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Cited by 208Bacteriophages and microbial interactionsCRISPR and Genetic EngineeringVirus-based gene therapy research

Enhancing phage therapy through synthetic biology and genome engineering

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Lenneman, Bryan R, Fernbach, Jonas, Loessner, Martin J, Lu, Timothy K, Kilcher, Samuel·2021·Current Opinion in Biotechnology
DOI 10.1016/j.copbio.2020.11.003PMC PMC11996084
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Abstract

The antimicrobial and therapeutic efficacy of bacteriophages is currently limited, mostly due to rapid emergence of phage-resistance and the inability of most phage isolates to bind and infect a broad range of clinical strains. Here, we discuss how phage therapy can be improved through recent advances in genetic engineering. First, we outline how receptor-binding proteins and their relevant structural domains are engineered to redirect phage specificity and to avoid resistance. Next, we summarize how phages are reprogrammed as prokaryotic gene therapy vectors that deliver antimicrobial 'payload' proteins, such as sequence-specific nucleases, to target defined cells within complex microbiomes. Finally, we delineate big data- and novel artificial intelligence-driven approaches that may guide the design of improved synthetic phage in the future.

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  • Phages exhibit low inherent toxicity and can be tailored to minimize off-target effects.

    “Phages exhibit low inherent toxicity and can be tailored to minimize off-target effects [15–17,62,99,100]”

    From Phage therapy in oncology: opportunities for cancer prevention and treatment (2026) · cites this paper as [99]

    The “Programming host specificity” section says phages can selectively infect and kill bacteria in complex communities and describes engineering a tunable host range, supporting the potential to limit off-target bacterial effects. The Introduction and designer-phage passages do not establish that phages have low inherent toxicity; the Introduction instead notes possible immunogenicity with prolonged treatment. Page numbers are unavailable in our copy. (LLM, conf=0.85)

