Abstract

   Growing evidence suggests that peripheral factors to the brain driving
   neuro-inflammation could affect Alzheimer’s Disease (AD) and
   Parkinson’s Disease (PD) severity. Herpes simplex virus type 1 (HSV1)
   infection has been associated with AD while other related viruses,
   including cytomegalovirus (CMV), Epstein-Bar virus and human
   herpesvirus 6 (HHV6), are known to infect neurons. Here we compare gene
   expression profiles between AD or PD patients to those afflicted with
   herpes viral infections as to discover novel potential
   neuro-inflammation pathways. We found multiple significant
   differentially expressed genes (DEGs) shared between AD/PD and viral
   infections including SESN3 which has a genetic association for
   increased AD risk. Pathway enrichment analysis revealed viruses shared
   Oxidative Stress Defense System and LRRK2 pathways with AD and PD,
   respectively. We further processed our data to identify novel target
   and drug-repurposing opportunities including anti-inflammatory therapy,
   immune-modulators and cholinesterase inhibitors which could lead to new
   therapeutics paradigms for these neurodegenerative diseases.

   Subject terms: Neuroimmunology, Computational biology and
   bioinformatics

Introduction

   Globally, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are
   among the most common causes of severe and fatal dementia^[28]1. While
   these diseases’ pathological hallmarks are neuronal loss, extracellular
   senile plaques containing the peptide β amyloid, and neurofibrillary
   tangles for AD^[29]1,[30]2, and the loss of neurons in the substantia
   nigra and elsewhere in association with the presence of Lewy bodies for
   PD^[31]1,[32]3, recent data indicate that neuroinflammation is involved
   in the progression of both neurodegenerative disorders^[33]2,[34]3.
   Neuroinflammation driven by activated microglial cells causes a vicious
   cycle of inflammatory reaction between microglia, astrocytes, and
   β-amyloid plaques leading to neuronal death^[35]1. Investigators have
   long suspected that pathogenic agents contribute to the onset and
   progression of AD and PD^[36]4,[37]5. However, only recently it was
   demonstrated that increased concentrations of proinflammatory cytokines
   occur in the initial stages of neurodegenerative diseases^[38]6.
   Moreover, certain genetic variants in the chromosome 6 region that
   specifies immune response human leukocyte antigens (HLAs)^[39]7 are
   associated with PD.

   Peripheral viral infections could elicit brain dysfunction through
   direct cytolytic effects on site or through whole body circulating
   inflammatory reactions^[40]8. Neurotropic viruses, such as arboviruses,
   influenza viruses and herpes viruses developed escape mechanisms from
   host immune surveillance enabling access to the central nervous system
   (CNS) which can result in long-lasting subclinical infections (reviewed
   in^[41]9). The systemic and local responses of the immune system to
   viral infection potentially contribute to neuronal damage, even in the
   absence of cell death^[42]9. Viruses can elicit CNS inflammation either
   by traversing a comprised Blood Brain Barrier (BBB), infecting
   peripheral nerves or broadly over-activating the peripheral innate and
   adaptive host immune system^[43]10.

   Multiple studies report the association between Herpes simplex virus
   type 1 (HSV1) and Alzheimer’s disease^[44]5,[45]11,[46]12. Other
   members of the Herpesviridae family have been implicated, particularly
   EBV, CMV and HHV6^[47]13–[48]16. Several viruses are capable of latent
   residency in the peripheral nervous system and target, in acute cases
   of encephalitis (for HSV1/2 and EBV), the same regions of the central
   nervous system (temporal and frontal cortex, and hippocampus) affected
   in AD^[49]17.

   In this study, we hypothesize that comparisons of gene expression
   profiles from patients afflicted with either AD or PD with those
   profiles resulting from Herpesviridae infection, specifically CMV, EBV
   or HHV6, might reveal new associated neuro-inflammation pathways. We
   performed direct transcriptome profile comparisons between gene
   expression changes and enriched pathways in patients with Herpesviridae
   viral infection and AD/PD patients. To prioritize individual targets
   and pharmacological opportunities of intervention, we leveraged
   additional biological information such as human genetic disease
   associations and drug-repurposing analyses. We report here multiple
   human host genes and pathways that were significantly shared by human
   immune system responses to viral infections and neurodegenerative
   pathology.

Results

Data set selection

   As described in detail in Methods, our computational analyses involved
   six steps (Fig. [50]1): (1) NCBI Gene Expression Omnibus (GEO) database
   querying and selection of AD and PD and CMV, EBV and HHV6 infection
   datasets; (2) stringent quality control and normalization for each
   dataset (Table S1); (3) differential expression analysis of individual
   datasets for healthy controls versus diseased individuals; (4)
   comparisons of differential expressed genes (DEGs) and pathways
   enriched across AD, PD and viral infection profiles; (5) integration of
   genetic associations and tissue-specific gene expression data
   integration and (6) target repositioning hypotheses generation using
   EMBL-EBI ChEMBL and Connectivity Map (CMAP) databases.

Figure 1.

   [51]Figure 1
   [52]Open in a new tab

   Flowchart of the cross-neurodegeneration (Alzheimer’s Disease and
   Parkinson’s Disease) and cross-viral infection (CMV, EBV and HHV6)
   transcriptome analysis pipeline. The computational analyses consist of
   six major steps which were presented in the blue boxes. Detailed
   criteria for each major step were described in the Methods.

   We found 35 GEO datasets with gene expression profiles by microarrays
   for AD, PD and human host response to CMV, EBV and HHV6 (Table [53]S1).
   We recalculated fold changes for AD and PD, and results are comparable
   with previous studies^[54]18,[55]19. Based on the filtering criteria
   described in the Methods section, six datasets were selected
   (Table [56]1): GSE636063 (132 whole blood samples from healthy vs 135
   AD patients^[57]18), [58]GSE99039 (whole blood samples from 232 healthy
   vs 204 PD patients^[59]19), [60]GSE81246 (peripheral blood mononuclear
   cells (PBMC) from 24 patients with latent CMV infection vs 10 patients
   with active disease^[61]20), [62]GSE20200^[63]21 and
   [64]GSE45829^[65]22 (7 B cell independent samples vs 7 EBV infected
   samples), and [66]GSE40396 (22 whole blood samples from seronegative
   patients vs 10 patients seropositive for HHV6 with fever^[67]23).
   Table [68]S1 summarizes all retrieved datasets along with the reasons
   for their inclusion or exclusion from our analyses.

Table 1.

