Spinal cord homogenates from SOD1 familial amyotrophic lateral sclerosis induce SOD1 aggregation in living cells

<div><p>Mutant Cu/Zn superoxide dismutase (SOD1) can confer its misfolding on wild-type SOD1 in living cells; the propagation of misfolding can also be transmitted between cells <i>in vitro</i>. Recent studies identified fluorescently-tagged SOD1^G85R as a promiscuous substrate that is highly prone to aggregate by a variety of templates, <i>in vitro</i> and <i>in vivo</i>. Here, we utilized several SOD1-GFP reporter proteins with G37R, G85R, or G93A mutations in SOD1. We observed that human spinal cord homogenates prepared from SOD1 familial ALS (FALS) can induce significantly more intracellular reporter protein aggregation than spinal cord homogenates from sporadic ALS, Alzheimer’s disease, multiple system atrophy or healthy control individuals. We also determined that the induction of reporter protein aggregation by SOD1-FALS tissue homogenates can be attenuated by incubating the cells with the SOD1 misfolding-specific antibody 3H1, or the small molecule 5-fluorouridine. Our study further implicates SOD1 as the seeding particle responsible for the spread of SOD1-FALS neurodegeneration from its initial onset site(s), and demonstrates two potential therapeutic strategies for SOD1-mediated disease. This work also comprises a medium-throughput cell-based platform of screening potential therapeutics to attenuate propagated aggregation of SOD1.</p></div

SOD1-misfolding specific antibodies and 5-fluorouridine reduce induced aggregation of SOD1-GFP by SOD1-FALS homogenates.

<p>Following a 4–6 h transfection of HEK293FT cells using SOD1^G85R-GFP reporter protein, 5-FUr or 3H1 were added to the cells at a final concentration of 5 μM or 20 μg/ml, respectively, shorty prior to incubation with SOD1-FALS tissue homogenates. Cells were then incubated for an additional 48 h period, and analyzed for the presence of induced aggregates (<b>A</b>). We find that 5-FUr and 3H1 are effective at reducing induced reporter protein aggregation by SOD1-D90A, G93S or I113T spinal cord homogenates. The summary bar graph (FALS-SOD1) includes all the repeats from the SOD1-D90A, G93S or I113T. Unpaired t-test was used to demonstrate statistically significant reduction in detectable reporter protein inclusions between untreated and treated cells (<b>B</b>). Arrowheads point towards visible reporter protein inclusions. Five biological repeats were performed for each homogenate. *, p < 0.05; **, p < 0.01. Scale bar: 40 μm.</p

Homogenates prepared from familial ALS spinal cord tissue induce SOD1 aggregation.

<p>A) SOD1 inclusions in 4 SOD1-FALS cases (A4V, D90A, G93S, I113T) were confirmed by immunohistochemistry. B) Homogenates prepared from human spinal cord tissue were incubated with HEK293FT cells pre-transfected with the indicated reporter protein (G37R, G85R or G93A-based). Cells were imaged 48 h post treatment and analyzed for the presence of inclusions using our algorithm. Representative immunocytochemistry micrographs demonstrate induced aggregation of SOD1^G85R-GFP in cells incubated with the indicated homogenate. Arrowheads point towards visible reporter protein inclusions. C) Summary of the effect of FALS, SALS and non-ALS control tissue homogenates on the reporter proteins. Bar graphs represent the percentage of reporter protein in inclusion form out of total reporter protein (area). Statistical significance was established using one way ANOVA followed by Dunnett’s test for multiple comparisons. D) Induced aggregation of the reporter protein using the individual homogenates grouped in (C). Each homogenate was tested 8–16 times with 2 technical repeat per run. *** p < 0.001, * p < 0.05. Scale bar: 40 μm.</p

The heterogeneity among included studies was reduced dramatically after excluding two studies from China.

<p>This meta-analysis was conducted using same protocol as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105534#pone-0105534-g003" target="_blank">Figure 3</a> except excluding two studies with patients with Chinese background. Please refer to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0105534#pone-0105534-g003" target="_blank">Figure 3</a>.</p

Initial Structural Models of the Aβ42 Dimer from Replica Exchange Molecular Dynamics Simulations

Experimental characterization of the molecular structure of small amyloid (A)­β oligomers that are currently considered as toxic agents in Alzheimer’s disease is a formidably difficult task due to their transient nature and tendency to aggregate. Such structural information is of importance because it can help in developing diagnostics and an effective therapy for the disease. In this study, molecular simulations and protein–protein docking are employed to explore a possible connection between the structure of Aβ monomers and the properties of the intermonomer interface in the Aβ42 dimer. A structurally diverse ensemble of conformations of the monomer was sampled in microsecond timescale implicit solvent replica exchange molecular dynamics simulations. Representative structures with different solvent exposure of hydrophobic residues and secondary structure content were selected to build structural models of the dimer. Analysis of these models reveals that formation of an intramonomer salt bridge (SB) between Asp23 and Lys28 residues can prevent the building of a hydrophobic interface between the central hydrophobic clusters (CHCs) of monomers upon dimerization. This structural feature of the Aβ42 dimer is related to the difference in packing of hydrophobic residues in monomers with the Asp23–Lys28 SB in on and off states, in particular, to a lower propensity to form hydrophobic contacts between the CHC domain and C-terminal residues in monomers with a formed SB. These findings could have important implications for understanding the difference between aggregation pathways of Aβ monomers leading to neurotoxic oligomers or inert fibrillar structures

Flow chart for ataxin-2 and ALS related article search, screen, evaluation, and data analysis.

<p>Flow chart for ataxin-2 and ALS related article search, screen, evaluation, and data analysis.</p

The synthesized OR of the presence of intermediate CAG repeats with meta-analysis is not different from FALS and SALS cases in the <i>ATXN2</i> gene.

<p>The relative risks (OR) among fALS cases (top panel) or sALS cases (lower Panel) compared to controls were synthesized with meta-analysis using extracted data from 4 included case-control studies. The results from random effects model were presented. Similar results were also obtained when use fixed effects model (data not shown).</p

Four-state model characterizing KKRR-ShPrP(120–232) kinetics in the pore.

<p>H, M_H, L, and M_L refer to the high, mid-high, low, and mid-low states respectively (N.B. PrP^C can escape from the pore from each state, which is not explicitly shown in the four-state model). HMM analysis of KKRR-ShPrP(120–232) in combination with the mid-state analysis (refer to text) yields the information on how the states are connected.</p

Cartoon illustrating the capture of an individual PrP^C molecule into an α-hemolysin nanopore.

<p>PrP^C is electrophoretically driven into the α-HL nanopore (voltage polarity given by the plus and minus signs) via its positively charged N-terminus. The <i>trans</i> chamber contains 0.3 M KCl, 45 mM NaPO₄, 10 mM HEPES, pH 8.0 solution at a PrP^C concentration of 13.4 µM. The <i>cis</i> chamber contains 3 M KCl, 10 mM HEPES, pH 8.0 solution. The salt-concentration gradient across the pore generates an osmotic flow from <i>trans</i>-to-<i>cis</i> and enhances the electric field around the entrance of the <i>trans</i>-side of the pore <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone.0054982-Wanunu1" target="_blank">[30]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone.0054982-Hatlo1" target="_blank">[31]</a> thereby substantially increasing the nanopore-capture rate of PrP^C in solution relative to symmetric salt conditions <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone.0054982-Wanunu1" target="_blank">[30]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054982#pone.0054982-Hatlo1" target="_blank">[31]</a>. Experiments were conducted (and maintained) at a temperature of 20°C ±0.1°C.</p

KKRR-ShPrP(120–232) event histogram and initial and optimal HMM models.

<p>(<b>a</b>) <b>(Left)</b> The KKRR-ShPrP(120–232) event histogram (blue) is divided into three regimes/states a high-state (black), mid-state (green) and low-state (red). The location of the peak and width of the distribution for each state in our initial model represents our best guess of the location and size of a given regime. <b>(Right)</b> The model parameters: <i>π</i> (i.e. the initial condition or probability that an event begins in a given state), <i>q</i> (the location of the peak of the Gaussian distribution, in terms of I/I0, for a given state), <i>b</i> (the standard deviation on the Gaussian of each state, which defines a state’s noise properties), and <i>A</i> (the state-to-state transition probability matrix). In our initial model we assume ignorance of the probabilities and therefore assume <i>π</i> to be uniformly distributed (i.e. an event is assumed equally likely to begin in any of the three states). Similarly with the transition probability matrix <i>A</i>, we assume all transitions to be equally likely (e.g. if in the low-state there is an equal probability for remaining in the low-state as there is for transitioning into the mid-state or the high-state). (<b>b</b>) <b>(Left)</b> After 40 iterations of the EM algorithm the optimal three-state model that best describes the data (i.e. the maximum likelihood model estimate) is converged upon. The low-state, far from encompassing all of the low current (as was presumed in our initial model) is very narrow and well defined, while the mid and high states both broaden out (the peak of the mid-state also shifts to a deeper conductance level relative to the initial model). <b>(Right)</b> The corresponding optimal model parameters.</p