Transcriptome analysis of Kluyveromyces marxianus under succinic acid stress and development of robust strains.

Kluyveromyces marxianus has become an attractive non-conventional yeast cell factory due to its advantageous properties such as high thermal tolerance and rapid growth. Succinic acid (SA) is an important platform molecule that has been applied in various industries such as food, material, cosmetics, and pharmaceuticals. SA bioproduction may be compromised by its toxicity. Besides, metabolite-responsive promoters are known to be important for dynamic control of gene transcription. Therefore, studies on global gene transcription under various SA concentrations are of great importance. Here, comparative transcriptome changes of K. marxianus exposed to various concentrations of SA were analyzed. Enrichment and analysis of gene clusters revealed repression of the tricarboxylic acid cycle and glyoxylate cycle, also activation of the glycolysis pathway and genes related to ergosterol synthesis. Based on the analyses, potential SA-responsive promoters were investigated, among which the promoter strength of IMTCP2 and KLMA_50231 increased 43.4% and 154.7% in response to 15 g/L SA. In addition, overexpression of the transcription factors Gcr1, Upc2, and Ndt80 significantly increased growth under SA stress. Our results benefit understanding SA toxicity mechanisms and the development of robust yeast for organic acid production. KEY POINTS: • Global gene transcription of K. marxianus is changed by succinic acid (SA) • Promoter activities of IMTCP2 and KLMA_50123 are regulated by SA • Overexpression of Gcr1, Upc2, and Ndt80 enhanced SA tolerance.

[Advances on the production of organic acids by yeast].

Organic acids are organic compounds that can be synthesized using biological systems. They often contain one or more low molecular weight acidic groups, such as carboxyl group and sulphonic group. Organic acids are widely used in food, agriculture, medicine, bio-based materials industry and other fields. Yeast has unique advantages of biosafety, strong stress resistance, wide substrate spectrum, convenient genetic transformation, and mature large-scale culture technology. Therefore, it is appealing to produce organic acids by yeast. However, challenges such as low concentration, many by-products and low fermentation efficiency still exist. With the development of yeast metabolic engineering and synthetic biology technology, rapid progress has been made in this field recently. Here we summarize the progress of biosynthesis of 11 organic acids by yeast. These organic acids include bulk carboxylic acids and high-value organic acids that can be produced naturally or heterologously. Finally, future prospects in this field were proposed.

Engineering yeast cell factories to produce biodegradable plastics and their monomers: Current status and prospects.

Traditional plastic products have caused serious environmental pollution due to difficulty to be degraded in the natural environment. In the recent years, biodegradable plastics are receiving increasing attention due to advantages in natural degradability and environmental friendliness. Biodegradable plastics have potential to be used in food, agriculture, industry, medicine and other fields. However, the high production cost of such plastics is the bottleneck that limits their commercialization and application. Yeasts, including budding yeast and non-conventional yeasts, are widely studied to produce biodegradable plastics and their organic acid monomers. Compared to bacteria, yeast strains are more tolerable to multiple stress conditions including low pH and high temperature, and also have other advantages such as generally regarded as safe, and no phage infection. In addition, synthetic biology and metabolic engineering of yeast have enabled its rapid and efficient engineering for bioproduction using various renewable feedstocks, especially lignocellulosic biomass. This review focuses on the recent progress in biosynthesis technology and strategies of monomeric organic acids for biodegradable polymers, including polylactic acid (PLA), polyhydroxyalkanoate (PHA), polybutylene succinate (PBS), and polybutylene adipate terephthalate (PBAT) using yeast cell factories. Improving the performance of yeast as a cell factory and strategies to improve yeast acid stress tolerance are also discussed. In addition, the critical challenges and future prospects for the production of biodegradable plastic monomer using yeast are also discussed.

[Strain engineering and fermentation technology for production of long-chain dicarboxylic acid: a review].

Long-chain dicarboxylic acid (DCA), a building block for synthesizing a variety of high value-added chemicals, has been widely used in agriculture, chemical, and pharmaceutical industries. The global demand for DCA is increasing in recent years. Compared with chemical synthesis which requires harsh conditions and complicated processes, fermentative production of DCA has many unparalleled advantages, such as low cost and mild reaction conditions. In this review, we summarized the chemical and microbial synthesis methods for DCA and the commercialization status of the fermentation process. Moreover, the advances of using molecular and metabolic engineering to create high-yielding strains for efficient production of DCA were highlighted. Furthermore, the challenges remaining in the microbial fermentation process were also discussed. Finally, the perspectives for developing high titer DCA producing strains by synthetic biology were proposed.

EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss.

Applications based on electroencephalography (EEG) signals suffer from the mutual contradiction of high classification performance vs. low cost. The nature of this contradiction makes EEG signal reconstruction with high sampling rates and sensitivity challenging. Conventional reconstruction algorithms lead to loss of the representative details of brain activity and suffer from remaining artifacts because such algorithms only aim to minimize the temporal mean-squared-error (MSE) under generic penalties. Instead of using temporal MSE according to conventional mathematical models, this paper introduces a novel reconstruction algorithm based on generative adversarial networks with the Wasserstein distance (WGAN) and a temporal-spatial-frequency (TSF-MSE) loss function. The carefully designed TSF-MSE-based loss function reconstructs signals by computing the MSE from time-series features, common spatial pattern features, and power spectral density features. Promising reconstruction and classification results are obtained from three motor-related EEG signal datasets with different sampling rates and sensitivities. Our proposed method significantly improves classification performances of EEG signals reconstructions with the same sensitivity and the average classification accuracy improvements of EEG signals reconstruction with different sensitivities. By introducing the WGAN reconstruction model with TSF-MSE loss function, the proposed method is beneficial for the requirements of high classification performance and low cost and is convenient for the design of high-performance brain computer interface systems.