   List of patient blood and in vitro B cells infected with EBV gene
   expression datasets selected in this study, and the number of samples
   and DEGs in each dataset.
   Dataset Phenotype Cell type PMID Platform Samples DEGs
   Cases Controls Outliers
   [69]GSE63063 Alzheimer’s Disease Whole Blood 26343147 Illumina 134 132
   6 906
   [70]GSE99039 Parkinson’s Disease Whole Blood 28916538 Affymetrix 204
   232 2 939
   [71]GSE81246 CMV host response PBMCs 28031361 Affymetrix 10 24 0 1910
   [72]GSE20200 EBV host response B cells 21147465 Exon Array 4 4 0 1491
   [73]GSE45829 EBV host response B cells 23724103 Exon Array 3 3 0 8589
   [74]GSE40396 HHV6 host response Whole Blood 23858444 Illumina 10 22 0
   545
   [75]Open in a new tab

   For each transcriptome dataset, we first determined the statistical
   significance of differentially expressed genes (DEGs) within each study
   by comparing disease to control samples. Subsequently, we compared
   individual study lists of significant DEGs to determine shared patterns
   in gene expression profiles for individual viruses compared to AD
   (Fig. [76]2A) and PD (Fig. [77]2B). This approach minimized any
   potential biases due to study differences in platforms or blood cell
   types.

Figure 2.

   [78]Figure 2
   [79]Open in a new tab

   Heatmaps of subset of CMV, EBV and HHV6 DEGs shared with AD (A) and PD
   (B). For each DEG, log2 fold changes were indicated in the heatmap. The
   genes were clustered using the UPGMA method.

CMV infection and Alzheimer’s disease shared molecular markers

   We identified 906 and 1,910 significant (False Discovery Rate adjusted
   [FDR-adj.] p-value < 0.05) DEGs in relation to AD and CMV human host
   response, respectively. Overall, 68 DEGs were shared in AD and CMV
   response signatures (Hypergeometric p-value (P[Hyper]) = 1.5 × 10^−7;
   Table [80]S2). SESN3 was the most down-regulated gene in patients
   infected with CMV and in active disease status (CMV: Fold Change
   [FC] = −2.7, FDR-adj. p-value = 6.3 × 10^−11; AD: FC = −1.3, FDR-adj.
   p-value = 5.0 × 10^−4, Fig. [81]2A). Sestrin 3 controls intracellular
   response to reactive oxygen species^[82]24 and acts as a trans-acting
   genetic regulator of a pro-convulsant gene network in the human
   epileptic hippocampus^[83]25.

   The full list of significant DEGs associated with AD and CMV (906 and
   1,910 DEGs respectively) were analyzed for enriched functional pathways
   using MetaCore/MetaBase (GeneGo) v6.0 (Thomson Reuters,
   [84]https://portal.genego.com/). In total, 5 human canonical pathways
   were significantly enriched in both AD and CMV DEG lists (Fig. [85]3A).
   The most significant pathway was “Role of Sirtuin1 and PGC1-alpha in
   activation of antioxidant defense system” (Fig. [86]4) (AD: FDR-adj.
   p-value = 0.01; CMV: FDR-adj. p-value = 2.1 × 10^−3).

Figure 3.

   [87]Figure 3
   [88]Open in a new tab

   Statistically significant (adjusted FDR p-value ≤ 0.05) shared CMV, EBV
   and HHV6 human host response pathways and differentially expressed
   genes in AD (A,B) and PD (C,D), respectively.

Figure 4.

   [89]Figure 4
   [90]Open in a new tab

   Pathway map for “Role of Sirtuin1 and PGC1-alpha in activation of
   antioxidant defense system”. Significant up-regulation of genes is
   denoted as up-pointing bars colored in red, and significant
   down-regulation of genes is denoted as down-pointing bars colored in
   blue. The height of the colored bar represents to the magnitude of the
   gene expression changes (fold change) between cases and controls.

EBV infection and Alzheimer’s Disease shared molecular markers

   We found 802 DEGs which were associated with EBV human host response.
   Of those, 36 genes were shared in AD and EBV host response signatures
   (P[Hyper] = 2.0 × 10^−3; Table [91]S3). As in CMV host response
   comparison with AD transcriptional profiles, SESN3 ranked as one of the
   top 20 genes associated with EBV human host response with same
   direction of association as seen in CMV human host response (EBV:
   FC = −6.9, FDR-adj. p-value = 1.1 × 10^−4) (Fig. [92]2A).

   Five canonical signaling pathways were enriched in both AD and EBV
   (Fig. [93]3A). Similar to that observed for CMV infection, the pathways
   “Role of Sirtuin1 and PGC1-alpha in activation of antioxidant defense
   system” (AD: FDR-adj. p-value = 0.01; EBV: FDR-adj. p-value = 0.049)
   and “Antigen presentation by MHC class II” were most significantly
   enriched in AD and EBV infection (AD: FDR-adj. p-value = 6.8 × 10^−3;
   CMV: FDR-adj. p-value = 0.04).

HHV6 infection and Alzheimer’s Disease shared molecular markers

   We identified 1,698 genes associated with HHV6 human host response.
   Comparisons of transcriptional profiles yielded 95 genes shared in AD
   and HHV6 host response (P[Hyper] = 0.038; Table [94]S4). IDO1 ranked as
   the top gene associated with HHV6 response (HHV6: FC = 4.5, FDR adj.
   p-value = 5.3 × 10^−5; AD: FC = −1.08, FDR adj. p-value = 0.001,
   Fig. [95]2A). Indoleamine 2, 3-dioxygenase (IDO1) catalyzes the first
   and rate limiting step in the kynurenin pathway^[96]26, which has been
   implicated in neuroinflammation and neurodegeneration^[97]27,[98]28.

   For AD and HHV6 (906 and 1,698 DEGs respectively), there were 9 common
   significantly enriched canonical pathways of which “Antigen
   presentation by MHC class II” had the highest significance (AD:
   FDR-adj. p-value = 6.8 × 10^−3; CMV: FDR-adj. p-value = 6.2 × 10^−6)
   (Fig. [99]3A).

Common genes and pathways across multiple viruses and Alzheimer’s Disease

   We identified 28 genes that were associated with AD and host response
   to at least two of the three viruses investigated in this study (CMV,
   EBV or HHV6) (Fig. [100]3B). Thioredoxin (TXN) was the most
   down-regulated gene in patients with AD (FC = −1.44, FDR adj.
   p-value = 1.20 × 10^−3), while up-regulated in patients with CMV
   (FC = 2.7, FDR adj. p-value = 3.0 × 10^−4) and HHV6 active infection
   (FC = 1.51, FDR adj. p-value = 2.4 × 10^−3) (Fig. [101]2A). Thioredoxin
   is crucial in maintaining a reduced oxygen intra-cellular environment
   and thus renders protection against oxidative stress^[102]29. For this
   reason, thioredoxin has been considered a promising early biomarker in
   the diagnosis of AD, suggesting the potential involvement of oxidative
   stress in the pathogenesis of the disease^[103]30.

   Three pathways were significantly enriched in all host response viruses
   and AD datasets: “Antigen presentation by MHC class II”, “Role of
   IAP-proteins in apoptosis” and “Role of Sirtuin1 and PGC1-alpha in
   activation of antioxidant defense system” (Fig. [104]3A,
   Table [105]S5). Two other pathways enriched in CMV and HHV6 host
   response as well as AD were “Induced oxidative stress and apoptosis in
   airway epithelial cells” and “Regulation of G1/S transition”.

