Unmasking the roles of N- and C-terminal flanking sequences from exon 1 of huntingtin as modulators of polyglutamine aggregation Scott L. Cricka, Kiersten M. Ruffa, Kanchan Garaia,b, Carl Friedenb,1, and Rohit V. Pappua,1 a Department of Biomedical Engineering and Center for Biological Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130; and bDepartment of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO 63110
Contributed by Carl Frieden, November 1, 2013 (sent for review September 25, 2013)
|
phase separation subsaturation tetramethyl rhodamine
the cellular toxicity of Htt exon 1 even when the polyglutamine tract is significantly expanded (8, 9). A molecular-level understanding of the synergy between the length of polyglutamine tracts and its flanking sequences is essential for inferring the roles of N17 and C38 in vivo. This requires a quantitative understanding of the driving forces, mechanisms, and morphologies for homopolymeric polyglutamine and their modulation by N17 and C38. Here, we report results from in vitro studies that use solubility measurements to quantify driving forces, kinetic assays to investigate aggregation mechanisms, and electron microscopy (EM) to study aggregate morphologies. Results Saturation Concentrations. Aqueous milieus are poor solvents for polyglutamine (10). In poor solvents, there exists a threshold concentration for polymers, c = cs, known as the saturation concentration (11). For concentrations that exceed cs the polymer plus solvent system separates into insoluble and soluble phases. At c = cs the chemical potentials of soluble and insoluble phases are equal providing the system is in thermodynamic equilibrium. The lower the value of cs, the stronger the driving force for aggregation and phase separation. Aggregation is a generic term that refers to intermolecular associations that give rise to species as small as dimers or aggregates that are large enough to be sedimentable. Conversely, phase separation refers to the phenomenon that leads to two distinct phases of distinct polymer densities. We used a micro-Bicinchoninic Acid (BCA) assay (12) to estimate cs as a function of temperature for 10 different
| supersaturation |
Significance How do N- and C-terminal flanking sequences from exon 1 of the huntingtin protein modulate the mechanisms of polyglutamine aggregation? We answer this question using approaches that combine distinct probes of aggregation mechanisms with measurements of solubility and aggregate morphologies. The N- and C-terminal flanking sequence modules from exon 1 of huntingtin act as gatekeepers, whereby the N-terminal flanking sequence accelerates fibril formation while destabilizing nonfibrillar species, whereas the C-terminal flanking sequence reduces the overall driving force for aggregation. These results provide a mechanistic underpinning for observations regarding naturally occurring sequence contexts as modulators of polyglutamine toxicity.
H
untington disease (HD) is caused by mutational expansion of the CAG trinucleotide within exon 1 of the huntingtin (Htt) gene (1). Mutations are translated as polyglutamine expansions within the Htt protein. Neuronal intranuclear inclusions are the pathological hallmarks of HD, and N-terminal fragments (NTFs) spanning exon 1 of the Htt protein are the main constituents of these inclusions (2). The Htt gene with expanded CAG tracts can undergo erroneous splicing, and the resultant aberrant messenger RNA is translated into a mutant exon 1 version of Htt that is similar to toxic NTFs found in neuronal intranuclear inclusions (3). Exon 1 spanning NTFs typically include a polyglutamine tract that is flanked on its N terminus by an amphipathic 17-residue stretch (MATLEKLMKAFESLKSF) denoted as N17 and by a 38-residue proline-rich stretch on its C terminus (P11-QLPQPPPQAQPLLPQPQ-P10) denoted as C38. The N17 sequence is conserved among higher mammals (SI Appendix, Fig. S1), and mutations within N17 impact the properties of NTFs (4, 5). N17 enhances the overall rate of aggregation, as measured by the rate of forming large insoluble species both in vitro (6) and in yeast (7). The C-terminal proline-rich region of exon 1 modulates polyglutamine aggregation and reduces www.pnas.org/cgi/doi/10.1073/pnas.1320626110
Author contributions: S.L.C., K.M.R., K.G., C.F., and R.V.P. designed research; S.L.C., K.M.R., and K.G. performed research; S.L.C., K.M.R., and K.G. contributed new reagents/analytic tools; S.L.C., K.M.R., and R.V.P. analyzed data; and S.L.C., K.M.R., and R.V.P. wrote the paper. The authors declare no conflict of interest. Freely available online through the PNAS open access option. 1
To whom correspondence may be addressed. E-mail: [email protected] or frieden@ biochem.wustl.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1320626110/-/DCSupplemental.
PNAS | December 10, 2013 | vol. 110 | no. 50 | 20075–20080
BIOPHYSICS AND COMPUTATIONAL BIOLOGY
Huntington disease is caused by mutational expansion of the CAG trinucleotide within exon 1 of the huntingtin (Htt) gene. Exon 1 spanning N-terminal fragments (NTFs) of the Htt protein result from aberrant splicing of transcripts of mutant Htt. NTFs typically encompass a polyglutamine tract flanked by an N-terminal 17residue amphipathic stretch (N17) and a C-terminal 38-residue proline-rich stretch (C38). We present results from in vitro biophysical studies that quantify the driving forces for and mechanisms of polyglutamine aggregation as modulated by N17 and C38. Although N17 is highly soluble by itself, it lowers the saturation concentration of soluble NTFs and increases the driving force, vis-à-vis homopolymeric polyglutamine, for forming insoluble aggregates. Kinetically, N17 accelerates fibril formation and destabilizes nonfibrillar intermediates. C38 is also highly soluble by itself, and it lends its high intrinsic solubility to lower the driving force for forming insoluble aggregates by increasing the saturation concentration of soluble NTFs. In NTFs with both modules, N17 and C38 act synergistically to destabilize nonfibrillar intermediates (N17 effect) and lower the driving force for forming insoluble aggregates (C38 effect). Morphological studies show that N17 and C38 promote the formation of ordered fibrils by NTFs. Homopolymeric polyglutamine forms a mixture of amorphous aggregates and fibrils, and its aggregation mechanisms involve early formation of heterogeneous distributions of nonfibrillar species. We propose that N17 and C38 act as gatekeepers that control the intrinsic heterogeneities of polyglutamine aggregation. This provides a biophysical explanation for the modulation of in vivo NTF toxicities by N17 and C38.
peptides. The sequence constructs for these cs measurements were (Gln)n-(Lys)2, (Lys)2-(Gln)n-(Lys)2, N17-(Gln)n-(Lys)2, (Gln)n-C38, and N17-(Gln)n-C38, and these are abbreviated as Qn-K2, K2-Qn-K2, N17-Qn-K2, Qn-C38, and N17-Qn-C38, respectively. In each of the constructs, the polyglutamine tract is either 30 or 40 residues long; i.e., n = 30 or 40. The choice of n brackets the mean threshold length that is relevant to the age-ofonset of HD. Each peptide was disaggregated using established protocols (13), and different concentrations of the peptide were quiescently incubated at a specified temperature in 50 mM phosphate buffer at pH 7 for a period of 2 wk. The material was then centrifuged to separate it into a supernatant and pellet, and the concentration in the supernatant was measured to estimate the cs for the peptide in question at the specified temperature. Using this method we can demonstrate that cs is a thermally reversible quantity as shown in SI Appendix, Fig. S2. Additionally, akin to brain-derived insoluble inclusions of Htt NTFs, the pellets stain positively with the fluorescent dye thioflavin T (ThT) (SI Appendix, Fig. S3). The poor solubility of polyglutamine requires the use of lysine residues to facilitate in vitro measurements of synthetic peptides (13). Data for K2-Qn-K2 are shown in the SI Appendix, Fig. S4, and they help quantify the effect of adding two additional lysine residues on cs values. Clearly, it is important to reduce the number of charged residues in synthetic constructs to ensure that their influence on the driving forces for aggregation is minimized (14, 15). Therefore, we selected Qn-K2 as the preferred mimic of homopolymeric polyglutamine. Fig. 1 shows the measured cs values for Qn-K2, N17-Qn-K2, Qn-C38, and N17-Qn-C38 at 30 °C and 40 °C. There is a fourfold reduction in cs for Q40-K2 versus Q30-K2 demonstrating that the magnitude of the driving force for forming insoluble aggregates
35
20
N17−Q30−K2 Q30−K2
Q40−K2 18
N17−Q30−C38 30
N17−Q40−K2
Q30−C38
N17−Q40−C38 Q40−C38
16
25
14
12
cs (μM)
cs (μM)
20 10
15 8
6
10
5 2
30ºC
40ºC
0
30ºC
40ºC
Fig. 1. Estimates of cs for Qn-K2, N17-Qn-K2, Qn-C38, and N17-Qn-C38 (n = 30, 40) at 30 °C and 40 °C.
