Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Transcriptomic analysis reveals cloquintocet-mexyl-inducible genes in hexaploid wheat (Triticum aestivum L.)

  • Olivia A. Landau ,

    Contributed equally to this work with: Olivia A. Landau, Brendan V. Jamison, Dean E. Riechers

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing

    olivia.landau@USDA.gov

    Affiliation Department of Crop Sciences, University of Illinois, Urbana, Illinois, United States of America

    ⨯
  • Brendan V. Jamison ,

    Contributed equally to this work with: Olivia A. Landau, Brendan V. Jamison, Dean E. Riechers

    Roles Formal analysis, Investigation, Methodology, Visualization

    Affiliation Department of Crop Sciences, University of Illinois, Urbana, Illinois, United States of America

    ⨯
  • Dean E. Riechers

    Contributed equally to this work with: Olivia A. Landau, Brendan V. Jamison, Dean E. Riechers

    Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Crop Sciences, University of Illinois, Urbana, Illinois, United States of America

    ⨯

Abstract

Identification and characterization of genes encoding herbicide-detoxifying enzymes is lacking in allohexaploid wheat (Triticum aestivum L.). Gene expression is frequently induced by herbicide safeners and implies the encoded enzymes serve a role in herbicide metabolism and detoxification. Cloquintocet-mexyl (CM) is a safener commonly utilized with halauxifen-methyl (HM), a synthetic auxin herbicide whose phytotoxic form is halauxifen acid (HA). Our first objective was to identify candidate HA-detoxifying genes via RNA-Seq by comparing untreated and CM-treated leaf tissue. On average, 81% of RNA-Seq library reads mapped uniquely to the reference genome and 76.4% of reads were mapped to a gene. Among the 103 significant differentially expressed genes (DEGs), functional annotations indicate the majority of DEGs encode proteins associated with herbicide or xenobiotic metabolism. This finding was further corroborated by gene ontology (GO) enrichment analysis, where several genes were assigned GO terms indicating oxidoreductase activity (34 genes) and transferase activity (45 genes). One of the significant DEGs is a member of the CYP81A subfamily of cytochrome P450s (CYPs; denoted as CYP81A-5A), which are of interest due to their ability to catalyze synthetic auxin detoxification. To investigate CYP expression induced by HM and/or CM, our second objective was to measure gene-specific expression of CYP81A-5A and its homoeologs (CYP81A-5B and CYP81A-5D) in untreated leaf tissue and leaf tissue treated with CM and HM over time using RT-qPCR. Relative to the reference gene (β-tubulin), basal CYP expression is high, expression among these CYPs varies over time, and expression for all CYPs is CM-inducible but not HM-inducible. Further analysis of CYP81A-5A, such as gene knock-out, overexpression experiments, or in vitro activity assays with purified enzyme are necessary to test the hypotheses that the encoded CYP detoxifies HA and that CM upregulates this reaction.

Citation: Landau OA, Jamison BV, Riechers DE (2025) Transcriptomic analysis reveals cloquintocet-mexyl-inducible genes in hexaploid wheat (Triticum aestivum L.). PLoS ONE 20(2): e0319151. https://doi.org/10.1371/journal.pone.0319151

Editor: Bahram Heidari, Shiraz University, IRAN, ISLAMIC REPUBLIC OF

Received: October 2, 2024; Accepted: January 29, 2025; Published: February 18, 2025

This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Data Availability: Illumina RNA-Seq data are available on NCBI (BioProject Accession #: PRJNA983309; https://www.ncbi.nlm.nih.gov/bioproject/983309). All other relevant data is included in the Supporting Information files.

Funding: This research was supported by an Undergraduate Research Award at the University of Illinois Urbana-Champaign to B.V.J. and funding from Corteva Agriscience to D.E.R. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The allohexaploid Triticum aestivum L. (wheat) genome (2n =  6x =  42; AABBDD) contains 21 homologous pairs of chromosomes spread among three homoeologous sets of seven chromosomes [1,2]. Like other cereal crops, wheat is tolerant to the synthetic auxin herbicides, which are commonly utilized for selective postemergence dicot weed control [3,4]. The primary mechanism behind selectivity is qualitative and quantitative differences in detoxification of these herbicides between grasses and dicots [4]. The general process for plant herbicide metabolism can be divided into three parts: Phase I, Phase II, and Phase III. Phase I typically involves oxidation reactions catalyzed by cytochrome P450s (CYPs), and these metabolites are subjected to Phase II conjugation reactions with endogenous substrates (e.g., glucose, reduced glutathione, or amino acids) [5–8]. During Phase III, Phase II metabolites are transported into the vacuole by transport proteins (e.g., ATP-binding cassette (ABC) transport proteins), and within the vacuole metabolites are detoxified further or incorporated into the cell wall [6–9]. Synthetic auxin herbicides exemplify of how differential herbicide metabolism allows for herbicide selectivity between grass crops and dicot weeds. Generally, grasses possess CYPs that catalyze irreversible ring-hydroxylation or dealkylation reactions of synthetic auxin herbicides, forming a less toxic compound and predisposing the herbicide to glucose conjugation by UDP-dependent glucosyltransferase (UGTs) and subsequent sequestration to the vacuole by ABC transport proteins [6,9,10]. By contrast, synthetic auxin herbicide metabolism in dicots mainly consists of reversible reactions, such as amino acid or glucose conjugation of the carboxylic acid, which results in some level of the phytotoxic form of the herbicide remaining in the cell [3,11].

While the role of CYPs in herbicide detoxification in tolerant crops and resistant weeds has been established [6,12,13], a specific gene encoding a synthetic auxin detoxifying CYP has not been characterized in wheat. However, several examples of CYPs governing tolerance to certain herbicides have been documented in grass crop [14–23] and weed species [19,24–29], including synthetic auxins in the case of maize (Zea mays) CYP81A9 [16]. These findings indicate members of the CYP81A subfamily are likely candidate genes associated with synthetic auxin detoxification.

The herbicide examined in this study, halauxifen-methyl (HM), was commercialized in 2015 and is a member of the 6-aryl-picolinic acid subclass of synthetic auxins [30,31]. HM is typically applied postemergence to wheat in tank mixtures with other herbicides [32]. Wheat is tolerant to HM through rapid detoxification of the phytotoxic form, halauxifen acid (HA) [33]. More specifically, once HM is de-esterified to HA, the HA is O-demethylated, and subsequently conjugated with glucose [33]. Previous HM phenotyping experiments [34] and results from liquid chromatography-mass spectrometry (LC-MS) experiments [35] indicate HA-detoxifying genes are located on chromosome 5A. Thus, further examination through RNA-Seq is warranted to identify candidate HA-detoxifying genes.

To prevent injury in wheat, cloquintocet-mexyl (CM) is typically included with postemergence applications of HM [32,33]. Safeners, like CM, are commonly applied with foliar herbicides to large-seeded cereals to reduce herbicide injury, which is accomplished by inducing expression and activity of herbicide detoxification and transporter enzymes [8,36,37]. While the phenotypic and metabolic effects of safeners are well documented, knowledge of safener regulation of corresponding genes or signaling pathways being induced for crop protection is still severely limited [7]. To date, transcriptome analyses of safeners in grass crops are relatively rare [38–42], and currently only one published paper has reported wheat transcriptomic data in response to safener, mefenpyr-diethyl [43]. Furthermore, gene expression induced by safeners implies the encoded enzymes may play a role in herbicide metabolism [7–9,44]; however, further biochemical studies are required to functionally validate the function of candidate genes. Thus, the first objective of this research is to identify candidate genes responsible for HA-detoxification via RNA-Seq by comparing the expression of genes in untreated (UT) and CM-treated tissue. The second objective is to validate CM-inducible gene expression of candidate gene(s) and to compare expression of candidate gene(s) in response to CM and/or HM treatments via TaqMan RT-qPCR.