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  1. [1]Zaman SB, Hussain MA, Nye R, Mehta V, Mamun KT, Hossain N: A review on antibiotic resistance: alarm bells are ringing. Cureus 2017, 9: e1403. 28852600 PMC5573035
  2. [2]Piddock LJ: The crisis of no new antibiotics–what is the way forward? Lancet Infect Dis 2012, 12: 249– 253. 22101066
  3. [3]Adegoke AA, Faleye AC, Singh G, Stenstrom TA: Antibiotic resistant superbugs: assessment of the interrelationship of occurrence in clinical settings and environmental niches. Molecules 2016, 22. PMC6155606 28035988
  4. [4]Hesse S, Adhya S: Phage therapy in the twenty-first century: facing the decline of the antibiotic era; is it finally time for the age of the phage? Annu Rev Microbiol 2019, 73: 155– 174. 31185183
  5. [5]Dedrick RM, Guerrero-Bustamante CA, Garlena RA, Russell DA, Ford K, Harris K, Gilmour KC, Soothill J, Jacobs-Sera D, Schooley RT: Engineered bacteriophages for treatment of a patient with a disseminated drug-resistant Mycobacterium abscessus. Nat Med 2019, 25: 730– 733. 31068712 PMC6557439
  6. [6]Schooley RT, Biswas B, Gill JJ, Hernandez-Morales A, Lancaster J, Lessor L, Barr JJ, Reed SL, Rohwer F, Benler S: Development and use of personalized bacteriophage-based therapeutic cocktails to treat a patient with a disseminated resistant Acinetobacter baumannii infection. Antimicrob Agents Chemother 2017, 61. PMC5610518 28807909
  7. [7]Rostol JT, Marraffini L: (Ph)ighting phages: how bacteria resist their parasites. Cell Host Microbe 2019, 25: 184– 194. 30763533 PMC6383810
  8. [8]Krut O, Bekeredjian-Ding I: Contribution of the immune response to phage therapy. J Immunol 2018, 200: 3037– 3044. 29685950
  9. [9]de Jonge PA, Nobrega FL, Brouns SJJ, Dutilh BE: Molecular and evolutionary determinants of bacteriophage host range. Trends Microbiol 2019, 27: 51– 63. 30181062
  10. [10]Mendoza SD, Nieweglowska ES, Govindarajan S, Leon LM, Berry JD, Tiwari A, Chaikeeratisak V, Pogliano J, Agard DA, Bondy-Denomy J: A bacteriophage nucleus-like compartment shields DNA from CRISPR nucleases. Nature 2020, 577: 244– 248. 31819262 PMC6949375
  11. [11]Pawluk A, Davidson AR, Maxwell KL: Anti-CRISPR: discovery, mechanism and function. Nat Rev Microbiol 2018, 16: 12– 17. 29062071
  12. [12]Le S, He X, Tan Y, Huang G, Zhang L, Lux R, Shi W, Hu F: Mapping the tail fiber as the receptor binding protein responsible for differential host specificity of Pseudomonas aeruginosa bacteriophages PaP1 and JG004. PLoS One 2013, 8:e68562. PMC3706319 23874674
  13. [13]Loessner MJ, Rees CE, Stewart GS, Scherer S: Construction of luciferase reporter bacteriophage A511::luxAB for rapid and sensitive detection of viable Listeria cells. Appl Environ Microbiol 1996, 62: 1133– 1140. 8919773 PMC167878
  14. [14]Mahichi F, Synnott AJ, Yamamichi K, Osada T, Tanji Y: Site-specific recombination of T2 phage using IP008 long tail fiber genes provides a targeted method for expanding host range while retaining lytic activity. FEMS Microbiol Lett 2009, 295: 211– 217. 19453513
  15. [15]Marinelli LJ, Hatfull GF, Piuri M: Recombineering: a powerful tool for modification of bacteriophage genomes. Bacteriophage 2012, 2: 5– 14. 22666652 PMC3357384
  16. [16]Marinelli LJ, Piuri M, Swigonova Z, Balachandran A, Oldfield LM, van Kessel JC, Hatfull GF: BRED: a simple and powerful tool for constructing mutant and recombinant bacteriophage genomes. PLoS One 2008, 3 e3957. 19088849 PMC2597740
  17. [17]Oda M, Morita M, Unno H, Tanji Y: Rapid detection of Escherichia coli O157:H7 by using green fluorescent protein-labeled PP01 bacteriophage. Appl Environ Microbiol 2004, 70: 527– 534. 14711684 PMC321238
  18. [18]Qimron U, Marintcheva B, Tabor S, Richardson CC: Genomewide screens for Escherichia coli genes affecting growth of T7 bacteriophage. Proc Natl Acad Sci U S A 2006, 103: 19039– 19044. 17135349 PMC1748173
  19. [19]Hatoum-Aslan A: Phage genetic engineering using CRISPR(−) Cas systems. Viruses 2018, 10. PMC6024849 29921752
  20. [20]Jaschke PR, Lieberman EK, Rodriguez J, Sierra A, Endy D: A fully decompressed synthetic bacteriophage oX174 genome assembled and archived in yeast. Virology 2012, 434: 278– 284. 23079106
  21. [21]Ando H, Lemire S, Pires DP, Lu TK: Engineering modular viral scaffolds for targeted bacterial population editing. Cell Syst 2015, 1: 187– 196. 26973885 PMC4785837
  22. [22]Kilcher S, Studer P, Muessner C, Klumpp J, Loessner MJ: Cross-genus rebooting of custom-made, synthetic bacteriophage genomes in L-form bacteria. Proc Natl Acad Sci U S A 2018, 115: 567– 572. 29298913 PMC5776983 ● By using cell wall-deficient bacteria as surrogate hosts for genome activation, this study expands the synthetic genome rebooting approach to several phages that infect Gram-positive host bacteria.
  23. [23]Meile S, Sarbach A, Du J, Schuppler M, Saez C, Loessner MJ, Kilcher S: Engineered reporter phages for rapid bioluminescence-based detection and differentiation of viable listeria cells. Appl Environ Microbiol 2020, 86. PMC7237785 32245761
  24. [24]Dunne M, Rupf B, Tala M, Qabrati X, Ernst P, Shen Y, Sumrall E, Heeb L, Pluckthun A, Loessner MJ: Reprogramming bacteriophage host range through structure-guided design of chimeric receptor binding proteins. Cell Rep 2019, 29: 1336– 1350 e1334. 31665644 ●● This is the first study to demonstrate targeted and structure-guided host range engineering in a non T7-like phage targeting a Gram-positive pathogen. The authors use structure-guided RBP chimera design and targeted rbp mutagenesis screens to modify Listeria phage host range.
  25. [25]Bundy BC, Franciszkowicz MJ, Swartz JR: Escherichia coli-based cell-free synthesis of virus-like particles. Biotechnol Bioeng 2008, 100: 28– 37. 18023052
  26. [26]Rustad M, Eastlund A, Jardine P, Noireaux V: Cell-free TXTL synthesis of infectious bacteriophage T4 in a single test tube reaction. Synth Biol 2018, 3. PMC7445788 32995511
  27. [27]Rustad M, Eastlund A, Marshall R, Jardine P, Noireaux V: Synthesis of infectious bacteriophages in an E. coli-based cell-free expression system. J Vis Exp 2017. PMC5614349 28872145
  28. [28]Wannier TM, Nyerges A, Kuchwara HM, Czikkely M, Balogh D, Filsinger GT, Borders NC, Gregg CJ, Lajoie MJ, Rios X: Improved bacterial recombineering by parallelized protein discovery. Proc Natl Acad Sci U S A 2020, 117: 13689– 13698. 32467157 PMC7306799
  29. [29]Noireaux V, Liu AP: The new age of cell-free biology. Annu Rev Biomed Eng 2020, 22: 51– 77. 32151150
