Structural defects in amyloid-β fibrils drive secondary nucleation

Domain: chemistry
Published: 2026-02-18
Editorial outcome: minor_revision

Abstract
Formation of new amyloid fibrils and oligomers from monomeric protein on the surfaces of existing fibrils is an important driver of many disorders such as Alzheimer’s and Parkinson’s diseases. The structural basis of this secondary nucleation process, however, is poorly understood. Here, we ask whether secondary nucleation sites are found predominantly at rare growth defects: irregularities in the fibril core structure incorporated during their original assembly. We first demonstrate using the specific inhibitor of secondary nucleation, Brichos, that secondary nucleation sites on Alzheimer’s disease-associated fibrils composed of Aβ40 and Aβ42 peptides are rare compared to the number of protein molecules they contain. We then grow Aβ40 fibrils under conditions designed to eliminate most growth defects while leaving the regular fibril morphology unchanged, and confirm the latter using cryo-electron microscopy. We measure both the ability of these annealed fibrils to promote secondary nucleation and the stoichiometry of their secondary nucleation sites, finding that both are greatly reduced as predicted. Re-analysis of published data for other proteins suggests that fibril growth defects may also drive secondary nucleation generally across most amyloids. These findings could unlock structure-based drug design of therapeutics that aim to halt amyloid disorders by inhibiting secondary nucleation sites.

Introduction
Amyloid fibrils are characterised by the stacking of proteins into β-sheet-rich aggregates with a hydrophobic core. They can serve functional purposes but are also frequently associated with pathology 1–4, making the mechanisms that control their proliferation a central problem in disease. Their spontaneous formation requires a slow primary nucleation process producing very short new fibrils from monomeric protein, and their subsequent rapid elongation by monomer addition (Fig. 1a) 5. Notably, in many systems, an additional reaction step, secondary nucleation of new fibrils on the surfaces of existing fibrils 6–9, dominates new oligomer and fibril production once aggregates appear 10,11. Secondary nucleation has been implicated across systems including type II diabetes-associated islet amyloid polypeptide (IAPP) 12,13, Parkinson’s disease-associated α-synuclein 14, and Alzheimer’s disease (AD)-associated tau and amyloid-β (Aβ) 10,15,16. Rapid secondary nucleation is characteristic of many disease-associated amyloids, due both to its auto-catalytic effect on fibril proliferation and to its ability to produce large quantities of toxic protein oligomers 9,17. Analogous surface-catalysed nucleation processes are well known in crystallisation, underscoring the generality of the underlying physics 18–20.

However, despite its importance, exactly how fibril surfaces catalyse secondary nucleation is still not known. A common assumption is that catalysis is uniform across the entire fibril surface, or at least that catalytic sites occur at every plane in the fibril (see Fig. 1b(i)). Nonetheless, several recent studies imply secondary nucleation sites on Aβ fibrils are rare and substoichiometric 21–24, casting doubt on this hypothesis, although the stoichiometry of these sites has never been quantified directly. One proposed explanation for this is that catalysis is localised at rare super-spreader fibrils (fibrils with dense secondary nucleation sites), with the remaining majority of fibrils being incapable of secondary nucleation (see Fig. 1b(ii)). However, recent studies indicate that secondary nucleation sites are spatially isolated from one another 25, that most Aβ42 fibrils have comparable catalytic activity 26, and that almost all Aβ42 fibrils possess non-periodic secondary nucleation sites 27, rendering this model unlikely for Aβ fibrils.

In light of the above findings, it has been proposed that these sites instead correspond to growth defects: defects generated in the fibril core during its initial assembly 25. Those types of defect structures that permit the next fibril plane to be assembled with the correct structure on top of the defective fibril plane without imposing an insurmountable free energy penalty should exist throughout the fibril length coexisting with otherwise uniform fibril morphology. One example of such a defect is when the monomers in a new fibril plane bind correctly with respect to one another and with the correct fold but laterally offset and potentially also out-of-register from the monomers in the preceding fibril plane during fibril elongation 28,29. The next fibril plane can then be assembled in perfect alignment with the offset plane, allowing correct growth to resume (see Fig. 1b(iii)). Small dislocations only partially exposing a filament’s cross-section are possible, as are larger dislocations that lead to entire filaments being misaligned, or even of fibril branching. Other plausible defect structures include (but are not limited to) fibril planes in which one or more of the monomers is only partially correctly folded, exposing part of the fibril’s hydrophobic core.

Near thermodynamic equilibrium, the stoichiometry of growth defects (the number of defects per monomer in the fibril) should follow the Boltzmann distribution, becoming the exponential of the free energy penalty for forming a given type of defect 30. Because fibrils are highly stable these penalties are expected to be large, leading to low defect incorporation into growing fibrils. Typically, however, experiments use high initial supersaturation (i.e. monomer concentration) to obtain sufficient fibril yield and reaction speed, driving fibril growth far from equilibrium. The average time between successive monomer binding events becomes much smaller than the average time taken for an incorrectly bound monomer to detach 29. Therefore, many fibril planes are bound sequentially on top of a defective fibril plane before it can detach or re-fold. The resultant very strong kinetic trapping can greatly increase defect stoichiometry 29,31. Increasing defect incorporation with increasing supersaturation is a well-documented feature of crystallisation 32, of which amyloid formation is often considered a one-dimensional analogue 33. Conversely, the common manufacturing technique of annealing can reduce defects in crystalline or pseudo-crystalline materials by using thermal treatment to remove kinetic trapping 34–36, suggesting an experimental lever to modulate growth defects.

Here, we quantify secondary nucleation site stoichiometries on Aβ42 and Aβ40 fibrils using BRICHOS, a ~100-residue mammalian chaperone that selectively binds to and blocks these sites 21,37,38, enabling a direct readout of site numbers per fibril mass. Inspired by annealing, we then use temperature control to grow Aβ40 fibrils under conditions designed to minimise growth defects whilst preserving their morphology as verified by high-resolution cryo-electron microscopy. Brichos binding measurements reveal that their secondary nucleation site stoichiometry is also greatly reduced, and kinetic assays demonstrate that the secondary nucleation rate of such annealed fibrils is reduced commensurately, causally linking defects to secondary nucleation sites. Together, these results identify growth defects as the predominant driver of secondary nucleation on Aβ fibrils, quantify their abundance, and suggest a general, defect-based mechanism for amyloid secondary nucleation supported by thermodynamic arguments and re-analysis of prior studies.

Results
Brichos binds Aβ42 amyloid fibrils tightly with low stoichiometry
We first characterised the interaction between the Brichos chaperone domain and Aβ42 fibrils using a microfluidic diffusional sizing (MDS) platform that was previously developed to quantify the interactions between biomolecules in solution under native conditions 39,40. In brief, this technique utilises the diffusion profiles of molecules in solution collected at multiple positions as they flow through a microfluidic channel (Fig. 2a). From the analysis of these diffusion profiles using the advection-diffusion equation, it is possible to extract the distribution of diffusion coefficients. Brichos molecules that are bound to amyloid fibrils diffuse through the channel cross-section much more slowly than unbound Brichos molecules, exhibiting two distinct diffusion profiles that can be detected and separated mathematically 40. Note, this separation becomes inaccurate when one species makes up <10% of the mixture.

Alexa-488 labelled proSP-C Brichos was titrated against 24 μM Aβ42 amyloid fibrils at 21°C. Two different incubation procedures were followed: addition of Brichos to Aβ42 fibrils generated in the absence of chaperone; and incubation of Brichos with monomeric Aβ42 at 37°C until essentially all monomers had converted into fibrils. In both procedures the mixtures were subsequently incubated for 48 h at 21°C prior to evaluation of binding. The resulting binding curves are shown in Fig. 2b, c; the data obtained through the two procedures are closely similar. We then fit to these data the Langmuir adsorption isotherm 41 [C]bound = KD [C]free / ([C]free + [S]tot), where KD is the dissociation constant, [C]bound and [C]free are the concentrations of the bound and free chaperone, respectively. [S]tot is the total concentration of binding sites available on an Aβ42 fibril surface, representing the maximum concentration of bound molecular chaperone. This procedure yielded KD = 314 nM at 21°C and [S]tot = 168 nM. Dividing [S]tot by the total concentration of monomer equivalents of Aβ42 in the fibrils revealed the binding stoichiometry s to be about one Brichos molecule per (24 μM)/(168 nM) = 143 Aβ42 monomers (Fig. 2c), i.e. s = 1/143. Brichos is predominantly trimeric in solution 38, but dissociates into monomers when binding to Aβ fibril secondary nucleation sites 22,42. This should not greatly affect the fitted stoichiometry, since each monomer is individually labelled and MDS measures total fluorescence. However, mild self-quenching of trimeric free Brichos is possible, in which case our fitted stoichiometry value would be a mild overestimate.