Materials and methods

Chemicals and plant materials

Chemicals used in the following experiments were provided by Corteva Agriscience and include CM (formulated as a 25% active ingredient wettable powder) and the ElevoreTM formulation of HM (contains 6.87% HM and equates to 68.5 grams halauxifen acid equivalent per liter of ElevoreTM). Seed for the winter wheat variety, ‘Kaskaskia’ [45], was provided by Dr. Frederic Kolb at the University of Illinois at Urbana-Champaign.

Seed sowing, treatment application and tissue collection

For both RNA-Seq and RT-qPCR experiments, seeds were planted in 382 cm3 pots containing a 1:1:1 soil mixture of soil, peat, and sand. Pots were placed in a greenhouse room with a 14-hour day length and a constant 21 to 23°C temperature band. Natural light was supplemented with halide lamps delivering 800 μmol m−2 s−1 photon flux to the plant canopy. Until the application of treatments, plants were watered at the soil surface once a day until the soil was uniformly moist. When seedlings produced 1–2 leaves (Zadoks stages 11–12), treatments were applied using a compressed air research sprayer calibrated to deliver 187 L ha−1 at 275 kPa with an even flat-fan nozzle.

For the RNA-Seq experiment UT plants were sprayed with a 0.1% solution of nonionic surfactant (NIS), while CM-treated plants were sprayed with a solution containing 15 g a.i. ha−1 of CM and 0.1% NIS [46]. After application of treatments, plants were returned to the greenhouse room. At 6 hours after treatment (HAT) the first leaves were cut at the collar from five plants (approximately 500 mg of leaf tissue) were collected, frozen with liquid nitrogen, and stored in a −80°C freezer until RNA extraction. This experiment was independently conducted three times with one UT sample and one CM sample from each experimental replication utilized for RNA-Seq sample submission. In total, three independent biological replicates for each treatment were submitted for RNA-Seq.

For the RT-qPCR experiment, treatments from the RNA-Seq experiment were included along with the addition of 5 g a.e. ha-1 of HM and a combination treatment of CM and HM (CM+HM). All treatments included 0.1% NIS. Harvesting procedures were the same as previously mentioned but performed at 3, 6, and 12 HAT. This experiment was conducted three times. One biological replicate per treatment per timepoint from each experimental replication was utilized for TaqMan RT-qPCR. In total, three independent biological replicates for each treatment at a given timepoint were utilized for TaqMan RT-qPCR.

RNA extraction, library construction, and transcriptomic analysis

Total RNA was isolated using previously described methods [34], and RNA concentration and purity were determined with a NanoDrop 1000 spectrophotometer (Thermo Scientific, USA). RNA samples with concentrations above 100 ng/µL, A260/A280 ratios above 1.8, and A260/A230 ratios between 2.0 and 2.3 were used in downstream processes. Each RNA sample (10 μg) was treated with TURBO™ DNase using the TURBO DNA-free™ Kit (Thermo Scientific, USA) using the manufacturer’s protocol to eliminate genomic DNA contamination. The concentration of DNase-treated RNA was determined with a Qubit 2.0 fluorometer (Invitrogen, USA) using the manufacturer’s protocol.

Six RNA samples (three UT biological replicates and three CM-treated biological replicates) were submitted to the Roy J. Carver Biotechnology Center for the construction of RNA-Seq libraries. An AATI Fragment Analyzer was used to evaluate integrity of the RNA samples, which indicated the 28S and 16S bands were prominent and degradation was not detected. Libraries were quantitated by qPCR and sequenced on one lane for 151 cycles from each end of the fragments on a NovaSeq 6000, generating 150-bp paired end reads. RNA-Seq data quality was estimated by FastQC v0.12.0 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) with low quality sequences filtered with fastp v0.17.0 [47]. None of the reads failed to pass the filter (Table 1). The ‘Chinese Spring’ reference genome (IWGSC RefSeq v1.1) and functional gene annotations were downloaded from URGI (https://wheat-urgi.versailles.inra.fr/). Salmon/1.10.1 was used to align clean reads to the reference genome and quantify reads [48]. All tests of significance for differential expression were analyzed with modules from the edgeR package [49] in R (version 4.2.0) using RStudio (Version 2023.03.0), in which the TMM normalization was utilized to adjust expression values to a common scale. Gene expression levels were calculated using counts-per-million and transformed to log2 counts per million. Significant DEGs were identified if fold changes in expression were ≥5 or ≤−5 and the false-discovery rate was <0.1. Illumina RNA-Seq data are available on NCBI (BioProject Accession #: PRJNA983309; https://www.ncbi.nlm.nih.gov/bioproject/983309). Molecular functions of DEGs were assigned gene ontology (GO) annotations with agriGO v2.0, which were used to perform GO enrichment analysis with default settings [50,51]. Of the 103 significant DEGs, 99 (96.1%) were assigned GO terms. The coding sequences of significant CYPs and UGTs were compared to Phytozome BLAST results (https://phytozome-next.jgi.doe.gov/) of CYP coding sequences maize [52] and rice (Oryza sativa) [53] using Phytozome BLAST (https://phytozome-next.jgi.doe.gov/).

thumbnail
Download:
Table 1. RNA-Seq libraries prepared from untreated (UT) and cloquintocet-mexyl (CM)-treated wheat leaf tissue.

https://doi.org/10.1371/journal.pone.0319151.t001

RNA preparation, primer design, thermal cycling conditions and data analysis for TaqMan RT-qPCR

RNA was prepared, evaluated for quality, and quantitated utilizing the same methods described in the previous section. However, instead of utilizing an AATI Fragment Analyzer to evaluate integrity, total RNA was examined for quality after denaturation at 55°C in the presence of formamide and formaldehyde, then integrity of rRNA bands were visualized with ethidium bromide in a 1.2% agarose gel containing 0.4 M formaldehyde [54].

Due to the high sequence identity (≥96%) among genes of interest, TaqMan RT-qPCR methodology was chosen to achieve homoeolog discrimination. TaqMan primers and probes were designed with AlleleID 7 (PREMIER Biosoft, USA) for the candidate gene TraesCS5A02G394800.1 and its homoeologs TraesCS5B02G402800.1 and TraesCS5D02G407300.1 (denoted as CYP81A-5A, CYP81A-5B, and CYP81A-5D, respectively; S1 and S2 Tables). Primers and probe were also designed for a β-tubulin gene (β-TUB; TraesCS7D02G454200.1) to serve as a reference gene (S1 and S2 Tables). This gene was chosen based on previous results indicating stable expression in wheat leaf tissue [55]. PCR efficiencies of CYPs and β-TUB primers were calculated in SDS 2.3 software (Applied Biosystems, USA) with six-point standard curves in a 10-fold dilution series of RNA (S1 Table).

RT-qPCR was conducted using a 7900 HT Sequence Detection System (Applied Biosystems, USA) and reactions were performed in 20 µL volumes, consisting of 10 µL TaqMan RT-PCR Mix (2x), 1.0 µL TaqMan Gene Expression Assay (20x), 0.5 µL TaqMan RT Enzyme Mix (40x), and 8.5 µL (170 ng) RNA template (TaqMan™ RNA-to-Ct™ 1-Step Kit; Applied Biosystems, USA). The following program was used for RT-qPCR: 48°C for 15 minutes, 95°C for 10 minutes, followed by 40 cycles at 95°C for 15 seconds and 60°C for 1 minute. Each sample was analyzed in three technical replicates and mean cycle threshold (Ct) values were calculated. Reverse-transcription negative controls were included to verify genomic DNA contamination was not contributing to Ct values. CM- and HM-induced gene expression for each CYP gene was calculated relative to transcript levels in the nontreated control samples (per treatment and timepoint) and normalized using β-TUB a reference gene with the 2-ΔΔCt method.

Comparisons of the ΔΔCt and Ct values of genes were performed with the lme4 package (Bates et al., 2015) in R (version 4.2.0) using RStudio (Version 2023.03.0). A mixed effects model was utilized for both experiments where gene, treatment, and their interactions were treated as fixed effects and replicates were treated as random effects. Data was subjected to ANOVA and means were separated with Fisher’s protected LSD (α =  0.05).

