Engineering a novel self-powering electrochemical biosensor

This paper records the efforts of a multi-disciplinary team of undergraduate students from Glasgow University to collectively design and carry out a 10 week project in Synthetic Biology as part of the international Genetic Engineered Machine competition (iGEM). The aim of the project was to design and build a self-powering electrochemical biosensor called ‘ElectrEcoBlu’. The novelty of this engineered machine lies in coupling a biosensor with a microbial fuel cell to transduce a pollution input into an easily measurable electrical output signal. The device consists of two components; the sensor element which is modular, allowing for customisation to detect a range of input signals as required, and the universal reporter element which is responsible for generating an electrical signal as an output. The genetic components produce pyocyanin, a competitive electron mediator for microbial fuel cells, thus enabling the generation of an electrical current in the presence of target chemical pollutants. The pollutants tested in our implementation were toluene and salicylate. ElectrEcoBlu is expected to drive forward the development of a new generation of biosensors. Our approach exploited a range of state-of-the-art modelling techniques in a unified framework of qualitative, stochastic and continuous approaches to support the design and guide the construction of this novel biological machine. This work shows that integrating engineering techniques with scientific methodologies can provide new insights into genetic regulation and can be considered as a reference framework for the development of biochemical systems in synthetic biology

Schematic overview of the models used for simulation.

<p>(<i>A</i>) Detailed scheme of the modeled metabolic pathways. The numbered arrows correspond to reactions from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi-1003371-t001" target="_blank">Table 1</a>. Extensions to the original model of glycolysis are indicated by colored shapes. Boundary metabolites are in bold, glycosomal Rib-5-P is a boundary metabolite in model C and D. (<i>B</i>) Schematic overview of the different models, each consisting of a unique combination of the colored modules described in (<i>A</i>) and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi-1003371-t001" target="_blank">Table 1</a>. Model C and D can alternatively utilize fructose (model C^fru and D^fru), but this branch is switched off unless specifically mentioned.</p

Simulations of oxidative stress in model C.

<p>(<i>A</i>) The steady state flux through the cytosolic pentose phosphate pathway in model C as a function of the oxidative stress by varying the kinetic constant <i>k_TOX</i>. (<i>B</i>) Fluxes through the cytosolic PPP enzymes as a function of time upon sudden oxidative stress. During the whole time-course, <i>k_TOX</i> = 2 µl·min⁻¹ · mg protein⁻¹. The system is removed from steady state at <i>t</i> = 0, by setting 99% of the NAD(P)H and trypanothione pools to the oxidized form. Shown is the relaxation of the cytosolic PPP fluxes. Solid lines indicate medians, shaded areas show interquartile ranges. Near identical results were obtained for model D (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi.1003371.s008" target="_blank">Figure S5</a>).</p

Simulations of 6PGDH inhibition and 6-PG accumulation.

<p>(<i>A–B</i>) The effects of inhibition of 6PGDH on 6-PG concentrations and metabolic fluxes were simulated by reducing <i>V</i>_max,6PGDH in model C and D at high oxidative stress (<i>k_TOX</i> = 200 µl·min⁻¹·mg protein⁻¹). Simulations at low oxidative stress (<i>k_TOX</i> = 2 µl·min⁻¹·mg protein⁻¹) are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi.1003371.s009" target="_blank">Figure S6</a>. ATP production flux as steady-state flux through PFK is indicated in red, while trypanothione reductase steady-state flux is indicated in yellow, both plotted on the left y-axis. Steady-state concentration of cytosolic (blue) and glycosomal (green) 6-phosphogluconate are plotted on the right y-axis. Shaded areas indicate interquartile ranges. (<i>C</i>) Steady-state flux through glycolysis as a function of the glycosomal 6-PG concentration in model A. A glycosomal 6-PG concentration of around 500 mM reduces the glycolytic flux by 50%.</p

Model stoichiometry by modules and their subsequent reactions.

<p>Bold characters indicate external metabolites which are kept fixed. Other metabolites are internal variables of the system.</p

Conserved moieties in the four models of Figure 1.

