A Workflow for Bridging the Genotype-Phenotype Relationship with Multi-omics Data and Genome-Scale Models of E. coli Metabolism Expressing Heterologous Pathways. (A) Multi-scale data types that are generally collected to elucidate changes in metabolic phenotypes of different strains. (B) The workflow involves a hierarchical staging of computational analysis methods: (1) basic strain differences; (2) relevant patterns and correlations in the data; (3) mechanisms of action in the context of a genome-scale network that can explain apparent differences in strain behavior. Brunk et al. Click to enlarge.

The findings were reported in an open-access paper published in the journal Cell Systems. Taek Soon Lee, JBEI’s Director of Metabolic Engineering and Deputy Vice President of the Fuels Synthesis Division is the study’s co-corresponding author along with Bernhard Palsson at University of California, San Diego. Elizabeth Brunk (formerly at JBEI, and currently at UC San Diego) is the study’s co-first author along with Kevin George at JBEI (currently at Amyris).

Understanding the complex interactions that occur between heterologous and native biochemical pathways represents a major challenge in metabolic engineering and synthetic biology. We present a workflow that integrates metabolomics, proteomics, and genome-scale models of Escherichia coli metabolism to study the effects of introducing a heterologous pathway into a microbial host. This workflow incorporates complementary approaches from computational systems biology, metabolic engineering, and synthetic biology; provides molecular insight into how the host organism microenvironment changes due to pathway engineering; and demonstrates how biological mechanisms underlying strain variation can be exploited as an engineering strategy to increase product yield. As a proof of concept, we present the analysis of eight engineered strains producing three biofuels: isopentenol, limonene, and bisabolene. Application of this workflow identified the roles of candidate genes, pathways, and biochemical reactions in observed experimental phenomena and facilitated the construction of a mutant strain with improved productivity.

—Brunk et al.

The authors note that a major challenge in both metabolic engineering and synthetic biology is understanding how the introduction of engineered or non-native components into a biochemical network influences the behavior of the entire system. Their three-stage workflow interprets complex multi-omics data for multi-strain characterization. Each of the three stages of the workflow works together as a concerted pipeline to efficiently process highly dimensional datasets.

The first two stages serve as a flexible framework to interpret raw, multi-omics data by sorting strain phenotypes based on dynamic difference profiles and correlating measurements based on distinct patterns derived from thousands of measurements.

These two stages of the workflow in particular are well suited for integration with high-throughput strain engineering and analysis pipelines, the authors said, where “manual” assessment of convoluted omics data is not feasible.

To account for the systems-level response to pathway engineering, the third stage of the workflow leverages statistics-based approaches in the context of a genome-scale metabolic model.

Synthetic biology and systems biology have been considered as two distinct technical frameworks. We hope the confluence of these two fields will benefit biofuels research and its scientific community. Our team is sharing this workflow as an open-source tool in the form of iPython notebooks, which allows anyone in the microbial engineering field to easily apply this workflow to their system.

—Taek Soon Lee

Resources

• Elizabeth Brunk, Kevin W. George, Jorge Alonso-Gutierrez, Mitchell Thompson, Edward Baidoo, George Wang, Christopher J. Petzold, Douglas McCloskey, Jonathan Monk, Laurence Yang, Edward J. O’Brien, Tanveer S. Batth, Hector Garcia Martin, Adam Feist, Paul D. Adams, Jay D. Keasling, Bernhard O. Palsson, Taek Soon Lee (2016) “Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow” Cell Systems doi: 10.1016/j.cels.2016.04.004