Heterotypic Amyloid β interactions facilitate amyloid assembly and modify amyloid structure

Abstract

It is still unclear why pathological amyloid deposition initiates in specific brain regions or why some cells or tissues are more susceptible than others. Amyloid deposition is determined by the self-assembly of short protein segments called aggregation-prone regions (APRs) that favour cross-β structure. Here, we investigated whether Aβ amyloid assembly can be modified by heterotypic interactions between Aβ APRs and short homologous segments in otherwise unrelated human proteins. Mining existing proteomics data of Aβ plaques from AD patients revealed an enrichment in proteins that harbour such homologous sequences to the Aβ APRs, suggesting heterotypic amyloid interactions may occur in patients. We identified homologous APRs from such proteins and show that they can modify Aβ assembly kinetics, fibril morphology and deposition pattern in vitro. Moreover, we found three of these proteins upon transient expression in an Aβ reporter cell line promote Aβ amyloid aggregation. Strikingly, we did not find a bias towards heterotypic interactions in plaques from AD mouse models where Aβ self-aggregation is observed. Based on these data, we propose that heterotypic APR interactions may play a hitherto unrealized role in amyloid-deposition diseases.

Keywords: Alzheimer’s disease; amyloid beta; heterotypic aggregation; toxicity.

Figure 1. Differential binding of Aβ‐aggregating species in Aβ cellulose peptide microarrays

1. A

   Aβ1‐42 sliding window membrane setup. Red indicates where the KLVFFA starts presented whole. Green where GAIIGL presented whole. Blue indicates the controls (4 proline breakers, 5 scrambled Aβ peptides, sequences Appendix Table S1).

2. B

   SEC‐MALS of Biot‐Aβ1‐42 preparation with 7 M GnHCL showing a clear monomeric peak and a smaller oligomeric.

3. C

   100 nM of Biot‐Aβ1‐42 monomers show no binding on membrane (down panel), TEM image shows no aggregating species in the sample (upper panel). Scale bar: 500 nm.

4. D

   Void fraction (oligomers) shows strong binding on first APR of Aβ1‐42. Scale bar: 500 nm.

5. E

   Normalized ThT kinetics of 10 μM Biot‐Aβ1‐42 with timepoints of samples that incubated with Aβ1‐42 membranes.

6. F–H

   Binding of different aggregating samples to Aβ membranes and their TEM images. 100 nM of sample1 (early oligomers) binds strongly to middle APR (F), 100 nM of sample 2 (late oligomers) binds in both middle and C‐terminal APR of Aβ1‐42 (down panel) while TEM images show fibrillar structures (upper panel) (G), 100 nM of sample 3 shows no specific binding to Aβ1‐42 membranes (H). Scale bars: 500 nm.

7. I

   ThT kinetics of Biot‐Aβ1‐42 seeding. 10 μM of Biot‐Aβ1‐42 incubated with 0.5 or 1 μM of Biot‐Aβ1‐42 seeds.

8. J

   100 nM of Biot‐Aβ1‐42 seeds show a strong binding in both APRs. TEM image (upper panel) slow clear fragmentation of fibrils. Scale bar: 1 μm.

Figure 2. Aβ binding to APR homologues derived from human proteins

1. A

   Sequence similarity in combination with peptide length for 1,000 random proteins derived from human proteome. Graph: bottom and top of the boxes are the first and third quartiles, central band represents median, whiskers encompass minimum and maximum.

2. B, C

   Distribution of amino acids in homologues to Aβ KLVFFA (B) and LVFFAE (C) proteins.

3. D

   Binding of Biot‐Aβ1‐42 to homologue peptides derived from ˜ 520 randomly selected proteins.

4. E

   Summary of Biot‐Aβ1‐42 binding throughout eight membranes, colour indicates the mean between membranes and the size of the outline of the standard deviation.

5. F

   Heatmap of amino acid substitutions in membrane hits.

6. G, H

   Membrane top binders spell AD and PLAK, white space consists of random sequences. (sequences in Appendix Table S2).

Figure 3. Homologous peptides can affect rAβ1‐42 kinetics and resulting fibril morphology

1. A–C

   ThT kinetics of 10 μM rAβ1‐42 alone or in the presence (1:1) of three homologue peptides (full screen on Appendix Figs S3 and S4, n = 2 independent experiments with 4 repeats). Reused images in Appendix Fig S3.

