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Transcription of putative tonoplast transporters in response to glyphosate and paraquat stress in Conyza bonariensis and Conyza canadensis and selection of reference genes for qRT-PCR

  • Marcelo L. Moretti ,

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

    Affiliations Department of Plant Sciences, University of California, Davis, California, United States of America, Department of Horticulture, Oregon State University, Corvallis, Oregon, United States of America

  • Rocio Alárcon-Reverte,

    Roles Data curation, Formal analysis, Investigation, Supervision, Writing – review & editing

    Affiliation Department of Plant Sciences, University of California, Davis, California, United States of America

  • Stephen Pearce,

    Roles Formal analysis, Investigation, Methodology, Validation, Writing – review & editing

    Affiliation Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, United States of America

  • Sarah Morran,

    Roles Formal analysis, Investigation, Supervision, Validation, Writing – review & editing

    Affiliation Department of Plant Sciences, University of California, Davis, California, United States of America

  • Bradley D. Hanson

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

    Affiliation Department of Plant Sciences, University of California, Davis, California, United States of America

Transcription of putative tonoplast transporters in response to glyphosate and paraquat stress in Conyza bonariensis and Conyza canadensis and selection of reference genes for qRT-PCR

  • Marcelo L. Moretti, 
  • Rocio Alárcon-Reverte, 
  • Stephen Pearce, 
  • Sarah Morran, 
  • Bradley D. Hanson


Herbicide resistance is a challenge for modern agriculture further complicated by cases of resistance to multiple herbicides. Conyza bonariensis and Conyza canadensis are invasive weeds of field crops, orchards, and non-cropped areas in many parts of the world. In California, USA, Conyza populations resistant to the herbicides glyphosate and paraquat have recently been described. Although the mechanism conferring resistance to glyphosate and paraquat in these species was not elucidated, reduced translocation of these herbicides was observed under experimental conditions in both species. Glyphosate and paraquat resistance associated with reduced translocation are hypothesized to be a result of sequestration of herbicides into the vacuole, with the possible involvement of over-expression of genes encoding tonoplast transporters of ABC-transporter families in cases of glyphosate resistance or cationic amino acid transporters (CAT) in cases of paraquat resistance. However, gene expression in response to herbicide treatment has not been studied in glyphosate and paraquat resistant populations. In the current study, we evaluated the transcript levels of genes possibly involved in resistance using real-time PCR. First, we evaluated eight candidate reference genes following herbicide treatment and selected three genes that exhibited stable expression profiles; ACTIN, HEAT-SHOCK-PROTEIN-70, and CYCLOPHILIN. The reference genes identified here can be used for further studies related to plant-herbicide interactions. We used these reference genes to assay the transcript levels of EPSPS, ABC transporters, and CAT in response to herbicide treatment in susceptible and resistant Conyza spp. lines. No transcription changes were observed in EPSPS or CAT genes after glyphosate or paraquat treatment, suggesting that these genes are not involved in the resistance mechanism. Transcription of the two ABC transporter genes increased following glyphosate treatment in all Conyza spp. lines. Transcription of ABC transporters also increased after paraquat treatment in all three lines of C. bonariensis. However, in C. canadensis, paraquat treatment increased transcription of only one ABC transporter gene in the susceptible line. The increase in transcription of ABC transporters after herbicide treatment is likely a stress response based on similar response observed across all Conyza lines regardless of resistance or sensitivity to glyphosate or paraquat, thus these genes do not appear to be directly involved in the mechanism of resistance in Conyza spp.


Herbicide resistance is widespread in modern agriculture and challenges the long-term sustainability of herbicide use [1]. Although many herbicides are commercially available, the market is dominated by a small number of widely used chemicals [2, 3]. Two of the most extensively used herbicides are glyphosate [4] and paraquat [5]. The repeated use of these herbicides has selected for the evolution in many weed species of resistance to individual herbicide modes of action and, increasingly, to multiple herbicides. Reports of dual resistance to both glyphosate and paraquat in weedy species are limited, but cases in Conyza bonariensis and Conyza canadensis were recently reported in several locations in California, USA [6, 7]. Members of the Conyza genus are listed among the most problematic herbicide-resistant weeds globally based on number of countries where they are present, number of herbicide modes of action with documented resistance, and estimated infested sites and cropping systems [8, 9]; these weeds affect corn, soybean, vineyards, orchards, and non-cropped areas across five continents [10]. Due to the agronomic and economic importance of C. bonariensis and C. canadensis in the U.S. and other parts of world, there is an urgent need to understand the mechanisms of glyphosate and paraquat resistance to help combat this threat.

Glyphosate is a mobile, foliar-applied herbicide which inhibits the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) [11]. Glyphosate resistance has been reported in several weed species with various mechanisms documented to date [12]. In Conyza spp., reduced translocation is the predominant resistance mechanism [12], which is associated with sequestration of the herbicide into the vacuole, thus neutralizing its activity [13]. Vacuole sequestration of glyphosate may be dependent on the activity of tonoplast-localized transporters [12, 13]. Although the specific transporters involved in glyphosate resistance in Conyza spp. have not been identified, two members of the ABC transporter super-family—M10 and M11—have been identified as candidate genes [14, 15]. These putative transporters are homologous to the Arabidopsis genes ABCC10 and ABCC8, respectively, which encode members of the ABCC sub-group of tonoplast-localized ABC transporters.[16]. In C. canadensis, M10 and M11 transcript levels exhibit a 45- to 300-fold increase in response to glyphosate treatment, with the response being greatest in glyphosate-resistant plants [14]. The increase in transcription of M10 and M11 in this species was reported to be synchronized with an increase in EPSPS transcription and affected by environmental conditions [15, 17]

