Macroscopic control of cell electrophysiology through ion channel expression

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Version of Record published: November 30, 2022 (This version)
Accepted Manuscript published: November 9, 2022 (Go to version)
Accepted: October 21, 2022
Received: February 22, 2022
Preprint posted: January 20, 2022 (Go to version)

1. Of interest
Flexible control of representational dynamics in a disinhibition-based model of decision making

Bo Shen, Kenway Louie, Paul W Glimcher

Research Article Jun 1, 2023
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Abstract
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Results and discussion
Materials and methods
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Abstract

Cells convert electrical signals into chemical outputs to facilitate the active transport of information across larger distances. This electrical-to-chemical conversion requires a tightly regulated expression of ion channels. Alterations of ion channel expression provide landmarks of numerous pathological diseases, such as cardiac arrhythmia, epilepsy, or cancer. Although the activity of ion channels can be locally regulated by external light or chemical stimulus, it remains challenging to coordinate the expression of ion channels on extended spatial–temporal scales. Here, we engineered yeast Saccharomyces cerevisiae to read and convert chemical concentrations into a dynamic potassium channel expression. A synthetic dual-feedback circuit controls the expression of engineered potassium channels through phytohormones auxin and salicylate to produce a macroscopically coordinated pulses of the plasma membrane potential. Our study provides a compact experimental model to control electrical activity through gene expression in eukaryotic cell populations setting grounds for various cellular engineering, synthetic biology, and potential therapeutic applications.

Editor's evaluation

The important contribution of this study is the ability to leverage engineered gene circuits to control cellular membrane potential. The presentation of the data in this work is convincing and the controls are in place to demonstrate that electrophysiological changes arise from external chemical stimuli. This study will be of interest to those working on non-neuronal bioelectricity, particularly synthetic biologists and bioengineers.

https://doi.org/10.7554/eLife.78075.sa0

Introduction

Electrical signals provide an active mechanism for the rapid delivery of information across noisy cells and tissues. Prominent examples of this phenomenon across kingdoms include excitable neuronal circuits (Aron and Yankner, 2016), plant defense signaling (Masatsugu, 2018), and metabolic coordination of biofilm growth (Prindle et al., 2015). These seemingly different forms of electrical signaling involve ion channels. Outward-rectifying potassium channels release potassium from the intracellular reservoir to the extracellular space, thereby allowing for potassium exchange between neighboring cells (Debanne et al., 1997). A rapid gating of ion channels at the subcellular level maintains the balance in the plasma membrane potential (PMP), which is central to cell electrophysiology (Naundorf et al., 2006).

The activity of potassium channels can be locally modulated by voltage-gating, mechanical or light stimulus, and external ligands. In the last decade, advances in optogenetics and chemical biology of ion channels have powered numerous medical applications through local regulation of electrical activity in living cells with the ultimate goal of treating major life-threatening diseases (Gradinaru et al., 2010; Häfner and Sandoz, 2022; Snyder, 2017; Montnach et al., 2022). Interestingly, recent studies indicate the spatial–temporal regulation of ion channel expression and membrane potential status is critical for landmarking pathological conditions such as cardiac arrhythmia, epilepsy, or various types of cancer (Rosati and McKinnon, 2004; Lastraioli et al., 2015; Zsiros et al., 2009; Niemeyer et al., 2001; Biasiotta et al., 2016). However, a major challenge is to achieve rational control of ion channels on extended spatial–temporal scales. Such a strategy would provide a basis foundation for advanced applications in treating epilepsy, chronic pain, irregular heartbeats, or potentially various types of cancer. Nevertheless, there is a lack of experimental models allowing a guided modulation of eukaryotic cell electrophysiology through modulation of ion channel expression.

To address this challenge, we build a synthetic gene regulatory mechanism that is capable of controlling ion channel expression in the cell populations of the model eukaryote Saccharomyces cerevisiae based on environmental changes. By combining live-cell imaging in microfluidic devices with computer modeling we tested a suitable eukaryotic model for macroscopic real-time modulation of ion channels and PMP in living cell collectives.

Results and discussion

Controlling macroscopic ion channel expression through plant hormones

Chemical messengers or light can selectively control local activity of ion channels on the plasma membrane (Gradinaru et al., 2010; Häfner and Sandoz, 2022; Snyder, 2017; Montnach et al., 2022). In contrast, we sought to implement an alternative system for ion channel modulation by chemically coordinating the ion channel expression at the macroscopic level. To test this concept, we used a model eukaryote yeast S. cerevisae. Previously, we have developed a synthetic two component circuit to control gene expression across cell populations based on chemical stimulation with phytohormones auxin and salicylate (Pérez-García et al., 2021). This circuit is composed of engineered Mar-type bacterial regulator (Will and Fang, 2020); IacR transcriptional activator and MarR transcriptional repressor (Pérez-García et al., 2021; Figure 1A), that are inhibited by auxin (IAA) and salicylate (SA), respectively. We sought this particular circuit is ideal for our application since it can coordinate gene expression in yeast on extended spatiotemporal scale (Pérez-García et al., 2021).

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Control of ion channel expression through open-loop circuit.

(A) Schematic of open-loop system driving expression of constitutively open KcsA* bacterial potassium channel. Activator IacR and repressor MarR (tagged with ODC degron; Takeuchi et al., 2008) are induced by galactose and repressed by auxin and salicylate, respectively (Pérez-García et al., 2021). (B) KcsA* contains plasma membrane localization (PM loc, green) and c-terminus mouse ODC degron (degron, black) sequences. (C) Schematic of microfluidic device used in the study. Flow channels widths were 120 μm for in the mixer module and 500 μm in main channels. The approximate height of channels was 25 μm. Cell traps had 500 × 500 μm size and height of approximately 7 μm. Loading direction is marked with green arrows, while anticipated flow in the experiment with black arrows. Time traces (D) and heat map (E) of Thioflavin T (ThT) fluorescence in control yeast strain do not show any organized features under 3-hr periodic phytohormone stimuli. (**F–I**) ThT fluorescence changes in the open-loop circuit (A) under 3-hr periodic phytohormone stimuli show distinct fluctuations (**F, G**), yet broad period distribution (H) and low synchronicity of ~50% (I). Periods of peaks were measured for all measured trapping regions without averaging as shown in (H). (I) Synchrony index is measured for all n = 23 trapping regions (communities) each containing ~10,000 yeast cells. Positions of phytohormone stimuli are shown with black (SA) and green (IAA) boxes above the time traces. Each technical experiment has been repeated at least two times with similar results. Violin plots represent medians (white dots), interquartile zones (gray bars) and 95% confidence levels (solid gray line).

Figure 1—source data 1 Source data for Figure 1H, I.: https://cdn.elifesciences.org/articles/78075/elife-78075-fig1-data1-v2.xlsx
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It is known that the overexpression of potassium channel TOK1 in yeast leads to PMP hyperpolarization (Sesti et al., 2001). Therefore, in theory, our strategy would allow a direct control of ion channel activity through coordinated modulation of gene expression levels, leading to global alterations of PMP. In our system changes in channel expression would cause associated changes in PMP primarily based on the temporal status of phytohormones in the environment (Figure 1A). Importantly, a voltage-gated channel opening, dependent on internal metabolic processes, could be attenuated by the use of constitutively open ion channel (Figure 1B). Thus, we used an open bacteria potassium channel KcsA* (Doyle et al., 1998; Cuello et al., 2010; Sun et al., 2020) as the output module of the synthetic circuit (Figure 1B, Figure 1—figure supplement 1). To create a dynamic environment, we employed controlled conditions on the microfluidic chip where yeast can grow under continuous media perfusion stimulated with antithetic pulses of SA and IAA with the regulable frequency (Figure 1C). To monitor PMP changes we have tested several commonly used dyes in yeast such as DIBAC4(3), DIS-C3(3), and cationic dye Thioflavin T (ThT) (Peña et al., 2020) and we found that a ThT outperforms other dyes in terms of stability, fluorescence level, low hydrophobicity (Peña et al., 2020), and near-linear response to potassium clamp experiments (Figure 1—figure supplement 2A–C). Finally, all these features of ThT were ideal for the long-term microfluidics experiments when stability, fluorescence, and low absorption in PDMS are critical.

Under coordinated changes of IAA and SA in the environment, the control strain that lacks the synthetic circuit (Figure 1A) does not show any regular changes in ThT fluorescence over time (Figure 1D, E and Figure 1—figure supplement 3A, Figure 1—video 1). Whereas the open-loop circuit driving KcsA channel in yeast (Figure 1A, B) showed noisy but recognizable fluctuations of ThT fluorescence with weak coupling between colonies detected by cumulative autocorrelation analysis (Figure 1F, G, Figure 1—figure supplement 3B, Figure 1—video 2). The period of ThT fluorescence showed a broad distribution around the phytohormone stimuli period (Figure 1H and Figure 1—figure supplement 3C). Also, there was a substantial variability in amplitudes (Figure 1—figure supplement 3E) but less of peak widths (Figure 1—figure supplement 3D). To complement autocorrelation analysis, we developed a quantitative metric of ‘synchrony index’ defined as 1R where R is the ratio of differences in subsequent ThT peak positions among cell communities (phase) to expected period. This metrics describes how well are yeast colonies synchronized with each other under guidance of the common environmental cue. Notably, we found that around 50% yeast colonies show synchronicity of ThT fluorescence (Figure 1I). Based on these data, we concluded that this open-loop system does not provide sufficiently robust changes in PMP to guide electrical activity on the macroscopic scale.

Encoding dual-feedback regulation increases speed and robustness of response in yeast collectives

To further improve the performance of our system we sought to design a closed-loop feedback circuit that could encode features such as fast responsiveness (due to feedback) and demonstrate noise filtering capability of excitable systems (Lindner et al., 2004). For that we coupled IacR and MarR through positive and negative feedback loops in the dual-feedback synthetic gene circuit (Figure 2A). To analyze the robustness and dynamics of the designed circuit we first performed computer model simulations of this IacR–MarR feedback system and identified regimes that demonstrate the prominence for the excitable dynamics (Figure 2B and Figure 2—figure supplement 1). Model predictions revealed minimal prerequisites for the transit between steady state (Figure 2—figure supplement 1A), excitability (Figure 2B, Figure 2—figure supplement 1B), and oscillations (Figure 2—figure supplement 1C). These conditions directly relate to the ratio of IacR–MarR deactivation which depends primarily on IAA and SA changes (Figure 2D, Figure 2—figure supplement 1A–C).

