Metallochaperones regulate intracellular copper levels

Abstract

Copper (Cu) is an important enzyme co-factor that is also extremely toxic at high intracellular concentrations, making active efflux mechanisms essential for preventing Cu accumulation. Here, we have investigated the mechanistic role of metallochaperones in regulating Cu efflux. We have constructed a computational model of Cu trafficking and efflux based on systems analysis of the Cu stress response of Halobacterium salinarum. We have validated several model predictions via assays of transcriptional dynamics and intracellular Cu levels, discovering a completely novel function for metallochaperones. We demonstrate that in addition to trafficking Cu ions, metallochaperones also function as buffers to modulate the transcriptional responsiveness and efficacy of Cu efflux. This buffering function of metallochaperones ultimately sets the upper limit for intracellular Cu levels and provides a mechanistic explanation for previously observed Cu metallochaperone mutation phenotypes.

Figure 1. Transcriptional response of components of the Cu efflux network and phenotypic consequences of their deletion.

Temporal changes in mRNA levels in response to a step increase in Cu to growth sub-inhibitory level (0.85 mM) reveals pulsed responses in transcript expression of the efflux pump yvgX and the two metallochaperones VNG0702H and VNG2581H. Expression of the metalloregulator VNG1179C does not change, suggesting that it is post-transcriptionally activated. Error bars are s.e.m for n = 5. Points (X) within each expression profile were normalized to the expression value at t = 0 (X₀). This ratio is plotted using a Log₁₀ transformation to accommodate large differences in dynamic range. Negative values indicate decreases in expression relative to expression at t = 0.

Figure 2. A model for the role of metallochaperones in modulating transcriptional dynamics and intracellular Cu levels.

(A) Schematic of the Cu efflux network with a single metallochaperone (Model 0). At the core is the interaction of the metallochaperones (yellow) with all known elements of the Cu efflux network – Cu ions (dark blue), efflux pump (gray), metalloregulator (light blue), and intracellular ion quota (scavenging/metabolizing enzymes) (green). (B) Model 0 recapitulates pulsed transcriptional induction of the efflux pump and metallochaperone. Simulated mRNA expression profiles for yvgX, metallochaperones, and gfp. (C) Simulated protein expression profiles for YvgX, metallochaperones, and GFP. Metallochaperone levels have been reduced by a factor of 10 for plotting. (D) Model 0 predicts that changes in metallochaperone abundance have significant consequences on steady state levels of YvgX and intracellular Cu. Simulated scan of steady state level for YvgX expression and intracellular Cu relative to metallochaperone abundance. Shaded region is the optimum range for metallochaperone abundance and corresponds to the levels observed in simulations of Cu efflux in a wt background. (E) Simulated scan of steady state level for YvgX expression in cells with normal (black), deficient (red), and high (green) metallochaperone expression levels. Blue dashed line indicates 5×10⁵ molecules of external Cu concentration in both simulations and experiments.

Figure 3. Transcriptional dynamics of YvgX and associated consequences on intracellular Cu levels.

(A) Experimental validation of perturbed yvgX activation sensitivity in cells with normal (wt, blue), deficient (Δ0702Δ2581, red), and increased (Ω0702Ω2581, green) metallochaperone levels, measured at 300 min post challenge with increasing concentrations of CuSO₄. Solid lines are averages of two independent biological replicates. Shaded areas show the spread between replicates. (B) Experimental validation of perturbed intracellular Cu concentrations in cells with normal, deficient, and increased metallochaperone levels after 300 min post stimulus with 0.85 mM CuSO₄ were measured using inductively coupled plasma mass spectrometry (ICP-MS).

Figure 4. Model for Cu trafficking, transcriptional regulation and efflux with nuanced and distinct functions for the two metallochaperone paralogs.

(A) Network for a model which assumes that the two metallochaperones have equivalent functions with nuances in their primary and secondary targets (Model 1.1). The delivery of Cu to primary targets is assumed to be “strong” or preferred but Cu hand-off to secondary targets also occurs albeit through “weaker” interactions. (B) Network for a model which assumes VNG0702H and VNG2581H have distinct roles in Cu trafficking (Model 1.2). Whereas Cu efflux is mediated only by VNG0702H, other trafficking roles are performed by VNG2581H. Notably, VNG0702H must receive Cu via a transfer from VNG2581H, which directly binds imported Cu. (C) Model 1.1 predictions do not recapitulate the experimental data – e.g. incorrect predictions of intracellular Cu levels in three of the four metallochaperones perturbed genetic backgrounds, i.e. Δ2581, Ω0702 and Ω0702Ω2581H. (D) Accurate predictions of Cu accumulation in all strains (with the exception of Ω0702Ω2581) by Model 1.2 support distinct roles for the two metallochaperones. Specifically, the model predicts relative intracellular Cu levels across all strains to be in the following order: Δ0702>Δ2581 = Δ0702Δ2581 = Ω2581 = Ω0702Ω2581>wt>Ω0702. (E) ICP-MS measured intracellular Cu concentrations after 300 min for single deletion and overexpression of metallochaperones. Double deletion and overexpression data are shown again for comparison. Here intracellular Cu levels nearly match Model 1.2 predictions: Δ0702Δ2581>Δ2581>Δ0702 = Ω2581>Ω0702Ω2581 = wt>Ω0702. (F) Predictions of intracellular Cu concentrations after 300 min by Model 1.3 (whose topology was determined by steady-state analysis to be a hybrid of Models 1.1 and 1.2) show a near 1∶1 match for experimentally measured results.

Figure 5. Three iterations of data-driven modeling, model prediction, and experimental testing has revealed novel insights into the role of metallochaperones in regulation of transcriptional dynamics of Cu-responsive efflux as well setting the homeostatic limits for intracellular Cu levels.

In Iteration 1, a systems scale environmental and genetic regulatory influence network (EGRIN) model for transcriptional control of physiology aided in the discovery of components of the Cu efflux circuit in H. salinarum. The experimental validations of EGRIN predictions was instrumental in the formulation of Model 0, which predicted consequences of deleting or overexpressing metallochaperones on transcriptional dynamics of the efflux pump as well as the intracellular Cu concentration. In Iteration 2, the specific predictions of Model 0 were experimentally tested by assaying transcriptional dynamics of the efflux pump over a range of Cu concentrations, and also by simultaneously measuring intracellular Cu in chaperone deletion and overexpression mutants. While the most of Model 0 predictions were validated by these experiments, disparity in the Model 0 prediction and experimental data vis-à-vis intracellular Cu level in the overexpression mutant initiated the next iteration. In Iteration 3, Model 1.3 was constructed based on predictions from models 1.1 (identically functional chaperones) and 1.2 (distinct chaperone functions); and topological inference from Model 2 (steady state generalized hill function). Measurements of intracellular Cu levels with ICP-MS directly match predictions from Model 1.3.