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SUPPLEMENTARY INFORMATIONdoi:10.1038/nature12415

AD

AD ɛ3

APOE4

Differential expression analysis (Fig.1)

< > < >

Unaff. ɛ3 Unaff. ɛ2 Unaff. ɛ4

DCA Analysis (Fig.2)

RNF219, SV2A…

Human Genetics (Fig.5)

Cell Models (Figs.3-4)

APOE4-dependent effect on APP trafficking/processing

Modulation of APOE4 effects on: -cerebral amyloid plaque load -AD age of onset.

Supplementary Figure 1 Schematic of the overall strategy. Initial differential expression approaches conducted in autopsied cortical brain samples identified a set of gene expression changes common to APOE4 carrier tissue (versus unaffected APOE2) and to LOAD APOE3 tissue (versus LOAD unaffected APOE3), termed the A/E pattern. DCA analysis identified candidate node factors that are play critical upstream regulatory roles in establishing the A/E pattern of change. Cellular and human geneti studies support a model in which DCA node candidates such as SV2A and RNF219 transduce the impact of APOE4, leading to modiied APP endocytosis and processing.

Transcriptional signatures overlap (Fig.1)

A/E pattern

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2/2 2/3 3/3

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Genome-wide gene expression analysis

APOE:

Unaffected individuals stratified by APOE genotype a

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3/4 4/4

LOAD risk:

Supplementary Figure 2. Genome-wide gene expression analysis of unaffected brain tissue samples describes a transcriptomic shift associated with APOE-dependent LOAD risk.a, Cerebral cortex brain samples from 185 unaffected individuals (GEO GSE15222 22 )) were categorized by their APOE genotype, color-coded in terms of low (blue) to high (red) LOAD risk. For each gene transcript, a correlation coefficient was calculated between expression levels and LOAD risk across the 185 individuals. b, Genes such as “Gene 1” (upper panel), which exhibit increased expression levels in the context of higher predicted LOAD risk, were assigned a high correlation coefficient (close to 1), while genes such as “Gene 3” (lower panel), which exhibit lower expression levels in the context of elevated LOAD risk, were assigned a low correlation coefficient (close to -1). c-d,GSEA functional annotation56 revealed that genes whose expression levels are positively correlated with APOE-associated LOAD risk in unaffected samples are, as a group, highly similar to a previously reported gene set that was found to be increased in incipient LOAD brain tissue (termed “Alzheimer_Enrichment_Up”; c) while those negatively correlated with APOE-associated LOAD risk are similar to a gene set that was found to be decreased in incipient LOAD tissue (termed “Alzheimer_Enrichment_Dn”; d). The x axis in (c-d) represents the continuum of all queried transcripts in our analysis (8560 in total), rank-ordered from those most upregulated in expression levels in the context of LOAD high-risk APOE genotypes (relative to LOAD low-risk APOE genotypes; presented at the red end of the color spectrum), to those most downregulated (presented at the blue end of the spectrum). Black bars signify the positions of all individual genes belonging to the previously defined gene sets “Alzheimer_Enrichment_Up” or “Alzheimer_Enrichment_Dn” in c and d, respectively. In (c), the individual black bars (corresponding to all of the members of the previously described Alzheimer_Enrichment_Up gene set) are predominantly at the red end of the spectrum, which corresponds to genes upregulated in our analysis of LOAD high-risk APOE genotypes; in (d), the individual black bars (here corresponding to all of the members of the previously described Alzheimer_Enrichment_Dn gene set) are predominantly at the blue end of the spectrum, which thus corresponds to the genes we find to be down-regulated in LOAD high-risk APOE genotypes.

