Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-05T04:14:47.619Z Has data issue: false hasContentIssue false
  • English
  • Français

Metabolism of halauxifen acid is regulated by genes located on wheat chromosome 5A

Published online by Cambridge University Press:  15 April 2024

Olivia A. Landau Open the ORCID record for Olivia A. Landau [Opens in a new window] ,
Jeanaflor Crystal T. Concepcion Open the ORCID record for Jeanaflor Crystal T. Concepcion [Opens in a new window]  and
Dean E. Riechers Open the ORCID record for Dean E. Riechers [Opens in a new window]
Olivia A. Landau
Affiliation:
Graduate Research Assistant, Department of Crop Sciences, University of Illinois, Urbana, IL, USA
Jeanaflor Crystal T. Concepcion
Affiliation:
Postdoctoral Research Associate, Department of Crop Sciences, University of Illinois, Urbana, IL, USA
Dean E. Riechers*
Affiliation:
Professor, Department of Crop Sciences, University of Illinois, Urbana, IL, USA
*
Corresponding author: Dean E. Riechers; Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Allohexaploid wheat (Triticum aestivum L.) is tolerant to halauxifen-methyl (HM) via rapid detoxification of the phytotoxic form of HM, halauxifen acid (HA), to non-phytotoxic metabolites. Previous research utilizing ‘Chinese Spring’ (CS) wheat, alien substitution (i.e., endogenous chromosome pair substituted with a homoeologous pair from diploid Sears’ goatgrass (Aegilops searsii M. Feldman & M. Kislev) (AS), or nullisomic-tetrasomic (NT) lines indicated plants lacking chromosome 5A are more sensitive than CS to HM. We hypothesized the increased HM sensitivity of these plants results from losing gene(s) on chromosome 5A associated with HA metabolism, which leads to a reduced HA detoxification rate relative to CS. To compare HA abundance among AS, CS, alien substitution, and NT lines during a time course, two excised-leaf studies using unlabeled HM and liquid chromatography–mass spectrometry analyses were performed. Aegilops searsii accumulated more HA than CS did, and each substitution line at 8, 12, and 24 h after treatment (HAT). Furthermore, only the wheat substitution line lacking chromosome 5A displayed greater abundance of HA relative to CS (2.4- to 3.8-fold, depending on the time point). In contrast, HA abundances in lines possessing chromosome 5A were not different than HA abundances in CS at all time points. When NT lines were compared with CS, the nullisomic 5D-tetrasomic 5A (N5D-T5A) line displayed similar HA abundance, whereas the nullisomic 5A-tetrasomic 5D (N5A-T5D) accumulated approximately 3-fold more HA at 12 and 24 HAT. These results biochemically support the hypothesis that genes encoding HA-detoxifying enzyme(s) are located on wheat chromosome 5A and corroborate findings from previous greenhouse phenotypic experiments. Future experimentation is needed to identify and characterize genes and enzymes on wheat chromosome 5A involved with HA detoxification, which may include cytochrome P450 monooxygenases, unknown oxidases, UDP-dependent glucosyltransferases, or, potentially, transcription factors that regulate expression of these genes associated with HA detoxification.

Keywords

Esteraseexcised-leaf assayherbicide detoxificationherbicide toleranceliquid chromatography–mass spectrometryoxidative metabolismsynthetic auxin
Type
Research Article
Information
Weed Science , Volume 72 , Issue 4 , July 2024 , pp. 339 - 345
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of Weed Science Society of America

Introduction

Halauxifen-methyl (HM) is categorized in the 6-aryl-picolinic acid subclass of synthetic auxin herbicides (Group 4) and was commercialized in 2015 (Epp et al. Reference Epp, Alexander, Balko, Buysse, Brewster, Bryan, Daeuble, Fields, Gast, Green, Irvine, Lo, Lowe, Renga and Richburg2016; Schmitzer et al. Reference Schmitzer, Balko, Daeuble, Epp, Satchivi, Siddall, Weimer and Yerkes2015). Like other synthetic auxins, HM mimics indole-3-acetic acid, which regulates numerous aspects of plant growth and development (Demeulenaere and Beeckman Reference Demeulenaere and Beeckman2014; McSteen Reference McSteen2010). Halauxifen-methyl provides selective postemergence control of dicot weeds in cereal crops (Dzikowski et al. Reference Dzikowski, Becker, Larelle, Kamerichs and Gast2016; Epp et al. Reference Epp, Alexander, Balko, Buysse, Brewster, Bryan, Daeuble, Fields, Gast, Green, Irvine, Lo, Lowe, Renga and Richburg2016). While HM was initially developed as a tank-mix partner for postemergence weed control in cereal crops at relatively low application rates (5 to 7.5 g ha−1), it can also be used as a burndown treatment before soybean [Glycine max (L.) Merr.] planting at even lower rates (1 to 2 g ha−1) due to its short soil half-life (10 to 25 d) and minimal soil residual activity (Epp et al. Reference Epp, Alexander, Balko, Buysse, Brewster, Bryan, Daeuble, Fields, Gast, Green, Irvine, Lo, Lowe, Renga and Richburg2016, Reference Epp, Schmitzer and Crouse2018).

Allohexaploid bread wheat (Triticum aestivum L.) (2n = 6x = 42; AABBDD) is tolerant to HM via rapid detoxification of the biologically active form, halauxifen acid (HA) (Dzikowski et al. Reference Dzikowski, Becker, Larelle, Kamerichs and Gast2016) following phase I and II metabolic reactions. Phase I reactions typically involve oxidation, reduction, or hydrolysis of phytotoxic parent molecules; most reactions are catalyzed by cytochrome P450 enzymes (CYPs) or bioactivation of proherbicide esters by carboxyl-esterases (CXEs) (Gershater and Edwards Reference Gershater and Edwards2007; Kreuz et al. Reference Kreuz, Tommasini and Martinoia1996). Phase II reactions involve conjugation of phytotoxic parent molecules or phase I metabolites with endogenous compounds, such as sugars or reduced glutathione (Kreuz et al. Reference Kreuz, Tommasini and Martinoia1996; Riechers et al. Reference Riechers, Kreuz and Zhang2010). Following de-esterification of HM to HA, HA is O-demethylated and subsequently conjugated with glucose (Figure 1). Although HM metabolism in wheat has been characterized (Dzikowski et al. Reference Dzikowski, Becker, Larelle, Kamerichs and Gast2016), the genes encoding HA-detoxifying enzymes have not been identified. Identification of such genes would be valuable for plant transformation to endow HM tolerance in HM-sensitive plants, such as soybean (Zobiole and Kalsing Reference Zobiole and Kalsing2017), cotton (Gossypium hirsutum L.), and other dicots. Additionally, these enzymes could potentially detoxify multiple herbicides, making them valuable in breeding programs aimed at improving herbicide tolerance in dicot crops (Dimaano and Iwakami Reference Dimaano and Iwakami2021; Gharabli et al. Reference Gharabli, Della Gala and Welner2023).

