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Article

Mechanism of Eriochloa villosa (Thunb.) Kunth Resistance to Nicosulfuron

by
Jing Guo
1,†,
Zeqian Xu
2,†,
Ting Jiao
1,†,
Hong Gao
1,
Yuechao Wang
3,
Liguo Zhang
3,
Mukai Li
4,
Xiaomin Liu
5,
Chunxiu Yan
1 and
Yujun Han
1,*,†
1
College of Plant Protection, Northeast Agricultural University, Harbin 150030, China
2
State Key Laboratory for Oncogenes and Bio-ID Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
3
Crop Resources Institute of Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
4
College of Agriculture, Heilongjiang Bayi Agricultural University, Daqing 163319, China
5
Key Laboratory of Crop Cultivation Physiology and Green Production of Hebei Province, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(10), 2210; https://doi.org/10.3390/agronomy14102210
Submission received: 7 August 2024 / Revised: 19 September 2024 / Accepted: 24 September 2024 / Published: 25 September 2024
(This article belongs to the Section Weed Science and Weed Management)
Browse Figures
Versions Notes

Abstract

:
Eriochloa villosa (Thunb.) Kunth, the main weed in corn fields, has gradually developed resistance to nicosulfuron due to continuous and extensive application. We identified a biotype showing resistance to ALS inhibitor nicosulfuron with a resistant index 13.83, but without any target spot mutation. Herein, transcriptome sequencing was used to analyze the differences in gene expression at the transcriptional level between nicosulfuron-resistant E. villosa HEK-40 varieties and sensitive E. villosa HEK-15 varieties. The resistant and sensitive varieties comparison revealed 9931 DEGs after nicosulfuron application, of which 5426 and 4505 genes were up-regulated and down-regulated, respectively. Some contigs related to metabolic resistance were identified based on differential expression via RNA-Seq, which includes ABC transporters (ko02010), glucosinolate biosynthesis (ko00966), 2-oxocarboxylic acid metabolism (ko01210), alanine, aspartate, and glutamate metabolism pathways (ko00250). Seven CYP450 genes, four GST genes, ten ABC transporter genes, and two GT genes related to metabolic resistance were identified. The 10 candidate genes screened were validated using q-PCR. This validation indicates that activities associated with P450 enzymes, ABC transporters, and glutathione S-transferases (GST) may play a role in conferring resistance, which is important for reducing the impact of weeds on corn fields and ensuring food security.