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APOE high-risk and LOAD status changes

APOE high-risk only changes

LOAD status only changes

LOAD

APOE

Regulation of transcription, DNA damage signaling, apoptosis, response to stress, GSK3 pathway, immune response, inflammatory response

Ageing brain down, mitochondrial membrane, neurogenesis, neurite development, PYK2 pathway, proteasome complex, microtubule based process

Adipocytes increased, Amino acid metabolic process, Cell surface receptor linked signal transduction, Cell adhesion molecule activity, Cellular lipid metabolic process, endoplasmic reticulum, Lipid binding

Microsome, Vesicular fraction, Voltage gated channel activity, Cation transport, Cellular component disassembly,

Alzheimer’s disease incipient up, ageing brain up, transcription factor activity, extracellular matrix, lipid raft, cell junction

Electron transport chain, Oxidative phosphorylation, Type III secretion system, Mitochondrial respiratory chain, Alzheimer’s disease incipient down,

Gene expression changes

Unaff.APOE2

Unaff.APOE3

Unaff.APOE4

LOADAPOE4

LOADAPOE3LOAD

APOE2

∆ LOAD

∆ APOE

LOAD risk0 1a

b

Supplementary Figure 3. Schematic of the cross-comparison of the effects of LOAD and of APOE variants on the brain transcriptome.a,To assess independently the impacts of LOAD status or APOE variants on gene expression in the cerebral cortex,two separate comparisons were performed. In one comparison, unaffected LOAD-free individuals homozygous for APOE3 (“Unaffected APOE3”, n=120) were contrasted with LOAD-affected APOE3 homozygous individuals (“AD APOE3”, n=52); this defined the transcriptomic shift in LOAD. In a second comparison, unaffected APOE ɛ2/ɛ2 or APOE ɛ2/ɛ3 individuals (“Unaffected APOE2”, n=28) were compared with unaffected APOE ɛ3/ɛ4 or APOE ɛ4/ɛ4individuals (“Unaffected APOE4”, n=37); this defined a transcriptomic shift associated with APOE status. b, A Venn diagram presents the overlap between the transcriptomic shift associated with LOAD and with APOE variant status as in (a). Gene ontology annotations of the gene sets that are consistently altered up- or down in each sector, derived using the DAVID algorithm57, 58, are presented.



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APOE4 Incipient Severe

Moderate

a

Supplementary Figure 4. Further brain transcriptomic comparisons. a-c, Scatter plots illustrate the common gene expression changes seen with LOAD and with APOE high-risk status. a: Gene expression changes in cerebral cortex tissue for individual genes are plotted as single points, and presented as a function of APOE4 carrier status (log-ratio versus non-carriers; y-axis) or LOAD (log-ratio versus unaffecteds; x-axis). Genes that are associated with coordinate changes in expression in both contexts fall within the quadrant highlighted in orange. b: A similar plot compares the impact of age (expression levels in individuals >85 as a log-ratio of levels in individuals <75, y-axis) to the impact of LOAD (as a log-ratio of expression levels in unaffecteds; all individuals >75 and <85) on individual gene expression profiles; quadrants in violet highlight genes coordinately impacted. c: The impact of APOE4 is plotted against the impact of age, and few genes appear coordinately impacted. d:The overlapping brain transcriptomic shift associated with both APOE4 and LOAD status (the A/E differential profile) is more similar to the transcriptomic shift previously reported in early-stage (incipient) LOAD than to the transcriptomic shifts associated with moderate or severe LOAD. The A/E differential profile was compared to the differential expression profiles previously associated with different stages of LOAD in post-mortem hippocampal samples, as assessed based on clinical Mini Mental Status Examination (MMSE) and neurofibrillary tangle scores on pathology (from GEO GSE1297). A dendrogram is presented based on hierarchical clustering of the A/E differential profile (“APOE”) compared to the transitions from unaffected to incipient LOAD (n=9 and 7, “Incipient”), from incipient to moderate (n=7 and 8, “Moderate”) and from moderate to severe (n=8 and 7, “Severe”).

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LOAD>85 yo.

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yo.

