A scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD + /NADH + imbalance
The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites. Controlled laboratory evolutions established chloramphenicol and...
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| Veröffentlicht in: | BMC systems biology 2017-04, Vol.11 (1), p.51-51, Article 51 |
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| creator | Banerjee, Deepanwita Parmar, Dharmeshkumar Bhattacharya, Nivedita Ghanate, Avinash D Panchagnula, Venkateswarlu Raghunathan, Anu |
| description | The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites.
Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance.
The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics - chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance. |
| doi_str_mv | 10.1186/s12918-017-0427-z |
| format | Article |
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Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance.
The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics - chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance.</description><identifier>ISSN: 1752-0509</identifier><identifier>EISSN: 1752-0509</identifier><identifier>DOI: 10.1186/s12918-017-0427-z</identifier><identifier>PMID: 28446174</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>Aeration ; Agar ; Alanine ; American Type Culture Collection ; Amino acids ; Aminodeoxychorismate lyase ; Ampicillin ; Animal models ; Anti-Bacterial Agents - pharmacology ; Antibiotic resistance ; Antibiotics ; Antimicrobial agents ; Benign ; Biological effects ; Biomarkers ; Chloromycetin ; Chromobacterium - drug effects ; Chromobacterium - genetics ; Chromobacterium - metabolism ; Computer Simulation ; Directed Molecular Evolution ; Drug resistance ; Drug Resistance, Bacterial - genetics ; E coli ; Enzymes ; Evolution ; Gene expression ; Gene sequencing ; Homeostasis ; Laboratories ; Metabolites ; Metabolomics ; Minimum inhibitory concentration ; Multidrug resistance ; Mutation ; NAD - metabolism ; Nicotinamide adenine dinucleotide ; Pathogens ; Phenotype ; Pigmentation ; Respiration ; Ribonucleic acid ; RNA ; Streptomycin ; Systems Biology</subject><ispartof>BMC systems biology, 2017-04, Vol.11 (1), p.51-51, Article 51</ispartof><rights>Copyright BioMed Central 2017</rights><rights>The Author(s). 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c427t-7509c4053592ef813e3321cde141279f8b41a29fcb25af1c16e2bb44ea9fc03a3</citedby><cites>FETCH-LOGICAL-c427t-7509c4053592ef813e3321cde141279f8b41a29fcb25af1c16e2bb44ea9fc03a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5405553/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5405553/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28446174$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Banerjee, Deepanwita</creatorcontrib><creatorcontrib>Parmar, Dharmeshkumar</creatorcontrib><creatorcontrib>Bhattacharya, Nivedita</creatorcontrib><creatorcontrib>Ghanate, Avinash D</creatorcontrib><creatorcontrib>Panchagnula, Venkateswarlu</creatorcontrib><creatorcontrib>Raghunathan, Anu</creatorcontrib><title>A scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD + /NADH + imbalance</title><title>BMC systems biology</title><addtitle>BMC Syst Biol</addtitle><description>The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites.
Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance.
The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics - chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance.</description><subject>Aeration</subject><subject>Agar</subject><subject>Alanine</subject><subject>American Type Culture Collection</subject><subject>Amino acids</subject><subject>Aminodeoxychorismate lyase</subject><subject>Ampicillin</subject><subject>Animal models</subject><subject>Anti-Bacterial Agents - pharmacology</subject><subject>Antibiotic resistance</subject><subject>Antibiotics</subject><subject>Antimicrobial agents</subject><subject>Benign</subject><subject>Biological effects</subject><subject>Biomarkers</subject><subject>Chloromycetin</subject><subject>Chromobacterium - drug effects</subject><subject>Chromobacterium - genetics</subject><subject>Chromobacterium - metabolism</subject><subject>Computer Simulation</subject><subject>Directed Molecular Evolution</subject><subject>Drug resistance</subject><subject>Drug Resistance, Bacterial - genetics</subject><subject>E coli</subject><subject>Enzymes</subject><subject>Evolution</subject><subject>Gene expression</subject><subject>Gene sequencing</subject><subject>Homeostasis</subject><subject>Laboratories</subject><subject>Metabolites</subject><subject>Metabolomics</subject><subject>Minimum inhibitory concentration</subject><subject>Multidrug resistance</subject><subject>Mutation</subject><subject>NAD - metabolism</subject><subject>Nicotinamide adenine dinucleotide</subject><subject>Pathogens</subject><subject>Phenotype</subject><subject>Pigmentation</subject><subject>Respiration</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>Streptomycin</subject><subject>Systems Biology</subject><issn>1752-0509</issn><issn>1752-0509</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNpdkcFq3DAQhkVpaNK0D9BLEfQSKE4kWV7Zl8KybZpASC_tWYy0410FW3IlObB5i75xtWwS0p7-YfTPaD5-Qj5wds55u7hIXHS8rRhXFZNCVQ-vyAlXjahYw7rXL-pj8jalO8aaWgj1hhyLVsoFV_KE_FnSZGEAMyAdMYMJg8tI0zxNA47oM2QXPE05QsbNjsIGnE-Zgs_OuJCdpRGTS7k06AR5Gzbo6WobwxgM2IzRzSO9d2EAi6Vyfj1bXFOzo7fLr_QzvShyVdSNppzhLb4jRz0MCd8_6in5dfnt5-qquvnx_Xq1vKlsQc2VKlhWFqKmE9i3vMa6FtyukUsuVNe3RnIQXW-NaKDnli9QGCMlQumxGupT8uWwd5rNiGtbWCMMeopuhLjTAZz-98W7rd6Ee92UX5umLgvOHhfE8HvGlPXoksWhUGCYk-ZtJ5QoblGsn_6z3oU5-oK3d8mFklLsXfzgsjGkFLF_PoYzvQ9cHwLXJXC9D1w_lJmPLymeJ54Srv8CMJqpjQ</recordid><startdate>20170426</startdate><enddate>20170426</enddate><creator>Banerjee, Deepanwita</creator><creator>Parmar, Dharmeshkumar</creator><creator>Bhattacharya, Nivedita</creator><creator>Ghanate, Avinash