https://rice.plantbiology.msu.edu/index.shtml) following Feng et al ³¹. Following this initial analysis, differential expression analysis was performed using the DESeq2 package (v1.30.1) in R. Genes with an adjusted p-value (False Discovery Rate, FDR) < 0.05 and an absolute log₂fold change > 1 were considered significantly differentially expressed.

2.3. Total RNA extraction, cDNA synthesis, and qRT-PCR

Rice samples were ground in liquid nitrogen, and total RNA was extracted using TRIzol reagent. Subsequently, cDNA was synthesized using a TransGen Kit (Tolo Biotechnology, Shanghai, China) according to the manufacturer’s protocol. Primers were designed using Primer Premier v6.0 (Premier Biosoft International, Palo Alto, CA, USA) ⁴. Next, qRT-PCR was performed using 2 × Q3 SYBR qPCR Master Mix (Universal; Tolo Biotechnology, Shanghai, China) with minor modifications to the manufacturer’s instructions, employing primers for the 12 OsSRF genes identified from FLUME/OFF stress transcriptome data and the reference gene (OsActin) (Table S2). Briefly, each reaction contained 0.5 μL of cDNA template in a 10-μL volume with 5 μL of 2 × Q3 SYBR qPCR Master Mix (Universal), 1 μL of primers, and 3.5 μL of RNase-free water. Thermal cycling conditions for qRT-PCR included 1 cycle at 95℃ for 30 s, followed by 40 cycles at 95℃ for 10 s and 60℃ for 30 s. Relative gene expression levels were calculated using the 2^−ΔΔCt method ³².

2.4. Phylogenetic analysis and multiple alignments

Twelve OsSRF genes responsive to OFF or FLUME were identified from the Rice Genome Annotation Project (https:// rice.uga.edu/), and their protein sequences were employed as queries. Homologous proteins from wheat (Triticum aestivum), soybean (Glycine max), maize (Zea mays), and Arabidopsis thaliana were retrieved using BLASTp (https://blast.ncbi.nlm.nih.gov/Blast.cgi), retaining sequences with > 40% identity. A phylogenetic tree was constructed using SRF protein sequences from these species and rice. A separate phylogenetic analysis was also performed for the 12 OsSRF proteins. MEGA7.0 was applied for sequence alignment and construction of a rootless neighbor-joining phylogenetic tree with 1,000 bootstrap replicates under the Poisson substitution (p-distance) model using default parameters. Evolview (http://www.evolgenius.info/evolview) was employed to visualize and refine the phylogenetic tree ³³.

2.5. Promoter, gene structure, and conserved motif analyses

The 2.0-kb upstream regions of each OsSRF gene were retrieved to analyze cis-elements in promoter sequences. Regulatory elements within promoter regions were predicted using PlantCARE (https://bioinformatics.psb.ugent.be/webtools/plantcare/html/) and visualized via RAWGraphs 2.0 (https://app.rawgraphs.io/) ³⁴. Conserved domains were analyzed using NCBI-CDD (https://www.ncbi.nlm.nih.gov/cdd/), with motifs identified using the MEME 5.5.7 online tool (https://meme-suite.org/meme/) based on full-length amino acid sequences ¹⁰. TBtools (https://github.com/CJ-Chen/TBtools) was employed to visualize the results.

2.6. Chromosomal location, Ka/Ks analysis, and synteny analysis of OsSRF genes

TBtools was applied to map the chromosomal locations of rice SRF genes (Zeng et al. 2025). The reference genome GFF3 file was downloaded from Ensembl Plants (https://plants.ensembl.org/index.html). The syntenic relationships of orthologous SRF genes in rice and among rice, wheat, Arabidopsis, soybean, and maize were analyzed using MCScanX. The ratios of nonsynonymous (Ka) to synonymous (Ks) nucleotide substitutions (Ka/Ks) for duplicated OsSRF gene pairs were calculated via TBtools ³⁵.

2.7. Biophysical properties, subcellular localization prediction, and protein–protein interaction analysis of OsSRF proteins

The Compute pI/Mw tool (https://web.expasy.org/protparam/) was employed to calculate the molecular weight, instability index, theoretical isoelectric point, amino acid count, and hydropathicity grand average of SRF proteins. The SWISS-MODEL workspace (https://swissmodel.expasy.org/) was applied for three-dimensional structure prediction ¹⁰. The subcellular localization of SRF proteins was predicted using Cell-Ploc (http://www.csbio.sjtu.edu.cn/bioinf/Cell-PLoc-2/) ²⁹. Protein–protein interactions were analyzed using the STRING database (https://cn.string-db.org/), and Cytoscape (https://cytoscape.org/) was employed to visualize the interaction network.

