Integrating transcriptomic data with metabolic model unravels the electron transfer mechanisms of Methanosarcina barkeri

• A metabolic model for Methanosarcina barkeri was developed • Integrating transcriptomic data with metabolic model improved model predictions • The CO 2 reduction pathway functioned during aceticlastic methanogenesis • Vht hydrogenase was responsible for electron uptake during DIET Methanogenic archaea, particularly Methanosarcina , are pivotal to the global carbon cycle and renewable energy production due to their versatile metabolic capabilities. Although transcriptomic analysis is widely employed to identify key genes and pathways in Methanosarcina under various methanogenic conditions—including the emerging direct interspecies electron transfer (DIET)-based methanogenesis—the weak correlation between gene expression levels and protein abundances poses challenges for interpreting transcriptomic data. To address this, we integrate transcriptomic data into a metabolic model of Methanosarcina barkeri for the first time, enabling more refined predictions and enhancing the interpretability of transcriptomic insights. This novel integrated model is subsequently utilized to simulate aceticlastic, hydrogenotrophic, and DIET-based methanogenesis. The results reveal that previous assumptions failed to account for the essential role of the oxidative CO 2 reduction pathway in aceticlastic methanogenesis. The model also successfully captures key transcriptomic features of DIET-based methanogenesis, clarifying the functional roles of crucial enzymes like the F 420 H 2 dehydrogenase Fpo and the transmembrane hydrogenase Vht in electron transfer. This integrative approach provides a deeper understanding of electron transfer mechanisms in M. barkeri and offeres valuable insights for advancing methanogen-based biotechnologies. Moreover, the study critically evaluates the relationship between gene expression and metabolic flux, establishing a practical framework for deriving meaningful insights from the growing volume of transcriptomic data.