  30. [30]Kilcher S, Loessner MJ: Engineering bacteriophages as versatile biologics. Trends Microbiol 2019, 27: 355– 367. 30322741
  31. [31]Lemire S, Yehl KM, Lu TK: Phage-based applications in synthetic biology. Annu Rev Virol 2018, 5: 453– 476. 30001182 PMC6953747
  32. [32]Chan BK, Abedon ST, Loc-Carrillo C: Phage cocktails and the future of phage therapy. Future Microbiol 2013, 8: 769– 783. 23701332
  33. [33]Chen M, Zhang L, Abdelgader SA, Yu L, Xu J, Yao H, Lu C, Zhang W: Alterations in gp37 expand the host range of a T4-like phage. Appl Environ Microbiol 2017, 83. PMC5691408 28939606
  34. [34]Lin TY, Lo YH, Tseng PW, Chang SF, Lin YT, Chen TS: A T3 and T7 recombinant phage acquires efficient adsorption and a broader host range. PLoS One 2012, 7:e30954. PMC3276506 22347414
  35. [35]Pouillot F, Blois H, Iris F: Genetically engineered virulent phage banks in the detection and control of emergent pathogenic bacteria. Biosecur Bioterror 2010, 8: 155– 169. 20569057 PMC2956564
  36. [36]Trojet SN, Caumont-Sarcos A, Perrody E, Comeau AM, Krisch HM: The gp38 adhesins of the T4 superfamily: a complex modular determinant of the phage’s host specificity. Genome Biol Evol 2011, 3: 674– 686. 21746838 PMC3157838
  37. [37]Yoichi M, Abe M, Miyanaga K, Unno H, Tanji Y: Alteration of tail fiber protein gp38 enables T2 phage to infect Escherichia coli O157:H7. J Biotechnol 2005, 115: 101– 107. 15607229
  38. [38]Yehl K, Lemire S, Yang AC, Ando H, Mimee M, Torres MT, de la Fuente-Nunez C, Lu TK: Engineering phage host-range and suppressing bacterial resistance through phage tail fiber mutagenesis. Cell 2019, 179: 459– 469 e459. 31585083 PMC6924272 ●● This work utilized a structure-guided rational design approach to introduce diversity to the host-range-determining regions of phage T3 tail fibers. The resulting phage libraries demonstrated altered host range profiles and suppressed the emergence of phage-resistant bacterial mutants.
  39. [39]Yosef I, Goren MG, Globus R, Molshanski-Mor S, Qimron U: Extending the host range of bacteriophage particles for DNA transduction. Mol Cell 2017, 66: 721– 728 e723. 28552617 ●● A T7 tail exchange strategy is employed to modify the range of target cells that can be transduced by non-replicative virions. In addition, the authors use their own targeted evolution approach to enhance transduction efficiencies.
  40. [40]Born Y, Fieseler L, Thony V, Leimer N, Duffy B, Loessner MJ: Engineering of bacteriophages Y2::dpoL1-C and Y2::luxAB for efficient control and rapid detection of the fire blight pathogen, Erwinia amylovora. Appl Environ Microbiol 2017, 83. PMC5452800 28389547
  41. [41]Lu TK, Collins JJ: Dispersing biofilms with engineered enzymatic bacteriophage. Proc Natl Acad Sci U S A 2007, 104: 11197– 11202. 17592147 PMC1899193
  42. [42]Pei R, Lamas-Samanamud GR: Inhibition of biofilm formation by T7 bacteriophages producing quorum-quenching enzymes. Appl Environ Microbiol 2014, 80: 5340– 5348. 24951790 PMC4136088
  43. [43]Citorik RJ, Mimee M, Lu TK: Sequence-specific antimicrobials using efficiently delivered RNA-guided nucleases. Nat Biotechnol 2014, 32: 1141– 1145. 25240928 PMC4237163
  44. [44]Bikard D, Euler CW, Jiang W, Nussenzweig PM, Goldberg GW, Duportet X, Fischetti VA, Marraffini LA: Exploiting CRISPR-Cas nucleases to produce sequence-specific antimicrobials. Nat Biotechnol 2014, 32: 1146– 1150. 25282355 PMC4317352
  45. [45]Park JY, Moon BY, Park JW, Thornton JA, Park YH, Seo KS: Genetic engineering of a temperate phage-based delivery system for CRISPR/Cas9 antimicrobials against Staphylococcus aureus. Sci Rep 2017, 7: 44929. 28322317 PMC5359561
  46. [46]Yosef I, Manor M, Kiro R, Qimron U: Temperate and lytic bacteriophages programmed to sensitize and kill antibiotic-resistant bacteria. Proc Natl Acad Sci U S A 2015, 112: 7267– 7272. 26060300 PMC4466736
  47. [47]Selle K, Fletcher JR, Tuson H, Schmitt DS, McMillan L, Vridhambal GS, Rivera AJ, Montgomery SA, Fortier LC, Barrangou R: In vivo targeting of Clostridioides difficile using phage-delivered CRISPR-Cas3 antimicrobials. mBio 2020, 11. PMC7064742 32156803 ●● Using a mouse model of C. difficile infection, Selle et al. demonstrate how combined CRISPR-Cas arming and phage lifestyle conversion enhance therapeutic efficacy, prevent the generation and expansion of phage-resistant lysogens, and reduce tissue damage in the host.
  48. [48]Lam KN, Spanogiannopoulos P, Soto-Perez P, Alexander M, Nalley MJ, Bisanz JE, Yu FB, Turnbaugh PJ: Phage-delivered CRISPR-Cas9 for strain-specific depletion and genomic deletions in the gut microbiota. bioRxiv 2020. 2020.2007.2009.193847. PMC8591988 34731631
  49. [49]Landhuis E: Deep learning takes on tumours. Nature 2020, 580: 551– 553. 32317799
  50. [50]Amgarten D, Braga LPP, da Silva AM, Setubal JC: MARVEL, a tool for prediction of bacteriophage sequences in metagenomic bins. Front Genet 2018, 9: 304. 30131825 PMC6090037
  51. [51]Auslander N, Gussow AB, Benler S, Wolf YI, Koonin EV: Seeker: alignment-free identification of bacteriophage genomes by deep learning. bioRxiv 2020. 2020.2004.2004.025783. PMC7708075 33045744
  52. [52]Leite DMC, Brochet X, Resch G, Que YA, Neves A, Pena-Reyes C: Computational prediction of inter-species relationships through omics data analysis and machine learning. BMC Bioinformatics 2018, 19: 420. 30453987 PMC6245486 ● This work demonstrates the application of supervised machine learning algorithms on phage sequence data to make phage-host interaction predictions.
  53. [53]Tynecki P, Guziński A, Kazimierczak J, Jadczuk M, Dastych J, Onisko A: PhageAI - bacteriophage life cycle recognition with machine learning and natural language processing. bioRxiv 2020. 2020.2007.2011.198606.
  54. [55]Bojar D, Camacho DM, Collins JJ: Using natural language processing to learn the grammar of glycans. bioRxiv 2020. 2020.2001.2010.902114.
  55. [56]Bojar D, Powers RK, Camacho DM, Collins JJ: SweetOrigins: extracting evolutionary information from glycans. bioRxiv 2020. 2020.2004.2008.031948.
  56. [57]Bielmann R, Habann M, Eugster MR, Lurz R, Calendar R, Klumpp J, Loessner MJ: Receptor binding proteins of Listeria monocytogenes bacteriophages A118 and P35 recognize serovar-specific teichoic acids. Virology 2015, 477: 110– 118. 25708539
  57. [58]Briers Y, Schmelcher M, Loessner MJ, Hendrix J, Engelborghs Y, Volckaert G, Lavigne R: The high-affinity peptidoglycan binding domain of Pseudomonas phage endolysin KZ144. Biochem Biophys Res Commun 2009, 383: 187– 191. 19348786

