Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER

Abstract

Precise control over microbial cell growth conditions could enable detection of minute phenotypic changes, which would improve our understanding of how genotypes are shaped by adaptive selection. Although automated cell-culture systems such as bioreactors offer strict control over liquid culture conditions, they often do not scale to high-throughput or require cumbersome redesign to alter growth conditions. We report the design and validation of eVOLVER, a scalable do-it-yourself (DIY) framework, which can be configured to carry out high-throughput growth experiments in molecular evolution, systems biology, and microbiology. High-throughput evolution of yeast populations grown at different densities reveals that eVOLVER can be applied to characterize adaptive niches. Growth selection on a genome-wide yeast knockout library, using temperatures varied over different timescales, finds strains sensitive to temperature changes or frequency of temperature change. Inspired by large-scale integration of electronics and microfluidics, we also demonstrate millifluidic multiplexing modules that enable multiplexed media routing, cleaning, vial-to-vial transfers and automated yeast mating.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: eVOLVER: an integrated framework for high-throughput, automated cell culture.
Figure 2: Design and performance of eVOLVER modules.
Figure 3: High-throughput experimental evolution across a multidimensional selection space.
Figure 4: Genome scale library fitness under temporally varying selection pressure.
Figure 5: Integrated millifluidic devices enable scaling of complex fluidic manipulation.

Similar content being viewed by others

Accession codes

Primary accessions

BioProject

References

  1. Elena, S.F. & Lenski, R.E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).

    Article  CAS  PubMed  Google Scholar 

  2. Nichols, R.J. et al. Phenotypic landscape of a bacterial cell. Cell 144, 143–156 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. Nevozhay, D., Adams, R.M., Van Itallie, E., Bennett, M.R. & Balázsi, G. Mapping the environmental fitness landscape of a synthetic gene circuit. PLoS Comput. Biol. 8, e1002480 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).

    Article  CAS  PubMed  Google Scholar 

  5. Li, Z. et al. Systematic exploration of essential yeast gene function with temperature-sensitive mutants. Nat. Biotechnol. 29, 361–367 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zuleta, I.A., Aranda-Díaz, A., Li, H. & El-Samad, H. Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry. Nat. Methods 11, 443–448 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Takahashi, C.N., Miller, A.W., Ekness, F., Dunham, M.J. & Klavins, E. A low cost, customizable turbidostat for use in synthetic circuit characterization. ACS Synth. Biol. 4, 32–38 (2015).

    Article  CAS  PubMed  Google Scholar 

  8. Feist, A.M., Herrgård, M.J., Thiele, I., Reed, J.L. & Palsson, B.Ø. Reconstruction of biochemical networks in microorganisms. Nat. Rev. Microbiol. 7, 129–143 (2009).

    Article  CAS  PubMed  Google Scholar 

  9. Lang, G.I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Maddamsetti, R., Lenski, R.E. & Barrick, J.E. Adaptation, clonal interference, and frequency-dependent interactions in a long-term evolution experiment with Escherichia coli. Genetics 200, 619–631 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Yona, A.H. et al. Chromosomal duplication is a transient evolutionary solution to stress. Proc. Natl. Acad. Sci. USA 109, 21010–21015 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wang, H.H. et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Esvelt, K.M., Carlson, J.C. & Liu, D.R. A system for the continuous directed evolution of biomolecules. Nature 472, 499–503 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ravikumar, A., Arrieta, A. & Liu, C.C. An orthogonal DNA replication system in yeast. Nat. Chem. Biol. 10, 175–177 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Crook, N. et al. In vivo continuous evolution of genes and pathways in yeast. Nat. Commun. 7, 13051 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bull, A.T. The renaissance of continuous culture in the post-genomics age. J. Ind. Microbiol. Biotechnol. 37, 993–1021 (2010).