Modulation of Aβ42 aggregation by Brichos reveals rare secondary nucleation sites
We next sought to relate our binding measurements to the ability of Brichos to inhibit secondary nucleation of Aβ42 fibrils. To this end, we incubated 3 μM of Aβ42 at 21°C and monitored the time course of amyloid fibril formation by Thioflavin T (ThT) fluorescence in the absence and presence of increasing concentrations of Brichos (Fig. 2d). We observe that the addition of Brichos delays aggregation by an amount that depends on the concentration of the chaperone, in line with previous reports 21. To disentangle the effect of Brichos on the rate constants for the individual microscopic steps of aggregation (primary nucleation kn, secondary nucleation k2 and elongation k+), we compared the experimental kinetic profiles with a theoretical chemical kinetics model of aggregation in the presence of Brichos (see Methods “Kinetic modelling”). In this model, chaperone molecules can bind surface sites of fibrils and in this manner reduce the rate of secondary nucleation, while leaving the rate of the other aggregation steps unaffected 43. A prediction from this model based on the unperturbed aggregation rate parameters obtained in the absence of Brichos (leftmost curve in Fig. 2d) and the rate parameters determined from the binding experiment shows good agreement with the experimental data. A comparison between the effective rate constant for secondary nucleation k2 with increasing Brichos concentration and the binding curve of Brichos to fibrils confirm that the extent of inhibition of Aβ42 by Brichos closely correlates with the amount of chaperone bound to fibrils (Fig. 2e). Strikingly, we see that almost complete suppression of secondary nucleation (reduction of k2 by >90%, see 2 and 3 μM Brichos data points) occurs with a Brichos coverage of <1% of the fibrils. We therefore obtain sub-stoichiometric inhibition of secondary nucleation by Brichos, with almost complete suppression of secondary nucleation obtained with only one fibril-bound chaperone molecule per about 150 monomers. These results imply that Brichos binding sites and secondary nucleation sites are one and the same. Under these fibril formation conditions, secondary nucleation sites therefore have a stoichiometry of s = 1/143, i.e. a frequency of approximately 1 site per 150 monomers in the fibril. With four monomers per plane with two monomers per filament plane and two such filaments in an Aβ42 fibril 44,45, this stoichiometry corresponds to one catalytic site per 36 fibril planes on average. A recent evaluation of the density of fibril protrusion during an ongoing Aβ42 aggregation process at pH 6.8 found a distribution peaking at around 1 protrusion per 28 planes, or per 112 monomers, i.e. similar to the rarity of secondary nucleation sites found here at pH 8.0 46.

Fibrils possessing fewer growth defects can be generated without measurably altering morphology
The low stoichiometry of Aβ42 secondary nucleation sites we have found in this study supports the hypothesis that these sites are rare growth defects in the fibril core structure. To verify this hypothesis, we sought to produce Aβ fibrils under conditions designed to reduce defect stoichiometry by removing their kinetic trapping, so we could subsequently test whether their secondary nucleation sites are also reduced.

Removal of supersaturation alone, e.g., by allowing an aggregation reaction to proceed to completion, is not expected to remove kinetic trapping of defects. This is because the prevalence of long fibrils (often containing >105 monomers) lead to very slow exchange of protein in the interiors of fibrils with the residual monomeric protein in solution 28–30,47,48. The need to depolymerise tens of thousands of monomers from the fibril to access the average defect imposes an insurmountable kinetic free-energy barrier for their removal. Therefore, fibril growth defects will persist in mature fibrils for very long periods of time unless experimental conditions are chosen to almost completely disassemble the fibrils. Also, in situ self-repair of defects is expected to be very slow even upon thermal treatment, since re-folding of defective monomers and alteration of their highly directional bonding to their neighbours is likely impractical without fibril disassembly. Instead, a more practical approach is to prevent kinetic trapping in the first place by growing the fibrils at very low supersaturation (Fig. 3a, b).

To do so, we took advantage of the fact that fibril solubility—the monomer concentration at which elongation and dissociation rates equalise—can be altered by changes in temperature. A previous study 49 reported a steady increase in Aβ40 solubility with temperature from 37 to 64°C in 20 mM sodium phosphate, pH 7.4 (Fig. 3c, Supplementary Table 2). We validated this trend by measuring the solubility of Aβ40 at 27, 45, 50, and 60°C under the same buffer conditions using NMR. In combination with the published solubility value at 37°C 50, these measurements confirmed that Aβ40 solubility increases steadily with temperature from 37 to 60°C (Supplementary Table 1). An annealing-type procedure involving incubating fibril seeds with a monomeric solution at a concentration slightly above the 60°C solubility, and gradually reducing the temperature from 60 to 37°C should then enable fibril growth at almost zero supersaturation (Fig. 3c). Such annealed fibrils are expected to have far fewer growth defects than fibrils produced from a similar (and therefore highly supersaturated) initial monomer concentration at 37°C. Here, we worked with Aβ40 rather than Aβ42 due to its higher solubility, making it easier to generate sufficient annealed fibril concentrations at low supersaturation.

First-generation Aβ40 fibrils were prepared by full aggregation of 57 μM Aβ40 monomer at 37°C. Second-generation annealed and control fibrils were produced by incubating 0.2 μM monomer-equivalent of the first generation fibrils with 10 μM fresh Aβ40 monomer. To make annealed fibrils, the incubation temperature was started at 60°C and was reduced by 1°C every hour until reaching 37°C (Fig. 3c and Supplementary Fig. 4c). To make control fibrils, the incubation temperature was maintained at 37°C throughout. Seeding bypasses primary nucleation 43,51, and at the low supersaturation used to make annealed fibrils, elongation dominates over secondary nucleation. Therefore, most annealed fibrils are expected to be elongated first-generation seeds. Since elongation typically preserves fibril morphology 52, using first-generation seeds therefore greatly reduces the risk that annealed and control fibrils will have different morphologies.

To confirm that the annealing procedure does not cause morphological changes, we performed high-resolution cryo-electron microscopy on both annealed and control fibrils. By design, the high-resolution algorithms can only detect regular, periodic structural features of amyloid fibrils. No matter how large a sample is analysed, irregular, nonperiodic defects can only ever appear as noise to the algorithms, and are therefore averaged away during image processing 53. We observed no significant differences in the raw images (Fig. 4a, b), and collated 2D class averages were indistinguishable between conditions (Fig. 4c-f). We finally collected statistics on these classes, and found that the proportions of twisted vs straight fibrils were almost identical in each sample (43:57 in annealed fibrils and 40:60 in control fibrils). Furthermore, the average crossover length was >300 nm in both samples, and the fibril widths matched within error. In other words, there are no detectable morphological differences between annealed and control fibrils. The only possible major differences between the annealed and control fibrils are therefore fibril length, and defect frequency and structure.

Despite their invisibility to the high-resolution cryo-EM algorithms, the existence of growth defects in Aβ40 and Aβ42 fibrils can nonetheless be confirmed directly from the raw cryo-EM images. This is because certain types of growth defects produce non local signatures visible in raw micrographs. These include defects that punctuate transitions from one fibril morphology to another possessing a different twist length (Fig. 4h-j), and branching sites (Fig. 4k). However, their quantification using the raw images is impractical due to their rarity combined with most fibrils being located in large, un-analysable clumps and tangles on the TEM grid 51. More importantly, this count would miss all growth defects that affect only the adjacent fibril planes, such as dislocations and partially misfolded monomers, and that could conceivably greatly outnumber visible defects.

Annealed fibrils possess far fewer secondary nucleation sites than normal fibrils
Since Brichos also strongly inhibits Aβ40 aggregation at very low stoichiometries 37, it is likely that, as was shown for Aβ42 fibrils, Brichos binds almost exclusively to secondary nucleation sites and not elsewhere along Aβ40 fibrils. We therefore again sought to quantify the number of secondary nucleation sites for both annealed and control fibrils by measuring the Brichos binding stoichiometry to Aβ40 fibrils. This time, we used fluorescence correlation spectroscopy (FCS) in addition to MDS to eliminate the possibility that differing Brichos binding stoichiometries for annealed and control fibrils are an artefact of either experimental technique.