Results

RNA-Seq and GO analysis of differentially expressed genes

Among all RNA-Seq libraries for UT and CM-treated leaf tissue, 81% of reads mapped uniquely to the reference genome, 76.4% of reads were mapped to a gene, 10% of reads were not mapped to a gene, 3% of reads were unmapped, 11% of reads were multi-mapped, and 0.1% of reads were ambiguous (Table 1). In terms of significant differentially expressed genes (DEGs), 101 genes were induced and two genes were repressed by CM (Fig 1). Based on their functional annotations, the majority of these DEGs encode proteins associated with Phase I, II or III herbicide/xenobiotic metabolism (81 genes upregulated and 1 gene downregulated) while the remaining DEGs are associated with stress/defense response (2 genes upregulated and 1 gene downregulated), amino acid metabolism (5 genes upregulated), heavy metal binding (3 genes upregulated), or encode transcription factors (3 genes upregulated; Fig 2). The two repressed genes, TraesCS3A02G042700 and TraesCS7D02G370400, are annotated as a peroxidase (potentially associated with Phase I herbicide/xenobiotic metabolism) and a “negative regulator of resistance” (a stress/defense response gene), respectively (Figs 1 and 2; S3 Table). These results indicate gene repression by CM is minimal in leaf tissue at 6 HAT, and we hypothesize that CM triggers a certain level of oxidative stress—possibly to fine-tune safener-mediated transcriptional regulation. Overall, these results indicate CM treatment at 6 HAT primarily induces gene expression with a relatively minor degree of gene repression.

thumbnail
Download:
Fig 1. Mean-difference plot showing the log2 fold change and mean abundance of each transcript in log2 counts per million (CPM).

Genes that were significantly induced (Up) and repressed (Down) by cloquintocet-mexyl are highlighted in red and blue, respectively. Genes that were not significantly differentially expressed (Non-DE) are highlighted in black. TraesCS5A02G397800.1 is circled in green.

https://doi.org/10.1371/journal.pone.0319151.g001

thumbnail
Download:
Fig 2. Tree map of functional annotations assigned to significant differentially expressed genes identified by RNA-Seq.

The main categories (bolded and underlined) include Phase I (light green), Phase II (orange), and Phase III (grey) metabolism, Amino Acid Metabolism (light blue), Transcription Factors (TFs; yellow), Proteins with only Domain Info (dark blue), and Stress/Defense Related (dark green). The number of identified genes is listed in parentheses or after the comma. Abbreviations: 2OG, 2-oxoglutarate; 5β-PORs, progesterone 5-beta-reductase; ABC, ATP-binding cassette; ADH, alcohol dehydrogenase; AzoR, azoreductase; CCR, cinnamoyl-CoA reductase 4; CSE, cystathionine gamma-lyase; CYP, cytochrome P450; GST, glutathione S-transferase; GT, glycosyltransferase; HTH, helix-turn-helix; IP, inhibitor protein; LAC, laccase; MTOX, N-methyl-L-tryptophan oxidase; OPR, 12-oxophytodienoate reductase; PLATZ, plant AT-rich protein and zinc-binding protein; PRP, pathogen-related protein; SBP, selenium-binding protein; SCP, serine carboxypeptidase; SDR, short chain dehydrogenase/reductase.

https://doi.org/10.1371/journal.pone.0319151.g002

The GO enrichment analysis identified 34 genes significantly associated with oxidoreductase activity and 45 genes significantly associated with transferase activity, including 11 genes associated with glutathione S-transferase (GST) and 19 genes associated with UGT activity (S1 and S2 Figs). Results of GO enrichment analysis corroborate with the results of the tree map (Fig 2) and further elaborate on the function of the encoded proteins of the DEGs by indicating which substrates or molecular bonds are potentially involved in the reactions catalyzed by the proteins. For example, the GO terms assigned to a specific gene may not only indicate it is a UGT, but also could catalyze glucose conjugations with abscisic acid or indole-3-acetic acid (S4 Table).

Based on the results of previous LC-MS [35] and phenotypic experiments [34], we hypothesze major candidate gene(s) encoding HA-detoxifying enzyme(s) are located on wheat chromosome 5A. Of the 103 significant DEGs, five genes (three UGTs and two CYPs) are located on the group 5 chromosomes with fold inductions for these genes ranging from approximately 5 to 23 (S3 Fig). The UGTs are TraesCS5D02G404200.1, TraesCS5A02G394800.1, and TraesCS5B02G305600.1, while the CYPs are TraesCS5A02G472300.1 and TraesCS5A02G397800.1.

Phytozome BLAST results of TraesCS5A02G472300.1 and TraesCS5A02G397800.1 display high sequence identity to members of CYP71C (≥70%) and CYP81A (≥80%), respectively. Both CYPs were assigned GO:0003824 (catalytic activity), GO:0016491 (oxidoreductase activity), and GO:0046914 (transition metal ion binding) (S4 Table), which are typical terms associated with CYPs. Based on resources from the International Wheat Genome Sequencing Consortium [2,56], the UGTs, TraesCS5D02G404200.1 and TraesCS5A02G394800.1, are homoeologs (94% identity), and Phytozome BLAST results indicate they have high sequence identity to members of UGT85A in maize and rice (≥71%). Both UGTs were assigned 23 GO terms specifying the glucosyltransferase activities for several compounds, including indole-3-acetate, benzoic acid, salicylic acid, abscisic acid and flavonol (S4 Table). Literature regarding the maize and rice UGT85As is not currently available, but other reports indicate that the UGT85As are in involved with plant stress responses in wheat, tobacco (Nicotiana tabacum), grapevine (Vitis vinifera ×  Vitis labrusca) [57–59]. TraesCS5B02G305600.1 shows high similarity to a salicylic acid glucosyltransferase 1 (SGT1) in rice (73% identity), which catalyzes the formation of glucoside and glucose esters of salicylic acid [60]. This UGT was assigned the same specific glucosyltransferase GO terms as the previous two UGTs, except for the terms associated with cytokinin, cis-zeatin, and hydroquinone activity (S4 Table). Furthermore, the assigned GO terms of GO:0052639 (salicylic acid glucosyltransferase (ester-forming) activity) and GO:0052640 (salicylic acid glucosyltransferase (glucoside-forming) activity) would suggest that TraesCS5B02G305600.1 is capable of catalyzing similar reactions associated with SGT1 (S4 Table).

With the exception of TraesCS5A02G472300, the other significant CYP and UGTs located on chromosome 5 (TraesCS5D02G404200, TraesCS5A02G394800, TraesCS5B02G305600, and TraesCS5A02G397800) are located in tandem gene clusters encoding similar enzymes (S5 Table). Gene clusters for TraesCS5A02G397800 and TraesCS5B02G305600 are relatively small in number with 3 other genes, while TraesCS5A02G394800 and its homoeolog TraesCS5D02G404200 exist in clusters with 9 to 10 other genes (S5 Table). It is common for the wheat genome to contain tandemly duplicated genes [61]. Likewise, genes encoding metabolic enzymes, such as CYPs and UGTs, commonly exist in tandem gene clusters in plants and they can range in number (approximately 3–15) [62–67]. Current results indicate that genes within these clusters vary in terms of CM induction since only the one gene within these clusters was identified in this research (S5 Table).

Given the previous LC-MS [35] and phenotypic results [34] along with the wealth of research regarding CYP81A involvement in herbicide detoxification in plants [13,24,27,29], TraesCS5A02G397800.1 and its homoeologs, TraesCS5B02G402800.1 and TraesCS5D02G407300.1 (denoted as CYP81A-5A, CYP81A-5B, and CYP81A-5D, respectively) were selected for further analysis via gene-specific RT-qPCR.

