Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Kinetic fingerprints differentiate the mechanisms of action of anti-Aβ antibodies

Abstract

The amyloid cascade hypothesis, according to which the self-assembly of amyloid-β peptide (Aβ) is a causative process in Alzheimer’s disease, has driven many therapeutic efforts for the past 20 years. Failures of clinical trials investigating Aβ-targeted therapies have been interpreted as evidence against this hypothesis, irrespective of the characteristics and mechanisms of action of the therapeutic agents, which are highly challenging to assess. Here, we combine kinetic analyses with quantitative binding measurements to address the mechanism of action of four clinical stage anti-Aβ antibodies, aducanumab, gantenerumab, bapineuzumab and solanezumab. We quantify the influence of these antibodies on the aggregation kinetics and on the production of oligomeric aggregates and link these effects to the affinity and stoichiometry of each antibody for monomeric and fibrillar forms of Aβ. Our results reveal that, uniquely among these four antibodies, aducanumab dramatically reduces the flux of Aβ oligomers.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Effects of the antibodies on the kinetics of aggregation of Aβ42 in the presence and absence of preformed fibrils.
Fig. 2: Effects of the antibodies on the production of Aβ42 oligomers.
Fig. 3: Binding affinity of the antibodies to Aβ42 monomers and fibrils.
Fig. 4: Kinetic fingerprints differentiate the mechanism of action of the anti-Aβ antibodies.

Data availability

The data presented in Figs. 14 are deposited in the DRYAD database https://doi.org/10.5061/dryad.79cnp5hsn

Code availability

Bayesian inference was performed using custom python code, which is available from the authors upon request.

References

  1. 1.

    Hardy, J. & Higgins, G. Alzheimer’s disease: the amyloid cascade hypothesis. Science 256, 184–185 (1992).

    CAS  PubMed  Google Scholar 

  2. 2.

    Selkoe, D. J. & Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8, 595–608 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Härd, T. & Lendel, C. Inhibition of amyloid formation. J. Mol. Biol. 421, 441–465 (2012).

    PubMed  Google Scholar 

  4. 4.

    Walsh, D. M. et al. Naturally secreted oligomers of amyloid β protein potently inhibit hippocampal long-term potentiation in vivo. Nature 416, 535–539 (2002).

    CAS  PubMed  Google Scholar 

  5. 5.

    Cohen, S. I. A. et al. A molecular chaperone breaks the catalytic cycle that generates toxic Aβ oligomers. Nat. Struct. Mol. Biol. 22, 207–213 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Benilova, I., Karran, E. & De Strooper, B. The toxic Aβ oligomer and Alzheimer’s disease: an emperor in need of clothes. Nat. Neurosci. 15, 349–357 (2012).

    CAS  PubMed  Google Scholar 

  7. 7.

    Lee, S. J. et al. Towards an understanding of amyloid-β oligomers: characterization, toxicity mechanisms, and inhibitors. Chem. Soc. Rev. 46, 310–323 (2017).

    CAS  PubMed  Google Scholar 

  8. 8.

    Reiss, A. B. et al. Amyloid toxicity in Alzheimer’s disease. Rev. Neurosci. 29, 613–627 (2018).

    CAS  PubMed  Google Scholar 

  9. 9.

    Arosio, P. et al. Kinetic analysis reveals the diversity of microscopic mechanisms through which molecular chaperones suppress amyloid formation. Nat. Commun. 7, 10948 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Cohen, S. I. et al. Proliferation of amyloid-β42 aggregates occurs through a secondary nucleation mechanism. Proc. Natl Acad. Sci. USA 110, 9758–9763 (2013).

    CAS  PubMed  Google Scholar 

  11. 11.

    Aprile, F. A. et al. Selective targeting of primary and secondary nucleation pathways in Aβ42 aggregation using a rational antibody scanning method. Sci. Adv. 3, e1700488 (2017).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Munke, A. et al. Phage display and kinetic selection of antibodies that specifically inhibit amyloid self-replication. Proc. Natl Acad. Sci. USA 114, 6444–6449 (2017).

    CAS  PubMed  Google Scholar 

  13. 13.

    Sevigny, J. et al. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature 537, 50–56 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Bohrmann, B. et al. Gantenerumab: a novel human anti-Aβ antibody demonstrates sustained cerebral amyloid-β binding and elicits cell-mediated removal of human amyloid-β. J. Alzheimer’s Dis. 28, 49 (2012).

