Study Cohort Characteristics
We obtained four publicly available RNA-seq data sets (Allen Brain Institute Aging Dementia and TBI study, Mayo Clinic RNA-seq, MSBB, and ROSMAP) from the brain (temporal cortex, parietal cortex, prefrontal cortex, and hippocampus) and three microarray datasets from whole blood (AddNeuroMed cohort 1, AddNeuroMed cohort 2 and ADNI). After outlier removal, we included a total of 1,084 brain samples (58% female; 26% apoE4 carriers) and 645 blood samples (58% female; 38% apoE4 carriers) in our analysis. Table 1 shows a summary of sample annotations including number of cases and controls, apoE carrier status, and number of males and females for brain datasets and blood datasets.
In the brain datasets, compared to controls, AD patients were significantly older (mean ± SD for AD: 86.5 ± 6.0 years and controls: 84.8 ± 7.4 years; two sample t-test, P < 0.001), more likely to be apoE4 carriers (AD: 38% carriers vs controls: 15% carriers; Chi-squared test, P < 0.001), and more likely to be females (AD: 65% female vs controls: 51% female; Chi-squared test, P < 0.001).
In the blood datasets, compared to controls, AD patients were significantly older (mean ± SD for AD: 77.0 ± 7.1 years and controls: 74.7 ± 5.7 years; two sample t-test, P < 0.001), more likely to be apoE4 carriers (AD: 60% carriers vs controls: 27% carriers; Chi-squared test, P < 0.001), more likely to be females (AD: 64% female vs controls: 55% female; Chi-squared test, P < 0.001), and had more years of education (mean ± SD for AD: 9.4 ± 4.8 years and controls: 13.9 ± 4.7 years; two sample t-test, P < 0.001).
Studies were merged and batch corrected using ComBat resulting in 13,500 common genes across 1,084 samples for brain studies and 3,371 common genes across 645 samples for blood studies. Supplementary Figure S1 and S2 show PCA plots before and after batch correction, demonstrating successful data merging and batch effect removal.
Table 1: Meta-analysis Study Characteristics
|
|
|
|
AD
|
CN
|
Study
|
Accession
|
Total participants
|
AD, no.
(%)
|
Female /Male
(% Female)
|
apoE4 Yes /No
(% Yes)
|
Female /Male
(% Female)
|
apoE4 Yes /No
(% Yes)
|
Brain Transcriptomic Studies
|
Allen
|
https://aging.brain-map.org/
|
212
|
72
(34)
|
29/43
(40)
|
22/50
(31)
|
54/86
(39)
|
19/121
(14)
|
Mayo Clinic RNA-Seq
|
syn5550404
|
154
|
80
(52)
|
49/31
(61)
|
42/38
(53)
|
36/38
(49)
|
9/65
(12)
|
MSBB
|
GSE52564
|
301
|
185
(62)
|
131/54
(71)
|
63/122
(34)
|
57/59
(49)
|
16/100
(13)
|
ROSMAP
|
syn3219045
|
417
|
218
(52)
|
151/67
(70)
|
83/135
(38)
|
122/77
(61)
|
33/166
(17)
|
Sum
|
|
1084
|
555
(52)
|
360/195
(65)
|
210/345
(38)
|
269/260
(51)
|
77/452
(15)
|
Whole Blood Transcriptomic Studies
|
ADNI
|
http://adni.loni.usc.edu/
|
301
|
43
(14)
|
17/26
(40)
|
32/11
(74)
|
135/125
(52)
|
71/189
(27)
|
AddNeuroMed1
|
GSE63060
|
182
|
91
(50)
|
65/26
(71)
|
52/39
(57)
|
55/36
(60)
|
30/61
(33)
|
AddNeuroMed2
|
GSE63061
|
160
|
86
(43)
|
59/27
(69)
|
47/39
(55)
|
45/29
(61)
|
15/59
(20)
|
Sum
|
|
645
|
220
(34)
|
141/79
(64)
|
131/89
(60)
|
235/190
(55)
|
116/309
(27)
|
Table 1
Meta-analysis Study Characteristics
Differential Gene Expression in the Brain Identifies a Distinct Sex-Specific Signature of AD
We observed distinct AD-associated transcriptomic signatures in the brain in males and females. A total of 981 genes were differentially expressed in females, including 583 upregulated genes and 398 downregulated genes (FC > 1.2, q < 0.05; Figures 2A-B; Supplementary Table 1). In males, 513 genes were differentially expressed, including 415 upregulated genes and 98 downregulated genes (FC > 1.2, q < 0.05; Figures 2A-B; Supplementary Table 1). Altogether, 631 genes were uniquely dysregulated in females, including 309 upregulated genes and 327 downregulated genes. In males, 166 genes were uniquely dysregulated, including 141 upregulated genes and 27 downregulated genes. There was a significant overlap of dysregulated genes across males and females (P < 0.05; hypergeometric test).