Expression of CYP81A-5A, CYP81A-5B, and CYP81A-5D

Overall, expression levels and fold inductions for the CYPs fluctuated throughout the time-course, and all three CYPs are CM-inducible but not HM-inducible (Fig 3). The CM and CM+HM treatments significantly induced expression of all CYPs at 3 HAT and 12 HAT where fold inductions ranged from approximately 4.8 to 28.6 (Fig 3). In contrast, HM treatments resulted in small (approximately 2.1-fold or less) fold changes in expression at all timepoints (Fig 3). At each timepoint, a CYP induced by CM was also induced by CM+HM and their fold inductions were not significantly different from each other (Fig 3). These results indicate a lack of additive or synergistic effects from the HM treatment. At 6 HAT, only the expression of CYP81A-5D is significantly induced by CM and CM+HM with both treatments resulting in an approximate 6.3- and 6.7-fold induction, respectively (Fig 3). Additionally, expression of all CYPs was always detected in UT tissue, especially at 3 HAT where all CYPs were not significantly different from β-TUB (S4 Fig). These results indicate CYP81A-5A expression is relatively high in UT tissue and, therefore, is likely required for yet-to-be identified endogenous function(s) at this growth stage in wheat.

thumbnail
Download:
Fig 3. Mean fold changes for CYP81A-5A, CYP81A-5B, and CYP81A-5D in response to cloquintocet-mexyl (CM) and halauxifen-methyl (HM) at 3, 6, and 12 hours after treatment (HAT).

Treatments include untreated control (0.1% nonionic surfactant (NIS)), CM (15 g a.i. ha−1 of CM), HM (5 g a.e. ha−1 of HM) and CM+HM (15 g a.i. ha−1 of CM and 5 g a.e. ha−1 of HM). All treatments included 0.1% NIS. Within each timepoint values that share the same letter are not significantly different (α =  0.05). Fold inductions for each gene at each timepoint were calculated by 2(−ΔΔCt) with β-tubulin (β-TUB) as a reference gene. Mean fold changes represent results from three biological replicates (n =  3). Error bars represent standard error of the mean.

https://doi.org/10.1371/journal.pone.0319151.g003

Discussion

CM-regulated genes associated with herbicide detoxification

We identified 103 DEGs whose expression is significantly altered (101 genes were induced and two genes were repressed) by CM treatments. The DEGs are associated with varying stages of herbicide metabolism, stress/defense response, amino acid metabolism, heavy metal binding, or transcription factors (Figs 1 and 2; S3 Table), which is further corroborated by GO enrichment analysis (S1 and S2 Figs; S4 Table). As mentioned previously, published transcriptomes for safener-treated grass species is rare; however, similar trends exist pertaining to the types of significant DEGs identified between the current and previous results. Regardless of safener or species, genes encoding herbicide-detoxifying enzymes, such as CYPs, UGTs, GSTs, and ABC transport proteins were in identified in both current and previous research [38–43]. While these enzymes are commonly associated with herbicide detoxification, other genes encoding enzymes capable of catalyzing Phase I oxidative and Phase II conjugation reactions were also identified (S3 Table). Other Phase I oxidative enzymes included 2-oxoglutarate/Fe(II)-dependent oxygenase superfamily proteins, 12-oxophytodienoate reductases, short-chain dehydrogenase/reductases [38,42], and peroxidases [42], while other Phase II conjugation enzymes included HXXXD-type acyl-transferase family proteins [38]. While other reports of transcriptomic data for CM-treated wheat are unavailable, CM-treated tissues were examined via Northern blot and protein expression analysis in wheat [46,68] and Aegilops tauschii [54,69], the D genome progenitor of wheat. In each case, GSTs were significantly induced by CM [54,68,69], and two GSTs identified by Xu et al. [54] (denoted as TtGSTU1 and TtGSTU2) were also identified in current results (Table S3). Differences in significant DEGs between current results and the results of previous experiments could be due to multiple differences in the experimental design, such as the choice of species, cultivar, safener, tissue type, plant growth stage, sampling timepoints, etc., could all influence results of transcriptome analysis. The effects of some of these factors have been explored in non-safener-related experiments in wheat [70,71], but the full extent of how these factors affect safener-induced gene expression has yet to be explored at the transcriptomic and proteomic levels.

CYPs and UGTs were anticipated as potential DEGs in the transcriptome data due to their common involvement in synthetic auxin herbicide detoxification [7,11,72,73] and their expression often induced by safeners [38–43]. CYPs are noteworthy due to their demonstrated ability to detoxify multiple classes of herbicides, especially in members of Poaceae: a plant family with the highest number of reported herbicide-detoxifying CYPs [13,27–29]. Given that members of the CYP81A subfamily commonly catalyze herbicide detoxification reactions [15–20,24–28,74], choosing to investigate CYP81A-5A in more detail was a reasonable hypothesis. However, members of CYP71C are also capable of herbicide-detoxification [23] and increased expression has been reported for nicosulfuron-tolerant Zea mays [75] and cyhalofop-butyl-resistant Leptochloa chinensis [76]. Thus, TraesCS5A02G472300.1 could also be examined in the future for roles in herbicide detoxification. Overall, further exploration into herbicide-detoxification capabilities of wheat CYPs is warranted since there are limited examples, including biochemical evidence of CYP involvement in diclofop-methyl detoxification [77–79], and wheat CYP71C6V1 detoxifying of several acetolactate synthase-inhibiting herbicides via an in vitro yeast assay [23].

To add the complexity of potential xenobiotic substrates, natural substrates of herbicide-detoxifying CYP81As are currently unknown [13], and this information could enhance the understanding of what herbicide characteristics determine its potential of being a CYP81A substrate. Given that CYPs are typically involved in the biosynthesis of both primary and secondary metabolites, such as sterols, fatty acids, carotenoids, and hormones [80–82], there are many potential candidates for CYP81A natural substrates.

The significant CM-induced UGTs identified by RNA-Seq also warrant examination in the future, especially since reactions catalyzed by CYPs may result in phytotoxic metabolites and still require further detoxification by UGTs [6,13]. In general, herbicide-detoxifying UGTs are not as well characterized compared to CYPs, especially in the Poaceae, but their expression is often induced by herbicides and safeners [38,44,83,84]. Some examples in wheat include detection of glucosylated metabolites of isoproturon [85] and florasulam [86]. In rice four UGTs are implicated in the detoxification of 2,4-D, and inhibitors of photosystem II, 4-hydroxyphenylpyruvate dioxygenase, and very-long-chain fatty acid elongases [87–90]. Lastly, a UGT in Alopecurus myosuroides was implicated in conferring cross-resistance to several herbicides [91].

CYP81A-5A, CYP81A-5B, and CYP81A-5D are CM-inducible but not HM-inducible

While the RT-qPCR experiment indicated that the expression of all examined CYPs is CM-inducible, expression between the three homoeologs varies over time and their expression is not equally induced by CM (Fig 3). Unequal homoeolog expression is not an unexpected result since expression patterns commonly vary among homoeologs in polyploids [70,92]. While CYP81A-5D displayed the largest inductions by CM across all timepoints (Fig 3), our previous results [34,35] indicate that CYP81A-5D alone is not sufficient for HA detoxification and that CYP81A-5A should be examined in future experiments. These differences between transcriptomic and phenotypic results could be partly explained by the generally weak correlation between mRNA transcript abundance and their corresponding proteins (usually around 40% in eukaryotes) [93]. We hypothesize that while CYP81A-5D has higher CM-inductions or comparable expression of CYP81A-5A, not all the CYP81A-5D transcripts are translated to functional, active proteins or the structural differences between the encoded proteins negatively affects HA binding for CYP81A-5D. Verifying increased protein abundance for any of the three gene products would require immunoblotting or ELISA assays.

The fluctuations in basal expression and CM inductions (Fig 3 and S4 Fig) during the time course are also not unexpected because many plant biological activities show diurnal variation, and the circadian clock coordinates plant activities in response to environmental cues, such as light and temperature [94]. Additionally, nearly all the genes associated with stress signaling pathways are regulated by the circadian clock, which synchronizes them for improved fitness and optimized development [95–97]. The objective of this experiment was not to measure the effect of the circadian clock on target genes; to address this objective, reference genes lacking variation over a time course experiment would be required [98] or absolute quantification of transcripts could be performed by making standards to extrapolate CYP transcript abundance in samples. The β-TUB gene would not be a suitable reference gene for this objective because it displays variation between timepoints (S4 Fig).