Figure 1. Structures of halauxifen-methyl (HM) and its metabolites in wheat: (A) HM; (B) halauxifen acid (HA); (C) O-demethylated metabolite of HA; and (D) glucose conjugate of HA.

Various genetic resources derived from the allohexaploid wheat cultivar ‘Chinese Spring’ (CS) are available for evaluating phenotypic responses via alteration of the base chromosome number or substituting chromosomes, including alien substitution and nullisomic-tetrasomic (NT) lines (Jiang et al. Reference Jiang, Friebe and Gill1994; Law et al. Reference Law, Snape and Worland1987). Alien substitution lines have one pair of endogenous homoeologous chromosomes replaced with the homoeologous chromosomes from another species, denoted as the “alien” genome (Jiang et al. Reference Jiang, Friebe and Gill1994). For NT lines, one pair of homoeologues are removed (nullisomic) and additional copies of another pair of homoeologues are added (tetrasomic) (Law et al. Reference Law, Snape and Worland1987; Sears Reference Sears1954, Reference Sears1966). These lines were utilized in previous HM phenotyping experiments with the goals of identifying lines with increased HM sensitivity and providing evidence concerning which chromosomes harbor genes necessary to confer HM tolerance in wheat (Obenland and Riechers Reference Obenland and Riechers2020).

Increased sensitivity to HM was clearly evident in wheat lines that lack chromosome 5A, relative to CS (Obenland and Riechers Reference Obenland and Riechers2020), but HM tolerance in wheat lines possessing 5A was similar to that of CS. In our prior research, we hypothesized that increased HM sensitivity in these wheat lines is due to reduced HA detoxification rates, which likely resulted from losing genes on chromosome 5A encoding HA-detoxifying enzymes (Obenland and Riechers Reference Obenland and Riechers2020). To directly address this hypothesis, the objective of this study was to measure and compare HA levels in CS and wheat lines utilized in our previous research via a combination of excised-leaf assays and liquid chromatography–mass spectrometry (LC-MS) analysis.

Materials and Methods

Chemicals, Plant Materials, and Growth Conditions

Analytical-grade standards of HM and HA (95.9% and 99.5% pure, respectively) were provided by Corteva Agriscience (Indianapolis, IN, USA). All other analytical-grade reagents were purchased through Fisher Scientific (Thermo-Fisher, Hanover Park, IL, USA) or Sigma Chemical (Millipore Sigma, St Louis, MO, USA). Seeds for CS, alien substitution lines (5A, 5B, and 5D), and NT lines were acquired from the Kansas State Wheat Genetics Resource Center; seed of Sears’ goatgrass (Aegilops searsii M. Feldman & M. Kislev) (AS; PI 599163), a diploid wheat relative (2n = 2x = 14; S genome), was obtained from the National Small Grains Collection (Aberdeen, ID, USA) of the U.S. Department of Agriculture–Agricultural Research Service. To promote uniform germination, seeds were subjected to a cold treatment by placing them on water-soaked filter paper in Petri dishes stored at 5 C for 3 d. Seedlings were cultivated in a growth chamber (Conviron Gen1000, Controlled Environments, Winnipeg, Canada) until seedlings produced two to three leaves (Zadoks stage 12-13) under conditions of 28/22 C day/night and a 16/8-h photoperiod. The LED lights in the growth chamber provided 550 μmol m−2 s−1 photon flux at the plant canopy level.

Excised-Leaf Assay, Tissue Harvest and Extraction, and LC-MS Analysis

The excised-leaf assay and metabolite extraction protocols utilized in our experiments are based on a protocol modified from previous studies (Concepcion et al. Reference Concepcion, Kaundun, Morris, Hutchings, Strom, Lygin and Riechers2021; Lygin et al. Reference Lygin, Kaundun, Morris, Mcindoe, Hamilton and Riechers2018; Ma et al. Reference Ma, Skelton and Riechers2015). For each experiment, the two oldest wheat leaves were cut at the collar, the cut surface of the leaf was washed with deionized water, transferred to 15-ml plastic tubes containing 400 µl of 0.1 M Tris-Cl (pH 6.0), and preincubated in the growth chamber for 1 h. After preincubation, leaves designated for HM treatment were transferred to tubes containing 400 µl of 300 μM HM (in 0.1 M Tris-Cl, pH 6.0) and untreated leaves were transferred to tubes containing only 400 µl of 0.1 M Tris-Cl (pH 6.0). Approximately 1.0 cm of tissue above the cut surface of the excised leaf was exposed to the uptake solution in these assays. At 2 h after treatment (HAT), leaves designated for the 2-HAT time point were rinsed with deionized water, and fresh weights were recorded (approx. 200 mg of fresh weight per sample). Samples designated for later time points (8, 12, and 24 HAT) were rinsed with deionized water and transferred to tubes containing 400 µl of quarter-strength Murashige and Skoog solution (MP Biomedicals, Solon, OH, USA), which provides mineral salts, vitamins, and organic compounds to support plant growth (Murashige and Skoog Reference Murashige and Skoog1962). Following each harvest time point, leaves were frozen in liquid nitrogen, and samples were stored at −80 C until metabolite extraction. Based on preliminary experiments quantifying HM remaining in the uptake solution at 2 HAT via high-performance liquid chromatography with photodiode array detection (HPLC-PDA) using methods (Supplementary Material 1) modified from a previous study (Concepcion et al. Reference Concepcion, Kaundun, Morris, Hutchings, Strom, Lygin and Riechers2021), at least 19% of HM supplied was absorbed by excised wheat leaves (Supplementary Material 2). However, AS absorbed a greater amount of HM than CS, which may be related to anatomical differences between these species. Substitution line 5D and the nullisomic 5D-tetrasomic 5A (N5D-T5A) line also absorbed more HM than CS (Supplementary Material 2), which positively correlates with a relatively higher degree of HM tolerance (Obenland and Riechers Reference Obenland and Riechers2020) and a putative faster rate of HM metabolism.

