Principal component analysis of proteomics (PCAP) as a tool to direct metabolic engineering
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Principal component analysis of proteomics (PCAP) as a tool to direct metabolic engineering. / Alonso-Gutierrez, Jorge; Kim, Eun-Mi; Batth, Tanveer S; Cho, Nathan; Hu, Qijun; Chan, Leanne Jade G; Petzold, Christopher J; Hillson, Nathan J; Adams, Paul D; Keasling, Jay D; Garcia Martin, Hector; Lee, Taek Soon.
In: Metabolic Engineering, Vol. 28, 2015, p. 123-133.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Principal component analysis of proteomics (PCAP) as a tool to direct metabolic engineering
AU - Alonso-Gutierrez, Jorge
AU - Kim, Eun-Mi
AU - Batth, Tanveer S
AU - Cho, Nathan
AU - Hu, Qijun
AU - Chan, Leanne Jade G
AU - Petzold, Christopher J
AU - Hillson, Nathan J
AU - Adams, Paul D
AU - Keasling, Jay D
AU - Garcia Martin, Hector
AU - Lee, Taek Soon
N1 - Copyright © 2014 International Metabolic Engineering Society. All rights reserved.
PY - 2015
Y1 - 2015
N2 - Targeted proteomics is a convenient method determining enzyme expression levels, but a quantitative analysis of these proteomic data has not been fully explored yet. Here, we present and demonstrate a computational tool (principal component analysis of proteomics, PCAP) that uses quantitative targeted proteomics data to guide metabolic engineering and achieve higher production of target molecules from heterologous pathways. The method is based on the application of principal component analysis to a collection of proteomics and target molecule production data to pinpoint specific enzymes that need to have their expression level adjusted to maximize production. We illustrated the method on the heterologous mevalonate pathway in Escherichia coli that produces a wide range of isoprenoids and requires balanced pathway gene expression for high yields and titers. PCAP-guided engineering resulted in over a 40% improvement in the production of two valuable terpenes. PCAP could potentially be productively applied to other heterologous pathways as well.
AB - Targeted proteomics is a convenient method determining enzyme expression levels, but a quantitative analysis of these proteomic data has not been fully explored yet. Here, we present and demonstrate a computational tool (principal component analysis of proteomics, PCAP) that uses quantitative targeted proteomics data to guide metabolic engineering and achieve higher production of target molecules from heterologous pathways. The method is based on the application of principal component analysis to a collection of proteomics and target molecule production data to pinpoint specific enzymes that need to have their expression level adjusted to maximize production. We illustrated the method on the heterologous mevalonate pathway in Escherichia coli that produces a wide range of isoprenoids and requires balanced pathway gene expression for high yields and titers. PCAP-guided engineering resulted in over a 40% improvement in the production of two valuable terpenes. PCAP could potentially be productively applied to other heterologous pathways as well.
KW - Escherichia coli/genetics
KW - Escherichia coli Proteins/biosynthesis
KW - Gene Expression Regulation, Bacterial
KW - Metabolic Engineering/methods
KW - Proteomics
KW - Terpenes/metabolism
U2 - 10.1016/j.ymben.2014.11.011
DO - 10.1016/j.ymben.2014.11.011
M3 - Journal article
C2 - 25554074
VL - 28
SP - 123
EP - 133
JO - Metabolic Engineering
JF - Metabolic Engineering
SN - 1096-7176
ER -
ID: 204047795