Paraquat is a foliar-applied herbicide with limited systemic mobility in the plant, which targets the chloroplasts, where it acts as a photosystem I electron acceptor generating reactive oxygen species [18]. Paraquat resistance in Conyza spp. and other weed species is also associated with reduced translocation of the herbicide and related to vacuolar sequestration [1821]. Although no specific transporter involved in paraquat sequestration into the vacuole has been identified, a study with C. canadensis suggested the involvement of a cationic amino acid transporter (CAT), [22]. In Arabidopsis, both CAT2 (AT1G58030) and CAT4 (AT3G03720) encode CATs which are primarily localized to the tonoplast [23], making them appropriate candidates for the vacuolar sequestration of paraquat.

In glyphosate-paraquat-resistant (GPR) Conyza spp., reduced translocation of glyphosate and paraquat was observed in both C. bonariensis and C. canadensis and paraquat resistance was always associated with glyphosate resistance [24]. However, this previous work did not investigate the molecular aspects of resistance. Characterizing the transcriptional responses of candidate genes in response to glyphosate and paraquat treatment is one approach that could shed light on the mechanism(s) of resistance to these herbicides in Conyza spp. A pre-requisite for the accurate determination of target gene transcript levels in qRT-PCR studies is the selection of stably-expressed reference genes [25, 26]. In model plant species, large, publicly-available databases describing temporal and spatial gene expression profiles facilitate the selection of reference genes [27]. However, non-model species such as Conyza lack these resources, meaning the selection and validation of stable reference genes must be performed empirically.

In the current study, we report the selection and validation of stably-expressed reference qRT-PCR assays for expression studies in Conyza spp. under glyphosate or paraquat stress. Using these reference genes, we evaluated the transcriptional profiles of candidate transporter genes in response to herbicide treatment to determine their involvement in the herbicide resistance mechanism.

Materials and methods

Plant material and growth conditions

Single seed descendants of previously characterized biotypes of three C. bonariensis and three C. canadensis were selected for these studies, hereafter referred to as lines. The phenotypes of these lines were: glyphosate and paraquat susceptible (GPS), glyphosate-resistant (GR), or glyphosate- and paraquat-resistant (GPR) [24]. The resistant lines of both species demonstrated similar reductions in the translocation of glyphosate (GR) or glyphosate and paraquat (GPR) from the treated leaf. All lines were self-pollinated for at least three generations and recurrently selected for the phenotype (GR, GPR, or GPS) to ensure homogeneous response. Seeds were placed in flats filled with commercial potting mix (Sun Gro Horticulture, Vancouver, BC, CAN) and kept in growth chambers (PGR15 Conviron, Manitoba, Canada) at 28°C/22°C day/night temperatures with 16 hours of light and 50% relative humidity. These conditions were maintained throughout the experiments. After germination, individual seedlings were transplanted into 0.5 L-pots and kept in the growth chamber. Plants were grown for 28 days before herbicide treatments were imposed on 5- to 7-leaf stage C. bonariensis or 8- to 10-leaf stage C. canadensis plants.

Herbicide treatment and sample collection

Response to glyphosate and paraquat was evaluated in independent experiments. In each experiment, three lines (GPS, GR, and GPR) of each species were either treated with the herbicide or left untreated. Six biological replicates were used for each treatment and line combination resulting in 72 samples per experiment. To evaluate the response to glyphosate, plants were treated with 29.56 mM of glyphosate acid, equivalent to a field rate of 1 kg of glyphosate acid ha-1 (glyphosate, Roundup PowerMax, 540 g acid equivalent L-1, Monsanto Co, St Louis, MO, USA). To evaluate the response to paraquat, plants were treated with 9.7 mM of the paraquat cation, equivalent to a field rate of 0.5 kg of paraquat ha-1 (Gramoxone Inteon®, 240 g ai L-1, Syngenta Crop Protection Inc., Greensboro, NC, USA). Herbicide solutions also included non-ionic surfactant (R-11®, 90% v/v, Wilbur-Ellis, Aurora, CO) at 0.25% v/v and ammonium sulfate at 1% w/v following label recommendations. Herbicide treatments were applied to foliage using a single-nozzle spray-chamber calibrated to deliver the equivalent of 200 L ha-1. Application occurred in the mornings between 8 and 10 am.

Leaf tissues were collected 24 hours after glyphosate application and 6 hours after paraquat application [14]. Although paraquat causes rapid desiccation of leaves in susceptible plants, functional activity of leaves was confirmed using maximum quantum of photosystem II within 6 hours after treatment in a preliminary study (S1 Fig), and by other researchers [22]. The sample collections consisted of excising the youngest, fully-expanded leaf at the base of the rosette for each biological replicate, and immediately placing it in liquid nitrogen. Samples were stored at -80°C until further analysis.

