Protein aggregation and protein instability govern familial amyotrophic lateral sclerosis patient survival

Abstract

The nature of the "toxic gain of function" that results from amyotrophic lateral sclerosis (ALS)-, Parkinson-, and Alzheimer-related mutations is a matter of debate. As a result no adequate model of any neurodegenerative disease etiology exists. We demonstrate that two synergistic properties, namely, increased protein aggregation propensity (increased likelihood that an unfolded protein will aggregate) and decreased protein stability (increased likelihood that a protein will unfold), are central to ALS etiology. Taken together these properties account for 69% of the variability in mutant Cu/Zn-superoxide-dismutase-linked familial ALS patient survival times. Aggregation is a concentration-dependent process, and spinal cord motor neurons have higher concentrations of Cu/Zn-superoxide dismutase than the surrounding cells. Protein aggregation therefore is expected to contribute to the selective vulnerability of motor neurons in familial ALS.

Figure 1. fALS Patients' Kaplan–Meier Survival Curves Illustrating Low and High Risk SOD1 Mutations

Kaplan–Meier survival curves from patients with A4V (red) and H46R (blue) SOD1 mutations and sALS (green) are as shown. Disease durations from 205 patients with A4V SOD1 mutation, 63 patients with H46R SOD1 mutation, and 269 patients with sALS were used to generate these Kaplan–Meier survival curves.

Figure 2. Rederivation and Validation of the Chiti–Dobson Equation

The original Chiti–Dobson equation was rederived by correlating all known empirically measured mutation-related changes in protein aggregation rates as of 2005 with the corresponding changes in the physicochemical properties of charge, hydrophobicity, and secondary structure. Importantly, aggregation rate data were not taken directly from the publication that presented the Chiti–Dobson equation; instead these values were calculated from their respective original publication if applicable (Table 4). The dependence of observed ln(ν_mut/ν_wt) on hydrophobicity, secondary structure, and charge changes were still observed after the addition of extra protein aggregation data. (A) The relationship between observed ln(ν_mut/ν_wt) and Δhydrophobicity. To insure that the effect of hydrophobicity change was considered independent of other physiochemical properties, only mutations that had a Δcharge of 0 and a |ΔΔG _coil-α + ΔΔG _β-coil| of less than 2.5 kJ/mol were considered. (B) The relationship between observed ln(ν_mut/ν_wt) and ΔΔG _coil-α + ΔΔG _β-coil. To insure that the effect of secondary structure change was considered independent of other physiochemical properties, only mutations which had a Δcharge of 0 and a |Δhydrophobicity| of less than 3 kcal/mol were considered. (C) The relationship between the observed ln(ν_mut/ν_wt) and Δcharge. To ensure that the effect of charge change was considered independent of other physiochemical properties, only mutations that had a |Δhydrophobicity| of less than 3 kcal/mol and a |ΔΔG _coil-α + ΔΔG _β-coil| of less than 2.5 kJ/mol were considered. Wild-type protein was used as a data point at (0,0) in all of the three graphs. The rederived slopes from this figure for the three factors, 0.95 for hydrophobicity, 0.18 for secondary structure, and −0.78 for charge, were applied to calculate aggregation propensities of fALS-causing SOD1 variants presented in Figure 4. Patient survival times were plotted against these aggregation propensities; the corresponding slope and R values differ less than 5% compared to the results in Figure 4 (unpublished data), validating the Chiti–Dobson equation.

Figure 3. Synergistic Increases in SOD1 Aggregation Propensity and Increases of Instability Are Associated with Decreases in Survival for fALS Patients (Data Weighted by the Number of Patients)

(A) An increase in aggregation propensity is associated with a decrease in fALS patient survival. Single point mutations of SOD1 found in fALS patients with corresponding reported stability values in (B) were considered. Aggregation propensities for each fALS mutation were calculated using the Chiti–Dobson equation, normalized such that 0 represents the least and 1 represents the most aggregation prone proteins, and the corresponding disease duration (survival) was plotted versus this value. (B) An increase in SOD1 instability is associated with severe disease. All instability values of apo SOD1 reported in the literature were normalized such that 0 represents the most and 1 represents the least stable proteins, and corresponding disease durations were plotted versus these values. (C) Increase in SOD1 aggregation propensity and gain of instability synergistically decrease patient survival. Normalized aggregation propensity in (A) and instability in (B) were summed and normalized to the range from 0 to 1, and patient survival was plotted versus this value. The data used in these three graphs (disease durations from 580 patients with 28 different fALS-causing SOD1 mutations with reported stability values) were weighted based on the number of patients for each mutation using SPSS version 15.0. Note that the correlation between the size of each data point and the number of patients for (A–C) is shown as an inset in (C).

Figure 4. Synergistic Increases in SOD1 Aggregation Propensity and Losses of Thermodynamic Stability Are Associated with Decreased Survival of fALS Patients (Using Unweighted Data)

(A) An increase in aggregation propensity is associated with decreased fALS patient survival. Single point mutations found in fALS patients with corresponding reported thermodynamic stabilities in (B) were considered. (B) A loss of SOD1 thermodynamic stability is associated with severe disease. (C) Increase in SOD1 aggregation propensity and gain of instability synergistically decrease patient survival. The data used in these three graphs (disease durations from 580 patients with 28 different fALS-causing SOD1 mutations with reported stability values) were treated equally regardless of the number of patients using the software SigmaPlot 9.0 (Systat Software, Inc.). SPSS 15.0 also was used on this analysis, and identical results were obtained.

Figure 5. Correlation between Measures of Stability, Specifically Normalized ΔT _m and ΔΔG, Reported from Different Groups for Apo SOD1 Variants

The variants with both measured ΔT _m and ΔΔG were plotted, and a good correlation between normalized ΔT _m and normalized ΔΔG was observed. This correlation provides the rationale for averaging ΔT _m and ΔΔG and taking the average value as an indicator for stability of fALS-related variants. The normalized ΔT _m and ΔΔG were obtained as described in the Materials and Methods section.