Current state of aromatics production using yeast: achievements and challenges

Aromatics find a range of applications in the chemical, food, cosmetic and pharmaceutical industries. While production of aromatics on the current market heavily relies on petroleum-derived chemical processes or direct extraction from plants, there is an increasing demand for establishing new renewable and sustainable sources of aromatics. To this end, microbial cell factories-mediated bioproduction using abundant feedstocks comprises a highly promising alternative to aromatics production. In this review, we provide the recent development of de novo biosynthesis of aromatics derived from the shikimate pathway in yeasts, including the model Saccharomyces cerevisiae as well as other non-conventional species. Moreover, we discuss how evolved metabolic engineering tools and strategies contribute to the construction and optimization of aromatics cell factories

DALNet: A Rail Detection Network Based on Dynamic Anchor Line

Rail detection is one of the key factors for intelligent train. In the paper, motivated by the anchor line-based lane detection methods, we propose a rail detection network called DALNet based on dynamic anchor line. Aiming to solve the problem that the predefined anchor line is image agnostic, we design a novel dynamic anchor line mechanism. It utilizes a dynamic anchor line generator to dynamically generate an appropriate anchor line for each rail instance based on the position and shape of the rails in the input image. These dynamically generated anchor lines can be considered as better position references to accurately localize the rails than the predefined anchor lines. In addition, we present a challenging urban rail detection dataset DL-Rail with high-quality annotations and scenario diversity. DL-Rail contains 7000 pairs of images and annotations along with scene tags, and it is expected to encourage the development of rail detection. We extensively compare DALNet with many competitive lane methods. The results show that our DALNet achieves state-of-the-art performance on our DL-Rail rail detection dataset and the popular Tusimple and LLAMAS lane detection benchmarks. The code will be released at https://github.com/Yzichen/mmLaneDet

De novo biosynthesis of bioactive isoflavonoids by engineered yeast cell factories

Isoflavonoids comprise a class of plant natural products with great nutraceutical, pharmaceutical and agricultural significance. Their low abundance in nature and structural complexity however hampers access to these phytochemicals through traditional crop-based manufacturing or chemical synthesis. Microbial bioproduction therefore represents an attractive alternative. Here, we engineer the metabolism of Saccharomyces cerevisiae to become a platform for efficient production of daidzein, a core chemical scaffold for isoflavonoid biosynthesis, and demonstrate its application towards producing bioactive glucosides from glucose, following the screening-reconstruction-application engineering framework. First, we rebuild daidzein biosynthesis in yeast and its production is then improved by 94-fold through screening biosynthetic enzymes, identifying rate-limiting steps, implementing dynamic control, engineering substrate trafficking and fine-tuning competing metabolic processes. The optimized strain produces up to 85.4 mg L−1 of daidzein and introducing plant glycosyltransferases in this strain results in production of bioactive puerarin (72.8 mg L−1) and daidzin (73.2 mg L−1). Our work provides a promising step towards developing synthetic yeast cell factories for de novo biosynthesis of value-added isoflavonoids and the multi-phased framework may be extended to engineer pathways of complex natural products in other microbial hosts

Metabolic reconfiguration enables synthetic reductive metabolism in yeast

Cell proliferation requires the integration of catabolic processes to provide energy, redox power and biosynthetic precursors. Here we show how the combination of rational design, metabolic rewiring and recombinant expression enables the establishment of a decarboxylation cycle in the yeast cytoplasm. This metabolic cycle can support growth by supplying energy and increased provision of NADPH or NADH in the cytosol, which can support the production of highly reduced chemicals such as glycerol, succinate and free fatty acids. With this approach, free fatty acid yield reached 40% of theoretical yield, which is the highest yield reported for Saccharomyces cerevisiae to our knowledge. This study reports the implementation of a synthetic decarboxylation cycle in the yeast cytosol, and its application in achieving high yields of valuable chemicals in cell factories. Our study also shows that, despite extensive regulation of catabolism in yeast, it is possible to rewire the energy metabolism, illustrating the power of biodesign

6-Gingerol attenuates sepsis-induced acute lung injury by suppressing NLRP3 inflammasome through Nrf2 activation

