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Weighted Correlation Network Analysis Mining Key Metabolites and Genes for Regulation of Root Growth and Secondary Metabolism of Astragalus membranaceus mongholicus

Xiaojie Li1,2, Yingtong Mu1,2, Junjie Wang1,2, Xiaoming Zhang1,2,*
1Key Laboratory of Grassland Resources, College of Grassland, Resource and Environmental Science, Inner Mongolia Agricultural University, Ministry of Education, Hohhot, 010021, P.R. of China.
2Engineering Research Center for the Seed breeding of Chinese and Mongolian Medicinal Materials in Inner Mongolia, Hohhot, 010011, Inner Mongolia, China.

  • Submitted03-07-2024|

  • Accepted13-09-2024|

  • First Online 10-01-2025|

  • doi 10.18805/LRF-824

ABSTRACT

Background: Harvesting period is an important feature that not only affects the yield quality of Astragalus herbs but also the content of astragaloside IV and calycosin 7-O-β-D-glucopyranoside which  are considered as the core indicators of the quality of Astragalus herbs. Herb yield and quality undergo rapid and substantial changes during the harvesting process. Part of enzymes and their encoding genes have been identified as potential candidates for the biosynthesis of herb quality-related triterpenoid saponins and isoflavonoids. However, wide gap still exists in the understanding of the pathways involving growth, development and biosynthesis of these two important active components in the roots of Astragalus.

Methods: In this study, we used Astragalus roots as material to analyze its metabolome and transcriptome of herbs at harvesting stage.

Result: The results indicated that changes in the harvesting period had a significant effect on the yield and quality of the herbs. Correlation of metabolome and RNA-seq results constructed using weighted gene co-expression network analysis (WGCNA) with herb growth and development indicated that the expression patterns of 19 hub metabolites (Oxamniquine, 4-ene-Valproic acid, Traumatic Acid, L-Histidinol, Salidroside, Temsavir, Retaspimycin, Isosakuranetin, etc.) and 16 structural genes with annotations (CNX2, talA, ABCF2, HIBCH, crtZ, etc.) had the highest connectivity with root growth and secondary metabolism of Astragalus. To search for potential regulatory genes related to root growth and development, we screened 54 relevant genes using 16 hub genes with annotations. The results advance the molecular basis of biosynthesis of secondary metabolites related to the harvesting period of Astragalus herbs, which can provide a theoretical basis for determining the regulatory mechanism of yield and quality.

INTRODUCTION

Astragalus membranaceus mongholicus (Astragalus) is a plant belonging to the genus Astragalus in the family Leguminosae, which is used as a medicine by roots (State Pharmacopeia Committee of China., 2020). It is a homologous plant used for medicine and food and has vast economic significance. Astragalus plants are widely used in clinical trials and other applications due to the high value of their medicinal components (Pham, 2015). Chemical composition of Astragalus mainly includes polysaccharides, flavonoids and triterpenoids. Its pharmacological effects are wide-ranging, including enhancement of immune function, anti-tumour,  regulation of body metabolism, protection of the nervous system (Xiao et al., 2020; Chen et al., 2022). One of the main effective components of Astragalus are triterpenoid saponins (Xiang et al., 2022). At present, more than 50 triterpenoid saponins have been obtained from Astragalus plants, mainly including astragalosides I-VIII, isoastragalosides I, II, IV, acetyl astragalosides, soyasaponins (Xiao et al., 2020). The accumulation of chemical components in Astragalus plants showed a dynamic change and the content of effective components varied greatly at different harvest times. Even in the same year and the same harvest time, the content of effective components varied greatly among different individuals (Wang et al., 2019; Xin et al., 2015). Therefore, it is particularly important to study the synthetic genes related to the growth, development and quantities of secondary metabolites in Astragalus roots and their expression regulation mechanisms, which can help in further research on ways to improve the quality of Astragalus herbs. 

