Steering and controlling evolution - from bioengineering to fighting pathogens.
Journal
Nature reviews. Genetics
ISSN: 1471-0064
Titre abrégé: Nat Rev Genet
Pays: England
ID NLM: 100962779
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
accepted:
30
05
2023
medline:
16
11
2023
pubmed:
4
7
2023
entrez:
3
7
2023
Statut:
ppublish
Résumé
Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.
Identifiants
pubmed: 37400577
doi: 10.1038/s41576-023-00623-8
pii: 10.1038/s41576-023-00623-8
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
851-867Informations de copyright
© 2023. Springer Nature Limited.
Références
Carroll, S. P. et al. Applying evolutionary biology to address global challenges. Science 346, 1245993 (2014).
pmcid: 4245030
doi: 10.1126/science.1245993
pubmed: 25213376
Nielsen, J. & Keasling, J. D. Engineering cellular metabolism. Cell 164, 1185–1197 (2016).
doi: 10.1016/j.cell.2016.02.004
pubmed: 26967285
Jouhten, P. et al. Predictive evolution of metabolic phenotypes using model-designed environments. Mol. Syst. Biol. 18, e10980 (2022). This study develops control protocols using environment switching and trait co-variation to elicit traits that are uncorrelated with cell fitness.
pmcid: 9536503
doi: 10.15252/msb.202210980
pubmed: 36201279
Esfahani, K. et al. A review of cancer immunotherapy: from the past, to the present, to the future. Curr. Oncol. 27, S87–S97 (2020).
pmcid: 7194005
doi: 10.3747/co.27.5223
pubmed: 32368178
Lässig, M. & Mustonen, V. Eco-evolutionary control of pathogens. Proc. Natl Acad. Sci. USA 117, 19694–19704 (2020). This study establishes optimal strategies for eco-evolutionary control that depend on the rate and size of the target population, quantifying how monitoring and computational prediction affect protocols and efficiency of control.
pmcid: 7443876
doi: 10.1073/pnas.1920263117
pubmed: 32737164
Nourmohammad, A. & Eksin, C. Optimal evolutionary control for artificial selection on molecular phenotypes. Phys. Rev. X 11, 011044 (2021). This study proposes an optimal control formalism to direct the evolution of multivariate traits with collateral effects, and discusses how to use predictive information to schedule monitoring of a population for control by artificial selection.
Lässig, M., Mustonen, V. & Walczak, A. M. Predicting evolution. Nat. Ecol. Evol. 1, 77 (2017).
doi: 10.1038/s41559-017-0077
pubmed: 28812721
Molina, R. S. et al. In vivo hypermutation and continuous evolution. Nat. Rev. Methods Prim. 2, 1–22 (2022).
Esvelt, K. M., Carlson, J. C. & Liu, D. R. A system for the continuous directed evolution of biomolecules. Nature 472, 499–503 (2011). This study presents an experimental platform for the directed evolution of molecules, using bacteriophages for feedback-controlled cell-to-cell transfer of genetic material.
pmcid: 3084352
doi: 10.1038/nature09929
pubmed: 21478873
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).
pmcid: 3708598
doi: 10.1038/nprot.2013.021
pubmed: 23429717
Badran, A. H. & Liu, D. R. In vivo continuous directed evolution. Curr. Opin. Chem. Biol. 24, 1–10 (2015).
doi: 10.1016/j.cbpa.2014.09.040
pubmed: 25461718
Packer, M. S., Rees, H. A. & Liu, D. R. Phage-assisted continuous evolution of proteases with altered substrate specificity. Nat. Commun. 8, 956 (2017).
pmcid: 5643515
doi: 10.1038/s41467-017-01055-9
pubmed: 29038472
Zhong, Z. et al. Automated continuous evolution of proteins in vivo. ACS Synth. Biol. 9, 1270–1276 (2020). This study presents an experimental platform for directed evolution of biomolecules in yeast, using targeted mutagenesis combined with artificial selection tuned by feedback from the molecular activity of interest.
pmcid: 7370864
doi: 10.1021/acssynbio.0c00135
pubmed: 32374988
Parts, L. et al. Revealing the genetic structure of a trait by sequencing a population under selection. Genome Res. 21, 1131–1138 (2011).
pmcid: 3129255
doi: 10.1101/gr.116731.110
pubmed: 21422276
Iwasawa, J. et al. Analysis of the evolution of resistance to multiple antibiotics enables prediction of the Escherichia coli phenotype-based fitness landscape. PLoS Biol. 20, e3001920 (2022). This study infers phenotype-based fitness landscapes for antibiotic resistance evolution, quantifying primary and collateral effects across different drugs.
pmcid: 9746992
doi: 10.1371/journal.pbio.3001920
pubmed: 36512529
Klumpp, S., Zhang, Z. & Hwa, T. Growth rate-dependent global effects on gene expression in bacteria. Cell 139, 1366–1375 (2009).
pmcid: 2818994
doi: 10.1016/j.cell.2009.12.001
pubmed: 20064380
Ceroni, F., Algar, R., Stan, G.-B. & Ellis, T. Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat. Methods 12, 415–418 (2015).
doi: 10.1038/nmeth.3339
pubmed: 25849635
Fedorec, A. J. H., Karkaria, B. D., Sulu, M. & Barnes, C. P. Single strain control of microbial consortia. Nat. Commun. 12, 1977 (2021).
pmcid: 8010080
doi: 10.1038/s41467-021-22240-x
pubmed: 33785746
Aoki, S. K. et al. A universal biomolecular integral feedback controller for robust perfect adaptation. Nature 570, 533–537 (2019).
doi: 10.1038/s41586-019-1321-1
pubmed: 31217585
Khammash, M. H. Perfect adaptation in biology. Cell Syst. 12, 509–521 (2021).
doi: 10.1016/j.cels.2021.05.020
pubmed: 34139163
Bier, E. Gene drives gaining speed. Nat. Rev. Genet. 23, 5–22 (2022).
doi: 10.1038/s41576-021-00386-0
pubmed: 34363067
Unckless, R. L., Clark, A. G. & Messer, P. W. Evolution of resistance against CRISPR/Cas9 gene drive. Genetics 205, 827–841 (2017).
doi: 10.1534/genetics.116.197285
pubmed: 27941126
Hutchings, M. I., Truman, A. W. & Wilkinson, B. Antibiotics: past, present and future. Curr. Opin. Microbiol. 51, 72–80 (2019).
doi: 10.1016/j.mib.2019.10.008
pubmed: 31733401
Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).
doi: 10.1016/j.cub.2019.04.024
pubmed: 31163166
Yang, D., Biragyn, A., Kwak, L. W. & Oppenheim, J. J. Mammalian defensins in immunity: more than just microbicidal. Trends Immunol. 23, 291–296 (2002).
doi: 10.1016/S1471-4906(02)02246-9
pubmed: 12072367
Selsted, M. E. & Ouellette, A. J. Mammalian defensins in the antimicrobial immune response. Nat. Immunol. 6, 551–557 (2005).
doi: 10.1038/ni1206
pubmed: 15908936
Adyns, L., Proost, P. & Struyf, S. Role of defensins in tumor biology. Int. J. Mol. Sci. 24, 5268 (2023).
pmcid: 10049535
doi: 10.3390/ijms24065268
pubmed: 36982340
Murray, C. J. L. et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 399, 629–655 (2022).
doi: 10.1016/S0140-6736(21)02724-0
Blair, J. M. A., Webber, M. A., Baylay, A. J., Ogbolu, D. O. & Piddock, L. J. V. Molecular mechanisms of antibiotic resistance. Nat. Rev. Microbiol. 13, 42–51 (2014).
doi: 10.1038/nrmicro3380
pubmed: 25435309
Hughes, D. & Andersson, D. I. Evolutionary consequences of drug resistance: shared principles across diverse targets and organisms. Nat. Rev. Genet. 16, 459–471 (2015).
doi: 10.1038/nrg3922
pubmed: 26149714
Andersson, D. I., Hughes, D. & Kubicek-Sutherland, J. Z. Mechanisms and consequences of bacterial resistance to antimicrobial peptides. Drug Resist. Updat. 26, 43–57 (2016).
doi: 10.1016/j.drup.2016.04.002
pubmed: 27180309
Pinheiro, F., Warsi, O., Andersson, D. I. & Lässig, M. Metabolic fitness landscapes predict the evolution of antibiotic resistance. Nat. Ecol. Evol. 5, 677–687 (2021).