1. Introduction

In agricultural production, some measures and technologies must be avoided to ensure the stability of food production, such as technologies related to the emergence of diseases, pests, and weeds in farmlands; these situations often require the use of pesticides, especially herbicides, which help control weeds and contribute significantly to global food security [1]. However, weed communities continue to evolve and weed resistance continues to increase due to the long-term and single application of herbicides [2,3], posing a serious threat to the security of food consumed by humans.
Since the first reports of 2,4-D-resistant Commelina diffusa and Daucus carota in the 1950s, more types and biotypes of herbicide-resistant weeds have emerged annually [4,5]. To date, 272 out of 100 crops in 72 countries worldwide (155 dicotyledonous plants and 117 monocotyledonous plants) have been reported to be resistant to herbicides, and 530 of these crops are herbicide-resistant biotypes. Weeds have developed resistance to 168 different herbicides in 21 categories of 31 known herbicide action sites [6]. At present, more resistant weeds are found in countries such as the United States [7], Europe [8], and Australia [9], especially weeds resistant to glyphosate [4]. Fifty-seven weed species, including Perennial ryegrass [10], Amaranthus palmeri [11], and Erigeron candidasis L [12], have developed resistance to glyphosate. The development of weed resistance limits the application of herbicides and seriously impacts sustainable agriculture worldwide; in addition, these impacts are becoming increasingly serious [13].
Nicosulfuron belongs to the sulfonylurea herbicide class, targets the ALS gene, and causes weed death by inhibiting the synthesis of branched chain amino acids [14]. The herbicide exhibits several characteristics, including high efficiency, low toxicity, broad spectrum effects, and strong selectivity [15]. It is mainly used for stem and leaf treatments to control common weeds in corn fields, such as Echinochloa crus-galli (L.) P. Beauv., E. villosa, Chenopodium album L., and Amaranthus retroflexus L. [16]. However, weeds in the field have developed severe resistance because the herbicide has been used continuously for a long period and exerts its activity on a single site.
Most of the weeds reported in China are resistant to glyphosate. Cao et al. [17] confirmed that 35.8% of Setaria viridis (L.) P. Beauv. plants, a resistant R376 population, are resistant to glyphosate, suggesting that R376′s resistance to glyphosate results from ALS gene mutations and enhanced metabolism. Yang et al. [18] reported that Amaranthus retroflexus L. is resistant to nicosulfuron and confirmed that cytochrome P450 and GST enhance herbicide metabolism. Digitaria sanguinalis in corn fields has recently developed resistance to nicosulfuron, which is produced through the overexpression of the ALS gene and enhanced metabolism [19]. In addition, Digitaria sanguinalis [20] and large crabgrass [21] have been identified. There are relatively few reports on the resistance of plants to nicosulfuron worldwide, and the resistance plants mainly include Sorghum halepense species [22].
Eriochloa villosa (Thunb.) Kunth is an annual weed among the gramineous weeds [23]. Due to the large size of E. villosa seeds, they mature and fall around the plant, allowing individual E. villosa plants to spread seeds on a large scale. Therefore, E. villosa can occur densely in the field, and in severe cases, it can reduce crop yield by up to 70% [24]. In addition, E. villosa can produce many dormant seeds. The occurrence time in the field is inconsistent, the depth of occurrence is large and uneven, and the duration of occurrence in the field is long. Even during the growing season, E. villosa seedlings continue to emerge. The leaves of E. villosa are fuzzy, and herbicides do not absorb well on the surface of the leaves; therefore, it is difficult to control the application of these herbicides in the field [25]. E. villosa originated in Eastern Asia, mainly in China and Russia, and has become competitive with crops due to its strong tillering ability; the weed even shows tolerance to some herbicides and has become an invasive species in Hungary and Australia [26].
Bunting et al. [27] experimented in the United States in 2003 and reported that E. villosa absorbs high amounts of formamide sulfur on methyl, but its control effect is poor. This resistance may be caused by nontargeted metabolism. Szilágyi et al. [28] reported that E. villosa exerts an allelopathic effect on maize and can significantly reduce the plant height, root length, root volume, and root dry weight of maize. In the main corn-growing areas of northern China, E. villosa weeds have become difficult to control locally, and corn fields in northern China have become increasingly damaged. Han et al. [29] found that E. villosa in Heilongjiang Province has developed resistance to nicosulfuron; furthermore, the content and activity of antioxidant enzymes (SOD, POD) and metabolic detoxification enzymes (GSH, GST) in highly nicosulfuron-resistant E. villosa populations are greater than those in medium-resistant and sensitive E. villosa populations. In addition, the target enzyme (ALS) activity and chlorophyll content recovered faster in these populations.
In this research, we found that E. villosa exhibits high resistance to nicosulfuron, and is related to nontargeted resistance. However, research on the specific mechanism that underlies nontarget resistance is incomplete and not comprehensive. Thus, this study focused on the differences in transcriptional gene expression between resistant and sensitive E. villosa HEK-40 and HEK-15 varieties. Specifically, a transcriptome sequencing method was used to explore the expression of genes related to the metabolic resistance of nicosulfuron. This study aimed to verify the expression levels of differentially expressed metabolic genes by RT-qPCR and clarify the metabolic resistance-related genes affected by E. villosa. This study provides a theoretical basis for the scientific prevention and control of resistant E. villosa and a reference for the chemical control of E. villosa in dryland crops, such as corn fields.

2. Materials and Methods

2.1. Materials

This experiment used E. villosa as the research plant, and it has been confirmed that the population with resistance to nicosulfuron and without the ALS mutation is HEK-40 (tolerant plants, R), while the sensitive population is HEK-15 (sensitive plants, S). The experimental herbicide used was 95.7% nicosulfuron, technically produced by Shandong Jingbo Agrochemical Technology Co., Ltd. (Binzhou, China). The spray equipment was the KNAPSACK hydraulic spray with four nozzles, and the nozzle model was TEEJET80015VS.

2.2. Cultivation of Experimental Materials

Plump E. villosa seeds were selected and placed in conical flasks. An appropriate amount of 3% sodium hypochlorite solution was poured into the conical flask, and the E. villosa seeds were disinfected for approximately 30 min. After disinfection, the sections were rinsed with sterile water three times. The test E. villosa seeds were soaked in warm water for 24 h to induce germination and sown after exposure. A nutrient bowl with a diameter of 20 cm and a depth of 20 cm was used, and the bowl was preloaded with soil that had not been contaminated with pesticides and was mixed with a 2/3 proportion of peat soil. The soaking pot watering method was used to saturate the soil moisture. Fifteen plump E. villosa seeds were sown (a total of 24 pots), covered with peat soil, and cultivated under outdoor conditions (the average temperature in Harbin in July is 24.4 °C, and the average daylight duration is 15.7 h [30]) with routine water management.
During the 4–5 leaf stage of E. villosa with consistent growth, the recommended upper limit dose of nicosulfuron in the field (54 g a.i./hm2) was sprayed, with clear water as the control. Each treatment was repeated three times, and the groups were as follows: S_CK (HEK-15 control group) and S-T (HEK-15 treatment group); R-CK (HEK-40 control group) and R-T (HEK-40 treatment group). Leaf tissues (three individuals of each biotype) were collected after 48 h, and samples without herbicide treatment were collected as controls (three individuals of each biotype). All 12 samples collected were snap frozen in liquid nitrogen and stored in a −80 °C refrigerator for future use.

