All analytical grade chemicals were purchased from Sigma-Aldrich (Castle Hill, Australia) or as otherwise stated. All solutions were prepared using 18 MΩ·cm ultrapure Milli-Q water (MQ; Merck-Millipore, Bayswater, Australia). All glassware was acid washed in 5% HCl (v/v) for at least 1 week, then thoroughly rinsed in MQ before use. Stock solutions were stored in amber glass bottles at 4°C prior to use. To mimic the intracellular environment and focus on the effect of pH on iron catalyzed oxidation of DA, all experiments were performed in a light-free environment at a controlled temperature of 22 ± 0.6°C. This temperature, rather than a physiological temperature of 37°C was selected to allow direct comparison with previous work at pH 7.4. Investigation of the effect of temperature on the processes of interest in this work would be worthwhile though it should be recognized that thermodynamic and kinetic data for many of the key reactions underpinning these processes has been obtained by other investigators in the temperature range 20–25°C. Details of the preparation of stock and working solutions can be found in Supplementary Materials.

The pH-dependent distributions of DA, Fe[III] and Fe[II] species were determined using the program Visual Minteq (Gustafsson, 2005). Details of the distributions are shown in Supplementary Figure 4 with stability constants used in this study provided in Supplementary Table 1.

The kinetic model developed to describe the experimental data at pH 6.5, 7.0, and 7.4 was implemented using the software Kintek Explorer (Johnson et al., 2009). Specifically, the kinetic model is a set of reactions describing the key processes involved in the interplay between Fe and DA. To apply the model for the quantification of the time-dependent transformation of reactants, intermediates and products, the rate constant for each key process was either adopted from previous work or fitted in this study. The “goodness of fit” was judged by the ability of the reaction set used (and the associated set of coupled differential equations representing the rate expressions for each reaction) to describe the time-dependent transformation of a range of substances. If the model provided a poor description of the data, it indicated either a flaw in the reaction set or rate constant(s) used. In addition to the data collected in the current work, portions of the experimental data collected at pH 7.4 described by Sun et al. (2016) were used to complement this dataset and fully elucidate the effect of pH on DA oxidation. Given the critical role of dissociation of DA bound Fe at lower pH and the improved constraints provided by analysis of DAC, slight amendments were applied to the kinetic model previously developed for pH 7.4 (Sun et al., 2016).

Given the relatively complicated model developed in this study, several intermediates were measured in order to better constrain the rate constants used herein. Briefly, (i) generation of DAC and H₂O₂ (shown in Figures 1–3) are used to constrain the rate constants for transformation of DA both in the absence and presence of iron including those for the oxidation of DA and leukoaminochrome (DAL) and cyclization of DAQ; (ii) decay of Fe[II] and formation of Fe-DA complexes in the presence of O₂ (shown in Figures 4A–C) are used to constrain the rate constant for the DA-induced transformation of Fe; and (iii) formation of Fe[III]-DA complexes (shown in Figure 4D and Supplementary Figure 5) in the absence of O₂ is used to constrain the rate constants for mono-complex formation, DA-induced reductive dissolution of ferrihydrite (amorphous ferric oxyhydroxide; or AFO) as well as the dynamic equilibrium between the mono- and bis-complexes. Details of the various reactions hypothesized to play a role are presented in Tables 1–3. Sensitivity analysis was used to determine the relative importance of the proposed reactions. Specifically, the greater the variation of the relative residuals that occurred with the change in magnitude of the rate constant, the more influential the reaction is. The lowest point or the “shift point” shown in the sensitivity analysis represents the optimal rate constant for the overall model applied. To simplify the applied model, rate constants were maintained consistent with those deduced in our previous study (Sun et al., 2016) with the exception of those with significant sensitivity to change of pH.

The sensitivity of the model to changes in individual rate constant values, defined as the relative residual, r, was determined using the program Kintecus (Ianni, 2003) combined with a Visual Basic for Applications (VBA) program. Note that the relative residual is defined as: r = 1n∑i=1n|MPi-EDi|EDi where MP_i is the model prediction, ED_i is the experimental data at the same condition and time interval and n represents the total number of measured data points.