doi: 10.1038/s41559-021-01397-0
pubmed: 33664488
Greulich, P., Scott, M., Evans, M. R. & Allen, R. J. Growth-dependent bacterial susceptibility to ribosome-targeting antibiotics. Mol. Syst. Biol. 11, 796 (2015). This study establishes a computable metabolic model of drug action and dosage response that can inform control protocols.
doi: 10.15252/msb.20145949
pubmed: 26146675
Roemhild, R., Bollenbach, T. & Andersson, D. I. The physiology and genetics of bacterial responses to antibiotic combinations. Nat. Rev. Microbiol. 20, 478–490 (2022).
doi: 10.1038/s41579-022-00700-5
pubmed: 35241807
Hansen, E., Karslake, J., Woods, R. J., Read, A. F. & Wood, K. B. Antibiotics can be used to contain drug-resistant bacteria by maintaining sufficiently large sensitive populations. PLoS Biol. 18, e3000713 (2020). This study establishes control strategies for antibiotic interventions that focus on containment rather than eradication of the target pathogen and delay the evolution of resistance.
pmcid: 7266357
doi: 10.1371/journal.pbio.3000713
pubmed: 32413038
Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).
doi: 10.1126/science.aaa4971
pubmed: 25838375
Hollingsworth, R. E. & Jansen, K. Turning the corner on therapeutic cancer vaccines. npj Vaccines 4, 1–10 (2019).
doi: 10.1038/s41541-019-0103-y
Łuksza, M. et al. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551, 517–520 (2017). This study establishes a predictive fitness model for cancer antigens interacting with T cell immune receptors that can guide cancer vaccine selection.
pmcid: 6137806
doi: 10.1038/nature24473
pubmed: 29132144
Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).
pmcid: 6954100
doi: 10.1038/s41586-019-1032-7
pubmed: 30894752
Saxena, M., van der Burg, S. H., Melief, C. J. M. & Bhardwaj, N. Therapeutic cancer vaccines. Nat. Rev. Cancer 21, 360–378 (2021).
doi: 10.1038/s41568-021-00346-0
pubmed: 33907315
Hoyos, D. et al. Fundamental immune–oncogenicity trade-offs define driver mutation fitness. Nature 606, 172–179 (2022).
pmcid: 9159948
doi: 10.1038/s41586-022-04696-z
pubmed: 35545680
Zapata, L. et al. Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors. Nat. Genet. 55, 451–460 (2023).
pmcid: 10011129
doi: 10.1038/s41588-023-01313-1
pubmed: 36894710
Łuksza, M. et al. Neoantigen quality predicts immunoediting in survivors of pancreatic cancer. Nature 606, 389–395 (2022).
pmcid: 9177421
doi: 10.1038/s41586-022-04735-9
pubmed: 35589842
Richman, L. P., Vonderheide, R. H. & Rech, A. J. Neoantigen dissimilarity to the self-proteome predicts immunogenicity and response to immune checkpoint blockade. Cell Syst. 9, 375–382.e4 (2019).
pmcid: 6813910
doi: 10.1016/j.cels.2019.08.009
pubmed: 31606370
Lakatos, E. et al. Evolutionary dynamics of neoantigens in growing tumors. Nat. Genet. 52, 1057–1066 (2020).
pmcid: 7610467
doi: 10.1038/s41588-020-0687-1
pubmed: 32929288
Sahin, U. & Türeci, Ö. Personalized vaccines for cancer immunotherapy. Science 359, 1355–1360 (2018).
doi: 10.1126/science.aar7112
pubmed: 29567706
Rojas, L. A. et al. Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer. Nature 618, 144–150 (2023).
pmcid: 10171177
doi: 10.1038/s41586-023-06063-y
pubmed: 37165196
Kolios, A. G. A., Tsokos, G. C. & Klatzmann, D. Interleukin-2 and regulatory T cells in rheumatic diseases. Nat. Rev. Rheumatol. 17, 749–766 (2021).
doi: 10.1038/s41584-021-00707-x
pubmed: 34728817
Schwartz, R. N., Stover, L. & Dutcher, J. P. Managing toxicities of high-dose interleukin-2. Oncology 16, 11–20 (2002).
pubmed: 12469935
Achar, S. R. et al. Universal antigen encoding of T cell activation from high-dimensional cytokine dynamics. Science 376, 880–884 (2022).
doi: 10.1126/science.abl5311
pubmed: 35587980
Nourmohammad, A. T cell immune responses deciphered. Science 376, 796–797 (2022).
doi: 10.1126/science.abq1679
pubmed: 35587975
Łuksza, M. & Lässig, M. A predictive fitness model for influenza. Nature 507, 57–61 (2014). This study predicts the antigenic evolution of influenza from one year to the next and is used to inform the biannual selection of global influenza vaccines.
doi: 10.1038/nature13087
pubmed: 24572367
Morris, D. H. et al. Predictive modeling of influenza shows the promise of applied evolutionary biology. Trends Microbiol. 26, 102–118 (2018).
doi: 10.1016/j.tim.2017.09.004
pubmed: 29097090
Huddleston, J. et al. Integrating genotypes and phenotypes improves long-term forecasts of seasonal influenza A/H3N2 evolution. eLife 9, e60067 (2020).
pmcid: 7553778
doi: 10.7554/eLife.60067
pubmed: 32876050
Wen, F. T., Bell, S. M., Bedford, T. & Cobey, S. Estimating vaccine-driven selection in seasonal influenza. Viruses 10, 509 (2018).
pmcid: 6165116
doi: 10.3390/v10090509
pubmed: 30231576
Meijers, M., Ruchnewitz, D., Łuksza, M. & Lässig, M. Vaccination shapes evolutionary trajectories of SARS-CoV-2. Preprint at bioRxiv https://doi.org/10.1101/2022.07.19.500637 (2022).
doi: 10.1101/2022.07.19.500637
Jardine, J. et al. Rational HIV immunogen design to target specific germline B cell receptors. Science 340, 711–716 (2013).
pmcid: 3689846
doi: 10.1126/science.1234150
pubmed: 23539181
Escolano, A. et al. Sequential immunization elicits broadly neutralizing anti-HIV-1 antibodies in Ig knockin mice. Cell 166, 1445–1458.e12 (2016).
pmcid: 5019122
doi: 10.1016/j.cell.2016.07.030
pubmed: 27610569
Saunders, K. O. et al. Targeted selection of HIV-specific antibody mutations by engineering B cell maturation. Science 366, eaay7199 (2019).
pmcid: 7168753
doi: 10.1126/science.aay7199
pubmed: 31806786
Steichen, J. M. et al. A generalized HIV vaccine design strategy for priming of broadly neutralizing antibody responses. Science 366, eaax4380 (2019).
pmcid: 7092357
doi: 10.1126/science.aax4380
pubmed: 31672916
Corey, L. et al. Two randomized trials of neutralizing antibodies to prevent HIV-1 acquisition. N. Engl. J. Med. 384, 1003–1014 (2021).
pmcid: 8189692
doi: 10.1056/NEJMoa2031738
pubmed: 33730454
Gilbert, P. B. et al. Neutralization titer biomarker for antibody-mediated prevention of HIV-1 acquisition. Nat. Med. 28, 1924–1932 (2022).
pmcid: 9499869
doi: 10.1038/s41591-022-01953-6
pubmed: 35995954
Haynes, B. F. et al. Strategies for HIV-1 vaccines that induce broadly neutralizing antibodies. Nat. Rev. Immunol. 23, 142–158 (2022).
pmcid: 9372928
doi: 10.1038/s41577-022-00753-w
pubmed: 35962033
Hai, R. et al. Influenza viruses expressing chimeric hemagglutinins: globular head and stalk domains derived from different subtypes. J. Virol. 86, 5774–5781 (2012).
pmcid: 3347257
doi: 10.1128/JVI.00137-12
pubmed: 22398287
Yassine, H. M. et al. Hemagglutinin-stem nanoparticles generate heterosubtypic influenza protection. Nat. Med. 21, 1065–1070 (2015).
doi: 10.1038/nm.3927
pubmed: 26301691
Krammer, F., García-Sastre, A. & Palese, P. Is it possible to develop a “universal” influenza virus vaccine? Potential target antigens and critical aspects for a universal influenza vaccine. Cold Spring Harb. Perspect. Biol. 10, a028845 (2018).