Next, we characterized the transcriptomic signatures observed in the brains of male and female AD patients. In females, among upregulated AD genes, we found 69 enriched pathways, many of them relating to components of the innate and adaptive immune system (Table 2; Supplementary Table 2). Several upregulated HLA system genes including HLA-DPB1, HPA-DRA, HLA-DOA, HLA-DRB5, HLA-DMA, HLA-DPA1 contributed to enrichment of a number of pathways relating to response to infection (Table 3). Components of the complement system including C1QA, C4B, and C4A were also uniquely dysregulated in females (Table 4; Supplementary Table 2). We also observed an enrichment of genes in the MAPK signaling pathway including MRAS, MK2, and MK3. Downregulated AD genes in females were enriched for a number of neurological signaling pathways including synaptic vesicle exocytosis, neuroactive ligand-receptor activation, and GnRH signaling (Table 2; Supplementary Table 3).
Strikingly, we observed an enrichment of fewer immune-related pathways in males with AD. Among upregulated genes in male AD patients, we found 12 enriched pathways, including amoebiasis and cytokine-cytokine receptor interaction, suggestive of adaptive and innate immune activation (Table 2; Supplementary Table 4). Similar to females, we also observed an enrichment of the MAPK signaling pathway, including MAP4K4 and MK2, in males. Among downregulated genes in male AD patients, we did not identify significantly enriched pathways. For a full list of enriched pathways, refer to Supplementary Tables S2-S4.
Lastly, we performed a non-stratified analysis comparing gene expression between AD and control samples irrespective of sex. Statistical models were adjusted for sex, apoE4 carrier status, and age. A total of 662 genes were upregulated and 430 genes were downregulated in patients with AD compared to controls (Figure S3, Table 2; Supplementary Table 1. Upregulated genes were enriched for several pathways previously implicated in AD including PI3K-Akt signaling and MAPK signaling as well as a number of immune related pathways including Staphylococcus aureus infection, human papillomavirus infection, and malaria (Supplementary Table S5). Several components of the complement system, including C4B, C4A, C1R, C3AR1, and C5AR1 also contributed to this enrichment (Supplementary Table S6). In our analysis of downregulated genes, we found several pathways related to neuroreceptor signaling and GABAergic transmission were enriched including the genes GABRA1, GNG3, GNG2, SLC32A1, GABRD, and GABRG2 (Supplementary Table S6).
