Back

Engineering protein expression dynamics with Tet-ON and dTAG degron systems: from precise control to oscillations

Noble, B.; Cottrell, O.; Rowntree, A.; Biga, V.; Woods, F.; Wang, X.; Papalopulu, N.; Miller, A.

2025-12-19 developmental biology
10.64898/2025.12.16.694651 bioRxiv
Show abstract

Precise temporal control of protein expression is essential for dissecting protein function and dynamic cellular processes. We present a framework for engineering tunable oscillatory protein expression (repeated pulses in expression) using widely adopted molecular tools, applying them to modulate NGN3 expression. Single-cell time-lapse microscopy reveals that the Tet-On system unexpectedly generates asynchronous oscillations in protein expression under continuous doxycycline administration. These oscillations are dependent on protein instability and are not tunable by doxycycline concentration. In contrast, the dTAG degron system enables precise, reversible, concentration-dependent control of protein degradation and reaccumulation. Coupled with a constitutive promoter, we achieve synchronous oscillatory protein expression (COD: Constituitive promoter driving Oscillations via Degradation). Mathematical modelling identifies optimal dTAG drug addition and removal timings using the COD system to flexibly tune NGN3 oscillation periods while maintaining other oscillatory parameters (mean level and peak-to-trough fold-change). Using microfluidics (COD+CHIPS) we reproduce model-predicted 5 and 10 hours periodicities while maintaining similar mean levels and peak-to-trough fold-changes. This work introduces a generalisable, programmable approach for generating and modulating protein oscillations, allowing investigation into how dynamic protein expression governs cellular function.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.