2.8. Molecular docking

Binding of the 12 OsSRF proteins to OFF and FLUME was evaluated using molecular docking. Structural files for FLUME (Compound CID: 54,745) and OFF (Compound CID: 39,327) were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/). Protein structure files (SDF format) were retrieved from UniProt (https://www.uniprot.org/), with accession numbers listed in Table S15. Docking simulations were conducted using AutoDockTools 1.5.7, with ligand and protein structure file conversions handled via OpenBabel (https://github.com/openbabel/openbabel/releases). PyMOL (https://pymol.org/) and Discovery Studio (https://www.3ds.com/products/biovia/discovery-studio) were applied to calculate ligand root-mean-square deviation (RMSD) ³⁶ and optimize ligand–protein visualization ³².

2.9. Statistical analysis

All statistical analyses were performed using SPSS v19.0 (IBM SPSS, Armonk, NY, USA). Differences between treatments and controls were assessed using one-way analysis of variance, with p < 0.05 defined as statistically significant. All experiments included three independent biological replicates.

3. Results

3.1. OsSRF gene identification in rice under OFF and FLUME stress

RNA sequencing analysis of eight libraries was conducted to investigate pesticide stress–induced OsSRF gene expression and identify genes responsive to FLUME or OFF exposure. In total, 12 OsSRF genes were identified and sequentially designated OsSRF1–OsSRF12 (Table S1). Based on pairwise comparisons using an expression threshold (fragments per kilobase of transcript per million mapped reads > 1), 10 genes were differentially expressed under Shoot ± OFF, 10 under Shoot ± FLUME, 11 under Root ± OFF, and 10 under Root ± FLUME conditions (Fig. S1A and B; Table S3). After FLUME treatment, further analysis identified one upregulated OsSRF gene (false discovery rate < 0.05, log₂ fold change > 1; Fig. S1C and D; Table S4). Compared with controls, OsSRF genes were expressed in all tissues following OFF or FLUME exposure, with treatment- and tissue-specific differences in expression levels.

3.2. Classification and phylogenetic analysis of OsSRF genes in rice

To explore phylogenetic relationships among OsSRF members, intraspecific and interspecific phylogenetic trees were constructed for rice, Arabidopsis, wheat, soybean, and maize. Based on SRF protein sequence similarity and tree topology, four major branches were identified, representing intraspecies and interspecies relationships (Fig. 1A and B). Interspecific phylogenetic analysis revealed that 117 SRF proteins clustered into four groups: 11 members in SRF-I, 37 in SRF-II, 21 in SRF-III, and 48 in SRF-IV (Fig. 1B). Comparative analysis of OsSRF family members and homologs from other species indicated that different OsSRF subfamily members clustered with SRF homologs from maize and wheat, suggesting a closer evolutionary relationship between OsSRF proteins and homologs from these two gramineous crops, likely reflecting shared ancestry or conserved traits.

Fig. 1.

(A) Phylogenetic analysis of SRF family genes in rice. (B) Neighbor-joining phylogeny tree of the SRF genes from rice (red star), Arabidopsis (blue circle), soybean (rect green), wheat (check, yellow) and maize (purple triangle). Different subgroups were marked using different colors. Neighbor-joining (NJ) bootstrap percentages (1000 replications) are indicated on the nodes. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.3. Chromosomal location and collinearity analysis of OsSRF genes

Gene duplication is a key driver of gene family expansion, increasing gene copy numbers and enhancing a species’ capacity to adapt to environmental stress and complex physiological processes ³⁷. Paralog relationships confirmed gene duplication events among OsSRF genes (Fig. 2). OsSRF genes from different subfamilies were distributed across several chromosomes: OsSRF4 (SRF-I) was located on chromosome 2; SRF-II, the largest group with five members (OsSRF5, OsSRF6, OsSRF10, OsSRF11, and OsSRF12), was distributed across chromosomes 3, 7, 9, and 10; SRF-III included OsSRF3 and OsSRF7 on chromosomes 2 and 6; and SRF-IV comprised OsSRF1, OsSRF2, OsSRF8, and OsSRF9 on chromosomes 2, 6, and 7.

