Introduction

Winter wild oat (Avena sterilis subsp. ludoviciana (Durieu) Gillet & Magne) (hereafter, A. ludoviciana) is an annual, high self-pollinating and hexaploid (2n = 6x = 42) plant, which is one of the most common prevalent grass weeds, with strong competitive ability and high damage level in many crops, especially in wheat fields1,2,3. In Iran, A. ludoviciana is a typical noxious weed, mainly infesting wheat (Triticum aestivumL.) fields, causing severe yield reductions3,4,5. Also over the past decades control of this weed has relied upon the post-emergence application of the acetyl-CoA carboxylase (ACCase, EC. 6.4.1.2) inhibitor including clodinafop-propargyl and pinoxaden and acetolactate synthase (ALS, EC. 2.2.1.6) inhibitor including mesosulfuron + iodosulfuron mesosulfuronmethyl4,6. However, consistent selective pressure imposed by the intensive and continuous application of ACCase and ALS-inhibitors has resulted in the evolution of herbicide resistance in A. ludovicianapopulations and thereby making it one of the top herbicide-resistant grass weed species in Iran1,3,4,6.

Two main terminologies have been used to describe the mechanisms of resistance to herbicides, including target site-based resistance (TSR) and non-target site-based resistance (NTSR). While TSR mechanism aims at herbicide with the same site of action, NTSR mechanism can lead to multiple resistances across different groups of herbicides, irrespective of their mechanisms of action. On the other hand, accurate identification is more challenging due to polygenicity of NTSR compared to TSR mechanisms7,8. Four gens families including cytochrome P450s, glutathione transferases, ABC transporter and glycosyl transferase play a major role in the response to herbicides and NTSR resistance, reducing the amount of herbicide reaching the target site through increasing the rate of herbicide metabolism (metabolic resistance), altering absorption or translocation, compensation, or changes in defense networks7,8,9.

Due to the extent of herbicide use and based on the research conducted in Iran, most herbicide-resistant populations of A. ludovicianaare exhibiting TSR mechanism. Also the different amino acid substitutions Ile-1781-Leu, Ile-2041-Asn and Asp-2078-Gly at key positions in the CT domain of ACCase gene have been reported as the genetic basis of herbicide resistance to ACCase inhibitors3,5,6,10. With the advancement of various branches of omics technologies such as genomics, proteomics and transcriptomics the demands for deciphering herbicide resistance mechanism, discovering new herbicide targets and introducing state-of-the-art approaches for weed management are expanding11,12,13.

Unlike transcriptomics, proteomics is a stable strategy that shows dynamic changes in the abundance of proteins, especially proteins with low expression14. Therefore, to better understand the mechanisms of herbicide resistance in weeds, proteomics approaches is essential for determining relevant protein-level changes in plants. The isobaric Tags for Relative and Absolute Quantitation (iTRAQ) technology is a high-throughput proteomic technique that allows simultaneous identification and quantification of proteins in multiple samples, with high coverage, more sensitive and precise than conventional proteomics methods15. During the past decade iTRAQ is one of the most robust techniques in plant quantitative proteomic and have been widely applied to identify proteins responsive to biotic and abiotic stresses16,17,18,19,20,21,22. Although this technique has been used in only a few studies related to resistance to herbicides in weeds such as Alopecurus aequalisSobol23. , Echinochloa crus-galliL24. , Beckmannia syzigachneSteud20. and Raphanus raphanistrum25.

In this regard, one of the major challenges in proteomic research in weeds is the lack of a reference database, which makes it difficult to identify and annotate all proteins12. Considering that resistance to herbicides leads to various agricultural problems such as the use of herbicides in excessive doses or their repeated use, and as a result, the economic cost is high. Based on this, accurate and comprehensive diagnosis of various aspects of resistance mechanisms is very necessary to monitor and manage this phenomenon. In this regard, one of the applications of different branches of Omics technologies is investigate the molecular and biochemical aspects of the evolution of resistance to herbicides in weeds in order to create suitable management methods to overcome it. A quantitative proteomics experiment is able to provide quantitative information for thousands of proteins simultaneously, provides novel insights of key proteins involved in plant abiotic stress response. Therefore, this research was conducted for the first time in A. ludoviciana with the aim of identifying the proteins that are involved in the herbicide resistance, using the iTRAQ technique.

