Proteolysis Degree of Protein Corona Affect Ultrasound-Induced Sublethal Effects on Saccharomyces cerevisiae: Transcriptomics Analysis and Adaptive Regulation of Membrane Homeostasis

Protein corona (PC) adsorbed on the surface of nanoparticles brings new research perspectives on the interaction between nanoparticles and fermentative microorganisms. Herein, the proteolysis of wheat PC adsorbed on a nano-Se surface using cell-free protease extract from S. cerevisiae was conducted. The proteolysis caused monotonic changes of ζ-potentials and surface hydrophobicity of PC. Notably, the innermost PC layer was difficult to be proteolyzed. Furthermore, when S. cerevisiae was stimulated by ultrasound + 0.1 mg/mL nano-Se@PC, the proportion of lethal and sublethal injured cells increased as a function of the proteolysis time of PC. The transcriptomics analysis revealed that 34 differentially expressed genes which varied monotonically were related to the plasma membrane, fatty acid metabolism, glycerolipid metabolism, etc. Significant declines in the membrane potential and proton motive force disruption of membrane were found with the prolonged proteolysis time; meanwhile, higher membrane permeability, membrane oxidative stress levels, membrane lipid fluidity, and micro-viscosity were triggered.

Screening and transcriptomic analysis of the ethanol-tolerant mutant Saccharomyces cerevisiae YN81 for high-gravity brewing

Ethanol stress is one of the major limiting factors for high-gravity brewing. Breeding of yeast strain with high ethanol tolerance, and revealing the ethanol tolerance mechanism of Saccharomyces cerevisiae is of great significance to the production of high-gravity beer. In this study, the mutant YN81 was obtained by ultraviolet-diethyl sulfate (UV-DES) cooperative mutagenesis from parental strain CS31 used in high-gravity craft beer brewing. The ethanol tolerance experiment results showed that cell growth and viability of YN81 were significantly greater than that of CS31 under ethanol stress. The ethanol tolerance mechanisms of YN81 were studied through observation of cell morphology, intracellular trehalose content, and transcriptomic analysis. Results from scanning electron microscope (SEM) showed alcohol toxicity caused significant changes in the cell morphology of CS31, while the cell morphology of YN81 changed slightly, indicating the cell morphology of CS31 got worse (the formation of hole and cell wrinkle). In addition, compared with ethanol-free stress, the trehalose content of YN81 and CS31 increased dramatically under ethanol stress, but there was no significant difference between YN81 and CS31, whether with or without ethanol stress. GO functional annotation analysis showed that under alcohol stress, the number of membrane-associated genes in YN81 was higher than that without alcohol stress, as well as CS31, while membrane-associated genes in YN81 were expressed more than CS31 under alcohol stress. KEGG functional enrichment analysis showed unsaturated fatty acid synthesis pathways and amino acid metabolic pathways were involved in ethanol tolerance of YN81. The mutant YN81 and its ethanol tolerance mechanism provide an optimal strain and theoretical basis for high-gravity craft beer brewing.

Accumulation and regulation of anthocyanins in white and purple Tibetan Hulless Barley (Hordeum vulgare L. var. nudum Hook. f.) revealed by combined de novo transcriptomics and metabolomics

BACKGROUND

Colored barley, which may have associated human health benefits, is more desirable than the standard white variety, but the metabolites and molecular mechanisms underlying seedcoat coloration remain unclear.

