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Transcriptome profiling to identify cytochrome P450 genes involved in penoxsulam resistance in Echinochloa glabrescens
Pesticide Biochemistry and Physiology 158 (2019) 112–120
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Transcriptome profiling to identify cytochrome P450 genes involved in penoxsulam resistance in Echinochloa glabrescens Bojun Yana,b,1, Yuhua Zhanga,b,1, Jun Lia,b, Jiapeng Fanga,b, Tingting Liua,b, Liyao Donga,b, a b
T
⁎
College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China Key Laboratory of Integrated Management of Crop Diseases and Pests (Nanjing Agricultural University), Ministry of Education, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Cytochrome P450s Echinochloa glabrescens Penoxsulam Transcriptome sequencing Metabolic resistance
Cytochrome P450s (P450s) confer resistance against herbicides, and this is increasingly becoming a concern for weed control. As a widespread Gramineae weed in paddy fields, Echinocloa glabrescens has become resistant to the acetolactate synthase (ALS)-inhibiting triazolopyrimidine herbicide penoxsulam. In this study, we found that the GR50 of the resistant population (SHQP-R) decreased substantially from 25.6 to 5.0 and 6.2 g a.i. ha−1 after treatment with the P450 inhibitors piperonyl butoxide (PBO) and malathion, respectively. However, P450 inhibitors almost had no effects on the susceptibility of the sensitive population (JYJD-S) to penoxsulam. To investigate the mechanisms of metabolic resistance, transcriptome sequencing analysis was performed to find candidate genes that may confer resistance to penoxsulam in E. glabrescens. A total of 233 P450 differentially expressed genes (DEGs) were identified by transcriptome sequencing. We found that the metabolic process and metabolic pathways were the most highly enriched in DEGs. Further, twenty-seven candidate P450 DEGs were selected for qPCR validation analyses. After penoxsulam treatment, the relative expression levels were significantly higher in SHQP-R than in JYJD-S. Among these, the relative expression of twenty-three P450 DEGs (eighteen from the CYP72A-71C-74A-96A-734A subfamily; five from CYP81E1-94C1–94B3-714C1-714C2) were upregulated and four P450 DEGs (from CYP724B1-711A1-707A7-97B2) were downregulated. Changes in the expression of these candidate P450 genes in E. glabrescens were in response to penoxsulam, which provides preliminary evidence for the role of P450s in herbicide metabolism in E. glabrescens. However, further functional studies on metabolic resistance to penoxsulam in a resistant E. glabrescens population are required.
1. Introduction Echinochloa glabrescens Munro ex Hook. f. is one of the most common Echinochloa weed species and is highly competitive with rice (Opeña et al., 2014). Herbicides are essential in modern agricultural production systems. However, the decreased effectiveness of chemical weed control measures due to rapid development of herbicide resistance is an increasing problem worldwide that impacts crop yields. Research has shown that uncontrolled E. glabrescens transplanted with rice reduced the yield by 7% and 87% at weed infestation levels of 5% and 50%, respectively (Opeña et al., 2014). Acetolactate synthase (ALS), also known as acetohydroxy acid synthase, is a key enzyme that catalyzes the biosynthesis of the branched-chain amino acids Val, Leu, and Ile (Duggleby et al., 2008). Penoxsulam, an ALS inhibitor, is one of
the most important herbicides in rice production and has been widely used in China since 2008 (Jabusch and Tjeerdema, 2005). However, long-term penoxsulam use results in the rapid development of resistance in Echinochloa species. For example, the increased prevalence of penoxsulam-resistant Echinochloa crus-galli is a serious challenge for weed management in rice fields (Chen et al., 2016). Currently, > 200 weed species have been reported to be herbicide resistant worldwide (Heap, 2019). Resistant weeds can survive herbicide applications via a variety of mechanisms, making the study of resistance mechanisms useful for adequate control strategies. The mechanisms involved in herbicide resistance are categorized as target-site resistance (TSR) and non-target-site resistance (NTSR) (Yuan et al., 2007). TSR is primarily involved in the modification of genes encoding proteins that are targets of herbicide molecules, as well as the
Abbreviations: ALS, acetolactate synthase; DEG, differentially expressed gene; FPKM, fragments per kilobase per million reads; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NTSR, non-target-site resistance; KO, KEGG Orthologs; OF, Oil miscible Flowable concentrate; PBO, piperonyl butoxide; qPCR, quantitative real-time PCR; TSR, target-site resistance. ⁎ Corresponding author at: College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China. E-mail address: [email protected] (L. Dong). 1 The first two authors contributed equally to this work. https://doi.org/10.1016/j.pestbp.2019.04.017 Received 4 March 2019; Received in revised form 29 April 2019; Accepted 30 April 2019 Available online 02 May 2019 0048-3575/ © 2019 Elsevier Inc. All rights reserved.
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Sunjoy AgroScience (Ningbo, China), respectively. PBO and malathion were dissolved and diluted in a 1% aqueous solution of Tween-80 (Solarbio Life Sciences, Beijing, China).
overexpression or amplification of target proteins; in contrast, NTSR mechanisms include herbicide metabolism and physiological processes that transport herbicide molecules away from their target proteins (Powles and Yu, 2010). NTSR mechanisms are more complicated than TSR mechanisms, and the molecular mechanisms of NTSR are not completely understood (Christophe, 2013; Délye et al., 2015). Therefore, it is very important to study NTSR mechanisms in terms of weed resistance management. Cytochrome P450s play a crucial role in NTSR (Ghanizadeh and Harrington, 2017). The P450 enzyme family is widely found in bacteria, fungi, plants, and animals (Nelson, 2009). P450s have many functions in plants. In addition to their physiological functions in the biosynthesis of hormones, lipids, and secondary metabolites, P450s help plants to cope with exposure to harmful exogenous chemicals including pesticides and industrial pollutants, decreasing their phytotoxicity (WerckReichhart et al., 2000; Nelson, 2009). The discovery that P450s might be involved in herbicide metabolism came from an analysis of herbicide residues formed in vivo (Werck-Reichhart et al., 2000). The major metabolites of most classes of herbicides are aryl- or alkyl-hydroxylated products, as well as N-, S-, or O-dealkylated products and their glucose conjugates (Werck-Reichhart et al., 2000; Yun et al., 2005). Research reported that many P450 genes may closely related to fenoxaprop-Pethyl resistance in Alopecurus japonicus (Chen et al., 2018). Höfer et al. (2014)) reported that CYP76C1, CYP76C2, and CYP76C4 could metabolize phenylurea herbicides. In addition, Iwakami et al. (2014)) reported that CYP81A12 and CYP81A21 could metabolize the ALS inhibitors bensulfuron and penoxsulam. Other P450 genes have also been reported as candidates for conferring resistance to different herbicides (Ghanizadeh and Harrington, 2017; Zhao et al., 2018). In this study, our objectives were to: (a) determine the sensitivity of E. glabrescens to penoxsulam following treatment with the P450 inhibitors PBO and malathion; (b) identify P450 DEGs in E. glabrescens after treatment with penoxsulam by transcriptome sequencing; (c) select candidate penoxsulam-related metabolic resistance P450 DEGs and verify their expression profiles by qPCR. We expect the findings of this study to provide background information for future studies on the metabolic role of P450s in the mechanism of penoxsulam resistance in E. glabrescens.
2.3. Sensitivity to penoxsulam following P450 inhibition Whole-plant dose-response experiments were carried out to determine the values for the 50% reduction in fresh weight of plants (GR50) in the SHQP-R and JYJD-S populations to penoxsulam in the absence and presence of PBO and malathion. E. glabrescens seedlings at the three- to four-leaf stage were used in the whole-plant dose response experiments. Penoxsulam was applied at 0, 1.875, 3.75, 7.5, 15, and 30 g a.i. ha−1 to the JYJD-S group, and at 0, 3.75, 7.5, 15, 30, and 60 g a.i. ha−1 to the SHQP-R group. To test the sensitivity of E. glabrescens populations to penoxsulam following P450 inhibition treatment, whole-plant dose-response experiments were conducted using the same method described above. Seedlings from the two E. glabrescens populations at the three- to fourleaf stage were treated with PBO, malathion, penoxsulam, PBO plus penoxsulam, or malathion plus penoxsulam, and corresponding control plants were treated with a mixture of acetone, Tween-80, and water (Wang et al., 2013; Pan et al., 2016b). PBO was applied twice at a dose of 2100 g a.i. ha−1 each in 97 L ha−1 water, for a total dose of 4200 g a.i. ha−1 in a total volume of 194 L ha−1 water; malathion was applied once at a dose of 1000 g a.i. ha−1 (Wang et al., 2013). Malathion and PBO were formulated in an emulsified mixture (Tween-80, 1 mL L−1 in water), and acetone and applied 1 h prior to penoxsulam application. Penoxsulam was also applied at the same doses as described above. All treated pots were then placed back in the incubator. Three weeks after treatment, the aboveground fresh weights of the plants were determined and expressed as percentages relative to the control group. This experiment was conducted with four pots per group, and each experiment was conducted twice. Data were then combined and averaged for all replicates. A three-parameter logistic function was fitted to weight for herbicide treatments using the “drc” add-on package in R 3.1.3 (R Core Team, Vienna, Austria) (Ritz and Streibig, 2005) as follows:
Y = d/[(1 + (x/GR50) b]
(1)
where Y is fresh weight response at dose x of the herbicide; d is the upper asymptote of fresh weight at dose zero; and b indicates the slope. SigmaPlot 10.0 (Systat Software Inc., San Jose, CA, USA) was used to construct the dose response curves.