    CAS  Google Scholar 

  15. 15.

    Bard, F. et al. Peripherally administered antibodies against amyloid β peptide enter the central nervous system and reduce pathology in a mouse model of Alzheimer disease. Nat. Med. 6, 916–919 (2000).

    CAS  PubMed  Google Scholar 

  16. 16.

    Hettmann, T. et al. Development of the clinical candidate PBD-C06, a humanized pGlu3-Aβ-specific antibody against Alzheimer’s disease with reduced complement activation. Sci. Rep. 10, 3294 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Crehan, H. et al. Effector function of anti-pyroglutamate-3 Aβ antibodies affects cognitive benefit, glial activation and amyloid clearance in Alzheimer’s-like mice. Alzheimers Res Ther. 12, 12 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Budd Haeberlein, S. et al. Topline results from Phase 3 aducanumab studies. In Clinical Trials on Alzheimer’s Disease (CTAD) San Diego, CA (2019).

  19. 19.

    Arndt, J. W. et al. Structural and kinetic basis for the selectivity of aducanumab for aggregated forms of amyloid-β. Sci. Rep. 8, 6412 (2018).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Lendel, C. et al. A hexameric peptide barrel as building block of amyloid-β protofibrils. Angew. Chem. Int. Ed. Engl. 53, 12756–12760 (2014).

    CAS  PubMed  Google Scholar 

  21. 21.

    Colvin, M. T. et al. Atomic resolution structure of monomorphic aβ42 amyloid fibrils. J. Am. Chem. Soc. 138, 9663–9674 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Wälti, M. A. et al. Atomic-resolution structure of a disease-relevant Aβ(1–42) amyloid fibril. Proc. Natl Acad. Sci. USA 113, E4976–E4984 (2016).

    PubMed  Google Scholar 

  23. 23.

    Brännström, K. et al. A generic method for design of oligomer-specific antibodies. PLoS ONE 9, e90857 (2014).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Ostrowitzki, S. et al. SCarlet RoAD investigators, a phase III randomized trial of gantenerumab in prodromal Alzheimer’s disease. Alzheimers Res. Ther. 9, 95 (2017).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Crespi, G. A., Hermans, S. J., Parker, M. W. & Miles, L. A. Molecular basis for mid-region amyloid-β capture by leading Alzheimer’s disease immunotherapies. Sci. Rep. 5, 9649 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Salloway, S. et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer’s disease. N. Engl. J. Med. 370, 322–333 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Honig, L. S. et al. Trial of solanezumab for mild dementia due to Alzheimer’s disease. New. Eng. Med. 378, 321–330 (2018).

    CAS  Google Scholar 

  28. 28.

    Bateman, R. J. et al. DIAN-TU pharma consortium for the dominantly inherited Alzheimer Network. The DIAN-TU next generation Alzheimer’s prevention trial: adaptive design and disease progression model. Alzheimer’s Dement. 13, 8–19 (2017).

    Google Scholar 

  29. 29.

    Knowles, T. P. et al. An analytical solution to the kinetics of breakable filament assembly. Science 326, 1533–1537 (2009).

    CAS  PubMed  Google Scholar 

  30. 30.

    Habchi, J. et al. An anticancer drug suppresses the primary nucleation reaction that initiates the production of the toxic Aβ42 aggregates linked with Alzheimer’s disease. Sci. Adv. 2, e1501244 (2016).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Månsson, C. et al. Interaction of the molecular chaperone DNAJB6 with growing amyloid-beta 42 (Aβ42) aggregates leads to sub-stoichiometric inhibition of amyloid formation. J. Biol. Chem. 289, 31066–31076 (2014).

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Cohen, S. I., Vendruscolo, M., Dobson, C. M. & Knowles, T. P. From macroscopic measurements to microscopic mechanisms of protein aggregation. J. Mol. Biol. 421, 160–171 (2012).

    CAS  PubMed  Google Scholar 

  33. 33.

    Arosio, P., Knowles, T. P. & Linse, S. On the lag phase in amyloid fibril formation. Phys. Chem. Chem. Phys. 17, 7606–7618 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Meisl, G. et al. Molecular mechanisms of protein aggregation from global fitting of kinetic models. Nat. Protoc. 11, 252–272 (2016).

    CAS  PubMed  Google Scholar 

  35. 35.

