Helminthosporium stenospilum, is one of the most serious threats to sugarcane production. However, its outbreaks and epidemics require suitable climatic conditions, resulting in the inefficient improvement of the SBS resistance by phenotype selection. The sugarcane F1 population of SBS-resistant YT93-159 × SBS-susceptible ROC22 was used for constructing the bulks. Bulked segregant RNA-seq (BSR-seq) was then performed on the parents YT93-159 (T01) and ROC22 (T02), and the opposite bulks of 30 SBS-susceptible individuals mixed bulk (T03) and 30 SBS-resistant individuals mixed bulk (T04) collected from 287 F1 individuals. A total of 170.00 Gb of clean data containing 297,921 SNPs and 70,426 genes were obtained. Differentially expressed genes (DEGs) analysis suggested that 7787 and 5911 DEGs were identified in the parents (T01 vs. T02) and two mixed bulks (T03 vs. T04), respectively. In addition, 25,363 high-quality and credible SNPs were obtained using the genome analysis toolkit GATK for SNP calling. Subsequently, six candidate regions with a total length of 8.72 Mb, which were located in the chromosomes 4B and 7C of sugarcane wild species Saccharum spontaneum, were identified, and 279 genes associated with SBS-resistance were annotated by ED algorithm and ΔSNP-index. Furthermore, the expression profiles of candidate genes were verified by quantitative real-time PCR (qRT-PCR) analysis, and the results showed that eight genes  and seven genes  of the 20 tested genes were significantly up-regulated in YT93-159 and ROC22, respectively. Finally, a potential molecular mechanism of sugarcane response to H. stenospilum infection is illustrate that the activations of ROS signaling, MAPK cascade signaling, Ca2+ signaling, ABA signaling, and the ASA-GSH cycle jointly promote the SBS resistance in sugarcane. This study provides abundant gene resources for the SBS resistance breeding in sugarcane.Sugarcane brown stripe (SBS), caused by the fungal pathogen  Saccharum spp. hybrids) is the most important sugar crop worldwide and accounts for 85–90% of sugar production in China. Pests and diseases, reducing the viability and affecting the growth of sugarcane, are the main factors limiting its yield and sugar content [Helminthosporium stenospilum Drechsler, is a fungal foliar disease [Sugarcane  [Modern sugarcane hybrids have a large genome, and their genetic background is highly complex, composed of a different number of chromosomes from 10×–12×) ,5,6,7. T10×–12×) . The rap10×–12×) ,10,11.Dm5 and Dm8 of Bremia lactucae in the F2 segregating population [Bulked segregant RNA-seq (BSR-seq) is a technical method based on the advantages of bulked segregant analysis (BSA) and high-throughput sequencing technology ,13. In 1pulation . In recepulation ,15, genepulation ,17, idenpulation ,19, and pulation ,21. In apulation  is an efpulation  and SNP-pulation ,24 metho1 population generated from a cross between YT9159 and ROC22. You et al. [1 population derived from sugarcane variety CP80-1827 and identified 23 QTLs tightly linked to the resistance of sugarcane ratoon stunting disease . In general, gene mapping is time-consuming and expensive, especially for crops with huge genomes and complex genetic backgrounds. The size of the mapping population, the number, type, and resolution of molecular markers are the main factors that limit gene mapping [Arabidopsis thaliana [Cucumis sativus [Oryza sativa [Triticum aestivum), maize (Zea mays), and sugarcane (Saccharum spp. hybrids). At present, BSR-seq has been used to locate major genes and related quantitative traits, and screen candidate genes related to traits in many crops [Sugarcane is an asexually reproductive plant. As far as QTL mapping of target traits is concerned, there is no near population such as doubled haploid (DH) or recombinant inbred line (RIL), and only self-crossing or segregating populations can be used. With the development of DNA molecular marker technology, QTL mapping has developed rapidly as well for different quantitative traits of sugarcane. For example, Lu et al.  located u et al.  used a sthaliana , Cucumisa sativa . Howeverny crops .1 segregating population derived from YT93-159  × ROC22 , respectively. Secondly, the differential gene expression by RNA-seq and association analysis were performed on all samples. Thirdly, the location of the chromosomal candidate regions associated with the trait for brown stripe resistance, functional annotation, and expression profiling of the candidate genes based on transcriptome data were performed. Finally, the