PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment
Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the trea...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Smug, Bogna J. [verfasserIn] Majkowska-Skrobek, Grazyna [verfasserIn] Drulis-Kawa, Zuzanna [verfasserIn] |
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| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of molecular biology - Amsterdam [u.a.] : Elsevier, 1959, 434 |
|---|---|
| Übergeordnetes Werk: |
volume:434 |
| DOI / URN: |
10.1016/j.jmb.2022.167670 |
|---|
| Katalog-ID: |
ELV00815743X |
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| 520 | |a Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the treated bacterial population can be postponed or reduced by the utilization of an effective phage cocktail. In this work, we present a publicly available web tool PhREEPred (Phage Resistance Emergence Prediction) (https://phartner.shinyapps.io/PhREEPred/), which will allow an informed choice of the composition of phage cocktails by predicting the outcome of phage cocktail or phage/depolymerase combination treatments against encapsulated bacterial pathogens given a mutating population that escapes single phage treatment. PhREEPred simulates solutions of our mathematical model calibrated and tested on the experimental Klebsiella pneumoniae setup and Klebsiella-specific lytic phages: K63 type-specific phage KP34 equipped with a capsule-degrading enzyme (KP34p57), capsule-independent myoviruses KP15 and KP27, and recombinant capsule depolymerase KP34p57. The model can calculate the phage-resistance emergence depending on the bacterial growth rate and initial density, the multiplicity of infection, phage latent period, its infectiveness and the cocktail composition, as well as initial depolymerase concentration and activity rate. This model reproduced the experimental results and showed that (i) the phage cocktail of parallelly infecting phages is less effective than the one composed of sequentially infecting phages; (ii) depolymerase can delay or prevent bacterial resistance by unveiling an alternative receptor for initially inactive phages. In our opinion, this customer-friendly web tool will allow for the primary design of the phage cocktail and phage-depolymerase combination effectiveness against encapsulated pathogens. | ||
| 650 | 4 | |a bacteriophage | |
| 650 | 4 | |a capsule depolymerase | |
| 650 | 4 | |a phage therapy | |
| 650 | 4 | |a phage/depolymerase resistance | |
| 650 | 4 | |a mathematical modelling | |
| 650 | 4 | |a encapsulated pathogens | |
| 700 | 1 | |a Majkowska-Skrobek, Grazyna |e verfasserin |4 aut | |
| 700 | 1 | |a Drulis-Kawa, Zuzanna |e verfasserin |4 aut | |
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| 912 | |a GBV_ILN_2050 | ||
| 912 | |a GBV_ILN_2056 | ||
| 912 | |a GBV_ILN_2059 | ||
| 912 | |a GBV_ILN_2061 | ||
| 912 | |a GBV_ILN_2064 | ||
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| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4242 | ||
| 912 | |a GBV_ILN_4251 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4326 | ||
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| 912 | |a GBV_ILN_4334 | ||
| 912 | |a GBV_ILN_4335 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4393 | ||
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2022 |
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2022 |
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10.1016/j.jmb.2022.167670 doi (DE-627)ELV00815743X (ELSEVIER)S0022-2836(22)00262-5 DE-627 ger DE-627 rda eng 570 610 DE-600 42.13 bkl Smug, Bogna J. verfasserin aut PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the treated bacterial population can be postponed or reduced by the utilization of an effective phage cocktail. In this work, we present a publicly available web tool PhREEPred (Phage Resistance Emergence Prediction) (https://phartner.shinyapps.io/PhREEPred/), which will allow an informed choice of the composition of phage cocktails by predicting the outcome of phage cocktail or phage/depolymerase combination treatments against encapsulated bacterial pathogens given a mutating population that escapes single phage treatment. PhREEPred simulates solutions of our mathematical model calibrated and tested on the experimental Klebsiella pneumoniae setup and Klebsiella-specific lytic phages: K63 type-specific phage KP34 equipped with a capsule-degrading enzyme (KP34p57), capsule-independent myoviruses KP15 and KP27, and recombinant capsule depolymerase KP34p57. The model can calculate the phage-resistance emergence depending on the bacterial growth rate and initial density, the multiplicity of infection, phage latent period, its infectiveness and the cocktail composition, as well as initial depolymerase concentration and activity rate. This model reproduced the experimental results and showed that (i) the phage cocktail of parallelly infecting phages is less effective than the one composed of sequentially infecting phages; (ii) depolymerase can delay or prevent bacterial resistance by unveiling an alternative receptor for initially inactive phages. In our opinion, this customer-friendly web tool will allow for the primary design of the phage cocktail and phage-depolymerase combination effectiveness against encapsulated pathogens. bacteriophage capsule depolymerase phage therapy phage/depolymerase resistance mathematical modelling encapsulated pathogens Majkowska-Skrobek, Grazyna verfasserin aut Drulis-Kawa, Zuzanna verfasserin aut Enthalten in Journal of molecular biology Amsterdam [u.a.] : Elsevier, 1959 434 Online-Ressource (DE-627)222605472 (DE-600)1355192-9 (DE-576)094059837 1089-8638 nnns volume:434 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 42.13 Molekularbiologie AR 434 |
| spelling |
10.1016/j.jmb.2022.167670 doi (DE-627)ELV00815743X (ELSEVIER)S0022-2836(22)00262-5 DE-627 ger DE-627 rda eng 570 610 DE-600 42.13 bkl Smug, Bogna J. verfasserin aut PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the treated bacterial population can be postponed or reduced by the utilization of an effective phage cocktail. In this work, we present a publicly available web tool PhREEPred (Phage Resistance Emergence Prediction) (https://phartner.shinyapps.io/PhREEPred/), which will allow an informed choice of the composition of phage cocktails by predicting the outcome of phage cocktail or phage/depolymerase combination treatments against encapsulated bacterial pathogens given a mutating population that escapes single phage treatment. PhREEPred simulates solutions of our mathematical model calibrated and tested on the experimental Klebsiella pneumoniae setup and Klebsiella-specific lytic phages: K63 type-specific phage KP34 equipped with a capsule-degrading enzyme (KP34p57), capsule-independent myoviruses KP15 and KP27, and recombinant capsule depolymerase KP34p57. The model can calculate the phage-resistance emergence depending on the bacterial growth rate and initial density, the multiplicity of infection, phage latent period, its infectiveness and the cocktail composition, as well as initial depolymerase concentration and activity rate. This model reproduced the experimental results and showed that (i) the phage cocktail of parallelly infecting phages is less effective than the one composed of sequentially infecting phages; (ii) depolymerase can delay or prevent bacterial resistance by unveiling an alternative receptor for initially inactive phages. In our opinion, this customer-friendly web tool will allow for the primary design of the phage cocktail and phage-depolymerase combination effectiveness against encapsulated pathogens. bacteriophage capsule depolymerase phage therapy phage/depolymerase resistance mathematical modelling encapsulated pathogens Majkowska-Skrobek, Grazyna verfasserin aut Drulis-Kawa, Zuzanna verfasserin aut Enthalten in Journal of molecular biology Amsterdam [u.a.] : Elsevier, 1959 434 Online-Ressource (DE-627)222605472 (DE-600)1355192-9 (DE-576)094059837 1089-8638 nnns volume:434 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 42.13 Molekularbiologie AR 434 |
| allfields_unstemmed |
10.1016/j.jmb.2022.167670 doi (DE-627)ELV00815743X (ELSEVIER)S0022-2836(22)00262-5 DE-627 ger DE-627 rda eng 570 610 DE-600 42.13 bkl Smug, Bogna J. verfasserin aut PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the treated bacterial population can be postponed or reduced by the utilization of an effective phage cocktail. In this work, we present a publicly available web tool PhREEPred (Phage Resistance Emergence Prediction) (https://phartner.shinyapps.io/PhREEPred/), which will allow an informed choice of the composition of phage cocktails by predicting the outcome of phage cocktail or phage/depolymerase combination treatments against encapsulated bacterial pathogens given a mutating population that escapes single phage treatment. PhREEPred simulates solutions of our mathematical model calibrated and tested on the experimental Klebsiella pneumoniae setup and Klebsiella-specific lytic phages: K63 type-specific phage KP34 equipped with a capsule-degrading enzyme (KP34p57), capsule-independent myoviruses KP15 and KP27, and recombinant capsule depolymerase KP34p57. The model can calculate the phage-resistance emergence depending on the bacterial growth rate and initial density, the multiplicity of infection, phage latent period, its infectiveness and the cocktail composition, as well as initial depolymerase concentration and activity rate. This model reproduced the experimental results and showed that (i) the phage cocktail of parallelly