2. Materials and methods 2.1. Plant materials and growth conditions Mature E. glabrescens seeds were collected from paddy fields in Yangzhou, Jiangsu Province, China (N 32°25′47.50″, E 119°33′2.38″), marked as JYJD-S (sensitive strain), as well as in Shanghai (N 31°09′22.52″, E 121°04′30.04″), marked as SHQP-R (resistant strain), in 2015. Seeds were cleaned, dried on open trays in the sun for 3 d, and then stored in paper bags at 4 °C until use. For the whole-plant bioassay, each pot (diameter: 9 cm, height: 10 cm) was filled with a 2:1 (w/w) mixture of sand and soil (pH 5.6, organic matter content: 1.4%) and sown with 30 pre-germinated E. glabrescens seeds (Chen et al., 2016). The pots were placed in an incubator with a 12-h light/dark photoperiod regime at 30 °C (light) and 25 °C (dark), and were watered every three days. The light intensity was 140 μmol m−2 s−1 photosynthetic photon flux density using fluorescent lamps, and the growth chamber was maintained at 60% relative humidity (Yuan et al., 2018). The seedlings were thinned to 15 plants per pot at the two-leaf stage and prepared for later experiments.
2.4. Transcriptome sequencing 2.4.1. Sample preparation, sequence library construction, and Illumina sequencing The seeds of SHQP-R and JYJD-S plants were germinated and cultivated to the three- to four-leaf stage under the experimental conditions described in section 2.1. Whole plants were sprayed with 7.5 g a.i. ha−1 penoxsulam. Leaf samples from the untreated control group were harvested (marked as JYJD-S0 and SHQP-R0); after 24 h and 72 h, the plant samples from the penoxsulam-treated group were collected (marked as JYJD-S24/SHQP-R24 and JYJD-S72/SHQP-R72, respectively). A total of 18 samples were thus collected (3 biological replicates × 3 treatment times × 2 populations). The samples were collected and frozen immediately in liquid nitrogen for RNA extraction. Total RNA from the 18 samples was extracted using Transzol Up (TransGen Biotech, Beijing, China) according to the manufacturer's protocol and treated with DNase I (Takara, Beijing, China). Total RNA degradation and contamination were monitored by electrophoresis on 1% agarose gels. The RNA integrity was assessed with an RNA Nano 6000 Assay Kit on the Agilent Bio analyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). High-quality RNA samples were selected to prepare the cDNA library. Sequencing libraries were generated using the
2.2. Herbicides and chemicals Penoxsulam (25 g/L Oil miscible Flowable concentrate [OF]) used in this study was provided by Dow AgroSciences (Indianapolis, IN, USA). Furthermore, the inhibitors of cytochrome P450, PBO (97%) and malathion (95%) were purchased from Aladdin (Shanghai, China) and 113
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Fig. 1. Dose-response curves of (A) susceptible (JYJD-S) and (B) resistant (SHQP-R) E. glabrescens populations to penoxsulam in the absence and presence of the cytochrome P450 inhibitors piperonyl butoxide (PBO) and malathion. Data are shown as the mean ± SE from two experiments.
sequenced on the Hiseq 2500, and 150-bp paired-end reads were generated.
Table 1 GR50 of the JYJD-S and SHQP-R E. glabrescens populations after penoxsulam treatment in the absence and presence of cytochrome P450 inhibitors PBO and malathion. Herbicides
Penoxsulam Penoxsulam + PBO Penoxsulam + Malathion
2.4.2. Mapping sequencing results and functional annotation Raw image data files from the Illumina Hiseq 2500 were transformed to raw reads by the Consensus Assessment of Sequence and Variation (CASAVA, v. 1.8.2) base recognition program. The clean reads were filtered from the raw reads by removing low-quality reads containing ambiguous nucleotides or adaptor sequences. All downstream analyses were based only on these high-quality clean reads, which were aligned to the reference genome sequence of the E. crus-galli genome (https://www.ncbi.nlm.nih.gov/bioproject/414998). This reference annotation was then used to predict new genes and novel exons, and for the optimization of gene structures. Gene function was annotated based on the following databases: NCBI non-redundant protein sequences (Nr), Protein family (Pfam), Clusters of Orthologous Groups of proteins (KOG/COG), Swiss-Prot, Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog database (KO), and Gene Ontology (GO).
GR50 ± SE (g a.i. ha−1)a JYJD-S
SHQP-R
3.09 ± 0.14 2.59 ± 0.31 3.05 ± 0.11
25.59 ± 1.16 5.00 ± 0.25 6.16 ± 0.36
a GR50, herbicide rate causing 50% growth reduction in plants; JYJD-S, sensitive samples; PBO, piperonyl butoxide SHQP-R, resistant samples. SE, standard error.
NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA) following the manufacturer's instructions. A total of 3 μg RNA per sample was used as the input material for RNA sample preparation. mRNA was purified from total RNA using poly-T oligo-attached magnetic beads, then fragmentation was carried out to create short sequences. First-strand cDNA was synthesized using M-MuLV Reverse Transcriptase (RNase H) and random hexamer primers. Second-strand cDNA synthesis was subsequently performed using DNA polymerase I and RNase H. Finally, the purified double-stranded cDNA samples were further amplified by PCR to construct the final cDNA libraries that were
2.4.3. Identification and analysis of DEGs associated with cytochrome P450s To infer the transcriptional changes over time in the genotype under penoxsulam stress conditions, all DEGs after penoxsulam treatment were identified and analyzed by GO and KEGG enrichment analyses. Both analyses were implemented using a hyper geometric test (Young
Fig. 2. Analysis of differentially expressed genes (DEGs) between sensitive (JYJD-S) and resistant (SHQP-R) biotypes before and after penoxsulam treatment. Volcano plots showing the DEGs between SHQP-R at 0 h after treatment (SHQP-R0) vs 24 h after treatment (SHQP-R24) (left) and JYJD-S0 vs JYJD-S24 (right). A value of q < 0.05 was used as the threshold for the significance of DEGs. Red dots represent upregulated genes, green dots show downregulated genes, and blue dots indicate genes with no significant differences before and after treatment. Venn diagram (center) shows the number of DEGs unique to JYJD-S0 vs JYJD-S24 and SHQP-R0 vs SHQP-R24, and the number of DEGs shared by the two biotypes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 114
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Table 2 Relative up- and downregulation of all DEGs and P450 DEGs. Samplea
All DEGs Up
All DEGs Down
P450 DEGs Up
P450 DEGs Down
JYJD-S0-vs-JYJD-S24 JYJD-S0-vs-JYJD-S72 SHQP-R0-vs-SHQP-R24 SHQP-R0-vs-SHQP-R72 Total DEGs
11,211 11,914 9096 9937 15,114
3442 4504 2550 3026 5963
66 68 73 106 157
50 39 33 18 76
a JYJD-S0, untreated sensitive samples; JYJD-S24, sensitive samples 24 h after treatment with 7.5 g a.i. ha−1 penoxsulam; JYJD-S72. sensitive samples 72 h after treatment with 7.5 g a.i. ha−1 penoxsulam; SHQP-R0, untreated resistant samples; SHQP-R24, resistant samples 24 h after treatment with 7.5 g a.i. ha−1 penoxsulam; SHQP-R72, resistant samples 72 h after treatment with 7.5 g a.i ha−1 penoxsulam.
Table 3 Number of genes at each level of relative expression. FPKMa interval 0–1 1–3 3–15 15–60 > 60 a b
JYJD-S 33,776 (40.07%) 14,801 (17.56%) 24,978 (29.63%) 8805 (10.44%) 1942 (2.30%)
b
Table 4 Sequence annotation of Echinochloa glabrescens transcriptome. SHQP-R
Public database
Number of genes
Percentage
34,003 (40.52%) 14,815 (17.65%) 24,619 (29.33%) 8572 (10.21%) 1915 (2.28%)
Annotated in Annotated in Annotated in Annotated in Annotated in Annotated in Annotated in Total genes
102,546 69,677 52,130 68,402 8201 99,923 103,181 131,259
78.12 53.08 39.71 52.11 6.24 76.12 78.60 100
FPKM, Fragments per kilobase per million reads. Ratios of gene number to total gene number are presented in parentheses.
et al., 2010). GO or KEGG terms with a value of q < 0.05 were considered significantly enriched (Storey and Tibshirani, 2003).
NR PFAM KOG SwissProt GO KEGG at least one database
group (Fig. 1 and Table 1). In contrast, P450 inhibitors had negligible effects on the susceptibility of the JYJD-S population to penoxsulam (Fig. 1 and Table 1). P450 inhibitors, Tween-80, and acetone alone had no visible effects on seedling growth in either SHQP-R or JYJD-S. This indicates that cytochrome P450s may mediate resistance to penoxsulam in the SHQP-R E. glabrescens strain.
2.5. qPCR confirmation of P450 DEGs A total of twenty-seven candidate P450 DEGs were selected to confirm their expression levels by qPCR. Samples at 0, 24, and 72 h after penoxsulam treatment were collected from the penoxsulam-sensitive and -resistant E. glabrescens strains. Total RNA extraction was performed as described in section 2.4.1, then RNA samples were reverse-transcribed to cDNA using the PrimeScript RT Reagent Kit with gDNA Eraser (TaKaRa, Otsu, Japan), and the concentrations of the cDNA samples were diluted to a uniform value. Primers for qPCR were designed using Primer3 (http://bioinfo.ut.ee/primer3-0.4.0/), and the EcActin gene (GenBank accession HQ395760) was used as a reference, and the following primers were used: EcActin-F: 5′-GTGCTGTTCCAGC CATCGTTCAT-3′, and -R: 5′-CTCCTTGCTCATACGGTCAGCAATA-3′ (Li et al., 2013). All primers used for qPCR are shown in Table S4. Realtime PCR was performed using the ABI-7500 Fast Real-Time PCR System (ABI, Life Technologies, Foster City, CA, USA) using the SYBR Premix Ex Taq kit (TaKaRa, Japan). The relative mRNA expression level of each gene in each biotype was subjected to analysis of variance (ANOVA), followed by Duncan's multiple range test (P < .05) for the separation of means. Data analysis was performed using SPSS version 20 (SPSS, Chicago, IL, USA). Two threshold values (Satoshi et al., 2014; Pan et al., 2016a), a significant result from Student's t-test (P < .05), and a 2-fold change were used to determine the up- or downregulation of gene expression caused by penoxsulam. Each experiment included three biological replicates and was repeated twice.
