Gamolenic acid

Understanding metabolic adaptations to seasonal fluctuations in ectothermic organisms is challenging, especially in tropical species where physiological responses are more pronounced than in temperate or polar counterparts. Traditional analytical methods often fail to account for the complex metabolic adjustments that are present in these substantial responses, and the high-dimensional characteristics of metabolomic data complicate the interpretation process when using conventional statistical methods. We utilized Non-negative Matrix Factorization (NMF), an unsupervised machine learning algorithm, to analyze monthly serum metabolomics data from tilapia over a year in order to overcome these limits. A deeper analysis using NMF demonstrated that carbohydrates gained prominence during warmer months, as evidenced by consistently elevated weights of glycolysis intermediates in our quantitative analysis. Furthermore, fatty acids remained an important factor in both warm and cold seasons. Amino acids emerged as particularly versatile metabolites, exhibiting adaptability during seasonal transitions. This flexibility suggests their crucial role in coordinating energy-related adaptations and potentially facilitating epigenetic and reproductive responses to changing environments. Serum aspartate composition during the warm-cold transition indicated sex-specific metabolic strategies, as sexual dimorphism was observed in the seasonal utilization of fatty acids and aspartate. Collectively, NMF objectively assesses the metabolic tactics of tropical fish and reveals latent patterns in real-world metabolic dynamics. Consequently, it possesses the potential to facilitate metabolomics-driven species conservation in response to environmental changes.

Polyunsaturated fatty acids (PUFAs), such as γ-linolenic acid, arachidonic acid, eicosapentaenoic acid, and docosahexaenoic acid, are highly valued in the global market due to their physiological effects and health benefits. Concerns related to overfishing and marine ecosystem degradation have driven interest in microalgal lipids as a sustainable and eco-friendly alternative for PUFA production. Despite some success in commercializing microalgal lipid products, they still fail to meet global demand. Advances in high-throughput omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have deepened the understanding of lipid biosynthesis in microalgae. This review explores the potential of multi-omics approaches to elucidate PUFA biosynthesis pathways, identify key regulatory genes, and optimize metabolic engineering strategies for enhanced lipid production. Additionally, this review discusses how multi-omics technologies address challenges in large-scale cultivation, promoting the industrialization of microalgal lipid productions. These insights provide a foundation for improving microalgal PUFA yields to meet growing global demand.