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A synthetic dual-feedback circuit coordinates ion channel expression in yeast communities.

(A) Schematic of dual-feedback circuit controlling downstream expression of bacterial potassium channel KcsA*. All components of the system are controlled by the same promoter to allow integration of both positive and negative feedbacks. (B) Computer model simulations of a synthetic dual-feedback circuit in the excitable regime. (C) Checking for a refractory period in dEGFP circuit reporter response under two long 12-hr pulses of auxin (green boxes). Time-lapse traces from microfluidic device are shown with the mean trend (black curve). Note inability of cell to respond to second pulse of IAA. (D) Computer model simulation of a circuit subjected to series of antithetic pulses of SA (black box inability) and IAA (green box). (E) Experimental time traces of dEGFP fluorescent marker under 2-hr antithetic pulses of SA and IAA. Note consistency of period between different yeast colonies and some level of variability in peak amplitudes. (F) Heat maps of time traces for n = 28 yeast communities reported simultaneously in the microfluidic device. (G) Cumulative autocorrelation trends for three different stimuli periods (2 hr [black], 1 hr [red], and 30 min [blue]). (H) Violin plot shows the consistency of dEGFP period for different stimuli shape and period. (I) Violin plot of synchrony index that was measured among all yeast communities (each containing ~10,000 cells) for different stimuli shape and period. Each experiment has been repeated at least two times with similar results. Violin plots represent medians (white dots), interquartile zones (gray bars) and 95% confidence levels (solid gray line).

Figure 2—source data 1 Source data for Figure 2H, I.: https://cdn.elifesciences.org/articles/78075/elife-78075-fig2-data1-v2.xlsx
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To test initially our computer model, we implemented a synthetic feedback circuit composed of IacR tagged with herpes simplex virus trans-activation domain (VP64) (Hagmann et al., 1997), MarR tagged with repression Mig1 silencing domain (Ostling et al., 1996; Figure 2A, Figure 2—figure supplement 2), and unstable dEGFP fluorescent reporter (Dantuma et al., 2000). All three components of the circuit were placed under control of the same synthetic promoter carrying MarR and IacR operator sites (Pérez-García et al., 2021). We found that this dual-feedback circuit responds sharply to SA–IAA gradient and shows excitable-like dynamics characterized by transient response peak in the absence of periodic hormone stimulus (Figure 2—figure supplements 3 and 4). Furthermore, we confirmed that the long step-like stimulation with IAA in dynamic microfluidic setup resulted in refractory dynamics of dEGFP signal after IAA removal (Figure 2C), characterized by transient response peak similar to that observed in the static environment (Figure 2—figure supplement 4) and in our computer model simulations (Figure 2B).

Next, we impose cyclic applications of IAA and SA with defined periods to guide gene expression patterns across yeast colonies. Spatially coordinated pulses of dEGFP were observed under various phytohormone stimulations of 2 hr, 1 hr, and 30 min (Figure 2E, F, and Figure 2—figure supplement 5, Figure 2—video 1) which are in a good agreement with computer model simulations (Figure 2D). The coordinated gene expression was confirmed by the analysis of cumulative dEGFP signal autocorrelation (Figure 2G), period distribution (Figure 2H, Figure 2—figure supplement 6A), complemented by peak characteristic analysis (Figure 2—figure supplement 6B, C). Importantly, we quantified that ~95% of all communities show coordinated peaks of dEGFP fluorescence (Figure 2I). Finally, to confirm that phytohormone changes directly control ion channel dynamics, we tested whether observed dynamics of circuit actually control of the ion channel expression. For that we fused KcsA* potassium channel (Figure 1B) with EGFP to monitor directly the life span of the channel under control of a feedback circuit. Indeed, phytohormone stimuli lead to coordinated spikes of KcsA-EGFP fluorescence mimicking that of dEGFP fluorescent marker, indicating that our circuit specifically controls the amount of heterologous ion channels inside the cell under guidance of phytohormones (Figure 2—figure supplement 7, Figure 2—video 2).

In summary, this chemically excited circuit presents a plausible regulatory module for engineering macroscopically coordinated ion channel expression in yeast cell populations.

Coupling feedback circuit to PMP changes at the population level by engineered potassium channels

We have demonstrated that dual-feedback circuit shows characteristics of excitable system that robustly controls the ion channel expression in yeast cell collectives. Next, we tested how these changes in ion channel presence would reflect upon PMP changes by measuring ThT cationic dye fluorescence dynamics. In particular, we asked if the close-loop system (Figure 3A) would outperform our open-loop system (Figure 1A).

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Global modulation of ion channel expression and plasma membrane potential (PMP) in yeast communities through phytohormones.

(A) Representative time traces of Thioflavin T (ThT) fluorescence per community (n = 56 yeast communities) under 2-hr hormone stimuli. Average trend is shown in black. SA and IAA peaks are shown as black and green boxes above the traces. Scale bar represents 20μm (B) Kymograph of ThT fluorescence equivalent to time traces (A) with color coded map. (C) Cumulative autocorrelation analysis for all n = 56 communities show clear periodic, synchronized ThT fluorescence changes across communities. (D) Synchrony index calculated for each of the communities for three different frequencies of phytohormone stimuli (1, 2, and 3 hr) show good agreement with autocorrelation analysis. (E) Variation in amplitudes of ThT fluorescence plotted against periods of ThT and ThT peak widths. (F) Schematic of dual-feedback circuit controlling the yeast potassium channel TOK1*. Time-lapse ThT fluorescence changes in TOK1* integrating synthetic circuit (G) and corresponding heat map (H). Violin plots represent medians (white dots), interquartile zones (gray bars) and 95% confidence levels (solid gray line).

Figure 3—source data 1 Source data for Figure 3D, E.: https://cdn.elifesciences.org/articles/78075/elife-78075-fig3-data1-v2.xlsx
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Next, we grew engineered yeasts carrying a dual-feedback circuit in the microfluidic device that was subjected to different frequencies of SA and IAA stimuli (3, 2, and 1 hr, respectively). We could observe cyclic bursts of ThT fluorescence that were remarkably consistent across cell populations in long-term experiments (Figure 3A, B, and Figure 3—videos 1–3). Cumulative autocorrelation and synchronicity analyses confirmed further the robustness of temporal response on the macroscopic scale (Figure 3C, D). The faster pace of environmental changes led to somehow less robust response possibly due to toxic effect of upregulation ion channels in short time periods (Figure 3D, Figure 3—figure supplement 1, Figure 3—video 3). Interestingly, while peak width and period were very consistent between different yeast colonies, we observed visible variation in amplitudes of ThT fluorescence (Figure 3E). These could be due to subtle differences in relative levels of KcsA* channels produced by each cell as observed in our experimental data (Figure 2—figure supplement 7) but also to inherent noise nature of gene expression. Nevertheless, our experiments indicate that dual-feedback integration in our original open-loop circuit significantly improves the robustness of ion channel control, resulting in coordinated modulation of ion channel expression and consequently organized changes of PMP across eukaryotic cell communities.

The PMP of yeast is regulated through voltage-gated potassium channels such as the outward-rectifier channel TOK1 (Martinac et al., 2008; Mackie and Brodsky, 2018). TOK1 is the main target for several toxins and volatile anesthetic agents (Ahmed et al., 1999), which cause uncontrolled opening and leakage of potassium ions to the extracellular space. Overexpression of TOK1 causes membrane hyperpolarization (more negative PMP) while tok1 mutation leads to membrane depolarization often accompanied by cell death (Sesti et al., 2001). Thereby, TOK1 controls potassium release from yeast cells and maintains a balance in the PMP. Next, we checked if our dual-feedback circuit could be plugged into the regulation of this native TOK1 potassium channel in yeast. This strategy would potentially allow to plug a synthetic circuit to any native potassium channels in eukaryotes.

For that purpose, we constructed TOK1* channel by tagging degron domain at c-terminus of TOK1 to decrease half-life of the channel and thus increase its dynamics similar to that of KcsA* construction. Then we plugged our feedback circuit to control TOK1* expression levels on the macroscopic level (Figure 3F). We recorded ThT fluorescence changes over time under pulses of IAA and SA. Similarly, to KcsA* we observed consistent ThT pulses across number of independent yeast colonies (Figure 3G, H, Figure 3—video 4) as exemplified by persistence of pulsing period and synchronicity (Figure 3—figure supplement 2). Again, the autocorrelation analysis revealed a dominant pattern of ThT changes monitored across yeast colonies (Figure 3—figure supplement 2). These data indicate that our dual-feedback circuit can indeed control also the expression of native channels to modulate its activity by controlling the number of TOK1* transcripts available in the cell. Therefore, our findings highlight a generality of strategy that includes engineered dual-feedback system for controlling potassium channels and PMP through environmental rhythms.

Concluding remarks

In the last decade, many efforts have been invested into developing new methods for the local control of ion channel activity with light or chemical signals. In contrast, mechanisms controlling ion channel expression have received substantially less attention, despite their importance in cardiac, neurological disorders and various forms of cancer. Here, we demonstrate a synthetic biology model for the control of potassium channel expression on macroscopic scales in eukaryotic cell populations. As a proof-of-concept, we tested this chemically driven mechanism in yeast to access the general suitability of our strategy for tuning cell electrophysiology at the population level. We demonstrated that a synthetic gene circuit controls ion channel dosage in each individual cell that is dictated by environmental input with selective frequencies. Changes in ion channel expression correlate with changes of PMP as shown by cationic dye translocation to the cell interior with increased fluorescence. Both heterologous and native channels were used as effectors of dual-feedback system to control PMP in yeast cells. Whereas the lack of feedback causes a noisy control of PMP, highlighting strong benefits of using dual-feedback regulation for the robust PMP control.

