Abstract

   Among the genetic factors known to increase the risk of late onset
   Alzheimer’s diseases (AD), the presence of the apolipoproteine e4
   (APOE4) allele has been recognized as the one with the strongest
   effect. However, despite decades of research, the pathogenic role of
   APOE4 in Alzheimer’s disease has not been clearly elucidated yet. In
   order to investigate the pathogenic action of APOE4, we applied a
   systems biology approach to the analysis of transcriptomic and genomic
   data of APOE44 vs. APOE33 allele carriers affected by Alzheimer’s
   disease. Network analysis combined with a novel technique for biomarker
   computation allowed the identification of an alteration in
   aging-associated processes such as inflammation, oxidative stress and
   metabolic pathways, indicating that APOE4 possibly accelerates
   pathological processes physiologically induced by aging. Subsequent
   integration with genomic data indicates that the Notch pathway could be
   the nodal molecular mechanism altered in APOE44 allele carriers with
   Alzheimer’s disease. Interestingly, PSEN1 and APP, genes whose mutation
   are known to be linked to early onset Alzheimer’s disease, are closely
   linked to this pathway. In conclusion, APOE4 role on inflammation and
   oxidation through the Notch signaling pathway could be crucial in
   elucidating the risk factors of Alzheimer’s disease.
     __________________________________________________________________

   Alzheimer’s disease (AD) is the most common cause of dementia,
   characterized clinically by a decline in cognitive function and by
   distinctive brain pathology with neuronal loss and the formation of
   amyloid plaques and neurofibrillary tangles.

   Early onset AD is rare and is caused by mutations in specific genes
   such as amyloid precursor protein (APP), presenilin 1 (PSEN1) and
   presenilin 2 (PSEN2). Late onset AD is the most common form but,
   although several putative susceptibility genes have been reported,
   APOE, coding for the Apolipoprotein E, is the most robust
   susceptibility gene known to date. Three common isoforms of APOE have
   been recognized: APOE2 (cys112, cys158), APOE3 (cys112, arg158) and
   APOE4 (arg112, arg158) and the presence of the alleles coding for the
   APOE4 isoform are associated with an increased risk (up to tenfold in
   homozygous cases[32]^1) of late onset AD when compared to the most
   common APOE3 allele or APOE2, a rarer allele, that appears to have,
   instead, a protective effect[33]^2,[34]^3.

   APOE is a multifunctional glycosylated protein with a major role in
   lipid transport and atherosclerosis pathogenesis and it is expressed in
   several organs, with the highest expression in the liver and brain. In
   the central nervous system, although neurons can produce APOE under
   certain conditions, non-neuronal cells, mainly astrocytes and to some
   extent microglia, are the major cell types that express APOE in the
   brain[35]^4,[36]^5.

   Numerous mechanisms by which APOE influences AD pathogenesis have been
   proposed, including a role in the clearance of Amyloid β[37]^6,[38]^7,
   but how this influences the pathogenic molecular processes remains to
   be clarified.

   To gain some insight into the molecular mechanisms responsible for the
   pathogenic impact of APOE, we used a systems biology approach to
   integrate pre-existing genomic and transcriptomic data in an unbiased
   fashion. A network-based analysis combined with a novel technique for
   biomarker identification revealed specific biological processes altered
   in APOE44 carriers affected by AD. Molecular processes related to
   inflammation, oxidative stress and metabolism were shown to be altered,
   demonstrating that APOE4 possibly quickens pathological alterations
   naturally induced by aging. The integration of genomic data evidenced
   that components of the Notch pathway could be among the upstream
   effectors involved in this action.

   Overall, this study provides a contribution to the long-standing APOE4
   debate by suggesting an evidence driven hypothesis on the mechanism by
   which APOE4 confers risk for the development of Alzheimer’s disease.

Material and Methods

   A schematic representation of the study workflow is shown in [39]Fig.
   1.

Figure 1. Schematic representation of the network analysis workflow.

