Artificial regulatory networks and cascades for discrete multilevel transgene control in mammalian cells.
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.
Kramer
Beat P
BP
Institute of Biotechnology, Swiss Federal Institute of Technology, ETH Zurich, CH-8093 Zurich, Switzerland.
Weber
Wilfried
W
Fussenegger
Martin
M
D000469:Alkaline Phosphatase / Q000235:genetics / Q000378:metabolism; D000818:Animals; D000900:Anti-Bacterial Agents / Q000494:pharmacology; D001709:Biotechnology; D016466:CHO Cells; D002477:Cells / Q000378:metabolism*; D002478:Cells, Cultured; D006224:Cricetinae; D003412:Cricetulus; D004847:Epithelial Cells / Q000187:drug effects / Q000378:metabolism; D005786:Gene Expression Regulation / Q000187:drug effects; D005818:Genetic Engineering; D006801:Humans; D008163:Luminescent Measurements; D018942:Macrolides / Q000494:pharmacology; D008322:Mammals; D016571:Neural Networks, Computer; D010957:Plasmids; D025361:Streptogramins / Q000494:pharmacology; D013752:Tetracycline / Q000494:pharmacology; D015534:Trans-Activators / Q000235:genetics; D014162:Transfection; D019076:Transgenes / Q000187:drug effects* / Q000235:genetics*; D000516:alpha-Amylases / Q000235:genetics / Q000378:metabolism
D000900:Anti-Bacterial Agents; D018942:Macrolides; D025361:Streptogramins; D015534:Trans-Activators; D000469:Alkaline Phosphatase; D000516:alpha-Amylases; D013752:Tetracycline