RESULTS

Here, the development of Tibetan hulless barley was monitored, and 18 biological samples at 3 seedcoat color developmental stages were analyzed by transcriptomic and metabolic assays in Nierumuzha (purple) and Kunlun10 (white). A total of 41 anthocyanin compounds and 4186 DEGs were identified. Then we constructed the proanthocyanin-anthocyanin biosynthesis pathway of Tibetan hulless barley, including 19 genes encoding structural enzymes in 12 classes (PAL, C4H, 4CL, CHS, CHI, F3H, F3'H, DFR, ANS, ANR, GT, and ACT). 11 DEGs other than ANR were significantly upregulated in Nierumuzha as compared to Kunlun10, leading to high levels of 15 anthocyanin compounds in this variety (more than 25 times greater than the contents in Kunlun10). ANR was significantly upregulated in Kunlun10 as compared to Nierumuzha, resulting in higher contents of three anthocyanins compounds (more than 5 times greater than the contents in Nierumuzha). In addition, 22 TFs, including MYBs, bHLHs, NACs, bZips, and WD40s, were significantly positively or negatively correlated with the expression patterns of the structural genes. Moreover, comparisons of homologous gene sequences between the two varieties identified 61 putative SNPs in 13 of 19 structural genes. A nonsense mutation was identified in the coding sequence of the ANS gene in Kunlun10. This mutation might encode a nonfunctional protein, further reducing anthocyanin accumulation in Kunlun10. Then we identified 3 modules were highly specific to the Nierumuzha (purple) using WGCNA. Moreover, 12 DEGs appeared both in the putative proanthocyanin-anthocyanin biosynthesis pathway and the protein co-expression network were obtained and verified.

CONCLUSION

Our study constructed the proanthocyanin-anthocyanin biosynthesis pathway of Tibetan hulless barley. A series of compounds, structural genes and TFs responsible for the differences between purple and white hulless barley were obtained in this pathway. Our study improves the understanding of the molecular mechanisms of anthocyanin accumulation and biosynthesis in barley seeds. It provides new targets for the genetic improvement of anthocyanin content and a framework for improving the nutritional quality of barley.

Mining transcriptomic data to identify Saccharomyces cerevisiae signatures related to improved and repressed ethanol production under fermentation

Saccharomyces cerevisiae is known for its outstanding ability to produce ethanol in industry. Underlying the dynamics of gene expression in S. cerevisiae in response to fermentation could provide informative results, required for the establishment of any ethanol production improvement program. Thus, representing a new approach, this study was conducted to identify the discriminative genes between improved and repressed ethanol production as well as clarifying the molecular responses to this process through mining the transcriptomic data. The significant differential expression probe sets were extracted from available microarray datasets related to yeast fermentation performance. To identify the most effective probe sets contributing to discriminate ethanol content, 11 machine learning algorithms from RapidMiner were employed. Further analysis including pathway enrichment and regulatory analysis were performed on discriminative probe sets. Besides, the decision tree models were constructed, the performance of each model was evaluated and the roots were identified. Based on the results, 171 probe sets were identified by at least 5 attribute weighting algorithms (AWAs) and 17 roots were recognized with 100% performance Some of the top ranked presets were found to be involved in carbohydrate metabolism, oxidative phosphorylation, and ethanol fermentation. Principal component analysis (PCA) and heatmap clustering validated the top-ranked selective probe sets. In addition, the top-ranked genes were validated based on GSE78759 and GSE5185 dataset. From all discriminative probe sets, OLI1 and CYC3 were identified as the roots with the best performance, demonstrated by the most weighting algorithms and linked to top two significant enriched pathways including porphyrin biosynthesis and oxidative phosphorylation. ADH5 and PDA1 were also recognized as differential top-ranked genes that contribute to ethanol production. According to the regulatory clustering analysis, Tup1 has a significant effect on the top-ranked target genes CYC3 and ADH5 genes. This study provides a basic understanding of the S. cerevisiae cell molecular mechanism and responses to two different medium conditions (Mg²⁺ and Cu²⁺) during the fermentation process.

Screening novel genes by a comprehensive strategy to construct multiple stress-tolerant industrial Saccharomyces cerevisiae with prominent bioethanol production

BACKGROUND

Strong multiple stress-tolerance is a desirable characteristic for Saccharomyces cerevisiae when different feedstocks are used for economical industrial ethanol production. Random mutagenesis or genome shuffling has been applied for improving multiple stress-tolerance, however, these techniques are generally time-consuming and labor cost-intensive and their molecular mechanisms are unclear. Genetic engineering, as an efficient technology, is poorly applied to construct multiple stress-tolerant industrial S. cerevisiae due to lack of clear genetic targets. Therefore, constructing multiple stress-tolerant industrial S. cerevisiae is challenging. In this study, some target genes were mined by comparative transcriptomics analysis and applied for the construction of multiple stress-tolerant industrial S. cerevisiae strains with prominent bioethanol production.

