Screening for genes involved in cellulase regulation by expression under the control of a novel constitutive promoter in Trichoderma reesei

Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges

The lignocellulosic biorefinery involves pretreatment, enzymatic hydrolysis, mixed sugar fermentation, and optional anaerobic digestion. This pipeline could be effectively implemented through machine learning (ML)-guided process optimization and strain modification rather than experimental or experience-based ones. This review takes a holistic perspective on the entire pipeline, discussing how ML could aid lignocellulosic, while other published work has focused on individual modules within the pipeline. This review also explores the model construction and evaluation strategies and highlights the emerging potential of transfer learning and hybrid ML models to address data insufficiency and improve model interpretability. Furthermore, challenges and future prospects of ML in lignocellulosic biorefinery will be elaborated in this review. Integrating ML into lignocellulosic biorefinery offers a promising pathway towards sustainable and competitive biorefinery systems.

Cumulative expression of heterologous XlnR regulatory modules and AraRA731V in Penicillium oxalicum enhances saccharification efficiency of corn stover and corn fiber

Penicillium oxalicum engineered strain DB2 and its mutant strains with multiple regulatory modules were constructed. Mutant strain RE-4-2 with two regulatory modules showed a significant increase in the reducing sugar released from corn stover and corn fiber as well as in the conversion of cellulose than DB2. RE-5-2 with three regulatory modules showed a further increase in reducing sugar released from corn stover and the conversion of cellulose on the basis of RE-4-2. RE-4-2-AraRA731V constructed by overexpressing AraRA731V in RE-4-2 showed an increase of 7.2 times and 1.2 times in arabinofuranosidase and xylosidase activities, respectively. Reducing sugar yield and cellulose conversion of corn stover and corn fiber by RE-4-2-AraRA731V were further increased.

Genetic engineering and raising temperature enhance recombinant protein production with the cdna1 promoter in Trichoderma reesei

The fungus Trichoderma reesei is a powerful host for secreted production of proteins. The promoter of cdna1 gene, which encodes a small basic protein of unknown function and high expression, is commonly used for constitutive protein production in T. reesei. Nevertheless, the production level of proteins driven by this promoter still needs to be improved. Here, we identified that the region 600- to 700-bp upstream of the start codon is critical for the efficiency of the cdna1 promoter. Increasing the copy number of this region to three improved the production of a heterologous β-mannanase by 37.5%. Screening of several stressful conditions revealed that the cdna1 promoter is heat inducible. Cultivation at 37 °C significantly enhanced the production of β-mannanase as well as a polygalacturonase with the cdna1 promoter compared with those at 30 °C. Combing the strategies of promoter engineering, multi-copy gene insertion, and control of cultivation temperature, β-mannanase of 199.85 U/mL and relatively high purity was produced in shake flask, which was 6.6 times higher than that before optimization. Taken together, the results advance the understanding of the widely used cdna1 promoter and provide effective strategies for enhancing the production of recombinant proteins in T. reesei.