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Mechanistic Origin of the Combined Effect of Surfaces and Mechanical Agitation on Amyloid Formation Fulvio Grigolato, Claudio Colombo, Raffaele Ferrari, Lenka Rezabkova, and Paolo Arosio ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.7b05895 • Publication Date (Web): 18 Oct 2017 Downloaded from http://pubs.acs.org on October 18, 2017

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Mechanistic Origin of the Combined Effect of Surfaces and Mechanical Agitation on Amyloid Formation Fulvio Grigolato§, Claudio Colombo§, Raffaele Ferrari, Lenka Rezabkova, and Paolo Arosio∗ Department of Chemistry and Applied Biosciences Swiss Federal Institute of Technology Zurich, Vladimir Prelog Weg 1, 8093, Zurich, Switzerland E-mail: [email protected]

∗

To whom correspondence should be addressed

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Abstract Interactions between proteins and surfaces in combination with hydrodynamic flow and mechanical agitation can often trigger the conversion of soluble peptides and proteins into aggregates, including amyloid fibrils. Despite the extensive literature on the empirical effects of surfaces and mechanical forces on the formation of amyloids, the molecular details of the mechanisms underlying this behavior are still elusive. This limitation is, in part, due to the complex reaction network underlying the formation of amyloids, where several microscopic reactions of nucleation and growth can occur both at the interfaces and in bulk. In this work, we design a high-throughput assay based on nanoparticles and we apply a chemical kinetic platform to analyze the mechanisms underlying the effect of surfaces and mechanical forces on the formation of amyloid fibrils from human insulin under physiological conditions. By considering a variety of polymeric nanoparticles with different surface properties we explore a broad range of repulsive and attractive interactions between insulin and surfaces. Our analysis shows that hydrophobic interfaces induce the formation of amyloid fibrils by specifically promoting the primary heterogeneous nucleation rate. In contrast, mechanical forces accelerate the formation of amyloid fibrils by favoring mass transport and further amplify the number of fibrils by promoting fragmentation events. Thus, surfaces and agitation have a combined effect on the kinetics of protein aggregation observed at the macroscopic level but, individually, they each affect distinct microscopic reaction steps: the presence of interfaces generates primary nucleation events of fibril formation, which is then amplified by mechanical forces. These results suggest that the inhibition of surface-induced heterogeneous nucleation should be considered a primary target to suppress aggregation and explain why in many systems the simultaneous presence of surfaces and hydrodynamic flow enhances protein aggregation.

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Keywords nanoparticles, hydrophobicity, mechanical agitation, interfaces, protein aggregation, amyloids, insulin

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The spontaneous self-assembly of a broad range of peptides and proteins into insoluble aggregates plays a crucial role in a variety of systems in biology and biotechnology. A particularly important class of protein aggregates are amyloid fibrils, which have been associated with many functional and aberrant activities in living systems. 1–3 Among the several factors that regulate aggregation reactions, interactions between proteins and several types of surfaces are well-recognized as key factors in determining aggregation rates in bulk solution. For instance, air-water interfaces 4–6 , lipid vesicles 7–9 or the surfaces of amyloid fibrils 10 can often promote nucleation and growth of protein aggregates. In addition to surfaces, mechanical forces induced by agitation and hydrodynamic flow are also well-known modulators of protein aggregation stability 11,12 . Understanding the combined effect of surfaces and hydrodynamic flow on protein aggregation is of fundamental importance for many biomedical and biotechnological systems, ranging from the circulation of amyloidogenic proteins in the blood stream 13,14 to the industrial manufacturing of monoclonal antibodies, to the delivery of therapeutic proteins 15,16 . In conventional aggregation assays it is challenging to extrapolate the individual effects of surfaces and shear due to the fact that the configuration of the experiment does not allow one to independently control the air/water interface and the mechanical forces induced by agitation. Recent studies have addressed this limitation by eliminating the air/water interfaces 4 or by introducing a controlled large amount of additional surfaces 17,18 . Following the latter approach, a particularly attractive strategy consists of using nanoparticles (NPs), 19 which exhibit a high surface to volume ratio and allow one to neglect the contribution of the air-water interface as well as the surfaces of the test tube. Moreover, the flexibility of polymer chemistry enables the synthesis of polymeric NPs with different surface properties and sizes, which, in turn, leads to the development of a flexible platform on which to selectively investigate the effect of surfaces on protein aggregation. Such aggregation studies in the presence of NPs have demostrated that these surfaces can

