Biosensor-based monitoring of the central metabolic pathway metabolites

Engineering microbial metabolic homeostasis for chemicals production

Microbial-based bio-refining promotes the development of a biotechnology revolution to encounter and tackle the enormous challenges in petroleum-based chemical production by biomanufacturing, biocomputing, and biosensing. Nevertheless, microbial metabolic homeostasis is often incompatible with the efficient synthesis of bioproducts mainly due to: inefficient metabolic flow, robust central metabolism, sophisticated metabolic network, and inevitable environmental perturbation. Therefore, this review systematically summarizes how to optimize microbial metabolic homeostasis by strengthening metabolic flux for improving biotransformation turnover, redirecting metabolic direction for rewiring bypass pathway, and reprogramming metabolic network for boosting substrate utilization. Future directions are also proposed for providing constructive guidance on the development of industrial biotechnology.

Monitoring kinetic processes of drugs and metabolites: Surface-enhanced Raman spectroscopy

Monitoring the kinetic changes of drugs and metabolites plays a crucial role in fundamental research, preclinical and clinical application. Raman spectroscopy (RS) is regarded as a fingerprinting technique that can reflect molecular structures but limited in applications due to poor sensitivity. Surface-enhanced Raman spectroscopy (SERS) significantly amplifies the detection sensitivity by plasmonic substrates, facilitating the identification and quantification of small molecules in biological samples, such as serum, urine, and living cells. This review will focus on advances in how SERS has been utilized to monitor the dynamic processes of small molecule drugs and metabolites in recent years. We first provide readers with a comprehensive overview of the mechanism and practical considerations of SERS, including enhancement theory, substrate design, sample pretreatment, molecule-substrate interactions and spectral analysis. Then we describe the latest advances in SERS for the detection and analysis of metabolites and drugs in cells, dynamic monitoring of drug in various biological matrices, and metabolic profiling for health assessment in biological fluids. We believe that high-performance SERS substrates, standardized technical regulations, and artificial intelligence spectral analysis will boost sensitive, accurate, reproducible, and universal molecular detection in the future. We hoped this review could inspire researchers working in related fields to better understand and utilize SERS for the analytical detection of drugs and metabolites.

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Biochemical implications of robotic surgery: a new frontier in the operating room

Robotic surgery represents a milestone in surgical procedures, offering advantages such as less invasive methods, elimination of tremors, scaled motion, and 3D visualization. This in-depth analysis explores the complex biochemical effects of robotic methods. The use of pneumoperitoneum and steep Trendelenburg positioning can decrease pulmonary compliance and splanchnic perfusion while increasing hypercarbia. However, robotic surgery reduces surgical stress and inflammation by minimizing tissue trauma. This contributes to faster recovery but may limit immune function. Robotic procedures also limit ischemia-reperfusion injury and oxidative damage compared to open surgery. They also help preserve native antioxidant defenses and coagulation. In a clinical setting, robotic procedures reduce blood loss, pain, complications, and length of stay compared to traditional procedures. However, risks remain, including device failure, the need for conversion to open surgery and increased costs. On the oncology side, there is still debate about margins, recurrence, and long-term survival. The advent of advanced technologies, such as intraoperative biosensors, localized drug delivery systems, and the incorporation of artificial intelligence, may further improve the efficiency of robotic surgery. However, ethical dilemmas regarding patient consent, privacy, access, and regulation of this disruptive innovation need to be addressed. Overall, this review sheds light on the complex biochemical implications of robotic surgery and highlights areas that require additional mechanistic investigation. It presents a comprehensive approach to responsibly maximize the potential of robotic surgery to improve patient outcomes, integrating technical skill with careful consideration of physiological and ethical issues.

Directed evolution of linker helix as an efficient strategy for engineering LysR-type transcriptional regulators as whole-cell biosensors

Whole-cell biosensors based on transcriptional regulators are powerful tools for rapid measurement, high-throughput screening, dynamic metabolic regulation, etc. To optimize the biosensing performance of transcriptional regulator, its effector-binding domain is commonly engineered. However, this strategy is encumbered by the limitation of diversifying such a large domain and the risk of affecting effector specificity. Molecular dynamics simulation of effector binding of LysG (an LysR-type transcriptional regulator, LTTR) suggests the crucial role of the short linker helix (LH) connecting effector- and DNA-binding domains in protein conformational change. Directed evolution of LH efficiently produced LysG variants with extended operational range and unaltered effector specificity. The whole-cell biosensor based on the best LysG^E58V variant outperformed the wild-type LysG in enzyme high-throughput screening and dynamic regulation of l-lysine biosynthetic pathway. LH mutations are suggested to affect DNA binding and facilitate transcriptional activation upon effector binding. LH engineering was also successfully applied to optimize another LTTR BenM for biosensing. Since LTTRs represent the largest family of prokaryotic transcriptional regulators with highly conserved structures, LH engineering is an efficient and universal strategy for development and optimization of whole-cell biosensors.

Engineering of global transcription factor FruR to redirect the carbon flow in Escherichia coli for enhancing L-phenylalanine biosynthesis

BACKGROUND

The catabolite repressor/activator protein (FruR) is a global regulatory protein known to control the expression of several genes concerned with carbon utilization and energy metabolism. This study aimed to illustrate effects of the FruR mutant on the _L-phenylalanine (_L-PHE) producing strain PHE01.

RESULTS

Random mutagenesis libraries of fruR generated in vitro were first integrated into the chromosome of PHE01 by CRISPR/Cas9 technique, and then the best mutant PHE07 (FruR^E173K) was obtained. With this mutant, a final _L-PHE concentration of 70.50 ± 1.02 g/L was achieved, which was 23.34% higher than that of PHE01. To better understand the mechanism, both transcriptomes and metabolomes of PHE07 were carried out and compared to that of PHE01. Specifically, the transcript levels of genes involved in gluconeogenesis pathway, pentose phosphate pathway, Krebs cycle, and glyoxylate shunt were up-regulated in the FruR^E173K mutant, whereas genes aceEF, acnB, and icd were down-regulated. From the metabolite level, the FruR^E173K mutation led to an accumulation of pentose phosphate pathway and Krebs cycle products, whereas the products of pyruvate metabolism pathway: acetyl-CoA and cis-aconic acid, were down-regulated. As a result of the altered metabolic flows, the utilization of carbon sources was improved and the supply of precursors (phosphoenolpyruvate and erythrose 4-phosphate) for _L-PHE biosynthesis was increased, which together led to the enhanced production of _L-PHE.

CONCLUSION

A novel strategy for _L-PHE overproduction by modification of the global transcription factor FruR in E. coli was reported. Especially, these findings expand the scope of pathways affected by the fruR regulon and illustrate its importance as a global regulator in _L-PHE production.

High-Level Production of Catechol from Glucose by Engineered Escherichia coli

Catechol (CA) is an aromatic compound with important applications in the fine chemical and pharmaceutical fields. As an alternative strategy to petroleum-based chemical synthesis, the production of catechol by using microbial cell factories has attracted great interest. However, the toxicity of catechol to microbial cells significantly limits the efficient production of bio-based catechol via one-step fermentation. Therefore, in this study, a two-step strategy for the efficient synthesis of CA was designed. Protocatechuic acid (PCA) was first efficiently produced by the engineered Escherichia coli strain AAA01 via fermentation, and then PCA in the fermentative broth was converted into CA by the whole-cell biocatalyst AAA12 with PCA decarboxylase. By optimizing the expression of flavin isoprenyl transferases and protocatechuic acid decarboxylases, the titer of CA increased from 3.4 g/L to 15.8 g/L in 12 h through whole-cell biocatalysis, with a 365% improvement; after further optimizing the reaction conditions for whole-cell biocatalysis, the titer of CA achieved 17.7 g/L within 3 h, which is the highest titer reported so far. This work provides an effective strategy for the green biomanufacturing of toxic compounds by Escherichia coli cell factories.

Development of bifunctional biosensors for sensing and dynamic control of glycolysis flux in metabolic engineering

Glycolysis is the primary metabolic pathway in all living organisms. Maintaining the balance of glycolysis flux and biosynthetic pathways is the crucial matter involved in the microbial cell factory. Few regulation systems can address the issue of metabolic flux imbalance in glycolysis. Here, we designed and constructed a bifunctional glycolysis flux biosensor that can dynamically regulate glycolysis flux for overproduction of desired biochemicals. A series of positive-and negative-response biosensors were created and modified for varied thresholds and dynamic ranges. These engineered glycolysis flux biosensors were verified to be able to characterize in vivo fructose-1,6-diphosphate concentration. Subsequently, the biosensors were applied for fine-tuning glycolysis flux to effectively balance the biosynthesis of two chemicals: mevalonate and N-acetylglucosamine. A glycolysis flux-dynamically controlled Escherichia coli strain achieved a 111.3 g/L mevalonate titer in a 1L fermenter.

Integrating thermodynamic and enzymatic constraints into genome-scale metabolic models

Stoichiometric genome-scale metabolic network models (GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric ratios, other constraints such as enzyme availability and thermodynamic feasibility can also limit the phenotype solution space. Extended GEM models considering either enzymatic or thermodynamic constraints have been shown to improve prediction accuracy. In this paper, we propose a novel method that integrates both enzymatic and thermodynamic constraints in a single Pyomo modeling framework (ETGEMs). We applied this method to construct the EcoETM (E. coli metabolic model with enzymatic and thermodynamic constraints). Using this model, we calculated the optimal pathways for cellular growth and the production of 22 metabolites. When comparing the results with those of iML1515 and models with one of the two constraints, we observed that many thermodynamically unfavorable and/or high enzyme cost pathways were excluded from EcoETM. For example, the synthesis pathway of carbamoyl-phosphate (Cbp) from iML1515 is both thermodynamically unfavorable and enzymatically costly. After introducing the new constraints, the production pathways and yields of several Cbp-derived products (e.g. L-arginine, orotate) calculated using EcoETM were more realistic. The results of this study demonstrate the great application potential of metabolic models with multiple constraints for pathway analysis and phenotype prediction.