RNA Compensation: A Positive Feedback Insulation Strategy for RNA-Based Transcription Networks

Competition and evolutionary selection among core regulatory motifs in gene expression control

Gene products that are beneficial in one environment may become burdensome in another, prompting the emergence of diverse regulatory schemes that carry their own bioenergetic cost. By ensuring that regulators are only expressed when needed, we demonstrate that autoregulation generally offers an advantage in an environment combining mutation and time-varying selection. Whether positive or negative feedback emerges as dominant depends primarily on the demand for the target gene product, typically to ensure that the detrimental impact of inevitable mutations is minimized. While self-repression of the regulator curbs the spread of these loss-of-function mutations, self-activation instead facilitates their propagation. By analyzing the transcription network of multiple model organisms, we reveal that reduced bioenergetic cost may contribute to the preferential selection of autoregulation among transcription factors. Our results not only uncover how seemingly equivalent regulatory motifs have fundamentally different impact on population structure, growth dynamics, and evolutionary outcomes, but they can also be leveraged to promote the design of evolutionarily robust synthetic gene circuits.

Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium

Through the implementation of designable genetic circuits, engineered probiotic microorganisms could be used as noninvasive diagnostic tools for the gastrointestinal tract. For these living cells to report detected biomarkers or signals after exiting the gut, the genetic circuits must be able to record these signals by using genetically encoded memory. Complex memory register circuits could enable multiplex interrogation of biomarkers and signals. A theory-based approach to create genetic circuits containing memory, known as sequential logic circuits, was previously established for a model laboratory strain of Escherichia coli, yet how circuit component performance varies for nonmodel and clinically relevant bacterial strains is poorly understood. Here, we develop a scalable computational approach to design robust sequential logic circuits in probiotic strain Escherichia coli Nissle 1917 (EcN). In this work, we used TetR-family transcriptional repressors to build genetic logic gates that can be composed into sequential logic circuits, along with a set of engineered sensors relevant for use in the gut environment. Using standard methods, 16 genetic NOT gates and nine sensors were experimentally characterized in EcN. These data were used to design and predict the performance of circuit designs. We present a set of genetic circuits encoding both combinational logic and sequential logic and show that the circuit outputs are in close agreement with our quantitative predictions from the design algorithm. Furthermore, we demonstrate an analog-like concentration recording circuit that detects and reports three input concentration ranges of a biochemical signal using sequential logic.

A portable regulatory RNA array design enables tunable and complex regulation across diverse bacteria

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 for E. coli function 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.

Identifying Antisense Oligonucleotides to Disrupt Small RNA Regulated Antibiotic Resistance via a Cell-Free Transcription-Translation Platform

Bacterial small RNAs (sRNAs) regulate many important physiological processes in cells, including antibiotic resistance and virulence genes, through base-pairing interactions with mRNAs. Antisense oligonucleotides (ASOs) have great potential as therapeutics against bacterial pathogens by targeting sRNAs such as MicF, which regulates outer membrane protein OmpF expression and limits the permeability of antibiotics. Here we devised a cell-free transcription-translation (TX-TL) assay to identify ASO designs that sufficiently sequester MicF. ASOs were then ordered as peptide nucleic acids conjugated to cell-penetrating peptides (CPP-PNA) to allow for effective delivery into bacteria. Subsequent minimum inhibitory concentration (MIC) assays demonstrated that simultaneously targeting the regions of MicF responsible for sequestering the start codon and the Shine-Dalgarno sequence of ompF with two different CPP-PNAs synergistically reduced the MIC for a set of antibiotics. This investigation offers a TX-TL-based approach to identify novel therapeutic candidates to combat intrinsic sRNA-mediated antibiotic resistance mechanisms.

A portable regulatory RNA array design enables tunable and complex regulation across diverse bacteria

A lack of composable and tunable gene regulators has hindered efforts to engineer non-model bacteria and consortia. To address this, we explore the broad-host potential of small transcription activating RNA (STAR) and propose a novel design strategy to achieve tunable gene control. First, we demonstrate that STARs optimized for E. coli function 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 a novel 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.