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Article

Reliable RT-qPCR Normalization in Polypogon fugax: Reference Gene Selection for Multi-Stress Conditions and ACCase Expression Analysis in Herbicide Resistance

1
College of Agriculture, Tarim University, Alaer 843300, China
2
Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alaer 843300, China
3
Key Laboratory of Integrated Pest Management of Xinjiang Production and Construction Corps in Southern Xinjiang, Alaer 843300, China
4
State Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Anyang 455000, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1813; https://doi.org/10.3390/agronomy15081813
Submission received: 25 June 2025 / Revised: 24 July 2025 / Accepted: 25 July 2025 / Published: 26 July 2025
(This article belongs to the Special Issue Adaptive Evolution in Weeds: Molecular Basis and Management)

Abstract

Asia minor bluegrass (Polypogon fugax), a widespread Poaceae weed, exhibits broad tolerance to abiotic stresses. Validated reference genes (RGs) for reliable RT-qPCR normalization in this ecologically and agriculturally significant species remain unidentified. This study identified eight candidate RGs using transcriptome data from seedling tissues. We assessed the expression stability of these eight RGs across various abiotic stresses and developmental stages using Delta Ct, BestKeeper, geNorm, and NormFinder algorithms. A comprehensive stability ranking was generated using RefFinder, with validation performed using the target genes COR413 and P5CS. Results identified EIF4A and TUB as the optimal RG combination for normalizing gene expression during heat stress, cold stress, and growth stages. EIF4A and ACT were most stable under drought stress, EIF4A and 28S under salt stress, and EIF4A and EF-1 under cadmium (Cd) stress. Furthermore, EIF4A and UBQ demonstrated optimal stability under herbicide stress. Additionally, application of validated RGs revealed higher acetyl-CoA carboxylase gene (ACCase) expression in one herbicide-resistant population, suggesting target-site gene overexpression contributes to resistance. This work presents the first systematic evaluation of RGs in P. fugax. The identified stable RGs provide essential tools for future gene expression studies on growth and abiotic stress responses in this species, facilitating deeper insights into the molecular basis of its weediness and adaptability.

Graphical Abstract

1. Introduction

Asia minor bluegrass (Polypogon fugax), a globally distributed Poaceae weed, exhibits significant dual relevance in agricultural and ecological contexts [1,2,3,4]. Although established as a competitive weed in key crops including canola (Brassica napus) and wheat (Triticum aestivum) [5], this species possesses considerable biotechnological promise. Its demonstrated capacity for cadmium (Cd) hyperaccumulation alongside other heavy metals [1,2], combined with extensive edaphic adaptability [4], designates it as a promising candidate for phytoremediation applications. Furthermore, superior genes derived from P. fugax have been shown to enhance abiotic stress tolerance in crops [6], emphasizing its value in the field of genetic improvement of crop tolerance to adversity.
Recent progress in molecular biological techniques has substantially heightened interest in deciphering the genetic and physiological foundations underpinning P. fugax stress tolerance. This encompasses investigations into its responses to salinity and drought [3], herbicide resistance mechanisms [7], and developmental processes [8]. Nevertheless, advancements in functional genomics research for this species remain constrained by a critical shortage of validated molecular methodologies. Precise gene expression analysis constitutes an indispensable step for elucidating the genetic mechanisms governing plant responses to biotic and abiotic stressors [9,10]. The revolution in accessibility to genome and transcriptome sequence data across numerous plant taxa has profoundly transformed the identification of stress-associated genes [11,12]. The real-time quantitative polymerase chain reaction (RT-qPCR) technique, recognized for its accuracy, efficiency, and practical utility, has become a predominant method for quantifying target gene transcript levels across diverse environmental conditions and developmental stages [13,14]. However, the reliability and precision of RT-qPCR data are influenced by multiple variables, including RNA integrity and concentration, reverse transcription efficiency, the selection of appropriate reference genes (RGs), and reaction conditions. Among these, RG expression stability is paramount for the accurate normalization and calibration of target gene expression profiles [9,15].
Several conventional RGs are commonly employed in plant studies, such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH), β-tubulin (TUB), actin (ACT), elongation factor 1-alpha (EF-1), and ubiquitin (UBQ), which have historically been utilized to standardize expression analyses in RT-qPCR [16,17,18]. However, substantial evidence from recent reports indicates that the expression stability of these traditional RGs is context-dependent. Their reliability varies significantly across plant species, environmental conditions, and even distinct developmental stages within the same species [18,19,20,21]. For instance, PP2C59 and UBC5B demonstrate optimal stability in Toona ciliata leaves subjected to biotic stress, whereas the HIS1-ACT7 pair is reliable in young stems of the same species [22]. Similarly, GAPDH proves unsuitable for normalization in ABA-treated Isodon rubescens yet remains effective in drought-stressed roots and salt-stressed leaves of Avena sativa [23,24]. Furthermore, GAPDH and ACTIN7 exhibit the highest stability in salt-treated leaves of Carex rigescens, while EIF4A and SAND are most stable in salt-stressed roots of the same species [25]. Collectively, these findings underscore the imperative for systematic, condition-specific evaluation of RGs across targeted plant species, developmental phases, and experimental stress treatments to ensure methodological rigor and precision in gene expression studies.
The current absence of comprehensively validated RGs for P. fugax presents a significant impediment to molecular investigations of its complex stress adaptation mechanisms. Consequently, this study employed a multifaceted experimental design, including heat stress, cold stress, drought stress, Cd stress, salt stress, herbicide stress, and distinct developmental stages. This approach was implemented to rigorously evaluate the expression stability of eight candidate RGs: TUB, ACT, EF-1, GAPDH, eukaryotic initiation factor 4A (EIF4A), ribulose-1,5-bisphosphate carboxylase (RUB), 28S ribosomal RNA (28S), and UBQ. The target genes COR413 and P5CS were utilized to experimentally validate the identified optimal RG pairs via RT-qPCR analysis. Furthermore, we applied these validated RGs to quantify acetyl-CoA carboxylase (ACCase) gene expression in three quizalofop-p-ethyl-resistant populations. Elevated ACCase transcript levels in one resistant population relative to susceptible controls implicate target gene overexpression in its resistance mechanism.