RNA extraction and cDNA synthesis

Total RNA was extracted using RNeasy Plant mini kit (Qiagen, Valencia, CA, USA) following the manufacturer’s protocol. RNA concentration and quality was assessed by measuring absorbance at 230, 260, and 280 nm using a ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). Sample quality was considered adequate when the absorbance ratio of A260/A280 was between 1.8 to 2 and the ratio of A260/A230 was greater than 2 [28]. RNA integrity was assessed by electrophoresis using a 2% agarose gel. cDNA synthesis was performed using 1 μg of template RNA using QuantiTect Reverse Transcription Kit (Qiagen, Valencia, CA, USA), including treatment with gDNA wipe-out buffer to mitigate DNA contamination, as recommended by the manufacturer.

Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis

Real-time PCR was conducted using the 7500 Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). Reactions were performed in 20 μL volumes comprised of 10 μL of Fast SYBR Green master mix (2X) (Applied Biosystems, Foster City, CA, USA), 200 nM of each primer and 20 ng of cDNA template. The reaction conditions consisted of single cycle of 20 s at 95°C followed by 40 cycles of two steps: first 3 s at 95°C followed by 30 s at 60°C.

Reference genes and target genes selection and validation for qRT-PCR

Eight genes commonly used as qRT-PCR reference genes in Arabidopsis thaliana were evaluated as candidate reference genes in C. bonariensis and C. canadensis [29]. The Arabidopsis thaliana nucleotide coding sequences of candidate reference genes were used as queries in a BLASTN search optimized for discontinuous megablast against the Conyza canadensis genome [30] from the NCBI assembly database [31]. The gene names, description, and details of the BLAST search are presented in Table 1.

Table 1. Candidate qRT-PCR reference and target genes used in this study.

Arabidopsis thaliana coding sequences were used to query C. canadensis genome assembly. The identity match (%) and e-value of the selected BLAST hit are listed.

Homolog detection was performed using a reciprocal BLAST search as described previously [32]. The C. canadensis genomic sequences most similar to the A. thaliana reference coding sequences were used to design qRT-PCR primers. Primer design was performed using the Primer-BLAST tool based on Primer 3 [33, 34]. The primers used for the ACTIN genes were previously published by Peng et al. [14]. The primers for EPSPS were designed to amplify conserved regions of all three known copies of the EPSPS gene from C. canadensis [35]. New primers were designed for M10 and M11 using as templates the direct sequences of the cDNA regions amplified by the primers originally developed by Peng et al. [14] (S1 Appendix). The primers designed used to amplify cDNA fragment were: M10 Forward (5’-TTGGCTCAACTTCGTGGTATCGGG-3′) and reverse (5′-CCAAGAAATTCCAAGCGGAACCCT-3′) with an amplified fragment of 253 bp; M11 Forward (5′-ATGCTGTCTTCTTTTACCTTTGC-3′) and reverse (5′-CGACTTCCCACTACCAGTTCTTC-3′) with an amplified fragment of 393 bp [14].

The thermocycle conditions were as follows: one initial cycle at 95°C for 2 min, followed by 35 cycles of 95°C for 30 seconds, 60°C for 30 seconds, 72°C for 2 minutes. The reaction was terminated with an extension cycle at 72°C for 7 minutes.

Primer pairs were selected based on low predicted likelihood of forming secondary structures (including self-dimerization, hairpin, and self-annealing), a prediction made by the OligoCalc online tool [36]. Primer specificities were tested by performing PCR using cDNA of both Conyza spp. as templates and running the amplified product on a 2% agarose gel. We also analyzed the shape of the dissociation curve to confirm each product had a single peak melting curve (S2 Fig). Amplification efficiencies were calculated using the slope of Ct values derived from a four-fold cDNA dilution series. Efficiencies were calculated using the formula: efficiency (%) = (10(-1/slope)-1)*100 and ranged from 84 to 99% (Table 2).

Table 2. Primer sequence and amplicon size of candidate reference genes and target genes evaluated.

Primer amplification efficiencies were calculated for each of the Conyza spp.

Reference gene stability

The cDNA samples from three biological replicates of each line and treatment combination were selected to analyze the stability of the candidate genes’ transcript levels across species, genotypes and herbicide treatments. Selection of candidate genes was performed independently for each herbicide and species. Evaluation of candidate genes was based on the number of cycles required for the fluorescence signal to surpass the threshold level, or cycle threshold (Ct). Ct values were assigned by a built-in algorithm in the qRT-PCR system using default settings. Ct values were assessed visually and no manual adjustments were necessary. Presence of outliers was tested using the Grubb’s test (p<0.05), and outliers were removed prior to further analysis [37]. Ct means and standard deviations for evaluated reference genes are available in S1 Dataset.

Many algorithms are available to evaluate gene stability, and the final outcome of gene stability is comparable among many of these algorithms [38]. Candidate gene stability was evaluated using the NormFinder algorithm because this algorithm can evaluate different sample groups and separate intra- versus inter-group variation [39]. The variation is summarized in a single stability parameter with smaller values indicating greater transcription stability. NormFinder stability parameters were calculated for each treatment and species and genes were ranked based on stability. A consensus ranking was calculated using the Monte Carlo algorithm based on the Spearman’s distances across rankings performed by RankAggreg package in R [40]. The three most stable genes were selected as reference genes to assay the transcript levels of target genes.