expression patterns of candidate genes were verified by qRT-PCR. This study is expected to provide new genetic resources for the molecular breeding of SBS resistance in sugarcane.The present study aimed to screen for differential expression of candidate genes in four different samples associated with brown stripe resistance in sugarcane via BSR-seq. According to the field performance, a total of 30 progenies of extreme resistance/susceptibility to brown stripe were selected from the F1 population were investigated. The SBS disease grade of each F1 progeny was evaluated across all tested individuals according to the established grading scales. Based on field disease severity surveys in the period 2015–2020, the environmental conditions in 2020 were most suitable to the epidemic of SBS, and the male parent ROC22 was identified as highly susceptible to SBS, while sugarcane female parent YT93-159 showed to be highly resistant. As a result, the individuals used for constructing resistant/susceptible bulks were selected mainly based on the performance of the tested progenies in 2020. Finally, 30 progenies with the extremely SBS-resistant and 30 progenies with the extremely SBS-susceptible were selected to construct resistant- and susceptible-bulk, respectively. Furthermore, the 1 individuals under six habitats. The phenotypic data of the disease severity survey of sugarcane brown stripe in 2020 are shown in In this study, a total of 287 individuals in the FS. spontaneum reference genome. The average percentage of sequence alignment ranged from 67.72 to 69.95% , 20.14 Gb from ROC22 (T02), 71.21 Gb from the SBS-susceptible bulk (T03), and 61.22 Gb from the SBS-resistant bulk (T04). The percentage of bases with Q30 was more than 93.00% and GC content ranged from 52.27 to 53.00%. Clean data were then individually aligned to the o 69.95% . These rAfter BSR-seq and quality control, a total of 70,426 genes were identified, of which 15,083 were differentially expressed genes (DEGs). In addition, 18,532 new genes were discovered by filtering out the sequences whose encoded peptide chains were too short (less than 50 amino acid residues) or contained only a single exon. Among them, the number of DEGs between the parents (T01 vs. T02) was 7787, which included 3747 significantly up-regulated genes and 4040 significantly down-regulated genes A. Then, A total of 635,094 SNPs, composed of SNPs between parents and those between the two bulks, were finally detected using the genome analysis toolkit GATK . Then, SAccording to the correlation threshold, a total of 12 SBS-resistance-associated regions were identified by ED algorithm analysis , coverinhttps://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi, accessed on 15 March 2022) and annotated the homologous function of these genes in A. thaliana with the TAIR database [To obtain gene function information, this study analyzed the conserved domains of candidate genes using NCBIs conserved domain database (CDD) . The following top three group categories were found: “general functional predictions only” (25), “transcription” (16), and “signal transduction mechanisms”, and “replication, recombination and repair” (15). In addition, DEGs analysis in the SBS resistance association region (including 279 genes) showed that 49 DEGs were differentially expressed  and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analysis. As shown in CIPK11, DHAR1, PR2, LRR-RLK, CBL2, CKA1, and MAGL9, while the genes with low expression were WRKY7, MDN1, FLS2, SDH1-2, MLO1, AGC1.5, and GLR2 in all four samples. In addition, the expression levels of PDLP1A, PAH1, LRR-RLK, and PMIR1 in T01 and T03 were higher than those in T02 and T04, while the expression patterns of CKA1, MAGL9, and WRKY31 were opposite. Interestingly, the expression levels of both CIPK11 and CDPK6 in T02 and T03 were higher than those in T01 and T04, respectively, while the expression patterns of NDA2, KP1, and BLH4 were opposite  were highly expressed, especially PR2, CBL2, and CDPK6, whereas eight genes  were lowly expressed. It is worth noting that WRKY31, CRK34, CPNIFS, CKA1, and CIPK11 were highly expressed in leaves, while CIPK11, CDPK6, PAH, and LRR-RLK were highly expressed in roots. DHAR1 was specifically expressed in buds. DRH1 and PR2 were highly expressed in stem piths. MCA2, NDA2, ASN1, and CDPK6 were highly expressed in the stem epidermis, and MCA2 and NDA2 were specifically expressed in the stem epidermis , SBS-susceptible parent ROC22 (T02), SBS-susceptible mixed bulk (T03), and SBS-resistance mixed bulk (T04) A. Among opposite A. Meanwhpidermis B. OveralKUP9, RLK7, GATA12, CIPK11, and FLS2 in T01 and T02 had no significant difference , but opposite for WRKY31, CIPK2, CKA1, CDPK6, PFK4, CBL2, and PR2, which were higher in T02 than in T01 (p < 0.05). The above results by qRT-PCR were consistent with their expression profiles in BSR-seq. Besides, the expression patterns of candidate genes were obviously different between SBS-resistant and susceptible sugarcane varieties.According to the results of the expression profiling of 39 candidate genes in the samples T01 and T02, 20 genes were selected for validation by qRT-PCR. The expression of fference . HoweverZ. mays) and identified 18 high-probability SNPs and six candidate genes in response to waterlogging stress. Wang et al. [Camellia sinensis) through BSR-seq. In addition, there are many reports that the sugarcane F1 population can be used in QTL mapping. For example, You et al. [Lxx) via QTL mapping. Yang et al. [1 population and screened 1574 potential genes linked to orange rust (Puccinia kuehnii) resistance in sugarcane. Besides, Zhang et al. [Didymella bryoniae) resistance in cucumber via QTL mapping. In the present study, we performed the BSR-seq analysis in four samples, and a total of 39 candidate genes related to SBS resistance were identified  inoculated with the pathogenic Xanthomonas oryzae pv. oryzae (Xoo), but the expression of PR5, PR6, PR15, and PR16 did not change significantly. In this study, one PR2 gene associated with SBS was rapidly identified, and the expression profiling showed that PR2 was highly expressed in the root, pith, leaf, epidermis, and the bud of sugarcane, as well as in T01, T02, T03, and T04  and the expression levels of immune genes, thus improving the resistance of N. benthamiana to tobacco bacterial wilt . The above results provide an important theoretical reference for the functional identification of the sugarcane PR2 gene detected here in the future.Plant resistance is a complex process between the host and the pathogen. Pathogenesis-related proteins (PRs) play an important role in the plant-pathogen interaction . Previou and T04 . Chu et syringae . The oveg et al.  can incrXa21 has been clearly reported as an effective bacterial blight disease resistance gene [ERECTA has been proven to not only regulate the process of ovule development but also participate in the resistance to bacterial wilt (R. solanacearum) in Arabidopsis. In addition, BAK1 binds to FLS2 and forms a specific ligand in vivo, thus causing the defense response of plants [RLK genes in sugarcane. Here in our study, six RLK candidate genes  were identified, and qRT-PCR analysis showed that LRR-RLK, CRK34, AK3, and RLK1 had higher expression in the SBS-resistant parent than in the SBS-susceptible parent  [Puccinia striiformis) [CIPK homologue gene, TaCIPK10, from wheat, which was rapidly induced by P. striiformis inoculation and salicylic acid (SA) treatment, was identified and cloned by Liu et al. [TaCIPK10 significantly reduced wheat resistance to P. striiformis, whereas its overexpression resulted in enhanced resistance of wheat to P. striiformis. Poddar et al. [CIPKs genes from chickpea (Cicer arietinum) and revealed that CaCIPK2 and CaCIPK11 were up-regulated at one or more time points under drought, ABA, and salt stress. In sugarcane, Su et al. [ScCIPK21 was up-regulated under ABA stress; ScCIPK1, −2, −15, −20, −21, and −28 were up-regulated under PEG treatment; ScCIPK1, −2, and −28 were up-regulated under NaCl stress. Besides, ScCIPK3 plays a vital role in the induction of immune-related genes after seven days post-inoculation with the tobacco bacteria R. solanacearum. In this study, the expression levels of CIPK2 in T02 were higher than those in T01, but there was no significant difference in the expression levels of CIPK11 between T01 and T02  , wheat siformis) ,54, coldiformis) , droughtiformis) ,57, and iformis) . A CIPK u et al. . The knor et al.  identifiu et al.  revealed and T02 . Therefocis-acting elements in the upstream promoter region with a special structure to exercise the function of gene regulation and expression at the transcription level, thereby activating or inhibiting the expression and regulating the downstream functional genes [WRKY genes, ZmWRKY19 and ZmWRKY53, were significantly up-regulated by Aspergillus flavus inoculation in the resistant maize line TZAR101 [ZmWRKY53 has also been shown to enhance drought and salt stress [WRKY22, WRKY13, WRKY47, WRKY45-1, WRKY45-2, WRKY30, WRKY53, WRKY55/WRKY31, and WRKY104/WRKY89 positively regulated rice resistance to blast (Magnaporthe oryzae) [WRKY76 negatively regulated rice resistance to M. oryzae [WRKY7 and WRKY31, were identified by BSR-seq. Among them, the expression levels of WRKY31 were high in both the SBS-resistant variety YT93-159 and the SBS-susceptible variety ROC22, while the expression of WRKY7 in YT93-159 was much higher than that in ROC22  ,64, wher. oryzae . In thisin ROC22 . Wang etH. stenospilum interaction was depicted  bind to pattern-recognition receptors (PRRs). It can respond to defensive responses in plants, such as the production of ethylene (ETH), the increase in reactive oxygen species (ROS) expression level in vivo, and then mitogen-activated protein kinases (MAPKs) cascade signal transduction, and the activation of plant hormone signal transduction such as salicylic acid (SA) and abscisic acid (ABA) to regulate the defense response of plants, thereby enhancing the resistance to SBS. When plants are infected by external pathogens, the expression of pathogenesis-related proteins (PRs) in vivo will be significantly up-regulated, and by regulating the expression of downstream disease-related genes, thereby defending against external pathogens, and finally making plants immune. Dehydroascorbate reductase (DHAR) is a key enzyme gene that can promote the regeneration of ASA in the ascorbic acid-glutathione (ASA-GSH) cycle, reduce the damage of ROS to plants, and enhance the resistance of plants under the infection by H. stenospilum. Upon sugarcane and H. stenospilum interaction, Ca2+ signal influx is generated within plant somatic cells. Ca2+ influx can activate the production of calcium sensor proteins, such as calcineurin B-like proteins (CBLs) and calcium-dependent protein kinases (CDPKs), which in turn activate the NADPH oxidase, and then promote the formation of ROS, and ultimately initiate plant defense responses. In addition, CBL-interacting protein kinases (CIPKs) can only respond to biotic and abiotic stress by interacting with Ca2+ sensor CBLs, such as by promoting cell wall reinforcement and regulating downstream resistance gene expression, thereby enhancing the resistance to SBS. In summary, when sugarcane interacts with H. stenospilum, the activation of MAPK cascade signaling, SA signaling, ABA signaling, ROS signaling, Ca2+ signaling, and the ASA-GSH cycle jointly promote the resistance to sugarcane brown stripe.A potential molecular mechanism of sugarcane and depicted . When su1 segregating population was developed from a cross of ‘ROC22’ (highly susceptible to SBS) and ‘YT93-159’ (highly resistant to SBS) in 2014, and a total of 287 F1 individuals were obtained [An Fobtained . Based o1 population. The grading scale and diagram were shown in Considering the lack of grading scales for the identification of disease resistance in sugarcane brown stripe and according to previous studies ,67, this1 population from the cross of YT93-159 × ROC22, parents and each of the 30 individuals of extreme SBS-resistance and extreme SBS-susceptibility were selected in December 2020 after the natural infection by the pathogen H. stenospilum. For the samples of the SBS-resistant parent, and 30 materials selected as the SBS-resistant mixed bulk, the sampling details were as follows: 5 individuals with similar growth vigor and without brown stripe disease symptom were selected, and leaf tissues located in the +2 leaf position and in the mid-leaf blade were collected for samples. Then, approximately 1.0 cm × 0.2 cm leaf tissues were taken via sterilized scissors from the mid-leaf blade, and quickly placed in a 2.0 mL centrifuge tube, and placed on dry ice. It is worth noting that the mass of each sample needs to reach about 10 g. In addition, especially for the SBS-susceptible parent and the SBS-susceptible mixed bulk  materials, the sampling details were as follows: 5 individuals with similar growth vigor and disease severity were selected. Then, half of the diseased spots and the healthy parts adjacent on the leaves were taken (approximately 1.0 cm × 0.2 cm) from the +2 leaf position and the mid-leaf blade when sampling. The mass of each sample needs to reach about 10 g. All samples were frozen in liquid nitrogen immediately after collection in the field, and then these samples were stored at −80 °C freezer pending extraction of total RNA.For RNA-seq, four groups of samples including two sugarcane parent cultivars, YT93-159 (T01) and ROC22 (T02), and the SBS-susceptible/-resistant mixed bulk (T03/T04) were collected, respectively. According to the results of the disease severity of each progeny in the F1 individuals was extracted using the Trizol reagent  following the manufacturer’s protocol. The concentration and quality of extracted RNA were checked using NanoDrop One , and their RNA Integrity Number (RIN) was also analyzed using an Agilent 2100 Bioanalyzer . Resistant and susceptible RNA bulks were constructed by separately mixing equal amounts of mRNA from the 30 resistant and susceptible F1 individuals, respectively. Two parents, 30 highly SBS-resistant individuals mixed bulk and 30 highly SBS-susceptible individuals mixed bulk, were submitted to Biomarker Technologies Co., Ltd.  for sequencing.Total RNA of ROC22, YT93-159, and 30 SBS-resistant and 30 SBS-susceptible FTM 2500 sequencing platform .All samples with a RIN ≥ 7 were regarded as meeting the sequencing standard and cDNA libraries were constructed using a TruSeq RNA sample preparation kit  according to the manufacturer’s instructions. The quality of the cDNA libraries was again assessed, and the acceptable cDNA libraries were sequenced on an Illumina HiSeqplatform . The rawS. spontaneum AP85-441 reference genome  using STAR [After the sequencing of the cDNA libraries, raw data were filtered, and poor-quality reads were eliminated to obtain high-quality clean data using FastQC and Trimmomatic software . The cleing STAR  for subsS. spontaneum AP85-441 reference genome sequence, the comparable reads were spliced using Cufflinks software and compared with the original genome annotation information to find the original unannotated transcript region and discover new transcripts and genes of the species. Clean reads were aligned to the reference genome, and the expression level was calculated with Fragments per KB of exon per million fragments mapped (FPKM). DESeq R package (version 1.18.0) [Based on the  1.18.0)  was then 1.18.0)  was used 1.18.0)  was usedGATK software  was used2 and then the ED value was fitted using the SNPNUM method [In this study, the association analysis on the candidate regions of SBS resistance was carried out based on ED  and ΔSNPM method  , trEMBL , Swiss-Prot , and COG  database with the maximum E-value of 1e-15 [http://search.cpan.org/dist/GO-TermFinder/, accessed on 10 March 2022) was used to describe the molecular function, biological process, and cellular component of the candidate gene. For KEGG pathway enrichment analysis, the KEGG database  was used to BLAST [https://www.ncbi.nlm.nih.gov/structure/cdd/wrpsb.cgi, accessed on 15 March 2022), and the functions of the candidate genes were predicted according to Arabidopsis TAIR database .According to the BSR-seq analysis, the candidate regions associated with the resistance of SBS were obtained, and in this region, the candidate genes associated with this trait were screened. Gene functional annotation of the candidate regions was carried out by BLAST  against of 1e-15 . GO and to BLAST  against To identify the key genes related to the SBS resistance, the expression patterns of the candidate genes were analyzed based on the BSR-seq datasets, and RNA-seq datasets of five different tissues  of sugarcane variety ROC22 (unpublished), and the expression heat map was drawn based on the FPKM expression values of the candidate genes using TBtools software .GAPDH [2O. The qRT-PCR reactions were performed in ABI QuantStudioTM 3 system  at 50 °C for 2 min pre-amplification; 95 °C for 10 min; followed by 40 cycles of 95 °C for 15 s, 60 °C for 1 min. Three biological replicates for each sample were conducted, and the relative expression level of each candidate gene was calculated from the 2−∆∆Ct value [According to the results of expression profiling analysis, 20 candidate genes differentially expressed between the resistant parent (T01) and the susceptible parent (T02) were selected for qRT-PCR verification. According to the instructions of Prime-ScriptTM RT Reagent Kit , first strand cDNA synthesis was performed. The GAPDH  was chosCt value . HistogrCt value . The sig1 individuals. We applied the BSR-seq technology to sequence the samples of YT93-159, ROC22, and two opposite mixed bulks. Then, a total of 1385 DEGs were successfully detected between T01 vs. T02 and T03 vs. T04. Six candidate regions spanning 8.72 Mb and associated with the SBS resistance were located on chromosomes 4B and 7C of sugarcane wild species S. spontaneum, in which 279 genes including 13 nonsynonymous genes were identified, and 39 candidate genes were selected for in-depth annotation. GO and KEGG enrichment analysis showed that the associated genes in the candidate regions participated in biological processes, such as immune system processes, metabolic processes, biological regulation, and response to external stimuli, and were mostly enriched in plant-pathogen interaction and plant hormone signaling metabolic pathways, suggesting that they could be potentially involved in disease resistance. Furthermore, the expression levels of 20 selected candidate genes were verified via qRT-PCR, and the results were consistent with the data in BSR-seq datasets, indicating the data reliability. Finally, a potential mechanism of molecular interaction between sugarcane and H. stenospilum was drawn, suggesting that the activations of ROS signaling, MAPK cascade signaling, Ca2+ signaling, ABA signaling, and the ASA-GSH cycle jointly promote the SBS resistance in sugarcane. Our results showed that BSR-seq has the full capacity to screen the candidate genes associated with the target traits, and the present study provides gene resources for sugarcane disease resistance breeding.In the present study, a field SBS disease severity survey was conducted on 287 F


Astragalus membranaceus has been used as a highly popular Chinese herbal medicine for centuries. Triterpenoids, namely astragalosides I, II, III, and IV, represent the main active compounds in this plant species. Transcription factors have a powerful effect on metabolite biosynthesis in plants. We investigated the effect of the Arabidopsis MYB12, production of anthocyanin pigment 1 (PAP1), and maize leaf color (LC) transcription factors in regulating the synthesis of astragaloside metabolites in A. membranaceus. Overexpression of these transcription factors in hairy roots differentially up-regulated these active compounds. Specifically, the overexpression of LC resulted in the accumulation of astragalosides I–IV. The content of astragalosides I and IV were, in particular, more highly accumulated. Overexpression of MYB12 increased the accumulation of astragaloside I in transgenic hairy roots, followed by astragaloside IV, and overexpression of PAP1 resulted in the increased synthesis of astragalosides I and IV. In addition, we found that overexpression of PAP1 together with LC increased astragaloside III levels. At the transcriptional level, several key genes of the mevalonate biosynthetic pathway, especially HMGR1, HMGR2, and HMGR3, were up-regulated differentially in response to these transcription factors, resulting in astragaloside synthesis in the hairy roots of A. membranaceus. Overall, our results indicated that heterologous expression of Arabidopsis MYB12, PAP1, and maize LC differentially affected triterpenoids biosynthesis, leading to the increased biosynthesis of active compounds in A. membranaceus. Astragalus membranaceus (Fisch.) Bunge. has been used as a popular Chinese herbal medicine for many years. The dried roots of A. membranaceus are known as “Huang Qi” or “Radix astragali” and are used in many traditional Chinese herbal decoctions. In Europe, the United States, and Asian countries, Radix astragali are commonly used as dietary supplements and additives [A. membranaceus has attracted the interest of many researchers due to their excellent pharmacological properties, including anticancer, anti-inflammatory, antioxidant and antiviral activities, immune enhancement, diabetic regulation and so on [A. membranaceus [Astragalus species and exhibit multiple pharmacological activities that contribute to this herb’s wide range of applications [dditives . A. membnd so on ,4,5,6,7.ranaceus ,9. Astraications ,12,13,14Astragalus species is usually low, and this limited level of production restricts the pharmaceutical application of these herbal remedies [A. membranaceus and other plants. For example, investigators have undertaken a feasibility study on treating the plants with elicitors, including methyl jasmonate (MeJA), salicylic acid, and acetylsalicylic acid, to promote AG production. Methyl jasmonate was shown to be the most effective at inducing AG production, with MeJA-treated plants exhibiting a 2.1-fold increase in AG concentration (5.5 ± 0.13 mg/g dry weight) than controls [O-β-d-glucoside were efficiently up-regulated in MeJA-treated A. membranaceus hairy roots [Agrobacterium-mediated A. membranaceus hairy root cultures [A. membranaceus hairy roots [Although AGs are important active ingredients, the total triterpene saponin content in remedies ,15. Howecontrols . Moreovery roots . Yeast ecultures . In othery roots . Howeverry roots ,19. Panax notoginseng [PjERF1 from Panax japonicus was also reported to promote saponin biosynthesis in PjERF1 overexpression cell lines [Salvia miltiorrhiza SmERF115 has been reported to be a positive regulator of phenolic acid biosynthesis in S. miltiorrhiza [SmMYB1 and SmMYB97 have been found to improve the accumulation of phenolic acids, anthocyanins, and tanshinones in S. miltiorrhiza [A. membranaceus using transcription factors has not yet been described thoroughly.Transcription factors are powerful regulators of metabolite biosynthesis in plants. It is well-recognized that transcription factors such as MYB, basic/helix-loop-helix (bHLH), and WD40 proteins can help researchers to engineer variations in metabolite biosynthesis ,21. The oginseng , and PjEll lines . Moreoveiorrhiza , while tiorrhiza ,32. HoweLeaf color (Lc), heterologously expressed in the A. membranaceus hairy root system, to evaluate whether they could be used to engineer AG metabolite biosynthesis. Our results indicated that MYB12, PAP1, and LC could differentially participate in triterpenoid biosynthesis. RT-PCR analysis revealed that overexpression of these three transcription factors induced the up-regulation of key structural genes in the AG biosynthetic pathway. This research provides useful information about the metabolic engineering of active compound production in the hairy roots of A. membranaceus using transcription factors. In this study, we used three transcription factors, the Arabidopsis transcription factor MYB12, Production of Anthocyanin Pigment1 (PAP1), and maize ZmLc, AtMYB12, and AtPAP1 were detected higher in the transgenic lines than in that of the control GUS hairy roots lines. In particular, expression in the hairy root line LC-7 was 66.6-fold higher than in the control line, followed by LC-8 and LC-13, which displayed 21.8-fold and 25.2-fold higher expression than the control  treatment, enhanced the content of AG IV was enhanced by 14.59-fold when compared with the control in A. membranaceus hairy root cultures [A. membranaceus using transcription factors via the hairy root system.Hairy roots are commonly considered to be an alternative system in which to study metabolite biosynthesis in plants. The system offers a useful way of producing increased amounts of metabolites within a relatively short time frame when compared with traditional culture methods . The A. eviously ,36,37. Twn roots . The usecultures ,19. Morecultures . HoweverAtMYB12 is considered to be a flavonol-specific activator of flavonoid biosynthesis in Arabidopsis [AtPAP1 has been reported to be a positive anthocyanin and flavonoid regulator [PAP1 was found to improve the production of anthocyanins and antioxidant activity in Ginseng hairy roots [PAP1/AtMYB75 in the transgenic hop (Humulus lupulus L.), canola (Brassica napus), and so on [Leaf color (Lc) belongs to the maize R gene family and was the first basic/helix-loop-helix (bHLH) transcription factor found in maize, Lc acts in the regulation of anthocyanin synthesis in maize [Lc can allow the engineering of flavonoid and/or anthocyanin biosynthesis in many plants, such as Arabidopsis and tobacco [ZmLc and AtPAP1 can be used as positive regulators in the metabolic engineering of flavonoid biosynthesis in S. baicalensis, resulting in 3- and 5-fold higher total flavonoids, respectively, than the control [ZmLc, AtMYB12 and AtPAP1 efficiently up-regulated AG biosynthesis in A. membranaceus, especially, AG I and AG IV were considered as common targets and significantly higher accumulation in overexpression hairy roots lines.Arabidopsis transcription factors MYB12, PAP1, and LC are three previously characterized flavonoid and/or anthocyanin-related transcription factors ,41,42,43egulator . The ectnd so on ,46. Leafin maize , and het tobacco , lisiant tobacco , rice [5 tobacco , sweet p tobacco , and gol tobacco . Park et control . In thisPAL, CHS, CHI, F3H, and FLS are upregulated in the flavonoid biosynthesis pathway and resulted in the accumulation of flavonol and flavonoid derivatives in AtMYB12 overexpressed tomato [PAP1/AtMYB75 in transgenic canola (Brassica napus) and hop (Humulus lupulus L.), by regulating the transcription level of flavonoid biosynthesis-related structural genes [AtMYB12 and PAP1 were overexpressed in buckwheat hairy root, only the AtMYB12 overexpressed lines resulted in rutin enhancement through the up-regulation of PAL, C4H, 4CL, CHS, CHI, F3H, F3′H, and FLS genes in the flavonoid biosynthesis pathway [PAP1 indeed enhanced the expression of various genes involved in the phenylpropanoid biosynthetic pathways (24 genes), and flavonoid biosynthetic pathways (17 genes) in ginseng (Panax ginseng C.A. Meyer) hairy roots [It has been shown in many plant species that transcription factors regulate the transcript levels of structural genes and contribute to the accumulation of metabolites ,21. Mored tomato . Anthocyal genes . However pathway , while, ry roots . A. membranaceus have previously been reported. [HMGR gene is directly related to triterpenoid biosynthesis [Panax ginseng HMGR (PgHMGR) in Platycodon grandiflorum [AACT to CAS of the AG biosynthesis pathway, only the higher expression of HMGR gene was induced by UV-B elicitation [HMGRs played a key target gene in A. membranaceus AGs biosynthesis and responded to the regulation of ZmLc, AtMYB12, and AtPAP1 [The key genes in the AG biosynthesis pathway in eported. ,36,55. Fynthesis ,57,58,59diflorum  reportedcitation . In thisd AtPAP1 . Arabidopsis thaliana, Panax ginseng, Saponaria vaccaria [According to previous research, the cytochrome P450 monooxygenase (P450) and the glycosyltransferases (GTs) play important roles in the triterpenoid pathway, mainly creating structural diversity of triterpenoids in plants ,61,62. Hvaccaria ,64,65, Gvaccaria ,34.Astragalus membranaceus seeds were purchased from Wofeng Chinese traditional Medicine Co., Ltd. , and were authenticated by Prof. Sangun Park. The Astragalus membranaceus sterilized seeds were obtained similarly as described before Li et al. [v/v) bleach solution, followed by rinsing several times in sterile water. The sterilized seeds were cultured on 1/2 MS medium in a growth chamber with a photoperiod of 16 h light/8 h dark at 24 °C. A. membranaceus 20 day old seedlings were used as the explant materials for hairy roots induction [i et al. . The surnduction .AtMYB12, ZmLc, and AtPAP1  were cloned, and the overexpression vector construction was accomplished through BP clonase II and LR clonase  according to the manufacturer’s instructions. Finally, these genes were successfully inserted into the overexpression vector pK7FWG2 (Invitrogen), expression clones pK7FWG2-ZmLc and pK7FWG2-AtPAP1 were then transformed into Agrobacterium rhizogenes strain R1000 by electroporation transformation. The GUS gene, which overexpresses beta-glucuronidase, was also constructed by the same method as control. The A. membranaceus hairy roots transformants were generated using the A. rhizogenes-mediated method [A. membranaceus infected by A. rhizogenes R1000 [The vector construction method has been described as previously reported . Brieflyd method . Brieflyes R1000 ,36. TranTotal RNA isolation and cDNA synthesis was finished as followed : Total R18 column  was employed for HPLC analysis. The HPLC was equipped with evaporating light scattering detection (ELSD). Astragalosides (AGs) chemical compounds including astragaloside I (AG I), astragaloside II (AG II), isoastragaloside II (IAG II), astragaloside III (AG III), astragaloside IV (AG IV) were obtained from ChromaDex . The HPLC-ELSD analysis method for Astragalosides (AGs) was established in the previous study [The control and confirmed overexpression hairy root lines were freeze-dried and ground into a fine powder. Samples (100 mg) were extracted with MeOH (1.5 mL) by sonicator for 60 min, then the supernatant was filtered by 0.45 μm PTFE syringe filter  for HPLC analysis. The Cus study ,36. The p < 0.05).All the experiments were independently conducted in triplicate, and the results for gene expression and metabolites content were shown as the mean ± standard error (SE). TB tools software was used for heatmap analysis. The ANOVA analysis was performed using SPSS version 22 . The significant differences between sample groups were conducted using Duncan’s Multiple Range Test. Values with different letters mean significantly different between groups (A. membranaceus, and show this to be a useful model for further exploring and investigating the function of transcription factors in triterpenoid accumulation in A. membranaceus and other plants.Taken together, the hairy root system is considered to be a useful and suitable tool for the genetic engineering of secondary metabolites enhancement. The efficient regulation of secondary metabolite production may be achieved by investigating the relevant transcription factors, especially in medicinal plants. In this study, we provide useful information about the use of transcription factors in the genetic engineering of AG biosynthesis in