The modification of heme special importer to improve the production of active hemoglobins in Escherichia coli.

To enhance the import of heme for the production of active hemoproteins in Escherichia coli C41 (DE3) lacking the special heme import system, heme receptor ChuA from E. coli Nissle 1917 was modified through molecular docking and the other components (ChuTUV) for heme import was overexpressed, while heme import was tested through growth assay and heme sensor HS1 detection. A ChuA mutant G360K was selected, which could import 3.91 nM heme, compared with 2.92 nM of the wild-type ChuA. In addition, it presented that the expression of heme transporters ChuTUV was not necessary for heme import. Based on the modification of ChuA (G360K), the titer of human hemoglobin and the peroxidase activity of leghemoglobin reached 1.19 µg g-1 DCW and 24.16 103 U g-1 DCW, compared with 1.09 µg g-1 DCW and 21.56 103 U g-1 DCW of the wild-type ChuA, respectively. Heme import can be improved through the modification of heme receptor and the engineered strain with improved heme import has a potential to efficiently produce high-active hemoproteins.

De Novo Biosynthesis of Lutein in Yarrowia lipolytica.

Lutein is a high-value tetraterpenoid carotenoid that is widely used in feed, cosmetics, food, and drugs. Microbial synthesis of lutein is an important method for green and sustainable production, serving as an alternative to plant extraction methods. However, an inadequate precursor supply and low catalytic efficiency of key pathway enzymes are the main reasons for the low efficacy of microbial synthesis of lutein. In this study, some strategies, such as enhancing the MVA pathway and localizing α-carotene synthase OluLCY within the subcellular organelles in Yarrowia lipolytica, were adopted to enhance the synthesis of precursor α-carotene, which resulted in a 10.50-fold increase in α-carotene titer, reaching 38.50 mg/L. Subsequently, by improving hydroxylase activity with truncated N-terminal transport peptide and locating hydroxylases to subcellular organelles, the final strain L9 producing 75.25 mg/L lutein was obtained. Eventually, a lutein titer of 675.40 mg/L (6.13 mg/g DCW) was achieved in a 5 L bioreactor by adding the antioxidant 2,6-ditert-butyl-4-methylphenol. This study realizes de novo synthesis of lutein in Y. lipolytica for the first time and achieves the highest lutein titer reported so far.

Modular assembly of an artificially concise biocatalytic cascade for the manufacture of phenethylisoquinoline alkaloids.

Plant-derived alkaloids are an important class of pharmaceuticals. However, they still rely on phytoextraction to meet their diverse market demands. Since multistep biocatalytic cascades have begun to revolutionize the manufacture of natural or unnatural products, to address the synthetic challenges of alkaloids, herein we establish an artificially concise four-enzyme biocatalytic cascade with avoiding plant-derived P450 modification for synthesizing phenethylisoquinoline alkaloids (PEIAs) after enzyme discovery and enzyme engineering. Efficient biosynthesis of diverse natural and unnatural PEIAs is realized from readily available substrates. Most importantly, the scale-up preparation of the colchicine precursor (S)-autumnaline with a high titer is achieved after replacing the rate-limiting O-methylation by the plug-and-play strategy. This study not only streamlines future engineering endeavors for colchicine biosynthesis, but also provides a paradigm for constructing more artificial biocatalytic cascades for the manufacture of diverse alkaloids through synthetic biology.

Research on predicting the diffusion of toxic heavy gas sulfur dioxide by applying a hybrid deep learning model to real case data.

Toxic heavy gas sulfur dioxide (SO2) is a specific life and environmental hazard. Predicting the diffusion of SO2 has become a research focus in fields such as environmental and safety studies. However, traditional methods, such as kinetic models, cannot balance precision and time. Thus, they do not meet the needs of emergency decision-making. Deep learning (DL) models are emerging as a highly regarded solution, providing faster and more accurate predictions of gas concentrations. To this end, this study proposes an innovative hybrid DL model, the parallel-connected convolutional neural network-gated recurrent unit (PC CNN-GRU). This model utilizes two CNNs connected in parallel to process gas release and meteorological datasets, enabling the automatic extraction of high-dimensional data features and handling of long-term temporal dependencies through the GRU. The proposed model demonstrates good performance (RMSE, MAE, and R2 of 20.1658, 10.9158, and 0.9288, respectively) with real data from the Project Prairie Grass (PPG) case. Meanwhile, to address the issue of limited availability of raw data, in this study, time series generative adversarial network (TimeGAN) are introduced for SO2 diffusion studies for the first time, and their effectiveness is verified. To enhance the practicality of the research, the contribution of drivers to SO2 diffusion is quantified through the utilization of the permutation importance (PIMP) and Sobol' method. Additionally, the maximum safe distance downwind under various conditions is visualized based on the SO2 toxicity endpoint concentration. The results of the analyses can provide a scientific basis for relevant decisions and measures.