    Padayachee, E. R. et al. Cerebrospinal fluid-induced retardation of amyloid β aggregation correlates with Alzheimer’s disease and the APOE ε4 allele. Brain Res. 1651, 11–16 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Frankel, R. et al. Autocatalytic amplification of Alzheimer-associated Aβ42 peptide aggregation in human cerebrospinal fluid. Nat. Commun. Biol. 2, 365 (2019).

    Google Scholar 

  37. 37.

    Michaels, T. C. T. et al. Dynamics of oligomer populations formed during the aggregation of Alzheimer’s Aβ42 peptide. Nat. Chem. 12, 445–451 (2020).

    CAS  PubMed  Google Scholar 

  38. 38.

    Arosio, P. et al. Microfluidic diffusion analysis of the sizes and interactions of proteins under native solution conditions. ACS Nano 10, 333–341 (2016).

    CAS  PubMed  Google Scholar 

  39. 39.

    Yates, E. V. et al. Latent analysis of unmodified biomolecules and their complexes in solution with attomole detection sensitivity. Nat. Chem. 7, 802–809 (2015).

    CAS  PubMed  Google Scholar 

  40. 40.

    Panza, F. et al. Are antibodies directed against amyloid-β (Aβ) oligomers the last call for the Aβ hypothesis of Alzheimer’s disease? Immunotherapy 11, 3–6 (2019).

    CAS  PubMed  Google Scholar 

  41. 41.

    Panza, F., Lozupone, M., Logroscino, G. & Imbimbo, B. P. A critical appraisal of amyloid-β-targeting therapies for Alzheimer disease. Nat. Rev. Neurol. 15, 73–88 (2019).

    PubMed  Google Scholar 

  42. 42.

    Wang, Y. et al. Lessons from anti-amyloid-β immunotherapies in Alzheimer disease: aiming at a moving target. Neurodegener. Dis. 17, 242–250 (2017).

    PubMed  Google Scholar 

  43. 43.

    Portelius, E., Westman-Brinkmalm, A., Zetterberg, H. & Blennow, K. Determination of β-amyloid peptide signatures in cerebrospinal fluid using immunoprecipitation-mass spectrometry. J. Proteome Res 5, 1010–1016 (2006).

    CAS  PubMed  Google Scholar 

  44. 44.

    Kummer, M. P. & Heneka, T. Truncated and modified amyloid-beta species. Alzheimers Res. Ther. 6, 28 (2014).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Brinkmalm, G. et al. Identification of neurotoxic cross-linked amyloid-β dimers in the Alzheimer’s brain. Brain 142, 1441–1457 (2019).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Koradi, R., Billeter, M. & Wüthrich, K. MOLMOL: a program for display and analysis of macromolecular structures. J. Mol. Graph. 14, 29–32 (1996).

    Google Scholar 

  47. 47.

    Kaar, W. et al. Refolding of Npro fusion proteins. Biotechnol. Bioeng. 104, 774–784 (2009).

    CAS  PubMed  Google Scholar 

  48. 48.

    Walsh, D. M. et al. A facile method for expression and purification of the Alzheimer’s disease-associated amyloid β-peptide. FEBS J. 276, 1266–1281 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Panza, F. et al. Bapineuzumab: anti-β-amyloid monoclonal antibodies for the treatment of Alzheimer’s disease. Immunother. 2, 767–782 (2010).

    CAS  Google Scholar 

  50. 50.

    Willander, H. et al. BRICHOS domains efficiently delay fibrillation of amyloid β-peptide. J. Biol. Chem. 287, 31608–31617 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Johansson, H., Nordling, K., Weaver, T. E. & Johansson, J. The Brichos domain-containing C-terminal part of pro-surfactant protein C binds to an unfolded poly-Val transmembrane segment. J. Biol. Chem. 281, 21032–21039 (2006).

    CAS  PubMed  Google Scholar 

  52. 52.

    Nasir, I., Linse, S. & Cabaleiro-Lago, C. Fluorescent filter-trap assay for amyloid fibril formation kinetics in complex solutions. ACS Chem. Neurosci. 6, 1436–1444 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the Swedish Research Council (grant no. VR 2015-00143), the European Research Council (advanced grant no. 340890), Knut and Alice Wallenberg Foundation (KAW 2016.0074) and the Novo Nordisk Foundation (grant no. NNF19OC0054635).

