Human serum transferrin (hTF) is a bilobal glycoprotein that plays a central role in iron metabolism. Each lobe of hTF (N- and C-lobe) can reversibly bind a single ferric iron. Iron binds to hTF at neutral pH in the plasma; diferric hTF binds to specific hTF receptors (TFR) on the cell surface and the complex undergoes receptor mediated endocytosis. The pH within the endosome is lowered to ~5.6 and iron is released from hTF. Apo hTF remains bound to the TFR and recycles back to the cell surface. Upon fusion with the plasma membrane, apo hTF dissociates from the TFR and is free to bind more iron and continue the cycle. The iron release process is complicated by various factors which include pH, anions, a chelator, lobe-lobe cooperativity and interaction with the TFR. All of these influence iron release in a complex manner. Because they are intricately linked, it is difficult to determine the effect of any single parameter. We have utilized stopped-flow and steady-state fluorescence and urea gel electrophoresis to dissect the iron release process as a function of lobe-lobe interactions, the presence of the TFR, and changes in pH and salt concentration. Application of recombinant protein production and site-directed mutagenesis has allowed us to generate a variety of hTF constructs in which the iron status of each lobe is completely controlled. Thus, we have created authentic monoferric hTFs unable to bind iron in one lobe, diferric hTFs with iron locked in one lobe and diferric hTF in which iron can be removed from both lobes. Importantly, we have produced the soluble portion of the TFR (sTFR) to analyze interactions between hTF and the sTFR and to monitor iron release from hTF/sTFR complexes. Together, we are able to provide a more precise picture of iron release from the two lobes of hTF in the presence and absence of the TFR. Steady-state fluorescence emission scans and urea gel electrophoresis provide a qualitative evaluation of the iron status of each construct after a predetermined incubation in iron removal buffer (i.e. an endpoint). However, these techniques do not provide information regarding the kinetic pathway to reach that endpoint. Combined with stopped-flow fluorescence time-based kinetics, a more precise assessment of the iron release process has been obtained. We have determined that changes in pH and salt affect endpoint iron release from the C-lobe, but not the N-lobe, however, the kinetics of iron release from both lobes are highly sensitive to pH and salt. Kinetic analysis in the absence and presence of the sTFR reveals the complexity of the iron release process. In the absence of the sTFR, the kinetics of iron release are insensitive to the iron status of the opposite lobe. However, in the presence of the sTFR, the kinetics of iron release from both lobes are affected by the iron status of the opposite lobe. Determination of conformational changes induced by anion binding, lobe-lobe communication and sTFR interactions have now been confidently assigned. We have created kinetic models of iron release from diferric hTF ± the sTFR and incorporated specific events pertaining to anion binding, lobe-lobe communication and conformational changes associated with sTFR interactions. We provide irrefutable evidence that a critical role of the sTFR is to accelerate the rate of iron release from the C-lobe, while decreasing the rate of iron release from the N-lobe such that the two lobes effectively release iron on a time scale relevant to one cycle of endocytosis.

A study was conducted to investigate and quantify the effects of corrosion inhibitors on metal release within a pilot distribution system while varying the source water. The pilot distribution system consisted of pre-existing facilities from Taylor et al (2005). Iron, copper, and lead release data were collected during four separate phases of operation. Each phase was characterized by the particular blend ratios used during the study. A blended source water represented a water that had been derived from a consistent proportion of three different source waters. These source waters included (1) surface water treated through enhanced coagulation/sedimentation/filtration, (2) conventionally treated groundwater, and (3) finished surface water treated using reverse osmosis membranes. The corrosion inhibitors used during the study were blended orthophosphate (BOP), orthophosphate (OP), zinc orthophosphate (ZOP), and sodium silicate (Si). This document was intended to cite the findings from the study associated with corrosion treatment using various doses of sodium silicate. The doses were maintained to 3, 6, and 12 mg/L as SiO2 above the blend-dependent background silica concentration. Sources of iron release within the pilot distribution system consisted of, in the following order of entry, (1) lined cast iron, (2) un-lined cast iron, and (3) galvanized steel. Iron release data from these materials was not collected for each individual iron source. Instead, iron release data represented the measurement of iron upon exposure to the pilot distribution system in general. There was little evidence to suggest that iron release was affected by sodium silicate. Statistical modeling of iron release suggested that iron release could be described by the water quality parameters of alkalinity, chlorides, and pH. The R2 statistic implied that the model could account for only 36% of the total variation within the iron release data set (i.e. R2 = 0.36). The model implies that increases in alkalinity and pH would be expected to decrease iron release on average, while an increase in chlorides would increase iron release. The surface composition of cast iron and galvanized steel coupons were analyzed using X-ray photoelectron spectroscopy (XPS). The surface analysis located binding energies consistent with Fe2O3, Fe3O4, and FeOOH for both cast iron and galvanized steel. Elemental scans detected the presence of silicon as amorphous silica; however, there was no significant difference between scans of coupons treated with sodium silicate and coupons simply exposed to the blended source water. The predominant form of zinc found on the galvanized steel coupons was ZnO. Thermodynamic modeling of the galvanized steel system suggested that zinc release was more appropriately described by Zn5(CO3)2(OH)6. The analysis of the copper release data set suggested that treatment with sodium silicate decreased copper release during the study. On average the low, medium, and high doses decreased copper release, when compared to the original blend source water prior to sodium silicate addition, by approximately 20%, 30%, and 50%, respectively. Statistical modeling found that alkalinity, chlorides, pH, and sodium silicate dose were significant variables (R2 = 0.68). The coefficients of the model implied that increases in pH and sodium silicate dose decreased copper release, while increases in alkalinity and chlorides increased copper release. XPS for copper coupons suggested that the scale composition consisted of Cu2O, CuO, and Cu(OH)2 for both the coupons treated with sodium silicate and those exposed to the blended source water. Analysis of the silicon elemental scan detected amorphous silica on 3/5 copper coupons exposed to sodium silicate. Silicon was not detected on any of the 8 control coupons. This suggested that sodium silicate inhibitor varied the surface composition of the copper scale. The XPS results seemed to be validated by the visual differences of the copper coupons exposed to sodium silicate. Copper coupons treated with sodium silicate developed a blue-green scale, while control coupons were reddish-brown. Thermodynamic modeling was unsuccessful in identifying a controlling solid that consisted of a silicate-based cupric solid. Lead release was generally decreased when treated with sodium silicate. Many of the observations were recorded below the detection limit (1 ppb as Pb) of the instrument used to measure the lead concentration of the samples during the study. The frequency of observations below the detection limit tended to increase as the dose of sodium silicate increased. An accurate quantification of the effect of sodium silicate was complicated by the observations recorded below detection limit. If the lead concentration of a sample was below detection limit, then the observation was recorded as 1 ppb. Statistical modeling suggested that temperature, alkalinity, chlorides, pH, and sodium silicate dose were important variables associated with lead release (R2 = 0.60). The exponents of the non-linear model implied that an increase in temperature, alkalinity, and chlorides increased lead release, while an increase in pH and sodium silicate dose were associated with a decrease in lead release. XPS surface characterization of lead coupons indicated the presence of PbO, PbO2, PbCO3, and Pb3(OH)2(CO3)2. XPS also found evidence of silicate scale formation. Thermodynamic modeling did not support the possibility of a silicate-based lead controlling solid. A solubility model assuming Pb3(OH)2(CO3)2 as the controlling solid was used to evaluate lead release data from samples in which lead coupons were incubated for long stagnation times. This thermodynamic model seemed to similarly describe the lead release of samples treated with sodium silicate and samples exposed to the blended source water. The pH of each sample was similar, thus sodium silicate, rather than the corresponding increase in pH, would appear to be responsible if a difference had been observed. During the overall study, the effects of BOP, OP, ZOP, and Si corrosion inhibitors were described by empirical models. Statistically, the model represented the expected value, or mean average, function. If these models are to be used to predict a dose for copper release, then the relationship between the expected value function and the 90th percentile must be approximated. The USEPA Lead and Copper Rule (LCR) regulates total copper release at an action level of 1.3 mg/L. This action level represents a 90th percentile rather than a mean average. Evaluation of the complete copper release data set suggested that the standard deviation was proportional to the mean average of a particular treatment. This relationship was estimated using a linear model. It was found that most of the copper data sub-sets (represented by a given phase, inhibitor, and dose) could be described by a normal distribution. The information obtained from the standard deviation analysis and the normality assumption validated the use of a z-score to relate the empirical models to the estimated 90th percentile observations. Since an analysis of the normality and variance (essentially contains the same information as the standard deviation) are required to assess the assumptions associated with an ANOVA, an ANOVA was performed to directly compare the effects of the inhibitors and corresponding doses. The findings suggested that phosphate-based inhibitors were consistently more effective than sodium silicate when comparing the same treatment levels (i.e. doses). Among the phosphate-based inhibitors, the effectiveness of each respective treatment level was inconsistent (i.e. there was no clear indication that any one phosphate-based inhibitor was more effective than the other). As the doses increased for each inhibitor, the results generally suggested that there was a corresponding tendency for copper release to decrease.