Yi Qin

De novo biosynthesis of (−)-limonene, (−)-carveol and (−)-carvone was achieved via rewiring the mevalonate pathway in the oleaginous yeast Rhodotorula toruloides. Strategies including enzyme selection, protein fusion, pathway optimization (MVA upstream enhancement, cofactor supply), and fermentation condition optimization were applied to improve the monoterpenoids production. Finally, the (−)-limonene titer of 1.6 g/L and (−)-carveol of 668.0 mg/L were achieved through fed-batch fermentation in a 3-L bioreactor. Furthermore, the feasibility of biosynthesis of (−)-carvone was also demonstrated. This study represents an example for de novo microbial production of (−)-limonene, (−)-carveol and (−)-carvone with the oleaginous red yeast R. toruloides, demonstrating its potential as a versatile chassis for monoterpenoid biosynthesis.

Higher alcohols are major volatile metabolites produced by Saccharomyces cerevisiae . They are traditionally regarded as end products of the Ehrlich pathway. Growing evidence indicates that higher alcohol formation represents a systemic phenotype shaped collectively by nutrient sensing, amino acid uptake, precursor partitioning, carbon source reprogramming, respiratory metabolism, redox homeostasis, and end-product stress. Therefore, higher alcohols serve not only as key aroma compounds, but also as a model system to investigate how yeast integrates environmental cues, metabolism and cellular homeostasis. This review dissects the transcriptional networks regulating higher alcohol biosynthesis. It summarizes nitrogen catabolite repressors Gln3p/Gat1p and Dal80p/Gzf3p; amino acid sensor proteins Stp1p/Stp2p; aromatic branch activator Aro80p; and regulatory proteins involved in precursor supply and metabolic reprogramming, including Leu3p, Gcn4p, Cat8p, Adr1p, Rds2p, Ert1p and the Hap complex. It further discusses the indirect effects of Pdr1p/Pdr3p, Msn2p/Msn4p, Yap1p, Haa1p and War1p via modulation of membrane integrity, redox balance and stress tolerance. Four functional modules are proposed here: substrate input, precursor supply, Ehrlich pathway output, and tolerance maintenance. Three distinct action modes are distinguished: direct pathway activation, precursor remodeling, and high-yield state preservation. This framework explains the condition-dependence and strain specificity of higher alcohol phenotypes. It provides theoretical guidance for subsequent time-resolved omics, chromatin and metabolic flux analyses.

The grape and wine industry faces prolonged barrel aging, microbial spoilage, protein haze, chemical additives, and inefficient by‐product valorization. These challenges motivate nonthermal technologies that reduce chemical inputs. Ultrasound operates in two mechanistically distinct regimes: sensing, based on acoustic propagation, reflection, and echo detection, and processing, driven mainly by acoustic cavitation and its physical and chemical effects, including microjets, shock waves, localized hotspots, and reactive radicals. These complementary modes provide a versatile physical platform. This review covers ultrasound across the grape‐wine continuum: acoustic imaging for precision viticulture; postharvest preservation with green activated water; green extraction enhanced by natural deep eutectic solvents; grape protein modification for ingredient assembly; modulation of wine color, aroma, and colloidal stability; selected aging‐associated reactions involving 1‐hydroxyethyl radicals; selective microbial inactivation and fermentation modulation; and scale‐up based on energy characterization, continuous‐flow reactors, and data‐driven modeling. Current evidence indicates that ultrasound is evolving from an empirically optimized unit operation toward a mechanistically informed and quantitatively characterized physical‐field platform. Reported effects include accelerated aging‐related transformations, modified protein structure and stability, and selective microbial responses, although they depend on treatment conditions and matrix properties. Absorbed energy density supports quantitative comparison of treatments, but reliable scale‐up also requires acoustic‐field distribution, residence time, temperature, and reactor geometry. Future progress will require standardized energy reporting, pilot‐ and continuous‐flow validation, physics‐informed data‐driven modeling, and integration of process analytical technologies with closed‐loop control. These advances would support more reproducible, scalable, and sustainable ultrasound processing in the grape and wine sector.

