Identifying Candidate Genes Related to Soybean (Glycine max) Seed Coat Color via RNA-Seq and Coexpression Network Analysis

被引:0
|
作者
Wang, Cheng [1 ]
Fu, Pingchun [2 ]
Sun, Tingting [1 ]
Wang, Yan [2 ]
Li, Xueting [1 ]
Lan, Shulin [1 ]
Liu, Hui [1 ]
Gou, Yongji [2 ]
Shang, Qiaoxia [2 ]
Li, Weiyu [1 ]
机构
[1] Beijing Univ Agr, Coll Plant Sci & Technol, Natl Demonstrat Ctr Expt Plant Prod Educ, Beijing Key Lab New Agr Technol Agr Applicat, Beijing 102206, Peoples R China
[2] Beijing Univ Agr, Key Lab Northern Urban Agr, Minist Agr & Rural Affairs, Beijing 102206, Peoples R China
关键词
<italic>Glycine max</italic>; seed coat color; RNA-seq; candidate genes; ANTHOCYANIN PIGMENTATION; PHOTORECEPTORS; PLANTS; MYBL2;
D O I
10.3390/genes16010044
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Background: The quality of soybeans is reflected in the seed coat color, which indicates soybean quality and commercial value. Researchers have identified genes related to seed coat color in various plants. However, research on the regulation of genes related to seed coat color in soybeans is rare. Methods: In this study, four lines of seed coats with different colors (medium yellow 14, black, green, and brown) were selected from the F2:5 population, with Beinong 108 as the female parent and green bean as the male parent, and the dynamic changes in the anthocyanins in the seed coat were stained with 4-dimethylaminocinnamaldehyde (DMACA) during the grain maturation process (20 days from grain drum to seed harvest). Through RNA-seq of soybean lines with four different colored seed coats at 30 and 50 days after seeding, we can further understand the key pathways and gene regulation modules between soybean seed coats of different colors. Results: DMACA revealed that black seed coat soybeans produce anthocyanins first and have the deepest staining. Clustering and principal component analysis (PCA) of the RNA-seq data divided the eight samples into two groups, resulting in 16,456 DEGs, including 5359 TFs. GO and KEGG enrichment analyses revealed that the flavonoid biosynthesis, starch and sucrose metabolism, carotenoid biosynthesis, and circadian rhythm pathways were significantly enriched. We also conducted statistical and expression pattern analyses on the differentially expressed transcription factors. Based on weighted gene coexpression network analysis (WGCNA), we identified seven specific modules that were significantly related to the four soybean lines with different seed coat colors. The connectivity and functional annotation of genes within the modules were calculated, and 21 candidate genes related to soybean seed coat color were identified, including six transcription factor (TF) genes and three flavonoid pathway genes. Conclusions: These findings provide a theoretical basis for an in-depth understanding of the molecular mechanisms underlying differences in soybean seed coat color and provide new genetic resources.
引用
收藏
页数:19
相关论文
共 50 条
  • [41] Screening for candidate genes related with histological microstructure, meat quality and carcass characteristic in pig based on RNA-seq data
    Ropka-Molik, Katarzyna
    Bereta, Anna
    Zukowski, Kacper
    Tyra, Miroslaw
    Piorkowska, Katarzyna
    Zak, Grzegorz
    Oczkowicz, Maria
    ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES, 2018, 31 (10): : 1565 - 1574
  • [42] Transcriptome sequencing and screening of genes related to glucose availability in Schizosaccharomyces pombe by RNA-seq analysis
    Tarhan, Cagatay
    Cakir, Ozgur
    GENETICS AND MOLECULAR BIOLOGY, 2021, 44 (03)
  • [43] RNA-seq analysis to identify genes related to resting egg production of panarctic Daphnia pulex
    Maruoka, Natsumi
    Makino, Takashi
    Urabe, Jotaro
    BMC GENOMICS, 2023, 24 (01)
  • [44] Putative Transcription Factor Genes Associated with Regulation of Carotenoid Biosynthesis in Chili Pepper Fruits Revealed by RNA-Seq Coexpression Analysis
    Guadalupe Villa-Rivera, Maria
    Martinez, Octavio
    Ochoa-Alejo, Neftali
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2022, 23 (19)
  • [45] Identification of stable QTLs and candidate genes involved in anaerobic germination tolerance in rice via high-density genetic mapping and RNA-Seq
    Yang, Jing
    Sun, Kai
    Li, Dongxiu
    Luo, Lixin
    Liu, Yongzhu
    Huang, Ming
    Yang, Guili
    Liu, Hong
    Wang, Hui
    Chen, Zhiqiang
    Guo, Tao
    BMC GENOMICS, 2019, 20 (1)
  • [46] RNA-seq of grafted near-isogenic soybean ( Glycine max) lines reveals root genotype drives shoot responses to iron deficiency chlorosis
    Kohlhase, Daniel R.
    O'Rourke, Jamie A.
    Graham, Michelle A.
    PLANT STRESS, 2025, 15
  • [47] Pinpointing MQTLs and candidate genes related to early maturity in upland cotton through the integration of meta-analysis, RNA-seq, and VIGS approaches
    Yuan, Wenmin
    Li, Ying
    Zhang, Wenjiao
    Ju, Jisheng
    Guo, Xuefeng
    Yang, Junning
    Lin, Hai
    Wang, Caixiang
    Ma, Qi
    Su, Junji
    INDUSTRIAL CROPS AND PRODUCTS, 2025, 223
  • [48] Combining Fine Mapping, Whole-Genome Re-Sequencing, and RNA-Seq Unravels Candidate Genes for a Soybean Mutant with Short Petioles and Weakened Pulvini
    Kong, Keke
    Xu, Mengge
    Xu, Zhiyong
    Sharmin, Ripa Akter
    Zhang, Mengchen
    Zhao, Tuanjie
    GENES, 2022, 13 (02)
  • [49] Identification of quantitative trait loci (QTLs) and candidate genes for seed shape and 100-seed weight in soybean [Glycine max (L.) Merr.]
    Kumar, Rahul
    Saini, Manisha
    Taku, Meniari
    Debbarma, Pulak
    Mahto, Rohit Kumar
    Ramlal, Ayyagari
    Sharma, Deepshikha
    Rajendran, Ambika
    Pandey, Renu
    Gaikwad, Kishor
    Lal, S. K.
    Talukdar, Akshay
    FRONTIERS IN PLANT SCIENCE, 2023, 13
  • [50] Integration of GWAS and RNA-Seq Analysis to Identify SNPs and Candidate Genes Associated with Alkali Stress Tolerance at the Germination Stage in Mung Bean
    Xu, Ning
    Chen, Bingru
    Cheng, Yuxin
    Su, Yufei
    Song, Mengyuan
    Guo, Rongqiu
    Wang, Minghai
    Deng, Kunpeng
    Lan, Tianjiao
    Bao, Shuying
    Wang, Guifang
    Guo, Zhongxiao
    Yu, Lihe
    GENES, 2023, 14 (06)