Introduction. Sepsis is characterized by an inappropriate inflammatory response. Acute lung injury (ALI) is the most common type of organ injury in sepsis, with high morbidity and mortality. 6-Gingerol is the main bioactive compound of ginger, and it possesses anti-inflammatory bioactivity in different diseases. This study is aimed to explore the specific function of 6-Gingerol in sepsis-induced ALI. Material and methods. Lipopolysaccharide (LPS) was intraperitoneally injected into Sprague-Dawley rats for establishing the ALI models in vivo. The ALI rats were intraperitoneally injected with 20 mg/kg 6-Gingerol. The contents of oxidative stress markers malondialdehyde (MDA), glutathione (GSH), and superoxide dismutase (SOD) were detected in the lung tissues of ALI rats. The concentrations of inflammatory factors [Tumor Necrosis Factor alpha (TNF-α), interleukin (IL)-6, and IL-1β] were measured by ELISA. Inflammatory cell infiltration in bronchoalveolar lavage fluid (BALF) of rats was tested. Western blot was utilized to test the protein levels of nuclear factor erythroid 2-related factor (Nrf2) and heme oxygenase-1 (HO-1) in lung tissues. Furthermore, immunohistochemical staining was applied for testing the expression of NLRP3 inflammasome in lung tissues. Results. The pathological changes in ALI rats were characterized by increased accumulation of inflammatory cells, alveolar hemorrhage, and pulmonary interstitial edema. However, the degree of pathological injury of lung tissues was significantly improved after 6-Gingerol treatment. Additionally, 6-Gingerol significantly attenuated the lung wet/dry ratio and protein permeability index (PPI) of LPS-induced rats. Furthermore, 6-Gingerol repressed oxidative stress and inflammatory reaction in LPS-induced rats by reducing the contents of MDA, GSH, SOD, TNF-α, IL-6, and IL-1β in the lung. LPS-induced infiltration of eosinophils, macrophages, neutrophils, and lymphocytes into lung was suppressed by 6-Gingerol administration. Moreover, 6-Gingerol activated Nrf2/HO-1 signaling and repressed LPS-induced‑NLRP3 inflammasome expression in lung tissues of LPS-induced rats. Intraperitoneal injection of ML385 (Nrf2 inhibitor) treatment into rats reversed the effects of 6-Gingerol on lung injury, inflammation, and oxidative stress in LPS-subjected rats. Conclusions. 6-Gingerol attenuates sepsis-induced ALI by suppressing NLRP3 inflammasome activation through Nrf2 activation

Amygdalin isolated from Amygdalus mongolica protects against hepatic fibrosis in rats

The aim of this research was to investigate the effect of amygdalin on hepatic fibrosis in rats. Amygdalin was purified and identified from the seeds of Amygdalus mongolica. Sprague Dawley rats in the control and model groups were administered water. Sprague Dawley rats were divided into the low-, middle-, and high-dose amygdalin groups that received 20, 40, and 80 mg kg–1 amygdalin, respectively. whereas the silymarin group was treated with 50 mg kg–1 silymarin. The control and model groups were administered water. Liver tissue analysis revealed significantly lower activities of ALT, AST, ALP, SOD, and MDA in the drug-treated groups compared to the model group. Serum analysis revealed significantly lower HYC and C-IV in the middle-dose amygdalin-treated group compared to the model group. The histopathological changes were less severe in the drug-treated groups as observed by the formation of pseudolobuli and decreased collagen fiber deposition. Hepatic fibrosis-related genes were expressed at significantly lower levels in the amygdalin-treated groups than in the model group. Amygdalin from A. mongolica represents a therapeutic candidate for hepatic fibrosis prevention and treatment

Metabolic engineering of Saccharomyces cerevisiae for production of very long chain fatty acid-derived chemicals

Production of chemicals and biofuels through microbial fermentation is an economical and sustainable alternative for traditional chemical synthesis. Here we present the construction of a Saccharomyces cerevisiae platform strain for high-level production of very-long-chain fatty acid (VLCFA)-derived chemicals. Through rewiring the native fatty acid elongation system and implementing a heterologous Mycobacteria FAS I system, we establish an increased biosynthesis of VLCFAs in S. cerevisiae. VLCFAs can be selectively modified towards the fatty alcohol docosanol (C22H46O) by expressing a specific fatty acid reductase. Expression of this enzyme is shown to impair cell growth due to consumption of VLCFA-CoAs. We therefore implement a dynamic control strategy for separating cell growth from docosanol production. We successfully establish high-level and selective docosanol production of 83.5 mg l(-1) in yeast. This approach will provide a universal strategy towards the production of similar high value chemicals in a more scalable, stable and sustainable manner

Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network

Background and Purpose: The risk of recurrent stroke following a transient ischemic attack (TIA) or minor stroke is high, despite of a significant reduction in the past decade. In this study, we investigated the feasibility of using artificial neural network (ANN) for risk stratification of TIA or minor stroke patients.Methods: Consecutive patients with acute TIA or minor ischemic stroke presenting at a tertiary hospital during a 2-year period were recruited. We collected demographics, clinical and imaging data at baseline. The primary outcome was recurrent ischemic stroke within 1 year. We developed ANN models to predict the primary outcome. We randomly down-sampled patients without a primary outcome to 1:1 match with those with a primary outcome to mitigate data imbalance. We used a 5-fold cross-validation approach to train and test the ANN models to avoid overfitting. We employed 19 independent variables at baseline as the input neurons in the ANN models, using a learning algorithm based on backpropagation to minimize the loss function. We obtained the sensitivity, specificity, accuracy and the c statistic of each ANN model from the 5 rounds of cross-validation and compared that of support vector machine (SVM) and Naïve Bayes classifier in risk stratification of the patients.Results: A total of 451 acute TIA or minor stroke patients were enrolled. Forty (8.9%) patients had a recurrent ischemic stroke within 1 year. Another 40 patients were randomly selected from those with no recurrent stroke, so that data from 80 patients in total were used for 5 rounds of training and testing of ANN models. The median sensitivity, specificity, accuracy and c statistic of the ANN models to predict recurrent stroke at 1 year was 75%, 75%, 75%, and 0.77, respectively. ANN model outperformed SVM and Naïve Bayes classifier in our dataset for predicting relapse after TIA or minor stroke.Conclusion: This pilot study indicated that ANN may yield a novel and effective method in risk stratification of TIA and minor stroke. Further studies are warranted for verification and improvement of the current ANN model

Ultra-rare genetic variation in common epilepsies: a case-control sequencing study

BACKGROUND:Despite progress in understanding the genetics of rare epilepsies, the more common epilepsies have proven less amenable to traditional gene-discovery analyses. We aimed to assess the contribution of ultra-rare genetic variation to common epilepsies. METHODS:We did a case-control sequencing study with exome sequence data from unrelated individuals clinically evaluated for one of the two most common epilepsy syndromes: familial genetic generalised epilepsy, or familial or sporadic non-acquired focal epilepsy. Individuals of any age were recruited between Nov 26, 2007, and Aug 2, 2013, through the multicentre Epilepsy Phenome/Genome Project and Epi4K collaborations, and samples were sequenced at the Institute for Genomic Medicine (New York, USA) between Feb 6, 2013, and Aug 18, 2015. To identify epilepsy risk signals, we tested all protein-coding genes for an excess of ultra-rare genetic variation among the cases, compared with control samples with no known epilepsy or epilepsy comorbidity sequenced through unrelated studies. FINDINGS:We separately compared the sequence data from 640 individuals with familial genetic generalised epilepsy and 525 individuals with familial non-acquired focal epilepsy to the same group of 3877 controls, and found significantly higher rates of ultra-rare deleterious variation in genes established as causative for dominant epilepsy disorders (familial genetic generalised epilepsy: odd ratio [OR] 2·3, 95% CI 1·7-3·2, p=9·1 × 10-8; familial non-acquired focal epilepsy 3·6, 2·7-4·9, p=1·1 × 10-17). Comparison of an additional cohort of 662 individuals with sporadic non-acquired focal epilepsy to controls did not identify study-wide significant signals. For the individuals with familial non-acquired focal epilepsy, we found that five known epilepsy genes ranked as the top five genes enriched for ultra-rare deleterious variation. After accounting for the control carrier rate, we estimate that these five genes contribute to the risk of epilepsy in approximately 8% of individuals with familial non-acquired focal epilepsy. Our analyses showed that no individual gene was significantly associated with familial genetic generalised epilepsy; however, known epilepsy genes had lower p values relative to the rest of the protein-coding genes (p=5·8 × 10-8) that were lower than expected from a random sampling of genes. INTERPRETATION:We identified excess ultra-rare variation in known epilepsy genes, which establishes a clear connection between the genetics of common and rare, severe epilepsies, and shows that the variants responsible for epilepsy risk are exceptionally rare in the general population. Our results suggest that the emerging paradigm of targeting of treatments to the genetic cause in rare devastating epilepsies might also extend to a proportion of common epilepsies. These findings might allow clinicians to broadly explain the cause of these syndromes to patients, and lay the foundation for possible precision treatments in the future. FUNDING:National Institute of Neurological Disorders and Stroke (NINDS), and Epilepsy Research UK