Non-targeted metabolomics and transcriptome sequencing technology can determine the functional metabolites and genes of individuals at different stages and their expression. It can establish a connection between the genetic information of the biological genome and functional metabolites, which can be helpful for the analysis of functional genes. Currently, metabolome and transcriptome sequencing technologies have been widely used in the fields of plants and animals, which greatly broaden the research methods of biological metabolites and beneficial to the study of the synthesis and accumulation mechanisms of active ingredients in medicinal plants, especially applicable to non-model plants like Astragalus (Wang et al., 2019). At present, the available research on transcriptome and metabolome of the medicinal parts (roots) of Astragalus  is very less and inadequate. Construction of co-expression network is an important tool for the efficient use of transcriptomic and metabolomic data. Combining multi-sample data with weighted gene co-expression network analysis (WGCNA) can cluster genes with similar expression patterns to construct a co-expression gene module. Then, the module associated with sample characteristics and the key module with the highest correlation with the research phenotype can be quickly found and the core genes in the key module can be excavated (Ji et al., 2022; Aidi et al., 2020; Na et al., 2024). These core genes and metabolites may be the key factors for the regulation of root phenotype and secondary metabolism in Astragalus.

Based on the academic background of the secondary metabolite biosynthesis of Astragalus, the objectives of this research aimed to identify the key metabolites and genes involved in the root growth, development, synthesis and accumulation of two important active ingredients in the herb. Key metabolites and key structural genes were identified by metabolomics and transcriptomics studies using Weighted Gene Co-Expression Network Analysis (WGCNA). These results not only filled some gaps in the knowledge regarding regulatory pathways of growth and development of Astragalus, but also identified some key genes and metabolites involved in the regulatory mechanisms of root secondary metabolism. Our findings can provide a basis for growth and development, active ingredient synthesis and breeding research of this plant.

MATERIALS AND METHODS

Plant material and treatments
 
The Astragalus roots harvested  during 2022 season from a test base (Inner Mongolia, Hohhot) were used as the test material for this study. We declare that the Astragalus roots in this study complied with institutional, national and international guidelines for the collection and cultivation of any plant materials. At each sampling period, six replicates of root were collected from each batch (period A, Oct.6; period B, Oct.16; period C, Oct.26; period D, Nov.6 respectively). The materials collected were frozen with liquid nitrogen and stored at -80oC. The treatment and sample collection, as well as the related indices (root length, root diameter, root fresh weight, thick lateral root, contents of astragaloside IV and calycosin 7-O-β-D-glucopyranoside) were estimated as described by Li et al., (2024).
 
Untargeted metabolome analysis, WGCNA and metabolite network visualization
 
xpression profile matrices of metabolites were derived from 24 metabolite expression samples from Astragalus roots of different harvesting periods. Co-expression networks were created using WGCNA (v1.29) package in R. A total of 1660 metabolites were used as input to the signed WGCNA network construction. The automatic network construction function block wise was used to build modules. In standard WGCNA networks, soft power was set to 6, Merge Module Threshold was 30 and the merge Cut Height value was 0.25. Metabolite expression adjacency matrices were constructed and used to analyze the network topology. In addition, module correlation analysis was performed on module eigenvalues and phenotypic trait data and Pearson correlation was used to calculate the correlation coefficient between phenotypic trait data and metabolite module eigenvalues and their correlation heat maps were drawn. In order to filter the modules with high correlation of tissue development, the threshold of correlation coefficient was defined as 0.70 and the significant level was 0.005. This means that any module with a correlation coefficient higher than 0.70 and a significant level of 0.005 will be regarded as a tissue development-specific module.
 
RNA-seq, WGCNA and gene network visualization
 
Expression profile matrices of genes were derived from 12 gene expression samples of Astragalus roots at different harvesting periods. The details of RNA-seq were described by Li et al (Li et al., 2024). The SRA accession numbers are SRR28617061-SRR28617072. Co-expression networks were created using WGCNA (v1.29) package in R. To ensure the specificity of the co-expression network, the data used for WGCNA co-expression network analysis were all RNAseqgenes after discarding none detectable genes (FPKM > 1) (Lin et al., 2017). A total of 23851 genes were used as input to the signed WGCNA network construction. The automatic network construction function block wise was used to build modules. In standard WGCNA networks, soft power was set to 5, min Module Size was 30 and the merge Cut Height value was 0.25.