20076 | www.pnas.org/cgi/doi/10.1073/pnas.1320626110
Supersaturation. We measured the kinetics of aggregation of different constructs using two different assays. Our goal was to compare the mechanisms of aggregation for polyglutamine constructs with and without the N17 and C38 modules. These studies require that kinetics experiments be performed under conditions where the magnitudes of driving forces for aggregation are equivalent for all constructs. The concept of supersaturation is useful in this regard, and the measured values of cs can be used to define the degree of supersaturation, S. For a given construct, if we denote the bulk concentration of fully dis c o aggregated monomers as co, then S = ln cs . If co > cs, then the uniformly mixed solution of monomers is metastable and supersaturated with respect to the soluble phase. The parameter S quantifies the degree of metastability of the predominantly monomeric solution and hence provides an estimate of the magnitude of the driving force for aggregation and phase separation (16). If co > cs, then S > 0, and the soluble phase is supersaturated, whereas if co < cs, then S < 0, and the soluble phase is subsaturated. When comparing the aggregation mechanisms of different constructs, we performed experiments at equivalent supersaturation values to ensure that comparisons between constructs are made for equivalent magnitudes of driving forces. This helps unmask mechanistic differences that are otherwise difficult to resolve if measurements are made at equivalent values of co rather than equivalent S values. Comparative Kinetics of Aggregation for Different Constructs Measured at Similar Supersaturation Values. For a given construct, the cs
4
0
increases with polyglutamine length. Fig. 1 also shows the cs values for N17-Qn-K2 and Qn-C38. The cs value for N17-Q40-K2 is in the submicromolar range for T ≤ 40 °C. At 40 °C the cs value for N17-Q30-K2 is fourfold smaller than that of Q30-K2. Clearly, N17 lowers the cs of polyglutamine-containing peptides. In contrast, we obtain a systematic increase in cs for Qn-C38 compared with the cs values for Qn-K2. The intrinsic solubilities of the N17 and C38 modules are in the millimolar range. Hence, the coupling between N17 and polyglutamine lowers the overall solubility to be below that of the individual modules, whereas C38 acts as a solubilizing module by increasing the cs of Qn-C38 vis-à-vis Qn-K2. Fig. 1 also shows measured cs values of N17-QnC38 peptides, which mimic exon 1 spanning NTFs. This construct combines the cs lowering effect of N17 and the cs elevating effect of C38. The cs values for N17-Qn-C38 increase relative to Qn-K2, implying a stronger contribution from the solubilizing effects of C38 compared with the cs diminishing effects of N17. Finally, in all of the constructs, cs decreases with increased polyglutamine length and increases with increased temperature (SI Appendix, Fig. S5). We have assumed that thermodynamic equilibrium between soluble and insoluble phases can be established within a 2-wk incubation period. The following criteria justify this assumption. First, we test for reliability by assessing if similar values of cs are reproduced when we incubate different amounts of starting material. Second, in all cases, visual inspection showed the development of precipitate well within the 2-wk incubation period. Third, all kinetics measurements (see below) show a plateauing of signals on time scales that are roughly an order of magnitude faster than the 2-wk incubation period.
values decrease with increasing polyglutamine length. The focus here is on the effects of flanking sequence modules. Accordingly, we present results for Q30 peptides in four different sequence contexts. Although the magnitudes of driving forces for aggregation increase, and the time scales for aggregation decrease, with increased polyglutamine length, our conclusions regarding comparative differences in mechanisms for different constructs should remain similar. Crick et al.
C
1.0
1.0
0.9
0.9
0.9
0.8
0.8
0.8
0.7
Q 30 -K2K*G N17-Q 30 -K2K*G
0.6
0.7
0.7
0.5
0.5
0.4
Q30 -K2: ThT Q30-K2K*G: TMR Complement of TMR
0.4
0.3
0.3
0.4
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0
5
10
15
20
25
30
35
40
45
50
0
N17-Q30-K2: ThT N17-Q30-K2K*G: TMR Complement of TMR
0.6
0.6
0.5
Extent of reaction
B
1.0
Extent of reaction
Normalized TMR fluorescence
A
5
10
15
20
t (hrs)
25
30
35
40
45
0
50
5
15
10
20
t (hrs)
25
30
35
40
50
45
t (hrs)
Crick et al.
fluorescence for the N17-Q30-K2K*G peptide at four different supersaturation values ranging from S ∼ 0.8 to S ∼ 2.2. The kinetic traces for the loss of TMR fluorescence of polyglutamine peptides show the absence of a lag phase, and the profiles are rather distinct from those for amyloid beta peptides that were studied using a similar approach (18). Fig. 2A compares the kinetics for the loss of TMR fluorescence measured for Q30-K2K*G and N17-Q30-K2K*G at equivalent supersaturation values, S ∼ 0.7–0.8. The time taken to achieve a normalized TMR fluorescence of 0.5 is approximately fourfold higher for N17-Q30-K2K*G compared with Q30-K2K*G. To further probe differences in aggregation mechanisms we compared the kinetics for the loss of TMR fluorescence to the kinetics of the gain in ThT fluorescence. The results are shown in Fig. 2 B and C for Q30-K2K*G and N17- Q30-K2K*G, respectively. N17 clearly accelerates fibril formation. This acceleration appears to derive from the destabilization of nonfibrillar intermediates—a conjecture that is supported by the comparative analysis of the kinetics of the loss of TMR fluorescence to the gain of ThT fluorescence shown in Fig. 2C. This figure also
1 0.9 0.8
Extent of reaction
We used two peptides, Q30-K2K*G and N17-Q30-K2K*G, to monitor the rate of loss of monomers into growing aggregates. Here, G denotes glycine and K* denotes a lysine residue that was modified by covalent attachment of tetramethyl rhodamine (TMR) through the amine. We assume that saturation concentrations for Q30-K2K*G and N17-Q30-K2K*G do not deviate appreciably from those of the unlabeled molecules. This is reasonable because the free energy of TMR dimerization is ∼2.8 kJ /mol (17), which is minimal compared with the strong interactions between polyglutamine molecules that give rise to low cs values (SI Appendix, Fig. S6). A pair of TMR molecules can stack to form fluorescently dark dimers. The percent probability of TMR forming a dimer is governed by the local concentration of other TMR molecules, which in micromolar aqueous solutions of TMR molecules will be less than 1% based on the known dissociation constant. However, if aggregation prone molecules, each with a single TMR label, were to form aggregates, then the probability of making TMR dimers increases because of the increased local concentration of TMR molecules that results from aggregation. Garai and Frieden (18) developed an assay that uses the loss of TMR fluorescence of labeled molecules to monitor aggregation, thus obviating the requirement that aggregates have to be species that are capable of binding ThT. This method affords higher temporal resolution of the aggregation process, especially with regard to the kinetic details of the early stages. Loss of fluorescence requires TMR dimerization either on the surface or within the interior of aggregates. Our computational analysis indicates that aggregates with approximately 20 Q30-K2K*G molecules will be 65% as bright as an individual molecule (SI Appendix, Fig. S7). All measurements of the rate of change of TMR fluorescence were initiated after disaggregation by dissolution of the peptides in 100% formic acid. These peptides were then diluted into water at twice the target concentration followed by further dilution to pH 7 in a 50-mM phosphate buffer. The measurements of TMR fluorescence were performed at 37 °C under continuous stirring. The disaggregation protocol had to be redesigned vis-àvis the standard protocol (13), established for use with K2-Qn-K2 peptides; the details are discussed in SI Appendix, section 2. SI Appendix, Fig. S8 shows the normalized TMR fluorescence for Q30-K2K*G measured as a function of time at 37 °C for three different supersaturation values that range from S ∼ 0.1 to S ∼ 0.8. SI Appendix, Fig. S9 shows data for the rate of loss of TMR
0.7 0.6 0.5 0.4 0.3
Q30-K2: co=10 μM, S≈0.8 N17-Q30-K2: co=2 μM, S≈0.7 Q 30-C38: co=50 μM, S≈0.7
0.2 0.1 0 0
N17-Q30-C38: co=12 μM, S≈0.7 5
10
15
20
25
t (hrs)
30
35
40
45
50
Fig. 3. Comparative analysis of the kinetics of gain in ThT fluorescence for four different Q30 constructs.
PNAS | December 10, 2013 | vol. 110 | no. 50 | 20077
BIOPHYSICS AND COMPUTATIONAL BIOLOGY
Fig. 2. (A) Comparison of the kinetics of the loss of TMR fluorescence for Q30-K2K*G and N17-Q30-K2K*G. For each construct, we normalized the TMR fluorescence using the t = 0 value as a reference. (B) Comparison of extents of aggregation obtained by following the loss in TMR fluorescence and gain in ThT fluorescence for Q30-K2K*G and Q30-K2, respectively. The extents of reaction range from 0 to 1 and were calculated using the minimum and maximum values for TMR and ThT fluorescence. The black points correspond to the complement of the TMR data, whereby we convert the loss of TMR fluorescence into a growth curve to compare this with the kinetics of the gain in ThT fluorescence. (C) Comparison of extents of aggregation obtained by following the loss in TMR fluorescence and gain in ThT fluorescence for N17-Q30-K2K*G and N17-Q30-K2, respectively. All other details are similar to that of B.
Q30-K2 10 M, S 0.8
N17-Q30-K2 2 M, S 0.7
Q30-C38 50 M, S 0.7
N17-Q30-C38 12 M, S 0.7
Fig. 4. Comparison of aggregate morphologies for different Q30 constructs obtained using EM. Additional comparisons using TMR-labeled constructs are shown in SI Appendix, Fig. S10. Visual inspection suggests that although N17 promotes long, ordered fibrils, these fibrils are thinner than those obtained for constructs with the C38 module.
