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Identifying metabolic elements that contribute to productivity of 1-propanol bioproduction using metabolomic analysis.

Metabolomics : Official journal of the Metabolomic Society

Putri SP, Nakayama Y, Shen C, Noguchi S, Nitta K, Bamba T, Pontrelli S, Liao J, Fukusaki E.
PMID: 30830363
Metabolomics. 2018 Jul 04;14(7):96. doi: 10.1007/s11306-018-1386-0.

INTRODUCTION: Previously constructed Escherichia coli strains that produce 1-propanol use the native threonine pathway, or a heterologous citramalate pathway. However, based on the energy and cofactor requirements of each pathway, a combination of the two pathways produces synergistic effects...

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Putri SP, Nakayama Y, Shen C, et al. Identifying metabolic elements that contribute to productivity of 1-propanol bioproduction using metabolomic analysis. Metabolomics. 2018;14(7):96doi: 10.1007/s11306-018-1386-0.
Putri, S. P., Nakayama, Y., Shen, C., Noguchi, S., Nitta, K., Bamba, T., Pontrelli, S., Liao, J., & Fukusaki, E. (2018). Identifying metabolic elements that contribute to productivity of 1-propanol bioproduction using metabolomic analysis. Metabolomics : Official journal of the Metabolomic Society, 14(7), 96. https://doi.org/10.1007/s11306-018-1386-0
Putri, Sastia Prama, et al. "Identifying metabolic elements that contribute to productivity of 1-propanol bioproduction using metabolomic analysis." Metabolomics : Official journal of the Metabolomic Society vol. 14,7 (2018): 96. doi: https://doi.org/10.1007/s11306-018-1386-0
Putri SP, Nakayama Y, Shen C, Noguchi S, Nitta K, Bamba T, Pontrelli S, Liao J, Fukusaki E. Identifying metabolic elements that contribute to productivity of 1-propanol bioproduction using metabolomic analysis. Metabolomics. 2018 Jul 04;14(7):96. doi: 10.1007/s11306-018-1386-0. PMID: 30830363.
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Membrane proteins, magic-angle spinning, and in-cell NMR.

Proceedings of the National Academy of Sciences of the United States of America

Pielak GJ, Tian F.
PMID: 22411816
Proc Natl Acad Sci U S A. 2012 Mar 27;109(13):4715-6. doi: 10.1073/pnas.1201502109. Epub 2012 Mar 12.

No abstract available.

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Pielak GJ, Tian F. Membrane proteins, magic-angle spinning, and in-cell NMR. Proc Natl Acad Sci U S A. 2012;109(13):4715-6doi: 10.1073/pnas.1201502109.
Pielak, G. J., & Tian, F. (2012). Membrane proteins, magic-angle spinning, and in-cell NMR. Proceedings of the National Academy of Sciences of the United States of America, 109(13), 4715-6. https://doi.org/10.1073/pnas.1201502109
Pielak, Gary J, and Tian, Fang. "Membrane proteins, magic-angle spinning, and in-cell NMR." Proceedings of the National Academy of Sciences of the United States of America vol. 109,13 (2012): 4715-6. doi: https://doi.org/10.1073/pnas.1201502109
Pielak GJ, Tian F. Membrane proteins, magic-angle spinning, and in-cell NMR. Proc Natl Acad Sci U S A. 2012 Mar 27;109(13):4715-6. doi: 10.1073/pnas.1201502109. Epub 2012 Mar 12. PMID: 22411816; PMCID: PMC3323951.
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Predicting novel substrates for enzymes with minimal experimental effort with active learning.

Metabolic engineering

Pertusi DA, Moura ME, Jeffryes JG, Prabhu S, Walters Biggs B, Tyo KEJ.
PMID: 29030274
Metab Eng. 2017 Nov;44:171-181. doi: 10.1016/j.ymben.2017.09.016. Epub 2017 Oct 10.

Enzymatic substrate promiscuity is more ubiquitous than previously thought, with significant consequences for understanding metabolism and its application to biocatalysis. This realization has given rise to the need for efficient characterization of enzyme promiscuity. Enzyme promiscuity is currently characterized...