In both FCS and MDS experiments, we mixed different concentrations of Alexa488-Brichos with fixed concentrations of non-fluorescent Aβ40 fibrils and subsequently measured the free Alexa488-Brichos concentration. In FCS this was done by positioning the confocal volume at a height where only diffusible Alexa488-Brichos can be detected, excluding those attached to the sedimented fibrils at the bottom of the dish (see Methods “Fluorescence correlation spectroscopy”). In MDS, this was done by centrifuging the samples to remove fibril-bound Brichos and performing measurements on the supernatant (see Methods “Microfluidic Diffusional Sizing”). Visual inspection of the resultant binding curves of free vs total Brichos (Fig. 5a, b) reveals annealed seeds to have a lower Brichos saturation concentration compared to control seeds, indicating a lower secondary nucleation site frequency, and supporting the hypothesis that they are growth defects.

We then globally fit these two binding curves using a simple protein-ligand binding model 54 (Eq. 5). We found one Brichos binding site per 99 monomers in control fibrils. This, alongside the fact that an additional and distinct site quantification technique was used compared to Aβ42, provides even more support for the rarity of Aβ secondary nucleation sites. In annealed fibrils, the stoichiometry of these sites was reduced to one per 834 monomers. This implies that the number of secondary nucleation sites is reduced by approximately 88% by the annealing treatment relative to fibrils formed entirely at 37°C. This large reduction validates a key prediction of the hypothesis that the secondary nucleation sites are growth defects.

Note that, if Brichos also binds elsewhere along Aβ40 fibrils, the secondary nucleation site stoichiometry would be even lower than what was inferred here from the Brichos binding stoichiometry. Since the annealed and control fibril morphologies have been demonstrated to be the same on average, this would in turn imply an even greater reduction in secondary nucleation site incidence in annealed fibrils compared to control fibrils if they are indeed growth defects. Trimerization of Brichos in solution 38 could scale the measured free Brichos concentrations by a constant factor, but this will not affect the fitted binding stoichiometry, instead only changing the y-axis proportionality constants in our binding model.

Annealed fibrils display considerably reduced secondary nucleation seeding efficiency compared to normal fibrils
We next used 0.1 or 2 μM monomer-equivalent of the second generation fibrils (control seeds or annealed seeds) as preformed seeds in new aggregation reactions of 5 μM monomeric Aβ40 at 37°C. We find that even though both control and annealed seeds accelerate the aggregation (Supplementary Fig. 1), the annealed seeds have much less seeding efficiency compared to control seeds in both lightly and heavily seeded conditions, Fig. 5c, d.

The decrease in seeding efficiency of annealed seeds compared to control seeds could, in principle, reflect annealed fibrils having greater length, and thus possessing fewer elongation-competent fibril ends per unit fibril mass, although such length differences should be limited by the iterative seeding approach used in their production. To test this possibility, aggregation data were fitted using the standard kinetic model for Aβ40 aggregation 16, allowing the seed fibril length to differ between control seeds and annealed seeds (by allowing the seed fibril number concentration P0 to differ between conditions at fixed seed mass concentration M0). This model was unable to globally fit the data, even assuming extreme (≫trillion-fold) length differences, yielding pronounced misfits particularly at 2 μM monomer-equivalent of seeds (Fig. 5c). In fact, lower-resolution cryo-TEM images show no sign that annealed and control fibrils differ significantly in length (Supplementary Fig. 5). More realistic upper bounds of their average length difference (e.g. 10× or 100×) lead to markedly worse misfits (Supplementary Fig. 5), further strengthening the conclusion that length differences alone cannot explain the large difference in seeding propensity of these fibrils. Note that directly eliminating seed length differences by sonication was not feasible, because Aβ40 fibrils generated under standard in vitro conditions are highly resistant to sonication 51. Moreover, vigorous sonication of annealed and control fibrils in weaker fibril types such as Aβ42 51 may never reduce them to the same length if fragmentation occurs preferentially at defect sites (see Supplementary Discussion).

The reduced number of secondary nucleation sites in annealed fibrils can be captured by a kinetic model that is identical in all respects to the standard model for Aβ40 aggregation 16, but where the seed mass concentration M0 is multiplied by fitting parameter r, the fraction of remaining secondary nucleation sites compared to control fibrils (See Methods Sec. “Kinetic modelling” for modified kinetic model equations). We find that such a model, where effectively M0 can vary but not P0, can fit the measured kinetic data very well (Fig. 5d), using a value for r of 0.08, which is consistent with a 92% reduction in secondary nucleation site numbers. This is remarkably consistent with the 88% reduction estimated using Brichos stoichiometry and is despite this model having no more degrees of freedom than the differing-seed-length model. In general, one might expect the ensemble of defect structures in annealed and control fibrils to differ somewhat. However, this close correspondence between secondary nucleation site stoichiometry and secondary nucleation propensity implies that there is little structural difference on average between the secondary nucleation sites in annealed and control Aβ40 fibrils.

Aβ40 secondary nucleation occurs predominantly at defects regardless of the supersaturation level
Brichos can increase the Aβ40 aggregation half-time by at least 10-fold under the conditions used to generate the control fibrils 37, which, given Aβ aggregation half times scale inversely with the cube root of the secondary nucleation rate 55,56, implies a ≥1000-fold reduction in total secondary nucleation. As demonstrated above, Brichos effects this inhibition by blocking defect sites, so its causing this ≥1000-fold reduction requires that nucleation from non-defect surface sites on control fibrils must be at least 1000-fold slower than nucleation at defects in the absence of Brichos. Annealed Aβ40 fibrils grown at near-zero supersaturation still possess growth defects at Boltzmann distribution-governed stoichiometry, and their secondary nucleation rates and defect abundance are reduced by 8- to 12-fold relative to control fibrils. Consequently, even in supersaturation-free Aβ40 fibrils, secondary nucleation at defects remains at least about 50-fold faster than nucleation from other, non-defect surface sites under the same conditions.

At equilibrium, the fraction of defective fibril planes vs total fibril planes peqdef is mathematically related to the free energy penalty ΔGdef for forming the least unstable possible types of defect by −RT ln(peqdef) = ΔGdef (assuming defective layers are rare, see Supplementary Methods Sec. 3.1). peqdef is related to the equilibrium defect site stoichiometry per monomer seq by seq = ndef peqdef / x, where x is the number of monomers in the fibril cross-section and ndef the number of discrete defect sites per defective layer (Supplementary Methods Sec. 3.1). For instance, ndef = 2 in dislocation-type defects because dislocations expose fibril core on both sides of the fibril. In Supplementary Methods Sec. 3.1, we use these numbers to estimate the equilibrium defect-free energy penalty for several plausible values of x and ndef, finding ΔGdef to range from −14.1 to −17.8 kJ/mol. This being many multiples of the thermal fluctuation energy RT, these defects are thus very thermodynamically unstable, as expected given their low stoichiometry. ΔGdef is also proportionally significant: approximately half of the per-monomer free energy for correct elongation (calculated in Supplementary Methods Sec. 3.1 as −32.3 kJ/mol at 45°C). Note this calculation applies to the majority of defect structures seen in annealed fibrils. However, as shown above, there is evidence to suggest that the defect structures in annealed and control fibrils are similar. If ΔGdef for Aβ42 fibrils is around half of the per-monomer free energy for correct elongation, as it is for Aβ40 fibrils, then switching to non-supersaturated growth conditions can be calculated to again reduce growth defect frequency by around 20-fold. Nonetheless, Brichos binding can also increase Aβ42 aggregation half-times by at least 6.5-fold 46, corresponding to a >250-fold reduction in secondary nucleation rate. So, fibril growth defects should remain the dominant source of both Aβ40 and likely also Aβ42 secondary nucleation even without any supersaturation. (Note, older and less detailed kinetic models predict that the aggregation half times scale inversely with the square root of the secondary nucleation rate instead of the cube root 10,16. Even if this were true, however, our conclusion would remain unchanged.)

Methods
Independent repeats in the plots correspond to separate measurement acquisitions performed on the same prepared sample, rather than measurements on independently prepared samples.

Chemicals and consumables
All Aβ42 or Aβ40 related experiments were carried out in 20 mM sodium phosphate buffer (sodium dihydrogen phosphate dihydrate and di-sodium hydrogen phosphate) at pH 8.0 or pH 7.4, respectively. The buffer components were purchased from Sigma-Aldrich (St. Louis, MO, USA), and all buffer solutions were prepared using Milli-Q water. The buffer was filtered through wwPTFE 0.2 μm 50 mm disc filters (Fisher Scientific, Pittsburgh, PA, USA) and degassed prior to use. The dye Thioflavin T (ThT) for monitoring amyloid formation was obtained from Calbiochem. Axygen® MaxyClear Snaplock Microtubes and Corning® 96-well Half Area Black/Clear Flat Bottom PEGylated Polystyrene Microplates (3881) were used as containers for protein solution to minimise protein adsorption onto container surfaces.