20078 | www.pnas.org/cgi/doi/10.1073/pnas.1320626110
1
Q30−K2K*G, S ≈ −2.2 Q30−K2K*G, S ≈ −1.5 Q30−K2K*G, S ≈ −0.6 N17−Q30−K2K*G, S ≈ −0.8 Q 30−C38K*G, S ≈ −2.1
0.9 0.8
Normalized TMR fluorescence
shows the expected profile for the rate of growth of aggregates that is obtained by computing the complement of the normalized profile for the loss of TMR fluorescence. This computed curve essentially coincides with the profile for the gain in ThT fluorescence profile, thereby implying that the loss of TMR fluorescence results almost entirely from fibril formation. In direct contrast, the loss in TMR fluorescence for Q30-K2K*G reaches a plateau value before there is discernible increase in ThT fluorescence (Fig. 2B). This implies a separation of time scales between two processes, namely, preequilibration of nonfibrillar species before their conversion into fibrillar aggregates. The time scales for the two processes, quantified as the times for the extents of distinct reactions to reach 50% completion, differ by at least an order of magnitude for Q30-K2K*G. To measure the rate of loss of TMR fluorescence for Q30-C38 and N17-Q30-C38 peptides at equivalent supersaturation values we would need TMR-labeled material at concentrations where inner filter effect confounds the interpretations of TMR fluorescence measurements (19). In addition, if we followed the protocol used for N17-Qn-K2K*G and Qn-K2K*G by labeling the C-terminal end, then we would be probing the likelihood of associations between the C38 modules as opposed to just the polyglutamine-mediated associations. We need detailed simulations to help guide the optimal placement of the TMR labels in constructs with the C38 module. These simulations are feasible with improvements to forcefields that describe proline-rich sequences and will be pursued in future work. Here, we focus on comparing the kinetics of changes in ThT fluorescence for all four constructs at equivalent supersaturation values. Fig. 3 presents our comparative analysis of data obtained for the extents of reaction derived from ThT fluorescence measurements as a function of time at 37 °C for the peptides, Q30-K2, N17-Q30-K2, Q30-C38, and N17-Q30-C38, respectively; S ∼ 0.7– 0.8 for all peptides. Although C38 lowers the cs value and hence decreases the magnitude of the driving force for aggregation, at equivalent supersaturation values the kinetic profile for the
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0
5
10
15
20
25
t (hrs)
30
35
40
45
Fig. 5. Comparing the kinetics of loss of TMR fluorescence in subsaturated solutions for Q30 constructs with and without N17 and C38.
change in ThT fluorescence for N17-Q30-C38 is essentially identical to that of N17-Q30-K2. This suggests that even in the presence of the solubilizing C38 module the mechanism of aggregation is controlled by N17, which accelerates fibril formation while destabilizing nonfibrillar intermediates. In contrast, the increase in ThT fluorescence for Q30-C38 shows a significant lag phase, which is also present for Q30-K2. Impact of Flanking Sequences on Morphologies. We asked if the observed differences in aggregation kinetics at equivalent supersaturation values lead to differences in the morphologies of aggregates. Fig. 4 shows the morphologies obtained using EM following long-time incubations of Q30-K2, N17-Q30-K2, Q30C38, and N17-Q30-C38. All data were collected following aggregation in supersaturated solutions, with S ∼ 0.8. Additional assessments of morphologies were obtained by imaging the end products of species obtained from TMR assays for Q30-K2K*G and N17-Q30-K2K*G (SI Appendix, Fig. S10). Q30-K2 forms a dense mesh of aggregates with truncated fibrils (Fig. 4). The rapid formation of nonfibrillar species by Q30-K2 appears to contribute to the disordered morphologies of higherorder aggregates. In contrast, the representative images in Fig. 4 show that N17-Q30-K2, Q30-C38, and N17-Q30-C38 form ordered fibrils with minimal nonspecific associations. Comparison of the morphologies for N17-Q30-K2 and Q30-C38 at equivalent magnifications suggests that these modules get differently incorporated into the fibrils leading to differences in morphologies. These differences are consistent with published studies, which suggest that C38 contributes to the formation of bottlebrush architectures (20). Aggregates Form in Subsaturated Solutions. The homogeneous nucleation model (16) has been used as a conceptual framework for explaining the kinetics of polyglutamine aggregation (21). In this model, an obligatory nucleus representing an embryo of the new aggregated/insoluble phase has to form within the homogeneously mixed, soluble phase. The free energy of nucleation depends on the supersaturation value and the free-energy penalty associated with creating an interface between the new and old phases (16). Supersaturation is a prerequisite for a nucleated process. It therefore follows that monomers have to be the predominant species in subsaturated solutions either because aggregates never form or because they are only marginally stable. To assess the applicability of homogeneous nucleation theory, we quantified the kinetics of the loss of TMR fluorescence in Crick et al.
100 nm
Fig. 6. Representative EM image of oligomers that form in subsaturated solutions of Q30-K2. Here, co = 1 μM and S ∼ –1.5. The black arrows point to small oligomers, and the red arrows point to larger oligomers that form via the aggregation of smaller oligomers. The sizes of oligomers are expected to be in the 10–50-nm range.
M1
F
S
I
M1
MCI
MCS
F
FS
t aggrega
FI
g
Crick et al.
B Schematic for N17-Qn-K2 and Ex1
A Schematic for Q n-K2
ear aggre
subsaturated solutions. Fig. 5 shows the results of these measurements for Q30-K2K*G, N17-Q30-K2K*G, and Q30-C38K*G. The data in Fig. 5 highlight the presence of stable aggregates that form without a lag phase on time scales that are comparable to those seen in supersaturated solutions. Fig. 6 shows a representative EM image of visible, nonfibrillar aggregates formed by Q30-K2K*G in subsaturated solutions. These aggregates are spherical and are roughly 10 nm in size. Increased concentration of such aggregates leads to the increased loss of TMR fluorescence that we observe for Q30-K2K*G as the solutions become less subsaturated, i.e., as S becomes less negative (Fig. 5). Taken together, the data in Figs. 5 and 6 provide evidence against the applicability of homogeneous nucleation for describing the aggregation of polyglutamine constructs. Instead the data point to a heterogeneous process that involves nonfibrillar species whose stabilities vary depending on the presence or absence of flanking sequences. Fig. 5 points to the existence of multiple phases in solution including one that should give rise to a distinct saturation concentration for soluble aggregates. These could either be liquidlike oligomers that arise from liquid–liquid demixing (22) or micellar structures that arise due to the possible existence of a critical micelle concentration. We used the plateau values for the TMR fluorescence in subsaturated solutions to estimate saturation concentrations for oligomers of Q30-K2K*G, N17Q30-K2K*G, and Q30-C38K*G, respectively (SI Appendix, section 2). We denote the oligomer saturation concentration as cc. It is appropriate to use the C-terminally labeled Q30-C38K*G peptide to estimate cc from the plateau value of the TMR fluorescence. This value should be indicative of molecules that remain unincorporated into aggregates, irrespective of where the TMR label is situated within the construct. The values we obtain for cc at 37 °C in 50 mM phosphate buffers at pH 7 are 250 nM, 165 nM, and 3 μM for Q30-K2K*G, N17-Q30-K2K*G, and Q30-C38K*G, respectively. Clearly, the oligomer saturation concentration is distinct from and lower
Discussion Fig. 7 shows schematic representations of our proposals for the aggregation mechanisms of homopolymeric polyglutamine and the modulation of these mechanisms by N17 and C38. The scheme shown in Fig. 7A is in accord with the data presented here for Q30-K2 and Q30-K2K*G and a recent two-stage nucleation model for polyglutamine aggregation (23). This model postulates the existence of heterogeneous distributions of oligomers that arise due to liquid–liquid demixing of intrinsically disordered polyglutamine. For homopolymers in poor solvents, intermolecular interfaces are favored over chain-solvent interfaces. Accordingly, disordered globules associate in a thermodynamically downhill fashion to form a heterogeneous distribution of liquid-like oligomers (23). The model assumes a slow conversion of oligomers into fibrillar species, providing co > cs. A finite concentration of suitably large oligomers is required to support a nucleated conformational conversion to generate a template for fibril elongation (24, 25). In the model, the size of the smallest fibril-competent oligomer is denoted as imin. The data presented here for Q30-K2 and Q30-K2K*G provide direct evidence in support of equilibrium distributions of oligomers that form in sub- and supersaturated solutions. This step precedes fibril formation in supersaturated solutions, and Fig. 7A proposes that flux into fibrils is decreased due to the collection of nonfibrillar species that form in the absence of flanking sequences. The model (23) also showed that if imin is decreased, which implies destabilization of oligomers that cannot convert to fibrils, then fibril formation is accelerated. In such a scenario, if aggregation is modeled as homogeneous nucleation, then the apparent nucleus size can become less than or equal to zero (23) as was shown by Thakur et al. (6) who applied a variant of homogeneous nucleation to model the kinetics of the loss of soluble species for N17-Qn-K2 peptides. The schematic in Fig. 7B summarizes the effects of N17 and C38 on the aggregation of Htt NTFs. We propose that N17 and C38 act as gatekeepers to destabilize nonfibrillar intermediates thus ensuring that the fluxes are distributed across fewer routes. This schematic is consistent with data presented here and with results from atomistic simulations (15) which show that N17 destabilizes nonspecific associations vis-à-vis homopolymeric polyglutamine. In vitro (6) and cellular measurements (7) showed
MCS
Fig. 7. Schematic representations for the distinct aggregation mechanisms expected for (A) Qn-K2, (B) N17-Qn-K2 and N17-Qn-C38. M1, MCS ,MCI , FS, and FI denote monomers, soluble oligomers, insoluble nonfibrillar aggregates, soluble fibrils, and insoluble fibrils, respectively. The boxed regions delineate insoluble aggregates. Small arrows within the boxed regions depict equilibria between insoluble and soluble species as well as equilibria between different forms of insoluble species. The proposal is that imin, i.e., the size of smallest oligomer that can convert to fibrils, is considerably larger for Qn-K2 than for polyglutamine constructs with the N17 and C38 flanking sequences. In B, the decrease in imin is depicted as a decrease in the stability of MCS . Glutamine, hydrophobic residues, positive and negatively charged residues, and proline are denoted using spheres that are orange, yellow, blue, red, and purple, respectively (B).
PNAS | December 10, 2013 | vol. 110 | no. 50 | 20079
BIOPHYSICS AND COMPUTATIONAL BIOLOGY
than cs. The ratio cccs quantifies the gap between cc and cs. For T ∼ 37 °C this ratio is 18.4, 6.5, and 8.3 for Q30-K2, N17-Q30-K2, and Q30-C38, respectively.
that N17 accelerates fibril formation. This was taken to mean that the “aggregation propensity” of homopolymeric polyglutamine is considerably weaker than that of NTF constructs containing both polyglutamine and N17. The data presented here provide a more nuanced explanation that connects the simulation results to previous experimental observations. Fibrils form slowly and inefficiently for homopolymeric polyglutamine because the gap between cc and cs is largest for these constructs. This gap gives rise to kinetic competition with and thermodynamic destabilization of ordered fibrillar aggregates and increases the overall heterogeneity due to the increased thermodynamic and kinetic stability of nonfibrillar species as sketched in Fig. 7A (26). The use of two separate assays that are designed to probe distinct features of the kinetics of aggregation combined with solubility and morphological measurements lead to a more complete picture of the heterogeneities inherent to polyglutamine aggregation and the modulation of these heterogeneities by N17 and C38. The data presented here reconcile results from theory (23), simulations (15), in vitro (6), and in-cell experiments (7). Both N17 and C38 contribute to narrowing the gap between cc and cs thereby destabilizing nonfibrillar intermediates. C38 contributes to increasing cs thus weakening the overall driving force for aggregation whereas N17 accelerates fibril formation and stabilizes these species while destabilizing nonfibrillar species. The aggregation mechanisms for polyglutamine-containing peptides are distinct from mechanisms reported for the amyloid beta (Aβ) system (18). For Aβ peptides, it has been argued that aggregation can be modeled using a quasi-homogeneous nucleation model (11) if co > cs and co < cc (27). Our results show that the combination of co > cs and co < cc cannot be achieved for polyglutamine-containing systems. Even in the presence of flanking sequences the ratio cccs remains greater than 1. As a result, in supersaturated solutions, the mechanisms are likely to
always involve heterogeneities such as nonfibrillar oligomers (23, 25) that form before or concomitantly with fibrils. It is possible that de novo sequence design can be used to generate sequence variants of Aβ for which cc < cs, as is the case with polyglutaminecontaining systems.
1. Walker FO (2007) Huntington’s disease. Lancet 369(9557):218–228. 2. Becher MW, et al. (1998) Intranuclear neuronal inclusions in Huntington’s disease and dentatorubral and pallidoluysian atrophy: Correlation between the density of inclusions and IT15 CAG triplet repeat length. Neurobiol Dis 4(6):387–397. 3. Sathasivam K, et al. (2013) Aberrant splicing of HTT generates the pathogenic exon 1 protein in Huntington disease. Proc Natl Acad Sci USA 110(6):2366–2370. 4. Zheng ZQ, Li AM, Holmes BB, Marasa JC, Diamond MI (2013) An N-terminal nuclear export signal regulates trafficking and aggregation of Huntingtin (Htt) protein exon 1. J Biol Chem 288(9):6063–6071. 5. Maiuri T, Woloshansky T, Xia J, Truant R (2013) The huntingtin N17 domain is a multifunctional CRM1 and Ran-dependent nuclear and cilial export signal. Hum Mol Genet 22(7):1383–1394. 6. Thakur AK, et al. (2009) Polyglutamine disruption of the huntingtin exon 1 N terminus triggers a complex aggregation mechanism. Nat Struct Mol Biol 16(4):380–389. 7. Tam S, et al. (2009) The chaperonin TRiC blocks a huntingtin sequence element that promotes the conformational switch to aggregation. Nat Struct Mol Biol 16(12): 1279–1285. 8. Duennwald ML, Jagadish S, Muchowski PJ, Lindquist S (2006) Flanking sequences profoundly alter polyglutamine toxicity in yeast. Proc Natl Acad Sci USA 103(29): 11045–11050. 9. Park S-H, et al. (2013) PolyQ proteins interfere with nuclear degradation of cytosolic proteins by sequestering the Sis1p chaperone. Cell 154(1):134–145. 10. Crick SL, Jayaraman M, Frieden C, Wetzel R, Pappu RV (2006) Fluorescence correlation spectroscopy shows that monomeric polyglutamine molecules form collapsed structures in aqueous solutions. Proc Natl Acad Sci USA 103(45):16764–16769. 11. Garai K, Sahoo B, Sengupta P, Maiti S (2008) Quasihomogeneous nucleation of amyloid beta yields numerical bounds for the critical radius, the surface tension, and the free energy barrier for nucleus formation. J Chem Phys 128(4):045102. 12. Smith PK, et al. (1985) Measurement of protein using bicinchoninic acid. Anal Biochem 150(1):76–85. 13. Chen SM, Wetzel R (2001) Solubilization and disaggregation of polyglutamine peptides. Protein Sci 10(4):887–891. 14. Walters RH, Murphy RM (2009) Examining polyglutamine peptide length: A connection between collapsed conformations and increased aggregation. J Mol Biol 393(4): 978–992.
15. Williamson TE, Vitalis A, Crick SL, Pappu RV (2010) Modulation of polyglutamine conformations and dimer formation by the N-terminus of huntingtin. J Mol Biol 396(5):1295–1309. 16. Kashchiev D, van Rosmalen GM (2003) Review: Nucleation in solutions revisited. Cryst Res Technol 38(7-8):555–574. 17. Corsepius NC, Lorimer GH (2013) Measuring how much work the chaperone GroEL can do. Proc Natl Acad Sci USA 110(27):E2451–E2459. 18. Garai K, Frieden C (2013) Quantitative analysis of the time course of Aβ oligomerization and subsequent growth steps using tetramethylrhodamine-labeled Aβ. Proc Natl Acad Sci USA 110(9):3321–3326. 19. Fanget B, Devos O, Draye M (2003) Correction of inner filter effect in mirror coating cells for trace level fluorescence measurements. Anal Chem 75(11):2790–2795. 20. Bugg CW, Isas JM, Fischer T, Patterson PH, Langen R (2012) Structural features and domain organization of huntingtin fibrils. J Biol Chem 287(38):31739–31746. 21. Chen SM, Ferrone FA, Wetzel R (2002) Huntington’s disease age-of-onset linked to polyglutamine aggregation nucleation. Proc Natl Acad Sci USA 99(18):11884–11889. 22. Ganazzoli F, Raos G, Allegra G (1999) Polymer association in poor solvents: from monomolecular micelles to clusters of chains and phase separation. Macromol. Theory Simul 8(1):65–84. 23. Vitalis A, Pappu RV (2011) Assessing the contribution of heterogeneous distributions of oligomers to aggregation mechanisms of polyglutamine peptides. Biophys Chem 159(1):14–23. 24. Serio TR, et al. (2000) Nucleated conformational conversion and the replication of conformational information by a prion determinant. Science 289(5483):1317–1321. 25. Lee J, Culyba EK, Powers ET, Kelly JW (2011) Amyloid-β forms fibrils by nucleated conformational conversion of oligomers. Nat Chem Biol 7(9):602–609. 26. Cheng SZD, Keller A (1998) The role of metastable states in polymer phase transitions: Concepts, principles, and experimental observations. Annu Rev Mater Sci 28:533–562. 27. Auer S, Ricchiuto P, Kashchiev D (2012) Two-step nucleation of amyloid fibrils: omnipresent or not? J Mol Biol 422(5):723–730. 28. Arrasate M, Finkbeiner S (2012) Protein aggregates in Huntington’s disease. Exp Neurol 238(1):1–11. 29. Landles C, et al. (2010) Proteolysis of mutant huntingtin produces an exon 1 fragment that accumulates as an aggregated protein in neuronal nuclei in Huntington disease. J Biol Chem 285(12):8808–8823.
20080 | www.pnas.org/cgi/doi/10.1073/pnas.1320626110
Implications for Htt NTFs in Vivo. We propose that N17 and C38 act as natural gatekeepers to minimize the deleterious effects of heterogeneities that are intrinsic to polyglutamine aggregation. Time-resolved microscopy data from neurons show that diffuse aggregates and smaller species of mutant Htt directly correlate with neuronal death, whereas the formation of large insoluble inclusions diminishes the levels of diffuse, heterogeneous aggregates and the risk of striatal neuron death (28). It is conceivable that variations in NTFs that result from aberrant splicing of exon 1 or proteolytic processing of mutant Htt (29) contribute to variations in the relative levels of small diffuse aggregates versus large insoluble inclusions, leading to the observed cell-to-cell variations in phenotype (28).
Materials and Methods Peptides were purchased in crude form from Yale University’s Keck Biotechnology center. In addition to the unlabeled peptides used for cs and ThT measurements, the labeled peptides were synthesized with the red TMR dye attached through the lysine amine. SI Appendix, section 2, provides all of the details of the preparation and handling of each peptide system. In addition, this section also details the protocols used for measuring cs values, TMR and ThT fluorescence, and imaging of aggregates using EM. ACKNOWLEDGMENTS. We thank Dorothy Beckett, Nicholas Corsepius, Marc Diamond, Tyler Harmon, Alex Holehouse, George Lorimer, M. Muthukumar, Dev Thirumalai, and Andreas Vitalis for insights and helpful discussions. This work was supported by Grant 5R01NS056114 from the National Institutes of Health (to R.V.P.).
