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Article

Comparative Gene Expression following 2,4-D Treatment in Two Red Clover (Trifolium pratense L.) Populations with Differential Tolerance to the Herbicide

by
Lucas Pinheiro de Araujo
1,†,
Michael Barrett
1,* and
Randy D. Dinkins
2,*
1
Department of Plant and Soil Sciences, University of Kentucky, Lexington, KY 40546-0312, USA
2
Forage-Animal Production Research Unit, United States Department of Agriculture Agricultural Research Service, Lexington, KY 40546-0091, USA
*
Authors to whom correspondence should be addressed.
†
Current address: Unicampo, Av. Carneiro Leão, 65-1409-1412-Zona 01, Maringá 87014-010, PR, Brazil.
Agronomy 2024, 14(6), 1198; https://doi.org/10.3390/agronomy14061198
Submission received: 30 April 2024 / Revised: 26 May 2024 / Accepted: 28 May 2024 / Published: 1 June 2024
(This article belongs to the Special Issue Integrated Ways to Improve Forage Production and Nutritional Value)
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Versions Notes

Abstract

:
Incorporation of red clover (Trifolium pratense L.) into grass pastures can reduce the need for nitrogen fertilizer applications and increase the nutritional value of the forage. However, red clover cultivars available for Kentucky producers are highly susceptible to herbicides, such as 2,4-D (2,4-dichlorophenoxy acetic acid), used for pasture broadleaf weed control. To overcome this problem, ‘UK2014’ red clover was selected for increased tolerance to 2,4-D. We employed a transcriptome analysis approach to compare the gene expression response following 2,4-D treatment of ‘UK2014’ to that of ‘Kenland’, a 2,4-D sensitive red clover and one of the parents of ‘UK2014’. The objectives were to first determine if the increased 2,4-D tolerance in ‘UK2014’ is reflected in a change of transcription response and/or a quicker recovery of a transcriptional response following 2,4-D treatment, and second, to identify genes, whether constitutively expressed or induced by 2,4-D, which could be the basis for the increased 2,4-D tolerance. Leaf tissue from the two red clovers grown in the field was collected at 4, 24, and 72 h after 2,4-D (1.12 kg 2,4-amine a.e. ha−1) treatment from both untreated and treated plants. Global gene expression was determined with reads from Illumina Hiseq 2500 mapped against the red clover draft genome, Tpv2.1 (GenBank Accession GCA_900079335.1). Genes that displayed differential expression (DEGs) following 2,4-D treatment were selected for further analysis. The number of DEGs was higher for ‘Kenland’ than for ‘UK2014’, suggesting that a lower transcriptional response corresponds with the higher 2,4-D tolerance in the ‘UK2014’ line. Similarly, gene ontology enrichment analysis revealed that expression of photosynthesis-related genes was less affected by 2,4-D in the ‘UK2014’ line than ‘Kenland’. Although we were not able to identify any specific genes that are the basis for the increased 2,4-D tolerance of ‘UK2014’, we concluded that the increased 2,4-D tolerance of ‘UK2014’ correlates with a decreased transcription response to 2,4-D. Additionally, expression of several cytochrome P450 genes that had different isoforms between ‘UK2014’ and ‘Kenland’ increased significantly in both following 2,4-D treatment, one or more of these P450s could be mediators of 2,4-D metabolism and tolerance in red clover.