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either promote or inhibit the aggregation of amyloidogenic peptides and proteins, depending on the relative reactivity of proteins in bulk with respect to surfaces. 19–24 In particular, the aggregation propensity of a given protein onto surfaces can be affected either by structural changes of the given protein or by modification of the given protein’s concentration upon adsorption 23,25–29 . Among the several surfaces which have been previously investigated, hydrophobic interfaces are recognized to be particularly effective in inducing protein aggregation, especially for globular proteins. 17,18,30 Despite the vast literature on the effects of surfaces and NPs on protein aggregation, the molecular mechanisms underpinning this behavior are still poorly understood. This is partially due to the fact that the formation of amyloid fibrils is the consequence of a cascade of nucleation and growth events which include several primary and secondary nucleation processes as well as elongation and fragmentation reactions. 31 In principle, the presence of surfaces could affect the global aggregation rate by modulating any of these individual reactions. Moreover, the partitioning of the protein between the interfaces and the bulk solution allows the reactions to potentially occur in both phases, 23 thereby generating a reaction network which is challenging to analyze. This work aims to address this aspect by applying a chemical kinetic strategy to investigate the individual effects of surfaces and mechanical agitation on the molecular mechanisms of amyloid formation. In doing so, we can provide insight on the system which is not accessible by experimental observation alone. We performed our study on the aggregation of human insulin, which is a common model amyloidogenic protein 31–35 in addition to being a relevant biotechnological product 36 . By synthesizing nanoparticles with different surface properties we explored a broad range of different intermolecular interactions between surfaces and insulin. In our experiments the ratio between the total surface of the nanoparticles and the surface of the air-water interface varies between ≈ 102 and ≈ 104 , and the contribution of the air-water interface can therefore be neglected. Our results show that hydrophobic surfaces can dramatically trigger the formation

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of amyloid fibrils of human insulin under physiological conditions by selectively increasing the primary heterogeneous nucleation rate. Moreover, we show that mechanical agitation provides positive feedback and promotes the generation of additional nuclei during the reaction.

Results and discussion Attractive interactions between surfaces and insulin promote amyloid formation To explore the effects of a broad range of interactions between insulin and surfaces, we synthesized several NPs, each with a different surface chemistry (Fig.1). Specifically, we produced PEGylated NPs to promote repulsive hydration forces, negative and positive NPs to induce long-range electrostatic coulombic interactions, and hydrophobic NPs to generate short-range attractive forces. Electrostatic interactions were further modulated by varying the net charge of insulin via the use of buffers with various pH values. The different conditions are summarized in Fig. 2, where we show the simulations of the interactions between insulin and the different surfaces according to colloidal coarse-grained models (Fig. 2B, see also Suppl. Info.). We investigated the aggregation behaviour in the absence and presence of an increasing amount of NPs by performing aggregation assays at 50 ◦C, at pH 1.6 or 7.4. We did so by monitoring the increase in the fluorescence signal of the Thioflavin T (ThT) dye, which is a common reporter of the formation of β-sheet structures characteristic of amyloid fibrils 10,32,37 . (Fig. 2C). The concentrations of NPs are expressed as insulin:NPs surface ratios, which vary in the experiments between 10 and 1000 and correspond to an excess of insulin monomers per single NP (between 103 and 105 ). The aggregation profiles show that in the presence of repulsive interaction forces between NPs and proteins, there is essentially no effect on the aggregation behavior during the 6