Gene expression adjacency matrices were constructed and used to analyze the network topology. In addition, module correlation analysis was performed on module eigenvalues and phenotypic trait data and Pearson correlation was used to calculate the correlation coefficient between phenotypic trait data and gene module eigenvalues and their correlation heat maps were drawn. In order to filter the modules with high correlation of tissue development, the threshold of correlation coefficient was defined as 0.70 and the significant level was 0.005. This means that any module with a correlation coefficient higher than 0.70 and a significant level of 0.005 will be regarded as a tissue development-specific module (Downs et al., 2013).
 
Enrichment pathway analysis
 
Firstly extracted the genes and metabolites in the modules significantly associated with the growth, development and secondary metabolism of Astragalus and then analysed by GO and KEGG enrichment using Goatools (Version 0.6.5, https://github.com/tanghaibao/GOatools) and KOBAS 2.1.1 (Version 2.1.1, http://kobas.cbi.pku.edu.cn/home.do) (Diao et al., 2019). Essentially, differential expression analysis was performed using the DESeq2. DEGs with |log2FC| > 1 and FDR < 0.05) were considered to be significantly different expressed genes.
 
Related modules on growth and secondary metabolic regulatory pathways
 
According to the topological overlap matrix, the weights of different genes in the module were calculated. The higher the weight value, the higher the degree of association between the genes. The genes with higher correlation coefficients among the modules were selected and at the same time, the genes that have already been annotated in the modules were selected. The genes were used to construct an interactions network, in which each node represented a gene and the two connecting lines were used to connect the two genes. Each node in the network represents a gene and the genes at both ends of the connection line have similar biological functions. As complex agronomic traits, growth development and secondary metabolism are not only related to external environmental signals, but also involve endogenous signal regulation. According to the existing literature, some keywords related to growth and metabolism regulation, such as pentose phosphate pathway, carotenoid biosynthesis, zeatin biosynthesis, plant hormone signal transduction, etc., were used to search for Look for metabolites and genes involved in regulation in tissue-specific modules (Tuan et al., 2015; Li et al., 2024; Zhang et al., 2022). The network of candidate target genes and metabolites (Top 10 percent connectivity) were visualized by Cytoscape (v3.7.1, USA).
 
qRT-PCR of genes
 
To further validate the analysis of the Illumina sequencing data, we randomly selected six DEGs for qRT-PCR amplification and specific primers were designed by Primer-BLAST from NCBI. Total root RNA was isolated from three biological replicate samples of each group by QIAzolLysisReagent (Qiagen, Germany). Reverse transcription and polymerase chain reaction were conducted by PrimeScriptTM 1st Strand cDNA Synthesis Kit and SYBR®Premix Ex TaqTM II (Tli RNaseH Plus) (Takara Biomedical Technology (Beijing) Co., Ltd., China). Targeted gene was measured using Heal Force RealTime PCR System (Heal Force CG-05, Hangzhou Jingle Scientific Instrument Co., Ltd., China) as described previously and each sample was measured for three times technical replicates. The reaction system was 20 µL, including 10 µL of Tli RNaseH Plus, 0.4 µL of Primer F (10 µM), 0.4 µL of Primer R (10 µM), 2 µL of cDNA, ROX Reference Dye 0.08 µL and 7.12 µL of ddH2O. All reactions were conducted in 96-well plates. The qPCR protocol included annealing at 95°C for 60 s, followed by 40 cycles of 95°C for 30 s, primer annealing at 60°C for 30 s and extension at 72°C for 20 s. A negative control without template for each primer pair was included in each qPCR analysis. Expression was normalized to GAPDH and calculated using 2-DDCt.
 
Statistical analysis
 
The R package from Bioconductor on Majorbio Cloud Platform was used to conduct a WGCNA analysis (https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/faq.html). The statistical significance was calculated with a Student’s t-test (*P<0.05, **P<0.01 and ***P<0.001). The figures were prepared using Excel 2019 software. Details of the data analysis are described in support file.

RESULTS AND DISCUSSION

Phenotypes and active ingredients analysis during harvesting
 
Understanding the formation of secondary metabolites is critical to the quality and marketability of Astragalus, especially the production of astragaloside IV and calycosin 7-O-β-D-glucopyranoside, two important metabolites that are major components in the formation of herb quality (Fig 1B,C,D). During the growth of Astragalus, secondary metabolite synthesis-related genes and metabolites showed up-regulated or down-regulated conditions with the extension of harvesting time. It was suggested that the accumulation of astragaloside IV and calycosin 7-O-β-D-glucopyranoside during ripening was likely driven by ACAT, HMGCS, HMGCR, MVK, MVAK2, MVD and other genes (Wang et al., 2023; Li, 2020; Zhang, 2020). The production of isofavone, especially calycosin 7-O-β-D-glucopyranoside, appeared to be strongly dependent on formonetin and calycosin metabolites (Zhang et al., 2022). In this study, the content of secondary metabolite astragaloside IV was significantly higher in C and D periods and calycosin 7-O-β-D-glucopyranoside content was significantly higher in D period.

Growth and development indicators and two important secondary metabolites of Astragalus roots at different harvesting periods were analysed according to the group’s previous research. Indicators related to herb yield and calycosin 7-O-β-D-glucopyranoside content were highest in period D (Fig 1A,B). Astragaloside IV stayed high level in Period C root and identified as the major contributors to Astragalus efficacy (Wu et al., 2020; Li et al., 2024). Besides, higher calycosin 7-O-β-D-glucopyranoside content in herbs after harvesting in periods B and D. Analyses of the contents of the two important active ingredients and the accumulation pattern of the herbs showed that the highest yield, astragaloside IV content and calycosin 7-O-β-D-glucopyranoside content of the herbs after harvesting in period D was the best harvesting period. If batch extraction of astragaloside IV or calycosin 7-O-β-D-glucopyranoside is required for subsequent studies, the herbs could be harvested in stages according to the harvesting strategy (Fig 1C,D).

​

Fig 1: Analysis of phenotypes and active Ingredients in Astragalus roots.


 
WGCNA of metabolomic data
 
The WGCNA was performed to investigate the coexpression networks, in which all co-expressed metabolites were connected to each other with varying correlation strengths. Metabolites were partitioned into 9 co-expression modules (Fig 2A,B). Period D were positively correlated with metabolites expression in the ‘yellow’ module and negatively correlated with metabolites expression in the ‘turquoise’ and ‘turquoise’ modules, with a coefficient of 0.91 and -0.82, respectively (Fig 2A). A total of 637 metabolites were identified based on the correlation (r > 0.8) of metabolites with period D in the tow modules.

​

Fig 2: Co-expression network analysis of metabolomic associated with growth, development and secondary metabolism during Astragalus roots harvesting.


 
Functional analysis of metabolites in correlated modules
 
KEGG compounds were classified for the two modules screened in relation to the Astragalus roots harvesting period D. Classification of KEGG analysis revealed that metabolites could be summarized in three main functional categories, including lipids, peptides and carbohydrates process (Figure 3A/B). We performed pathway enrichment analyses on the relevant modules and selected significantly enriched metabolic pathways. KEGG enrichment revealed that the modules related to growth and development were mainly enriched in Phenylpropanoid biosynthesis, Flavonoid biosynthesis, Nucleotide metabolism, Stilbenoid, diarylheptanoid and gingerol biosynthesis, Glycerophospholipid metabolism, alpha-Linolenic acid metabolism, Plant hormone signal transduction, Linoleic acid metabolism and Arachidonic acid metabolism (Figure 3C/D). We speculated that these pathways may play an important role in regulating the root growth and development and secondary metabolic processes of Astragalus.

​

Fig 3: Classification of KEGG compounds co-expressing metabolites and analysis of KEGG enrichment of metabolites in target modules.


 
Regulation of growth and development and two major active components by key metabolites
 
Metabolites networks were visualised and metabolite connectivity analysed for metabolites in modules related to period D in Astragalus. To determine the Hub metabolites related to period D, we classified the ten metabolites with the highest kME values in each module as hub metabolites and utilized the hub metabolites and their interacting indicators of phenotype and active ingredient content to map the metabolites co-expression network (Fig 4). 19 highly connected metabolites screened by WGCNA analysis using period D tissue-specific modules (yellow turquoise) for multifactorial correlation were highly correlated with indicators of growth development and active ingredient content of Astragalus (Fig 4A,B).

​

Fig 4: Analysis of multifactorial interactions of hub metabolites network interaction in period D significant enrichment module.


 
WGCNA of RNA-seq data
 
The WGCNA was performed to investigate the coexpression networks, in which all co-expressed genes were connected to each other with varying correlation strengths. Genes were partitioned into 36 co-expression modules (Figure 5A/B). Growth and development indicators were positively correlated with gene expression in the ‘blue’ module and negatively correlated with gene expression in the ‘brown’ and ‘turquoise’ modules. The content of CI was negatively correlated with gene expression in the ‘grey60’ module, with a coefficient of -0.76. The content of CC was positively correlated with gene expression in the ‘pink’ module and negatively correlated in the ‘brown’ and ‘yellow’ modules, with a coefficient of 0.71, -0.86 and -0.78, respectively (Fig 5A). A total of 14701 genes were identified based on the correlation (r > 0.7) of genes with growth and development and active ingredient content in the six modules. A total of 14,701 genes were identified based on the correlation between genes and growth and development in the 3 modules. A total of 5096 genes were identified based on the correlation between genes and active ingredient content in the 4 modules.

​

Fig 5: Co-expression network analysis of transcriptomes associated with growth, development and secondary metabolism during Astragalus roots harvesting.


 
Functional analysis of genes in correlated modules
 
GO annotation of three types of modules related to root growth and development, astragaloside IV content and calycosin 7-O-β-D-glucopyranoside content of screened Astragalus roots, respectively. GO annotation analysis revealed that genes could be summarized in three main functional categories, including cellular component, molecular function and biological process. Five groups, including macromolecule metabolic process, integral component of membrane, intracellular organelle, cellular macromolecule metabolic process and organonitrogen compound metabolic process, were the main classifications for more than 50% of the genes in the GO annotations related to growth and development (Fig 6A). Six groups, including macromolecule metabolic process, integral component of membrane, intracellular organelle, intracellular membrane-bounded organelle, organonitrogen compound metabolic process and cellular macromolecule metabolic process, were the main classifications for more than 50% of the genes in the GO annotations related to calycosin 7-O-β-D-glucopyranoside content (Fig 6B). Molecular function is the main classifications for more than 50% of the genes in the GO annotations related to Astragaloside IV content (Fig 6C).

We performed pathway enrichment analysis on growth and development and active ingredient-related modules and selected the top 20 metabolic pathways from each comparison. KEGG enrichment revealed that the modules related to growth and development were mainly enriched in plant hormone signal transduction, Glycosyl phosphatidylinositol (GPI)-anchor biosynthesis, Homologous recombination, Folate biosynthesis, Terpenoid backbone biosynthesis and Aminoacyl-tRNA biosynthesis (Fig 6D). The modules related to the content of calycosin 7-O-β-D-glucopyranoside were mainly enriched in Plant hormone signal transduction, Terpenoid backbone biosynthesis, Non-homologous end-joining, Phenylalanine, tyrosine and tryptophan biosynthesis and Plant-pathogen interaction (Fig 6E). The modules related to astragaloside IV content were mainly enriched in RNA degradation, Linoleic acid metabolism and N-Glycan biosynthesis (Fig 6F). Thus, we speculated that these pathways may play an important role in regulating the growth and development process of Astragalus.

​

Fig 6: GO annotation of co-expressed genes and KEGG enrichment analysis of genes in target module.