CMV infection and Parkinson’s Disease shared molecular markers

   There were 939 genes associated with PD and 1910 genes associated with
   CMV host response. Of those, 152 DEGs were shared between PD and CMV
   host response (P[Hyper] = 0.04; Table [106]S6). Amyloid beta precursor
   like protein 2 (APLP2) was the most up-regulated gene in CMV host
   response (FC = 8.8, FDR adj. p-value = 4.1 × 10^−7) and was also
   associated with PD (FC = 1.13, FDR adj. p-value = 3.6 × 10^−3)
   (Fig. [107]2B). APLP2 belongs to the Alzheimer’s-associated amyloid
   beta-protein precursor gene family, which interacts with the synaptic
   release machinery, suggesting a role in neurotransmission^[108]31.

   We found 28 canonical signaling pathways enriched in both PD and CMV
   using the full list of significant DEGs. The most significant pathways
   were “Integrin inside-out signaling in neutrophils” (PD: FDR-adj.
   p-value = 4.1 × 10^−11; CMV: FDR-adj. p-value = 3.0 × 10^−3) and
   “Inhibition of neutrophil migration by pro-resolving lipid mediators”
   (PD: FDR-adj. p-value = 4.1 × 10^−11; CMV: FDR-adj.
   p-value = 3.4 × 10^−3; Fig. [109]3C).

EBV infection and Parkinson’s Disease shared molecular markers

   Comparisons of transcriptional profiles yielded 60 genes shared in PD
   and EBV host response (P[Hyper] = 0.02; Table [110]S7). As seen in the
   CMV host response comparison with PD transcriptional profiles, APLP2
   ranked as one of the top 5 genes associated with EBV human host
   response (EBV: FC = −6.5, FDR-adj. p-value = 1.2 × 10^−4;
   Fig. [111]2B).

   From the list of genes associated with PD and EBV (939 and 802 DEGs
   respectively), there were 53 pathways significantly enriched in both
   diseases with “Reverse signaling by Ephrin-B” as the most significant
   one (PD: FDR-adj. p-value = 3.8 × 10^−7; EBV: FDR-adj.
   p-value = 5.4 × 10^−3; Fig. [112]3C).

HHV6 infection and Parkinson’s Disease shared molecular markers

   Overall, 181 DEGs were shared in PD and HHV-6 response signatures
   (P[Hyper] = 6.4 × 10^−9; Fig. [113]2B; Table [114]S8). IL1RN was the
   most highly over-expressed gene in patients infected with HHV6 and
   significantly expressed in PD (HHV6: FC = 8.47, FDR-adj.
   p-value = 1.7 × 10^−6; PD: FC = 0.27, p-value = 1.7 × 10^−3).
   Interleukin 1 (IL-1) receptor antagonist (IL-1RN) is a naturally
   occurring anti-inflammatory agent that binds to the IL-1 receptor but
   lacks agonist activity and therefore functions like a competitive
   inhibitor of IL-1^[115]32.

   Considering the total genes associated with PD and HHV6 (939 and 1697
   DEGs respectively), 253 human canonical pathways significantly enriched
   in both DEG lists. As seen for CMV, the pathway entitled “Inhibition of
   neutrophil migration by pro-resolving lipid mediator” ranked as the
   most significant result (PD: FDR-adj. p-value = 4.3 × 10^−11; EBV:
   FDR-adj. p-value = 3.0 × 10^−3).

Common genes and pathways across multiple viruses and Parkinson’s Disease

   We identified 54 genes associated with PD and host response to at least
   two of the three viruses investigated in this study (CMV, EBV or HHV6)
   (Fig. [116]3D). BCL6, GYG1, RBCK1, TIMP2 and CIRBP were common DEGs
   across all viruses tested and associated with PD. Tissue inhibitors of
   metalloproteinases (TIMPs) are endogenous inhibitors of matrix
   metalloproteinases (MMPs), and the aberrant expressions of MMPs are
   strongly associated with neuroinflammation and neuronal cell
   death^[117]33.

   In total, 15 human canonical pathways were significantly enriched from
   DEGs associated with PD and human host response to CMV, EBV and HHV6;
   many of which are involved in host immune response (Table [118]S9). The
   most significant pathway was “Reverse signaling by Ephrin-B” (PD
   FDR-adj. p-value = 3.3 × 10^−6; HHV6 FDR-adj. p-value = 3.7 × 10^−2;
   EBV FDR-adj. p-value = 1.9 × 10^−2; CMV FDR-adj.
   p-value = 1.5 × 10^−3). Previous studies have indicated that ephrin
   signaling pathway is involved in the inflammatory process following CNS
   injury by serving roles in the maintenance of endothelial junction
   integrity and cytoskeletal structure. Remarkably, there was significant
   enrichment for several pathways commonly associated with PD and
   neurodegenerative diseases in general. These include the leucine rich
   repeat kinase 2 (LRRK2) pathway (PD FDR-adj. p-value = 3.7 × 10^−6;
   HHV6 FDR-adj. p-value = 4.2 × 10^−2; p-value = EBV FDR-adj.
   p-value = 2.0 × 10^−2; CMV FDR-adj. p-value = 1.8 × 10^−3;
   Fig. [119]5). The G2019S mutation within the LRRK2 kinase domain is the
   most common causal mutation in PD patients^[120]34, and it results in
   substantial increase in LRRK2 kinase activity^[121]35. The mechanism by
   which LRRK2-G2019S induces PD pathology remains unclear, although
   several studies have implicated this mutation in the dysregulation of
   autophagic function^[122]36.

Figure 5.

   [123]Figure 5
   [124]Open in a new tab

   Pathway map for “LRRK2 in neurons in Parkinson’s disease”. Significant
   up-regulation of genes is denoted as up-pointing bars colored in red,
   and significant down-regulation of genes is denoted as down-pointing
   bars colored in blue. The height of the colored bar represents to the
   magnitude of the gene expression changes (fold change) between cases
   and controls.

Control analyses for disease relevance of detected molecular signatures

   To evaluate potential biases in our approach, we performed further
   analyses. First, we also compared CMV, EBV and HHV6 host response DEGs
   to genes associated with Huntington’s Disease (HD) and Type 2 diabetes
   mellitus (T2DM; FDR adj. p-value ≤ 0.05), as control analyses with
   another neurodegenerative condition (HD) and a disease unrelated to
   neurodegenerative and infectious disease (T2DM), to examine for
   potential spurious comparisons. We selected three publicly available
   datasets with peripheral blood gene expression samples:
   [125]GSE9006^[126]37 (11 children diseased for T2DM and 23 healthy
   children), [127]GSE69528^[128]38 (23 adults diseased for T2DM and 27
   healthy controls) and [129]GSE34721^[130]39 (150 samples from patients
   with HD and 70 samples from healthy controls). By contrast with the
   comparisons with AD and PD, these control analyses showed no
   statistically significant enrichment with T2DM or HD in the 3 datasets
   tested (P[Hyper] > 0.1, Fig. S1) which suggests that our common
   molecular signatures between AD or PD and Herpesviridae infections are
   robust and non-spurious.