As shown in Supplementary Figures 6A,B, significant influence of DAQ cyclization and DAC decay on the transformation of DA was observed at the two pH values investigated here (the results of previous studies at pH 7.4 are provided by Sun et al., 2016). The most sensitive point of the relative residual r increases in line with pH. This is in agreement with the proposed rate constants (Tables 1–3) in the main text as deprotonation is generally the prerequisite for DAQ cyclization and DAC decay. In contrast, a relatively insensitive relative residual r is observed below the deduced upper rate constant (k₅ = 5.3 × 10⁶) for the redox exchange between DAQ and DAL (Land et al., 2003; Sun et al., 2016), especially at pH 7.0 (Supplementary Figure 6C). As such, the rate constant of the redox exchange reaction at both pH 6.5 and 7.0 was consistent with these previously reported findings.

As shown in Supplementary Figure 6D, the DA-induced dissolutionanalysis shown in of AFO is an important process as the relative residual r is very sensitive to the change in the magnitude of the rate constant. A slight increase in the shift point with increase in pH is observed with this result in accord with the proposed rate constants applied. However, compared with that of DA-induced dissolution of AFO, the reductive dissolution after the adsorption of DA onto the surface of AFO (Reaction 12, Table 2) is relatively insensitive at both pH values used (Supplementary Figure 6E), indicating that reductive dissolution is not as important as DA-induced dissolution of AFO at each pH value investigated herein. As shown in Supplementary Figures 6F,G, the formation of both Fe^[III]DA (Reaction 13, Table 2) and Fe^[II]DA (Reaction 24, Table 3) are key reactions in this study as can be seen from the relative sensitivities of the relative residuals for these reactions. The rate constant for the formation of the bis-complex from Fe^[III]DA with another DA molecule (Reaction 14, Table 2) is assumed to be similar to the rate constant for water-loss from Fe(OH)(H₂O)52+ of 4.50 × 10⁵ M⁻¹s⁻¹ (Blesa and Matijevi, 1989) in view of the fact that the replacement of a coordinated H₂O by the additional DA molecule is generally faster than the formation of the mono-complex (Ludwig et al., 1995). The results of sensitivity analysis shown in Supplementary Figure 6H indicate that this assigned value should be reasonable for the sensitivity of relative residual r. Similar to the formation of the Fe-DA complexes, dissociation of these complexes is also of great significance given the considerable sensitivity of the rate constants over several orders of magnitude (Supplementary Figures 6I–K). In contrast to increase in the optimal value on decrease in pH (Supplementary Figure 6I), the increase in the dissociation rate constant shown in Supplementary Figure 6J is much less significant. This supports the supposition that dissociation is generally important at low pH with the bis-complex much more stable in view of the iron sequestration. As shown in Supplementary Figure 6L, relative insensitivity of the relative residual r is evident for the deduced rate constant (3.7 × 10⁵ M⁻¹s⁻¹) for the reaction between superoxide and AFO (Reaction 19, Table 2). As such, in order to simplify the model, the rate constant of this reaction at high pH is considered to be the same as that proposed previously (Sun et al., 2016). However, a value for this rate constant of one order of magnitude lower is deduced from model fitting in this study at pH 6.5 with this lower value possibly a result of the increased proportion of HO2• at this lower pH and the subsequent reduced electrostatic attraction between superoxide and the AFO surface.

Theoretically, change in pH would typically result in variation of the Fe[II] oxidation rate in the presence of oxidants such as DA^•−. However, as shown in Supplementary Figure 6M, considerable insensitivity of the relative residual is observed around the value proposed at physiological pH. As such, a consistent rate constant was used. Reduction of Fe[III] by DA^•− is important at pH 6.5 given the significant decrease in relative residuals on variation of the rate constant (from 1 to 10¹⁰ M⁻¹s⁻¹; shown in Supplementary Figure 6N). Therefore, the reaction between Fe[III] and DA^•− is considered in the proposed reaction scheme. The reduction of DA bound Fe[III] by DA^•− is not as significant as the reduction of Fe[III] in view of its lower reduction potential. As such, reduction of organically complexed Fe[III] by DA^•− is not considered in the reaction scheme.

