Global metabolome analysis of Dunaliella tertiolecta, Phaeobacter italicus R11 Co-cultures using thermal desorption - Comprehensive two-dimensional gas chromatography - Time-of-flight mass spectrometry (TD-GC×GC-TOFMS). (March 2022)

• Record Type: Journal Article
• Authors: Sorochan Armstrong, Michael D.; Arredondo Campos, O. René; Bannon, Catherine C.; de la Mata, A. Paulina; Case, Rebecca J.; Harynuk, James J.
• Is Part Of: Phytochemistry. Volume 195(2022)
• Publication Date: 2022-03
• Subjects: Dunaliella tertiolecta -- Dunaliellaceae -- Phaeobacter italicus -- Rhodobacteraceae -- Biofuels -- Machine learning -- Metabolomics -- GC×GC; Botanical chemistry -- Periodicals; Biochemistry -- Periodicals; Botany -- Periodicals; Chimie végétale -- Périodiques; 572.2
• DOI: 10.1016/j.phytochem.2021.113052
• Languages: English
• ISSNs: 0031-9422

Abstract

Abstract: Dunaliella tertiolecta is a marine microalgae that has been studied extensively as a potential carbon-neutral biofuel source (Tang et al., 2011). Microalgae oil contains high quantities of energy-rich fatty acids and lipids, but is not yet commercially viable as an alternative fuel. Carefully optimised growth conditions, and more recently, algal-bacterial co-cultures have been explored as a way of improving the yield of D. tertiolecta microalgae oils. The relationship between the host microalgae and bacterial co-cultures is currently poorly understood. Here, a complete workflow is proposed to analyse the global metabolomic profile of co-cultured D. tertiolectra and Phaeobacter italicus R11, which will enable researchers to explore the chemical nature of this relationship in more detail. To the best of the authors' knowledge this study is one of the first of its kind, in which a pipeline for an entirely untargeted analysis of the algal metabolome is proposed using a practical sample preparation, introduction, and data analysis routine. Graphical abstract: Image 1 Highlights: A method for direct microalgae sample introduction is proposed. Simple large volume injection for dilute samples. Methodology maintains system cleanliness and reproducible performance. Advanced chemometric tools can extract useful and discriminating metabolomic features even from very noisy complex datasets.