Future experiments and implications for CYP81A-5A

Given the results of previous experiments [34,35] and the abundant evidence of CYP81A involvement in the detoxification of multiple herbicides [16–19,29,99], CYP81A-5A will be examined for future herbicide detoxification characterization. If CYP81A-5A also catalyzes detoxification of multiple herbicides, it would have great potential for genetic transformation of relevant crop genomes lacking natural herbicide tolerance. Genes encoding herbicide detoxification enzymes could be utilized for in vitro metabolism assays with E. coli or yeast cells [24,100] to screen and predict whole-plant tolerance to numerous experimental herbicides in wheat prior to performing whole-plant phenotyping.

Functional validation of CYP81A-5A with clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9) methods is desired, which has been successfully utilized in wheat [101–104]. We therefore hypothesize CRISPR/Cas9-mediated modifications in CYP81A-5A that result in either a knockout or altered expression will have a commensurate effect on natural or CM-induced HM tolerance and possibly tolerance to other wheat-selective herbicides. When the function of CYP81A-5A has been validated, further exploration into its transcriptional regulation, expression in other tissues, and substrate binding will be performed.

Conclusions

RNA-Seq identified 103 DEGs whose expression can be significantly altered (101 genes were induced and two genes were repressed) by CM treatments. These genes and encoded proteins are associated with varying stages of herbicide metabolism, stress/defense response, amino acid metabolism, heavy metal binding, or transcription factors. Further investigation with RT-qPCR indicated that CYP81A-5A expression is CM-inducible but not HM-inducible. Further characterization of CYP81A-5A will improve our understanding of what herbicide characteristics determine its potential of being a CYP81A substrate. This information could be utilized when determining herbicide rotations that minimize the chance for selecting grass weeds possessing CYP81As that are capable of detoxifying multiple different herbicides.

Supporting information

S1 Fig. Results of agriGO v2.0 related to transferase activity.

The color of the box indicates the significance level of the false discovery rate (reported in parentheses), with the yellow indicating relatively low significance and the gradation intensifies towards red to indicate higher significance. At the bottom of each significant box, the first fraction represents the number of significant differentially expressed genes assigned the specified GO term (out of 99), and the second fraction indicates the number of genes in the Triticum aestivum L. reference background with the same GO annotation.

https://doi.org/10.1371/journal.pone.0319151.s001

(TIF)

S2 Fig. Results of agriGO v2.0 related to oxidoreductase activity.

The color of the box indicates the significance level of the false discovery rate (reported in parentheses), with the yellow indicating relatively low significance and the gradation intensifies towards red to indicate higher significance. At the bottom of each significant box, the first fraction represents the number of significant differentially expressed genes assigned the specified GO term (out of 99), and the second fraction indicates the number of genes in the Triticum aestivum L. reference background with the same GO annotation.

https://doi.org/10.1371/journal.pone.0319151.s002

(TIF)

S3 Fig. Mean fold inductions of significant cytochrome P450s (CYPs) and UDP-dependent glucosyltransferase (UGTs) located on the group 5 wheat chromosomes.

Green bars represent UGTs and blue bars represent CYPs. Genes were induced by 15 g a.i. ha-1 of cloquintocet-mexyl relative to untreated controls. Error bars indicate standard error of the mean.

https://doi.org/10.1371/journal.pone.0319151.s003

(TIF)

S4 Fig. Mean cycle threshold (Ct) value for β-tubulin (β-TUB), CYP81A-5A, CYP81A-5B, and CYP81A-5D at 3, 6, and 12 hours after treatment (HAT) in untreated (UT) samples.

The UT treatment consisted of 0.1% nonionic surfactant, which is an adjuvant that was included in all other treatment utilized for this experiment. Within each timepoint, means that share the same letter are not significantly different (Fisher’s LSD α = 0.05). Mean Ct values represent results from three biological replicates (n =  3). Error bars represent standard error of the mean.

https://doi.org/10.1371/journal.pone.0319151.s004

(TIF)

S1 Table. Reference and target gene primers and probes utilized for TaqMan RT-qPCR experiment.

https://doi.org/10.1371/journal.pone.0319151.s005

(XLSX)

S2 Table. Predicted amplified sequences of TaqMan primers and probes.

The underlined sequences correspond to the binding sites of the forward and reverse primers, respectively. Underlined and bolded sequences correspond to the binding sites of the TaqMan probes. Sequences are based on ‘Chinese Spring’ reference genome (IWGSC RefSeq v1.1) and functional gene annotations were downloaded from URGI (https://wheat-urgi.versailles.inra.fr/).

https://doi.org/10.1371/journal.pone.0319151.s006

(XLSX)

S3 Table. All differentially expressed genes (DEGs) with Functional Annotations and Expression Data Sorted by Highest Fold Change (FC).

https://doi.org/10.1371/journal.pone.0319151.s007

(XLSX)

S4 Table. All GO Terms for each differentially expressed genes (DEGs).

https://doi.org/10.1371/journal.pone.0319151.s008

(XLSX)

S5 Table. List of significant differentially expressed genes (DEGs) identified by RNA-Seq and genes sharing the same functional annotation tandemly located on the same chromosome.

https://doi.org/10.1371/journal.pone.0319151.s009

(XLSX)

S1 File. Data associated with RT-qPCR experiments.

https://doi.org/10.1371/journal.pone.0319151.s010

(XLSX)

Acknowledgments

We thank Dr. Alvaro Hernandez and Dr. Jenny Drnevich of the Roy J. Carver Biotechnology Center for providing technical advice for the construction of RNA-Seq libraries and statistical analysis. We also thank Dr. Kris N. Lambert, Dr. Norbert Satchivi and Dr. Carla Yerkes for providing technical support.

