Literature DB >> 33452393

Weighted gene co-expression network analysis of the salt-responsive transcriptomes reveals novel hub genes in green halophytic microalgae Dunaliella salina.

Bahman Panahi1, Mohammad Amin Hejazi2.   

Abstract

Despite responses to salinity stress in Dunaliella salina, a unicellular halotolerant green alga, being subject to extensive study, but the underlying molecular mechanism remains unknown. Here, Empirical Bayes method was applied to identify the common differentially expressed genes (DEGs) between hypersaline and normal conditions. Then, using weighted gene co-expression network analysis (WGCNA), which takes advantage of a graph theoretical approach, highly correlated genes were clustered as a module. Subsequently, connectivity patterns of the identified modules in two conditions were surveyed to define preserved and non-preserved modules by combining the Zsummary and medianRank measures. Finally, common and specific hub genes in non-preserved modules were determined using Eigengene-based module connectivity or module membership (kME) measures and validation was performed by using leave-one-out cross-validation (LOOCV). In this study, the power of beta = 12 (scale-free R2 = 0.8) was selected as the soft-thresholding to ensure a scale-free network, which led to the identification of 15 co-expression modules. Results also indicate that green, blue, brown, and yellow modules are non-preserved in salinity stress conditions. Examples of enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in non-preserved modules are Sulfur metabolism, Oxidative phosphorylation, Porphyrin and chlorophyll metabolism, Vitamin B6 metabolism. Moreover, the systems biology approach was applied here, proposed some salinity specific hub genes, such as radical-induced cell death1 protein (RCD1), mitogen-activated protein kinase kinase kinase 13 (MAP3K13), long-chain acyl-CoA synthetase (ACSL), acetyl-CoA carboxylase, biotin carboxylase subunit (AccC), and fructose-bisphosphate aldolase (ALDO), for the development of metabolites accumulating strains in D. salina.

Entities:  

Year:  2021        PMID: 33452393      PMCID: PMC7810892          DOI: 10.1038/s41598-020-80945-3

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  36 in total

1.  N-Acetyl Cysteine Functions as a Fast-Acting Antioxidant by Triggering Intracellular H2S and Sulfane Sulfur Production.

Authors:  Daria Ezeriņa; Yoko Takano; Kenjiro Hanaoka; Yasuteru Urano; Tobias P Dick
Journal:  Cell Chem Biol       Date:  2018-02-08       Impact factor: 8.116

2.  Decreasing fructose-1,6-bisphosphate aldolase activity reduces plant growth and tolerance to chilling stress in tomato seedlings.

Authors:  Bingbing Cai; Qiang Li; Fengjiao Liu; Huangai Bi; Xizhen Ai
Journal:  Physiol Plant       Date:  2018-04-23       Impact factor: 4.500

3.  Expressed sequence tag (EST) profiling in hyper saline shocked Dunaliella salina reveals high expression of protein synthetic apparatus components.

Authors:  Fadi Alkayal; Rebecca L Albion; Richard L Tillett; Leyla T Hathwaik; Mark S Lemos; John C Cushman
Journal:  Plant Sci       Date:  2010-07-14       Impact factor: 4.729

4.  Algal Functional Annotation Tool: a web-based analysis suite to functionally interpret large gene lists using integrated annotation and expression data.

Authors:  David Lopez; David Casero; Shawn J Cokus; Sabeeha S Merchant; Matteo Pellegrini
Journal:  BMC Bioinformatics       Date:  2011-07-12       Impact factor: 3.169

5.  Salinity-Induced Palmella Formation Mechanism in Halotolerant Algae Dunaliella salina Revealed by Quantitative Proteomics and Phosphoproteomics.

Authors:  Sijia Wei; Yangyang Bian; Qi Zhao; Sixue Chen; Jiawei Mao; Chunxia Song; Kai Cheng; Zhen Xiao; Chuanfang Zhang; Weimin Ma; Hanfa Zou; Mingliang Ye; Shaojun Dai
Journal:  Front Plant Sci       Date:  2017-05-23       Impact factor: 5.753

6.  Coexpression network and phenotypic analysis identify metabolic pathways associated with the effect of warming on grain yield components in wheat.

Authors:  Christine Girousse; Jane Roche; Claire Guerin; Jacques Le Gouis; Sandrine Balzegue; Said Mouzeyar; Mohamed Fouad Bouzidi
Journal:  PLoS One       Date:  2018-06-25       Impact factor: 3.240

7.  WGCNA Analysis of Salt-Responsive Core Transcriptome Identifies Novel Hub Genes in Rice.

Authors:  Mingdong Zhu; Hongjun Xie; Xiangjin Wei; Komivi Dossa; Yaying Yu; Suozhen Hui; Guohua Tang; Xiaoshan Zeng; Yinghong Yu; Peisong Hu; Jianlong Wang
Journal:  Genes (Basel)       Date:  2019-09-17       Impact factor: 4.096

8.  Integration of Cross Species RNA-seq Meta-Analysis and Machine-Learning Models Identifies the Most Important Salt Stress-Responsive Pathways in Microalga Dunaliella.

Authors:  Bahman Panahi; Mohammad Frahadian; Jacob T Dums; Mohammad Amin Hejazi
Journal:  Front Genet       Date:  2019-08-29       Impact factor: 4.599

9.  WGCNA: an R package for weighted correlation network analysis.

Authors:  Peter Langfelder; Steve Horvath
Journal:  BMC Bioinformatics       Date:  2008-12-29       Impact factor: 3.169

10.  Peroxisome Proliferator-Activated Receptor-γ Modulates the Response of Macrophages to Lipopolysaccharide and Glucocorticoids.

Authors:  Michael Heming; Sandra Gran; Saskia-L Jauch; Lena Fischer-Riepe; Antonella Russo; Luisa Klotz; Sven Hermann; Michael Schäfers; Johannes Roth; Katarzyna Barczyk-Kahlert
Journal:  Front Immunol       Date:  2018-05-08       Impact factor: 7.561

View more
  11 in total

1.  Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis.

Authors:  Juexing Li; Lei Zhou; Zhenhua Li; Shangneng Yang; Liangyue Tang; Hui Gong
Journal:  Front Genet       Date:  2021-12-24       Impact factor: 4.599

2.  The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter.

Authors:  Keping Chai; Xiaolin Zhang; Huitao Tang; Huaqian Gu; Weiping Ye; Gangqiang Wang; Shufang Chen; Feng Wan; Jiawei Liang; Daojiang Shen
Journal:  Front Neurol       Date:  2022-02-24       Impact factor: 4.003

3.  Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis.

Authors:  Guoju Fan; Zhihai Jin; Kaiqiang Wang; Huitang Yang; Jun Wang; Yankui Li; Bo Chen; Hongwei Zhang
Journal:  BMC Cardiovasc Disord       Date:  2021-12-03       Impact factor: 2.298

4.  Transcriptome Analysis Revealed the Molecular Response Mechanism of Non-heading Chinese Cabbage to Iron Deficiency Stress.

Authors:  Jingping Yuan; Daohan Li; Changwei Shen; Chunhui Wu; Nadeem Khan; Feifei Pan; Helian Yang; Xin Li; Weili Guo; Bihua Chen; Xinzheng Li
Journal:  Front Plant Sci       Date:  2022-03-11       Impact factor: 5.753

5.  Identification of stress-related genes by co-expression network analysis based on the improved turbot genome.

Authors:  Xi-Wen Xu; Weiwei Zheng; Zhen Meng; Wenteng Xu; Yingjie Liu; Songlin Chen
Journal:  Sci Data       Date:  2022-06-29       Impact factor: 8.501

6.  Identification of hub genes related to CD4+ memory T cell infiltration with gene co-expression network predicts prognosis and immunotherapy effect in colon adenocarcinoma.

Authors:  Lingxue Tang; Sheng Yu; Qianqian Zhang; Yinlian Cai; Wen Li; Senbang Yao; Huaidong Cheng
Journal:  Front Genet       Date:  2022-08-29       Impact factor: 4.772

7.  Integrative Identification of Crucial Genes Associated With Plant Hormone-Mediated Bud Dormancy in Prunus mume.

Authors:  Ping Li; Tangchun Zheng; Zhiyong Zhang; Weichao Liu; Like Qiu; Jia Wang; Tangren Cheng; Qixiang Zhang
Journal:  Front Genet       Date:  2021-07-06       Impact factor: 4.599

8.  Comprehensive Transcriptome Analysis Uncovers Distinct Expression Patterns Associated with Early Salinity Stress in Annual Ryegrass (Lolium Multiflorum L.).

Authors:  Guangyan Feng; Pengqing Xiao; Xia Wang; Linkai Huang; Gang Nie; Zhou Li; Yan Peng; Dandan Li; Xinquan Zhang
Journal:  Int J Mol Sci       Date:  2022-03-18       Impact factor: 5.923

Review 9.  Application of machine learning in bacteriophage research.

Authors:  Yousef Nami; Nazila Imeni; Bahman Panahi
Journal:  BMC Microbiol       Date:  2021-06-26       Impact factor: 3.605

10.  Expression of 5-methylcytosine regulators is highly associated with the clinical phenotypes of prostate cancer and DNMTs expression predicts biochemical recurrence.

Authors:  Lin Wang; Guoping Ren; Biaoyang Lin
Journal:  Cancer Med       Date:  2021-07-05       Impact factor: 4.452

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.