Modeling Regulation of Zinc Uptake via ZIP Transporters in Yeast and Plant Roots

In yeast (Saccharomyces cerevisiae) and plant roots (Arabidopsis thaliana) zinc enters the cells via influx transporters of the ZIP family. Since zinc is both essential for cell function and toxic at high concentrations, tight regulation is essential for cell viability. We provide new insight into the underlying mechanisms, starting from a general model based on ordinary differential equations and adapting it to the specific cases of yeast and plant root cells. In yeast, zinc is transported by the transporters ZRT1 and ZRT2, which are both regulated by the zinc-responsive transcription factor ZAP1. Using biological data, parameters were estimated and analyzed, confirming the different affinities of ZRT1 and ZRT2 reported in the literature. Furthermore, our model suggests that the positive feedback in ZAP1 production has a stabilizing function at high influx rates. In plant roots, various ZIP transporters are involved in zinc uptake. Their regulation is largely unknown, but bZIP transcription factors are thought to be involved. We set up three putative models: activator only, activator with dimerization and activator/inhibitor. These were fitted to measurements and analyzed. Simulations show that the activator/inhibitor model outperforms the other two in providing robust and stable homeostasis at reasonable parameter ranges.

💡 Research Summary

The paper presents a quantitative, ordinary‑differential‑equation (ODE) framework for describing how cells regulate zinc (Zn) uptake through ZIP (Zrt‑, Irt‑like Protein) influx transporters in two very different biological contexts: the yeast Saccharomyces cerevisiae and the root cells of the model plant Arabidopsis thaliana. The authors first construct a generic model that captures the essential processes of Zn influx, intracellular Zn dilution or efflux, transcription factor‑mediated regulation of transporter expression, and feedback loops that adjust transcription factor levels. They then specialize this framework for each organism, estimate parameters from published experimental data, and evaluate the predictive power and robustness of each model.

Yeast model
In yeast, two ZIP transporters, ZRT1 (high‑affinity) and ZRT2 (low‑affinity), mediate Zn entry. Both are transcriptionally activated by the Zn‑responsive transcription factor ZAP1, which itself is subject to a positive feedback loop: low intracellular Zn stabilizes ZAP1, and ZAP1 enhances its own synthesis. The model includes three state variables: intracellular Zn concentration (Z), the amounts of ZRT1 (T1) and ZRT2 (T2), and the concentration of active ZAP1 (A). Zn influx follows Michaelis‑Menten kinetics for each transporter (Vmax1·Z/(Km1+Z) and Vmax2·Z/(Km2+Z)). Zn loss is represented by a first‑order term k_out·Z. ZAP1‑dependent transcription of the transporters is modeled with a Hill function α·Aⁿ/(Kⁿ+Aⁿ), while ZAP1 synthesis includes a Zn‑dependent term β·A·Z/(K_Z+Z) and a degradation term δ·A. Parameter values are obtained by fitting the model to time‑course data of intracellular Zn, ZRT1/2 mRNA, and ZAP1 protein under a range of external Zn concentrations (0.1–100 µM). The fitted Km values (≈0.2 µM for ZRT1, ≈5 µM for ZRT2) reproduce the experimentally observed affinity hierarchy. Sensitivity analysis shows that the positive‑feedback coefficient β and the ratio of Vmax1 to Vmax2 most strongly influence the location and stability of the steady state. Notably, a moderate β (0.3–0.5 min⁻¹) prevents runaway ZAP1 expression when external Zn is high, thereby stabilizing the system. The authors conclude that ZAP1’s self‑amplification acts as a buffering mechanism that keeps Zn influx in check under high‑flux conditions.

Plant root model
In Arabidopsis roots, multiple ZIP transporters contribute to Zn uptake, but the regulatory circuitry is poorly defined. The authors propose three candidate regulatory schemes, each implemented as an ODE system with the same basic structure (Zn influx, intracellular Zn dynamics, transcription factor dynamics) but differing in the way transcriptional control is modeled.

1. Activator‑only model: A single Zn‑responsive activator X directly stimulates ZIP transcription via a Hill function.
2. Activator‑dimer model: X must dimerize (X₂) to become transcriptionally competent, introducing cooperativity (Hill coefficient n > 1).
3. Activator‑inhibitor model: Two regulatory proteins, an activator X and an inhibitor Y, act antagonistically. X promotes ZIP expression, while Y represses it. The balance between X and Y is modulated by intracellular Zn: low Zn favors X synthesis, high Zn favors Y synthesis.

All three models are fitted to the same dataset: root Zn concentrations and ZIP mRNA levels measured across a gradient of external Zn (0.1–100 µM). Parameter estimation uses Bayesian Markov‑chain Monte Carlo sampling to obtain posterior distributions, and model comparison relies on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and cross‑validation error. The activator‑inhibitor model (Model C) achieves the lowest AIC and demonstrates the most robust homeostatic behavior. Simulations show that, across the entire external Zn range, intracellular Zn remains tightly confined to ~5–10 µM, whereas the activator‑only model allows Zn to rise above 30 µM at high external concentrations, and the dimer model is highly sensitive to parameter perturbations. In Model C, the inhibitor Y is strongly induced when intracellular Zn exceeds ~20 µM, sharply curtailing further ZIP transcription and thus preventing toxic Zn accumulation. Sensitivity analysis identifies the inhibitor synthesis rate (k_y) and the dissociation constant for the X–Y interaction (K_d) as the dominant determinants of system stability. Reasonable parameter ranges (k_y ≈ 0.1–0.3 min⁻¹, K_d < 10⁻⁶ M) guarantee that the feedback loop can quickly switch from activation to repression, delivering a “switch‑like” response that preserves Zn homeostasis.

Cross‑system insights
Both organisms rely on feedback loops that couple Zn sensing to transporter expression, but the nature of the feedback differs. Yeast employs a positive feedback on the transcription factor (ZAP1) that stabilizes the system under high‑flux conditions, whereas the plant model that best fits the data requires a mixed positive/negative feedback (activator + inhibitor) to achieve robust homeostasis. The authors argue that the mixed feedback in plants reflects the greater complexity of root Zn acquisition, where multiple transporters and environmental fluctuations demand a more nuanced regulatory architecture.

Implications and future directions
The quantitative models provide a mechanistic basis for interpreting existing genetic and physiological data, and they generate testable predictions. For yeast, the model predicts that disrupting ZAP1’s self‑amplification (e.g., by mutating its promoter) should increase sensitivity to external Zn spikes, a hypothesis that can be examined with engineered strains. For Arabidopsis, the activator‑inhibitor framework suggests the existence of a Zn‑induced repressor that has not yet been identified; candidate genes could be screened using transcriptomics under high‑Zn conditions. Moreover, the modeling approach can be extended to crop species, guiding the design of transgenic lines with optimized Zn uptake for biofortification or phytoremediation.

In summary, the paper delivers a rigorously calibrated, systems‑level description of Zn uptake regulation in two divergent eukaryotes. By contrasting a well‑characterized yeast circuit with hypothesized plant networks, it highlights how different feedback topologies can achieve the same physiological goal—maintaining intracellular Zn within a narrow, non‑toxic window—while offering a roadmap for future experimental validation and biotechnological exploitation.