Expression and purification of Aβ42, Aβ40 and Brichos
Recombinant peptide Aβ (M1-42) (MDAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA), referred to here as Aβ42, Aβ(1-40) (DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVV), referred to here as Aβ40, and Aβ(M1-40) (MDAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVV) were expressed in inclusion bodies in E. coli BL21(DE3)pLysS Star and purified using denaturation, anion-exchange chromatography, and size-exclusion chromatography to obtain highly pure monomeric peptide 72,73. Aβ(1-40) was expressed in fusion with the EDDIE tag to enable a free Asp1, and its purification required additional steps for refolding/autocleavage of the EDDIE tag 73. The Aβ(1-40) peptide was used in all Aβ40 experiments, except in the measurements of solubility using NMR spectroscopy, in which case Aβ(M1-40) was used. The proSP-C BRICHOS domain was expressed in E. coli BL21(DE3), isolated under denaturing conditions by Ni2+ affinity chromatography, and further purified following thrombin cleavage and ion-exchange chromatography 37. Purified Aβ peptides were aliquoted and stored at −80°C. Purified BRICHOS was aliquoted and stored at −80°C until use. Protein concentrations were determined by UV absorbance.

Immediately prior to each experiment, Aβ42 or Aβ40 powder was dissolved in 6 M guanidine hydrochloride (pH 8.5), and monomers were isolated by size exclusion chromatography (Superdex 75 column) in 20 mM sodium phosphate buffer as described in chemicals and consumables section.

ProSP-C Brichos domain was covalently labelled with the Alexa-488 dye (Thermo Fisher Scientific, Waltham, US) by incubating the protein for three hours in 20 mM sodium phosphate buffer, pH 8.0, at room temperature under gentle agitation with one equivalent of Alexa-488 carboxylic acid succinimidyl ester to label covalently primary amines. Following the labelling reaction, excess dye was removed by desalting on a G25 gel filtration column, followed by purification by anion exchange to remove any protein with multiple labels, and an additional desalting step on a G25 gel filtration column before use. The labelling efficiency of Alexa-488 on Brichos is around 90% as indicated by relative absorbance measurements at 280 and 490 nm, see Supplementary Fig. 6. Stated Brichos concentrations always refer to both labelled and unlabelled chaperone monomers together.

Microfluidic diffusional sizing
The fabrication and operation of the microfluidic diffusion device followed established procedures 39,40. Briefly, the microfluidic chips were fabricated by using standard soft lithography techniques into polydimethylsiloxane 74. The analyte sample and the buffer were introduced in the device through reservoirs connected to the inlets. The flow rates in the channels were controlled by applying a negative pressure at the single outlet channel by means of a syringe pump (neMESYS, Cetoni GmbH, Korbussen, Germany). Typical flow rates were in the range 60 μl/h to 90 μl/h and lateral diffusion profiles were recorded at twelve different positions (3.52, 5.29, 8.57, 10.33, 18.61, 20.37, 28.65, 30.41, 58.69, 60.45, 88.73 and 90.5 mm) by standard epifluorescence microscopy using a cooled CCD camera (Evolve 512, Photometrics, Tucson, AZ, USA). The diffusion profiles were fitted by model simulations based on advection-diffusion equations under the assumption that the diffusivities of the species in the mixture are distributed according to a bimodal Gaussian distribution 39. In this approach, the fitting parameters are the mean and the standard deviation of at least two size distributions, corresponding to the populations of the free chaperone (small size range) and of the chaperone bound to the fibrils (large size range).

In order to evaluate the binding between Brichos and Aβ42 fibrils during the aggregation process, samples with Alexa-488 labelled Brichos at concentrations of 0.25, 0.35 or 0.50 μM were mixed with 24 μM Aβ42 monomers and 20 μM ThT and incubated in a 96-well plate in a Fluostar Optima plate reader (BMG Labtech, Ortenberg, Germany) at 37°C with double orbital rotation (400 rpm) in order to generate sufficiently short fibrils for injecting into the microfluidic channels 10. The aggregation kinetics were monitored by following the associated increase in ThT fluorescence. After completion of the reaction, the samples were incubated for at least 2 days at 21°C to ensure equilibrium conditions. They were subsequently analysed by microfluidic diffusion sizing at 21°C.

To evaluate the binding between Brichos and mature Aβ42 fibrils, the same Aβ42 aggregation reaction was performed at 37°C but without Brichos. After completion of the reaction, aliquots of Alexa-488 labelled Brichos in the concentration range between 0 and 1.5 μM were added to samples of the mature Aβ42 fibrils and incubated for at least 2 days at 21°C to ensure equilibrium conditions. They were subsequently analysed by microfluidic diffusion sizing at 21°C. To better constrain the Langmuir model during subsequent fitting, more measurements were taken at lower total Brichos concentrations, where the bound concentration changes more rapidly.

Brichos binding to Aβ40 fibrils was evaluated by incubating 5 μM Aβ40 fibrils (control or annealed) with Alexa-488 labelled Brichos at 12.5, 25, 50 or 100 nM for 10 min at 37°C. Before loading the samples into MDS, Aβ40 samples were centrifuged and only supernatant was injected, as Aβ40 fibrils are shake-resistant and remain too long in the microfluidic channels 51. Samples were centrifuged at 17,850 rpm (~29,000g) for 15 min at 37°C, and 4 μL of each supernatant was loaded into microfluidic channels for analysis on a Fluidity One-M Serum instrument (Fluidics Inc., Cambridge, UK). The intensity of Alexa-488 was measured, and the artefactual contribution from free dye was removed using the free dye fractions determined in the FCS experiments.

Kinetic experiments
The kinetic assay for Aβ40 displayed in Fig. 5b, c starts with 5 μM monomer in 20 mM pH 7.4 phosphate buffer with 10 μM ThT, with or without 0.1 or 2 μM monomer-equivalent of seeds. The seeds were generated as shown in Supplementary Fig. 4. After mixing the monomer and the seeds, the solutions were immediately incubated in a 96-well half-area clear flat-bottom PEGylated polystyrene microplate at 37°C. The fluorescence was recorded in a Fluostar Omega plate reader (BMG Labtech) using a 440 nm excitation filter and a 480 nm emission filter, and used to monitor the extent of aggregation. The waiting time between the reading cycles was set to 0, meaning that the wells were continuously monitored throughout the kinetic measurements. This setup is designed to control the agitation frequency caused by reading wells consistently across all experiments 46.

The kinetic assays for Aβ42 were performed by incubating a solution of 3 μM Aβ42 peptide, 1% seeds, and 20 μM ThT with or without different concentrations of Brichos in a non-binding 96 well plate in a plate reader Fluostar Optima (BMG Labtech, Ortenberg, Germany) at 37°C with double orbital rotation (400 rpm). The validity of ThT fluorescence as a quantitative reporter of Aβ40 and Aβ42 fibril formation using this protocol has been established in multiple mechanistic studies 10,16,75. Its use here complies with community requirements on chemical probes.

Kinetic modelling
Aβ42 aggregation. We describe Aβ42 aggregation using a chemical kinetics framework that accounts for the various microscopic steps of aggregation (Fig. 1a) 16. The unperturbed kinetic equations for the aggregate number concentration P(t) and aggregate mass concentration M(t) are:
dP/dt = k1 mn1 + k2 mn2 M, (1a)
dM/dt = 2k+ mP = (d/dt) M, (1b)
where m(t) is the monomer concentration, k1, k2, k+ are the rate constants for primary nucleation, secondary nucleation and elongation, and n1, n2 are the reaction orders of primary and secondary nucleation with respect to the monomer concentration.

Fits of the aggregation curves in the presence of Brichos in Fig. 2d were performed using the Amylofit platform (which implements analytical solutions to the kinetic equations (1a), (1b)) 43. The presence of Brichos was captured by means of an effective rate constant for secondary nucleation k2 that depends on chaperone concentration.

Aβ40 aggregation. Equation (1) apply unmodified to Aβ40 aggregation seeded with control fibrils. When an Aβ40 aggregation reaction is seeded with annealed fibrils, Eqs. (1) still apply, but with modified initial condition M(0) = rM0, where M0 is the mass concentration of seed fibrils and r is the fractional reduction in secondary nucleation sites caused by the annealing process. The analytical solution to such equations is then identical to that used in Amylofit 43, but with all instances of M(0) replaced by rM0. We used an offline version of Amylofit to globally fit the modified and unmodified analytical solutions to the kinetic data for unseeded reactions and for reactions seeded with annealed and control fibrils, and to determine r. Full fits are in Supplementary Fig. 1; Figure 5d omits the unseeded kinetic curve to provide greater resolution on the seeded kinetic curves. Misfits in Fig. 5c were performed using the standard kinetic model in the AmyloFit platform with different average seed fibril lengths allowed for the annealed and control fibrils.