This work opens new avenues in the field of synthetic biology and cellular engineering by enabling a transformation of clocked chemical cues into coordinated ion channel expression without need for apparent cell-to-cell coupling. Our work proposes that coherent changes of ion channel expression can adjust electrical activity in growing cell populations and on extended spatial–temporal scales. Furthermore, our circuit could be potentially plugged into native context where timed processes would control circuit components endogenously and connect circuit output to native ion channel dosage. Nevertheless, our initial, proof-of-concept study produces some variability in response amplitudes. Therefore, future studies are needed to establish systems with improved response robustness to timed inputs, potentially leading to the creation of powerful toolboxes for controlling macroscopic electrophysiology across the tree of life. Practically, controlling locally and globally ion channel expression holds a key to designing more effective therapies providing the control of electrical status of abnormal cells in a broad spectrum of diseases.

Materials and methods

Strains and plasmid constructions

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Constructs were cloned using isothermal Gibson assembly cloning. A middle-copy (~10–30 copies) episomal plasmid pGADT7 (Takara Bio Inc) was used to increase the concentration of proteins to buffer for the effects of intrinsic molecular noise and selected using different auxotrophic selection markers (Leucine, Uracil, and Histidine). Iacr_o/MarR_o promoter and either standard CYC1 or ADH1 yeast terminators were cloned into activator or repressor plasmids (Figure 1—figure supplement 1). MarR, IacR, and KcsA* were codon optimized for yeast and synthesized using services delivered by Integrated DNA Technologies (IDT). The reporter plasmids include synthetic minimal promoters (synthesized with IDT) with previously identified MarR or IacR operator (Alekshun et al., 2001; Shu et al., 2015) sequences upstream TATA-box and minimal CYC1 promoter and fast-degradable UBG76V-EGFP (dEGFP) (Dantuma et al., 2000). KcsA bacterial potassium channel was engineered to include open configuration mutations as previously described (Cuello et al., 2010; Sun et al., 2020) and modified further to include plasma membrane localization signals and c-terminus mouse ODC degron signal (Takeuchi et al., 2008). KcsA* or TOK1* replaces the dEGFP gene on the reporter plasmid, respectively (Figure 1—figure supplement 1 and Figure 2—figure supplement 2). PCR reactions were performed using Q5 high fidelity polymerase (New England Biolabs). Correct PCR products were digested with DpnI (New England Biolabs) to remove the template and subsequently cleaned up with a DNA cleanup kit (Zymo Research) before Gibson assembly. Constructs were transformed in ultra-competent cells from E. coli DH5a strain using standard protocols. All plasmids were confirmed by colony PCR and validated with sequencing. The BY4741 laboratory yeast strain (a kind gift from Dr. Luis Rubio) carrying integrated copy constitutively expressed mCherry reporter was used to prepare competent cells and transformation of plasmids using Frozen-EZ Yeast Transformation II Kit (Zymo Research). DNA sequences used in this study are summarized in Supplementary file 1. Yeast strains are embedded in Key Resource Table.

Multiwell plate and microscopy fluorescence measurements

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Overnight culture of the yeast grown in 2% sucrose low fluorescence media (Formedium, UK) was diluted 100× and pipetted directly to a 96-well plate containing 2% sucrose and gradient of SA and IAA concentrations. Plates were incubated at 30° C overnight and well mixed by shaking before performing measurements. Measurements were done with the Thermo Scientific VarioskanTM LUX multimode microplate reader after 24 hr or were recorded every 10 min to generate a time-lapse profile of the dEGFP and OD₆₀₀. OD₆₀₀ was set at an absorbance of 600 nm wavelength, the fluorescence excitation and emission light at 488 and 517 nm wavelength for dEGFP. The PMP reporter activity was analyzed by measuring dye expression using potassium clamp experiments 24 hr after staining. Overnight cultures were diluted to a total OD₆₀₀ of 0.1. KCl at different concentrations was added to the diluted cultures. Aliquotes of 200 µl were pipetted from these diluted cultures to a multi-well plate containing different KCl concentrations (0, 50, 100, 200, and 400 mM) and 10 µM Thioflavin T, 10 µM DIBAC4(3), and 10 µM DIS-C3(3) for a direct comparison. Plates were incubated at 30°C overnight. The next day, each well was imaged in two different channels: differential interference contrast (DIC) and GFP (λ_Ex = 488 nm; λ_Em = 515 nm). The image acquisition was controlled by the software µManager and Leica DMI9 and images were captured using a ×10 dry objective (NA = 0.32).

Time-lapse imaging, growth conditions, and data analysis

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Live-cell imaging was performed on the Automated inverted Leica DMi8 fluorescence microscope equipped with Hamamatsu Orca Flash V3 camera that was controlled by Micro-Manager v.2.0 (https://micro-manager.org/). Images were captured with ×40 dry objective NA = 0.8 (Leica Inc). Traps containing cells were imaged every 10 min on three different channels (DIC, GFP Excitation: 488, Emission: 515, and mCherry Excitation: 583, Emission: 610) with CoolLed pE600 LED excitation source and standard Chroma epifluorescent filter set. Experiments were run for up to 72 hr under the continuous supply of nutrients in the microfluidic device. Acquired images were initially processed in Fiji 2.0 (https://imagej.net/Fiji) using custom scripting to extract positions with exponentially growing yeast cells. Constitutively expressed mCherry marker was used to identify exponentially growing cells and used to derive normalized dEGFP fluorescence: Dead or non-growing individuals were discarded by correcting dEGFP or KcsA-EGFP signal according to the formula dEGFP/(dEGFP + mCherry). Each image was divided into 25 regions of interest and analyzed separately to isolate regions where cells were actively growing and could be tracked over time. The posterior analysis was done with custom R-studio scripts. Firstly, raw data were detrended using the detrend function from ‘pracma’ R-studio v4.0.3 package and then smoothed with Savitzky-Golay Smoothing function (savgol), from the same package, with a filter length of 15 was applied and the signal was normalized between 0 and 1 to generate heat maps across cell traps. Amplitudes were calculated with find peaks within the Process Data using the ‘findpeaks’ function from ‘pracma’ R package with nups and ndowns of 6, and periods were calculated by calculating distances between successive dEGFP peaks. Phase drift was calculated by comparing time differences of successive dEGFP peaks between cell communities in a microfluidic device to derive inter-community measures. Cumulative autocorrelations traces and power spectrum densities were calculated on mean dEGFP trajectories per colony calculated for n yeast communities (n > 20, ~10,000 cells each) using standard calculations with Matlab 2018b derived packages autocorrect and Fast Fourier Transformation (FFT). A quantitative metric of ‘synchrony index’ is defined as 1 − R, where R is the ratio of differences in subsequent ThT peak positions among cell communities (phase) to expected period. This metrics describes how well are yeast colonies synchronized with each other under guidance of the common environmental cue. The identical image analysis procedures were used to evaluate ThT fluorescence dynamics, autocorrelation function, and frequency responses.

Mathematical model description

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To derive a mathematical model of the excitable circuit (Figure 2B and Figure 2—figure supplement 1), we used a system of coupled ordinary differential equations adapted from previous theoretical studies (Lindner et al., 2004). Briefly, IacR and MarR protein concentrations change over time according to the following formulas:

\frac{\partial I a c R}{\partial t} = a_{1} + \frac{b_{1} \cdot I a c R^{2}}{K_{A}^{2} + I a c R^{2} + {(γ \cdot M a r R)}^{2}} - d_{1} \cdot (1 + ϵ_{I} \cdot I A A) \cdot I a c R

\frac{\partial M a r R}{\partial t} = a_{2} + \frac{b_{2} \cdot I a c R^{2}}{K_{B}^{2} + I a c R^{2}} - d_{2} \cdot (1 + ϵ_{M} \cdot S A) \cdot M a r R

where a₁ and a₂ are basal IacR and MarR production rates. b₁ and b₂ are IacR-dependent protein production rates. K_A and K_B are half-max hill function coefficients, and γ is the rate of MarR-dependent repression. d₁ and d₂ are protein turnover rates, respectively. $ϵ_{I}$ and $ϵ_{M}$ are the rates of phytohormone effect on a total protein turnover. IAA and SA are modeled using square wave and sine signal generators (Matlab Inc). The ratio of protein turnover $\frac{d_{1}}{d_{2}}$ (Figure 2—figure supplement 1A–C) represents a key bifurcation parameter that enables the transition of the system into oscillatory and excitable regimes. Model parameters are summarized in Supplementary file 2.

To calculate nullclines we set the left-hand side of Equations 1 and 2 to 0 and setting $ϵ_{I}$ and $ϵ_{M}$ to 0. One can derive the following terms for calculation of phase portraits MarR*(IacR) and MarR**(IacR) (Figure 2—figure supplement 1A–C):

M a r R^{*} = \frac{1}{γ} \cdot \sqrt{- \frac{K_{A}^{2} \cdot a_{1} + a_{1} \cdot I a c R^{2} + b_{1} \cdot I a c R^{2} - d_{1} \cdot I a c R^{3} - K_{A}^{2} \cdot d_{1} \cdot I a c R}{a_{1} - d_{1} \cdot I a c R}}

M a r R^{* *} = \frac{a_{2} + \frac{b_{2} \cdot I a c R^{2}}{K_{B}^{2} + I a c R^{2}}}{d_{2}}

Cell loading procedure

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All tubing lines were sterilized with ethanol and plugged into syringes or introduced in falcon tubes under sterile conditions. Fresh yeast colony was grown in low fluorescence media composition (Formedium, UK) with 2% sucrose as a carbon source or 2% glucose (K1 toxin-producing strain). The next day, yeast cultures were diluted 10–20 times approximately to 0.2–0.4 OD₆₀₀ to obtain highly concentrated cells that were transferred to a 50-ml falcon tube for loading. 60-ml media syringes were filled with 25 ml of inducing media (2% sucrose + 0.5% galactose) with or without compounds and 50-ml waste falcon tubes were filled with 10 ml of Distilled De-Ionized water. Before loading, devices were vacuumed for at least 20 min to remove all the air from the channels and traps. Syringes and falcon tubes were placed on the height control system and lines were connected as follows: media syringes were plugged first and kept above all other inputs to prevent media contamination. Adjusting the height of the cells containing falcon tube as well as media and waste aids in controlling the cell seeding in the traps. Although many cells pass through the chip directly toward the waste port, few cells got captured via microvalves and seeded the traps. Once 10–20 cells were captured in each trapping region, the flow from the cell loading port was reverted by decreasing the height to the same level as for the auxiliary waste.

Microfluidic mold fabrication

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Molds for the production of microfluidic device (Pérez-García et al., 2021) were designed in Inkscape and printed on plastic sheets with the monochrome laser printer at 1200 dpi resolution as described in Pérez-García et al., 2021. A density of Ink deposition was used to control the feature height. Plastic wafers were cut and transferred to the thermal oven set to 160°C to shrink by one-third of the original size, then baked again for 10 min to smoothen and harden the ink. Finally, molds were cleaned with soap, rinsed with isopropanol and DDI water and dried using a nitrogen gun, and secured with Scotch tape before use. Each cell trap is 500 × 500 × 7 μm size, hosting ~10,000 haploid yeast cells.

Soft lithography

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Molds were introduced in plastic 90-mm Petri dishes and fixed with double-sided tape. Dowsil Sylgard 184 Polydimethylsiloxane (PDMS) was properly mixed in a 10:1 (wt/wt) ratio of elastomer and curing agent and stirred until the uniform consistency was achieved. Approximately 27 ml of the homogeneous mixture was poured into each Petri dish and completely degassed using the 8 CFM 2-stage vacuum pump for approximately 20 min. Degassed PDMS was cured at 80°C for 2 hr. Cured PDMS was removed from the Petri dish, separated from the wafer, and cut to extract the individual chips. Fluid access ports were punched with a 0.7-mm diameter World Precision Instruments (WPI) biopsy puncher and flushed with ethanol to remove any remaining PDMS. Individual chips were cleaned with ethanol and DDI water and Scotch tape to remove any remaining dirt particles.

Microfluidic device bonding

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At least 1 day before use, individual chips and coverslips were cleaned in the sonic bath and rinsed in ethanol, isopropanol, and water. Both surfaces were exposed to Corona SB plasma treater (ElectroTechnics Model BD-20AC Hand-Held Laboratory Corona Treater) between 45 s and 1 min, then surfaces were brought together and introduced at 80°C in an oven overnight to obtain the enhanced bond strength.

Appendix 1

Appendix 1—key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Strain, BY4741 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 (Saccharomyces cerevisiae)	sRedM	Pérez-García et al., 2021	BY4741 pGK1:: mCherry	Selection: KanMX (G418)
Strain, sRedM (Saccharomyces cerevisiae)	cLPdGFP	This study	sRedM pMarOIacO:: IacR-VP64 (pEX1004, ADDGENE_194950) pMarOIacO::MarR-RD (pEX1005, ADDGENE_ 194951) pMarOIacO::dEGFP (UbG76V-EGFP) (pEX1006, ADDGENE_194952)	Selection: KanMX (G418) -Leu, -Ura, -His
Strain, BY4741 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 (Saccharomyces cerevisiae)	oLPKcsA*	This study	BY4741 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 pGal7:: IacR-VP64 (pEX1001, ADDGENE_ 194713) pGal7::MarR-RD (pEX1002 ADDGENE_ 194714) pMarOIacO::KcsA*(pEX1003, ADDGENE_194949)	Selection: -Leu, -Ura, -His
Strain, BY4741 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 (Saccharomyces cerevisiae)	sEmpty (control strain)	This study	BY4741 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 Empty insert plasmids pGADT7 (Takara Bio) backbone with auxotrophic markers	Selection: -Leu, -Ura, -His
Strain, BY4741 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 (Saccharomyces cerevisiae)	cLPKcsA*	This study	sRedM pMarOIacO:: IacR-VP64 (pEX1004, ADDGENE_194950) pMarOIacO::MarR-RD (pEX1005, ADDGENE_ 194951) pMarOIacO::KcsA*(pEX1003, ADDGENE_194949)	Selection: -Leu, -Ura, -His
Strain, BY4741 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 (Saccharomyces cerevisiae)	cLPTOK1*	This study	pMarOIacO:: IacR-VP64 (pEX1004, ADDGENE_194950) pMarOIacO::MarR-RD (pEX1005, ADDGENE_ 194951) pMarOIacO::TOK1*(pEX1008, ADDGENE_194954)	Selection: -Leu, -Ura, -His
Strain, BY4741 MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 (Saccharomyces cerevisiae)	cLPKcsA-EGFP*	This study	sRedM pMarOIacO:: IacR-VP64 (pEX1004, ADDGENE_194950) pMarOIacO::MarR-RD (pEX1005, ADDGENE_ 194951) pMarOIacO::KcsA- GFP*(pEX1007, ADDGENE_194953)	Selection: KanMX (G418) -Leu, -Ura, -His
Other, PMP dye: Thioflavin T	ThT	Fisher Scientific, Thermo Scientific	CAS: 2390-54-7
Other, PMP dye: Bis-(1,3-Dibutylbarbituric Acid)Trimethine Oxonol	DIBAC4(3)	VWR INTERNATIONAL EUROLAB, S.L	CAS: 70363-83-6
Other, PMP dye: 3,3'-di-n-propylthiacarbocyanine iodide	DIS-C3(3)	FISHER SCIENTIFIC	CAS: 53336-12-2

Data availability

All data are shown in the manuscript, figure supplements, or the supplementary files. Plasmids have been deposited to Addgene lab database https://www.addgene.org/browse/article/28233359/.

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Decision letter

Arthur Prindle

Reviewing Editor; Northwestern University, United States
Naama Barkai

Senior Editor; Weizmann Institute of Science, Israel
Joseph Larkin

Reviewer; Boston University, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Macroscopic control of synchronous electrical signaling with chemically-excited gene expression" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Reviewing Editor and Naama Barkai as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Joseph Larkin (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) The title/abstract should be refocused to de-emphasize communication/electrical signaling/synchronous, since the coordinated behavior is largely driven by external chemical stimuli. The mechanistic basis of the external chemical stimuli -> membrane potential change is the priority and is impactful enough on its own.

2) With that goal in mind, the rationale behind the excitable dynamics, as well as each of the molecular mechanistic steps, should be made clearer:

a) Why are excitable dynamics important here? This should be justified more clearly in terms of the synthetic biology application, as it makes implementation by other researchers more complex. Does the circuit as constructed truly exhibit excitable dynamics? This should be probed using their computational model along with further experiments. For example, by trying other non-periodic inputs or checking for a refractory period.

b) What is the clear causal chain of molecular events for producing a membrane potential change using chemical stimuli, including inducer uptake, excitable circuit activation, ion channel expression, ionic flux, membrane potential change, and finally membrane potential reporter fluorescence? This is the core contribution of the manuscript and is necessary for others to use this toolbox. This should be clearly illustrated with a schematic figure and each of these mechanistic steps should be tested with experimental controls. For example, by using a more standard voltage dye (such as TMRM or a voltage-sensitive protein) to track membrane potential, a K⁺ selective dye to track ionic flux, and matching degradation tags between KCsA and GFP to demonstrate the expression lifetime of the channel. Is it possible that membrane channels are more difficult to degrade than free proteins? Is it also possible that other ionic fluxes and/or cellular metabolism related to PMP could be interacting with the excitable circuit dynamics?

Reviewer #1 (Recommendations for the authors):

Overall, I think that this is a promising manuscript that should be accepted following minor experimental revisions to supplement the results with additional mechanistic controls (described below).

Specific Questions/Suggestions:

1) The authors should use a more standard voltage-sensitive dye (such as TMRM) or a voltage-sensitive protein. ThT can act as a voltage-sensitive dye due to its positive charge but is nonstandard

2) The authors should use a K+ dye to confirm that voltage changes are due to K+ flux

3) KcsA and GFP have different degradation tags. Can you show an equivalent expression in KcsA system to show it tracks the dynamics? What if KcsA is always present but some other cellular feedback (such as YMC) results in the dynamics? Is the yeast metabolic cycle contributing to these dynamics? How do the timescales compare and can YMC oscillations be observed in this setting?

4) The time histogram for periods is intuitive in Figure 1, why isn't that continued in Figures 2/3 instead using a power spectra? I think using the period histograms would give more confidence in the results.

5) Is the TOK1 channel constitutively expressed based on literature data or data from the authors? Is there a chelator of K1 toxin to confirm its role as a relevant diffusible signal? Or pulsing external K1 toxin and/or other channel blocker?

6) Scales bars and scale information would be useful, particularly for the colony experiments. Additionally, images in Figure 3b could be improved, and (along with movies) suggest heterogeneity of voltage response and toxicity of KcsA expression and K1 toxin in general. How long can this strategy be sustained due to toxicity? Is this a challenge to bioengineering/synthetic biology applications?

Reviewer #2 (Recommendations for the authors):

Overall, I think this is good work that should be published in eLife, as many in the community could benefit from novel approaches to synthetic biology – much more needs to be done in this emerging area. I do believe that the authors have supported their claim of control of synchronous electrical signaling via channel expression. However, overall, the presentation can be improved in a way that leads to a clear comprehension of what has been achieved. While synchronous fluorescence is achieved, what does this mean, that Vmem is identical across cells? Or just that Vmem is changing at the same rate? Or is it synchronous gene expression? It's not explicitly made clear but should be the key part of the introduction or methods. The greatest addition that can be made is a clear causal chain in Figure 1 drawing out the steps of channel expression, channel function, Vmem change, fluorescence, etc. If the goal is truly a tool or toolbox for others to use, this is necessary. Furthermore, the limited rigor in which the electrical activity was characterized, and the light discussion on drawbacks/limitations, reduces the impact of the claim that it is a 'robust synthetic transcriptional toolbox'. I think this is very nice work but needs to be presented a bit more thoughtfully.

General Remarks:

1. I would like to see an experimental null model that is not simply a control population (Sup 2-2, B), but where chemical stimulants are delivered in a manner seeking to abolish the periodicity.

2. In Figure 3D, it seems that this K1->TOK is slightly less reliable than the previous 2 experiments. There are a couple of communities that don't seem to sync as much. Why? This should be discussed.

3. It seems that the shorter the cycle, the less reliable the method (see figure S1, 5). I didn't see this mentioned anywhere.

4. In keeping with (2) and (3), there is little discussion of drawbacks/limitations/etc. – please add.

5. I'm not sure of the difficulty of the experiments, but many times you list that each experiment has been repeated, 'at least two times.' Why not give a precise sample size? N = 2 seems low, and perhaps the authors want to state what the limitations/difficulties were (which in turn bears on the issue of this being a toolbox – people need to know how many N's are reasonable).

6. While phase difference is a fine measure, there are many ways that periodic signals can be analyzed (wave shape, amplitude, etc.) including other measures of synchronicity. It may be useful to measure/characterize other aspects of how these electrical signals are related. I think this may be useful, as in Figure 2B and 3C that the mean dark line doesn't well represent the data spread.

Line Remarks:

1. Line 50-51: while I agree that ion expression may be noisy, it may also be attributed to biological degeneracy. It would be interesting to address this and how it may affect the results.

2. Line 69-71: I do not know why you chose Mar receptors, why it matters, the upsides/downsides, etc. Due to the earlier claim that this is a 'toolbox', please say more about these choices and what other choices could be made. As it stands, this is a single 'tool'.

3. Line 114-115: Does anything else contribute to potassium release? Are there any other mechanisms by which the PMP remains balanced? How does your method affect these, if at all?

4. Line 179-181: I do not understand the claim that this methodology is non-invasive. How would I do this in-vivo – don't you need a way to stimulate cells with chemicals in a periodic fashion?

5. Line 186-187: I would like to see in the discussion the author's thoughts on how this may disrupt electrical communication. In neuroscience, for example, electrical signaling is paramount for proper brain function. Would any system that depends on timed electrical communication not be eligible for this method?

Figure Remarks:

Overall, I think the figures need a bit more work and care put into them. They do not always communicate the ideas clearly, which is a shame given the valuable work:

1. Figure 1 – why is there a cyan channel inside the cell – what does this indicate?

2. Figure 1 – Most critical is to add a 'flow diagram' to walk me through what is happening overall. Figure A-B leave too much for my imagination. Especially for someone not familiar with the subject. Specifically, the causal chain downstream of the chemical stimulation – what happens next to the channel, the PMP, and eventually fluorescence – an explicit block diagram (and text) of what's driving what in this circuit.

3. Figure 1 (and others) the tiny boxes above C with SA/IAA are not obvious to see, nor to what they're doing. Again, more care should go into explaining the method and the results, as presenting this methodology is the entire point of the paper.

4. Figure 1D could have 'flow arrows' that better describe what's happening.

5. Figure 1 – The heat map is not labeled on the Y axis, and you reference specific community numbers a couple of times.

6. Figure 1E – The dashed colored lines make this too hard to read.

7. Figure S1, 3-4: These graphs are directly comparable, but have flipped y-axis. Why?

8. Figure 2E – I may be wrong, but the PSD seems strange. The peak of the dotted lines is ~0.002 Hz, which is around 8.3 minutes. However, this a 1h induced period. Is this off by a factor of 10? 0.0002 is closer to 1.3 hours.

9. Figure 2E – The entire point of this graph is to show that you can make a signal with a given frequency. However, I have no way to know what those peaks are, because they are not labeled, and the x-axis is making readers guess.

10. Figure S1, 6 A – please mark peaks or give me a x-axis that lets me guess better.

11. Figure S1, 6 D – I would not consider the variation here low. In fact, the title of the figure seems misleading. While yes, there is little change across stimulation period/shape the actual values are quite variable.

Small typos:

1. Line 37, 'which in turn provides (a?) power reservoir'

Reviewer #3 (Recommendations for the authors):

We would like to reiterate that this paper impressed us and we are enthusiastic about it.

First, here are some suggestions for addressing the two major issues we mentioned;

– To address this first issue, we think it is possible to remove references to signaling or communication within the text and focus it on chemical control of membrane potential. Again, we think that result in itself is impactful. The text and figures should make it clearer that the data show a group of cells all independently responding to the same driving stimulus. This is not engineering communication. It is a step toward that goal.

Another option would be for the authors to present analysis of the existing data that demonstrates spatial signal propagation.

We do not think Figure 2 supplement 3 should be included in the paper unless there is clear observation of a spatially propagating excitable signal.

– We suggest multiple approaches to argue for excitable dynamics. First, the authors could experimentally test several predictions of the excitable model with the microfluidic system. Do they observe a refractory period? Do they observe the expected behavior from the model if only one of the phytohormones is added or taken away? Supplements 3 and 4 of Figure 1 provide some support, but those results are not compared to specific predictions or a non-excitable scenario.

We have several overall questions and suggestions:

– Please describe the device in more detail. How physically large is each well? Roughly how many cells are contained in each well? When reporting average fluorescence values from colonies, roughly how many cells are being averaged over?

– The text often remarks about noise and how the system buffers noise. However, the Figure 1 video shows notable heterogeneity in GFP expression. Some cells have low signal, others very high. Is this expected for the excitable circuit? At the same time, the ThT movies from Figure 2 appear less heterogeneous, which is interesting given that the experiments have the same underlying circuit. Is this due to some buffering of noise by physiology that maintains membrane potential? Could it be due to buffering of cells by each other when they all release or take up potassium? What do the authors think about this? Or are we wrong about our observations of heterogeneity? The text presents no analysis, so one can only guess by looking at the movies.

– As described above, is it possible to perform a co-culture experiment of wild-type cells with the engineered KcsA* strain and drive the engineered strain with chemical stimuli? This would result in collective potassium leak by the engineered cells. Figure 2 supplement 1 suggests that this may modulate the membrane potential of the WT cells. While similar to the experiments of Figure 3, it may come closer to demonstrating electrical communication.

– The early discussion of TOK1 was distracting. We believe that TOK1 can be introduced with Figure 3.

– What do we know about the relevance of membrane potential in yeast? Given what we know, does this system offer any way to control yeast physiology? If the authors have any thoughts on this, it would be great to include those in the concluding remarks.

There are some components of the paper that were highlighted, but we didn't fully grasp their importance. It would be great if the authors could describe the importance of these aspects more. Here are the components whose importance we would like to better understand:

– Why is construction of an excitable circuit central to this result? Reasons to do this would be to synchronize cells and to create a spatially propagating wave. However, as we have indicated, it does not appear in the data that the system does these things.

– What is the importance of the phase drift measurements? Does the different phase drift for different stimulation patterns tell us something about the synthetic circuit?

We have several comments on the figures:

– Figure 1A and 1B are confusing. Figure 1A shows control of ion channels, which is the point of the paper, but not of Figure 1. This sets up the expectation that the results of Figure 1 are with ion channels. Figure 1B is very difficult to read. Perhaps color-coding the regulatory arrows for the two parts of the circuit would make it more clear? Or showing a simplified version like that of Figure 2A? As is, it takes a lot of examination and thought to understand what Figure 1B is showing.

– Is it possible to show where the pulses of the phytohormones are happening on the time trace graphs as shading in the background throughout the time trace? As the figures are now, it is difficult to tell that the chemical stimuli are periodic.

– In the autocorrelation graphs, why is one curve a heavy black line and the others light, colored, dotted lines? This makes it difficult to read the colored lines and leads the reader to believe there is something fundamentally different about those conditions from the black line.

– A small comment: is it possible to use a different color scale for ThT and GFP heatmaps? Or add color bar scales to the heatmaps with labels like "GFP Intensity" or "ThT Intensity"?

We believe some panels in the supplements could be brought into the main figures:

– Figure 1 – supplement 1B and D, could be added to main text Figure 1 to illustrate the excitable dynamics of the circuit.

– Figure 2 supplement 2A and B are essential and support what we believe is the most impressive result here, engineering the ability to dynamically control cellular membrane potential. Perhaps ACFs could be computed and compared for the two examples in this supplementary figure also.

https://doi.org/10.7554/eLife.78075.sa1

Author response

Essential revisions:

1) The title/abstract should be refocused to de-emphasize communication/electrical signaling/synchronous, since the coordinated behavior is largely driven by external chemical stimuli. The mechanistic basis of the external chemical stimuli -> membrane potential change is the priority and is impactful enough on its own.

We thank all three Reviewers for all constructive suggestions that with no doubt helped us to improve the original manuscript. We concur that coordinated behavior is guided by external stimuli in our systems, however, individual communities synchronize with each other by maintaining the low phase drift over time (guided by phytohormone stimuli). This synchronization mechanism has noise-filtering capacity, and it is based on Mar proteins (Perez-Garcia et al., Nat Comm, 2021). On contrary, classically driven systems build in bacteria (i.e., TetR, AraC, LacI) are limited by increasing phase drift which eventually leads to desynchrony among individual cells and populations (i.e., Mondragón-Palomino et al., 2011, Science). This is a key distinction between these classic driven systems and the coordinated system guided by external stimuli presented in our work.

In the revised version of the manuscript, we also demonstrate that the incorporation of feedbacks provides significantly more robust solution for coordinated expression of ion channels than that of the open-loop system.

Generally, we have substantially revised the manuscript including its structure, key message and title by focusing on the main finding; the coordinated regulation of ion channel expression that allows controllable modulation of PMP at the macroscopic scale in the eukaryotic model system.

2) With that goal in mind, the rationale behind the excitable dynamics, as well as each of the molecular mechanistic steps, should be made clearer:

a) Why are excitable dynamics important here? This should be justified more clearly in terms of the synthetic biology application, as it makes implementation by other researchers more complex. Does the circuit as constructed truly exhibit excitable dynamics? This should be probed using their computational model along with further experiments. For example, by trying other non-periodic inputs or checking for a refractory period.

In the revised version we added several new experimental datasets. For instance, we tested the open-loop system (revised Figure 1) against the closed-loop system which incorporates positive and negative feedbacks (revised Figures 2 and 3). This additional control revealed the importance of circuit architecture for translating external stimuli into coordinated cellular behavior. We demonstrate that open-loop system can control ion channel expression but it seems considerably less robust then the closed-loop system when it comes to controlling changes in PMP which now has been discussed in the revised version (lines 107-120). Positive and negative feedbacks allow noise filtering and increases the responsiveness of the system which is reflected in coordinated PMP changes in yeast communities (revised Figure 3). Furthermore, we tested putative excitability using a long-term pulse of auxin. We found that our system could mimic the refractory dynamics seen in other excitable systems and our system was not able to respond to the subsequent pulse of phytohormone (Figure 2B and 2C, lines 138-149). Consistently, we observed a pulse of reporter response in the static environment (Figure 2—figure supplement 4). These observations suggest there may be a putative excitability that is an intrinsic property of our closed-loop system.

b) What is the clear causal chain of molecular events for producing a membrane potential change using chemical stimuli, including inducer uptake, excitable circuit activation, ion channel expression, ionic flux, membrane potential change, and finally membrane potential reporter fluorescence? This is the core contribution of the manuscript and is necessary for others to use this toolbox. This should be clearly illustrated with a schematic figure and each of these mechanistic steps should be tested with experimental controls. For example, by using a more standard voltage dye (such as TMRM or a voltage-sensitive protein) to track membrane potential, a K⁺ selective dye to track ionic flux, and matching degradation tags between KCsA and GFP to demonstrate the expression lifetime of the channel. Is it possible that membrane channels are more difficult to degrade than free proteins? Is it also possible that other ionic fluxes and/or cellular metabolism related to PMP could be interacting with the excitable circuit dynamics?

As suggested, we have now improved presentation of system components in Figures to clarify sequence of molecular events. We would also like to stress that the systems presented in this work is a first step towards control of ion channel expression and PMP in eukaryotes, as thus should not be considered as a ‘ready to use’ toolbox as certainly more work is required to establish such proper toolbox with exchangeable components and tunable dynamics. The following revisions and experiments were added to the corrected version of the manuscript:

1) Open-loop synthetic circuit integrating MarR and IacR controls expression of KcsA channel and this circuit is sensitive to both IAA and SA (revised Figure 1). Closed-loop synthetic circuit controlling heterologous KcsA* or native TOK1* channels, respectively (Figures 2 and 3).

2) Cyclic changes in ion channel expression increases or decrease potassium channels expression (Figure 1A) with is typically associated with change in PMP, for instance the overproduction of potassium channels cause membrane hyperpolarization while channels mutants cause PMP depolarization (i.e., Ahmed, A. et al. 1999, Cell; Mackie, T. D. and Brodsky, J. L, 2018, Genetics; Sesti, F et al., 2001, Cell).

3) We found that commonly used plasma membrane potential dyes used in yeast (i.e. Peña et al., 2020), such as DIBAC4(3) and DIS-C(3)3 are inferior to ThT in terms of signal strength, stability, hydrophobicity and linearity (dynamic range) of potassium change detection (Figure 1—figure supplement 2, lines 97-103). We did not use TMRM as this was used primarily to monitor mitochondrial membrane potential, and not plasma membrane potential which is outside of the scope of this work.

4) We have constructed KcsA-EGFP fusion construct maintaining ODC degradation tag; the same tag that used for both MarR and IacR. We demonstrate that KcsA* channel follows similar dynamics of that of dGFP or ThT fluorescence characterized by peaks of expression coordinated in a macroscopic context (Figure 2—figure supplement 7, main text lines 159165). Therefore, degradation tags that we used provide robust changes in the half-life time of both dGFP reporter and KcsA* ion channel. It is important to note that degradation tag used for the construction of dGFP is based on N-terminal degron rule and cannot be used for construction of KcsA as KcsA requires N-terminal signal peptide for the proper membrane targeting.

5) We constructed an engineered version of native yeast potassium channel (TOK1*) and we tested whether our circuit could control native voltage-sensitive channel by directly modulating TOK1 expression levels (Figure 3F, lines 209-220). We found that regulated expression of TOK1 provided organized changes in ThT fluorescence similar to those observed for synthetic KcsA*, indicating that control of ion channel dosage provides a more general strategy to control PMP dynamics.

6) We cannot completely exclude that cell metabolism or other ions could influence PMP changes in yeast. However, we have now tested three different periods of phytohormone stimuli (1h, 2h,3h) and we see coherent changes of ThT fluorescence that adapt to changes in ion channel expression. Furthermore, the response robustness was dependent on circuit architecture (open-loop versus closed-loop). In our view it is very unlikely some other metabolic processes or ions could provide such selective control of PMP changes that we observed in our engineered yeast populations.

7) We attempted to measure K⁺ flux using commercial ION Potassium Green-2 AM, K⁺ indicator (Abcam) in yeast which, to our knowledge, was never used before in yeast. However, we were unsuccessful in detecting any fluorescence even after control treatments with KCL. It is possible that dye is quenched or yeast does not have esterases to hydrolyse the AM. Again, to our knowledge this is the only fluorescent dye able to monitor K⁺ changes, thus more work is needed to establish robust K⁺ dyes in yeast before they could be used as robust track K⁺ changes in yeast. We feel this is currently outside of scope of this work.

Reviewer #1 (Recommendations for the authors):

Overall, I think that this is a promising manuscript that should be accepted following minor experimental revisions to supplement the results with additional mechanistic controls (described below).

Specific Questions/Suggestions:

1) The authors should use a more standard voltage-sensitive dye (such as TMRM) or a voltage-sensitive protein. ThT can act as a voltage-sensitive dye due to its positive charge but is nonstandard

We tested several dyes including most commonly used PMP marker in yeast such as DIBAC4(3) and DIS-C3(3) (i.e. Peña et al., 2020) and we found ThT stands out in terms of stability, fluorescence strength, dynamic range, low hydrophobicity and absorption properties as compared to other dyes (Figure 1- figure supplement 2, and lines 97-103)., which is consistent with previous studies (i.e. Peña et al., 2020). TMRM was so far used to monitor static potential of mitochondrial membrane and thus is not suitable for long-term live imaging of plasma membrane potential. In early stage of this study, we indeed tested some voltage sensitive proteins i.e., ArcLight (Walrati Limapichat, et al., 2020) but signal intensities were marginal and dynamic range was very poor making these markers unsuitable for time-lapse cell imaging in microfluidic devices.

2) The authors should use a K+ dye to confirm that voltage changes are due to K+ flux

We attempted to measure K+ flux using commercial ION Potassium Green-2 AM, K+ indicator (Abcam) for first time in yeast. However, we were unsuccessful in detecting any fluorescence even after control treatments with KCL. It is possible that dye is quenched or yeast does not have esterases to hydrolyse the AM. To our knowledge this is the only dye able to monitor K+ changes, thus more studies is needed to establish working K+ dyes in yeast before they could be used as robust track K+ changes in yeast. We feel it is currently outside of scope of this work.

3) KcsA and GFP have different degradation tags. Can you show an equivalent expression in KcsA system to show it tracks the dynamics? What if KcsA is always present but some other cellular feedback (such as YMC) results in the dynamics? Is the yeast metabolic cycle contributing to these dynamics? How do the timescales compare and can YMC oscillations be observed in this setting?

We have constructed KcsA*-EGFP fusion construct maintaining ODC degradation tag that was also used for both MarR and IacR circuit components. We demonstrate that KcsA* channel follows similar dynamics of that of dGFP or ThT fluorescence characterized by peaks of expression coordinated in a macroscopic context (Figure 2- figure supplement 7, lines 159-166). Therefore, degradation tags that we used provide robust changes in the halflife time of both dGFP and KcsA* ion channel. It is important to note that degradation tag used for the construction of dGFP is based on N-terminal degron rule and cannot be used for the construction of KcsA as KcsA requires N-terminal signal peptide for proper membrane targeting.

4) The time histogram for periods is intuitive in Figure 1, why isn't that continued in Figures 2/3 instead using a power spectra? I think using the period histograms would give more confidence in the results.

In the revised manuscript, we have used violin plots to demonstrate distribution of periods in all experiments to ease comparison between different scenarios.

5) Is the TOK1 channel constitutively expressed based on literature data or data from the authors? Is there a chelator of K1 toxin to confirm its role as a relevant diffusible signal? Or pulsing external K1 toxin and/or other channel blocker?

We have included new experiments and remove K1 data from the manuscript as it may indeed confuse readers. Currently, we are unable to control K1 toxin half-life due to its extracellular, and middle-man nature. Instead, we decided to control TOK1 expression directly with our synthetic circuit (revised Figure 3). We constructed an engineered version of native yeast potassium channel (TOK1*) and we tested whether our circuit could control native voltage-sensitive channel by modulating TOK1 expression levels (Figure 3F-H, lines 198-220). We found that regulated expression of TOK1 provided organized changes in ThT fluorescence similar to those observed for synthetic KcsA*, indicating that control of ion channel dosage provides a more general strategy to control PMP dynamics.

6) Scales bars and scale information would be useful, particularly for the colony experiments. Additionally, images in Figure 3b could be improved, and (along with movies) suggest heterogeneity of voltage response and toxicity of KcsA expression and K1 toxin in general. How long can this strategy be sustained due to toxicity? Is this a challenge to bioengineering/synthetic biology applications?

We have included scale bars to the figures. Answering the toxicity question, yes, the toxicity is a recognizable challenge in synthetic biology applications, therefore it is critical to increase degradation rates of effectors to maintain low level of toxicity in such synthetic systems. This issue was one of main reasons for integrating degron tags into KcsA* and TOK1* in our study.

Reviewer #2 (Recommendations for the authors):

Overall, I think this is good work that should be published in eLife, as many in the community could benefit from novel approaches to synthetic biology – much more needs to be done in this emerging area. I do believe that the authors have supported their claim of control of synchronous electrical signaling via channel expression. However, overall, the presentation can be improved in a way that leads to a clear comprehension of what has been achieved. While synchronous fluorescence is achieved, what does this mean, that Vmem is identical across cells? Or just that Vmem is changing at the same rate? Or is it synchronous gene expression? It's not explicitly made clear but should be the key part of the introduction or methods. The greatest addition that can be made is a clear causal chain in Figure 1 drawing out the steps of channel expression, channel function, Vmem change, fluorescence, etc. If the goal is truly a tool or toolbox for others to use, this is necessary. Furthermore, the limited rigor in which the electrical activity was characterized, and the light discussion on drawbacks/limitations, reduces the impact of the claim that it is a 'robust synthetic transcriptional toolbox'. I think this is very nice work but needs to be presented a bit more thoughtfully.

We do not measure Vmem directly, instead we use cationic dye that translocate in the event of PMP change. We see coordinated changes in ThT fluorescence which are of course the proxy for Vmem changes. But the underlying mechanism is the synchronized expression of ion channels. We show that changing dosage of ion channels in the cells may be sufficient to induce macroscopically organized changes in PMP. In the revised manuscript we improved and clarify sequence of events that led us conclude that coordinated changes in ion channels expression can control PMP on the extended spatial-temporal scale.

General Remarks:

1. I would like to see an experimental null model that is not simply a control population (Sup 2-2, B), but where chemical stimulants are delivered in a manner seeking to abolish the periodicity.

Since our synthetic circuit responds directly to phytohormone changes it is expected that any unregular changes in stimuli will disrupt regular expression of ion channels. In our current flow setup, we are unable to generate chaotic unregular stimuli. However, we show that extending time of pulses can reveal underlying the refractory-like dynamics (Figure 2B, C). Importantly, we have included one more important control for testing influence of circuit architecture on the ion channel expression. In particular, we removed the feedback layers, creating open-loop systems and we demonstrate that it is substantially less reliable than its feedback-integrating circuit counterpart (please compare revised Figure 1 vs Figure 3).

2. In Figure 3D, it seems that this K1->TOK is slightly less reliable than the previous 2 experiments. There are a couple of communities that don't seem to sync as much. Why? This should be discussed.

We have included new experiments and remove K1 data from the manuscript as it may indeed confuse readers. In fact, we cannot control K1 toxin half-life due to its extracellular, middle-man nature, and toxicity effects are pronounced compared to a direct use of potassium channel such as KcsA*. Instead, we provided new data on the control of TOK1 expression with our synthetic circuit (see revised Figure 3) using an engineered version of native yeast potassium channel (TOK1*). We tested whether our circuit could control native voltagesensitive channel by modulating TOK1 expression levels. We found that regulated expression of TOK1 provided organized changes in ThT fluorescence similar to those observed for synthetic KcsA*, indicating that control of ion channel dosage may provide a more general strategy to control PMP dynamics (lines 209-220).

3. It seems that the shorter the cycle, the less reliable the method (see figure S1, 5). I didn't see this mentioned anywhere.

Shortest cycles of 30min are close to half-life time of fluorescence marker dGFP which is most likely the limiting factor for robust measurements of super rapid fluorescence changes. In fact, in our previous work, we show that the faster the cycle the more reliable system becomes (Perez-Garcia et al., 2021, Nat Comm) up to the limits of degradation machinery.

4. In keeping with (2) and (3), there is little discussion of drawbacks/limitations/etc. – please add.

We discuss limitations of this approach in the revised concluding remarks section.

5. I'm not sure of the difficulty of the experiments, but many times you list that each experiment has been repeated, 'at least two times.' Why not give a precise sample size? N = 2 seems low, and perhaps the authors want to state what the limitations/difficulties were (which in turn bears on the issue of this being a toolbox – people need to know how many N's are reasonable).

We have clarified the descriptions. n in many cases such as synchrony index is the number of analyzed communities (biological replicates). Each community consists of around 10000 cells (500um x 500um traps). Then, each experiment was repeated at least two time (technical replicates) on independent days. For instance, if we analyzed n=25* communities that means we took into account 250000 cells biological replicates x 2 technical replicates. For measurements of amplitudes, periods, peak widths we took all data for each of communities to present cumulative distributions. Therefore, we believe that represents statistically sounds numbers. We clarified description of biological and technical replicates in the revised manuscript.

6. While phase difference is a fine measure, there are many ways that periodic signals can be analyzed (wave shape, amplitude, etc.) including other measures of synchronicity. It may be useful to measure/characterize other aspects of how these electrical signals are related. I think this may be useful, as in Figure 2B and 3C that the mean dark line doesn't well represent the data spread.

Now we analyzed synchronicity using three different methods:

1) we calculate cumulative autocorrelations of response between communities.

2) To complement autocorrelation analysis, we developed a quantitative metric of

‘synchrony index’ defined as 1 – R where R is the ratio of differences in subsequent ThT peak positions amongst cell communities (phase) to expected period. This metrics describes how well synchronized are fungi colonies with each other under guidance of the common environmental signal.

3) we analyzed amplitudes and peak widths for all presented scenarios and we conclude that while periods and peak widths are robust across communities there is a noticeable variation in amplitudes (i.e., Figure 3E).

Based on those, we present multi-step rigorous approach to explore different aspects of oscillatory signal characteristics.

Line Remarks:

1. Line 50-51: while I agree that ion expression may be noisy, it may also be attributed to biological degeneracy. It would be interesting to address this and how it may affect the results.

We thank Reviewer for this interesting comment.

To demonstrate importance of noise filtering, we show how the open-loop system that lacks noise filtering capacity (no feedback) performs much worse than its closed-loop counterpart in terms of PMP modulation on the macroscopic scale (compare Figure 1 and Figure 3). This indicates that a feedback-controlled ion expression is necessary to steer PMP in a coherent manner (lines 193-196).

2. Line 69-71: I do not know why you chose Mar receptors, why it matters, the upsides/downsides, etc. Due to the earlier claim that this is a 'toolbox', please say more about these choices and what other choices could be made. As it stands, this is a single 'tool'.

In revised manuscript we elaborated on the rationale for using Mar-based system that directly relates to our recent work (Perez-Garcia et al., 2021, Nat Comm) (lines 75-85). Again, we would like to stress that we propose a foundation for development of new tools or toolboxes for rational engineering of electrophysiology in eukaryotes. Therefore, this work should be treated as initial proof of concept rather than ‘ready to use’ modular toolbox, we concur more work is needed in the future to establish standard for this new approach (lines 244-250). We apologize for misunderstanding and adjust the text accordingly.

3. Line 114-115: Does anything else contribute to potassium release? Are there any other mechanisms by which the PMP remains balanced? How does your method affect these, if at all?

We are not aware of any other mechanism that could provide such selective regulation of PMP in yeast that we can control thorough environment. We believe that changes of PMP that we recorded are attributed to the architecture of synthetic circuit as we reporter significant differences in open-loop and closed-loop circuit variants. In particular feedback integrating circuits representation can robustly control ion channel expression thorugh environmental stimuli.

4. Line 179-181: I do not understand the claim that this methodology is non-invasive. How would I do this in-vivo – don't you need a way to stimulate cells with chemicals in a periodic fashion?

We apologize for the confusion. Now we removed that claim from the revised manuscript.

5. Line 186-187: I would like to see in the discussion the author's thoughts on how this may disrupt electrical communication. In neuroscience, for example, electrical signaling is paramount for proper brain function. Would any system that depends on timed electrical communication not be eligible for this method?

Our system could be integrated to control native ion channels to either disrupt or coordinate electrophysiology of cells. Furthermore, native system could be plugged to control timing of Mar protein dynamics to synchronize output with timed input as suggested by Reviewer. We included that speculation in revised manuscript (lines 238-250).

Figure Remarks:

Overall, I think the figures need a bit more work and care put into them. They do not always communicate the ideas clearly, which is a shame given the valuable work:

1. Figure 1 – why is there a cyan channel inside the cell – what does this indicate?

This has been clarified in the revised version of the manuscript.

2. Figure 1 – Most critical is to add a 'flow diagram' to walk me through what is happening overall. Figure A-B leave too much for my imagination. Especially for someone not familiar with the subject. Specifically, the causal chain downstream of the chemical stimulation – what happens next to the channel, the PMP, and eventually fluorescence – an explicit block diagram (and text) of what's driving what in this circuit.

This has been improved in revised version (Figures 1A and 2A and 3F).

3. Figure 1 (and others) the tiny boxes above C with SA/IAA are not obvious to see, nor to what they're doing. Again, more care should go into explaining the method and the results, as presenting this methodology is the entire point of the paper.

Amended in the revised version. We would like to comment that ‘toolbox’ creation is not the key point of this paper but rather the concept of using ion channel expression to control electrophysiology in cellular collectives.

4. Figure 1D could have 'flow arrows' that better describe what's happening.

Amended in the revised version of Figure 1C panel and legend.

5. Figure 1 – The heat map is not labeled on the Y axis, and you reference specific community numbers a couple of times.

Amended in the revised version.

6. Figure 1E – The dashed colored lines make this too hard to read.

Amended in the revised version.

7. Figure S1, 3-4: These graphs are directly comparable, but have flipped y-axis. Why?

These diagrams are not directly comparable. One is a heat map of SA-IAA concentration gradient (3) at given time point (entrance in stationary phase) while (4) has additional time component.

8. Figure 2E – I may be wrong, but the PSD seems strange. The peak of the dotted lines is ~0.002 Hz, which is around 8.3 minutes. However, this a 1h induced period. Is this off by a factor of 10? 0.0002 is closer to 1.3 hours.

Yes, we thank Reviewer for spotting this error it should be 10x less indeed, however we decided to not to use PSD diagrams in revised version to increase clarity and simplicity of analysis.

9. Figure 2E – The entire point of this graph is to show that you can make a signal with a given frequency. However, I have no way to know what those peaks are, because they are not labeled, and the x-axis is making readers guess.

It has been corrected in revised version.

10. Figure S1, 6 A – please mark peaks or give me a x-axis that lets me guess better.

This panel has been removed in revised version as all other PSD diagrams.

11. Figure S1, 6 D – I would not consider the variation here low. In fact, the title of the figure seems misleading. While yes, there is little change across stimulation period/shape the actual values are quite variable.

In revised version we removed CV analysis and focused on direct analysis of peak period, widths and amplitudes to clarify and simplify overall analysis of wave patterns and show cumulative distributions among all communities (space) and in time.

Small typos:

1. Line 37, 'which in turn provides (a?) power reservoir'

Corrected accordingly.

Reviewer #3 (Recommendations for the authors):

We would like to reiterate that this paper impressed us and we are enthusiastic about it.

First, here are some suggestions for addressing the two major issues we mentioned;

– To address this first issue, we think it is possible to remove references to signaling or communication within the text and focus it on chemical control of membrane potential. Again, we think that result in itself is impactful. The text and figures should make it clearer that the data show a group of cells all independently responding to the same driving stimulus. This is not engineering communication. It is a step toward that goal.

As suggested, we focused the story on coordinated regulation of ion channel expression and its impact on electrophysiology of individual cells in the collective. We removed connotation to communication or signaling between cells.

Another option would be for the authors to present analysis of the existing data that demonstrates spatial signal propagation.

We do not think Figure 2 supplement 3 should be included in the paper unless there is clear observation of a spatially propagating excitable signal.

