Chemogenomics and orthology‐based design of antibiotic combination therapies
Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computat...
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| Autor*in: |
Chandrasekaran, Sriram [verfasserIn] Cokol‐Cakmak, Melike [verfasserIn] Sahin, Nil [verfasserIn] Yilancioglu, Kaan [verfasserIn] Kazan, Hilal [verfasserIn] Collins, James J [verfasserIn] Cokol, Murat [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Molecular Systems Biology - Nature Publishing Group UK, 2023, 12(2016), 5 vom: 23. Mai |
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| Übergeordnetes Werk: |
volume:12 ; year:2016 ; number:5 ; day:23 ; month:05 |
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| DOI / URN: |
10.15252/msb.20156777 |
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| Katalog-ID: |
SPR058028366 |
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| 520 | |a Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms. | ||
| 520 | |a Synopsis Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). | ||
| 520 | |a Graphical Abstract Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. | ||
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| 700 | 1 | |a Collins, James J |e verfasserin |4 aut | |
| 700 | 1 | |a Cokol, Murat |e verfasserin |4 aut | |
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10.15252/msb.20156777 doi (DE-627)SPR058028366 (SPR)msb.20156777-e DE-627 ger DE-627 rakwb eng Chandrasekaran, Sriram verfasserin (orcid)0000-0002-8405-5708 aut Chemogenomics and orthology‐based design of antibiotic combination therapies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms. Synopsis Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). Graphical Abstract Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. chemogenomics (dpeaa)DE-He213 combination therapy (dpeaa)DE-He213 drug resistance (dpeaa)DE-He213 Cokol‐Cakmak, Melike verfasserin aut Sahin, Nil verfasserin aut Yilancioglu, Kaan verfasserin aut Kazan, Hilal verfasserin aut Collins, James J verfasserin aut Cokol, Murat verfasserin aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 12(2016), 5 vom: 23. Mai (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:12 year:2016 number:5 day:23 month:05 https://dx.doi.org/10.15252/msb.20156777 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 12 2016 5 23 05 |
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10.15252/msb.20156777 doi (DE-627)SPR058028366 (SPR)msb.20156777-e DE-627 ger DE-627 rakwb eng Chandrasekaran, Sriram verfasserin (orcid)0000-0002-8405-5708 aut Chemogenomics and orthology‐based design of antibiotic combination therapies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms. Synopsis Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). Graphical Abstract Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. chemogenomics (dpeaa)DE-He213 combination therapy (dpeaa)DE-He213 drug resistance (dpeaa)DE-He213 Cokol‐Cakmak, Melike verfasserin aut Sahin, Nil verfasserin aut Yilancioglu, Kaan verfasserin aut Kazan, Hilal verfasserin aut Collins, James J verfasserin aut Cokol, Murat verfasserin aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 12(2016), 5 vom: 23. Mai (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:12 year:2016 number:5 day:23 month:05 https://dx.doi.org/10.15252/msb.20156777 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 12 2016 5 23 05 |
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10.15252/msb.20156777 doi (DE-627)SPR058028366 (SPR)msb.20156777-e DE-627 ger DE-627 rakwb eng Chandrasekaran, Sriram verfasserin (orcid)0000-0002-8405-5708 aut Chemogenomics and orthology‐based design of antibiotic combination therapies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms. Synopsis Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). Graphical Abstract Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. chemogenomics (dpeaa)DE-He213 combination therapy (dpeaa)DE-He213 drug resistance (dpeaa)DE-He213 Cokol‐Cakmak, Melike verfasserin aut Sahin, Nil verfasserin aut Yilancioglu, Kaan verfasserin aut Kazan, Hilal verfasserin aut Collins, James J verfasserin aut Cokol, Murat verfasserin aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 12(2016), 5 vom: 23. Mai (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:12 year:2016 number:5 day:23 month:05 https://dx.doi.org/10.15252/msb.20156777 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 12 2016 5 23 05 |
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10.15252/msb.20156777 doi (DE-627)SPR058028366 (SPR)msb.20156777-e DE-627 ger DE-627 rakwb eng Chandrasekaran, Sriram verfasserin (orcid)0000-0002-8405-5708 aut Chemogenomics and orthology‐based design of antibiotic combination therapies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms. Synopsis Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). Graphical Abstract Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. chemogenomics (dpeaa)DE-He213 combination therapy (dpeaa)DE-He213 drug resistance (dpeaa)DE-He213 Cokol‐Cakmak, Melike verfasserin aut Sahin, Nil verfasserin aut Yilancioglu, Kaan verfasserin aut Kazan, Hilal verfasserin aut Collins, James J verfasserin aut Cokol, Murat verfasserin aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 12(2016), 5 vom: 23. Mai (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:12 year:2016 number:5 day:23 month:05 https://dx.doi.org/10.15252/msb.20156777 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 12 2016 5 23 05 |
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10.15252/msb.20156777 doi (DE-627)SPR058028366 (SPR)msb.20156777-e DE-627 ger DE-627 rakwb eng Chandrasekaran, Sriram verfasserin (orcid)0000-0002-8405-5708 aut Chemogenomics and orthology‐based design of antibiotic combination therapies 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2016 Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms. Synopsis Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). Graphical Abstract Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. chemogenomics (dpeaa)DE-He213 combination therapy (dpeaa)DE-He213 drug resistance (dpeaa)DE-He213 Cokol‐Cakmak, Melike verfasserin aut Sahin, Nil verfasserin aut Yilancioglu, Kaan verfasserin aut Kazan, Hilal verfasserin aut Collins, James J verfasserin aut Cokol, Murat verfasserin aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 12(2016), 5 vom: 23. Mai (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:12 year:2016 number:5 day:23 month:05 https://dx.doi.org/10.15252/msb.20156777 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 12 2016 5 23 05 |
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Chandrasekaran, Sriram @@aut@@ Cokol‐Cakmak, Melike @@aut@@ Sahin, Nil @@aut@@ Yilancioglu, Kaan @@aut@@ Kazan, Hilal @@aut@@ Collins, James J @@aut@@ Cokol, Murat @@aut@@ |
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chemogenomics and orthology‐based design of antibiotic combination therapies |
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Chemogenomics and orthology‐based design of antibiotic combination therapies |
| abstract |
Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms. Synopsis Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). Graphical Abstract Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. © The Author(s) 2016 |
| abstractGer |
Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms. Synopsis Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). Graphical Abstract Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. © The Author(s) 2016 |
| abstract_unstemmed |
Abstract Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms. Synopsis Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. INDIGO approach identifies antibiotic combinations that interact synergistically or antagonistically using chemogenomics.The analysis reveals a small set of genes in E. coli that are predictive of interaction outcomes and are surprisingly conserved between distant bacterial species.INDIGO can estimate the interaction outcomes in pathogens such as M. tuberculosis and S. aureus, based on the conservation of drug‐interaction‐related genes in E. coli.Novel predictions were experimentally validated in E. coli (66 combinations) and S. aureus (45 combinations). Graphical Abstract Novel combination therapies are needed to counter antibiotic resistance and reduce treatment times. The INDIGO algorithm enables the discovery of effective antibiotic combinations in less‐studied pathogens by leveraging chemogenomics data in model organisms. © The Author(s) 2016 |
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Chemogenomics and orthology‐based design of antibiotic combination therapies |
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Cokol‐Cakmak, Melike Sahin, Nil Yilancioglu, Kaan Kazan, Hilal Collins, James J Cokol, Murat |
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