pmcid: 6028071
doi: 10.1101/cshperspect.a028845
pubmed: 28663209
Corbett Kizzmekia, S. et al. Design of nanoparticulate group 2 influenza virus hemagglutinin stem antigens that activate unmutated ancestor B cell receptors of broadly neutralizing antibody lineages. MBio 10, e02810–e02818 (2019).
pmcid: 6391921
pubmed: 30808695
Wu, N. C. & Wilson, I. A. Influenza hemagglutinin structures and antibody recognition. Cold Spring Harb. Perspect. Med. 10, a038778 (2020).
pmcid: 7397844
doi: 10.1101/cshperspect.a038778
pubmed: 31871236
Arevalo, C. P. et al. A multivalent nucleoside-modified mRNA vaccine against all known influenza virus subtypes. Science 378, 899–904 (2022).
doi: 10.1126/science.abm0271
pubmed: 36423275
Wang, S. et al. Manipulating the selection forces during affinity maturation to generate cross-reactive HIV antibodies. Cell 160, 785–797 (2015).
pmcid: 4357364
doi: 10.1016/j.cell.2015.01.027
pubmed: 25662010
Shaffer, J. S., Moore, P. L., Kardar, M. & Chakraborty, A. K. Optimal immunization cocktails can promote induction of broadly neutralizing Abs against highly mutable pathogens. Proc. Natl Acad. Sci. USA 113, E7039–E7048 (2016).
pmcid: 5111661
doi: 10.1073/pnas.1614940113
pubmed: 27791170
Sprenger, K. G., Louveau, J. E., Murugan, P. M. & Chakraborty, A. K. Optimizing immunization protocols to elicit broadly neutralizing antibodies. Proc. Natl Acad. Sci. USA 117, 20077–20087 (2020).
pmcid: 7443869
doi: 10.1073/pnas.1919329117
pubmed: 32747563
Zhou, T. et al. Structural basis for broad and potent neutralization of HIV-1 by antibody VRC01. Science 329, 811–817 (2010).
pmcid: 2981354
doi: 10.1126/science.1192819
pubmed: 20616231
Klein, F. et al. Antibodies in HIV-1 vaccine development and therapy. Science 341, 1199–1204 (2013).
pmcid: 3970325
doi: 10.1126/science.1241144
pubmed: 24031012
Subbaraman, H., Schanz, M. & Trkola, A. Broadly neutralizing antibodies: what is needed to move from a rare event in HIV-1 infection to vaccine efficacy? Retrovirology 15, 52 (2018).
pmcid: 6064177
doi: 10.1186/s12977-018-0433-2
pubmed: 30055627
Luo, S. & Perelson, A. S. Competitive exclusion by autologous antibodies can prevent broad HIV-1 antibodies from arising. Proc. Natl Acad. Sci. USA 112, 11654–11659 (2015).
pmcid: 4577154
doi: 10.1073/pnas.1505207112
pubmed: 26324897
Nourmohammad, A., Otwinowski, J. & Plotkin, J. B. Host–pathogen coevolution and the emergence of broadly neutralizing antibodies in chronic infections. PLoS Genet. 12, e1006171 (2016).
pmcid: 4956326
doi: 10.1371/journal.pgen.1006171
pubmed: 27442127
Planas, D. et al. Considerable escape of SARS-CoV-2 Omicron to antibody neutralization. Nature 602, 671–675 (2021).
doi: 10.1038/s41586-021-04389-z
pubmed: 35016199
Garcia-Beltran, W. F. et al. mRNA-based COVID-19 vaccine boosters induce neutralizing immunity against SARS-CoV-2 Omicron variant. Cell 185, 457–466.e4 (2022).
pmcid: 8733787
doi: 10.1016/j.cell.2021.12.033
pubmed: 34995482
Gruell, H. et al. mRNA booster immunization elicits potent neutralizing serum activity against the SARS-CoV-2 Omicron variant. Nat. Med. 28, 477–480 (2022).
pmcid: 8767537
doi: 10.1038/s41591-021-01676-0
pubmed: 35046572
Hachmann, N. P. et al. Neutralization escape by SARS-CoV-2 omicron subvariants BA.2.12.1, BA.4, and BA.5. N. Engl. J. Med. 387, 86–88 (2022).
doi: 10.1056/NEJMc2206576
pubmed: 35731894
Yang, L. et al. Antigen presentation dynamics shape the antibody response to variants like SARS-CoV-2 Omicron after multiple vaccinations with the original strain. Cell Rep. 42, 112256 (2023).
pmcid: 9986127
doi: 10.1016/j.celrep.2023.112256
pubmed: 36952347
Schaefer-Babajew, D. et al. Antibody feedback regulates immune memory after SARS-CoV-2 mRNA vaccination. Nature 613, 735–742 (2023).
doi: 10.1038/s41586-022-05609-w
pubmed: 36473496
Futuyma, D. J. Evolutionary constraint and ecological consequences. Evolution 64, 1865–1884 (2010).
doi: 10.1111/j.1558-5646.2010.00960.x
pubmed: 20659157
Jia, X., Ma, Y., Bu, R., Zhao, T. & Wu, K. Directed evolution of a transcription factor PbrR to improve lead selectivity and reduce zinc interference through dual selection. AMB Express 10, 67 (2020).
pmcid: 7148400
doi: 10.1186/s13568-020-01004-8
pubmed: 32277291
Yokobayashi, Y. & Arnold, F. H. A dual selection module for directed evolution of genetic circuits. Nat. Comput. 4, 245–254 (2005).
doi: 10.1007/s11047-004-7442-x
Read, A. F., Day, T. & Huijben, S. The evolution of drug resistance and the curious orthodoxy of aggressive chemotherapy. Proc. Natl Acad. Sci. USA 108, 10871–10877 (2011).
pmcid: 3131826
doi: 10.1073/pnas.1100299108
pubmed: 21690376
Hansen, E., Woods, R. J. & Read, A. F. How to use a chemotherapeutic agent when resistance to it threatens the patient. PLoS Biol. 15, e2001110 (2017).
pmcid: 5300106
doi: 10.1371/journal.pbio.2001110
pubmed: 28182734
Li, X. et al. Mitochondria shed their outer membrane in response to infection-induced stress. Science 375, eabi4343 (2022).
doi: 10.1126/science.abi4343
pubmed: 35025629
Gatenby, R. A., Gillies, R. J. & Brown, J. S. The evolutionary dynamics of cancer prevention. Nat. Rev. Cancer 10, 526–527 (2010).
pmcid: 3744108
doi: 10.1038/nrc2892
pubmed: 21137109
Zhang, J., Cunningham, J. J., Brown, J. S. & Gatenby, R. A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun. 8, 1–9 (2017). This study introduces an eco-evolutionary protocol for cancer control that adaptively incorporates feedback from the target cell population, resulting in substantial clinical improvements over previous approaches.
doi: 10.1038/s41467-017-01968-5
Day, T., Huijben, S. & Read, A. F. Is selection relevant in the evolutionary emergence of drug resistance? Trends Microbiol. 23, 126–133 (2015).
pmcid: 4494118
doi: 10.1016/j.tim.2015.01.005
pubmed: 25680587
Strelkowa, N. & Lässig, M. Clonal interference in the evolution of influenza. Genetics 192, 671–682 (2012).
pmcid: 3454888
doi: 10.1534/genetics.112.143396
pubmed: 22851649
Gong, L. I., Suchard, M. A. & Bloom, J. D. Stability-mediated epistasis constrains the evolution of an influenza protein. eLife 2, e00631 (2013).
pmcid: 3654441
doi: 10.7554/eLife.00631
pubmed: 23682315
Koelle, K. & Rasmussen, D. A. The effects of a deleterious mutation load on patterns of influenza A/H3N2’s antigenic evolution in humans. eLife 4, e07361 (2015).
pmcid: 4611170
doi: 10.7554/eLife.07361
pubmed: 26371556
Gajewski, T. F., Schreiber, H. & Fu, Y.-X. Innate and adaptive immune cells in the tumor microenvironment. Nat. Immunol. 14, 1014–1022 (2013).
pmcid: 4118725
doi: 10.1038/ni.2703
pubmed: 24048123
Wang, S. & Dai, L. Evolving generalists in switching rugged landscapes. PLoS Comput. Biol. 15, e1007320 (2019).
pmcid: 6771975
doi: 10.1371/journal.pcbi.1007320
pubmed: 31574088
Sachdeva, V., Husain, K., Sheng, J., Wang, S. & Murugan, A. Tuning environmental timescales to evolve and maintain generalists. Proc. Natl Acad. Sci. USA 117, 12693–12699 (2020).