1. Introduction

Red clover (Trifolium pratense L.) is a legume crop predominantly utilized in forage systems for grazing or hay production [1]. Rhizobia (Rhizobium leguminosarum) associated with red clover can fix atmospheric nitrogen, which can reduce the need for nitrogen fertilizer applications to pastures and increase the nutritional value of the forage, ultimately creating a more sustainable pasture system [2,3]. However, introduction of red clover into a grass-based pasture system significantly complicates weed management. Weed management in pastures relies heavily on products containing synthetic auxin herbicides (WSSA group 4) [4] as active ingredients. The synthetic auxin 2,4-D (2,4-dichlorophenoxy acid) is often the herbicide of choice for pastures, likely due to its relatively lower cost compared to alternative herbicides. Red clover is sensitive to most herbicidal options for broadleaf weed control in pastures [5], including 2,4-D.
Auxin herbicides, such as 2,4-D, are structurally similar to natural plant auxins. At low concentrations, synthetic auxins mimic the physiological effects of these plant hormones [6,7]. At high doses, synthetic auxins exhibit herbicidal activity in sensitive plants by causing abnormal growth, epinasty, and plant death [8]. A three-phase cascade of plant responses to synthetic auxins, starting from herbicide entry into the plant tissue and ending with plant death, was proposed by Grossmann [9]. The first response is a stimulation phase, occurring within 1–8 h following synthetic auxin application. This response is characterized by increased gene expression accompanied by increased ethylene biosynthesis and an accumulation of abscisic acid (ABA) [9,10]. The second response to synthetic auxins occurs within 24 h following application and includes general growth inhibition and reduced biosynthesis. Accumulation of ABA has been shown to drive stomatal aperture closure and this, in turn, causes aperture closure, which in turn inhibits photosynthesis [11]. In a third response, senescence is driven by the overproduction of reactive oxygen species (ROS), leading to chlorosis, localized necrosis, and plant death [9,12].
Recent breakthroughs have elucidated how auxin perception and signaling take place in higher plants. In the absence of auxins, transcriptional repressors (Aux/IAA) bind to auxin transcriptional factors (ARFs), suppressing the transcription of auxin-responsive genes [13]. Both 2,4-D and the natural auxin indole-3-acetic acid (IAA) are recognized by TIR1, a promiscuous receptor site in a Skp-Culling-F-box protein (SCFTIR1) E3 Ubiquitin ligase [14]. Auxins, either natural or synthetic, stabilize the complex formed by the F-box protein and Aux/IAAs (SCFTIR1/AFB). ARF repressors are tagged by poly-ubiquitination and subsequently degraded in proteosomes [15,16]. With the degradation of Aux/IAAs, expression of auxin-responsive genes ensues. Aux/IAAs are likely present in this suite of early activated genes, aiding in reestablishing homeostasis after a rapid increase in auxin-responsive gene expression, possibly acting as a negative regulation mechanism [6]. A relevant factor is the tight regulation of IAA homeostasis. IAA is a substrate for Gretchen Hagen 3 (GH3), which targets IAA for amino acid conjugation and storage [17,18]. 2,4-D is not a substrate for GH3s, thus resulting in a persistent auxin response likely responsible for its herbicidal effect and lethality [6,18].
At the molecular level, the downstream consequence of the rapid gene activation by synthetic auxins is the overexpression of 1-aminocyclopropane-1-carboxylic acid (ACC) synthase and 9-cis-epoxycarotenoid dioxygenase (NCED) [9]. ACC synthases have been long described as mediators of the first step in the ethylene biosynthesis pathway [19]. ACC synthases are known to be rapidly upregulated and overproduced within the first hours following auxin treatment in sensitive plants [9,20]. NCEDs catalyze the transformation of xanthophylls to xanthoxins, leading to the biosynthesis of ABA [21]. A recent study on the transcriptomics of horseweed (Erigeron canadensis L.) following synthetic auxin exposure challenges the traditional view of the crosstalk between ABA and ethylene [22,23]. In the alternative view, NCED overexpression, and consequent ABA accumulation, occur independently of the ethylene overproduction and are not triggered by it. However, the consensus remains that the general phytotoxic effects following synthetic auxin treatment are critically related to ABA accumulation in plant tissues [10,22].
Knowledge of the plant responses to synthetic auxins and how they cause plant death provides a platform for understanding the basis for enhanced 2,4-D tolerance in red clover. Many of the plant 2,4-D resistance mechanisms described in the literature are non-target sites (NTSR). These include rapid necrosis [24], reduced 2,4-D uptake [25], reduced 2,4-D translocation [26,27], and increased 2,4-D metabolism [28,29,30,31,32,33,34]. A review of herbicide resistance in Lolium spp. by Suzukawa et al. [35] discussed the three phases of herbicide metabolism involved in herbicide resistance. These phases typically occur in the order: metabolism (Phase I), processing/conjugation (Phase II), and storage (Phase III). Enzymes such as cytochrome P450s (CYP450s) and glutathione S-transferases (GSTs) (Phase I enzymes), glucosyltransferases (GTs) (Phase II enzymes), and ABC transporters (Phase III) are suggested as some of the mediators of these processes.
Three red clover breeding initiatives have focused on increasing 2,4-D tolerance in red clover in North America [36]. A 2,4-D tolerant red clover was identified following in vitro techniques [37] and has been used as parental material in the development of 2,4-D lines adapted to the southern [38], the transition [39] and northern zones [40] in the United States. In Kentucky, a red clover population was selected from open-pollinated plots containing mixtures of the Florida 2,4-D tolerant population and the 2,4-D sensitive cultivar ‘Kenland’, a traditional cultivar adapted to the transition zone. Following the initial selection, eight additional cycles of selection were performed using selective pressure as high as 2.24 kg a.e. ha−1 2,4-D to identify tolerant red clover plants resulting from crosses between the two populations [39]. The population resulting after the selection cycles was designated as ‘UK2014’, and it is the population utilized in this study. In addition, previous work with the red clover ‘UK2014’ line suggested that increased 2,4-D metabolism may be responsible for the increased 2,4-D tolerance [39]. In this study, we utilize RNA sequencing to compare the transcriptomic profiles of ‘UK2014’ and the red clover cultivar ‘Kenland’ (2,4-D sensitive) following applications of 2,4-D in a field setting. We hypothesize that, given ‘UK2014’s increased tolerance, the gene expression response of ‘UK2014’ following 2,4-D application would be less or would recover more quickly, or both, than ‘Kenland’. A second hypothesis is that a constitutive or 2,4-D induced, or both, increase in enzyme classes implicated in 2,4-D metabolism, particularly CYP450s, is expected in ‘UK2014’ compared to ‘Kenland’.

2. Materials and Methods

2.1. Field Growth Conditions

Two red clover populations were used in this study: ‘Kenland’, 2,4-D sensitive, (Victory Seed Company, PO Box 192, Molalla, OR, USA) and ‘UK2014’, a 2,4-D tolerant selection (University of Kentucky, Lexington, KY, USA) [41]. Plants were grown in a field setting at the University of Kentucky’s Spindletop Research Farm in Lexington, Kentucky (38°07′44″ N, 84°29′46″ W). The soil at the study site is a Maury silt loam (fine, mixed, active, mesic Typic Paleudalfs) with 2.6% organic matter and pH 6.5–7. No supplemental fertilization was performed during the study. Weeds initially present in the area were controlled by two sequential applications of 1.12 kg ae ha−1 of glyphosate (Mad Dog® Plus, Loveland Products, Greeley, CO, USA) approximately four weeks prior to initiating each study. Weed control applications were made utilizing an all-terrain vehicle sprayer equipped with TeeJet® XR11004 flat fan nozzles (P.O. Box 7900 Wheaton, IL, USA) and applying 243 L of spray solution per hectare at 275 kPa pressure. Red clover seeds were drilled into prepared seedbeds at a rate of 13 kg ha−1 with a drill-disk plot planter. The 2015 study was seeded on 23 April and the 2018 study was seeded on April 3. Both studies were structured in a randomized complete block design (RCBD), with each experimental unit measuring 1.5 × 6.0 m with six red clover rows spaced 0.20 m apart.

2.2. Treatments and Tissue Sampling

2,4-D was applied at the bud to the early-flowering stage, corresponding to 25% of the plants flowering, on 10 August 2015, and 18 August 2018 [41]. Each field plot was sprayed once, either with a mock treatment (water only) or 2,4-D amine (Loveland Products Inc.® P.O. Box 1286 Greeley, CO, USA) at 1.12 ae kg ha−1. All 2,4-D treatments were applied using a CO2 pressurized sprayer equipped with a 1.8 m wide boom and TeeJet® 8002 flat fan tip nozzles (P.O. Box 7900 Wheaton, IL, USA) spaced 51 cm apart. The carrier volume was 243 L ha−1 at 207 kPa spray pressure.
Leaf samples were collected from three experimental blocks in both 2015 and 2018. Ten leaflets were randomly collected from red clover plants from individual experimental units within blocks. The functional sampling area was 1.0 × 4.0 m, which encompassed the four central clover rows and the middle four meters of each field plot. The ten red clover leaflets were bulked in one composite sample, representative of one biological replicate. The composite samples were immediately flash frozen in liquid nitrogen and stored at −80 °C until RNA extraction. Experimental units were recurrently sampled at 4, 24, and 72 h after 2,4-D treatment (HAT), in a repeated measure fashion. The 2,4-D treatment was applied at 11:00 EDT. Plots with 2,4-D treatment or treated with water only were sampled at each time point to allow comparison within time points. A total of 36 samples were collected from each of the 2015 and 2018 field studies, with a grand total of 72 samples for downstream work.