3.2. Quantitative assessment of transcriptome sequencing The numbers of raw reads from the 18 samples ranged from 5.172–6.423 × 107 with an error rate of approximately 2%, yielding 7.76–9.64 G clean bases (Table S1). Each sample that produced clean reads was aligned to the recently released E. crus-galli reference genome (Guo et al., 2017). About 85% of total reads were mapped to the reference genome (Table S2). Among them, uniquely mapped reads comprised about 79% of total reads, and multiple mapped reads comprised about 6% (Table S2). The proportion of clean reads in each sample mapped to reference genome ranged from 4.304–5.370 × 107 (Table S2). All raw-sequence read data were uploaded to the NCBI Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/Traces/ sra) with accession number SRP187315. 3.3. DEGs and transcriptome profiles Transcriptome sequencing data showed that the expression levels of a large number of genes were changed after penoxsulam treatment. A total of 21,077 DEGs (15,114 upregulated, 5963 downregulated) were obtained. In JYJD-S, there were 14,653 DEGs (11,211 upregulated, 3442 downregulated) (|log2.Fold_change| > 1, q < 0.05), compared to 11,646 DEGs (9096 upregulated, 2550 downregulated) (|log2.Fold_change| > 1, q < 0.05) in SHQP-R at 24 h after penoxsulam treatment; after 72 h, JYJD-S showed 16,418 DEGs (11,914 upregulated, 4504 downregulated) (|log2.Fold_change| > 1, q < 0.05) compared to 12,963 DEGs (9937 upregulated, 3026 downregulated) (|log2.Fold_change| > 1, q < 0.05) in SHQP-R (Fig. 2, Fig. S1 and Table 2). From these, we obtained 233 P450 DEGs. A total of 116 and 107 P450 DEGs were identified in JYJD-S at 24 h and 72 h after penoxsulam treatment compared to the control, respectively. Similarly, 106 and 124 P450 DEGs were identified in the SHQP-R group at 24 h and 72 h after treatment, respectively (Table 2).
3. Results 3.1. Impact of P450 inhibitors on penoxsulam resistance Whole-plant dose-response experiments showed that the GR50 values of the SHQP-R and JYJD-S strains for penoxsulam were 25.59 and 3.09 g a.i. ha−1, respectively. When P450 inhibitors were added prior to penoxsulam treatment, the resistance of the SHQP-R population was greatly reduced. The GR50 of SHQP-R to penoxsulam decreased from 25.59 to 5.00 g a.i. ha−1 in the PBO plus penoxsulam treatment group, and to 6.16 g a.i. ha−1 in the malathion plus penoxsulam treatment 115
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Fig. 3. Gene ontology (GO) classifications of E. glabrescens DEGs according to their involvement in biological process (BP), cellular component (CC), and molecular function (MF). (A) GO classifications of sensitive (JYJD-S) E. glabrescens samples without treatment (JYJD-S0) and 24 h after penoxsulam treatment (JYJD-S24)·(B) GO classifications of resistant (SHQP-R) E. glabrescens samples without treatment (SHQP-R0) and 24 h after tpenoxsulam treatment (SHQP-R24).
of DEGs using GO enrichment analysis; a total of 8201 (6.24%) sequences were assigned to GO terms (Table 4). In the two populations, metabolic process was significantly enriched in the DEGs (Fig. 3 and Fig. S2). Using KEGG pathway tools, we found that 99,923 (76.12%) sequences were assigned to KEGG terms (Table 4), the pathway of biosynthesis of secondary metabolites and metabolic pathways were significantly enriched in DEGs (Fig. 4 and Fig. S3). Similarly, KEGG pathway enrichment analysis in P450 DEGs showed that 61.37% (143/ 233) were annotated to twenty-seven KEGG pathways (Table S3). Of them, metabolic pathways (53.85%; 77/143) (ko01100) and biosynthesis of secondary metabolites (67.13%; 96/143) (ko01110) were enriched for the most P450 genes (Table S3).
The JYJD-S and SHQP-R genes from the mapped libraries were normalized using the fragments per kilobase per million reads (FPKM) method. At FPKM values of 0–1, the genes were regarded as having a low expression level; genes with FPKMs of 3–15 were regarded as having a medium expression level; and genes with FPKM values of > 60 were regarded as having a very high expression level (Table 3). 3.4. Functional annotation of DEGs The assembled sequences were annotated using Nr, KO, SwissProt, PFAM, GO, and KOG. A total of 1.031 × 105 (78.60%) sequences were annotated in at least one database (Table 4). We evaluated the functions 116
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Fig. 4. Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog database (KO) analysis of penoxsulam-sensitive (JYJD-S) and -resistant (SHQP-R) E. glabrescens biotypes. The vertical axis represents the top 20 enriched pathways, and the horizontal axis represents the rich factor. The size of the circle represents the number of genes, and the color of the circle represents the q-value. The suffix −0 indicates untreated samples; −24 indicate samples tested 24 h after penoxsulam treatment, respectively.
ALS-inhibiting herbicides has long been reported and is increasingly prevalent in many weed species. However, studies on metabolic herbicide resistance in E. glabrescens are limited, and the genes controlling this process remain largely unknown. The E. crus-galli genome was recently reported to provide a new understanding of the molecular mechanisms underlying the extreme adaptation of Echinochloa weed species to herbicides (Guo et al., 2017). Compared to the 246 full-length P450 genes in the Arabidopsis genome and the 328 full-length P450 genes in the rice genome, the E. crus-galli genome contained significantly more P450 genes, at 917. P450 gene diversity may be an important consideration in assessing the role of P450s in the evolution of herbicide resistance. The identified P450 genes found to be involved in NTSR were beneficial to understanding the evolution of metabolic resistance in E. glabrescens species, and for developing resistance mitigation strategies. Transcriptome sequencing has been successfully used to screen genes involved in metabolic resistance to ALS herbicides in many grass weed species, including Descurainia sophia L. and Alopecurus aequalis (Yang et al., 2016; Zhao et al., 2017). In this study, a total of 233 P450 DEGs were identified by transcriptome sequencing. KEGG analysis indicated that 143 P450 DEGs were annotated to twenty-seven KEGG pathways, of which metabolic pathways (ko01100) and biosynthesis of secondary metabolites (ko01110) were the most enriched KEGG pathways in response to penoxsulam (Table S3). CYP74A1 and CYP74A2 were annotated to these two KEGG pathways (Table S3), and their relative expression levels were upregulated and significantly higher in SHQP-R than in JYJD-S after penoxsulam treatment (Fig. 5 and Table 5). Studies have shown that the upregulation of P450 genes in resistant plants may enhance their herbicide metabolism capacity (Werck-Reichhart and Feyereisen, 2000; Delye, 2013). For example, several P450 genes were found to have high expression levels in an E. phyllopogon population after bispyribac treatment (Iwakami et al., 2014) and the expression of four P450 genes was consistently upregulated in resistant A. aequalis plants after mesosulfuron-methyl treatment (Zhao et al., 2017). The upregulated expression of CYP74A1 and
3.5. Verification of expression profiles of 27 candidate metabolic resistance P450 genes by qPCR To further explore the expression patterns of these candidate P450 genes in JYJD-S and SHQP-R, we performed qPCR at the same timepoints as those for transcriptome sequencing. The results showed that the relative expression levels of 23/27 candidate P450 genes were upregulated and significantly higher (P < .05) in SHQP-R than in JYJD-S for at least one time point (Fig. 5). Of them, four genes from CYP72A15-74A1-81E1-94B3 (Fig. 5E, Q, S, W, and Table 5) and six genes from the CYP71C-734A subfamily (Fig. 5G, I, J, K, M, N, and Table 5) showed a continuous increase in expression from 24 h to 72 h in SHQP-R. Furthermore, three genes from CYP734A1-74A2-94C1 (Fig. 5L, R, T, and Table 5) and ten genes from the CYP72A-71C-96A714C subfamily (Fig. 5A–D, F, H, O, P, U, V, and Table 5) were upregulated at 24 h, their relative expression levels were reduced at 72 h in SHQP-R and most of them also showed decreased expression at 72 h in JYJD-S. However, although 4/27 candidate P450 genes (Fig. 5X, Y, Z, AA, and Table 5) were downregulated compared to the controls, their relative expression levels were higher in SHQP-R than in JYJD-S for at least one time point. The expression levels of these twenty-seven genes in the two biotypes, as determined by qPCR, were consistent with the results of transcriptome sequencing (Table 5). 4. Discussion This study aimed to identify changes in the expression of P450 genes in E. glabrescens in response to penoxsulam in order to establish a transcriptomic resource for herbicide-resistant weed research. We demonstrated that resistance of E. glabrescens to penoxsulam was related to NTSR mechanisms by showing that the P450 inhibitors PBO and malathion could largely reverse penoxsulam resistance (Fig. 1). It has been well established that PBO and malathion can inhibit the metabolism of ALS-inhibitor herbicides, thus reversing metabolism-based resistance (Christopher et al., 1994). Metabolism-based resistance to 117
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Fig. 5. Relative transcript levels of 27 candidate cytochrome P450 genes after penoxsulam treatment in sensitive (JYJD-S) and resistant (SHQP-R) E. glabrescens populations. ★,significant up- or downregulated compared to JYJD-S at the same time point.
genes have been identified or characterized that could be linked to herbicide resistance in grass weed species. The P450s identified so far in ALS-inhibiting herbicide-tolerant crops or -resistant weeds include CYP72A31 in Oryza sativa or A. thaliana, which metabolizes bensulfuron-methyl (BSM) (Hiroaki et al., 2014); CYP81A12 and CYP81A21 in E. phyllopogon, which metabolizes BSM and penoxsulam (Iwakami et al., 2014); and CYP71C6v1, which acts on the wheat-metabolizing sulfonylurea herbicides chlorsulfuron, triasulfuron and metsulfuronmethyl via phenyl ring hydroxylase (Xiang et al., 2006). In our study, the relative expression levels of nine genes from the CYP72A and CYP71C subfamilies were upregulated and significantly higher in SHQP-R than in JYJD-S after penoxsulam treatment (Fig. 5 and Table 5). This indicates these nine genes could be major candidates for further study of herbicide metabolic resistance; however, the role of these genes remains unknown, and more work is needed to elucidate their roles in penoxsulam resistance. In this study, the relative expression of five P450 genes (CYP81E1, CYP94C1, CYP94B3, CYP714C1, and CYP714C2) were upregulated and those of four P450 genes (CYP97B2, CYP707A7, CYP711A1, and CYP724B1) were downregulated after penoxsulam treatment, and their relative expression levels in SHQP-R were significantly higher than those in JYJD-S (Fig. 5 and Table 5). However, the involvement of these genes in herbicide metabolism and resistance has not yet been reported. A possible reason for this could be that different P450s function in metabolizing specific ALS herbicides; alternatively, this may be owing to insufficient study of these genes and mechanisms in weeds.
CYP74A2 in our study could play a key role in reducing the sensitivity of resistant populations to penoxsulam and may enhance their penoxsulam metabolism capacity. In addition, some P450s have been reported to play important roles in the metabolism of secondary metabolites and in the catalysis of diverse reactions in plants. In our study, we found the relative expression level of CYP96A15 were significantly higher in SHQP-R than in JYJD-S after penoxsulam treatment (Fig. 5 and Table 5). CYP96A15 was reported to catalyze the hydroxylation of alkane substrates and was found to be involved in the biosynthesis of wax secondary alcohols and ketones (Greer et al., 2007). The hydroxylation and epoxidation catalyzed by P450s are also important degradation pathways for herbicides (Werck-Reichhart and Feyereisen, 2000). Furthermore, P450 genes are also involved in the brassinosteroid (BR) biosynthesis and catabolism. BRs are classified as safeners, which are known to induce the activity of numerous plant P450s involved in the biodegradation of herbicides (Hatzios and Burgos, 2004). We found that five genes from the CYP734A subfamily were annotated to the BR biosynthesis pathway (Table S4). Their relative expression levels were upregulated and significantly higher in SHQP-R than in JYJD-S in response to penoxsulam treatment (Fig. 5 and Table 5). Previous studies have reported that the CYP734A subfamily typically functions in BR catabolism in plants, and BRs also reported to protect crop plants against toxicity from herbicides (Toshiyuki et al., 2006; Xia et al., 2006). However, the identification of herbicide-metabolizing and resistance-endowing genes has been slow, and a limited number of P450 118
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providing E. glabrescens seeds. We also thank Editage for their professional English language editing services.
Table 5 qPCR validation of metabolic resistance candidate P450 genes in E. glabrescens. Gene ID
EC_v6.g045841 EC_v6.g024973 EC_v6.g060073 EC_v6.g082320 EC_v6.g038024 EC_v6.g098149 EC_v6.g044361 EC_v6.g101296 EC_v6.g077391 EC_v6.g037193 EC_v6.g010859 EC_v6.g030816 EC_v6.g030813 EC_v6.g037194 EC_v6.g106048 EC_v6.g099783 EC_v6.g038942 EC_v6.g065252 EC_v6.g096098 EC_v6.g040417 EC_v6.g094355 EC_v6.g036347 EC_v6.g103571 EC_v6.g006185 EC_v6.g021538 EC_v6.g098730 EC_v6.g000762
Functional annotationa
CYP74A1 CYP74A2 CYP734A1 CYP734A1 CYP734A1 CYP734A6 CYP734A6 CYP96A15 CYP96A15 CYP72A14 CYP72A14 CYP72A14 CYP72A15 CYP72A15 CYP71C1 CYP71C2 CYP71C2 CYP71C4 CYP81E1 CYP94C1 CYP94B3 CYP714C1 CYP714C2 CYP97B2 CYP707A7 CYP711A1 CYP724B1
q-valueb
3.70E-13 1.90E-12 6.40E-86 1.30E-76 1.50E-85 4.80E-75 6.60E-38 7.90E-46 3.50E-54 8.90E-37 1.70E-87 5.60E-59 5.80E-84 9.10E-43 4.80E-92 9.30E-88 9.20E-09 1.70E-94 2.90E-91 2.40E-53 1.30E-06 1.30E-78 2.90E-74 4.10E-81 8.50E-13 1.60E-20 1.20E-57
Relative expression change: RNA-seq
Relative expression change: qPCR
(R_24/ S_24)
(R_72/ S_72)
(R_24/ S_24)
(R_72/ S_72)
2.35 4.92 2.07 10.07 4.28 2.23 2.72 3.13 8.56 4.06 7.30 2.61 9.47 9.48 1.04 3.81 2.67 15.84 3.34 4.33 2.06 3.30 5.68 2.39 2.44 4.38 9.56
2.99 1.70 1.57 1.94 2.62 1.35 8.53 1.59 2.66 5.39 4.27 1.44 4.72 13.00 6.76 4.22 2.52 1.68 3.96 1.78 3.00 2.03 2.97 5.25 1.55 3.43 5.96
2.11⁎ 2.68⁎ 14.08⁎ 3.89⁎ 10.21⁎ 22.57⁎ 2.15⁎ 2.73⁎ 1.61⁎ 3.56⁎ 2.46⁎ 7.81⁎ 3.22⁎ 2.90⁎ 2.86⁎ 1.99⁎ 2.41⁎ 2.12⁎ 4.87⁎ 13.72⁎ 1.90 2.12⁎ 2.95⁎ 10.67⁎ 6.75⁎ 1.29 1.74⁎
13.84⁎ 1.35 2.04⁎ 2.75⁎ 4.99⁎ 10.56⁎ 2.94⁎ 0.72 2.04⁎ 1.00 1.15 2.61⁎ 3.31⁎ 1.29 1.42 2.42⁎ 1.49 1.75⁎ 8.37⁎ 3.53⁎ 3.25⁎ 1.16 3.26⁎ 1.17 8.92⁎ 3.50⁎ 2.41⁎
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Plant Physiol. 166, 1149. Iwakami, S., Endo, M., Saika, H., Okuno, J., Nakamura, N., Yokoyama, M., et al., 2014. Cytochrome P450 CYP81A12 and CYP81A21 are associated with resistance to two acetolactate synthase inhibitors in Echinochloa phyllopogon. Plant Physiol. 165, 618–629. Jabusch, T.W., Tjeerdema, R.S., 2005. Partitioning of penoxsulam, a new sulfonamide herbicide. J. Agr. Food. Chem. 53, 7179. Li, G., Wu, S., Cai, L., Wang, Q., Zhao, X., Wu, C., 2013. Identification and mRNA expression profile of glutamate receptor-like gene in quinclorac-resistant and susceptible Echinochloa crus-galli. Gene 531, 489–495. Nelson, D.R., 2009. The cytochrome p450 homepage. Hum. Genomics 4, 59–65. Opeña, J.L., Chauhan, B.S., Baltazar, A.M., 2014. Seed germination ecology of Echinochloa glabrescens and its implication for management in rice (Oryza sativa L.). PLoS One 9, e92261. Pan, L., Gao, H., Xia, W., Zhang, T., Dong, L., 2016a. Establishing a herbicide-metabolizing enzyme library in Beckmannia syzigachne to identify genes associated with metabolic resistance. J. Exp. Bot. 67, 1745. Pan, L., Li, J., Zhang, T., Zhang, D., Dong, L., 2016b. Cross-resistance patterns to acetyl coenzyme A carboxylase (ACCase) inhibitors associated with different ACCase mutations in Beckmannia syzigachne. Weed Res. 55, 609–620. Powles, S.B., Yu, Q., 2010. Evolution in action: plants resistant to herbicides. Ann. Rev. Plant Biol. 61, 317–347. Ritz, C., Streibig, J.C., 2005. Bioassay analysis using R. J. Stat. Softw. 12, 1–22. Satoshi, I., Akira, U., Yukiko, K., Hiroyuki, S., Hiroaki, W., Tatsuya, I., 2014. Cytochrome P450 genes induced by bispyribac-sodium treatment in a multiple-herbicide-resistant biotype of Echinochloa phyllopogon. Pest Manag. Sci. 70, 549–558. Storey, J.D., Tibshirani, R., 2003. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U. S. A. 100, 9440–9445. Toshiyuki, O., Takahito, N., Bunta, W., Daisaku, O., Takao, Y., Hisashi, M., Kanzo, S., Masaharu, M., 2006. Tomato cytochrome P450 CYP734A7 functions in brassinosteroid catabolism. Phytochemistry 67, 1895–1906. Wang, H., Li, J., Bo, L., Lou, Y., Dong, L., 2013. The role of cytochrome P450 monooxygenase in the different responses to fenoxaprop-P-ethyl in annual bluegrass (Poa annua L.) and short awned foxtail (Alopecurus aequalis Sobol.). Pestic. Biochem. Physiol. 107, 334–342.
a The subfamily classification of CytP450 genes was annotated using the NCBI non-redundant protein sequences (Nr) database. b The resulting p-value was adjusted and expressed as the q-value using the Benjamini-Hochberg procedure to control the false discovery rate. ⁎ p < .05.