Frozen tissue was ground in liquid nitrogen using a mortar and pestle, and compounds were extracted in 1 ml of 90% (v/v) methanol. The supernatant was removed after centrifugation at 12,000 × g, and a second extraction was performed with the remaining plant material by adding 1 ml of 90% (v/v) methanol. After another centrifugation at 12,000 × g, the pellet was discarded, and the second supernatant was combined with the first supernatant, resulting in a final volume of 2 ml. All samples were spiked with 4-chloro-DL-PHE as an internal standard before extraction. Samples were dried and concentrated under nitrogen gas and reconstituted with 250 μl of water:acetonitrile (1:1, v/v) containing 0.1% formic acid. Quality-control (QC) samples were then prepared by combining aliquots of each sample for injection throughout each experimental run (Dunn et al. Reference Dunn, Broadhurst, Begley, Zelena, Francis-Mcintyre, Anderson, Brown, Knowles, Halsall, Haselden, Nicholls, Wilson, Kell and Goodacre2011). An HA standard was also prepared. All samples were stored at −80 C until further analysis.

Samples were submitted to the Roy J. Carver Metabolomics Facility, University of Illinois at Urbana-Champaign, for LC-MS analysis using previously described methods (Concepcion et al. Reference Concepcion, Kaundun, Morris, Hutchings, Strom, Lygin and Riechers2021; Elolimy et al. Reference Elolimy, Alharthi, Zeineldin, Parys, Helmbrecht and Loor2019). Samples were analyzed with a Q-Exactive MS system (Thermo, Bremen, Germany), and separation was conducted with a Dionex Ultimate 3000 series HPLC equipped with a Phenomenex C18 column (4.6 by100 mm, 2.6-μm particle size). Mobile phases consisted of A (H2O with 0.1% formic acid [v/v]) and B (acetonitrile with 0.1% formic acid [v/v]). The flow rate was 0.25 ml min−1 with a linear gradient starting at 100% A for 3 min. The gradient then transitioned to 100% B (20 to 30 min) and returned to 100% A (31 to 36 min). Twenty microliters of each sample was injected, and the autosampler temperature was 15 C. Mass spectra were then acquired under both positive (sheath gas flow rate: 45; auxiliary gas flow rate: 11; sweep gas flow rate: 2; spray voltage: 3.5 kV; capillary temp: 250 C; auxiliary gas heater temp: 415 C) and negative electrospray ionization (sheath gas flow rate: 45; auxiliary gas flow rate: 11; sweep gas flow rate: 2; spray voltage: −2.5 kV; capillary temp: 250 C; auxiliary gas heater temp: 415 C). The full-scan mass-spectrum resolution was set to 70,000 with a scan range of m/z 67 to 1,000, and the automatic gain control target was 1 × 106 with a maximum injection time of 200 ms. Chromatographic analysis was conducted in a randomized sequence, including QC samples injected initially to equilibrate the analytical platform and after every 10 test samples to evaluate the stability of the experimental procedure (Dunn et al. Reference Dunn, Broadhurst, Begley, Zelena, Francis-Mcintyre, Anderson, Brown, Knowles, Halsall, Haselden, Nicholls, Wilson, Kell and Goodacre2011; Godzien et al. Reference Godzien, Alonso-Herranz, Barbas and Armitage2015; Sangster et al. Reference Sangster, Major, Plumb, Wilson and Wilson2006; Wehrens et al. Reference Wehrens, Hageman, van Eeuwijk, Kooke, Flood, Wijnker, Keurentjes, Lommen, van Eekelen, Hall, Mumm and de Vos2016). Raw data files obtained in full-MS mode (samples, procedural blank, and QC) and data obtained in full-MS followed by data-dependent MS2 were processed using MS-DIAL v. 4.9221218 following previously described parameters (Concepcion et al. Reference Concepcion, Kaundun, Morris, Hutchings, Strom, Lygin and Riechers2021; Tsugawa et al. Reference Tsugawa, Cajka, Kind, Ma, Higgins, Ikeda, Kanazawa, Vandergheynst, Fiehn and Arita2015). The peak area of each metabolite feature was normalized based on QC samples and peak area of the internal standard (Supplementary Material 2). Normalized peak areas for HA were then exported into a .csv file for further statistical analysis.

Experimental Setup and Statistical Analysis

The first experiment included CS, AS, and three alien substitution lines derived from CS wheat but with endogenous chromosomes 5A, 5B, or 5D replaced with the corresponding homoeologous chromosome (5S) from AS (Obenland and Riechers Reference Obenland and Riechers2020). Based on the results of the first experiment, NT lines with different numbers of chromosomes 5A and 5D (i.e., 5A or 5D were either omitted [nullisomic] or doubled [tetrasomic]) were used for the second experiment, which included CS, nullisomic 5A-tetrasomic 5D (N5A-T5D), and N5D-T5A. Both experiments were designed to meet or exceed minimum requirements for replicating and reporting metabolite profiling results (Sumner et al. Reference Sumner, Amberg, Barrett, Beale, Beger, Daykin, Fan, Fiehn, Goodacre, Griffin, Hankemeier, Hardy, Harnly, Higashi and Kopka2007). The first experiment was independently conducted twice using either three or four biological replicates per treatment per time point (seven total biological replicates). The second experiment was conducted once and utilized five biological replicates per treatment per time point. Chinese Spring served as a positive control in both experiments, as it is HM tolerant, while AS served as a negative control due to its sensitivity to HM (Obenland and Riechers Reference Obenland and Riechers2020). Comparisons of HA normalized peak areas among populations and time points were performed with the lme4 package (Bates et al. Reference Bates, Mächler, Bolker and Walker2015) in R (v. 4.2.0) using RStudio (v. 2023.03.0). Both experiments used a mixed-effects model in which population, time point, and their interactions were treated as fixed effects and replicates were random effects. Homogeneity of variance was confirmed with the Levene’s test. Data were subjected to ANOVA, and treatment means were separated with Fisher’s LSD (α = 0.05).

Results and Discussion

LC-MS Analysis of HA Levels in CS, AS, Alien Substitution, and NT Lines

Peak abundances of HA in the first experiment with CS and AS exhibited anticipated results as positive and negative controls, respectively; the amounts of HA in CS remained relatively low throughout the time course, while HA peak abundances in AS increased during the time course and were greater than HA levels in CS and all substitution lines at 8, 12, and 24 HAT (Figure 2). At these same time points, HA levels in AS were 8.5-, 7.4-, and 5.6-fold higher, respectively, than HA levels in CS. Among substitution lines, only 5A accumulated HA relative to CS at 8, 12, and 24 HAT (Figure 2), with HA peak abundances 3.8-, 3.2- and 2.4-fold higher than HA levels in CS, respectively. In contrast, the level of HA in 5D was not different from HA levels in CS at any time point.

