4.7 Article

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

Journal

METABOLIC ENGINEERING
Volume 61, Issue -, Pages 360-368

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymben.2020.07.002

Keywords

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

Funding

  1. Novo Nordisk Foundation [NNF10CC1016517]

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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.

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