Intelligent identification of natural gas pipeline defects based on improved pollination algorithm.

As a natural gas pipeline approaches the end of its service life, the integrity of the pipeline starts failing because of corrosion or cracks. These and other defects affect the normal production and operation of the pipeline. Therefore, the identification of pipeline defects is critical to ensure the normal, safe, and efficient operation of these pipelines. In this study, a combination of adaptive adjustment based on conversion probability and Gaussian mutation strategy was used to improve the flower pollination algorithm (FPA) and enhance the search ability of traditional flower pollination. The adaptive adjustment of the transition probability effectively balances the development and exploration abilities of the algorithm. The improved flower pollination algorithm (IFPA) outperformed six classical benchmark functions that were used to verify the superiority of the improved algorithm. A Gaussian mutation strategy was integrated with IFPA to optimise the initial input weights and thresholds of the extreme learning machine (ELM), improve the balance and exploration ability of the algorithm, and increase the efficiency and accuracy for identifying pipeline defects. The proposed IFPA-ELM model for pipeline defect identification effectively overcomes the tendency of FPA to converge to local optima and that of ELM to engage in overfitting, which cause poor recognition accuracy. The identification rates of various pipeline defects by the IFPA-ELM algorithm are 97% and 96%, which are 34% and 13% higher, respectively, than those of FPA and FPA-ELM. The IFPA-ELM model may be used in the intelligent diagnosis of pipeline defects to solve practical engineering problems. Additionally, IFPA could be further optimised with respect to the time dimension, parameter settings, and general adaptation for application to complex engineering optimisation problems in various fields.

Visible-light organophotoredox-mediated intermolecular formal [4 + 2] cycloadditions of arylcyclobutylamines with olefins.

We have developed a visible-light-mediated formal [4 + 2] cycloaddition of arylcyclobutylamines with olefins, using QXPT-NPhCN as an organic photocatalyst. The corresponding cycloadducts could be obtained from electron-deficient olefins, aryl olefins and exocyclic olefins. We found that the addition of K3PO4 could significantly promote the cycloadditions. Using this method, 2-functionalized cyclohexylamines including the ones with spiro-skeletons can be expediently obtained. Based on the "3D-bioisostere" principle, we designed and synthesized three cyclohexylamine 2-sulfonylurea compounds.

Kluyveromyces as promising yeast cell factories for industrial bioproduction: From bio-functional design to applications.

As the two most widely used Kluyveromyces yeast, Kluyveromyces marxianus and K. lactis have gained increasing attention as microbial chassis in biocatalysts, biomanufacturing and the utilization of low-cost raw materials owing to their high suitability to these applications. However, due to slow progress in the development of molecular genetic manipulation tools and synthetic biology strategies, Kluyveromyces yeast cell factories as biological manufacturing platforms have not been fully developed. In this review, we provide a comprehensive overview of the attractive characteristics and applications of Kluyveromyces cell factories, with special emphasis on the development of molecular genetic manipulation tools and systems engineering strategies for synthetic biology. In addition, future avenues in the development of Kluyveromyces cell factories for the utilization of simple carbon compounds as substrates, the dynamic regulation of metabolic pathways, and for rapid directed evolution of robust strains are proposed. We expect that more synthetic systems, synthetic biology tools and metabolic engineering strategies will adapt to and optimize for Kluyveromyces cell factories to achieve green biofabrication of multiple products with higher efficiency.

Investigation of the Solubility of Elemental Sulfur (S) in Sulfur-Containing Natural Gas with Machine Learning Methods.

Some natural gases are toxic because they contain hydrogen sulfide (H2S). The solubility pattern of elemental sulfur (S) in toxic natural gas needs to be studied for environmental protection and life safety. Some methods (e.g., experiments) may pose safety risks. Measuring sulfur solubility using a machine learning (ML) method is fast and accurate. Considering the limited experimental data on sulfur solubility, this study used consensus nested cross-validation (cnCV) to obtain more information. The global search capability and learning efficiency of random forest (RF) and weighted least squares support vector machine (WLSSVM) models were enhanced via a whale optimization-genetic algorithm (WOA-GA). Hence, the WOA-GA-RF and WOA-GA-WLSSVM models were developed to accurately predict the solubility of sulfur and reveal its variation pattern. WOA-GA-RF outperformed six other similar models (e.g., RF model) and six other published studies (e.g., the model designed by Roberts et al.). Using the generic positional oligomer importance matrix (gPOIM), this study visualized the contribution of variables affecting sulfur solubility. The results show that temperature, pressure, and H2S content all have positive effects on sulfur solubility. Sulfur solubility significantly increases when the H2S content exceeds 10%, and other conditions (temperature, pressure) remain the same.