Figure 2. Mean normalized peak abundance of halauxifen acid (HA) in ‘Chinese Spring’ (CS) wheat, Aegilops searsii (AS), and wheat group 5 alien substitution lines 5A, 5B, and 5D. Means were determined from seven total biological replicates from two independent experiments at 2, 8, 12, and 24 h after treatment (HAT). Error bars represent the standard error of the mean. For each time point, means that are not significantly different are bracketed and marked “ns”; two asterisks (**) indicate a significant difference from all other means; a single black asterisk (*) indicates a significant difference from CS as well as means marked “ns”; and a single yellow asterisk (*) indicates means are not significantly different from CS, 5A, or 5D. Significant differences were determined using Fisher’s LSD (α = 0.05).

By comparison, HA levels in the 5B line (Figure 2) varied throughout the time course relative to CS, 5A, and 5D, which were more consistent between 8 and 24 HAT. The peak abundance of HA in 5B was not different from HA levels in CS or 5D at 8, 12, and 24 HAT, but HA abundance in 5B increased to levels also not different from HA levels in 5A at 12 and 24 HAT (Figure 2). This finding indicates the rate of HA metabolism in 5B at 12 and 24 HAT is slower than that in CS and 5D compared with its rate of metabolism at 8 HAT or, alternatively, that rates of HA formation from HM are higher in 5B at 12 and 24 HAT compared with 8 HAT. These metabolism results are in accord with previous phenotypic experiments in the greenhouse, where the 5B line displayed intermediate HM sensitivity relative to the highly sensitive 5A line and relatively tolerant 5D line (Obenland and Riechers Reference Obenland and Riechers2020). The variation in HA peak abundance in the 5B line during the time course may indicate competing CXE versus CYP enzyme activities (further described later), but additional research is needed to investigate this hypothesis. However, because HA peak abundances in lines 5A and 5D consistently differed at 8, 12, and 24 HAT, we focused on NT lines with varying numbers of 5A and 5D chromosomes (e.g., N5A-T5D and N5D-T5A) for subsequent LC-MS analysis.

In the second experiment, HA peak abundance in CS remained relatively low and did not significantly change throughout the time course (Figure 3), which was also noted in the previous experiment (Figure 2). Only line N5A-T5D displayed higher amounts of HA relative to CS at 12 and 24 HAT (approximately 3-fold; Figure 3). HA peak abundances in N5D-T5A did not differ from HA levels in CS throughout the time course (Figure 3).

Figure 3. Mean normalized peak abundance of halauxifen acid (HA) in ‘Chinese Spring’ (CS) wheat and nullisomic-tetrasomic (NT) lines N5A-T5D and N5D-T5A. Means were determined from five biological replicates from one experiment at 2, 8, 12, and 24 h after treatment (HAT). Error bars represent the standard error of the mean. For each time point, means that are not significantly different are bracketed and marked “ns”; a single black asterisk (*) indicates a significant difference from CS and N5D-T5A. Significant differences were determined using Fisher’s LSD (α = 0.05).

These results indicate that species and lines lacking chromosome 5A (AS, N5A-T5D, and the 5A substitution line) have a relatively slow rate of HA metabolism. By contrast, lines possessing chromosome 5A (CS, N5D-T5A, and the 5B and 5D substitution lines) maintain HA metabolism rates similar to rates in CS. These findings support the hypothesis that genes encoding HA-detoxifying enzymes are located on wheat chromosome 5A, and as a result, lines lacking 5A consistently accumulate greater amounts of phytotoxic HA relative to lines possessing 5A through 24 HAT. Additionally, these results corroborate findings from previous greenhouse experiments in which 5A, N5A-T5D, and AS were relatively sensitive to HM, while CS, N5D-T5A, 5B, and 5D maintained tolerance to HM (Obenland and Riechers Reference Obenland and Riechers2020).

As HM is absorbed by the cuticle and underlying leaf tissue, it is de-esterified to HA (Figure 1) at an unknown rate. HA is a phytotoxic, transient, phase I compound that is subsequently O-demethylated, then conjugated with glucose (Dzikowski et al. Reference Dzikowski, Becker, Larelle, Kamerichs and Gast2016; Figure 1). O-demethylation and glucose conjugation reactions are likely catalyzed by CYPs and UDP-dependent glucosyltransferases (UGTs), respectively, which commonly mediate synthetic auxin herbicide–detoxification reactions in tolerant grasses (Frear Reference Frear1995; Mithila et al. Reference Mithila, Hall, Johnson, Kelley and Riechers2011; Sterling and Hall Reference Sterling and Hall1997; Zhang and Yang Reference Zhang and Yang2021). The presence of HA in all species and lines investigated (Figures 2 and 3) implies the activity of CXEs that convert HM to HA (Gershater and Edwards Reference Gershater and Edwards2007). However, the activity of CXEs, CYPs, and UGTs and resulting metabolic flux could vary, which could shed more light on our current results. Given differential absorption of HM among some species and lines (Supplementary Material 2), future investigation of CXEs is warranted. For example, the abundance of HA in the 5B or 5D substitution lines during the time course (Figure 2) could be relatively low due to loss of an active CXE that converts HM to HA. Conducting a follow-up metabolism study and supplying HA instead of HM in our assay may help elucidate the possible role of differential CXE activity among wheat lines in future research.

Additional biokinetic analyses should also measure abundances of the O-demethylated metabolite and glucose conjugate of HA (Figure 1) to comprehensively understand altered HA detoxification rates or patterns in these substitution and NT lines. Such investigations would require analytical metabolite standards or radiolabeled HM, which were not available for the current study. Because these initial metabolites could be present but occur in low abundance—particularly the O-demethylated HA metabolite if it is rapidly turned over to phase II conjugates—a whole-plant assay may be more informative, especially if quantifying HM absorption, HA translocation, or measuring HA metabolites beyond 24 h is desired.

The discovery of HM sensitivity in AS was fortuitous for our prior research (Obenland and Riechers Reference Obenland and Riechers2020). Detection of a relatively low HA metabolism rate (Figure 2) is notable because grasses are generally tolerant to synthetic auxins, with the exception of quinoline carboxylic acids (Grossmann Reference Grossmann2010; Sterling and Hall Reference Sterling and Hall1997) and a different 6-aryl-picolinic acid herbicide, florpyrauxifen-benzyl (Epp et al. Reference Epp, Alexander, Balko, Buysse, Brewster, Bryan, Daeuble, Fields, Gast, Green, Irvine, Lo, Lowe, Renga and Richburg2016; Lim et al. Reference Lim, Kim, Noh, Lim, Yook, Kim, Yi and Kim2021; Miller et al. Reference Miller, Norsworthy and Scott2018; Takano et al. Reference Takano, Greenwalt, Ouse, Zielinski and Schmitzer2023). Interestingly, the wheat safener cloquintocet-mexyl (3.75 g ai ha−1) reduced injury in AS even at an elevated rate of HM (60 g ae ha−1 plus adjuvants; Obenland and Riechers Reference Obenland and Riechers2020), which indicates enhancement of herbicide tolerance (presumably via induction of metabolic enzymes) by safeners is not limited to cultivated cereal crops (Riechers et al. Reference Riechers, Kreuz and Zhang2010).