Fig. 2.

Chromosome distribution of OsSRF genes. Different colors represent different subfamilies: green for SRF-I, orange for SRF-II, blue for SRF-III, and purple for SRF-IV. The connecting lines represent paralogs. A Scale on the left represents the length of the chromosome in Mb. The thin vertical bars on the right represent the chromosomes of rice, the chromosome number is shown above each chromosome, and the internal color represents the gene density on the chromosome, the bluer the color, the lower the gene density at that position, and the redder the color, the higher the gene density at that position. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

In intraspecific collinearity analysis of 12 OsSRF genes, 6 genes were involved in segmental duplications, forming 5 homologous gene pairs on distinct chromosomes (Fig. 3A; Table S5). These findings indicate that segmental duplication likely contributed substantially to SRF gene expansion in rice. To further examine OsSRF gene evolution, collinearity analysis was performed between rice and each of Arabidopsis, soybean, maize, and wheat, with 27, 1, 11, and 39 collinear gene pairs identified between OsSRF genes and those from the other species, respectively (Fig. 3B–E; Table S6). The greater number of orthologous relationships with monocots (maize and wheat) relative to dicots (Arabidopsis and soybean) indicates stronger evolutionary conservation among gramineous species. Notably, OsSRF9 formed a conserved collinear gene pair in all comparisons, underscoring its evolutionary conservation across plant species. synonymous (Ks) and non-synonymous (Ka) substitution rates, as well as the Ka/Ks ratios were calculated for paralogous and orthologous SRF genes (Tables S5 and S6), with all Ka/Ks ratios < 1, indicating that purifying selection removed deleterious mutations and maintained protein conservation during gramineous evolution.

Fig. 3.

Synteny analysis of OsSRF genes. (A) Collinearity analysis of OsSRF genes. The red line links the OsSRF genes that have experienced fragment duplication, and grey lines depicts all fragment duplication genes on the genome. Synteny analysis of OsSRF genes between rice and Arabidopsis (B), soybean (C), maize (D) and wheat (E). The species are rice, maize, wheat, Arabidopsis, and soybean. The red lines are the syntenic SRF gene pairs for dicotyledon and monocotyledon crop genomes. Gray lines are colinear blocks for dicotyledon and monocotyledon crop genomes. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3.4. OsSRF gene structure and protein conserved domains and motifs

In the 12 OsSRF proteins, conserved domains were identified using NCBI-CDD (Fig. S2). The PKc_like superfamily and PLN00113 superfamily domains were present in all proteins, supporting functional conservation across the family. The SRF-II subfamily was characterized by the LRRNT_2 domain, whereas OsSRF12 also contained an STKc_IRAK domain. SRF-III subfamily proteins exhibited highly conserved STKc_IRAK domains without intermember variation. The SRF-IV subfamily mainly contained these two core domains, although OsSRF8 also possessed a weakly conserved LRRNT_2 domain. The SRF-I protein OsSRF4 lacked additional specific domains but retained strong conservation of the PKc_like domain (Fig. S2A). E-values and Bit-scores for all conserved domains are provided in Table S8.

Gene structure analysis revealed that all 12 OsSRF genes possessed multi-intron–exon structures. SRF-II subfamily members contained 18 exons and 15 introns, harboring complete coding sequences (CDS) and untranslated regions (UTRs). The SRF-I gene OsSRF4 included 15 exons and 14 introns but lacked UTRs. SRF-III subfamily genes contained complete 5′-UTRs and 3′-UTRs, with lengths notably greater than those in other subfamilies. SRF-IV subfamily genes exhibited the greatest structural diversity, with substantial variation in exon number and UTR integrity, indicating high alternative splicing potential (Fig. S2B).

Using the MEME web server, OsSRF protein conserved motifs were predicted, identifying 6 conserved motifs across the 12 proteins (Fig. S3). Only Motif 5 showed copy number variation: OsSRF1, OsSRF5, and OsSRF10 contained one copy, whereas the remaining proteins contained two copies. These findings indicate that conserved motif distribution, exon–intron organization, domains, and protein structures remain largely conserved within each subfamily.

