Cell-Free Characterization of Coherent Feed-Forward Loop-Based Synthetic Genetic Circuits

Abstract

Regulatory pathways inside living cells employ feed-forward architectures to fulfill essential signal processing functions that aid in the interpretation of various types of inputs through noise-filtering, fold-change detection and adaptation. Although it has been demonstrated computationally that a coherent feed-forward loop (CFFL) can function as noise filter, a property essential to decoding complex temporal signals, this motif has not been extensively characterized experimentally or integrated into larger networks. Here we use post-transcriptional regulation to implement and characterize a synthetic CFFL in an Escherichia coli cell-free transcription-translation system and build larger composite feed-forward architectures. We employ microfluidic flow reactors to probe the response of the CFFL circuit using both persistent and short, noise-like inputs and analyze the influence of different circuit components on the steady-state and dynamics of the output. We demonstrate that our synthetic CFFL implementation can reliably repress background activity compared to a reference circuit, but displays low potential as a temporal filter, and validate these findings using a computational model. Our results offer practical insight into the putative noise-filtering behavior of CFFLs and show that this motif can be used to mitigate leakage and increase the fold-change of the output of synthetic genetic circuits.

Decentralizing Cell-Free RNA Sensing With the Use of Low-Cost Cell Extracts

“…Lehr et al have shown the implementation of CFTS in commercially available cell-free transcription-translation systems (TX-TL) based on cell extracts ( Lehr et al, 2019 ), which are also sourced from the US and shipped under cold chain. Encouragingly, Silverman et al (2019) , McNerney et al (2019) and Pieters et al (2021) have recently shown the successful implementation of CFTS in home-made cell extracts, offering a path for local prototyping de novo sensors of interest ( McNerney et al, 2019 ; Silverman et al, 2019 ; Pieters et al, 2021 ).…”

DNA Input Classification by a Riboregulator-Based Cell-Free Perceptron

“… 55 , 56 All of the genetic constructs for the inputs, weights, and the independent reporter were constructed using a Golden Gate assembly-based cloning method. 57 The DNA constructs used were optimized for RNA stability as well as the efficacy of the input–weight pair, as reported by Pieters et al 62 Polymerase chain reactions (PCR) were used to prepare linear DNA templates for use in the experiments (see the Methods section).…”

“…Furthermore, continuous flow reactions can be implemented to provide sufficient transcription and translation resources over prolonged durations. 62 , 77 …”

“…The cell lysate, energy mixture, and amino acid solutions used to prepare the TXTL reaction solution were prepared identically to the protocol described by Pieters et al 62 To prepare a master mix of all reaction components, excluding the DNA templates, the following were combined (with the final concentration given in brackets): cell lysate (33% of the final reaction volume), energy mixture 55 (7.1% of the final reaction volume), a constant distribution amino acid solution 82 (37.5 mM), magnesium l -glutamate (10 mM), potassium l -glutamate (40 mM), PEG-8000 (2%), and GamS protein (3 μM). When combined, this solution comprised 69% of the total reaction volume, with the linear DNA templates supplemented with Milli-Q water accounting for the remaining 31%.…”

End-to-end computational approach to the design of RNA biosensors for detecting miRNA biomarkers of cervical cancer

“…The plot of intensity against the complex concentration over a 2-h interval obtained a sigmoidal fit with a steep rise between 10 nM and 100 nM (Section S4, Supplementary File S7). This showed that the model is sensitive to changes in the concentration of the trigger miRNA biomarker in the range of interest, satisfying a necessary biosensor property [ 25 , [55] , [56] , [57] , [58] , [59] , [60] ]. A similar trend of sigmoidal kinetics was observed for the hsa-miR-20a-5p biomarker over a complex concentration range of 100 nM and 1 μM (Supplementary File S7).…”