Light response of gametophyte in Adiantum flabellulatum: transcriptome analysis and identification of key genes and pathways

被引:0
|
作者
Cai, Zeping [1 ]
Wang, Xiaochen [1 ]
Xie, Zhenyu [1 ]
Wen, Zhenyi [2 ]
Yu, Xudong [3 ]
Xu, Shitao [3 ]
Su, Xinyu [1 ]
Luo, Jiajia [4 ]
机构
[1] Hainan Univ, Coll Forestry, Key Lab Genet & Germplasm Innovat Trop Special For, Minist Educ, Haikou, Hainan, Peoples R China
[2] Hainan Univ, Coll Ecol & Environm, Haikou, Hainan, Peoples R China
[3] Hainan Univ, Coll Forestry, Key Lab Germplasm Resources Biol Trop Special Orna, Haikou, Peoples R China
[4] Chinese Acad Trop Agr Sci, Trop Crops Genet Resources Inst, Haikou, Hainan, Peoples R China
来源
基金
中国国家自然科学基金;
关键词
fern gametophyte; light signal; photosynthesis; photoprotection; weighted gene co-expression network analysis; ARABIDOPSIS; EXPRESSION; FERN; PROANTHOCYANIDIN; FAMILY; GROWTH;
D O I
10.3389/fpls.2023.1222414
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Light serves not only as a signaling cue perceived by plant photoreceptors but also as an essential energy source captured by chloroplasts. However, excessive light can impose stress on plants. Fern gametophytes possess the unique ability to survive independently and play a critical role in the alternation of generations. Due to their predominantly shaded distribution under canopies, light availability becomes a limiting factor for gametophyte survival, making it imperative to investigate their response to light. Previous research on fern gametophytes' light response has been limited to the physiological level. In this study, we examined the light response of Adiantum flabellulatum gametophytes under different photosynthetic photon flux density (PPFD) levels and identified their high sensitivity to low light. We thereby determined optimal and stress-inducing light conditions. By employing transcriptome sequencing, weighted gene co-expression network analysis, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, we identified 10,995 differentially expressed genes (DEGs). Notably, 3 PHYBs and 5 Type 1 CRYs (CRY1s) were significantly down-regulated at low PPFD (0.1 mmol m(-2) s(-1)). Furthermore, we annotated 927 DEGs to pathways related to photosynthesis and 210 to the flavonoid biosynthesis pathway involved in photoprotection. Additionally, we predicted 34 transcription factor families and identified a close correlation between mTERFs and photosynthesis, as well as a strong co-expression relationship between MYBs and bHLHs and genes encoding flavonoid synthesis enzymes. This comprehensive analysis enhances our understanding of the light response of fern gametophytes and provides novel insights into the mechanisms governing their responses to light.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Transcriptome Analysis Reveals Key Genes and Pathways Associated with Metastasis in Breast Cancer
    Li, Wei
    Liu, Jianling
    Zhang, Bin
    Bie, Qingli
    Qian, Hui
    Xu, Wenrong
    ONCOTARGETS AND THERAPY, 2020, 13 : 323 - 335
  • [22] Transcriptome analysis reveals key genes and pathways for prickle development in Zanthoxylum armatum
    Wang, Yi
    Jiang, Yuhui
    Feng, Fayu
    Guo, Yongqing
    Hao, Jiabo
    Huyan, Li
    Du, Chunhua
    Xu, Liang
    Lu, Bin
    HELIYON, 2024, 10 (05)
  • [23] Identification of key pathways and candidate genes in gliomas by bioinformatics analysis
    Cao, Yuan
    Song, Yali
    Quan, Juan
    Zhang, Liyu
    Tian, Qianqian
    Wu, Shuang
    Zhao, Chuanmei
    Li, Qiao
    INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL MEDICINE, 2019, 12 (11): : 12679 - 12692
  • [24] Identification of Key Genes and Pathways in Cervical Cancer by Bioinformatics Analysis
    Wu, Xuan
    Peng, Li
    Zhang, Yaqin
    Chen, Shilian
    Lei, Qian
    Li, Guancheng
    Zhang, Chaoyang
    INTERNATIONAL JOURNAL OF MEDICAL SCIENCES, 2019, 16 (06): : 800 - 812
  • [25] Identification of key genes and pathways in diabetic nephropathy by bioinformatics analysis
    Geng, Xiao-dong
    Wang, Wei-wei
    Feng, Zhe
    Liu, Ran
    Cheng, Xiao-long
    Shen, Wan-jun
    Dong, Zhe-yi
    Cai, Guang-yan
    Chen, Xiang-mei
    Hong, Quan
    Wu, Di
    JOURNAL OF DIABETES INVESTIGATION, 2019, 10 (04) : 972 - 984
  • [26] Multiple Myeloma: Bioinformatic Analysis for Identification of Key Genes and Pathways
    Saadoune, Chaimaa
    Nouadi, Badreddine
    Hamdaoui, Hasna
    Chegdani, Fatima
    Bennis, Faiza
    BIOINFORMATICS AND BIOLOGY INSIGHTS, 2022, 16
  • [27] Transcriptome analysis reveals key genes and pathways associated with piglet fetal mummification
    Wang, Shujie
    Wu, Pingxian
    Wang, Kai
    Ji, Xiang
    Chen, Dong
    Jiang, Anan
    Liu, Yihui
    Xiao, Weihang
    Jiang, Yanzhi
    Zhu, Li
    Xu, Xu
    Li, Mingzhou
    Li, Xuewei
    Tang, Guoqing
    GENOME, 2021, 64 (12) : 1029 - 1040
  • [28] Multiple Myeloma: Bioinformatic Analysis for Identification of Key Genes and Pathways
    Saadoune, Chaimaa
    Nouadi, Badreddine
    Hamdaoui, Hasna
    Chegdani, Fatima
    Bennis, Faiza
    BIOINFORMATICS AND BIOLOGY INSIGHTS, 2022, 16
  • [29] IDENTIFICATION OF KEY GENES AND PATHWAYS IN GBM THROUGH BIOINFORMATICS ANALYSIS
    Wang, Ziheng
    FRESENIUS ENVIRONMENTAL BULLETIN, 2019, 28 (07): : 5248 - 5252
  • [30] Identification of key genes and pathways associated with feed efficiency of native chickens based on transcriptome data via bioinformatics analysis
    Lei Yang
    Tingting He
    Fengliang Xiong
    Xianzhen Chen
    Xinfeng Fan
    Sihua Jin
    Zhaoyu Geng
    BMC Genomics, 21