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time scale of the experiments, independently of the molecular origin of the repulsive forces. By contrast, the presence of attractive interactions between NPs and insulin promotes the aggregation process. We note that in the absence of NPs insulin solutions are stable at pH 7.4, while acidic pH conditions are well known to induce formation of amyloid fibrils 32 . At both pH values, however, the addition of NPs that promote repulsive forces does not induce appreciable differences in the aggregation profiles with respect to the situation in which NPs are absent. Moreover, we note that hydrophobic NPs are particularly effective in initiating aggregation, despite the fact that the extent of the calculated attractive interaction potential is three orders of magnitude smaller with respect to the potential of the charged NPs. This observation is consistent with previous reports discussing the destabilizing effect of hydrophobic interfaces on protein stability. 14,17,18,30 FTIR spectroscopy (Fig. 3A) and imaging analysis (Fig. 3B) confirm that the fibrils generated in the presence of NPs exhibit similar structures to the amyloids prepared under conventional acidic conditions 31 . Moreover, we further verified the absence of alternative pathways with respect to fibrillization by isolating the soluble fraction via precipitation and analyzing the residual monomer concentration by absorbance measurements. The time evolution of the monomer conversion (Fig. 3C and Suppl. Fig. S3) exhibits a sigmoidal shape which is symmetric with respect to the profile of the formation of amyloid fibrils, confirming that essentially every monomeric unit is recruited into fibrillar, ThT-positive structures, and no additional aggregation pathways leading to amorphous aggregates are present in the solution. The dramatic ability of hydrophobic NPs to trigger aggregation was further validated by experiments performed under the physiological conditions of 37 ◦C and pH 7.4. While most globular proteins are stable under these conditions, the presence of the hydrophobic NPs induces the formation of amyloid fibrils (Fig. 4A) to an extent which is larger with respect

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to charged NPs (Suppl. Fig. S4). This peculiar effect of the hydrophobic forces was further probed by a series of experiments in which we synthesized and tested NPs composed of hydrophobic molecules with increasing carbon chain length. These experiments demonstrated that the aggregation rates increase with the hydrophobicity of the NPs (Fig. 4B).

Hydrophobic surfaces trigger amyloid formation by specifically affecting primary heterogeneous nucleation events We next aimed to go beyond the empirical observation of the effect of the NPs and investigate the molecular mechanisms underlying the powerful ability of hydrophobic NPs to induce amyloid formation at physiological conditions. To do so, we applied a chemical kinetic model and analyzed the ThT aggregation reaction profiles monitored at fixed concentration of insulin and increasing concentrations of hydrophobic NPs at both 37 ◦C (Fig. 4A) and 50 ◦C (data shown in Suppl. Fig. S4). In addition to the elongation of fibrils via incorporation of monomeric units, insulin amyloids also grow via secondary nucleation processes that include fragmentation events 31,34 , which form additional reactive fibril ends and provide positive feedback for fibril growth. The presence of fibril fragmentation in the presence of shaking is confirmed by the measurement of the length distribution of fibrils incubated at 400 rpm in the absence of monomers. The fibril length distributions were obtained by a single-fibril analysis of atomic force microscopy images using the open-source FiberApp software 38 . The results (Suppl. Fig. S5) show a shift of the fibril length distribution towards smaller sizes with increasing incubation time. The contribution of the individual microscopic reactions to the global aggregation profiles is described in the kinetic model mainly through two combined parameters: k+ kn and k+ k− , where kn , k+ and k− are the primary nucleation, elongation and fragmentation rate constants, respectively. The core of the analysis consists of monitoring the changes in the kinetic parameters 8


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required to describe the experimental profiles at different concentrations of NPs. The fitted simulated profiles were compared to the experimental data in Fig. 4A, and the corresponding kinetic parameters are shown in Fig. 4C (the full list of kinetic parameters used in our model simulations is reported in Suppl. Info). The parameter k+ kn increases with increasing the concentration of hydrophobic NPs, while the parameter k+ k− remains constant. This important result shows that the presence of the NPs significantly and selectively affects only the primary heterogeneous nucleation rate. Indeed, modifications of the elongation rate constant k+ would affect both parameters, while changes in the fragmentation rate constant should modify the parameter k+ k− . This observation indicates that growth and fragmentation processes are independent of the hydrophobic surfaces, and therefore occur mainly in bulk solution. We conclude that the presence of hydrophobic NPs induces amyloid formation by promoting the nucleation of the first oligomers, either by inducing conformational changes upon adsorption or by locally increasing the concentration. The measurement of the residual soluble fraction by NPs precipitation (Suppl. Fig. S3) indicates that about 20% of insulin monomers are adsorbed on the surfaces of the NPs in the early stages of aggregation, corresponding to about 102 − 104 monomers per NP, a value which is equal or larger than the number of molecules required to form a monolayer on the NP surface (equal to about 102 ). Although the local increase of insulin concentration could be responsible for increasing the nucleation process, a similar adsorption is also present in the case of attractive interactions induced by charged NPs. The fact that hydrophobic NPs have a larger effect than charged NPs suggests that in addition to surface concentration, conformational modifications of insulin monomers upon adsorption may also be present. Circular dichroism spectroscopy analysis did not show any significant structural changes in the structure of bulk insulin in the absence and presence of NPs after several hours of incubation in a temperature range from 25 to 50 ◦C (Fig. 4D), indicating that no major conformational changes occur in solution. This observation implies that changes in insulin structure must be confined to the surfaces of the NPs, and these

