A De Novo Optimized Cell-Free System for the Expression of Soluble and Active Human Tumor Necrosis Factor-Alpha.
Nawal Abd El-BakyEsmail M El-FakharanySoraya A SabryEhab R El-HelowElrashdy Mustafa RedwanAmira SabryPublished in: Biology (2022)
Cell-free (in vitro) expression is a robust alternative platform to the cell-based (in vivo) system for recombinant protein production. Tumor necrosis factor-alpha (TNF-α) is an effective pro-inflammatory cytokine with pleiotropic effects. The aim of the current study was de novo optimized expression of soluble and active human TNF-α by an in vitro method in an E. coli -based cell-free protein synthesis (CFPS) system and its biological activity evaluation. The codon-optimized synthetic human TNF-α gene was constructed by a two-step PCR, cloned into pET101/D-TOPO vector and then expressed by the E. coli CFPS system. Cell-free expression of the soluble protein was optimized using a response surface methodology (RSM). The anticancer activity of purified human TNF-α was assessed against three human cancer cell lines: Caco-2, HepG-2 and MCF-7. Data from RSM revealed that the lowest value (7.2 µg/mL) of cell-free production of recombinant human TNF-α (rhTNF-α) was obtained at a certain incubation time (6 h) and incubation temperature (20 °C), while the highest value (350 µg/mL) was recorded at 4 h and 35 °C. This rhTNF-α showed a significant anticancer potency. Our findings suggest a cell-free expression system as an alternative platform for producing soluble and functionally active recombinant TNF-α for further research and clinical trials.
Keyphrases
- cell free
- rheumatoid arthritis
- endothelial cells
- poor prognosis
- circulating tumor
- induced pluripotent stem cells
- clinical trial
- pluripotent stem cells
- binding protein
- escherichia coli
- squamous cell carcinoma
- stem cells
- computed tomography
- artificial intelligence
- gene expression
- recombinant human
- pet ct
- genome wide
- dna methylation
- transcription factor
- papillary thyroid
- young adults
- lymph node metastasis
- data analysis