Microbiomes influence tea quality, safety, and health from production to consumption, yet most studies and reviews examine individual stages. This review uses the Tea Plant‐Fermentation‐Gut Continuum to assess cross‐stage microbial and metabolic connectivity, not as an established transmission chain. It separates microbial origin and persistence from processing‐associated chemical transformation and host responses. At the leaf‐to‐processing interface, phyllosphere and endophytic communities may contribute to the processing microbial pool, but taxonomic overlap does not demonstrate directional transmission, and strain‐level persistence remains unproven. During processing, plant enzymes and microbial activities shape tea chemistry differently across tea types, affecting pigment and aroma development. At the processing‐to‐gut interface, current evidence mainly supports chemical and metabolic continuity: tea‐ and fermentation‐derived compounds reach the gut and are metabolized by resident microbiota, whereas evidence that fermentation‐associated microorganisms survive, persist, or colonize the gut remains insufficient. These metabolites are linked to FXR‐FGF15, AMPK, and AhR signaling and to metabolic, anti‐inflammatory, and neuroprotective effects, mostly in preclinical models; human evidence remains limited. Together, the evidence does not support an uninterrupted microbial transmission pathway across these stages. Key gaps remain in microbial origin, persistence, activity, causality, and host relevance. Shared taxa may reflect persistence, environmental acquisition, or compositional overlap rather than direct transmission; tea type, cultivar, processing, dose, and host microbiota limit generalization. Strain‐resolved tracking, controlled experiments, functional omics, and metabolic tracing are needed to distinguish association from persistence, activity, and causal function. Addressing these gaps will support evidence‐based tea processing, precision fermentation, and microbiome‐informed nutrition.

Hanseniaspora uvarum is a representative non- Saccharomyces species that plays a significant role in fermentation processes such as winemaking. In recent years, this species has gained attention in food engineering and evolutionary biology. However, the population genomic signatures in this species remain poorly understood. In this study, a population genomics analysis was conducted on 151 H. uvarum strains (45 from Ningxia, China; 21 from other regions of China; 67 from Australia; and 18 from other regions or of unspecified origin), and a pangenome analysis was performed on 159 strains, incorporating eight additional genome assemblies. Phylogenetic analysis, ancestry coefficient analysis, and principal component analysis generally distinguished Chinese strains from those sampled on other continents. However, substantial post-divergence gene flow and introgression were inferred between intercontinentally paired clades. Positively selected candidate genes exhibited region-specific patterns: GO terms related to the positive regulation of filamentous growth in response to external stimuli were significantly enriched in the Ningxia strains; the stress-related GO term “cytoplasmic stress granule” was significantly enriched in both the Ningxia and Australian strains, but with distinct sets of associated genes. Although the samples were primarily isolated from anthropogenic environments, H. uvarum exhibited an open pangenome, indicating substantial adaptive potential to diverse stresses. This study advances our understanding of the evolutionary dynamics of H. uvarum and establishes a genomic foundation for future ecological and industrial research on this yeast.

Machine learning is transforming the grape and wine industry by shifting traditional experience‐driven practices toward data‐driven and intelligent decision making across the entire value chain. This review provides a conceptually driven synthesis of machine learning‐enabled technologies spanning vineyard sensing, precision viticulture, fermentation monitoring, and winemaking optimization. An integrated analytical framework is proposed, linking multisource data acquisition, preprocessing and representation learning, model development, and intelligent decision making into a unified end‐to‐end pipeline. Building on this framework, the review critically examines advances related to five key scientific challenges: multimodal data integration, model interpretability, cross‐domain generalization, whole‐chain decision coordination, and scalable industrial deployment. Particular emphasis is placed on the mechanisms and trade‐offs of multiscale sensing technologies (including spectroscopy, chromatography–mass spectrometry, imaging, and electronic sensing), as well as data preprocessing, feature engineering, and multimodal fusion strategies. A task‐oriented and data‐structured perspective on model selection is highlighted, covering linear models, kernel methods, ensemble learning, deep neural networks, and probabilistic frameworks, alongside evaluation protocols and interpretability approaches. In contrast to fragmented task‐specific studies, this review highlights the importance of cross‐stage integration and closed‐loop decision systems linking vineyard management with downstream vinification and quality evaluation. Despite rapid progress, key challenges remain, including data scarcity and heterogeneity, limited model transferability, and high implementation costs. Emerging directions such as knowledge‐guided machine learning, causal inference, small‐sample learning, and human–AI collaboration are expected to enhance robustness and interpretability. Overall, this review provides a structured roadmap for advancing intelligent and sustainable practices in the grape and wine industry.

Fengxiang Taibai baijiu (FTB) fermentation involves complex microbial interactions that critically influence aroma formation; however, the efficient selection of high ester‐producing yeasts and the prediction of their fermentation performance remain challenging. In this study, yeasts were systematically isolated from different stages of FTB fermentation and evaluated using a combination of fermentation experiments and machine learning approaches. A total of 156 strains were obtained, among which seven Wickerhamomyces anomalus strains exhibited high ester‐producing capacity (> 1.15 g/L). Among them, strain F12D‐38 demonstrated the strongest tolerance and aroma‐producing potential under ethanol–acetic acid co‐stress conditions. An XGBoost model achieved high predictive accuracy ( R ² = 0.89) and was applied to guide strain screening and process optimization, identifying an optimal inoculation range of 14%–16% (v/v). Fermentation validation showed that five strains outperformed the traditional control, with F12D‐38 achieving an approximately 15.0% increase in total volatile compounds. Overall, this study demonstrates that integrating machine learning with experimental validation enables efficient strain selection and fermentation optimization, providing a practical strategy for improving aroma quality and process control in FTB production.

This study aimed to modify metabolite synthesis in Saccharomyces cerevisiae (S. cerevisiae) under simulated wine fermentation conditions by regulating the glycerol metabolic pathway. We systematically analyzed the effects of overexpressing the aquaporin gene AQY1 and co-expressing AQY1 with the glycerol-3-phosphate dehydrogenase gene GPD1 on the metabolism of ethanol, higher alcohols, and esters. Our results indicate that AQY1 overexpression increased glycerol yield by 6.58%, reduced higher alcohol content by 14.60%, and elevated ester content by 7.15%. The downregulation of related amino acid metabolism genes correlated with the observed decrease in higher alcohol levels. Notably, co-expression of AQY1 and GPD1 further enhanced glycerol yield by 10.66% while decreasing ethanol content by 6.32%. By analyzing changes in gene expression alongside metabolic mechanisms, we hypothesize that the redistribution of carbon flux and NADH toward the glycerol pathway not only decreases the precursors for ethanol synthesis but also directly inhibits the activity of aldehyde dehydrogenase (ALD2/3/4/6), thereby constraining ethanol production. In comparison to AQY1 overexpression alone, the co-expression strategy did not significantly alter glycerol accumulation; however, it reduced both ethanol and ester content by 8.38% and 8.40%, respectively, while markedly increasing higher alcohol content by 22.30%. This increase may result from enhanced glycolytic flux and pyruvate accumulation, which promote metabolic flow toward amino acid synthesis pathways. In summary, this study effectively remodeled the central carbon metabolism network by targeting glycerol metabolism, achieving diverse metabolic product synthesis and providing important references for the selection and breeding of industrial S. cerevisiae strains.

The flavor complexity of mead is influenced by various factors, including the geographic origin of honey, botanical sources, fermentation processes, and the addition of fruits and herbs. Chemical characteristics of mead are regulated by factors such as honey origin, yeast metabolism, latitude, climate, light intensity, and fermentation parameters, which collectively affect the formation of volatile and nonvolatile compounds. This review examines the chemical profiling of mead, with a particular focus on detecting volatile compounds. Advanced analytical techniques like HS–SPME–GC–MS, RP–UHPLC–ESI–MS, and GC × GC–HRMS are employed to detect and quantify these compounds. Quantitative methods, such as the internal standard method and standard addition method, effectively mitigate interference from high‐sugar matrices in mead, ensuring accurate results. The flavor characteristics of mead, especially pure mead, are primarily determined by the choice of honey, typically exhibiting floral, fruity, and sweet honey flavors. European meads are marked by phenolic compounds and fruity esters, Asian meads are rich in terpenes and phenolic compounds, North American and South American meads are characterized by methoxy‐aromatic compounds and lactones, while African meads contain unique ester and sulfur compounds. Despite challenges like the lack of standardized methods and regional imbalances in research, standardizing methods and integrating “omics” technologies will help uncover regional characteristics in mead production and promote its global recognition and development.