Author information

Affiliations

Authors

Contributions

S.L. and O.H. designed the study. S.L. expressed and purified recombinant Aβ1–42 and Aβ(MC1–42) peptides. S.L., M.L. and E.S. developed and optimized the protocol for recombinant Aβ1–42 production and purification. F.Q., T.O’M., T.B. and P.H.W. generated and purified the antibodies. O.H. provided the pooled CSF samples. S.L. isolated Aβ42 monomers and antibodies by SEC and performed the aggregation kinetics experiments before unblinding. S.L. performed kinetic analysis of the data before unblinding. G.M., S.I.A.C., T.P.J.K. and C.M.D. performed additional kinetic analysis of the data after unblinding. S.L. produced and purified the Alexa-647-labeled forms of Aβ42 and all antibodies. S.L. performed the oligomer quantification experiments, and K.B. performed all mass spectrometry analyses before unblinding. T.S. performed the diffusion measurements of KD and stoichiometry before unblinding. S.L., T.S., S.R.A.D., C.K.X. and G.M. analyzed the diffusion data. S.L., M.V., S.I.A.C., T.P.J.K. and O.H. wrote the paper with input from all coauthors.

Corresponding author

Correspondence to Sara Linse.

Ethics declarations

Competing interests

S.L. acquired research support (for the institution) from Biogen to cover the costs for the kinetic experiments and analyses as well as the oligomer generation experiments and analyses. O.H. has acquired research support (for the institution) from Roche, GE Healthcare, Biogen, AVID Radiopharmaceuticals and Euroimmun. In the past 2 years, he has received consultancy/speaker fees from Biogen and Roche. S.L., S.I.A.C., M.V., C.M.D. and T.P.J.K. are founders and shareholders of Wren Therapeutics Ltd, where S.L., S.I.A.C., M.V., E.S. and T.P.J.K. are also employees. P.H.W., F.Q. and T.B. are employees and shareholders of Biogen. T.P.J.K. is a founder and S.R.A.D. is an employee of Fluidic Analytics.

Additional information

Peer review information Inês Chen was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Microscopic mechanism of inhibition.

The action of an inhibitor can be assessed through the analysis of macroscopic aggregation curves5,9. A-C) Predicted changes to the macroscopic aggregation curve if the rate constant for one specific microscopic step is selectively reduced by a factor of 3 (marine blue), 10 (blue), 30 (cyan), 100 (green), 1000 (orange) or 10000 (red). The reference curve is calculated for 3 µM Aβ42 in 20 mM HEPES, 140 mM NaCl, 1 mM CaCl2 pH 8.018 with knk+ = 2.5.10-3 M-2s-2, k2k+ = 2.1.1013 M-3s-2, √KM = 0.25 µM; nc = 1, n2 = 2. (a) Inhibition of secondary nucleation (rate constant k2) causes little lag-phase extension but a reduction of the transition slope. This route for therapeutic intervention may substantially reduce toxicity5, and requires inhibitors that block the catalytic surface of fibrils, or bind to on-pathway oligomers to prevent their conversion to fibrils5,9,11,30. (b) Inhibition of primary nucleation (kn) causes a lag-phase prolongation with no effect on the transition steepness, thereby delaying the generation of oligomeric species through secondary nucleation. This requires compounds that bind Aβ monomers, nuclei or other oligomers9,12,30,31. (c). Inhibition of elongation (k+) causes both a lag-phase extension and a reduction in transition slope5,9, an effect that may increase toxicity over time5. When the ends of fibrils are blocked, a larger fraction of the monomers bind to their sides, where secondary nucleation and oligomer generation is catalyzed. The macroscopic curves are sensitive to rate constant products (knk+ and k2k+); precisely the same effect as displayed in C can arise if kn and k2 are reduced in parallel by the same factor. (df) Total nucleation rate underlying each trace in panel A, B and C, respectively.

Extended Data Fig. 2 Isolation of monomeric Aβ42 and antibodies by size exclusion chromatography.

Isolation of the antibodies on a 1 × 30 cm Superdex 200 column (GE Healthcare) in 20 mM HEPES/NaOH, 140 mM NaCl, 1 mM CaCl2, pH 8.0 just prior to the kinetics experiment (first five panels). Isolation of recombinant Aβ1-42 on a 1 × 30 cm Superdex 75 column (GE Healthcare) in 20 mM HEPES/NaOH, 1 mM CaCl2, pH 8.0 just prior to the kinetics experiment (last panel). 140 mM NaCl was added from a 4.2 M NaCl stock after size exclusion to limit losses on the column. In each case, the fraction between the vertical red lines was collected.