pmcid: 7293598
doi: 10.1073/pnas.1914586117
pubmed: 32457160
Yang, L., Caradonna, T. M., Schmidt, A. G. & Chakraborty, A. K. Mechanisms that promote the evolution of cross-reactive antibodies upon vaccination with designed influenza immunogens. Cell Rep. 42, 112160 (2023). This study combines theory with experiments to show that influenza vaccines containing a chimera of multiple epitopes can induce broadly reactive antibodies.
pmcid: 10184763
doi: 10.1016/j.celrep.2023.112160
pubmed: 36867533
Brown, S. P., Le Chat, L., De Paepe, M. & Taddei, F. Ecology of microbial invasions: amplification allows virus carriers to invade more rapidly when rare. Curr. Biol. 16, 2048–2052 (2006).
doi: 10.1016/j.cub.2006.08.089
pubmed: 17055985
Duerkop, B. A., Clements, C. V., Rollins, D., Rodrigues, J. L. M. & Hooper, L. V. A composite bacteriophage alters colonization by an intestinal commensal bacterium. Proc. Natl Acad. Sci. USA 109, 17621–17626 (2012).
pmcid: 3491505
doi: 10.1073/pnas.1206136109
pubmed: 23045666
Gama, J. A. et al. Temperate bacterial viruses as double-edged swords in bacterial warfare. PLoS ONE 8, e59043 (2013).
pmcid: 3594171
doi: 10.1371/journal.pone.0059043
pubmed: 23536852
Li, X.-Y. et al. Temperate phages as self-replicating weapons in bacterial competition. J. R. Soc. Interface 14, 20170563 (2017).
pmcid: 5746566
doi: 10.1098/rsif.2017.0563
pubmed: 29263125
Frazão, N. et al. Two modes of evolution shape bacterial strain diversity in the mammalian gut for thousands of generations. Nat. Commun. 13, 5604 (2022).
pmcid: 9509342
doi: 10.1038/s41467-022-33412-8
pubmed: 36153389
Lei, J. et al. The antimicrobial peptides and their potential clinical applications. Am. J. Transl. Res. 11, 3919–3931 (2019).
pmcid: 6684887
pubmed: 31396309
Lazzaro, B. P., Zasloff, M. & Rolff, J. Antimicrobial peptides: application informed by evolution. Science 368, eaau5480 (2020).
pmcid: 8097767
doi: 10.1126/science.aau5480
pubmed: 32355003
Baym, M. et al. Spatiotemporal microbial evolution on antibiotic landscapes. Science 353, 1147–1151 (2016).
pmcid: 5534434
doi: 10.1126/science.aag0822
pubmed: 27609891
Castle, S. D., Grierson, C. S. & Gorochowski, T. E. Towards an engineering theory of evolution. Nat. Commun. 12, 3326 (2021).
pmcid: 8185075
doi: 10.1038/s41467-021-23573-3
pubmed: 34099656
Xie, L. & Shou, W. Steering ecological-evolutionary dynamics to improve artificial selection of microbial communities. Nat. Commun. 12, 6799 (2021).
pmcid: 8611069
doi: 10.1038/s41467-021-26647-4
pubmed: 34815384
Kuosmanen, T. et al. Drug-induced resistance evolution necessitates less aggressive treatment. PLoS Comput. Biol. 17, e1009418 (2021).
pmcid: 8491903
doi: 10.1371/journal.pcbi.1009418
pubmed: 34555024
Lin, A. et al. Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Sci. Transl. Med. 11, eaaw8412 (2019).
pmcid: 7717492
doi: 10.1126/scitranslmed.aaw8412
pubmed: 31511426
Force, T. & Kolaja, K. L. Cardiotoxicity of kinase inhibitors: the prediction and translation of preclinical models to clinical outcomes. Nat. Rev. Drug Discov. 10, 111–126 (2011).
doi: 10.1038/nrd3252
pubmed: 21283106
Harrison, R. K. Phase II and phase III failures: 2013–2015. Nat. Rev. Drug Discov. 15, 817–818 (2016).
doi: 10.1038/nrd.2016.184
pubmed: 27811931
Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429 (2011).
doi: 10.1126/science.1193954
pubmed: 21273479
Cairns, J., Jokela, R., Becks, L., Mustonen, V. & Hiltunen, T. Repeatable ecological dynamics govern the response of experimental communities to antibiotic pulse perturbation. Nat. Ecol. Evol. 4, 1385–1394 (2020).
doi: 10.1038/s41559-020-1272-9
pubmed: 32778754
Gore, J., Youk, H. & van Oudenaarden, A. Snowdrift game dynamics and facultative cheating in yeast. Nature 459, 253–256 (2009).
pmcid: 2888597
doi: 10.1038/nature07921
pubmed: 19349960
Janeway, C. A., Jr, Travers, P., Walport, M. & Shlomchik, M. J. Immunobiology (Garland Science, 2001).
Shinnakasu, R. et al. Regulated selection of germinal-center cells into the memory B cell compartment. Nat. Immunol. 17, 861–869 (2016).
doi: 10.1038/ni.3460
pubmed: 27158841
Viant, C. et al. Antibody affinity shapes the choice between memory and germinal center B cell fates. Cell 183, 1298–1311.e11 (2020).
pmcid: 7722471
doi: 10.1016/j.cell.2020.09.063
pubmed: 33125897
Mayer, A., Balasubramanian, V., Walczak, A. M. & Mora, T. How a well-adapting immune system remembers. Proc. Natl Acad. Sci. USA 116, 8815–8823 (2019). This theoretical work studies how adaptive immune repertoires should be organized to minimize the cost of infections in a given environment of pathogens.
pmcid: 6500122
doi: 10.1073/pnas.1812810116
pubmed: 30988203
Röschinger, T., Tovar, R. M., Pompei, S. & Lässig, M. Adaptive ratchets and the evolution of molecular complexity. Preprint at arXiv https://doi.org/10.48550/arXiv.2111.09981 (2021).
doi: 10.48550/arXiv.2111.09981
Schnaack, O. H. & Nourmohammad, A. Optimal evolutionary decision-making to store immune memory. eLife 10, e61346 (2021).
pmcid: 8116052
doi: 10.7554/eLife.61346
pubmed: 33908347
Schnaack, O. H., Peliti, L. & Nourmohammad, A. Learning and organization of memory for evolving patterns. Phys. Rev. X 12, 021063 (2022).
Chardès, V., Vergassola, M., Walczak, A. M. & Mora, T. Affinity maturation for an optimal balance between long-term immune coverage and short-term resource constraints. Proc. Natl Acad. Sci. USA 119, e2113512119 (2022).
pmcid: 8872716
doi: 10.1073/pnas.2113512119
pubmed: 35177475
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Current status and applications of genome-scale metabolic models. Genome Biol. 20, 121 (2019).
pmcid: 6567666
doi: 10.1186/s13059-019-1730-3
pubmed: 31196170
Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. & Hwa, T. Interdependence of cell growth and gene expression: origins and consequences. Science 330, 1099–1102 (2010). This study establishes a quantitative model for growth-dependent allocation of proteome resources, which is an important prerequisite for metabolic control approaches.
doi: 10.1126/science.1192588
pubmed: 21097934
Weiße, A. Y., Oyarzún, D. A., Danos, V. & Swain, P. S. Mechanistic links between cellular trade-offs, gene expression, and growth. Proc. Natl Acad. Sci. USA 112, E1038–E1047 (2015). This study develops a growth model for microbial cells that includes cell metabolism and nutrient intake, providing a computable link between environmental changes and eco-evolutionary dynamics.
pmcid: 4352769
doi: 10.1073/pnas.1416533112
pubmed: 25695966
Dourado, H., Mori, M., Hwa, T. & Lercher, M. J. On the optimality of the enzyme-substrate relationship in bacteria. PLoS Biol. 19, e3001416 (2021).
pmcid: 8547704
doi: 10.1371/journal.pbio.3001416
pubmed: 34699521
Posfai, A., Taillefumier, T. & Wingreen, N. S. Metabolic trade-offs promote diversity in a model ecosystem. Phys. Rev. Lett. 118, 028103 (2017).
pmcid: 5743855
doi: 10.1103/PhysRevLett.118.028103
pubmed: 28128613
Good, B. H., Martis, S. & Hallatschek, O. Adaptation limits ecological diversification and promotes ecological tinkering during the competition for substitutable resources. Proc. Natl Acad. Sci. USA 115, E10407–E10416 (2018).