2. D

   Lag time difference between rAβ1‐42 alone (rAβ1‐42 = 0) and in the presence of peptides (Statistics: Brown–Forsythe and Welch ANOVA tests with Dunnett’s T3 multiple comparison corrections, n = 2 independent experiments with 4 repeats).Graph: Mean difference and 95% CI. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

3. E

   Fluorescence amplitude difference between rAβ1‐42 alone (rAβ1‐42 = 0) and in the presence of the peptides (Statistics: Brown–Forsythe and Welch ANOVA tests with Dunnett’s T3 multiple comparison correction, n = 2 independent experiments with 4 repeats). Graph: Mean difference and 95% CI. *P ≤ 0.05, ***P ≤ 0.001, ****P ≤ 0.0001.

4. F

   Representative TEM images of fibrils made in the presence of 1:1 rAβ1‐42:peptides. Scale bars: 500 nm.

5. G

   Fibril length difference between Aβ alone and in the presence of peptides (Statistics: Brown–Forsythe and Welch ANOVA test with Games‐Howell multiple comparison correction). Graph: Mean difference and 99% CI. **P ≤ 0.01, ****P ≤ 0.0001. At least 9 different positions on grid and at least 100 fibrils were counted for each condition (except Aβ+P23, Aβ+P24). Fibril length distribution in Appendix Fig S7.

6. H

   Curcumin binding to Aβ fibrils alone or in presence of P5. (n = 2, at least 4 repeats, statistics: Kolmogorov‐Smirnov test). Graph: mean ± SD. P = 0.049. Reused image in Appendix Fig S9.

7. I

   Representative AFM height images of Aβ fibrils alone or in a 1:1 mixture with P3, P5, P8 and P12 peptides are shown in the top row. The boxes indicate the magnified regions shown in the second row. Arrows indicate the locations of representative individual fibrils shown in magnified detail, each shown as a 200 nm digitally straightened segment and a 100 nm segment of the corresponding 3D surface envelope model that was calculated from the image data. The scale bar for each row is shown to the left, with both the 3D model and the straightened image data representing 10 nm. The colour scale of the 3D models from blue to yellow indicates the distance (from low to high) between the fibril surface and fibril centre axis to demonstrate their twist patterns. The average fibril height distribution of around 80 manually selected filaments per sample that showed twist patterns characteristic of single, not fragmented, amyloid fibrils are shown in the bottom row.

Figure 4. Presence of proteins with homologous regions to Aβ APRs in human amyloid plaques

1. Over‐representation of Aβ APRs in amyloid plaques of AD brains (statistics: hypergeometric test with Bonferroni correction) *P ≤ 0.05, ***P ≤ 0.001, ****P ≤ 0.0001. The original data were taken from Xiong et al (2019a, b) and included three biological replicates.

2. Log‐odd ratio of random sampling from mass spectrometry background. In blue is the distribution upon random sampling (×1,000) from tissue proteins. Red dot indicates the true values of analysis. In APR regions the actual value (red) resides either in the edges or outside of the random distribution. (statistics: Z‐test). *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

3. Log‐odd ratio of random sampling from human proteome. In blue is the distribution upon random sampling (x1000) from proteome. Red dot indicates the true values of analysis. (statistics: Z‐test) *P ≤ 0.05, **P ≤ 0.01.

4. TANGO scores of homologue APRs in amyloid plaques and non‐amyloid plaques proteins. (statistics: Kolmogorov–Smirnov test).

5. Biological pathways enrichment of Aβ APR homologue‐related and non‐related proteins derived from AD amyloid plaques.

6. APR homologue proteins identified in other MS studies.

7. Over‐representation of Aβ APRs in amyloid plaques of non‐AD brains. (statistics: hypergeometric test with Bonferroni correction). *P ≤ 0.05, **P ≤ 0.01 The original data were taken from Xiong et al (2019a, b) and included three biological replicates.

8. Log‐odd ratio of random sampling from mass spectrometry background. In blue is the distribution upon random sampling (×1,000) from tissue proteins. Red dot indicates the true values of analysis. (statistics: Z‐test) *P ≤ 0.05, **P ≤ 0.01.

9. Log‐odd ratio of random sampling from human proteome. In blue is the distribution upon random sampling (×1,000) from proteome. Red dot indicates the true values of analysis. (statistics: Z‐test) *P ≤ 0.05, **P ≤ 0.01.

