Integration of Cross Species RNA-seq Meta-Analysis and Machine-Learning Models Identifies the Most Important Salt Stress–Responsive Pathways in Microalga Dunaliella - Panahi, Bahman; Frahadian, Mohammad; Dums, Jacob T.; Hejazi, Mohammad Amin (Frontiers in Genetics, 2019)
Photosynthetic microalgae are potentially yielding sources of different high value secondary metabolites. Salinity is a complex stress that influences various metabolite related pathways in microalgae. To obtain a clear view of the underlying metabolic pathways and resolve contradictory information concerning the transcriptional regulation of Dunaliella species in salt stress conditions, RNA-seq meta-analysis along with systems levels analysis was conducted. A p-value combination technique with Fisher method was used for cross species meta-analysis on the transcriptomes of two D. salina and D. tertiolecta species. The potential functional impacts of core meta-genes were surveyed based on gene ontology and network analysis. Different attribute weighting algorithms were used to verify the importance of the meta-genes. In the current study, the integration of supervised machine learning algorithms with RNA-seq meta-analysis was performed. The analysis shows that the lipid and nitrogen metabolism, structural proteins of photosynthesis apparatus, chaperone-mediated autophagy and ROS related genes are the key and core elements of the Dunaliella salt stress response system. Cross-talk between Ca2+ signal transduction, lipid accumulation, and ROS signaling network in salt stress conditions is also proposed. Our novel approach opens new avenues for better understanding of microalgae stress response mechanisms and for selection of candidate gene targets for metabolite production in microalgae.