Expression of ABC-transporters, CAT, and EPSPS genes

Candidate gene transcript levels were quantified in the youngest fully-expanded leaf tissue 24 hours after treatment with glyphosate or six hours after treatment with paraquat. The genes ACT7, CYP5, and HPS70 were used as reference genes and the geometric means of all three genes was used for normalization. For each line and herbicide, target gene transcript levels were compared between treated and untreated plants using six biological replicates. Ct means and standard deviations for evaluated target genes are available on S1 Dataset. Transcription of the four target genes was compared using the 2-ΔΔCT method for each gene and line [41]. A t-test (p <0.05) was used to compare the effect of herbicide treatment on transcription of target genes within line. To compare the basal expression level of target genes among lines before herbicide treatment, we used the 2-ΔCT method [42]. These expression values are presented relative to the geometric mean of the three reference genes ACT7, CYP5, and HPS70 for each line. A t-test (p <0.05) was used to make pair-wise comparison of lines for each species and target gene combination.


Validation of qRT-PCR assays for reference and target genes in Conyza spp.

To develop stable qRT-PCR reference gene assays for herbicide treatment studies in Conyza spp. we selected eight candidate genes previously used in qRT-PCR studies in Arabidopsis [29]. The coding sequences for each gene was queried against the Conyza canadensis genome assembly and three pairs of qRT-PCR primers were designed. Based on several quality parameters (S2 Fig) and affinity for both species, the best primer pair was selected (Table 2).

We evaluated the expression stability of each candidate reference gene using cDNA samples from the leaves of three lines (GPR, GR, and GPS) of both species from untreated plants and plants treated with either glyphosate or paraquat. In response to glyphosate treatment, the three most stable genes were ACT7, HPS70, and CYP5 in C. bonariensis, and HPS70, CYP5, and ACT7 in C. canadensis (Table 3). In response to paraquat treatment, the three most stable genes were CYP5, ACT7, and HPS70 in C. bonariensis, and CYP5, ACT7, and HPS70 in C. canadensis (Table 3).

Table 3. Transcription stability values and stability ranking of candidate reference genes calculated using the NormFinder algorithm.

Transcription was evaluated on young leaves of Conyza bonariensis and Conyza canadensis lines after glyphosate or paraquat treatments.

The consensus ranking of gene stability identified the genes CYP5, ACT7, and HPS70 as the most stable across species and herbicide treatments (Fig 1), and therefore these genes were used as reference genes in the subsequent experiments.

Fig 1. Consensus ranking of reference gene stability in Conyza spp. using Monte Carlo algorithm based on Spearman distances.

Genes are ordered by decreasing stability values based on their rankings (grey lines) calculated by Normfinder in C. bonariensis and C. canadensis treated with glyphosate or paraquat. The mean ranking is indicated by the black line, and the consensus ranking computed by Cross-Entropy (CE) Monte Carlo algorithm is indicated by the red line. The genes cyclophilin 5 (CYP5), actin 7 (ACT7), and heat-shock protein 70 (HPS70) were the three most stable genes.

Expression of ABC-transporters, CAT, and EPSPS genes in response to herbicide treatment

We next analyzed the transcript levels of four target genes in response to herbicide treatment, which were selected based on previous reports suggesting their involvement in glyphosate or paraquat resistance in Conyza spp. [14, 22]. Glyphosate treatment increased transcript levels of the putative ABC-transporter gene M10 by 18-fold and 14-fold in the GR and GPR lines of C. bonariensis, respectively (p <0.05, Fig 2A). Transcript levels of M11 were also significantly increased following glyphosate treatment in all C. bonariensis lines (8- to 23-fold, p <0.05). No significant changes (p >0.05) in transcript levels of CAT4 or EPSPS were observed in response to glyphosate in C. bonariensis (Fig 2A). In C. canadensis, both ABC transporter genes were induced in response to glyphosate treatment. M10 transcript levels increased in all lines by 16- to 32-fold (Fig 2B), whereas the increase in M11 transcript levels was greater in GR and GPS lines (16-fold) compared to the GPR line (4-fold). Consistent with results in C. bonariensis, CAT4 and EPSPS transcript levels were unaffected by glyphosate treatment (Fig 2B).

Fig 2. Transcripts levels of EPSPS, CAT4, and two ABC transporters in glyphosate treated plants relative to untreated plants in lines of Conyza bonariensis (A) and Conyza canadensis (B).

Lines were glyphosate-paraquat-susceptible (GPS), glyphosate-resistant (GR), and glyphosate-paraquat-resistant (GPR). Transcript levels were measured 24 h after glyphosate treatment and normalized using the geometric mean of actin (ACT), heat-shock-protein (HPS70), and cyclophilin 5 (CYP5) genes within each line. Significant differences (p <0.05) in transcript levels between treated and untreated plants indicated by *.

An increase in the transcript levels of both ABC transporter genes was also observed in response to paraquat treatment. In C. bonariensis, transcript levels of both M10 and M11 were significantly increased after treatment in all lines (p <0.05). In C. canadensis, M11 transcript levels in the GPS biotype were 4.2 fold higher following paraquat treatment (p <0.05, Fig 3B), but were not significantly different in the other biotypes. No significant differences in the transcript levels of M10, EPSPS or CAT4 were observed in response to paraquat treatment in any of the lines of either species (Fig 3B).

Fig 3. Transcript levels of EPSPS, CAT4, and two ABC transporters (M10 and M11) in paraquat treated plants relative to untreated plants in lines of Conyza bonariensis (A) and Conyza canadensis (B).

Lines were glyphosate-paraquat-susceptible (GPS), glyphosate-resistant (GR), and glyphosate-paraquat-resistant (GPR). Transcript levels were measure 6 h after paraquat treatment and were normalized using the geometric mean of actin, heat-shock-protein, and cyclophilin genes within each line. Significant differences (p <0.05) in transcript levels between treated and untreated plants indicated by *.

To determine whether differences in resistance between lines could be attributed to differential basal transcript levels of target genes, we compared their expression in untreated plants. Among C. bonariensis lines, transcript levels of both M10 and M11 were significantly higher in the GPS line than in either GR or GPR lines (Fig 4A). No significant differences in CAT4 or EPSPS transcript levels were found. Among C. canadensis lines, there were no significant differences in the basal transcript levels of any target gene (Fig 4B). These results suggest that differences in the basal transcript levels of the assayed target genes are unlikely to be responsible for the herbicide resistance phenotype.

Fig 4. Transcript levels of EPSPS, CAT4, and two ABC transporters (M10 and M11) in untreated plants of lines of Conyza bonariensis (A) and Conyza canadensis (B).

Lines were glyphosate-paraquat-susceptible (GPS), glyphosate-resistant (GR), and glyphosate-paraquat-resistant (GPR). Transcript levels were normalized using the geometric mean of actin, heat-shock-protein, and cyclophilin genes within each line. Significant differences (p<0.05) in transcript levels among lines are indicated by *.


Validation of qRT-PCR reference genes in Conyza spp.

Altered transcriptional responses to treatments can provide important clues regarding the mechanism underlying biological responses, such as herbicide tolerance. Despite rapid advances in next-generation sequencing technologies, developing individual qRT-PCR assays remains the most cost effective approach to characterize changes in expression for a small number of target genes. To accurately quantify changes in transcript levels at high sensitivity using qRT-PCR, the selection of appropriate reference genes is imperative [43]. Accuracy is further improved when multiple reference genes are selected [44].

However, even commonly used reference genes can exhibit large differences in expression depending on environmental conditions, tissue sample, and organism making it important to evaluate genes prior to normalization [45]. Because target gene expression is measured relative to the expression of the reference gene, variation in the expression of the reference gene between treatments, genotypes and environments can introduce inaccuracies into expression data. Therefore, for each analysis selection of reference genes must be carefully considered based on the experimental conditions, since suitable reference genes may vary depending on a number of factors. For example, in Alopercurus myosuroidis Huds., a monocot weed species, TUB, GADPH, and UBQ were identified as the most stable reference genes after application of an acetyl-CoA carboxylase inhibitor herbicide [46]. Similarly, in Lolium sp., another monocot weed, GADPH and UBQ were identified as the most stable reference genes following treatment with herbicides that inhibit both acetolactate synthase or acetyl-CoA carboxylase [47]. Therefore, TUB was identified as a stable reference gene in A. myosuroides, but not in Lolium sp. [47]. In Conyza spp., transcriptional responses have only been evaluated in C. canadensis after glyphosate treatment. The gene ACT was used as reference gene, but no indication of gene stability was provided [14]. In the current study, ACT was identified as stable after glyphosate treatment in both C. canadensis and C. bonariensis. However, GADPH, TUB, and UBQ were not among the most stable genes in Conyza spp. (Fig 1). Likewise, GAPDH has been ranked unsuitable as a reference gene in bamboo [48], brachypodium [49] and pearl millet [50], while in rice [51], and soybean [52], none of these three genes were suitable as references [51, 52].

Here, we analyzed the transcript levels of eight candidate genes in Conyza spp. under different herbicide stress conditions, and identified three genes which showed the greatest stability across all tested conditions (ACT7, HPS70, and CYP5). The selected genes can be applied for further studies to investigate the mechanism of herbicide resistance in Conyza spp.

Expression of EPSPS, CAT and ABC-transporter genes in response to herbicide treatment

In a previous study, we identified glyphosate and paraquat resistant lines of C. bonariensis and C. canadensis which was associated with reduced translocation of these herbicides, possibly as a result of herbicide sequestration into the vacuoles [24]. To test the hypothesis that increased expression of putative transporter genes were associated with this phenotype, we assayed the transcript levels of four target genes in response to herbicide treatment in susceptible and resistant lines.

We found that EPSPS transcript levels were unaffected by glyphosate or paraquat treatment in any line (Figs 2 and 3). These findings are in agreement with a previous study in glyphosate-resistant C. canadensis, which similarly showed no changes in EPSPS expression following herbicide application [35]. However, another study reported increased transcription of EPSPS after glyphosate treatment in GR C. canadensis [15]. In general, over-expression of genes encoding target enzymes is a rare mechanism of herbicide resistance in plants [1]. Increased EPSPS expression has been reported in other GR weed species, such as Amaranthus palmeri S. Watson, generally as a result of increased EPSPS gene copy number [12].

Many classes of transporters are involved in paraquat movement across cell membranes, including members of the Cationic Amino acid Transporter (CAT) family which are more commonly known for their role in polyamine transport [53]. Their dual role in transporting paraquat and polyamines may be a result of the similarities between these molecules [53, 54]. CAT4 transcript levels were previously shown to be elevated in Conyza following paraquat treatment [22]. However, in the current study, we found no significant changes in CAT4 transcript levels in response to paraquat treatment (Fig 3). Although the association between CAT4 and paraquat transport has not previously been reported, another sub-family of polyamine transporters, the L-amino acid transporters, has been shown to play a role in paraquat transport [55, 56]. It is important to note that none of the L-amino acid transporters were reported to be localized to the tonoplast.