Crick et al.
Contributed by Carl Frieden, November 1, 2013 (sent for review September 25, 2013)
|
phase separation subsaturation tetramethyl rhodamine
the cellular toxicity of Htt exon 1 even when the polyglutamine tract is significantly expanded (8, 9). A molecular-level understanding of the synergy between the length of polyglutamine tracts and its flanking sequences is essential for inferring the roles of N17 and C38 in vivo. This requires a quantitative understanding of the driving forces, mechanisms, and morphologies for homopolymeric polyglutamine and their modulation by N17 and C38. Here, we report results from in vitro studies that use solubility measurements to quantify driving forces, kinetic assays to investigate aggregation mechanisms, and electron microscopy (EM) to study aggregate morphologies. Results Saturation Concentrations. Aqueous milieus are poor solvents for polyglutamine (10). In poor solvents, there exists a threshold concentration for polymers, c = cs, known as the saturation concentration (11). For concentrations that exceed cs the polymer plus solvent system separates into insoluble and soluble phases. At c = cs the chemical potentials of soluble and insoluble phases are equal providing the system is in thermodynamic equilibrium. The lower the value of cs, the stronger the driving force for aggregation and phase separation. Aggregation is a generic term that refers to intermolecular associations that give rise to species as small as dimers or aggregates that are large enough to be sedimentable. Conversely, phase separation refers to the phenomenon that leads to two distinct phases of distinct polymer densities. We used a micro-Bicinchoninic Acid (BCA) assay (12) to estimate cs as a function of temperature for 10 different
| supersaturation |
Significance How do N- and C-terminal flanking sequences from exon 1 of the huntingtin protein modulate the mechanisms of polyglutamine aggregation? We answer this question using approaches that combine distinct probes of aggregation mechanisms with measurements of solubility and aggregate morphologies. The N- and C-terminal flanking sequence modules from exon 1 of huntingtin act as gatekeepers, whereby the N-terminal flanking sequence accelerates fibril formation while destabilizing nonfibrillar species, whereas the C-terminal flanking sequence reduces the overall driving force for aggregation. These results provide a mechanistic underpinning for observations regarding naturally occurring sequence contexts as modulators of polyglutamine toxicity.
H
untington disease (HD) is caused by mutational expansion of the CAG trinucleotide within exon 1 of the huntingtin (Htt) gene (1). Mutations are translated as polyglutamine expansions within the Htt protein. Neuronal intranuclear inclusions are the pathological hallmarks of HD, and N-terminal fragments (NTFs) spanning exon 1 of the Htt protein are the main constituents of these inclusions (2). The Htt gene with expanded CAG tracts can undergo erroneous splicing, and the resultant aberrant messenger RNA is translated into a mutant exon 1 version of Htt that is similar to toxic NTFs found in neuronal intranuclear inclusions (3). Exon 1 spanning NTFs typically include a polyglutamine tract that is flanked on its N terminus by an amphipathic 17-residue stretch (MATLEKLMKAFESLKSF) denoted as N17 and by a 38-residue proline-rich stretch on its C terminus (P11-QLPQPPPQAQPLLPQPQ-P10) denoted as C38. The N17 sequence is conserved among higher mammals (SI Appendix, Fig. S1), and mutations within N17 impact the properties of NTFs (4, 5). N17 enhances the overall rate of aggregation, as measured by the rate of forming large insoluble species both in vitro (6) and in yeast (7). The C-terminal proline-rich region of exon 1 modulates polyglutamine aggregation and reduces www.pnas.org/cgi/doi/10.1073/pnas.1320626110
Author contributions: S.L.C., K.M.R., K.G., C.F., and R.V.P. designed research; S.L.C., K.M.R., and K.G. performed research; S.L.C., K.M.R., and K.G. contributed new reagents/analytic tools; S.L.C., K.M.R., and R.V.P. analyzed data; and S.L.C., K.M.R., and R.V.P. wrote the paper. The authors declare no conflict of interest. Freely available online through the PNAS open access option. 1
To whom correspondence may be addressed. E-mail: [email protected] or frieden@ biochem.wustl.edu.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. 1073/pnas.1320626110/-/DCSupplemental.
PNAS | December 10, 2013 | vol. 110 | no. 50 | 20075–20080
BIOPHYSICS AND COMPUTATIONAL BIOLOGY
Huntington disease is caused by mutational expansion of the CAG trinucleotide within exon 1 of the huntingtin (Htt) gene. Exon 1 spanning N-terminal fragments (NTFs) of the Htt protein result from aberrant splicing of transcripts of mutant Htt. NTFs typically encompass a polyglutamine tract flanked by an N-terminal 17residue amphipathic stretch (N17) and a C-terminal 38-residue proline-rich stretch (C38). We present results from in vitro biophysical studies that quantify the driving forces for and mechanisms of polyglutamine aggregation as modulated by N17 and C38. Although N17 is highly soluble by itself, it lowers the saturation concentration of soluble NTFs and increases the driving force, vis-à-vis homopolymeric polyglutamine, for forming insoluble aggregates. Kinetically, N17 accelerates fibril formation and destabilizes nonfibrillar intermediates. C38 is also highly soluble by itself, and it lends its high intrinsic solubility to lower the driving force for forming insoluble aggregates by increasing the saturation concentration of soluble NTFs. In NTFs with both modules, N17 and C38 act synergistically to destabilize nonfibrillar intermediates (N17 effect) and lower the driving force for forming insoluble aggregates (C38 effect). Morphological studies show that N17 and C38 promote the formation of ordered fibrils by NTFs. Homopolymeric polyglutamine forms a mixture of amorphous aggregates and fibrils, and its aggregation mechanisms involve early formation of heterogeneous distributions of nonfibrillar species. We propose that N17 and C38 act as gatekeepers that control the intrinsic heterogeneities of polyglutamine aggregation. This provides a biophysical explanation for the modulation of in vivo NTF toxicities by N17 and C38.
peptides. The sequence constructs for these cs measurements were (Gln)n-(Lys)2, (Lys)2-(Gln)n-(Lys)2, N17-(Gln)n-(Lys)2, (Gln)n-C38, and N17-(Gln)n-C38, and these are abbreviated as Qn-K2, K2-Qn-K2, N17-Qn-K2, Qn-C38, and N17-Qn-C38, respectively. In each of the constructs, the polyglutamine tract is either 30 or 40 residues long; i.e., n = 30 or 40. The choice of n brackets the mean threshold length that is relevant to the age-ofonset of HD. Each peptide was disaggregated using established protocols (13), and different concentrations of the peptide were quiescently incubated at a specified temperature in 50 mM phosphate buffer at pH 7 for a period of 2 wk. The material was then centrifuged to separate it into a supernatant and pellet, and the concentration in the supernatant was measured to estimate the cs for the peptide in question at the specified temperature. Using this method we can demonstrate that cs is a thermally reversible quantity as shown in SI Appendix, Fig. S2. Additionally, akin to brain-derived insoluble inclusions of Htt NTFs, the pellets stain positively with the fluorescent dye thioflavin T (ThT) (SI Appendix, Fig. S3). The poor solubility of polyglutamine requires the use of lysine residues to facilitate in vitro measurements of synthetic peptides (13). Data for K2-Qn-K2 are shown in the SI Appendix, Fig. S4, and they help quantify the effect of adding two additional lysine residues on cs values. Clearly, it is important to reduce the number of charged residues in synthetic constructs to ensure that their influence on the driving forces for aggregation is minimized (14, 15). Therefore, we selected Qn-K2 as the preferred mimic of homopolymeric polyglutamine. Fig. 1 shows the measured cs values for Qn-K2, N17-Qn-K2, Qn-C38, and N17-Qn-C38 at 30 °C and 40 °C. There is a fourfold reduction in cs for Q40-K2 versus Q30-K2 demonstrating that the magnitude of the driving force for forming insoluble aggregates
35
20
N17−Q30−K2 Q30−K2
Q40−K2 18
N17−Q30−C38 30
N17−Q40−K2
Q30−C38
N17−Q40−C38 Q40−C38
16
25
14
12
cs (μM)
cs (μM)
20 10
15 8
6
10
5 2
30ºC
40ºC
0
30ºC
40ºC
Fig. 1. Estimates of cs for Qn-K2, N17-Qn-K2, Qn-C38, and N17-Qn-C38 (n = 30, 40) at 30 °C and 40 °C.