pmcid: 6217437
doi: 10.1073/pnas.1807530115
pubmed: 30322918
Ansari, A. F., Reddy, Y. B. S., Raut, J. & Dixit, N. M. An efficient and scalable top-down method for predicting structures of microbial communities. Nat. Comput. Sci. 1, 619–628 (2021).
doi: 10.1038/s43588-021-00131-x
van den Berg, N. I. et al. Ecological modelling approaches for predicting emergent properties in microbial communities. Nat. Ecol. Evol. 6, 855–865 (2022).
pmcid: 7613029
doi: 10.1038/s41559-022-01746-7
pubmed: 35577982
Mora, T., Walczak, A. M., Bialek, W. & Callan, C. G. Jr. Maximum entropy models for antibody diversity. Proc. Natl Acad. Sci. USA 107, 5405–5410 (2010).
pmcid: 2851784
doi: 10.1073/pnas.1001705107
pubmed: 20212159
Desponds, J., Mora, T. & Walczak, A. M. Fluctuating fitness shapes the clone-size distribution of immune repertoires. Proc. Natl Acad. Sci. USA 113, 274–279 (2016).
doi: 10.1073/pnas.1512977112
pubmed: 26711994
DeWitt, W. S. et al. Dynamics of the cytotoxic T cell response to a model of acute viral infection. J. Virol. 89, 4517–4526 (2015).
pmcid: 4442358
doi: 10.1128/JVI.03474-14
pubmed: 25653453
Pogorelyy, M. V. et al. Detecting T cell receptors involved in immune responses from single repertoire snapshots. PLoS Biol. 17, e3000314 (2019).
pmcid: 6592544
doi: 10.1371/journal.pbio.3000314
pubmed: 31194732
Nourmohammad, A., Otwinowski, J., Łuksza, M., Mora, T. & Walczak, A. M. Fierce selection and interference in B-cell repertoire response to chronic HIV-1. Mol. Biol. Evol. 36, 2184–2194 (2019).
pmcid: 6759071
doi: 10.1093/molbev/msz143
pubmed: 31209469
Snyder, T. M. et al. Magnitude and dynamics of the T-cell response to SARS-CoV-2 infection at both individual and population levels. Preprint at medRxiv https://doi.org/10.1101/2020.07.31.20165647 (2020).
doi: 10.1101/2020.07.31.20165647
pmcid: 7418734
pubmed: 32793919
Nolan, S. et al. A large-scale database of T-cell receptor β (TCRβ) sequences and binding associations from natural and synthetic exposure to SARS-CoV-2. Res. Sq. https://doi.org/10.21203/rs.3.rs-51964/v1 (2020).
doi: 10.21203/rs.3.rs-51964/v1
pmcid: 7418738
pubmed: 32793896
Minervina, A. A. et al. Primary and secondary anti-viral response captured by the dynamics and phenotype of individual T cell clones. eLife 9, e53704 (2020).
pmcid: 7060039
doi: 10.7554/eLife.53704
pubmed: 32081129
Montague, Z. et al. Dynamics of B cell repertoires and emergence of cross-reactive responses in patients with different severities of COVID-19. Cell Rep. 35, 109173 (2021).
pmcid: 8106887
doi: 10.1016/j.celrep.2021.109173
pubmed: 33991510
Minervina, A. A. et al. Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T-cell memory formation after mild COVID-19 infection. eLife 10, e63502 (2021).
pmcid: 7806265
doi: 10.7554/eLife.63502
pubmed: 33399535
Pogorelyy, M. V. et al. Resolving SARS-CoV-2 CD4
pmcid: 9247234
doi: 10.1016/j.xcrm.2022.100697
pubmed: 35841887
Mayer, A., Balasubramanian, V., Mora, T. & Walczak, A. M. How a well-adapted immune system is organized. Proc. Natl Acad. Sci. USA 112, 5950–5955 (2015).
pmcid: 4434741
doi: 10.1073/pnas.1421827112
pubmed: 25918407
Bradde, S., Nourmohammad, A., Goyal, S. & Balasubramanian, V. The size of the immune repertoire of bacteria. Proc. Natl Acad. Sci. USA 117, 5144–5151 (2020).
pmcid: 7071851
doi: 10.1073/pnas.1903666117
pubmed: 32071241
Mayer, A., Mora, T., Rivoire, O. & Walczak, A. M. Diversity of immune strategies explained by adaptation to pathogen statistics. Proc. Natl Acad. Sci. USA 113, 8630–8635 (2016).
pmcid: 4978245
doi: 10.1073/pnas.1600663113
pubmed: 27432970
Vogwill, T. & MacLean, R. C. The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evol. Appl. 8, 284–295 (2015).
doi: 10.1111/eva.12202
pubmed: 25861386
Melnyk, A. H., Wong, A. & Kassen, R. The fitness costs of antibiotic resistance mutations. Evol. Appl. 8, 273–283 (2015).
doi: 10.1111/eva.12196
pubmed: 25861385
Lawley, T. D. et al. Targeted restoration of the intestinal microbiota with a simple, defined bacteriotherapy resolves relapsing Clostridium difficile disease in mice. PLoS Pathog. 8, e1002995 (2012).
pmcid: 3486913
doi: 10.1371/journal.ppat.1002995
pubmed: 23133377
de Visser, J. A. G. M. & Krug, J. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15, 480–490 (2014).
doi: 10.1038/nrg3744
pubmed: 24913663
Bitbol, A.-F. & Schwab, D. J. Quantifying the role of population subdivision in evolution on rugged fitness landscapes. PLoS Comput. Biol. 10, e1003778 (2014).
pmcid: 4133052
doi: 10.1371/journal.pcbi.1003778
pubmed: 25122220
Freitas, O., Wahl, L. M. & Campos, P. R. A. Robustness and predictability of evolution in bottlenecked populations. Phys. Rev. E 103, 042415 (2021).
doi: 10.1103/PhysRevE.103.042415
pubmed: 34005989
Berg, J., Willmann, S. & Lässig, M. Adaptive evolution of transcription factor binding sites. BMC Evol. Biol. 4, 42 (2004).
pmcid: 535555
doi: 10.1186/1471-2148-4-42
pubmed: 15511291
Sella, G. & Hirsh, A. E. The application of statistical physics to evolutionary biology. Proc. Natl Acad. Sci. USA 102, 9541–9546 (2005).
pmcid: 1172247
doi: 10.1073/pnas.0501865102
pubmed: 15980155
Rotem, A. et al. Evolution on the biophysical fitness landscape of an RNA virus. Mol. Biol. Evol. 35, 2390–2400 (2018).
pmcid: 6188569
doi: 10.1093/molbev/msy131
pubmed: 29955873
Maynard Smith, J. Evolution and the Theory of Games (Cambridge Univ. Press, 1982).
Stanková, K., Brown, J. S., Dalton, W. S. & Gatenby, R. A. Optimizing cancer treatment using game theory: a review. JAMA Oncol. 5, 96–103 (2019).
pmcid: 6947530
doi: 10.1001/jamaoncol.2018.3395
pubmed: 30098166
LaMont, C. et al. Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1. eLife 11, e76004 (2022). This study introduces a computational population genetics model to predict HIV escape from bNAbs and to devise optimal combination therapies of bNAbs that suppress HIV escape and rebound within patients.
pmcid: 9467514
doi: 10.7554/eLife.76004
pubmed: 35852143
Meijers, M., Vanshylla, K., Gruell, H., Klein, F. & Lässig, M. Predicting in vivo escape dynamics of HIV-1 from a broadly neutralizing antibody. Proc. Natl Acad. Sci. USA 118, e2104651118 (2021).
pmcid: 8325275
doi: 10.1073/pnas.2104651118
pubmed: 34301904
Lee, J. M. et al. Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants. Proc. Natl Acad. Sci. USA 115, E8276–E8285 (2018).
pmcid: 6126756
doi: 10.1073/pnas.1806133115
pubmed: 30104379
Wu, N. C. et al. Major antigenic site B of human influenza H3N2 viruses has an evolving local fitness landscape. Nat. Commun. 11, 1233 (2020).
pmcid: 7060233
doi: 10.1038/s41467-020-15102-5
pubmed: 32144244
Starr, T. N. et al. Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding. Cell 182, 1295–1310.e20 (2020).
pmcid: 7418704
doi: 10.1016/j.cell.2020.08.012
pubmed: 32841599
Wang, Y., Lei, R., Nourmohammad, A. & Wu, N. C. Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing. eLife 10, e72516 (2021).