Taken together these results do not provide any indication of the involvement of EPSPS or CAT4 genes in glyphosate or paraquat resistance in Conyza spp. from California within the first six hours after treatment for paraquat or 24 hours for glyphosate.

We also found that paraquat treatment affected the transcript levels of ABC transporters in Conyza spp. M10 and M11 transcript levels were higher in all C. bonariensis lines following paraquat treatment (Fig 3A), while in C. canadensis, M11 transcript levels were higher in GPS lines, but not in either of the resistant lines (Fig 3B). However, because these changes in expression were observed in both resistant and susceptible lines, they are unlikely to be associated with the resistance phenotype and are indicative of a more general stress response (Fig 2A and 2B). Changes in expression in response to abiotic stresses, including glyphosate stress, have been documented previously, for example in Arabidopsis, where glyphosate stress results in changes in transcript levels of several ABC transporter genes [57, 58].

The results presented establish no association between paraquat resistance and two members of the ABC transporter superfamily, M10 and M11. These findings are consistent with previous studies, which were recently reviewed [18]. Nonetheless, the involvement of ABC transporters cannot be confirmed or discarded based on these data, since only two putative genes were evaluated out of the large ABC transporter gene family. For instance, a transcriptome profiling study of the monocot weed Eleusine indica L., reported the transcript levels of many transporters, including members of the ABC transporter family, after paraquat treatment; however, only a small subset of up-regulated genes were associated with paraquat-resistant lines [59]. ABC transporters are known to transport paraquat and have been associated with paraquat resistance in other species. For example, in Arabidopsis, loss-of-function mutations in AtPDR11, which encodes an ABC-transporter, reduces cellular uptake of paraquat and confers resistance to this herbicide [60].

To aid in the identification of candidate genes associate with resistance mechanism, future studies could benefit from an approach combining transcriptome analysis and forward genetics, an example of which was recently described in Lolium rigidum [61]. In this study, a non-targeted approach in diclofop resistant and susceptible lines first to identify changes in expression which were associated with resistance and then to validate their findings using a segregating F2 population [58]. Using this approach, candidate genes were identified which were strongly associated with the resistance phenotype [61]. It is also important to note that herbicide resistance phenotypes can result from other changes in gene regulation, such as at the translational or post-translation level. These analyses were beyond the scope of the current study.

Supporting information

S1 Fig. Functional activity of leaves measure by dark adapted chlorophyll fluorescence.

Glyphosate-paraquat resistant (GPR) and glyphosate-paraquat-susceptible (GPS) were monitored up to 48 hours after foliar treatment with 9.7 mM of the paraquat cation, equivalent to a field rate of 0.5 kg of paraquat ha-1.


S2 Fig. Primer specificity test.

Melting curves generated for ACT7 (A, B), TUA6 (C, D), eEF1α (E, F), eIF4α (G, H), GADPH (I, J), HPS70 (K, L), UBQ3 (M, N), CYP5 (O, P), CAT4 (Q, R), M10 (S, T), M11 (U, V), EPSPS (W, X) in C. bonariensis and C. canadensis, respectively.


S1 Appendix. Consensus sequencing of Conyza bonariensis cDNA M10 and M11.

Direct sequencing of M10 and M11 product using primers developed by Peng et al. 2010. Sequences highlighted in grey indicate introns.


S1 Dataset. Cycle threshold (Ct) means and standard error (SE) of reference genes and target genes for each Conyza species, lines, and herbicides tested.




