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

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.

Molecular Characteristics of Choledochal Cysts in Children: Transcriptome Sequencing

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Identification of the Real Hub Gene and Construction of a Novel Prognostic Signature for Pancreatic Adenocarcinoma Based on the Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator Algorithms

“…However, most researchers have failed to focus on the considerable interconnection between genes when constructing prognostic signatures, and thus, the weighted gene co-expression network analysis (WGCNA) was developed. WGCNA provides a new approach in performing higher-resolution analysis, which can more accurately predict hub genes in a disease, thus, providing a novel field of vision for the exploration of disease pathophysiology and the construction of disease prognostic signatures ( Panahi and Hejazi, 2021 ). For instance, in 2021, Farhadian et al (2021) highlighted the pivotal role of GJA1, AP2A2, and NPAS3 in the lactation process using WGCNA algorithms.…”

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

“…The higher the value of a Z summary , the stronger the evidence that the module is preserved. Z summary values >10 marked the most preservative modules 17–19 …”