D</creator><creator>Panchagnula, Venkateswarlu</creator><creator>Raghunathan, Anu</creator><general>BioMed Central</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QL</scope><scope>7TM</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170426</creationdate><title>A scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD + /NADH + imbalance</title><author>Banerjee, Deepanwita ; Parmar, Dharmeshkumar ; Bhattacharya, Nivedita ; Ghanate, Avinash D ; Panchagnula, Venkateswarlu ; Raghunathan, Anu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c427t-7509c4053592ef813e3321cde141279f8b41a29fcb25af1c16e2bb44ea9fc03a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Aeration</topic><topic>Agar</topic><topic>Alanine</topic><topic>American Type Culture Collection</topic><topic>Amino acids</topic><topic>Aminodeoxychorismate lyase</topic><topic>Ampicillin</topic><topic>Animal models</topic><topic>Anti-Bacterial Agents - pharmacology</topic><topic>Antibiotic resistance</topic><topic>Antibiotics</topic><topic>Antimicrobial agents</topic><topic>Benign</topic><topic>Biological effects</topic><topic>Biomarkers</topic><topic>Chloromycetin</topic><topic>Chromobacterium - drug effects</topic><topic>Chromobacterium - genetics</topic><topic>Chromobacterium - metabolism</topic><topic>Computer Simulation</topic><topic>Directed Molecular Evolution</topic><topic>Drug resistance</topic><topic>Drug Resistance, Bacterial - genetics</topic><topic>E coli</topic><topic>Enzymes</topic><topic>Evolution</topic><topic>Gene expression</topic><topic>Gene sequencing</topic><topic>Homeostasis</topic><topic>Laboratories</topic><topic>Metabolites</topic><topic>Metabolomics</topic><topic>Minimum inhibitory concentration</topic><topic>Multidrug resistance</topic><topic>Mutation</topic><topic>NAD - metabolism</topic><topic>Nicotinamide adenine dinucleotide</topic><topic>Pathogens</topic><topic>Phenotype</topic><topic>Pigmentation</topic><topic>Respiration</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>Streptomycin</topic><topic>Systems Biology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Banerjee, Deepanwita</creatorcontrib><creatorcontrib>Parmar, Dharmeshkumar</creatorcontrib><creatorcontrib>Bhattacharya, Nivedita</creatorcontrib><creatorcontrib>Ghanate, Avinash D</creatorcontrib><creatorcontrib>Panchagnula, Venkateswarlu</creatorcontrib><creatorcontrib>Raghunathan, Anu</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>BMC systems biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Banerjee, Deepanwita</au><au>Parmar, Dharmeshkumar</au><au>Bhattacharya, Nivedita</au><au>Ghanate, Avinash D</au><au>Panchagnula, Venkateswarlu</au><au>Raghunathan, Anu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD + /NADH + imbalance</atitle><jtitle>BMC systems biology</jtitle><addtitle>BMC Syst Biol</addtitle><date>2017-04-26</date><risdate>2017</risdate><volume>11</volume><issue>1</issue><spage>51</spage><epage>51</epage><pages>51-51</pages><artnum>51</artnum><issn>1752-0509</issn><eissn>1752-0509</eissn><abstract>The leading edge of the global problem of antibiotic resistance necessitates novel therapeutic strategies. This study develops a novel systems biology driven approach for killing antibiotic resistant pathogens using benign metabolites.
Controlled laboratory evolutions established chloramphenicol and streptomycin resistant pathogens of Chromobacterium. These resistant pathogens showed higher growth rates and required higher lethal doses of antibiotic. Growth and viability testing identified malate, maleate, succinate, pyruvate and oxoadipate as resensitising agents for antibiotic therapy. Resistant genes were catalogued through whole genome sequencing. Intracellular metabolomic profiling identified violacein as a potential biomarker for resistance. The temporal variance of metabolites captured the linearized dynamics around the steady state and correlated to growth rate. A constraints-based flux balance model of the core metabolism was used to predict the metabolic basis of antibiotic susceptibility and resistance.
The model predicts electron imbalance and skewed NAD/NADH ratios as a result of antibiotics - chloramphenicol and streptomycin. The resistant pathogen rewired its metabolic networks to compensate for disruption of redox homeostasis. We foresee the utility of such scalable workflows in identifying metabolites for clinical isolates as inevitable solutions to mitigate antibiotic resistance.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>28446174</pmid><doi>10.1186/s12918-017-0427-z</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Aeration Agar Alanine American Type Culture Collection Amino acids Aminodeoxychorismate lyase Ampicillin Animal models Anti-Bacterial Agents - pharmacology Antibiotic resistance Antibiotics Antimicrobial agents Benign Biological effects Biomarkers Chloromycetin Chromobacterium - drug effects Chromobacterium - genetics Chromobacterium - metabolism Computer Simulation Directed Molecular Evolution Drug resistance Drug Resistance, Bacterial - genetics E coli Enzymes Evolution Gene expression Gene sequencing Homeostasis Laboratories Metabolites Metabolomics Minimum inhibitory concentration Multidrug resistance Mutation NAD - metabolism Nicotinamide adenine dinucleotide Pathogens Phenotype Pigmentation Respiration Ribonucleic acid RNA Streptomycin Systems Biology |
| title | A scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD + /NADH + imbalance |
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