Stochastic Simulations as a Tool for Assessing Signal Fidelity in Gene Expression in Synthetic Promoter Design

The design and development of synthetic biology applications in a workflow often involve connecting modular components. Whereas computer-aided design tools are picking up in synthetic biology as in other areas of engineering, the methods for verifying the correct functioning of living technologies are still in their infancy. Especially, fine-tuning for the right promoter strength to match the design specifications is often a lengthy and expensive experimental process. In particular, the relationship between signal fidelity and noise in synthetic promoter design can be a key parameter that can affect the healthy functioning of the engineered organism. To this end, based on our previous work on synthetic promoters for the E. coli PhoBR two-component system, we make a case for using chemical reaction network models for computational verification of various promoter designs before a lab implementation. We provide an analysis of this system with extensive stochastic simulations at a single-cell level to assess the signal fidelity and noise relationship. We then show how quasi-steady-state analysis via ordinary differential equations can be used to navigate between models with different levels of detail. We compare stochastic simulations with our full and reduced models by using various metrics for assessing noise. Our analysis suggests that strong promoters with low unbinding rates can act as control tools for filtering out intrinsic noise in the PhoBR context. Our results confirm that even simpler models can be used to determine promoters with specific signal to noise characteristics.

Droplet-based microfluidic platform for high-throughput screening of Streptomyces

Streptomyces are one of the most important industrial microorganisms for the production of proteins and small-molecule drugs. Previously reported flow cytometry-based screening methods can only screen spores or protoplasts released from mycelium, which do not represent the filamentous stationary phase Streptomyces used in industrial cultivation. Here we show a droplet-based microfluidic platform to facilitate more relevant, reliable and rapid screening of Streptomyces mycelium, and achieved an enrichment ratio of up to 334.2. Using this platform, we rapidly characterized a series of native and heterologous constitutive promoters in Streptomyces lividans 66 in droplets, and efficiently screened out a set of engineered promoter variants with desired strengths from two synthetic promoter libraries. We also successfully screened out several hyperproducers of cellulases from a random S. lividans 66 mutant library, which had 69.2–111.4% greater cellulase production than the wild type. Our method provides a fast, simple, and powerful solution for the industrial engineering and screening of Streptomyces in more industry-relevant conditions.

Mediator Subunits MED16, MED14, and MED2 Are Required for Activation of ABRE-Dependent Transcription in Arabidopsis

The Mediator complex controls transcription of most eukaryotic genes with individual subunits required for the control of particular gene regulons in response to various perturbations. In this study, we reveal the roles of the plant Mediator subunits MED16, MED14, and MED2 in regulating transcription in response to the phytohormone abscisic acid (ABA) and we determine which cis elements are under their control. Using synthetic promoter reporters we established an effective system for testing relationships between subunits and specific cis-acting motifs in protoplasts. Our results demonstrate that MED16, MED14, and MED2 are required for the full transcriptional activation by ABA of promoters containing both the ABRE (ABA-responsive element) and DRE (drought-responsive element). Using synthetic promoter motif concatamers, we showed that ABA-responsive activation of the ABRE but not the DRE motif was dependent on these three Mediator subunits. Furthermore, the three subunits were required for the control of water loss from leaves but played no role in ABA-dependent growth inhibition, highlighting specificity in their functions. Our results identify new roles for three Mediator subunits, provide a direct demonstration of their function and highlight that our experimental approach can be utilized to identify the function of subunits of plant transcriptional regulators.

Dissection of PRC1 and PRC2 recruitment in Arabidopsis connects EAR repressome to PRC2 anchoring

PcG complexes ensure that every cell in an organism expresses the genes needed at a particular stage, time or condition. However, it is still not fully understood how PRC1 and PRC2 are recruited to target genes in plants. Recent results in Arabidopsis support that PRC2 recruitment is mediated by different TFs. However, it is unclear how all these TFs interact with PRC2 and whether they can also recruit PRC1 activity. Here, by using a system to in vivo bind selected factors to a synthetic promoter lacking the complexity of PcG target promoters, we show that while VAL1 binding recapitulates PRC1 and PRC2 marking, the binding of other TFs only render PRC2 marking. Interestingly, all these TFs contain an EAR domain that acts as docking point for PRC2 and HDACs, connecting two different repressive mechanisms. Furthermore, we show that different TFs act synergistically in PRC2 anchoring to maintain a long-term repression.

Synthetic promoter based azide biosensor toolkit to advance chemical-biology

Real-time azide or azido-functionalized molecular detection inside living cells using bioorthogonal chemistry-based approaches has been revolutionary to advancing chemical-biology. These methods have enabled diverse applications ranging from understanding the role of cellular glycosylation pathways, identifying diseased cells, and targeting delivery of azido-based therapeutic drugs. However, while classical techniques were applicable only to in-vitro detection of such functional groups, even recent bioorthogonal based-detection methods require expensive sensing reagents and also cannot selectively identify inorganic azide. Here, we report an in-vivo synthetic promoter based azide biosensor toolkit to selectively detect azide anions. A promiscuous cyanate-specific promoter was engineered to detect azide and rapidly induce expression of green fluorescent protein (GFP) in Escherichia coli. Our synthetic azide operon allows highly-tunable GFP expression, outperforming the classic lac-operon, and also offers an alternative low-cost protein expression system. Finally, we showcase the utility of this toolkit for in-vivo bioorthogonal reaction biosensing and glycoengineering based applications.

Model-driven promoter strength prediction based on a fine-tuned synthetic promoter library in Escherichia coli

Promoters are one of the most critical regulatory elements controlling metabolic pathways. However, in recent years, researchers have simply perfected promoter strength, but ignored the relationship between the internal sequences and promoter strength. In this context, we constructed and characterized a mutant promoter library of Ptrc through dozens of mutation-construction-screening-characterization engineering cycles. After excluding invalid mutation sites, we established a synthetic promoter library, which consisted of 3665 different variants, displaying an intensity range of more than two orders of magnitude. The strongest variant was 1.52-fold stronger than a 1 mM isopropyl-β-D-thiogalactoside driven PT7 promoter. Our synthetic promoter library exhibited superior applicability when expressing different reporters, in both plasmids and the genome. Different machine learning models were built and optimized to explore relationships between the promoter sequences and transcriptional strength. Finally, our XgBoost model exhibited optimal performance, and we utilized this approach to precisely predict the strength of artificially designed promoter sequences. Our work provides a powerful platform that enables the predictable tuning of promoters to achieve the optimal transcriptional strength.