Identification of reference genes for real-time quantitative PCR in different development stage in chili pepper fruits（Capsicum annuum and Capsicum chinense）

Identification of reference genes for real-time quantitative PCR in different development stage in chili pepper fruits（Capsicum annuum and Capsicum chinense） | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of reference genes for real-time quantitative PCR in different development stage in chili pepper fruits（Capsicum annuum and Capsicum chinense） Yuanhao Xu, Chuan Wang, Bingchun Xie, Chu Ye, Xiaomei Xu, Tao Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9157895/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The selection of appropriate reference genes is a fundamental prerequisite for reliable gene expression analysis using real-time quantitative polymerase chain reaction (RT-qPCR). To date, no systematic study has identified reference genes for normalizing RT-qPCR data in pepper fruits across different developmental stages. In this study, we evaluated the expression stability of seven commonly used internal control genes and nine additional candidates derived from available transcriptomic datasets in two widely cultivated chili pepper species (Capsicum annuum and Capsicum chinense) at various fruit developmental stages. The expression stability of the 16 candidate genes was assessed using four established algorithms: NormFinder, ΔCt, BestKeeper, and GeNorm. Our results demonstrated that CT8 and EIF were the most stably expressed genes during fruit development in Capsicum annuum ‘TJ’ and Capsicum chinense ‘7054’, respectively. This study provides a foundation for accurate normalization of RT-qPCR analyses in pepper fruits at different developmental stages and will help ensure the reliability of subsequent gene expression studies in these species. Biological sciences/Biological techniques Biological sciences/Biotechnology Biological sciences/Genetics Biological sciences/Molecular biology Biological sciences/Plant sciences Reference genes qRT-PCR Capsicum annuue Fruit development expression analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Quantitative real-time polymerase chain reaction (qRT-PCR) has become the gold standard method for quantifying gene expression and validating RNA-seq results. In qRT-PCR analysis, reference genes serve as internal controls to normalize the expression levels of target genes. Ideally, reference genes should remain unaffected by experimental conditions and exhibit stable expression across various treatments, tissues, and organs [ 1 ]. Commonly used reference genes in plant studies include actin (Actin), transcriptional elongation factor 1 (EF1), 18S ribosomal RNA (18S rRNA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), cyclophilin (CYP), ribulose-1,5-bisphosphate carboxylase (RuBP), adenine phosphoribosyltransferase (APRT), and α-tubulin (α-TUA) [ 2 – 4 ]. However, numerous studies have demonstrated that no single reference gene is universally applicable across all experimental conditions [ 5 – 7 ]. Therefore, the selection of appropriate reference genes is critical for accurate gene expression analysis in specific crops, such as chili pepper. Chili pepper (Capsicum spp.) is one of the most economically important crops in the Solanaceae family. Among the cultivated species, C. annuum and C. chinense are the most widely grown and commercially significant. Several studies have sought to identify optimal reference genes in chili peppers. For example, Wan et al. [ 8 ] used the bell pepper cultivar ‘PBC631B’ (Capsicum annuum) to evaluate reference genes and found that β-TUB and UBI-3were stable across all treatments, while UBI-3and GAPDHwere stable in different tissues and organs. In contrast, ACT and UBI-1were deemed unsuitable as reference genes. In another study, Wang [ 9 ] employed ‘CMS 21A’ (Capsicum annuum), a cytoplasmic male-sterile line, as experimental material and evaluated ten candidate reference genes—including ACT, ACT1, ACT2, 18S rRNA, PPR1, TEF1A, EF1α, UEP, CYC, and GAPDH—in roots, stems, leaves, flowers of mature plants, as well as in seedlings subjected to abiotic stress and hormone treatments. The results indicated that ACT was the most suitable reference gene across different tissues of mature plants, whereas EF1α was optimal for studies involving abiotic stress and hormone treatments. Among all samples examined, EF1α and UEP together demonstrated the highest expression stability. However, there have been no systematic investigations identifying optimal reference genes for normalizing gene expression during different developmental stages of chili pepper fruits. In the present study, we used two cultivars, ‘TJ’ (Capsicum annuum) and ‘7054’ (Capsicum chinense), as experimental materials. We evaluated the expression stability of seven commonly used reference genes (GAPDH, UBI-3, UBI-1, ACT, EIF, β-TUB, α-TUB) along with nine additional genes (CT4–CT12) selected from published transcriptome data based on their stable expression during fruit development. The stability of these 16 candidate reference genes was comprehensively assessed using four independent algorithms: NormFinder, ΔCt, BestKeeper, and GeNorm. This study establishes a foundation for accurate normalization of RT-qPCR data throughout fruit development in Capsicum annuumand Capsicum chinense. 2. Material and Methods 2.1 Plant materials Three biological replicates of chili pepper fruits from Capsicum annuum‘TJ’ and *Capsicum chinense’ ‘7054’ were collected weekly post-anthesis (designated as W1 through W6) from a cultivation site in Zhongluotan Town, Baiyun District, Guangzhou, Guangdong Province, China (23°23′24.5″N, 113°26′19.4″E). All collected samples were immediately frozen in liquid nitrogen upon collection and stored at − 80°C for subsequent analysis. 2.2 Candidate gene selection and primer design We identified genes demonstrating consistent expression throughout all stages of fruit development based on published transcriptome datasets [ 10 – 13 ] (designated as CT1–CT12), and evaluated their expression stability relative to commonly used reference genes (GAPDH, UBI-3, UBI-1, ACT, EIF, β-TUB, α-TUB), with the aim of selecting the most stable reference genes during pepper fruit development. 2.3 RNA extraction and cDNA synthesis Total RNA was extracted from three biological replicates using a QIAGEN RNeasy® Plant Mini Kit (Cat. No. 74904, QIAGEN, Hilden, Germany) and treated with the RNase-Free DNase Set (QIAGEN) to remove genomic DNA contamination. RNA integrity was assessed by 1% agarose gel electrophoresis, and concentration and purity were determined using a NanoPhotometer® spectrophotometer (N120, IMPLEN, Westlake Village, CA, USA). Complementary DNA (cDNA) was synthesized from the purified RNA using the PrimeScript™ RT Reagent Kit with gDNA Eraser (RR037A, Takara, Dalian, China) according to the manufacturer’s instructions. The resulting cDNA was stored at − 20°C for subsequent use. 2.4 qRT‑PCR analyses Primers for the candidate reference genes were designed using Primer Premier 5.0 software (listed in Table 1 ). Primer specificity was verified by conventional PCR using a standard DNA polymerase (GenStar). A 10-µL reaction volume was used, consisting of 5 µL of polymerase, 4 µL of sterile water, 0.25 µL of each forward and reverse primer, and 0.5 µL of DNA template. The amplification protocol was as follows: initial denaturation at 94°C for 5 min; 35 cycles of 94°C for 30 s, 55°C for 30 s, and 72°C for 1 min; followed by a final extension at 72°C for 10 min. Amplification products were analyzed by 1% agarose gel electrophoresis to confirm primer specificity using a 4100 Gel Imaging System (Tanon, Shanghai, China). For quantitative PCR, the SYBR Premix Ex Taq™ kit (Takara Bio, Dalian, China) was employed according to the manufacturer’s instructions. Reactions were performed in a 25-µL volume under a two-step amplification program: 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s. A melting curve analysis was subsequently conducted by heating from 65°C to 