Term
|
Adjusted P
|
Genes
|
Female Upregulated Genes (n= 583)
|
Staphylococcus aureus infection
|
<0.001
|
C1QB;C1QA;HLA-DRB5;CFH;CFI;PTAFR;C4B;C4A;HLA-DMA;HLA-DMB;
FCGR2A;HLA-DPB1;HLA-DRA;MASP1;HLA-DOA;HLA-DPA1;C1QC
|
MAPK signaling pathway
|
<0.001
|
CSF1;FLT4;HSPB1;FGF1;FGF2;EGFR;RASGRP3;MECOM;RPS6KA1;GNA12;
MAP3K20;CD14;MAP3K6;PDGFRB;TGFB2;ANGPT2;IL1R1;DUSP1;
HGF;GNG12;NFKB1;TGFBR1;GADD45G;TGFBR2;EFNA1;MRAS;MAPKAPK3;
MAPKAPK2;TAB2;MYD88;EPHA2;HSPA1A
|
PI3K-Akt signaling pathway
|
<0.001
|
CDKN1A;CSF1;ITGB5;FLT4;TNC;LPAR3;FGF1;THBS2;FGF2;EGFR;SPP1;ITGB8;
IL6R;MCL1;PDGFRB;ANGPT2;SYK;LAMB2;NOS3;HGF;FN1;GNG12;
OSMR;GNG11;NFKB1;EFNA1;COL1A2;ITGA10;ITGA6;SGK1;TLR4;EPHA2;TLR2
|
Leishmaniasis
|
<0.001
|
TGFB2;HLA-DRB5;NFKB1;HLA-DMA;HLA-DMB;FCGR2A;HLA-DPB1;HLA-DRA;
TAB2;HLA-DOA;TLR4;MYD88;HLA-DPA1;TLR2
|
Inflammatory bowel disease (IBD)
|
<0.001
|
TGFB2;HLA-DRB5;NFKB1;HLA-DMA;HLA-DMB;HLA-DPB1;HLA-DRA;HLA-DOA;
TLR5;TLR4;IL18R1;HLA-DPA1;TLR2
|
Toxoplasmosis
|
<0.001
|
TGFB2;HLA-DRB5;LAMB2;NFKB1;HLA-DMA;HLA-DMB;HLA-DPB1;HLA-DRA;
TAB2;ITGA6;HLA-DOA;TLR4;MYD88;HLA-DPA1;TLR2;HSPA1A
|
Cell adhesion molecules (CAMs)
|
<0.001
|
HLA-DRB5;SDC2;HLA-E;CLDN11;OCLN;VCAN;HLA-DMA;PTPRC;HLA-DMB;
CLDN15;HLA-DPB1;HLA-DRA;ITGB8;ITGA6;CD58;HLA-DOA;CD34;HLA-DPA1
|
Epstein-Barr virus infection
|
<0.001
|
LYN;HLA-DRB5;CDKN1A;SYK;DDX58;TAP1;TNFAIP3;NFKB1;HLA-E;GADD45G;
HLA-DMA;HLA-DMB;HLA-DPB1;HLA-DRA;TAB2;HES1;CD58;HLA-DOA;
MYD88;HLA-DPA1;TLR2
|
Pathways in cancer
|
<0.001
|
NOTCH2;CDKN1A;NOTCH4;FLT4;LEF1;SLC2A1;CXCR4;LPAR3;FGF1;FGF2;
DLL1;GLI3;EGFR;GLI2;RASGRP3;MECOM;GNA12;HES1;RXRG;IL6R;PDGFRB;
CDKN2B;TGFB2;LAMB2;FZD7;HGF;FN1;MITF;GNG12;GNG11;NFKB1;TGFBR1;
GADD45G;TGFBR2;HEYL;SMO;ITGA6
|
Systemic lupus erythematosus
|
<0.001
|
C1QB;C1QA;HLA-DRB5;C4B;C4A;HLA-DMA;HLA-DMB;FCGR2A;HLA-DPB1;
HIST1H4H;HLA-DRA;HLA-DOA;HIST1H2AC;HIST1H2BD;HLA-DPA1;C1QC
|
69 more..
