Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity

Abstract

Increasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome. Transforming organisms such as Escherichia coli into microbial factories is achieved via several engineering strategies, used individually or in combination, with the goal of maximizing the production of chosen target compounds. One technique relies on suppressing or overexpressing selected genes; another involves introducing heterologous enzymes into a microbial host. These modifications steer mass flux towards the set of desired metabolites but may create unexpected interactions. In this work, we develop a computational method, termed M etabolic D isruption Work flow ( MDFlow ), for discovering interactions and network disruptions arising from enzyme promiscuity – the ability of enzymes to act on a wide range of molecules that are structurally similar to their native substrates. We apply MDFlow to two experimentally verified cases where strains with essential genes knocked out are rescued by interactions resulting from overexpression of one or more other genes. We demonstrate how enzyme promiscuity may aid cells in adapting to disruptions of essential metabolic functions. We then apply MDFlow to predict and evaluate a number of putative promiscuous reactions that can interfere with two heterologous pathways designed for 3-hydroxypropionic acid (3-HP) production. Using MDFlow , we can identify putative enzyme promiscuity and the subsequent formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high biosynthetic productivity and yield. As we demonstrate, MDFlow provides an innovative workflow to systematically identify incompatibilities between the native metabolism of the host and its engineered modifications due to enzyme promiscuity.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation

“…This wider range of applications includes biofuel production studies, such as for quantifying butyric acid and other products of fermentation by Clostridium acetobutylicum ( Jang et al, 2013 ; Liu et al, 2019 ) and for studying the microbial bioconversion of plant polymers such as pectin and lignocellulose to biofuels ( Kuivanen et al, 2019 ; Lu et al, 2022 ). The MCD could also provide a useful vehicle for systematically screening large gene knockout libraries in microbial engineering projects ( Porokhin et al, 2021 ). Although the MCD was evaluated here in the context of bacterial metabolism, we anticipate it could be readily adapted to studying mammalian cell culture models ( Allen et al, 2003 ; Zukunft et al, 2018 ; Lagziel et al, 2019 ; Wright Muelas et al, 2020 ), biomarker discovery ( Tolstikov et al, 2020 ), pharmaceutical lead screening ( Tomita et al, 2018 ), environmental monitoring ( Lankadurai et al, 2013 ), microbiology ( Ye et al, 2022 ), plant biology ( Kumar et al, 2017 ), and food chemistry ( Cevallos-Cevallos et al, 2009 ).…”

Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products

“…Most, if not all, enzymes are promiscuous, acting on substrates other than the ones they evolved to catalyze ( Nobeli et al , 2009 ; Tawfik, 2020 ). Three applications, constructing of de novo synthesis pathways ( Otero-Muras and Carbonell, 2021 ), creating extended metabolic models (EMMs) that account for enzyme promiscuity ( Porokhin et al , 2021 ), and identifying metabolic products measured through metabolomics ( Strutz et al , 2022 ), have driven the development of tools to analyze broad promiscuity. The prevailing approach is to first identify a set of reaction rules (e.g.…”

Pickaxe: a Python library for the prediction of novel metabolic reactions

“…Promiscuous enzymes have also been used in engineered pathways to produce new-to-nature products [ 7 , 8 ]. However, promiscuity may have unintended consequences of byproduct formulation leading to low yield of a desired product or the production of chemicals that may be toxic to the cell [ 9 , 10 ]. Although there have been successes, in order to fully utilize the potential of promiscuity for determining the metabolism of an organism as well as designing novel pathways, it is paramount to be able to understand and enumerate the possible reactions stemming from promiscuous enzymes [ 11 , 12 ].…”