95°C in 0.5°C increments with continuous fluorescence acquisition. All samples were run in three technical replicates. Table 1 Primer information of candidate reference gene Primer name Forward primer sequence(5’→3’) Reverse primer sequence(5’→3’) Gene ID GAPDH ATGATGATGTGAAAGCAGCG TTTCAACTGGTGGCTGCTAC AJ246013.1 UBI-3 TGTCCATCTGCTCTCTGTTG CACCCCAAGCACAATAAGAC AY486137.1 UBI-1 AAGGAAATGTGTGTCTCAAC TCCAAATGCCAAACTTCTAG DQ924970.1 ACT TGTTATGGTAGGGATGGGTC TTCTCTCTATTTGCCTTGGG AY572427.1 EIF CCTGTTATCGTGCTACTTTG GTTTCATTGCCNTGCCAGAT AY484392.1 β-TUB GAGGGTGAGTGAGCAGTTC CTTCATCGTCATCTGCTGTC EF495259.1 α-TUB ATCAACTATCAGCCACCAAC TACCCTCACCAACATACCAG EF495257.1 CT1 GCACGGTTCTTCTTACTG AAAGTCTGCGAAACCTCT gene-BC332_08861 CT2 AGGCGAGAACGAAGACAG TTGCCAATACCACCCATC gene-BC332_23789 CT3 AGCAGCCACTATCAAAGC ACGCATCTGGACCGTAAT gene-BC332_06775 CT4 TCCTCCATCTCGTCCAAA CATCACGGCGGTAGGTAA CA08g01440 CT5 CGTGTTGTGGGAACTGGA CTCGTCGTTGAATAGAAGCA CA11g11330 CT6 TTTGCCTCTGTCCTTTGG GCACCTCTTCAGCTACCG CA01g00780 CT7 TAAGCATTGCCAGGAGTT AAGCATCGCATCATTTCA Capana02g001723 CT8 AACAAATACACCCTACGC GACGGAAACATCTGAACC Capana03g004217 CT9 GCTCCATCCTTCATCTCC GAGCGGCACTACTACGAC Capana02g002361 CT10 ATGCCAGGACTCAACACT CTCAGATCGGGAACAACA Capana08g002835 CT11 CTTTGGTTGTGGCTCTGC TTGCCTTCTTCGTCTCGT Capana02g000368 CT12 ATCCTTGATGCGTTCTTC GGCTTGTTTGGCTCTTTC Capana06g000596 2.5 Transcript stability analysis Primer amplification efficiency was determined using LinRegPCR software. Subsequently, the expression stability of the 16 candidate reference genes was evaluated using four independent algorithms: NormFinder, ΔCt, BestKeeper, and GeNorm. The results were then integrated and comprehensively ranked via the online tool RefFinder ( http://blooge.cn/RefFinder/ ) to obtain a consensus stability order. 3. Result 3.1. Candidate reference genes selection and amplification specificity Candidate reference genes were selected based on their stable expression across different developmental stages, as identified from previously published transcriptome data, and were designated CT1 to CT12 (Table S1 ). PCR amplification of the 19 candidate genes followed by agarose gel electrophoresis revealed non-specific amplification products for CT1, CT2, and CT3, leading to their exclusion from further analysis (Fig. 1 ). In accordance with the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines [ 14 ], non-specific amplification was also observed for CT9 and CT10 (Fig. 1 ). However, the non-specific products were approximately 500 bp in length, which does not interfere with qRT-PCR analysis. Therefore, CT9 and CT10 were retained for subsequent experiments. Melting curve analysis of real-time PCR products demonstrated that all candidate genes exhibited a single peak, confirming amplification specificity (Fig. 2 ). Based on these results, all candidate genes except CT1–CT3 were selected for further study. 3.2 Expression levels of candidate reference genes 3.2.1. Expression levels of candidate reference genes in “TJ” pepper fruits The cycle threshold (Ct) value represents the number of PCR cycles required for the fluorescence signal to cross a predefined threshold, serving as a key parameter for assessing gene expression abundance. Lower Ct values correspond to higher expression levels. The stability of Ct values is a critical criterion for selecting appropriate reference genes. As illustrated in Fig. 3 , the Ct values of the 16 candidate reference genes across various developmental stages of ‘TJ’ pepper fruit ranged from 23.28 (UBI-3) to 36.30 (CT6). Four genes—α-TUB, EIF, β-TUB, and UBI-3—exhibited significantly lower Ct values compared to the others, indicating high expression abundance throughout fruit development. In contrast, CT6 showed a markedly higher Ct value, reflecting the lowest expression level among all candidate reference genes. 3.2.2. Expression level of candidate reference gene in “7054” pepper fruit The Ct values of the 16 candidate reference genes across the six developmental stages of ‘7054’ pepper fruit are presented in Fig. 4 . The Ct values ranged from 19.18 (UBI-3) to 30.78 (CT6). Five genes—α-TUB, EIF, GAPDH, UBI-3, and UBI-1—exhibited significantly lower Ct values compared to the others, indicating high expression abundance during fruit development. In contrast, CT6 showed a markedly higher Ct value, reflecting the lowest expression level among all candidate reference genes. This pattern of expression was consistent with that observed in ‘TJ’ pepper fruit. 3.3 Transcript stability of candidate reference genes 3.3.1 Stability evaluation of candidate reference genes in “TJ” pepper fruit Analysis with NormFinder and the ΔCt method identified EIFas the most stably expressed gene (Table 2 ). In BestKeeper, CT7demonstrated the highest stability, whereas GeNorm analysis indicated CT8as the most suitable reference gene. Notably, UBI-1exhibited inconsistent expression across all algorithms. Comprehensive ranking using the RefFinder online tool generated a stability order (from most to least stable) for the candidate reference genes across the six developmental stages of ‘TJ’ pepper fruit as follows: CT8 > EIF > CT4 > CT7 > CT12 > CT11 > CT6 > β-TUB > CT5 > UBI-3 > ACT > CT9 > GAPDH > α-TUB > CT10 > UBI-1. Table 2 The stability of reference genes for “TJ” pepper fruit development candidates Stability values Gene Comprehensive NormFinder Delta CT BestKeeper GeNorm CT8 1.86(1) 0.255(2) 1.25(2) 0.418(3) 0.308(1) EIF 2.06(2) 0.128(1) 1.24(1) 0.700(6) 0.489(3) CT4 2.59(3) 0.323(3) 1.29(3) 0.642(5) 0.308(1) CT7 3.87(4) 0.704(8) 1.48(7) 0.392(1) 0.695(4) CT12 3.98(5) 0.536(5) 1.41(5) 0.417(2) 0.764(5) CT11 5.26(6) 0.396(4) 1.37(4) 0.999(8) 0.873(6) CT6 7.14(7) 0.610(6) 1.47(6) 1.082(9) 1.026(8) β-TUB 7.91(8) 0.690(7) 1.56(8) 1.087(10) 0.958(7) CT5 8.54(9) 0.919(11) 1.67(11) 0.423(4) 1.228(11) UBI-3 10.13(10) 0.821(10) 1.62(9) 1.318(13) 1.120(9) ACT 10.19(11) 0.806(9) 1.65(10) 1.231(12) 1.172(10) CT9 10.90(12) 1.045(14) 1.83(12) 0.746(7) 1.287(12) GAPDH 13.24(13) 1.038(13) 1.83(12) 1.330(14) 1.349(13) α-TUB 13.71(14) 1.017(12) 1.88(14) 1.501(15) 1.407(14) CT10 13.88(15) 1.514(15) 2.36(15) 1.230(11) 1.515(15) UBI-1 16.00(16) 1.773(16) 2.71(16) 2.273(16) 1.664(16) 3.3.2. Stability evaluation of candidate reference genes in “7054” pepper fruit Analysis by NormFinder, GeNorm, and the ΔCt method indicated that the EIFgene exhibited the highest expression stability (Table 3 ). In contrast, the CT4gene was identified as the most stable according to BestKeeper. The β-TUBgene was consistently identified as unstable across all analytical tools. Comprehensive evaluation using the RefFinder online platform revealed that the top three most stable reference genes across the six developmental stages of ‘7054’ pepper fruit were EIF, CT4, and UBI-3. Table 3 Stability of reference genes for “7054” pepper fruit development candidates Stability values Gene Comprehensive NormFinder Delta CT BestKeeper GeNorm EIF 1.19(1) 0.086(1) 0.77(1) 0.465(2) 0.112(1) CT4 1.57(2) 0.151(3) 0.78(2) 0.442(1) 0.112(1) UBI-3 3.46(3) 0.159(4) 0.80(3) 0.513(4) 0.209(3) CT5 3.72(4) 0.098(2) 0.80(3) 0.582(6) 0.265(4) GAPDH 5.38(5) 0.201(5) 0.87(7) 0.472(3) 0.429(8) CT6 6.16(6) 0.2586) 0.84(5) 0.697(8) 0.341(6) CT8 6.19(7) 0.282(7) 0.85(6) 0.693(7) 0.308(5) CT7 6.88(8) 0.364(8) 0.92(8) 0.515(5) 0.372(7) ACT 10.82(9) 0.649(9) 1.23(9) 1.074(13) 0.855(13) CT9 10.95(10) 0.696(10) 1.26(10) 0.958(12) 0.800(12) CT12 11.49(11) 0.748(11) 1.27(11) 1.307(16) 0.555(9) CT11 12.16(12) 0.761(13) 1.29(12) 1.137(14) 0.643(10) UBI-1 12.67(13) 0.755(12) 1.31(13) 1.179(15) 0.732(11) CT10 13.18(14) 0.875(14) 1.46(14) 0.937(11) 0.950(14) α-TUB 13.55(15) 0.919(15) 1.47(15) 0.715(9) 1.030(15) β-TUB 13.86(16) 0.938(16) 1.49(16) 0.770(10) 1.088(16) 3.3.3. Analysis of pairing variants of reference genes In real-time PCR analysis, the use of a single reference gene is often insufficient to ensure reliable normalization[ 7 ]. To address this, the present study employed geNorm software to evaluate the optimal number of reference genes required for accurate normalization in two pepper cultivars (Capsicum annuum‘TJ’ and Capsicum chinense‘7054’). In geNorm, the pairwise variation value Vₙ/Vₙ₊₁ is used to determine whether the inclusion of an additional reference gene is necessary. A threshold of Vₙ/Vₙ₊₁ < 0.15 indicates that n reference genes are sufficient for accurate normalization, whereas a value above 0.15 suggests that n + 1 genes should be used. As shown in Fig. 5 , the V₂/₃ value exceeded 0.15 in ‘TJ’ pepper, indicating that three reference genes are required to normalize target gene expression reliably during fruit development. In contrast, two reference genes were sufficient to ensure data accuracy in ‘7054’ pepper. 4. Discussion qRT-PCR is widely employed for analyzing relative gene expression due to its high accuracy, efficiency, and reproducibility[ 15 ]. However, the selection of appropriate and stable reference genes is critical for the reliable quantification of target gene expression[ 16 ]. Ideally, reference genes should exhibit consistent expression across different tissues, organs, and developmental stages within the same species, and remain unaffected by internal biological conditions or external environmental factors[ 1 ]. In practice, however, the expression stability of reference genes is relative, and even within the same species, their expression can be influenced by various internal and external variables. Thus, it is essential to identify and validate reference genes under specific experimental conditions. To date, suitable reference genes have been identified for various plants during distinct developmental stages of the same organ. For example, Yang[ 17 ] reported that genes such as UBC23, CYP38, and GAPDH2in Euscaphis konishiiare stably expressed during fruit development and are suitable as reference genes. Chen[ 18 ] demonstrated that Actinand EF1-αare appropriate reference genes for pitaya (dragon fruit) development. Similarly, Kanakachari found that in eggplant, SANDand TBPare suitable reference genes across fruit developmental stages, whereas the Ubiquitin gene exhibited instability. Choi[ 19 ] identified three genes—EXPRESSED, RPL8, and GAPDH—as effective reference genes during tomato fruit development. Consistent with findings in eggplant[ 20 ], the present study also observed that UBI-1expression was unstable in pepper, rendering it unsuitable as an endogenous control during fruit development. In contrast, several genes identified in this study—including CT4, UBI-3, CT8, CT6, and CT11—demonstrated higher expression stability compared to commonly used reference genes such as ACT and GAPDH. These genes have been rarely reported previously, underscoring the need for more extensive and in-depth screening of reference genes across species and conditions. Based on comprehensive analysis, CT4is recommended as the most reliable reference gene for studying pepper fruit development. Declarations Funding National Key Research and Development Program Project (2023YFD1201502); Guangdong Provincial Department of Agriculture Seed Industry Revitalization and Agricultural Technology Promotion Project (2024-440000-87020100-8753); Guangzhou Science and Technology Program Project (2023B03J1082); Bijie Science and Technology Project with Open Call for Proposals (Bijie Science and Technology Cooperation Major Project [2022] No.3) CRediT authorship contribute statement Yuanhao Xu: Conceptualization , Methodology,Investigation,Formal analysis,Data Curation,Writing-original draft, Writing-review & editing. Bingchun Xie :Conceptualization,Methodology,Investigation,Data Curation,Visualization,Writing-review & editing. Chu Ye :Validation,Formal analysis,Data Curation. Xiaowan Xu :Project administration. Xiaomei Xu :Validation.. Tao Li and Zhou Heng : Funding acquisition, Project administration, Supervision. Declaration of competing interest The authors declare no competing interests. Data availability All data generated or analysed during this study are included in this published article [and its supplementary information files]. References A. Radonic, S. Thulke, I.M. Mackay, O. Landt, W. Siegert, A. Nitsche, Guideline to reference gene selection for quantitative real-time PCR, BIOCHEM BIOPH RES CO, 313 (2004) 856-62.https://doi.org/10.1016/j.bbrc.2003.11.177 M. Expósito-Rodríguez, A.A. Borges, A. Borges-Pérez, J.A. 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Kumar, Evaluation of suitable reference genes for normalization of qPCR gene expression studies in brinjal (Solanum melongena L.) during fruit developmental stages, APPL BIOCHEM BIOTECH, 178 (2016) 433-450.https://doi.org/ 10.1007/s12010-015-1884-8 Additional Declarations No competing interests reported. Supplementary Files TableS1.docx Appendix A. 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"Wang","suffix":""},{"id":629464324,"identity":"c4e4bb0e-ed04-42b0-8d8b-b86ae0e6ee5b","order_by":2,"name":"Bingchun Xie","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bingchun","middleName":"","lastName":"Xie","suffix":""},{"id":629464326,"identity":"832fd5b6-add1-4d3a-887e-def8dc802531","order_by":3,"name":"Chu Ye","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chu","middleName":"","lastName":"Ye","suffix":""},{"id":629464329,"identity":"f810c661-7f88-42b3-9b5e-851d876140df","order_by":4,"name":"Xiaomei Xu","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaomei","middleName":"","lastName":"Xu","suffix":""},{"id":629464334,"identity":"99685963-7e6a-4e7b-9af0-b6b785d964b9","order_by":5,"name":"Tao Li","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Li","suffix":""},{"id":629464335,"identity":"14778af0-9085-4c26-9ab2-ee6be2efaa98","order_by":6,"name":"Xiaowan Xu","email":"","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaowan","middleName":"","lastName":"Xu","suffix":""},{"id":629464342,"identity":"3fa40823-edb6-4cb3-96c9-29cd45a436da","order_by":7,"name":"Zhou Heng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACAxDB2MDAwA9lk6BFsoFkLQYHiNVizt57+OXPHTaJm88v3vjhxx8GeXNCWix7zqVZSJ5JS9x241mxZG8bg+HOBkIOu5FjZmDYdhio5YyBBG8DQwLQhQS03H9jZpAI1LJ5xhnjn3/+EKPlBo/xg4NALRv4e8ykediI0XImx4yxsS3NeMYNtjJr2TYJww0EtRw/Y/zxZ5uNbH//4c033/yxkSdoCxCwSYApiQQwSVg9EDB/AFP8RJg+CkbBKBgFIxMAADkcSLdpmU76AAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Heng","suffix":""}],"badges":[],"createdAt":"2026-03-18 10:08:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9157895/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9157895/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108099014,"identity":"64c13e6c-0492-49f3-b3d9-e78e92fc0f19","added_by":"auto","created_at":"2026-04-29 10:30:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91771,"visible":true,"origin":"","legend":"\u003cp\u003eSpecific test of 19 candidate reference genes\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9157895/v1/cef254f1e069bc0538de8da6.png"},{"id":108182643,"identity":"72455956-b93f-4e38-b391-7726ce4da5e4","added_by":"auto","created_at":"2026-04-30 08:59:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":960469,"visible":true,"origin":"","legend":"\u003cp\u003eMelting curve peaks of 16 candidate genes\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9157895/v1/350774af391a9e570f22f02f.png"},{"id":108099016,"identity":"207cf394-3ae4-4047-b286-f194435e9ba2","added_by":"auto","created_at":"2026-04-29 10:30:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47567,"visible":true,"origin":"","legend":"\u003cp\u003eCt values of 16 candidate reference genes at different developmental stages of “TJ”pepper fruit\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9157895/v1/5368364eb73d4b7f25f57b5c.png"},{"id":108099017,"identity":"b368e582-ab8b-447e-b7d5-fcdb817c8e2c","added_by":"auto","created_at":"2026-04-29 10:30:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53405,"visible":true,"origin":"","legend":"\u003cp\u003eCt values of 16 candidate reference genes at different developmental stages of “7054” pepper fruit\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9157895/v1/67324a4424f42f489ab2ffd9.png"},{"id":108182053,"identity":"d0f19059-3a53-4140-9fd1-d83433398ea1","added_by":"auto","created_at":"2026-04-30 08:59:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":426348,"visible":true,"origin":"","legend":"\u003cp\u003eDetermination of the optimal number of reference genes by pairwise variation analysis using geNorm.Pairwise variation(Vn/Vn+1）was analyzed the normalization factors NFn and NFn+1,in all condition tested.