3.5. Cis-regulatory element analysis

To investigate the potential functions of OsSRF genes, PlantCARE was employed to identify cis-acting elements within promoter regions. In total, 271 cis-regulatory elements were detected and classified into 11 types across three categories: growth-, phytohormone-, and stress-related (Fig. 4; Table S9). Among these, phytohormone-related elements were most abundant (151 sites), including ABRE (35), AUX (3), GARE (11), MeJARE (94), and SARE (8). Growth-related elements (60 sites) comprised LRE (47) and MERE (13). Stress-related elements (60 sites) included AIRE (20), DSRE (1), LTRE (9), and MYB (30). These results indicate that OsSRF genes respond to hormonal, light, and stress signals, integrating multiple pathways to coordinate rice growth and defense responses.

Fig. 4.

Cis-element analysis of the SRF gene family in rice. Each colored stream connects a specific cis-acting element type to the OsSRF genes that contain it, with the stream width indicating the number of that element present in the corresponding gene. ABRE: ABA responsive element; AIRE: anaerobic induction responsive element; AUX: auxin responsive element; DSRE: defense and stress responsive element; GARE: gibberellin-responsive element; LRE: light responsive element; LTRE: low-temperature responsive element; MERE: meristem expression responsive element; MeJARE: methyl jasmonate responsive element; SARE: salicylic acid responsive element.

3.6. Physiochemical properties, subcellular localization, and protein–protein interaction analysis of OsSRF proteins

The physicochemical properties of OsSRF proteins are summarized in Table S10. Predicted proteins contained 700–782 amino acids with molecular weights of 76,109.09–84,644.95 Da (mean: 80,465.12 Da). Their theoretical isoelectric points were 5.56–8.84, with instability indices of 37.02–57.36 and aliphatic indices of 82.1–95.1, indicating variation in hydrophobic side-chain composition. Subcellular localization analysis predicted localization of all 12 OsSRF proteins to the cell membrane (Table S11), consistent with their functions as membrane-bound receptors in plant processes ³⁸ and stress signal perception ²⁹.

To examine functional relationships among OsSRF proteins, the STRING database was employed to construct a protein–protein interaction network (Fig. 5). The network included several interacting partners, such as glyceraldehyde-3-phosphate dehydrogenase (GAPD_ORYSJ, OsJ_03778, B1150F11.17, OJ1579_G03.13, and Q5JME6_ORYSJ), 3,4-dihydroxy-2-butanone kinase (Q6ASW3_ORYSJ), and 1-acyl-sn-glycerol-3-phosphate acyltransferase (Q337H3_ORYSJ). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is closely associated with plant stress responses, acting through posttranslational modifications, such as acetylation and S-nitrosylation, to mediate signal transduction and reduce oxidative damage or regulate stress resistance pathways, thereby improving stress tolerance ³⁹. These protein–protein interactions further support the involvement of OsSRF proteins in plant stress response pathways.

Fig. 5.

Protein interactions network diagram of OsSRF genes in rice. The minimum required interaction score was set to 0.400. Node size and color intensity: Correlate with the degree of connectivity (larger, darker nodes have more interacting partners).

3.7. Molecular docking

To elucidate interactions between OsSRF proteins and pesticides, molecular docking was performed for 12 OsSRF proteins with both OFF and FLUME (Fig. 6, Fig. 7). Docking with FLUME revealed that all proteins relied on alkyl and π-alkyl interactions as core binding forces but also displayed interaction variability: eight proteins formed conventional hydrogen bonds (Fig. 6A and D–H), six formed fluorine–halogen bonds (Fig. 6A, D, E, H, J, and K), two formed π–π stacking interactions (Fig. 6A and B), four formed π–σ interactions (Fig. 6b and I–K), two exhibited C–H hydrogen bonds (Fig. 6A and A), and three showed π–cation interactions (Fig. 6H, J, and K). OsSRF3 contained only three interacting residues, whereas OsSRF10 contained up to nine, with most proteins displaying five residues. Binding energies ranged from − 1.26 to − 3.87 kcal/mol, with OsSRF8 showing the lowest value (−3.87 kcal/mol), followed by OsSRF9 (−3.37 kcal/mol), whereas OsSRF5 showed the highest (−1.26 kcal/mol). The remaining proteins displayed intermediate values (all kcal/mol; OsSRF4: −2.74; OsSRF12: −2.70; OsSRF7: −2.67; OsSRF2: −2.66; OsSRF11: −2.61; OsSRF3: −2.54; OsSRF10: −2.29; OsSRF1: −1.76; OsSRF6: −1.67).

Fig. 6.