RESULTS

Twenty-eight shared differentially expressed genes (DEGs) were identified by comparative analysis of the transcriptomes of a multiple stress-tolerant strain E-158 and its original strain KF-7 under five stress conditions (high ethanol, high temperature, high glucose, high salt, etc.). Six of the shared DEGs which may have strong relationship with multiple stresses, including functional genes (ASP3, ENA5), genes of unknown function (YOL162W, YOR012W), and transcription factors (Crz1p, Tos8p), were selected by a comprehensive strategy from multiple aspects. Through genetic editing based on the CRISPR/Case9 technology, it was demonstrated that expression regulation of each of these six DEGs improved the multiple stress-tolerance and ethanol production of strain KF-7. In particular, the overexpression of ENA5 significantly enhanced the multiple stress-tolerance of not only KF-7 but also E-158. The resulting engineered strain, E-158-ENA5, achieved higher accumulation of ethanol. The ethanol concentrations were 101.67% and 27.31% higher than those of the E-158 when YPD media and industrial feedstocks (straw, molasses, cassava) were fermented, respectively, under stress conditions.

CONCLUSION

Six genes that could be used as the gene targets to improve multiple stress-tolerance and ethanol production capacities of S. cerevisiae were identified for the first time. Compared to the other five DEGs, ENA5 has a more vital function in regulating the multiple stress-tolerance of S. cerevisiae. These findings provide novel insights into the efficient construction of multiple stress-tolerant industrial S. cerevisiae suitable for the fermentation of different raw materials.

Comparative transcriptomic and metabolic profiling provides insight into the mechanism by which the autophagy inhibitor 3-MA enhances salt stress sensitivity in wheat seedlings

BACKGROUND

Salt stress hinders plant growth and production around the world. Autophagy induced by salt stress helps plants improve their adaptability to salt stress. However, the underlying mechanism behind this adaptability remains unclear. To obtain deeper insight into this phenomenon, combined metabolomics and transcriptomics analyses were used to explore the coexpression of differentially expressed-metabolite (DEM) and gene (DEG) between control and salt-stressed wheat roots and leaves in the presence or absence of the added autophagy inhibitor 3-methyladenine (3-MA).

RESULTS

The results indicated that 3-MA addition inhibited autophagy, increased ROS accumulation, damaged photosynthesis apparatus and impaired the tolerance of wheat seedlings to NaCl stress. A total of 14,759 DEGs and 554 DEMs in roots and leaves of wheat seedlings were induced by salt stress. DEGs were predominantly enriched in cellular amino acid catabolic process, response to external biotic stimulus, regulation of the response to salt stress, reactive oxygen species (ROS) biosynthetic process, regulation of response to osmotic stress, ect. The DEMs were mostly associated with amino acid metabolism, carbohydrate metabolism, phenylalanine metabolism, carbapenem biosynthesis, and pantothenate and CoA biosynthesis. Further analysis identified some critical genes (gene involved in the oxidative stress response, gene encoding transcription factor (TF) and gene involved in the synthesis of metabolite such as alanine, asparagine, aspartate, glutamate, glutamine, 4-aminobutyric acid, abscisic acid, jasmonic acid, ect.) that potentially participated in a complex regulatory network in the wheat response to NaCl stress. The expression of the upregulated DEGs and DEMs were higher, and the expression of the down-regulated DEGs and DEMs was lower in 3-MA-treated plants under NaCl treatment.

CONCLUSION

3-MA enhanced the salt stress sensitivity of wheat seedlings by inhibiting the activity of the roots and leaves, inhibiting autophagy in the roots and leaves, increasing the content of both H₂O₂ and O₂•-, damaged photosynthesis apparatus and changing the transcriptome and metabolome of salt-stressed wheat seedlings.

Tolerance against butanol stress by disrupting succinylglutamate desuccinylase inEscherichia coli

The four-carbon alcohol, butanol, is emerging as a promising biofuel and efforts have been undertaken to improve several microbial hosts for its production.