Extended Data Fig. 3 Aggregation kinetics data of Aβ1-42 in the absence and presence of antibodies.

All panels show data acquired using recombinant Aβ1-42 at 37 °C under quiescent conditions in 20 mM HEPES/NaOH, 140 mM NaCl, 1 mM CaCl2, pH 8.0 in PEGylated plates. (ac) Normalized data with chaducanumab (A,B) and isotype control (C). The same data are shown in non-normalized form in panels (d, e) and (g), respectively. Panels (f, h, i) show the same data as shown in Fig. 1, and in addition panel H includes data at 500–2000 nM 3D6. The fitted lines in panel A are with reduced values of k2. The fits in panel B, to normalized data in the absence and presence 250–1000 nM chaducanumab, allow for a decrease in k2 and an increase in kn. We note that at very high concentrations (>500 nM), which are unlikely to be realized in vivo, chaducanumab accelerates the bulk Aβ42 aggregation process by increasing the primary nucleation rate. Indeed, the data at 250–1000 nM chaducanumab are best fitted assuming that the effect on k2 is combined with an increase in kn (Extended Data Fig. 4). Interestingly, at high concentrations of each antibody, the ThT intensity is significantly reduced, which for m266 and 3D6, is found to be related to a substantial concentration of Aβ1-42 remaining in solution (Extended Data Fig. 5), implying the possibility of monomer binding which we assess directly in solution (Fig. 3).

Extended Data Fig. 4 Effects of the antibodies on the rate constants.

The rate constant values relative to Aβ42 alone are shown as a function of the antibody concentration for the case that produced the best fit to the data for each antibody (see Fig. 1). For chaducanumab (a) we thus show the effect on the rate constant for secondary nucleation, k2, up to 125 nM antibody, and thereafter the effects in kn and k2. For m266 (b) we show the effect on the rate constant for primary nucleation, kn. For 3D6 (c) and chgantenerumab (d) we show the effect on the elongation rate constant, k+.

Extended Data Fig. 5 Measurements of the amounts of Aβ42 monomers remaining in solution at the end of the aggregation reaction.

We measured the Aβ1-42 monomers remaining in solution after reaching the ThT plateau in the presence of antibodies that cause a drop in final ThT intensity. Sample from multiple wells (300–400 µl) were collected after reaching the ThT plateau and fibrils were removed by centrifugation. A small sample (10 µl) from the top of the supernatant was analyzed using SDS PAGE (the standard, S, in panel C and B is at 10 kDa). Lane Aβ shows monomers before aggregation, the lane labeled 0 shows the remaining monomer at the plateau for Aβ1-42 alone. Lanes 1–5 correspond to 1:1 serial dilutions of antibody from 500 nM (chaducanumab, 3D6 and chgantenerumab) or 1 µM (m266). Remaining Aβ1-42 monomers at the plateau are clearly detected with 1 µM, 500 nM and 250 nM m266, and with 500 nM and 250 nM 3D6. Faint Aβ1-42 bands are seen with 125 nM m266 and 125 nM 3D6. Thus, the reduced ThT intensity at the plateau intensity is, at least in part, due to less monomer being consumed in the reaction when high concentration of m266 or 3D6 are present. No Aβ1-42 monomer is detected by this method at the plateau in the presence of chaducanumab or chgantenerumab, indicating that the effect on the ThT plateau intensity is not due to less monomer being consumed in the reaction. Instead, it is likely an effect of these antibodies interfering with the ThT signal. All data were acquired using Aβ1-42 at 37 °C under quiescent conditions in 20 mM HEPES/NaOH, 140 mM NaCl, 1 mM CaCl2, pH 8.0 in PEGylated plates.

Extended Data Fig. 6 Effects of the antibodies on the aggregation kinetics of Aβ1-42 in CSF.