Results

Dose–response assays

The results obtained from the dose-response studies of susceptible and resistant populations of A. ludoviciana with iodosulfuron-methyl sodium + mesosulfuron-methyl and clodinafop-propargyl herbicides show that the slope of the line related to susceptible populations in response to the application of both mentioned herbicides is faster compared to the resistant populations, which shows that it has faced a greater weight loss (Fig. 1A, B), so that the slope of the susceptible and resistant plants to herbicides were (0.87 and 0.60) and (1.36 and 1.25) respectively (Table 1). Furthermore, based on the ED50 values of the populations, it can be stated that the herbicide dosage required for a 50% weight reduction of A. ludoviciana populations was lower for iodosulfuron-methyl sodium + mesosulfuron-methyl compared to clodinafop-propargyl. The ED50 values for susceptible and resistant populations in response to the application of iodosulfuron-methyl sodium + mesosulfuron-methyl and clodinafop-propargyl herbicides were (5.75 and 26) and (30.55 and 251), respectively. In addition to the aforementioned findings based on the resistance index of the mentioned herbicides, it can be inferred that A. ludoviciana populations exhibit higher resistance to clodinafop-propargyl (9.56) compared to iodosulfuron-methyl sodium + mesosulfuron-methyl (5.31).

Fig. 1
figure 1

Dose–response curves of the fresh weight reduction (% of control) in susceptible (S) and resistant (R) winter wild Oat populations. (A) clodinafop-propargyl. (B) iodosulfuron-methyl sodium + mesosulfuron-methyl. Each data point is the mean ± SE of two experiments.

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Table 1 Coefficients of log-logistic dose-response curve of Avena ludoviciana population based on the fresh weight for describing efficacy of different ratios of herbicides (iodosulfuron-methyl sodium + mesosulfuron-methyl and clodinafop-propargyl).
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Leaf gas exchange of S and R in responding to herbicide treatment

According to the results obtained from physiological parameter measurements, the rate of CO2 assimilation in the resistant population was reduced by 64% compared to the susceptible population (Fig. 2A). Also, intercellular CO2 concentration (Ci) is higher compared to the susceptible population (Fig. 2C). Furthermore, herbicide application resulted in a 44% decrease in stomatal conductance (Gs) and transpiration rates (Tr) in the resistant population compared to the susceptible population in both cases (Fig. 2B, D).

Fig. 2
figure 2

Leaf gas exchange analysis in A. ludoviciana R and S biotypes under herbicide treatment. (A) Net photosynthetic rate (Pn). (B) Stomata conductance (Gs). (C) Intercellular CO2 concentration (Ci). (D) Transpiration rate (Tr). Values are means ± standard error (n = 3). *: significantly different at the P < 0.05 level.

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Identification of DEPs

A total of 286,474 spectra were generated from the iTRAQ- LC-MS/MS proteomic analysis of all samples. After removing the low-scoring spectra, our results included 99,693 identified spectrums (34.8% of the total) of 18,313 unique peptides, 2,421 pre-clustered proteins and 484 clustered proteins (Additional file 1: Figure. S1). To evaluate replicates, hierarchical clustering and principal component analysis (PCA) were applied to the iTRAQ.

data. The clustering analysis of the samples shows that the replicates of the untreated susceptible samples (SC1, 2), herbicide-treated susceptible samples (ST1, 2) and the treated resistant samples (RT1, 2) are well clustered together, while the replicates of the untreated resistant samples are among the others (Additional file 2: Figure. S2A). Also, the results of PCA analysis show that the groups are categorized differently (Additional file 2: Figure. S2B).