  1. 1. Powles SB, Yu Q. Evolution in action: plants resistant to herbicides. Annu Rev Plant Biol. 2010;61:317–47. pmid:20192743
  2. 2. Gianessi LP. The increasing importance of herbicides in worldwide crop production. Pest Manag Sci. 2013;69(10):1099–105. pmid:23794176
  3. 3. Green JM. The benefits of herbicide-resistant crops. Pest Manag Sci. 2012;68(10):1323–31. pmid:22865693
  4. 4. Benbrook CM. Trends in glyphosate herbicide use in the United States and globally. Env Sci Eur. 2016;28(1):3. pmid:27752438
  5. 5. Shoham J. Quantifying the economic and environmental benefits of paraquat. Outlook on Pest Manag. 2013;24(2):64–9.
  6. 6. Heap IM. The international survey of herbicide resistant weeds [Internet]. 2017 [updated 2017 March 14; cited 2017 March 14].
  7. 7. Moretti ML, Sosnoskie LM, Shrestha A, Wright SD, Hembree KJ, Jasieniuk M, et al. Distribution of Conyza sp. in orchards of California and response to glyphosate and paraquat. Weed Sci. 2016;64(2):339–47.
  8. 8. Holm LG, Doll J, Holm E, Pancho JV, Herberger JP. World weeds: natural histories and distribution. New York, NY: Wiley; 1997. 1129 p.
  9. 9. Heap I. Herbicide resistant weeds. In: Pimentel D, Peshin R, editors. Integrated pest management. Pesticide Problems. 3. Ithaca, New York: Springer; 2014. p. 281–301.
  10. 10. Karlsson L, Milberg P. Comparing after-ripening response and germination requirements of Conyza canadensis and C. bonariensis (Asteraceae) through logistic functions. Weed Res. 2007;47(5):433–41.
  11. 11. Steinrücken HC, Amrhein N. The herbicide glyphosate is a potent inhibitor of 5-enolpyruvylshikimic acid-3-phosphate synthase. Biochem Biophys Res Commun. 1980;94(4):1207–12. pmid:7396959
  12. 12. Sammons RD, Gaines TA. Glyphosate resistance: state of knowledge. Pest Manag Sci. 2014;70(9):1367–77. pmid:25180399
  13. 13. Ge X, d’Avignon DA, Ackerman JJ, Sammons RD. In Vivo 31P-nuclear magnetic resonance studies of glyphosate uptake, vacuolar sequestration, and tonoplast pump activity in glyphosate-resistant horseweed. Plant Physiol. 2014;166(3):1255–68. pmid:25185124
  14. 14. Peng YH, Abercrombie LLG, Yuan JS, Riggins CW, Sammons RD, Tranel PJ, et al. Characterization of the horseweed (Conyza canadensis) transcriptome using GS-FLX 454 pyrosequencing and its application for expression analysis of candidate non-target herbicide resistance genes. Pest Manag Sci. 2010;66(10):1053–62. pmid:20715018
  15. 15. Tani E, Chachalis D, Travlos IS. A glyphosate resistance mechanism in Conyza canadensis involves synchronization of EPSPS and ABC-transporter genes. Plant Mol Biol Report. 2015;33(6):1–10.
  16. 16. Verrier PJ, Bird D, Burla B, Dassa E, Forestier C, Geisler M, et al. Plant ABC proteins–a unified nomenclature and updated inventory. Trends Plant Sci. 2008;13(4):151–9. pmid:18299247
  17. 17. Tani E, Chachalis D, Travlos IS, Bilalis D. Environmental conditions influence induction of iey ABC-iransporter genes affecting glyphosate resistance mechanism in Conyza canadensis. Int J Mol Sci. 2016;17(4):342. pmid:27104532
  18. 18. Hawkes TR. Mechanisms of resistance to paraquat in plants. Pest Manag Sci. 2014;70(9):1316–23. pmid:24307186
  19. 19. Lasat MM, DiTomaso JM, Hart JJ, Kochian LV. Evidence for vacuolar sequestration of paraquat in roots of a paraquat‐resistant Hordeum glaucum biotype. Physiol Plant. 1997;99(2):255–62.
  20. 20. Hart JJ, DiTomaso JM. Sequestration and oxygen radical detoxification as mechanisms of paraquat resistance. Weed Sci. 1994;42(2):277–84.
  21. 21. Yu Q, Huang S, Powles SB. Direct measurement of paraquat in leaf protoplasts indicates vacuolar paraquat sequestration as a resistance mechanism in Lolium rigidum. Pestic Biochem Physiol. 2010;98(1):104–9.
  22. 22. Jóri B, Soós V, Szego D, Páldi E, Szigeti Z, Rácz I, et al. Role of transporters in paraquat resistance of horseweed (Conyza canadensis (L.) Cronq). Pestic Biochem Physiol. 2007;88(1):57–65.
  23. 23. Yang H, Krebs M, Stierhof Y-D, Ludewig U. Characterization of the putative amino acid transporter genes AtCAT2, 3 & 4: the tonoplast localized AtCAT2 regulates soluble leaf amino acids. J Plant Physiol. 2014;171(8):594–601. pmid:24709150
  24. 24. Moretti ML, Hanson BD. Reduced translocation is involved in resistance to glyphosate and paraquat in Conyza bonariensis and Conyza canadensis from California. Weed Res. 2017;57(1):25–34.
  25. 25. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611–22. pmid:19246619
  26. 26. Bustin S. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol. 2002;29(1):23–39. pmid:12200227
  27. 27. Pruitt KD, Tatusova T, Brown GR, Maglott DR. NCBI Reference Sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res. 2012;40(D1):D130–D5.
  28. 28. Wilfinger WW, Mackey K, Chomczynski P. Effect of pH and ionic strength on the spectrophotometric assessment of nucleic acid purity. BioTechniques. 1997;22(3):474–6, 8–81. pmid:9067025
  29. 29. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible W-R. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 2005;139(1):5–17. pmid:16166256
  30. 30. Peng Y, Lai Z, Lane T, Rao M, Okada M, Jasieniuk M, et al. De novo genome assembly of the economically-important weed Conyza canadensis using integrated data from multiple sequencing platforms. Plant Physiol. 2014;166(3):1241–54. pmid:25209985
  31. 31. Kitts PA, Church DM, Thibaud-Nissen F, Choi J, Hem V, Sapojnikov V, et al. Assembly: a resource for assembled genomes at NCBI. Nucleic Acids Res. 2016;44(D1):D73–D80. pmid:26578580