2.3. RNA Extraction and Library Preparation

Frozen tissue samples were transferred to 17 mL polycarbonate vials, partially filled with metal beads, both previously cooled in liquid nitrogen. Tissue was pulverized by running a Geno/Grinder 2000 (SPEX CertiPrep, Metuchen, NJ, USA) for 60s at 700 strokes/min. Total RNA was extracted by a hybrid method that included an organic phase separation in TRIzol™ reagent (Molecular Research Center, Inc. Cincinnati, OH, USA) and a spin column purification with Qiagen (Qiagen Sciences Inc., Germantown, MD, USA) reagents described in Dinkins et al. [42]. Briefly, approximately 1g of the leaf tissue powder was suspended in 2.0 mL centrifuge tubes containing 200 µL of chloroform and 1 mL of TRIzol™ reagent. The suspension was homogenized and centrifuged at 8C to separate its organic and aqueous phases. The aqueous phase, containing the RNA, was transferred to a 1.0 mL tube. An equal amount of 100% ethanol was added to the aqueous phase, and the resulting solution was homogenized and transferred to the spin column. The column was washed once with 700 µL Qiagen RW1 buffer and twice with 500 µL Qiagen RPE buffer. The resulting RNA extracts were resuspended with 20 µL of RNAase-free water. The resulting suspensions, containing total RNA extracts, were stored at -80C until library preparation.
The total RNA concentration in each sample was measured with a spectrophotometer (Model 1000TM Nanodrop, Thermo Fisher Scientific, Wilmington, DE, USA). A 260:280 nm ratio of approximately 2.0 was considered a threshold value for acceptable RNA purity. The RNA quality was also visually checked by chip capillarity electrophoresis in an Agilent 2100 Bioanalyzer (Agilent Technologies, Waldbroon, Germany). The RNA Integrity Number (RIN), quantified by the bioanalyzer, also served as a measure of the quality of the RNA in the extracts. We established a threshold of 9.8 to 10 for an acceptable RIN to move forward with library preparation. Total RNA samples that were stored for longer than a year were subject to purity, quality, and concentration reassessment immediately before proceeding to the synthesis of cDNA libraries.
For the 2015 samples, frozen RNA was shipped to Novogene (Novogene Corporation Inc., Sacramento, CA, USA) for library preparation and sequencing. These cDNA libraries were constructed from 150 base paired-end mRNAs and subsequently sequenced following Novogene’s protocols. Briefly, following the quality checks, ribosomal RNA was removed utilizing the Ribo-Zero kit and the resulting mRNA was enriched using oligo(DT) beads. Enriched mRNA was fragmented by adding a fragmentation buffer. Random hexamer primers were added to the fragmented mRNA for the first-strand synthesis of cDNAs, followed by second-strand synthesis with Illumina buffers, dNTPs, RNase H and DNA polymerase I. The following steps included terminal repairs, adapter ligation, PCR enrichment and several size selections. The resulting cDNA libraries were sequenced in Illumina Hi-Seq.
For the 2018 samples, cDNA library preparation was performed in the Forage and Animal Production Research Unit of the United Department of Agriculture Agricultural Research Service unit located at the University of Kentucky. Libraries of cDNA were constructed utilizing the Illumina TruSeq RNA Library Prep v2 (Illumina, San Diego, CA, USA). In the TruSeq v2 protocol, mRNA is purified by poly-A binding beads and fragmented by Illumina premade master mixes. The first cDNA strand was synthesized by Invitrogen’s SuperScrip™ IV (Thermo Fisher Scientific, 3411 Silverside Road, Wilmington, DE, USA) in Illumina buffers. The complementary double-strand cDNA was synthesized with the Illumina Second Strand Master Mix. Further steps in the protocol were taken to ligate Illumina adapters with different sequence tags (bar-codes) from the Illumina TruSeq RNA preparation kit, which allow demultiplexing during downstream analysis. The resulting double-stranded cDNA was submitted to PCR amplification and multiple size selection cycles with MAGBIO beads (MagBio Genomics Inc., Gaithersburg, MD, USA). Each cDNA library was validated in the Agilent 2100 Bioanalyzer. The concentration of cDNA was measured using Thermo Scientific’s Qubit dsDNA high-sensitivity assay (Thermo Fisher Scientific, 3411 Silverside Road, Wilmington, DE, USA). DNA concentration in each library was normalized to 50nM in DNase-free water and six samples were combined and sequenced per lane (single read, 100 bp) on an Illumina HiSeq 2500 at the University of Kentucky HealthCare Genomics Core Laboratory (UKHC Genomics Core, Lexington, KY, USA).

2.4. Analysis of RNA-seq Reads

Raw fastq reads returned from both Novogene and the UKHC Genomics Core were uploaded into the CLC Genomics Workbench (v12.0, CLC Genomics Workbench, Qiagen Aarhus A/S, Aarhus, Denmark). The sequence data are available at NCBI under BioProject PRJNA1090547. Reads were mapped against the reference red clover genome Tpv2.1 (GenBank Accession GCA_90079335.1) [43]. All gene annotations are based on the Tp_TGAC_v2_gene annotations and are abbreviated as Tp_gene_xxxx in the text. Mapping parameters were set to a minimum fragment similarity of 0.8 and a minimum fragment length of 0.5. Reads were transformed by the addition of one (1) to eliminate zeros from the data. Genes were prefiltered in CLC for a minimum of 10 reads (RPKM) across the three replications of each treatment combination (red clover, 2,4-D treatment, and time of collection after treatment). The average number of mapped reads for the 2015 sequencing was 21 M reads and 16 M reads for the 2018 samples (Table S1). Filtering out the low-expressing genes resulted in 27,388 gene models retained from the Tpv2.1 red clover annotation for analysis. Analysis of the RNA-seq reads was performed separately for each year, 2015 or 2018, due to differences in sequencing parameters at each of the facilities and concomitant differences in the output number of mapped reads.
The prefiltered gene list was loaded into JMP® Genomics 13.0 (SAS Institute Inc., Cary, NC, USA) for differential gene expression analysis. Reads were then normalized by the Trimmed Mean of M-values (TMM) algorithm in JMP. Normalized reads for each sample were log-transformed (log2) using the JMP RNA-seq Basic Workflow Analysis. Within the workflow, principal component variance analysis was performed for all individual factors and their interactions (red clover|2,4-D treatment|time), before and after TMM normalization. The normalized data were submitted to an Analysis of Variance (ANOVA) utilizing a Gaussian/normal distribution. The generalized linear model considered three fixed effects (red clover, 2,4-D treatment, and time) and their interaction: Y = red clover + 2,4-D treatment + time + red clover × 2,4-D treatment + red clover × time + 2,4-D treatment × red clover × time. The ANOVA model was adjusted by considering blocks as a random factor.
Comparisons were performed between the two red clover lines (‘UK2014’ and ‘Kenland’), treated either with 2,4-D or water only, within each time point of sampling. This was executed through orthogonal contrasts in the JMP Genomics option to compare simple LS-means. The criteria to identify Differentially Expressed Genes (DEGs) was a minimum 2-fold-change in expression between comparison groups (|log2(fold-change)| ≥ 1). A false discovery rate (FDR) at log10p-value of 0.5 was set to account for false positives.
Expression differences of interest included comparisons between 2,4-D treated ‘UK2014’ and its respective ‘UK2014’ control, and between 2,4-D treated ‘Kenland’ and its respective control, within each time point (4, 24, or 72 h after treatment either with 2,4-D or water only). These comparisons allowed the identification of specific genes differentially expressed due to 2,4-D treatment, particularly the commonalities and differences in each genotype response. In order to identify gene candidates possibly related to mediated 2,4-D metabolism, which could contribute to increased tolerance, we compared ‘UK2014’ and ‘Kenland’ under 2,4-D treatment, at each time of sampling. Annotated gene lists for graphical representation in heat maps were obtained from the DEG sets and mapped using JMP Genomics.
Gene Ontology (GO) enrichment analysis was used to identify biological processes affected by 2,4-D treatment. GO analysis compared only 2,4-D treated and Control conditions, for each red clover, separately by time point. Analysis was performed separately by over or underexpressed and overexpressed DEGs. The web base AgriGO v2.0 [44] was used with a stringent cutoff (Yekutieli, FDR under dependency, FDR < 0.0005). Additionally, GO terms containing a perfect overlap of the same set of genes were aggregated to representative broader terms. Results are displayed in bubble plots containing the number of genes and significance level.

3. Results and Discussion

3.1. Effect of 2,4-D on Gene Expression

‘Kenland’ had a higher number of 2,4-D overexpressed genes at all time points (Figure 1). In 2015, there were 1148, 496, and 490 unique overexpressed genes in ‘Kenland’ at 4, 24, and 72 HAT compared to 23, 311 and 115 unique overexpressed genes in ‘UK2014’ at the same time points, respectively (Table S2). In 2018, there were 310, 1696, and 516 overexpressed genes unique to ‘Kenland’ at 4, 24, and 72 HAT compared to 40, 55, and 272 in ‘UK2014’ at the same time points, respectively (Table S3). There were generally more underexpressed genes unique to ‘Kenland’ than those unique to ‘UK2014’. In 2015, 24 and 72 HAT, there were 1320 and 708 unique underexpressed genes in ‘Kenland’ compared to 290 and 45 in ‘UK2014’ at the same time points. In 2018, 4 and 72 HAT, there were 316 and 838 unique underexpressed genes in ‘Kenland’ compared to 87 and 198 unique underexpressed genes in ‘UK2014’, at the same time points, respectively. However, in two instances, 4 HAT in 2015 and, more significantly, 24 HAT in 2018 there were more unique underexpressed genes in ‘UK2014’ than ‘Kenland’. ‘UK2014’ had 100 unique underexpressed genes compared to 39 in ‘Kenland’ 4 HAT in 2015. In 2018 HAT, ‘UK2014’ had 1771 unique underexpressed genes compared to 184 in ‘Kenland’. Overall, measured by the number of DEGs at each time point in each year, the response of ‘UK2014’ to 2,4-D was less than that of ‘Kenland’. This result was similar to that observed for the phenotypic response between ‘Kenland and ‘UK2014’ by Araujo et al. [41] and between the more and less 2,4-D sensitive genotypes of Eurasian watermilfoil (Myriophyllum spicatum) [45].
Comparisons between the 2,4-D treated ‘Kenland’ and ‘UK2014’ plants revealed a similar expression profile (Figure 2). Most genes were below the FDR (0.05) cutoff line and were not differentially expressed following 2,4-D treatment, although large fold-changes were observed in some genes. Tp_gene_23536 and Tp_gene_8999, as examples, were expressed 17 and 14 times more in 2,4-D treated ‘UK2014’ than 2,4-D treated ‘Kenland’, respectively, in 2018 (Table S4). These would be interesting genes to explore because their expression levels were among the highest in the DEG lists comparing treated plants (Tables S4 and S5). However, this differential expression was not consistent in both years. In addition, the current annotations for Tp_gene_23536, Tp_gene_8999, Tp_gene_26375, and Tp_gene_8804 gene are: hypothetical protein, eukaryotic aspartyl protease, serine-type endopeptidase, and expansin A1, respectively; and we could not identify an obvious relationship between these genes and 2,4-D metabolism. These genes would be interesting to explore because their expression levels were among the highest in the DEG lists comparing treated red clovers (Tables S4 and S5) with a link between 2,4-D metabolism be identified.

3.2. DEG Ontology

Gene Ontology analysis on genes that were underexpressed following 2,4-D application did not reveal any significant associated biological processes at 4 HAT (Figure 2A; Table S6). Significant terms were only detected at 24 and 72 HAT. The largest effect of 2,4-D treatment was on genes related to photosynthesis and related subprocesses. These terms were significantly overrepresented in both ‘Kenland’ and ‘UK2014’, and both years at 24 HAT (Figure 3A). A total of 21 and 49 genes related to photosynthesis were common to both ‘UK2014’ and ‘Kenland’ (Table S7). Examples of genes within these terms were annotated as photosystem I (genes Tp_gene_26533, Tp_gene_16174), photosystem II (genes 10344, 11252), and light-harvesting chlorophyll (genes Tp_gene_1283, Tp_gene_13606). In addition to genes that were common to both red clovers, ‘Kenland’ had 32 (24 HAT, 2015), 30 (24 HAT, 2018), and 25 (72 HAT, 2018) unique genes in the photosynthesis process. That term was not significant for genes unique to ‘UK2014’ (Figure 3, Table S7). 2,4-D affected photosynthesis-related genes more in ‘Kenland’ than for ‘UK2014’. Additionally, the 2,4-D effect on photosynthesis genes was generalized since the annotations of these genes covered several steps of photosynthesis.
Other significant terms common to both ‘Kenland’ and ‘UK2014’ included processes related to biosynthesis and were concentrated at 24 HAT, but only in 2015. Most genes within these terms were reported as ribosomal proteins. Annotations of the ontology for these genes were unspecific, for example, organonitrogen compound biosynthetic process and translation. It was not possible to determine specific biosynthetic pathways affected by 2,4-D in the study.
Recent studies in horseweed (Erigeron canadensis L.) [22] and Gossypium sp. [46] reported a decreased abundance of transcripts related to photosynthesis following 2,4-D treatment. This effect was reported as early as 6 h after synthetic auxin treatment in horseweed [22]. Our gene ontology analysis agrees with these reports, but a decrease in photosynthesis transcripts occurred later than 6 HAT. Further, in the more 2,4-D sensitive Eurasian genotype of watermilfoil, compared to the less 2,4-D sensitive watermilfoil hybrid genotype (M. spicatum × M. sibiricum), the downregulation of photosynthetic genes following 2,4-D treatment was more pronounced in the sensitive genotype [45]. McCauley et al. [22] also suggested a whole-scale downregulation of the photosynthetic process in plants treated with auxin herbicides. We also found a generalized decrease in photosynthetic-related transcripts following 2,4-D application in ‘Kenland’. The same did not occur for the 2,4-D tolerant ‘UK2014’. Together, GO analysis (Figure 3) and Venn diagram representations (Figure 1 and Table 1) confirm that the response of ‘UK2014’ to 2,4-D is less than that of ‘Kenland’ at the transcriptome level.
Gene Ontology analysis was also performed in DEG lists where expression increased following 2,4-D treatment (Figure 3B) in relation to controls. Results were inconsistent between years and times of collection, but a relevant overlap occurred for the oxidation-reduction process, at the 0.0005 FDR cutoff. The term “single-organism metabolic process” included several of the genes in “oxidation-reduction’ (Table S7). This term was also overrepresented in both ‘Kenland’ and ‘UK2014’ at 24 HAT in 2015 and 72 HAT in 2015 (Figure 3B). In addition to limiting photosynthetic activity, 2,4-D has been shown to induce biosynthesis of abscisic acid (ABA) and ethylene auxin-responsive genes as well as the accompanied overproduction of reactive oxygen species (ROS) [8,11].

3.3. Effect of 2,4-D on Early Auxin Responsive Genes

Changes in expression of the early auxin-responsive genes such as Auxin/Indole-3-Acetic Acid response factors (Aux/IAA, ARFs) occur following the application of 2,4-D [47]. Genes with annotations related to auxins, according to LIS descriptions [48], were selected from DEG lists that compared 2,4-D treated clovers against their untreated controls (Tables S2 and S3). With these criteria, 42 genes in 2015 and 64 genes in 2018 were identified. Representative examples of these auxin-responsive DEGs are displayed in Table 2.
In general, auxin-responsive genes were overexpressed following 2,4-D treatment in both red clovers, compared to untreated controls (Table 2). Gene Tp_gene_8381, for example, codes an auxin response factor 9. This gene was overexpressed 116 times more in 2,4-D treated ‘Kenland’ than untreated ‘Kenland’ at 4 HAT in 2015. In 2018, the same gene was 177 times overexpressed also comparing treated against untreated ‘Kenland’. At 72 HAT, Tp_gene_8381 was overexpressed by 97 and 29 factors in 2015 and 2019, respectively. The same overexpression of 8381 following 2,4-D treatment was observed in 2,4-D treated ‘UK2014’ against its untreated controls. In ‘UK2014’, the application of 2,4-D increased the expression of Tp_gene_8381 by a factor of 70 in 2015 and 284 in 2018. This overexpression seemed to decline over time. Some genes, for example Tp_gene_3144 (auxin response factor 18), were overexpressed by 2,4-D treatment in ‘Kenland’ at 4 and 24 HAT but not at 72 HAT (Table 2). This pattern of decrease of fold-change over time was consistent for most of the other identified auxins (Table 2, Tables S2, and S3).
Auxins, such as 2,4-D, interact with the SCFTIR1/AFB complex in plants, leading to the ubiquitination and degradation of Aux/IAA transcriptional repressors [15]. Auxin Response Factors (ARFs) are tagged for polyubiquitination, leading to the expression of early auxin response genes [49,50]. It has been suggested that Aux/IAAs are likely present in this suite of early activated genes, acting as a negative regulation mechanism [6]. Our results agree with the literature that an increase in the expression of auxin-related genes was observed following 2,4-D treatment in red clover. This effect also decreased over time, suggesting a decline in the 2,4-D stimulus on the auxin response gene expression response in red clover.
Gretchen Hagen 3 (GH3) are a class of enzymes involved in the formation of hormone amino acid conjugates and have a key role in auxin regulation and homeostasis in the plant [17,49,51]. GH3 have been shown to be rapidly transcribed under high auxin concentrations [49,51]. The putative red clover GH3 genes were highly expressed in our study and had some of the largest increases following 2,4-D application, both in ‘UK2014’ and ‘Kenland’, compared to untreated controls (Figure 4, Tables S4 and S5). For example, the Tp_gene_20529 that encodes a putative indole-3-acetic acid-amido synthetase (GH3.9) had an approximately a 259 and 100-fold increase compared to the untreated control, at 4 HAT in 2015 for ‘Kenland’ and ‘UK2014’, respectively (Figure 4). Another representative GH3 example was Tp_gene_26900, which had a similar induction pattern. However, no differences in expression were observed between 2,4-D treated ‘Kenland’ and ‘UK2014’ (Tables S5 and S6). Previous work in Raphanus raphanistrum L. [52] and Glycine max (L.) Merr. [53] indicated the main metabolites of 2,4-D were amino acid conjugates of 2,4-D. In Arabidopsis thaliana (L.) Heynh., a study identified the 2,4-D conversion to 2,4-D amino acid conjugates involving the activity of 60 different GH3 proteins [54]. De Figueiredo, Barnes, Boot, De Figueiredo, Nissen, Dayan and Gaines [34] also suggested the activity of GH3’s formed 2,4-D aspartyl conjugates in waterhemp.

3.4. Expression of Genes for Key Enzymes Involved in Absisic Acid and Ethylene Biosynthesis

Phytohormones, particularly abscisic acid (ABA) and ethylene, are critical components of processes that lead to plant death following the application of auxin herbicides [7,9]. We evaluated the expression of three gene families that encode key enzymes in the biosynthesis of these hormones: 9-cis-epoxycarotenoid dioxygenases (NCED), 1-aminocyclopropane-1-carboxylate synthases (ACS), and 1-aminocyclopropane-1-carboxylate oxidases (ACO) [19,20,21,55]. In 2015, 5 ACCs, 2 ACOs, and 1 NCEDs were differentially expressed either between 2,4-D treated and untreated ‘Kenland’ or ‘UK2014’, in at least one time point. In 2018, we detected 7ACCs, 5 ACOs, and 1 NCED that were differentially expressed. (Table 3). Generally, expression of these genes increased in each red clover following the application of 2,4-D. None of these were differentially expressed comparing 2,4-D treated ‘UK2014’ against 2,4-D treated ‘Kenland’.
ACS catalyzes the first step in biosynthesis of ethylene [19,56] and ACOs catalyze the next step downstream in the ethylene pathway [55]. NCED enzymes catalyze the rate-limiting step for ABA production [57]. Previous studies with catchweed bedstraw (Gallium aparine L.) and Arabidopsis thaliana found an increase in NCED expression following IAA or 2,4-D application [21,58]. Accumulation of ethylene, which would lead to ABA overproduction, has been commonly accepted as the primary process leading to plant death after the application of synthetic auxins [9]. A recent study on the transcriptome of horseweed found a rapid upregulation of NCEDs independent of ABA accumulation following treatment with auxin, 2,4-D, dicamba (3,6-dichloro-2-methoxybenzoic acid), and halauxyfen-methyl (methyl 4-amino-3-chloro-6-(4-chloro-2-fluoro-3-methoxyphenyl)-2-pyridinecarboxylate) [22,23]. We found that expression of one NCED rapidly increased upon 2,4-D treatment in ‘UK2014’ and ‘Kenland’ red clovers. However, this happened only in 2015 and it was not detected in 2018. These results suggest that 2,4-D elicits a rapid response in red clover, increasing the expression of genes involved in ethylene and ABA biosynthesis.

3.5. Differential Expression of CYP450s

The most commonly reported enzymes shown to be involved in the metabolism and detoxification of xenobiotics include glutathione S-transferases, CYP450s, esterases, and glucosyl-transferases [59]. While the results were not consistent across years, both ‘Kenland’ and ‘UK2014’ appeared to respond to the 2,4-D induced stress in a similar way by upregulating genes related to oxidation-reduction biological processes. The GO oxidation-reduction term included 106 and 91 genes that were overexpressed in both ‘Kenland’ and ‘UK2014’ (Table S7). These genes were mainly annotated as CYP450s (e.g., Tp_gene_10731, Tp_gene_13828, Tp_gene_14936), peroxidases (e.g., Tp_gene_15344, Tp_gene_17748, Tp_gene_18979), and dehydrogenases (e.g., Tp_gene_10042, Tp_gene_14165, Tp_gene_12649) (Tables S2 and S3).
The role of CYP450s as mediators of enhanced metabolism was suggested as a mechanism for tolerance to 2,4-D in dicotyledonous weed species such as corn poppy (Papaver rhoeas L.) [29] and waterhemp (Amaranthus tuberculatus (Moq.) J. D. Sauer) [28,60]. Additionally, CYP450 enzymes have been implicated in Phase I herbicide metabolism and promiscuity in metabolizing a broad range of herbicides [61]. Thus, we filtered the CYP450s that were differentially expressed following 2,4-D treatment for both ‘Kenland’ and ‘UK2014’ (Tables S8 and S9). A list of 104 and 150 genes were extracted from 2015 and 2018, and some of the highly differentially expressed DEGs are shown in Figure 5. The increase in selected CYP450s expression was evident for both red clover lines following 2,4-D application in relation to controls. The Tp_gene_26877, for example, was expressed 27 times more in 2,4-D treated ‘Kenland’ than in its control and 11 times more in 2,4-D treated ‘UK2014’ than in its control, 4 HAT in 2015. The same pattern was observed in 2018, with changes of 8-fold and 4-fold, for ‘Kenland’ and ‘UK2014’, respectively. This pattern, higher expression in 2,4-D treated ‘Kenland’ than ‘UK2014’ compared to their controls, was also found for other selected CYP450s (Figure 5). The only differentially expressed CYP450 genes between ‘Kenland’ and ‘UK2014’ lines were seen in the 2018 experiment where three low expressing P450 genes (Tp_gene_34608, Tp_gene_19702, Tp_gene_19696) were detected. Overall, differential CYP450 expression was similar in both ‘Kenland’ and ‘UK2014’ lines.
Kenland’ was shown to metabolize 2,4-D but at a slower rate than ‘UK2014’ [39]. However, the RNAseq results suggest that we cannot conclude that the increased 2,4-D metabolism in ‘UK2014’ compared with ‘Kenland’ is associated with higher expression of a specific CYP450. CYP450s have promiscuous catalytic binding sites and perform various biological functions, the metabolism of xenobiotics being only one of them [61,62]. Some specific classes of CYP450s have been identified as herbicide metabolism mediators [63]. CYP450s belonging to family 81 were overexpressed following 2,4-D treatment in waterhemp [64] and rigid ryegrass (Lolium rigidum Gaudin) [65]. We identified several CYP450s belonging to family 81. Two of them, genes Tp_gene_26880 and Tp_gene_26877, described above, were found neighboring a significant mapped single nucleotide polymorphism (SNP) on linkage group 2 that was associated with 2,4-D resistance by Benevenuto et al. [66]. As red clover is an obligate outcrossing species, red clover varieties contain a high level of heterozygosity and a high number of identifiable SNPs [67]. While the primary focus of the current study was to determine if differential expression levels between the two red clover lines were responsible for the higher 2,4-D tolerance in the ‘UK2014’ line, RNA-seq data are also able to provide sequence information in the transcribed regions of the genome. Using our sequence data, we confirmed the SNP allele identified by Benevenuto et al. [66] in the vicinity of the CYP450 gene cluster on linkage group 2 described above and identified other SNPs in other genes, including the Tp_gene_26877 gene, when comparing ‘‘Kenland’’ to ‘UK2014’. While most of these SNPs are silent and do not result in amino acid changes, three SNPs in the Tp_gene_26877 gene were identified that would result in amino acid changes. For example, one base, in the 3’ end of the Tp_gene_26877 gene (CDS base 1289), has an “A”-“T” transversion, resulting in a Met to Leu amino acid change. ‘UK2014’ is homozygous at this locus and encodes the Leu isoform, whereas ‘‘Kenland’’ is heterozygous and expresses roughly 50% of each of the Met and Leu alleles. Interestingly, the “very 2,4-D sensitive” cultivar ‘Southern Belle’ is homozygous for the Met isoform at the locus. Currently, it is not known what effect, if any, this, and the other, amino acid changes, might have on the function of this CYP450, if any, or how it might be involved in 2,4-D metabolism. Additional screening for SNPs in other targeted genes, including other CYP450 enzymes in red clover utilizing newer technologies [68], together with expression studies in model organisms would aid in determining substrates, and metabolism efficiency, of the different isoforms of these enzymes.

4. Conclusions

In summary, the increased tolerance of the ‘UK2014’ was observed at the transcriptional level whereby ‘UK2014’ appeared less affected by the herbicide application as observed in the change in overall gene expression, in particular photosynthesis-related genes when compared to the 2,4-D susceptible red clover ‘Kenland’. The application of 2,4-D elicited an increased expression of several CYP450s in red clover, but the increased expression of no single CYPP450 explained the increased 2,4-D tolerance of ‘UK2014’. Rather, the tolerance seems to be conferred by multiple genes, either by altered expression or altered enzyme isoforms, which is in agreement with the results shown by Benevenuto et al. [66] that 2,4-D resistance in red clover is a quantitative trait. Further investigation of the CYP450 variants between ‘UK2014’ and ‘Kenland’ red clover could aid in elucidating the basis for the 2,4-D tolerance. Further development of additional SNP arrays would allow for a more rapid selection for increased 2,4-D tolerance in red clover. This in turn, would provide for cost-effective broadleaf week control in pastures without losing the reduced pasture nitrogen fertilizer requirements and improved forage nutritional values provided by red clover.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14061198/s1, The Supplemental Excel File contains supplemental Tables S1–S9. Table S1. The total number of mapped reads from sequencing of RNA extracted from red clover leaf tissue, untreated (control) or treated with 1.12 kg a.e. of 2,4-D amine from ‘Kenland’ and ‘UK2014’ red clover genotypes. Table S2. Expression of genes in red clovers UK2014 or Kenland, with or without 2,4-D treatment at 1.12 kg ae/ha and comparisons on the expression between treated red clovers (UT and KT) and their respective controls (UC and KC) in 2015. Table S3. Expression of genes in red clovers UK2014 or Kenland, with or without 2,4-D treatment at 1.12 kg ae/ha and comparisons on the expression between treated red clovers (UT and KT) and their respective controls (UC and KC) in 2018. Table S4. Comparison of genes that were overexpressed in 2,4-D treated UK2014 clover in relation to 2,4-D treated Kenland clover in 2015. Table S5. Comparison of genes that were overexpressed in 2,4-D treated UK2014 clover in relation to 2,4-D treated Kenland clover in 2018. Table S6. Enriched Gene Ontology terms, related to biological processes, from downregulated genes with significance in comparisons between 2,4-D treated and untreated red clover germplasms; results are split by tissue samplings from either 2015 or 2018 field studies. Table S7. Enriched Gene Ontology term, related to biological processes, from upregulated genes with significance in comparisons between 2,4-D treated and untreated red clover cultivars; results are split by tissue samplings from either 2015 or 2018 field studies. Table S8. Selection of cytochrome P450s that were overexpressed in 2,4-D treated UK2014 and 2,4-D Kenland clovers in relation to their respective controls in 2015; Table S9. Selection of cytochrome P450s that were overexpressed in 2,4-D treated UK2014 and 2,4-D Kenland clovers in relation to their respective controls in 2018.

Author Contributions

Conceptualization, M.B. and R.D.D.; methodology, L.P.d.A., R.D.D. and M.B.; software, L.P.d.A. and R.D.D.; validation, R.D.D.; formal analysis, L.P.d.A.; investigation, L.P.d.A.; resources, M.B.; data curation, R.D.D.; writing—original draft preparation, L.P.d.A.; writing—review and editing, R.D.D. and M.B.; visualization, L.P.d.A.; supervision, M.B.; project administration, M.B.; funding acquisition, M.B. and R.D.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by University of Kentucky Non-Assistance Cooperative Agreements 5864403005 and 5850428003 to M.B, and by the United States Department of Agriculture USDA–ARS CRIS project 5042-21000-004-000D to R.D. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the University of Kentucky or the U.S. Department of Agriculture. The University of Kentucky and the USDA are equal opportunity providers and employers.

Data Availability Statement

The raw read sequence data are available under BioProject PRJNA1090547 at NCBI.

Acknowledgments

The authors wish to thank Linda Williams and Gene Olson who both provided significant help in the conduct of this study and to Troy Bass for his help with the laboratory RNA isolations and RNAseq library preparations. This manuscript is dedicated to Norman L. Taylor who initiated this project in his belief in the inclusion of red clover for increased productivity into pasture-based sustainable agriculture. He worked tirelessly post-retirement until his death in 2010 in the selection and development a 2,4-D tolerant red clover line to aid producer weed management.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The number of differentially expressed genes (DEGs) expressed either more (up) or less (down) in response to 2,4-D treatment in Kenland (pink) and UK2014 (green); RNA for DEGs determination was collected at 4, 24, or 72 h after 2,4-D treatment from two distinct field studies, one in 2015 (A,C,E) and one in 2018 (B,D,F).
Figure 1. The number of differentially expressed genes (DEGs) expressed either more (up) or less (down) in response to 2,4-D treatment in Kenland (pink) and UK2014 (green); RNA for DEGs determination was collected at 4, 24, or 72 h after 2,4-D treatment from two distinct field studies, one in 2015 (A,C,E) and one in 2018 (B,D,F).
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Figure 2. Representation of genes that were more expressed in 2,4-D treated UK2014 over Kenland (UT > KT) or 2,4-D treated Kenland over UK2014 (KT > UT); (A,C,E) correspond to 4, 24, and 72 HAT in 2015, respectively; (B,D,F) correspond to 4, 24, and 72 HAT in 2018, respectively; each dot represents a gene on the volcano plot and the cytochrome P450s are highlighted in green; vertical lines mark a 2-fold-change in expression (log2) and the horizontal line is the significance cutoff (FDR > 0.05).
Figure 2. Representation of genes that were more expressed in 2,4-D treated UK2014 over Kenland (UT > KT) or 2,4-D treated Kenland over UK2014 (KT > UT); (A,C,E) correspond to 4, 24, and 72 HAT in 2015, respectively; (B,D,F) correspond to 4, 24, and 72 HAT in 2018, respectively; each dot represents a gene on the volcano plot and the cytochrome P450s are highlighted in green; vertical lines mark a 2-fold-change in expression (log2) and the horizontal line is the significance cutoff (FDR > 0.05).
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Figure 3. Bubble plots of overrepresented Gene Ontology terms (FDR < 0.0005) for genes expressed less (A) or more (B) after 2,4-D treatment in common between UK2014 and Kenland or unique to each red clover.
Figure 3. Bubble plots of overrepresented Gene Ontology terms (FDR < 0.0005) for genes expressed less (A) or more (B) after 2,4-D treatment in common between UK2014 and Kenland or unique to each red clover.
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Figure 4. Distribution of normalized reads (TMM) of GH3 genes that were differentially expressed (log2|Fold-change| > 2 and FDR > 0.05) at least one of the time points, 4, 24, and 72 h after treatment, between either 2,4-D treated Kenland (in green) or UK2014 (in purple) compared to the respective untreated controls.
Figure 4. Distribution of normalized reads (TMM) of GH3 genes that were differentially expressed (log2|Fold-change| > 2 and FDR > 0.05) at least one of the time points, 4, 24, and 72 h after treatment, between either 2,4-D treated Kenland (in green) or UK2014 (in purple) compared to the respective untreated controls.
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Figure 5. Heat maps of highly expressed annotated P450s (Lsmeans higher than 27 in at least one of the treatment combinations) that were differentially expressed following 2,4-D application in either Kenland or UK2014 for 2015 (A) and 2018 (B) experiments.
Figure 5. Heat maps of highly expressed annotated P450s (Lsmeans higher than 27 in at least one of the treatment combinations) that were differentially expressed following 2,4-D application in either Kenland or UK2014 for 2015 (A) and 2018 (B) experiments.
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Table 1. Summary of the total numbers of Differentially Expressed Genes (DEGs) in Kenland (susceptible to 2,4-D) and UK2014 (tolerant to 2,4-D) after treatment with 2,4-D.
Table 1. Summary of the total numbers of Differentially Expressed Genes (DEGs) in Kenland (susceptible to 2,4-D) and UK2014 (tolerant to 2,4-D) after treatment with 2,4-D.
HAT *2015 Field Study2018 Field Study
Unique toUnique to UK2014CommonUnique to KenlandUnique to UK2014Common
Kenland
41187123293626127413
2418166011545188018262528
72119816024613544701166
* Leaf tissue for RNA extractions and DEGs determination was collected at 4, 24, or 72 h after treatment (HAT) with 2,4-D.
Table 2. Fold-change in the expression of selected early auxin response genes (Tp_Gene_FestureID#) in UK2014 and Kenland 4, 24 and 72 h after 2,4-D treatment, from RNA sequencing of leaf tissue from the 2015 field study *.
Table 2. Fold-change in the expression of selected early auxin response genes (Tp_Gene_FestureID#) in UK2014 and Kenland 4, 24 and 72 h after 2,4-D treatment, from RNA sequencing of leaf tissue from the 2015 field study *.
Feature IDFold-ChangeSignificance of Fold-Changes [–log10 (p-Value)]LIS Description
4 HAT24 HAT72 HAT4 HAT24 HAT72 HAT
KT/KCUT/UCKT/KCUT/UCKT/KCUT/UCKT/KCUT/UCKT/KCUT/UCKT/KCUT/UC
838111670553346979411.4110.3914.0713.3411.0710.99auxin response factor 9
30395211128349.475.765.824.652.363.14auxin response factor 1
3144372345229.548.393.363.891.491.36auxin response factor 18
160732213116625.634.454.052.672.760.74auxin efflux carrier family protein
15558121099459.318.788.188.234.85.69auxin response factor 4
838111799284133292811.5211.1913.1211.768.498.39auxin response factor 9
30391591810449.067.489.547.693.834.2auxin response factor 1
314430281192310.8710.647.957.111.213.03auxin response factor 18
1607311141677117.768.438.925.945.937.73auxin efflux carrier family protein
1555810921118.277.841.40.330.580.84auxin response factor 4
* Cells colored in blue represent a significant positive fold-change (FDR < 0.05 and |log2FC| ≥ 2).
Table 3. Fold-change in the expression of selected abscisic acid and ethylene biosynthesis genes (Tp_gene_FeatureID) in UK2014 and Kenland 4, 24 and 72 h after 2,4-D treatment, from RNA sequencing of leaf tissue from the 2015 and 2018 field studies.
Table 3. Fold-change in the expression of selected abscisic acid and ethylene biosynthesis genes (Tp_gene_FeatureID) in UK2014 and Kenland 4, 24 and 72 h after 2,4-D treatment, from RNA sequencing of leaf tissue from the 2015 and 2018 field studies.
YearLIS DescriptionFeature IDFold-Changes **Significance of Differences –log10 (p-Value)
4 HAT24 HAT72 HAT4 HAT24 HAT72 HAT
KT/KCUT/UCKT/KCUT/UCKT/KCUT/UCKT/KCUT/UCKT/KCUT/UCKT/KCUT/UC
2015NCED297613134133.620.623.751.093.484.65
ACC9324223159123992110.479.909.449.060.920.12
3720122132199146223011.6711.8112.5712.007.928.68
26986321713361.660.686.125.401.893.50
128833163113.390.646.343.110.720.22
18207221812240.860.516.155.151.172.12
253082122111.800.891.653.200.330.02
195972223113.783.733.394.610.630.94
ACO354954131115.100.773.900.310.370.24
272893362221.951.383.511.351.180.94
2564932116333.101.207.395.242.942.75
193252142321.010.603.741.252.201.25
138491123230.210.001.072.800.893.14
2018NCED297612222112.412.162.101.230.552.03
52642131111.871.672.452.751.341.26
ACC37201992926316943958.625.349.058.376.057.43
93241561333041588.177.925.355.911.972.86
1288351910344.340.636.516.772.684.02
269862254231.361.594.513.421.683.06
256495267445.421.946.356.564.494.95
ACO193251132110.960.725.102.940.451.06
195972222211.903.582.891.971.791.23
** Cells colored in blue represent a significant positive fold-change (FDR < 0.05 and |log2FC| ≥ 2); cells colored in orange represent a significant negative fold-change (FDR < 0.05 and |log2FC| ≥ 2). Fold-change values were back-transformed from the log to the linear scale with 2 log2(FC).
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Araujo, L.P.d.; Barrett, M.; Dinkins, R.D. Comparative Gene Expression following 2,4-D Treatment in Two Red Clover (Trifolium pratense L.) Populations with Differential Tolerance to the Herbicide. Agronomy 2024, 14, 1198. https://doi.org/10.3390/agronomy14061198

AMA Style

Araujo LPd, Barrett M, Dinkins RD. Comparative Gene Expression following 2,4-D Treatment in Two Red Clover (Trifolium pratense L.) Populations with Differential Tolerance to the Herbicide. Agronomy. 2024; 14(6):1198. https://doi.org/10.3390/agronomy14061198

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Araujo, Lucas Pinheiro de, Michael Barrett, and Randy D. Dinkins. 2024. "Comparative Gene Expression following 2,4-D Treatment in Two Red Clover (Trifolium pratense L.) Populations with Differential Tolerance to the Herbicide" Agronomy 14, no. 6: 1198. https://doi.org/10.3390/agronomy14061198

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