To assess how pH influences the generation of toxic DA metabolites, the autoxidation of DA over a range of pH was initially investigated with attention given to the accumulation of H₂O₂ and DAC. As shown in Figure 1, spontaneous oxidation of DA is highly pH-dependent with 20 μM DA producing H₂O₂ at rates of 0.08, 0.12, and 0.24 μM h⁻¹ at pH 6.5, 7.0, and 7.4, respectively. On the other hand, <0.1 μM of DAC was produced at pH 7.0 in the absence of Fe (Figure 2A).

The oxidation of DA by O₂: DA+O2→DA•-+O2•- is often considered to be a critical step in the production of H₂O₂ and DAC. Firstly, DA^•− can disproportionate to form DAQ which subsequently cyclizes to DAL. DAC and H₂O₂ can then be produced either through the redox exchange between DAQ and DAL (Equation 7) or as a result of the direct oxidation of DAL (Equation 8), O2•- disproportionation (Equation 9) and/or the O2•--mediated transformation of radicals such as DA^•− (Equation 10; Hawley et al., 1967; Graham, 1978; Sun et al., 2016). DAQ+DAL→DA+DAC DAL+O2→DAC+H2O2 O2•−+O2•−→H2O2+O2 O2•−+DA•−→DAQ+H2O2

Among the various reactions shown above, the direct oxidation of DA by O₂ to DA^•− and O2•- (Equation 6) is expected to be rate limiting in view of the spin restriction inherent between DA and O₂ and, accordingly, exhibits a small rate constant (Table 1). With this in mind, any factors that influence this rate-limiting step (such as the presence of Fe[II] and Fe[III]; see Section Iron Accelerates Dopamine Oxidation) will alter the rate of formation of products arising from the oxidation of DA.

In general, the apparent oxidation rate of DA is determined by the contribution of different DA species. The pH-dependent formation of H₂O₂ shown in Figure 1 is attributed, at least partially, to the increase in the proportion of deprotonated DA ions on increase in pH; i.e., kapp=αH2DAkH2DA+O2+αHDAkHDA-+O2+αDA2-kDA2-+O2 where k_app is the apparent oxidation rate constant, α_i is the fraction of total DA species present as species i and k_i is the intrinsic oxidation rate constant of species i. Previous work has suggested that the abstraction of a hydrogen atom from the mono-deprotonated form of DA is the rate limiting step in the oxidation of DA (Herlinger et al., 1995). As such, it is not unexpected that an increase in the proportion of deprotonated DA on increase in pH would give rise to the enhanced generation of both DA^•− and O2•- via Equation 6, eventually resulting in the subsequent increase in the concentration of H₂O₂. Compared with the significantly enhanced production of H₂O₂ in the presence of iron (shown in section Iron Accelerates Dopamine Oxidation), while continuously formed, the toxicity induced by the autoxidation of DA is unlikely to be significant given the presence of in vivo oxidant removal enzymes, such as superoxide dismutase 1 (SOD1) and the glutathione peroxidases (GPx), though there is evidence that both enzymes are dysfunctional in Parkinson's disease (Cardoso et al., 2017; Trist et al., 2017, 2018). Additionally, the presence of DA-derived quinones is expected to be negligible (as shown in Figure 2A).

In order to quantify the rate of generation of ROS and reactive quinones arising from the interaction between Fe and DA over pHs ranging from those of the physiological neuronal cytosol to those that have experienced disease-related and post-mortem acidosis, the accumulation of DAC and the concentrations of the key Fenton reagents–Fe[II] and H₂O₂-were subsequently investigated.

In contrast to the negligible production of DAC in the absence of Fe, a substantially higher concentration was generated in the presence of 5 μM iron at pH 7.0. In general, our results show that Fe[II] was more effective in catalyzing DAC production than was Fe[III] (Figure 2A). The initial rate of formation of DAC in the presence of both Fe[II] and Fe[III] increased significantly at pH 7.0 and 7.4 (Figures 2B,C). By contrast, the concentration of DAC plateaued in the latter stages of the 120 min assessment period at pH 7.4, more so in the presence of Fe[II].

