High titer production of gastrodin enabled by systematic refactoring of yeast genome and an antisense-transcriptional regulation toolkit.

Gastrodin, a phenolic glycoside, is a prominent component of Gastrodia elata, which is renowned for its sedative, hypnotic, anticonvulsant, and neuroprotective activities. Engineering heterologous production of plant natural products in microbial host represents a safe, cost-effective, and scalable alternative to plant extraction. Here, we present the construction of an engineered Yarrowia lipolytica yeast that achieves a high-titer production of gastrodin. We systematically refactored the yeast genome by enhancing the flux of the shikimate pathway and optimizing the glucosyl transfer system. We introduced more than five dozen of genetic modifications onto the yeast genome, including enzyme screening, alleviation of rate-limiting steps, promoter selection, genomic integration site optimization, downregulation of competing pathways, and elimination of gastrodin degradation. Meanwhile, we developed a Copper-induced Antisense-Transcriptional Regulation (CATR) tool. The developed CATR toolkit achieved dynamic repression and activation of violacein synthesis through the addition of copper in Y. lipolytica. This strategy was further used to dynamically regulate the pyruvate kinase node to effectively redirect glycolytic flux towards the shikimate pathway while maintaining cell growth at proper rate. Taken together, these efforts resulted in 9477.1 mg/L of gastrodin in shaking flaks and 13.4 g/L of gastrodin with a yield of 0.149 g/g glucose in a 5-L bioreactor, highlighting the potential for large-scale and sustainable production of gastrodin from microbial fermentation.

Optimization and multi-uncertainty analysis of best management practices at the watershed scale: A reliability-level based bayesian network approach.

Best management practices (BMPs) have been widely adopted to mitigate diffuse source pollutants, and the simulated processes of its pollutant reduction effectiveness suffer from manifold uncertainties, such as watershed model parameters and climate change. We presented a novel Bayesian modeling framework for BMPs planning, integrating process-based watershed modeling and Bayesian optimization algorithm to reveal the impact of multiple uncertainties. The proposed framework was applied to a BMPs planning case study in the Erhai watershed, the seventh-largest freshwater lake in China. Firstly, priority management areas (PMAs) were identified for BMPs siting using a simulation-optimization approach. Bayesian networks were subsequently embedded to reveal the multiple uncertainty sources in the optimal planning and the reliability level (RL) is introduced to represent the probability to meet the water quality target with BMPs implementation. The results suggest that ENS of discharge and nutrients concentration simulation by LSPC are both greater than 0.5, which displays satisfactory performance. The identified PMAs account for 0.8% of the total watershed areas while contribute to more than 15% of nutrient loadings reduction. The analysis of multiple uncertainty sources reveals that precipitation is the most influential source of uncertainties in BMP effectiveness. The construction of hedgerows plays an important role in the nutrient reduction. With the improvement of the reliability levels, the cost increases sharply, indicating that the implementation of BMPs has a marginal utility. The study addressed the urgent need for effective and efficient BMPs planning by identifying PMAs and addressing multi-source uncertainties.

Attention biases the process of risky decision-making: Evidence from eye-tracking.

Attention determines what kind of option information is processed during risky choices owing to the limitation of visual attention. This paper reviews research on the relationship between higher-complexity risky decision-making and attention as illustrated by eye-tracking to explain the process of risky decision-making by the effect of attention. We demonstrate this process from three stages: the pre-phase guidance of options on attention, the process of attention being biased, and the impact of attention on final risk preference. We conclude that exogenous information can capture attention directly to salient options, thereby altering evidence accumulation. In particular, for multi-attribute risky decision-making, attentional advantages increase the weight of specific attributes, thus biasing risk preference in different directions. We highlight the significance of understanding how people use available information to weigh risks from an information-processing perspective via process data.

Advances and perspectives in genetic expression and operation for the oleaginous yeast Yarrowia lipolytica.

The utilization of industrial biomanufacturing has emerged as a viable and sustainable alternative to fossil-based resources for producing functional chemicals. Moreover, advancements in synthetic biology have created new opportunities for the development of innovative cell factories. Notably, Yarrowia lipolytica, an oleaginous yeast that is generally regarded as safe, possesses several advantageous characteristics, including the ability to utilize inexpensive renewable carbon sources, well-established genetic backgrounds, and mature genetic manipulation methods. Consequently, there is increasing interest in manipulating the metabolism of this yeast to enhance its potential as a biomanufacturing platform. Here, we reviewed the latest developments in genetic expression strategies and manipulation tools related to Y. lipolytica, particularly focusing on gene expression, chromosomal operation, CRISPR-based tool, and dynamic biosensors. The purpose of this review is to serve as a valuable reference for those interested in the development of a Y. lipolytica microbial factory.

Internal positive feedback promotes water quality improvement for a recovering hyper-eutrophic lake: A three-dimensional nutrient flux tracking model.

Nutrient dynamics in lakes are determined by the combined effects of external and internal nutrient fluxes. However, the feedback loop of nutrient fluxes and water quality changes is still an open question. An integrated three-dimensional flux tracking approach based on the Environmental Fluid Dynamics Code model was established to quantify the long-term dynamic changes in external and internal processes in Lake Dianchi (one of the three most hyper-eutrophic lakes in China), and to explore the potential causes of water quality improvement during 2012-2018. The long-term trends and relative influences of nutrient fluxes on water quality were identified. The results showed that the inflow flux was the largest input source and declined by 50% between 2003 and 2012, which was followed by a stable trend from 2012 to 2018. The second largest input source was benthic release, which exhibited a significant interannual reduction. Algae sedimentation was the largest removal process, and declined by 45% between 2012 and 2018. An integrated analysis demonstrated that, following an external loading reduction prior to 2012, the positive feedback of internal fluxes promoted water quality improvement during 2012-2018. Considering the long timescale of the nutrient-flux feedback mechanism, reducing external loading is still the top priority for a long-term virtuous cycle of water quality in the process of eutrophic lake restoration.