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LOAD75-85

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∆ LOAD

∆ Age a LOAD risk0 1

LOAD aging related expression changes

LOAD-unrelated aging expression changes

Aging-unrelated LOAD expression changes AD

Aging

DNA binding, MAPK pathway, DNA damage signaling, apoptosis, response to stress, GSK3 pathway

ATP synthesis, Type III secretion system, neurogenesis, neurite development, mitochondrial membrane, PYK2 pathway, envelope, mitochondria matrix, mitochondria lumen, membrane organization and biogenesis, mitochondria ribosome, proteasome pathway, microtubule base process, coated vesicle, endoplasmic reticulum

mRNA processing reactome, Nucleoplasm, DNA replication, chromosomal part, nuclear lumen, Chromatin modification, lysosome Synaptic transmission, Cell-cell signaling, Transmembrane receptor activity, Plasma membrane, GTPase activator activity, Regulation of MAP kinase activity, Wnt singaling, Cell surface receptor linked signal transduction

Gene expression changes Enriched gene categories

Alzheimer’s disease incipient up, ageing brain up, transcription factor activity, extracellular matrix, lipid raft, response to stress, immune response, inflammatory response, cell junction

Ageing brain down, Electron transport chain, Neuron development, Cellular respiration, Glycolysis and gluconeogenesis, Mitochondria, Alzheimer’s disease incipient down, Creb pathway, GTP acivity

b

Supplementary Figure 5.Independent cross-comparison of the effects of LOAD and aging on the human brain transcriptome.a, To assess independently the effects of LOAD onset and aging on gene expression in cerebral cortex, two comparisons were performed : one between unaffected individuals of ages between between 75 and 85 years old (“Unaff. 75-85 yo.”, n=67) and LOAD-affected individuals in the same age range (“AD 75-85 yo”, n=86) and a second comparison between unaffected individuals aged less than 75 years old (“Unaff. <75 yo.”, n=56) and unaffected individuals aged more than 85 years old (“Unaff. >85 yo.”, n=64). b, Gene ontology annotation of the gene sets that are consistently either up- or downregulated by either aging in unaffected individuals or LOAD onset in age-matched individuals or both are presented as a Venn diagram. Gene ontology annotation of sets for each group were determined using DAVID algorithm57, 58 and are presented on right.

LOAD<75 yo.

Unaff.<75 yo.



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Supplementary Figure 6. Gene co-expression correlation matrix for unaffected individuals in the context of APOE2. Individual transcript co-expression correlation coefficients are presented as a table; high co-expression correlations are in red, low co-expression correlations are in blue. The table includes genes found to be consistently upregulated (“DE Up”, light green), downregulated (“DE Down, dark green”) or differentially co-expressed (also termed differentially wired; “DW1”, purple; “DW2”, orange; “DW3”, yellow) both in the comparison of LOAD to non-LOAD (all in APOE ɛ3/eɛ3), and in correlating APOE-associated risk with expression levels (as schematized in Supplementary Fig. 2). Across the sample set of APOE ɛ2/ɛ3 or ɛ2/ɛ2 LOAD-unaffected individuals (n=28), some genes are consistently highly correlated in expression with members of the DCA nodal genes. However, even on a broad visual comparison to the coexpression correlation matrices for APOE4 LOAD-free or APOE3 LOAD individuals (Supplementary Figures 8and 9 respectively), it can be seen that the overall pattern changes. This underlies the identification of the DCA nodal genes.



Supplementary Figure 7. Gene co-expression correlation matrix for unaffected individuals in the context of APOE3. Individual transcript co-expression correlation coefficients are presented as a table; high co-expression correlations are in red, low co-expression correlations are in blue. The table includes genes found to be consistently upregulated (“DE Up”, light green), downregulated (“DE Down, dark green”) or differentially co-expressed (also termed differentially wired; “DW1”, purple; “DW2”, orange; “DW3”, yellow) both in the comparison of LOAD to non-LOAD (all in APOEɛ3/ɛ3), and in correlating APOE-associated risk with expression levels (as schematized in Supplementary Fig. 2). Across the sample set of APOEɛ3/ɛ3 LOAD-unaffecteindividuals (n=52), some genes are consistently highly correlated in expression with members of the DCA nodal genes. However, even on a broad visual comparison to the coexpression correlation matrices for APOE ɛ3/ɛ4 LOAD-free or APOE ɛ3/ɛ3LOAD individuals (Supplementary Figures 8 and 9 respectively), it can be seen that the overall pattern changes. This underlies the identification of the DCA nodal genes.