Accordingly, labile Fe also accelerated the production of H₂O₂, again with Fe[II] being generally more efficient than Fe[III], particularly during the initial ~10 min following addition of reagents (Figure 3A). While H₂O₂ was continuously produced at pH 7.0 and 7.4 in the presence of both Fe species, at pH 6.5 only Fe[III] showed an increase in H₂O₂ production though the concentration present at t = 120 min was only ~10% of that produced at pH 7.4 (Figure 3B). For Fe[II], H₂O₂ concentration remained stable after a subtle increase from t = 0–10 min.

Accompanying the rapid generation of both DAC and H₂O₂ was a significant decrease in the concentration of Fe[II]. The apparent oxidation rate of Fe[II] significantly increased in the presence of DA. In the absence of DA, the concentration of Fe[II] halved over 2 h at pH 7.0 (Supplementary Figure 7A) while Fe[II] levels were near-totally depleted within 30 min in the presence of 10 μM DA at the same pH (Figure 4A).

According to the rate law: -d[Fe[II]]dt = k[Fe[II]][O2] the oxidation of Fe[II] depends on the concentration of dissolved O₂ present. As the O₂ concentration in brain tissue is typically in the range of 10–60 μM (Koppenol and Butler, 1985; Ndubuizu and Lamanna, 2007), which corresponds to < 5% O₂ saturation in aqueous solutions, the effect of O₂ (at 2.5 and 5% saturation) on the transformation of Fe[II] in the presence of DA was examined at pH 7.0. As expected, both the rate and extent of Fe[II] oxidation decreased with lower O₂ concentration, with only a 10% decrease in Fe[II] concentration at pH 7.0 in 5% O₂ at t = 2 h compared with a 40% decrease at 21% O₂ saturation (Supplementary Figure 7B). Oxidation of Fe[II] followed an exponential decay on addition of 10 μM DA at pH 7.0 with initial Fe[II] exhausted after ~70 min in 21% O₂ and 80–90% loss at 2 h for physiologically-relevant O₂ concentrations (Figure 4B). Accordingly, the initial formation of Fe^IIIDA₂ was strongly dependent on O₂ concentration with markedly lower rates observed at 2.5 and 5% O₂ (Figure 4C).

As predicted by thermodynamic and kinetic data, the majority of Fe[III] should be present in an insoluble phase, even in the presence of 20 μM DA. As such, the continuous generation of both DAC and H₂O₂ in the presence of Fe[III] shown in Figures 2C, 3B suggests that DA is capable of inducing the slow release of reactive soluble Fe species. Indeed, dissolution of precipitated Fe by DA may pose a potential risk in view of its contribution to the labile iron pool (Dixon and Stockwell, 2014) and subsequent DA oxidation. Thus, to understand the extent of DA-induced mobilization of Fe[III], the temporal change in concentration of Fe^[III]DA₂ was measured over the pH range of 6.5 to 7.4. To eliminate potential confounding effects of O₂-mediated transformation of both Fe and reducing radicals, Fe^[III]DA₂ formation was investigated under anoxic conditions. As shown in Figure 4D, Fe^[III]DA₂ formation increased gradually at pH 7.0 and 7.4 in the presence of high initial DA concentration (50 μM) though not at pH 6.5. At this pH 6.5, Fe^[III]DA₂ formation was ~60% of that produced at circumneutral pH; a trend conserved at a lower DA concentration (20 μM; Supplementary Figure 5).

Compared with pH 6.5, the increase in the overall concentration of Fe^IIIDA₂ at pH 7.0 and 7.4 can most likely be attributed to: (i) the reduced rate of LMCT resulting from the increased proportion of Fe^IIIDA₂ present, and (ii) more efficient formation of transient >Fe[III]-DA surface complexes that precede thermal detachment of DA-bound Fe (Equation 13) and/or DA-induced reductive dissolution (Equation 14): >Fe[III]n+DA→>Fe[III]n-1+Fe[III]DA >Fe[III]n+DA→>Fe[III]n-1+Fe[II]+DA•-

