A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli

StressME: Unified computing framework of Escherichia coli metabolism, gene expression, and stress responses

Generalist microbes have adapted to a multitude of environmental stresses through their integrated stress response system. Individual stress responses have been quantified by E. coli metabolism and expression (ME) models under thermal, oxidative and acid stress, respectively. However, the systematic quantification of cross-stress & cross-talk among these stress responses remains lacking. Here, we present StressME: the unified stress response model of E. coli combining thermal (FoldME), oxidative (OxidizeME) and acid (AcidifyME) stress responses. StressME is the most up to date ME model for E. coli and it reproduces all published single-stress ME models. Additionally, it includes refined rate constants to improve prediction accuracy for wild-type and stress-evolved strains. StressME revealed certain optimal proteome allocation strategies associated with cross-stress and cross-talk responses. These stress-optimal proteomes were shaped by trade-offs between protective vs. metabolic enzymes; cytoplasmic vs. periplasmic chaperones; and expression of stress-specific proteins. As StressME is tuned to compute metabolic and gene expression responses under mild acid, oxidative, and thermal stresses, it is useful for engineering and health applications. The modular design of our open-source package also facilitates model expansion (e.g., to new stress mechanisms) by the computational biology community.

Lung but not brain cancer cell malignancy inhibited by commonly used anesthetic propofol during surgery: Implication of reducing cancer recurrence risk

Introduction

Intravenous anesthesia with propofol was reported to improve cancer surgical outcomes when compared with inhalational anesthesia. However, the underlying molecular mechanisms largely remain unknown.

Objectives

The anti-tumor effects of propofol and the possible underlying mechanism including altered metabolic and signaling pathways were studied in the current study.

Methods

The cell viability, proliferation, migration, and invasion of cancer cells were analyzed with CCK-8, Ki-67 staining, wound healing, and Transwell assay, respectively. The protein changes were analyzed with Western blot and immunofluorescent staining. The metabolomics alteration was studied with 1H-NMR spectroscopy. The gene expression regulations were analyzed with PCR gene array and qRT-PCR experiments.

Results

In this study, we found that propofol reduced cell viability and inhibited cell proliferation, migration and invasion of lung cancer cells, but not neuroglioma cells. In lung cancer cells, propofol downregulated glucose transporter 1 (GLUT1), mitochondrial pyruvate carrier 1 (MPC1), p-Akt, p-Erk1/2, and hypoxia- inducible factor 1 alpha (HIF-1 α ) expressions and upregulated pigment epithelium-derived factor (PEDF) expression. Propofol increased intracellular glutamate and glycine but decreased acetate and formate whilst increased glucose, lactate, glutamine, succinate, pyruvate, arginine, valine, isoleucine, and leucine and glycerol, and decreased acetate, ethanol, isopropanol in the culture media of lung cancer cells. Furthermore, VEGFA, CTBP1, CST7, CTSK, CXCL12, and CXCR4 gene expressions were downregulated, while NR4A3, RB1, NME1, MTSS1, NME4, SYK, APC, and FAT1 were upregulated following the propofol treatment. Consistent with the phenotypical changes, these molecular and metabolic changes were not found in the neuroglioma cells.

Conclusion

Our findings indicated anti-tumor effects of propofol on the lung cancer but not brain cancer, through the regulation of tumor metastasis-related genes, multi-cellular signaling and cellular metabolism.