|
|
|
Female Downregulated Genes (n= 398)
|
Neuroactive ligand-receptor interaction
|
0.001
|
CHRNB2;GABRB2;GABRA1;CHRNA2;GABRA4;PTH2R;CCK;GRIK2;HTR5A;RXFP1;
GABRG2;MCHR2;MAS1;GLRA3;GLRB;CNR1;SST;NPY;TAC1;VIP;GABRD
|
Retrograde endocannabinoid signaling
|
0.01
|
RIMS1;GABRB2;MAPK9;GABRA1;NDUFA5;CNR1;GABRA4;ITPR1;ADCY1;GABRD;GABRG2
|
Synaptic vesicle cycle
|
0.01
|
RIMS1;SLC6A7;ATP6V1G2;ATP6V1B2;ATP6V1H;ATP6V0E2;CPLX1;STX1A
|
Aldosterone synthesis and secretion
|
0.01
|
CAMK2D;STAR;PRKCE;CAMK4;CAMK2A;ITPR1;ADCY1;ATP1B1;CAMK1G
|
Nicotine addiction
|
0.02
|
CHRNB2;GABRB2;GABRA1;GABRA4;GABRD;GABRG2
|
GnRH signaling pathway
|
0.03
|
MAPK9;EGR1;CAMK2D;PRKCD;CAMK2A;ITPR1;PTK2B;ADCY1
|
Male Upregulated Genes (n= 415)
|
PI3K-Akt signaling pathway
|
0.009
|
PDGFRB;CSF3R;CSF1;ITGB5;IRS1;LAMB2;FLT4;FN1;LAMC1;GNG12;OSMR;FGF2;
NFKB1;BCL2L11;ITGA10;KDR;SPP1;ITGA6;EPHA2;TLR2
|
MAPK signaling pathway
|
0.01
|
PDGFRB;TGFB2;CSF1;IL1R1;DUSP1;FLT4;GNG12;FGF2;NFKB1;TGFBR2;MRAS;
MECOM;MAPKAPK2;KDR;CD14;MYD88;EPHA2;MAP4K4
|
NOD-like receptor signaling pathway
|
0.03
|
NEK7;CARD6;ERBIN;ANTXR2;GBP2;ANTXR1;IKBKE;GBP1;NFKB1;MYD88;GBP4;GBP3
|
ECM-receptor interaction
|
0.03
|
ITGB5;LAMB2;ITGA10;SPP1;FN1;ITGA6;LAMC1;HSPG2
|
Proteoglycans in cancer
|
0.04
|
TGFB2;MRAS;ITGB5;FZD7;KDR;FN1;GAB1;HCLS1;WNT7A;HSPG2;FGF2;TLR2
|
Focal adhesion
|
0.04
|
VAV3;PDGFRB;ITGB5;LAMB2;ITGA10;FLT4;KDR;FN1;SPP1;CAPN2;ITGA6;LAMC1
|
Fc gamma R-mediated phagocytosis
|
0.04
|
VAV3;PTPRC;FCGR2A;MYO10;INPP5D;DOCK2;WASF2;PLPP1
|
Amoebiasis
|
0.04
|
TGFB2;IL1R1;LAMB2;FN1;LAMC1;CD14;NFKB1;TLR2
|
Pathways in cancer
|
0.04
|
NOTCH2;PDGFRB;TGFB2;CSF3R;LAMB2;FZD7;FLT4;LEF1;FN1;SLC2A1;WNT7A;
CXCR4;MITF;LAMC1;GNG12;FGF2;NFKB1;GLI2;TGFBR2;BCL2L11;MECOM;ITGA6;NFE2L2
|
Cytokine-cytokine receptor interaction
|
0.04
|
TGFB2;CSF3R;CSF1;IL1R1;CXCR4;LIFR;INHBB;OSMR;TNFRSF1B;IL17RB;TGFBR2;
IL1RL1;ACKR3;TNFRSF25;IL18R1
|
2 more..
|
|
|
Male Downregulated Genes (n= 98)
|
No enriched pathways
|
Table 2
Meta-analysis Study Characteristics
Network Analysis in the Brain Identifies a Stronger Disease Signature in Females
To assess transcriptomic changes on a gene network level, we utilized WGCNA. Gene networks were derived separately for male and female samples and compared using network preservation methods, as previously described49. We identified two AD-associated modules in males and 11 AD-associated modules in females (Figure 3A) that met the significance threshold (FDR < 0.05) and were either positively or negatively correlated with case/control status. Among the male modules, a 463-gene module (termed black) was upregulated in AD, and a 151-gene module (termed tan) was downregulated in AD. The black module in males had significant overlap with two modules in females (termed yellow and pink) (P < 0.001; hypergeometric test) as indicated by asterisks in Figure 3B. The black module also had strong preservation in the female network (Z-summary score > 10). Among the female-specific disease associated modules, four modules (termed green, red, black and turquoise) were downregulated in AD, while seven were upregulated (Figure 3A).
Enrichment analysis of disease-associated modules using the 2019 KEGG Human pathway database revealed pathways relevant to AD that were consistent with those identified in the single gene analysis (Figure 3A). For example, in both males and females, an upregulated module was enriched for Akt signaling related pathways and downregulated modules were enriched for oxidative phosphorylation and thermogenesis related pathways, consistent with single gene level analyses.