<p>Where moieties are present in multiple models, only the number is indicated. Moieties 1–4 are conserved in all four model versions. Moieties 6–8 are a result of the extension with the pentose phosphate pathway. Moiety 9 is a modified version of moiety 5, now including the phosphorylated metabolites of the pentose-phosphate pathway. The latter appears when the glycosomal PPP is completed with ribokinase. When the glycosomal PPP is linked to ATP transport instead, moiety 5 disappears altogether.</p

ATP:ADP antiporter mimics turbo-state.

<p>(<i>A</i>) Overview of the models used in this figure. Model A and D are from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi-1003371-g001" target="_blank">Figure 1</a>, model A–glyc is model A without glycosomal localization, as described in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi.1003371-Haanstra2" target="_blank">[31]</a>, model A+AAT is model A with an ATP:ADP antiporter. (<i>B–C</i>) Steady-state concentrations of glycosomal Glc-6-P and Fru-1,6-BP are depicted in the various models. (<i>D</i>) Increasing the activity of the ATP:ADP antiporter (V_{max,ATP:ADP antiporter}) in model D leads to a high risk of accumulation of hexose phosphates. The green line indicates the concentration of Fru-1,6-BP in the original model of glycolysis (17.2 mM, panel C, model A). Glc_e in this simulation is 25 mM. (<i>E</i>) Time course simulation of model D at 25 mM Glc_e and various values for the V_{max,ATP:ADP antiporter} parameter. Plotted is the concentration of glycosomal phosphates (ΣP similar as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi-1003371-g002" target="_blank">Figure 2</a>, moiety 5 in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi-1003371-t002" target="_blank">Table 2</a>). ATP:ADP antiporter activity values below 1 nmol·min⁻¹·mg protein⁻¹ result in depletion of glycosomal phosphates (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi-1003371-g002" target="_blank">Figure 2</a>). <i>k_TOX</i> = 2 µl·min⁻¹·mg protein⁻¹ in all models. Solid lines indicate medians, shaded areas and error bars show interquartile ranges, as derived from the uncertainty modeling.</p

Ablation of 6PGDH.

<p>(<i>A</i>) Effect of 6PGDH ablation on the growth rate. A non-induced 6PGDH^RNAi culture, grown in glucose-containing HMI-9 was split at 0 h, +tet is induced with tetracycline, while −tet is the non-induced control. <i>(B)</i> Specific activities of 6PGDH in induced and control 6PGDH^RNAi parasites. <i>(C)</i> Western blot showing predominant co-localization of 6PGDH with the glycosomal marker aldolase (fraction S), while a faint band can also be observed in the cytosolic fraction P with the marker enolase. <i>(D)</i> Cell densities during growth on different substrates. At <i>t</i> = 0 h, a 6PGDH^RNAi culture grown on glucose was split at 1·10⁵ ml⁻¹ to HMI-9 with either glucose or fructose, and in the absence and presence of tetracycline. Plotted cell densities are cumulative, as −tet cultures were split at 48 h to 1·10⁵ ml⁻¹. A higher starting cell density was used to allow the parasites to adapt to the change in carbon source. Growth on fructose is slower than on glucose, but is unable to rescue the induced cultures.</p

Phosphate leak in model with PPP.

<p>Time course simulation of model B, in which the reactions of the glycosomal PPP are switched on at <i>t</i> = 0 by increasing their V_max value from zero to the value given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi.1003371.s013" target="_blank">Table S1</a>. Glc_e is 5 mM and <i>k_TOX</i> = 2 µl·min⁻¹·mg protein⁻¹. Solid lines indicate medians, shaded areas show interquartile ranges. Fluxes (J) are plotted on the left y-axis and are indicative of glucose uptake (GlcT_{plasma membrane}), glycerol (GK) and pyruvate production (PyrT) and the two branches of pentose phosphate pathways (G6PDH_c/g). The sum of bound phosphates in the glycosome (ΣP_g), as exists in the model of glycolysis (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003371#pcbi-1003371-t002" target="_blank">Table 2</a>, moiety 5), is plotted on the right y-axis. Within 25 minutes, all bound phosphates within the glycosome are depleted and all metabolic fluxes subsequently drop to zero.</p