The race to harness hydrogen as a clean energy source is on, and a recent study has shed light on a crucial aspect of this endeavor: the intricate balance between microbial growth and hydrogen production. This research, led by a team from the National Technology Innovation Center of Synthetic Biology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, and other institutions, introduces an enzyme-constrained genome-scale metabolic model (ecGEM) that offers a novel perspective on optimizing biohydrogen production.
The study focuses on the bacterium Ethanoligenens harbinense YUAN-3, a hydrogen-producing microbe. The team constructed a genome-scale metabolic model (GEM) named ixeh674, covering 674 genes, 977 metabolites, and 1,063 reactions. To enhance the model's biological realism, they rebuilt the biomass equation using experimentally measured protein, DNA, RNA, glycogen, and amino acid composition, significantly improving prediction accuracy from 64.71% to 91.42%.
The key innovation was the integration of enzyme turnover numbers (kcat values) predicted using DLkcat, a deep-learning-based tool. This led to the creation of the enzyme-constrained model ecixeh674, which accounted for finite enzyme resources. The model revealed a fascinating trade-off: rapid growth demands enzyme capacity for precursor synthesis, leaving fewer resources for hydrogen-producing pathways. During the stationary phase, growth slows, and hydrogen yield increases, aligning with experimental observations.
The study identified amino acid biosynthesis and selected gene targets as promising avenues for enhancing fermentative biohydrogen production. Interestingly, diverting carbon and reduced nicotinamide adenine dinucleotide (NADH) flux toward glutamate and glutamine biosynthesis reduced ethanol formation and boosted hydrogen production. Single-gene knockout simulations further highlighted the potential of specific gene targets, such as Ethha_1547, encoding phosphoglycerate kinase, to significantly increase hydrogen flux under low-carbon conditions.
The authors emphasize that improving biohydrogen production is not a simple matter of maximizing one pathway. Instead, microbial cells must allocate limited enzyme and energy resources among growth, survival, and product formation. The ecGEM model, by revealing these hidden trade-offs, provides a practical approach for selecting engineering targets. It helps explain when hydrogen production can be enhanced, when growth becomes a limiting cost, and why balanced strain design is crucial for future fermentation systems.
This research offers a model-guided path for engineering hydrogen-producing microbes, moving beyond trial-and-error optimization. By identifying metabolic routes that redirect carbon, NADH, and adenosine triphosphate (ATP), the ecGEM framework can support rational strain design to improve hydrogen yield while maintaining viable growth. The approach's potential extends to mixed-substrate fermentation, microbial communities, and reactor-scale process design, where substrate competition and community stability are significant challenges.
As biological hydrogen production advances toward industrial applications, enzyme-constrained modeling could become a valuable decision-making platform. It bridges the gap between microbial metabolism and cleaner energy production, offering insights into the complex interplay between growth, survival, and product formation in hydrogen-producing microbes.