The receptor–ligand interaction of pesticides with the OsSRF protein active site. (A–L) Visualized using PyMol, representing molecular docking of FLUME with OsSRF1 (A), OsSRF2 (B), OsSRF3 (C), OsSRF4 (D), OsSRF5 (E), OsSRF6(F), OsSRF7 (G), OsSRF8 (H), OsSRF9 (I), OsSRF10 (J), OsSRF11 (K), and OsSRF12 (L), respectively. Different colored dashed lines represent distinct types of non-covalent interactions between the ligand and the protein, including conventional hydrogen bonds, carbon hydrogen bonds, halogen bonds, alkyl, pi-alkyl, pi-cation, pi-sigma and pi-pi stacked interactions.

Fig. 7.

The receptor–ligand interaction of pesticides with the OsSRF protein active site. (A–L) Visualized using PyMol, representing molecular docking of OFF with OsSRF1 (A), OsSRF2 (B), OsSRF3 (C), OsSRF4 (D), OsSRF5 (E), OsSRF6 (F), OsSRF7 (G), OsSRF8 (H), OsSRF9 (I), OsSRF10 (J), OsSRF11 (K), and OsSRF12 (L), respectively. Different colored dashed lines represent distinct types of non-covalent interactions between the ligand and the protein, including conventional hydrogen bonds, carbon hydrogen bonds, halogen bonds, alkyl, pi-alkyl, pi-cation, pi-sigma and pi-pi stacked interactions.

Based on molecular docking of the 12 OsSRF proteins with OFF, π-alkyl interactions were common to all proteins, whereas alkyl interactions occurred in 10 proteins, excluding OsSRF11 and OsSRF12. Additional interactions included conventional hydrogen bonds (Fig. 7A, B, D, and F–J), fluorine–halogen bonds (Fig. 7C–G, K, and L), π–π stacking (Fig. 7A, B, D, E, H, and J), carbon hydrogen bonds (Fig. 7B, C, F, I, and L), π–σ interactions (Fig. 7C, D, and H), and a π–cation interaction detected only in OsSRF8 (Fig. 7H). The number of residues ranged from three to six, with OsSRF2, OsSRF3, OsSRF11, and OsSRF12 each containing three, whereas OsSRF10 contained six. Binding energies ranged from − 3.10 to − 4.57 kcal/mol, with OsSRF8 showing the lowest value (−4.57 kcal/mol), whereas OsSRF4 showed the highest (−3.10 kcal/mol). The remaining proteins showed intermediate values (all kcal/mol; OsSRF1: −4.34; OsSRF2: −3.31; OsSRF3: −3.67; OsSRF5: −3.33; OsSRF6: −4.03; OsSRF7: −4.21; OsSRF9: −4.20; OsSRF10: −3.99; OsSRF11: −3.67; OsSRF12: −4.27).

RMSD values were used to evaluate docking accuracy. Ligand poses from the referenced crystal structures served as references for RMSD calculations. Table S17 reports the number of top 10 ranked poses for each docking that satisfied the RMSD ≤ 2.00 Å threshold, considered reliable ³⁶. For FLUME, >70% of the top 10 poses for each OsSRF protein met this criterion, indicating stable complex formation in vivo; for example, OsSRF8 (−3.87 kcal/mol) had its first 8 poses within RMSD values of 0.509–1.766 Å, linking strong binding affinity with conformational reliability. For OFF, agreement with RMSD ≤ 2.00 Å was even higher: >90% of the top 10 poses for each protein showed RMSD < 2.5 Å, with approximately 60% falling below 1.5 Å. Most initial top-ranked OFF poses showed high precision; for instance, OsSRF10′s first pose had an RMSD of 0.008 Å, approaching the theoretical docking accuracy limit.