 
Analysis of hub genes interaction network in the module
 
Mining and identification of key genes regulating root growth and development and analysing the regulatory pathways of plant growth and development are important elements of biological breeding of Astragalus. Members of the major plant transcription factor families, including the bHLH and AP2/ERF families, have been validated for their roles in the regulation of plant growth and development (Yue, 2022). However, genes and metabolites related to the regulation of root growth and development in Astragalus less well studied. Metabolomic and transcriptomic WGCNA analyses in this study indicated that 19 metabolites (Fig 4) and 8 annotated key genes (CNX2, talA, ABCF2, HIBCH, crtZ, AXY4, TTC5 and UNKL) are essential for the regulation of growth development of Astragalus roots.

In this study, gene networks were visualised and gene connectivity analysed for genes in modules related to growth and development, calycosin 7-O-β-D-glucopyranoside content and astragaloside IV content in Astragalus. To determine the Hub genes related to growth development and secondary metabolism, we classified the ten genes with the highest kME values in each module as hub genes and utilized the hub genes and their interacting genes to map the gene co-expression network (Fig 7). 22 highly connectivity genes were screened by WGCNA analysis using 8 annotated genes (CNX2, talA, ABCF2, HIBCH, crtZ, AXY4, TTC5, UNKL) in the tissue-specific modules (blue brown turquoise) related to growth and development (Fig 7A). 6 annotated genes (ETR, IPT, AP3B, talB, AXY4, CYP707A) in the tissue-specific modules (brown yellow pink) related to calycosin 7-O-β-D-glucopyranoside content were used to screen 24 candidate genes with high connectivity (Fig 7B). 8 high connectivity candidates were screened using 2 annotated genes (copA, JAR) in the tissue-specific module related to astragaloside IV content (Fig 7C). 16 annotated high connectivity candidates and 54 related high connectivity candidates were screened in the seven modules.

​

Fig 7: Analysis of hub genes network interaction in phenotypic and active ingredients significant enrichment module.


 
Validation genes by qRT-PCR analysis
 
To evaluate the reliability of the gene expression profile of Astragalus at different harvesting periods, qRT-PCR was used to verify the gene expression level. We selected six genes: TRINITY_DN34490_c0_g3, TRINITY_DN184_c0_g2, TRINITY_DN6890_c0_g1, TRINITY_DN31161_c0_g1, TRINITY_DN3009_c1_g1 and TRINITY_DN1975_c0_g1 to be upregulated or down regulated under different harvesting periods. We found that the expression levels of these 6 genes in Astragalus herbs roots at different times were consistent with the transcriptome sequencing results, which indicated that our RNA-seq data were reliable (Fig 8).

​

Fig 8: The expression pattern of 6 selected genes identified by RNA-seq was verified by qRT-PCR.

CONCLUSION

In this study, metabolomic and transcriptomic WGCNA analyses were used to show the roles of 19 highly-connected hub metabolites and 16 highly-connected hub genes with annotations in the growth and development of the roots of Astragalus and in the accumulation of the two important active components. The 16 annotated hub genes were also used to screen 54 key genes with high connectivity related to growth development and secondary metabolism. Overall, these findings provide new insights into the regulation of growth, development and secondary metabolism in the roots of Astragalus, as well as the roles of specific structural genes and metabolites in the accumulation of herb yield and active ingredient content.

ACKNOWLEDGEMENT

Xiaojie Li and Xiaoming Zhang designed the experiments. Xiaojie Li and Yingtong Mu conducted the experiments. Xiaojie Li wrote the manuscript. Junjie Wang and Xiaoming Zhang reviewed the manuscript. All authors approved the paper. All co-authors reviewed the final version and approved the manuscript before submission. This work was supported by a grant from “Identifcation of high yielding and-quality germplasm and breeding technology of Astragalus Membranaceus Mongholicus” (2023-Society-2), “Efcient Breeding and Processing Technology Integration of Multifunctional Grass Seeds for Grassland” (2022YFDZ0025) and “Scientifc and Technological Achievements Transformation Project of Inner Mongolia Autonomous Region” (CGZH2018134).

CONFLICT OF INTEREST

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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