A Compartmentalized Out-of-Equilibrium Enzymatic Reaction Network for Sustained Autonomous Movement

Abstract

Every living cell is a compartmentalized out-of-equilibrium system exquisitely able to convert chemical energy into function. In order to maintain homeostasis, the flux of metabolites is tightly controlled by regulatory enzymatic networks. A crucial prerequisite for the development of lifelike materials is the construction of synthetic systems with compartmentalized reaction networks that maintain out-of-equilibrium function. Here, we aim for autonomous movement as an example of the conversion of feedstock molecules into function. The flux of the conversion is regulated by a rationally designed enzymatic reaction network with multiple feedforward loops. By compartmentalizing the network into bowl-shaped nanocapsules the output of the network is harvested as kinetic energy. The entire system shows sustained and tunable microscopic motion resulting from the conversion of multiple external substrates. The successful compartmentalization of an out-of-equilibrium reaction network is a major first step in harnessing the design principles of life for construction of adaptive and internally regulated lifelike systems.

Figure 1

Rational design and experimental assembly of a compartmentalized metabolic network. (A) Schematic representation of the nanoreactors containing four enzymatic cycles which are able to convert glucose and phosphoenolpyruvate (PEP) into movement of the construct. (B) Rational design of a metabolic pathway for double cycling of natural substrates leading to autonomous movement. The activation cycle, starting with glucose and phosphoenolpyruvate, feeds forward the pyruvate–l-lactate cycle with regeneration of β-NADH, and is controlled by the amount of ATP present in the system. The negative feedforward regulation by pyruvate enables a tunable continuous local production of oxygen by the motor cycle. Sufficient concentrations of glucose and PEP as well as positive and negative feedforward mechanisms are crucial for maintaining a prolonged out-of-equilibrium state.

Figure 2

Characterization of the metabolic pathway. (A) Reaction scheme of the proposed assay for the pyruvate–l-lactate cycle and for the comparison with LDH activity. (B) β-NADH consumption rates of the pyruvate–l-lactate cycle and for LDH at varied concentrations of pyruvate. Below 0.5 mM a positive effect on the consumption rate is observed due to pyruvate regeneration. Negative substrate inhibition is observed for values higher than 0.5 mM pyruvate. (C, D) Time courses in H₂O₂ production rates for various values of initial [ATP] (C) and [glucose] (D) from model predictions. The inset shows an expansion of the graph near the coordinate origin. (E) Progress curves of β-NADH production and consumption per mg of enzyme mixture, obtained experimentally for different initial concentrations of ATP (0.13–1.0 mM) at fixed glucose concentrations (2.5 mM). Three regimes (*, **, and ***) are defined and represent different phases of operation of the enzymatic network. Solid lines represent model predictions using optimized parameters. (F) Progress curves of β-NADH production and consumption obtained experimentally for different initial concentrations of glucose (0.5–5.0 mM), while ATP concentrations were kept fixed at 0.5 mM. Solid lines represent model predictions using optimized parameters.

Figure 3

Characterization of the nanoreactors. (A) Cryo-transmission electron microscopy (cryo-TEM) image of a nanoreactor (left). TEM image of nanoreactors loaded with the enzymatic network (middle). TEM coupled with energy dispersive X-ray spectroscopy showing the mapping of sulfur (S), specific to the cysteines and methionines in the enzymes and their localization inside the nanoreactors (right). Scale bars 100 nm (left) and 1 μm (middle and right). (B) Intensity profile of an SDS–PAGE lane, loaded with enzymes recovered from reopened nanoreactors (left). SDS–PAGE shows the bands from the enzymes of the network (right). (C) Time courses for the encapsulated enzymatic network, depicting β-NADH production and consumption for various initial concentrations of ATP.

Figure 4

Nanoreactors movement analysis. (A) Average speeds of the nanoreactors over time, with 10 mM glucose as starting concentration. For every time point, average speeds were calculated from the MSDs of 60 particles over 90 s. (B) Experimentally determined glucose concentrations over time. The depletion of glucose does not influence the nanoreactor speed (A). (C) Average initial speeds (first 90 s) of nanoreactors loaded with an enzymatic cascade and fueled with different glucose concentrations. (D) Average speeds (first 90 s) of nanoreactors containing enzymatic network at different glucose concentrations. (E) Nanoreactor movement in human serum with the full network compartmentalized. (F) Motion of nanoreactors loaded with catalase only; the remainder of the network is added to the serum.