Therefore, studying these genes in more detail is important. Whether they confer resistance to penoxsulam will require further comprehensive testing. In conclusion, one E. glabrescens population (SHQP-R) showing resistance to the ALS herbicide penoxsulam was identified. This population displayed CytP450-induced metabolic resistance. A total of twentyseven candidate P450 DEGs were selected by transcriptome sequencing of penoxsulam-sensitive and -resistant biotypes. qPCR verification showed that their relative expression level was significantly higher in SHQP-R than in JYJD-S after penoxsulam treatment. Our data provide preliminary insights into the changes in the gene expression of various P450s and their involvement in penoxsulam resistance. Considering the complexity of most plant defense mechanisms, future studies should focus on the functional characterization of these candidates. This study may greatly improve our understanding of herbicide metabolism resistance mechanisms, and also help identify the P450 genes involved in herbicide metabolism. Moreover, these results are potentially beneficial for developing methods to counter herbicide-resistant weeds. Declarations of interest None. Acknowledgements This research was supported by the National Natural Science Foundation of China (grant number: 31871993). The authors thank Zhang Lin (Qingpu Plant Protection Station, China) and Wang Hongchun (Jiangsu Academy of Agricultural Sciences, China) for 119
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Yuan, J.S., Tranel, P.J., C Neal, S., 2007. Non-target-site herbicide resistance: a family business. Trends Plant Sci. 12, 6–13. Yuan, G., Li, J., Pan, X., Liu, D., Napier, R., Dong, L., 2018. Quinclorac resistance induced by the suppression of the expression of 1-aminocyclopropane-1-carboxylic acid (ACC) synthase and ACC oxidase genes in Echinochloa crus-galli var. zelayensis. Pestic. Biochem. Physiol. 146, 25. Yun, M.S., Yogo, Y., Miura, R., Yamasue, Y., Fischer, A.J., 2005. Cytochrome P-450 monooxygenase activity in herbicide-resistant and -susceptible late watergrass (Echinochloa phyllopogon). Pestic. Biochem. Physiol. 83, 107–114. Zhao, N., Li, W., Bai, S., Guo, W., Yuan, G., Wang, F., Liu, W., Wang, J., 2017. Transcriptome profiling to identify genes involved in mesosulfuron-methyl resistance in Alopecurus aequalis. Front. Plant Sci. 8, 1391. Zhao, N., Yan, Y., Ge, L., Zhu, B., Liu, W., Wang, J., 2018. Target site mutations and cytochrome P450s confer resistance to fenoxaprop-P-ethyl and mesosulfuron-methyl in Alopecurus aequalis. Pest Manag. Sci. 75, 204–214.
Werck-Reichhart, D., Feyereisen, R., 2000. Cytochromes P450: a success story. Genome Biol. 1, 1–8. Werck-Reichhart, D., Hehn, A., Didierjean, L., 2000. Cytochromes P450 for engineering herbicide tolerance. Trends Plant Sci. 5, 116–123. Xia, X., Huang, Y., Wang, L., Huang, L., Yu, Y., Zhou, Y., Yu, J., 2006. Pesticides-induced depression of photosynthesis was alleviated by 24-epibrassinolide pretreatment in Cucumis sativus L. Pestic. Biochem. Physiol. 86, 42–48. Xiang, W., Wang, X., Ren, T., 2006. Expression of a wheat cytochrome P450 monooxygenase cDNA in yeast catalyzes the metabolism of sulfonylurea herbicides. Pestic. Biochem. Physiol. 85, 1–6. Yang, Q., Deng, W., Li, X., Yu, Q., Bai, L., Zheng, M., 2016. Target-site and non-target-site based resistance to the herbicide tribenuron-methyl in flixweed (Descurainia sophia L.). BMC Genomics 17, 551. Young, M.D., Wakefield, M.J., Smyth, G.K., Oshlack, A., 2010. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11 R14-R14.
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Transcriptome profiling to identify cytochrome P450 genes involved in penoxsulam resistance in Echinochloa glabrescens Bojun Yana,b,1, Yuhua Zhanga,b,1, Jun Lia,b, Jiapeng Fanga,b, Tingting Liua,b, Liyao Donga,b, a b
T
⁎
College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China Key Laboratory of Integrated Management of Crop Diseases and Pests (Nanjing Agricultural University), Ministry of Education, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Cytochrome P450s Echinochloa glabrescens Penoxsulam Transcriptome sequencing Metabolic resistance
Cytochrome P450s (P450s) confer resistance against herbicides, and this is increasingly becoming a concern for weed control. As a widespread Gramineae weed in paddy fields, Echinocloa glabrescens has become resistant to the acetolactate synthase (ALS)-inhibiting triazolopyrimidine herbicide penoxsulam. In this study, we found that the GR50 of the resistant population (SHQP-R) decreased substantially from 25.6 to 5.0 and 6.2 g a.i. ha−1 after treatment with the P450 inhibitors piperonyl butoxide (PBO) and malathion, respectively. However, P450 inhibitors almost had no effects on the susceptibility of the sensitive population (JYJD-S) to penoxsulam. To investigate the mechanisms of metabolic resistance, transcriptome sequencing analysis was performed to find candidate genes that may confer resistance to penoxsulam in E. glabrescens. A total of 233 P450 differentially expressed genes (DEGs) were identified by transcriptome sequencing. We found that the metabolic process and metabolic pathways were the most highly enriched in DEGs. Further, twenty-seven candidate P450 DEGs were selected for qPCR validation analyses. After penoxsulam treatment, the relative expression levels were significantly higher in SHQP-R than in JYJD-S. Among these, the relative expression of twenty-three P450 DEGs (eighteen from the CYP72A-71C-74A-96A-734A subfamily; five from CYP81E1-94C1–94B3-714C1-714C2) were upregulated and four P450 DEGs (from CYP724B1-711A1-707A7-97B2) were downregulated. Changes in the expression of these candidate P450 genes in E. glabrescens were in response to penoxsulam, which provides preliminary evidence for the role of P450s in herbicide metabolism in E. glabrescens. However, further functional studies on metabolic resistance to penoxsulam in a resistant E. glabrescens population are required.
1. Introduction Echinochloa glabrescens Munro ex Hook. f. is one of the most common Echinochloa weed species and is highly competitive with rice (Opeña et al., 2014). Herbicides are essential in modern agricultural production systems. However, the decreased effectiveness of chemical weed control measures due to rapid development of herbicide resistance is an increasing problem worldwide that impacts crop yields. Research has shown that uncontrolled E. glabrescens transplanted with rice reduced the yield by 7% and 87% at weed infestation levels of 5% and 50%, respectively (Opeña et al., 2014). Acetolactate synthase (ALS), also known as acetohydroxy acid synthase, is a key enzyme that catalyzes the biosynthesis of the branched-chain amino acids Val, Leu, and Ile (Duggleby et al., 2008). Penoxsulam, an ALS inhibitor, is one of
the most important herbicides in rice production and has been widely used in China since 2008 (Jabusch and Tjeerdema, 2005). However, long-term penoxsulam use results in the rapid development of resistance in Echinochloa species. For example, the increased prevalence of penoxsulam-resistant Echinochloa crus-galli is a serious challenge for weed management in rice fields (Chen et al., 2016). Currently, > 200 weed species have been reported to be herbicide resistant worldwide (Heap, 2019). Resistant weeds can survive herbicide applications via a variety of mechanisms, making the study of resistance mechanisms useful for adequate control strategies. The mechanisms involved in herbicide resistance are categorized as target-site resistance (TSR) and non-target-site resistance (NTSR) (Yuan et al., 2007). TSR is primarily involved in the modification of genes encoding proteins that are targets of herbicide molecules, as well as the
Abbreviations: ALS, acetolactate synthase; DEG, differentially expressed gene; FPKM, fragments per kilobase per million reads; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; NTSR, non-target-site resistance; KO, KEGG Orthologs; OF, Oil miscible Flowable concentrate; PBO, piperonyl butoxide; qPCR, quantitative real-time PCR; TSR, target-site resistance. ⁎ Corresponding author at: College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China. E-mail address: [email protected] (L. Dong). 1 The first two authors contributed equally to this work. https://doi.org/10.1016/j.pestbp.2019.04.017 Received 4 March 2019; Received in revised form 29 April 2019; Accepted 30 April 2019 Available online 02 May 2019 0048-3575/ © 2019 Elsevier Inc. All rights reserved.
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Sunjoy AgroScience (Ningbo, China), respectively. PBO and malathion were dissolved and diluted in a 1% aqueous solution of Tween-80 (Solarbio Life Sciences, Beijing, China).
overexpression or amplification of target proteins; in contrast, NTSR mechanisms include herbicide metabolism and physiological processes that transport herbicide molecules away from their target proteins (Powles and Yu, 2010). NTSR mechanisms are more complicated than TSR mechanisms, and the molecular mechanisms of NTSR are not completely understood (Christophe, 2013; Délye et al., 2015). Therefore, it is very important to study NTSR mechanisms in terms of weed resistance management. Cytochrome P450s play a crucial role in NTSR (Ghanizadeh and Harrington, 2017). The P450 enzyme family is widely found in bacteria, fungi, plants, and animals (Nelson, 2009). P450s have many functions in plants. In addition to their physiological functions in the biosynthesis of hormones, lipids, and secondary metabolites, P450s help plants to cope with exposure to harmful exogenous chemicals including pesticides and industrial pollutants, decreasing their phytotoxicity (WerckReichhart et al., 2000; Nelson, 2009). The discovery that P450s might be involved in herbicide metabolism came from an analysis of herbicide residues formed in vivo (Werck-Reichhart et al., 2000). The major metabolites of most classes of herbicides are aryl- or alkyl-hydroxylated products, as well as N-, S-, or O-dealkylated products and their glucose conjugates (Werck-Reichhart et al., 2000; Yun et al., 2005). Research reported that many P450 genes may closely related to fenoxaprop-Pethyl resistance in Alopecurus japonicus (Chen et al., 2018). Höfer et al. (2014)) reported that CYP76C1, CYP76C2, and CYP76C4 could metabolize phenylurea herbicides. In addition, Iwakami et al. (2014)) reported that CYP81A12 and CYP81A21 could metabolize the ALS inhibitors bensulfuron and penoxsulam. Other P450 genes have also been reported as candidates for conferring resistance to different herbicides (Ghanizadeh and Harrington, 2017; Zhao et al., 2018). In this study, our objectives were to: (a) determine the sensitivity of E. glabrescens to penoxsulam following treatment with the P450 inhibitors PBO and malathion; (b) identify P450 DEGs in E. glabrescens after treatment with penoxsulam by transcriptome sequencing; (c) select candidate penoxsulam-related metabolic resistance P450 DEGs and verify their expression profiles by qPCR. We expect the findings of this study to provide background information for future studies on the metabolic role of P450s in the mechanism of penoxsulam resistance in E. glabrescens.