ThT fluorescence as a function of time for reactions starting from 6 µM Aβ1-42 in 66% CSF, 20 mM HEPES/NaOH, 140 mM NaCl, 1 mM CaCl2, pH 8.0 in the absence and presence of chaducanumab (a), m266 (b), 3D6 (c) or chgantenerumab (d). The colour codes for the antibody concentrations are given in nM in each panel. The left column shows data obtained in the presence of 30% preformed seeds, with linear fits, and the following three columns show non-seeded data fitted three times with the selective variation of one rate constant in each column. For chaducanumab, the heavy seeded data rule out an effect on k+, and we can identify a reduction in k2 as the model which best fits the chaducanumab data in CSF; this is verified using light seeding in CSF (Extended Data Fig. 7). Likewise, for m266 the heavy seeded data rule out an effect on k+, and we can identify a reduction in kn as the model which best fits the m266 data in CSF. For 3D6 as well as chgantenerumab, the seeded data show an effect on k+, which indeed explains also the non-seeded data in CSF. Note that the x-axis covers 2.2, 6 or 8 h depending on the magnitude of the effect of each antibody.

Extended Data Fig. 7 Effects of chaducanumab on light seeded (2%) aggregation kinetics of Aβ1-42 in CSF.

ThT fluorescence as a function of time for reactions starting from 6 µM Aβ1-42 with 120 nM preformed seed fibrils in 66% CSF, 20 mM HEPES/NaOH, 140 mM NaCl, 1 mM CaCl2, pH 8.0 in the absence and presence of chaducanumab at 125 and 250 nM. The fits in the left panel assumes a constant value of k2 and curve-specific values of k+. The fits in the right panel assumes a constant value of k+ and curve-specific values of k2. The inclusion of a low amount of seeds passes primary nucleation and makes the data independent of this step; these data thus validate a reduction of secondary nucleation rate as the primary role of chaducanumab in CSF.

Extended Data Fig. 8 Aggregation kinetics in the presence of preformed seed fibrils at low concentration (2%).

Normalized ThT fluorescence as a function of time for reactions starting from 3 to 4 µM Aβ1-42 monomer and 2% (60 to 80 nM) Aβ1-42 fibrils in 20 mM HEPES/NaOH, 140 mM NaCl, 1 mM CaCl2, pH 8.0 in the absence (black) and presence of a) chgantenerumab, b) m266, c) isotype control antibody (yellow), d) chaducanumab, or e) 3D6 at five concentrations as given by the colour code in panel A. The fitted curves in panels A-C allow only a variation of k2, whereas the fitted curves in panels D and E allow only a variation of k+. f). ThT fluorescence as a function of time for reactions starting from 3 μM Aβ1-42 in the absence (grey) or presence of 60 nM preformed seed fibrils made in the absence (black) or presence of 6 nM chaducanumab (green). The fits assume a constant value of k+ and curve-specific values of k2. The relative values of k2 obtained are 1.0 (grey), 1.0 (black) and 0.67 (green). Thus, forming the seeds in the presence of chaducanumab leads to a 33% reduction in apparent k2, very similar to what we observed with 6 nM chaducanumab in the non-seeded data (Extended Data Fig. 4a). We conclude that a substantial fraction of the inhibitory effect on secondary nucleation originates from interaction of the antibody with fibrillar aggregates.

Extended Data Fig. 9 MALDI TOF/TOF analyses.

Top) Aβ1-42 in the presence of chaducanumab. Example of a MALDI TOF/TOF spectrum with direct spotting of samples collected at the half-time of aggregation of Aβ1-42 in the presence of chaducanumab. Middle and bottom) Aβ1-42 in the presence of m266. Example of a MALDI TOF-TOF spectra with HPLC separation before spotting for samples collected at the half-time of aggregation of Aβ1-42 in the presence of m266. In Fraction 9 there is so much 14N peptide that the signal from 15N is suppressed.

Extended Data Fig. 10 Antibody binding analysis in solution.

a) Diffusion of Alexa-647-Aβ(MC1-42) in the absence and presence of increasing concentration of isotype control antibody. b) Diffusion of Alexa-647-labeled isotype control antibody in the absence and presence of increasing concentrations of unlabelled Aβ1-42 fibrils. In both panels are shown the fraction of fluorescence appearing in the diffused half at the channel outlet as a function of total concentration of the non-labeled species. The solid lines are fitted straight lines. c) The same data as in Fig. 3c, e, f replotted with the ratio of fibril to antibody concentration on the X-axis. The fibril concentration is in monomer units. d) Examples of results of Bayesian analysis.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Linse, S., Scheidt, T., Bernfur, K. et al. Kinetic fingerprints differentiate the mechanisms of action of anti-Aβ antibodies. Nat Struct Mol Biol 27, 1125–1133 (2020). https://doi.org/10.1038/s41594-020-0505-6

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing