Parallel adaptive evolution cultures of Escherichia coli lead to convergent growth phenotypes with different gene expression states
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Laboratory evolution can be used to address fundamental questions about adaptation to selection pressures and, ultimately, the process of evolution. In this study, we investigated the reproducibility of growth phenotypes and global gene expression states during adaptive evolution. The results from parallel, replicate adaptive evolution experiments of Escherichia coli K-12 MG1655 grown on either lactate or glycerol minimal media showed that (1) growth phenotypes at the endpoint of evolution are convergent and reproducible; (2) endpoints of evolution have different underlying gene expression states; and (3) the evolutionary gene expression response involves a large number of compensatory expression changes and a smaller number of adaptively beneficial expression changes common across evolution strains. Gene expression changes initially showed a large number of differentially expressed genes in response to an environmental change followed by a return of most genes to a baseline expression level, leaving a relatively small set of differentially expressed genes at the endpoint that varied between evolved populations.
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During scale-up of microbial processes, cells are exposed to altered environmental conditions and environmental stress regimes.
“Whereas small-scale cultivations and initial engineering steps are often performed in complex media in batch culture, during the scale-up of the process, cells are exposed to altered environmental conditions and environmental stress regimes [57,58].”
From Adaptive laboratory evolution—Principles and applications for biotechnology (2013) · cites this paper as [58]
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A CENTENNIAL CELEBRATION FOR QUANTITATIVE GENETICS
Roff, D. A. (2007) · Evolution · doi:10.1111/j.1558-5646.2007.00100.x · Cited by 102 · see citing works
Convergent Metabolic Specialization through Distinct Evolutionary Paths in Pseudomonas aeruginosa
Rosa, R. L., Johansen, H. K., & Molin, S. (2018) · mBio · doi:10.1128/mbio.00269-18 · Cited by 93 · see citing works
Zhang, J., Wu, C., Du, G., & Chen, J. (2012) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-011-0346-6 · Cited by 97 · see citing works
“many microbial phenomena, such as the emergence of new pathogens and the acquisition of environmental resistance factors, can address fundamental questions on adaptation to selection pressures and evolution [15,16].”
BackgroundKishimoto, T., Iijima, L., Tatsumi, M., Ono, N., Oyake, A., Hashimoto, T., Matsuo, M., Okubo, M., Suzuki, S., Mori, K., et al. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001164 · Cited by 96 · see citing works
“Evolution experiments conducted in the laboratory allow direct temporal observation of the genetic and phenotypic alterations and the precise verification of evolutionary mechanisms [1–3].”
BackgroundBarrett, C. L., Herring, C. D., Reed, J. L., & Palsson, B. Ø. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0505231102 · Cited by 95 · see citing works
Self-citationGenomics of Parallel Experimental Evolution in Drosophila
Graves, J. L., Hertweck, K. L., Phillips, M., Han, M., Cabral, L. G., Barter, T. T., Greer, L. F., Burke, M. K., Mueller, L. D., & Rose, M. R. (2016) · Molecular Biology and Evolution · doi:10.1093/molbev/msw282 · Cited by 91 · see citing works
“The terms parallelism and convergence are often confused in the literature (Fong et al. 2005; Arendt and Reznick 2008), and in any case are terms of shifting usage.”
BackgroundAngione, C. (2019) · BioMed Research International · doi:10.1155/2019/8304260 · Cited by 88 · see citing works
“Following this approach, FBA and its multiomic modifications have been used to predict the metabolic response to a given condition in light of the multiple cellular objectives that a cell is required to meet [67, 68].”
MethodThe effect of phenotypic plasticity on evolution in multipeaked fitness landscapes
Borenstein, E., Meilijson, I., & Ruppin, E. (2006) · Journal of Evolutionary Biology · doi:10.1111/j.1420-9101.2006.01125.x · Cited by 80 · see citing works
“It is only in a multipeaked landscape (Wright, 1932; Kauffman & Levin, 1987; Kauffman, 1993; Korona et al., 1994; Burch & Chao, 1999; Lenski et al., 1999; Fong et al., 2005), where the overall evolutionary rate is dominated by the exponential passage time in the negative selection regions, that the beneficial effect o…”
BackgroundInfluentialSpor, A., Kvitek, D. J., Nidelet, T., Martin, J., Legrand, J., Dillmann, C., Bourgais, A., de Vienne, D., Sherlock, G., & Sicard, D. (2013) · Evolution · doi:10.1111/evo.12302 · Cited by 79 · see citing works
“Fong et al. (2005) demonstrated that the transcriptional states of their evolved 7 8 6 EVOLUTION MARCH 2014 populations were very different from each other, despite similarity in endpoint growth phenotypes.”
BackgroundQuantifying Organismal Complexity using a Population Genetic Approach
Tenaillon, O., Silander, O., Uzan, J., & Chao, L. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000217 · Cited by 77 · see citing works
“Several experimental studies using microbes have shown that a considerable amount of phenotypic convergence occurs during evolution [24–27], even when organisms begin from different starting points in the landscape [28].”
BackgroundHarcombe, W. R., Delaney, N. F., Leiby, N., Klitgord, N., & Marx, C. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003091 · Cited by 74 · see citing works
“Fong et al, 2005 [44]; Lewis et al, 2010 [25]”
Swindell, W. R., Huebner, M., & Weber, A. P. M. (2007) · Heredity · doi:10.1038/sj.hdy.6800975 · Cited by 74 · see citing works
“This methodology has been used to study stress effects in several bacterial species (Riehle et al., 2003; Fong et al., 2005), but to our knowledge, has not yet been implemented in Arabidopsis or other plant species.”
BackgroundMethodProgress in engineering acid stress resistance of lactic acid bacteria
Wu, C., Huang, J., & Zhou, R. (2013) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-013-5435-3 · Cited by 74 · see citing works
Shifts in mutation spectra enhance access to beneficial mutations
Sane, M., Diwan, G. D., Bhat, B. A., Wahl, L. M., & Agashe, D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2207355120 · Cited by 66 · see citing works
“Several studies also show very rapid and large fitness increases during laboratory experimental evolution of E. coli , consistent with the availability of large-effect beneficial mutations (35, 46).”
BackgroundLee, D., Feist, A. M., Barrett, C. L., & Palsson, B. Ø. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026172 · Cited by 71 · see citing works
“coli endpoint strains (named GA, GB, GC, GD, GE) [13] reached the maximum growth rate Figure 1.”
BackgroundMethodInfluentialSelf-citationQuantification provides a conceptual basis for convergent evolution
Speed, M. P., & Arbuckle, K. (2016) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12257 · Cited by 71 · see citing works
“In one of a growing number of examples, Fong et al. (2005) found microbial populations evolving similar growth phenotypes on lactate or glycerol minimal media but, as evidenced by mRNA transcriptomics, often by diverse genomic routes.”
BackgroundA genome-scale, constraint-based approach to systems biology of human metabolism
Mo, M. L., Jamshidi, N., & Palsson, B. Ø. (2007) · Molecular BioSystems · doi:10.1039/b705597h · Cited by 71 · see citing works
Self-citationHändel, N., Schuurmans, J. M., Feng, Y., Brul, S., & ter Kuile, B. H. (2014) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.02892-14 · Cited by 69 · see citing works
“coli to 265 exposure to antibiotics of this and other studies (5, 6, 10, 25, 31-34), a picture emerges of the 266 cell exploring all possible escape routes both at the transcriptional and at the mutational level.”
Uncovering a Hidden Distributed Architecture Behind Scale-free Transcriptional Regulatory Networks
Balaji, S., Iyer, L. M., Aravind, L., & Babu, M. M. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.04.026 · Cited by 68 · see citing works
Metabolic Characterization of <i>Escherichia coli</i> Strains Adapted to Growth on Lactate
Hua, Q., Joyce, A. R., Palsson, B. Ø., & Fong, S. S. (2007) · Applied and Environmental Microbiology · doi:10.1128/aem.00527-07 · Cited by 67 · see citing works
“To provide more-comprehensive views of evolutionary growth on lactate, both metabolic pathway and transcriptional responses in lactate metabolism- and gluconeogenesis-related genes were dissected based on the flux states and gene expression profiling of the wild-type strain and seven evolved populations obtained at da…”
BackgroundMethodResultInfluentialSelf-citationEvolutionary engineering of industrial microorganisms-strategies and applications
Zhu, Z., Zhang, J., Ji, X., Fang, Z., Wu, Z., Chen, J., & Du, G. (2018) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-018-8937-1 · Cited by 67 · see citing works
Kok, S., Nijkamp, J. F., Oud, B., Roque, F. C., Ridder, D., Daran, J.-M., Pronk, J. T., & Maris, A. J. A. (2012) · FEMS Yeast Research · doi:10.1111/j.1567-1364.2011.00787.x · Cited by 65 · see citing works
Functional and Metabolic Effects of Adaptive Glycerol Kinase (GLPK) Mutants in Escherichia coli
Applebee, M. K., Joyce, A. R., Conrad, T. M., Pettigrew, D. W., & Palsson, B. Ø. (2011) · Journal of Biological Chemistry · doi:10.1074/jbc.m110.195305 · Cited by 64 · see citing works
Self-citationGenetic Enablers Underlying the Clustered Evolutionary Origins of C4 Photosynthesis in Angiosperms
Christin, P., Arakaki, M., Osborne, C. P., & Edwards, E. J. (2015) · Molecular Biology and Evolution · doi:10.1093/molbev/msu410 · Cited by 63 · see citing works
A Practical Guide to Genome-Scale Metabolic Models and Their Analysis
dos Santos, F. B., Boele, J., & Teusink, B. (2011) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/b978-0-12-385118-5.00024-4 · Cited by 62 · see citing works
Gysi, D. M., Voigt, A., Fragoso, T. D. M., Almaas, E., & Nowick, K. (2018) · NORA - Norwegian Open Research Archives · doi:10.17169/refubium-922 · Cited by 62 · see citing works
Predicting evolution from genomics: experimental evolution of bacteriophage T7
Bull, J. J., & Molineux, I. J. (2008) · Heredity · doi:10.1038/sj.hdy.6801087 · Cited by 59 · see citing works
“It is also now possible to obtain genome-wide sequences of bacteria to identify evolutionary changes (Honisch et al., 2004; Herring et al., 2006; Velicer et al., 2006) and to obtain genome-wide expression levels (Fong et al., 2005b).”
BackgroundInfluentialAdaptive Evolution of Synthetic Cooperating Communities Improves Growth Performance
Zhang, X., & Reed, J. L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108297 · Cited by 59 · see citing works
“during evolution of individual strains [40]; however, the evolu-”
BackgroundGenotype networks in metabolic reaction spaces
Samal, A., Rodrigues, J. F. M., Jost, J., Martin, O., & Wagner, A. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-30 · Cited by 56 · see citing works
Superessential reactions in metabolic networks
Barve, A., Rodrigues, J. F. M., & Wagner, A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1113065109 · Cited by 55 · see citing works
Gysi, D. M., Voigt, A., Fragoso, T. D. M., Almaas, E., & Nowick, K. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2351-7 · Cited by 70 · see citing works
Evolution alters ecological mechanisms of coexistence in experimental microcosms
Zhao, L., Zhang, Q., & Zhang, D. (2015) · Functional Ecology · doi:10.1111/1365-2435.12611 · Cited by 52 · see citing works
Landaeta, R., Aroca, G. E., Acevedo, F., Teixeira, J. A., & Mussatto, S. I. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.06.048 · Cited by 56 · see citing works
“Strains obtained by adaptive evolution, while can converge to similar phenotypes, have different gene expression patterns due to random mutations during the adaptation [35], thus diminishing the usability of repeats for these cultures.”
BackgroundMaharjan, R. P., Seeto, S., & Ferenci, T. (2006) · Journal of Bacteriology · doi:10.1128/jb.01414-06 · Cited by 55 · see citing works
“Our bioenergetic and metabolomic results are indicative of rapid, multiple metabolic adaptations not previously recognized in experimental bacterial populations (16, 39).”
BackgroundJu, S. Y., Kim, J., & Lee, P. C. (2016) · Biotechnology for Biofuels · doi:10.1186/s13068-016-0662-3 · Cited by 58 · see citing works
Carroll, S. M., & Marx, C. J. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003427 · Cited by 54 · see citing works
Predictive analytics of environmental adaptability in multi-omic network models
Angione, C., & Lió, P. (2015) · Scientific Reports · doi:10.1038/srep15147 · Cited by 53 · see citing works
“Then, we use the flow matrix (12) and a spectral method for community detection to further investigate each component (see Methods).”
MethodMultiplex methods provide effective integration of multi-omic data in genome-scale models
Angione, C., Conway, M., & Lió, P. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0912-1 · Cited by 54 · see citing works
“This multi-level structure accounts for the fact that the actual bacterial response to any environmental condition is highly dependent on multiple cellular objectives that the bacterium is required to meet [31, 32].”
BackgroundSnitkin, E. S., Dudley, A. M., Janse, D. M., Wong, K., Church, G. M., & Segrè, D. (2008) · Genome biology · doi:10.1186/gb-2008-9-9-r140 · Cited by 52 · see citing works
“The mutations underlying the improved glycerol growth phenotype caused major regulatory changes, likely detrimental to growth under more commonly encountered conditions, and therefore absent in the wild type [51,52].”