Quantification of identified proteins

Identification of proteins involved in herbicide resistance was done based on the comparison of protein abundance between herbicide-treated and control samples using iTRAQ data in resistant and susceptible populations. To identify differentially expressed proteins between herbicide treatment and control, the fold change, (|log2FC| > 0.6; FC > 1.5; [fold change, FC]; p < 0.05) was used (Table 2). Then the comparison of DEPs in four main groups including (TS-TR, CS-CR, TS-CS, TR-CR) was conducted using Venn diagram (Fig. 3A, B). Comparison in the resistant group (TR-CR) indicated that there are 138 DEPs in which 68 proteins were up-regulated and 70 proteins were down-regulated. Also, in the susceptible group (TS-CS), a total of 93 DEPs were identified, in which 47 proteins were up-regulated and 46 proteins were down-regulated.

Table 2 The differentially changed proteins identified using iTRAQ in susceptible (S) and resistant (R) biotype.
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Fig. 3
figure 3

Identification of differentially expressed proteins (DEPs) between susceptible biotype (S) and resistant biotype (R) exposed to untreated (C) and Herbicide-treated (T). (A) Number of up or down-regulated proteins between the groups (TR-CR, TS-CS, TS-TR, CR-CS). (B) Venn diagram showing the number of DEPs between resistant (R) and susceptible (S) in four treatment comparisons (TR-CR, TS-CS, TS-TR, CR-CS).

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GO and KEGG Enrichment of DEPs

Functional annotation of DEPs under herbicide treatments was performed in both the susceptible and resistant populations using GO. Based on this annotation, the proteins were classified into three attributes: Biological Processes (BP), Molecular Functions (MF) and Cellular Components (CC) (Fig. 4 and Additional file 3).

GO analysis revealed that the Biological processes accounted for 64 GO, with dominant terms including cellular process (GO:0009987), metabolic process (GO:0008152), cellular metabolic process (GO:0044237), organic substance metabolic process (GO:0071704), primary metabolic process (GO:0044238), nitrogen compound metabolic process (GO:0006807), organic substance biosynthetic process (GO:1901576), cellular biosynthetic process (GO:0044249), response to stimulus (GO:0050896), oxidation-reduction process (GO:0055114), small molecule metabolic process (GO:0044281) (Fig. 4A and Additional file 3: Table S1).

Molecular functions accounted for 17 GO terms, with dominant terms including catalytic activity (GO:0003824), binding (GO:0005488), oxidoreductase activity (GO:0016491), ion binding (GO:0043167), anion binding (GO:0043168) (Fig. 4B and Additional file 3: Table S2) .And Cellular components accounted for 20 GO terms, with main terms including Cell (GO:0005623), intracellular (GO:0005622), cytoplasm (GO:0005737), intracellular organelle (GO:0043229), chloroplast (GO:0009507) (Fig. 4C and Additional file 3:Table S3).

Fig. 4
figure 4

GO enrichment for all of the identified DEPs. (A) The functional classification of DEPs involved in biological processes. (B) The functional classification of DEPs involved in molecular functions. (C) The functional classification of DEPs involved in cellular components.

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Furthermore, to further investigate the metabolic pathways associated with herbicide resistance, the analysis of enriched pathways of DEPs was conducted using KEGG enrichment analysis26,27 (Additional file 4). The results showed that in the resistant population (R), there are ten pathways, including metabolic pathways, Biosynthesis of secondary metabolites, Carbon metabolism, Carbon fixation in photosynthetic organisms, Glyoxylate and dicarboxylate metabolism. The enriched pathways are as follows (Additional file 4: Table S4). In contrast, in the susceptible population (S), it only includes two pathways: Metabolic pathways and amino sugar and nucleotide sugar metabolism (Additional file 4: Table S5).

Discussion

Winter wild oat (A. sterilis subsp. ludoviciana) is a problematic weed for many agricultural crops, especially wheat, due to its physiological characteristics and high competitive abilities.

In this study, several of the key proteins involved in herbicide resistance in A. ludoviciana were identified using the iTRAQ technique.