Notably, several additional pathways not seen through single gene analysis were observed in the network analyses. An upregulated module in both males and females was highly enriched for zinc finger nuclease genes related to Herpes simplex viral infection, consistent with recent work demonstrating Herpes virus infection in AD brains54.
Consistent with the single gene analysis, we observed greater number of disease associated modules in females with AD than in males. For example, an upregulated female module was enriched for cell structural processes related to adherens junctions, actin cytoskeleton and axonal guidance. An additional downregulated female module was enriched for neurological signaling pathways including synaptic vesicle exocytosis, aldosterone synthesis and secretion and morphine addiction. Interestingly, an additional female downregulated module was enriched for autophagy and proteolysis pathways, consistent with molecular studies demonstrating decreased autophagy in AD, particularly in females55 (Figure 3A).
We also conducted an analysis identifying modules with apoE4:disease interactive effect to understand differential penetrance of the apoE 4 allele in males and females. In the male gene network, we were unable to identify modules with significant apoE4:disease interactive effect. Interestingly, in the female network, we identified one module that was downregulated (2211 genes) in AD, and two modules (329 genes and 439 genes) that were upregulated in AD and exhibited a significant apoE4:disease interactive effect (Figure 3A). The two upregulated modules (termed pink and purple) were significantly enriched for several zinc finger nuclease genes related to Herpes simplex viral infection. The downregulated module was enriched for metabolic pathways including oxidative phosphorylation and the TCA cycle. Together these results suggest a female-specific network dysregulation involving zinc finger nucleases and metabolic alteration supporting differential apoE4 penetrance in males and females.
There were 102 hub genes among disease associated modules in the female network identified as module membership greater than 0.8, gene significance greater than 0.2, and differentially expressed between AD and controls (Figure 3C; Supplementary Table S7). In contrast, zero hub genes were identified in the male gene network. Protein-protein interaction maps generated by STRING v11 suggest several Ca+2- and G protein-dependent interconnected genes including ITPKB, PDGFRB, GNG12, and GNA12 among the female disease associated modules (Figure 3C). Among modules with apoE4:disease interactive effect in females, 35 hub genes were identified, including ITPKB as a highly connected regulator (Figure 3D). For a full list of genes in each module, including hub genes, please refer to Supplementary Table S7).
Differential Gene Expression in Whole Blood Identifies Stronger Disease Signatures in Females with AD in Comparison to Males
Similar to the brain, we observed distinct AD-associated transcriptomic signatures between males and females with AD in whole blood. We observed a total of 599 differentially expressed genes in females with AD, including 294 upregulated genes and 305 downregulated genes (q < 0.05; Figures 2C-D; Supplementary Table 8). In males, 98 genes were differentially expressed in AD, including 38 upregulated genes and 50 downregulated genes (q < 0.05; Figures 2C-D; Supplementary Table 8). Altogether, 542 genes were uniquely dysregulated in females, including 271 upregulated genes and 271 downregulated genes. In males, 31 genes were uniquely dysregulated, including 15 upregulated genes and 16 downregulated genes. There was a significant overlap of dysregulated genes across males and females with AD (P < 0.05; hypergeometric test).
Next, we characterized the transcriptomic signatures observed in the blood of male and female AD patients. Among upregulated genes in female AD patients, we found 14 enriched pathways, many of them relating to components of the innate and adaptive immune system (Table 3; Supplementary Table S9). Several cytokine response elements including STAT5B, STAT6, and IL10RB contributed to enrichment of a number of pathways relating to response to infection (Table 3). Similar to the brain, components of actin cytoskeleton regulation were also dysregulated in females. (Table 3; Supplementary Table S9). Downregulated genes in female AD patients were enriched for a number of metabolism related processes including oxidative phosphorylation and thermogenesis, consistent with the single-gene and network analysis in the brain (Supplementary Table S10).
Similar to the brain analysis, we observed dramatically fewer enriched pathways in males with AD. Among upregulated genes in male AD patients, we did not identify any enriched pathways. Among downregulated genes in male AD patients, components of the proteasome were enriched including PSMD4 and PSMC3 (Table 3; Supplementary Table S11). For a full list of enriched pathways, refer to Supplementary Tables S9-S11.
