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Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry CCMS
Published on: December 20, 2010
Mechanistic insights into sodium citrate-induced metabolic rewiring for enhanced S-adenosylmethionine synthesis via
Le Dong1, Weijing Song1, Zhongyue Li1
1State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, People's Republic of China.
Abstract:
S-adenosylmethionine (SAM) is a high-value metabolite with widespread applications in medicine and nutrition, yet its microbial production remains constrained by high energy demands and inefficient precursor utilization. In this study, we investigated sodium citrate supplementation as a strategy to enhance SAM biosynthesis in Pichia pastoris under methanol induction. Integrating transcriptomics with a newly reconstructed genome-scale metabolic model (iLD1283), we systematically elucidated the molecular and metabolic mechanisms underlying citrate-mediated improvements. Physiological analysis revealed that sodium citrate supplementation significantly increased biomass accumulation, methanol and L-methionine assimilation, and intracellular ATP levels, resulting in a 70% enhancement in SAM titer. Transcriptomic profiling demonstrated global metabolic reprogramming, including the upregulation of glycolysis, the tricarboxylic acid cycle, oxidative phosphorylation, and amino acid biosynthesis, collectively supporting improved energy supply and precursor availability. Constraint-based simulations using iLD1283 identified an optimal citrate feeding rate that balanced energy generation and SAM production, which was validated in 5-L fed-batch fermentation, achieving a peak SAM concentration of 10.87 g/L. Metabolic flux analysis further confirmed increased flux through central carbon pathways and elevated cofactor regeneration. Together, these findings provide mechanistic insight into sodium citrate-induced metabolic rewiring and establish a model-guided framework for rational optimization of energy-intensive microbial processes. This work highlights the potential of combining omics data and metabolic modeling to guide precision feeding strategies for enhanced bioproduction.
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