  32. 32. Moreno-Hagelsieb G, Latimer K. Choosing BLAST options for better detection of orthologs as reciprocal best hits. Bioinformatics. 2008;24(3):319–24. pmid:18042555
  33. 33. Ye J, Coulouris G, Zaretskaya I, Cutcutache I, Rozen S, Madden TL. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics. 2012;13(1):134. pmid:22708584
  34. 34. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 2012;40(15):e115–e. pmid:22730293
  35. 35. Nol N, Tsikou D, Eid M, Livieratos I, Giannopolitis C. Shikimate leaf disc assay for early detection of glyphosate resistance in Conyza canadensis and relative transcript levels of EPSPS and ABC transporter genes. Weed Res. 2012;52(3):233–41.
  36. 36. Kibbe WA. OligoCalc: an online oligonucleotide properties calculator. Nucleic Acids Res. 2007;35(suppl 2):W43–W6. pmid:17452344
  37. 37. Burns MJ, Nixon GJ, Foy CA, Harris N. Standardisation of data from real-time quantitative PCR methods–vevaluation of outliers and comparison of calibration curves. BMC Biotechnol. 2005;5(1):31. pmid:16336641
  38. 38. De Spiegelaere W, Dern-Wieloch J, Weigel R, Schumacher V, Schorle H, Nettersheim D, et al. Reference gene validation for RT-qPCR, a note on different available software packages. PLOS ONE. 2015;10(3):e0122515. pmid:25825906
  39. 39. Andersen CL, Jensen JL, Ørntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64(15):5245–50. pmid:15289330
  40. 40. Pihur V, Datta S, Datta S. RankAggreg, an R package for weighted rank aggregation. BMC Bioinformatics. 2009;10(1):1. pmid:19228411
  41. 41. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods. 2001;25(4):402–8. pmid:11846609
  42. 42. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative CT method. Nat Protocols. 2008;3(6):1101–8. pmid:18546601
  43. 43. Taylor S, Wakem M, Dijkman G, Alsarraj M, Nguyen M. A practical approach to RT-qPCR-Publishing data that conform to the MIQE guidelines. Methods. 2010;50(4):S1–S5. pmid:20215014
  44. 44. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7):research0034.1–11.
  45. 45. Kozera B, Rapacz M. Reference genes in real-time PCR. J Appl Genet. 2013;54(4):391–406. pmid:24078518
  46. 46. Petit C, Pernin F, Heydel J-M, Délye C. Validation of a set of reference genes to study response to herbicide stress in grasses. BMC research notes. 2012;5(1):18. pmid:22233533
  47. 47. Duhoux A, Délye C. Reference genes to study herbicide stress response in Lolium sp.: up-regulation of P450 genes in plants resistant to acetolactate-synthase inhibitors. PLOS ONE. 2013;8(5):e63576. pmid:23696834
  48. 48. Fan C, Ma J, Guo Q, Li X, Wang H, Lu M. Selection of reference genes for quantitative real-time PCR in bamboo (Phyllostachys edulis). PLOS ONE. 2013;8(2):e56573. pmid:23437174
  49. 49. Hong S-Y, Seo PJ, Yang M-S, Xiang F, Park C-M. Exploring valid reference genes for gene expression studies in Brachypodium distachyon by real-time PCR. BMC Plant Biol. 2008;8(1):1. pmid:18992143
  50. 50. Saha P, Blumwald E. Assessing reference genes for accurate transcript normalization using quantitative real-time PCR in pearl millet [Pennisetum glaucum (L.) R. Br.]. PLOS ONE. 2014;9(8):e106308. pmid:25170776
  51. 51. Jain M, Nijhawan A, Tyagi AK, Khurana JP. Validation of housekeeping genes as internal control for studying gene expression in rice by quantitative real-time PCR. Biochem Biophys Res Commun. 2006;345(2):646–51. pmid:16690022
  52. 52. Jian B, Liu B, Bi Y, Hou W, Wu C, Han T. Validation of internal control for gene expression study in soybean by quantitative real-time PCR. BMC Mol Biol. 2008;9(1):1–14. pmid:18573215
  53. 53. Fujita M, Shinozaki K. Identification of polyamine transporters in plants: paraquat transport provides crucial clues. Plant Cell Physiol. 2014;55(5):855–61. pmid:24590488
  54. 54. Hart JJ, DiTomaso JM, Linscott DL, Kochian LV. Transport interactions between paraquat and polyamines in roots of intact maize seedlings. Plant Physiol. 1992;99(4):1400–5. pmid:16669051
  55. 55. Fujita M, Fujita Y, Iuchi S, Yamada K, Kobayashi Y, Urano K, et al. Natural variation in a polyamine transporter determines paraquat tolerance in Arabidopsis. PNAS. 2012;109(16):6343–7. pmid:22492932
  56. 56. Li J, Mu J, Bai J, Fu F, Zou T, An F, et al. Paraquat resistant 1, a golgi-localized putative transporter protein, is involved in intracellular transport of paraquat. Plant Physiol. 2013;162(1):470–83. pmid:23471133
  57. 57. Glombitza S, Dubuis P-H, Thulke O, Welzl G, Bovet L, Götz M, et al. Crosstalk and differential response to abiotic and biotic stressors reflected at the transcriptional level of effector genes from secondary metabolism. Plant Mol Biol. 2004;54(6):817–35. pmid:15604654
  58. 58. Faus I, Zabalza A, Santiago J, Nebauer SG, Royuela M, Serrano R, et al. Protein kinase GCN2 mediates responses to glyphosate in Arabidopsis. BMC Plant Biol. 2015;15(1):1. pmid:25603772
  59. 59. An J, Shen X, Ma Q, Yang C, Liu S, Chen Y. Transcriptome profiling to discover putative genes associated with paraquat resistance in goosegrass (Eleusine indica L.). PLOS ONE. 2014;9(6):e99940. pmid:24927422
  60. 60. Xi J, Xu P, Xiang CB. Loss of AtPDR11, a plasma membrane-localized ABC transporter, confers paraquat tolerance in Arabidopsis thaliana. Plant J. 2012;69(5):782–91. pmid:22026747
  61. 61. 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