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Supplementary Figure 8. Gene co-expression correlation matrix for unaffected individuals in the context of APOE4. Individual transcript co-expression correlation coefficients are presented as a table; high co-expression correlations are in red, low co-expression correlations are in blue. The table includes genes found to be consistently upregulated (“DE Up”, light green), downregulated (“DE Down, dark green”) or differentially co-expressed (also termed differentially wired; “DW1”, purple; “DW2”, orange; “DW3”, yellow) both in the comparison of LOAD to non-LOAD (all in APOE ɛ3/ɛ3), and in correlating APOE-associated risk with expression levels (as schematized in Supplementary Fig. 2). Across the sample set of APOE ɛ3/ɛ4 or ɛ4/ɛ4 LOAD-unaffected individuals (n=37), some genes are consistently highly correlated in expression with members of the DCA nodal genes. However, even on a broad visual comparison to the coexpression correlation matrices for APOE2 LOAD-free individuals (Supplementary Figures 6), it can be seen that the overall pattern changes. This underlies the identification of the DCA nodal genes.



Supplementary Figure 9. Gene co-expression correlation matrix for LOAD-affected individuals in the context of APOE3. Individual transcript co-expression correlation coefficients are presented as a table; high co-expression correlations are in red, low co-expression correlations are in blue. The table includes genes found to be consistently upregulated (“DE Up”, light green), downregulated (“DE Down, dark green”) or differentially co-expressed (also termed differentially wired; “DW1”, purple; “DW2”, orange; “DW3”, yellow) both in the comparison of LOAD to non-LOAD (all in APOE3/3), and in correlating APOE-associated risk with expression levels (as schematized in Supplementary Fig. 2). Across the sample set of APOE ɛ3/ɛ3 LOAD-affected individuals (n=120), some genes are consistently highly correlated in expression with members of the DCA nodal genes. However, even on a broad visual comparison to the coexpression correlation matrices for APOE 2 or APOE3 LOAD-free individuals (Supplementary Figures 6 and 7respectively), it can be seen that the overall pattern changes. This underlies the identification of the DCA nodal genes.


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Supplementary Figure 10

Supplementary Figure 10. DCA candidate nodal regulators modify Aβ production in an APOE4-dependent fashion. a, rhAPOE4-stimulation of APP-N2a cells (100 µg/ml for 18 hrs) but not rhAPOE2 and rhAPOE3 (100 µg/ml for 18 hrs) increased Aβ40 (left panel) and Aβ42 (right panel) accumulation in the culture media, as quantified by standard ELISA. Means are represented. Errors bars are SEM. n=4 per group. *, p<0.05 as determined by ANOVA followed by Tukey HSD. b,c. Effect of knockdown of individual DCA candidate genes on Aβ40 (upper panel) and (lower panel) Aβ42 levels in untreated APP-N2a cells (c) or in APP-N2a cells treated with rhAPOE2 (100 µg/ml, “E2”) or rhAPOE4 (100 µg/ml, “E4”) for 18 hrs (b). n=4 per group. Aβ40 and Aβ42 levels in supernatant were measured by ELISA, and normalized to the total protein content. Means are represented. Errors bars are SEM.d, Effect of knockdown of individual DCA candidate genes on Aβ42/Aβ40 ratio in APP-N2a cells treated with rhAPOE2 (100 µg/ml, “E2”) or recombinant hAPOE4 (100 µg/ml, “E4”) as in (b,c).