20076 | www.pnas.org/cgi/doi/10.1073/pnas.1320626110
Supersaturation. We measured the kinetics of aggregation of different constructs using two different assays. Our goal was to compare the mechanisms of aggregation for polyglutamine constructs with and without the N17 and C38 modules. These studies require that kinetics experiments be performed under conditions where the magnitudes of driving forces for aggregation are equivalent for all constructs. The concept of supersaturation is useful in this regard, and the measured values of cs can be used to define the degree of supersaturation, S. For a given construct, if we denote the bulk concentration of fully dis c o aggregated monomers as co, then S = ln cs . If co > cs, then the uniformly mixed solution of monomers is metastable and supersaturated with respect to the soluble phase. The parameter S quantifies the degree of metastability of the predominantly monomeric solution and hence provides an estimate of the magnitude of the driving force for aggregation and phase separation (16). If co > cs, then S > 0, and the soluble phase is supersaturated, whereas if co < cs, then S < 0, and the soluble phase is subsaturated. When comparing the aggregation mechanisms of different constructs, we performed experiments at equivalent supersaturation values to ensure that comparisons between constructs are made for equivalent magnitudes of driving forces. This helps unmask mechanistic differences that are otherwise difficult to resolve if measurements are made at equivalent values of co rather than equivalent S values. Comparative Kinetics of Aggregation for Different Constructs Measured at Similar Supersaturation Values. For a given construct, the cs
4
0
increases with polyglutamine length. Fig. 1 also shows the cs values for N17-Qn-K2 and Qn-C38. The cs value for N17-Q40-K2 is in the submicromolar range for T ≤ 40 °C. At 40 °C the cs value for N17-Q30-K2 is fourfold smaller than that of Q30-K2. Clearly, N17 lowers the cs of polyglutamine-containing peptides. In contrast, we obtain a systematic increase in cs for Qn-C38 compared with the cs values for Qn-K2. The intrinsic solubilities of the N17 and C38 modules are in the millimolar range. Hence, the coupling between N17 and polyglutamine lowers the overall solubility to be below that of the individual modules, whereas C38 acts as a solubilizing module by increasing the cs of Qn-C38 vis-à-vis Qn-K2. Fig. 1 also shows measured cs values of N17-QnC38 peptides, which mimic exon 1 spanning NTFs. This construct combines the cs lowering effect of N17 and the cs elevating effect of C38. The cs values for N17-Qn-C38 increase relative to Qn-K2, implying a stronger contribution from the solubilizing effects of C38 compared with the cs diminishing effects of N17. Finally, in all of the constructs, cs decreases with increased polyglutamine length and increases with increased temperature (SI Appendix, Fig. S5). We have assumed that thermodynamic equilibrium between soluble and insoluble phases can be established within a 2-wk incubation period. The following criteria justify this assumption. First, we test for reliability by assessing if similar values of cs are reproduced when we incubate different amounts of starting material. Second, in all cases, visual inspection showed the development of precipitate well within the 2-wk incubation period. Third, all kinetics measurements (see below) show a plateauing of signals on time scales that are roughly an order of magnitude faster than the 2-wk incubation period.
values decrease with increasing polyglutamine length. The focus here is on the effects of flanking sequence modules. Accordingly, we present results for Q30 peptides in four different sequence contexts. Although the magnitudes of driving forces for aggregation increase, and the time scales for aggregation decrease, with increased polyglutamine length, our conclusions regarding comparative differences in mechanisms for different constructs should remain similar. Crick et al.
C
1.0
1.0
0.9
0.9
0.9
0.8
0.8
0.8
0.7
Q 30 -K2K*G N17-Q 30 -K2K*G
0.6
0.7
0.7
0.5
0.5
0.4
Q30 -K2: ThT Q30-K2K*G: TMR Complement of TMR
0.4
0.3
0.3
0.4
0.3
0.2
0.2
0.2
0.1
0.1
0.1
0
5
10
15
20
25
30
35
40
45
50
0
N17-Q30-K2: ThT N17-Q30-K2K*G: TMR Complement of TMR
0.6
0.6
0.5
Extent of reaction
B
1.0
Extent of reaction
Normalized TMR fluorescence
A
5
10
15
20
t (hrs)
25
30
35
40
45
0
50
5
15
10
20
t (hrs)
25
30
35
40
50
45
t (hrs)
Crick et al.
fluorescence for the N17-Q30-K2K*G peptide at four different supersaturation values ranging from S ∼ 0.8 to S ∼ 2.2. The kinetic traces for the loss of TMR fluorescence of polyglutamine peptides show the absence of a lag phase, and the profiles are rather distinct from those for amyloid beta peptides that were studied using a similar approach (18). Fig. 2A compares the kinetics for the loss of TMR fluorescence measured for Q30-K2K*G and N17-Q30-K2K*G at equivalent supersaturation values, S ∼ 0.7–0.8. The time taken to achieve a normalized TMR fluorescence of 0.5 is approximately fourfold higher for N17-Q30-K2K*G compared with Q30-K2K*G. To further probe differences in aggregation mechanisms we compared the kinetics for the loss of TMR fluorescence to the kinetics of the gain in ThT fluorescence. The results are shown in Fig. 2 B and C for Q30-K2K*G and N17- Q30-K2K*G, respectively. N17 clearly accelerates fibril formation. This acceleration appears to derive from the destabilization of nonfibrillar intermediates—a conjecture that is supported by the comparative analysis of the kinetics of the loss of TMR fluorescence to the gain of ThT fluorescence shown in Fig. 2C. This figure also
1 0.9 0.8
Extent of reaction
We used two peptides, Q30-K2K*G and N17-Q30-K2K*G, to monitor the rate of loss of monomers into growing aggregates. Here, G denotes glycine and K* denotes a lysine residue that was modified by covalent attachment of tetramethyl rhodamine (TMR) through the amine. We assume that saturation concentrations for Q30-K2K*G and N17-Q30-K2K*G do not deviate appreciably from those of the unlabeled molecules. This is reasonable because the free energy of TMR dimerization is ∼2.8 kJ /mol (17), which is minimal compared with the strong interactions between polyglutamine molecules that give rise to low cs values (SI Appendix, Fig. S6). A pair of TMR molecules can stack to form fluorescently dark dimers. The percent probability of TMR forming a dimer is governed by the local concentration of other TMR molecules, which in micromolar aqueous solutions of TMR molecules will be less than 1% based on the known dissociation constant. However, if aggregation prone molecules, each with a single TMR label, were to form aggregates, then the probability of making TMR dimers increases because of the increased local concentration of TMR molecules that results from aggregation. Garai and Frieden (18) developed an assay that uses the loss of TMR fluorescence of labeled molecules to monitor aggregation, thus obviating the requirement that aggregates have to be species that are capable of binding ThT. This method affords higher temporal resolution of the aggregation process, especially with regard to the kinetic details of the early stages. Loss of fluorescence requires TMR dimerization either on the surface or within the interior of aggregates. Our computational analysis indicates that aggregates with approximately 20 Q30-K2K*G molecules will be 65% as bright as an individual molecule (SI Appendix, Fig. S7). All measurements of the rate of change of TMR fluorescence were initiated after disaggregation by dissolution of the peptides in 100% formic acid. These peptides were then diluted into water at twice the target concentration followed by further dilution to pH 7 in a 50-mM phosphate buffer. The measurements of TMR fluorescence were performed at 37 °C under continuous stirring. The disaggregation protocol had to be redesigned vis-àvis the standard protocol (13), established for use with K2-Qn-K2 peptides; the details are discussed in SI Appendix, section 2. SI Appendix, Fig. S8 shows the normalized TMR fluorescence for Q30-K2K*G measured as a function of time at 37 °C for three different supersaturation values that range from S ∼ 0.1 to S ∼ 0.8. SI Appendix, Fig. S9 shows data for the rate of loss of TMR
0.7 0.6 0.5 0.4 0.3
Q30-K2: co=10 μM, S≈0.8 N17-Q30-K2: co=2 μM, S≈0.7 Q 30-C38: co=50 μM, S≈0.7
0.2 0.1 0 0
N17-Q30-C38: co=12 μM, S≈0.7 5
10
15
20
25
t (hrs)
30
35
40
45
50
Fig. 3. Comparative analysis of the kinetics of gain in ThT fluorescence for four different Q30 constructs.
PNAS | December 10, 2013 | vol. 110 | no. 50 | 20077
BIOPHYSICS AND COMPUTATIONAL BIOLOGY
Fig. 2. (A) Comparison of the kinetics of the loss of TMR fluorescence for Q30-K2K*G and N17-Q30-K2K*G. For each construct, we normalized the TMR fluorescence using the t = 0 value as a reference. (B) Comparison of extents of aggregation obtained by following the loss in TMR fluorescence and gain in ThT fluorescence for Q30-K2K*G and Q30-K2, respectively. The extents of reaction range from 0 to 1 and were calculated using the minimum and maximum values for TMR and ThT fluorescence. The black points correspond to the complement of the TMR data, whereby we convert the loss of TMR fluorescence into a growth curve to compare this with the kinetics of the gain in ThT fluorescence. (C) Comparison of extents of aggregation obtained by following the loss in TMR fluorescence and gain in ThT fluorescence for N17-Q30-K2K*G and N17-Q30-K2, respectively. All other details are similar to that of B.
Q30-K2 10 M, S 0.8
N17-Q30-K2 2 M, S 0.7
Q30-C38 50 M, S 0.7
N17-Q30-C38 12 M, S 0.7
Fig. 4. Comparison of aggregate morphologies for different Q30 constructs obtained using EM. Additional comparisons using TMR-labeled constructs are shown in SI Appendix, Fig. S10. Visual inspection suggests that although N17 promotes long, ordered fibrils, these fibrils are thinner than those obtained for constructs with the C38 module.