Lastly, we performed a non-stratified analysis comparing gene expression between AD and control samples irrespective of sex in whole blood. Analyses were adjusted for sex, apoE4 carrier status, age and education. A total of 339 genes were upregulated and 360 genes were downregulated in patients with AD compared to controls (Figure S3B, Supplementary Table S8). Upregulated genes were enriched for several pathways previously implicated in AD, including MAPK signaling, autophagy and NFkB signaling (Supplementary Table S12). In addition, a number of immune related pathways were enriched including tuberculosis, Escherichia coli infection, salmonella infection, and inflammatory bowel disease. Several components of the NFkB cascade and antigen presentation system including NFKBIA, ITGAM, STAT5B, TLR5, TLR4, CD14 and C4A, contributed to this enrichment (Supplementary Table S12). Among downregulated genes, pathways related to protein synthesis and metabolism, including ribosome, proteasome, protein export, thermogenesis, and oxidative phosphorylation were enriched. Included in these pathways were several oxidation phosphorylation related genes including NDUFA9, NDUFA8, COX4I2 (Supplementary Table S13).
Term
|
Adjusted P
|
Genes
|
Female Upregulated Genes (n= 294)
|
Tuberculosis
|
<0.001
|
ATP6V0B;CEBPB;ITGAM;IL10RB;IFNGR2;TCIRG1;CTSS;CREB1;IRAK1;LAMP2;
ITGAX;RAF1;CAMK2G
|
Necroptosis
|
0.004
|
PYCARD;STAT5B;MLKL;H2AFJ;IFNGR2;STAT6;TYK2;CFLAR;CAMK2G;
HIST1H2AC;HIST2H2AC
|
Fc gamma R-mediated phagocytosis
|
0.006
|
HCK;PTPRC;ARPC1A;PRKCD;RAC2;ASAP1;ARPC5;RAF1
|
Pathogenic Escherichia coli infection
|
0.01
|
ARPC1A;NCK2;ARHGEF2;ARPC5;TLR5;TUBA4A
|
TNF signaling pathway
|
0.01
|
CEBPB;RPS6KA5;CREB1;MLKL;MAP3K8;FOS;CFLAR;CREB5
|
Regulation of actin cytoskeleton
|
0.02
|
FGD3;ITGAM;SPATA13;ARPC1A;RAC2;ITGAX;IQGAP1;ARPC5;RAF1;SSH2;PAK2
|
Lysosome
|
0.02
|
GNPTG;CD63;ATP6V0B;LAMP2;IDS;TCIRG1;GNS;CTSS
|
Phagosome
|
0.02
|
ATP6V0B;ITGAM;LAMP2;CANX;TAP1;TCIRG1;TUBA4A;CTSS;ATP6V1F
|
JAK-STAT signaling pathway
|
0.02
|
STAT5B;CCND3;CSF3R;IL10RB;IFNGR2;STAT6;TYK2;RAF1;MCL1
|
Estrogen signaling pathway
|
0.03
|
CREB1;PRKCD;FOS;KRT10;RAF1;ADCY7;FKBP5;CREB5
|
4 more..