Recent advances and prospects of Bacillus amyloliquefaciens as microbial cell factories: from rational design to industrial applications.

Bacillus amyloliquefaciens is one of the most characterized Gram-positive bacteria. This species has unique characteristics that are beneficial for industrial applications, including its utilization of: cheap carbon as a substrate, a transparent genetic background, and large-scale robustness in fermentation. Indeed, the productivity characteristics of B. amyloliquefaciens have been thoroughly analyzed and further optimized through systems biology and synthetic biology techniques. Following the analysis of multiple engineering design strategies, B. amyloliquefaciens is now considered an efficient cell factory capable of producing large quantities of multiple products from various raw materials. In this review, we discuss the significant potential advantages offered by B. amyloliquefaciens as a platform for metabolic engineering and industrial applications. In addition, we systematically summarize the recent laboratory research and industrial application of B. amyloliquefaciens, including: relevant advances in systems and synthetic biology, various strategies adopted to improve the cellular performances of synthetic chemicals, as well as the latest progress in the synthesis of certain important products by B. amyloliquefaciens. Finally, we propose the current challenges and essential strategies to usher in an era of broader B. amyloliquefaciens use as microbial cell factories.

Manipulation of fungal cell wall integrity to improve production of fungal natural products.

Fungi, as an important industrial microorganism, play an essential role in the production of natural products (NPs) due to their advantages of utilizing cheap raw materials as substrates and strong protein secretion ability. Although many metabolic engineering strategies have been adopted to enhance the biosynthetic pathway of NPs in fungi, the fungal cell wall as a natural barrier tissue is the final and key step that affects the efficiency of NPs synthesis. To date, many important progresses have been achieved in improving the synthesis of NPs by regulating the cell wall structure of fungi. In this review, we systematically summarize and discuss various strategies for modifying the cell wall structure of fungi to improve the synthesis of NPs. At first, the cell wall structure of different types of fungi is systematically described. Then, strategies to disrupt cell wall integrity (CWI) by regulating the synthesis of cell wall polysaccharides and binding proteins are summarized, which have been applied to improve the synthesis of NPs. In addition, we also summarize the studies on the regulation of CWI-related signaling pathway and the addition of exogenous components for regulating CWI to improve the synthesis of NPs. Finally, we propose the current challenges and essential strategies to usher in an era of more extensive manipulation of fungal CWI to improve the production of fungal NPs.

Visible-Light Organophotoredox-Mediated [3 + 2] Cycloaddition of Arylcyclopropylamine with Structurally Diverse Olefins for the Construction of Cyclopentylamines and Spiro[4.n] Skeletons.

We developed a visible-light-mediated [3 + 2] cycloaddition of arylcyclopropylamine with structurally diverse olefins using QXPT-NPh as a highly efficient organic photoredox catalyst. We first achieved the use of various alkyl-substituted alkenes in intermolecular [3 + 2] cycloadditions with cyclopropylamine. We also developed a general and efficient strategy for the construction of structurally diverse cyclopentane-based spiro[4.n] skeletons with 1,3-difunctional groups, which broadly exist in natural products and synthetic molecules. Furthermore, we proposed a hydrogen-bond mode between the arylcyclopropylamine and the photocatalyst QXPT-NPh.

Synergistic Improvement of 5-Aminolevulinic Acid Production with Synthetic Scaffolds and System Pathway Engineering.