20078 | www.pnas.org/cgi/doi/10.1073/pnas.1320626110
1
Q30−K2K*G, S ≈ −2.2 Q30−K2K*G, S ≈ −1.5 Q30−K2K*G, S ≈ −0.6 N17−Q30−K2K*G, S ≈ −0.8 Q 30−C38K*G, S ≈ −2.1
0.9 0.8
Normalized TMR fluorescence
shows the expected profile for the rate of growth of aggregates that is obtained by computing the complement of the normalized profile for the loss of TMR fluorescence. This computed curve essentially coincides with the profile for the gain in ThT fluorescence profile, thereby implying that the loss of TMR fluorescence results almost entirely from fibril formation. In direct contrast, the loss in TMR fluorescence for Q30-K2K*G reaches a plateau value before there is discernible increase in ThT fluorescence (Fig. 2B). This implies a separation of time scales between two processes, namely, preequilibration of nonfibrillar species before their conversion into fibrillar aggregates. The time scales for the two processes, quantified as the times for the extents of distinct reactions to reach 50% completion, differ by at least an order of magnitude for Q30-K2K*G. To measure the rate of loss of TMR fluorescence for Q30-C38 and N17-Q30-C38 peptides at equivalent supersaturation values we would need TMR-labeled material at concentrations where inner filter effect confounds the interpretations of TMR fluorescence measurements (19). In addition, if we followed the protocol used for N17-Qn-K2K*G and Qn-K2K*G by labeling the C-terminal end, then we would be probing the likelihood of associations between the C38 modules as opposed to just the polyglutamine-mediated associations. We need detailed simulations to help guide the optimal placement of the TMR labels in constructs with the C38 module. These simulations are feasible with improvements to forcefields that describe proline-rich sequences and will be pursued in future work. Here, we focus on comparing the kinetics of changes in ThT fluorescence for all four constructs at equivalent supersaturation values. Fig. 3 presents our comparative analysis of data obtained for the extents of reaction derived from ThT fluorescence measurements as a function of time at 37 °C for the peptides, Q30-K2, N17-Q30-K2, Q30-C38, and N17-Q30-C38, respectively; S ∼ 0.7– 0.8 for all peptides. Although C38 lowers the cs value and hence decreases the magnitude of the driving force for aggregation, at equivalent supersaturation values the kinetic profile for the
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0
5
10
15
20
25
t (hrs)
30
35
40
45
Fig. 5. Comparing the kinetics of loss of TMR fluorescence in subsaturated solutions for Q30 constructs with and without N17 and C38.
change in ThT fluorescence for N17-Q30-C38 is essentially identical to that of N17-Q30-K2. This suggests that even in the presence of the solubilizing C38 module the mechanism of aggregation is controlled by N17, which accelerates fibril formation while destabilizing nonfibrillar intermediates. In contrast, the increase in ThT fluorescence for Q30-C38 shows a significant lag phase, which is also present for Q30-K2. Impact of Flanking Sequences on Morphologies. We asked if the observed differences in aggregation kinetics at equivalent supersaturation values lead to differences in the morphologies of aggregates. Fig. 4 shows the morphologies obtained using EM following long-time incubations of Q30-K2, N17-Q30-K2, Q30C38, and N17-Q30-C38. All data were collected following aggregation in supersaturated solutions, with S ∼ 0.8. Additional assessments of morphologies were obtained by imaging the end products of species obtained from TMR assays for Q30-K2K*G and N17-Q30-K2K*G (SI Appendix, Fig. S10). Q30-K2 forms a dense mesh of aggregates with truncated fibrils (Fig. 4). The rapid formation of nonfibrillar species by Q30-K2 appears to contribute to the disordered morphologies of higherorder aggregates. In contrast, the representative images in Fig. 4 show that N17-Q30-K2, Q30-C38, and N17-Q30-C38 form ordered fibrils with minimal nonspecific associations. Comparison of the morphologies for N17-Q30-K2 and Q30-C38 at equivalent magnifications suggests that these modules get differently incorporated into the fibrils leading to differences in morphologies. These differences are consistent with published studies, which suggest that C38 contributes to the formation of bottlebrush architectures (20). Aggregates Form in Subsaturated Solutions. The homogeneous nucleation model (16) has been used as a conceptual framework for explaining the kinetics of polyglutamine aggregation (21). In this model, an obligatory nucleus representing an embryo of the new aggregated/insoluble phase has to form within the homogeneously mixed, soluble phase. The free energy of nucleation depends on the supersaturation value and the free-energy penalty associated with creating an interface between the new and old phases (16). Supersaturation is a prerequisite for a nucleated process. It therefore follows that monomers have to be the predominant species in subsaturated solutions either because aggregates never form or because they are only marginally stable. To assess the applicability of homogeneous nucleation theory, we quantified the kinetics of the loss of TMR fluorescence in Crick et al.
100 nm
Fig. 6. Representative EM image of oligomers that form in subsaturated solutions of Q30-K2. Here, co = 1 μM and S ∼ –1.5. The black arrows point to small oligomers, and the red arrows point to larger oligomers that form via the aggregation of smaller oligomers. The sizes of oligomers are expected to be in the 10–50-nm range.
M1
F
S
I
M1
MCI
MCS
F
FS
t aggrega
FI
g
Crick et al.
B Schematic for N17-Qn-K2 and Ex1
A Schematic for Q n-K2
ear aggre
subsaturated solutions. Fig. 5 shows the results of these measurements for Q30-K2K*G, N17-Q30-K2K*G, and Q30-C38K*G. The data in Fig. 5 highlight the presence of stable aggregates that form without a lag phase on time scales that are comparable to those seen in supersaturated solutions. Fig. 6 shows a representative EM image of visible, nonfibrillar aggregates formed by Q30-K2K*G in subsaturated solutions. These aggregates are spherical and are roughly 10 nm in size. Increased concentration of such aggregates leads to the increased loss of TMR fluorescence that we observe for Q30-K2K*G as the solutions become less subsaturated, i.e., as S becomes less negative (Fig. 5). Taken together, the data in Figs. 5 and 6 provide evidence against the applicability of homogeneous nucleation for describing the aggregation of polyglutamine constructs. Instead the data point to a heterogeneous process that involves nonfibrillar species whose stabilities vary depending on the presence or absence of flanking sequences. Fig. 5 points to the existence of multiple phases in solution including one that should give rise to a distinct saturation concentration for soluble aggregates. These could either be liquidlike oligomers that arise from liquid–liquid demixing (22) or micellar structures that arise due to the possible existence of a critical micelle concentration. We used the plateau values for the TMR fluorescence in subsaturated solutions to estimate saturation concentrations for oligomers of Q30-K2K*G, N17Q30-K2K*G, and Q30-C38K*G, respectively (SI Appendix, section 2). We denote the oligomer saturation concentration as cc. It is appropriate to use the C-terminally labeled Q30-C38K*G peptide to estimate cc from the plateau value of the TMR fluorescence. This value should be indicative of molecules that remain unincorporated into aggregates, irrespective of where the TMR label is situated within the construct. The values we obtain for cc at 37 °C in 50 mM phosphate buffers at pH 7 are 250 nM, 165 nM, and 3 μM for Q30-K2K*G, N17-Q30-K2K*G, and Q30-C38K*G, respectively. Clearly, the oligomer saturation concentration is distinct from and lower
Discussion Fig. 7 shows schematic representations of our proposals for the aggregation mechanisms of homopolymeric polyglutamine and the modulation of these mechanisms by N17 and C38. The scheme shown in Fig. 7A is in accord with the data presented here for Q30-K2 and Q30-K2K*G and a recent two-stage nucleation model for polyglutamine aggregation (23). This model postulates the existence of heterogeneous distributions of oligomers that arise due to liquid–liquid demixing of intrinsically disordered polyglutamine. For homopolymers in poor solvents, intermolecular interfaces are favored over chain-solvent interfaces. Accordingly, disordered globules associate in a thermodynamically downhill fashion to form a heterogeneous distribution of liquid-like oligomers (23). The model assumes a slow conversion of oligomers into fibrillar species, providing co > cs. A finite concentration of suitably large oligomers is required to support a nucleated conformational conversion to generate a template for fibril elongation (24, 25). In the model, the size of the smallest fibril-competent oligomer is denoted as imin. The data presented here for Q30-K2 and Q30-K2K*G provide direct evidence in support of equilibrium distributions of oligomers that form in sub- and supersaturated solutions. This step precedes fibril formation in supersaturated solutions, and Fig. 7A proposes that flux into fibrils is decreased due to the collection of nonfibrillar species that form in the absence of flanking sequences. The model (23) also showed that if imin is decreased, which implies destabilization of oligomers that cannot convert to fibrils, then fibril formation is accelerated. In such a scenario, if aggregation is modeled as homogeneous nucleation, then the apparent nucleus size can become less than or equal to zero (23) as was shown by Thakur et al. (6) who applied a variant of homogeneous nucleation to model the kinetics of the loss of soluble species for N17-Qn-K2 peptides. The schematic in Fig. 7B summarizes the effects of N17 and C38 on the aggregation of Htt NTFs. We propose that N17 and C38 act as gatekeepers to destabilize nonfibrillar intermediates thus ensuring that the fluxes are distributed across fewer routes. This schematic is consistent with data presented here and with results from atomistic simulations (15) which show that N17 destabilizes nonspecific associations vis-à-vis homopolymeric polyglutamine. In vitro (6) and cellular measurements (7) showed
MCS
Fig. 7. Schematic representations for the distinct aggregation mechanisms expected for (A) Qn-K2, (B) N17-Qn-K2 and N17-Qn-C38. M1, MCS ,MCI , FS, and FI denote monomers, soluble oligomers, insoluble nonfibrillar aggregates, soluble fibrils, and insoluble fibrils, respectively. The boxed regions delineate insoluble aggregates. Small arrows within the boxed regions depict equilibria between insoluble and soluble species as well as equilibria between different forms of insoluble species. The proposal is that imin, i.e., the size of smallest oligomer that can convert to fibrils, is considerably larger for Qn-K2 than for polyglutamine constructs with the N17 and C38 flanking sequences. In B, the decrease in imin is depicted as a decrease in the stability of MCS . Glutamine, hydrophobic residues, positive and negatively charged residues, and proline are denoted using spheres that are orange, yellow, blue, red, and purple, respectively (B).
