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A quantitative method for proteome reallocation using minimal regulatory interventions

Nature Chemical Biology volume 16, pages 1026–1033 (2020)Cite this article

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An Author Correction to this article was published on 09 September 2020

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Abstract

Engineering resource allocation in biological systems is an ongoing challenge. Organisms allocate resources for ensuring survival, reducing the productivity of synthetic biology functions. Here we present a new approach for engineering the resource allocation of Escherichia coli by rationally modifying its transcriptional regulatory network. Our method (ReProMin) identifies the minimal set of genetic interventions that maximizes the savings in cell resources. To this end, we categorized transcription factors according to the essentiality of its targets and we used proteomic data to rank them. We designed the combinatorial removal of transcription factors that maximize the release of resources. Our resulting strain containing only three mutations, theoretically releasing 0.5% of its proteome, had higher proteome budget, increased production of an engineered metabolic pathway and showed that the regulatory interventions are highly specific. This approach shows that combining proteomic and regulatory data is an effective way of optimizing strains using conventional molecular methods.

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Fig. 1: Gene essentiality and TRN analysis in the predefined condition.
Fig. 2: Emerging properties from proteomics data integration.
Fig. 3: Proteome liberation calculations using ReProMin.
Fig. 4: Deleted TF proteome fraction mass estimation.
Fig. 5: Synthetic circuit characterization.
Fig. 6: Violacein pathway evaluation.

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Data availability

RNA-seq data from this study have been deposited in NCBI’s Gene Expression Omnibus (GSE134335). Additional data is available from the corresponding author upon reasonable request.

Code availability

The code and data to run ReProMin can be found at: https://github.com/utrillalab/repromin

Change history

  • 09 September 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

We thank E. Marquez-Zavala and C. Lloyd for metabolism and gene-expression model simulations support. Y. Castillo-Franco and C. F. Mendez-Cruz for computational support and G. Hernandez-Chavez, H. King, M. Hughes and A. Sicilia for technical support. We acknowledge the funding provided by UNAM–DGAPA-PAPIIT projects IA200716 and IA201518. Newton advanced Fellowship Project NA 160328. J.J. and J.K. acknowledge the support received from the Biology and Biotechnology Research Council (grant nos. BB/M009769/1 and BB/T011289/1) and European Union’s Horizon 2020 research and innovation program for the project P4SB (grant agreement no. 633962). G.L.P. acknowledges the Programa de Doctorado en Ciencias Biomédicas, Universidad Nacional Autónoma de México (UNAM), and the PhD scholarship 434655 from CONACyT.

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Authors and Affiliations

  1. Systems and Synthetic Biology Program, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México, Cuernavaca, Mexico

    Gustavo Lastiri-Pancardo, Jonathan S. Mercado-Hernández & José Utrilla

  2. Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK

    Juhyun Kim & José I. Jiménez

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  1. Gustavo Lastiri-Pancardo
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Contributions

J.U. and G.L.P. designed ReProMin. G.L.P. developed computational methods and performed data analysis. G.L.P. and J.S.M.H. carried out experiments. G.L.P., J.K. and J.I.J. analyzed flow cytometry experiments, isocost lines and violacein production. J.U. supervised the study. J.U., G.L.P. and J.I.J. wrote the manuscript.

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Correspondence to José Utrilla.

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J.U. and G.L.P. are inventors in a MX patent application filled by UNAM.

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Extended data

Extended Data Fig. 1 ME-model simulations and proteome sector response to reducing the unmodeled protein fraction (UPF).

The ME-model iJL1678b-ME was used to simulate the effect of the reduction of the UPF and different expression levels of an unused recombinant model protein (GFP) (see methods). Similar to the maintenance energy coefficient, the hedging proteome and other non-growth related (thus not modeled) functions are accounted for in ME-models as a part of the UPF. Each panel shows a, growth rate and the corresponding fraction of each proteome sector b, core sector and the alternative element dependent sector: c, the carbon sector d, the nitrogen sector e, the phosphate sector f, the sulphur sector g, the non-ME sector h, the recombinant sector, comprised by the maximum attained GFP expression, i, the other sector (non-classified) and j, the UPF sector. The simulation shows an increased availability of cellular resources for recombinant protein production by reducing the UPF.

Extended Data Fig. 2 ReProMin proteome liberation landscapes corresponding to the UT case.

Potential proteome liberation landscape corresponding to a, Galactose, b, Acetate, c, Glycerol + casAA and d, Rich Medium (LB).

Extended Data Fig. 3 Regulatory subnetwork of ReProMin predicted gene targets.

Subnetwork corresponding to a, ST case PYC mutant and b, UT case PFC mutant; blue circles represent predicted silenced targets, yellow circles predicted induced targets and gray circles genes with no proteomic coverage; size of the circles is proportional to the PL of the target.

Extended Data Fig. 4 Transcriptomic analysis of the UT case designed strain.

a, Correlation plot for PFC and WT strains transcripts. Green squares represent the three deleted TFs. b, Volcano plot showing differential gene expression. In both cases, statistically significant genes are highlighted (blue – downregulated, yellow—upregulated) (log2 Fold Change ≥ 1 or ≤−1, P ≤ 0.01, n = 2). c, Integration of transcriptomics with computational tool predictions. The size of the circle corresponds to the fold change of each target (the largest circles represent fully silenced genes), in all cases blue circles represent targets releasing resources (down regulated), yellow circles represent targets generating burden (upregulated) and grey circles targets that were not found expressed. d, Accuracy of computational tool predictions based on RNAseq data. Yellow circles represent wrong predictions, blue circles represent accurate predictions and grey circles represent unmapped predictions (expression was not detected).

Extended Data Fig. 5 Phenotypic evaluation generated strains based on glucose ReProMin predictions (UT and ST cases) and control.

Growth on different carbon source supplemented M9 medium and rich medium (LB). a-d, shows max growth rate and f-i, shows max O.D. Points represent the Gaussian fitted value ± 2 s.d. for n = 9.

Extended Data Fig. 6 Metabolic burden evaluation of strains based on glucose ReProMin predictions (UT and ST cases) and control.

Metabolic burden while carrying empty, circuit plasmid and induced circuit plasmid, a, shows max growth rate and b, shows max O.D. Points represent the Gaussian fitted value ± 2 s.d. for n = 9.

Extended Data Fig. 7 Synthetic circuit characterization in glucose M9 medium.

Isocost lines showing mean fluorescence per cell measured by flow cytometry during balanced growth (~5 h). Points represent the Gaussian fitted fluorescence value ± 2 s.d. for n = 9 of red reporter (x axis) plotted against the green reporter (y axis) in an increasing inducer concentration (0, 2.5, 5, 20 nM AHL). A linear regression was used to fit the points to a line.

Extended Data Fig. 8 Isocost lines during balanced growth (~5 h).

Isocost lines of the generated mutant strains for two growth conditions: a, Glucose M9 medium and b, Rich medium. Left: absolute fluorescence, Right: normalized fluorescence. Points represent the Gaussian fitted fluorescence value ± 2 s.d. for n = 9 of red reporter (x axis) plotted against the green reporter (y axis) in an increasing inducer concentration (0, 2.5, 5, 20 nM AHL). A linear regression was used to fit the points to a line.

Extended Data Fig. 9 Isocost lines during stationary phase (~22 hrs).

Isocost lines of the generated mutant strains for two growth conditions: a, Glucose M9 medium and b, Rich medium. Left: absolute fluorescence, Right: normalized fluorescence Points represent the Gaussian fitted fluorescence value ± 2 s.d. for n = 9 of red reporter (x axis) plotted against the green reporter (y axis) in an increasing inducer concentration (0, 2.5, 5, 20 nM AHL). A linear regression was used to fit the points to a line.

Extended Data Fig. 10 Violacein production characterization.

a, Protein normalized violacein production using 2 g/L tryptophan in the presence of AHL (20 nM) (mean ± s.d., n = 9). b, Total violacein production without adding tryptophan after 24 h in the presence of increasing inducer (AHL) concentrations (mean ± s.d., n = 9). Asterisks *, ** and *** denote significant differences between WT and PFC using a two-tailed unpaired Student’s t-test. The following P values were obtained for normalized violacein production: 1 h, P = 0.0003; 2 h, P = 0.5647; 6 h, P < 0.0001. The following P values were obtained for violacein production with different AHL concentrations: No AHL, P = 0.0599; 1.25 nM, P = 0.0146; 2.5 nM, P = 0.0021; 5 nM, P < 0.0001; 10 nM, P = 0.0014; 20 nM, P = 0.0005.

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Lastiri-Pancardo, G., Mercado-Hernández, J.S., Kim, J. et al. A quantitative method for proteome reallocation using minimal regulatory interventions. Nat Chem Biol 16, 1026–1033 (2020). https://doi.org/10.1038/s41589-020-0593-y

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