2.3. Sensitivity to penoxsulam following P450 inhibition Whole-plant dose-response experiments were carried out to determine the values for the 50% reduction in fresh weight of plants (GR50) in the SHQP-R and JYJD-S populations to penoxsulam in the absence and presence of PBO and malathion. E. glabrescens seedlings at the three- to four-leaf stage were used in the whole-plant dose response experiments. Penoxsulam was applied at 0, 1.875, 3.75, 7.5, 15, and 30 g a.i. ha−1 to the JYJD-S group, and at 0, 3.75, 7.5, 15, 30, and 60 g a.i. ha−1 to the SHQP-R group. To test the sensitivity of E. glabrescens populations to penoxsulam following P450 inhibition treatment, whole-plant dose-response experiments were conducted using the same method described above. Seedlings from the two E. glabrescens populations at the three- to fourleaf stage were treated with PBO, malathion, penoxsulam, PBO plus penoxsulam, or malathion plus penoxsulam, and corresponding control plants were treated with a mixture of acetone, Tween-80, and water (Wang et al., 2013; Pan et al., 2016b). PBO was applied twice at a dose of 2100 g a.i. ha−1 each in 97 L ha−1 water, for a total dose of 4200 g a.i. ha−1 in a total volume of 194 L ha−1 water; malathion was applied once at a dose of 1000 g a.i. ha−1 (Wang et al., 2013). Malathion and PBO were formulated in an emulsified mixture (Tween-80, 1 mL L−1 in water), and acetone and applied 1 h prior to penoxsulam application. Penoxsulam was also applied at the same doses as described above. All treated pots were then placed back in the incubator. Three weeks after treatment, the aboveground fresh weights of the plants were determined and expressed as percentages relative to the control group. This experiment was conducted with four pots per group, and each experiment was conducted twice. Data were then combined and averaged for all replicates. A three-parameter logistic function was fitted to weight for herbicide treatments using the “drc” add-on package in R 3.1.3 (R Core Team, Vienna, Austria) (Ritz and Streibig, 2005) as follows:
Y = d/[(1 + (x/GR50) b]
(1)
where Y is fresh weight response at dose x of the herbicide; d is the upper asymptote of fresh weight at dose zero; and b indicates the slope. SigmaPlot 10.0 (Systat Software Inc., San Jose, CA, USA) was used to construct the dose response curves.
2. Materials and methods 2.1. Plant materials and growth conditions Mature E. glabrescens seeds were collected from paddy fields in Yangzhou, Jiangsu Province, China (N 32°25′47.50″, E 119°33′2.38″), marked as JYJD-S (sensitive strain), as well as in Shanghai (N 31°09′22.52″, E 121°04′30.04″), marked as SHQP-R (resistant strain), in 2015. Seeds were cleaned, dried on open trays in the sun for 3 d, and then stored in paper bags at 4 °C until use. For the whole-plant bioassay, each pot (diameter: 9 cm, height: 10 cm) was filled with a 2:1 (w/w) mixture of sand and soil (pH 5.6, organic matter content: 1.4%) and sown with 30 pre-germinated E. glabrescens seeds (Chen et al., 2016). The pots were placed in an incubator with a 12-h light/dark photoperiod regime at 30 °C (light) and 25 °C (dark), and were watered every three days. The light intensity was 140 μmol m−2 s−1 photosynthetic photon flux density using fluorescent lamps, and the growth chamber was maintained at 60% relative humidity (Yuan et al., 2018). The seedlings were thinned to 15 plants per pot at the two-leaf stage and prepared for later experiments.
2.4. Transcriptome sequencing 2.4.1. Sample preparation, sequence library construction, and Illumina sequencing The seeds of SHQP-R and JYJD-S plants were germinated and cultivated to the three- to four-leaf stage under the experimental conditions described in section 2.1. Whole plants were sprayed with 7.5 g a.i. ha−1 penoxsulam. Leaf samples from the untreated control group were harvested (marked as JYJD-S0 and SHQP-R0); after 24 h and 72 h, the plant samples from the penoxsulam-treated group were collected (marked as JYJD-S24/SHQP-R24 and JYJD-S72/SHQP-R72, respectively). A total of 18 samples were thus collected (3 biological replicates × 3 treatment times × 2 populations). The samples were collected and frozen immediately in liquid nitrogen for RNA extraction. Total RNA from the 18 samples was extracted using Transzol Up (TransGen Biotech, Beijing, China) according to the manufacturer's protocol and treated with DNase I (Takara, Beijing, China). Total RNA degradation and contamination were monitored by electrophoresis on 1% agarose gels. The RNA integrity was assessed with an RNA Nano 6000 Assay Kit on the Agilent Bio analyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). High-quality RNA samples were selected to prepare the cDNA library. Sequencing libraries were generated using the
2.2. Herbicides and chemicals Penoxsulam (25 g/L Oil miscible Flowable concentrate [OF]) used in this study was provided by Dow AgroSciences (Indianapolis, IN, USA). Furthermore, the inhibitors of cytochrome P450, PBO (97%) and malathion (95%) were purchased from Aladdin (Shanghai, China) and 113
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Fig. 1. Dose-response curves of (A) susceptible (JYJD-S) and (B) resistant (SHQP-R) E. glabrescens populations to penoxsulam in the absence and presence of the cytochrome P450 inhibitors piperonyl butoxide (PBO) and malathion. Data are shown as the mean ± SE from two experiments.
sequenced on the Hiseq 2500, and 150-bp paired-end reads were generated.
Table 1 GR50 of the JYJD-S and SHQP-R E. glabrescens populations after penoxsulam treatment in the absence and presence of cytochrome P450 inhibitors PBO and malathion. Herbicides
Penoxsulam Penoxsulam + PBO Penoxsulam + Malathion
2.4.2. Mapping sequencing results and functional annotation Raw image data files from the Illumina Hiseq 2500 were transformed to raw reads by the Consensus Assessment of Sequence and Variation (CASAVA, v. 1.8.2) base recognition program. The clean reads were filtered from the raw reads by removing low-quality reads containing ambiguous nucleotides or adaptor sequences. All downstream analyses were based only on these high-quality clean reads, which were aligned to the reference genome sequence of the E. crus-galli genome (https://www.ncbi.nlm.nih.gov/bioproject/414998). This reference annotation was then used to predict new genes and novel exons, and for the optimization of gene structures. Gene function was annotated based on the following databases: NCBI non-redundant protein sequences (Nr), Protein family (Pfam), Clusters of Orthologous Groups of proteins (KOG/COG), Swiss-Prot, Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog database (KO), and Gene Ontology (GO).
GR50 ± SE (g a.i. ha−1)a JYJD-S
SHQP-R
3.09 ± 0.14 2.59 ± 0.31 3.05 ± 0.11
25.59 ± 1.16 5.00 ± 0.25 6.16 ± 0.36
a GR50, herbicide rate causing 50% growth reduction in plants; JYJD-S, sensitive samples; PBO, piperonyl butoxide SHQP-R, resistant samples. SE, standard error.
NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA) following the manufacturer's instructions. A total of 3 μg RNA per sample was used as the input material for RNA sample preparation. mRNA was purified from total RNA using poly-T oligo-attached magnetic beads, then fragmentation was carried out to create short sequences. First-strand cDNA was synthesized using M-MuLV Reverse Transcriptase (RNase H) and random hexamer primers. Second-strand cDNA synthesis was subsequently performed using DNA polymerase I and RNase H. Finally, the purified double-stranded cDNA samples were further amplified by PCR to construct the final cDNA libraries that were
2.4.3. Identification and analysis of DEGs associated with cytochrome P450s To infer the transcriptional changes over time in the genotype under penoxsulam stress conditions, all DEGs after penoxsulam treatment were identified and analyzed by GO and KEGG enrichment analyses. Both analyses were implemented using a hyper geometric test (Young
Fig. 2. Analysis of differentially expressed genes (DEGs) between sensitive (JYJD-S) and resistant (SHQP-R) biotypes before and after penoxsulam treatment. Volcano plots showing the DEGs between SHQP-R at 0 h after treatment (SHQP-R0) vs 24 h after treatment (SHQP-R24) (left) and JYJD-S0 vs JYJD-S24 (right). A value of q < 0.05 was used as the threshold for the significance of DEGs. Red dots represent upregulated genes, green dots show downregulated genes, and blue dots indicate genes with no significant differences before and after treatment. Venn diagram (center) shows the number of DEGs unique to JYJD-S0 vs JYJD-S24 and SHQP-R0 vs SHQP-R24, and the number of DEGs shared by the two biotypes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 114
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Table 2 Relative up- and downregulation of all DEGs and P450 DEGs. Samplea
All DEGs Up
All DEGs Down
P450 DEGs Up
P450 DEGs Down
JYJD-S0-vs-JYJD-S24 JYJD-S0-vs-JYJD-S72 SHQP-R0-vs-SHQP-R24 SHQP-R0-vs-SHQP-R72 Total DEGs
11,211 11,914 9096 9937 15,114
3442 4504 2550 3026 5963
66 68 73 106 157
50 39 33 18 76
a JYJD-S0, untreated sensitive samples; JYJD-S24, sensitive samples 24 h after treatment with 7.5 g a.i. ha−1 penoxsulam; JYJD-S72. sensitive samples 72 h after treatment with 7.5 g a.i. ha−1 penoxsulam; SHQP-R0, untreated resistant samples; SHQP-R24, resistant samples 24 h after treatment with 7.5 g a.i. ha−1 penoxsulam; SHQP-R72, resistant samples 72 h after treatment with 7.5 g a.i ha−1 penoxsulam.
Table 3 Number of genes at each level of relative expression. FPKMa interval 0–1 1–3 3–15 15–60 > 60 a b
JYJD-S 33,776 (40.07%) 14,801 (17.56%) 24,978 (29.63%) 8805 (10.44%) 1942 (2.30%)
b
Table 4 Sequence annotation of Echinochloa glabrescens transcriptome. SHQP-R
Public database
Number of genes
Percentage
34,003 (40.52%) 14,815 (17.65%) 24,619 (29.33%) 8572 (10.21%) 1915 (2.28%)
Annotated in Annotated in Annotated in Annotated in Annotated in Annotated in Annotated in Total genes
102,546 69,677 52,130 68,402 8201 99,923 103,181 131,259
78.12 53.08 39.71 52.11 6.24 76.12 78.60 100
FPKM, Fragments per kilobase per million reads. Ratios of gene number to total gene number are presented in parentheses.
et al., 2010). GO or KEGG terms with a value of q < 0.05 were considered significantly enriched (Storey and Tibshirani, 2003).