Cited by

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Authors
Lenneman, B. R., Fernbach, J., Loessner, M. J., Lu, T. K., & Kilcher, S.
Affiliations
  • Massachusetts Institute of Technology (US)
  • ETH Zurich (CH)
Corresponding author
Kilcher, Samuel
ORCID
Venue
Current Opinion in Biotechnology
Publisher
Elsevier BV
Published
2021-04
Volume
68
Pages
151-159
ISSN
0958-1669 (ISSN-L)
DOI
10.1016/j.copbio.2020.11.003
PMC
PMC11996084
OpenAlex
W3112990061
Cited by
208
References
1
Citations by year
2026: 49 · 2025: 46 · 2024: 40 · 2023: 31 · 2022: 30 · 2021: 11
FWCI
1.58
Type
journal article
Language
en
Open access
Licence
Locations
  • Current Opinion in Biotechnology (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (acceptedVersion, cc-by-nc-nd)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-nd)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc-nd)
  • ETH Zürich Research Collection
Funders
  • National Institutes of Health
  • Defense Threat Reduction Agency
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Topics
  • Physical Sciences › Environmental Science › Ecology › Bacteriophages and microbial interactions
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Virus-based gene therapy research
Keywords
phage therapy, bacteriophages, phage resistance, receptor binding protein, sequence-specific nucleases
MeSH terms
Phage Therapy, Artificial Intelligence, Bacteriophages, Genetic Engineering, Synthetic Biology

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