infecting phages is less effective than the one composed of sequentially infecting phages; (ii) depolymerase can delay or prevent bacterial resistance by unveiling an alternative receptor for initially inactive phages. In our opinion, this customer-friendly web tool will allow for the primary design of the phage cocktail and phage-depolymerase combination effectiveness against encapsulated pathogens. bacteriophage capsule depolymerase phage therapy phage/depolymerase resistance mathematical modelling encapsulated pathogens Majkowska-Skrobek, Grazyna verfasserin aut Drulis-Kawa, Zuzanna verfasserin aut Enthalten in Journal of molecular biology Amsterdam [u.a.] : Elsevier, 1959 434 Online-Ressource (DE-627)222605472 (DE-600)1355192-9 (DE-576)094059837 1089-8638 nnns volume:434 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 42.13 Molekularbiologie AR 434 |
| allfieldsGer |
10.1016/j.jmb.2022.167670 doi (DE-627)ELV00815743X (ELSEVIER)S0022-2836(22)00262-5 DE-627 ger DE-627 rda eng 570 610 DE-600 42.13 bkl Smug, Bogna J. verfasserin aut PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the treated bacterial population can be postponed or reduced by the utilization of an effective phage cocktail. In this work, we present a publicly available web tool PhREEPred (Phage Resistance Emergence Prediction) (https://phartner.shinyapps.io/PhREEPred/), which will allow an informed choice of the composition of phage cocktails by predicting the outcome of phage cocktail or phage/depolymerase combination treatments against encapsulated bacterial pathogens given a mutating population that escapes single phage treatment. PhREEPred simulates solutions of our mathematical model calibrated and tested on the experimental Klebsiella pneumoniae setup and Klebsiella-specific lytic phages: K63 type-specific phage KP34 equipped with a capsule-degrading enzyme (KP34p57), capsule-independent myoviruses KP15 and KP27, and recombinant capsule depolymerase KP34p57. The model can calculate the phage-resistance emergence depending on the bacterial growth rate and initial density, the multiplicity of infection, phage latent period, its infectiveness and the cocktail composition, as well as initial depolymerase concentration and activity rate. This model reproduced the experimental results and showed that (i) the phage cocktail of parallelly infecting phages is less effective than the one composed of sequentially infecting phages; (ii) depolymerase can delay or prevent bacterial resistance by unveiling an alternative receptor for initially inactive phages. In our opinion, this customer-friendly web tool will allow for the primary design of the phage cocktail and phage-depolymerase combination effectiveness against encapsulated pathogens. bacteriophage capsule depolymerase phage therapy phage/depolymerase resistance mathematical modelling encapsulated pathogens Majkowska-Skrobek, Grazyna verfasserin aut Drulis-Kawa, Zuzanna verfasserin aut Enthalten in Journal of molecular biology Amsterdam [u.a.] : Elsevier, 1959 434 Online-Ressource (DE-627)222605472 (DE-600)1355192-9 (DE-576)094059837 1089-8638 nnns volume:434 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 42.13 Molekularbiologie AR 434 |
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PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment |
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PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment |
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Smug, Bogna J. |
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Journal of molecular biology |
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Smug, Bogna J. Majkowska-Skrobek, Grazyna Drulis-Kawa, Zuzanna |
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phreepred: phage resistance emergence prediction web tool to foresee encapsulated bacterial escape from phage cocktail treatment |
| title_auth |
PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment |
| abstract |
Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the treated bacterial population can be postponed or reduced by the utilization of an effective phage cocktail. In this work, we present a publicly available web tool PhREEPred (Phage Resistance Emergence Prediction) (https://phartner.shinyapps.io/PhREEPred/), which will allow an informed choice of the composition of phage cocktails by predicting the outcome of phage cocktail or phage/depolymerase combination treatments against encapsulated bacterial pathogens given a mutating population that escapes single phage treatment. PhREEPred simulates solutions of our mathematical model calibrated and tested on the experimental Klebsiella pneumoniae setup and Klebsiella-specific lytic phages: K63 type-specific phage KP34 equipped with a capsule-degrading enzyme (KP34p57), capsule-independent myoviruses KP15 and KP27, and recombinant capsule depolymerase KP34p57. The model can calculate the phage-resistance emergence depending on the bacterial growth rate and initial density, the multiplicity of infection, phage latent period, its infectiveness and the cocktail composition, as well as initial depolymerase concentration and activity rate. This model reproduced the experimental results and showed that (i) the phage cocktail of parallelly infecting phages is less effective than the one composed of sequentially infecting phages; (ii) depolymerase can delay or prevent bacterial resistance by unveiling an alternative receptor for initially inactive phages. In our opinion, this customer-friendly web tool will allow for the primary design of the phage cocktail and phage-depolymerase combination effectiveness against encapsulated pathogens. |
| abstractGer |
Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the treated bacterial population can be postponed or reduced by the utilization of an effective phage cocktail. In this work, we present a publicly available web tool PhREEPred (Phage Resistance Emergence Prediction) (https://phartner.shinyapps.io/PhREEPred/), which will allow an informed choice of the composition of phage cocktails by predicting the outcome of phage cocktail or phage/depolymerase combination treatments against encapsulated bacterial pathogens given a mutating population that escapes single phage treatment. PhREEPred simulates solutions of our mathematical model calibrated and tested on the experimental Klebsiella pneumoniae setup and Klebsiella-specific lytic phages: K63 type-specific phage KP34 equipped with a capsule-degrading enzyme (KP34p57), capsule-independent myoviruses KP15 and KP27, and recombinant capsule depolymerase KP34p57. The model can calculate the phage-resistance emergence depending on the bacterial growth rate and initial density, the multiplicity of infection, phage latent period, its infectiveness and the cocktail composition, as well as initial depolymerase concentration and activity rate. This model reproduced the experimental results and showed that (i) the phage cocktail of parallelly infecting phages is less effective than the one composed of sequentially infecting phages; (ii) depolymerase can delay or prevent bacterial resistance by unveiling an alternative receptor for initially inactive phages. In our opinion, this customer-friendly web tool will allow for the primary design of the phage cocktail and phage-depolymerase combination effectiveness against encapsulated pathogens. |
| abstract_unstemmed |
Phages, as well as phage-derived proteins, especially lysins and depolymerases, are intensively studied to become prospective alternatives or supportive antibacterials used alone or in combination. In the common phage therapy approach, the unwanted emergence of phage-resistant variants from the treated bacterial population can be postponed or reduced by the utilization of an effective phage cocktail. In this work, we present a publicly available web tool PhREEPred (Phage Resistance Emergence Prediction) (https://phartner.shinyapps.io/PhREEPred/), which will allow an informed choice of the composition of phage cocktails by predicting the outcome of phage cocktail or phage/depolymerase combination treatments against encapsulated bacterial pathogens given a mutating population that escapes single phage treatment. PhREEPred simulates solutions of our mathematical model calibrated and tested on the experimental Klebsiella pneumoniae setup and Klebsiella-specific lytic phages: K63 type-specific phage KP34 equipped with a capsule-degrading enzyme (KP34p57), capsule-independent myoviruses KP15 and KP27, and recombinant capsule depolymerase KP34p57. The model can calculate the phage-resistance emergence depending on the bacterial growth rate and initial density, the multiplicity of infection, phage latent period, its infectiveness and the cocktail composition, as well as initial depolymerase concentration and activity rate. This model reproduced the experimental results and showed that (i) the phage cocktail of parallelly infecting phages is less effective than the one composed of sequentially infecting phages; (ii) depolymerase can delay or prevent bacterial resistance by unveiling an alternative receptor for initially inactive phages. In our opinion, this customer-friendly web tool will allow for the primary design of the phage cocktail and phage-depolymerase combination effectiveness against encapsulated pathogens. |
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| title_short |
PhREEPred: Phage Resistance Emergence Prediction Web Tool to Foresee Encapsulated Bacterial Escape from Phage Cocktail Treatment |
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