Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model

Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite developm...
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Autor*in:	Yan, Qiang [verfasserIn] Robert, Seth Brooks, J. Paul Fong, Stephen S.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Genome-scale metabolic model 2,3-butanediol -acetylneuraminic acid RNASeq -butanol Chitin Flux balance analysis

Anmerkung:	© The Author(s). 2019

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 20(2019), 1 vom: 06. Mai
Übergeordnetes Werk:	volume:20 ; year:2019 ; number:1 ; day:06 ; month:05

Links:	Volltext

DOI / URN:	10.1186/s12859-019-2826-1

Katalog-ID:	SPR026923335

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520			\|a Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. Results In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens.
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700	1		\|a Brooks, J. Paul \|4 aut
700	1		\|a Fong, Stephen S. \|4 aut
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allfields	10.1186/s12859-019-2826-1 doi (DE-627)SPR026923335 (SPR)s12859-019-2826-1-e DE-627 ger DE-627 rakwb eng Yan, Qiang verfasserin (orcid)0000-0002-5038-5776 aut Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. Results In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens. Genome-scale metabolic model (dpeaa)DE-He213 2,3-butanediol (dpeaa)DE-He213 -acetylneuraminic acid (dpeaa)DE-He213 RNASeq (dpeaa)DE-He213 -butanol (dpeaa)DE-He213 Chitin (dpeaa)DE-He213 Flux balance analysis (dpeaa)DE-He213 Robert, Seth aut Brooks, J. Paul aut Fong, Stephen S. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 20(2019), 1 vom: 06. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:20 year:2019 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12859-019-2826-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 06 05
spelling	10.1186/s12859-019-2826-1 doi (DE-627)SPR026923335 (SPR)s12859-019-2826-1-e DE-627 ger DE-627 rakwb eng Yan, Qiang verfasserin (orcid)0000-0002-5038-5776 aut Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. Results In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens. Genome-scale metabolic model (dpeaa)DE-He213 2,3-butanediol (dpeaa)DE-He213 -acetylneuraminic acid (dpeaa)DE-He213 RNASeq (dpeaa)DE-He213 -butanol (dpeaa)DE-He213 Chitin (dpeaa)DE-He213 Flux balance analysis (dpeaa)DE-He213 Robert, Seth aut Brooks, J. Paul aut Fong, Stephen S. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 20(2019), 1 vom: 06. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:20 year:2019 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12859-019-2826-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 06 05
allfields_unstemmed	10.1186/s12859-019-2826-1 doi (DE-627)SPR026923335 (SPR)s12859-019-2826-1-e DE-627 ger DE-627 rakwb eng Yan, Qiang verfasserin (orcid)0000-0002-5038-5776 aut Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. Results In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens. Genome-scale metabolic model (dpeaa)DE-He213 2,3-butanediol (dpeaa)DE-He213 -acetylneuraminic acid (dpeaa)DE-He213 RNASeq (dpeaa)DE-He213 -butanol (dpeaa)DE-He213 Chitin (dpeaa)DE-He213 Flux balance analysis (dpeaa)DE-He213 Robert, Seth aut Brooks, J. Paul aut Fong, Stephen S. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 20(2019), 1 vom: 06. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:20 year:2019 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12859-019-2826-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 06 05
allfieldsGer	10.1186/s12859-019-2826-1 doi (DE-627)SPR026923335 (SPR)s12859-019-2826-1-e DE-627 ger DE-627 rakwb eng Yan, Qiang verfasserin (orcid)0000-0002-5038-5776 aut Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. Results In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens. Genome-scale metabolic model (dpeaa)DE-He213 2,3-butanediol (dpeaa)DE-He213 -acetylneuraminic acid (dpeaa)DE-He213 RNASeq (dpeaa)DE-He213 -butanol (dpeaa)DE-He213 Chitin (dpeaa)DE-He213 Flux balance analysis (dpeaa)DE-He213 Robert, Seth aut Brooks, J. Paul aut Fong, Stephen S. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 20(2019), 1 vom: 06. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:20 year:2019 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12859-019-2826-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 06 05
allfieldsSound	10.1186/s12859-019-2826-1 doi (DE-627)SPR026923335 (SPR)s12859-019-2826-1-e DE-627 ger DE-627 rakwb eng Yan, Qiang verfasserin (orcid)0000-0002-5038-5776 aut Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2019 Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. Results In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens. Genome-scale metabolic model (dpeaa)DE-He213 2,3-butanediol (dpeaa)DE-He213 -acetylneuraminic acid (dpeaa)DE-He213 RNASeq (dpeaa)DE-He213 -butanol (dpeaa)DE-He213 Chitin (dpeaa)DE-He213 Flux balance analysis (dpeaa)DE-He213 Robert, Seth aut Brooks, J. Paul aut Fong, Stephen S. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 20(2019), 1 vom: 06. Mai (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:20 year:2019 number:1 day:06 month:05 https://dx.doi.org/10.1186/s12859-019-2826-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 20 2019 1 06 05
language	English
source	Enthalten in BMC bioinformatics 20(2019), 1 vom: 06. Mai volume:20 year:2019 number:1 day:06 month:05
sourceStr	Enthalten in BMC bioinformatics 20(2019), 1 vom: 06. Mai volume:20 year:2019 number:1 day:06 month:05
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Genome-scale metabolic model 2,3-butanediol -acetylneuraminic acid RNASeq -butanol Chitin Flux balance analysis
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Yan, Qiang @@aut@@ Robert, Seth @@aut@@ Brooks, J. Paul @@aut@@ Fong, Stephen S. @@aut@@
publishDateDaySort_date	2019-05-06T00:00:00Z
hierarchy_top_id	326644814
id	SPR026923335
language_de	englisch
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Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. 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author	Yan, Qiang
spellingShingle	Yan, Qiang misc Genome-scale metabolic model misc 2,3-butanediol misc -acetylneuraminic acid misc RNASeq misc -butanol misc Chitin misc Flux balance analysis Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model
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topic_title	Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model Genome-scale metabolic model (dpeaa)DE-He213 2,3-butanediol (dpeaa)DE-He213 -acetylneuraminic acid (dpeaa)DE-He213 RNASeq (dpeaa)DE-He213 -butanol (dpeaa)DE-He213 Chitin (dpeaa)DE-He213 Flux balance analysis (dpeaa)DE-He213
topic	misc Genome-scale metabolic model misc 2,3-butanediol misc -acetylneuraminic acid misc RNASeq misc -butanol misc Chitin misc Flux balance analysis
topic_unstemmed	misc Genome-scale metabolic model misc 2,3-butanediol misc -acetylneuraminic acid misc RNASeq misc -butanol misc Chitin misc Flux balance analysis
topic_browse	misc Genome-scale metabolic model misc 2,3-butanediol misc -acetylneuraminic acid misc RNASeq misc -butanol misc Chitin misc Flux balance analysis
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title	Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model
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title_full	Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model
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author_browse	Yan, Qiang Robert, Seth Brooks, J. Paul Fong, Stephen S.
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title_sort	metabolic characterization of the chitinolytic bacterium serratia marcescens using a genome-scale metabolic model
title_auth	Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model
abstract	Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. Results In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens. © The Author(s). 2019
abstractGer	Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. Results In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens. © The Author(s). 2019
abstract_unstemmed	Background Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications. Results In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle. Conclusions Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens. © The Author(s). 2019
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title_short	Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model
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Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model

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