2. Materials and Methods

2.1. Plant Materials

Seeds of P. fugax, derived from the previously documented SC-S population [26], were subjected to surface sterilization through immersion in 2.5% (v/v) sodium hypochlorite solution (Aladdin, Shanghai, China, active chlorine ≥5.0%). Following sterilization, seeds were thoroughly rinsed with sterile distilled water to eliminate residual disinfectant. Germination was initiated in 9 cm diameter Petri dishes prepared with double-layered sterile filter paper, moistened with 5 mL of sterile distilled water. Upon radicle emergence, ten uniformly germinated seeds were transferred to individual plastic pots containing a growth matrix composed of organic matter and vermiculite in a 2:1 (v/v) ratio. Plants were cultivated under strictly controlled environmental conditions within a programmable artificial climate chamber (Boxun, Shanghai, China) with the fluorescent lamps producing a photo-synthetic photon flux density of 140 μmol m−2 s−1 and a 16 h photoperiod at 20 °C and an 8 h dark period at 15 °C. At the 3–4-leaf stage, 28-day-old seedlings were exposed to the abiotic stress treatments described below.

2.2. Sample Collection Under Diverse Stresses and Growth Stages

2.2.1. Cold and Heat Stress Applications

Twenty-eight-day-old seedlings were subjected to constant 4 °C (cold stress) or 40 °C (heat stress) exposure [27]. Aboveground tissues were collected prior to treatment initiation (0 h) and at 12 h and 24 h post-exposure. All samples were immediately flash-frozen in liquid nitrogen and maintained at −80 °C for subsequent RNA isolation.

2.2.2. Salt and Drought Stress Induction

Twenty-eight-day-old seedlings received either salt or drought stress treatments. Salt stress was imposed via soil irrigation with 100 mL of 500 mM NaCl (Aladdin, Shanghai, China, purity ≥ 99.8%) solution. Drought conditions were simulated through application of 100 mL of 12.87% polyethylene glycol 6000 (PEG) (Solarbio, Beijing, China, purity ≥ 99.0%) solution, inducing observable leaf wilting within 1 h that persisted throughout the experiment. Aboveground tissues were harvested at 0 h, 12 h, and 24 h following stress initiation, rapidly frozen in liquid nitrogen, and archived at −80 °C.

2.2.3. Cd and Herbicide Exposure

Twenty-eight-day-old seedlings were exposed to Cd or herbicide treatments. Cd stress was administered by amending the growth substrate with CdCl2 (Aladdin, Shanghai, China, purity ≥ 99.9%) solution to attain a final concentration of 20 mg kg−1 [28]. Herbicide treatment involved foliar application of quizalofop-p-ethyl (Aladdin, Shanghai, China, purity ≥ 98.0%) at 52.5 g active ingredient (a.i.) ha−1 (field-recommended dose) [5]. Aboveground tissues were collected at 0 h, 12 h, and 24 h post-treatment, flash-frozen in liquid nitrogen, and stored at −80 °C.

2.2.4. Developmental Stage Sampling

Plants cultivated under standardized environmental conditions were sampled at four distinct stages: 3–4-leaf stage, tillering stage, heading stage, and maturity stage. Leaf tissues were harvested at each developmental phase, immediately frozen in liquid nitrogen, and preserved at −80 °C for RNA extraction.

2.3. Reference Gene Selection

Eight candidate RGs (EIF4A, ACT, TUB, EF-1, 28S, UBQ, GAPDH, and RUB) were identified from P. fugax RNA-seq datasets [5]. Expression stability metrics were calculated from FPKM values (Fragments Per Kilobase per Million mapped reads) using Excel, including mean FPKM (MF), standard deviation (SD), and coefficient of variation (CV = SD/MF). Genes meeting dual criteria of credible functional annotation and substantial expression (MF ≥ 10) with low variability (CV ≤ 20%) were selected [29].

2.4. RNA Isolation and RT-qPCR Profiling

Total RNA was extracted using the Trelief® Hi-Pure Plant RNA Kit (Tsingke, Xi’an, China). Complementary DNA synthesis employed the Goldenstar® RT6 cDNA Synthesis Kit (Tsingke, Xi’an, China). Quantitative amplification was performed on a QuantStudio™ 1 Plus system (Thermo Fisher, Guangzhou, China) with ArtiCanCEO SYBR qPCR Mix (Tsingke, Xi’an, China) under manufacturer-specified two-step cycling parameters. Gene-specific primers (Table 1) were designed using Primer3 v. 0.4.0 (https://bioinfo.ut.ee/primer3-0.4.0/) (accessed on 5 May 2024) and validated through 1% agarose gel electrophoresis and melt curve analysis. All RT-qPCR assays incorporated three biological replicates with duplicate technical replicates.

2.5. Reference Gene Stability Assessment

Expression stability of candidate RGs was evaluated using four independent algorithms: BestKeeper [25], geNorm [30], NormFinder [25], and the comparative Delta Ct method [31]. RefFinder [32] integrated computational outputs to generate comprehensive stability rankings. Validation was performed by profiling target genes COR413 and P5CS using identical RNA samples. Relative expression was calculated via the 2−ΔΔCt method, comparing normalization approaches using (1) optimal RG pairs for each treatment and (2) the least stable RG identified per condition.

2.6. ACCase Expression Profiling Across the Different Populations

Three quizalofop-p-ethyl-resistant populations (R1 [26], R2 [33] and R3 [5]) and one quizalofop-p-ethyl-susceptible population (SC-S) were used in this study. Seedlings at the 3–4-leaf stage were exposed to quizalofop-p-ethyl treatments at 52.5 g a.i. ha−1. Aboveground tissues were collected at 0 h and 12 h post-treatment, flash-frozen in liquid nitrogen, and stored at −80 °C. The quantification primers of ACCase gene (Table S1) were designed using Primer3 v. 0.4.0 (https://bioinfo.ut.ee/primer3-0.4.0/) (accessed on 20 June 2024). RNA extraction and RT-qPCR procedures were as described in Section 2.4.

3. Results

3.1. Verification of Primer Specificity and Effectiveness

Primer specificity was confirmed by 1% agarose gel electrophoresis, demonstrating single amplicons for all eight candidate RGs (TUB, ACT, EF-1, GAPDH, EIF4A, RUB, 28S, and UBQ) (Figure S1). Melt curve analysis further validated primer performance through single-peak profiles with high reproducibility (Figure S2). Amplification efficiencies of 90–110% and correlation coefficients (R2) of 0.945–1 (Table 1) validated robust PCR consistency. These data confirm the primer suitability for precise RT-qPCR quantification. Ct values exhibited an inverse relationship with transcript abundance, where diminished values corresponded to elevated expression levels. The Ct distribution spanned 14.73–35.86 (Figure S3), with RUB demonstrating the lowest mean Ct values (indicating maximal constitutive expression) across experimental conditions, while 28S showed the highest baseline expression (Figure S3).

3.2. Expression Stability Analysis of Candidate Reference Genes

The stability of eight candidate RGs was systematically assessed across six abiotic stresses (heat, drought, salt, cold, Cd, quizalofop-p-ethyl) and developmental stages using four analytical approaches: Delta Ct, NormFinder, BestKeeper and geNorm.

3.2.1. Delta Ct Method Analysis

Stability was evaluated through mean standard deviation (SD) of pairwise ΔCt values, where lower SD indicates higher stability (Table 2). EIF4A exhibited optimal stability across all conditions. GAPDH ranked least stable under heat stress and growth stages, while RUB showed minimal stability during salt and Cd stress. 28S (drought, quizalofop-p-ethyl) and UBQ (cold) demonstrated the lowest stability within respective treatments (Table 2).

3.2.2. NormFinder Analysis

This algorithm evaluates stability through intra- and inter-group expression variance, with lower values indicating higher consistency. NormFinder identified TUB as most stable under heat stress, diverging from Delta Ct results. Remaining stability rankings aligned with Delta Ct outcomes (Table 2 and Table 3).

3.2.3. BestKeeper Analysis

BestKeeper evaluated RG stability through pairwise correlation analysis, ranking candidates based on SD values (lower SD indicates higher stability; Table 4). EIF4A emerged as the most stable under heat stress and Cd stress. 28S showed optimal stability under drought and cold stress. RUB demonstrated the highest stability under quizalofop-p-ethyl stress and across developmental stages, while EF-1 was most stable under salt stress (Table 4). UBQ displayed the lowest stability under drought stress, cold stress, and developmental stages. RUB ranked as the least reliable under salt stress and Cd stress. Additionally, GAPDH and EF-1 showed minimal stability under heat stress and quizalofop-p-ethyl stress, respectively (Table 4).

3.2.4. geNorm Analysis

The geNorm program determined stability by calculating M-values, where lower values signify higher expression consistency. Stability rankings varied across conditions. TUB and ACT constituted the optimal RG pair under drought, salt, and cold stress (Figure 1B,C,E). TUB and EF-1 exhibited superior stability during heat stress, while EIF4A and EF-1 were optimal during Cd stress (Figure 1A,D). UBQ and GAPDH formed the most stable pair under quizalofop-p-ethyl stress, and EIF4A and TUB demonstrated the highest stability across the developmental stages (Figure 1F,G).

3.2.5. Comprehensive Ranking of RGs

Comparative analysis revealed discrepancies in the optimal RG selection among the four methods, attributable to their distinct statistical frameworks. To reconcile these variations, RefFinder generated a consolidated stability ranking by computing the geometric mean (geomean) of algorithm-specific scores. The top two RGs in the overall ranking were selected as the optimal combinations [22,34]. EIF4A and TUB emerged as the most stable under heat stress (Figure 2A), while EIF4A and ACT ranked highest under drought stress (Figure 2B). For salt stress, EIF4A and 28S exhibited superior stability (Figure 2C). EIF4A and EF-1 were optimal under Cd exposure (Figure 2D), TUB and EIF4A under cold stress (Figure 2E), EIF4A and UBQ under quizalofop-p-ethyl exposure (Figure 2F), and EIF4A and TUB across different growth stages (Figure 2G).

3.3. Validation of the Recommended RGs

To validate the selected RGs, we analyzed the expression of two well-established stress-responsive marker genes, COR413 (implicated in cold tolerance) and P5CS (implicated in drought tolerance through proline biosynthesis), based on their documented roles in plant stress responses [35,36,37,38]. Normalization was performed using the optimal RG pairs for cold stress (TUB/EIF4A) and drought stress (EIF4A/ACT), alongside the least stable genes, UBQ (cold stress) and RUB (drought stress). Under cold stress, normalization with TUB/EIF4A revealed significant upregulation of COR413 expression, particularly 24 h post-treatment, whereas normalization with UBQ showed no significant changes (Figure 3A). Similarly, normalization with EIF4A/ACT demonstrated significant upregulation of P5CS expression 24 h after drought stress initiation. In contrast, normalization using RUB yielded no significant differential expression. These results underscore the critical impact of RG selection on expression profiling accuracy and emphasize the necessity for rigorous, stress-specific validation of reference genes in P. fugax research.

3.4. Expression Level of the ACCase Gene in P. fugax Under Quizalofop-p-Ethyl Exposure

This investigation employed four P. fugax populations (one susceptible, SC-S; three resistant, R1-R3) to examine the relationship between ACCase expression and quizalofop-p-ethyl resistance at 0 h (pre-treatment) and 12 h (early response) using optimal versus least-stable RG normalization. These timepoints were selected to capture initial transcriptional changes following herbicide application. Relative to SC-S controls, the R1 population exhibited no significant differential ACCase expression regardless of normalization approach (Figure 4A,B), while the R2 population demonstrated significantly higher ACCase transcript levels at 0 h and 12 h post-herbicide treatment (Figure 4C,D). The R3 population showed comparable ACCase expression at 0 h but significant upregulation at 12 h when normalized with 28S (Figure 4E,F). Notably, ACCase expression levels responded more markedly to 28S normalization than to EIE4A/UBQ normalization.

4. Discussion

Prior research has established that no single RG universally quantifies target gene expression across all experimental conditions or plant species. Suitable RGs have been characterized for several grass species, including Lolium spp. [27,39,40], Agrostis stolonifera [41], Cynodon dactylon [42], Paspalum vaginatum [15], and Poa pratensis [43]. This investigation represents the first systematic identification of multiple RGs appropriate for RT-qPCR normalization in P. fugax leaves subjected to six distinct abiotic stresses (heat, cold, drought, Cd, salt, and herbicide) and across various developmental stages.
Previous reports indicate that the Delta Ct method, geNorm, NormFinder, and BestKeeper algorithms can yield divergent outcomes due to their differing computational approaches [41]. RefFinder integrates data from these four programs to screen RGs and achieve robust evaluations [22,44]. In the present work, the EIF4A gene exhibited exceptional stability across all stress treatments, supporting its reliability as an RG (Figure 2). Comparable findings for EIF4A have been documented in studies involving shortawn foxtail [45], Sudan grass [34], and Italian ryegrass [46]. EIF4A, encoding a key translation initiation factor in eukaryotes, typically demonstrates minimal responsiveness to environmental perturbations [47], underpinning its frequent selection as an RG [25,27,48]. Conversely, while the ACT gene is widely regarded as a conventional RG [49,50], our data revealed low expression stability for ACT under heat, salt, and quizalofop-p-ethyl stress, despite higher stability under other conditions (Figure 2). This significant fluctuation in ACT stability may stem from its variable sensitivity to different stressors. Generally, relying on a single gene for normalization introduces substantial errors in a considerable proportion of samples [30]. Therefore, employing two or more RGs in parallel for a given sample or condition is essential to detect systematic biases and ensure the accuracy of RT-qPCR data [30,51]. Our findings align with reports for P. vaginatum [15] and Carex rigescens [25], indicating that two RGs are sufficient for precise normalization in most P. fugax tissues and conditions, as determined by geNorm analysis. The study provides optimal RG pairs for P. fugax under specific scenarios (Figure 2): EIF4A and TUB for heat stress, EIF4A and ACT for drought stress, EIF4A and 28S for salt stress, EIF4A and EF-1 for Cd exposure, TUB and EIF4A for cold stress, EIF4A and UBQ for quizalofop-p-ethyl treatment, and EIF4A and TUB for different growth stages. Nevertheless, the optimal number of stable RGs depends on experimental variables. Utilizing multiple RGs during RT-qPCR data analysis is recommended to eliminate deviations and fluctuations potentially arising from using only one or two reference genes.
A notable observation is the differential expression patterns exhibited by several RGs in P. fugax compared to other grass species under identical abiotic stresses. For instance, while EF-1 was reported as the most stable RG under salt and drought stress in soybean and Vigna mungo [52,53], its stability in P. fugax under these stresses was lower than several other candidates (Figure 2). Other commonly used RGs, including RUB, GAPDH, and UBQ [27,54,55], employed for normalization in diverse plant species, also displayed variable expression across species and environmental conditions. In this study, RUB, GAPDH, and UBQ consistently ranked lower in stability than other RGs across nearly all conditions. These comparisons underscore the necessity of identifying species-specific RGs for accurate gene expression quantification in P. fugax.
Significant variations in normalized target gene expression levels were observed when stable versus unstable RGs were used, potentially leading to misinterpretation of results. To further validate RG stability in this work, the expression profiles of two target genes (COR413 and P5CS) were examined. The cold-responsive COR413 gene encodes a membrane protein potentially functioning as a putative G-protein-coupled receptor, implicated in stress signaling or membrane stabilization during cold stress [35,36]. The delta 1-pyrroline-5-carboxylate synthetase gene (P5CS) encodes a key enzyme in proline biosynthesis, responsive to water-related stresses [37,38]. Results demonstrated that expression patterns of these target genes under drought and cold stress varied considerably depending on the reference gene selected (Figure 3), highlighting the critical importance of appropriate internal control genes for reliable RT-qPCR analysis.
Quizalofop-p-ethyl resistance in Polypogon fugax poses a significant threat to canola production. While qualitative alterations in the ACCase gene confer resistance in some weeds [33,56], the contribution of quantitative expression changes remains incompletely characterized. Accurate assessment of transcriptional regulation in herbicide resistance genes is hindered by the absence of validated reference genes for normalization. This study identified EIF4A/UBQ as the optimal RG combination for quizalofop-p-ethyl-exposed P. fugax. Utilizing this pair, we quantified ACCase transcription in three resistant populations, revealing elevated transcript levels in one resistant population (R2) relative to susceptible controls. Notably, this population simultaneously harbors an Asp-2078-Gly mutation [33]. This finding aligns with documented ACCase upregulation patterns across multiple herbicide-resistant species. In resistant Digitaria sanguinalis, ACCase expression increased 3.4–9.3 times more than the susceptible biotype [57]. Similarly, constitutive and inducible overexpression contributes significantly to cyhalofop resistance in Echinochloa crus-galli [58], while playing a more limited role in highly resistant E. crus-galli [59] and Eleusine indica [60] populations. Critically, our RG-validated approach revealed the coexistence of target-site mutation (Asp-2078-Gly) and ACCase overexpression in the R2 population—a resistance mechanism that would likely remain undetectable when normalized with unstable RGs. Beyond P. fugax, these findings demonstrate that overlooking RG validation in weed systems risks (1) misdiagnosing resistance mechanisms (e.g., attributing resistance solely to mutations while missing expression contributions) and (2) underestimating resistance complexity (particularly in populations with polygenic traits).

5. Conclusions

This research constitutes the first comprehensive identification and validation of RGs for P. fugax under diverse abiotic stresses and growth stages, establishing a vital methodological framework for future gene function studies in this species. The stable RGs identified and validated herein provide a reliable basis for accurate RT-qPCR quantification of target gene expression in P. fugax experiencing various abiotic stresses. These findings will facilitate the discovery of stress-responsive genes and elucidate molecular mechanisms underlying stress tolerance in P. fugax.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15081813/s1, Figure S1: PCR products of eight reference genes (TUB, ACT, EF-1, GAPDH, EIF4A, RUB, 28S, and UBQ). M, DNA marker; Figure S2: Melting curves for eight reference genes, including (A) TUB, (B) ACT, (C) EF-1, (D) GAPDH, (E) EIF4A, (F) RUB, (G) 28S and (H) UBQ; Figure S3: Ct values of eight reference genes in P. fugax under diverse stresses and growth stages. (A) Heat stress, (B) Drouth stress, (C) Salt stress, (D) Cd exposure, (E) Cold stress, (F) Quizalofop-p-ethyl stress, (G) Growth stage; Table S1: Primers used in this study.

Author Contributions

Conceptualization, S.W. and W.C.; data curation, Y.Z. and X.Y.; formal analysis, Y.Z., X.Y., Q.H. and J.Z.; funding acquisition, S.W. and W.C.; investigation, Y.Z., X.Y., Q.H. and J.Z.; resources, W.C.; supervision, Y.Z., X.Y., Q.H., J.Z., S.W. and W.C.; validation, W.C.; visualization, Y.Z. and X.Y.; writing—original draft, Y.Z., X.Y. and W.C.; writing—review and editing, Y.Z., X.Y. and W.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Key R&D Program of China (2023YFD2301200), the President’s Fund Project of Tarim University (TDZKBS202501), the State Key Laboratory of Cotton Bio-breeding and Integrated Utilization Open Fund (CB2025A20), and the National College Student Innovation and Entrepreneurship Training Program (202510757009).

Data Availability Statement

Data are contained within the article.

Acknowledgments

We thank the State Key Laboratory of Cotton Bio-breeding and Integrated Utilization for granting us access to its facilities.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The RGs were ranked based on their expression stability values determined using the geNorm analysis. (A) Heat stress, (B) drought stress, (C) salt stress, (D) Cd exposure, (E) cold stress, (F) quizalofop-p-ethyl stress, and (G) growth stage.
Figure 1. The RGs were ranked based on their expression stability values determined using the geNorm analysis. (A) Heat stress, (B) drought stress, (C) salt stress, (D) Cd exposure, (E) cold stress, (F) quizalofop-p-ethyl stress, and (G) growth stage.
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Figure 2. The comprehensive ranking of RGs in four groups using RefFinder program. (A) Heat stress, (B) drought stress, (C) salt stress, (D) Cd exposure, (E) cold stress, (F) quizalofop-p-ethyl stress, and (G) growth stage.
Figure 2. The comprehensive ranking of RGs in four groups using RefFinder program. (A) Heat stress, (B) drought stress, (C) salt stress, (D) Cd exposure, (E) cold stress, (F) quizalofop-p-ethyl stress, and (G) growth stage.
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Figure 3. Relative expression of two target genes (COR413 and P5CS) normalized with different sets of RGs under (A) cold stress and (B) drought stress. In cold stress, the TUB/EIF4A group and UBQ served as the optimal and least stable reference genes, respectively. In drought stress, the EIF4A/ACT group and RUB served as the optimal and least stable reference genes, respectively. Error bars represent the standard error of the means (n = 3 independent biological replicates). * indicates significant difference (p < 0.05) between 12 h or 24 h and 0 h, by Student’s t-test.
Figure 3. Relative expression of two target genes (COR413 and P5CS) normalized with different sets of RGs under (A) cold stress and (B) drought stress. In cold stress, the TUB/EIF4A group and UBQ served as the optimal and least stable reference genes, respectively. In drought stress, the EIF4A/ACT group and RUB served as the optimal and least stable reference genes, respectively. Error bars represent the standard error of the means (n = 3 independent biological replicates). * indicates significant difference (p < 0.05) between 12 h or 24 h and 0 h, by Student’s t-test.
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Figure 4. Relative expression levels of the ACCase gene across three quizalofop-p-ethyl-resistant populations (R1, R2 and R3) and one quizalofop-p-ethyl-susceptible population (SC-S) at 0 h (A,C,E) and 12 h (B,D,F) after quizalofop-p-ethyl treatment. The relative expression levels are evaluated by the ratio of the normalized expression of the ACCase gene in resistant populations to that in the susceptible SC-S population using the 2−ΔΔCt method. The EIF4A/UBQ group and 28S served as the optimal and least stable reference genes, respectively. Error bars represent the standard error of the means (n = 3 independent biological replicates). * indicates significant difference (p < 0.05) between the susceptible population and the corresponding resistant population, by Student’s t-test.
Figure 4. Relative expression levels of the ACCase gene across three quizalofop-p-ethyl-resistant populations (R1, R2 and R3) and one quizalofop-p-ethyl-susceptible population (SC-S) at 0 h (A,C,E) and 12 h (B,D,F) after quizalofop-p-ethyl treatment. The relative expression levels are evaluated by the ratio of the normalized expression of the ACCase gene in resistant populations to that in the susceptible SC-S population using the 2−ΔΔCt method. The EIF4A/UBQ group and 28S served as the optimal and least stable reference genes, respectively. Error bars represent the standard error of the means (n = 3 independent biological replicates). * indicates significant difference (p < 0.05) between the susceptible population and the corresponding resistant population, by Student’s t-test.
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Table 1. Primer sequences and descriptions of eight reference genes.
Table 1. Primer sequences and descriptions of eight reference genes.
IDGene SymbelGene DescriptionPrimer Sequences (5′→3′)Amplicon
Length (bp)
Amplification Efficiency (%)R2
TRINITY_DN5506_c0_g1EF-1Elongation factor 1-alphaGATGAAAATGCCTCCAGAACG
AGCCCCACAACGAATACATC
128105.070.994
TRINITY_DN35447_c0_g128S28S ribosomal RNACCATCCATCTCGCAAGAAAT
CGCAATATCTTCACCCGTTT
22191.880.989
TRINITY_DN11218_c0_g1ACTActinGCACAATGTTGCCATACAGG
AAGAACAGCTCCTCCGTTGA
20599.560.999
TRINITY_DN6226_c0_g1TUBβ-tubulinGACTCCTTCAACTCAGCCAG
CCCTACGATCTCAAGCAACAG
125103.550.986
TRINITY_DN51183_c0_g1GAPDHGlyceraldehyde-3-phosphate dehydrogenaseTGCCCTGTGATTTTCCGATAG
AAGCCTAATTCGAGAGTTCAGAC
15895.570.993
TRINITY_DN15195_c1_g2EIF4AEukaryotic initiation factor 4ATGCTTGGTTTCTGACTTCTGG
TTGTGGAGTTGTCGCTAGTG
17690.520.947
TRINITY_DN10120_c0_g1UBQubiquitinAGCGGTGTCAAAGGTGTC
TGGCTGAGTGGAAAGATCAAG
18097.240.993
TRINITY_DN3966_c0_g1RUBribulose-1,5-bisphosphate carboxylaseAACGGTGGAAGGATCAGATG
GAAGATGAACCAAACCTTGCTG
17493.780.997
Table 2. The RGs were ranked based on their suitability for normalization, and their expression stability values were determined using the Delta Ct method.
Table 2. The RGs were ranked based on their suitability for normalization, and their expression stability values were determined using the Delta Ct method.
RankHeat Stress
Subset
Drought Stress
Subset
Salt Stress
Subset
Cd Stress
Subset
Cold Stress
Subset
Quizalofop-p-ethyl Stress SubsetGrowth Stage
Subset
GeneSDGeneSDGeneSDGeneSDGeneSDGeneSDGeneSD
1EIF4A0.59EIF4A0.65EIF4A0.68EIF4A0.6EIF4A0.65EIF4A0.68EIF4A0.79
2TUB0.63ACT0.67EF-10.77ACT0.63TUB0.69TUB0.69TUB0.84
3EF-10.65TUB0.6828S0.78EF-10.67ACT0.73UBQ0.75ACT0.97
4RUB0.66GAPDH0.68UBQ0.78GAPDH0.67EF-10.81GAPDH0.7528S0.99
5ACT0.68EF-10.92TUB0.8328S0.79RUB0.91ACT0.79EF-11.00
628S0.78UBQ0.93ACT0.89TUB0.80GAPDH1.00EF-10.84RUB1.16
7UBQ0.90RUB0.94GAPDH0.94UBQ0.8528S1.03RUB0.90UBQ1.16
8GAPDH1.5728S1.06RUB1.1RUB1.03UBQ1.0928S1.04GAPDH1.34
Table 3. The RGs were ranked based on their expression stability values determined using the NormFinder analysis.
Table 3. The RGs were ranked based on their expression stability values determined using the NormFinder analysis.
RankHeat Stress
Subset
Drought Stress
Subset
Salt Stress
Subset
Cd Stress
Subset
Cold Stress
Subset
Quizalofop-p-ethyl Stress SubsetGrowth Stage
Subset
GeneSVGeneSVGeneSVGeneSVGeneSVGeneSVGeneSV
1TUB0.047EIF4A0.165EIF4A0.269EIF4A0.243EIF4A0.181EIF4A0.344EIF4A0.254
2EIF4A0.226ACT0.273UBQ0.455ACT0.277TUB0.296TUB0.346TUB0.427
3EF-10.239TUB0.32028S0.466EF-10.423ACT0.391GAPDH0.483ACT0.656
4RUB0.330GAPDH0.335EF-10.498GAPDH0.434EF-10.545UBQ0.49528S0.684
528S0.408EF-10.745TUB0.627TUB0.604RUB0.650ACT0.546EF-10.753
6ACT0.410UBQ0.784ACT0.71228S0.646GAPDH0.824EF-10.628RUB0.914
7UBQ0.803RUB0.794GAPDH0.799UBQ0.68028S0.915RUB0.725UBQ0.939
8GAPDH1.53828S0.954RUB0.942RUB0.907UBQ0.95328S0.903GAPDH1.181
Table 4. The RGs were ranked based on their expression stability values determined using the BestKeeper analysis.
Table 4. The RGs were ranked based on their expression stability values determined using the BestKeeper analysis.
RankHeat Stress
Subset
Drought Stress
Subset
Salt Stress
Subset
Cd Stress
Subset
Cold Stress
Subset
Quizalofop-p-ethyl Stress SubsetGrowth Stage
Subset
GeneSDGeneSDGeneSDGeneSDGeneSDGeneSDGeneSD
1EIF4A0.3028S0.55EF-10.55EIF4A0.5528S0.49RUB0.60RUB0.16
2EF-10.30EF-10.5628S0.36ACT0.56TUB0.52ACT0.85EIF4A0.74
3RUB0.35ACT0.79ACT0.95EF-10.59ACT0.56UBQ0.89GAPDH0.82
4ACT0.39EIF4A0.91TUB0.93GAPDH0.65EF-10.57TUB0.9328S0.85
5TUB0.40TUB1.07GAPDH0.6128S0.73EIF4A0.70EIF4A1.06TUB0.86
628S0.41GAPDH1.07EIF4A0.53TUB0.82RUB0.74GAPDH1.10EF-10.96
7UBQ0.62RUB1.27UBQ0.94UBQ0.94GAPDH1.0728S1.14ACT0.96
8GAPDH1.31UBQ1.45RUB1.05RUB1.06UBQ1.30EF-11.28UBQ1.29
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Zhao, Y.; Yang, X.; Hu, Q.; Zhang, J.; Wan, S.; Chen, W. Reliable RT-qPCR Normalization in Polypogon fugax: Reference Gene Selection for Multi-Stress Conditions and ACCase Expression Analysis in Herbicide Resistance. Agronomy 2025, 15, 1813. https://doi.org/10.3390/agronomy15081813

AMA Style

Zhao Y, Yang X, Hu Q, Zhang J, Wan S, Chen W. Reliable RT-qPCR Normalization in Polypogon fugax: Reference Gene Selection for Multi-Stress Conditions and ACCase Expression Analysis in Herbicide Resistance. Agronomy. 2025; 15(8):1813. https://doi.org/10.3390/agronomy15081813

Chicago/Turabian Style

Zhao, Yufei, Xu Yang, Qiang Hu, Jie Zhang, Sumei Wan, and Wen Chen. 2025. "Reliable RT-qPCR Normalization in Polypogon fugax: Reference Gene Selection for Multi-Stress Conditions and ACCase Expression Analysis in Herbicide Resistance" Agronomy 15, no. 8: 1813. https://doi.org/10.3390/agronomy15081813

APA Style

Zhao, Y., Yang, X., Hu, Q., Zhang, J., Wan, S., & Chen, W. (2025). Reliable RT-qPCR Normalization in Polypogon fugax: Reference Gene Selection for Multi-Stress Conditions and ACCase Expression Analysis in Herbicide Resistance. Agronomy, 15(8), 1813. https://doi.org/10.3390/agronomy15081813

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