Genome-scale metabolic modelling common cofactors metabolism in microorganisms

High Titer of (S)-Equol Synthesis from Daidzein in Escherichia coli

(S)-Equol is the terminal metabolite of daidzein and plays important roles in human health. However, due to anaerobic inefficiency, limited productivity in (S)-equol-producing strains often hinders (S)-equol mass production. Here, a multi-enzyme cascade system was designed to generate a higher (S)-equol titer. First, full reversibility of the (S)-equol synthesis pathway was found and a blocking reverse conversion strategy was established. As biosynthetic genes are present in the microbial genome, an effective daidzein reductase was chosen using evolutionary principles. And our analyses showed that NADPH was crucial for the pathway. In response to this, a novel NADPH pool was redesigned after analyzing a cofactor metabolism model. By adjusting synthesis pathway genes at the right expression level, the entire synthesis pathway can take place smoothly. Thus, the cascade system was optimized by regulating the gene expression intensity. Finally, after optimizing fermentation conditions, a 5 L bioreactor was used to generate a high (S)-equol production titer (3418.5 mg/L), with a conversion rate of approximately 85.9%. This study shows a feasible green process route for the production of (S)-equol.

Genome-scale metabolic network models: from first-generation to next-generation

Over the last two decades, thousands of genome-scale metabolic network models (GSMMs) have been constructed. These GSMMs have been widely applied in various fields, ranging from network interaction analysis, to cell phenotype prediction. However, due to the lack of constraints, the prediction accuracy of first-generation GSMMs was limited. To overcome these limitations, the next-generation GSMMs were developed by integrating omics data, adding constrain condition, integrating different biological models, and constructing whole-cell models. Here, we review recent advances of GSMMs from the first generation to the next generation. Then, we discuss the major application of GSMMs in industrial biotechnology, such as predicting phenotypes and guiding metabolic engineering. In addition, human health applications, including understanding biological mechanisms, discovering biomarkers and drug targets, are also summarized. Finally, we address the challenges and propose new trend of GSMMs. KEY POINTS: •This mini-review updates the literature on almost all published GSMMs since 1999. •Detailed insights into the development of the first- and next-generation GSMMs. •The application of GSMMs is summarized, and the prospects of integrating machine learning are emphasized.

Machine learning predicts the impact of antibiotic properties on the composition and functioning of bacterial community in aquatic habitats

In the past decades, hundreds of antibiotics have been isolated from microbial metabolites or have been artificially synthesized for protecting humans, animals and crops from microbial infections. Their everlasting usage results in impacts on the microbial community composition and causes well-known collateral damage to the functioning of microbial communities. Nevertheless, the impact of different antibiotic properties on aquatic microbial communities have so far only poorly been disentangled. Here we characterized the environmental risk of 50 main kinds of antibiotics from 9 classes at a concentration of 10 μg/L for aquatic bacterial communities via metadata analysis combined with machine learning. Metadata analysis showed that the alpha diversity of the bacterial community increased only after treatment with aminoglycoside and β-lactam antibiotics, while its structure was changed by almost all tested antibiotics. The antibiotic treatment also disturbed the functions of the bacterial community, especially with regard to metabolic pathways, including amino acids, cofactors, vitamins, xenobiotics and carbohydrate metabolism. The critical characteristics (atom stereocenter count, number of hydrogen atoms in the antibiotic, and the adipose water coefficient) of antibiotics affecting the composition of the bacterial community in aquatic habitats were screened by machine learning. The key characteristics of antibiotics affecting the function bacterial communities were the number of hydrogen atoms, molecular weight and complexity. In summary, by developing machine learning models and by performing metadata analysis, this study provides the relationship between the properties of antibiotics and their adverse impacts on aquatic microbial communities from a macro perspective. The study also provides guidance for the rational design of antibiotics.