DEPs involved in photosynthesis

The results of our study demonstrated differential regulation of photosynthesis-related proteins in the susceptible and resistant populations. In the resistant population, the protein subunit psaA associated with PSI and two subunits (petA, petB) related to the cytochrome b6 complex were found to be down-regulated. Conversely, in the susceptible population, two subunits (psaA, psaB) associated with PSI, two subunits (psbD, psbB) associated with PSII, and one subunit (petB) related to the cytochrome b6 complex showed up-regulated (Table 2 and 5). Another protein that exhibited down-regulation in the resistant population is chlorophyll a-b binding (Q6YWJ7), which plays a role in the light-harvesting complex and the transfer of captured energy to the central complex of PSII (Table 2). The results of our study showed that in the resistant population, in addition to the enzymes involved in the light phase of photosynthesis, enzymes related to the Calvin-Benson cycle such as triosephosphate isomerase, fructose-1, 6-bisphosphatase, RuBisCO large subunit, and RuBisCO activase were down-regulated (Table 2). In contrast, the susceptible population exhibited up-regulation of photosynthesis-related proteins and the results from photosynthetic parameter measurements also showed higher rates of photosynthesis and CO2 assimilation. Reduced photosynthesis in the resistant population due to herbicide application can also leads to a decrease in CO2 consumption, and as a result, the concentration of intercellular CO2(Ci) was higher compared to the susceptible biotype. These findings confirm previous studies regarding the reduction of photosynthesis in response to herbicide application in the resistant population compared to the susceptible population25,28.

Fig. 5
figure 5

The KEGG photosynthesis pathway map for A. ludoviciana subjected to herbicide stress conditions. (A) The green highlights indicate the corresponding DEPs were significantly downregulated in herbicide-treated resistant samples to untreated resistant samples (RT vs. RC). (B) The yellow highlight indicate the corresponding DEPs were significantly upregulated in herbicide-treated susceptible samples to untreated susceptible samples (ST vs. SC). This figure (KEGG: map00195) was downloaded from the KEGG website with copy-right permission. The KEGG photosynthesis pathway map can be found online at http://www.kegg.jp/pathway/map00195.

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The correlation between reduced photosynthesis and resistance to herbicides can be explained in the context of ecological fitness29,30. Studies have shown that in many herbicide-resistant weeds, various traits such as height, germination ability, growth rate, biomass production, competitive ability, and reproductive have changed31,32,33.

DEPs involved in glycolysis and TCA cycle

The results of this study showed that two enzymes, succinate dehydrogenase and formate dehydrogenase, exhibited up-regulation in the resistant population under herbicide application (Table 2). Studies have shown that SDH plays an important role in stress response through signaling pathways, possibly mediated by salicylic acid34. The up-regulation of these two enzymes (FDH, SDH) in this study, along with the down-regulation of other enzymes in the TCA cycle, appears to provide the necessary intermediate metabolites and energy in response to herbicide-induced stress.

Other enzymes of the cycle, including isocitrate dehydrogenase and pyruvate dehydrogenase, showed down-regulation in the resistant population under herbicide application, while they remained unchanged in the susceptible population (Table 2). Studies suggest that the reduction in TCA cycle activity, which is associated with reduced consumption of organic acids, can serve as a strategy to conserve carbon structures for the synthesis of compounds needed to cope with stressful conditions35.

DEPs involved in stress response and detoxification

In this study, several proteins related to stress, herbicide detoxification, and ROS scavenging were identified, and they exhibited different regulation patterns in both susceptible and resistant populations. One of the key enzymes in this pathway is SOD [Cu-Zn], which showed up-regulation by 1.7-fold in the resistant population and down-regulation by the same amount in the susceptible population (Table 2). Studies have shown that SOD activity is higher in plants exposed to environmental stresses such as drought, salinity, heat, and toxic metals36. Another protein that plays a key and important role in the metabolism and detoxification of herbicides in plants is Cytochrome P450 monooxygenase (P450)37,38. In this study, Cytochrome P450 (Q6YSB4) had up-regulation in the resistant population (1.9-fold) and down-regulated in the susceptible population (1.8-fold) (Table 2), indicating its role in the evolution of resistance to the herbicides used in this experiment.