3.8. Expression profiles of OsSRF genes in rice under FLUME and OFF stress

To further examine OsSRF gene expression, qRT-PCR was performed on 12 genes showing strong induction under FLUME and OFF treatments. Expression levels of the 12 OsSRF genes increased significantly under pesticide stress, reaching maximum values at 0.15 mg/L OFF or 0.08 mg/L FLUME exposures (Fig. 8, Fig. 9). The upregulation fold changes in roots were generally higher than those in shoots for most genes. Under 0.15 mg/L OFF, OsSRF1, OsSRF8, and OsSRF10 showed the strongest responses [3.95/2.31-fold, 5.62/2.71-fold, and 3.95/2.33-fold (root/shoot), respectively], whereas OsSRF2, OsSRF4, OsSRF5, OsSRF6, OsSRF9, and OsSRF11 exhibited milder upregulation [2.51–3.63/1.44–2.67-fold (root/shoot)], with OsSRF3, OsSRF7 and OsSRF12 showed relatively low induction [2.06–2.09/1.64–2.17-fold (root/shoot)]. In contrast, under 0.08 mg/L FLUME, OsSRF1, OsSRF8, and OsSRF12 showed the strongest responses [4.67/2.07-fold, 11.98/1.53-fold, and 5.60/2.11-fold (root/shoot), respectively], whereas OsSRF2, OsSRF3, OsSRF4, OsSRF6, OsSRF7, OsSRF10 and OsSRF11 showed moderate upregulation [3.06–3.94/1.87–3.38-fold (root/shoot)]. OsSRF5 and OsSRF9 displayed milder responses [2.46/1.92-fold and 2.44/2.02-fold (root/shoot)].

Fig. 8.

Effects of FLUME induced change in genes expression levels of 12 SRF genes in rice roots and shoots, Ten-day-old rice seedlings were exposed to 0–0.14 mg/L FLUME for 6 d. Different colors in the figure correspond to different concentrations of the pesticides. The results are the mean values ± SD (n = 3). Different lowercase and uppercase letters indicate a significant difference between each treatment concentration of FLUME in the roots and shoots, respectively (p < 0.05, ANOVA).

Fig. 9.

Effects of OFF induced change in genes expression levels of 12 SRF genes in rice roots and shoots, Ten-day-old rice seedlings were exposed to 0–0.25 mg/L OFF for 6 d. Different colors in the figure correspond to different concentrations of the pesticides. The results are the mean values ± SD (n = 3). Different lowercase and uppercase letters indicate a significant difference between each treatment concentration of OFF in the roots and shoots, respectively (p < 0.05, ANOVA).

4. Discussion

SRF genes have been identified in numerous plant species, including Arabidopsis, soybean, maize, wheat, and rice, where they perform essential roles in diverse biological processes. Advances in whole-genome analysis and sequencing technologies have facilitated identification of these gene families38, 40, 41, 42, 43. Multiple studies have explored SRF protein responses to abiotic stresses, such as heat and drought 21, 44; however, the role of the SRF family in pesticide-exposed rice remains understudied. In this research, we systematically investigated the responses of rice SRF genes and proteins to pesticide treatments using bioinformatics approaches. Twelve OsSRF genes were identified, with one significantly upregulated under OFF and FLUME treatments (Fig. S1). We also observed increased OsSRF gene expression in response to both pesticides (Fig. 8, Fig. 9). Therefore, OsSRF genes likely play key roles in mitigating OFF and FLUME toxicity in rice by regulating metabolic processes. We characterized gene structures, cis-acting elements, conserved domains, motif compositions, transcriptional expression patterns, chromosomal locations, and subcellular localization for the 12 OsSRF genes and proteins, along with phylogenetic and collinearity analyses. We also performed molecular docking and protein–protein interaction network analyses.

To clarify the evolutionary relationships within and among species, we constructed a phylogenetic tree using SRF gene sequences from maize, rice, soybean, Arabidopsis, and wheat (Fig. 1; Table S7). Based on 117 proteins from these species, 4 distinct clades were identified. Among them, one, five, two, and four OsSRF genes were classified into the SRF-I, SRF-II, SRF-III, and SRF-IV subgroups, respectively.

Previous research has indicated that gene duplication is central to genome evolution and gene family expansion, with tandem and segmental duplications driving diversification ⁴⁵. Such duplication events allow plants to rapidly adapt to environmental stresses ⁴⁶. The observation that six OsSRF genes participate in segmental duplications indicates uneven distribution and highlights segmental duplication as a major driver of OsSRF gene evolution and stress adaptation in rice. Furthermore, selective pressure analysis revealed that Ka/Ks ratios were consistently < 1, indicating strong purifying selection. This pattern suggests that nonsynonymous (Ka) substitutions occur far less frequently than synonymous (Ks) substitutions, reflecting high evolutionary conservation of OsSRF protein sequences through preferential fixation of synonymous mutations. We also detected strong evolutionary relationships among rice SRF gene pairs, including OsSRF4–OsSRF7 and OsSRF5–OsSRF12, as well as between rice and maize SRF gene pairs, i.e., OsSRF5–Zm00001eb006270 and OsSRF12–Zm00001eb027970. Intraspecies collinearity within rice and interspecies collinearity between rice and maize supported these relationships (Fig. 3). However, rice SRF genes shared fewer homologs with Arabidopsis; only one orthologous gene pair was identified between rice and Arabidopsis, compared with 27 between rice and maize (Fig. 3b and d). These patterns were corroborated by phylogenetic analysis, showing that SRF genes and proteins within the same evolutionary subgroup share similar functional domains, conserved motifs, and gene architectures, emphasizing their evolutionary relatedness.