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modified monomers are likely to be highly reactive and rapidly recruited into oligomers and fibrils. In order to validate the conclusions of the kinetic analysis, we predicted the behavior of seeded aggregation experiments from the mechanisms derived under unseeded conditions. In Fig. 5, we show the aggregation profiles of insulin solutions in the absence and presence of hydrophobic NPs seeded by the presence of pre-formed fibrils. The model parameters evaluated under unseeded conditions accurately predict the aggregation curve in the presence of seeds (Fig. 5A). Moreover, the conclusion that primary nucleation events do not occur in the absence of NPs is consistent with the fact that the seeded aggregation curve in Fig. 5B could be well described by simply setting the primary nucleation rate kn to zero and keeping the same kinetic parameters of the simulations in Fig. 5A (see Table S6). An important insight of this experiment is the confirmation that hydrophobic surfaces do not affect elongation and fragmentation events, and instead specifically promote heterogeneous primary nucleation.This result has implications not only for the formation of the final fibrils but also for the soluble intermediate oligomers.

Mechanical agitation provides a positive feedback by increasing both primary and secondary nucleation processes We next applied the chemical kinetic platform to analyze the combined effect of hydrophobic surfaces and mechanical forces on the microscopic mechanisms of aggregation. For this purpose, we designed a series of kinetic experiments where we varied both the amount of NPs and the shaking speed at 50 ◦ C and pH 7.4 (Fig. 6A and 6B). At constant shaking frequency, the results show an increase in the aggregation rate at higher concentrations of hydrophobic NPs, in agreement with the results at 37 ◦ C showed in Fig. 4A. Notably, the effect of decreasing the shaking frequency, and therefore the mechanical forces, is drastically more evident at the lower NPs concentration. In particular, at high NP 10


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concentrations the progessive reduction in the shaking speed from 400 to 0 rpm decreases the rate of aggregation but does not suppress the final formation of fibrils during the time scale of the experiment. In contrast, at low NP concentrations, no significant aggregation is observed at low shaking frequencies. To explain the observed behavior, we applied the kinetic model to describe the ThT aggregation profiles observed at the high NP concentrations. The changes in the aggregation profiles monitored at different shaking rates can be sufficiently described by changing the rate constants k+ k− and k+ kn , as shown by the simulated profiles in Fig. 6A-6B and the kinetic parameters reported in Fig. 6C. The analysis of the kinetic rate constants shows that the mechanical agitation increases both the fragmentation rate of the fibrils and the effective primary nucleation rate, most likely by promoting the detachment of nuclei generated on the surfaces of the NPs (see Table S7). The findings discussed in the previous paragraph revealed that hydrophobic surfaces affect only the primary nucleation rate without modifying the fragmentation processes. Taken together, these results suggest that the effects of surfaces and mechanical agitation are decoupled at the microscopic level, such that these factors affect different reactions in the aggregation network: namely, the presence of hydrophobic surfaces specifically triggers heterogeneous primary nucleation events, while mechanical forces generate a positive amplification of the formation of fibrils by promoting both fragmentation and primary nucleation events, likely by favoring the detachment of the nuclei from the surfaces. Due to the fact that the elongation and the fragmentation rate constants are independent of the hydrophobic surfaces, we predicted that the estimated rate constants k+ and k− at high concentrations of NPs (Fig. 6A) would be capable of describing the profiles shown in Fig. 6B. Indeed, we could simulate the aggregation curves at low NP concentrations by significantly changing only the heterogeneous primary nucleation constant, kn (see Table S7). In this case, the number of fibrils generated by primary nucleation is reduced, and the applied low frequency shaking is insufficient in producing amplification.