pmcid: 8683081
doi: 10.7554/eLife.72516
pubmed: 34878407
Starr, T. N. et al. Shifting mutational constraints in the SARS-CoV-2 receptor-binding domain during viral evolution. Science 377, 420–424 (2022).
doi: 10.1126/science.abo7896
pubmed: 35762884
Phillips, A. M. et al. Binding affinity landscapes constrain the evolution of broadly neutralizing anti-influenza antibodies. eLife 10, e71393 (2021).
pmcid: 8476123
doi: 10.7554/eLife.71393
pubmed: 34491198
Moulana, A. et al. Compensatory epistasis maintains ACE2 affinity in SARS-CoV-2 Omicron BA.1. Nat. Commun. 13, 7011 (2022).
pmcid: 9668218
doi: 10.1038/s41467-022-34506-z
pubmed: 36384919
Maher, M. C. et al. Predicting the mutational drivers of future SARS-CoV-2 variants of concern. Sci. Transl. Med. 14, eabk3445 (2022).
doi: 10.1126/scitranslmed.abk3445
pubmed: 35014856
Madani, A. et al. Large language models generate functional protein sequences across diverse families. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01618-2 (2023).
doi: 10.1101/2020.03.07.982272
pmcid: 10400306
pubmed: 36702895
Hie, B., Zhong, E. D., Berger, B. & Bryson, B. Learning the language of viral evolution and escape. Science 371, 284–288 (2021).
doi: 10.1126/science.abd7331
pubmed: 33446556
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
pmcid: 8371605
doi: 10.1038/s41586-021-03819-2
pubmed: 34265844
Rives, A. et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl Acad. Sci. USA 118, e2016239118 (2021).
pmcid: 8053943
doi: 10.1073/pnas.2016239118
pubmed: 33876751
Pun, M. N. et al. Learning the shape of protein micro-environments with a holographic convolutional neural network. Preprint at arXiv https://doi.org/10.48550/arXiv.2211.02936 (2022).
doi: 10.48550/arXiv.2211.02936
Dauparas, J. et al. Robust deep learning-based protein sequence design using ProteinMPNN. Science 378, 49–56 (2022).
pmcid: 9997061
doi: 10.1126/science.add2187
pubmed: 36108050
Vaishnav, E. D. et al. The evolution, evolvability and engineering of gene regulatory DNA. Nature 603, 455–463 (2022).
doi: 10.1038/s41586-022-04506-6
pubmed: 35264797
Treloar, N. J., Fedorec, A. J. H., Ingalls, B. & Barnes, C. P. Deep reinforcement learning for the control of microbial co-cultures in bioreactors. PLoS Comput. Biol. 16, e1007783 (2020).
pmcid: 7176278
doi: 10.1371/journal.pcbi.1007783
pubmed: 32275710
Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019). This study demonstrates that machine learning can guide the directed evolution of proteins by in silico fitness predictions.
doi: 10.1038/s41592-019-0496-6
pubmed: 31308553
Sinai, S. & Kelsic, E. D. A primer on model-guided exploration of fitness landscapes for biological sequence design. Preprint at arXiv https://doi.org/10.48550/arXiv.2010.10614 (2020).
doi: 10.48550/arXiv.2010.10614
Udrescu, S.-M. & Tegmark, M. AI Feynman: a physics-inspired method for symbolic regression. Sci. Adv. 6, eaay2631 (2020).
pmcid: 7159912
doi: 10.1126/sciadv.aay2631
pubmed: 32426452
Stengel, R. F. Optimal Control and Estimation (Courier, 1994).
Black, F. & Scholes, M. The pricing of options and corporate liabilities. J. Polit. Econ. 81, 637–654 (1973).
doi: 10.1086/260062
Merton, R. C. Theory of rational option pricing. Bell J. Econ. Manag. Sci. 4, 141–183 (1973).
doi: 10.2307/3003143
Bellman, R. On the theory of dynamic programming. Proc. Natl Acad. Sci. USA 38, 716–719 (1952).
pmcid: 1063639
doi: 10.1073/pnas.38.8.716
pubmed: 16589166
Kappen, H. J. An introduction to stochastic control theory, path integrals and reinforcement learning. AIP Conf. Proc. 887, 149–181 (2007).
doi: 10.1063/1.2709596
Fischer, A., Vázquez-García, I. & Mustonen, V. The value of monitoring to control evolving populations. Proc. Natl Acad. Sci. USA 112, 1007–1012 (2015).
pmcid: 4313848
doi: 10.1073/pnas.1409403112
pubmed: 25587136
Iram, S. et al. Controlling the speed and trajectory of evolution with counterdiabatic driving. Nat. Phys. 17, 135–142 (2020).
doi: 10.1038/s41567-020-0989-3
Champer, J. et al. Molecular safeguarding of CRISPR gene drive experiments. eLife 8, e41439 (2019).
pmcid: 6358215
doi: 10.7554/eLife.41439
pubmed: 30666960
Wright, O., Stan, G.-B. & Ellis, T. Building-in biosafety for synthetic biology. Microbiology 159, 1221–1235 (2013).
doi: 10.1099/mic.0.066308-0
pubmed: 23519158
Daley, G. Q., Lovell-Badge, R. & Steffann, J. After the storm — a responsible path for genome editing. N. Engl. J. Med. 380, 897–899 (2019).
doi: 10.1056/NEJMp1900504
pubmed: 30649993
Mandell, D. J. et al. Biocontainment of genetically modified organisms by synthetic protein design. Nature 518, 55–60 (2015).
pmcid: 4422498
doi: 10.1038/nature14121
pubmed: 25607366
Chan, C. T. Y., Lee, J. W., Cameron, D. E., Bashor, C. J. & Collins, J. J. “Deadman” and “Passcode” microbial kill switches for bacterial containment. Nat. Chem. Biol. 12, 82–86 (2016).
doi: 10.1038/nchembio.1979
pubmed: 26641934
zur Wiesch, P. A., Kouyos, R., Engelstädter, J., Regoes, R. R. & Bonhoeffer, S. Population biological principles of drug-resistance evolution in infectious diseases. Lancet Infect. Dis. 11, 236–247 (2011).
doi: 10.1016/S1473-3099(10)70264-4
pubmed: 21371657
Larsen, A. C. et al. A general strategy for expanding polymerase function by droplet microfluidics. Nat. Commun. 7, 11235 (2016).
pmcid: 4822039
doi: 10.1038/ncomms11235
pubmed: 27044725
Chen, H. et al. Efficient, continuous mutagenesis in human cells using a pseudo-random. DNA editor. Nat. Biotechnol. 38, 165–168 (2020).
doi: 10.1038/s41587-019-0331-8
pubmed: 31844291
Cravens, A., Jamil, O. K., Kong, D., Sockolosky, J. T. & Smolke, C. D. Polymerase-guided base editing enables in vivo mutagenesis and rapid protein engineering. Nat. Commun. 12, 1579 (2021).
pmcid: 7952560
doi: 10.1038/s41467-021-21876-z
pubmed: 33707425
Rix, G. & Liu, C. C. Systems for in vivo hypermutation: a quest for scale and depth in directed evolution. Curr. Opin. Chem. Biol. 64, 20–26 (2021).
pmcid: 8464631
doi: 10.1016/j.cbpa.2021.02.008
pubmed: 33784581
Shi, C., Wang, C., Lu, J., Zhong, B. & Tang, J. Protein sequence and structure co-design with equivariant translation. Paper presented at the 11th International Conference on Learning Representations https://openreview.net/forum?id=pRCMXcfdihq (2023).