Previous studies on SRF genes in rice, Arabidopsis, and maize demonstrate that this gene family plays important roles in the stress responses of dicot and monocot plants while maintaining conserved structures, functions, and evolutionary patterns 41, 47. Combined with our expression profile results (Fig. 8, Fig. 9), we propose that these SRF genes participate in responses to FLUME and OFF stress. Nonetheless, substantial variation among phylogenetic subgroups suggests that the functional diversity of rice SRF proteins has gradually evolved. Evidence tracing the long evolutionary history of SRF genes supports this interpretation 21, 48, indicating that their origin and diversification date to the early divergence of monocots and dicots. Motif analysis revealed consistent motif patterns within subfamilies, indicating functional conservation. The presence of all six motifs in every SRF protein highlights conservation of the SRF domain in the rice genome. Prior studies also reported highly conserved SRF domains in other plant species 21, 48.

Domain analysis also identified STKc and LRR domains in several SRF proteins, both known to be critical for plant adaptation and function 44, 49 (Fig. S2; Table S8). SRF proteins are well-established as regulators of abiotic stress tolerance 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44. Additionally, they are closely associated with plant development, growth, and stress responses 21, 38, 50. Therefore, the identified domains may facilitate rice pesticide metabolism and detoxification.

Subcellular localization of specific proteins plays a crucial role in regulating various plant biological processes 51, 52. Our results showed that all 12 OsSRF proteins localized to the cell membrane (Table S11), suggesting that they function in plant signaling regulation and transmembrane processes. These roles align with the typical functions of membrane-associated proteins, which sense external stress cues, maintain ion homeostasis, regulate lipid remodeling for stress adaptation, and activate downstream signaling pathways involved in plant stress responses. Moreover, cell membranes detect OFF- and FLUME-induced stress signals through membrane components and photoreceptors; in response, they help maintain cellular homeostasis and sustain photosynthesis by regulating lipid 53, 54.

To enhance plant stress tolerance, cis-acting elements are critical for regulating gene expression and influencing how SRF genes respond to external stimuli ⁵⁵. Analysis of the 12 OsSRF genes showed that abscisic acid (ABA)-responsive elements (ABREs) displayed the greatest sequence variation among identified elements (Fig. 4). Multiple ABREs occurred in the upstream regions of 11 genes. Their high frequency and broad distribution imply that ABREs regulate plant responses by modulating synthesis of gibberellic acid (GA), salicylic acid (SA), methyl jasmonate (MeJA), and ABA, thereby enhancing resistance to abiotic stresses, such as salinity, low temperature, drought, and pesticide exposure 7, 56.

Liu et al. (2013) ⁵⁷ reported that ABA improves rice tolerance to abiotic stress, whereas SA and MeJA are key regulators of pesticide metabolism and detoxification 58, 59. For instance, SA promotes the degradation of fomesafen, glyphosate, and isoproturon, thereby reducing their phytotoxicity in plants ⁵⁸. Similarly, MeJA enhances isoproturon resistance in wheat by promoting detoxification and degradation ⁵⁹. We previously demonstrated that the cis-acting element of acetyltransferase responds to ABA and GA under mesotrione and fomesafen stress ⁵⁶, confirming its role in mesotrione and fomesafen degradation and metabolism in rice 56, 60. These findings indicate that SRF genes participate in abiotic and biotic stress responses, including OFF and FLUME metabolism in rice.

Comparative analyses of SRF gene sequences and expression patterns across model plants may further clarify SRF functions in rice. GAPDH plays a central role in plant stress tolerance; beyond its glycolytic role, oxidative modification of its active-site cysteine under stress converts it into a regulatory protein involved in reactive oxygen species scavenging, stress signal transduction, and cellular homeostasis ³⁹. Notably, our results show that 11 of 12 OsSRF proteins interact with GAPDH (Fig. 5). Given GAPDH’s role in stress signaling, these interactions suggest that OsSRF proteins function as GAPDH regulators or partners in plant stress adaptation, facilitating signal transmission, metabolic adjustment, or homeostasis maintenance. This further underscores the importance of OsSRF genes in plant responses to biotic and abiotic stresses, including pesticide exposure.

Further investigation of pesticide–receptor ligand interactions is required to clarify the molecular functions of SRF domains in pesticide metabolism and degradation. The likelihood that an enzyme participates in pesticide metabolism increases with the strength of its interaction with the compound ⁶¹. In this study, proteins encoded by all 12 OsSRF genes, particularly OsSRF8, showed strong interactions with OFF and FLUME molecules (Fig. 6, Fig. 7). OsSRF8 exhibited strong binding to both OFF and FLUME, with low binding energies of − 4.57 and − 3.87 kcal/mol, respectively, among the lowest values in the OsSRF family (Table S16). These negative binding energies indicate thermodynamically favorable and spontaneous formation of stable OsSRF8–pesticide complexes. Notably, OsSRF8 binding to both pesticides also displayed high conformational stability, as indicated by ligand RMSD values for the top 10 docking conformations (Table S17). This finding provides insight into how the SRF gene family contributes to pesticide metabolism in plants. However, the functional or regulatory complexity of OsSRF genes and proteins in pesticide metabolism may not be fully captured by bioinformatics and expression analyses without experimental validation. Future studies employing overexpression and mutation experiments may further clarify the molecular mechanisms through which OsSRF genes regulate detoxification and metabolism of FLUME and OFF in rice.

5. Conclusions

SRF genes mediate plant stress responses, yet their roles in rice metabolism and detoxification of pesticides remain uncharacterized. This study aimed to identify OFF/FLUME-responsive SRF genes in rice and clarify their structural, evolutionary characteristics, expression profiles and binding activity to OFF/FLUME. we identified 12 OsSRF genes that respond to OFF and FLUME stress in the rice genome. Based on structural and functional characteristics, these genes were classified into four subfamilies: SRF-I–IV. Several cis-acting elements were detected in upstream promoter regions, linking transcriptional activation of OsSRF genes with stress responses to FLUME and OFF in rice. Most OsSRF proteins contained distinct motifs or domains that interact with various substrates, enabling adaptation to diverse environmental circumstances. Classification, chromosomal distribution, and collinearity analyses showed that OsSRF genes are distributed across 6 chromosomes in 4 groups, sharing 27 collinear genes with maize and barley and 1 each with soybean and Arabidopsis. Intraspecific collinearity analysis identified 5 homologous pairs among the 12 OsSRF genes. All collinear pairs exhibited Ka/Ks ratios < 1, indicating strong purifying selection during evolution. Molecular docking and RMSD analyses demonstrated that OFF and FLUME bind to OsSRF proteins through multiple amino acid residues. Furthermore, qRT-PCR results confirmed that OsSRF genes likely play critical roles in defense responses to FLUME and OFF stress. These findings highlight the molecular and physiological processes underlying OsSRF function in rice and provide a foundation for future research on OsSRF gene roles in pesticide metabolism and detoxification.

8. Clinical trial number

Not applicable.

9. Consent for publication

Not applicable.

CRediT authorship contribution statement

Zhi Zhong Zhou: Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation. Yang Yang Mo: Visualization, Validation, Software, Data curation. Li Qing Zeng: Visualization, Validation, Software, Data curation. Xiao Lu Wang: Validation, Data curation. Ying Yu Zeng: Validation, Software, Data curation. Kaikai Mao: Validation, Software. Xiao Liang Liu: Validation, Supervision, Project administration. He Chen: Writing – review & editing, Supervision, Resources, Project administration. Zhao Jie Chen: Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

7. Ethics approval and consent to participate

Not applicable.

Funding

National Natural Science Foundation of China (32402414).

Authorship contribution

Zhi Zhong Zhou and Yang Yang Mo, conducted the RNA-seq and RT-PCR analysis of rice exposed to OFF or FLUME. Li Qing Zeng and Zhi Jiang He, carried out the biochemical properties analysis of SRF protein genes. Xiao Lu Wang and Ying Yu Zeng, carried out protein interaction analysis of SRF protein genes. Xiao Liang Liu and Kaikai Mao, carried out the data analysis. He Chen and Zhao Jie Chen, conceived and designed the study, drafted, edited and proofread the manuscript.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

The following are the Supplementary data to this article: