Next Article in Journal
Genome-Wide Identification and Expression Analyses of the MADS-Box Gene During Flowering in Primulina huaijiensis
Previous Article in Journal
Transcriptome and Metabolome Reveal Ferulic Acid as a Critical Phenylpropanoid for Drought Resistance in Dendrobium sinense
Previous Article in Special Issue
Genome-Wide Dissection of Sorghum B3 Transcription Factor Family Identifies SbLAV1 as a Critical Transcriptional Regulator of Starch Biosynthesis in Developing Sorghum Grains
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 14
Issue 12
10.3390/plants14121842
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Root Development Genes (RDGs) Network in Brassica napus and the Role of BnaSHR-6 in Response to Low Nitrogen

by
Xingying Chen
1,2,†,
Sining Zhou
1,2,†,
Shuang Ye
1,2,
Zhuo Chen
1,2,
Zexuan Wu
1,2,
Shiying Liu
1,2,
Liping Hu
1,2,
Xiwen Yang
1,2,
Xiaoya Yang
1,2,
Peiji He
1,2,
Xingzhi Qian
1,2,
Huafang Wan
1,2,
Ti Zhang
1,2,
Nengwen Ying
1,2,
Huiyan Zhao
1,2,
Jiana Li
1,2,
Cunmin Qu
1,2,* and
Hai Du
1,2,*
1
College of Agronomy and Biotechnology, Southwest University, Chongqing 400715, China
2
Academy of Agricultural Sciences, Southwest University, Chongqing 400715, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Plants 2025, 14(12), 1842; https://doi.org/10.3390/plants14121842
Submission received: 19 May 2025 / Revised: 9 June 2025 / Accepted: 13 June 2025 / Published: 15 June 2025
(This article belongs to the Special Issue Crop Genetics and Breeding)
Browse Figures
Versions Notes

Abstract

:
The root system is vital for Brassica napus water/nutrient uptake and anchorage, highlighting the importance of identifying root development genes (RDGs). In this study, we identified 218 RDGs in B. napus through homology-based retrieval. Phylogenetic analysis of 22 representative species revealed that the RDGs are widely present in plants ranging from aquatic algae to angiosperms. RDGs in B. napus expanded through whole-genome duplication (WGD) events between Brassica rapa and Brassica oleracea ancestors and smaller duplications specific to B. napus. Promoter analysis identified 115 cis-elements, mainly abiotic stress-related and light-responsive. Transcription factor networks showed regulation by BBR-BPC, MIKC_MADS, AP2, and GRAS families. Transcriptome analysis under multiple stresses revealed that low nitrogen (LN) induced the most pronounced changes, with >50% (109/218) of RDGs differentially expressed in roots. Furthermore, we screened the BnaSHR-6 gene, which is co-localized in both primary roots (PR) and lateral roots (LR), and responds strongly to LN. Phenotypic analysis revealed that the BnaSHR-6 gene regulates the growth and development of both PR and LR under LN conditions, and confers a degree of resistance. These findings advance our understanding of RDGs in B. napus and provide valuable gene resources for subsequent molecular breeding.

1. Introduction

Root systems serve as the primary entry point for plants to acquire essential nutrients and provide mechanical stability [1,2]. Under nutrient deficiency or abiotic stresses such as soil salinization, roots exhibit adaptive responses [3,4]. During the development of plant roots, a multitude of genes play crucial roles in regulating processes such as root initiation, elongation, branching, cell differentiation, and responses to environmental stimuli. These genes are collectively referred to as Root Development Genes (RDGs) [5]. Therefore, the systematic identification of RDGs is crucial for improving crop stress tolerance and enhancing resource utilization efficiency.
There are two main types of root systems in plants: the taproot systems of dicots and the fibrous root systems of monocots [6]. The fibrous root system consists of a dense network of adventitious roots originating from the stem [7]. However, research on this architecture is limited, primarily focusing on Poaceae species such as rice and maize [8,9,10,11]. In taproot systems, the primary root (PR) serves as the main root, with branching consisting of smaller lateral roots (LR) and root hairs (RH) [12]. Current root research predominantly centers on taproot systems, particularly in the model plant Arabidopsis. The development of the primary root in Arabidopsis is driven by the root apical meristem (RAM), where stem cells maintained by the WOX5-dependent quiescent center drive growth [13,14,15,16]. LR development involves a regulatory network centered on *ARF7/19* alongside transcription factors like PLTs, SCR, and SHR [17,18,19]. RH formation in Arabidopsis is determined by cell-position specificity and controlled by R2R3-MYB/bHLH/WD40 complexes [20,21,22,23]. These studies in Arabidopsis have provided a framework for understanding conserved molecular mechanisms in plant root development and laid the foundation for studying RDGs in related crops.
As a representative taproot plant of agricultural importance, Brassica napus (genome AACC, 2n = 38) is a globally cultivated oilseed crop with significant value for vegetable consumption, edible oil production, and animal feed [24,25,26]. However, current research on B. napus has predominantly focused on aboveground traits, while root system research lags significantly, with existing work primarily addressing nutrient uptake and stress resistance [27,28,29], leaving RDG networks and developmental mechanisms poorly explored. Therefore, constructing a network of RDGs in B. napus is of great significance to elucidate the mechanisms underlying root development and to provide guidance for high-yield breeding.
In this study, we systematically analyzed the network of RDGs in B. napus at the genome-wide level, providing insights into the distribution, evolution, regulation, and expression patterns of RDGs. Additionally, we analyzed the expression patterns of RDGs under soil environmental stresses. BnaSHR-6 stands out for its dual role in PR and LR development and its sustained up-regulation. This gene exhibits positive regulation under normal conditions and demonstrates an active response to low nitrogen (LN) stress. We subsequently cloned its coding sequence, generating transgenic lines to confirm its positive regulation of root architecture under LN. This study provides a thorough overview of the distribution, evolution, and expression patterns of RDGs in B. napus, alongside the functional validation of candidate genes, thereby establishing a robust foundation for future functional analyses of RDGs.

2. Results

2.1. Identification of RDGs in B. napus

Based on the RDGs in Arabidopsis and the genomic data of B. napus publicly available on the BnIR online website (http://cbi.hzau.edu.cn/cgi-bin/bnapus/gb2/gbrowse/ZS11v0/, accessed on 1 September 2022), we identified a total of 218 RDGs in B. napus. Among these, 70 genes were associated with the PR network, 116 with the LR network, and 58 with the RH network (Figure 1 and Table S1). Notably, 26 genes overlapped between the PR and LR networks, including 14 genes involved in apical meristem growth (e.g., BnaWOX5s and BnaSHRs) and 12 genes regulating auxin concentration (e.g., BnaPIN3s and BnaPLT1s). The 70 candidate genes in the PR network originated from 17 gene families, including 7 TF families. The LR network comprised 116 candidate genes from 26 families (19 TF families), while the RH network included 58 candidate genes from 16 families (14 TF families).
Subcellular localization analyses indicated that proteins associated with root development in B. napus are predominantly nuclear-localized, suggesting that nuclear regulation plays a central role in the development of the root system in this species. Additionally, regulatory proteins were mainly localized in the nucleus, whereas functional proteins were distributed in non-nuclear subcellular structures. Members of the same protein family consistently exhibited identical subcellular localization patterns (Figure S1). Chromosomal localization results demonstrated that RDGs were evenly distributed between the An (106 genes) and Cn (108 genes) subgenomes. However, there was an uneven distribution across individual chromosomes and a higher concentration of genes at the chromosome ends (Figure S2).

2.2. Evolutionary Process of RDGs in the Plant Kingdom

To investigate the distribution pattern of RDGs across the plant kingdom, we selected 16 representative species spanning major plant lineages, from aquatic green algae to angiosperms. From these species, 979 RDGs were identified, with an overall trend of amplification from algal plants to angiosperms. Based on phylogenetic distribution patterns, the evolutionary trajectory of RDGs can be categorized into three distinct stages (Figure 2 and Table S2). In the first stage, aquatic green algae contained only homologous genes for CDKA1, which are associated with cell division and the cell cycle, but lacked true RDGs specific to root development. In the second stage, the transition from aquatic to terrestrial plants, the RDG network initially formed in bryophytes and ferns. The number of RDGs significantly increased, and genes including PINs, PLTs, SHRs, and SCRs, which are related to the establishment of root infrastructure, emerged. Among these, the newly added genes were primarily associated with primary and LR, while fewer genes related to RH appeared, resulting in a relatively simple RDG network. In the third stage, the RDG network was further refined. The number of RDGs increased further in both gymnosperms and angiosperms, and the networks for PRs and LRs became more extensive. Simultaneously, due to the increase in the number and diversity of RH-related genes, the RDG network for RHs was gradually enriched and refined. RDGs differ between dicotyledonous and monocotyledonous plants, and the genus Brassica within dicotyledonous plants encompasses all types of RDG networks.
Building on the premise that U’s triangle serves as a robust model for studying gene expansion, we further identified RDGs across the six target species. The results revealed that these RDGs are highly conserved and stable within their evolutionary relationships. Although the majority of RDGs remained evolutionarily stable, a subset of genes underwent tandem repeats, such as the copy number increase of CDKA1s and the copy number decrease of TRYs observed in B. napus (Table S3).

2.3. Linear Relationship Analysis of RDGs in B. napus

To further explore the genetic relationships within the B. napus genome, which is composed of AACC subgenomes, we conducted an intraspecific collinearity analysis (Figure 3A). Preliminary analysis of the evolutionary mechanisms revealed that RDGs had a total of 486 linear pairs between the B. napus and B. oleracea genomes, and 446 linear pairs between the B. napus and B. rapa genomes. Among the RDGs in the PR network, approximately 34.16% (166 pairs) of linear relationships were found between B. napus and B. rapa genomes, and about 38.79% (173 pairs) between B. napus and B. oleracea genomes. Within the LR network, the distribution of root development genes (RDGs) revealed that approximately 53.29% (259 pairs) were identified between B. napus and B. rapa genomes, and 55.61% (248 pairs) were identified between B. napus and B. oleracea genomes. Among the RDGs in the RH network, the linear associations between B. napus and B. rapa genomes accounted for approximately 27.16% (132 pairs), and that between B. napus and B. oleracea genomes accounted for about 22.20% (99 pairs). These results indicate a significant degree of covariance between B. napus and both of its parental species. The ratio of genes from the two parental backgrounds in the LR network was nearly equal (1:1). In contrast, the PR network retained a higher proportion of genes from the B. oleracea and B. rapa background.
Meanwhile, we analyzed the sequence identity of 204 duplicate gene pairs generated via WGD (Figure 3B). Approximately 90.96% (185 genes) of the duplicated genes exhibited ≥60% sequence identity at the CDS level. However, sequence identity was significantly lower in the promoter regions, with only 74.02% (151 genes) of the duplicated genes showing ≥60% sequence identity. This suggests that the evolution of the RDG network in B. napus primarily involves functional differentiation and regulatory changes in promoter regions, while the coding regions remain relatively conserved and exhibit evolutionary lagging.
The Ka/Ks ratio was used to evaluate selection pressure during molecular evolution. Results indicated that most RDGs (~68.75%) underwent purifying selection in B. napus, while a smaller proportion (~31.25%) underwent neutral selection (Figure 3C). These findings indicate that the evolution of RDGs is primarily driven by purifying selection or neutral selection.

2.4. Potential Transcriptional Regulation Profile of RDGs in B. napus

We identified 115 types of 29,401 cis-regulatory elements (CREs) in the promoter regions (−1500 bp) of RDGs in B. napus using the PlantCARE online software (https://bioinformatics.psb.ugent.be/webtools/plantcare/html/, accessed on 2 September 2022) (Figure 4A). In addition to the common basic core cis-elements (e.g., TA-TA-box, CAAT-box, and GC-box) and light-responsive cis-elements (e.g., G-box and GATA-motif), we identified a total of 937 cis-elements associated with seven abiotic stress-responsive cis-elements, including low-temperature-responsive cis-elements, salt-stress-responsive cis-elements, and injury-responsive cis-elements. Additionally, we identified 1514 cis-elements related to 15 hormone responses across five categories, including growth hormone response cis-elements, abscisic acid response cis-elements, gibberellic acid response cis-elements, salicylic acid response cis-elements, and methyl jasmonate response cis-elements.
Furthermore, we predicted the TF binding sites in the promoter regions of RDGs in B. napus. The results showed that there were 5065 pairs of potential transcriptional regulatory relationships between 333 genes from 31 TF families and RDGs in B. napus (Figure 4B). Among these, the RDG network might appear to be complexly regulated by a large number of upstream TF, primarily BBR-BPC, MIKC_MADS, AP2, and GRAS TF families. Interestingly, in the PR network, the GRAS family regulates 23 RDGs with only one member, while the 66 members of the ERF family regulate only 14 RDGs. This quantitative regulation relationship also exists in RH and LR networks, suggesting that different TF families have a more complex approach to regulating root systems.

2.5. Analysis of DEGs and LN Stress Expression Profiles of RDGs in B. napus

We utilized RNA-seq datasets from the BnaGADB website (http://www.bnagadb.cn/, accessed on 1 September 2022) for ZS11 seedling leaves and roots under low nitrogen (LN), low phosphorus (LP), and low potassium (LK) treatments, and from the BnTIR website (http://yanglab.hzau.edu.cn/BnTIR/, accessed on 1 September 2022) to obtain data related to heat stress, salt stress, and osmotic stress. The number of differentially expressed genes (DEGs) compared to the control group under different treatment conditions was quantified (Figure 5A). The results showed that the number of up-regulated genes in roots was higher than leaves under nutrient stress conditions. Notably, the number of up-regulated genes in roots under LN increased over time.
Based on the DEGs analysis, we further investigated the expression patterns under LN (Figure 5B). In the LN stress expression profile, 109 (~50.00%) of the 218 RDGs in B. napus exhibited differential expression in roots (FC ≥ 2, FDR < 0.01). Among the PR network, 23 (~51.11%) genes were up-regulated, and 8 (~17.78%) genes were down-regulated. In the LR network, 10 (~21.28%) genes were up-regulated, and 27 (~57.45%) genes were down-regulated. In the RH network, 19 (~59.38%) genes were up-regulated, and 5 (~15.63%) genes were down-regulated. These results indicate that half of the RDGs in B. napus were differentially expressed under LN stress, with more DEGs up-regulated in PR and more DEGs down-regulated in LR. Thus, RDGs in B. napus widely respond to LN stress, with expression trends varying across the RDG network in different root networks.

2.6. Functional Validation of the BnaSHR-6 Gene in Arabidopsis Mutants

The analysis of 26 genes common to both PR and LR networks revealed differential expression of 14 genes under low nitrogen (LN) stress. Among these, 10 genes were up-regulated, and 4 were down-regulated (Figure 6A). Notably, of the 10 up-regulated genes, BnaSHR-2, BnaSHR-4, BnaSHR-6, BnaSCR-1, and BnaSCR-2 demonstrated up-regulation on both days 5 and 12. The remaining genes were up-regulated exclusively on day 5 or day 12. Given that BnaSHR-6 displayed relatively higher expression levels in B. napus roots and exhibited higher and more stable up-regulation, it was considered a candidate gene for further molecular function validation studies.
To explore the function of BnaSHR-6, we generated 35Sp::BnaSHR-6 transgenic lines and cultured them on both standard Hoagland’s solid medium and LN media, alongside the wild-type (Col-0). Under control conditions, the 35Sp::BnaSHR-6 line showed longer PR length and greater LR densities compared to Col-0 (Figure 6C). This suggests that BnaSHR-6 positively regulates both PR and LR development. Phenotypic analyses under LN conditions revealed increased PR and LR lengths in Col-0, whereas the 35Sp::BnaSHR-6 line exhibited no significant change in PR length but showed an increase in LR length. This indicates that BnaSHR-6 responds positively and exhibits resistance to LN stress. These findings suggest that BnaSHR-6 is up-regulated under LN conditions, supporting its role in root development under such conditions.

3. Discussion

3.1. Genome Evolution and Functional Diversification of RDGs in B. napus

B. napus is an allotetraploid formed by the hybridization between B. rapa and B. oleracea, both of which have experienced ancestral whole-genome triplication (WGT) events common in Brassicaceae [29,30]. Given the high conservation of homologous genomic regions, the gene number in B. napus is theoretically approximately six times that of Arabidopsis. For example, the 53 RDGs identified in Arabidopsis should expand to approximately 318 genes in B. napus through polyploidization. However, only 218 orthologs were identified, indicating that approximately one-third of genes were lost through large-scale homologous recombination following WGT. The diploid progenitors B. rapa and B. oleracea, contain 103 and 107 root initiation-related genes, respectively. In the B. napus subgenomes, 106 genes are located in the An subgenome and 110 in the Cn subgenome, with two remaining unknown, showing varying retention rates between subgenomes, likely due to subgenome dominance selection. Sequence analysis of the 218 genes in B. napus revealed that their CDS are highly conserved (>90% identity), while promoter sequences exhibit significant diversity. This suggests that the functional diversification of these genes may enhance environmental adaptability during root development. Additionally, frequent small-scale duplication events (17.43% tandem repeats) and positive selection in promoter regions were found to continuously introduce new variation into the RDG network.

3.2. Expression Patterns of RDGs in B. napus

Analysis of DEGs related to RDGs in B. napus under abiotic and nutrient stresses revealed that DEGs were present under almost all stress conditions, suggesting that these genes may be involved in regulating root stress adaptation and nutrient acquisition. For example, studies have shown that the transcription factors ARF7 and ARF19 respond to phosphorus deficiency by positively regulating PHR1, a key regulator of phosphorus starvation responses in Arabidopsis roots [31]. In Arabidopsis, GL2 directly modulates ETO1 expression and function under LN, LP, or LK stress [23,32]. TCP20 binds to the DNA of over 100 nitrate-regulated genes, playing a crucial role in systemic nitrate responses [33,34]. Notably, a relatively higher number of DEGs was observed under LN stress, suggesting that RDGs under LN stress may positively influence root growth and development, indicating potential applications in agriculture. Among the six genes consistently up-regulated in roots at both 5 and 12 days of LN treatment, all belonged to the GRAS family. Two family members, SHR and SCR, interact to specify endodermal cell identity and regulate root apical meristem establishment and maintenance. BnaSHR-6 exhibited higher expression levels and more stable up-regulation under LN, implying its potential role in modulating root growth under LN conditions.

3.3. The Critical Function of BnaSHR-6 in B. napus

Short Root (SHR) acts as a mobile TF and plays a crucial role in regulating the asymmetric division of root cortex/endodermis stem cells, maintaining the activity of the root apical meristematic [30,31]. The duplication of the SHR gene is widespread, with extensive homologs found in Oryza sativa, Brachypodium distachyon, Zea maize, Medicago truncatula, and various other plant species [35]. In B. napus, BnaSHR-6 was identified as a crucial factor responsive to LN conditions. Under control conditions, the 35Sp::BnaSHR-6 transgenic line showed increased PR length and higher LR density compared to the wild type (Col-0). Under LN conditions, Col-0 exhibited a significant increase in both PR and LR length, consistent with previous findings [32]. In contrast, the 35Sp::BnaSHR-6 line did not show a notable change in PR length but did show a significant increase in LR density. This further confirms that BnaSHR-6 positively responds to LN, influencing the development of both PR and LR. The up-regulation of BnaSHR-6 under LN highlights its essential role in maintaining root architecture under LN stress. As a key regulator of root development under LN, BnaSHR-6 may also preliminarily affect the growth of aboveground leaves. Under control conditions, the aboveground leaves of the 35Sp::BnaSHR-6 lines showed enhanced growth compared to Col-0. Under LN stress, transgenic lines maintained relatively superior leaf growth despite overall reduction. Given that SHR is expressed in vascular tissues of Arabidopsis (inflorescences, stems, leaves) and regulates leaf cell proliferation and vascularization [36,37], we hypothesize that BnaSHR-6 could extend its regulatory role beyond roots to modulate leaf development under stress conditions. This potential mechanism warrants further validation in B. napus.

4. Materials and Methods

4.1. Identification and Subcellular Localization of RDGs in B. napus

To identify RDGs in B. napus, we conducted a detailed review of the relevant literature reports and summarized the RDGs that have been functionally defined in Arabidopsis, and retrieved the protein and CDS sequences of these genes from the TAIR website (https://www.arabidopsis.org/, accessed on 1 September 2022) and the Phytozome v12.1 database (http://www.Phytozome.net/, accessed on 1 September 2022) [38]. The genome database of the rapeseed variety “ZS11” was searched for homology using BLASTP. The Expectation Value threshold was set to 0.0001, and the rest of the parameters were set by default [39,40]. Then, multiple sequence alignment was performed using MAFFT version 7 (https://mafft.cbrc.jp/alignment/server/, accessed on 1 September 2022), and after deletion of sequences with large gaps (>60%) and redundancies, all homologous genes in the RDG network in B. napus were analyzed and obtained in combination with sequence similarity (>80%) [41]. The accuracy of the data was further checked by predicting and analyzing the protein structures encoded by the candidate genes using SMART (https://smart.embl.de/, accessed on 1 September 2022) and ExPaSy websites (https://www.expasy.org/, accessed on 1 September 2022). RDGs in B. rapa and B. oleracea were also characterized using the methods described above. The predicted subcellular localization of root initial development-related proteins in B. napus was analyzed using the Plant-mPLoc (http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/, accessed on 1 September 2022) and WoLF PSORT (https://wolfpsort.hgc.jp, accessed on 1 September 2022) websites [42,43].

4.2. Chromosomal Localization and Collinearity Analysis of RDGs in B. napus

This study employed the GBROWSE tool from the BnPIR website (http://cbi.hzau.edu.cn/cgi-bin/bnapus/gb2/gbrowse/ZS11v0/, accessed on 1 September 2022) to conduct a comprehensive analysis of the chromosomal localization of RDGs in B. napus, and the chromosome distributions of RDGs were drawn using MapChart 2.3.2 software [44,45]. The One Step MCScanX plugin within the TBtool v2.056 software was used to perform an in-depth analysis and visualization of the collinearity between RDGs in ZS11 and those in the genomes of B. rapa, B. oleracea, and B. napus [46]. The duplication events of RDGs were defined based on the collinearity relationship.

4.3. Analysis of Amplification and Evolutionary Mechanism of RDGs in B. napus

The CDS sequences and promoter sequences (−1500 bp) of RDGs in B. napus were obtained using the BnPIR website (http://cbi.hzau.edu.cn/cgi-bin/bnapus//blast, accessed on 1 September 2022) [44]. Similarity and identity between duplicated gene pairs across CDS and promoter sequences were subsequently analyzed using MatGAT 2.01 software, thus assessing the conservation of gene duplications [47]. Also, based on the obtained CDS sequences of RDGs and the replication relationships among RDGs in B. napus, selection pressure analysis was performed using KaKs_Calculator 2.0 software (https://bigd.big.ac.cn/tools/kaks, accessed on 1 September 2022) to calculate the model with LWL [48].

4.4. Identification of RDGs in the Plant Kingdom

Based on the protein sequences of the RDGs in Arabidopsis, homology searches of the remaining representative species of the Kingdom Plantae and representative species of Brassicaceae were performed using BLASTP to identify the RDGs in the corresponding species. Among them, the genomic data for Chlamydomonas reinhardtii, Volvox carteri, Physcomitrella patens, Selaginella moellendorffii, Amborella trichopoda, Oryza sativa, Zea mays, Aquilegia coerulea, Solanum tuberosum, M. truncatula, and Populus trichocarpa were obtained from the Phytozome v12.1 database (https://phytozome-next.jgi.doe.gov, accessed on 1 September 2022). Picea abies genomic data was obtained from the TreeGenes website (https://treegenesdb.org/FTP, accessed on 1 September 2022). Brassica nigra, Brassica juncea, Brassica carinata, B. napus were obtained from the BnTIR website (http://yanglab.hzau.edu.cn/BnTIR, accessed on 1 September 2022). Data processing and preliminary analysis of RDGs in the above species were performed using Microsoft Excel software.

4.5. Functional Prediction Analysis of RDGs in B. napus

To elucidate the regulatory mechanism of RDGs in B. napus, we used the PlantTFDB website (http://planttfdb.gao-lab.org, accessed on 1 September 2022) to predict the potential transcription factor binding sites (p-value ≤ 1 ×10−6) of the promoter sequences (−1500 bp) of RDGs in B. napus [49]. Cis-elements of the promoter sequences (−1500 bp) of RDGs in B. napus were predicted using the PlantCARE website (http://bioinformatics.psb.ugent.be/webtools/plantcare/html, accessed on 1 September 2022) [50]. Cytoscape 3.8.2 software was used to visualize the prediction results [51].

4.6. Expression Pattern Analysis of RDGs in B. napus

To explore the nutrient-responsive expression patterns of RDGs in B. napus, the RNA-seq dataset of ZS11 seedling leaves and roots under LN, LP, and LK treatments was obtained from the BnaGADB website (http://www.bnagadb.cn/, accessed on 1 September 2022). The expression patterns were further analyzed utilizing abiotic stress expression profile data sourced from the BnTIR website (http://yanglab.hzau.edu.cn/BnTIR/, accessed on 1 September 2022), which encompasses profiles related to heat stress, salt stress, and osmotic stress. We focused on RDGs exhibiting maximum fragments per kilobase of transcript per million mapped reads (FPKM) values greater than or equal to 1 across all samples for subsequent differential expression analysis. We employed the DESeq2 package to conduct this analysis, applying a threshold of log2 (fold change) ≥ 1 and p-value < 0.05 to identify significantly differentially expressed genes. Data processing and subsequent analysis were performed using Microsoft Excel 2022. The expression data of the above groups were processed separately using Cluster 3.0 and visualized using Java Treeview 3.0 and TBtools v2.121 [52,53].

4.7. Phenotypic Analysis of Transgenic Arabidopsis

We used the pEASY-BnaSHR-6 vector as a template and designed specific primers (F: 5′-ATGGATACTCTCTTTAGACTAGTCAGTCTCC-3′; R: 5′-CGTTGGCCGCCACGCACTG-3′) to amplify the BnaSHR-6 gene fragment by PCR. The amplified fragment was digested with Kpn I (N-terminal) and BamH I (C-terminal) restriction enzymes, and the full-length CDS of BnaSHR-6 was cloned into the middle of the CaMV35Sp promoter and the Flag gene by T4-DNA ligase in the pC1300-Flag vector to construct the 35Sp:: BnaSHR-6-Flag vector. Then, we conducted a phenotypic analysis on the Arabidopsis line obtained, including the wild type (Col-0) and transgenic Arabidopsis (35Sp::BnaSHR-6). Seeds were surface sterilized with 75% ethanol for 15 min, then washed 3–5 times with sterile deionized water (ddH2O) and sown on full nutrient Hoagland medium and LN solid media (Table S4). After 3 days of 4 °C vernalization, they were planted in an artificial climate culture chamber. The specific conditions were set at an illumination intensity of 20,000 Lux, a 16 h photoperiod at 23 °C, followed by an 8 h dark period at 20 °C, and 70% humidity. Transgenic Arabidopsis and Col-0 were germinated in Hoagland medium for four days in the same environment and then transferred to LN medium for eight days for phenotypic analysis. PR and LR were observed and measured for Transgenic Arabidopsis and Col-0, and the resulting data were statistically analyzed and visualized using GraphPad Prism version 9.5 software.

4.8. RT-qPCR Analysis of 35Sp::BnaSHR-6 Overexpressing Transgenic Arabidopsis

The qRT-PCR method was applied to analyze the expression levels of 35Sp::BnSHR-6 and Col-0, using AtActin as control. Seedlings grown on full nutrient Hoagland medium for 10 days were used. Each treatment was performed with three biological replicates, each replicate containing five plants. The primers of 35Sp::BnSHR-6 and AtActin used in this analysis are listed in Table S5. The RNA simple total RNA Extraction Kit (TIANGEN, Beijing, China) was used to extract the total RNA in each sample. Then, the quality and concentration of the total RNA were examined using 1% gel electrophoresis and a Thermo Fisher ScientificTM spectrophotometer (Thermo, Beijing, China), respectively. Subsequently, about 1.0 μg of the total RNA was applied to synthesize first-strand cDNA in a 20 μL reaction system, according to the manufacturer’s instructions for HiScript® III RT SuperMix for qPCR (+gDNA wiper) HiScript® III RT kit (Vazyme, Nanjing, China). The cDNA concentration was diluted to 35 ng/μL for subsequent experiments. The Taq Pro Universal SYBR qPCR Master Mix Kit (Vazyme, Nanjing, China) was used to perform real-time PCR analysis in a CFX Connect™ Real-Time System (Bio-Rad, Hercules, CA, USA). The cDNA was amplified using a 40-cycle program (95 °C, 5 s; 58 °C, 20 s per cycle) following denaturation. Expression levels were calculated as the mean signal intensity across the three biological replicates using the 2−ΔΔCt method. One-way ANOVA analyses of variance were used to assess the difference in the expression level of each gene.

5. Conclusions

In this study, a total of 218 RDGs were identified within the B. napus genome, and the RDG network in B. napus was systematically mapped and constructed. We conducted a comprehensive analysis of the amplification and evolutionary mechanisms, phyletic distribution patterns, transcriptional regulation network characteristics, and expression properties of this network. Notably, we identified the gene BnaSHR-6, which is part of both the PR and LR networks and exhibits significant up-regulation under LN stress. In this study, a homology search in B. napus based on known RDGs in Arabidopsis was performed with high conservation and reference value. However, the exclusive reliance on Arabidopsis homologs may have led to the omission of RDGs specific to B. napus. To mitigate this limitation, future research should incorporate de novo transcriptomic analyses of root development stages alongside proteomic studies. The integration of multi-omics datasets with forward genetics has the potential to delineate a detailed roadmap of root development for specific species. Ultimately, this systems biology framework can expedite the application of RDGs from fundamental research to the precision breeding of high-yielding rapeseed cultivars, thereby providing both theoretical insights and practical genetic resources essential for sustainable agriculture.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants14121842/s1, Table S1: Features of the Root development genes (RDGs) from Brassica napus and Arabidopsis thaliana identified in this study. Table S2: Specific distribution of RDGs in 12 typical plants. Table S3: Specific distribution of RDGs in six species in the U’s triangle. Table S4: List of the normal and LN Hoagland’s components used in this study. Figure S1: Subcellular localization of root development-related proteins in B. napus. Figure S2: Number of RDGs on each chromosome.

Author Contributions

Conceptualization, X.C. and S.Z.; methodology, S.Z.; software, S.Y. and Z.C.; validation, Z.C., Z.W. and S.L.; formal analysis, X.C., P.H. and X.Q.; investigation, L.H. and X.Q.; resources, S.L. and X.Y. (Xiwen Yang); data curation, X.Y. (Xiaoya Yang) and S.Z.; writing—original draft preparation, X.C.; writing—review and editing, T.Z. and H.W.; visualization, H.Z. and N.Y.; supervision, C.Q.; project administration, J.L.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (2023YFF1000701), the 2024 Key Core Agricultural Technologies R&D Program of Chongqing: Development, Application of Short-Cycle Rapeseed Germplasm Resources, and the Natural Science Foundation of Chongqing, China (CSTB2022NSCQ-MSX0790) and the National Training Program of Innovation and Entrepreneurship for Undergraduates (202410635011).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hostetler, A.N.; Khangura, R.S.; Dilkes, B.P.; Sparks, E.E. Bracing for sustainable agriculture: The development and function of brace roots in members of Poaceae. Curr. Opin. Plant Biol. 2021, 59, 101985. [Google Scholar] [CrossRef] [PubMed]
  2. Lyzenga, W.J.; Liu, Z.; Olukayode, T.; Zhao, Y.; Kochian, L.V.; Ham, B.K. Getting to the roots of N, P, and K uptake. J. Exp. Bot. 2023, 74, 1784–1805. [Google Scholar] [CrossRef]
  3. Karlova, R.; Boer, D.; Hayes, S.; Testerink, C. Root plasticity under abiotic stress. Plant Physiol. 2021, 187, 1057–1070. [Google Scholar] [CrossRef]
  4. Oldroyd, G.E.D.; Leyser, O. A plant’s diet, surviving in a variable nutrient environment. Science 2020, 368, aba0196. [Google Scholar] [CrossRef]
  5. Benfey, P.N.; Scheres, B. Root development. Curr. Biol. CB 2000, 10, R813–R815. [Google Scholar] [CrossRef]
  6. Liu, Q.; Liang, Z.; Feng, D.; Jiang, S.; Wang, Y.; Du, Z.; Li, R.; Hu, G.; Zhang, P.; Ma, Y.; et al. Transcriptional landscape of rice roots at the single-cell resolution. Mol. Plant 2021, 14, 384–394. [Google Scholar] [CrossRef] [PubMed]
  7. Coudert, Y.; Périn, C.; Courtois, B.; Khong, N.G.; Gantet, P. Genetic control of root development in rice, the model cereal. Trends Plant Sci. 2010, 15, 219–226. [Google Scholar] [CrossRef]
  8. Li, J.; Zhu, S.; Song, X.; Shen, Y.; Chen, H.; Yu, J.; Yi, K.; Liu, Y.; Karplus, V.J.; Wu, P.; et al. A rice glutamate receptor-like gene is critical for the division and survival of individual cells in the root apical meristem. Plant Cell 2006, 18, 340–349. [Google Scholar] [CrossRef] [PubMed]
  9. Steffens, B.; Wang, J.; Sauter, M. Interactions between ethylene, gibberellin and abscisic acid regulate emergence and growth rate of adventitious roots in deepwater rice. Planta 2006, 223, 604–612. [Google Scholar] [CrossRef]
  10. Qin, T.; Kazim, A.; Wang, Y.; Richard, D.; Yao, P.; Bi, Z.; Liu, Y.; Sun, C.; Bai, J. Root-Related Genes in Crops and Their Application under Drought Stress Resistance-A Review. Int. J. Mol. Sci. 2022, 23, 11477. [Google Scholar] [CrossRef]
  11. Li, L.; Hey, S.; Liu, S.; Liu, Q.; McNinch, C.; Hu, H.C.; Wen, T.J.; Marcon, C.; Paschold, A.; Bruce, W.; et al. Characterization of maize roothairless6 which encodes a D-type cellulose synthase and controls the switch from bulge formation to tip growth. Sci. Rep. 2016, 6, 34395. [Google Scholar] [CrossRef] [PubMed]
  12. Bellini, C.; Pacurar, D.I.; Perrone, I. Adventitious roots and lateral roots: Similarities and differences. Annu. Rev. Plant Biol. 2014, 65, 639–666. [Google Scholar] [CrossRef] [PubMed]
  13. Chiatante, D.; Rost, T.; Bryant, J.; Scippa, G.S. Regulatory networks controlling the development of the root system and the formation of lateral roots: A comparative analysis of the roles of pericycle and vascular cambium. Ann. Bot. 2018, 122, 697–710. [Google Scholar] [CrossRef]
  14. Jones, B.; Ljung, K. Subterranean space exploration: The development of root system architecture. Curr. Opin. Plant Biol. 2012, 15, 97–102. [Google Scholar] [CrossRef]
  15. Petricka, J.J.; Winter, C.M.; Benfey, P.N. Control of Arabidopsis root development. Annu. Rev. Plant Biol. 2012, 63, 563–590. [Google Scholar] [CrossRef] [PubMed]
  16. Sarkar, A.K.; Luijten, M.; Miyashima, S.; Lenhard, M.; Hashimoto, T.; Nakajima, K.; Scheres, B.; Heidstra, R.; Laux, T. Conserved factors regulate signalling in Arabidopsis thaliana shoot and root stem cell organizers. Nature 2007, 446, 811–814. [Google Scholar] [CrossRef]
  17. Banda, J.; Bellande, K.; von Wangenheim, D.; Goh, T.; Guyomarc’h, S.; Laplaze, L.; Bennett, M.J. Lateral Root Formation in Arabidopsis: A Well-Ordered LRexit. Trends Plant Sci. 2019, 24, 826–839. [Google Scholar] [CrossRef]
  18. Motte, H.; Vanneste, S.; Beeckman, T. Molecular and Environmental Regulation of Root Development. Annu. Rev. Plant Biol. 2019, 70, 465–488. [Google Scholar] [CrossRef]
  19. Slovak, R.; Ogura, T.; Satbhai, S.B.; Ristova, D.; Busch, W. Genetic control of root growth: From genes to networks. Ann. Bot. 2016, 117, 9–24. [Google Scholar] [CrossRef]
  20. Cui, S.; Suzaki, T.; Tominaga-Wada, R.; Yoshida, S. Regulation and functional diversification of root hairs. Semin. Cell Dev. Biol. 2018, 83, 115–122. [Google Scholar] [CrossRef]
  21. Long, Y.; Schiefelbein, J. Novel TTG1 Mutants Modify Root-Hair Pattern Formation in Arabidopsis. Front. Plant Sci. 2020, 11, 383. [Google Scholar] [CrossRef] [PubMed]
  22. Marzec, M.; Melzer, M.; Szarejko, I. Root hair development in the grasses: What we already know and what we still need to know. Plant Physiol. 2015, 168, 407–414. [Google Scholar] [CrossRef] [PubMed]
  23. Serna, L. Evolution of gene regulatory network architectures regulating root hair patterning in superrosid species. New Phytol. 2023, 240, 940–944. [Google Scholar] [CrossRef]
  24. Chen, Y.Y.; Lu, H.Q.; Jiang, K.X.; Wang, Y.R.; Wang, Y.P.; Jiang, J.J. The Flavonoid Biosynthesis and Regulation in Brassica napus: A Review. Int. J. Mol. Sci. 2022, 24, 357. [Google Scholar] [CrossRef] [PubMed]
  25. Arifuzzaman, M.; Oladzadabbasabadi, A.; McClean, P.; Rahman, M. Shovelomics for phenotyping root architectural traits of rapeseed/canola (Brassica napus L.) and genome-wide association mapping. Mol. Genet. Genom. MGG 2019, 294, 985–1000. [Google Scholar] [CrossRef]
  26. Wang, W.; Liu, H.; Xie, Y.; King, G.J.; White, P.J.; Zou, J.; Xu, F.; Shi, L. Rapid identification of a major locus qPRL-C06 affecting primary root length in Brassica napus by QTL-seq. Ann. Bot. 2023, 131, 569–583. [Google Scholar] [CrossRef]
  27. Yuan, P.; Liu, H.; Wang, X.; Hammond, J.P.; Shi, L. Genome-wide association study reveals candidate genes controlling root system architecture under low phosphorus supply at seedling stage in Brassica napus. Mol. Breed. New Strateg. Plant Improv. 2023, 43, 63. [Google Scholar] [CrossRef]
  28. Ibrahim, S.; Ahmad, N.; Kuang, L.; Tian, Z.; Sadau, S.B.; Iqbal, M.S.; Wang, X.; Wang, H.; Dun, X. Genome-Wide Association Studies of Root-Related Traits in Brassica napus L. under Low-Potassium Conditions. Plants 2022, 11, 1826. [Google Scholar] [CrossRef]
  29. Boter, M.; Pozas, J.; Jarillo, J.A.; Piñeiro, M.; Pernas, M. Brassica napus Roots Use Different Strategies to Respond to Warm Temperatures. Int. J. Mol. Sci. 2023, 24, 1143. [Google Scholar] [CrossRef]
  30. Levesque, M.P.; Vernoux, T.; Busch, W.; Cui, H.; Wang, J.Y.; Blilou, I.; Hassan, H.; Nakajima, K.; Matsumoto, N.; Lohmann, J.U.; et al. Whole-genome analysis of the SHORT-ROOT developmental pathway in Arabidopsis. PLoS Biol. 2006, 4, e143. [Google Scholar] [CrossRef]
  31. Hao, Y.; Cui, H. SHORT-ROOT regulates vascular patterning, but not apical meristematic activity in the Arabidopsis root through cytokinin homeostasis. Plant Signal. Behav. 2012, 7, 314–317. [Google Scholar] [CrossRef] [PubMed]
  32. Jia, Z.; Giehl, R.F.H.; von Wirén, N. The Root Foraging Response under Low Nitrogen Depends on DWARF1-Mediated Brassinosteroid Biosynthesis. Plant Physiol. 2020, 183, 998–1010. [Google Scholar] [CrossRef] [PubMed]
  33. Guan, P.; Wang, R.; Nacry, P.; Breton, G.; Kay, S.A.; Pruneda-Paz, J.L.; Davani, A.; Crawford, N.M. Nitrate foraging by Arabidopsis roots is mediated by the transcription factor TCP20 through the systemic signaling pathway. Proc. Natl. Acad. Sci. USA 2014, 111, 15267–15272. [Google Scholar] [CrossRef] [PubMed]
  34. Liu, B.; Wu, J.; Yang, S.; Schiefelbein, J.; Gan, Y. Nitrate regulation of lateral root and root hair development in plants. J. Exp. Bot. 2020, 71, 4405–4414. [Google Scholar] [CrossRef]
  35. Shaar-Moshe, L.; Brady, S.M. SHORT-ROOT and SCARECROW homologs regulate patterning of diverse cell types within and between species. New Phytol. 2023, 237, 1542–1549. [Google Scholar] [CrossRef]
  36. Dhondt, S.; Coppens, F.; De Winter, F.; Swarup, K.; Merks, R.M.; Inzé, D.; Bennett, M.J.; Beemster, G.T. SHORT-ROOT and SCARECROW regulate leaf growth in Arabidopsis by stimulating S-phase progression of the cell cycle. Plant Physiol. 2010, 154, 1183–1195. [Google Scholar] [CrossRef]
  37. Yoon, E.K.; Oh, J.; Lim, J. (Don’t) Look Up!: Is short-root just a short-root plant? Front. Plant Sci. 2022, 13, 1069996. [Google Scholar] [CrossRef]
  38. Goodstein, D.M.; Shu, S.; Howson, R.; Neupane, R.; Hayes, R.D.; Fazo, J.; Mitros, T.; Dirks, W.; Hellsten, U.; Putnam, N.; et al. Phytozome: A comparative platform for green plant genomics. Nucleic Acids Res. 2012, 40, D1178–D1186. [Google Scholar] [CrossRef]
  39. Camacho, C.; Coulouris, G.; Avagyan, V.; Ma, N.; Papadopoulos, J.; Bealer, K.; Madden, T.L. BLAST+: Architecture and applications. BMC Bioinform. 2009, 10, 421. [Google Scholar] [CrossRef]
  40. Song, J.M.; Guan, Z.; Hu, J.; Guo, C.; Yang, Z.; Wang, S.; Liu, D.; Wang, B.; Lu, S.; Zhou, R.; et al. Eight high-quality genomes reveal pan-genome architecture and ecotype differentiation of Brassica napus. Nat. Plants 2020, 6, 34–45. [Google Scholar] [CrossRef]
  41. Katoh, K.; Misawa, K.; Kuma, K.; Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002, 30, 3059–3066. [Google Scholar] [CrossRef] [PubMed]
  42. Chou, K.C.; Shen, H.B. Plant-mPLoc: A top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS ONE 2010, 5, e11335. [Google Scholar] [CrossRef] [PubMed]
  43. Horton, P.; Park, K.J.; Obayashi, T.; Fujita, N.; Harada, H.; Adams-Collier, C.J.; Nakai, K. WoLF PSORT: Protein localization predictor. Nucleic Acids Res. 2007, 35, W585–W587. [Google Scholar] [CrossRef]
  44. Song, J.M.; Liu, D.X.; Xie, W.Z.; Yang, Z.; Guo, L.; Liu, K.; Yang, Q.Y.; Chen, L.L. BnPIR: Brassica napus pan-genome information resource for 1689 accessions. Plant Biotechnol. J. 2021, 19, 412–414. [Google Scholar] [CrossRef] [PubMed]
  45. Voorrips, R.E. MapChart: Software for the graphical presentation of linkage maps and QTLs. J. Hered. 2002, 93, 77–78. [Google Scholar] [CrossRef]
  46. Chen, C.; Chen, H.; Zhang, Y.; Thomas, H.R.; Frank, M.H.; He, Y.; Xia, R. TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data. Mol. Plant 2020, 13, 1194–1202. [Google Scholar] [CrossRef]
  47. Campanella, J.J.; Bitincka, L.; Smalley, J. MatGAT: An application that generates similarity/identity matrices using protein or DNA sequences. BMC Bioinform. 2003, 4, 29. [Google Scholar] [CrossRef]
  48. Wang, D.; Zhang, Y.; Zhang, Z.; Zhu, J.; Yu, J. KaKs_Calculator 2.0: A toolkit incorporating gamma-series methods and sliding window strategies. Genom. Proteom. Bioinform. 2010, 8, 77–80. [Google Scholar] [CrossRef]
  49. Tian, F.; Yang, D.C.; Meng, Y.Q.; Jin, J.; Gao, G. PlantRegMap: Charting functional regulatory maps in plants. Nucleic Acids Res. 2020, 48, D1104–D1113. [Google Scholar] [CrossRef]
  50. Lescot, M.; Déhais, P.; Thijs, G.; Marchal, K.; Moreau, Y.; Van de Peer, Y.; Rouzé, P.; Rombauts, S. PlantCARE, a database of plant cis-acting regulatory elements and a portal to tools for in silico analysis of promoter sequences. Nucleic Acids Res. 2002, 30, 325–327. [Google Scholar] [CrossRef]
  51. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef] [PubMed]
  52. de Hoon, M.J.; Imoto, S.; Nolan, J.; Miyano, S. Open source clustering software. Bioinformatics 2004, 20, 1453–1454. [Google Scholar] [CrossRef] [PubMed]
  53. Saldanha, A.J. Java Treeview—Extensible visualization of microarray data. Bioinformatics 2004, 20, 3246–3248. [Google Scholar] [CrossRef] [PubMed]
Plants 14 01842 g001
Figure 1. Primary roots (PR), lateral roots (LR), and root hairs (RH) gene networks of RDGs in B. napus. The numbers following the gene name indicate the gene number of the homologous gene in B. napus.
Figure 1. Primary roots (PR), lateral roots (LR), and root hairs (RH) gene networks of RDGs in B. napus. The numbers following the gene name indicate the gene number of the homologous gene in B. napus.
Plants 14 01842 g001
Plants 14 01842 g002
Figure 2. The distribution of RDGs in 16 typical plant species. (A) Homologous genes are marked at the bottom. The number in each square represents the number of genes. (B) The evolution of RDGs in 16 typical plant species. The evolution trend is divisible into three stages (Stage I, Stage II, and Stage III). Stage I is Chlamydomonas reinhardtii (Cr) to Volvox carteri (Vc), which contains only homologous genes of CDKA1. Stage II is Physcomitrella patens (Pp) to Selaginella moellendorffii (Sm), with the transition from aquatic to terrestrial plants and the initial formation of the RGD network. Stage III is Picea abies (Pa) to Brassica napus (Bn), where the number of RGDs further increased.
Figure 2. The distribution of RDGs in 16 typical plant species. (A) Homologous genes are marked at the bottom. The number in each square represents the number of genes. (B) The evolution of RDGs in 16 typical plant species. The evolution trend is divisible into three stages (Stage I, Stage II, and Stage III). Stage I is Chlamydomonas reinhardtii (Cr) to Volvox carteri (Vc), which contains only homologous genes of CDKA1. Stage II is Physcomitrella patens (Pp) to Selaginella moellendorffii (Sm), with the transition from aquatic to terrestrial plants and the initial formation of the RGD network. Stage III is Picea abies (Pa) to Brassica napus (Bn), where the number of RGDs further increased.
Plants 14 01842 g002
Plants 14 01842 g003
Figure 3. Evolutionary analysis of RDGs in B. napus. (A) Linear relationship analysis of B. napus RDGs and homologous genes in B. rapa and B. oleracea genomes. (B) Comparison of CDS and promoter identity in PR, LR, and RH. p value was calculated by t tests. *, ***, and **** mean significant differences at the 0.05, 0.001, and 0.0001 probability levels, respectively. (C) Comparison of Ka, Ks, and Ka/Ks values among PR, LR, and RH. p value was calculated by one-way ANOVA. * mean significant differences at the 0.05 probability level. ns means no significance.
Figure 3. Evolutionary analysis of RDGs in B. napus. (A) Linear relationship analysis of B. napus RDGs and homologous genes in B. rapa and B. oleracea genomes. (B) Comparison of CDS and promoter identity in PR, LR, and RH. p value was calculated by t tests. *, ***, and **** mean significant differences at the 0.05, 0.001, and 0.0001 probability levels, respectively. (C) Comparison of Ka, Ks, and Ka/Ks values among PR, LR, and RH. p value was calculated by one-way ANOVA. * mean significant differences at the 0.05 probability level. ns means no significance.
Plants 14 01842 g003
Plants 14 01842 g004
Figure 4. Transcription and post-transcription regulation analysis of RDGs. (A) The 22 types of important CREs in the promoter regions of RDGs. The X-axis represents the number of RDGs. (B) The potential regulatory interaction between TF and RDGs. Large circles represent predicted TF genes. Small blue circles represent genes in the PR network, small green circles represent genes in the LR network, and small red circles represent genes in the RH network. The green, yellow, purple, red, blue, orange, and black lines indicate the regulation of RIDGs by BBR-BPC, ERF, MIKC_MADS, Dof, AP2, GRAS, and B3 genes, respectively.
Figure 4. Transcription and post-transcription regulation analysis of RDGs. (A) The 22 types of important CREs in the promoter regions of RDGs. The X-axis represents the number of RDGs. (B) The potential regulatory interaction between TF and RDGs. Large circles represent predicted TF genes. Small blue circles represent genes in the PR network, small green circles represent genes in the LR network, and small red circles represent genes in the RH network. The green, yellow, purple, red, blue, orange, and black lines indicate the regulation of RIDGs by BBR-BPC, ERF, MIKC_MADS, Dof, AP2, GRAS, and B3 genes, respectively.
Plants 14 01842 g004
Plants 14 01842 g005
Figure 5. Expression of RDGs under abiotic and nutrient stress. (A) Number of DEGs in RDGs under abiotic and nutrient stresses. 1 h, 3 h, 6 h, 12 h, 24 h, 1 d, 3 d, 5 d, and 12 d represent the time elapsed after different treatments. L: leaf; R: root. (B) LN stress expression profile of RDGs in B. napus. There are 130 genes in RDGs with detectable expression levels in B. napus roots and/or leaves (FPKM ≥ 1). In the color bar, orange represents a high level of expression, and yellow represents little or no expression. LN: low nitrogen treatment.
Figure 5. Expression of RDGs under abiotic and nutrient stress. (A) Number of DEGs in RDGs under abiotic and nutrient stresses. 1 h, 3 h, 6 h, 12 h, 24 h, 1 d, 3 d, 5 d, and 12 d represent the time elapsed after different treatments. L: leaf; R: root. (B) LN stress expression profile of RDGs in B. napus. There are 130 genes in RDGs with detectable expression levels in B. napus roots and/or leaves (FPKM ≥ 1). In the color bar, orange represents a high level of expression, and yellow represents little or no expression. LN: low nitrogen treatment.
Plants 14 01842 g005
Plants 14 01842 g006
Figure 6. Phenotype analyses of the BnaSHR-6 transgenic lines under CK and LN treatments in Arabidopsis. CK: full nutrition; LN: low nitrogen. (A) DEGs among shared genes in the PR and LR networks under LN stress. L: leaf; R: root. (B) The gene expression of BnaSHR-6 in 35Sp:: BnaSHR-6 transgenic lines and Col-0. Data from 10-day-old seedlings. n = 3 biological replicates, p value was calculated by one-way ANOVA. **** mean significant differences at the 0.0001 probability level. L: line. (C) Phenotypic analysis of Arabidopsis under CK and LN. (D) Statistical data of the PR length and LR density at Col-0 and 35Sp:: BnaSHR-6 transgenic lines. Data from 12-day-old seedlings. n ≥ 30, p value was calculated by one-way ANOVA. *, **, ***, and **** mean significant differences at the 0.05, 0.01, 0.001, and 0.0001 probability levels, respectively. Scale bar: 1 cm.
Figure 6. Phenotype analyses of the BnaSHR-6 transgenic lines under CK and LN treatments in Arabidopsis. CK: full nutrition; LN: low nitrogen. (A) DEGs among shared genes in the PR and LR networks under LN stress. L: leaf; R: root. (B) The gene expression of BnaSHR-6 in 35Sp:: BnaSHR-6 transgenic lines and Col-0. Data from 10-day-old seedlings. n = 3 biological replicates, p value was calculated by one-way ANOVA. **** mean significant differences at the 0.0001 probability level. L: line. (C) Phenotypic analysis of Arabidopsis under CK and LN. (D) Statistical data of the PR length and LR density at Col-0 and 35Sp:: BnaSHR-6 transgenic lines. Data from 12-day-old seedlings. n ≥ 30, p value was calculated by one-way ANOVA. *, **, ***, and **** mean significant differences at the 0.05, 0.01, 0.001, and 0.0001 probability levels, respectively. Scale bar: 1 cm.
Plants 14 01842 g006
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, X.; Zhou, S.; Ye, S.; Chen, Z.; Wu, Z.; Liu, S.; Hu, L.; Yang, X.; Yang, X.; He, P.; et al. The Root Development Genes (RDGs) Network in Brassica napus and the Role of BnaSHR-6 in Response to Low Nitrogen. Plants 2025, 14, 1842. https://doi.org/10.3390/plants14121842

AMA Style

Chen X, Zhou S, Ye S, Chen Z, Wu Z, Liu S, Hu L, Yang X, Yang X, He P, et al. The Root Development Genes (RDGs) Network in Brassica napus and the Role of BnaSHR-6 in Response to Low Nitrogen. Plants. 2025; 14(12):1842. https://doi.org/10.3390/plants14121842

Chicago/Turabian Style

Chen, Xingying, Sining Zhou, Shuang Ye, Zhuo Chen, Zexuan Wu, Shiying Liu, Liping Hu, Xiwen Yang, Xiaoya Yang, Peiji He, and et al. 2025. "The Root Development Genes (RDGs) Network in Brassica napus and the Role of BnaSHR-6 in Response to Low Nitrogen" Plants 14, no. 12: 1842. https://doi.org/10.3390/plants14121842

APA Style

Chen, X., Zhou, S., Ye, S., Chen, Z., Wu, Z., Liu, S., Hu, L., Yang, X., Yang, X., He, P., Qian, X., Wan, H., Zhang, T., Ying, N., Zhao, H., Li, J., Qu, C., & Du, H. (2025). The Root Development Genes (RDGs) Network in Brassica napus and the Role of BnaSHR-6 in Response to Low Nitrogen. Plants, 14(12), 1842. https://doi.org/10.3390/plants14121842

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop