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Population-level amplification of gene regulation by programmable gene transfer

Nature Chemical Biology volume 21, pages 939–948 (2025)Cite this article

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Abstract

Engineering cells to sense and respond to environmental cues often focuses on maximizing gene regulation at the single-cell level. Inspired by population-level control mechanisms like the immune response, we demonstrate dynamic control and amplification of gene regulation in bacterial populations using programmable plasmid-mediated gene transfer. By regulating plasmid loss rate, transfer rate and fitness effects via Cas9 endonuclease, F conjugation machinery and antibiotic selection, we modulate the fraction of plasmid-carrying cells, serving as an amplification factor for single-cell-level regulation. This approach expands the dynamic range of gene expression and allows orthogonal control across populations. Our platform offers a versatile strategy for dynamically regulating gene expression in engineered microbial communities.

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Fig. 1: ADEPT: amplification of population-level gene expression control.
Fig. 2: ADEPT tunes plasmid abundance and population-level gene expression.
Fig. 3: Targeted cutting tunes plasmid abundance and total gene expression levels at the population level.
Fig. 4: Temporal control of population-level gene expression.
Fig. 5: Orthogonal control of population-level gene expression.
Fig. 6: Amplification of the response to an inflammatory bowel disease biomarker TTR.

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Data availability

The data that support the findings of this study are available via Zenodo at https://doi.org/10.5281/zenodo.14188774 (ref. 68). Sequence information of the promoter and RBS can be found at http://parts.igem.org/Promoters/Catalog/Anderson and https://parts.igem.org/Ribosome_Binding_Sites/Prokaryotic/Constitutive/Community_Collection, respectively. Source data are provided with this paper.

Code availability

Model simulation and data analysis code used in this study are deposited on Zenodo at https://doi.org/10.5281/zenodo.14188774 (ref. 68).

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Acknowledgements

We thank H. Ma, M. Lynch, E. Chory, C. Gersbach and J. Granek for insightful comments and suggestions. This work was supported by the National Institutes of Health (R01EB031869 and R01AI125604 to L.Y.) and a National Science Foundation Graduate Fellowship (to G.S.H.).

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Duke University, Durham, NC, USA

    Hye-In Son, Grayson S. Hamrick, Ashwini R. Shende, Kyeri Kim & Lingchong You

  2. Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA

    Kaichun Yang & Tony Jun Huang

  3. Center for Quantitative Biodesign, Duke University, Durham, NC, USA

    Lingchong You

  4. Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA

    Lingchong You

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Contributions

H.-I.S. and L.Y. conceived the idea. H.-I.S. and L.Y. designed the experimental and computational studies. H.-I.S., G.S.H. and K.K. cloned the plasmids. H.-I.S., G.S.H. and A.R.S. performed Fp and GFP measurements, persistence testing and temporal programming experiments. H.-I.S. conducted sgRNA screening and biosensor testing. H.-I.S. and L.Y. analyzed ordinary differential equation models. K.Y. fabricated the microfluidic devices. H.-I.S. and K.K. conducted timelapse microscopy experiments. H.-I.S., G.S.H. and L.Y. wrote and revised multiple versions of the manuscript. All the authors read and contributed revisions. L.Y. and T.J.H. supervised the work and acquired funding.

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Correspondence to Lingchong You.

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Extended data

Extended Data Fig. 1 Microscopic analysis of GFP distribution.

We tested three circuit variants: (A) the variant circuit encoding a perfectly matching spacer and constitutively expressing GFP-ssrA (circuit used in Fig. 2); (B) the variant circuit encoding a perfectly matching spacer and expressing GFP under pLac (circuit used in Extended Data Fig. 2); and (C) the variant circuit encoding a mismatching spacer expressing GFP under pLac (circuit used in Fig. 3). Overnight culture carrying each of the three circuits were first primed by diluting 1/100-fold in LB + 25 µg/mL Cm for 3 hours (37 °C, 225 rpm) in 50 mL Erlenmeyer flasks. Then, the cells were diluted another 1/10-fold and distributed into 4 types of media: (1) LB + 25 µg/mL Cm, (2) LB + 25 µg/mL Cm + 100 ng/mL ATc, (3) LB + 25 µg/mL Cm + 50 µg/mL Kan, and (4) LB + 25 µg/mL Cm + 50 µg/mL Kan + 100 ng/mL ATc in a 2-mL deep well plate. After another 3 hours of incubation (37 °C, 700 rpm), 1.5 µl of each sample was loaded on glass sides and was covered with cover slips. Cells were imaged on a microscope (Keyence, BZ-X800). At least five frames with moderate cell density were taken for each sample type. The acquired images were analyzed using standard Python packages (Numpy (version 1.25.2), Pandas (version 2.0.3), and Skimage (version 0.23.2)). The GFP values were log transformed before plotting the histogram for clearer visualization. All circuits showed a bimodality when the CRISPR/Cas9 interference was induced. When the spacer is perfectly matching, the bimodality is stronger, especially when Cas9-mediated cutting is not induced, suggesting a substantial basal level cutting activity.

