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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00752

Integration of cross species RNA-seq Meta-analysis and Machine Learning Models identifies the most important salt stress responsive pathways in microalga Dunaliella

 Bahman Panahi1*, mohammad Farhadian2, JacobT Dumas3 and Mohmmadamin Hejazi1
  • 1Agricultural Biotechnology Research Institute of Iran, Iran
  • 2University of Tabriz, Iran
  • 3University of Delaware, United States

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.

Keywords: Microalgae, dunaliella, RNA-seq meta-analysis, machine learning, network, retrograde signaling, ROS, tetrapyrrole

Received: 06 Apr 2019; Accepted: 17 Jul 2019.

Copyright: © 2019 Panahi, Farhadian, Dumas and Hejazi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Bahman Panahi, Agricultural Biotechnology Research Institute of Iran, Karaj, Alborz, Iran, panahibahman@ymail.com