2.3. Total RNA Extraction and cDNA Library Preparation

Total RNA was extracted from E. villosa leaves using an RNA Ure Plant Kit (Illumina, San Diego, CA, USA), and three biological replicates were used for each treatment. Agarose gel electrophoresis was used to analyze the integrity of the sample RNA and determine whether there was DNA contamination, RNA purity was detected using a NanoPhotometer (Implen, Munich, Germany) spectrophotometer, RNA concentration was measured with high precision using a Qubit 2.0 fluorometer, and an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA) was used to accurately detect RNA integrity. After the samples passed the inspection, the next step of testing was performed. A NEBNext® Ultra TM RNA Library Prep Kit (Gene Biotechnology International Trade Co., Ltd., Shanghai, China) was used to construct the library. Then, detection was performed after the library was constructed. The library that met the standards was subjected to Illumina sequencing, and the entire sequencing process was completed by Metwell Biotechnology Co., Ltd. Trans Decoder (5.3.0) (Wuhan, China) was used to perform CDS prediction on the transcripts assembled by Trinity and generate the corresponding amino acid sequences of the transcripts.

2.4. Unigene Feature Annotations

Due to the lack of a reference genome for E. villosa, Diamond software (v2.0.9) was used to compare the unigenes with those in the NR database, and HMMER software (3.3.2) was used to compare the amino acid sequences with those in the Pfam database to obtain annotation information.

2.5. Screening and Analysis of Differentially Expressed Genes (DEGs)

Gene expression levels were evaluated using the FPKM (expected number of fragments per kilobase of transcript sequence per million base pairs sequenced; it explicitly accommodates sequencing data with one, two, or, if needed for future sequencing platforms, higher numbers of reads from single-source molecules) calculation method [31], and the resulting data were analyzed using DESeq software (1.44.0) [32]. Each gene (transcript) has its own FPKM value, which is used to represent the expression level of the gene (transcript) in the test sample. DEGs were corrected for p value (padj) < 0.05 and |log2 (fold change)| ≥ 1, where log2 (fold change) > 1 indicates up-regulated genes (top); Log2 (fold change) < −1 indicates down-regulated genes (bottom), while the rest are not DEGs. In this study, the DEGs of sensitive population S and resistant population R of E. villosa were treated and analyzed with nicosulfuron.

2.6. Functional Annotation and Enrichment Analysis of DEGs

GO functional annotation and KEGG enrichment can be performed on the screened DEGs to clarify the differences between samples at the gene level and facilitate the exploration of target genes and key pathways. During bioinformatics analyses, the R package clusterProfiler is generally used to perform GO data enrichment analysis with DEGs. After GO enrichment analysis, DEGs can be classified according to their biological process (BP), molecular function (MF), or cellular component (CC) status. Thus, enrichment analysis of DEGs was performed based on hypergeometric tests, and KEGG enrichment analysis was performed on a pathway basis.

2.7. Screening and qRT-PCR Validation of Metabolic Resistance Genes in E. villosa

Using the HiScript® III 1st Strand cDNA Synthesis Kit, we performed reverse transcription on RNA extracted from E. villosa (Sophora miltiorrhiza), which was not used for transcriptome sequencing. Eleven highly expressed transcripts (P450s, GSTs, ABC transporters, and UDP glycyltransferases) were selected from among the DEGs, and the E. villosa Actin gene was selected as the reference gene to verify the reliability of the transcriptome sequencing data. The 2−ΔΔCT method [33] was used to calculate the relative expression levels of the genes. Microsoft Excel 2019 software was used to organize the data, and graphs were drawn to compare the RT-qPCR and transcriptome data. Each treatment performed at least three independent biological replicates, and each replicate consisted of three technical replicates. The primer sequences used are detailed in Supplementary Table S1.
In this study, the Anosim analysis method and Tukey’s Honest Significant Difference (HSD) test in DPS 7.05 software were applied for data processing. In this study, each treatment included 3 biological replicates.

3. Results

3.1. Analysis of the Resistance of Different E. villosa Populations to Nicosulfuron

Our research group continuously collected E. villosa seeds from various places between 2010 and 2017 and conducted resistance testing. The resistance index of HEK-40 E. villosa seeds collected in Harbin, Heilongjiang Province to nicosulfuron was 13.83 (Table 1). E. villosa in the HEK-15 population showed sensitivity to 3.75 g a.i./hm2 nicosulfuron. At a concentration of 54 g a.i./hm2, the E. villosa in the HEK-15 population withered and died, while the E. villosa in the resistant population HEK-40 survived at 13.5–162 g a.i./hm2 (Figure 1). Therefore, subsequent sequencing analysis was conducted on the resistant population HEK-40 (tolerant plants, R) and the sensitive population HEK-40 (sustainable plants, S). The results showed that the ALS gene was not mutated (Supplementary Table S2), indicating that it was not caused by target resistance.

3.2. Determining the Quality of E. villosa Seedlings with Different Resistance Levels

The transcriptomes of tolerant and sensitive E. villosa seedlings were sequenced before and after treatment with nicosulfuron to detect differences in gene expression. As shown in Table 2, the Q20 values of each sample were greater than 96%, and the Q30 values of each sample were greater than 91%. The proportion of bases with a sequencing error rate was less than 0.1%. These results indicate that the genetic sequences of each sample had high accuracy and quality, meeting the requirements for follow-up tests.

3.3. Principal Component and Dissimilarity Analysis

Individual samples from each of the four groups (R, R_CK, S, and S_CK) were collected together, indicating that the individual differences between the groups were very small. In addition, the main components of R and R_CK were similar and achieved good aggregation effects. However, the main components of S and S_CK were different, confirming that the mRNA expression of sensitive varieties was affected by treatment with nicosulfuron (Figure 2A). Figure 2B shows that there was a significant difference in the diversity between R_CK and S_CK without nicosulfuron and between S and S_CK. However, the difference in diversity between the resistant varieties R and R_CK was relatively small, and there was a significant difference between the four groups, with an R value of 0.463 and a p value of 0.013.

3.4. Differential Expression Analysis of mRNA

This experiment used standard squares to screen DEGs with p values < 0.05 and a |log2 (fold change)| > 1 for DEGs, in which genes with a log2 (fold change) > 1 were considered up-regulated genes (Up) and genes with a log2 (fold change) < −1 were considered down-regulated genes (Down). In the sensitive variety (S vs. S_CK), there were more DEGs before and after treatment; in total, 26,557 DEGs were detected, including 13,396 up-regulated genes and 12,561 down-regulated genes. Before and after treatment, a total of 20,958 DEGs were detected among the resistant varieties (R vs. R_CK), of which 12,319 were up-regulated and 8639 were down-regulated. A total of 9677 DEGs were detected in the sensitive and resistant varieties (R_CK vs. S_CK) before comparison, of which 2751 were up-regulated and 6926 were down-regulated. However, when the two varieties were compared, the difference between the two varieties (R vs. S) was more significant; in total, 9931 DEGs were detected, including 5426 up-regulated and 4505 down-regulated DEGs (Figure 3, Table 3). In summary, the greatest changes in DEGs were detected between the sensitive varieties before and after treatment with nicosulfuron, and there were more DEGs between the resistant and sensitive varieties after treatment than before treatment. There were 1302 DEGs in the S vs. S_CK, R vs. R_CK, and R vs. S comparisons (Figure 4). These findings indicate that sensitive varieties exhibit significant changes in DEGs after exposure to nicosulfuron, suggesting that sensitive varieties need to activate more defense genes to combat nicosulfuron.

3.5. Homologous Species Comparison of E. villosa Unigenes in the NR Database

A total of 187,611 unigenes were identified as homologous species through comparison with the NR database (Figure 5). The seven species with the greatest number of comparisons were Setaria italica, Panicum hallii, Dichantelium oligosanthes, Zea mays, Sorghum bicolor, Oryza sativa Japonica Group, and Brachypodium distachyon. Among them, 102,557 (54.66%) were homologous to Setaria italica, 36,405 (19.4%) were homologous to Panicum hallii, 14,259 (7.6%) were homologous to Dichantelium oligosanthes, 14,165 (7.55%) were homologous to Zea mays, 6993 (3.73%) were homologous to Sorghum bicolor, 3851 (2.05%) were homologous to Oryza sativa Japonica Group, and 849 (0.45%) were homologous to Brachypodium distachyon.

3.6. Functional Annotation and KEGG Enrichment Analysis of E. villosa GO

In this study, the DEGs of nicosulfuron-resistant and nicosulfuron-susceptible E. villosa were annotated before and after treatment with GO. The results revealed the functional distribution characteristics of the genes related to resistance to nicosulfuron in E. villosa. Based on the above analysis, DEGs were screened and annotated using GO for the DEGs of S vs. S_CK, R vs. R_CK, R_CK vs. S_CK, and R vs. S, and a GO annotation bar chart was drawn. The DEGs mainly participated in biological processes, such as cellular processes (GO: 0009987), metabolic processes (GO: 0008152), and response to stimuli (GO: 0050896). Moreover, most of the DEGs were annotated as biological regulation (GO: 0065007) or regulation of biological processes (GO: 0050789). Regarding cellular components, most of the DEGs were annotated as cell structure (GO: 0110165), while the remaining DEGs were annotated as protein complexes (GO: 0032991). Regarding molecular functions, binding (GO: 0005488) and catalytic activity (GO: 0003824) accounted for 80% of the DEGs (Figure 6).
To further investigate the functions of the DEGs, we conducted KEGG pathway analysis on the DEGs between S and S_CK, R and R_CK, R_CK and S_CK, R and S, and R and S. We selected the top 20 KEGG pathways enriched with DEGs for display (Figure 7). KEGG analysis revealed S vs. S_CK, and R vs. R_CK after treatment with nicosulfuron were significantly enriched in ribosome (ko03010), C5 branched dicarboxylic acid metabolism (ko00660), photosynthesis (ko00195), ribosome biogenesis in eukaryotes (ko03008), 2-oxocarboxylic acid metabolism (ko01210), valine, Leucine and isoleucine biosynthesis (ko00290), biosynthesis of amino acids (ko01230), alanine, aspartate, and glutamate metabolism (ko00250), cysteine and methionine metabolism (ko00270), arginine biosynthesis (ko00220), ABC transporters (ko02010), and glucosinolate biosynthesis (ko00966) (Figure 7A,B). R_CK vs. S_CK and R vs. S were significantly enriched in the biosynthesis of secondary metabolites (ko01110), flavonoid biosynthesis (ko00941), alanine, aspartate, and glutamate metabolism pathways (ko00250) (Figure 7C,D). The metabolism of alanine, aspartic acid, and glutamic acid (ko00250) was significantly enriched in the following groups: S vs. S_CK, R vs. R_CK, R_CK vs. S_CK, and R vs. S.

3.7. Identification of DEGs Involved in the Resistance of E. villosa to Nicotinic Sulfur Metal

The DEGs related to metabolic resistance selected from the DEGs are shown in Table 4. We identified seven DEGs related to CYP450s, four DEGs related to GST, 10 DEGs related to ABC transporters, and two DEGs related to GTs (Table 4).

3.8. RT-qPCR Validation of DEGs Involved in the Resistance of E. villosa to Nicosulfuron

Ten genes involved in the metabolic resistance of nicosulfuron were randomly selected from the DEGs between the different control groups of E. villosa and subjected to RT-qPCR validation. As shown in Figure 8, the RT-qPCR results were consistent with the transcriptome results, confirming the authenticity and reliability of the transcriptome results. These 10 genes were up-regulated in R vs. S. Except for the cyp450 72A15 (Cluster-73381.1) and cyp450 72A15 (Cluster-73381.5) genes, the relative gene expression levels of the other eight genes in R vs. R_CK were all up-regulated. The relative expression changes in genes cyp94B3 (Cluster-30135.4), cyp450 71A1 (Cluster-86594.1), GST U6 (Cluster-1471.7), GST U6 (Cluster-8157.4), GST U6 (Cluster-73413.4), ABC transporter G (Cluster-24334.4), and UDP 74F2 (Cluster-58399.985) in resistant varieties were significantly different from those in sensitive varieties after the application of nicosulfuron (p < 0.05).

4. Discussion

Eriochloa villosa (Thunb.) Kunth is a common annual weed among the gramineous weeds found in spring corn fields in northern China; Kunth seriously impacts corn yield and urgently needs to be controlled in corn fields [29]. Nicosulfuron has gradually become one of the most important varieties of herbicides used in maize fields due to its high herbicidal activity, strong selectivity, and minimal impact on subsequent crops. However, due to the continuous application of nicosulfuron over the years, some weed populations in maize fields have gained resistance though evolution. In this study, the GR50 value of HEK-40, a resistant population of maize field millet, was 87.69 g a.i./hm2, with a resistance index of 13.83; therefore, applying the recommended dosage in the field is no longer sufficient to prevent the spread of resistant E. villosa.
Based on sequencing analysis of the ALS sequences for the resistant R and sensitive S populations, no site mutations were found, indicating that the resistance of the E. villosa HEK-40 population to drugs was not caused by targeted mutations. In this study, transcriptome sequencing technology was used to sequence and analyze the nonmutated ALS gene of the nicosulfuron-resistant E. villosa HEK-40 strain and the sensitive E. villosa HEK-15 strain. No DEGs related to ALS were found through functional annotation, and previous studies revealed that spraying the P450 inhibitor malathion can reduce the resistance index of the resistant population. It is speculated that the presence of HEK-40 in the E. villosa population is related to both nicosulfuron resistance and metabolic resistance. Previous studies revealed that P450 mediates the enhanced metabolic resistance of Setaria viridis to nicosulfuron [34], while Cao et al. [17] reported that P450 monooxygenase also mediates the metabolic resistance of Setaria viridis R376 populations to nicosulfuron.
Cytochrome P450 monooxygenase, the largest protease superfamily widely present in plants and animals, is involved in the first stage of herbicide metabolism and detoxification as well as various biosynthetic pathways in weeds [35]. Zhao et al. [36] reported that CYP94A1 and CYP71A4 were overexpressed in mesosulfuron-resistant Alopecurus aequalis. Then, Iwakami et al. [37] conducted transcriptome sequencing on Echinochloa phyllopogon (Stapf) Koss., which is resistant to bispyribac-sodium, and found that the overexpression of CYP71AK2 and CYP72A254 caused metabolic resistance; the results also indicated that Lolium rigidum Gaudich became resistant to diclofop-methyl after CYP72A and CYP81B1 were overexpressed [38]. In addition, cytochrome P450 participates in multiple metabolic pathways to resist stress in plants in different environments [39]. In this study, transcriptome sequencing revealed that the DEGs involved in the drug metabolism–cytochrome P450 pathway in resistant E. villosa HEK-40 varieties were significantly enriched after nicosulfuron treatment, indicating that cytochrome P450 oxidase plays an important role in the metabolic resistance of E. villosa to nicosulfuron. This study revealed that the expression of the CYP450 genes CYP71A1, CYP94B3, etc., which may play important roles in nicosulfuron metabolism and resistance, significantly increased. Compared with those in the HEK-40 population, the expression levels of the CYP94B3 and CYP71A1 genes were lower in the S population before and after nicosulfuron treatment.
Glutathione-S-transferase (GST), which is involved in the second stage of herbicide detoxification, can catalyze glutathione to convert various herbicides into nontoxic and water-soluble forms, increasing plant resistance and reducing the damage caused by herbicides to crops [40]. Wang et al. [33] reported that GST T3, GST U6, and GST U14 are involved in the nontargeting resistance of Beckmannia syzigachne (Steud.) Fern. to mesosulfuron; similarly, Vennapusa et al. [41] confirmed that AtuGSTF is related to atrazine resistance in Amaranthus tuberculatus. In this study, four genes related to glutathione transferase were detected in resistant E. villosa; these genes were annotated with GST U6, which is highly consistent with the findings of Wang et al. [33]. Therefore, the glutathione pathway may play an important role in the metabolic resistance of E. villosa to nicosulfuron. Like GSTs, glycosyltransferases (GTs) participate in the second stage of herbicide detoxification. GTs can directly bind to herbicides and are another important enzyme for herbicide detoxification. Glycosyltransferases have been shown to detoxify various toxic chemicals, including herbicides [42]. Gaines et al. [38] conducted RNA-seq analysis on hard ryegrass resistant to glyphosate and reported that two CYP450 genes, one GT gene and one nitrogenase monooxygenase (NMO) gene, play a role in glyphosate metabolism resistance. Pan et al. [43] reported that 15 genes, including CYP450s, GST, UDP, and esterase, were up-regulated or mutated in the nontargeting resistance of Beckmannia syzigachne (Steud.) Fern. to Fenoxaprop-P, confirming that multiple enzymes play a regulatory role in the nontargeted resistance of weeds to ACCase inhibitors. Huang et al. [44] overexpressed UGT91C1 in Arabidopsis, which increased the resistance of Arabidopsis to mesotrione. Transcriptome sequencing revealed that two GT-related genes were significantly up-regulated in resistant E. villosa; these genes were annotated as UDP glycosyltransfer 73C1 and UDP glycosyltransfer 74F2. This up-regulation may be related to the nontargeted resistance of Eriochloa villosa (Thunb.) Kunth to nicosulfuron.
ABC transporters play an important role in cellular detoxification, clearing herbicides and other toxic substances from the cytoplasm. These transporters have been confirmed to participate in different biological processes in plants and are mainly localized on the outer membrane, mitochondria, and peroxisomes of the plasma membrane, vacuoplasts, or chloroplasts [45]. The activity of ABC transporter proteins on herbicide metabolites has been fully demonstrated in model species and resistant weeds, such as barnyard grass, which participate in the third stage of herbicide detoxification [46]. With the development of molecular biology and bioinformatics, an increasing number of studies have confirmed that ABC transporters are associated with nontargeted herbicide resistance in weeds. Through GS-FLX 454 pyrophosphate sequencing, Peng et al. [47] reported that the expression level of the ABC transporter gene in resistant Erigeron canadensis L. increased after glyphosate application. Pan et al. [48] reported that EcABCC8, which is located on the plasma membrane, can excrete glyphosate from varieties in glyphosate-resistant Echinochloa crus-galli (L.) P. Beauv. When the EcABCC8 gene sequence is consistent, the expression level of EcABCC8 is greater in resistant plants, and the glyphosate content in varieties is lower. These studies indicate that the active transport of glyphosate by transporter proteins to achieve vacuolar sealing or eliminate varieties can increase the resistance of weeds to glyphosate. Zhao et al. [49] reported high expression levels of ABCC8 and ABCB11 in a study of Alopecurus aequalis Sobol. resistance to mesosulfuron. Several genes were screened in this study, such as ABCC3, ABCC4, ABCB4, and ABCG25, and it was found that the expression levels of ABCC3 and ABCG25 varied significantly in the R population. These genes may be related to the metabolic resistance of nicosulfuron and should be further examined in future research.

5. Conclusions

Transcriptome sequencing was performed to identify many key candidate genes involved in the response of E. villosa to nicosulfuron stress. E. villosa may detoxify nicosulfuron by up-regulating detoxifying enzymes such as CYP450s, GST, GTs, and ABC transporters. Plants may develop metabolic resistance to nicosulfuron by regulating amino acids and their derivatives, secondary metabolites, and other substances to cope with nicosulfuron-induced stress. This process involves a single gene or metabolite and a complex regulatory and signaling mechanism.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/agronomy14102210/s1, Table S1, Primer sequences; Table S2, ALS genes sequence comparison of HEK-15 and HEK-40.