Extended Data Fig. 2 Alternative circuit design for maximizing total GFP dynamic range.

(A) Schematic of the inducible ADEPT circuit. In this design, GFP is no longer ssrA-tagged, allowing a higher accumulation, and is regulated under the lactose-inducible promoter pLac, adding a new layer for gene expression control. All other control mechanisms (ATc, Kan, and Lin) remain the same. (B-D) The alternative design was tested under various inducer combinations by measuring the pTarget-carrying fraction (Fp) (B), normalized GFP intensity (C), and total GFP intensity (D). (B) For Fp measurement, cells were grown overnight in LB media supplemented with 25 μg/mL Cm + 50 μg/mL Kan in a 2-mL deep well plate. Cultures were diluted 1/1000-fold into 20 media conditions with combinations of ATc (0, 0.01, 0.1, 1, 10, and 100 ng/mL), Lin (0 and 3.2 mM), and Kan (0 and 50 μg/mL). After 4 hours, samples were plated on LB agar plates containing 25 μg/mL Cm ± 50 μg/mL Kan for CFU counting. Fp was calculated as the ratio of CFU from Cm + Kan plates from Cm-only plates. Dashed and solid lines represent the mean of four technical replicates. (C-D) For GFP measurement, overnight cultures were diluted 10-4-fold into the same 20 media conditions. No inducer was added for GFP expression since leaky expression under pLac was sufficient to observe the effect of ATc, Kan, and Lin. Samples were placed in a black-walled 96-well plate with 50 µl of mineral oil to prevent evaporation. After 15 hours at 37 °C, cell density (OD600) and GFP intensity (Ex: 488 nm; Em: 510 nm) were measured. The normalized GFP signal (C) showed a trend similar to total GFP intensity (D), demonstrating the robustness and modularity of the ADEPT design.

Source data

Extended Data Fig. 3 Suppressed state culture recovering to recapture the original dynamic trend.

Each of the suppressed state cultures from Fig. 3c were reinoculated in the same 4 types of media (10-3 fold dilution) and cultured for another 24 hours. GFP was measured from the recovered cultures at t = 48 hours. We observed the recovered cultures exhibited a large dynamic range as observed from the initial culture shown in Fig. 3c.

Source data

Extended Data Fig. 4 Additional persistence test results.

(A) Extended data from Fig. 3d, including showing two additional samples. The first four samples are identical to the those shown in Fig. 3d, with data from two more samples (Msp2 and Msp3) added. All samples are MG1655 + FHR cells carrying pCas9 and one of six pTargets: (1) pTarget encoding a targeting spacer (sp) that perfectly matches the target region but does not encode oriT; (2) pTarget carrying sp and oriT; (3) pTarget carrying a non-targeting spacer (NT) and oriT; (4) – (6): pTarget carrying sp with 1 or 2 base pair mismatches and oriT. The mismatched spacers are the same as those used in the first three panels of Fig. 3c, containing different sets of mismatches (see Supplementary Table 3 for sequences). Cells carrying pCas9 and one of the six pTargets were cultured in LB with 25 µg/mL Cm and diluted 1/1000 every 24 hours for 6 days. The fraction of cells carrying pTarget (Fp) were measured daily by plating on LB agar with 25 µg/mL Cm or 25 µg/mL Cm + 50 µg/mL Kan for CFU counting. Plasmid abundance (Fp) was calculated as: \({Fp}=\scriptstyle\frac{\frac{{CFU}}{{ml}}\text{counted from LB}+\text{Cm}+\text{Kan plates}}{\frac{{CFU}}{{ml}}\text{counted from LB}+\text{Cm plates}}\). The data shows that not all mismatches ensure plasmid persistence. The line plot represents the mean of four technical replicates. (B) All six maintained FHR after 6 days of passaging. The gray bar represents the mean of four technical replicates. Using Day 6 cultures, we measured the fraction of cells carrying FHR on blank LB agar plates and LB plates supplemented with 10 µg/mL tetracycline, which selects for FHR. Despite variability among replicates, nearly 100 % of samples maintained FHR.

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Statistical source data. Microscope images are deposited on Zenodo68.

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Source Data Extended Data Fig. 2

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Source Data Extended Data Fig. 4

Statistical source data. Microscope images are deposited on Zenodo68.

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Son, HI., Hamrick, G.S., Shende, A.R. et al. Population-level amplification of gene regulation by programmable gene transfer. Nat Chem Biol 21, 939–948 (2025). https://doi.org/10.1038/s41589-024-01817-9

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