Constraints-based analysis identifies NAD+ recycling through metabolic reprogramming in antibiotic resistant Chromobacterium violaceum
In the post genomic era, high throughput data augment stoichiometric flux balance models to compute accurate metabolic flux states, growth and energy phenotypes. Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resi...
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description | In the post genomic era, high throughput data augment stoichiometric flux balance models to compute accurate metabolic flux states, growth and energy phenotypes. Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resistance and induce susceptibility to existing antibiotics. A genome-scale metabolic model (GSMM) for Chromobacterium violaceum, an opportunistic human pathogen, was reconstructed using legacy data. Experimental constraints were used to represent antibiotic susceptible and resistant populations. Model predictions were validated using growth and respiration data successfully. Differential flux distribution and metabolic reprogramming were identified as a response to antibiotics, chloramphenicol and streptomycin. Streptomycin resistant populations (StrpR) redirected tricarboxylic acid (TCA) cycle flux through the glyoxylate shunt. Chloramphenicol resistant populations (ChlR) resorted to overflow metabolism producing acetate and formate. This switch to fermentative metabolism is potentially through excess reducing equivalents and increased NADH/NAD ratios. Reduced proton gradients and changed Proton Motive Force (PMF) induced by antibiotics were also predicted and verified experimentally using flow cytometry based membrane potential measurements. Pareto analysis of NADH and ATP maintenance showed the decoupling of electron transfer and ATP synthesis in StrpR. Redox homeostasis and NAD+ cycling through rewiring metabolic flux was implicated in re-sensitizing antibiotic resistant C. violaceum. These approaches can be used to probe metabolic vulnerabilities of resistant pathogens. On the verge of a post-antibiotic era, we foresee a critical need for systems level understanding of pathogens and host interaction to extend shelf life of antibiotics and strategize novel therapies. |
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Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resistance and induce susceptibility to existing antibiotics. A genome-scale metabolic model (GSMM) for Chromobacterium violaceum, an opportunistic human pathogen, was reconstructed using legacy data. Experimental constraints were used to represent antibiotic susceptible and resistant populations. Model predictions were validated using growth and respiration data successfully. Differential flux distribution and metabolic reprogramming were identified as a response to antibiotics, chloramphenicol and streptomycin. Streptomycin resistant populations (StrpR) redirected tricarboxylic acid (TCA) cycle flux through the glyoxylate shunt. Chloramphenicol resistant populations (ChlR) resorted to overflow metabolism producing acetate and formate. This switch to fermentative metabolism is potentially through excess reducing equivalents and increased NADH/NAD ratios. Reduced proton gradients and changed Proton Motive Force (PMF) induced by antibiotics were also predicted and verified experimentally using flow cytometry based membrane potential measurements. Pareto analysis of NADH and ATP maintenance showed the decoupling of electron transfer and ATP synthesis in StrpR. Redox homeostasis and NAD+ cycling through rewiring metabolic flux was implicated in re-sensitizing antibiotic resistant C. violaceum. These approaches can be used to probe metabolic vulnerabilities of resistant pathogens. On the verge of a post-antibiotic era, we foresee a critical need for systems level understanding of pathogens and host interaction to extend shelf life of antibiotics and strategize novel therapies.