1 Introduction
The use of metabolic engineering to develop efficient microbial cell
factories has been proved to be an attractive and powerful way to
produce valuable chemicals and materials that are important for our
society (Chae et al., 2017; Liu & Nielsen, 2019). However, the inherent
complexity of cellular metabolism and the corresponding difficulties in
balancing the trade-off between product formation and cell growth and
viability have greatly slowed down
the
design-build-test (DBT) cycles (Nielsen & Keasling, 2016). This
challenge
motivates
the need for new methods to accelerate the design and optimization of
biosynthetic systems (Bowie et al., 2020; Bundy et al., 2018).
In recent years, cell-free systems have developed rapidly and gradually
showed their strengths for speeding up DBT cycles (Dudley et al., 2015;
Moore et al., 2018; Morgado et al., 2016; Sun et al., 2014).
Cell-free
systems are not constrained by the
requirement of maintaining cellular viability and growth, thereby
allowing the full allocation of carbon and energy resources to the
product formation. Moreover, the openness of the cell-free systems
allows direct access to the reaction conditions and cellular contents,
providing great flexibility and freedom in the design and adjustment of
biosynthetic reactions (Rasor et al., 2021; Vilkhovoy et al., 2020).
Given the above superiorities,
purified
enzyme systems, which are the most common examples of cell-free
biochemical synthesis, have been widely used to study enzymatic pathways
and inform cellular expression (Bogorad et al., 2013; Dudley et al.,
2015; Zhu et al., 2014). On the other hand, crude cell lysates have
increasingly
gained
popularity for prototyping metabolism because they provide the
endogenous metabolism for cofactor recycling and energy regeneration
(Dudley et al., 2016, 2019; Jewett et al., 2008), which is limited in
purified enzyme systems. Additionally, crude lysates also have the
capability to build biosynthetic pathways by expressing functional
catalytic enzymes directly in vitro by cell-free protein
synthesis (CFPS) (Dudley et al., 2020; Grubbe et al., 2020; Karim et
al., 2020; Rasor et al., 2022). The hybrid approach of CFPS driving
metabolic engineering (CFPS-ME) has been successfully used to prototype
the synthesis of polyhydroxyalkanoate (Kelwick et al., 2018), styrene
(Grubbe et al., 2020), indole alkaloids (Khatri et al., 2020),
valinomycin (Zhuang et al., 2020), and acetone (Rasor et al., 2022). In
addition, the Jewett group developed an elegant approach termed in
vitro Prototyping and Rapid Optimization of Biosynthetic Enzymes
(iPROBE)
in the context of CFPS-ME. In
iPROBE,
dozens of enzyme variants in hundreds of pathway
combinations
were rapidly
tested
to improve the productivity of butanol and limonene (Dudley et al.,
2020; Karim et al., 2020). To build and
assess
different pathway combinations, the amounts of enzyme homologs that were
produced by CFPS needed to be determined by the incorporation of14C-leucine in
iPROBE.
However, the procedure of radioactive incorporation is laborious, and
radioactive 14C-leucine is
unavailable
for many laboratories. These constraints significantly limit the
usefulness of iPROBE. Thus, there remains a great demand for an approach
that enables testing and screening many enzyme homologs in a fast and
technically simple manner.
β-Nicotinamide mononucleotide (NMN)
is a key intermediate in nicotinamide adenine dinucleotide
(NAD+) biosynthesis and exists in all living species.
NMN has been demonstrated to have effective pharmacological activities
in the treatment of various diseases, such as obesity, Alzheimer’s
disease, and high fat diet-induced type 2 diabetes (Poddar et al., 2019;
Yoshino et al., 2011). However, the current high price of NMN
hampers the widespread use and
practical implementation of this molecule. While there have been many
efforts to improve the production of NMN by engineering biosynthetic
pathways in vivo (Marinescu
et al., 2018; Shoji et al., 2021) or in vitro (Qian et al., 2022;
Zhou et al., 2022), these efforts have typically explored only a small
set of
enzyme
homologs in their
optimization
strategies. Hence, productive
enzyme
homologs and combinations for efficient synthesis of NMN are still
required.
The
self-complementing split GFP, engineered from superfolder green
fluorescent protein (sfGFP), was first developed by
Waldo
and his coworkers for protein tagging (Cabantous et al., 2005). In this
system, sfGFP was asymmetrically split between β-strands 10 and 11 into
a large (GFP1–10) and a small (GFP11) fragment. The two fragments were
not individually fluorescent, but they could spontaneously interact with
each other to form a functional GFP. By fusing GFP11 fragment on a
target protein and detecting its association with GFP1–10 fragment,
this system has been used in numerous biological studies including
protein solubility assays (Cabantous & Waldo, 2006), screening of
enzyme mutant libraries (Santos-Aberturas et al., 2015), and imaging
protein localization in living cells (Kamiyama et al., 2016; R. Tamura
et al., 2021). In addition, Karim and colleagues recently showed the
possibility for quantification of protein produced in vitro by
split-GFP (Karim & Jewett, 2018). However, no one
to
our knowledge has yet practically applied the
split
GFP system to
prototype
enzyme homologs.
In this work, a novel strategy, which combined CFPS with split GFP, was
developed for prototyping enzyme homologs (Figure
1).
The key idea was that the most
productive
enzyme homolog for each step in the candidate pathway was rapidly
identified
by
using a
normalized
screening procedure. In this procedure, enzyme homologs were produced
in
parallel by CFPS in a few hours, and the expression level and activity
of each homolog were determined simultaneously by using the split GFP
assay. As a proof of concept, the capacity of this strategy was
demonstrated by optimizing a three-step pathway for synthesizing NMN.
By
using this strategy, the time for testing 10 enzyme homologs of each
catalytic step was reduced from a few weeks to 72
hours.
Additionally,
NMN biosynthesis was further optimized by
improving
physiochemical conditions, tuning enzyme ratios and
cofactor
concentrations, and decreasing the feedback inhibition to reach a
12-fold improvement over our initial setup. As a result, it was expected
that this strategy would accelerate the timeline of DBT cycles and
enhance efforts to optimize the production of desired products in
cell-free systems.