Our results inform future experiments, including RNA-seq or genome-wide association studies, aimed at identifying potential candidate genes on chromosome 5A. However, these experiments often yield numerous (>50) potential candidate genes (Baek et al. Reference Baek, Goodrich, Brown, James, Moose, Lambert and Riechers2019). This is especially true for wheat, which has a relatively large (approximately 16 GB) allohexaploid genome (IWGSC Reference Epp, Alexander, Balko, Buysse, Brewster, Bryan, Daeuble, Fields, Gast, Green, Irvine, Lo, Lowe, Renga and Richburg2018), meaning a significant gene might be identified along with its homoeologues. This concern is exacerbated since CYPs and UGTs are encoded by large multigene families per diploid genome (Casey and Dolan Reference Casey and Dolan2023; Gharabli et al. Reference Gharabli, Della Gala and Welner2023). Previous studies utilizing various wheat species, alien substitution lines, or NT lines have identified differential homoeologous chromosome contributions to herbicide tolerance, metabolism, and gene expression patterns (Riechers et al. Reference Riechers, Yang, Irzyk, Jones and Fuerst1996, Reference Riechers, Irzyk, Jones and Fuerst1997; Xu et al. Reference Xu, Lagudah, Moose and Riechers2002; Yu et al. Reference Yu, Guo, Cui, Chen and Li2023).

Potential candidate genes encoding HA-detoxifying enzymes include CYPs and UGTs (Frear Reference Frear1995; Mithila et al. Reference Mithila, Hall, Johnson, Kelley and Riechers2011; Riechers et al. Reference Riechers, Kreuz and Zhang2010; Zhang and Yang Reference Zhang and Yang2021). CYPs have been implicated in detoxification of numerous herbicides in the Poaceae, which has the highest number of herbicide-detoxifying plant CYPs (Brazier-Hicks et al. Reference Brazier-Hicks, Franco-Ortega, Watson, Rougemont, Cohn, Dale, Hawkes, Goldberg-Cavalleri, Onkokesung and Edwards2022; Casey and Dolan Reference Casey and Dolan2023; Dimaano and Iwakami Reference Dimaano and Iwakami2021; Han et al. Reference Han, Yu, Beffa, González, Maiwald, Wang and Powles2020; Pan et al. Reference Pan, Guo, Wang, Shi, Yang, Zhou, Yu and Bai2022; Yu et al. Reference Yu, Guo, Cui, Chen and Li2023; Zheng et al. Reference Zheng, Yu, Sun, Zhang, Zhang, Tang, Lin and Shen2022). Members of the plant CYP81A subfamily are the most common, but CYP72A and CYP71C members have also been identified (Supplementary Material 1). Compared with CYPs, genes encoding herbicide-detoxifying UGTs in plants are generally not as well characterized. UGT activities in wheat have been directly studied with natural product and xenobiotic substrates (Brazier et al. Reference Brazier, Cole and Edwards2002), and the activities of herbicide-detoxifying UGTs have been inferred from metabolism studies in wheat in which glycosylated metabolites of isoproturon (Lu et al. Reference Lu, Zhang and Yang2015) and florasulam (DeBoer et al. Reference DeBoer, Thornburgh and Ehr2006) were detected.

Regulatory elements and factors, such as promoters, untranslated regions (UTRs), and transcription factors (TFs), of candidate genes may also contribute to the prominent role of genes on wheat chromosome 5A in controlling HA detoxification compared with its homoeologous chromosomes 5B and 5D. The role of TFs in herbicide detoxification is often inferred, as RNA-seq has identified TFs regulated by herbicides and safeners (Baek et al. Reference Baek, Goodrich, Brown, James, Moose, Lambert and Riechers2019; Brazier-Hicks et al. Reference Brazier-Hicks, Howell, Cohn, Hawkes, Hall, McIndoe and Edwards2020; Chen et al. Reference Chen, Su, Yang, Peng, Wu, Zhao, Lin, Bai and Wang2023; Duhoux et al. Reference Duhoux, Carrère, Gouzy, Bonin and Délye2015; Gaines et al. Reference Gaines, Lorentz, Figge, Herrmann, Maiwald, Ott, Han, Busi, Yu, Powles and Beffa2014; Zhang et al. Reference Zhang, Gao, Fang, Wang, Chen, Li and Dong2022). Homoeologue expression bias is commonly observed in allopolyploid plants (Grover et al. Reference Grover, Gallagher, Szadkowski, Yoo, Flagel and Wendel2012), partly attributed to variations in cis- and trans-regulatory elements between homoeologues (He et al. Reference He, Wang, Rutter, Jordan, Ren, Taagen, DeWitt, Sehgal, Sukumaran, Dreisigacker, Reynolds, Halder, Sehgal, Liu and Chen2022; Kremling et al. Reference Kremling, Chen, Su, Lepak, Romay, Swarts, Lu, Lorant, Bradbury and Buckler2018). We therefore hypothesize potential candidate genes on chromosome 5A and their 5B and 5D homoeologues vary in promoter/UTR sequences and/or TF binding that regulates gene expression, resulting in relatively higher expression and activity of 5A homoeologues. However, gene copy number variation or differences in encoded protein sequences among homoeologous genes should not be precluded without further experimentation.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/wsc.2024.24

Data availability

Data from LC-MS analyses are available on MassIVE (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp; reference numbers are MSV000092323, MSV000092320, and MSV000092256).

Acknowledgments

We thank Norbert Satchivi and Carla Yerkes (Corteva Agrisciences) for providing technical advice and standards for experimentation, Harold Bockelman of the USDA-ARS and Jon Raupp of the Kansas State Wheat Genetics Resource Center for assistance in obtaining wheat seeds, and Michael La Frano and Alexander Ulanov (University of Illinois) for providing LC-MS services.

Funding statement

This research was partially funded by Corteva Agrisciences.

Competing interests

The authors declare no competing interests.