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Supplementary Figure 10. DCA candidate nodal regulators modify Aβ production in an APOE4-dependent fashion. a, rhAPOE4-stimulation of APP-N2a cells (100 µg/ml for 18 hrs) but not rhAPOE2 and rhAPOE3 (100 µg/ml for 18 hrs) increased Aβ40 (left panel) and Aβ42 (right panel) accumulation in the culture media, as quantified by standard ELISA. Means are represented. Errors bars are SEM. n=4 per group. *, p<0.05 as determined by ANOVA followed by Tukey HSD. b,c. Effect of knockdown of individual DCA candidate genes on Aβ40 (upper panel) and (lower panel) Aβ42 levels in untreated APP-N2a cells (c) or in APP-N2a cells treated with rhAPOE2 (100 µg/ml, “E2”) or rhAPOE4 (100 µg/ml, “E4”) for 18 hrs (b). n=4 per group. Aβ40 and Aβ42 levels in supernatant were measured by ELISA, and normalized to the total protein content. Means are represented. Errors bars are SEM.d, Effect of knockdown of individual DCA candidate genes on Aβ42/Aβ40 ratio in APP-N2a cells treated with rhAPOE2 (100 µg/ml, “E2”) or recombinant hAPOE4 (100 µg/ml, “E4”) as in (b,c).



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Supplementary Figure 11. DCA candidate shRNA knock-down validation. Effect of knockdown of individual DCA candidate genes on the targets genes expression levels in APP-N2a cells. mRNA levels were measured by RT-qPCR, normalized to GAPDH mRNA levels. Levels are represented relatively to the one observed in cells transfected with a control, non-targeting shRNA (“Ctl shRNA”). n=3 per group. Means are represented. Errors bars are SEM. ***: p<0.001, **: p<0.01, *: p<0.05, by two-tailed t-test vs the corresponding control, non-targeting shRNA condition. b-d, Quantification of Aβ40 (b), Aβ42 (c) and sAPPβ (d) in supernatants from APP-N2a cell cultures transfected with RNF219 shRNA or vector control. Error bars are SEM; n=12-18 cultures per group. *, P < 0.05. ANOVA followed by SNK test. e, SV2A expression was significantly reduced by shRNA vectors #1 and #2, but not #3 shRNA . n=5 per group. Quantification of SV2A levels. Error bars are SEM; n=5 per group. *, P < 0.05 compared with control vector (“NS”) . Analyses by ANOVA followed by SNK test. f-h, Quantification of Aβ40 (f), Aβ42 (g) and sAPPβ (h) in supernatants from APP-N2a cells transfected with SV2A shRNA vectors as in (e). Error bars are SEM; n=12-18 per group. *, P < 0.05. Analyses by ANOVA followed by SNK test.

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Supplementary Figure 12 RNF219 and SV2A modulate APOE4-induced APP processinga, Schematic of APP processing. β-secretase cleavage of APP holoprotein generates extracellular sAPPβ andtransmembrane C99 fragments (left schematic); C99 may subsequently be cleaved by γ-secretase activity to generate AICD and Aβ fragments. α-secretase cleavage of the APP holoprotein (right schematic), which precludes β-secretase processing, leads to generation of the extracellular sAPPα and the transmembrane C83 fragments; subsequently, C83 may be processed by the γ-secretase complex to generate AICD and P3 fragments. b, Western Blot analysis of lysates from APP-N2a cells transfected with an RNF219 or SV2A shRNA vector, or control non-silencing (NS) vector, and exposed to rhAPOE4 (100 µg/ml for 18 hrs) or vehicle only. Blots were probed with antibodies for APP holoprotein, BACE1, APP C99 β-cleavage or C83 α-cleavage transmembrane fragment, or β-actin. RNF219 knock-down by shRNA reduced APP C99 fragment accumulation in the context of APOE4 treatment but not in the context of vehicle-only treatment; RNF219 knockdown did not alter APP holoprotein or BACE1 levels. SV2A knockdown by shRNA reduced APP C99 fragment accumulation, but did not significantly alter APP holoprotein or BACE1 levels. d-f,Quantification of BACE1 (d) , C83 (e) and C99/C83 ratio (f) in APP-N2a cell lysates. a.u., arbitrary units. Error bars are SEM; n=5-6. *, P < 0.05; analyses by ANOVA followed by SNK test.