References

  1. 1. Glover NM, Redestig H, Dessimoz CH. Homoeologs: what are they and how do we infer them? Trends Plant Sci. 2016;21:609–21. pmid:27021699
  2. 2. IWGSC. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science. 2018;361(6403):eaar7191. pmid:30115783
  3. 3. Mithila J, Hall JC, Johnson WG, Kelley KB, Riechers DE. Evolution of resistance to auxinic herbicides: historical perspectives, mechanisms of resistance, and implications for broadleaf weed management in agronomic crops. Weed Sci. 2011;59:445–57.
  4. 4. Grossmann K. Auxin herbicides: current status of mechanism and mode of action. Pest Manag Sci. 2010;66(2):113–20. pmid:19823992
  5. 5. Siminszky B. Plant cytochrome P450-mediated herbicide metabolism. Phytochem Rev. 2006;5(2-3):445–58.
  6. 6. Gaines TA, Duke SO, Morran S, Rigon CAG, Tranel PJ, Kuepper A, et al. Mechanisms of evolved herbicide resistance. J Biol Chem. 2020;295:10307–30. pmid:32430396
  7. 7. Riechers DE, Kreuz K, Zhang Q. Detoxification without intoxication: herbicide safeners activate plant defense gene expression. Plant Physiol. 2010;153(1):3–13. pmid:20237021
  8. 8. Hatzios KK, Burgos N. Metabolism-based herbicide resistance: regulation by safeners. Weed Sci. 2004;52(3):454–67.
  9. 9. Yuan JS, Tranel PJ, Stewart CN. Non-target-site herbicide resistance: a family business. Trends Plant Sci. 2007;12(1):6–13. pmid:17161644
  10. 10. Davies J, Caseley J. Herbicide safeners: a review. Pestic Sci. 1999;55:1043–58.
  11. 11. Sterling TM, Hall JC. Mechanism of action of natural auxins and the auxinic herbicides. In: Roe R, Burton J, Kuhr R, editors. Herbicide activity: toxicology, biochemistry and molecular biology. Amsterdam: IOS Press; 1997. p. 111–41.
    • 12. Nandula VK, Riechers DE, Ferhatoglu Y, Barrett M, Duke SO, Dayan FE, et al. Herbicide metabolism: crop selectivity, bioactivation, weed resistance, and regulation. Weed Sci. 2019;67(2):149–75.
    • 13. Dimaano N, Iwakami S. Cytochrome P450-mediated herbicide metabolism in plants: current understanding and prospects. Pest Manag Sci. 2021;77:22–32. PMID: 32776423
    • 14. Brazier-Hicks M, Franco-Ortega S, Watson P, Rougemont B, Cohn J, Dale R, et al. Characterization of cytochrome P450s with key roles in determining herbicide selectivity in maize. ACS Omega. 2022;7(20):17416–31. pmid:35647462
    • 15. Choe E, Williams MM. Expression and comparison of sweet corn CYP81A9s in relation to nicosulfuron sensitivity. Pest Manag Sci. 2020;76(9):3012–9. pmid:32248609
    • 16. Nordby JN, Williams MM, Pataky JK, Riechers DE, Lutz JD. A common genetic basis in sweet corn inbred Cr1 for cross sensitivity to multiple cytochrome P450-metabolized herbicides. Weed Sci. 2008;56(3):376–82.
    • 17. Pan G, Zhang X, Liu K, Zhang J, Wu X, Zhu J, et al. Map-based cloning of a novel rice cytochrome P450 gene CYP81A6 that confers resistance to two different classes of herbicides. Plant Mol Biol. 2006;61(6):933–43. pmid:16927205
    • 18. Zhang J, Xu Y, Wu X, Zhu L. A bentazon and sulfonylurea sensitive mutant: breeding, genetics and potential application in seed production of hybrid rice. Theor Appl Genet. 2002;105(1):16–22. pmid:12582557
    • 19. Iwakami S, Kamidate Y, Yamaguchi T, Ishizaka M, Endo M, Suda H, et al. CYP81A P450s are involved in concomitant cross-resistance to acetolactate synthase and acetyl-CoA carboxylase herbicides in Echinochloa phyllopogon. New Phytol. 2019;221(4):2112–22. pmid:30347444
    • 20. Zhang L, Lu Q, Chen H, Pan G, Xiao S, Dai Y, et al. Identification of a cytochrome P450 hydroxylase, CYP81A6, as the candidate for the bentazon and sulfonylurea herbicide resistance gene, Bel, in rice. Mol Breed. 2007;19:59–68.
    • 21. Saika H, Horita J, Taguchi-Shiobara F, Nonaka S, Nishizawa-Yokoi A, Iwakami S, et al. A novel rice cytochrome P450 gene, CYP72A31, confers tolerance to acetolactate synthase-inhibiting herbicides in rice and Arabidopsis. Plant Physiol. 2014;166(3):1232–40. pmid:24406793
    • 22. Imaishi H, Matumoto S. Isolation and functional characterization in yeast of CYP72A18, a rice cytochrome P450 that catalyzes (ω-1)-hydroxylation of the herbicide pelargonic acid. Pestic Biochem Physiol. 2007;88(1):71–7.
    • 23. Xiang W, Wang X, Ren T. Expression of a wheat cytochrome P450 monooxygenase cDNA in yeast catalyzes the metabolism of sulfonylurea herbicides. Pestic Biochem Physiol. 2006;85(1):1–6.
    • 24. Dimaano N, Yamaguchi T, Fukunishi K, Tominaga T, Iwakami S. Functional characterization of cytochrome P450 CYP81A subfamily to disclose the pattern of cross-resistance in Echinochloa phyllopogon. Plant Mol Biol. 2020;102:403–16. pmid:31898147
    • 25. Iwakami S, Endo M, Saika H, Okuno J, Nakamura N, Yokoyama M, et al. Cytochrome P450 CYP81A12 and CYP81A21 are associated with resistance to two acetolactate synthase inhibitors in Echinochloa phyllopogon. Plant Physiol. 2014;165(2):618–29. pmid:24760819
    • 26. Guo F, Iwakami S, Yamaguchi T, Uchino A, Sunohara Y, Matsumoto H. Role of CYP81A cytochrome P450s in clomazone metabolism in Echinochloa phyllopogon. Plant Sci. 2019;283:321–8. pmid:31128703
    • 27. Pan L, Guo Q, Wang J, Shi L, Yang X, Zhou Y, et al. CYP81A68 confers metabolic resistance to ALS and ACCase-inhibiting herbicides and its epigenetic regulation in Echinochloa crus-galli. J Hazard Mater. 2022;428:128225. pmid:35032953
    • 28. Han H, Yu Q, Beffa R, González S, Maiwald F, Wang J, et al. Cytohrome P450 CYP81A10v7 in Lolium rigidum confers metabolic resistance to herbicides across at least five modes of action. Plant J. 2020;105(1):79–92.
    • 29. Zheng T, Yu X, Sun Y, Zhang Q, Zhang X, Tang M, et al. Expression of a cytochrome P450 gene from bermuda grass Cynodon dactylon in soybean confers tolerance to multiple herbicides. Plants (Basel, Switzerland). 2022;11(7):949. pmid:35406929
    • 30. Schmitzer PR, Balko TW, Daeuble JF, Epp JB, Satchivi NM, Siddall TL, et al. Discovery and SAR of halauxifen methyl: a novel auxin herbicide. In: Maienfisch P, Stevenson T, editors. Discovery and synthesis of crop protection products. Washington, D.C.: ACS Books; 2015. p. 247–60. https://doi.org/10.1021/bk-2015-1204.ch018
      • 31. Epp JB, Alexander AL, Balko TW, Buysse AM, Brewster WK, Bryan K, et al. The discovery of ArylexTM active and RinskorTM active: two novel auxin herbicides. Bioorg Med Chem. 2016;24(3):362–71. pmid:26321602
      • 32. Epp JB, Schmitzer PR, Crouse GD. Fifty years of herbicide research: comparing the discovery of trifluralin and halauxifen-methyl. Pest Manag Sci. 2018;74(1):9–16. pmid:28675627
      • 33. Dzikowski M, Becker J, Larelle D, Kamerichs B, Gast R. ArylexTM active - new herbicide active and base for new cereals herbicides: ZyparTM and PixxaroTM EC to control wide range of broadleaf weeds in cereals in Europe. Julius-Kühn-Archiv. 2016;452:297–304.
      • 34. Obenland OA, Riechers DE. Identification of chromosomes in Triticum aestivum possessing genes that confer tolerance to the synthetic auxin herbicide halauxifen-methyl. Sci Rep. 2020;10(1):8713. pmid:32457385
      • 35. Landau OA, Concepcion JCT, Riechers DE. Metabolism of halauxifen acid is regulated by genes located on wheat chromosome 5A. Weed Sci. 2024;72:339–5.
