Independent component analysis of E. coli's transcriptome reveals the cellular processes that respond to heterologous gene expression

Justin Tan, Anand V. Sastry, Karoline S. Fremming, Sara P. Bjørn, Alexandra Hoffmeyer, Sangwoo Seo, Bjørn G. Voldborg, Bernhard O. Palsson*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Achieving the predictable expression of heterologous genes in a production host has proven difficult. Each heterologous gene expressed in the same host seems to elicit a different host response governed by unknown mechanisms. Historically, most studies have approached this challenge by manipulating the properties of the heterologous gene through methods like codon optimization. Here we approach this challenge from the host side. We express a set of 45 heterologous genes in the same Escherichia coli strain, using the same expression system and culture conditions. We collect a comprehensive RNAseq set to characterize the host's transcriptional response. Independent Component Analysis of the RNAseq data set reveals independently modulated gene sets (iModulons) that characterize the host response to heterologous gene expression. We relate 55% of variation of the host response to: Fear vs Greed (16.5%), Metal Homeostasis (19.0%), Respiration (6.0%), Protein folding (4.5%), and Amino acid and nucleotide biosynthesis (9.0%). If these responses can be controlled, then the success rate with predicting heterologous gene expression should increase.

Original languageEnglish
Pages (from-to)360-368
Number of pages9
JournalMetabolic Engineering
Publication statusPublished - Sept 2020

Bibliographical note

Funding Information:
This work was funded by the Novo Nordisk Foundation Grant Number NNF10CC1016517 . We would like to thank Rebecca Lennen for her contribution of the gene templates and help in designing the plasmid backbone. We would also like to Stefan Kol, Laurence Yang and Daniel Zielinski for many valuable discussions. We would like to thank Marc Abrams for assistance with manuscript editing.

Publisher Copyright:
© 2020 The Authors

Other keywords

  • Big data
  • Heterologous gene expression
  • Host cell response
  • Independent component analysis
  • Metabolic burden
  • Plasmid


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