Mechanism of Thioesterase-Catalyzed Chain Release in the Biosynthesis of the Polyether Antibiotic Nanchangmycin

SummaryThe polyketide backbone of the polyether ionophore antibiotic nanchangmycin (1) is assembled by a modular polyketide synthase in Streptomyces nanchangensis NS3226. The ACP-bound polyketide is thought to undergo a cascade of oxidative cyclizations to generate the characteristic polyether. Deletion of the glycosyl transferase gene nanG5 resulted in accumulation of the corresponding nanchangmycin aglycone (6). The discrete thioesterase NanE exhibited a nearly 17-fold preference for hydrolysis of 4, the N-acetylcysteamine (SNAC) thioester of nanchangmycin, over 7, the corresponding SNAC derivative of the aglycone, consistent with NanE-catalyzed hydrolysis of ACP-bound nanchangmycin being the final step in the biosynthetic pathway. Site-directed mutagenesis established that Ser96, His261, and Asp120, the proposed components of the NanE catalytic triad, were all essential for thioesterase activity, while Trp97 was shown to influence the preference for polyether over polyketide substrates

Lipid engineering combined with systematic metabolic engineering of <i>Saccharomyces cerevisiae</i> for high-yield production of lycopene

Saccharomyces cerevisiae is an efficient host for natural-compound production and preferentially employed in academic studies and bioindustries. However, S. cerevisiae exhibits limited production capacity for lipophilic natural products, especially compounds that accumulate intracellularly, such as polyketides and carotenoids, with some engineered compounds displaying cytotoxicity. In this study, we used a nature-inspired strategy to establish an effective platform to improve lipid oil–triacylglycerol (TAG) metabolism and enable increased lycopene accumulation. Through systematic traditional engineering methods, we achieved relatively high-level production at 56.2 mg lycopene/g cell dry weight (cdw). To focus on TAG metabolism in order to increase lycopene accumulation, we overexpressed key genes associated with fatty acid synthesis and TAG production, followed by modulation of TAG fatty acyl composition by overexpressing a fatty acid desaturase (OLE1) and deletion of Seipin (FLD1), which regulates lipid-droplet size. Results showed that the engineered strain produced 70.5 mg lycopene/g cdw, a 25% increase relative to the original high-yield strain, with lycopene production reaching 2.37 g/L and 73.3 mg/g cdw in fed-batch fermentation and representing the highest lycopene yield in S. cerevisiae reported to date. These findings offer an effective strategy for extended systematic metabolic engineering through lipid engineering

A Comparative Study of Systolic and Diastolic Mechanical Synchrony in Canine, Primate, and Healthy and Failing Human Hearts.

Aim: Mechanical dyssynchrony (MD) is associated with heart failure (HF) and may be prognostically important in cardiac resynchronization therapy (CRT). Yet, little is known about its patterns in healthy or diseased hearts. We here investigate and compare systolic and diastolic MD in both right (RV) and left ventricles (LV) of canine, primate and healthy and failing human hearts. Methods and Results: RV and LV mechanical function were examined by pulse-wave Doppler in 15 beagle dogs, 59 rhesus monkeys, 100 healthy human subjects and 39 heart failure (HF) patients. This measured RV and LV pre-ejection periods (RVPEP and LVPEP) and diastolic opening times (Q-TVE and Q-MVE). The occurrence of right (RVMDs) and left ventricular systolic mechanical delay (LVMDs) was assessed by comparing RVPEP and LVPEP values. That of right (RVMDd) and left ventricular diastolic mechanical delay (LVMDd) was assessed from the corresponding diastolic opening times (Q-TVE and Q-MVE). These situations were quantified by values of interventricular systolic (IVMDs) and diastolic mechanical delays (IVMDd), represented as positive if the relevant RV mechanical events preceded those in the LV. Healthy hearts in all species examined showed greater LV than RV delay times and therefore positive IVMDs and IVMDd. In contrast a greater proportion of the HF patients showed both markedly increased IVMDs and negative IVMDd, with diastolic mechanical asynchrony negatively correlated with LVEF. Conclusion: The present IVMDs and IVMDd findings have potential clinical implications particularly for personalized setting of parameter values in CRT in individual patients to achieve effective treatment of HF

Recent advances in the elucidation of enzymatic function in natural product biosynthesis [version 1; referees: 2 approved]

With the successful production of artemisinic acid in yeast, the promising potential of synthetic biology for natural product biosynthesis is now being realized. The recent total biosynthesis of opioids in microbes is considered to be another landmark in this field. The importance and significance of enzymes in natural product biosynthetic pathways have been re-emphasized by these advancements. Therefore, the characterization and elucidation of enzymatic function in natural product biosynthesis are undoubtedly fundamental for the development of new drugs and the heterologous biosynthesis of active natural products. Here, discoveries regarding enzymatic function in natural product biosynthesis over the past year are briefly reviewed

Recent advances in the elucidation of enzymatic function in natural product biosynthesis [version 2; referees: 2 approved]

With the successful production of artemisinic acid in yeast, the promising potential of synthetic biology for natural product biosynthesis is now being realized. The recent total biosynthesis of opioids in microbes is considered to be another landmark in this field. The importance and significance of enzymes in natural product biosynthetic pathways have been re-emphasized by these advancements. Therefore, the characterization and elucidation of enzymatic function in natural product biosynthesis are undoubtedly fundamental for the development of new drugs and the heterologous biosynthesis of active natural products. Here, discoveries regarding enzymatic function in natural product biosynthesis over the past year are briefly reviewed

Simulation Study of New Buckling Restraint Bracing Based on Bayesian Optimization Algorithm to Identify the Parameters of Modified Bouc–Wen Model

The introduction of new materials can significantly improve the performance of existing dampers, but the constitutive model of new materials is difficult to be obtained in a short time, which makes its numerical simulation difficult. Bouc–Wen–Baber–Noori (BWBN) model can well describe the stress-strain relationship of materials, and it is helpful to introduce new materials into numerical simulation. In this paper, a new high manganese steel material is used to fabricate buckling restrained brace, and based on the improved Transition Markov Chain Monte Carlo (iTMCMC) sampling method and Bayesian reasoning, a new parameter identification of BWBN model is completed; model parameters are introduced into OpenSees to complete the response analysis of the new steel damper in the structure. It provides a new method and approach for introducing new materials into structural seismic resistance and proves its reliability

MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area

To improve the poor accuracy of the MODIS (Moderate Resolution Imaging Spectroradiometer) daily fractional snow cover product over the complex terrain of the Tibetan Plateau (RMSE = 0.30), unmanned aerial vehicle and machine learning technologies are employed to map the fractional snow cover based on MODIS over this terrain. Three machine learning models, including random forest, support vector machine, and back-propagation artificial neural network models, are trained and compared in this study. The results indicate that compared with the MODIS daily fractional snow cover product, the introduction of a highly accurate snow map acquired by unmanned aerial vehicles as a reference into machine learning models can significantly improve the MODIS fractional snow cover mapping accuracy. The random forest model shows the best accuracy among the three machine learning models, with an RMSE (root-mean-square error) of 0.23, especially over forestland and shrubland, with RMSEs of 0.13 and 0.18, respectively. Although the accuracy of the support vector machine and back-propagation artificial neural network models are worse over forestland and shrubland, their average errors are still better than that of MOD10A1. Different fractional snow cover gradients also affect the accuracy of the machine learning algorithms. Nevertheless, the random forest model remains stable in different fractional snow cover gradients and is, therefore, the best machine learning algorithm for MODIS fractional snow cover mapping in Tibetan Plateau areas with complex terrain and severely fragmented snow cover