Author Contributions

Methodology, Y.H.; software, J.G.; validation, T.J., J.G., Z.X. and H.G.; formal analysis, Y.W.; investigation, Y.H. and C.Y.; resources, L.Z. and X.L.; data curation, J.G. and M.L.; writing—original draft preparation, J.G.; writing—review and editing, M.L. and Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the National Natural Science Foundation of China (32072434); the Natural Science Foundation of Heilongjiang Province of China (LH2023C009); the School Backbone Project of Northeast Agricultural University (19XG02).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding authors. Additionally, the RNA-seq raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors wish to thank all the colleagues who assisted in this research and provided technical advice and Xiaomin Liu for technical assistance.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. Growth of HEK-40 and HEK-15 varieties treated with different doses of nicosulfuron. HEK-40 varieties constitute a resistant population, and HEK-15 varieties constitute a sensitive population.
Figure 1. Growth of HEK-40 and HEK-15 varieties treated with different doses of nicosulfuron. HEK-40 varieties constitute a resistant population, and HEK-15 varieties constitute a sensitive population.
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Figure 2. (A) Contains principal component analysis plots for the experimental and control group samples, and (B) shows the dissimilarity values between the 4 groups. S, treated susceptible plants; S_CK, nontreated susceptible plants; R, treated tolerant plants; R_CK, nontreated tolerant plants. Anosim analysis method was applied for data processing.
Figure 2. (A) Contains principal component analysis plots for the experimental and control group samples, and (B) shows the dissimilarity values between the 4 groups. S, treated susceptible plants; S_CK, nontreated susceptible plants; R, treated tolerant plants; R_CK, nontreated tolerant plants. Anosim analysis method was applied for data processing.
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Figure 3. Differential gene expression volcano maps. The horizontal axis represents the fold change (log (B/A)) in gene expression differences between different groups of samples, while the vertical axis represents the p value, indicating a statistically significant difference in gene expression. The smaller the p value is, the larger the -log (p value) is, and the more significant the difference. Each point in the graph represents a gene, where red represents up-regulated genes, green represents down-regulated genes, and black represents non DEGs. S, treated susceptible plants; S_CK, nontreated susceptible plants; R, treated tolerant plants; R_CK, nontreated tolerant plants. (A) shows S vs. S_CK; (B) shows R vs. R_CK; (C) shows R_CK vs. S_CK; and (D) shows R vs. S.
Figure 3. Differential gene expression volcano maps. The horizontal axis represents the fold change (log (B/A)) in gene expression differences between different groups of samples, while the vertical axis represents the p value, indicating a statistically significant difference in gene expression. The smaller the p value is, the larger the -log (p value) is, and the more significant the difference. Each point in the graph represents a gene, where red represents up-regulated genes, green represents down-regulated genes, and black represents non DEGs. S, treated susceptible plants; S_CK, nontreated susceptible plants; R, treated tolerant plants; R_CK, nontreated tolerant plants. (A) shows S vs. S_CK; (B) shows R vs. R_CK; (C) shows R_CK vs. S_CK; and (D) shows R vs. S.
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Figure 4. Venn diagram showing the number of DEGs common or specific to treated and nontreated tolerant and susceptible plants. S, treated susceptible plants; S_CK, nontreated susceptible plants; R, treated tolerant plants; R_CK, nontreated tolerant plants.
Figure 4. Venn diagram showing the number of DEGs common or specific to treated and nontreated tolerant and susceptible plants. S, treated susceptible plants; S_CK, nontreated susceptible plants; R, treated tolerant plants; R_CK, nontreated tolerant plants.
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Figure 5. Alignment of the unigenes of Eriochloa villosa with those of homologous species in the NR database.
Figure 5. Alignment of the unigenes of Eriochloa villosa with those of homologous species in the NR database.
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Figure 6. GO functional annotation and classification of DEGs. (A) S vs. S_CK; (B) R vs. R_CK; (C) R_CK vs. S_CK; and (D) R vs. S. The blue area represents the Biological Process, orange represents the Cellular Component, and green represents the Molecular Function.
Figure 6. GO functional annotation and classification of DEGs. (A) S vs. S_CK; (B) R vs. R_CK; (C) R_CK vs. S_CK; and (D) R vs. S. The blue area represents the Biological Process, orange represents the Cellular Component, and green represents the Molecular Function.