– We suggest multiple approaches to argue for excitable dynamics. First, the authors could experimentally test several predictions of the excitable model with the microfluidic system. Do they observe a refractory period? Do they observe the expected behavior from the model if only one of the phytohormones is added or taken away? Supplements 3 and 4 of Figure 1 provide some support, but those results are not compared to specific predictions or a non-excitable scenario.

To explore putative excitability in our system we present two observations. Firstly, a transient peak of dGFP expression that was recorded in the static environment (Figure 2 —figure supplement 4) and has not been seen in our previous study that involve open-loop system (Perez-Garcia et al., 2021, Nat Comm). Secondly, we added new data when we stimulated cells with long 12h peak of auxin followed by second peak and revealed refractory-like dynamics characterized by lack of response capability to subsequent stimuli (Figure 2B and 2C). We believe these datasets provide further evidence towards existence of excitability in our dual feedback circuit.

We have several overall questions and suggestions:

– Please describe the device in more detail. How physically large is each well? Roughly how many cells are contained in each well? When reporting average fluorescence values from colonies, roughly how many cells are being averaged over?

This description has been improved in revised version each trap/community contains ~10000 cells. Traps are approximately 500umx500umx7um.

– The text often remarks about noise and how the system buffers noise. However, the Figure 1 video shows notable heterogeneity in GFP expression. Some cells have low signal, others very high. Is this expected for the excitable circuit? At the same time, the ThT movies from Figure 2 appear less heterogeneous, which is interesting given that the experiments have the same underlying circuit. Is this due to some buffering of noise by physiology that maintains membrane potential? Could it be due to buffering of cells by each other when they all release or take up potassium? What do the authors think about this? Or are we wrong about our observations of heterogeneity? The text presents no analysis, so one can only guess by looking at the movies.

We believe dual feedback provide noise-filtering capabilities and responsiveness as compared to non-feedback alternative (see now revised Figure 1 and Figure 3). This is an eminent feature of putatively excitable system. We also show while periods and peak widths are very robust across communities, the amplitude may be quite variable (Figure 3E). Therefore, our circuit filters undesired frequencies but pass the noise in the amplitude of response (lines 193196, concluding remarks and various places in main text).

– As described above, is it possible to perform a co-culture experiment of wild-type cells with the engineered KcsA* strain and drive the engineered strain with chemical stimuli? This would result in collective potassium leak by the engineered cells. Figure 2 supplement 1 suggests that this may modulate the membrane potential of the WT cells. While similar to the experiments of Figure 3, it may come closer to demonstrating electrical communication.

As mentioned above, we have decided to revise structure of the story and remove connotation to electrical communication at present and focus on regulation of ion channel expression and non-coupled coordination of cell electrophysiology for a general clarity. We would prefer to explore this exciting possibility in the follow-up study.

– The early discussion of TOK1 was distracting. We believe that TOK1 can be introduced with Figure 3.

We rearranged and put more context to TOK1 in revised version (lines 198-220). By engineering TOK1 into our circuit (Figure 3F-H) we show that our concept could apply more generally to native potassium channels.

– What do we know about the relevance of membrane potential in yeast? Given what we know, does this system offer any way to control yeast physiology? If the authors have any thoughts on this, it would be great to include those in the concluding remarks.

In revised manuscript we discussed how this concept could be use more generally to control cell electrophysiology (lines 229-250).

There are some components of the paper that were highlighted, but we didn't fully grasp their importance. It would be great if the authors could describe the importance of these aspects more. Here are the components whose importance we would like to better understand:

– Why is construction of an excitable circuit central to this result? Reasons to do this would be to synchronize cells and to create a spatially propagating wave. However, as we have indicated, it does not appear in the data that the system does these things.

In revised manuscript we discuss that putative excitability via dual feedback design could offer noise buffering capability supported by direct comparison with the open-loop system (Figures 1 and 3).

– What is the importance of the phase drift measurements? Does the different phase drift for different stimulation patterns tell us something about the synthetic circuit?

To remove confusions, we have analyzed synchronicity using following measures in the revised version:

1) we calculate cumulative autocorrelations of response between each community.

2) To complement autocorrelation analysis, we developed a quantitative metric of

3) we analyzed amplitudes and peak widths for all presented scenarios.

All these metrics show that circuit is robust in terms of period and width of response but show variability in amplitudes. We do not see big changes in overall synchronicity for different stimuli frequencies (i.e. Figure 3D). However, we do see significant decrease of SI after removing feedback layer (Figure 1I) which tells us that circuit architecture is central to the performance of our system.

We have several comments on the figures:

– Figure 1A and 1B are confusing. Figure 1A shows control of ion channels, which is the point of the paper, but not of Figure 1. This sets up the expectation that the results of Figure 1 are with ion channels. Figure 1B is very difficult to read. Perhaps color-coding the regulatory arrows for the two parts of the circuit would make it more clear? Or showing a simplified version like that of Figure 2A? As is, it takes a lot of examination and thought to understand what Figure 1B is showing.

We have simplified and clarified circuit diagrams in the revised version of figures to further guide readers.

– Is it possible to show where the pulses of the phytohormones are happening on the time trace graphs as shading in the background throughout the time trace? As the figures are now, it is difficult to tell that the chemical stimuli are periodic.

We provided pulses of phytohormones on the top of each time-course datasets (black box for SA and green box for IAA) in all adapted figures

– In the autocorrelation graphs, why is one curve a heavy black line and the others light, colored, dotted lines? This makes it difficult to read the colored lines and leads the reader to believe there is something fundamentally different about those conditions from the black line.

Color corresponds to different observable periods. Figure has been improved.

– A small comment: is it possible to use a different color scale for ThT and GFP heatmaps? Or add color bar scales to the heatmaps with labels like "GFP Intensity" or "ThT Intensity"?

Now, we provide separate heatmap labels for both dGFP and ThT.

We believe some panels in the supplements could be brought into the main figures:

– Figure 1 – supplement 1B and D, could be added to main text Figure 1 to illustrate the excitable dynamics of the circuit.

This has been amended in revised Figure 1.

– Figure 2 supplement 2A and B are essential and support what we believe is the most impressive result here, engineering the ability to dynamically control cellular membrane potential. Perhaps ACFs could be computed and compared for the two examples in this supplementary figure also.

Now, we added critical datasets with cyclic stimuli for two different ion channels and two circuit architectures (open-loop (Figure 1) and closed-loop (Figures 2 and 3). Data on control condition have been also presented in revised Figure 1).

https://doi.org/10.7554/eLife.78075.sa2

Article and author information

Author details

Mario García-Navarrete

Centro de Biotecnologıa y Genomica de Plantas (Universidad Politecnica de Madrid – Instituto Nacional de Investigacion y Tecnologıa Agraria y Alimentaria), Pozuelo de Alarcón, Spain

Contribution
Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft

Contributed equally with
Merisa Avdovic and Sara Pérez-Garcia

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-1899-8206
Merisa Avdovic

Centro de Biotecnologıa y Genomica de Plantas (Universidad Politecnica de Madrid – Instituto Nacional de Investigacion y Tecnologıa Agraria y Alimentaria), Pozuelo de Alarcón, Spain

Contribution
Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

Contributed equally with
Mario García-Navarrete and Sara Pérez-Garcia

Competing interests
No competing interests declared
Sara Pérez-Garcia

Centro de Biotecnologıa y Genomica de Plantas (Universidad Politecnica de Madrid – Instituto Nacional de Investigacion y Tecnologıa Agraria y Alimentaria), Pozuelo de Alarcón, Spain

Contribution
Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology

Contributed equally with
Mario García-Navarrete and Merisa Avdovic

Competing interests
No competing interests declared
Diego Ruiz Sanchis

Centro de Biotecnologıa y Genomica de Plantas (Universidad Politecnica de Madrid – Instituto Nacional de Investigacion y Tecnologıa Agraria y Alimentaria), Pozuelo de Alarcón, Spain

Contribution
Formal analysis, Investigation, Methodology, Writing - original draft

Competing interests
No competing interests declared
Krzysztof Wabnik

Centro de Biotecnologıa y Genomica de Plantas (Universidad Politecnica de Madrid – Instituto Nacional de Investigacion y Tecnologıa Agraria y Alimentaria), Pozuelo de Alarcón, Spain

Contribution
Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing - original draft, Project administration, Writing - review and editing

For correspondence
k.wabnik@upm.es

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-7263-0560

Funding

Comunidad de Madrid (Programa de Atraccion de Talento 2017-2023 2017-T1/BIO-5654)

Krzysztof Wabnik

Ministerio de Ciencia, Innovación y Universidades (PGC2018-093387-A-I00)

Krzysztof Wabnik

Agencia Estatal de Investigación (SEV-2016-0672)

Krzysztof Wabnik

Agencia Estatal de Investigación (SEV-2016-0672-18-3:PRE2018-084946)

Merisa Avdovic

Universidad Politécnica de Madrid (Plan Propio Predoctoral fellow)

Mario García-Navarrete

The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Acknowledgements

We would like to thank Dr. Luis Rubio for providing BY4741 laboratory yeast strain used in this work. This work was supported by the Programa de Atraccion de Talento 2017 (Comunidad de Madrid, 2017-T1/BIO-5654 to KW), Severo Ochoa (SO) Programme for Centres of Excellence in R&D from the Agencia Estatal de Investigacion of Spain (grant SEV-2016-0672 (2017–2021) to KW via the CBGP). In the frame of SEV-2016-0672 funding MA received a PhD fellowship (SEV-2016-0672-18-3: PRE2018-084946). KW was supported by Programa Estatal de Generacion del Conocimiento y Fortalecimiento Cientıfico y Tecnologico del Sistema de I+D+I 2019 (PGC2018-093387-A-I00) from MICIU (to KW). UPM Plan Propio Predoctoral fellow finances MGN.

Senior Editor

Naama Barkai, Weizmann Institute of Science, Israel

Reviewing Editor

Arthur Prindle, Northwestern University, United States

Reviewer

Joseph Larkin, Boston University, United States

Publication history

Preprint posted: January 20, 2022 (view preprint)
Received: February 22, 2022
Accepted: October 21, 2022
Accepted Manuscript published: November 9, 2022 (version 1)
Version of Record published: November 30, 2022 (version 2)

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.