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These results highlight the combined effect of surfaces and mechanical agitation, as demonstrated by the calculation of the number of fibrils generated by primary and secondary nucleation events at different NP concentrations and shaking speeds (Fig. 6D). At the lower NP concentrations, the number of fibrils generated by primary nucleation events at 100 rpm is about 0.001 % of the amount produced at higher NP concentrations. Furthermore, the decrease of the shaking speed does not provide sufficient amplification to sustain the aggregation process. Although conventional macroscopic measurements cannot detect aggregation, this analysis demonstrates that under these conditions, hydrophobic surfaces can trigger the formation of a small amount of oligomers which may exhibit toxic functions and/or seed the growth of fibrils upon changes in conditions. Overall, these findings suggest that the inhibition of surface-induced heterogeneous nucleation should be considered a primary target to suppress aggregation in contexts where this process has negative consequences. In summary, our results provide a mechanistic description of the combined effect of hydrophobic surfaces and mechanical agitation on amyloid formation, and reveal how these factors independently influence distinct microscopic processes (Fig. 7). Although these factors influence distinct microscopic reactions, the effects of these two factors are combined at the macroscopic level because of the non-linearity of the complex aggregation network underlying the formation of amyloid fibrils, where secondary nucleation processes induced by fragmentation provide a positive feedback for the formation of fibrils initially generated by primary nucleation events. We expect that similar mechanisms could also extend to several other biomedical and biotechnological systems in which the presence of surfaces and shear plays a crucial role in triggering and modulating protein aggregation. In particular, the results of this work have been obtained with a model amyloidogenic protein, and it remains to be established whether or not similar conclusions could apply to any generic protein. This possibility can be explored by applying experimental strategies similar to the platform developed in this work.

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Conclusions Using NPs of markedly different surface chemistry, we have explored the effects of a broad range of repulsive and attractive protein-surface interactions on insulin’s amyloid formation propensity. We further probed this system by studying these effects in the absence and presence of mechanical agitation. We have applied a chemical kinetic platform to go beyond the empirical observation of the effect of surfaces and mechanical agitation on protein aggregation and provide a molecular, mechanistic description of this process. We have shown that hydrophobic NPs promote the formation of amyloid fibrils under physiological conditions by specifically altering the heterogeneous primary nucleation reaction. In constrast, mechanical forces increase the rate of fragmentation events and promote primary nucleation events, likely by favoring the turnover of monomers and nuclei on the surfaces. Despite the fact that surfaces and mechanical forces affect distinct microscopic processes, the effects of these two factors are combined at the macroscopic level because of the nonlinearity of the aggregation network underlying the formation of amyloid fibrils, where secondary nucleation processes induced by fragmentation provide a positive feedback for the formation of fibrils initially generated by primary nucleation events. Our analysis also demonstrates that hydrophobic surfaces could trigger the formation of a small amount of oligomers under conditions in which conventional macroscopic measurements cannot detect aggregation. Such oligomers may exhibit toxic functions and/or promote fibril formation upon changes in conditions, suggesting that the inhibition of surface-induced heterogeneous nucleation should be considered a primary target to suppress aggregation in contexts where this process has negative consequences. We expect that similar mechanisms may explain why protein aggregation is often promoted by the simultaneous presence of hydrophobic surfaces and hydrodynamic flow, while the presence of only one of these two factors is less detrimental to protein stability.