The kinetic model developed in this study was used to infer potential long-term effects of the combined mechanisms of DA oxidation in the presence of Fe. Particular emphasis was given to the effect of system conditions on Fe turnover rate (TOR; used as a measure of Fe-specific catalysis) and associated hydroxyl radical and DAC production, encompassing changing DA and H₂O₂ concentrations and steady-state dissolved O₂. In this study, given the overall goodness of the model fit to the experimental data (Figures 1–5, Supplementary Figures 5, 7), the TOR was determined from the oxidation rate of Fe[II] as predicted by the model under particular conditions (e.g., pH, total concentration of Fe, the concentration ratio of Fe to DA and O₂ content).

Iron accumulates in multiple regions of the aging human brain (Acosta-Cabronero et al., 2016), and is most marked in the Fe-rich deep gray matter within the basal ganglia (Li et al., 2014). The degree of accumulation in Parkinson's disease patients is markedly higher in the SNc and putamen (Wang et al., 2016), where dopaminergic neurons project, and is associated with disease severity (Wallis et al., 2008), suggesting elevated levels of Fe are intrinsically linked to the biochemical mechanism underlying neuron loss (Hare et al., 2015). The disease-specific hyperaccumulation of Fe throughout the nigrostriatal pathway (He et al., 2015) indicates a particular vulnerability of dopaminergic neurons, and a lack of apparent pathology in non-Parkinson's-affected regions that also accumulate Fe during normal aging (Acosta-Cabronero et al., 2016) support the hypothesis that DA oxidation and free radical generation, occurring either independently of, or concurrent to, classical Fenton chemistry is a major source of oxidative stress in Parkinson's disease.

This characteristic feature may explain how two essential neurochemicals, which are normally segregated by vesicular confinement of DA to prevent oxidation by cytoplasmic Fe, are able to interact in the Parkinson's disease brain, and how they relate to α-synuclein dysfunction. Both mutations to the SNCA gene encoding α-synuclein and post-translational oxidative damage to the protein (Lotharius and Brundin, 2002b), the latter being an effect of elevated levels of Fe and Fenton-type ROS production, can lead to permeabilized vesicles that effectively “leak” DA into the pro-oxidant environment of the cytoplasm (Lotharius and Brundin, 2002a). This initial ^•OH-driven oxidative damage may trigger a cascade of free radical production that accelerates the rate of α-synuclein modifications, impaired vesicular transport, and DA oxidation, in turn, aggravating cell loss by triggering DAC-induced neurotoxicity.

Central to the view that elevated Fe facilitates DA breakdown is the notion of a labile Fe pool. The propensity of unbound cytoplasmic Fe[II] to react with by-products (such as H₂O₂) of mitochondrial respiration necessitates tight metabolic regulation of neuronal Fe levels involving various regulatory proteins (Moos et al., 2007), a number of which have been recognized as being dysfunctional in Parkinson's disease (Hare et al., 2013). Whether a labile Fe pool exists in actuality is the subject of much debate and both rapid oxidation of Fe[II] in the O₂-rich environment and obvious difficulties in directly speciating Fe within a living human neuron represent two analytical challenges that preclude obtaining conclusive evidence that Fe dyshomeostasis actively promotes increased oxidative stress in Parkinson's disease. Regardless, as nigral Fe accumulation is now recognized as an indisputable pathological feature of Parkinson's disease (Ayton and Lei, 2014; Wang et al., 2016), understanding the mechanism of how Fe promotes oxidative stress in dopaminergic neurons is critical as Fe chelation therapies enter clinical trials (Devos et al., 2014; Martin-Bastida et al., 2017; Moreau et al., 2018; Sun et al., 2018c).

Iron-mediated DA oxidation differs from Fenton/Haber-Weiss chemistry in that, in addition to redox cycling of Fe[II]/Fe[III], both species are capable of forming intermediary complexes immediately preceding free radical production. For instance, both Fe[II] and Fe[III] are capable of d orbital “bridging” between DA and O₂, and inducing the subsequent accelerated transfer of electrons and generation of DA^•− and O2•- free radicals (Miller et al., 1990). The reactions and associated rate constants involved in Fe[II]/Fe[III] redox cycling and DA oxidation are listed in Tables 2, 3.