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Pertusi DA, Moura ME, Jeffryes JG, et al. Predicting novel substrates for enzymes with minimal experimental effort with active learning. Metab Eng. 2017;44:171-181doi: 10.1016/j.ymben.2017.09.016.
Pertusi, D. A., Moura, M. E., Jeffryes, J. G., Prabhu, S., Walters Biggs, B., & Tyo, K. E. J. (2017). Predicting novel substrates for enzymes with minimal experimental effort with active learning. Metabolic engineering, 44171-181. https://doi.org/10.1016/j.ymben.2017.09.016
Pertusi, Dante A, et al. "Predicting novel substrates for enzymes with minimal experimental effort with active learning." Metabolic engineering vol. 44 (2017): 171-181. doi: https://doi.org/10.1016/j.ymben.2017.09.016
Pertusi DA, Moura ME, Jeffryes JG, Prabhu S, Walters Biggs B, Tyo KEJ. Predicting novel substrates for enzymes with minimal experimental effort with active learning. Metab Eng. 2017 Nov;44:171-181. doi: 10.1016/j.ymben.2017.09.016. Epub 2017 Oct 10. PMID: 29030274; PMCID: PMC7055960.
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Escherichia coli as a host for metabolic engineering.

Metabolic engineering

Pontrelli S, Chiu TY, Lan EI, Chen FY, Chang P, Liao JC.
PMID: 29689382
Metab Eng. 2018 Nov;50:16-46. doi: 10.1016/j.ymben.2018.04.008. Epub 2018 Apr 22.

Over the past century, Escherichia coli has become one of the best studied organisms on earth. Features such as genetic tractability, favorable growth conditions, well characterized biochemistry and physiology, and availability of versatile genetic manipulation tools make E. coli...

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Pontrelli S, Chiu TY, Lan EI, et al. Escherichia coli as a host for metabolic engineering. Metab Eng. 2018;50:16-46doi: 10.1016/j.ymben.2018.04.008.
Pontrelli, S., Chiu, T. Y., Lan, E. I., Chen, F. Y., Chang, P., & Liao, J. C. (2018). Escherichia coli as a host for metabolic engineering. Metabolic engineering, 5016-46. https://doi.org/10.1016/j.ymben.2018.04.008
Pontrelli, Sammy, et al. "Escherichia coli as a host for metabolic engineering." Metabolic engineering vol. 50 (2018): 16-46. doi: https://doi.org/10.1016/j.ymben.2018.04.008
Pontrelli S, Chiu TY, Lan EI, Chen FY, Chang P, Liao JC. Escherichia coli as a host for metabolic engineering. Metab Eng. 2018 Nov;50:16-46. doi: 10.1016/j.ymben.2018.04.008. Epub 2018 Apr 22. PMID: 29689382.
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Development of high throughput 96-blade solid phase microextraction-liquid chromatrography-mass spectrometry protocol for metabolomics.

Analytica chimica acta

Mousavi F, Bojko B, Pawliszyn J.
PMID: 26388479
Anal Chim Acta. 2015 Sep 10;892:95-104. doi: 10.1016/j.aca.2015.08.016. Epub 2015 Aug 22.

In metabolomics, the workflow for quantitative and comprehensive metabolic mapping of cellular metabolites can be a very challenging undertaking. Sampling and sample preparation play a significant role in untargeted analysis, as they may affect the composition of the analyzed...

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Mousavi F, Bojko B, Pawliszyn J. Development of high throughput 96-blade solid phase microextraction-liquid chromatrography-mass spectrometry protocol for metabolomics. Anal Chim Acta. 2015;892:95-104doi: 10.1016/j.aca.2015.08.016.
Mousavi, F., Bojko, B., & Pawliszyn, J. (2015). Development of high throughput 96-blade solid phase microextraction-liquid chromatrography-mass spectrometry protocol for metabolomics. Analytica chimica acta, 89295-104. https://doi.org/10.1016/j.aca.2015.08.016
Mousavi, Fatemeh, et al. "Development of high throughput 96-blade solid phase microextraction-liquid chromatrography-mass spectrometry protocol for metabolomics." Analytica chimica acta vol. 892 (2015): 95-104. doi: https://doi.org/10.1016/j.aca.2015.08.016
Mousavi F, Bojko B, Pawliszyn J. Development of high throughput 96-blade solid phase microextraction-liquid chromatrography-mass spectrometry protocol for metabolomics. Anal Chim Acta. 2015 Sep 10;892:95-104. doi: 10.1016/j.aca.2015.08.016. Epub 2015 Aug 22. PMID: 26388479.
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The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli.

Nucleic acids research

Sundararaj S, Guo A, Habibi-Nazhad B, Rouani M, Stothard P, Ellison M, Wishart DS.
PMID: 14681416
Nucleic Acids Res. 2004 Jan 01;32:D293-5. doi: 10.1093/nar/gkh108.

The CyberCell Database (CCDB: http://redpoll. pharmacy.ualberta.ca/CCDB) is a comprehensive, web-accessible database designed to support and coordinate international efforts in modeling an Escherichia coli cell on a computer. The CCDB brings together both observed and derived quantitative data from numerous...

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Sundararaj S, Guo A, Habibi-Nazhad B, et al. The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli. Nucleic Acids Res. 2004;32:D293-5doi: 10.1093/nar/gkh108.
Sundararaj, S., Guo, A., Habibi-Nazhad, B., Rouani, M., Stothard, P., Ellison, M., & Wishart, D. S. (2004). The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli. Nucleic acids research, 32D293-5. https://doi.org/10.1093/nar/gkh108
Sundararaj, Shan, et al. "The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli." Nucleic acids research vol. 32 (2004): D293-5. doi: https://doi.org/10.1093/nar/gkh108
Sundararaj S, Guo A, Habibi-Nazhad B, Rouani M, Stothard P, Ellison M, Wishart DS. The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli. Nucleic Acids Res. 2004 Jan 01;32:D293-5. doi: 10.1093/nar/gkh108. PMID: 14681416; PMCID: PMC308842.
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The influence of medium composition on the microbial secretory production of hydroxyalkanoate oligomers.

The Journal of general and applied microbiology

Mizuno S, Sakurai T, Nabasama M, Kawakami K, Hiroe A, Taguchi S, Tsuge T.
PMID: 33952784
J Gen Appl Microbiol. 2021 Oct 22;67(4):134-141. doi: 10.2323/jgam.2020.09.002. Epub 2021 May 01.

With the aid of a chain transfer (CT) reaction, hydroxyalkanoate (HA) oligomers can be secreted by recombinant Escherichia coli carrying the gene encoding a lactate-polymerizing enzyme (PhaC1

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Mizuno S, Sakurai T, Nabasama M, et al. The influence of medium composition on the microbial secretory production of hydroxyalkanoate oligomers. J Gen Appl Microbiol. 2021;67(4):134-141doi: 10.2323/jgam.2020.09.002.
Mizuno, S., Sakurai, T., Nabasama, M., Kawakami, K., Hiroe, A., Taguchi, S., & Tsuge, T. (2021). The influence of medium composition on the microbial secretory production of hydroxyalkanoate oligomers. The Journal of general and applied microbiology, 67(4), 134-141. https://doi.org/10.2323/jgam.2020.09.002
Mizuno, Shoji, et al. "The influence of medium composition on the microbial secretory production of hydroxyalkanoate oligomers." The Journal of general and applied microbiology vol. 67,4 (2021): 134-141. doi: https://doi.org/10.2323/jgam.2020.09.002
Mizuno S, Sakurai T, Nabasama M, Kawakami K, Hiroe A, Taguchi S, Tsuge T. The influence of medium composition on the microbial secretory production of hydroxyalkanoate oligomers. J Gen Appl Microbiol. 2021 Oct 22;67(4):134-141. doi: 10.2323/jgam.2020.09.002. Epub 2021 May 01. PMID: 33952784.
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Protein cost allocation explains metabolic strategies in Escherichia coli.

Journal of biotechnology

Grigaitis P, Olivier BG, Fiedler T, Teusink B, Kummer U, Veith N.
PMID: 33309962
J Biotechnol. 2021 Feb 10;327:54-63. doi: 10.1016/j.jbiotec.2020.11.003. Epub 2020 Dec 10.

In-depth understanding of microbial growth is crucial for the development of new advances in biotechnology and for combating microbial pathogens. Condition-specific proteome expression is central to microbial physiology and growth. A multitude of processes are dependent on the protein...

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Grigaitis P, Olivier BG, Fiedler T, et al. Protein cost allocation explains metabolic strategies in Escherichia coli. J Biotechnol. 2020;327:54-63doi: 10.1016/j.jbiotec.2020.11.003.
Grigaitis, P., Olivier, B. G., Fiedler, T., Teusink, B., Kummer, U., & Veith, N. (2021). Protein cost allocation explains metabolic strategies in Escherichia coli. Journal of biotechnology, 32754-63. https://doi.org/10.1016/j.jbiotec.2020.11.003
Grigaitis, Pranas, et al. "Protein cost allocation explains metabolic strategies in Escherichia coli." Journal of biotechnology vol. 327 (2021): 54-63. doi: https://doi.org/10.1016/j.jbiotec.2020.11.003
Grigaitis P, Olivier BG, Fiedler T, Teusink B, Kummer U, Veith N. Protein cost allocation explains metabolic strategies in Escherichia coli. J Biotechnol. 2021 Feb 10;327:54-63. doi: 10.1016/j.jbiotec.2020.11.003. Epub 2020 Dec 10. PMID: 33309962.
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Showing 49 to 56 of 56 entries