The phytohormone auxin plays crucial roles in nearly every aspect of plant growth and development. Auxin signaling is activated through the phytohormone-induced proteasomal degradation of the Auxin/INDOLE-3-ACETIC ACID (Aux/IAA) family of transcriptional repressors. Notably, many auxin-modulated physiological processes are also regulated by nitric oxide (NO) that executes its biological effects predominantly through protein S-nitrosylation at specific cysteine residues. However, little is known about the molecular mechanisms in regulating the interactive NO and auxin networks. Here, we show that NO represses auxin signaling by inhibiting IAA17 protein degradation. NO induces the S-nitrosylation of Cys-70 located in the intrinsically disordered region of IAA17, which inhibits the TIR1-IAA17 interaction and consequently the proteasomal degradation of IAA17. The accumulation of a higher level of IAA17 attenuates auxin response. Moreover, an IAA17^C70W nitrosomimetic mutation renders the accumulation of a higher level of the mutated protein, thereby causing partial resistance to auxin and defective lateral root development. Taken together, these results suggest that S-nitrosylation of IAA17 at Cys-70 inhibits its interaction with TIR1, thereby negatively regulating auxin signaling. This study provides unique molecular insights into the redox-based auxin signaling in regulating plant growth and development.

As one of the newest fields of engineering, synthetic biology relies upon a trial-and-error Design-Build-Test-Learn approach to simultaneously learn how function is encoded in biology and attempt to engineer it. Many software and hardware platforms have been developed to automate, optimize, and algorithmically perform each step of the Design-Build-Test-Learn cycle. However, there are many fewer options for automating the Build step. Build typically involves DNA assembly, which remains manual, low throughput, and unreliable in most cases and limits our ability to advance the science and engineering of biology. Here, we present AssemblyTron: an open-source python package to integrate j5 DNA assembly design software outputs with build implementation in Opentrons liquid handling robotics with minimal human intervention. We demonstrate the versatility of AssemblyTron through several scarless, multipart DNA assemblies beginning from fragment amplification. We show that AssemblyTron can perform PCRs across a range of fragment lengths and annealing temperatures by using an optimal annealing temperature gradient calculation algorithm. We then demonstrate that AssemblyTron can perform Golden Gate and homology-dependent in vivo assemblies with comparable fidelity to manual assemblies by simultaneously building four four-fragment assemblies of chromoprotein reporter expression plasmids. Finally, we used AssemblyTron to perform site-directed mutagenesis reactions via homology-dependent in vivo assembly also achieving comparable fidelity to manual assemblies as assessed by sequencing. AssemblyTron can reduce the time, training, costs, and wastes associated with synthetic biology, which along with open-source and affordable automation, will further foster the accessibility of synthetic biology and accelerate biological research and engineering.

The exquisite specificity of proteins is a key feature driving their application to anticancer therapies. The therapeutic potential of another fundamental property of proteins, their ability to be regulated by molecular cues in their environment, is unknown. Here, we describe a synthetic biology strategy for designing protein therapeutics that autonomously activate a therapeutic function in response to a specific cancer marker of choice. We demonstrate this approach by creating a prodrug-activating enzyme that selectively kills human cancer cells that accumulate the marker hypoxia-inducible factor 1α. This property arises primarily through increased cellular accumulation of the enzyme in the presence of the marker. Our strategy offers a platform for the development of inherently selective protein therapeutics for cancer and other diseases.

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.