Solubility measurement
The aggregation of 106, 106, 132, and 101 μM Aβ(M1-40) monomers at 27, 45, 50, and 60°C, respectively, was monitored by integrating the methyl group region of the Aβ(M1-40) 1H NMR spectra. The solubility was estimated from the relative integral of the methyl signals at the final plateau compared to the initial value. The concentration of Aβ(M1-40) at the initial value of NMR was determined using UV absorbance. A detailed description of the solubility measurements is provided in Supplementary method sec. 3.3, and the solubility of Aβ(M1-40) at 27, 45, 50, and 60°C is provided in the Supplementary table 1. The solubility of Aβ(M1-40) at 60°C is plotted as a single dot in Fig. 3c. NMR experiments were performed on a Bruker Avance III HD 900 spectrometer (Bruker Biospin, Rheinstetten, Germany), operating at a 1H resonance frequency of 899.8 MHz and fitted with a 5 mm cold probe.

Fluorescence correlation spectroscopy
Before FCS measurements, samples of 4500 nM Aβ40 fibrils and 3–100 nM Alexa488-Brichos were incubated together in MatTek dishes (35 mm, 10 mm glass bottom, No. 1.5 glass) at 37°C for 2 h in a desiccator with water below to avoid evaporation. The measurements were conducted using a Zeiss 980 confocal laser scanning microscope (LSM 980, Carl Zeiss) using ZEN 3.10 software, equipped with a Zeiss C-Apochromat 40×−/1.2 NA water immersion objective. Excitation was performed at 488 nm, and fluorescence emission was collected within the 499-622 nm range. Each data point in Fig. 5a is based on 3–4 FCS measurements, 90 s measurements each. FCS data were acquired using Zeiss ZEN microscopy software (Zeiss). The free Brichos concentration was determined by fitting the correlation curves at a time range of 10−5 to 1 s to theoretical models for diffusion of two species of Brichos (Fig. S2) 76, using the diffusion of free dye and Brichos:
Y = 1/N * (f1 / (1 + X/τD1) * sqrt(1 + X/(τD1 * S2)) + f2 / (1 + X/τD2) * sqrt(1 + X/(τD2 * S2)) + 1)
where N is the total number of particles, f is the fractional contribution of the component, X is correlation time, S is the structural parameter fixed to 5, τD1 and τD2 are the diffusion times of the two components, which are fixed to 27 μs and 126 μs, respectively, representing the diffusion time of free dye and Brichos. The resulting free Brichos particle number is shown in Supplementary Fig. 2c, and the average fraction of Brichos at the baseline or above the baseline (4% and 10%) is used to calculate the free Brichos concentration, which is fitted by the ligand-protein binding model for stoichiometry.

Fitting of ligand-protein binding model
We consider the 1:1 binding reaction: P + L ⇌kon/koff PL, where our ligand L is Brichos and our protein P is a secondary nucleation site. At binding equilibrium, the total ligand concentration cL is:
cL = [L] + [PL] = [L] + [L] * cP / (K D + [L]), (2)
where [L] is the concentration of free Brichos, [PL] is the concentration of Brichos bound to secondary nucleation sites, cP is the total concentration of secondary nucleation sites, and KD is the dissociation constant. This can be rearranged to:
[L]2 + [L] * (KD + cP − cL) − cL KD = 0: (3)
Finally:
[L] = −0.5 * (KD + M0 s − cL) + sqrt(0.25 * (KD + M0 s − cL)2 + cL KD), (4)
where s is the number of secondary nucleation sites per monomer residue in the fibrils, and M0 is the fibril mass concentration. To determine the number of secondary nucleation sites per monomeric residue (s) in Fig. 5, the free Brichos concentration y as a function of total Brichos concentration x was fitted with the following ligand binding equation, which is a rearranged form of Eq. (4):
y = a * (−0.5 * B + sqrt(0.25 * B2 + x * KD)) (5a)
B = KD + s M0 − x, (5b)
where a is an arbitrary proportionality constant, KD is the dissociation constant for Brichos binding, and the quantity in the brackets is the concentration of free Brichos. The concentration of Aβ40 fibrils is M0 = 4500 nM in FCS experiments, and M0 = 5000 nM in MDS experiments. FCS and MDS datasets were fitted globally, sharing KD, s(annealed), and s(control), while allowing separate scaling factors aFCS and aMDS for the two techniques.

To assess parameter uncertainty, we used nonparametric stratified bootstrap resampling: one replicate per Brichos concentration was randomly drawn for each dataset, the global fit was repeated (Q = 1000 iterations), and the distributions of fitted parameters were summarised by medians and 95% confidence intervals. Confidence bands in the plots were generated as the spread of bootstrap-predicted curves.

Cryo-EM sample preparation
The samples were imaged using cryo-EM as described previously 73. Specimens were plunged using an automated plunge freezer (Leica EM GP) set at 20°C with 90% relative humidity. Thin liquid films were created on glow-discharged lacey carbon 300 mesh grids (Agar Scientific) and blotted with filter paper before being plunged into liquid ethane at −184°C, ensuring vitrification in a glass-like state that prevents ice crystal formation and preserves the original microstructures. The specimens were stored in liquid nitrogen until further use.

Cryo-EM imaging
The prepared specimen grids were transferred into the electron microscope (JEM 2200FS) using a Fischione model 2550 cryo-transfer tomography holder. Imaging was performed at an acceleration voltage of 200 kV, with zero-loss images captured digitally using a TVIPS F416 camera and SerialEM 4.0.27 under low-dose conditions with a 10 eV energy filter. Image contrast was adjusted automatically using ImageJ version 1.53a.

Cryo-EM data collection and processing
The cryo EM datasets were collected at the SciLifeLab node in Umeå, Sweden, using a Titan Krios electron microscope (Thermo Fisher) operated at 300 kV with a Falcon 4 detector and a Selectris energy filter (Thermo Fisher) was used operating with a 10 eV slit. Datasets of size 8106 movies and 7990 movies were collected for control and annealed fibrils, respectively, at a nominal magnification of 130,000× was set yielding a pixel size of 0.92 Å. A defocus range of −1.4 to −2.6 μm and a total dose of 40 e−/Å2 were used over an exposure time of 4.39 seconds.

The raw EER movies were fractionated, aligned and summed using motion correction in RELION-4 77 and CTF estimation was done for micrographs using CTFFIND4 78. Manual picking of fibrils was done until roughly 100,000 particles of 300 pixel box size were picked, and the extracted segment was used to train a separate picking model for each dataset in Topaz, which is incorporated in RELION-4.0 77. Iterative rounds of 2D classification were performed to remove picking artefacts, such as carbon edges and ice contamination, until only classes that could be protein fibrils remained. For 2D classification, a box size of 600 pixels was used. Twisted and straight fibrils were then separated out and a final round of 2D classification was performed separately for both sets of fibrils.