Engineered synthetic scaffolds to organize metabolic pathway enzymes and system pathway engineering to fine-tune metabolic fluxes play essential roles in microbial production. Here, we first obtained the most favorable combination of key enzymes for 5-aminolevulinic acid (5-ALA) synthesis through the C5 pathway by screening enzymes from different sources and optimizing their combination in different pathways. Second, we successfully constructed a multienzyme complex assembly system with PduA*, which spatially recruits the above three key enzymes for 5-ALA synthesis in a designable manner. By further optimizing the ratio of these key enzymes in synthetic scaffolds, the efficiency of 5-ALA synthesis through the C5 pathway was significantly improved. Then, the competitive metabolism pathway was fine-tuned by rationally designing different antisense RNAs, further significantly increasing 5-ALA titers. Furthermore, for efficient 5-ALA synthesis, obstacles of NADH and NADPH imbalances and feedback inhibition of the synthesis pathway were also overcome through engineering the NADPH regeneration pathway and transport pathway, respectively. Finally, combining these strategies with further fermentation optimization, we achieved a final 5-ALA titer of 11.4 g/L. These results highlight the importance of synthetic scaffolds and system pathway engineering to improve the microbial cell factory production performance.

Based on abnormal fluctuations in user-side flow simulation analysis of low- and medium-pressure gas pipeline leakage monitoring.

Due to the weak monitoring equipment for low- and medium-pressure gas pipelines, it is not easy to identify small flow leaks. The detection methods are mostly traditional manual inspections or night pressure-maintaining leak detection methods, which cannot be automatically monitored and sensed immediately. The abnormal fluctuations in client traffic caused by pipeline leaks studied in this paper can locate and detect leak locations more effectively. This paper analyzes the theoretical formula of leakage location based on flow data and finds out how to use the abnormal fluctuation of user-side flow to detect and locate gas pipeline leakage. First of all, this article uses the simulation software Pipeline Studio to construct the medium and low-pressure pipeline model. On this basis, 6 sets of leakage conditions were designed and simulated dynamically. Finally, the simulation results are analyzed, and the results show that: 1) The advisable monitoring period for monitoring abnormal fluctuations of user-side traffic is 10s. 2) There are two relationships between abnormal flow fluctuations and leakage position. They are: when the leakage point is at the first 40% of the relative distance from the gas source, the disturbance amplitude first increases and then decreases, and at the last 60%, it continues to decrease.; The closer the leak is to the user end, the more significant the abnormal flow fluctuations will be. On the contrary, the smaller the abnormal flow fluctuations will be; 3) No matter where the leakage occurs, the abnormal flow fluctuations in the 2nd and 3rd seconds after the leak occurs tend to be consistent. The proposal of the advisable monitoring period and the relationship between abnormal fluctuations of flow and the location of leakage provides a theoretical basis for the use of abnormal fluctuations of user-side flow for gas pipeline leakage detection and location.

Metabolic Engineering of Bacillus amyloliquefaciens to Efficiently Synthesize L-Ornithine From Inulin.

Bacillus amyloliquefaciens is the dominant strain used to produce γ-polyglutamic acid from inulin, a non-grain raw material. B. amyloliquefaciens has a highly efficient tricarboxylic acid cycle metabolic flux and glutamate synthesis ability. These features confer great potential for the synthesis of glutamate derivatives. However, it is challenging to efficiently convert high levels of glutamate to a particular glutamate derivative. Here, we conducted a systematic study on the biosynthesis of L-ornithine by B. amyloliquefaciens using inulin. First, the polyglutamate synthase gene pgsBCA of B. amyloliquefaciens NB was knocked out to hinder polyglutamate synthesis, resulting in the accumulation of intracellular glutamate and ATP. Second, a modular engineering strategy was applied to coordinate the degradation pathway, precursor competition pathway, and L-ornithine synthesis pathway to prompt high levels of intracellular precursor glutamate for l-ornithine synthesis. In addition, the high-efficiency L-ornithine transporter was further screened and overexpressed to reduce the feedback inhibition of L-ornithine on the synthesis pathway. Combining these strategies with further fermentation optimizations, we achieved a final L-ornithine titer of 31.3 g/L from inulin. Overall, these strategies hold great potential for strengthening microbial synthesis of high value-added products derived from glutamate.

Systematic engineering of Bacillus amyloliquefaciens for efficient production of poly-γ-glutamic acid from crude glycerol.

Microbial production of poly-γ-glutamic acid (γ-PGA) from non-food raw materials is a promising alternative to food feedstocks-based biosynthesis. A superior cell factory of Bacillus amyloliquefaciens for the efficient synthesis of γ-PGA from crude glycerol was constructed through systematic metabolic engineering. Firstly, some phase-dependent promoters were screened from B. amyloliquefaciens, which can be used for fine regulation of subsequent metabolic pathways. Secondly, the glycerol utilization pathway and the γ-PGA synthesis pathway were co-optimized utilizing the above-screened promoters, which increased the titer of γ-PGA by 1.75-fold. Then, the titer of γ-PGA increased to 15.6 g/L by engineering transcription factors degU and blocking competitive pathways. Finally, combining these strategies with an optimized fermentation process, 26.4 g/L γ-PGA was obtained from crude glycerol as a single carbon source (a 3.72-fold improvement over the initial strain). Overall, these strategies will have great potential for synthesizing other products from crude glycerol in B. amyloliquefaciens.

[Advances in the biosynthesis of tetrapyrrole compounds].

Tetrapyrrole compounds are a class of compounds with important functions. They exist in living organisms and have been widely used in agriculture, food, medicine, and other fields. The cumbersome process and high cost of chemical synthesis, as well as the shortcomings of unstable quality of animal and plant extraction methods, greatly hampered the industrial production and applications of tetrapyrrole compounds. In recent years, the rapid development of synthetic biology has provided new tools for microorganisms to efficiently synthesize tetrapyrrole compounds from renewable biomass resources. This article summarizes various strategies for the biosynthesis of tetrapyrrole compounds, discusses methods to improve its biosynthesis efficiency and future prospects, with the aim to facilitate the research on biosynthesis of tetrapyrrole compounds.

Metabolic Engineering of Escherichia coli for Ectoine Production With a Fermentation Strategy of Supplementing the Amino Donor.

Ectoine, an osmotic pressure-compensated solute, is used in the food, agriculture, medicine, and cosmetics industries due to its ability to protect macromolecules. In this study, an ectoine-producing variant of Escherichia coli, ET08, was genetically constructed by introducing the ectABC gene cluster and eliminating metabolic pathways involving lysine and pyruvate. Medium optimization enhanced ectoine production from 1.87 to 10.2 g/L. Analysis of the transcriptional levels revealed that supplementation with ammonium sulfate enhanced the metabolic flux towards the biosynthesis of ectoine. Furthermore, by optimizing the copy number of ectA, ectB, and ectC, the recombinant E. coli ET11 (ectA:ectB:ectC = 1:2:1) produced 12.9 g/L ectoine in the shake flask and 53.2 g/L ectoine in a fed-batch fermenter, representing the highest ectoine titer produced by E. coli, which has great industrial prospects.

Modeling the Solubility of Sulfur in Sour Gas Mixtures Using Improved Support Vector Machine Methods.

The study of sulfur solubility is of great significance to the safe development of sulfur-containing gas reservoirs. However, due to measurement difficulties, experimental research data on sulfur solubility thus far are limited. Under the research background of small samples and poor information, a weighted least-squares support vector machine (WLSSVM)-based machine learning model suitable for a wide temperature and pressure range is proposed to improve the prediction accuracy of sulfur solubility in sour gas. First, we use the comprehensive gray relational analysis method to extract important factors affecting sulfur solubility as the model input parameters. Then, we use the whale optimization algorithm (WOA) and gray wolf optimizer (GWO) intelligence algorithms to find the optimal solution of the penalty factor and kernel coefficient and bring them into three common kernel functions. The optimal kernel function is calculated, and the final WOA-WLSSVM and GWO-WLSSVM models are established. Finally, four evaluation indicators and an outlier diagnostic method are introduced to test the proposed model's performance. The empirical results show that the WOA-WLSSVM model has better performance and reliability; the average absolute relative deviation is as low as 3.45%, determination coefficient (R 2) is as high as 0.9987, and the prediction accuracy is much higher than that of other models.

Comparative analysis of the chemical and biochemical synthesis of keto acids.

Keto acids are essential organic acids that are widely applied in pharmaceuticals, cosmetics, food, beverages, and feed additives as well as chemical synthesis. Currently, most keto acids on the market are prepared via chemical synthesis. The biochemical synthesis of keto acids has been discovered with the development of metabolic engineering and applied toward the production of specific keto acids from renewable carbohydrates using different metabolic engineering strategies in microbes. In this review, we provide a systematic summary of the types and applications of keto acids, and then summarize and compare the chemical and biochemical synthesis routes used for the production of typical keto acids, including pyruvic acid, oxaloacetic acid, α-oxobutanoic acid, acetoacetic acid, ketoglutaric acid, levulinic acid, 5-aminolevulinic acid, α-ketoisovaleric acid, α-keto-γ-methylthiobutyric acid, α-ketoisocaproic acid, 2-keto-L-gulonic acid, 2-keto-D-gluconic acid, 5-keto-D-gluconic acid, and phenylpyruvic acid. We also describe the current challenges for the industrial-scale production of keto acids and further strategies used to accelerate the green production of keto acids via biochemical routes.