Diamond IFFLs are a Signaling Decoder’s Best Friend

Summary by Allison Saul: Benzinger, D., Ovinnikov, S. & Khammash, M. Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression. Cell Syst 13, 353-364 e356 (2022). https://doi.org:10.1016/j.cels.2022.02.004

Image credit: Midjourney

Biological signaling can be highly dynamic and the decoding of signaling dynamics is important for proper cell function[1,2]. For example, calcium oscillations can activate different genes depending on oscillation dynamics[3]. However, how cells are decoding complex signals isn’t fully understood. De novo synthetic circuit engineering provides a promising approach to investigate how cells decode signaling.

To investigate signal decoding, Benzinger et al.[4] designed and characterized a suite of light-responsive transcription activators and repressors with different response kinetics. These optogenetic tools provide a platform for understanding signaling decoding because light is easily manipulatable, capable of creating different input levels and dynamics. They used these tools to create a system called a “diamond-shaped incoherent feedforward loop (diamond-IFFL)”[5]. In this genetic diamond-IFFL, a single stimulus (light) activates both a transcriptional activator and repressor that act on the same RFP reporter gene. They next measured RFP reporter activity before, during, and after a light pulse. Before and during a light pulse, reporter activity was low. Interestingly, regardless of pulse length, the post-pulse response was identical: cells demonstrated increased reporter activity for ~15 minutes after the end of light exposure. How can this system detect when a pulse ends? Differential activator and repressor reversion rates to inactive states explains this behavior: both turn on in response to the light stimulus, but when the light pulse is terminated, the repressor returns to an inactive state quicker than the activator. Therefore, the activator acts longer than the inhibitor, inducing reporter transcription. The diamond-IFFL therefore acts as a “falling edge detector”, a system whose activity is indicative of the end of the stimulus[5].

Benzinger et al. then explored the behavior of the diamond-IFFL when altering specific components. They altered two features of the repressor: 1. Expression levels, and 2. Deactivation kinetics. When expression levels of repressors were increased, the range of signal inputs capable of generating a reporter response decreased. When increasing the deactivation kinetics of the repressor, they found the range of signal inputs resulting in a response broadened. Together, these results demonstrate that components of a genetic diamond-IFFL can be tuned to achieve a desired response.

Finally, they explored a synthetic gene expression demultiplexer system. They used their diamond-IFFL in combination with an additional blue-light responsive gene regulation system with different kinetics to individually control expression of two reporters. Identical inputs drove different outputs: Higher intensity light resulted in the diamond-IFFL displaying low expression as opposed to the second reporter, which maintained higher levels of expression. Similarly for intermediate pulse inputs, as the duty cycle increased, diamond-IFFL expression dropped and secondary reporter expression grew. This demonstrates that extending the diamond-IFFL model can imitate behavior of a cellular demultiplexer.

Benzinger et al. developed a simple, elegant experimental platform to probe how synthetic gene networks can decode signaling, including multiplexing. Continuing to build a suite of optogenetically-driven synthetic gene networks could provide intricate details describing how signal decoding occurs in developing organisms to form unique tissues and organs, expanding the horizons of the field of developmental biology.

1. Farahani, P. E., Reed, E. H., Underhill, E. J., Aoki, K. & Toettcher, J. E. Signaling, Deconstructed: Using Optogenetics to Dissect and Direct Information Flow in Biological Systems. Annu Rev Biomed Eng 23, 61-87 (2021). https://doi.org:10.1146/annurev-bioeng-083120-111648

2. Purvis, J. E. & Lahav, G. Encoding and decoding cellular information through signaling dynamics. Cell 152, 945-956 (2013). https://doi.org:10.1016/j.cell.2013.02.005

3. Dolmetsch, R. E., Xu, K. & Lewis, R. S. Calcium oscillations increase the efficiency and specificity of gene expression. Nature 392, 933-936 (1998).

4. Benzinger, D., Ovinnikov, S. & Khammash, M. Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression. Cell Syst 13, 353-364 e356 (2022). https://doi.org:10.1016/j.cels.2022.02.004

5. Tabor, J. J. et al. A synthetic genetic edge detection program. Cell 137, 1272-1281 (2009). https://doi.org:10.1016/j.cell.2009.04.048