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Figure 7. KEGG enrichment analysis of DEGs. (A) S vs. S_CK; (B) R vs. R_CK; (C) R_CK vs. S_CK; and (D) R vs. S.
Figure 7. KEGG enrichment analysis of DEGs. (A) S vs. S_CK; (B) R vs. R_CK; (C) R_CK vs. S_CK; and (D) R vs. S.
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Figure 8. RT-qPCR validation of 10 DEGs associated with metabolic resistance to nicosulfuron in Eriochloa villosa. Error bars represent the mean ± SE of three biological replicates. Significant difference at each treatment between R and S by Tukey’s test (p < 0.05). Lowercase letters indicates the significance between different treatments by Tukey’s test (p < 0.05).
Figure 8. RT-qPCR validation of 10 DEGs associated with metabolic resistance to nicosulfuron in Eriochloa villosa. Error bars represent the mean ± SE of three biological replicates. Significant difference at each treatment between R and S by Tukey’s test (p < 0.05). Lowercase letters indicates the significance between different treatments by Tukey’s test (p < 0.05).
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Table 1. Information about Eriochloa villosa for experience.
Table 1. Information about Eriochloa villosa for experience.
PopulationCollection TimeCollection SiteLongitude and LatitudeGR50
g a.i./hm2		Relational Coefficient R2Resistance Index
HEK-402017.9Yilan County, Harbin City46°6′50″ N, 130°6′8″ E87.690.994313.83
HEK-152017.9Beixing Village, Tangyuan County, Jiamusi City46°57′46″ N, 130°1′54″ E6.340.99571.00
Table 2. Summary of the sequencing data obtained after quality control.
Table 2. Summary of the sequencing data obtained after quality control.
SampleRaw ReadsClean ReadsClean Base (G)Error Rate (%)Q20 (%)Q30 (%)GC Content
(%)
S_CK163274590582742248.740.0396.5491.3151.68
S_CK271658798658270169.870.0396.7591.8752.81
S_CK361974484578206688.670.0396.7791.8852.35
S_112977222012036123818.050.0396.6491.5652.27
S_2885318107966322011.950.0396.6291.5051.36
S_3836413227676178211.510.0396.6191.4852.25
R_CK1722350826778504410.170.0396.5591.3352.13
R_CK265608280602188489.030.0396.6391.5651.57
R_CK3757660507023134810.530.0396.591.2552.1
R_1928958948516182012.770.0396.4391.0751.86
R_2746936426807301610.210.0396.6291.5351.93
R_3731113806786226610.180.0396.7291.8052.03
Note: S_CK, nontreated susceptible plants; S, treated susceptible plants; R_CK, nontreated tolerant plants; R, treated tolerant plants.
Table 3. DEGs putatively involved in differential tolerance to nicosulfuron in E. villosa.
Table 3. DEGs putatively involved in differential tolerance to nicosulfuron in E. villosa.
Level of Gene ExpressionNumber of DEGs
R vs. R_CKS vs. S_CKR_CK vs. S_CKR vs. S
UpDownUpDownUpDownUpDown
>1–511,169838012,61512,1412428334350993219
>5–101011240124138629029993001124
>1013919140343358427162
Total12,319863913,99612,5612751692654264505
20,95826,55796779931
Note: S_CK, nontreated susceptible plants; S, treated susceptible plants; R_CK, nontreated tolerant plants; R, treated tolerant plants.
Table 4. Identification of genes related to metabolic resistance in E. villosa.
Table 4. Identification of genes related to metabolic resistance in E. villosa.
GeneAnnotationDifference Multiple Log2FC
S vs. S_CK_R vs. R_CK_R_CK vs. S_CKR vs. S
Cluster-73381.1CYP 72A15−0.85−0.311.041.54
Cluster-73381.5CYP 72A15−0.84−0.571.451.68
Cluster-30135.10CYP 94B3−1.281.62−0.612.27
Cluster-30135.3CYP 94B3−0.551.70−0.301.94
Cluster-30135.4CYP 94B3−1.761.28−0.552.48
Cluster-30135.9CYP 94B3−1.071.58−0.492.14
Cluster-86594.1CYP 71A10.221.760.211.73
Cluster-1471.7GST U63.052.791.641.34
Cluster-1471.8GST U62.793.051.011.24
Cluster-73413.4GST U60.891.630.381.09
Cluster-8157.4GST U60.401.030.611.20
Cluster-14424.28ABC B47.006.042.651.68
Cluster-15344.4ABC C42.732.781.011.02
Cluster-14424.33ABC B45.737.29−0.281.25
Cluster-24334.4ABC G25−0.981.65−0.462.15
Cluster-43295.2ABC G370.221.80−0.101.46
Cluster-74965.22ABC G361.773.140.191.53
Cluster-74965.32ABC G401.653.180.291.78
Cluster-74965.6ABC G401.192.870.321.96
Cluster-9837.12ABC C30.751.710.291.21
Cluster-9837.19ABC C32.522.990.851.29
Cluster-14780.5UGT73C1−1.681.52−0.282.90
Cluster-58399.985UGT74F23.534.940.181.57
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Guo, J.; Xu, Z.; Jiao, T.; Gao, H.; Wang, Y.; Zhang, L.; Li, M.; Liu, X.; Yan, C.; Han, Y. Mechanism of Eriochloa villosa (Thunb.) Kunth Resistance to Nicosulfuron. Agronomy 2024, 14, 2210. https://doi.org/10.3390/agronomy14102210

AMA Style

Guo J, Xu Z, Jiao T, Gao H, Wang Y, Zhang L, Li M, Liu X, Yan C, Han Y. Mechanism of Eriochloa villosa (Thunb.) Kunth Resistance to Nicosulfuron. Agronomy. 2024; 14(10):2210. https://doi.org/10.3390/agronomy14102210

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Guo, Jing, Zeqian Xu, Ting Jiao, Hong Gao, Yuechao Wang, Liguo Zhang, Mukai Li, Xiaomin Liu, Chunxiu Yan, and Yujun Han. 2024. "Mechanism of Eriochloa villosa (Thunb.) Kunth Resistance to Nicosulfuron" Agronomy 14, no. 10: 2210. https://doi.org/10.3390/agronomy14102210

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