|
|
|
Female Downregulated Genes (n= 305)
|
Ribosome
|
<0.001
|
RPL4;RPL5;RPL30;RPL41;RPL32;RPL12;RPL22;RPL11;RPL35A;MRPL36;
MRPL24;RPL6;MRPL33;RPS25;RPL36AL;RPL35;RPL24;RPS20;RPL26;RPS27A;
RPL39;RPS24;RPS12
|
Proteasome
|
<0.001
|
PSMB6;PSMA5;PSMB7;PSMA3;PSMD4;PSMC3;PSMC1;POMP;PSMB1;PSMC2;
PSMD1;PSMF1
|
Spliceosome
|
<0.001
|
ISY1;HSPA8;SF3B5;CCDC12;BUD31;DDX42;PLRG1;PQBP1;SNRPD2;ZMAT2;
SYF2;SNRPG;PPIH;SNRPA1;SNRPB2;SLU7;CTNNBL1
|
Protein export
|
<0.001
|
SRP19;SEC61G;SRPRB;SRP68;SRP14;SEC11A
|
Oxidative phosphorylation
|
<0.001
|
NDUFA9;NDUFA8;NDUFS5;COX17;NDUFB2;NDUFA1;COX6A1;ATP6V1E1;
NDUFV2;COX6C;ATP6V1D;UQCRH
|
Huntington disease
|
<0.001
|
NDUFA9;NDUFA8;NDUFB2;NDUFA1;CLTA;COX6C;COX6A1;UQCRH;SOD1;
SIN3A;NDUFS5;VDAC3;BAX;NDUFV2
|
Non-alcoholic fatty liver disease (NAFLD)
|
<0.001
|
NDUFA9;NDUFA8;NDUFS5;NDUFB2;NDUFA1;BAX;PIK3R1;COX6A1;NDUFV2;
COX6C;ADIPOR2;UQCRH
|
Protein processing in endoplasmic reticulum
|
0.002
|
DNAJA1;ATXN3;HSPA8;HSP90AA1;HSPH1;HSP90AB1;EIF2AK1;SEC61G;
ERP29;BAX;UBXN6
|
Parkinson disease
|
0.002
|
NDUFA9;NDUFA8;NDUFS5;VDAC3;NDUFB2;NDUFA1;COX6A1;NDUFV2;
COX6C;UQCRH
|
Thermogenesis
|
0.007
|
NDUFA9;COA3;NDUFA8;SMARCC1;NDUFS5;COX17;NDUFB2;NDUFA1;
COX6C;COX6A1;NDUFV2;UQCRH
|
3 more…
|
|
|
Male Upregulated Genes (n=38)
|
No enriched pathways
|
Male Downregulated Genes (n=50)
|
Proteasome
|
0.06
|
PSMD4;PSMC3;POMP
|
Table 3
Enriched Pathways in Blood
Network Analysis in Whole Blood Identifies a Stronger Disease Signature in Females
We identified five AD-associated modules in females and zero AD-associated modules in males (Figure 4) that met the significance threshold (FDR < 0.05) and were either positively or negatively correlated with case/control status. Among the modules in female samples, three modules including a 483-gene module (termed turquoise), a 129-gene module (termed pink) and 153-gene module (termed black) were upregulated in AD. Two modules including a 270-gene module (termed blue) and 119-gene module (termed magenta) were downregulated in AD (Figure 4A). No modules with significant apoE4:disease interaction effect were found in female or male network analyses from the blood datasets.
Enrichment analysis of disease-associated modules using the 2019 KEGG Human pathway database revealed pathways relevant to AD that were consistent with those identified in the single gene analysis (Figures 4A and 3A). For example, upregulated modules in females were strongly enriched for innate immune system activity, neutrophil degranulation, CSF signaling, IL2 signaling, and cytokine signaling. Consistent with single gene analyses, downregulated modules in females were enriched for metabolic processes including metabolism of RNA and metabolism of amino acids (Figure 4A).
There were 35 hub genes among disease associated modules in the female-specific network identified as module membership greater than 0.8, gene significance greater than 0.2 and differentially expressed between AD and controls (Figure 4B). In contrast, zero hub genes were identified in the male-specific gene network. Protein-protein interaction maps generated by STRING v11 suggest several interconnected genes including the B cell development related protein, IGLL1, and ribosomal proteins RPS20, RPS25, RPL4, and RPL35A (Figure 4B).
For a full list of genes in each module, including hub genes, please refer to Supplementary Table S14).
Comparison of Brain and Blood Transcriptomic Signatures Reveals Common Immune Related Signals in Females
We next identified genes that were commonly dysregulated in both blood and brain (Figure 2E). In females, a total of 23 genes were dysregulated in the brain and blood in the same direction (two downregulated and 21 upregulated). Several genes among the commonly upregulated genes have roles in antigen presentation including TAP1, CTSS, and PTPRC. Enrichment analysis of commonly upregulated genes revealed an enrichment of the KEGG terms primary immunodeficiency, phagosome, and cell adhesion molecules (adjusted P < 0.1; Figure 2F). In addition, eight genes were dysregulated but in different directions in the brain and blood including PRKCD, VAMP8, GIMAP7, LAPTM5, HLA-DOA, TNS1, DBI, GIMAP7, TUbA4A (Figure 2E). In contrast, in males we found one upregulated gene, VCAN encoding vesican, dysregulated in both the blood and brain (Figure 2E).
Cell-type Deconvolution Identifies Sex-specific Immune Cell Dysregulation in Females with AD
Differences in 22 immune blood cell types (Figures 5A-B) were evaluated by deconvolving the transcriptomic signature obtained via meta-analysis of blood studies. Analysis of cell type proportions adjusting for age, sex, and apoE4 status revealed an increase in neutrophils and naïve B cells, and a decrease in M2 macrophages and CD8+ T cells in AD patients compared to controls in pooled male and female samples (Figure 5C, FDR P <0.05). Among females with AD, relative to controls, we observed an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples (Figure 5C, FDR P <0.05). Interestingly, among males with AD, we did not find any significant differences in immune cell proportions compared to controls.
Sex-specific Transcriptomic Data Improves AD Classification Accuracy
To assess the value of sex-specific transcriptomic data in developing a blood-based classifier in AD, we trained a linear SVM model to classify AD patients controls using the transcriptomic signature obtained via meta-analysis of blood studies. We trained a ‘clinical model’ with age, sex, education, and apoE4 status and a ‘clinical + molecular model’ with age, sex, education, apoE4 status, and blood transcriptomic data. Using pooled male and female samples, the ‘clinical + molecular model’ achieved a higher AUROC compared to the ‘clinical model’ (AUROC = 0.88 for ‘clinical + molecular model’; AUROC = 0.77 for ‘clinical model’) on a test set composed of 25% of samples (Figures 6A and S4A).
Interestingly, a model trained with only female data achieved a higher AUROC (‘clinical + molecular model’: 0.90 and ‘clinical model’: 0.86; Figures 6B and S4B) than the pooled male and female model. In contrast, a model trained with only male data obtained a lower AUROC (‘clinical + molecular’ model 0.81 and ‘clinical model’ 0.83; Figures 6C and S4C) than the pooled male and female model.
Figures 6G-H summarizes shared features between models. In all simple models (pooled male and female, female only, and male only), age and apoE4 status had a positive feature importance while education had a negative feature importance. A positive feature importance means that the expression of that feature increases the likelihood of being classified as AD (termed risk factor). A negative feature importance means that expression of the feature expression reduces the likelihood of being classified as AD (termed protective factor). In the female ‘clinical + molecular model’, 57 features, including known risk factors including apoE4 and age, had a positive feature importance (Supplementary Table S15). In addition, 50 features had negative feature importance. Among these were education and previously implicated AD risk genes including CETN2 (Supplementary Table S15). In the male ‘clinical + molecular model’, 103 features, including apoE4, had positive feature importance. (Supplementary Table S16). In addition, 105 features, including education, had negative feature importance (Supplementary Table S16).
Altogether, we observed a significant overlap (P < 0.001, hypergeometric test) in features with non-zero feature importance between the pooled male and female ‘clinical + molecular model’ and female ‘clinical + molecular model’; female ‘clinical + molecular model’ and male ‘clinical + molecular model’; and pooled male and female ‘clinical + molecular model’ and male ‘clinical + molecular model’ (Figure 6G).
Functional annotation of features with a non-zero feature importance was performed via enrichment analysis using the 2019 KEGG database of human pathways. Among features with non-zero feature importance, we did not identify any enriched biological pathways in the male only and female only complex models. In the male and female pooled complex model, features with positive feature importance (risk factors), were enriched for staphylococcus aureus infection, graft-vs-host disease, and antigen presentation and processing KEGG pathways (adjusted P < 0.05; Figure 6H). The HLA genes HLA-DRB4 and HLA-DQA1 contributed to this enrichment. In addition, the P-selection glycoprotein ligand–1 gene (SELPLG) and killer cell immunoglobulin-like receptor (KIR2DL3) also contributed to enrichment, suggesting a role for leukocyte recruitment and natural killer cell activity in AD pathology.