NR PFAM KOG SwissProt GO KEGG at least one database
group (Fig. 1 and Table 1). In contrast, P450 inhibitors had negligible effects on the susceptibility of the JYJD-S population to penoxsulam (Fig. 1 and Table 1). P450 inhibitors, Tween-80, and acetone alone had no visible effects on seedling growth in either SHQP-R or JYJD-S. This indicates that cytochrome P450s may mediate resistance to penoxsulam in the SHQP-R E. glabrescens strain.
2.5. qPCR confirmation of P450 DEGs A total of twenty-seven candidate P450 DEGs were selected to confirm their expression levels by qPCR. Samples at 0, 24, and 72 h after penoxsulam treatment were collected from the penoxsulam-sensitive and -resistant E. glabrescens strains. Total RNA extraction was performed as described in section 2.4.1, then RNA samples were reverse-transcribed to cDNA using the PrimeScript RT Reagent Kit with gDNA Eraser (TaKaRa, Otsu, Japan), and the concentrations of the cDNA samples were diluted to a uniform value. Primers for qPCR were designed using Primer3 (http://bioinfo.ut.ee/primer3-0.4.0/), and the EcActin gene (GenBank accession HQ395760) was used as a reference, and the following primers were used: EcActin-F: 5′-GTGCTGTTCCAGC CATCGTTCAT-3′, and -R: 5′-CTCCTTGCTCATACGGTCAGCAATA-3′ (Li et al., 2013). All primers used for qPCR are shown in Table S4. Realtime PCR was performed using the ABI-7500 Fast Real-Time PCR System (ABI, Life Technologies, Foster City, CA, USA) using the SYBR Premix Ex Taq kit (TaKaRa, Japan). The relative mRNA expression level of each gene in each biotype was subjected to analysis of variance (ANOVA), followed by Duncan's multiple range test (P < .05) for the separation of means. Data analysis was performed using SPSS version 20 (SPSS, Chicago, IL, USA). Two threshold values (Satoshi et al., 2014; Pan et al., 2016a), a significant result from Student's t-test (P < .05), and a 2-fold change were used to determine the up- or downregulation of gene expression caused by penoxsulam. Each experiment included three biological replicates and was repeated twice.
3.2. Quantitative assessment of transcriptome sequencing The numbers of raw reads from the 18 samples ranged from 5.172–6.423 × 107 with an error rate of approximately 2%, yielding 7.76–9.64 G clean bases (Table S1). Each sample that produced clean reads was aligned to the recently released E. crus-galli reference genome (Guo et al., 2017). About 85% of total reads were mapped to the reference genome (Table S2). Among them, uniquely mapped reads comprised about 79% of total reads, and multiple mapped reads comprised about 6% (Table S2). The proportion of clean reads in each sample mapped to reference genome ranged from 4.304–5.370 × 107 (Table S2). All raw-sequence read data were uploaded to the NCBI Sequence Read Archive (SRA, http://www.ncbi.nlm.nih.gov/Traces/ sra) with accession number SRP187315. 3.3. DEGs and transcriptome profiles Transcriptome sequencing data showed that the expression levels of a large number of genes were changed after penoxsulam treatment. A total of 21,077 DEGs (15,114 upregulated, 5963 downregulated) were obtained. In JYJD-S, there were 14,653 DEGs (11,211 upregulated, 3442 downregulated) (|log2.Fold_change| > 1, q < 0.05), compared to 11,646 DEGs (9096 upregulated, 2550 downregulated) (|log2.Fold_change| > 1, q < 0.05) in SHQP-R at 24 h after penoxsulam treatment; after 72 h, JYJD-S showed 16,418 DEGs (11,914 upregulated, 4504 downregulated) (|log2.Fold_change| > 1, q < 0.05) compared to 12,963 DEGs (9937 upregulated, 3026 downregulated) (|log2.Fold_change| > 1, q < 0.05) in SHQP-R (Fig. 2, Fig. S1 and Table 2). From these, we obtained 233 P450 DEGs. A total of 116 and 107 P450 DEGs were identified in JYJD-S at 24 h and 72 h after penoxsulam treatment compared to the control, respectively. Similarly, 106 and 124 P450 DEGs were identified in the SHQP-R group at 24 h and 72 h after treatment, respectively (Table 2).
3. Results 3.1. Impact of P450 inhibitors on penoxsulam resistance Whole-plant dose-response experiments showed that the GR50 values of the SHQP-R and JYJD-S strains for penoxsulam were 25.59 and 3.09 g a.i. ha−1, respectively. When P450 inhibitors were added prior to penoxsulam treatment, the resistance of the SHQP-R population was greatly reduced. The GR50 of SHQP-R to penoxsulam decreased from 25.59 to 5.00 g a.i. ha−1 in the PBO plus penoxsulam treatment group, and to 6.16 g a.i. ha−1 in the malathion plus penoxsulam treatment 115
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Fig. 3. Gene ontology (GO) classifications of E. glabrescens DEGs according to their involvement in biological process (BP), cellular component (CC), and molecular function (MF). (A) GO classifications of sensitive (JYJD-S) E. glabrescens samples without treatment (JYJD-S0) and 24 h after penoxsulam treatment (JYJD-S24)·(B) GO classifications of resistant (SHQP-R) E. glabrescens samples without treatment (SHQP-R0) and 24 h after tpenoxsulam treatment (SHQP-R24).
of DEGs using GO enrichment analysis; a total of 8201 (6.24%) sequences were assigned to GO terms (Table 4). In the two populations, metabolic process was significantly enriched in the DEGs (Fig. 3 and Fig. S2). Using KEGG pathway tools, we found that 99,923 (76.12%) sequences were assigned to KEGG terms (Table 4), the pathway of biosynthesis of secondary metabolites and metabolic pathways were significantly enriched in DEGs (Fig. 4 and Fig. S3). Similarly, KEGG pathway enrichment analysis in P450 DEGs showed that 61.37% (143/ 233) were annotated to twenty-seven KEGG pathways (Table S3). Of them, metabolic pathways (53.85%; 77/143) (ko01100) and biosynthesis of secondary metabolites (67.13%; 96/143) (ko01110) were enriched for the most P450 genes (Table S3).
The JYJD-S and SHQP-R genes from the mapped libraries were normalized using the fragments per kilobase per million reads (FPKM) method. At FPKM values of 0–1, the genes were regarded as having a low expression level; genes with FPKMs of 3–15 were regarded as having a medium expression level; and genes with FPKM values of > 60 were regarded as having a very high expression level (Table 3). 3.4. Functional annotation of DEGs The assembled sequences were annotated using Nr, KO, SwissProt, PFAM, GO, and KOG. A total of 1.031 × 105 (78.60%) sequences were annotated in at least one database (Table 4). We evaluated the functions 116
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Fig. 4. Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog database (KO) analysis of penoxsulam-sensitive (JYJD-S) and -resistant (SHQP-R) E. glabrescens biotypes. The vertical axis represents the top 20 enriched pathways, and the horizontal axis represents the rich factor. The size of the circle represents the number of genes, and the color of the circle represents the q-value. The suffix −0 indicates untreated samples; −24 indicate samples tested 24 h after penoxsulam treatment, respectively.
ALS-inhibiting herbicides has long been reported and is increasingly prevalent in many weed species. However, studies on metabolic herbicide resistance in E. glabrescens are limited, and the genes controlling this process remain largely unknown. The E. crus-galli genome was recently reported to provide a new understanding of the molecular mechanisms underlying the extreme adaptation of Echinochloa weed species to herbicides (Guo et al., 2017). Compared to the 246 full-length P450 genes in the Arabidopsis genome and the 328 full-length P450 genes in the rice genome, the E. crus-galli genome contained significantly more P450 genes, at 917. P450 gene diversity may be an important consideration in assessing the role of P450s in the evolution of herbicide resistance. The identified P450 genes found to be involved in NTSR were beneficial to understanding the evolution of metabolic resistance in E. glabrescens species, and for developing resistance mitigation strategies. Transcriptome sequencing has been successfully used to screen genes involved in metabolic resistance to ALS herbicides in many grass weed species, including Descurainia sophia L. and Alopecurus aequalis (Yang et al., 2016; Zhao et al., 2017). In this study, a total of 233 P450 DEGs were identified by transcriptome sequencing. KEGG analysis indicated that 143 P450 DEGs were annotated to twenty-seven KEGG pathways, of which metabolic pathways (ko01100) and biosynthesis of secondary metabolites (ko01110) were the most enriched KEGG pathways in response to penoxsulam (Table S3). CYP74A1 and CYP74A2 were annotated to these two KEGG pathways (Table S3), and their relative expression levels were upregulated and significantly higher in SHQP-R than in JYJD-S after penoxsulam treatment (Fig. 5 and Table 5). Studies have shown that the upregulation of P450 genes in resistant plants may enhance their herbicide metabolism capacity (Werck-Reichhart and Feyereisen, 2000; Delye, 2013). For example, several P450 genes were found to have high expression levels in an E. phyllopogon population after bispyribac treatment (Iwakami et al., 2014) and the expression of four P450 genes was consistently upregulated in resistant A. aequalis plants after mesosulfuron-methyl treatment (Zhao et al., 2017). The upregulated expression of CYP74A1 and
3.5. Verification of expression profiles of 27 candidate metabolic resistance P450 genes by qPCR To further explore the expression patterns of these candidate P450 genes in JYJD-S and SHQP-R, we performed qPCR at the same timepoints as those for transcriptome sequencing. The results showed that the relative expression levels of 23/27 candidate P450 genes were upregulated and significantly higher (P < .05) in SHQP-R than in JYJD-S for at least one time point (Fig. 5). Of them, four genes from CYP72A15-74A1-81E1-94B3 (Fig. 5E, Q, S, W, and Table 5) and six genes from the CYP71C-734A subfamily (Fig. 5G, I, J, K, M, N, and Table 5) showed a continuous increase in expression from 24 h to 72 h in SHQP-R. Furthermore, three genes from CYP734A1-74A2-94C1 (Fig. 5L, R, T, and Table 5) and ten genes from the CYP72A-71C-96A714C subfamily (Fig. 5A–D, F, H, O, P, U, V, and Table 5) were upregulated at 24 h, their relative expression levels were reduced at 72 h in SHQP-R and most of them also showed decreased expression at 72 h in JYJD-S. However, although 4/27 candidate P450 genes (Fig. 5X, Y, Z, AA, and Table 5) were downregulated compared to the controls, their relative expression levels were higher in SHQP-R than in JYJD-S for at least one time point. The expression levels of these twenty-seven genes in the two biotypes, as determined by qPCR, were consistent with the results of transcriptome sequencing (Table 5). 4. Discussion This study aimed to identify changes in the expression of P450 genes in E. glabrescens in response to penoxsulam in order to establish a transcriptomic resource for herbicide-resistant weed research. We demonstrated that resistance of E. glabrescens to penoxsulam was related to NTSR mechanisms by showing that the P450 inhibitors PBO and malathion could largely reverse penoxsulam resistance (Fig. 1). It has been well established that PBO and malathion can inhibit the metabolism of ALS-inhibitor herbicides, thus reversing metabolism-based resistance (Christopher et al., 1994). Metabolism-based resistance to 117
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Fig. 5. Relative transcript levels of 27 candidate cytochrome P450 genes after penoxsulam treatment in sensitive (JYJD-S) and resistant (SHQP-R) E. glabrescens populations. ★,significant up- or downregulated compared to JYJD-S at the same time point.