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0210008</identifier><identifier>PMID: 30608971</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acetic acid ; Anti-Bacterial Agents - pharmacology ; Antibiotic resistance ; Antibiotics ; ATP ; Bacteria ; BASIC BIOLOGICAL SCIENCES ; Biology and Life Sciences ; Biomass ; Chemical engineering ; Chloramphenicol ; Chloromycetin ; Chromobacterium ; Chromobacterium - drug effects ; chromobacterium violaceum ; Citric Acid - metabolism ; Citric Acid Cycle - drug effects ; Data Mining ; Decoupling ; drug metabolism ; Drug resistance ; Electron transfer ; Energy metabolism ; Flow cytometry ; Fluctuations ; Flux ; Genes ; Genetic engineering ; Genomes ; Genomics ; Genotypes ; Glucose ; Glucose - metabolism ; Homeostasis ; Infections ; Laboratories ; Mathematical models ; Medicine and Health Sciences ; Membrane potential ; Metabolic flux ; Metabolism ; Metabolites ; NAD ; NAD - metabolism ; NADH ; Nicotinamide adenine dinucleotide ; Opportunist infection ; Overflow ; Oxalic Acid - metabolism ; oxidation-reduction reactions ; oxygen metabolism ; Pareto analysis ; Pathogens ; Phenotypes ; Physical Sciences ; Physiology ; Populations ; Predictions ; Proteins ; Protonmotive force ; pyruvate ; Redox properties ; Rewiring ; Sensitizing ; Shelf life ; Shunt resistance ; Streptomycin ; Tricarboxylic acid cycle</subject><ispartof>PloS one, 2019-01, Vol.14 (1), p.e0210008-e0210008</ispartof><rights>2019 Banerjee, Raghunathan. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Banerjee, Raghunathan 2019 Banerjee, Raghunathan</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c553t-a5b6a476e62dbefc590b25acb4ac55aa98d86e12b96d99c6b80ab0f1693140753</citedby><cites>FETCH-LOGICAL-c553t-a5b6a476e62dbefc590b25acb4ac55aa98d86e12b96d99c6b80ab0f1693140753</cites><orcidid>0000-0002-7063-9606 ; 0000000270639606</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6319732/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6319732/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30608971$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1599794$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><contributor>Singh, Pankaj K</contributor><creatorcontrib>Banerjee, Deepanwita</creatorcontrib><creatorcontrib>Raghunathan, Anu</creatorcontrib><creatorcontrib>Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)</creatorcontrib><title>Constraints-based analysis identifies NAD+ recycling through metabolic reprogramming in antibiotic resistant Chromobacterium violaceum</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In the post genomic era, high throughput data augment stoichiometric flux balance models to compute accurate metabolic flux states, growth and energy phenotypes. Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resistance and induce susceptibility to existing antibiotics. A genome-scale metabolic model (GSMM) for Chromobacterium violaceum, an opportunistic human pathogen, was reconstructed using legacy data. Experimental constraints were used to represent antibiotic susceptible and resistant populations. Model predictions were validated using growth and respiration data successfully. Differential flux distribution and metabolic reprogramming were identified as a response to antibiotics, chloramphenicol and streptomycin. Streptomycin resistant populations (StrpR) redirected tricarboxylic acid (TCA) cycle flux through the glyoxylate shunt. Chloramphenicol resistant populations (ChlR) resorted to overflow metabolism producing acetate and formate. This switch to fermentative metabolism is potentially through excess reducing equivalents and increased NADH/NAD ratios. Reduced proton gradients and changed Proton Motive Force (PMF) induced by antibiotics were also predicted and verified experimentally using flow cytometry based membrane potential measurements. Pareto analysis of NADH and ATP maintenance showed the decoupling of electron transfer and ATP synthesis in StrpR. Redox homeostasis and NAD+ cycling through rewiring metabolic flux was implicated in re-sensitizing antibiotic resistant C. violaceum. These approaches can be used to probe metabolic vulnerabilities of resistant pathogens. On the verge of a post-antibiotic era, we foresee a critical need for systems level understanding of pathogens and host interaction to extend shelf life of antibiotics and strategize novel therapies.</description><subject>Acetic acid</subject><subject>Anti-Bacterial Agents - pharmacology</subject><subject>Antibiotic resistance</subject><subject>Antibiotics</subject><subject>ATP</subject><subject>Bacteria</subject><subject>BASIC BIOLOGICAL SCIENCES</subject><subject>Biology and Life Sciences</subject><subject>Biomass</subject><subject>Chemical engineering</subject><subject>Chloramphenicol</subject><subject>Chloromycetin</subject><subject>Chromobacterium</subject><subject>Chromobacterium - drug effects</subject><subject>chromobacterium violaceum</subject><subject>Citric Acid - metabolism</subject><subject>Citric Acid Cycle - drug effects</subject><subject>Data Mining</subject><subject>Decoupling</subject><subject>drug metabolism</subject><subject>Drug resistance</subject><subject>Electron transfer</subject><subject>Energy metabolism</subject><subject>Flow cytometry</subject><subject>Fluctuations</subject><subject>Flux</subject><subject>Genes</subject><subject>Genetic engineering</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Genotypes</subject><subject>Glucose</subject><subject>Glucose - metabolism</subject><subject>Homeostasis</subject><subject>Infections</subject><subject>Laboratories</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Membrane potential</subject><subject>Metabolic flux</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>NAD</subject><subject>NAD - metabolism</subject><subject>NADH</subject><subject>Nicotinamide adenine dinucleotide</subject><subject>Opportunist infection</subject><subject>Overflow</subject><subject>Oxalic Acid - metabolism</subject><subject>oxidation-reduction reactions</subject><subject>oxygen metabolism</subject><subject>Pareto analysis</subject><subject>Pathogens</subject><subject>Phenotypes</subject><subject>Physical Sciences</subject><subject>Physiology</subject><subject>Populations</subject><subject>Predictions</subject><subject>Proteins</subject><subject>Protonmotive force</subject><subject>pyruvate</subject><subject>Redox properties</subject><subject>Rewiring</subject><subject>Sensitizing</subject><subject>Shelf life</subject><subject>Shunt resistance</subject><subject>Streptomycin</subject><subject>Tricarboxylic acid cycle</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</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><sourceid>DOA</sourceid><recordid>eNptkt1u1DAQhSMEoqXwBggiuEFCu9jxT-IbpGr5q1TBDVxbY2ey61USL7ZTaV-A58a7m1Yt4sqWz5lvPKNTFC8pWVJW0w9bP4UR-uXOj7gkFSWENI-Kc6pYtZAVYY_v3c-KZzFuCRGskfJpccaIJI2q6XnxZ-XHmAK4McWFgYhtCZm6jy6WrsUxuc5hLL9ffnpfBrR727txXaZN8NN6Uw6YwPje2aztgl8HGIaD7sZMSc44n45apqX8UK5y3eAN2ITBTUN543wPFqfhefGkgz7ii_m8KH59-fxz9W1x_ePr1eryemGFYGkBwkjgtURZtQY7KxQxlQBrOGQDgGraRiKtjJKtUlaahoAhHZWKUU5qwS6K1yfurvdRzyuMuqKSMc7rimbH1cnRetjqXXADhL324PTxwYe1hpCn6lHXRoHirZC8YhwFqlbUtWjQWNko0nWZ9XHuNpkBW5vXGaB_AH2ojG6j1_5GS0ZVzaoMeHMC-JicjtYltBvrxxFt0lQoVSueTe_mLsH_njAmPbhose9hRD8dh-OUZKDK1rf_WP-_An5y2eBjDNjd_ZgSfcjebZU-ZE_P2ctlr-5Pe1d0Gzb2Fw532v4</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Banerjee, Deepanwita</creator><creator>Raghunathan, Anu</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</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>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</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>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>OIOZB</scope><scope>OTOTI</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-7063-9606</orcidid><orcidid>https://orcid.org/0000000270639606</orcidid></search><sort><creationdate>20190101</creationdate><title>Constraints-based analysis identifies NAD+ recycling through metabolic reprogramming in antibiotic resistant Chromobacterium violaceum</title><author>Banerjee, Deepanwita ; Raghunathan, Anu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c553t-a5b6a476e62dbefc590b25acb4ac55aa98d86e12b96d99c6b80ab0f1693140753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Acetic acid</topic><topic>Anti-Bacterial Agents - pharmacology</topic><topic>Antibiotic resistance</topic><topic>Antibiotics</topic><topic>ATP</topic><topic>Bacteria</topic><topic>BASIC BIOLOGICAL SCIENCES</topic><topic>Biology and Life Sciences</topic><topic>Biomass</topic><topic>Chemical engineering</topic><topic>Chloramphenicol</topic><topic>Chloromycetin</topic><topic>Chromobacterium</topic><topic>Chromobacterium - drug effects</topic><topic>chromobacterium violaceum</topic><topic>Citric Acid - metabolism</topic><topic>Citric Acid Cycle - drug effects</topic><topic>Data Mining</topic><topic>Decoupling</topic><topic>drug metabolism</topic><topic>Drug resistance</topic><topic>Electron transfer</topic><topic>Energy metabolism</topic><topic>Flow cytometry</topic><topic>Fluctuations</topic><topic>Flux</topic><topic>Genes</topic><topic>Genetic engineering</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Genotypes</topic><topic>Glucose</topic><topic>Glucose - metabolism</topic><topic>Homeostasis</topic><topic>Infections</topic><topic>Laboratories</topic><topic>Mathematical models</topic><topic>Medicine and Health Sciences</topic><topic>Membrane potential</topic><topic>Metabolic flux</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>NAD</topic><topic>NAD - 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Investigating altered metabolism in the context of evolved resistant genotypes potentially provide simple strategies to overcome drug resistance and induce susceptibility to existing antibiotics. A genome-scale metabolic model (GSMM) for Chromobacterium violaceum, an opportunistic human pathogen, was reconstructed using legacy data. Experimental constraints were used to represent antibiotic susceptible and resistant populations. Model predictions were validated using growth and respiration data successfully. Differential flux distribution and metabolic reprogramming were identified as a response to antibiotics, chloramphenicol and streptomycin. Streptomycin resistant populations (StrpR) redirected tricarboxylic acid (TCA) cycle flux through the glyoxylate shunt. Chloramphenicol resistant populations (ChlR) resorted to overflow metabolism producing acetate and formate. This switch to fermentative metabolism is potentially through excess reducing equivalents and increased NADH/NAD ratios. Reduced proton gradients and changed Proton Motive Force (PMF) induced by antibiotics were also predicted and verified experimentally using flow cytometry based membrane potential measurements. Pareto analysis of NADH and ATP maintenance showed the decoupling of electron transfer and ATP synthesis in StrpR. Redox homeostasis and NAD+ cycling through rewiring metabolic flux was implicated in re-sensitizing antibiotic resistant C. violaceum. These approaches can be used to probe metabolic vulnerabilities of resistant pathogens. On the verge of a post-antibiotic era, we foresee a critical need for systems level understanding of pathogens and host interaction to extend shelf life of antibiotics and strategize novel therapies.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30608971</pmid><doi>10.1371/journal.pone.0210008</doi><orcidid>https://orcid.org/0000-0002-7063-9606</orcidid><orcidid>https://orcid.org/0000000270639606</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Acetic acid Anti-Bacterial Agents - pharmacology Antibiotic resistance Antibiotics ATP Bacteria BASIC BIOLOGICAL SCIENCES Biology and Life Sciences Biomass Chemical engineering Chloramphenicol Chloromycetin Chromobacterium Chromobacterium - drug effects chromobacterium violaceum Citric Acid - metabolism Citric Acid Cycle - drug effects Data Mining Decoupling drug metabolism Drug resistance Electron transfer Energy metabolism Flow cytometry Fluctuations Flux Genes Genetic engineering Genomes Genomics Genotypes Glucose Glucose - metabolism Homeostasis Infections Laboratories Mathematical models Medicine and Health Sciences Membrane potential Metabolic flux Metabolism Metabolites NAD NAD - metabolism NADH Nicotinamide adenine dinucleotide Opportunist infection Overflow Oxalic Acid - metabolism oxidation-reduction reactions oxygen metabolism Pareto analysis Pathogens Phenotypes Physical Sciences Physiology Populations Predictions Proteins Protonmotive force pyruvate Redox properties Rewiring Sensitizing Shelf life Shunt resistance Streptomycin Tricarboxylic acid cycle |
title | Constraints-based analysis identifies NAD+ recycling through metabolic reprogramming in antibiotic resistant Chromobacterium violaceum |
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