Note that, due to current software limitations, the average crossover length is too large for 3D fibril structures to be calculated; however, in this study, these 3D structures are not needed since the 2D classes already rule out significant structural differences in the fibrils.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript from Hu et al investigates the structural origin of secondary nucleation of amyloid beta (A-beta) peptide, which is closely connected to Alzheimer's Disease. The secondary nucelation process has in several previous papers been shown to have a key role in the amyloid formation of A-beta and the manuscript thereby have important impact for understanding of the basic biochemical mechanisms behind the disease. I believe this is an important and insightful paper that should be published after addressing the following questions/issues:\n1. The kinetically trapping of defects (as illustrated in figure 3) would need some further motivation. Since the binding free energy between defect monomer and the fibril is significantly lowered, it would be an obvious sites for fibril breakage (discussed on page 16, lines 513-515). This should open for preferential fibril breakage at these sites with a much faster exchange of the defect monomer or a (even faster) structural reorganisation of the monomer at the fibril end. Even in the case of a two filament fibril, partial breakage of the defect filament could allow for monomer reorganisation and re-formation of a non-defect fibril.\n2. Investigating the differences in fragmentation between annealed and non-annealed fibrils could give further support for the reduced defect frequency of annealed fibrils. For example, length distributions after gentle fragmentation (stirring or gentle sonication) could reflect the frequency of defects.\n3. Comparing the length distributions (statistical analysis of images from TEM or AFM) of the annealed and non-annealed fibrils (section 2.5) would also strengthen the conclusion that it is not the number of fibril ends that give rise to the observed differences in seeding capacity.\n4. Much of the discussion relies on the solubility data of Ab40 presented in table S1. Why do the authors use solubility data from a 20 years old Masters thesis (most likely not peer-reviewed) when they i. have their own data from recent publications and ii. show that they can easily measure the data by 1H NMR experiments?\n5. When discussing the binding stoichiometry of proSP-C BRICHOS, it should be mentioned that it under normal conditions exists as trimer and how this affect the determined binding stoichiometry (see e.g. Biverstal H, et al. Biochim Biophys Acta 1854, 835-843 (2015) or Willander H, et al. Proc Natl Acad Sci U S A 109, 2325-2329 (2012))\n6. The discussion highlights the implication of the structural defects for AD pathology. What level of supersaturation is expected in vivo? Normal CSF concentrations of Abeta are clearly below the solubility limit, and the concentration decreases further during the disease. This would indicate that defects, and thereby secondary nucleation, may not have a major role in vivo.\nMinor issues:\nPage 7, line 197: should it be '< 90%' ?\nPage 16, line 483: change Kd to KD\nPage 24, Ref 39: missing details\nPage 26, Ref 55: missing details\nSI, page 4, line100: should it be 'x = 2'?\nSI, Figure S3: What is the temperature for the NMR data? Both 60 and 37 deg are mentioned in the legend.", "reviewer_2": "In this manuscript the authors describe the mechanisms by which structural defects on amyloid-β fibrils act as secondary nucleation sites through analyzing binding kinetics of Brichos chaperon domain onto fibril surfaces. They also demonstrate that these defects and thereby secondary nucleation can be strongly inhibited by decreasing monomer concentration or by steadily decreasing reaction temperature. These so-called \"annealed\" Aβ fibrils are structurally indistinguishable from control fibrils and yet contain far fewer secondary nucleation sites and have much lower secondary nucleation propansity.\nThe manuscript is well-written and clear. Considering the recent approval of anti-Aβ antibodies for symptomatic treatment of Alzheimer's disease, the manuscript is quite timely and moves the field forward. No major issues in methodology or interpretation is evident, but a number of minor issues should be addressed:\n1. Data shown in Fig. 2b should be further clarified. First, individual data points should be reported instead of mean±SD, considering that N=2 for the blue curve. Second, the accuracy of the data points for the blue curve needs to be double-checked for (i) Brichos concentrations tested for the two different conditions do not overlap, (ii) the concentrations tested for the \"blue\" condition are not well spaced (please pay particular attention to the 6th data point). Third, the authors should specify whether the regression line is calculated for the blue curve only.\n2. Also in Fig. 2b, for the 7th Brichos concentration tested, the higher of the two data points is likely above the maximum coverage value (based on the binding stoichiometry determined from the fit curve). The authors should explain the potential causes of this apparent measurement error. Also, the coefficients of determination (R2) should be reported for curves in Fig 2 as was done in Fig. 5.\n3. In SI Section 1.2.2, for 2-monomer-thick fibril, x should be 2, not 4. Fortunately, free energy differences were calculated using x = 2; so, a textual correction will be sufficient.\n4. In SI Section 3, K should be K (equations 16 and 17) to be consistent with the rest of the manuscript. The same goes for line 483 in the main text.\n5. In SI Fig. 3 caption, NMR spectra is reported to be obtained at 37°C, but the text and title of the legend suggest 60°C.\n6. Several paragraphs of the Results section (starting in lines 283, 295, 375 and 407) rather belong to the Discussion section, some of which contain material that are repeated in the Discussion.\n7. Brichos should be adequately introduced and relevant reference(s) should be cited.\n8. Fig. 1a could be cited at the end of the sentence starting in line 65.", "reviewer_3": "This study presents a compelling investigation into the role of structural defects in amyloid-β (Aβ) fibrils as catalysts for secondary nucleation, a key process in amyloid aggregation associated with neurodegenerative diseases. The authors employ an impressive combination of biophysical techniques including microfluidic diffusional sizing, fluorescence correlation spectroscopy, and cryo-EM, coupled with kinetic modeling, to demonstrate that secondary nucleation sites correspond to rare growth defects in fibrils. The findings significantly advance our understanding of amyloid aggregation mechanisms and have important implications for therapeutic strategies targeting secondary nucleation.\nThe study's major strengths lie in its novel mechanistic insights and methodological rigor. By quantitatively showing that annealed fibrils exhibit dramatically reduced secondary nucleation activity while maintaining standard morphology, the authors provide evidence linking defects to nucleation sites.\nHowever, I would like clarification from the authors on several improtant issues before reccomending publication. They ae listed below:\n1. The demonstration that Brichos binds to these sites at substoichiometric ratios is based on accurate determination of [Stot]. However, no mention is made about the labelling efficiency of the A488-Brichos sample and there is no dicsussion on whether this can significantly impact the conclusion.\n2. What do the Brichos concentration represent – are the reported values total protein concentrations or concentration of the labelled fractions?\n3. In Fig. 2D the fibril bound Brichos % increase with concentration but data is shown only upto 1.5  M. It would be informative to see results at higher concentrations to assess saturation behavior.\n4. The authors strangely use two different techniques for estimating Brichos binding to amyloid fibrils for experiments in Fig. 2 (MDS) and Fig. 5 (FCS). For proper comparison and consistency MDS and FCS should be used for both experiments.\n5. The \"annealing\" concept, while central to the study, isn't clearly defined until the Results section and would benefit from earlier introduction.\n6. While data shown to infer Brichos binding to defect sites is compelling it can benefit from a more direct experimental evidence. For example recent work using single molecule TIRF measurments have directly captured secondary nucelation events from visualisation of branching (https://pubs.acs.org/doi/full/10.1021/jacs.1c07228). These types of direct measurments could complement the results described here.\n7. The study assumes all secondary nucleation sites as identical between annealed and control fibrils, differing only in frequency. This point needs clarification."}, "author_response": "RESPONSE: We are very grateful for the overall positive assessment of our manuscript.\n1. The kinetically trapping of defects (as illustrated in figure 3) would need some further motivation. Since the binding free energy between defect monomer and the fibril is significantly lowered, it would be an obvious sites for fibril breakage (discussed on page 16, lines 513-515). This should open for preferential fibril breakage at these sites with a much faster exchange of the defect monomer or a (even faster) structural reorganisation of the monomer at the fibril end. Even in the case of a two filament fibril, partial breakage of the defect filament could allow for monomer reorganisation and re-formation of a non-defect fibril.\nRESPONSE: Although it is plausible that most fibril fragmentation occurs at defects, it is not plausible that most defects cause a fragmentation event. This would cause average fibril lengths to be at most a few times higher than defect incorporation stoichiometry. Under typical production conditions, including the quiescent conditions used here, fibril average lengths are micron-scale, consisting of tens of thousands to tens of millions of monomers, but our measured secondary nucleation site stoichiometry is c. hundreds of monomers. We are also unconvinced the removal of defects by breakage of a single defective filament in a 2-filament fibril will be significantly faster than fragmentation of the entire fibril. First, it is unclear that mechanical forces could break just one filament in the fibril. Second, the newly broken filament ends would presumably also need to detach from the neighbouring filaments for there to be sufficient space between them to enable re-folding or reattachment of the defective monomers, imposing an additional free energy barrier.\nACTION: We have added an SI section (SI Sec. 10) outlining the above arguments and signposted it in the Discussion (Page 17, Line 465-471).