Footnotes

Associate Editor: Christopher Preston, University of Adelaide

References

Baek, YS, Goodrich, L V., Brown, PJ, James, BT, Moose, SP, Lambert, KN, Riechers, DE (2019) 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 10:192 10.3389/fpls.2019.00192CrossRefGoogle ScholarPubMed
Bates, D, Mächler, M, Bolker, BM, Walker, SC (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48 10.18637/jss.v067.i01CrossRefGoogle Scholar
Brazier, M, Cole, DJ, Edwards, R (2002) O-glucosyltransferase activities toward phenolic natural products and xenobiotics in wheat and herbicide-resistant and herbicide-susceptible black-grass (Alopecurus myosuroides). Phytochemistry 59:149–156 10.1016/S0031-9422(01)00458-7CrossRefGoogle ScholarPubMed
Brazier-Hicks, M, Franco-Ortega, S, Watson, P, Rougemont, B, Cohn, J, Dale, R, Hawkes, TR, Goldberg-Cavalleri, A, Onkokesung, N, Edwards, R (2022) Characterization of cytochrome P450s with key roles in determining herbicide selectivity in maize. ACS Omega 7:17416–17431 10.1021/acsomega.2c01705CrossRefGoogle ScholarPubMed
Brazier-Hicks, M, Howell, A, Cohn, J, Hawkes, T, Hall, G, McIndoe, E, Edwards, R (2020) Chemically induced herbicide tolerance in rice by the safener metcamifen is associated with a phased stress response. J Exp Bot 71:411–421 10.1093/jxb/erz438CrossRefGoogle ScholarPubMed
Casey, A, Dolan, L (2023) Genes encoding cytochrome P450 monooxygenases and glutathione S-transferases associated with herbicide resistance evolved before the origin of land plants. PLoS ONE 18:e0273594 10.1371/journal.pone.0273594CrossRefGoogle ScholarPubMed
Chen, K, Su, X, Yang, H, Peng, Y, Wu, L, Zhao, Z, Lin, T, Bai, L, Wang, L (2023) Multi-omics analyses reveal the crosstalk between the circadian clock and the response to herbicide application in Oryza sativa . Front Plant Sci 14:1155258 10.3389/fpls.2023.1155258CrossRefGoogle ScholarPubMed
Concepcion, JCT, Kaundun, SS, Morris, JA, Hutchings, SJ, Strom, SA, Lygin, AV, Riechers, DE (2021) Resistance to a nonselective 4-hydroxyphenylpyruvate dioxygenase-inhibiting herbicide via novel reduction–dehydration–glutathione conjugation in Amaranthus tuberculatus . New Phytol 232:2089–2105 10.1111/nph.17708CrossRefGoogle ScholarPubMed
DeBoer, GJ, Thornburgh, S, Ehr, RJ (2006) Uptake, translocation and metabolism of the herbicide florasulam in wheat and broadleaf weeds. Pest Manag Sci 62:316–324 10.1002/ps.1163CrossRefGoogle ScholarPubMed
Demeulenaere, M, Beeckman, T (2014) The interplay between auxin and the cell cycle during plant development. Pages 119–142 in Zažímalová E, Petrášek J, Benková E, eds. Auxin and Its Role in Plant Development. Vienna: Springer-Verlag10.1007/978-3-7091-1526-8_7CrossRefGoogle Scholar
Dimaano, N, Iwakami, S (2021) Cytochrome P450-mediated herbicide metabolism in plants: current understanding and prospects. Pest Manag Sci 77:22–32 10.1002/ps.6040CrossRefGoogle Scholar
Duhoux, A, Carrère, S, Gouzy, J, Bonin, L, Délye, C (2015) 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 87:473–487 10.1007/s11103-015-0292-3CrossRefGoogle Scholar
Dunn, WB, Broadhurst, D, Begley, P, Zelena, E, Francis-Mcintyre, S, Anderson, N, Brown, M, Knowles, JD, Halsall, A, Haselden, JN, Nicholls, AW, Wilson, ID, Kell, DB, Goodacre, R (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6:1060–1083 10.1038/nprot.2011.335CrossRefGoogle ScholarPubMed
Dzikowski, M, Becker, J, Larelle, D, Kamerichs, B, Gast, R (2016) Arylex™ active—new herbicide active and base for new cereals herbicides: Zypar™ and Pixxaro™ EC to control wide range of broadleaf weeds in cereals in Europe. Julius-Kühn-Archiv 452:297–304 Google Scholar
Elolimy, A, Alharthi, A, Zeineldin, M, Parys, C, Helmbrecht, A, Loor, JJ (2019) Supply of methionine during late-pregnancy alters fecal microbiota and metabolome in neonatal dairy calves without changes in daily feed intake. Front Microbiol 10:2159 10.3389/fmicb.2019.02159CrossRefGoogle ScholarPubMed
Epp, JB, Alexander, AL, Balko, TW, Buysse, AM, Brewster, WK, Bryan, K, Daeuble, JF, Fields, SC, Gast, RE, Green, RA, Irvine, NM, Lo, WC, Lowe, CT, Renga, JM, Richburg, JS, et al. (2016) The discovery of Arylex™ active and Rinskor™ active: two novel auxin herbicides. Bioorg Med Chem 24:362–371 10.1016/j.bmc.2015.08.011CrossRefGoogle ScholarPubMed
Epp, JB, Schmitzer, PR, Crouse, GD (2018) Fifty years of herbicide research: comparing the discovery of trifluralin and halauxifen-methyl. Pest Manag Sci 74:9–16 10.1002/ps.4657CrossRefGoogle ScholarPubMed
Frear, DS (1995) Wheat microsomal cytochrome P450 monooxygenases: characterization and importance in the metabolic detoxification and selectivity of wheat herbicides. Drug Metabol Drug Interact 12:329–358 10.1515/DMDI.1995.12.3-4.329CrossRefGoogle ScholarPubMed
Gaines, TA, Lorentz, L, Figge, A, Herrmann, J, Maiwald, F, Ott, MC, Han, H, Busi, R, Yu, Q, Powles, SB, Beffa, R (2014) RNA-Seq transcriptome analysis to identify genes involved in metabolism-based diclofop resistance in Lolium rigidum . Plant J 78:865–876 10.1111/tpj.12514CrossRefGoogle ScholarPubMed
Gershater, MC, Edwards, R (2007) Regulating biological activity in plants with carboxylesterases. Plant Sci 173:579–588 10.1016/j.plantsci.2007.08.008CrossRefGoogle Scholar
Gharabli, H, Della Gala, V, Welner, DH (2023) The function of UDP-glycosyltransferases in plants and their possible use in crop protection. Biotechnol Adv 67:108182 10.1016/j.biotechadv.2023.108182CrossRefGoogle ScholarPubMed
Godzien, J, Alonso-Herranz, V, Barbas, C, Armitage, EG (2015) Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample. Metabolomics 11:518–528 10.1007/s11306-014-0712-4CrossRefGoogle Scholar
Grossmann, K (2010) Auxin herbicides: current status of mechanism and mode of action. Pest Manag Sci 66:113–120 10.1002/ps.1860CrossRefGoogle ScholarPubMed
Grover, CE, Gallagher, JP, Szadkowski, EP, Yoo, MJ, Flagel, LE, Wendel, JF (2012) Homoeolog expression bias and expression level dominance in allopolyploids. New Phytol 196:966–971 10.1111/j.1469-8137.2012.04365.xCrossRefGoogle ScholarPubMed
Han, H, Yu, Q, Beffa, R, González, S, Maiwald, F, Wang, J, Powles, SB (2020) Cytochrome P450 CYP81A10v7 in Lolium rigidum confers metabolic resistance to herbicides across at least five modes of action. Plant J 178:1081–1095 Google Scholar
He, F, Wang, W, Rutter, WB, Jordan, KW, Ren, J, Taagen, E, DeWitt, N, Sehgal, D, Sukumaran, S, Dreisigacker, S, Reynolds, M, Halder, J, Sehgal, SK, Liu, S, Chen, J, et al. (2022) Genomic variants affecting homoeologous gene expression dosage contribute to agronomic trait variation in allopolyploid wheat. Nat Commun 13:826 10.1038/s41467-022-28453-yCrossRefGoogle ScholarPubMed
[IWGSC] International Wheat Genome Sequencing Consortium (2018) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361:eaar7191 10.1126/science.aar7191CrossRefGoogle Scholar
Jiang, JM, Friebe, B, Gill, BS (1994) Recent advances in alien gene-transfer in wheat. Euphytica 73:199–212 10.1007/BF00036700CrossRefGoogle Scholar
Kremling, KAG, Chen, SY, Su, MH, Lepak, NK, Romay, MC, Swarts, KL, Lu, F, Lorant, A, Bradbury, PJ, Buckler, ES (2018) Dysregulation of expression correlates with rare-allele burden and fitness loss in maize. Nature 555:520–523 10.1038/nature25966CrossRefGoogle ScholarPubMed
