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Abstract RNA-driven phase separation is emerging as a promising approach for engineering biomolecular condensates with diverse functionalities. Condensates form thanks to weak yet specific RNA–RNA interactions established by design via complementary sequence domains. Here, we demonstrate how RNA condensates formed by star-shaped RNA motifs, or nanostars, can be dynamically controlled when the motifs include additional linear or branch-loop domains that facilitate access of regulatory RNA molecules to the nanostar interaction domains. We show that condensates dissolve in the presence of RNA “invaders” that occlude selected nanostar bonds and reduce the valency of the nanostars, preventing phase separation. We further demonstrate that the introduction of “anti-invader” strands, complementary to the invaders, makes it possible to restore condensate formation. An important aspect of our experiments is that we demonstrate these behaviors in one-pot reactions, where RNA nanostars, invaders, and anti-invaders are simultaneously transcribed in vitro using short DNA templates. Our results lay the groundwork for engineering RNA-based assemblies with tunable, reversible condensation, providing a promising toolkit for synthetic biology applications requiring responsive, self-organizing biomolecular materials. 

Abstract A lack of composable and tunable gene regulators has hindered efforts to engineer non-model bacteria and consortia. Toward addressing this, we explore the broad-host potential of small transcription activating RNA (STAR) and propose a design strategy to achieve tunable gene control. First, we demonstrate that STARs optimized forE. colifunction across different Gram-negative species and can actuate using phage RNA polymerase, suggesting that RNA systems acting at the level of transcription are portable. Second, we explore an RNA design strategy that uses arrays of tandem and transcriptionally fused RNA regulators to precisely alter regulator concentration from 1 to 8 copies. This provides a simple means to predictably tune output gain across species and does not require access to large regulatory part libraries. Finally, we show RNA arrays can be used to achieve tunable cascading and multiplexing circuits across species, analogous to the motifs used in artificial neural networks. 

Recent discoveries in biology have highlighted the importance of protein and RNA-based condensates as an alternative to classical membrane-bound organelles. Here, we demonstrate the design of pure RNA condensates from nanostructured, star-shaped RNA motifs. We generate condensates using two different RNA nanostar architectures: multi-stranded nanostars whose binding interactions are programmed via linear overhangs, and single-stranded nanostars whose interactions are programmed via kissing loops. Through systematic sequence design, we demonstrate that both architectures can produce orthogonal (distinct and immiscible) condensates, which can be individually tracked via fluorogenic aptamers. We also show that aptamers make it possible to recruit peptides and proteins to the condensates with high specificity. Successful co-transcriptional formation of condensates from single-stranded nanostars suggests that they may be genetically encoded and produced in living cells. We provide a library of orthogonal RNA condensates that can be modularly customized and offer a route toward creating systems of functional artificial organelles for the task of compartmentalizing molecules and biochemical reactions. 

Condensation of RNA and proteins is central to cellular functions, and the ability to program it would be valuable in synthetic biology and synthetic cell science. Here we introduce a modular platform for engineering synthetic RNA condensates from tailor-made, branched RNA nanostructures that fold and assemble co-transcriptionally. Up to three orthogonal condensates can form simultaneously and selectively accumulate fluorophores through embedded fluorescent light-up aptamers. The RNA condensates can be expressed within synthetic cells to produce membrane-less organelles with a controlled number and relative size, and showing the ability to capture proteins using selective protein-binding aptamers. The affinity between otherwise orthogonal nanostructures can be modulated by introducing dedicated linker constructs, enabling the production of bi-phasic RNA condensates with a prescribed degree of interphase mixing and diverse morphologies. The in situ expression of programmable RNA condensates could underpin the spatial organization of functionalities in both biological and synthetic cells. 

The development of fluorescent light-up RNA aptamers (FLAPs) has paved the way for the creation of sensors to track RNA in live cells. A major challenge with FLAP sensors is their brightness and limited signal-to-background ratio both in vivo and in vitro. To address this, we develop sensors using the Pepper aptamer, which exhibits superior brightness and photostability when compared to other FLAPs. The sensors are designed to fold into a low fluorescence conformation and to switch to a high fluorescence conformation through toehold or loop-mediated interactions with their RNA target. Our sensors detect RNA targets as short as 20 nucleotides in length with a wide dynamic range over 300-fold in vitro, and we describe strategies for optimizing the sensor’s performance for any given RNA target. To demonstrate the versatility of our design approach, we generated Pepper sensors for a range of specific, biologically relevant RNA sequences. Our design and optimization strategies are portable to other FLAPs and offer a promising foundation for future development of RNA sensors with high specificity and sensitivity for detecting RNA biomarkers with multiple applications. 

Cellular signaling pathways are responsible for decision making that sustains life. Most signaling pathways include post-translational modification cycles, that process multiple inputs and are tightly interconnected. Here we consider a model for phosphorylation/dephosphorylation cycles, and we show that under some assumptions they can operate as molecular neurons or perceptrons, that generate sigmoidal-like activation functions by processing sums of inputs with positive and negative weights. We carry out a steady-state and structural stability analysis for single molecular perceptrons as well as for feedforward interconnections, concluding that interconnected phosphorylation/dephosphorylation cycles may work as multilayer biomolecular neural networks (BNNs) with the capacity to perform a variety of computations. As an application, we design signaling networks that behave as linear and non-linear classifiers.