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Methods Synthesis and characterization of polymeric nanoparticles Methyl methacrylate (MMA, Sigma Aldrich, 99%), butyl acrylate (BA, Sigma Aldrich, 99%), lauryl acrylate (LA, Acros Organics, 96%), 2-ethylhexil acrylate (EHA, ABCR, 98%), poly ethylene glycol methyl ether methacrylate (PEGMA, Mw = 2080 Da, 50% solution in water), 3-sulfopropyl methacrylate potassium salt (MA−SO–3 , Sigma Aldrich, 98%), sodium dodecyl sulfate (SDS, Sigma Aldrich, 99, 85%), [2-(methacryloyloxy)ethyl]trimethylammonium chloride (MA−Ch+ , Sigma Aldrich, 80% solution in water), potassium persulfate (KPS, Merck), 2,2’-azobis(2-methylpropionamidine) dihydrochloride (V50, Sigma Aldrich, 97%), azoisobutyronitrile (AIBN, Sigma Aldrich, 99%) were purchased commercially and used without further purification. Positive, negative and PEGylated NPs were synthesized using MMA as the main component adopting either a batch or semibatch procedure 39,40 . In particular, for the PEGylated NPs, 500 mg of PEGMA were dissolved in 45 ml of distilled water in a three neck flask. The system was incubated in an oil bath equipped with a glass condenser and a thermocouple and purged with a nitrogen flux for 20 minutes at room temperature under stirring. After rising the temperature to 70 ◦C and introducing the radical initiator (KPS, 50 mg dissolved in 2.5 ml of distilled water), MMA (2 g) was added with a syringe pump over one hour and the reaction was allowed to continue for four hours. Positive and negative NPs were produced with a batch process by dissolving 2.375 g of MMA and 0.125 mg of either MA−Ch+ or MA−SO–3 in 45 g of distilled water and carring out the reaction under the same conditions of the PEGylated NPs. V50 and KPS were used as radical initiators for positive and negative NPs, respectively. For hydrophobic NPs, a batch emulsion polymerization was employed using SDS as a stabilizing agent. 75 mg of SDS and 2.5 g of hydrophobic monomer (either BA, EHA) were mixed in 45 ml of water. In analogy with the previous cases, the solution was first flushed with

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nitrogen and then heated to 70 ◦C. KPS (30 mg dissolved in 2.5 ml of water) was used as the radical initiator. The reaction was stopped after four hours and the dispersions were cleaned with ion exchange resins (Dowex Marathon M3, Sigma Aldrich) to remove the surfactant as well as all the possible electrolytes 41 . The effectiveness of the surfactant removal step was verified by measuring the electric conductivity of the dispersion before and after the ion exchange (data not shown). The sizes and the Zeta potentials of the NPs were both characterized by dynamic light scattering (Zetasizer, Malvern, UK) (Fig. 1B, solid red line, and Fig. 1C ). Measurements were performed in triplicates. The size of the NPs was further characterized by scanning electron microscopy (SEM) for positive and negative NPs, and by cryo-SEM for hydrophobic and PEGylated NPs. SEM images were acquired by adsorbing samples on a Si wafer. After drying, the samples were investigated on a Gemini 1530 (Zeiss) operated at low acceleration voltage (U = 1 kV) to minimize charging. For cryo-SEM analysis samples on carbon coated grids (Quantifoil, Germany) were manually plunge frozen in a mix of liquid ethane/propane, freeze dried up to −80 ◦C in a freeze-fracturing system BAF 060 (Bal-Tec/Leica, Vienna), coated with tungsten and imaged at −120 ◦C and 2kV in a field emission SEM Leo Gemini 1530 (Carl Zeiss, Germany) equipped with a cold stage (VCT Cryostage, Bal-Tec/Leica). The size distributions were extracted from the images with an in-house software written in Matlab (Fig. 1B, solid black line). The hydrophobicity of the NPs in 25 mM phosphate buffer at pH 7.4 was assessed semiquantitatively via measuring interactions with an hydrophobic dye and via a salt-precipitaiton assay. In particular, the increase in the fluorescence signal of Nile Red upon binding to the NPs was monitored in a 96-well plate (non-binding µclear Greiner bio-one microplate, PS, F-Bottom, Chimney wells) by recording fluorescence at 660 nm after excitation at 560 nm in a ClarioStar pleate reader (BMG Labtech, DE) (Fig. 1D, left, see also Supp. Info.). The hydrophobicity was also assessed by investigating the stability of 5% v/v NPs dispersions upon addition of a 10% v/v 50 mM MgCl2 solution (Fig. 1D, right).

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The final NPs solid content was determined via thermogravimetric analysis.