The particularly strong ability of Fe[II] to generate DAC and H₂O₂ evident in Figures 2, 3 may result from the relative insolubility and precipitation of Fe[III] as AFO over the pH range investigated. This is an important factor to consider, as ferritin sequesters Fe within a protein nanocage in a chemical state resembling AFO (Jian et al., 2016). However, the ready oxidation of Fe[II] in the presence of DA (Reaction 24 and Reaction 25 in Table 3) favors the conversion of Fe[II] to the Fe^[III]DA complex and subsequent O2•- generation (Sun et al., 2016). Fe^[III]DA is not particularly stable and may either react with another DA molecule to form Fe^IIIDA₂ (shown in Figure 4C and Supplementary Figure 7C), reversibly react with O2•- to reform Fe^[II]DA and O₂ or undergo ligand-to-metal charge transfer (LMCT) with release of Fe[II] and DA^•− as follows: Fe[III]DA→Fe[II]+DA•-

Compared to the relatively inactive iron oxyhydroxide precipitates, the more soluble DA-bound Fe[III] oxidation product formed from ferrous DA complexes should enhance the production of both DAC and H₂O₂ by accelerating generation of both DA^•− and O2•- radicals.

Distinguishing iron-mediated dopamine oxidation from Fenton-generated ROS is important, as the free radicals produced do not have the multiple redundant detoxification pathways that exist for H₂O₂. While quinones are a relatively minor component of dopamine metabolism, which ordinarily favors providing a precursor for adrenaline biosynthesis or excretion from the central nervous system as homovanillic acid, it is unclear precisely how these highly-reactive species are neutralized. Several endogenous enzymes have been shown to interact with dopamine o-quinones, including SOD1, glutathione transferase (by way of glutathione conjugation) and macrophage migration inhibitory factor (Solano et al., 1999; Haque et al., 2003), though quinone detoxification would be considered a secondary function. In the absence of metal catalysis, polymerization of quinones to neuromelanin is a remarkably slow process with a normal aged brain containing only around 5 mg g⁻¹ (Aime et al., 2000). Without an efficient mechanism of quinone removal, these products have greater neurotoxic potential. This is especially the case in human dopaminergic neurons, in light of recent data indicating that dopaminergic neurons derived from fibroblasts of sporadic and familial Parkinson's disease patients contain higher concentrations of DA and oxidation products than similarly-prepared murine cell cultures (Burbulla et al., 2017).

The rapid formation of both DAC and H₂O₂ may be attributed to the pH-dependent oxidation of DA-bound Fe[II] shown in Figure 4A. Variation in pH reflecting transition from a physiologically normal microchemical environment to Parkin-associated mitochondrial dysfunction (Pickrell and Youle, 2015) and post-mortem acidosis (Donaldson and Lamont, 2013) had a marked effect on DAC and H₂O₂ production concomitant to pH-dependent oxidation of Fe[II]. The catechol moiety of DA is capable of forming a strong complex with Fe[III] (Avdeef et al., 1978; Sever and Wilker, 2004): Fe[III]+DA→Fe[III]DA with the mono-complex (Fe^[III]DA) dominant at acidic pH and the bis Fe^[III]DA₂ complex favored at circumneutral pH (Supplemental Figure 4). At pH 6.5, the ratio of [Fe^[III]DA₂]/[Fe^[III]DA] is ~5 in conditions reflective of otherwise physiologically normal concentrations of Fe and DA. Once formed, radical generation would also occur, increasing potential protein and lipid peroxidation events with the rate of LMCT for Fe^[III]DA (0.23 s⁻¹) substantially higher than that for Fe^[III]DA₂ (7.26 × 10⁻⁵ s⁻¹; El-Avaan et al., 1997; Sun et al., 2016). From the rate law d [Fe[II]]dt = d [DA•-]dt = k[Fe[III]DA] continuous generation of DAC at pH 6.5, especially in the presence of Fe[II], was unsurprising. The slightly higher concentrations of DAC formed in the presence of Fe[III], particularly during the initial half of the total reaction time assessed (Figure 2C), may be attributed to the more efficient Fe mobilization induced by DA at higher pH.

In contrast to the slower yet continuous generation of H₂O₂ at pH 7.0 and 7.4 (Figure 3A), the non-linear formation of DAC at these pHs (Figure 2B) suggests that DAC levels are maintained at a steady-state under physiological conditions through rearrangement of DAC and the subsequent formation of indoles or even possibly neuromelanin (Supplementary Figure 8). It is reasonable to deduce that the rearrangement of DAC coupled with the ensuing polymerization on increase in pH plays an important role in the removal of DAC.

It should be noted that the presence of metals, such as Fe and calcium (as Ca²⁺) can also promote this process (Sun et al., 2018a). The model developed adequately describes the generation of reactive oxygen species and accumulation of DAC but comprehensive description of the specific role of Fe in DAC decomposition will require an expanded model. Regardless, the accumulation of protein-modifying DAC is likely to be enhanced at low pHs as a result of the presence of high concentrations of the active Fe[II] catalyst seen in both parkinsonian animal models (Hare et al., 2014; Billings et al., 2016) and the human Parkinson's disease brain (Dexter et al., 1991).

The cessation of H₂O₂ accumulation observed at pH 6.5 (Figure 3A) may be related to the high concentration of Fe[II] present in the solution. Under these conditions, the H₂O₂ initially generated as a result of DA-enhanced oxidation of Fe[II] is subsequently consumed via peroxidation of the remaining Fe[II]. Apart from the obvious pH-dependent oxidation process, attenuation of Fe[II] oxidation at pH 6.5 (Figure 4A), especially during the later stages of the reaction, suggests that Fe[II] may be regenerated under these conditions with the regeneration of Fe[II] related to formation of DA intermediates such as DA^•−. Given the continuous generation of DAC (Figure 2B), the high concentrations of Fe[II] observed may, in turn, result from DA^•−-induced reduction of ferric iron. Thermodynamically, the low reduction potential of the DAQ/DA^•− couple indicates that reduction of aqueous Fe[III] is feasible (Pham and Waite, 2014). Indeed, the rapid reduction of Fe[III] by 6-hydroxydopamine (6-OHDA) semiquinones has been proposed (Jameson and Linert, 2001) with 6-OHDA itself a neurotoxic breakdown product of DA that is facilitated by Fe (Hare and Double, 2016). However, as pH increases, it is expected that the majority of the Fe present would be efficiently converted into AFO and, with a low reduction potential at pH 7.0 (Watt et al., 1985), DA^•− is unable to mobilize Fe from AFO. It is of note that neuronal ferritin iron is stored in an AFO-like ferrihydrite core (Hagen et al., 2017) which is prone to mobilization under acidic conditions (La et al., 2017).

When viewed within the context of existing literature, the results described here present something of a conundrum for those wishing to quantify DAC and related DA oxidation products in human SNc tissue. While acidosis occurring in degenerating neurons may contribute to the progression of Parkinson's disease by increasing the rate of oxidant generation and accumulation of DAC, post-mortem decreases in tissue pH, stemming from sample handling, exposure to the environment after removal at autopsy, and even post-mortem interval may present inaccurate assessment of DA oxidation.

Considering mitochondrial dysfunction is common to most neurodegenerative diseases (Lin and Beal, 2006), it is not surprising that data supplied from tissue housed in the Sydney Brain Bank, where 72% of cases with a neurodegenerative disease, including pathologically-diagnosed Parkinson's disease, exhibited tissue pH as low as 5.86 (Genoud et al., 2017). Tissue pH that deviates from normal physiology, regardless of whether the cause is endogenous or artifact, will undoubtedly alter the speciation of DA and Fe, and rate of formation, reactivity and fate of toxic DA intermediates (El-Avaan et al., 1997; Pham et al., 2006; Sun et al., 2015, 2016, 2018a). Accordingly, to avoid any misinterpretation of DA metabolites, quantitative assessment of DA metabolism in post-mortem tissue should account for multiple confounding factors that influence tissue pH.

DH receives research and materials support from Agilent Technologies. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.