PNAS | December 10, 2013 | vol. 110 | no. 50 | 20079
BIOPHYSICS AND COMPUTATIONAL BIOLOGY
than cs. The ratio cccs quantifies the gap between cc and cs. For T ∼ 37 °C this ratio is 18.4, 6.5, and 8.3 for Q30-K2, N17-Q30-K2, and Q30-C38, respectively.
that N17 accelerates fibril formation. This was taken to mean that the “aggregation propensity” of homopolymeric polyglutamine is considerably weaker than that of NTF constructs containing both polyglutamine and N17. The data presented here provide a more nuanced explanation that connects the simulation results to previous experimental observations. Fibrils form slowly and inefficiently for homopolymeric polyglutamine because the gap between cc and cs is largest for these constructs. This gap gives rise to kinetic competition with and thermodynamic destabilization of ordered fibrillar aggregates and increases the overall heterogeneity due to the increased thermodynamic and kinetic stability of nonfibrillar species as sketched in Fig. 7A (26). The use of two separate assays that are designed to probe distinct features of the kinetics of aggregation combined with solubility and morphological measurements lead to a more complete picture of the heterogeneities inherent to polyglutamine aggregation and the modulation of these heterogeneities by N17 and C38. The data presented here reconcile results from theory (23), simulations (15), in vitro (6), and in-cell experiments (7). Both N17 and C38 contribute to narrowing the gap between cc and cs thereby destabilizing nonfibrillar intermediates. C38 contributes to increasing cs thus weakening the overall driving force for aggregation whereas N17 accelerates fibril formation and stabilizes these species while destabilizing nonfibrillar species. The aggregation mechanisms for polyglutamine-containing peptides are distinct from mechanisms reported for the amyloid beta (Aβ) system (18). For Aβ peptides, it has been argued that aggregation can be modeled using a quasi-homogeneous nucleation model (11) if co > cs and co < cc (27). Our results show that the combination of co > cs and co < cc cannot be achieved for polyglutamine-containing systems. Even in the presence of flanking sequences the ratio cccs remains greater than 1. As a result, in supersaturated solutions, the mechanisms are likely to
always involve heterogeneities such as nonfibrillar oligomers (23, 25) that form before or concomitantly with fibrils. It is possible that de novo sequence design can be used to generate sequence variants of Aβ for which cc < cs, as is the case with polyglutaminecontaining systems.
1. Walker FO (2007) Huntington’s disease. Lancet 369(9557):218–228. 2. Becher MW, et al. (1998) Intranuclear neuronal inclusions in Huntington’s disease and dentatorubral and pallidoluysian atrophy: Correlation between the density of inclusions and IT15 CAG triplet repeat length. Neurobiol Dis 4(6):387–397. 3. Sathasivam K, et al. (2013) Aberrant splicing of HTT generates the pathogenic exon 1 protein in Huntington disease. Proc Natl Acad Sci USA 110(6):2366–2370. 4. Zheng ZQ, Li AM, Holmes BB, Marasa JC, Diamond MI (2013) An N-terminal nuclear export signal regulates trafficking and aggregation of Huntingtin (Htt) protein exon 1. J Biol Chem 288(9):6063–6071. 5. Maiuri T, Woloshansky T, Xia J, Truant R (2013) The huntingtin N17 domain is a multifunctional CRM1 and Ran-dependent nuclear and cilial export signal. Hum Mol Genet 22(7):1383–1394. 6. Thakur AK, et al. (2009) Polyglutamine disruption of the huntingtin exon 1 N terminus triggers a complex aggregation mechanism. Nat Struct Mol Biol 16(4):380–389. 7. Tam S, et al. (2009) The chaperonin TRiC blocks a huntingtin sequence element that promotes the conformational switch to aggregation. Nat Struct Mol Biol 16(12): 1279–1285. 8. Duennwald ML, Jagadish S, Muchowski PJ, Lindquist S (2006) Flanking sequences profoundly alter polyglutamine toxicity in yeast. Proc Natl Acad Sci USA 103(29): 11045–11050. 9. Park S-H, et al. (2013) PolyQ proteins interfere with nuclear degradation of cytosolic proteins by sequestering the Sis1p chaperone. Cell 154(1):134–145. 10. Crick SL, Jayaraman M, Frieden C, Wetzel R, Pappu RV (2006) Fluorescence correlation spectroscopy shows that monomeric polyglutamine molecules form collapsed structures in aqueous solutions. Proc Natl Acad Sci USA 103(45):16764–16769. 11. Garai K, Sahoo B, Sengupta P, Maiti S (2008) Quasihomogeneous nucleation of amyloid beta yields numerical bounds for the critical radius, the surface tension, and the free energy barrier for nucleus formation. J Chem Phys 128(4):045102. 12. Smith PK, et al. (1985) Measurement of protein using bicinchoninic acid. Anal Biochem 150(1):76–85. 13. Chen SM, Wetzel R (2001) Solubilization and disaggregation of polyglutamine peptides. Protein Sci 10(4):887–891. 14. Walters RH, Murphy RM (2009) Examining polyglutamine peptide length: A connection between collapsed conformations and increased aggregation. J Mol Biol 393(4): 978–992.
15. Williamson TE, Vitalis A, Crick SL, Pappu RV (2010) Modulation of polyglutamine conformations and dimer formation by the N-terminus of huntingtin. J Mol Biol 396(5):1295–1309. 16. Kashchiev D, van Rosmalen GM (2003) Review: Nucleation in solutions revisited. Cryst Res Technol 38(7-8):555–574. 17. Corsepius NC, Lorimer GH (2013) Measuring how much work the chaperone GroEL can do. Proc Natl Acad Sci USA 110(27):E2451–E2459. 18. Garai K, Frieden C (2013) Quantitative analysis of the time course of Aβ oligomerization and subsequent growth steps using tetramethylrhodamine-labeled Aβ. Proc Natl Acad Sci USA 110(9):3321–3326. 19. Fanget B, Devos O, Draye M (2003) Correction of inner filter effect in mirror coating cells for trace level fluorescence measurements. Anal Chem 75(11):2790–2795. 20. Bugg CW, Isas JM, Fischer T, Patterson PH, Langen R (2012) Structural features and domain organization of huntingtin fibrils. J Biol Chem 287(38):31739–31746. 21. Chen SM, Ferrone FA, Wetzel R (2002) Huntington’s disease age-of-onset linked to polyglutamine aggregation nucleation. Proc Natl Acad Sci USA 99(18):11884–11889. 22. Ganazzoli F, Raos G, Allegra G (1999) Polymer association in poor solvents: from monomolecular micelles to clusters of chains and phase separation. Macromol. Theory Simul 8(1):65–84. 23. Vitalis A, Pappu RV (2011) Assessing the contribution of heterogeneous distributions of oligomers to aggregation mechanisms of polyglutamine peptides. Biophys Chem 159(1):14–23. 24. Serio TR, et al. (2000) Nucleated conformational conversion and the replication of conformational information by a prion determinant. Science 289(5483):1317–1321. 25. Lee J, Culyba EK, Powers ET, Kelly JW (2011) Amyloid-β forms fibrils by nucleated conformational conversion of oligomers. Nat Chem Biol 7(9):602–609. 26. Cheng SZD, Keller A (1998) The role of metastable states in polymer phase transitions: Concepts, principles, and experimental observations. Annu Rev Mater Sci 28:533–562. 27. Auer S, Ricchiuto P, Kashchiev D (2012) Two-step nucleation of amyloid fibrils: omnipresent or not? J Mol Biol 422(5):723–730. 28. Arrasate M, Finkbeiner S (2012) Protein aggregates in Huntington’s disease. Exp Neurol 238(1):1–11. 29. Landles C, et al. (2010) Proteolysis of mutant huntingtin produces an exon 1 fragment that accumulates as an aggregated protein in neuronal nuclei in Huntington disease. J Biol Chem 285(12):8808–8823.
20080 | www.pnas.org/cgi/doi/10.1073/pnas.1320626110
Implications for Htt NTFs in Vivo. We propose that N17 and C38 act as natural gatekeepers to minimize the deleterious effects of heterogeneities that are intrinsic to polyglutamine aggregation. Time-resolved microscopy data from neurons show that diffuse aggregates and smaller species of mutant Htt directly correlate with neuronal death, whereas the formation of large insoluble inclusions diminishes the levels of diffuse, heterogeneous aggregates and the risk of striatal neuron death (28). It is conceivable that variations in NTFs that result from aberrant splicing of exon 1 or proteolytic processing of mutant Htt (29) contribute to variations in the relative levels of small diffuse aggregates versus large insoluble inclusions, leading to the observed cell-to-cell variations in phenotype (28).
Materials and Methods Peptides were purchased in crude form from Yale University’s Keck Biotechnology center. In addition to the unlabeled peptides used for cs and ThT measurements, the labeled peptides were synthesized with the red TMR dye attached through the lysine amine. SI Appendix, section 2, provides all of the details of the preparation and handling of each peptide system. In addition, this section also details the protocols used for measuring cs values, TMR and ThT fluorescence, and imaging of aggregates using EM. ACKNOWLEDGMENTS. We thank Dorothy Beckett, Nicholas Corsepius, Marc Diamond, Tyler Harmon, Alex Holehouse, George Lorimer, M. Muthukumar, Dev Thirumalai, and Andreas Vitalis for insights and helpful discussions. This work was supported by Grant 5R01NS056114 from the National Institutes of Health (to R.V.P.).
Crick et al.