Schuler, T. H., Poppy, G. M., Kerry, B. R. & Denholm, I. Insect-resistant transgenic plants. Trends Biotechnol. 16, 168–175 (1998).
doi: 10.1016/S0167-7799(97)01171-2
Castle, L. A. et al. Discovery and directed evolution of a glyphosate tolerance gene. Science 304, 1151–1154 (2004).
doi: 10.1126/science.1096770
pubmed: 15155947
Douglas, A. E. Strategies for enhanced crop resistance to insect pests. Annu. Rev. Plant. Biol. 69, 637–660 (2018).
doi: 10.1146/annurev-arplant-042817-040248
pubmed: 29144774
Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).
pmcid: 5577644
doi: 10.1038/nature22991
pubmed: 28678778
Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).
doi: 10.1038/s41586-018-0792-9
pubmed: 30568305
Perelson, A. S. et al. Decay characteristics of HIV-1-infected compartments during combination therapy. Nature 387, 188–191 (1997).
doi: 10.1038/387188a0
pubmed: 9144290
Baym, M., Stone, L. K. & Kishony, R. Multidrug evolutionary strategies to reverse antibiotic resistance. Science 351, aad3292 (2016).
pmcid: 5496981
doi: 10.1126/science.aad3292
pubmed: 26722002
Wang, K. K. et al. A hybrid drug limits resistance by evading the action of the multiple antibiotic resistance pathway. Mol. Biol. Evol. 33, 492–500 (2016).
doi: 10.1093/molbev/msv243
pubmed: 26538141
Feder, A. F. et al. More effective drugs lead to harder selective sweeps in the evolution of drug resistance in HIV-1. eLife 5, e10670 (2016).
pmcid: 4764592
doi: 10.7554/eLife.10670
pubmed: 26882502
Zhang, F. et al. Optimal combination treatment regimens of vaccine and radiotherapy augment tumor-bearing host immunity. Commun. Biol. 4, 78 (2021).
pmcid: 7815836
doi: 10.1038/s42003-020-01598-6
pubmed: 33469123
Malherbe, D. C. et al. Sequential immunization with a subtype B HIV-1 envelope quasispecies partially mimics the in vivo development of neutralizing antibodies. J. Virol. 85, 5262–5274 (2011).
pmcid: 3094990
doi: 10.1128/JVI.02419-10
pubmed: 21430056
Klasse, P. J. et al. Sequential and simultaneous immunization of rabbits with HIV-1 envelope glycoprotein SOSIP.664 trimers from clades A, B and C. PLoS Pathog. 12, e1005864 (2016).
pmcid: 5023125
doi: 10.1371/journal.ppat.1005864
pubmed: 27627672
Mohan, T., Berman, Z., Kang, S.-M. & Wang, B.-Z. Sequential immunizations with a panel of HIV-1 Env virus-like particles coach immune system to make broadly neutralizing antibodies. Sci. Rep. 8, 7807 (2018).
pmcid: 5958130
doi: 10.1038/s41598-018-25960-1
pubmed: 29773829
Miyamoto, S. et al. Vaccination-infection interval determines cross-neutralization potency to SARS-CoV-2 Omicron after breakthrough infection by other variants. Med 3, 249–261.e4 (2022).
doi: 10.1016/j.medj.2022.02.006
pubmed: 35261995
Lu, C.-L. et al. Enhanced clearance of HIV-1-infected cells by broadly neutralizing antibodies against HIV-1 in vivo. Science 352, 1001–1004 (2016).
pmcid: 5126967
doi: 10.1126/science.aaf1279
pubmed: 27199430
Ragheb, M. N. et al. Inhibiting the evolution of antibiotic resistance. Mol. Cell 73, 157–165.e5 (2019).
pmcid: 6320318
doi: 10.1016/j.molcel.2018.10.015
pubmed: 30449724
Bozic, I. et al. Evolutionary dynamics of cancer in response to targeted combination therapy. eLife 2, e00747 (2013).
pmcid: 3691570
doi: 10.7554/eLife.00747
pubmed: 23805382
Marchi, J., Lässig, M., Walczak, A. M. & Mora, T. Antigenic waves of virus-immune coevolution. Proc. Natl Acad. Sci. USA 118, e2103398118 (2021).
pmcid: 8271616
doi: 10.1073/pnas.2103398118
pubmed: 34183397
Tuerk, C. & Gold, L. Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase. Science 249, 505–510 (1990).
doi: 10.1126/science.2200121
pubmed: 2200121
Fernandez-Gacio, A., Uguen, M. & Fastrez, J. Phage display as a tool for the directed evolution of enzymes. Trends Biotechnol. 21, 408–414 (2003).
doi: 10.1016/S0167-7799(03)00194-X
pubmed: 12948674
Brudno, Y., Birnbaum, M. E., Kleiner, R. E. & Liu, D. R. An in vitro translation, selection and amplification system for peptide nucleic acids. Nat. Chem. Biol. 6, 148–155 (2010).
doi: 10.1038/nchembio.280
pubmed: 20081830
van Bloois, E., Winter, R. T., Kolmar, H. & Fraaije, M. W. Decorating microbes: surface display of proteins on Escherichia coli. Trends Biotechnol. 29, 79–86 (2011).
doi: 10.1016/j.tibtech.2010.11.003
pubmed: 21146237
Rovner, A. J. et al. Recoded organisms engineered to depend on synthetic amino acids. Nature 518, 89–93 (2015).
pmcid: 4590768
doi: 10.1038/nature14095
pubmed: 25607356
Blind, M. & Blank, M. Aptamer selection technology and recent advances. Mol. Ther. Nucleic Acids 4, e223 (2015).
pmcid: 4345306
doi: 10.1038/mtna.2014.74
pubmed: 28110747
Wong, B. G., Mancuso, C. P., Kiriakov, S., Bashor, C. J. & Khalil, A. S. Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER. Nat. Biotechnol. 36, 614–623 (2018).
pmcid: 6035058
doi: 10.1038/nbt.4151
pubmed: 29889214
Rice, L. B. The clinical consequences of antimicrobial resistance. Curr. Opin. Microbiol. 12, 476–481 (2009).
doi: 10.1016/j.mib.2009.08.001
pubmed: 19716760
Laxminarayan, R. Antibiotic effectiveness: balancing conservation against innovation. Science 345, 1299–1301 (2014).
doi: 10.1126/science.1254163
pubmed: 25214620
Moura de Sousa, J., Balbontín, R., Durão, P. & Gordo, I. Multidrug-resistant bacteria compensate for the epistasis between resistances. PLoS Biol. 15, e2001741 (2017).
pmcid: 5395140
doi: 10.1371/journal.pbio.2001741
pubmed: 28419091
Wistrand-Yuen, E. et al. Evolution of high-level resistance during low-level antibiotic exposure. Nat. Commun. 9, 1599 (2018).
pmcid: 5913237
doi: 10.1038/s41467-018-04059-1
pubmed: 29686259
Durão, P., Balbontín, R. & Gordo, I. Evolutionary mechanisms shaping the maintenance of antibiotic resistance. Trends Microbiol. 26, 677–691 (2018).
doi: 10.1016/j.tim.2018.01.005
pubmed: 29439838
Vasan, N., Baselga, J. & Hyman, D. M. A view on drug resistance in cancer. Nature 575, 299–309 (2019).
pmcid: 8008476
doi: 10.1038/s41586-019-1730-1
pubmed: 31723286
Szybalski, W. & Bryson, V. Genetic studies on microbial cross resistance to toxic agents. I. Cross resistance of Escherichia coli to fifteen antibiotics. J. Bacteriol. 64, 489–499 (1952).
pmcid: 169383
doi: 10.1128/jb.64.4.489-499.1952
pubmed: 12999676
Oz, T. et al. Strength of selection pressure is an important parameter contributing to the complexity of antibiotic resistance evolution. Mol. Biol. Evol. 31, 2387–2401 (2014).
pmcid: 4137714
doi: 10.1093/molbev/msu191
pubmed: 24962091
Levin-Reisman, I. et al. Antibiotic tolerance facilitates the evolution of resistance. Science 355, 826–830 (2017).
doi: 10.1126/science.aaj2191
pubmed: 28183996
Levin-Reisman, I., Brauner, A., Ronin, I. & Balaban, N. Q. Epistasis between antibiotic tolerance, persistence, and resistance mutations. Proc. Natl Acad. Sci. USA 116, 14734–14739 (2019).
pmcid: 6642377
doi: 10.1073/pnas.1906169116
pubmed: 31262806
Vega, N. M. & Gore, J. Collective antibiotic resistance: mechanisms and implications. Curr. Opin. Microbiol. 21, 28–34 (2014).
pmcid: 4367450
doi: 10.1016/j.mib.2014.09.003
pubmed: 25271119
Sorg, R. A. et al. Collective resistance in microbial communities by intracellular antibiotic deactivation. PLoS Biol. 14, e2000631 (2016).
pmcid: 5189934
doi: 10.1371/journal.pbio.2000631
pubmed: 28027306
de Vos, M. G. J., Zagorski, M., McNally, A. & Bollenbach, T. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. Proc. Natl Acad. Sci. USA 114, 10666–10671 (2017).
pmcid: 5635929
doi: 10.1073/pnas.1713372114
pubmed: 28923953
Klümper, U. et al. Selection for antimicrobial resistance is reduced when embedded in a natural microbial community. ISME J. 13, 2927–2937 (2019).
pmcid: 6864104
doi: 10.1038/s41396-019-0483-z
pubmed: 31384011
Bottery, M. J., Pitchford, J. W. & Friman, V.-P. Ecology and evolution of antimicrobial resistance in bacterial communities. ISME J. 15, 939–948 (2020).
pmcid: 8115348
doi: 10.1038/s41396-020-00832-7
pubmed: 33219299
Witte, W. Medical consequences of antibiotic use in agriculture. Science 279, 996–997 (1998).
doi: 10.1126/science.279.5353.996
pubmed: 9490487
Bawa, A. S. & Anilakumar, K. R. Genetically modified foods: safety, risks and public concerns — a review. J. Food Sci. Technol. 50, 1035–1046 (2013).
doi: 10.1007/s13197-012-0899-1
pubmed: 24426015
Gilbert, N. Case studies: a hard look at GM crops. Nature https://doi.org/10.1038/497024a (2013).
doi: 10.1038/497024a
pubmed: 23985872
Hawkins, N. J., Bass, C., Dixon, A. & Neve, P. The evolutionary origins of pesticide resistance. Biol. Rev. Camb. Philos. Soc. 94, 135–155 (2018).
pmcid: 6378405
doi: 10.1111/brv.12440
pubmed: 29971903
Aarestrup, F. M. and Schwarz, S. in Antimicrobial Resistance in Bacteria of Animal Origin (ed. Aarestrup, F. M.) 187–212 (ASM Press, 2019).
Mann, A., Nehra, K., Rana, J. S. & Dahiya, T. Antibiotic resistance in agriculture: perspectives on upcoming strategies to overcome upsurge in resistance. Curr. Res. Microb. Sci. 2, 100030 (2021).
pmcid: 8610298
pubmed: 34841321
Flynn, J. L. & Chan, J. Tuberculosis: latency and reactivation. Infect. Immun. 69, 4195–4201 (2001).
pmcid: 98451
doi: 10.1128/IAI.69.7.4195-4201.2001
pubmed: 11401954
Bailey, J., Blankson, J. N., Wind-Rotolo, M. & Siliciano, R. F. Mechanisms of HIV-1 escape from immune responses and antiretroviral drugs. Curr. Opin. Immunol. 16, 470–476 (2004).
doi: 10.1016/j.coi.2004.05.005
pubmed: 15245741
Lin, P. L. & Flynn, J. L. Understanding latent tuberculosis: a moving target. J. Immunol. 185, 15–22 (2010).
doi: 10.4049/jimmunol.0903856
pubmed: 20562268
Perng, G.-C. & Jones, C. Towards an understanding of the herpes simplex virus type 1 latency-reactivation cycle. Interdiscip. Perspect. Infect. Dis. 2010, 262415 (2010).
pmcid: 2822239
doi: 10.1155/2010/262415
pubmed: 20169002
Cohn, L. B., Chomont, N. & Deeks, S. G. The biology of the HIV-1 latent reservoir and implications for cure strategies. Cell Host Microbe 27, 519–530 (2020).
pmcid: 7219958
doi: 10.1016/j.chom.2020.03.014
pubmed: 32272077
Chen, Y., Jungsuwadee, P., Vore, M., Butterfield, D. A. & St Clair, D. K. Collateral damage in cancer chemotherapy: oxidative stress in nontargeted tissues. Mol. Interv. 7, 147–156 (2007).
doi: 10.1124/mi.7.3.6
pubmed: 17609521
Kostine, M. et al. Opportunistic autoimmunity secondary to cancer immunotherapy (OASI): an emerging challenge. Rev. Med. Interne 38, 513–525 (2017).
doi: 10.1016/j.revmed.2017.01.004
pubmed: 28214182
Pauken, K. E., Dougan, M., Rose, N. R., Lichtman, A. H. & Sharpe, A. H. Adverse events following cancer immunotherapy: obstacles and opportunities. Trends Immunol. 40, 511–523 (2019).
pmcid: 6527345
doi: 10.1016/j.it.2019.04.002
pubmed: 31053497
Albero, B., Tadeo, J. L., Escario, M., Miguel, E. & Pérez, R. A. Persistence and availability of veterinary antibiotics in soil and soil-manure systems. Sci. Total. Environ. 643, 1562–1570 (2018).
doi: 10.1016/j.scitotenv.2018.06.314
pubmed: 30189572
Iwu, C. D., Korsten, L. & Okoh, A. I. The incidence of antibiotic resistance within and beyond the agricultural ecosystem: a concern for public health. Microbiologyopen 9, e1035 (2020).
pmcid: 7520999
doi: 10.1002/mbo3.1035
pubmed: 32710495
Galon, J., Angell, H. K., Bedognetti, D. & Marincola, F. M. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity 39, 11–26 (2013).
doi: 10.1016/j.immuni.2013.07.008
pubmed: 23890060
Gire, S. K. et al. Genomic surveillance elucidates Ebola virus origin and transmission during the 2014 outbreak. Science 345, 1369–1372 (2014).
pmcid: 4431643
doi: 10.1126/science.1259657
pubmed: 25214632
Gardy, J., Loman, N. J. & Rambaut, A. Real-time digital pathogen surveillance — the time is now. Genome Biol. 16, 155 (2015).
pmcid: 4531805
doi: 10.1186/s13059-015-0726-x
pubmed: 27391693
Zanini, F. et al. Population genomics of intrapatient HIV-1 evolution. eLife 4, e11282 (2015).
pmcid: 4718817
doi: 10.7554/eLife.11282
pubmed: 26652000
Kugelman, J. R. et al. Monitoring of Ebola virus Makona evolution through establishment of advanced genomic capability in Liberia. Emerg. Infect. Dis. 21, 1135–1143 (2015).
pmcid: 4816332
doi: 10.3201/eid2107.150522
pubmed: 26079255
Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).
pmcid: 6247931
doi: 10.1093/bioinformatics/bty407
pubmed: 29790939
Havel, J. J., Chowell, D. & Chan, T. A. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19, 133–150 (2019).
pmcid: 6705396
doi: 10.1038/s41568-019-0116-x
pubmed: 30755690
Wyres, K. L. et al. Genomic surveillance of antimicrobial resistant bacterial colonisation and infection in intensive care patients. BMC Infect. Dis. 21, 683 (2021).
pmcid: 8278603
doi: 10.1186/s12879-021-06386-z
pubmed: 34261450
Lam, M. M. C. et al. A genomic surveillance framework and genotyping tool for Klebsiella pneumoniae and its related species complex. Nat. Commun. 12, 4188 (2021).
pmcid: 8263825
doi: 10.1038/s41467-021-24448-3
pubmed: 34234121
Chen, Z. et al. Global landscape of SARS-CoV-2 genomic surveillance and data sharing. Nat. Genet. 54, 499–507 (2022).
pmcid: 9005350
doi: 10.1038/s41588-022-01033-y
pubmed: 35347305
Tate, J. G. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2019).
doi: 10.1093/nar/gky1015
pubmed: 30371878
Scott, M. & Hwa, T. Bacterial growth laws and their applications. Curr. Opin. Biotechnol. 22, 559–565 (2011).
pmcid: 3152618
doi: 10.1016/j.copbio.2011.04.014
pubmed: 21592775
Dourado, H. & Lercher, M. J. An analytical theory of balanced cellular growth. Nat. Commun. 11, 1226 (2020).
pmcid: 7060212
doi: 10.1038/s41467-020-14751-w
pubmed: 32144263
Gowda, K., Ping, D., Mani, M. & Kuehn, S. Genomic structure predicts metabolite dynamics in microbial communities. Cell 185, 530–546.e25 (2022).
doi: 10.1016/j.cell.2021.12.036
pubmed: 35085485
Kinney, J. B. & McCandlish, D. M. Massively parallel assays and quantitative sequence-function relationships. Annu. Rev. Genomics Hum. Genet. 20, 99–127 (2019).
doi: 10.1146/annurev-genom-083118-014845
pubmed: 31091417
Verkuil, R. et al. Language models generalize beyond natural proteins. Preprint at bioRxiv https://doi.org/10.1101/2022.12.21.521521 (2022).
doi: 10.1101/2022.12.21.521521
Bialek, W. & Tishby, N. Predictive Information. Preprint at arXiv https://doi.org/10.48550/arXiv.cond-mat/9902341 (1999).
doi: 10.48550/arXiv.cond-mat/9902341