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Supplementary Figure 13. RNF219 and SV2A modulate APOE4-induced APP trafficking.a, Double immunostaining for the early endosomal marker EEA1 (red) and for APP (green) in APP-N2a cells transfected with an RNF219 shRNA vector or control non-silencing (NS) vector and treated with rhAPOE4 or vehicle-only. Quantification of the data showed increased colocalization of APP and EEA1 in APP-N2a cells. Insets show high-magnification views for visualization of colocalization of APP and EEA1. All results represent the means ± SEM (n=35-48 cells in 3-6 independent wells per group). *, P < 0.05. ANOVA followed by SNK test. b, Quantification of cell-surface BACE1 in APP-N2a cells in the context of SV2A knockdown. APP-N2a cells, transfected with an SV2A shRNA or control vector, were incubated with rhAPOE4 or vehicle-only for 18 hr. Cell surface proteins were biotinylated, and biotinylated proteins were then isolated using avidin beads and quantified by Western blotting with antibodies to BACE1. c, Quantification of cell surface BACE1. a.u., arbitrary units. Error bars are SEM; n=5-6independent wells per group. *, P < 0.05. ANOVA followed by SNK test.

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Supplementary Figure 14. Several DCA candidate node genes encode modifiers of APP localization in an APOE4-dependent fashion.a, Double staining of APP and BACE1 in rhAPOE4 stimulated APP-N2a cells. rhAPOE4 (100 µg/ml, 18 hrs) or vehicle treated APP-N2a were transfected by shRNA expression vectors targeting HDLBP, ROGDI, CALU, PTK2B or by a non-silencing control vector (“NS”). After fixation, cells were immunostained with anti-APP (N-terminal amino acid target: 22C11; red) and anti-BACE1 (green) antibodies. Colocalization is visualized as yellow in the merged images. b, APP and BACE1 colocalization quantification. Increased co-localization between APP and BACE1 was observed in the cell soma in the context of rhAPOE4 stimulation Scale bar 5 µm. Co-localization between APP and BACE1 was quantified by image J analysis software (NIH). Error bars are SEM; (n=12-38 cells in a total of 3 wells per group). c, Quantification of APP internalization away from the cell surface by image J analysis (defined as >0.5 µm from surface of cell), as in (b). Error bars are SEM; (n=12-38 cells in a total of 3 wells per group). *, P < 0.05. ANOVA followed by Tukey HSD post-hoc analysis.



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Supplementary Figure 15. SV2A modulates APP processing and trafficking.a, Schematic of RNF219 primary structure that includes RING-finger and coiled-coil motifs. Mutant vectors that harbor mutations in essential cysteines within the RING finger motif (C18A/C21A), or a deletion of the entire RING domain (D1-77), are depicted. b,c, Quantification of Aβ42 (b) and sAPPβ (c) in supernatants from APP-N2a cell cultures transfected with wild-type or mutant mRNF219 constructs as in Fig. 3g. Expression of the mutant RNF219 vectors inhibited the impact of rhAPOE4 treatment on Aβ42 and sAPPβ expression. Error bars are SEM; n=12-18wells per group. *, P < 0.05. ANOVA followed by SNK test. h,i, Quantification of Aβ42 (d) and sAPPβ (e) in media of APP-N2a cells transfected with SV2A-EGFP or Y46A mutant expression vectors. Error bars are SEM; n=12-18 per group. *, P < 0.05. ANOVA followed by SNK test. f, g, Analysis of cell-surface APP (f) or BACE1 (g) in APP-N2a cells transfected with expression vectors for wild-type SV2A-EGFP, Y46A mutant SV2A-EGFP, or with a control vector (EGFP) as labeled. Cells were exposed to rhAPOE4 (100 µg/ml for 18 hrs) or vehicle-only, processed for biotin surface labeling as described in i, and lysates were analyzed by Western blotting for APP or BACE1. Quantifications of cell-surface APP or BACE1 levels are presented below each blot. Error bars are SEM; n=5-6independent wells per group. *, P < 0.05. Analysis by ANOVA followed by SNK test. h,i, Localization of APP (h) or BACE1 (i) with SV2A in APP-N2a cells under the rhAPOE4 condition. APP-N2a cell transfected with SV2A-EGFP or Y46A (green) were treated with rhAPOE4 (100 µg/ml, 18 hr) or vehicle. After incubation, cells were fixed and immunostained with an antibody to the APP N-terminus(red) or to BACE1 (red), and then visualized by confocal fluorescent microscopy. Scale bar, 5 µm. Representative images as labeled are presented in the 4 left panels; co-localization was quantified (right panel) by image J analysis. Error bars are SEM; n=32 to 60 cells in 4-6 wells per group. *, P < 0.05. ANOVA followed by SNK test.