Calculation of interaction potentials The interactions between insulin and NPs under different conditions were quantified by coarsegrained colloidal interaction potentials considering the contribution of steric, electrostatic, hydration and hydrophobic forces. 42 The details of the model simulations of the several non-covalent interactions and the corresponding parameters are described in Suppl. Info.

Sample preparation, aggregation assays and fibril characterization Lyophilized human insulin was kindly donated by NovoNordisk (Copenhagen, DN). The powder was freshly dissolved before each experiment in 25 mM HCl at pH 1.6. When required, the solution was exchanged in 25 mM phosphate buffer at pH 7.4 by overnight dialysis at 4 ◦C using Slide-A-Lyzer Dialysis Cassettes (2K MWCO, 3 ml, Thermo Scientific). The solution was filtered with a 200 nm cut-off syringe filter (Millex LG Syringe Driven Filter Unit) to remove the presence of any potential seeds for aggregation. The quaternary state and the concentration of human insulin were checked by size exclusion chromatography (Superdex 75/100 column, GE Healthcare) coupled with in line multi-angle light scattering (Wyatt, DE) (Suppl. Fig. S2). 140 µl of insulin solution with 20 µM Thioflavin T (ThT) in the absence and presence of NPs were incubated in a 96-well plate (non-binding µclear Greiner bio-one microplate, PS, F-Bottom, Chimney wells) and the aggregation was monitored by recording ThT fluorescence at 480 nm after excitation at 440 nm in a ClarioStar pleate reader (BMG Labtech, DE). For control experiments, the time evolution of the residual monomer was evaluated by UV absorbance after separating the NPs by a combination of salt-induced precipitation and centrifugation. Changes in protein structure during aggregation were monitored by circular dichroism spectroscopy on a Jasco-815 CD spectrophotometer (Jasco, Easton, MD). Far-UV CD spectra 16


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were recorded from 250 to 195 nm with a quartz cuvette of 0.1 cm path length. Samples were further analyzed by Fourier Transform Infrared spectroscopy (FTIR) on a Cary 630 instrument (Agilent Technologies) and by atomic force microscopy (AFM) on a Cypher instrument (Asylum research) working in contact mode with a T4400PB type tip (Asylum research) with frequency range 20 − 50 kHz.

Kinetic model analysis The aggregation profiles in the presence of different concentrations of NPs were simulated individually according to the following equation:

M (t) =1− M (∞)

B+ + C+ B− + C+ eκt B+ + C+ eκt B− + C+

κ2 κ˜ ∞ κ

∞

e−κ∞ t ,

(1)

where the kinetic parameters B± , C± , κ, κ∞ , and κ ˜ ∞ are functions of the two combinations of the microscopic rate constants k+ k− and k+ kn , where kn , k+ , and k− are the primary nucleation, elongation, and fragmentation rate constants, respectively. 43–45 The rate constants in the presence of different concentrations of NPs were determined by fitting the individual reaction profiles by minimizing a least-squared error function defined tP exp as y = (Msim (ti ) − Mexp (ti ))2 , where Msim (ti ) and Mexp (ti ) are the simulated and the i=1

experimental total fibril mass fraction at time ti , respectively. 46,47 More details are reported in Suppl. Inform.

Associated Content Supporting experimental section, Tables S1-S7, and Figures S1-S5 as noted in the text.

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Acknowledgments The authors gratefully acknowledge NovoNordisk (DE) for kind donation of material, Dr. Tommaso Casalini (SUPSI, CH) and Prof. M. Muschol (University of South Florida, US) for useful discussions, Prof. R. Riek (ETH Zurich) for use of equipment, Dr. Frank Krumeich and Dr. Stephan Handschin of the ScopeM (ETH Zurich) for SEM and cryo-SEM images, respectively. This work has been supported by ETH Zurich.

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Figure Captions Figure 1: Characterization of the nanoparticles (NPs) used in this work. A) Schematic representing the composition of the different NPs. B) Size distributions of the different NPs measured by DLS (red solid line, see also Supp. Info.) and reconstructed from SEM or cryo-SEM images (black solid lines); corresponding images are reported on the right. C) Zeta potential distributions of the different NPs measured by electrophoresis. D) Hydrophobicity of the different NPs: increase in the fluorescence signal of Nile Red (percent) upon binding to NPs (see also Supp. Info.); stability of NPs dispersion (5% v/v) in a 5 mM MgCl2 solution. All the numerical values represent averages of independent triplicates.

Figure 2: Attractive interactions between surfaces and insulin promote amyloid formation. A) Schematic representing the repulsive and attractive intermolecular interactions between insulin and the various NP conditions. B) Simulations of the interactions between insulin and the various surfaces using colloidal course-grained models. The continuous lines correspond to the calculated global interaction potentials originating from the contribution of the individual non-covalent forces (dashed lines); C) Aggregation reaction profiles observed in the absence and presence of different NPs at 50 ◦C and 400 rpm shaking at the reference insulin concentration of 1 g l−1 . From left to right, experiments were performed at the following insulin:NP surface ratios: for PEGylated NPs: 100 : 1, 25 : 1, 10 : 1; for positive NPs at pH 1.6 and negative NPs at pH 7.4: 1000 : 1, 100 : 1, 50 : 1; for positive NPs at pH 7.4: 1000 : 1, 50 : 1, 25 : 1; for negative NPs at pH 1.6: 1000 : 1, 100 : 1, 50 : 1; and for hydrophobic NPs: 1000 : 1, 50 : 1, 10 : 1. The arrow indicates increasing concentrations of NPs. Black points correspond to insulin in the absence of NPs.

Figure 3: Formation of amyloid fibrils in the presence of hydrophobic NPs. A) FTIR and B) AFM imaging analysis of fibrils generated in the absence of NPs at pH 1.6 (left, scale bar: 1 µm) and in the presence of NPs at pH 7.4 (right, scale bar: 0.5 µm); C) The time 23


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evolution of insulin monomer conversion (red circles) exhibits a symmetric behavior with respect to the amount of fibrils formed over time (blue circles), indicating that no alternative, non-amyloid aggregation pathways are present in the system.

Figure 4: A) ThT aggregation profiles measured at 37 ◦C at the reference insulin concentration of 1 g l−1 in the absence (black symbols) and presence of NPs at insulin:NPs surface ratios of 1000 : 1 (yellow symbols), 100 : 1 (blue symbols), and 25 : 1 (violet symbols). Continuous lines represent model simulations. B) Aggregation rates are faster in the presence of NPs consisting of molecules with increasing hydrophobicity as shown by kinetic ThT aggregation experiments. C) Scaling of the kinetic rate constants k+ kn and k+ k− corresponding to the simulated aggregation profiles at 37 ◦C shown in A) and at 50 ◦C (data and simulated profiles shown in Suppl. Fig. 3). D) CD spectroscopy analysis of insulin solutions at 50 ◦C in the absence and presence of hydrophobic NPs for different incubation times; the insert shows the CD ellipsometry value measured at the reference wavelength λ = 208 nm in the temperature interval from 25 to 50 ◦C.

Figure 5: Unseeded (black) and seeded (red) aggregation profiles of insulin (1 g l−1 ) measured at 50 ◦C in the presence (A) and absence (B) of hydrophobic NPs at the insulin:NPs surface ratio of 100 : 1 with 2.5% monomer equivalent of pre-formed fibrils. The continuous lines represent predictions from model simulations.

Figure 6: Analysis of the combined effect of hydrophobic surfaces and shaking. Aggregation profiles at different shaking frequencies at high (25 : 1) (A) and low (1000 : 1) (B) NP concentrations. Circles indicate experimental data and continuous lines represent model simulations. The experimental aggregation profile of insulin in the absence of NPs at 400 rpm is shown in both panel A and B for reference as a light green line. C) Kinetic parameters corresponding to the model simulations shown in panel A. D) Calculated number

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of fibrils generated via primary and secondary nucleation processes at high and low NP concentration at various shaking frequencies. Experiments were performed at the reference insulin concentration of 1 g l−1 at 50 ◦C, pH 7.4

Figure 7: Schematic representation of the combined effect of surfaces and mechanical forces on the aggregation mechanisms underlying the formation of amyloids from human insulin under physiological conditions.

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Mechanistic Origin of the Combined Effect of ... - ACS Publications

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