Although Pichia pastoris was successfully used for heterologous gene expression for more than twenty years, many factors influencing protein expression remain unclear. Here, we optimized the expression of a thermophilic endoglucanase from Thermothielavioides terrestris (TtCel45A) for cost-effective production in Pichia pastoris. To achieve this, we established a multifactorial regulation strategy that involved selecting a genome-editing system, utilizing neutral loci, incorporating multiple copies of the heterologous expression cassette, and optimizing high-density fermentation for the co-production of single-cell protein (SCP). Notably, even though all neutral sites were used, there was still a slight difference in the enzymatic activity of heterologously expressed TtCel45A. Interestingly, the optimal gene copy number for the chromosomal expression of TtCel45A was found to be three, indicating limitations in translational capacity, post-translational processing, and secretion, ultimately impacting protein yields in P. pastoris. We suggest that multiple parameters might influence a kinetic competition between protein elongation and mRNA degradation. During high-density fermentation, the highest protein concentration and endoglucanase activity of TtCel45A with three copies reached 15.8 g/L and 9640 IU/mL, respectively. At the same time, the remaining SCP of P. pastoris exhibited a crude protein and amino acid content of up to 59.32% and 46.98%, respectively. These findings suggested that SCP from P. pastoris holds great promise as a sustainable and cost-effective alternative for meeting the global protein demand, while also enabling the production of thermophilic TtCel45A in a single industrial process.

Methanol, which produced in large quantities from low-quality coal and the hydrogenation of CO₂, is a potentially renewable one-carbon (C1) feedstock for biomanufacturing. The methylotrophic yeast Pichia pastoris is an ideal host for methanol biotransformation given its natural capacity as a methanol assimilation system. However, the utilization efficiency of methanol for biochemical production is limited by the toxicity of formaldehyde. Therefore, reducing the toxicity of formaldehyde to cells remains a challenge to the engineering design of a methanol metabolism. Based on genome-scale metabolic models (GSMM) calculations, we speculated that reducing alcohol oxidase (AOX) activity would re-construct the carbon metabolic flow and promote balance between the assimilation and dissimilation of formaldehyde metabolism processes, thereby increasing the biomass formation of P. pastoris. According to experimental verification, we proved that the accumulation of intracellular formaldehyde can be decreased by reducing AOX activity. The reduced formaldehyde formation upregulated methanol dissimilation and assimilation and the central carbon metabolism, which provided more energy for the cells to grow, ultimately leading to an increased conversion of methanol to biomass, as evidenced by phenotypic and transcriptome analysis. Significantly, the methanol conversion rate of AOX-attenuated strain PC110-AOX1-464 reached 0.364 g DCW/g, representing a 14% increase compared to the control strain PC110. In addition, we also proved that adding a co-substrate of sodium citrate could further improve the conversion of methanol to biomass in the AOX-attenuated strain. It was found that the methanol conversion rate of the PC110-AOX1-464 strain with the addition of 6 g/L sodium citrate reached 0.442 g DCW/g, representing 20% and 39% increases compared to AOX-attenuated strain PC110-AOX1-464 and control strain PC110 without sodium citrate addition, respectively. The study described here provides insight into the molecular mechanism of efficient methanol utilization by regulating AOX. Reducing AOX activity and adding sodium citrate as a co-substrate are potential engineering strategies to regulate the production of chemicals from methanol in P. pastoris.

Thermophilic β-1,4-endoglucanases (Cel5A) have garnered significant interest due to their potential applications in various industries, particularly in biofuel production and biorefineries. However, despite inherent stability, thermophilic Cel5A still face challenges in terms of further enhancing their catalytic efficiency and thermostability. In this study, a novel B-factor analysis method was used to predict beneficial amino acid substitutions within a 4 Å radius of the catalytic site in the tunnel of thermophilic Cel5A from Acidomyces richmondensis (ArCel5A). A combined strategy involving site-saturation mutagenesis and high-throughput screening was employed to identify the variants with the highest endoglucanase activity. Genomic sequencing revealed a mutation at position 299 in the starting strain T. reesei A2H, where the nucleotide sequence changed from TAC to TGC, resulting in a corresponding amino acid substitution from Tyrosine(Y) to Cystine(C). The endoglucanase activity of the mutant ArCel5A reached 3251 IU/mL, representing an 85.2% increase compared to wild-type ArCel5A at the fermentation time of 94 h. Significantly, the ArCel5A-Y299C mutant showed superior thermostability, retaining 93.8% of its initial activity after 30 min at 70 °C, and 91.5% after 10 min at 80 °C. Various computational simulation methods confirmed that the Y299C mutation enhanced the stability of the catalytic pocket, thereby improving the overall stability and catalytic efficiency of ArCel5A. This study offers an effective strategy for mining target sites for rational mutagenesis based on highly conserved sequences, which simultaneously improves both the thermostability and catalytic efficiency of thermophilic Cel5A.

Background The aim of this study was to utilize machine learning techniques to identify biomarkers associated with the diagnosis of bladder cancer, providing valuable insights into its early pathogenesis and exploring their potential as prognostic markers and therapeutic targets. Methods Initially, we conducted a comparative analysis of the genomes between bladder cancer samples, focusing on identifying the most significant differences between the cancer group and the normal group. Next, we employed machine learning techniques for feature selection and identified a key gene by integrating ferroptosis-related genes into our analysis. Moreover, we integrated transcriptome data, somatic mutation data, and clinical data to perform comprehensive analyses, including functional enrichment analysis, tumor mutation load analysis, immune infiltration analysis, and pan-cancer analysis. These analyses aimed to elucidate the pathological relevance of the candidate genes. Furthermore, we constructed a ceRNA network to identify the genes and regulatory pathways associated with these candidate genes. Results We initially conducted screening using the Weighted Gene Co-expression Network Analysis and machine learning techniques, resulting in the identification of six candidate genes: NR4A1, PAMR1, CFD, RAI2, ALG3, and HAAO. Subsequently, by integrating data from the FerrDB database, we identified NR4A1 as a gene associated with ferroptosis. Additionally, our analysis revealed a correlation between the expression of NR4A1 and tumor mutations as well as immune infiltration in patients with bladder cancer. Conclusion Our data strongly suggest that NR4A1 could serve as a crucial prognostic biomarker for bladder cancer and may also play a role in the development of various other cancers.