genes have been identified or characterized that could be linked to herbicide resistance in grass weed species. The P450s identified so far in ALS-inhibiting herbicide-tolerant crops or -resistant weeds include CYP72A31 in Oryza sativa or A. thaliana, which metabolizes bensulfuron-methyl (BSM) (Hiroaki et al., 2014); CYP81A12 and CYP81A21 in E. phyllopogon, which metabolizes BSM and penoxsulam (Iwakami et al., 2014); and CYP71C6v1, which acts on the wheat-metabolizing sulfonylurea herbicides chlorsulfuron, triasulfuron and metsulfuronmethyl via phenyl ring hydroxylase (Xiang et al., 2006). In our study, the relative expression levels of nine genes from the CYP72A and CYP71C subfamilies were upregulated and significantly higher in SHQP-R than in JYJD-S after penoxsulam treatment (Fig. 5 and Table 5). This indicates these nine genes could be major candidates for further study of herbicide metabolic resistance; however, the role of these genes remains unknown, and more work is needed to elucidate their roles in penoxsulam resistance. In this study, the relative expression of five P450 genes (CYP81E1, CYP94C1, CYP94B3, CYP714C1, and CYP714C2) were upregulated and those of four P450 genes (CYP97B2, CYP707A7, CYP711A1, and CYP724B1) were downregulated after penoxsulam treatment, and their relative expression levels in SHQP-R were significantly higher than those in JYJD-S (Fig. 5 and Table 5). However, the involvement of these genes in herbicide metabolism and resistance has not yet been reported. A possible reason for this could be that different P450s function in metabolizing specific ALS herbicides; alternatively, this may be owing to insufficient study of these genes and mechanisms in weeds.
CYP74A2 in our study could play a key role in reducing the sensitivity of resistant populations to penoxsulam and may enhance their penoxsulam metabolism capacity. In addition, some P450s have been reported to play important roles in the metabolism of secondary metabolites and in the catalysis of diverse reactions in plants. In our study, we found the relative expression level of CYP96A15 were significantly higher in SHQP-R than in JYJD-S after penoxsulam treatment (Fig. 5 and Table 5). CYP96A15 was reported to catalyze the hydroxylation of alkane substrates and was found to be involved in the biosynthesis of wax secondary alcohols and ketones (Greer et al., 2007). The hydroxylation and epoxidation catalyzed by P450s are also important degradation pathways for herbicides (Werck-Reichhart and Feyereisen, 2000). Furthermore, P450 genes are also involved in the brassinosteroid (BR) biosynthesis and catabolism. BRs are classified as safeners, which are known to induce the activity of numerous plant P450s involved in the biodegradation of herbicides (Hatzios and Burgos, 2004). We found that five genes from the CYP734A subfamily were annotated to the BR biosynthesis pathway (Table S4). Their relative expression levels were upregulated and significantly higher in SHQP-R than in JYJD-S in response to penoxsulam treatment (Fig. 5 and Table 5). Previous studies have reported that the CYP734A subfamily typically functions in BR catabolism in plants, and BRs also reported to protect crop plants against toxicity from herbicides (Toshiyuki et al., 2006; Xia et al., 2006). However, the identification of herbicide-metabolizing and resistance-endowing genes has been slow, and a limited number of P450 118
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providing E. glabrescens seeds. We also thank Editage for their professional English language editing services.
Table 5 qPCR validation of metabolic resistance candidate P450 genes in E. glabrescens. Gene ID
EC_v6.g045841 EC_v6.g024973 EC_v6.g060073 EC_v6.g082320 EC_v6.g038024 EC_v6.g098149 EC_v6.g044361 EC_v6.g101296 EC_v6.g077391 EC_v6.g037193 EC_v6.g010859 EC_v6.g030816 EC_v6.g030813 EC_v6.g037194 EC_v6.g106048 EC_v6.g099783 EC_v6.g038942 EC_v6.g065252 EC_v6.g096098 EC_v6.g040417 EC_v6.g094355 EC_v6.g036347 EC_v6.g103571 EC_v6.g006185 EC_v6.g021538 EC_v6.g098730 EC_v6.g000762
Functional annotationa
CYP74A1 CYP74A2 CYP734A1 CYP734A1 CYP734A1 CYP734A6 CYP734A6 CYP96A15 CYP96A15 CYP72A14 CYP72A14 CYP72A14 CYP72A15 CYP72A15 CYP71C1 CYP71C2 CYP71C2 CYP71C4 CYP81E1 CYP94C1 CYP94B3 CYP714C1 CYP714C2 CYP97B2 CYP707A7 CYP711A1 CYP724B1
q-valueb
3.70E-13 1.90E-12 6.40E-86 1.30E-76 1.50E-85 4.80E-75 6.60E-38 7.90E-46 3.50E-54 8.90E-37 1.70E-87 5.60E-59 5.80E-84 9.10E-43 4.80E-92 9.30E-88 9.20E-09 1.70E-94 2.90E-91 2.40E-53 1.30E-06 1.30E-78 2.90E-74 4.10E-81 8.50E-13 1.60E-20 1.20E-57
Relative expression change: RNA-seq
Relative expression change: qPCR
(R_24/ S_24)
(R_72/ S_72)
(R_24/ S_24)
(R_72/ S_72)
2.35 4.92 2.07 10.07 4.28 2.23 2.72 3.13 8.56 4.06 7.30 2.61 9.47 9.48 1.04 3.81 2.67 15.84 3.34 4.33 2.06 3.30 5.68 2.39 2.44 4.38 9.56
2.99 1.70 1.57 1.94 2.62 1.35 8.53 1.59 2.66 5.39 4.27 1.44 4.72 13.00 6.76 4.22 2.52 1.68 3.96 1.78 3.00 2.03 2.97 5.25 1.55 3.43 5.96
2.11⁎ 2.68⁎ 14.08⁎ 3.89⁎ 10.21⁎ 22.57⁎ 2.15⁎ 2.73⁎ 1.61⁎ 3.56⁎ 2.46⁎ 7.81⁎ 3.22⁎ 2.90⁎ 2.86⁎ 1.99⁎ 2.41⁎ 2.12⁎ 4.87⁎ 13.72⁎ 1.90 2.12⁎ 2.95⁎ 10.67⁎ 6.75⁎ 1.29 1.74⁎
13.84⁎ 1.35 2.04⁎ 2.75⁎ 4.99⁎ 10.56⁎ 2.94⁎ 0.72 2.04⁎ 1.00 1.15 2.61⁎ 3.31⁎ 1.29 1.42 2.42⁎ 1.49 1.75⁎ 8.37⁎ 3.53⁎ 3.25⁎ 1.16 3.26⁎ 1.17 8.92⁎ 3.50⁎ 2.41⁎
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a The subfamily classification of CytP450 genes was annotated using the NCBI non-redundant protein sequences (Nr) database. b The resulting p-value was adjusted and expressed as the q-value using the Benjamini-Hochberg procedure to control the false discovery rate. ⁎ p < .05.
Therefore, studying these genes in more detail is important. Whether they confer resistance to penoxsulam will require further comprehensive testing. In conclusion, one E. glabrescens population (SHQP-R) showing resistance to the ALS herbicide penoxsulam was identified. This population displayed CytP450-induced metabolic resistance. A total of twentyseven candidate P450 DEGs were selected by transcriptome sequencing of penoxsulam-sensitive and -resistant biotypes. qPCR verification showed that their relative expression level was significantly higher in SHQP-R than in JYJD-S after penoxsulam treatment. Our data provide preliminary insights into the changes in the gene expression of various P450s and their involvement in penoxsulam resistance. Considering the complexity of most plant defense mechanisms, future studies should focus on the functional characterization of these candidates. This study may greatly improve our understanding of herbicide metabolism resistance mechanisms, and also help identify the P450 genes involved in herbicide metabolism. Moreover, these results are potentially beneficial for developing methods to counter herbicide-resistant weeds. Declarations of interest None. Acknowledgements This research was supported by the National Natural Science Foundation of China (grant number: 31871993). The authors thank Zhang Lin (Qingpu Plant Protection Station, China) and Wang Hongchun (Jiangsu Academy of Agricultural Sciences, China) for 119
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