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Supplementary Figure 16. Levetiracetam modulates APP processing in the presence of rhAPOE4. a-c, Levetiracetam (LEV; 1, 10, 100, or 1000 nM as indicated, for 18 hr) suppressed rhAPOE4-associated Aβ40 (a)and Aβ42 (b) accumulation in APP-N2a cells. However, the Aβ42/Aβ40 ratio (c) was not affected by LEV treatment. Error bars are SEM; n=6-12 wells per group. *, P < 0.05. Analysis by ANOVA followed by SNK test. d-f,Quantification of APP-positive total puncta area (d), number (e), and diameter (f), in individual UND E3/3 and UND E4 hiN cell cultures as labeled. Total APP-positive puncta area and puncta number per cell were significantly greater in APOE4 carrier hiN cultures, relative to E4 UND cultures (see Fig. 4b). Treatment with LEV (1 µM) suppressed these phenotypes in APOE4 allele hiN cell cultures. Results represent the means ± SEM by ANOVA with post-hoc Tukey HSD. *, P < 0.05. Puncta are defined here as distinct signal intensities 0.1 to 1 µm in diameter using Image J analysis software (NIH).

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Supplementary Figure 17. rs2248663 at the RNF219 locus modifies amyloid deposition in human brain in APOE4 non-carriers.a: Manhattan plot of a genome-wide scan for SNP variants modifying LOAD age of onset in an APOE4-dependent manner. The interaction was evaluated for 286,177 SNPs (all SNPs present in at least 3 out of the 4 datasets analyzed; see Supplementary Table 3). The X-axis represents chromosomal location, whereas the Y-axis represents -log10 of the p-value for the APOE4 interaction of each SNP, determined by meta-analysis across the datasets using METAL software. SNP variants at the FYN and RNF219 loci (arrow) exhibited among the strongest genome-wide interactions with APOE4. The focused analysis presented in Figure 5a was based on the specific hypothesis that DCA hits are associated with earlier LOAD age-of-onset, and was thus not performed genome-wide. To further illustrate the strength of the interaction between variants at the DCA node genes and APOE4, agenome-wide analysis is additionally presented herein.b: Local Manhattan plots of the FYN locus SNP variants that modulate LOAD age-of-onset in an APOE4-dependent manner. X-axis represents chromosomal location, Y-axis represents -log10 of the p-value for the APOE4 interaction of each SNP, combined across the different datasets. Such an interaction term reflects an association of the specific SNP variant with LOAD age-of-onset that is observed in APOE4 non-carriers but not APOE4 carriers. c: Effect of RNF219 rs2248663 genotypes on Aβ brain amyloid load, as a function of APOE genotype, in cognitively intact elderly individuals from the ADNI dataset. Aβ load was quantified by florbetapir-PET and presented as the standardized uptake value ratio (SUVR) in brain regions as indicated. The effect of rs2248663 allelic load on the SUVR was evaluated for each group using an additive model within the plink linear function, and adjusted for gender and age. Regression coefficients associated with the rs2248663 minor allele load (β), as well as the 95% confidence interval (C.I.) and associated p-value, are presented. Power analysis using R pwr package done for each region but cerebellum in the APOE4 carrier group revealed sensitivities between 74% (frontal cortex) and 86% (brainstem) to detect an effect of similar size as the one observed in ɛ4 non-carriers with alpha=5%.