   Second, since the gene expression datasets used in our analyses were
   obtained from blood samples of AD and PD patients we needed to evaluate
   the co-expression of shared viral host response genes with the most
   important immune function cell type found in the brain, the microglia.
   We re-analyzed and evaluated the gene expression profiles of 161 CMV,
   EBV or HHV6 host response genes shared with AD, and 329 genes shared
   with PD in 37 human microglia post-mortem samples, whose donors had
   history of normal cognitive function and no apparent neuropathological
   abnormalities^[131]40 (Fig. [132]6). We found the majority of genes
   (139 of 161 genes) 86.3% in AD and (308 of 329 genes) 93.6% in PD were
   actively expressed in human microglia (log2 TPM > 2) (Fig. [133]6). Of
   the DEGs shared by at least two of the three viruses, we found 82.1%
   (23 out of 28 genes) in AD and 90.7% (49 out of 54 genes) in PD were
   expressed in microglia. These findings lend further support for
   sampling of the blood as a surrogate for direct microglia gene
   expression profiling.

Figure 6.

   [134]Figure 6
   [135]Open in a new tab

   Microglia gene expression levels (log2 transcripts per million reads
   (TPM)) of CMV, EBV and HHV6 DEGs common with AD (A) or PD (B).

Potential drug targets with human genetic evidence

   It was previously reported that drug targets with robust human genetics
   support regarding disease pathology could boost success rates in
   clinical development^[136]41. Therefore, we surveyed the public
   genome-wide association studies present in the GWAS catalog for genetic
   evidence for the 172 and 329 DEGs that associated with host response to
   viruses (CMV, EBV or HHV6) and AD or PD, respectively. Each SNP was
   interposed to the linkage disequilibrium (LD) region
   upstream/downstream within 1 kb of the DEG coding region. A total of 19
   genes were proximal to at least one SNP associated with
   neurodegenerative diseases (AD: 11 genes PD: 8 genes) (Table [137]2).
   Rs6430538, the most significant variant (p-value = 8 × 10^−24)
   associated with PD, is located near the gene HNMT on chromosome 2. HNMT
   encodes histamine N-methyltransferase which has a key leading role in
   histamine metabolism in the central nervous system^[138]42, and was
   pointed as a genomic biomarker for PD increased susceptibility^[139]43.
   Rs2373115, an intronic variant near GRB2 associated binding protein 2
   (GAB2), was the most significant variant (p-value = 1 × 10^−10)
   associated with AD. Multiple genetic variants in the GAB2 region are
   associated with late-AD onset, which could be involved in multiple
   pathways leading to the formation of neurofibrillary tangles^[140]44.

Table 2.

   List of 19 DEGs associated with viral host response and Alzheimer’s
   Disease or Parkinson’s Disease proximal to SNPs associated with
   neurodegenerative diseases in the GWAS catalog.
   DEG Gene Description EBV DEG CMV DEG HHV6 DEG Disease associated Most
   significant variant P-value PUBMED IDs
   HNMT histamine N-methyltransferase x Parkinson’s disease rs6430538
   8.00E-24 28892059; 25064009; 22451204
   SIPA1L2 signal induced proliferation associated 1 like 2 x Parkinson’s
   disease rs10797576 8.00E-13 28892059
   RAB29 RAB29, member RAS oncogene family x Parkinson’s disease rs947211
   2.00E-12 19915576
   ZNF626 zinc finger protein 626 x Alzheimer’s disease rs561655 7.00E-11
   21460841
   GAB2 GRB2 associated binding protein 2 x Alzheimer’s disease rs2373115
   1.00E-10 17553421
   VRK1 vaccinia related kinase 1 x Alzheimer’s disease rs150511909
   4.00E-09 26830138
   MRPL58 mitochondrial ribosomal protein x x Alzheimer’s disease
   rs9899728 2.00E-08 26913989
   CLMN calmin x Alzheimer’s disease rs115102486 2.00E-08 26830138
   PLEKHM1 pleckstrin homology and RUN domain containing M1 x Parkinson’s
   disease rs11012 6.00E-08 20070850
   SESN3 sestrin 3 x x Alzheimer’s disease rs3911569 3.00E-07 26830138
   ADRM1 adhesion regulating molecule 1 x Alzheimer’s disease rs73310256
   3.00E-07 26830138
   MX2 MX dynamin like GTPase 2 x Parkinson’s disease rs78736162 3.00E-07
   25663231
   ACTN4 actinin alpha 4 x Parkinson’s disease rs62120679 6.00E-07
   28892059
   SQSTM1 sequestosome 1 x Alzheimer’s disease rs72807343 7.00E-07
   24162737
   RAB3D RAB3D, member RAS oncogene family x Alzheimer’s disease
   rs148273964 8.00E-07 26830138
   RAB11FIP4 RAB11 family interacting protein 4 x Alzheimer’s disease
   rs142835438 8.00E-07 27770636
   STAP1 Signal transducing adaptor family member 1 x Parkinson’s disease
   rs2242330 2.00E-06 17052657
   IER2 immediate early response 2 x Alzheimer’s disease rs72998574
   2.00E-06 27770636
   GRN granulin precursor x Parkinson’s disease rs63750043; g.103432 C > T
   NA 17923627
   [141]Open in a new tab

   In addition, we identified several SNPs in close proximity (<1 KB to
   the coding region) of the 172 and 329 DEGs showed genetic association
   with other non-relevant conditions to neurodegenerative diseases. For
   example, 20 genes were proximal to SNPs associate with T2DM
   (Table [142]S10).

Drug repurposing analysis

   The 401 targets identified in this study (172 and 329 DEGs associated
   with host response to viruses and AD and PD, respectively) were mapped
   to public compounds by searching the EMBL-EBI ChEMBL database for
   approved and marketed drugs targeting these genes. Overall, we
   identified 55 drug-target pairs in 20 DEGs (Table [143]3). Most genes
   were associated with multiple drugs, for example, TUBB6 was targeted by
   12 unique compounds with diverse therapeutic indications such as
   oncology and acute coronary syndrome. Alfacalcidol, a vitamin D
   receptor (VDR) agonist with therapeutic indication for PD is also
   included in our list as a potential repurposing opportunity to treat
   peripheral drivers of neurodegeneration.

Table 3.

