Principal component analysis of proteomics (PCAP) as a tool to direct metabolic engineering

Research output: Contribution to journalJournal articleResearchpeer-review

  • Jorge Alonso-Gutierrez
  • Eun-Mi Kim
  • Batth, Tanveer Singh
  • Nathan Cho
  • Qijun Hu
  • Leanne Jade G Chan
  • Christopher J Petzold
  • Nathan J Hillson
  • Paul D Adams
  • Jay D Keasling
  • Hector Garcia Martin
  • Taek Soon Lee

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.

Original languageEnglish
JournalMetabolic Engineering
Pages (from-to)123-133
Number of pages11
Publication statusPublished - Mar 2015

    Research areas

  • Escherichia coli/genetics, Escherichia coli Proteins/biosynthesis, Gene Expression Regulation, Bacterial, Metabolic Engineering/methods, Proteomics, Terpenes/metabolism

ID: 204047795