Deciphering the mechanism by which the yeast Phaffia rhodozyma responds adaptively to environmental, nutritional, and genetic cues

Phaffia rhodozyma is a basidiomycetous yeast that synthesizes astaxanthin (ASX), which is a powerful and highly valuable antioxidant carotenoid pigment. P. rhodozyma cells accrue ASX and gain an intense red-pink coloration when faced with stressful conditions such as nutrient limitations (e.g., nitrogen or copper), the presence of toxic substances (e.g., antimycin A), or are affected by mutations in the genes that are involved in nitrogen metabolism or respiration. Since cellular accrual of ASX occurs under a wide variety of conditions, this yeast represents a valuable model for studying the growth conditions that entail oxidative stress for yeast cells. Recently, we proposed that ASX synthesis can be largely induced by conditions that lead to reduction-oxidation (redox) imbalances, particularly the state of the NADH/NAD+ couple together with an oxidative environment. In this work, we review the multiple known conditions that elicit ASX synthesis expanding on the data that we formerly examined. When considered alongside the Mitchell's chemiosmotic hypothesis, the study served to rationalize the induction of ASX synthesis and other adaptive cellular processes under a much broader set of conditions. Our aim was to propose an underlying mechanism that explains how a broad range of divergent conditions converge to induce ASX synthesis in P. rhodozyma. The mechanism that links the induction of ASX synthesis with the occurrence of NADH/NAD+ imbalances may help in understanding how other organisms detect any of a broad array of stimuli or gene mutations, and then adaptively respond to activate numerous compensatory cellular processes.

Comprehensive understanding of Saccharomyces cerevisiae phenotypes with whole-cell model WM_S288C

Biological network construction for Saccharomyces cerevisiae is a widely used approach for simulating phenotypes and designing cell factories. However, due to a complicated regulatory mechanism governing the translation of genotype to phenotype, precise prediction of phenotypes remains challenging. Here, we present WM_S288C, a computational whole-cell model that includes 15 cellular states and 26 cellular processes and which enables integrated analyses of physiological functions of Saccharomyces cerevisiae. Using WM_S288C to predict phenotypes of S. cerevisiae, the functions of 1140 essential genes were characterized and linked to phenotypes at five levels. During the cell cycle, the dynamic allocation of intracellular molecules could be tracked in real-time to simulate cell activities. Additionally, one-third of non-essential genes were identified to affect cell growth via regulating nucleotide concentrations. These results demonstrated the value of WM_S288C as a tool for understanding and investigating the phenotypes of S. cerevisiae.

Current research trends and challenges in tissue engineering for mending broken hearts

Cardiovascular disease (CVD) is among the leading causes of mortality worldwide. The shortage of donor hearts to treat end-stage heart failure patients is a critical problem. An average of 3500 heart transplant surgeries are performed globally, half of these transplants are performed in the US alone. Stem cell therapy is growing rapidly as an alternative strategy to repair or replace the damaged heart tissue after a myocardial infarction (MI). Nevertheless, the relatively poor survival of the stem cells in the ischemic heart is a major challenge to the therapeutic efficacy of stem-cell transplantation. Recent advancements in tissue engineering offer novel biomaterials and innovative technologies to improve upon the survival of stem cells as well as to repair the damaged heart tissue following a myocardial infarction (MI). However, there are several limitations in tissue engineering technologies to develop a fully functional, beating cardiac tissue. Therefore, the main goal of this review article is to address the current advancements and barriers in cardiac tissue engineering to augment the survival and retention of stem cells in the ischemic heart.

Genomic sequencing, genome-scale metabolic network reconstruction, and in silico flux analysis of the grape endophytic fungus Alternaria sp. MG1

BACKGROUND

Alternaria sp. MG1, an endophytic fungus isolated from grape, is a native producer of resveratrol, which has important application potential. However, the metabolic characteristics and physiological behavior of MG1 still remains mostly unraveled. In addition, the resveratrol production of the strain is low. Thus, the whole-genome sequencing is highly required for elucidating the resveratrol biosynthesis pathway. Furthermore, the metabolic network model of MG1 was constructed to provide a computational guided approach for improving the yield of resveratrol.

RESULTS

Firstly, a draft genomic sequence of MG1 was generated with a size of 34.7 Mbp and a GC content of 50.96%. Genome annotation indicated that MG1 possessed complete biosynthesis pathways for stilbenoids, flavonoids, and lignins. Eight secondary metabolites involved in these pathways were detected by GC-MS analysis, confirming the metabolic diversity of MG1. Furthermore, the first genome-scale metabolic network of Alternaria sp. MG1 (named iYL1539) was reconstructed, accounting for 1539 genes, 2231 metabolites, and 2255 reactions. The model was validated qualitatively and quantitatively by comparing the in silico simulation with experimental data, and the results showed a high consistency. In iYL1539, 56 genes were identified as growth essential in rich medium. According to constraint-based analysis, the importance of cofactors for the resveratrol biosynthesis was successfully demonstrated. Ethanol addition was predicted in silico to be an effective method to improve resveratrol production by strengthening acetyl-CoA synthesis and pentose phosphate pathway, and was verified experimentally with a 26.31% increase of resveratrol. Finally, 6 genes were identified as potential targets for resveratrol over-production by the recently developed methodology. The target-genes were validated using salicylic acid as elicitor, leading to an increase of resveratrol yield by 33.32% and the expression of gene 4CL and CHS by 1.8- and 1.6-fold, respectively.

CONCLUSIONS

This study details the diverse capability and key genes of Alternaria sp. MG1 to produce multiple secondary metabolites. The first model of the species Alternaria was constructed, providing an overall understanding of the physiological behavior and metabolic characteristics of MG1. The model is a highly useful tool for enhancing productivity by rational design of the metabolic pathway for resveratrol and other secondary metabolites.

Genome-scale biological models for industrial microbial systems

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

Impairment of NADH dehydrogenase and regulation of anaerobic metabolism by the small RNA RyhB and NadE for improved biohydrogen production in Enterobacter aerogenes

BACKGROUND

Enterobacter aerogenes is a facultative anaerobe and is one of the most widely studied bacterial strains because of its ability to use a variety of substrates, to produce hydrogen at a high rate, and its high growth rate during dark fermentation. However, the rate of hydrogen production has not been optimized. In this present study, three strategies to improve hydrogen production in E. aerogenes, namely the disruption of nuoCDE, overexpression of the small RNA RyhB and of NadE to regulate global anaerobic metabolism, and the redistribution of metabolic flux. The goal of this study was to clarify the effect of nuoCDE, RyhB, and NadE on hydrogen production and how the perturbation of NADH influences the yield of hydrogen gas from E. aerogenes.

RESULTS

NADH dehydrogenase activity was impaired by knocking out nuoCD or nuoCDE in E. aerogenes IAM1183 using the CRISPR-Cas9 system to explore the consequent effect on hydrogen production. The hydrogen yields from IAM1183-CD(∆nuoC/∆nuoD) and IAM1183-CDE (∆nuoC/∆nuoD/∆nuoE) increased, respectively, by 24.5 and 45.6% in batch culture (100 mL serum bottles). The hydrogen produced via the NADH pathway increased significantly in IAM1183-CDE, suggesting that nuoE plays an important role in regulating NADH concentration in E. aerogenes. Batch-cultivating experiments showed that by the overexpression of NadE (N), the hydrogen yields of IAM1183/N, IAM1183-CD/N, and IAM1183-CDE/N increased 1.06-, 1.35-, and 1.55-folds, respectively, compared with IAM1183. Particularly worth mentioning is that the strain IAM118-CDE/N reached 2.28 mol in H₂ yield, per mole of glucose consumed. IAN1183/R, IAM1183-CD/R, and IAM1183-CDE/R showed increasing H₂ yields in batch culture. Metabolic flux analysis indicated that increased expression of RyhB led to a significant shift in metabolic patterns. We further investigated IAM1183-CDE/N, which had the best hydrogen-producing traits, as a potential candidate for industry applications using a 5-L fermenter; hydrogen production reached up to 1.95 times greater than that measured for IAM1183.

CONCLUSIONS

Knockout of nuoCD or nuoCDE and the overexpression of nadE in E. aerogenes resulted in a redistribution of metabolic flux and improved the hydrogen yield. Overexpression of RyhB had an significant change on the hydrogen production via NADH pathway. A combination of strategies would be a novel approach for developing a more economic and efficient bioprocess for hydrogen production in E. aerogenes. Finally, the latest CRISPR-Cas9 technology was successful for editing genes in E. aerogenes to develop our engineered strain for hydrogen production.