      • 36. Kraehmer H, Laber B, Rosinger C, Schulz A. Herbicides as weed control agents: state of the art: I. Weed control research and safener technology: the path to modern agriculture. Plant Physiol. 2014;166(3):1119–31. pmid:25104723
      • 37. Riechers DE, Green JM. Crop selectivity and herbicide safeners historical perspectives and development, safener-regulated gene expression, and new directions. In: Jugulam M, editor. Biology, physiology and molecular biology of weeds. Boca Raton: CRC Press; 2017. p. 123–43.
        • 38. Baek YS, Goodrich LV, Brown PJ, James BT, Moose SP, Lambert KN, et al. Transcriptome profiling and genome-wide association studies reveal GSTs and other defense genes involved in multiple signaling pathways induced by herbicide safener in grain sorghum. Front Plant Sci. 2019;10:192. pmid:30906302
        • 39. Brazier-Hicks M, Howell A, Cohn J, Hawkes T, Hall G, McIndoe E, et al. Chemically induced herbicide tolerance in rice by the safener metcamifen is associated with a phased stress response. J Exp Bot. 2020;71(1):411–21. pmid:31565749
        • 40. Sun L, Yang M, Su W, Xu H, Xue F, Lu C, et al. Transcriptomic analysis of maize uncovers putative genes involved in metabolic detoxification under four safeners treatment. Pestic Biochem Physiol. 2023;194:105465. pmid:37532342
        • 41. Zhao Y, Li W, Sun L, Xu H, Su W, Xue F, et al. Transcriptome analysis and the identification of genes involved in the metabolic pathways of fenoxaprop-P-ethyl in rice treated with isoxadifen-ethyl hydrolysate. Pestic Biochem Physiol. 2022;183:105057. pmid:35430061
        • 42. Hu L, Yao Y, Cai R, Pan L, Liu K, Bai L. Effects of fenclorim on rice physiology, gene transcription and pretilachlor detoxification ability. BMC Plant Biol. 2020;20(1):1–12.
        • 43. Yuan L, Ma G, Geng Y, Liu X, Wang H, Li J, et al. Seed dressing with mefenpyr-diethyl as a safener for mesosulfuron-methyl application in wheat: the evaluation and mechanisms. PLoS One. 2021;16(8):e0256884. pmid:34460856
        • 44. Edwards R, Buono DD, Fordham M, Skipsey M, Brazier M, Dixon DP, et al. Differential induction of glutathione transferases and glucosyltransferases in wheat, maize and Arabidopsis thaliana by herbicide safeners. Zeitschrift für Naturforschung C. 2005;60(3–4):307–16.
        • 45. Kolb FL, Smith NJ. Registration of “Kaskaskia” wheat. Crop Sci. 2001;41(1):256–256.
        • 46. Taylor VL, Cummins I, Brazier-Hicks M, Edwards R. Protective responses induced by herbicide safeners in wheat. Environ Exp Bot. 2013;88(C):93–9. pmid:23564986
        • 47. Chen S, Zhou Y, Chen Y, Gu J. FASTP: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90. pmid:30423086
        • 48. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14(4):417–9. pmid:28263959
        • 49. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40. pmid:19910308
        • 50. Du Z, Zhou X, Ling Y, Zhang Z, Su Z. AgriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010;38:W64–70. pmid:20435677
        • 51. Tian T, Liu Y, Yan H, You Q, Yi X, Du Z, et al. AgriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res. 2017;45(W1):W122–9. pmid:28472432
        • 52. Hufford MB, Seetharam AS, Woodhouse MR, Chougule KM, Ou S, Liu J, et al. De novo assembly, annotation, and comparative analysis of 26 diverse maize genomes. Science. 2021;373(6555):655–62. pmid:34353948
        • 53. Ouyang S, Zhu W, Hamilton J, Lin H, Campbell M, Childs K, et al. The TIGR rice genome annotation resource: improvements and new features. Nucleic Acids Res. 2007;35:D883–7. pmid:17145706
        • 54. Xu F, Lagudah ES, Moose SP, Riechers DE. Tandemly duplicated safener-induced glutathione S-transferase genes from Triticum tauschii contribute to genome-and organ-specific expression in hexaploid wheat. Plant Physiol. 2002;130:362–73. pmid:12226515
        • 55. Zhang K, Niu S, Di D, Shi L, Liu D, Cao X, et al. Selection of reference genes for gene expression studies in virus-infected monocots using quantitative real-time PCR. J Biotechnol. 2013;168(1):7–14. pmid:23954326
        • 56. Alaux M, Rogers J, Letellier T, Flores R, Alfama F, Pommier C, et al. Linking the international wheat genome sequencing consortium bread wheat reference genome sequence to wheat genetic and phenomic data. Genome Biol. 2018;19:1–10.
        • 57. Morant M, Hehn A, Werck-Reichhart D. Conservation and diversity of gene families explored using the CODEHOP strategy in higher plants. BMC Plant Biol. 2002;2(1):1–12. Available: http://www.biomedcentral.com/1471-2229/2/7
        • 58. Yang Q, Zhang Y, Qu X, Wu F, Li X, Ren M, et al. Genome-wide analysis of UDP-glycosyltransferases family and identification of UGT genes involved in abiotic stress and flavonol biosynthesis in Nicotiana tabacum. BMC Plant Biol. 2023;23(1):204. pmid:37076827
        • 59. Cao X, Jiang D, Wang H, Wu B, Cheng J, Zhang B. Identification of UGT85A glycosyltransferases associated with volatile conjugation in grapevine (Vitis vinifera × Vitis labrusca). Hortic Plant J. 2023;9(6):1095–107.
        • 60. Li D, Zhou J, Zheng C, Zheng E, Liang W, Tan X, et al. OsTGAL1 suppresses the resistance of rice to bacterial blight disease by regulating the expression of salicylic acid glucosyltransferase OsSGT1. Plant Cell Environ. 2022;45(5):1584–602. pmid:35141931
        • 61. Jia Y, Xu M, Hu H, Chapman B, Watt C, Buerte B, et al. Comparative gene retention analysis in barley, wild emmer, and bread wheat pangenome lines reveals factors affecting gene retention following gene duplication. BMC Biol. 2023;21(1):25. pmid:36747211
        • 62. Zhan C, Shen S, Yang C, Liu Z, Fernie AR, Graham IA, et al. Plant metabolic gene clusters in the multi-omics era. Trends Plant Sci. 2022;27(10):981–1001. pmid:35365433
        • 63. Wang M, Yuan J, Qin L, Shi W, Xia G, Liu S. TaCYP81D5, one member in a wheat cytochrome P450 gene cluster, confers salinity tolerance via reactive oxygen species scavenging. Plant Biotechnol J. 2020;18(3):791–804. pmid:31472082
        • 64. Nelson D, Werck-Reichhart D. A P450-centric view of plant evolution. Plant J. 2011;66(1):194–211. pmid:21443632
        • 65. Yu J, Tehrim S, Wang L, Dossa K, Zhang X, Ke T, et al. Evolutionary history and functional divergence of the cytochrome P450 gene superfamily between Arabidopsis thaliana and Brassica species uncover effects of whole genome and tandem duplications. BMC Genom. 2017;18:1–21.
        • 66. Wang F, Su Y, Chen N, Shen S. Genome-wide analysis of the UGT gene family and identification of flavonoids in Broussonetia papyrifera. Molecules. 2021;26(11):3449. pmid:34204142
        • 67. Wilson AE, Tian L. Phylogenomic analysis of UDP-dependent glycosyltransferases provides insights into the evolutionary landscape of glycosylation in plant metabolism. Plant J. 2019;100(6):1273–88. pmid:31446648
        • 68. Theodoulou FL, Clark IM, He XL, Pallett KE, Cole DJ, Hallahan DL. Co-induction of glutathione-S-transferases and multidrug resistance associated protein by xenobiotics in wheat. Pest Manag Sci. 2003;59(2):202–14. pmid:12587874
        • 69. Zhang Q, Xu F, Lambert KN, Riechers DE. Safeners coordinately induce the expression of multiple proteins and MRP transcripts involved in herbicide metabolism and detoxification in Triticum tauschii seedling tissues. Proteomics. 2007;7(8):1261–78. pmid:17380533