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Supplementary Figure 18. The DCA candidate core regulator ‘hits’ define a set of genetic loci that are enriched for associations with LOAD age-of-onset. Sets of genes were defined by virtue of simple differential expression, as detailed in Supplementary Fig. 3, in the context of LOAD status (« AD DE », upper left quadrant, corresponding to the pink group in Supplementary Fig. 3a), in the context of APOE genotype (« APOE DE », upper right quadrant , corresponding to the blue group in Supplementary Fig.3a), or by the overlap of the APOE genotype and LOAD status gene sets (« APOExAD DE », corresponding to the purple group in Supplementary Fig. 3a). In addition, a gene set was defined by DCA (« DCA », genes listed in Fig. 2b). For each gene in each set, SNP variants within a 50 kb radius were evaluated for an association with AD age-of-onset in an APOE4 dependent fashion (as in Fig. 5a). The null distribution of normalized enrichment scores (see Methods), representing the analysis of 10000 gene labels permutation, are presented as a black histogram. The red arrow, in each histogram, indicates the observed normalized enrichment score for the gene set as labeled, along with the empirical p-value derived from the comparison with the null distribution generated.



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Supplementary Table 1 Illumina probesets significantly altered in expression and in the same direction by APOE4 and LOAD in cerebral cortical tissue (p< 0.05 for differential expression in each condition with an overall false discovery rate [FDR] of 5.6%). For each condition, the effect on the gene expression level is represented as a log2-ratio. APOE4 effect is evaluated in LOAD unaffected individuals (“APOE4”, versus non-carriers, n=28 and 37 in the two groups respectively; using GEO dataset GSE15222), LOAD effect is evaluated in APOE4 non-carriers(“LOAD”, versus unaffected, n=120 and n=52 respectively; GEO GSE15222).



Supplementary Table 2. Common genetic variants within a 50 kb range of the DCA candidate nodal genes that modify LOAD age of onset in an APOE4-specific manner. Interaction with APOE4 for LOAD age of onset was evaluated as the interaction term between each SNP and APOE4 presence in a linear equation that encompasses gender and APOE4 load as covariates. Analyses were done independently in each dataset analyzed (TGEN Discovery cohort, TGEN replication cohort, GenADA cohort, ADNI cohort; only variants present in at least 3 out of the 4 studies were considered before assembly (see Methods for details).



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Supplementary Table 3 Common genetic variants that modify AD age of onset in a APOE4-specific manner. Interaction with APOE4 for age of onset was evaluated for each SNP in a linear equation as per the Methods. Genome-wide analyses were done independently in each dataset (TGEN Discovery cohort, TGEN replication cohort, GenADA cohort, ADNI cohort) before meta-analysis. Presented are the valence of the interaction term with APOE4 in each dataset (“Direction”) for the SNPs that reached a nominal p-value below 10-4 in the combined genome-wide meta-analysis (“P-value”).



Supplementary Table 4 GWAS datasets used for the age of onset genetic analysis.



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Supplementary Table 5: Effects of RNF219 rs2248663 and APOE genotypes on brain amyloid deposition.Effect of RNF219 rs2248663 (upper panel) and APOE genotype (lower panel) on Aβ brain amyloid load, in cognitively intact elderly (unaffected) and Mild Cognitively Impaired (MCI) individuals from the ADNI dataset. Aβload was quantified by florbetapir-PET and presented as the standardized uptake value ratio (SUVR) in brain regions as indicated. The effect of rs2248663 or APOE4 allelic load on the SUVR was evaluated for each group using an additive model within the plink linear function, and adjusted for gender and age. Regression coefficients associated with the rs2248663 minor allele load (β), as well as the 95% confidence interval (C.I.) and associated p-value, are presented.

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