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. 2006 Aug 5;124(4):732-46.
doi: 10.1016/j.jbiotec.2006.01.003. Epub 2006 Feb 20.

Improved transgene expression fine-tuning in mammalian cells using a novel transcription-translation network

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Improved transgene expression fine-tuning in mammalian cells using a novel transcription-translation network

Laetitia Malphettes et al. J Biotechnol. .

Abstract

Following the discovery of RNA interference (RNAi) and related phenomena, novel regulatory processes, attributable to small non-protein-coding RNAs, continue to emerge. Capitalizing on the ability of artificial short interfering RNAs (siRNAs) to trigger degradation of specific target transcripts, and thereby silence desired gene expression, we designed and characterized a generic transcription-translation network in which it is possible to fine-tune heterologous protein production by coordinated transcription and translation interventions using macrolide and tetracycline antibiotics. Integration of siRNA-specific target sequences (TAGs) into the 5' or 3' untranslated regions (5'UTR, 3'UTR) of a desired constitutive transcription unit rendered transgene-encoded protein (erythropoietin, EPO; human placental alkaline phosphatase, SEAP; human vascular endothelial growth factor 121, VEGF(121)) production in mammalian cells responsive to siRNA levels that can be fine-tuned by macrolide-adjustable RNA polymerase II- or III-dependent promoters. Coupling of such macrolide-responsive siRNA-triggered translation control with tetracycline-responsive transcription of tagged transgene mRNAs created an antibiotic-adjustable two-input transcription-translation network characterized by elimination of detectable leaky expression with no reduction in maximum protein production levels. This transcription-translation network revealed transgene mRNA depletion to be dependent on siRNA and mRNA levels and that translation control was able to eliminate basal expression inherent to current transcription control modalities. Coupled transcription-translation circuitries have the potential to lead the way towards composite artificial regulatory networks, to enable complex therapeutic interventions in future biopharmaceutical manufacturing, gene therapy and tissue engineering initiatives.

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