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9157895/v1/a3638d7e930535dc43cebb4e.png"},{"id":108490832,"identity":"704495c9-1e46-4c9b-a5c7-a7995aedf696","added_by":"auto","created_at":"2026-05-05 09:49:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1686567,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9157895/v1/1fc39a0d-9ea2-4243-a638-f8c08185be11.pdf"},{"id":108182458,"identity":"3d508263-6953-4c46-80c8-b7339dfe08f5","added_by":"auto","created_at":"2026-04-30 08:59:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAppendix A. Supplementary data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAddition file:Table S1\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9157895/v1/aee841f1523581d3c5286b1d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of reference genes for real-time quantitative PCR in different development stage in chili pepper fruits（Capsicum annuum and Capsicum chinense）","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eQuantitative real-time polymerase chain reaction (qRT-PCR) has become the gold standard method for quantifying gene expression and validating RNA-seq results. In qRT-PCR analysis, reference genes serve as internal controls to normalize the expression levels of target genes. Ideally, reference genes should remain unaffected by experimental conditions and exhibit stable expression across various treatments, tissues, and organs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Commonly used reference genes in plant studies include actin (Actin), transcriptional elongation factor 1 (EF1), 18S ribosomal RNA (18S rRNA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), cyclophilin (CYP), ribulose-1,5-bisphosphate carboxylase (RuBP), adenine phosphoribosyltransferase (APRT), and α-tubulin (α-TUA) [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, numerous studies have demonstrated that no single reference gene is universally applicable across all experimental conditions [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, the selection of appropriate reference genes is critical for accurate gene expression analysis in specific crops, such as chili pepper.\u003c/p\u003e \u003cp\u003eChili pepper (Capsicum spp.) is one of the most economically important crops in the Solanaceae family. Among the cultivated species, C. annuum and C. chinense are the most widely grown and commercially significant. Several studies have sought to identify optimal reference genes in chili peppers. For example, Wan et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] used the bell pepper cultivar \u0026lsquo;PBC631B\u0026rsquo; (Capsicum annuum) to evaluate reference genes and found that β-TUB and UBI-3were stable across all treatments, while UBI-3and GAPDHwere stable in different tissues and organs. In contrast, ACT and UBI-1were deemed unsuitable as reference genes. In another study, Wang [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] employed \u0026lsquo;CMS 21A\u0026rsquo; (Capsicum annuum), a cytoplasmic male-sterile line, as experimental material and evaluated ten candidate reference genes\u0026mdash;including ACT, ACT1, ACT2, 18S rRNA, PPR1, TEF1A, EF1α, UEP, CYC, and GAPDH\u0026mdash;in roots, stems, leaves, flowers of mature plants, as well as in seedlings subjected to abiotic stress and hormone treatments. The results indicated that ACT was the most suitable reference gene across different tissues of mature plants, whereas EF1α was optimal for studies involving abiotic stress and hormone treatments. Among all samples examined, EF1α and UEP together demonstrated the highest expression stability. However, there have been no systematic investigations identifying optimal reference genes for normalizing gene expression during different developmental stages of chili pepper fruits.\u003c/p\u003e \u003cp\u003eIn the present study, we used two cultivars, \u0026lsquo;TJ\u0026rsquo; (Capsicum annuum) and \u0026lsquo;7054\u0026rsquo; (Capsicum chinense), as experimental materials. We evaluated the expression stability of seven commonly used reference genes (GAPDH, UBI-3, UBI-1, ACT, EIF, β-TUB, α-TUB) along with nine additional genes (CT4\u0026ndash;CT12) selected from published transcriptome data based on their stable expression during fruit development. The stability of these 16 candidate reference genes was comprehensively assessed using four independent algorithms: NormFinder, ΔCt, BestKeeper, and GeNorm. This study establishes a foundation for accurate normalization of RT-qPCR data throughout fruit development in Capsicum annuumand Capsicum chinense.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant materials\u003c/h2\u003e \u003cp\u003eThree biological replicates of chili pepper fruits from Capsicum annuum\u0026lsquo;TJ\u0026rsquo; and *Capsicum chinense\u0026rsquo; \u0026lsquo;7054\u0026rsquo; were collected weekly post-anthesis (designated as W1 through W6) from a cultivation site in Zhongluotan Town, Baiyun District, Guangzhou, Guangdong Province, China (23\u0026deg;23\u0026prime;24.5\u0026Prime;N, 113\u0026deg;26\u0026prime;19.4\u0026Prime;E). All collected samples were immediately frozen in liquid nitrogen upon collection and stored at \u0026minus;\u0026thinsp;80\u0026deg;C for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Candidate gene selection and primer design\u003c/h2\u003e \u003cp\u003eWe identified genes demonstrating consistent expression throughout all stages of fruit development based on published transcriptome datasets [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] (designated as CT1\u0026ndash;CT12), and evaluated their expression stability relative to commonly used reference genes (GAPDH, UBI-3, UBI-1, ACT, EIF, β-TUB, α-TUB), with the aim of selecting the most stable reference genes during pepper fruit development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 RNA extraction and cDNA synthesis\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from three biological replicates using a QIAGEN RNeasy\u0026reg; Plant Mini Kit (Cat. No. 74904, QIAGEN, Hilden, Germany) and treated with the RNase-Free DNase Set (QIAGEN) to remove genomic DNA contamination. RNA integrity was assessed by 1% agarose gel electrophoresis, and concentration and purity were determined using a NanoPhotometer\u0026reg; spectrophotometer (N120, IMPLEN, Westlake Village, CA, USA). Complementary DNA (cDNA) was synthesized from the purified RNA using the PrimeScript\u0026trade; RT Reagent Kit with gDNA Eraser (RR037A, Takara, Dalian, China) according to the manufacturer\u0026rsquo;s instructions. The resulting cDNA was stored at \u0026minus;\u0026thinsp;20\u0026deg;C for subsequent use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 qRT‑PCR analyses\u003c/h2\u003e \u003cp\u003ePrimers for the candidate reference genes were designed using Primer Premier 5.0 software (listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Primer specificity was verified by conventional PCR using a standard DNA polymerase (GenStar). A 10-\u0026micro;L reaction volume was used, consisting of 5 \u0026micro;L of polymerase, 4 \u0026micro;L of sterile water, 0.25 \u0026micro;L of each forward and reverse primer, and 0.5 \u0026micro;L of DNA template. The amplification protocol was as follows: initial denaturation at 94\u0026deg;C for 5 min; 35 cycles of 94\u0026deg;C for 30 s, 55\u0026deg;C for 30 s, and 72\u0026deg;C for 1 min; followed by a final extension at 72\u0026deg;C for 10 min. Amplification products were analyzed by 1% agarose gel electrophoresis to confirm primer specificity using a 4100 Gel Imaging System (Tanon, Shanghai, China).\u003c/p\u003e \u003cp\u003eFor quantitative PCR, the SYBR Premix Ex Taq\u0026trade; kit (Takara Bio, Dalian, China) was employed according to the manufacturer\u0026rsquo;s instructions. Reactions were performed in a 25-\u0026micro;L volume under a two-step amplification program: 95\u0026deg;C for 30 s, followed by 40 cycles of 95\u0026deg;C for 5 s and 60\u0026deg;C for 30 s. A melting curve analysis was subsequently conducted by heating from 65\u0026deg;C to 95\u0026deg;C in 0.5\u0026deg;C increments with continuous fluorescence acquisition. All samples were run in three technical replicates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer information of candidate reference gene\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward primer sequence(5\u0026rsquo;\u0026rarr;3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse primer sequence(5\u0026rsquo;\u0026rarr;3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGene ID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGATGATGTGAAAGCAGCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTTCAACTGGTGGCTGCTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAJ246013.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUBI-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGTCCATCTGCTCTCTGTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCACCCCAAGCACAATAAGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAY486137.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUBI-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGGAAATGTGTGTCTCAAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCCAAATGCCAAACTTCTAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDQ924970.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGTTATGGTAGGGATGGGTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTCTCTCTATTTGCCTTGGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAY572427.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCTGTTATCGTGCTACTTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTTTCATTGCCNTGCCAGAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAY484392.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-TUB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAGGGTGAGTGAGCAGTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTTCATCGTCATCTGCTGTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEF495259.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eα-TUB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATCAACTATCAGCCACCAAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTACCCTCACCAACATACCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEF495257.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCACGGTTCTTCTTACTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAAGTCTGCGAAACCTCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003egene-BC332_08861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGGCGAGAACGAAGACAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTGCCAATACCACCCATC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003egene-BC332_23789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGCAGCCACTATCAAAGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACGCATCTGGACCGTAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003egene-BC332_06775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCCTCCATCTCGTCCAAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCATCACGGCGGTAGGTAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCA08g01440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGTGTTGTGGGAACTGGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTCGTCGTTGAATAGAAGCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCA11g11330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTTGCCTCTGTCCTTTGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCACCTCTTCAGCTACCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCA01g00780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAAGCATTGCCAGGAGTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAGCATCGCATCATTTCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCapana02g001723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAACAAATACACCCTACGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGACGGAAACATCTGAACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCapana03g004217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCTCCATCCTTCATCTCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAGCGGCACTACTACGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCapana02g002361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGCCAGGACTCAACACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTCAGATCGGGAACAACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCapana08g002835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTTTGGTTGTGGCTCTGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTGCCTTCTTCGTCTCGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCapana02g000368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATCCTTGATGCGTTCTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCTTGTTTGGCTCTTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCapana06g000596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Transcript stability analysis\u003c/h2\u003e \u003cp\u003ePrimer amplification efficiency was determined using LinRegPCR software. Subsequently, the expression stability of the 16 candidate reference genes was evaluated using four independent algorithms: NormFinder, ΔCt, BestKeeper, and GeNorm. The results were then integrated and comprehensively ranked via the online tool RefFinder (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://blooge.cn/RefFinder/\u003c/span\u003e\u003cspan address=\"http://blooge.cn/RefFinder/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain a consensus stability order.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Candidate reference genes selection and amplification specificity\u003c/h2\u003e\n \u003cp\u003eCandidate reference genes were selected based on their stable expression across different developmental stages, as identified from previously published transcriptome data, and were designated CT1 to CT12 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). PCR amplification of the 19 candidate genes followed by agarose gel electrophoresis revealed non-specific amplification products for CT1, CT2, and CT3, leading to their exclusion from further analysis (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn accordance with the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], non-specific amplification was also observed for CT9 and CT10 (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, the non-specific products were approximately 500 bp in length, which does not interfere with qRT-PCR analysis. Therefore, CT9 and CT10 were retained for subsequent experiments.\u003c/p\u003e\n \u003cp\u003eMelting curve analysis of real-time PCR products demonstrated that all candidate genes exhibited a single peak, confirming amplification specificity (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Based on these results, all candidate genes except CT1\u0026ndash;CT3 were selected for further study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Expression levels of candidate reference genes\u003c/h2\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1. Expression levels of candidate reference genes in \u0026ldquo;TJ\u0026rdquo; pepper fruits\u003c/h2\u003e\n \u003cp\u003eThe cycle threshold (Ct) value represents the number of PCR cycles required for the fluorescence signal to cross a predefined threshold, serving as a key parameter for assessing gene expression abundance. Lower Ct values correspond to higher expression levels. The stability of Ct values is a critical criterion for selecting appropriate reference genes.\u003c/p\u003e\n \u003cp\u003eAs illustrated in Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the Ct values of the 16 candidate reference genes across various developmental stages of \u0026lsquo;TJ\u0026rsquo; pepper fruit ranged from 23.28 (UBI-3) to 36.30 (CT6). Four genes\u0026mdash;\u0026alpha;-TUB, EIF, \u0026beta;-TUB, and UBI-3\u0026mdash;exhibited significantly lower Ct values compared to the others, indicating high expression abundance throughout fruit development. In contrast, CT6 showed a markedly higher Ct value, reflecting the lowest expression level among all candidate reference genes.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2. Expression level of candidate reference gene in \u0026ldquo;7054\u0026rdquo; pepper fruit\u003c/h2\u003e\n \u003cp\u003eThe Ct values of the 16 candidate reference genes across the six developmental stages of \u0026lsquo;7054\u0026rsquo; pepper fruit are presented in Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The Ct values ranged from 19.18 (UBI-3) to 30.78 (CT6). Five genes\u0026mdash;\u0026alpha;-TUB, EIF, GAPDH, UBI-3, and UBI-1\u0026mdash;exhibited significantly lower Ct values compared to the others, indicating high expression abundance during fruit development. In contrast, CT6 showed a markedly higher Ct value, reflecting the lowest expression level among all candidate reference genes. This pattern of expression was consistent with that observed in \u0026lsquo;TJ\u0026rsquo; pepper fruit.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Transcript stability of candidate reference genes\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Stability evaluation of candidate reference genes in \u0026ldquo;TJ\u0026rdquo; pepper fruit\u003c/h2\u003e\n \u003cp\u003eAnalysis with NormFinder and the \u0026Delta;Ct method identified EIFas the most stably expressed gene (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In BestKeeper, CT7demonstrated the highest stability, whereas GeNorm analysis indicated CT8as the most suitable reference gene. Notably, UBI-1exhibited inconsistent expression across all algorithms.\u003c/p\u003e\n \u003cp\u003eComprehensive ranking using the RefFinder online tool generated a stability order (from most to least stable) for the candidate reference genes across the six developmental stages of \u0026lsquo;TJ\u0026rsquo; pepper fruit as follows: CT8\u0026thinsp;\u0026gt;\u0026thinsp;EIF\u0026thinsp;\u0026gt;\u0026thinsp;CT4\u0026thinsp;\u0026gt;\u0026thinsp;CT7\u0026thinsp;\u0026gt;\u0026thinsp;CT12\u0026thinsp;\u0026gt;\u0026thinsp;CT11\u0026thinsp;\u0026gt;\u0026thinsp;CT6\u0026thinsp;\u0026gt;\u0026thinsp;\u0026beta;-TUB\u0026thinsp;\u0026gt;\u0026thinsp;CT5\u0026thinsp;\u0026gt;\u0026thinsp;UBI-3\u0026thinsp;\u0026gt;\u0026thinsp;ACT\u0026thinsp;\u0026gt;\u0026thinsp;CT9\u0026thinsp;\u0026gt;\u0026thinsp;GAPDH\u0026thinsp;\u0026gt;\u0026thinsp;\u0026alpha;-TUB\u0026thinsp;\u0026gt;\u0026thinsp;CT10\u0026thinsp;\u0026gt;\u0026thinsp;UBI-1.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe stability of reference genes for \u0026ldquo;TJ\u0026rdquo; pepper fruit development candidates\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\n \u003cp\u003eStability values\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eComprehensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNormFinder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDelta CT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eBestKeeper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eGeNorm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.86(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.255(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.25(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.418(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.308(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.06(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.128(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.24(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.700(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.489(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.59(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.323(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.29(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.642(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.308(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.87(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.704(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.48(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.392(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.695(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.98(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.536(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.41(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.417(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.764(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5.26(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.396(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.37(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.999(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.873(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7.14(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.610(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.47(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.082(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.026(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026beta;-TUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e7.91(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.690(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.56(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.087(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.958(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e8.54(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.919(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.67(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.423(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.228(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUBI-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10.13(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.821(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.62(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.318(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.120(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10.19(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.806(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.65(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.231(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.172(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10.90(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.045(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.83(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.746(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.287(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13.24(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.038(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.83(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.330(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.349(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026alpha;-TUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13.71(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.017(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.88(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.501(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.407(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13.88(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.514(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.36(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.230(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.515(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUBI-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e16.00(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.773(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.71(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2.273(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.664(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2. Stability evaluation of candidate reference genes in \u0026ldquo;7054\u0026rdquo; pepper fruit\u003c/h2\u003e\n \u003cp\u003eAnalysis by NormFinder, GeNorm, and the \u0026Delta;Ct method indicated that the EIFgene exhibited the highest expression stability (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, the CT4gene was identified as the most stable according to BestKeeper. The \u0026beta;-TUBgene was consistently identified as unstable across all analytical tools.\u003c/p\u003e\n \u003cp\u003eComprehensive evaluation using the RefFinder online platform revealed that the top three most stable reference genes across the six developmental stages of \u0026lsquo;7054\u0026rsquo; pepper fruit were EIF, CT4, and UBI-3.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStability of reference genes for \u0026ldquo;7054\u0026rdquo; pepper fruit development candidates\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\n \u003cp\u003eStability values\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eComprehensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNormFinder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDelta CT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eBestKeeper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eGeNorm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.19(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.086(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.77(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.465(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.112(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.57(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.151(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.78(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.442(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.112(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUBI-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.46(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.159(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.80(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.513(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.209(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.72(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.098(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.80(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.582(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.265(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e5.38(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.201(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.87(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.472(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.429(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6.16(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.2586)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.84(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.697(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.341(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6.19(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.282(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.85(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.693(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.308(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6.88(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.364(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.92(8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.515(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.372(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eACT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10.82(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.649(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.23(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.074(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.855(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e10.95(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.696(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.26(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.958(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.800(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e11.49(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.748(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.27(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.307(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.555(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12.16(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.761(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.29(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.137(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.643(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eUBI-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e12.67(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.755(12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.31(13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1.179(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.732(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCT10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13.18(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.875(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.46(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.937(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.950(14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026alpha;-TUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13.55(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.919(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.47(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.715(9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.030(15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u0026beta;-TUB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13.86(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.938(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.49(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.770(10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1.088(16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3. Analysis of pairing variants of reference genes\u003c/h2\u003e\n \u003cp\u003eIn real-time PCR analysis, the use of a single reference gene is often insufficient to ensure reliable normalization[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To address this, the present study employed geNorm software to evaluate the optimal number of reference genes required for accurate normalization in two pepper cultivars (Capsicum annuum\u0026lsquo;TJ\u0026rsquo; and Capsicum chinense\u0026lsquo;7054\u0026rsquo;).\u003c/p\u003e\n \u003cp\u003eIn geNorm, the pairwise variation value Vₙ/Vₙ₊₁ is used to determine whether the inclusion of an additional reference gene is necessary. A threshold of Vₙ/Vₙ₊₁ \u0026lt; 0.15 indicates that n reference genes are sufficient for accurate normalization, whereas a value above 0.15 suggests that n\u0026thinsp;+\u0026thinsp;1 genes should be used.\u003c/p\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the V₂/₃ value exceeded 0.15 in \u0026lsquo;TJ\u0026rsquo; pepper, indicating that three reference genes are required to normalize target gene expression reliably during fruit development. In contrast, two reference genes were sufficient to ensure data accuracy in \u0026lsquo;7054\u0026rsquo; pepper.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eqRT-PCR is widely employed for analyzing relative gene expression due to its high accuracy, efficiency, and reproducibility[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the selection of appropriate and stable reference genes is critical for the reliable quantification of target gene expression[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Ideally, reference genes should exhibit consistent expression across different tissues, organs, and developmental stages within the same species, and remain unaffected by internal biological conditions or external environmental factors[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In practice, however, the expression stability of reference genes is relative, and even within the same species, their expression can be influenced by various internal and external variables. Thus, it is essential to identify and validate reference genes under specific experimental conditions.\u003c/p\u003e \u003cp\u003eTo date, suitable reference genes have been identified for various plants during distinct developmental stages of the same organ. For example, Yang[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] reported that genes such as UBC23, CYP38, and GAPDH2in \u003cem\u003eEuscaphis konishiiare\u003c/em\u003e stably expressed during fruit development and are suitable as reference genes. Chen[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] demonstrated that Actinand EF1-αare appropriate reference genes for pitaya (dragon fruit) development. Similarly, Kanakachari found that in eggplant, SANDand TBPare suitable reference genes across fruit developmental stages, whereas the Ubiquitin gene exhibited instability. Choi[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] identified three genes\u0026mdash;EXPRESSED, RPL8, and GAPDH\u0026mdash;as effective reference genes during tomato fruit development.\u003c/p\u003e \u003cp\u003eConsistent with findings in eggplant[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the present study also observed that UBI-1expression was unstable in pepper, rendering it unsuitable as an endogenous control during fruit development. In contrast, several genes identified in this study\u0026mdash;including CT4, UBI-3, CT8, CT6, and CT11\u0026mdash;demonstrated higher expression stability compared to commonly used reference genes such as ACT and GAPDH. These genes have been rarely reported previously, underscoring the need for more extensive and in-depth screening of reference genes across species and conditions. Based on comprehensive analysis, CT4is recommended as the most reliable reference gene for studying pepper fruit development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;National Key Research and Development Program Project (2023YFD1201502); Guangdong Provincial Department of Agriculture Seed Industry Revitalization and Agricultural Technology Promotion Project (2024-440000-87020100-8753); Guangzhou Science and Technology Program Project (2023B03J1082); Bijie Science and Technology Project with Open Call for Proposals (Bijie Science and Technology Cooperation Major Project [2022] No.3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribute statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYuanhao Xu:\u0026nbsp;\u003c/strong\u003eConceptualization\u003cstrong\u003e,\u003c/strong\u003eMethodology,Investigation,Formal analysis,Data Curation,Writing-original draft, Writing-review \u0026amp; editing.\u003cstrong\u003eBingchun Xie\u003c/strong\u003e:Conceptualization,Methodology,Investigation,Data Curation,Visualization,Writing-review \u0026amp; editing. \u003cstrong\u003eChu Ye\u003c/strong\u003e:Validation,Formal analysis,Data Curation.\u003cstrong\u003eXiaowan Xu\u003c/strong\u003e:Project administration. \u003cstrong\u003eXiaomei Xu\u003c/strong\u003e:Validation..\u003cstrong\u003eTao Li and Zhou Heng\u003c/strong\u003e: Funding acquisition, Project administration, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eA. 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Ritter, Selection of housekeeping genes for qRT-PCR analysis in potato tubers under cold stress, MOL BREEDING, 31 (2013) 39-45.https://doi.org/10.1007/s11032-012-9766-z\u003c/li\u003e\n \u003cli\u003eO. Thellin, W. Zorzi, B. Lakaye, B. De Borman, B. Coumans, G. Hennen, T. Grisar, A. Igout, E. Heinen, Housekeeping genes as internal standards: use and limits, J BIOTECHNOL, 75 (1999) 291-295.https://doi.org/10.1016/S0168-1656(99)00163-7\u003c/li\u003e\n \u003cli\u003eL. Thorrez, K. Van Deun, L.C. Tranchevent, L. Van Lommel, K. Engelen, K. Marchal, Y. Moreau, I. Van Mechelen, F. Schuit, Using ribosomal protein genes as reference: a tale of caution, PLOS ONE, 3 (2008) e1854.https://doi.org/10.1371/journal.pone.0001854\u003c/li\u003e\n \u003cli\u003eS. Selvey, E.W. Thompson, K. Matthaei, R.A. Lea, M.G. Irving, L.R. 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Kumar, Evaluation of suitable reference genes for normalization of qPCR gene expression studies in brinjal (Solanum melongena L.) during fruit developmental stages, APPL BIOCHEM BIOTECH, 178 (2016) 433-450.https://doi.org/ 10.1007/s12010-015-1884-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Reference genes, qRT-PCR, Capsicum annuue, Fruit development, expression analysis","lastPublishedDoi":"10.21203/rs.3.rs-9157895/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9157895/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe selection of appropriate reference genes is a fundamental prerequisite for reliable gene expression analysis using real-time quantitative polymerase chain reaction (RT-qPCR). To date, no systematic study has identified reference genes for normalizing RT-qPCR data in pepper fruits across different developmental stages. In this study, we evaluated the expression stability of seven commonly used internal control genes and nine additional candidates derived from available transcriptomic datasets in two widely cultivated chili pepper species (Capsicum annuum and Capsicum chinense) at various fruit developmental stages. The expression stability of the 16 candidate genes was assessed using four established algorithms: NormFinder, ΔCt, BestKeeper, and GeNorm. Our results demonstrated that CT8 and EIF were the most stably expressed genes during fruit development in Capsicum annuum \u0026lsquo;TJ\u0026rsquo; and Capsicum chinense \u0026lsquo;7054\u0026rsquo;, respectively. This study provides a foundation for accurate normalization of RT-qPCR analyses in pepper fruits at different developmental stages and will help ensure the reliability of subsequent gene expression studies in these species.\u003c/p\u003e","manuscriptTitle":"Identification of reference genes for real-time quantitative PCR in different development stage in chili pepper fruits（Capsicum annuum and Capsicum chinense）","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 10:30:01","doi":"10.21203/rs.3.rs-9157895/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-19T11:26:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230010262548158358900339017152812986407","date":"2026-04-29T07:26:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T07:05:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154514023429127002886703293762324359817","date":"2026-04-25T12:36:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33510837742840841413290079899965407153","date":"2026-04-22T16:01:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265813820990426095228289278398197621570","date":"2026-04-21T13:15:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T12:25:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-21T12:24:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T16:02:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T12:19:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-02T11:04:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e94f4c2-3a53-4452-9b28-e51dc19e659e","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-19T11:26:13+00:00","index":96,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67000574,"name":"Biological sciences/Biological techniques"},{"id":67000575,"name":"Biological sciences/Biotechnology"},{"id":67000576,"name":"Biological sciences/Genetics"},{"id":67000577,"name":"Biological sciences/Molecular biology"},{"id":67000578,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-04-29T10:30:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 10:30:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9157895","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9157895","identity":"rs-9157895","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}