   List of launched drugs targeting DEGs associated with viral host
   response and Alzheimer’s Disease or Parkinson’s Disease.
   Target EBV_DEG CMV_DEG HHV6_DEG Drug Disease Molecule type
   VDR x ALFACALCIDOL diabetic nephropathy Small molecule
   VDR x CHOLECALCIFEROL type I diabetes mellitus; Parkinson’s Disease
   Small molecule
   VDR x CALCIPOTRIENE psoriasis Small molecule
   VDR x ERGOCALCIFEROL acute coronary syndrome Small molecule
   VDR x PARICALCITOL chronic kidney disease Small molecule
   VDR x CALCITRIOL Hypocalcemia Small molecule
   VDR x DOXERCALCIFEROL secondary hyperparathyroidism Small molecule
   VDR x CALCIFEDIOL secondary hyperparathyroidism Small molecule
   TYMP x TIPIRACIL metastatic colorectal cancer Small molecule
   TUBB6 x VINCRISTINE acute lymphoblastic leukemia Small molecule
   TUBB6 x DOCETAXEL squamous cell carcinoma Small molecule
   TUBB6 x PACLITAXEL breast carcinoma Small molecule
   TUBB6 x COLCHICINE acute coronary syndrome Small molecule
   TUBB6 x VINORELBINE breast carcinoma Small molecule
   TUBB6 x TRASTUZUMAB EMTANSINE breast carcinoma Antibody
   TUBB6 x BRENTUXIMAB VEDOTIN lymphoma Antibody
   TUBB6 x CABAZITAXEL prostate carcinoma Small molecule
   TUBB6 x VINBLASTINE neoplasm Small molecule
   TUBB6 x ERIBULIN breast carcinoma Small molecule
   TUBB6 x VINFLUNINE neoplasm Small molecule
   TUBB6 x IXABEPILONE neoplasm Small molecule
   TOP1MT x TOPOTECAN acute myeloid leukemia Small molecule
   RARA x x TRETINOIN acne Small molecule
   RARA x x ADAPALENE acne Small molecule
   RARA x x ACITRETIN psoriasis Small molecule
   RARA x x ISOTRETINOIN acne Small molecule
   RARA x x ALITRETINOIN Eczema Small molecule
   RARA x x TAZAROTENE psoriasis Small molecule
   RARA x x ETRETINATE psoriasis Small molecule
   PSMB10 x BORTEZOMIB multiple myeloma; Glycogen storage disease due to
   acid maltase deficiency Small molecule
   PSMB10 x IXAZOMIB CITRATE multiple myeloma Small molecule
   PSMB10 x CARFILZOMIB neoplasm Protein
   PDK3 x SODIUM DICHLOROACETATE lactic acidosis Small molecule
   IL4R x DUPILUMAB Eczema Antibody
   IL23A x x USTEKINUMAB psoriasis Antibody
   IL17RA x BRODALUMAB psoriasis Antibody
   IFNGR2 x INTERFERON GAMA-1B relapsing-remitting multiple sclerosis
   Protein
   IFNGR2 x INTERFERON GAMMA-1B idiopathic pulmonary fibrosis Protein
   HCK x BOSUTINIB neoplasm Small molecule
   FGR x x DASATINIB chronic myelogenous leukemia Small molecule
   EPHB6 x x VANDETANIB thyroid carcinoma Small molecule
   EPHB6 x x PREDNIMUSTINE lymphoma Small molecule
   CSF3R x x PEGFILGRASTIM breast carcinoma Protein
   CSF3R x x FILGRASTIM myocardial infarction Protein
   CSF3R x x LIPEGFILGRASTIM lymphoma Small molecule
   CD52 ALEMTUZUMAB diabetes mellitus Antibody
   CD3D x x BLINATUMOMAB acute lymphoblastic leukemia Antibody
   CD3D x x MUROMONAB-CD3 immune system disease Antibody
   CD3D x x CATUMAXOMAB neoplasm Antibody
   BCR x PONATINIB neoplasm Small molecule
   ALOX5 x SULFASALAZINE rheumatoid arthritis Small molecule
   ALOX5 x ZILEUTON asthma Small molecule
   ALOX5 x MESALAMINE ulcerative colitis Small molecule
   ALOX5 x BALSALAZIDE ulcerative colitis Small molecule
   ALOX5 x OLSALAZINE ankylosing spondylitis Small molecule
   [144]Open in a new tab

   CMAP is another drug repurposing approach which deploys the
   anti-correlation relationship across disease gene expression signatures
   and pharmacological in vitro perturbations^[145]45. We performed
   separate analyses using CMV, EBV and HHV6 human host response gene
   expression signatures to assess anti-correlation of approximately 5000
   small-molecule compounds and 300 reagents from the Broad Institute
   public library ([146]www.broadinstitute.org/connectivity-map-cmap).
   Overall, 16, 24 and 16 compounds were significantly anti-correlated to
   the CMV, EBV and HHV6 host response signature respectively
   (p-value < 0.05, Specificity < 0.1; Table [147]4). Of those, 14
   compounds (highlighted in Table [148]4) showed evidence in the
   literature of neuro-protection to Alzheimer’s or Parkinson’s Disease
   through multiple mechanisms, such as dopamine receptor agonism,
   monoamine oxidase and cholinesterase inhibition. In addition, multiple
   compounds identified showed anti-inflammatory properties, which were
   previously considered potential pharmacological options for AD
   prevention^[149]46.

Table 4.

   List of the Broad Institute public library of compounds associated with
   gene targets in CMV, EBV or HHV6 human host response based on
   CMAP^[150]45 analysis of contrary gene expression profiles.
   Compound Mechanism of Action Indication CMV Host response signature EBV
   Host response signature HHV6 Host response signature
   Compound Score Enrichment Score P-Value Compound Score Enrichment Score
   P-Value Compound Score Enrichment Score P-Value
   Quinostatin PI3-Kinase/mTOR inhibitors Oncology 1.00 −0.87 0.0337 1
   −0.94 0.0085 NA NA NA
   Cortisone Corticosteroid Hormone Receptor Agonists Anti-inflammatory
   0.50 −0.88 0.0285 NA NA NA NA NA NA
   Quinethazone Sodium/chloride tranporter inhibitor Antihypertension 0.50
   −0.84 0.0492 NA NA NA NA NA NA
   Metrifonate Cholinesterase inhibitor Neuro protection ^[151]86, [152]87
   0.37 −0.95 0.0054 NA NA NA NA NA NA
   Cicloheximide Protein synthesis inhibitor Antibiotics 0.33 −0.99 0.0002
   0.33 −0.93 0.0107 NA NA NA
   Anisomycin MAP kinase activator Neuro protection ^[153]71 0.33 −0.97
   0.0023 0.33 −0.96 0.0038 NA NA NA
   Molindone Dopamine receptor antagonist Neuro protection ^[154]69 0.33
   −0.94 0.0059 NA NA NA 0.33 −0.90 0.0216
   Hydroflumethiazide Na-Cl cotransporter inhibitor Antihypertensive 0.33
   −0.94 0.0077 NA NA NA NA NA NA
   Pronetalol Adrenoreceptor blocker (beta) Neuro protection69 0.33 −0.93
   0.0089 NA NA NA NA NA NA
   Picotamide Eicosenoid receptor antagonist Anti-inflammatory 0.33 −0.93
   0.0103 NA NA NA NA NA NA
   Mephenytoin Sodium channel blocker Antihypertensive 0.33 −0.89 0.0231
   NA NA NA NA NA NA
   Dipivefrine Adrenergic agonist Neuro protection ^[155]69 0.33 −0.87
   0.0343 NA NA NA NA NA NA
   Etamsylate Prostaglandin synthesis inhibitor Anti-inflammatory 0.33
   −0.85 0.0422 NA NA NA NA NA NA
   Mebeverine Phosphodiesterase inhibitor Neuro protection ^[156]88 0.33
   −0.85 0.045 NA NA NA NA NA NA
   Prasterone Estrogen receptor (ER) agonists Androgen receptor (AR)
   agonists Neuro protection ^[157]89 0.33 −0.85 0.0467 NA NA NA NA NA NA
   Pirenzepine Muscarinic M1 receptor antagonist Neuro protection ^[158]69
   0.30 −0.95 0.0045 NA NA NA NA NA NA
   Calmidazolium Calmodulin binding inhibitor Immunosuppressant NA NA NA
   1.00 −0.9023 0.0193 NA NA NA
   Antazoline Histamine H1 receptor antagonist Allergy NA NA NA 0.33
   −0.8696 0.0342 NA NA NA
   Beta-escin Vasoconstriction Cardiovascular Agent [Pubchem] NA NA NA
   0.33 −0.7912 0.0181 NA NA NA
   Betahistine Histamine H1 receptor agonist Anti-vertigo NA NA NA 0.33
   −0.8763 0.0312 NA NA NA
   Cephaeline Anti-neoplastic Oncology NA NA NA 0.33 −0.9537 0.0002 NA NA
   NA
   Cetirizine Histamine H1 receptor antagonist Allergy NA NA NA 0.33
   −0.8937 0.023 NA NA NA
   Dequalinium chloride Anti-bacterial Antibiotics NA NA NA 0.33 −0.9666
   0.0021 NA NA NA
   Domperidone Dopamine receptor D2 antagonist Antiemetic NA NA NA 0.33
   −0.8436 0.0495 NA NA NA
   Emetine anti-parasitic Anti-parasitic NA NA NA 0.33 −0.9978 0 NA NA NA
   Felodipine Calcium channel (L-type) blocker Antihypertensive NA NA NA
   0.33 −0.6897 0.0066 NA NA NA
   Flunarizine Sodium channel antagonist; Calmodulin binding and H1
   antagonist Severe Migraine NA NA NA 0.33 −0.9181 0.0137 NA NA NA
   Metergoline Serotonin and Dopamine receptors ligand Pychoactive (Drug
   Bank) NA NA NA 0.33 −0.9576 0.0036 NA NA NA
   Natamycin Anti-infective agent Anti-parasitic NA NA NA 0.33 −0.9229
   0.0121 NA NA NA
   Pargyline Monoamine oxidase inhibitor Neuro protection ^[159]87 NA NA
   NA 0.33 −0.937 0.0081 NA NA NA
   Perhexiline CPT inhibitor Cardiovascular Agent [Pubchem] NA NA NA 0.33
   −0.8921 0.0238 NA NA NA
   Phenyl propanolamine Adrenoreceptor agonist (alpha) Allergy NA NA NA
   0.33 −0.9258 0.0113 NA NA NA
   Piribedil Dopamine receptor agonist Parkinson’s Treatment NA NA NA 0.33
   −0.9197 0.0132 NA NA NA
   Pyrvinium Anthelmintic Anti-parasitic NA NA NA 0.33 −0.7906 0.0039 NA
   NA NA
   Saquinavir HIV Protease inhibitor Anti-retroviral NA NA NA 0.33 −0.8712
   0.0335 NA NA NA
   Talampicillin Peptidoglycan synthesis inhibitor Antibiotics NA NA NA
   0.33 −0.8465 0.0476 NA NA NA
   Tretinoin Retinoid Skin related conditions NA NA NA 0.33 −0.4968 0.0017
   NA NA NA
   Cyclopenthiazide Sodium Chloride Symporter Inhibitor Antihypertensive
   NA NA NA NA NA NA 0.33 −0.9846 0.0004
   Alcuronium chloride Cholinergic receptor antagonist Muscle relaxant NA
   NA NA NA NA NA 1.00 −0.956 0.0036
   Pergolide Dopamine receptor agonist Pychoactive (Drug Bank) NA NA NA NA
   NA NA 0.33 −0.957 0.0036
   Staurosporine Protein kinase inhibitor Oncology NA NA NA NA NA NA 1.00
   −0.8538 0.006
   Hemicholinium Acetylcholine stores depletor Neuro protection ^[160]1 NA
   NA NA NA NA NA 0.33 −0.9296 0.0094
   Suramin sodium Topoisomerase inhibitor Oncology NA NA NA NA NA NA 0.45
   −0.9171 0.013
   Pyrantel antihelmintic Anti-parasitic NA NA NA NA NA NA 0.33 −0.9011
   0.0191
   Arachidonyl trifluoromethane Phospholipase A2 inhibitor
   Anti-inflammatory NA NA NA NA NA NA 1.00 −0.9004 0.0193
   Triprolidine Histamine H1 receptor antagonist Allergy NA NA NA NA NA NA
   0.33 −0.8889 0.0242
   Ethambutol Chelating agent Antibiotics NA NA NA NA NA NA 0.30 −0.886
   0.0255
   Minaprine 5-HT2 receptor inhibitor; Dopamine receptor agonist
   Parkinson’s Treatment NA NA NA NA NA NA 0.37 −0.8763 0.0303
   Nimesulide Cyclooxygenase-2 inhibitor Anti-inflammatory NA NA NA NA NA
   NA 0.33 −0.8728 0.0322
   Ganciclovir DNA synthesis inhibitor Antibiotics NA NA NA NA NA NA 0.33
   −0.8612 0.0382
   Sparteine Anti-inflammatory diuretic Anti-infective agent
   Anti-inflammatory NA NA NA NA NA NA 0.33 −0.85 0.0446
   Pentoxifylline Phosphodiesterase inhibitor Intermittent Claudication NA
   NA NA NA NA NA 0.33 −0.849 0.0454
   [161]Open in a new tab

   Compounds with neuro protection evidence were highlighted (in bold).

Discussion

   Understanding the causal basis for neurodegenerative diseases is
   challenged by its extended preclinical stage, and the unfeasible task
   to sample brain tissues routinely. Currently, it is known that
   neurodegenerative diseases, such as AD and PD, could result from
   multiple risk factors including genetic susceptibility^[162]47,[163]48,
   age^[164]49, and toxins and inflammatory responses as environmental
   triggers for microglia and astrocyte activation^[165]50. In this
   context, pervasive viral infections could precipitate peripheral
   inflammatory reactions or immune dysregulation that are often
   associated with AD and PD^[166]9,[167]36,[168]51. We report here a
   systematic study of common molecular markers between viral
   perturbations to human immune response and clinical AD and PD. Our
   strategy was to examine multiple public transcriptome datasets from
   patients seropositive/seronegative for CMV, EBV or HHV6, and AD/PD
   patients with the goal of identifying novel biology mechanisms suited
   for therapeutic modulation.

   The concept of utilizing datasets with blood samples for the detection
   of disease associated molecular changes in gene expression relies on
   the natural role of peripheral blood cells in immune response to
   circulating pathogens. This enabled our blood-to-blood sample gene
   expression comparisons between human host response to CMV, EBV or HHV-6
   infection to that of AD and PD patients. In addition, recent studies
   demonstrated significant correlation in gene expression between
   multiple brain tissues and peripheral blood cells^[169]52–[170]56. We
   confirmed that the majority of DEGs from the blood are also actively
   expressed in human microglia. Therefore, we feel there is validity in
   our approach of inferring genes and pathways involved in AD/PD
   pathology through comparative blood differential gene expression
   analyses with host response to viral pathogens.

   Our results provide evidence of the involvement of oxidative stress
   mechanisms in the pathologies of our representative viruses and AD
   through the activation of the Sirtuin and PGC1-alpha pathway. In
   addition, SESN3 and TXN, which play important roles in this pathway,
   ranked among the top genes associated with CMV and EBV, and CMV and
   HHV6 host responses, respectively. Further support is provided by
   genetic evidence from GWAS which show an association of the SNP
   rs3911569 located near the gene SESN3 with a 5-fold increased risk for
   AD. These findings support the emerging “mitochondrial cascade
   hypothesis” based on growing evidence for AD-related mitochondrial
   dysfunction^[171]57, and the potential impact of CMV, EBV and HHV6 host
   response in oxidative stress.

   Our analyses also highlighted BCL6, GYG1, RBCK1, TIMP2 and CIRBP, which
   were DEGs shared between all viruses and PD. TIMP2 was associated with
   neuroprotection through inhibition of matrix
   metalloproteinases^[172]33, which were involved in neuropathological
   processes such as inflammation, BBB damage and neuronal cell death,
   leading to multiple CNS disorders such as PD^[173]58. To our knowledge,
   none of the other genes have been previously linked to PD,
   neurodegeneration or neuroinflammation. BCL6, a sequence specific
   transcriptional repressor which is a key player in B cell
   differentiation, has recently gained attention due to the association
   of EBV latent proteins with BCL6 down-regulation^[174]59. These
   findings have implications for emerging strategies targeting B cell
   differentiation but how they could influence neurodegeneration still
   needs further investigation.

   Recent studies show that LRRK2, a kinase mutated in PD clinical
   cases^[175]60–[176]62, modulates inflammation in response to different
   pathological stimuli. LRRK2 plays a potential role in cytoskeleton
   remodeling and vesicle trafficking in microglia cells toward a
   pro-inflammatory state and, consequently, neurodegeneration^[177]63.
   The LRRK2 pathway was significantly enriched from DEGs associated with
   PD and human host response to CMV, EBV and HHV6. LRRK2 gene expression
   is regulated by IFN-γ and potentially mediates immune responses to
   pathogens^[178]64,[179]65. Recently, we reported that this pathway was
   also linked to human host response to Mycobacterium
   tuberculosis^[180]66. LRRK2 knock-outs in mouse models, displays
   phenotypes of hyperactive immune responses and increased risk to
   inflammatory bowel disease by regulating the transcriptional regulatory
   protein nuclear factor of activated T cells^[181]67. Our findings
   further support the potential roles of LRRK2 in host response to
   infection and neurodegeneration.

   By mapping the 401 DEGs identified in this study to compounds listed in
   the ChEMBL database, we identified 55 drug-target pairs for 20 genes.
   Of those, 12 drug-target pairs showed primary therapeutic indication
   for auto-immune disease or chronic inflammatory conditions, such as
   psoriasis and rheumatoid arthritis. These results highlight the role of
   immune dysregulation in neurodegeneration, particularly, in AD and
   PD^[182]68. Thus, our findings suggest the use of immunomodulators as
   potential therapeutic strategies for AD and PD. Pro-inflammatory
   cytokines and chemokines as well as reactive oxygen and nitrogen
   species secreted by activated microglia can trigger a neurotoxic
   cascade leading to neuronal lesions and significant damage to the CNS.
   Therefore, therapies targeting neuroinflammation either directly or
   indirectly warrant further investigation^[183]68.

   From our CMAP analysis, we identified several clinically used drugs
   that could be potentially repurposed for targeting human host factors
   in CMV, EBV and HHV6 infections. Overall, 14 of those compounds showed
   evidence in the literature of neuro-protection to AD or PD through
   multiple mechanisms, such as dopamine receptor agonism, and monoamine
   oxidase and cholinesterase inhibition^[184]69–[185]71. Moreover, other
   CMAP compounds identified showed anti-inflammatory properties, many of
   which have shown promising results in experimental models of the
   disease^[186]70,[187]72. These findings suggest several relevant
   mechanisms pertinent to both viral infection and neurodegeneration that
   need to be further explored.

   Multiple epidemiological reports have associated AD or PD with diverse
   bacterial and viral pathogens^[188]4,[189]5,[190]73. Most of them
   connect Herpesviridae to AD, particularly HSV-1^[191]74–[192]76, EBV,
   CMV, and HHV6^[193]13–[194]16. In aggregate, these studies are
   suggestive of a viral contribution to neurodegenerative diseases
   although their findings offer little insight into potential mechanisms.
   Recently, Readhead et al.^[195]77 compared computational networks
   between AD and the RNA-Seq abundance of multiple viruses. Their
   findings implicate HHV6 and HHV7 contribution to the development of
   neuropathology and AD. Differently, our study provides a direct gene
   expression comparison of changes in expression in patients with
   documented evidence for viral infection (and active disease) and AD/PD.
   Arguably, using host gene expression signatures might be a more
   “agonistic” approach which overcomes the limitations of “virus hunting”
   for specific pathogens and could reveal the participation of both known
   and unknown viruses (or other pathogens) in neurodegenerative disease
   pathology based on overall host response.

   Our study has some limitations to be considered. Most importantly, we
   were limited by the sample size and quality of publicly available
   datasets. For instance, we wished to investigate HSV-1 human host
   response comparing blood gene expression with the AD/PD DEGs/pathways
   but none of the available datasets were generated from patients.
   Moreover, the results presented here are not enough to conclusively
   prove causality relationship between viral host response and
   neurodegeneration. For that to occur, further clinical trials and
   interventional studies are necessary. Lastly, drug repurposing
   compounds obtained from CMAP analyses are derived from Broad Institute
   gene expression data on fibroblasts and tumor cell lines, which may not
   be the most relevant tissue for this study. Validating these results on
   microglia or brain tissues or even specific blood cells would be ideal.

   Our study adds to the growing evidence of the role of immune
   dysfunction in neurodegenerative diseases. Moreover, gene expression
   systematic comparisons between host response to EBV, CMV and HHV6 and
   AD/PD provide new insights into host genes and pathways important for
   neurodegeneration and convey potential drug repurposing opportunities
   promoting neuroprotection. Experimental validation of the pharmacologic
   interventions proposed here would constitute the next stage in the drug
   development for the proposed targets and compounds. Further evolution
   of this paradigm shift viewing peripheral immunity dysregulation as a
   potential driver of neurodegeneration could lead to novel therapeutic
   approaches for the treatment of PD and AD.

Methods

Selection of gene expression datasets

   Data analysis workflows broadly followed our previously published
   studies on human host response to various intra-cellular residing
   pathogens including bacteria^[196]78, viruses^[197]79 and
   tuberculosis^[198]66. Gene Expression Omnibus (GEO) database (as of
   June 2018) was queried for human blood microarray gene expression
   datasets in response to Herpesviridae infection, Alzheimer’s and
   Parkinson’s Disease. The specific search terms used were: “HSV”, “EBV”,
   “HHV6”, “CMV”, “Alzheimer’s Disease”, “Parkinson’s Disease”, “Homo
   sapiens”, and “Whole blood”. The retrieved datasets were filtered based
   on the following criteria: gene expression profiles from published
   studies which were: 1) raw data available and derived from human cells
   of AD/PD or single virus infected patients; 2) there was at least one
   control group (healthy subjects) and one diseased group and; 3) data
   originated from human array platforms. Additionally, type 2 diabetes
   mellitus (T2DM) and Huntington’s Disease (HD) datasets were included to
   allow gene expression comparisons with unrelated neurodegenerative and
   non-infectious diseases and serve as controls for spurious comparisons.
   Table [199]S1 summarizes all retrieved datasets along with the reasons
   for their inclusion or exclusion from our analyses.

   Raw (intra-slide normalized) gene expression data, study design table
   and annotation table of each dataset were obtained from the
   GEO/ArrayExpress databases and processed using ArrayStudio v10.0
   (OmicSoft, USA). The datasets retrieved are microarray datasets
   obtained from the following platforms: Illumina Human HT, Affymetrics
   Human Genome U133 and Affymetrics Exon Array (Table [200]S1). Several
   datasets ([201]GSE42834, [202]GSE56153, [203]GSE31348 and
   [204]GSE36238) were further excluded due to both a noisy kernel density
   plot and low within group pairwise correlation (correlation cutoff 0.9,
   Table [205]S1), but were included as independent datasets for
   validation purposes. After quality filtering, six microarray datasets
   (GSE636063, [206]GSE99039, [207]GSE81246, [208]GSE202007, GSE458298,
   and [209]GSE40396) from either whole blood or peripheral blood
   mononuclear cells (PBMCs) were retained for further analysis.

Data processing and statistical analysis

   Quality Control analyses were performed in all datasets
   selected^[210]66. Data imported was previously normalized. Intra-slide
   normalization was assessed by: 1) kernel density; 2) Principal
   Component Analysis or PCA (showing divergent samples within groups); 3)
   Median Absolute Deviation (MAD) score and; 4) within group pairwise
   correlation. Samples were considered outliers if failed at least two of
   these assessments. Samples irrelevant to our study design (such as
   samples from host response to bacteria and viruses other than HHV6 in
   [211]GSE40396) were also excluded. In total, 6 samples from AD, 2
   samples from PD datasets were excluded. All samples in CMV, EBV, HHV6
   and T2DM datasets passed QC.

   For each dataset, scale quantile inter-slide normalization (fixed
   target median value to 500), log2 transformation and probe differential
   expression analysis was performed in ArrayStudio v10.0 (OmicSoft, USA).
   When more than one probe mapped to a gene, the expression value of the
   lowest p-value was used for that gene (the “aggregate” R function was
   applied). Differentially expressed genes (DEG) passed the false
   discovery rate adjusted [FDR-adj.] p-value threshold of 0.05. The AD or
   PD DEG list was compared with the list of DEGs associated with CMV, EBV
   or HHV6 host response to identify shared gene expression signatures.
   The statistical significance of the overlap between AD/PD DEGs with
   CMV, EBV or HHV6 DEGs was assessed with a hypergeometric test (using
   the “phyper” R function).

Pathway enrichment analysis

   Pathway enrichment analysis was performed for all DEGs from each
   dataset using MetaCore/MetaBase (GeneGo) v6.34 (Thomson Reuters,
   [212]https://portal.genego.com/)^[213]66. The p-value for each of the
   1480 human canonical pathways in MetaCore was generated using a
   hypergeometric test with an FDR-adj. p-value cutoff of 0.01. The
   Compare Experiments Workflow tool was used for comparing gene
   expression data across different datasets (AD/PD DEG with CMV, EBV or
   HHV6 DEGs) by analyzing their intersections in terms of their mappings
   onto MetaCore’s ontologies, including canonical pathway maps.

Genetic variants enriched in candidate gene region

   Based on the shared genes across AD or PD and CMV, EBV and HHV6 DEGs,
   we searched the Open Targets^[214]80 validation platform to identify
   genetic variants proximal to these candidate gene targets associated
   with AD or PD. Sources of genetic associations in Open Targets include
   the following: the GWAS catalog, Genomics England PanelApp, the PheWAS
   catalog, the European Variation Archive (EVA) and
   Gene2Phenotype^[215]80. Variant-gene assignment considered deleterious
   consequences within the gene coding region, and the variant location
   within introns or regulatory regions. Intergenic variants assigned to
   the promoter region of the nearest gene were also retrieved in this
   search.

Gene expression validation in human microglia available datasets

   To validate the expression profile of blood sample DEGs, publicly
   available datasets with human microglia gene expression datasets were
   identified in GEO database. Raw gene expression files were downloaded
   from [216]GSE99074^[217]40.An initial quality check of host RNA-Seq
   data was performed using FastQC^[218]81. Quality filtered reads were
   mapped to the human reference genome GRCh38 ensembl 86 using
   STAR^[219]82, and quantified with featureCounts^[220]83. The data was
   annotated with Biomart^[221]84, and gene expression measurements were
   reported in log2 transcripts per million (TPM).

Drug-target prioritization

   To link putative targets (DEGs) to public compounds, we obtained
   evidence from approved and marketed drugs that are associated to 11,538
   targets from the EMBL-EBI ChEMBL database v23^[222]85. This analysis
   included data on drugs that have been approved for marketing by the
   U.S. Food and Drug Administration (FDA) and direct clinical evidence of
   interaction with the encoded DEG.

   In addition, we performed drug repurposing analysis with the
   Connectivity Map^[223]45 (CMAP,
   [224]https://www.broadinstitute.org/cmap/). For each gene expression
   profile to host response to CMV, EBV and HHV6, the 500 genes that
   ranked (based on FDR-adj. p-values) at the very top and bottom of each
   list were selected and compared against the gene expression profiles
   from the Broad CMAP compound library. Significant compounds were
   prioritized based on anti-correlated compound enrichment score, which
   represents compounds inversely matched to the disease signatures
   surveyed (score < 0; FDR-adj. p-value ≤ 0.05; compound
   specificity < 0.1). To enable result interpretation, ChEMBL
   data^[225]85 on target, mechanism of action, and drug indication was
   integrated. Compounds with unknown mechanism of action, no clinical use
   or antibacterial effect were excluded from our results.

Supplementary information

   [226]Supplementary Figures and Table Labels^ (261.8KB, pdf)
   [227]Supplementary Table S1.^ (17KB, xlsx)
   [228]Supplementary Table S2.^ (17.5KB, xlsx)
   [229]Supplementary Table S3.^ (14.6KB, xlsx)
   [230]Supplementary Table S4.^ (16.1KB, xlsx)
   [231]Supplementary Table S5.^ (14.5KB, xlsx)
   [232]Supplementary Table S6.^ (22KB, xlsx)
   [233]Supplementary Table S7.^ (15KB, xlsx)
   [234]Supplementary Table S8.^ (22.6KB, xlsx)
   [235]Supplementary Table S9.^ (58KB, xlsx)
   [236]Supplementary Table S10.^ (12.2KB, xlsx)

Acknowledgements