10. Biological pathways enrichment in of Aβ APR homologue‐related and non‐related proteins derived from non‐AD amyloid plaques.

Figure 5. No over‐representation is observed in amyloid plaques from APP/PS1 mouse or in Glial cytoplasmic inclusions from

1. Over‐representation of Aβ APRs in amyloid plaques of APP/PS1 mouse brains (mean ± SD) from two biological replicates. (statistics: hypergeometric test with Bonferroni correction).

2. Log‐odd ratio of random sampling from mass spectrometry background for both replicates. Red dot indicates the true values of analysis. (statistics: Z‐test) *P ≤ 0.05.

3. Log‐odd ratio of random sampling from mouse proteome for both replicates. Red dot indicates the true values of analysis. (statistics: Z‐test) *P ≤ 0.05.

4. No over‐representation of Aβ APRs was observed in proteins from Glial cytoplasmic inclusions (α‐synuclein aggregates). (statistics: hypergeometric test with Bonferroni correction).

Figure EV1. Over‐representation of Aβ APRs in tau tangles and amyloid plaques

1. Subcellular localization of proteins found in amyloid plaques (Xiong et al, 2019a, b).

2. No over‐representation was observed of any Aβ regions in Tau tangles isolated from human AD brains (Drummond et al, 2020a, b). Hippocampal formation data set from Human Protein Atlas was used as background. Statistics: Hypergeometric test with Bonferroni correction. *P ≤ 0.05.

3. Over‐representation of C‐terminal APR in Aβ amyloid plaques isolated from human AD brains. (Drummond et al, 2017a, b). Hippocampal formation data set from Human Protein Atlas was used as background. Statistics: Hypergeometric test with Bonferroni correction. *P ≤ 0.05, **P ≤ 0.01, ****P ≤ 0.0001.

Figure 6. Proteins with homologues to Aβ regions can induce the aggregation of Aβ1‐42 in HEK293T cells Aβ biosensor

1. A

   Experimental setup of inducing aggregation in Aβ biosensor cell line.

2. B

   Representative images of Aβ biosensor seeding with three different seed concentration (10, 50, 100 nM) Scale bars: 100 μm.

3. C

   Treating Aβ biosensor with different concentrations of rAβ1‐42 seeds induces the aggregation of mCherry‐Aβ1‐42 in a dose‐dependent matter (n = 3 independent experiments, graph: mean and 95% CI). FRAP of Aβ spots shows limited recovery confirming that are aggregates (in detail at Appendix Fig S10B and C). Scale bar: 2.5 μm.

4. D

   Representative images of three proteins that can induce aggregation of Aβ1‐42 in biosensor cell. Increased aggregation is observed in cells expressing the construct but not GFP alone (left panels). Scale bar: 100 μm. FRAP of the resulting aggregates shows no recovery (in detail at Appendix Fig S11). Scale bar: 2.5 μm Removal of the homologue regions resulting in reduced aggregation (right panels). (n = 3 independent experiments).

5. E

   Quantification of a number of spots per cell in cells expressing/not expressing the construct (identified by GFP, transfection reporter) (n = 3 independent experiments, statistics: ordinary one‐way ANOVA with Dunnett’s T3 multiple comparison correction, unpaired t‐test for transfected/non‐transfected cells). Bar plot: mean with 95% CI. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.

6. F, G

   Quantification of Spots per cell for construct6 and construct6 control (6Ctrl, removal of homologue region) (F) and construct10 and construct10 control (10ctrl, removal of homologue region) (G). (n = 4 independent experiments, statistics: ordinary one‐way ANOVA). Bar plot: mean with 95% CI. ****P ≤ 0.0001.

7. H, I

   Confocal images of colocalization of protein of interest with Aβ aggregates. (Contrast of images were enhanced to 0.1% saturated pixels using Fiji). Scale bars: 22 μm.

Figure EV2. Effect of expression of amyloidogenic proteins in aggregation of Aβ

1. Quantification of spots per cell for aggregation inducer constructs (6, 9, 10) and known amyloidogenic proteins (α‐synuclein, tauRD‐P301S, SOD1‐A4V). Graph: mean with 95% CI. (n = 4 independent experiments, statistics: ordinary one‐way ANOVA). *P ≤ 0.05, **P ≤ 0.01, ****P ≤ 0.0001.

2. Representative images of cells expressing the different constructs. mCherry indicates Aβ, Alexa647 the constructs. Scale bars: 100 μm.