BackgroundPhenotypic plasticity as a long-term memory easing readaptations to ancestral environments
Ho, W.-C., Li, D., Zhu, Q., & Zhang, J. (2020) · Science Advances · doi:10.1126/sciadv.aba3388 · Cited by 52 · see citing works
Applebee, M. K., Herrgård, M. J., & Palsson, B. Ø. (2008) · Journal of Bacteriology · doi:10.1128/jb.01976-07 · Cited by 51 · see citing works
“The role of the rpoB/C mutations is believed to be linked to stabilizing the initially large-scale changes in the transcription profile observed in each evolution’s population, which eventually stabilized and largely returned to the preperturbation expression state by the end of the adaptation period (5).”
BackgroundSelf-citationCharacterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow
Brunk, E., Chang, R. L., Xia, J., Hefzi, H., Yurkovich, J. T., Kim, D., Buckmiller, E., Wang, H. H., Cho, B., Yang, C., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1811971115 · Cited by 51 · see citing works
Self-citationRatib, N. R., Seidl, F., Ehrenreich, I. M., & Finkel, S. E. (2021) · mBio · doi:10.1128/mbio.03337-20 · Cited by 45 · see citing works
“Global dysregulation followed by fine-tuning to restore transcription to a prestress or new optimal state has been proposed as a strategy to adapt to stressful environments (42, 43).”
Lewis, N. E., Cho, B., Knight, E. M., & Palsson, B. Ø. (2009) · Journal of Bacteriology · doi:10.1128/jb.00034-09 · Cited by 49 · see citing works
“Interestingly, when parallel cultures are evolved in this way, their evolutionary paths are not the same, though they usually evolve to have similar growth rates, substrate uptake rates, and oxygen uptake rates at the group level (31, 32).”
BackgroundMethodInfluentialSelf-citationNguyen-Vo, T. P., Liang, Y., Sankaranarayanan, M., Seol, E., Chun, A. Y., Ashok, S., Chauhan, A. S., Kim, J. R., & Park, S. (2019) · Metabolic Engineering · doi:10.1016/j.ymben.2019.02.001 · Cited by 48 · see citing works
Metabolic Networks and Their Evolution
Wagner, A. (2012) · Advances in experimental medicine and biology · doi:10.1007/978-1-4614-3567-9_2 · Cited by 47 · see citing works
“coli cells adapt evolutionarily to new nutrients show that such change can occur in a few hundred generations, can alter the transcription of many genes, and can occur differently in parallel experiments [58].”
BackgroundPrinciples for the design of multicellular engineered living systems
Aydin, O., Passaro, A. P., Raman, R., Spellicy, S. E., Weinberg, R. P., Kamm, R. D., Sample, M., Truskey, G. A., Zartman, J. J., Dar, R. D., et al. (2022) · APL Bioengineering · doi:10.1063/5.0076635 · Cited by 45 · see citing works
Rate, accuracy and cost of ribosomes in bacterial cells
Lovmar, M., & Ehrenberg, M. (2006) · Biochimie · doi:10.1016/j.biochi.2006.04.019 · Cited by 47 · see citing works
Spontaneous Reaction Silencing in Metabolic Optimization
Nishikawa, T., Gulbahce, N., & Motter, A. E. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000236 · Cited by 46 · see citing works
Adaptive evolution by optimizing expression levels in different environments
Babu, M. M., & Aravind, L. (2005) · Trends in Microbiology · doi:10.1016/j.tim.2005.11.005 · Cited by 46 · see citing works
Kim, K., Hou, C. Y., Choe, D., Kang, M., Cho, S., Sung, B. H., Lee, D., Lee, S., Kang, T. J., & Cho, B. (2021) · Metabolic Engineering · doi:10.1016/j.ymben.2021.11.004 · Cited by 47 · see citing works
Pang, T. Y., & Lercher, M. J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1718997115 · Cited by 45 · see citing works
- Authors
- Fong, S. S., Joyce, A. R., & Palsson, B. Ø.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Palsson, Bernhard Ø.
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2005-10
- Volume
- 15
- Issue
- 10
- Pages
- 1365-1372
- ISSN
- 1088-9051 (ISSN-L)
- PMC
- PMC1240078
- OpenAlex
- W2031681107
- Cited by
- 265
- References
- 37
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 6 · 2023: 7 · 2022: 6 · 2021: 10 · 2020: 18 · 2019: 14 · 2018: 13 · 2017: 9 · 2016: 8 · 2015: 14 · 2014: 14 · 2013: 23 · 2012: 24
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (GM-57089, R01GM062791, R01GM057089, GM62791)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- adaptive evolution, Escherichia coli, experimental evolution, parallel evolution, reproducibility, growth phenotype, gene expression, differentially expressed genes
- MeSH terms
- Escherichia coli, Biological Evolution, Phenotype, RNA, Messenger, Transcription, Genetic, Gene Expression Profiling
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