ArticleBioresources and bioprocessing2026
Valorization of Miscanthus × giganteus hydrolysates into heme-enriched yeast biomass and extract using xylose-fermenting Saccharomyces cerevisiae.
Article in Bioresources and bioprocessing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Heme-enriched microbial products represent value-added bioproducts that can be produced through sustainable fermentation processes. Current microbial heme production platforms rely mainly on refined sugars and downstream purification, limiting cost-effectiveness. Here, we developed a lignocellulosic hydrolysate-based bioprocess for producing heme-enriched yeast extract using Saccharomyces cerevisiae. The xylose-fermenting S. cerevisiae SR8 accumulated approximately twofold higher specific intracellular heme on xylose than on glucose, reaching 0.25 ± 0.02 mg/g dry cell weight (DCW) compared with 0.13 ± 0.01 mg/g DCW, consistent with reduced Crabtree repression and enhanced respiratory metabolism. During fed-batch cultivation with Miscanthus × giganteus hydrolysates, residual glucose control suppressed ethanol overflow and promoted biomass formation, resulting in 17.5 ± 0.4 g/L DCW, a specific heme content of 0.20 ± 0.01 mg/g DCW, and a heme titer of 3.4 ± 0.1 mg/L. This performance exceeded that of a mock hydrolysate with identical sugar composition. Autolysis of the resulting biomass produced a lyophilized yeast extract containing 36 ± 2 µg heme/g, exceeding the heme content of the commercial yeast extract tested and supported comparable microbial growth. These results indicate that downstream heme recovery during yeast extract production remains a key bottleneck for improving the overall process efficiency and product heme density. Among the downstream extraction methods tested, high-pressure homogenization most effectively enhanced heme release. Overall, these results demonstrate lignocellulosic hydrolysate valorization into heme-enriched yeast extract and identify downstream recovery as a key target for process optimization.
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