\n2. Investigating the differences in fragmentation between annealed and non-annealed fibrils could give further support for the reduced defect frequency of annealed fibrils. For example, length distributions after gentle fragmentation (stirring or gentle sonication) could reflect the frequency of defects.\nRESPONSE: Sonication or shaking can not significantly shorten average Aβ40 fibril length, since Aβ40 fibrils are extremely resistant to fragmentation even under heavy sonication, as shown in [50]. However, for weaker fibrils such as Aβ42, this could be a viable option and we agree it is worth stating this.\nACTION: We have added material outlining these potential future experiments to the aforementioned new SI section (SI Sec. 10) and signposted it in the Discussion (Page 17, Line 469-471).\n3. Comparing the length distributions (statistical analysis of images from TEM or AFM) of the annealed and non-annealed fibrils (section 2.5) would also strengthen the conclusion that it is not the number of fibril ends that give rise to the observed differences in seeding capacity.\nRESPONSE: The misfits of the kinetic data in section 2.5 shows that even if the average annealed fibril length is 100 trillion times that of control fibrils, this still cannot explain the differences in seeding capacity of annealed and control fibrils. We agree with the reviewer that this argument could potentially be strengthened by using a more realistic difference in fibril length to generate even worse misfits. Statistical analysis of TEM images of Ab40 to provide a precise factor for the difference in average length is not feasible due to the tangling of the fibrils and their large length relative to the observation field even under very low magnification. However, such images can at least provide an upper bound for these differences.\nACTION: We have added a new section SI Sec.7 and figure. S5 to the SI containing low magnification cryoTEM images, showing that the annealed fibrils are not significantly shorter than the 37 degree fibrils. Also included in the supplemental figure are revised, and worse, misfits to the kinetic data assuming more realistic differences in average fibril length (10x-1000x). We reference these new findings in Results section 2.5 (Page 14, Line 332-335).\n4. Much of the discussion relies on the solubility data of Ab40 presented in table S1. Why do the authors use solubility data from a 20 years old Masters thesis (most likely not peer-reviewed) when they i. have their own data from recent publications and ii. show that they can easily measure the data by 1H NMR experiments?\nRESPONSE: We originally used these data for convenience as several temperatures were studied in the same experiments. We nonetheless take the reviewer’s point that this is not optimal, especially considering the peptide preparation and purification was quite different compared to our own.\nACTION: We have replaced the solubility data in the main text Fig3 with new and published solubility data from our lab. Our results (Table 1 in SI) display a similar trend to that described in the referenced Master’s thesis (Table 2 in SI), showing increased solubility with rising temperature above 37 degrees. We now retain the data from the Masters thesis in the SI for comparison/validation purposes only. In the same SI section we give an expanded account of the methodology in the various experiments performed. We have also added Methods section (Solubility measurement, line 594-602, page 20), and sentences to the Results (line 225-229, page 9) explaining that we measured the solubility with NMR at temps 27, 45, 50, and 60 degrees and used our group’s previously published data for 37 celsius. The new solubility measurements also result in small changes to calculated free energy penalties in the SI.\n5. When discussing the binding stoichiometry of proSP-C BRICHOS, it should be mentioned that it under normal conditions exists as trimer and how this affect the determined binding stoichiometry (see e.g. Biverstal H, et al. Biochim Biophys Acta 1854, 835-843 (2015) or Willander H, et al. Proc Natl Acad Sci U S A 109, 2325-2329 (2012))\nRESPONSE: For Fig 2: What is measured here is the total fluorescence of free vs fibril-bound Brichos. The trimeric state of free Brichos does not change this other than possible mild self-quenching. So the percentage of free Brichos could be underestimated a little, causing a modest overestimate of the Brichos binding stoichiometry. For Fig 5: What is measured here is the total fluorescence of free Brichos. Although self-quenching in trimers could artefactually reduce this, we use arbitrary units for the free Brichos concentration so the only potential effect on our fitting results is to change the arbitrary proportionality constants. There is therefore no effect on the fitted binding stoichiometry.\nACTION: We have added the effects of Brichos trimerisation on fitting of Figs 2 and 5 to the corresponding results sections (line 165-167, page 6 and line 309-312, page 12).\n6. The discussion highlights the implication of the structural defects for AD pathology. What level of supersaturation is expected in vivo? Normal CSF concentrations of Abeta are clearly below the solubility limit, and the concentration decreases further during the disease. This would indicate that defects, and thereby secondary nucleation, may not have a major role in vivo.\nRESPONSE: The chemical environments in CSF and in the brain interstitial fluid are very different to the chemical environment in vitro. It is therefore not expected that the solubility of Aβ40 with respect to fibrils in vitro and in vivo will be especially similar. Clearly, for it to be possible for Aβ40 fibrils to accumulate, the concentrations of Aβ40 must be above the local solubility limit. The extent of supersaturation in these environments is however not known. Nonetheless, even at low or no supersaturation, the laws of thermodynamics guarantee that defects still form, with stoichiometry governed by the Boltzmann distribution.\nACTION: We have rewritten the final Results section to now focus on proving that the defect site stoichiometry is still high enough in Aβ40 fibrils produced without supersaturation that most secondary nucleation still occurs at defects regardless of Aβ40 supersaturation levels. We have also added a new Discussion paragraph that points out that in vivo relevance must therefore be determined by factors other than supersaturation-level-control of defect stoichiometry.\nMinor issues:\nPage 7, line 197: should it be '< 90%' ?\nRESPONSE+ACTION: We have now rewritten this sentence for greater clarity.\nPage 16, line 483: change Kd to KD\nRESPONSE+ACTION: We thank the reviewer for catching this. We have now done this.\nPage 24, Ref 39: missing details\nRESPONSE+ACTION: It is a master’s thesis rather than a publication, therefore it has fewer details than might be expected. In keeping with citing conventions, we have now added the following details to the citation: university name, department name, and a url.\nPage 26, Ref 55: missing details\nRESPONSE+ACTION: We have now added the details https://doi.org/10.1039/D3SC06343G\nSI, page 4, line100: should it be 'x = 2'?\nRESPONSE+ACTION: Yes indeed, we thank the reviewer for catching this.\nSI, Figure S3: What is the temperature for the NMR data? Both 60 and 37 deg are mentioned in the legend.\nRESPONSE+ACTION: We have changed the legend to mention only 60 degrees, which is the correct temperature.\nRESPONSE: We are very grateful for the overall positive assessment of our manuscript.\n1. Data shown in Fig. 2b should be further clarified. First, individual data points should be reported instead of mean±SD, considering that N=2 for the blue curve. Second, the accuracy of the data points for the blue curve needs to be double-checked for (i) Brichos concentrations tested for the two different conditions do not overlap, (ii) the concentrations tested for the \"blue\" condition are not well spaced (please pay particular attention to the 6th data point). Third, the authors should specify whether the regression line is calculated for the blue curve only.\nRESPONSE (i): in Fig 2d (now 2e) this is an error: for blue the x values are free Brichos and for red they are total Brichos. In Fig 2b (now 2c) all data points use measured free Brichos as x values and therefore do not automatically overlap.\nACTION (i): We have replotted Fig 2b (now 2c) as individual data points, and correctly labelled the x axis as “Free Brichos concentration”.\nRESPONSE (ii): the apparently unusual spacing is not an error. It comes from three factors. First, we sampled more frequently at lower concentrations where the curvature is greater, to better constrain the regression to the Langmuir model. Second, the x values are free Brichos concentrations measured, not total Brichos concentrations tested, and so some apparent clustering is caused by random measurement errors. Third, data points 6 and 7 used relatively similar total Brichos concentrations. Also, if we include the red dataset in the regressions then the spacing becomes more visually even.\nACTION (ii): We have added to the Methods (Line 567-569) a note that more measurements were taken at lower total Brichos concentrations, to better constrain the fits to the Langmuir model.\nRESPONSE (iii): the regression curve was calculated on the blue dataset only. Given the close similarity between datasets, and since our model does not predict stoichiometry or KD differences for the underlying methods, on reflection we think it is more appropriate to instead fit to both datasets globally.\nACTION (iii): We have re-performed the regression so that it is now applied to both red and blue datasets simultaneously, and stated this in the caption of Figure 2.\n2. Also in Fig. 2b, for the 7th Brichos concentration tested, the higher of the two data points is likely above the maximum coverage value (based on the binding stoichiometry determined from the fit curve). The authors should explain the potential causes of this apparent measurement error. Also, the coefficients of determination (R2) should be reported for curves in Fig 2 as was done in Fig. 5.\nRESPONSE: This has happened because at this datapoint we are near the 10% detection limit of MDS, with only 12% of the fluorescent signal coming from bound Brichos according to the gaussian fitting. The re-fitting of the Langmuir model to all data rather than just the blue data has increased the fitted maximum coverage value just enough that the outlier is no longer above it. Nonetheless such data points still carry significant error.\nACTION: We have added the R2 values to the caption. We have also added a new panel before Fig 2b in response to another reviewer’s comment showing % bound vs total Brichos, which is what is actually directly inferred from the MDS measurements, and illustrated the detection limit on this. We explain the likely cause of measurement error in the outlier in Results 2.1 first paragraph.\n3. In SI Section 1.2.2, for 2-monomer-thick fibril, x should be 2, not 4. Fortunately, free energy differences were calculated using x = 2; so, a textual correction will be sufficient.\nRESPONSE+ACTION: We thank the reviewer for catching this typo; we will correct it. As the reviewer points out, the calculations were performed with the correct x value.\n4. In SI Section 3, K should be K (equations 16 and 17) to be consistent with the rest of the manuscript. The same goes for line 483 in the main text.\n5. In SI Fig. 3 caption, NMR spectra is reported to be obtained at 37°C, but the text and title of the legend suggest 60°C.\nRESPONSE+ACTION (4&5): We thank the reviewer for catching these typos; we have corrected them both.\n6. Several paragraphs of the Results section (starting in lines 283, 295, 375 and 407) rather belong to the Discussion section, some of which contain material that are repeated in the Discussion.\nRESPONSE (line 283): This paragraph was written with a Discussion-like tone, but it is key to understanding the cryo-EM analysis that defects will not be detectable but morphological differences will.\nACTION: We have reduced this paragraph to two sentences and merged it into the preceding paragraph to make this point clear.\nRESPONSE (line 295): This paragraph describes novel results presented graphically in the SI, although it is not written in a way that makes this clear.\nACTION: In response to another reviewer’s comment we have merged the relevant SI figure into Fig 4. We have also rewritten this paragraph to remove any potential discussion-like tone.\nRESPONSE (line 375): This paragraph uses a new although simple calculation to demonstrate an important new result that even without supersaturation secondary nucleation still occurs predominantly at growth defects.\nACTION: In response to another reviewer’s comment we have heavily rewritten this Results section to focus on this proof. In response to this comment we have also attempted to remove any discussion-like tone from the paragraph.\nRESPONSE (line 407): This paragraph could indeed be reasonably moved to the Discussion and we thank the reviewer for pointing this out.\nACTION: We ultimately decided to remove this paragraph due to its speculative nature and the need to satisfy journal length constraints.\n7. Brichos should be adequately introduced and relevant reference(s) should be cited.\nRESPONSE+ACTION: We have now introduced Brichos with suitable references at the end of the Introduction.\n8. Fig. 1a could be cited at the end of the sentence starting in line 65.\nRESPONSE+ACTION: We have now cited Fig. 1a at the end of this sentence.\nRESPONSE: We are again very grateful for the overall positive assessment of our manuscript.\n1. The demonstration that Brichos binds to these sites at substoichiometric ratios is based on accurate determination of [Stot]. However, no mention is made about the labelling efficiency of the A488-Brichos sample and there is no dicsussion on whether this can significantly impact the conclusion.\nRESPONSE: This is a good point. The Brichos concentrations we use refer to the concentration of labelled + unlabelled Brichos. Therefore, if the labelling efficiency is <100%, this will not affect the fitting results assuming labelled + unlabelled Brichos have similar binding thermodynamics.\nACTION: We have now measured the labelling efficiency using UV-Vis, finding it to be 90%. We have added these experiments as a new supplemental figure (S6) in a new SI section (SI Sec. 8). We briefly explain in the accompanying SI section that a lower labeling efficiency does not affect fitting results assuming labeled and unlabeled Brichos bind fibrils similarly. We now refer to this SI figure in the Methods section where Brichos labeling is introduced (line 530-531, page 18).\n2. What do the Brichos concentration represent – are the reported values total protein concentrations or concentration of the labelled fractions?\nRESPONSE: The Brichos concentration represents the total protein concentration (labeled+unlabeled Brichos).\nACTION: this is now stated explicitly in the Methods (line 532-533, page 18).\n3. In Fig. 2D the fibril bound Brichos % increase with concentration but data is shown only up to 1.5 mM. It would be informative to see results at higher concentrations to assess saturation behavior.\nRESPONSE: The saturation is already directly observable in the MDS data even without performing a regression although this is not clear from the way the data are plotted. The fraction of free vs bound Brichos was directly estimated using MDS, and this was later converted into concentrations. At the highest Brichos concentrations used, we are already at <10% of Brichos being bound. MDS cannot reliably distinguish 2 species when one is present at <10% of the concentration of the other. Therefore, using higher concentrations will not be able to demonstrate levels of saturation higher than this with any statistical significance.\nACTION: We have now inserted a new panel before panel 2b showing a plot of the % of Brichos that is free vs the total Brichos concentration, and showing that at the highest Brichos concentrations the bound Brichos % already drops below the detection limit. In the caption we clarify that the free vs bound brichos plot is then calculated from this.\n4. The authors strangely use two different techniques for estimating Brichos binding to amyloid fibrils for experiments in Fig. 2 (MDS) and Fig. 5 (FCS). For proper comparison and consistency MDS and FCS should be used for both experiments.\nRESPONSE: We used different techniques for Ab40 and Ab42 for historical reasons. Although comparison of Ab40 and Ab42 Brichos binding stoichiometry was never a key goal of the study, we nonetheless see the value in using both methods on one of the fibril types to check consistency between methods. We have therefore now also performed MDS on Ab40 fibrils in addition to the original FCS. (We have not included FCS experiments on Ab42 fibrils since this would take several more months for us to complete, and is not needed to demonstrate consistency now that MDS has been used on both fibril types.)\nACTION: We added the new MDS data on Ab40 as a new panel in Fig 5 and fitted globally for the stoichiometries along with the FCS data. We found similar stoichiometry values to before when we solely used FCS data, although with increased accuracy. We now motivate the FCS data in the text (Results Sec. 2.4) as a confirmatory experiment to ensure that the different stoichiometries of annealed and control fibrils are not somehow an artefact of the MDS technique.\n5. The \"annealing\" concept, while central to the study, isn't clearly defined until the Results section and would benefit from earlier introduction.\nRESPONSE: We take the reviewer’s point - currently Results section 3 is both too long and contains much material which is already known and better-placed in the Introduction.\nACTION: We have moved substantial material concerning defect stoichiometry and their removal through annealing from Results section 3 to the Introduction.\n6. While data shown to infer Brichos binding to defect sites is compelling it can benefit from a more direct experimental evidence. For example recent work using single molecule TIRF measurements have directly captured secondary nucleation events from visualisation of branching (https://pubs.acs.org/doi/full/10.1021/jacs.1c07228). These types of direct measurements could complement the results described here.\nRESPONSE: Most plausible defect structures are expected to be too small and irregular to visualize at the resolution of TIRF or other microscopy techniques such as AFM or cryo-TEM. However, we in fact already found direct evidence of defects using cryo-TEM, via their nonlocal effects on fibril structure. We previously relegated these images to the SI, where they may have gone unnoticed.\nACTION: We have added the direct cryo-TEM images of defects to figure 4, and added text to the associated Results section to explain that defect stoichiometry can nonetheless not be reliably quantified by microscopy (line 268-273, page 11).\n7. The study assumes all secondary nucleation sites as identical between annealed and control fibrils, differing only in frequency. This point needs clarification.\nRESPONSE: In Results section 2.3 it is explicitly stated that defect structure may differ between annealed and control fibrils (lines 280-282 of the submitted manuscript). In section 2.5 (lines 371-374 of the submitted manuscript) it is explained that the kinetic and binding data implies there is little difference in structure: “The close correspondence between secondary nucleation site stoichiometry and secondary nucleation propensity implies that there is little if any structural difference between the secondary nucleation sites in annealed and control fibrils”. No other important results or conclusions in the manuscript depend in turn on this conclusion, however.\nACTION: We have added some sentences to Results sections 2.5 and 2.6 to make this point clearer."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed all my comments.", "reviewer_2": "The authors have revised the manuscript to answer to all my (minor) comments. The manuscript is well-organized and timely, and it moves the field forward. Publication is recommended.\nAdditional minor issues that arose during revision:\n- In line 575, the centrifugation speed should be given in g-force not in rpm, since the latter depends on the instrument.\n- In their rebuttal, the authors stated that they added R2 values of the fit curves to Fig. 2 caption, but this is only true for the fit curve in panel C but not for the fit curves in panel E.\n- In Fig. S5, panel B appears to have considerably better contrast than panel A. Can the authors improve the cryo-TEM image in panel A?\n- Considering that the solubility data reported in Tables S1 and S2 are generally consistent with each other, I see no clear reason for replacing Fig. 3d in the original manuscript with Fig. 3c in the revised manuscript (which has less data points and is less informative overall). The manuscript would improve if the authors replaced Fig. 3c with the original Fig. 3d and added 95% CI error bars (now reported in Table S2).", "reviewer_3": "I am happy the way authors addressed my concerns. I have no further questions."}, "author_response": "We have now written the centrifugation speed in g-force.\nPanel E actually features exclusively model projections, not fitting. These models use parameters fitted in panels c and d. We have now added a mean-squared-error to panel d (in line with convention in amyloid kinetic curve fitting instead of r squared). We have also changed the caption for panel e to clarify that it contains only projections, not fits, and added r-squared values.\nThe contrast in figure S5a was already adjusted and represents the best that we were able to achieve.\nWe have replaced Fig.3c with the original Fig.3d with 95% CI error bars."}]

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