DEPs involved in protein metabolism and regulation

In this study, proteins related to the large subunit of plastid ribosomes, including 50 S ribosomal protein L19-2, chloroplastic, and 50 S ribosomal protein L12, chloroplastic, exhibited up-regulation in the resistant population and down-regulation in the susceptible population.

Considering that rapid changes in translation may enable cells to respond more effectively to unfavorable conditions and reduce the damage caused by ROS, it can be inferred that the up-regulated and meaningful expression of ribosomal proteins in the resistant population plays an important role in its resistance to herbicides.

DEPs involved in lipid metabolism

In this study, the protein 3-ketoacyl-CoA thiolase 2, peroxisomal, involved in beta-oxidation of fatty acids, was up-regulated in both the susceptible and resistant populations following herbicide application, although the increase was greater in the resistant population. These results indicate that the up-regulation of proteins associated with fatty acid synthesis partially compensates for the effects of ACCase-inhibiting herbicides, which hinder fatty acid synthesis in the weeds. Furthermore, it can be inferred that when facing herbicide stress, particularly under conditions of reduced photosynthetic rates, may utilize lipids as an energy source.

DEPs involved in lignin biosynthesis

The results of this study showed that the key proteins in the phenylpropanoid pathway, including probable cinnamyl alcohol dehydrogenase 6 and phenylalanine ammonia-lyase, are down-regulated in the resistant population, while the phenylalanine ammonia-lyase-like is down-regulated in the susceptible population (Table 2). On the other hand, considering the reduction in the activity of photosynthetic enzymes, particularly Rubisco, in the resistant population, which leads to decreased CO2fixation, it can be inferred that the accumulation of lignin, which is a major carbon sink in plants, is reduced through the down-regulation of the PAL and CAD enzymes. Studies have shown that the reduction in the activity of PAL and CAD enzymes, resulting in decreased lignin, strengthens the biosynthesis pathway and accumulation of other secondary metabolites in the phenylpropanoid pathway, such as flavonoids and anthocyanins, which play a crucial role in stress tolerance39. Therefore, it can be concluded that the reduction in the activity of enzymes in the phenylpropanoid pathway through the increase of secondary metabolites serves as a strategy for herbicide resistance in the resistant population.

DEPs involved in Signal Transduction

Studies have shown that kinases play an important role in membrane signaling associated with stress resistance, growth, adaptation to diverse environmental conditions, and reproduction40. Based on the results of the present study, the serine/threonine kinase protein (B7F9F7) exhibited up-regulation in the resistant population, while (Q6YY75) showed down-regulation in the susceptible population (Table 2). Calreticulin is another protein that specifically plays an important role in Ca2+homeostasis and signaling network41. In this study, calreticulin-like protein (Q7Y140) was induced in the resistant population by herbicide treatment, suggesting that altered CRT expression may act as a signaling molecule in modulating the herbicidal effects on the plant and consequently enhancing herbicide resistance.

DEPs involved in transcription

Transcription factors play a crucial role in plant growth, cell cycle, cellular signaling, metabolic pathways, and response to various environmental stresses in all organisms42. The results from this study showed that, Q84Q84, annotated as RNA polymerase sigma factor (sigB), and the protein transcription factor TEOSINTE BRANCHED (A0A0N7KQS6) showed significant up-regulation in the resistant population (Table 2). Investigations demonstrate that SigB is a general component in plant responses to different stresses, aiding the cell in resisting oxidative pressure43,44. Therefore, considering the role of sigma factors in responding to various stresses in plants, it seems that the up-regulation of this protein may play a significant role in herbicide resistance in the resistant population.

DEPs involved in ion channel proteins

In this study, three proteins related to ion channels, including probable ion channel CASTOR (Q75LD5), mechanosensitive ion channel protein 10 (Q69NN6), and two-pore potassium channel c (Q69TN4), showed up-regulation in the resistant population (Table 2). One of the mechanisms of herbicide resistance in resistant weed populations is the sequestration of herbicides through vacuolar sequestration7. Similar strategies are employed by salt-resistant plants to reduce the toxic effects of ions by sequestering them into vacuoles through ion channels45. Additionally, the role of cationic and anionic channels in response to biotic and abiotic stresses through signaling pathways is also significant46, and it appears that the up regulation of proteins associated with ion channels in this study may play a crucial role in herbicide resistance in the resistant biotype.

Conclusions

The results of this study show that the resistant population has different strategies to reduce herbicide stress, so that after herbicide treatment, plants change their carbon metabolism and amino acid metabolism in order to reduce costs. In other words, plant defense in resistant plants is based on draining energy from growth to defensive metabolites. Also, enzyme and antioxidant systems moderate excessive production of ROS and oxidative stress caused by herbicides. The results obtained from the antioxidant and enzymatic defense system of A. ludoviciana show that the molecular response of susceptible and resistant populations are different when exposed to herbicides, so that key proteins, especially SOD [Cu-Zn] and cytochrome P450, are down-regulated in the susceptible population and up-regulation in resistant population. It seems that the plant’s defense system is suppressed by the herbicide in the susceptible population, while activated in the resistant population. Although in susceptible population, another group of proteins such as dehydrin, U-box, dnaJ, and F-box were identified, this had an increased expression under the conditions of herbicide stress. However, it seems that their role in herbicide resistance is not to the extent of increasing resistance in the susceptible population and preventing it’s from death. In general, the defense system against herbicides is evolved and active in the resistant population and weak and non-evolved in the susceptible population. As the analysis of differential protein enrichment pathways using KEGG also shows, DEPs are involved in ten pathways in the resistant population but just two pathways in the susceptible population. In total, in the resistant A. ludoviciana population, in addition to the activation of enzymatic and non-enzymatic defense systems, other strategies such as reduced photosynthesis and respiration, increased transcription and translation activity, enhanced lipid metabolism, regulation of cellular processes and homeostasis, and up-regulation of proteins associated with signaling and ion channels play a role in resistance to herbicide. These findings provide new insights into the role of different proteins in resistance to herbicide and contribute to a comprehensive understanding of herbicide resistance in A. ludoviciana.

Methods

Plant materials and herbicide treatment

The flowchart corresponding to the different parts of this experiment is shown in Additional files 5: Figure S3. In this study, confirmed seeds belonging to two A. ludovicianapopulations were used. The resistance and susceptibility of these populations to the studied herbicides were previously confirmed in an experiment. A resistant biotype (R) with a history of resistances to ACCase and ALS inhibiting herbicides including clodinafop-propargyl and iodosulfuron-methyl-sodium + mesosulfuron-methyl collected from within a winter wheat field, and a susceptible biotype (S) collected from the field margins where no herbicide was used. The resistance and susceptibility state of the R and S to clodinafop-propargyl (ACCase inhibitor) and iodosulfuron-methylsodium + mesosulfuron-methyl (ALS inhibitor) were previously confirmed in a whole-plant experiment. Populations R (W-KZ-DAAZ3-97, 231515.10; 3495978.54) and S (W-TE-PI1-97, 3907421.01; 566443.22) were collected under the supervision of a colleague (Hamidreza Sasanfar) at the Iranian Research Institute of Plant Protection (IRIPP), from Tehran and Khouzestan provinces in 2018. Also the collected populations have been kept under standard conditions (stable humidity and constant low temperature) at the National Seed Bank of Resistance weeds, Department of Weed Research, IRIPP. Initially, the seeds were disinfected with a 5% sodium hypochlorite solution (NaOCl) for 5 min, and after breaking seed dormancy (scarification and pre-chilling at 4 °C for one week), they were sown in half-liter pots with three replications and grown in a greenhouse under a 16-hour light and 8-hour dark photoperiod, and a temperature of 15 °C47. After approximately three weeks, the seedlings at the two to three-leaf stage were treated with both herbicides at recommended rates, namely clodinafop-propargyl (ACCase inhibitor) and iodosulfuron-methyl sodium + mesosulfuron-methyl (ALS inhibitor) at rate of 80 g a.i. ha−1 and 18 g a.i. ha−1, respectively (each herbicide sprayed separately and with the minimum possible time interval between applications). Approximately 24 h after herbicide application, sampling was conducted from the treated and untreated (control) seedlings, and after placing them in liquid nitrogen, they were stored at −80 °C until further experimental steps were performed24.

Dose-response test

In order to evaluate the level of resistance among A. ludoviciana populations, an experiment was conducted in a completely randomized design. The number of individuals in each treatment was eight, and there were six replications for each herbicide separately in a whole-plant assay in the greenhouse. In this experiment, resistant and susceptible A. ludoviciana populations were exposed to different doses of the herbicides clodinafop-propargyl and iodosulfuron-methyl sodium + mesosulfuron-methyl. Four weeks after spraying the plants in each pot were harvested by cutting from the soil surface and fresh matters were immediately weighed. To obtain the resistance index and dose-response curves in the resistant and susceptible populations, the weight data were fitted using the three-parameter log-logistic equation (Eq. 1):

$$Y=d+exp[b(log\: x-log\: e)]$$
(1)

In this equation, Y = response rate (percent compared to the control) in dose = x concentration of herbicide (grams of effective substance per hectare), d = the upper limit of the curve, b = the slope of the line and e = the ED50 or dose of the herbicide that causes a 50% reduction in fresh or dry weight curves between the upper and lower limits.

Measurements of Leaf Gas Exchange

A. ludoviciana with herbicide-resistant and susceptible populations were planted in plastic pots in a climate controlled chamber using the same conditions. Seedlings at the six-leaf stage.

were used to obtain Gas exchange parameters including net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO2 concentration (Ci) and transient transpiration rate (Tr). Measurements were made under the leaves of two mature leaves of each sample with three repetitions using a portable infrared gas analyzer (Li-6400, Li-COR Inc., USA) from 10:00 to 12:00 h on a sunny day. Conditions in the chamber were: PAR of 1200–1400 µmol (photon) m–2 s–1, leaf temperature of 25 °C, relative humidity of 60–75%, CO2 concentration of 380 µmol mol–1.

Protein extraction

To extract proteins from leaf tissue, the Trichloroacetic acid (TCA)/acetone method was performed with slight modifications48. For this purpose, one gram of leaf tissue was finely ground using a mortar and pestle with liquid nitrogen. Then, 10 mL of acetone containing 10% (w/v) TCA and 0.07% (w/v) 2-mercaptoethanol was added to the ground sample. The resulting mixture was kept at a temperature of −20 °C for two hours. Subsequently, the samples were centrifuged at 25,000 g for 20 min. To the obtained protein pellet, 10 mL of acetone solution was added and incubated at 20 °C for one hour. The samples were centrifuged again at 12,000 g, and the washing step was repeated twice to remove non-protein contaminants. The remaining acetone residue in the protein pellet was removed by lyophilization and the protein pellet was dried at 80 °C. Additionally, the protein concentration was determined using the Bradford assay (Bio-Rad) with bovine serum albumin as the standard49. The extraction of protein, reduction, alkylation, digestion, and iTRAQ labeling of proteins carried out in Institute of Plant Protection and Agricultural Research, Organization (AREEO) in Iran and LC-MS/MS analysis performed at PhenoSwitch Bioscience’s company in Canada.

Reduction, alkylation, digestion, and iTRAQ labeling of proteins

The iTRAQ protocol generally includes the steps of reduction, alkylation, and protein digestion prior to their labeling. Briefly, protein samples were reduced with with 10 mM dithiothreitol at 56 °C for 1 h. followed by alkylation through the addition of iodoacetamide. Iodoacetamide (IAM) is small organic electrophiles intended to stably block cysteine thiols through covalent modification. Subsequently, enzymatic digestion of each of the samples (100 micrograms) was performed by adding trypsin (Promega, USA) at a ratio of (w/w) (50:1).

This study includes a total of eight samples, where two susceptible and resistant populations of A. ludoviciana were evaluated with and without herbicide treatment, each with two biological replicates. After initial preparation, the samples (100 µg) were labeled using iTRAQ 8-plex kits (AB Sciex, USA) according to the manufacturer’s instructions as follows: untreated susceptible samples (SC-1, SC-2) were labeled with tags 113 and 114, herbicide-treated susceptible samples (ST-1, ST-2) were labeled with tags 115 and 116, untreated resistant samples (RC-1, RC-2) were labeled with tags 117 and 118, and herbicide-treated resistant samples (RT-1, RT-2) were labeled with tags 119 and 121. After labeled with the isobaric iTRAQ reagents–8plex, the eight individual digested protein samples were pooled for further processing and MS analysis.

Strong cation exchange (SCX) chromatography separation

The LC-20AB HPLC Pump system (Shimadzu Corporation, Kyoto, Japan) was used for peptides fractionation. The iTRAQ-labeled peptide mixture was reconstituted with 4 mL of buffer A (25 mM NaH2PO4 in 25% ACN, pH 3.0) and loaded onto a 4.6 × 250-mmUltremex SCX column (Phenomenex, Torrance, CA) containing 5-µm particles. The peptides were eluted at a flow rate of 1 mL·min − 1 with a gradient of buffer A for 10 min, 5–35% buffer B (25 mM NaH2PO4, 1 M KCl in 25% ACN, pH 3.0) for 11 min, and 35–80% buffer B for 1 min. The system was then maintained in 80% buffer B for 3 min before equilibrating with buffer A for 10 min prior to the next injection. The elution was monitored by measuring the absorbance at 214 nmUV spectroscopy. SCX-fractionated peptides were desalted using Strata X C18 column (Phenomenex, Torrance, CA) and dried in a vacuum centrifuge for the Mass Spectrum (MS) analysis20.

Liquid chromatography-mass spectrometry (LC-MS/MS) measurements

Equisition was performed with an ABSciex TripleTOF 6600 (ABSciex, Foster City, CA, USA) equipped with an electrospray interface with a 25 μm iD capillary and coupled to an Eksigent µUHPLC (Eksigent, Redwood City, CA, USA). Analyst TF 1.8 software was used to control the instrument and for data processing and acquisition. Acquisition was performed in Information Dependant Acquisition (IDA) mode. The source voltage was set to 5.5 kV and maintained at 325 °C, curtain gas was set at 45 psi, gas one at 25 psi and gas two at 25 psi. Separation was performed on a reversed phase Kinetex XB C18 column 0.3 μm i.d., 2.6 μm particles, 150 mm which was maintained at 60 °C. Samples were injected by loop overfilling into a 5µL loop. For the 15 min (IDA) LC gradient, the mobile phase consisted of the following solvent A (0.2% v/v formic acid and 3% DMSO v/v in water) and solvent B (0.2% v/v formic acid and 3% DMSO in Ethanol (EtOH) at a flow rate of 3 µL/min.

Protein identification and data analysis

For peptide analysis, the raw data files were initially converted to mgf files using Proteome Discoverer 1.4. Subsequently, ProteinPilot 5.0 software (AB SCIEX, USA) was utilized for analyzing the mgf files, and the Paragon algorithm was employed for searching against the Rice protein database UniProt-Rice_UP000059680 (48,903 entries)50. Comparisons between treated and control samples of the same species were performed to calculate the DEPs using the limma package in the R software, with crteria of (|log2FC| > 0.6; FC > 1.5; [fold change, FC]; p < 0.05). Data normalization was conducted using the quantile method, and box plots were generated (Aditional files 6: Figure S4). Protein enrichment analysis of DEPs in different pathways was performed using the DAVID database (https://david.ncifcrf.gov/).

iTRAQ quantitative identified DEPs

Firstly, all identified proteins were uploaded onto UniProt database to get their IDs and then were searched against the GO database (http://www.geneontology.org). GO annotation of each protein was derived from the UniProt-GOA database (http://www.ebi.ac.uk/GOA/) and pathways enrichment analysis of differentially expressed proteins was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database26,27 (http://www.genome.jp/kegg/).