        • 70. Ramírez-González RH, Borrill P, Lang D, Harrington SA, Brinton J, Venturini L, et al. International Wheat Genome Sequencing Consortium. The transcriptional landscape of polyploid wheat. Science. 2018;361(6403):eaar6089. pmid:30115782
        • 71. Kaur S, Shamshad M, Jindal S, Kaur A, Singh S, Sharma A, et al. RNA-Seq-based transcriptomics study to investigate the genes governing nitrogen use efficiency in Indian wheat cultivars. Front Genet. 2022;13:853910. pmid:35432475
        • 72. Frear DS. Wheat microsomal cytochrome P450 monooxygenases: characterization and importance in the metabolic detoxification and selectivity of wheat herbicides. Drug Metabol Drug Interact. 1995;12(3–4):329–57. pmid:8820860
        • 73. Zhang JJ, Yang H. Metabolism and detoxification of pesticides in plants. Sci Total Environ. 2021;790:148034. pmid:34111793
        • 74. Dimaano N, Tominaga T, Iwakami S. Thiobencarb resistance mechanism is distinct from CYP81A-based cross-resistance in late watergrass (Echinochloa phyllopogon). Weed Sci. 2022;70:160–6.
        • 75. Liu X, Xu X, Li B, Yao X, Zhang H, Wang G, et al. Genomic and transcriptomic insights into cytochrome P450 monooxygenase genes involved in nicosulfuron tolerance in maize (Zea mays L.). Journal of Integrative Agriculture. 2018;17(8):1790–9.
        • 76. Zhang Y, Chen L, Song W, Cang T, Xu M, Wu C. Diverse mechanisms associated with cyhalofop-butyl resistance in Chinese sprangletop (Leptochloa chinensis (L.) Nees): characterization of target-site mutations and metabolic resistance-related genes in two resistant populations. Front Plant Sci. 2022;13:990085. pmid:36518516
        • 77. Tanaka FS, Hoffer BL, Shimabukuro RH, Wien RG, Walsh WC. Identification of the isomeric hydroxylated metabolites of methyl 2-[4-(2, 4-dichlorophenoxy) phenoxy] propanoate (diclofop-methyl) in wheat. J Agric Food Chem. 1990;38(2):559–65.
        • 78. Zimmerlin A, Durst F. Xenobiotic metabolism in plants: aryl hydroxylation of diclofop by a cytochrome P-450 enzyme from wheat. Phytochem. 1990;29(6):1729–32.
        • 79. Zimmerlin A, Durst F. Aryl hydroxylation of the herbicide diclofop by a wheat cytochrome P-450 monooxygenase. Plant Physiol. 1992;100(2):874–81. pmid:16653070
        • 80. Mizutani M, Ohta D. Diversification of P450 genes during land plant evolution. Annu Rev Plant Biol. 2010;61:291–315. pmid:20192745
        • 81. Mizutani M, Sato F. Unusual P450 reactions in plant secondary metabolism. Arch Biochem Biophys. 2011;507(1):194–203. pmid:20920462
        • 82. Wakabayashi T, Hamana M, Mori A, Akiyama R, Ueno K, Osakabe K, et al. Direct conversion of carlactonoic acid to orobanchol by cytochrome P450 CYP722C in strigolactone biosynthesis. Sci Adv. 2019;5(12):9067–85.
        • 83. Duhoux A, Carrère S, Gouzy J, Bonin L, Délye C. RNA-Seq analysis of rye-grass transcriptomic response to an herbicide inhibiting acetolactate-synthase identifies transcripts linked to non-target-site-based resistance. Plant Mol Biol. 2015;87(4–5):473–87. pmid:25636204
        • 84. Gaines TA, Lorentz L, Figge A, Herrmann J, Maiwald F, Ott MC, et al. RNA-Seq transcriptome analysis to identify genes involved in metabolism-based diclofop resistance in Lolium rigidum. Plant J. 2014;78(5):865–76. pmid:24654891
        • 85. Lu YC, Zhang S, Yang H. Acceleration of the herbicide isoproturon degradation in wheat by glycosyltransferases and salicylic acid. J Hazard Mater. 2015;283:806–14. pmid:25464323
        • 86. DeBoer GJ, Thornburgh S, Ehr RJ. Uptake, translocation and metabolism of the herbicide florasulam in wheat and broadleaf weeds. Pest Manag Sci. 2006;62(4):316–24. pmid:16506146
        • 87. Brazier-Hicks M, Gershater M, Dixon D, Edwards R. Substrate specificity and safener inducibility of the plant UDP-glucose-dependent family 1 glycosyltransferase super-family. Plant Biotechnol J. 2018;16(1):337–48. pmid:28640934
        • 88. Su XN, Zhang JJ, Liu JT, Zhang N, Ma LY, Lu FF, et al. Biodegrading two pesticide residues in paddy plants and the environment by a genetically engineered approach. J Agric Food Chem. 2019;67(17):4947–57. pmid:30994343
        • 89. Zhang JJ, Gao S, Xu JY, Lu YC, Lu FF, Ma LY, et al. Degrading and phytoextracting atrazine residues in rice (Oryza sativa) and growth media intensified by a phase II mechanism modulator. Environ Sci Technol. 2017;51(19):11258–68. pmid:28872855
        • 90. Liu Q, Chen TT, Xiao DW, Zhao SM, Lin JS, Wang T, et al. OsIAGT1 is a glucosyltransferase gene involved in the glucose conjugation of auxins in rice. Rice. 2019;12(1):1–13.
        • 91. Brazier M, Cole DJ, Edwards R. O-Glucosyltransferase activities toward phenolic natural products and xenobiotics in wheat and herbicide-resistant and herbicide-susceptible black-grass (Alopecurus myosuroides). Phytochem. 2002;59(2):149–56. pmid:11809449
        • 92. Leach LJ, Belfield EJ, Jiang C, Brown C, Mithani A, Harberd NP. Patterns of homoeologous gene expression shown by RNA sequencing in hexaploid bread wheat. BMC Genom. 2014;15:1–19.
        • 93. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13(4):227–32. pmid:22411467
        • 94. McClung CR. Plant circadian rhythms. Plant Cell. 2006;18(4):792–803. pmid:16595397
        • 95. Covington MF, Maloof JN, Straume M, Kay SA, Harmer SL. Global transcriptome analysis reveals circadian regulation of key pathways in plant growth and development. Genome Biol. 2008;9(8):1–18.
        • 96. Nozue K, Maloof JN. Diurnal regulation of plant growth. Plant Cell Environ. 2006;29(3):396–408. pmid:17080594
        • 97. Green RM, Tingay S, Wang ZY, Tobin EM. Circadian rhythms confer a higher level of fitness to Arabidopsis plants. Plant Physiol. 2002;129(2):576–84. pmid:12068102
        • 98. Jain N, Vergish S, Khurana JP. Validation of house-keeping genes for normalization of gene expression data during diurnal/circadian studies in rice by RT-qPCR. Sci Rep. 2018;8(1):3208.
        • 99. Li J, Yu H, Zhang F, Lin C, Gao J, Fang J, et al. A built-in strategy to mitigate transgene spreading from genetically modified corn. PLoS One. 2013;8(12):e81645. pmid:24324711
        • 100. Abdollahi F, Alebrahim MT, Ngov C, Lallemand E, Zheng Y, Villette C, et al. Innate promiscuity of the CYP706 family of P450 enzymes provides a suitable context for the evolution of dinitroaniline resistance in weed. New Phytol. 2021;229(6):3253–68. pmid:33253456
        • 101. Okada A, Arndell T, Borisjuk N, Sharma N, Watson-Haigh NS, Tucker EJ, et al. CRISPR/Cas9-mediated knockout of Ms1 enables the rapid generation of male-sterile hexaploid wheat lines for use in hybrid seed production. Plant Biotechnol J. 2019;17(10):1905–13. pmid:30839150
        • 102. Zhang Y, Bai Y, Wu G, Zou S, Chen Y, Gao C, et al. Simultaneous modification of three homoeologs of TaEDR1 by genome editing enhances powdery mildew resistance in wheat. Plant J. 2017;91(4):714–24. pmid:28502081
        • 103. Zhang Y, Li D, Zhang D, Zhao X, Cao X, Dong L, et al. Analysis of the functions of TaGW2 homoeologs in wheat grain weight and protein content traits. Plant J. 2018;94(5):857–66. pmid:29570880
        • 104. Wang W, Simmonds J, Pan Q, Davidson D, He F, Battal A, et al. Gene editing and mutagenesis reveal inter-cultivar differences and additivity in the contribution of TaGW2 homoeologues to grain size and weight in wheat. Theor Appl Gen. 2018;131:2463–75.