Metabolic and Evolutionary Engineering of Diploid Yeast for the Production of First- and Second-Generation Ethanol

High titer (>100 g/L) ethanol production from pretreated corn stover hydrolysate by modified yeast strains

Developing a reliable lignocellulose pretreatment method to extract mixed sugars and engineering efficient strains capable of utilizing xylose are crucial for advancing cellulosic ethanol production. In this study, chemical and characterization analyses revealed that alkali cooking can significantly remove lignin from lignocellulose crops. The highest amount of mixed sugar was obtained from corn stover hydrolysates with a 15 % solid loading. Our genetically engineered yeast strain ΔsnR4, derived from a well-staged WXY70, demonstrated excellent performance in low 10 % solids loading corn stover hydrolysate, producing a high ethanol yield of 0.485 g/g total sugars. When a combined NaOH-ball milling pretreatment strategy was applied at high solids loading, ΔsnR4 exhibited the maximum ethanol titer of 110.9 g/L within 36 h, achieving an ethanol yield of 92.9 % theoretical maximum. Therefore, ΔsnR4 is highly compatible with high solid loading NaOH-ball milling pretreatment, making it a potential candidate for industrial cellulosic ethanol.

From Saccharomyces cerevisiae to Ethanol: Unlocking the Power of Evolutionary Engineering in Metabolic Engineering Applications

Increased human population and the rapid decline of fossil fuels resulted in a global tendency to look for alternative fuel sources. Environmental concerns about fossil fuel combustion led to a sharp move towards renewable and environmentally friendly biofuels. Ethanol has been the primary fossil fuel alternative due to its low carbon emission rates, high octane content and comparatively facile microbial production processes. In parallel to the increased use of bioethanol in various fields such as transportation, heating and power generation, improvements in ethanol production processes turned out to be a global hot topic. Ethanol is by far the leading yeast output amongst a broad spectrum of bio-based industries. Thus, as a well-known platform microorganism and native ethanol producer, baker's yeast Saccharomyces cerevisiae has been the primary subject of interest for both academic and industrial perspectives in terms of enhanced ethanol production processes. Metabolic engineering strategies have been primarily adopted for direct manipulation of genes of interest responsible in mainstreams of ethanol metabolism. To overcome limitations of rational metabolic engineering, an alternative bottom-up strategy called inverse metabolic engineering has been widely used. In this context, evolutionary engineering, also known as adaptive laboratory evolution (ALE), which is based on random mutagenesis and systematic selection, is a powerful strategy to improve bioethanol production of S. cerevisiae. In this review, we focus on key examples of metabolic and evolutionary engineering for improved first- and second-generation S. cerevisiae bioethanol production processes. We delve into the current state of the field and show that metabolic and evolutionary engineering strategies are intertwined and many metabolically engineered strains for bioethanol production can be further improved by powerful evolutionary engineering strategies. We also discuss potential future directions that involve recent advancements in directed genome evolution, including CRISPR-Cas9 technology.

Biological valorization of lignin-derived vanillin to vanillylamine by recombinant E. coli expressing ω-transaminase and alanine dehydrogenase in a petroleum ether-water system

Vanillylamine, as an important drug precursor and fine chemical intermediate, has great economic value. By constructing a strategy of double enzyme co-expression, one newly constructed recombinant E. coli HNIQLE-AlaDH expressing ω-transaminase from Aspergillus terreus and alanine dehydrogenase from Bacillus subtilis was firstly used aminate lignin-derived vanillin to vanillylamine by using a relatively low dosage of amine donors (vanillin:L-alanine:isopropylamine = 1:1:1, mol/mol/mol). In addition, in a two-phase system (water:petroleum ether = 80:20 v/v), the bioconversion of vanillin to vanillylamine was catalyzed by HNIQLE-AlaDH cell under the ambient condition, and the vanillylamine yield was 71.5%, respectively. This double-enzyme HNIQLE-AlaDH catalytic strategy was applied to catalyze the bioamination of furfural and 5-hydroxymethylfurfural with high amination efficiency. It showed that the double-enzyme catalytic strategy in this study promoted L-alanine to replace D-Alanine to participate in bioamination of vanillin and its derivatives, showing a great prospect in the green biosynthesis of biobased chemicals from biomass.

Deletion of NGG1 in a recombinant Saccharomyces cerevisiae improved xylose utilization and affected transcription of genes related to amino acid metabolism

Production of biofuels and biochemicals from xylose using yeast cell factory is of great interest for lignocellulosic biorefinery. Our previous studies revealed that a natural yeast isolate Saccharomyces cerevisiae YB-2625 has superior xylose-fermenting ability. Through integrative omics analysis, NGG1, which encodes a transcription regulator as well as a subunit of chromatin modifying histone acetyltransferase complexes was revealed to regulate xylose metabolism. Deletion of NGG1 in S. cerevisiae YRH396h, which is the haploid version of the recombinant yeast using S. cerevisiae YB-2625 as the host strain, improved xylose consumption by 28.6%. Comparative transcriptome analysis revealed that NGG1 deletion down-regulated genes related to mitochondrial function, TCA cycle, ATP biosynthesis, respiration, as well as NADH generation. In addition, the NGG1 deletion mutant also showed transcriptional changes in amino acid biosynthesis genes. Further analysis of intracellular amino acid content confirmed the effect of NGG1 on amino acid accumulation during xylose utilization. Our results indicated that NGG1 is one of the core nodes for coordinated regulation of carbon and nitrogen metabolism in the recombinant S. cerevisiae. This work reveals novel function of Ngg1p in yeast metabolism and provides basis for developing robust yeast strains to produce ethanol and biochemicals using lignocellulosic biomass.

Classification prediction of early pulmonary nodes based on weighted gene correlation network analysis and machine learning

OBJECTIVE

To use weighted gene correlation network analysis (WGCNA) and machine learning algorithm to predict classification of early pulmonary nodes with public databases.

METHODS

The expression data and clinical data of lung cancer patients were firstly extracted from public database (GTEx and TCGA) to study the differentially expressed genes (DEGs) of lung adenocarcinoma (LUAD). The intersection of three R packages (Dseq2, Limma, EdgeR) methods were selected as candidate DEGs for further study. WGCNA was used to obtain relevant modules and key genes of lung cancer classification, GO and KEGG enrichment analysis was performed. The model was built using two machine learning methods, Least Absolute Shrinkage and Selection Operator (LASSO) regression and tumor classification was also predicted with extreme Gradient Boosting (XGBoost) algorithm.

RESULTS

DEGs analysis revealed that there were 1306 LUAD genes. WGCNA module analysis showed that a total of 116 genes were significantly related to classification, and module genes were mainly related to 14 KEGG pathways. The machine learning algorithm identified 10 target genes by LASSO regression analysis of differential genes, and 18 genes were identified by XGBoost model. A total of 6 genes were found from the intersection of the above methods as classification signatures of early pulmonary nodules, including "HMGB3" "ARHGAP6" "TCF21" "FCN3" "COL6A6" "GOLM1".

CONCLUSION

Using DEGs analysis, WGCNA method and machine learning algorithm, six gene signatures related to early stage of LUAD, which can assist clinicians in disease classification prediction.