Kreuz, K, Tommasini, R, Martinoia, E (1996) Old enzymes for a new job: herbicide detoxification in plants. Plant Physiol 111:349–353 10.1104/pp.111.2.349CrossRefGoogle ScholarPubMed
Law, CN, Snape, JW, Worland, AJ (1987) Aneuploidy in wheat and its uses in genetic analysis. Pages 71–108 in Lupton FGH, ed. Wheat Breeding. Dordrecht, Netherlands: Springer10.1007/978-94-009-3131-2_4CrossRefGoogle Scholar
Lim, SH, Kim, H, Noh, TK, Lim, JS, Yook, MJ, Kim, JW, Yi, JH, Kim, DS (2021) Baseline sensitivity of Echinochloa crus-gall and E. oryzicola to florpyrauxifen-benzyl, a new synthetic auxin herbicide, in Korea. Front Plant Sci 12:656642 10.3389/fpls.2021.656642CrossRefGoogle Scholar
Lu, YC, Zhang, S, Yang, H (2015) Acceleration of the herbicide isoproturon degradation in wheat by glycosyltransferases and salicylic acid. J Hazard Mater 283:806–814 10.1016/j.jhazmat.2014.10.034CrossRefGoogle ScholarPubMed
Lygin, A V., Kaundun, SS, Morris, JA, Mcindoe, E, Hamilton, AR, Riechers, DE (2018) Metabolic pathway of topramezone in multiple-resistant waterhemp (Amaranthus tuberculatus) differs from naturally tolerant maize. Front Plant Sci 9:1644 10.3389/fpls.2018.01644CrossRefGoogle ScholarPubMed
Ma, R, Skelton, JJ, Riechers, DE (2015) Measuring rates of herbicide metabolism in dicot weeds with an excised leaf assay. J Vis Exp 103:e53236 Google Scholar
McSteen, P (2010) Auxin and monocot development. Cold Spring Harb Perspect Biol 2:a001479 10.1101/cshperspect.a001479CrossRefGoogle ScholarPubMed
Miller, MR, Norsworthy, JK, Scott, RC (2018) Evaluation of florpyrauxifen-benzyl on herbicide-resistant and herbicide-susceptible barnyardgrass accessions. Weed Technol 32:126–134 10.1017/wet.2017.100CrossRefGoogle Scholar
Mithila, J, Hall, JC, Johnson, WG, Kelley, KB, Riechers, DE (2011) Evolution of resistance to auxinic herbicides: historical perspectives, mechanisms of resistance, and implications for broadleaf weed management in agronomic crops. Weed Sci 59:445–457 10.1614/WS-D-11-00062.1CrossRefGoogle Scholar
Murashige, T, Skoog, F (1962) A revised medium for rapid growth and bio assays with tobacco tissue cultures. Physiol Plant 15:473–497 10.1111/j.1399-3054.1962.tb08052.xCrossRefGoogle Scholar
Obenland, OA, Riechers, DE (2020) Identification of chromosomes in Triticum aestivum possessing genes that confer tolerance to the synthetic auxin herbicide halauxifen-methyl. Sci Rep 10:8713 10.1038/s41598-020-65434-xCrossRefGoogle Scholar
Pan, L, Guo, Q, Wang, J, Shi, L, Yang, X, Zhou, Y, Yu, Q, Bai, L (2022) CYP81A68 confers metabolic resistance to ALS and ACCase-inhibiting herbicides and its epigenetic regulation in Echinochloa crus-galli . J Hazard Mater 428:128225 10.1016/j.jhazmat.2022.128225CrossRefGoogle ScholarPubMed
Riechers, DE, Irzyk, GP, Jones, SS, Fuerst, EP (1997) Partial characterization of glutathione S-transferases from wheat (Triticum spp.) and purification of a safener-induced glutathione S-transferase from Triticum tauschii . Plant Physiol 114:1461–1470 10.1104/pp.114.4.1461CrossRefGoogle ScholarPubMed
Riechers, DE, Kreuz, K, Zhang, Q (2010) Detoxification without intoxication: herbicide safeners activate plant defense gene expression. Plant Physiol 153:3–13 10.1104/pp.110.153601CrossRefGoogle ScholarPubMed
Riechers, DE, Yang, K, Irzyk, GP, Jones, SS, Fuerst, EP (1996) Variability of glutathione S-transferase levels and dimethenamid tolerance in safener-treated wheat and wheat relatives. Pestic Biochem Physiol 56:88–101 10.1006/pest.1996.0063CrossRefGoogle Scholar
Sangster, T, Major, H, Plumb, R, Wilson, AJ, Wilson, ID (2006) A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst 131:1075–1078 10.1039/b604498kCrossRefGoogle ScholarPubMed
Schmitzer, PR, Balko, TW, Daeuble, JF, Epp, JB, Satchivi, NM, Siddall, TL, Weimer, MR, Yerkes, CN (2015) Discovery and SAR of halauxifen methyl: a novel auxin herbicide. Pages 247–260 in Maienfisch P, Stevenson T, eds. Discovery and Synthesis of Crop Protection Products. Washington, DC: ACS Books10.1021/bk-2015-1204.ch018CrossRefGoogle Scholar
Sears, ER (1954) The aneuploids of common wheat. Mo Agric Exp Stn Res Bull 572:1–58 Google Scholar
Sears, ER (1966) Nullisomic-tetrasomic combinations in hexaploid wheat. Pages 29–45 in Riley R, Lewis KR, eds. Chromosome Manipulations and Plant Genetics. Boston: Springer10.1007/978-1-4899-6561-5_4CrossRefGoogle Scholar
Sterling, TM, Hall, JC (1997) Mechanism of action of natural auxins and the auxinic herbicides. Pages 111–141 in Roe R, Burton J, Kuhr R, eds. Herbicide Activity: Toxicology, Biochemistry and Molecular Biology. Amsterdam: IOS PressGoogle Scholar
Sumner, LW, Amberg, A, Barrett, D, Beale, MH, Beger, R, Daykin, CA, Fan, TW-M, Fiehn, O, Goodacre, R, Griffin, JL, Hankemeier, T, Hardy, N, Harnly, J, Higashi, R, Kopka, J, et al. (2007) Proposed minimum reporting standards for chemical analysis. Metabolomics 3:211–221 10.1007/s11306-007-0082-2CrossRefGoogle ScholarPubMed
Takano, HK, Greenwalt, S, Ouse, D, Zielinski, M, Schmitzer, P (2023) Metabolic cross-resistance to florpyrauxifen-benzyl in barnyardgrass (Echinochloa crus-galli) evolved before the commercialization of Rinskor™. Weed Sci 71:77–83 10.1017/wsc.2023.11CrossRefGoogle Scholar
Tsugawa, H, Cajka, T, Kind, T, Ma, Y, Higgins, B, Ikeda, K, Kanazawa, M, Vandergheynst, J, Fiehn, O, Arita, M (2015) MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12:523–526 10.1038/nmeth.3393CrossRefGoogle ScholarPubMed
Wehrens, R, Hageman, JA, van Eeuwijk, F, Kooke, R, Flood, PJ, Wijnker, E, Keurentjes, JJB, Lommen, A, van Eekelen, HDLM, Hall, RD, Mumm, R, de Vos, RCH (2016) Improved batch correction in untargeted MS-based metabolomics. Metabolomics 12:88 10.1007/s11306-016-1015-8CrossRefGoogle ScholarPubMed
Xu, F-X, Lagudah, ES, Moose, SP, Riechers, DE (2002) Tandemly duplicated safener-induced glutathione S-transferase genes from Triticum tauschii contribute to genome- and organ-specific expression in hexaploid wheat. Plant Physiol 130:362–373 10.1104/pp.004796CrossRefGoogle ScholarPubMed
Yu, H, Guo, X, Cui, H, Chen, J, Li, X (2023) Metabolism difference is involved in mesosulfuron-methyl selectivity between Aegilops tauschii and Triticum aestivum . J Agric Food Chem 71:186–196 10.1021/acs.jafc.2c05809CrossRefGoogle ScholarPubMed
Zhang, JJ, Yang, H (2021) Metabolism and detoxification of pesticides in plants. Sci Total Environ 790:148034 10.1016/j.scitotenv.2021.148034CrossRefGoogle ScholarPubMed
Zhang, Y, Gao, H, Fang, J, Wang, H, Chen, J, Li, J, Dong, L (2022) Up-regulation of bZIP88 transcription factor is involved in resistance to three different herbicides in both Echinochloa crus-galli and E. glabrescens . J Exp Bot 73:6916–6930 10.1093/jxb/erac319CrossRefGoogle ScholarPubMed
Zheng, T, Yu, X, Sun, Y, Zhang, Q, Zhang, X, Tang, M, Lin, C, Shen, Z (2022) Expression of a cytochrome P450 gene from bermuda grass Cynodon dactylon in soybean confers tolerance to multiple herbicides. Plants 11:949 10.3390/plants11070949CrossRefGoogle ScholarPubMed
Zobiole, LHS, Kalsing, A (2017) Efficacy of halauxifen-methyl formulations to control volunteer soybean DAS-444Ø6-6 (Enlist E3™). Rev Bras Herb 16:192–197 Google Scholar
Figure 0

Figure 1. Structures of halauxifen-methyl (HM) and its metabolites in wheat: (A) HM; (B) halauxifen acid (HA); (C) O-demethylated metabolite of HA; and (D) glucose conjugate of HA.

Figure 1

Figure 2. Mean normalized peak abundance of halauxifen acid (HA) in ‘Chinese Spring’ (CS) wheat, Aegilops searsii (AS), and wheat group 5 alien substitution lines 5A, 5B, and 5D. Means were determined from seven total biological replicates from two independent experiments at 2, 8, 12, and 24 h after treatment (HAT). Error bars represent the standard error of the mean. For each time point, means that are not significantly different are bracketed and marked “ns”; two asterisks (**) indicate a significant difference from all other means; a single black asterisk (*) indicates a significant difference from CS as well as means marked “ns”; and a single yellow asterisk (*) indicates means are not significantly different from CS, 5A, or 5D. Significant differences were determined using Fisher’s LSD (α = 0.05).

Figure 2

Figure 3. Mean normalized peak abundance of halauxifen acid (HA) in ‘Chinese Spring’ (CS) wheat and nullisomic-tetrasomic (NT) lines N5A-T5D and N5D-T5A. Means were determined from five biological replicates from one experiment at 2, 8, 12, and 24 h after treatment (HAT). Error bars represent the standard error of the mean. For each time point, means that are not significantly different are bracketed and marked “ns”; a single black asterisk (*) indicates a significant difference from CS and N5D-T5A. Significant differences were determined using Fisher’s LSD (α = 0.05).

Supplementary material: File

Landau et al. supplementary material

Landau et al. supplementary material
Download Landau et al. supplementary material(File)
File 325.9 KB