    Article  CAS  PubMed  Google Scholar 

  17. Gresham, D. & Dunham, M.J. The enduring utility of continuous culturing in experimental evolution. Genomics 104, 399–405 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Piper, M.D.W. et al. Reproducibility of oligonucleotide microarray transcriptome analyses. An interlaboratory comparison using chemostat cultures of Saccharomyces cerevisiae. J. Biol. Chem. 277, 37001–37008 (2002).

    Article  CAS  PubMed  Google Scholar 

  19. Cressey, D. The DIY electronics transforming research. Nature 544, 125–126 (2017).

    Article  CAS  PubMed  Google Scholar 

  20. Kong, D.S. et al. Open-source, community-driven microfluidics with Metafluidics. Nat. Biotechnol. 35, 523–529 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Adamo, A. et al. On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system. Science 352, 61–67 (2016).

    Article  CAS  PubMed  Google Scholar 

  22. Perkel, J.M. The Internet of Things comes to the lab. Nature 542, 125–126 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Toprak, E. et al. Evolutionary paths to antibiotic resistance under dynamically sustained drug selection. Nat. Genet. 44, 101–105 (2012).

    Article  CAS  Google Scholar 

  24. Milias-Argeitis, A., Rullan, M., Aoki, S.K., Buchmann, P. & Khammash, M. Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat. Commun. 7, 12546 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hope, E.A. et al. Experimental Evolution Reveals Favored Adaptive Routes to Cell Aggregation in Yeast. Genetics 206, 1153–1167 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Toprak, E. et al. Building a morbidostat: an automated continuous-culture device for studying bacterial drug resistance under dynamically sustained drug inhibition. Nat. Protoc. 8, 555–567 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Thorsen, T., Maerkl, S.J. & Quake, S.R. Microfluidic large-scale integration. Science 298, 580–584 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Melin, J. & Quake, S.R. Microfluidic large-scale integration: the evolution of design rules for biological automation. Annu. Rev. Biophys. Biomol. Struct. 36, 213–231 (2007).

    Article  CAS  PubMed  Google Scholar 

  29. Unger, M.A., Chou, H.P., Thorsen, T., Scherer, A. & Quake, S.R. Monolithic microfabricated valves and pumps by multilayer soft lithography. Science 288, 113–116 (2000).

    Article  CAS  PubMed  Google Scholar 

  30. Grover, W.H., Skelley, A.M., Liu, C.N., Lagally, E.T. & Mathies, R.A. Monolithic membrane valves and diaphragm pumps for practical large-scale integration into glass microfluidic devices. Sens. Actuators B Chem. 89, 315–323 (2003).

    Article  CAS  Google Scholar 

  31. Kryazhimskiy, S., Rice, D.P., Jerison, E.R. & Desai, M.M. Microbial evolution. Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344, 1519–1522 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Mitchell, A. et al. Adaptive prediction of environmental changes by microorganisms. Nature 460, 220–224 (2009).

    Article  CAS  PubMed  Google Scholar 

  33. Ketola, T. et al. Fluctuating temperature leads to evolution of thermal generalism and preadaptation to novel environments. Evolution 67, 2936–2944 (2013).

    PubMed  Google Scholar 

  34. Sæther, B.-E. & Engen, S. The concept of fitness in fluctuating environments. Trends Ecol. Evol. 30, 273–281 (2015).

    Article  PubMed  Google Scholar 

  35. Bennett, M.R. et al. Metabolic gene regulation in a dynamically changing environment. Nature 454, 1119–1122 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Salignon, J., Richard, M., Fulcrand, E. & Yvert, G. Genomics of cellular proliferation in periodic environmental fluctuations. Mol. Syst. Biol. 14, e7823 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Giaever, G. & Nislow, C. The yeast deletion collection: a decade of functional genomics. Genetics 197, 451–465 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Smith, A.M. et al. Quantitative phenotyping via deep barcode sequencing. Genome Res. 19, 1836–1842 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Gibney, P.A., Lu, C., Caudy, A.A., Hess, D.C. & Botstein, D. Yeast metabolic and signaling genes are required for heat-shock survival and have little overlap with the heat-induced genes. Proc. Natl. Acad. Sci. USA 110, E4393–E4402 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Cherry, J.M. et al. Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res. 40, D700–D705 (2012).

    Article  CAS  PubMed  Google Scholar 

  41. Morano, K.A., Grant, C.M. & Moye-Rowley, W.S. The response to heat shock and oxidative stress in Saccharomyces cerevisiae. Genetics 190, 1157–1195 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Loar, J.W. et al. Genetic and biochemical interactions among Yar1, Ltv1 and Rps3 define novel links between environmental stress and ribosome biogenesis in Saccharomyces cerevisiae. Genetics 168, 1877–1889 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Escalante-Chong, R. et al. Galactose metabolic genes in yeast respond to a ratio of galactose and glucose. Proc. Natl. Acad. Sci. USA 112, 1636–1641 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Rice, S.A. et al. Biofilm formation and sloughing in Serratia marcescens are controlled by quorum sensing and nutrient cues. J. Bacteriol. 187, 3477–3485 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Anderson, J.B., Sirjusingh, C. & Ricker, N. Haploidy, diploidy and evolution of antifungal drug resistance in Saccharomyces cerevisiae. Genetics 168, 1915–1923 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. González, C. et al. Stress-response balance drives the evolution of a network module and its host genome. Mol. Syst. Biol. 11, 827 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Brauer, M.J., Saldanha, A.J., Dolinski, K. & Botstein, D. Homeostatic adjustment and metabolic remodeling in glucose-limited yeast cultures. Mol. Biol. Cell 16, 2503–2517 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Alper, H., Fischer, C., Nevoigt, E. & Stephanopoulos, G. Tuning genetic control through promoter engineering. Proc. Natl. Acad. Sci. USA 102, 12678–12683 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hutchison, C.A. III et al. Design and synthesis of a minimal bacterial genome. Science 351, aad6253–aad6253 (2016).

    Article  PubMed  CAS  Google Scholar 

  50. Richardson, S.M. et al. Design of a synthetic yeast genome. Science 355, 1040–1044 (2017).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to B. Stafford for his assistance in design architecture of the system. We thank H. Khalil, A. Soltanianzadeh, A. Sun, S. Pipe, and A. Cavale for help on construction of the system. We are indebted to the Electronics Design Facility (EDF) and Engineering Product Innovation Center (EPIC) at Boston University for their services. We also thank D. Segrè, J. Ngo, J. Tytell, W. Wong, and members of the Khalil lab for insightful comments on the manuscript. This work was supported by a NSF CAREER Award (MCB-1350949 to A.S.K.) and a DARPA grant (HR0011-15-C-0091 to A.S.K.). A.S.K. also acknowledges funding from the NIH Director's New Innovator Award (1DP2AI131083-01), DARPA Young Faculty Award (D16AP00142), and NSF Expeditions in Computing (CCF-1522074).

Author information

Authors and Affiliations

Authors

Contributions

B.G.W., C.J.B., and A.S.K. conceived the study. B.G.W. developed the system with guidance and input from all authors. B.G.W. and C.P.M. performed and analyzed experiments. S.K. generated reagents. C.J.B. and A.S.K. oversaw the study. All authors wrote the paper.

Corresponding authors

Correspondence to Caleb J Bashor or Ahmad S Khalil.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–37, Supplementary Notes 1–17, Supplementary Tables 1–3 (PDF 13265 kb)

Life Sciences Reporting Summary (PDF 205 kb)

Supplementary Code

Supplementary Code (ZIP 2103 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wong, B., Mancuso, C., Kiriakov, S. et al. Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER. Nat Biotechnol 36, 614–623 (2018). https://doi.org/10.1038/nbt.4151

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.4151

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research