Artificial regulatory networks and cascades for discrete multilevel transgene control in mammalian cells

Abstract

Prototype drug-adjustable heterologous transcription control systems designed for gene therapy applications typically show sigmoid dose-response characteristics and enable fine-tuning of therapeutic transgenes only within a narrow inducer concentration range of a few nanograms. However, the design of clinical dosing regimes which achieve tissue-specific concentrations with nanogram precision is yet a "mission impossible." Therefore, most of today's transcription control systems operate as ON/OFF switches and not in a true adjustable mode. The availability of robust transcription control configurations which lock expression of a single therapeutic transgene at desired levels in response to fixed clinical doses of different inducers rather than minute concentration changes of a single inducer would be highly desirable. Based on in silico predictions, we have constructed a variety of mammalian artificial regulatory networks by interconnecting the tetracycline- (TET(OFF)), streptogramin- (PIP(OFF)), and macrolide- (E(OFF)) repressible gene regulation systems as linear (auto)regulatory cascades. These networks enable multilevel expression control of several transgenes in response to different antibiotics or allow titration of a single transgene to four discrete expression levels by clinical dosing of a single antibiotic: 1) high expression in the absence of any antibiotic (+++), 2) medium level expression following addition of tetracycline (++), 3) low level expression in response to the macrolide erythromycin (+), and 4) complete repression by streptogramins such as pristinamycin (-). The first-generation artificial regulatory networks exemplify modular interconnections of different heterologous gene regulations systems to achieve multigene expression, fine-tuning, or to design novel control networks with unprecedented transgene regulation properties. Such higher-level transcription control modalities will lead the way towards composite artificial regulatory networks able to effect complex therapeutic interventions in future gene therapy and tissue engineering scenarios.