Cell Systems
Previews Omics Meets Metabolic Pathway Engineering Guo-Qiang Chen1,2,3,* 1Peking-Tsinghua
Center for Life Sciences, School of Life Science, Tsinghua University, Beijing 100084, China for Synthetic and Systems Biology, Tsinghua University, Beijing 100084, China 3MOE Key Lab of Industrial Biocatalysis, Tsinghua University, Beijing 100081, China *Correspondence:
[email protected] http://dx.doi.org/10.1016/j.cels.2016.05.005 2Center
A principled approach to integrating metabolomics, proteomics, and genome-scale metabolic modeling facilitaties rational pathway engineering of E. coli. Powerful molecular tools make it easier than ever to introduce and alter foreign and native biochemical pathways. But getting these pathways to function as desired is still challenging, in part because every tweak to a pathway may unfavorably change the micro-environment within the host organism. In this issue of Cell Systems, two leading research groups describe a workflow for using various sources of omics data to understand how the host micro-environment changes in response to a heterologous pathway (Brunk et al., 2016). They apply the workflow to Escherichia coli producing three biofuels isopentenol, limonene, and bisabolene to understand the differences between high- and lowproducing strains and identify potential targets for engineering improved strains. Overall, this work demonstrates how techniques from systems biology can complement conventional synthetic biology and pathway engineering approaches (Figure 1). A typical pathway-engineering project follows an iterative design-build-testanalyze cycle. In this approach, in silico metabolic pathway design will be conducted first, followed by construction of the engineered strains containing the designed pathways. Subsequently, the strains are grown to test their abilities for the required properties of product formations. Finally, the strains will be analyzed to establish the property and engineering pathway relationship (Bernstein and Carlson 2014). These engineering efforts are often focused on a narrow set of one or two experimental outputs, such as product titer, growth rate, or substrate-to-product conversion efficiency. For example, most strain optimization is focused primarily on the final product synthesis pathway, such as the mevalonate 362 Cell Systems 2, June 22, 2016
pathway for biofuel formation studied by Brunk et al. As a result, it is difficult to know why in many cases the final product titer is not improved significantly as expected.
Figure 1. The Confluence of Two Accelerating Fields—Synthetic Biology/ Pathway Engineering and Systems Biology—Presents Exciting Opportunities for the Discovery of Novel, Economic Solutions to Industrial Challenges Conceptually, these ‘‘two worlds’’ have commonly operated under the pretense of distinct technical and theoretical frameworks, despite common engineering origins. Whereas systems biologists largely define a host organism by the underlying metabolic pathways and biological components, synthetic biologists commonly focus on the design of parts or modules according to biochemical and biological principles. Brunk et al. describe a workflow that integrates these strengths, or forces these two worlds to collide, to facilitate highthroughout generation, analysis, and improvement of engineered microorganisms. Image by Elizabeth Brunk.
In contrast, it has been proposed that a systematic exploration of the interplay between foreign pathway engineering and endogenous metabolism could allow clearer identifications of strain variation, perturbed metabolic nodes, and ultimately produce new engineering targets (Lee et al., 2012). These new engineering targets could be more effective for engineering compared with those identified by the consideration of only the product synthesis pathways. A key challenge to this approach lies in knowing how to interpret the wealth of data generated by omics assays to identify the genes or pathways on which to focus one’s engineering efforts. Brunk et al. describe such a workflow for marrying systems-biology analysis with pathway engineering. They began with several production E. coli strains that use a mevalonate pathway from Saccharomyces cerevisiae to make precursors to terpene fuels and chemicals at different levels. Next, the authors grew these strains and collected time course data on >80 intracellular and extracellular metabolites using quantitative mass spectrometry and >50 proteins or protein complexes using selected reaction monitoring proteomics. Data were collected for both heterologous and endogenous metabolic pathways. To compare basic differences among strains, Brunk et al. first rapidly ‘‘filtered’’ these data into distinct profiles, which they call dynamic difference profiles. These profiles provided valuable insight into the metabolic responses to engineering by highlighting maximally perturbed nodes. Second, the authors used principle component analysis to identify patterns in how these changes were correlated over time. Third, the authors contextualized these perturbations within
Cell Systems
Previews a biochemical network using genomescale modeling. Notably, the genome-scale modeling allowed the authors to elucidate the biological impacts of pathway optimization. For example, one analysis suggested that the introduction of the heterologous mevalonate pathway depleted NADPH levels in E. coli, leading to an NADPH bottleneck that limited that amount of biofuel produced. Certain high-producing strains were found to have increased metabolic flux through reactions that regenerate NADPH, thereby alleviating the NADPH bottleneck. This suggested an engineering strategy to enhance product yields by disrupting pathways that consume NADPH and boosting flux through pathways that produce NADPH. Although the workflow combining various omic data provides benefits for metabolic pathway engineering, it is mainly useful for some model organisms
such as E. coli with well studies omics. For many industrially important strains, such as Corynebacterium glutamicum and Ralstonia eutropha, the rich omic data needed for this type of workflow may not be readily available. In addition, other optimizations, such as promoters, ribosome binding sites, gene order arrangements, and expression control strategy, could be considered in the workflow. These pose challenges for future pathway engineering that need to be overcome. Overall, the study of Brunk et al. demonstrates that a workflow that pairs synthetic pathway construction with a systems-level, model-driven analysis can successfully reconcile metabolomics data, proteomics data, and predictions from genome-scale models. The mevalonate pathway engineering used as a case study suggests that other traditional microbial products such as amino acids, polyhydroxyalkanoates
(PHA), fatty acids and the likes could be studied using similar approaches to improve yields and productivities. Toward this effort, Brunk et al. should be commended for documenting their workflow and all of their analyses in iPython notebooks, which should allow others to more easily reproduce and extend these results.
REFERENCES Bernstein, H.C., and Carlson, R.P. (2014). Design, construction, and characterization methodologies for synthetic microbial consortia. In Engineering and Analyzing Multicellular Systems: Methods and Protocols, Vol. 1151, L. Sun, and W. Shou, eds. (Springer), pp. 49–68. Brunk, E., George, K.W., Alonso-Gutierrez, J., Thompson, M., Baidoo, E., Wang, G., Petzold, C.J., McCloskey, D., Monk, J., Yang, L., et al. (2016). Cell Syst. 2, 335–346. Lee, J.W., Na, D., Park, J.M., Lee, J., Choi, S., and Lee, S.Y. (2012). Nat. Chem. Biol. 8, 536–546.
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