   [40]Figure 1
   [41]Open in a new tab

   The molecular biomarker associated to APOE44-AD was extracted from
   transcriptomic data of post-mortem cerebral cortices of AD affected
   individuals carrying the APOE33 or APOE44 alleles. Network analysis was
   then performed using NetWalker technique with the reference
   protein-protein interaction (PPI) network derived from HPRD data. The
   functionality of the network was then performed by testing
   over-represented Gene Ontology biological process terms and pathways.
   Genomic data analysis was then performed with the analysis of genes in
   the network displaying a genetic interaction with APOE.

Transcriptomic and Genomic data

   The microarray dataset (study number 1, [42]Table 1) was downloaded
   from the Myers laboratory website
   ([43]http://labs.med.miami.edu/myers/LFuN/data.html, GEO reference:
   [44]GSE15222). The complete dataset consists of transcriptome-wide gene
   expression data from post-mortem cerebral cortical area (temporal,
   parietal and frontal cortices), all cortical areas involved in the
   disease, of Alzheimer’s disease affected subjects at all stages of the
   disease (Braak stages ≥3) and matched controls (188 controls and 176
   AD), stratified by their APOE genotype[45]^8. In the study, data from
   77 subjects affected by AD (43 APOE33 and 34 APOE44) was used
   ([46]Table 1) matched for age (average age APOE33-AD 83.58 years
   [72–102 years]; APOE44-AD 83.62 years [68–92 years]) and post-mortem
   interval (average PMI APOE33-AD 10.54 hours [1.3–26.9 hours]; APOE44-AD
   10.39 hours [1.2–17.7 hours]).

Table 1. Datasets used in the study.

   Study Type of data/Technology #Subjects in the original study #Subjects
   selected for the study and APOE genotype Brain Region Data source
   1-Mayer’s lab Transcriptomic/Illumina ref-seq 8 364 77 (43APOE33 34
   APOE44) Temporal, Parietal, Frontal Cortex NCBI GEO [47]GSE15222
   2-Mayo Transcriptomic/Illumina HiSeq 2000 278 39 (30 APOE33 9 APOE44)
   Temporal Cortex AMP AD knowledge Portal - syn3163039
   3- Astrocyte study Transcriptomic/2.0 Affymetrix gene arrays 18 18 (9
   APOE4-9 APOE4+) Frontal Cortex (Astrocyte) NCBI GEO [48]GSE29652
   4-ADNI Genomic/ILLUMINA Human610-Quad BeadChip 757 139 (APOE4:
   0 = 45;1 = 65;2 = 29) NA adni.loni.usc.edu
   5-Mayo LOAD GWAS Genomic/ILLUMINA HumanHap300 2099 580 (APOE4: 0 = 175;
   1 = 289; 2 = 98) NA AMP AD Knowledge Portal - syn3157238
   [49]Open in a new tab

   Table describing the datasets used in the study, type of data and
   technology platform used, total number of subjects in the original
   study and number of subjects selected for the present study including
   APOE alleles composition. Brain regions analyzed in the study (when
   applicable) and data source are also described.

   For the validation studies, an additional dataset was evaluated: the
   Mayo RNA sequencing study (study number 2 [50]Table 1) of temporal
   cortex accessed through the AMP AD Knowledge Portal (syn3163039;
   [51]https://www.synapse.org/#!Synapse:syn2580853/wiki/77552). The
   original study consisted of RNA sequencing data of 278 samples from
   post-mortem temporal cortical tissue of AD affected individuals at all
   stages of the disease (Braak stages ≥4) and matched controls. In the
   present study a sub-population of 39 subjects was used (30 APOE33 and 9
   APOE44 carriers) all subjects were affected by AD (average age
   APOE33-AD 76.5 years [65–80 years]; APOE44-AD 72.3 years [66–78 years]
   ([52]Table 1). No information on post-mortem interval was provided.

   In order to evaluate the role of the specific pathways highlighted in
   our network analysis in astrocytes of APOE4 individuals, an additional
   dataset was used (GEO accession: [53]GSE29652; study number 3,
   [54]Table 1). This dataset includes microarray profiles of
   laser-captured microdissected astrocytes of post-mortem human temporal
   cortex of AD affected individuals APOE4- and APOE4+ carriers at
   different Braak stages. NCBI GEO’s tool GEO2R was used to compare
   expression between the APOE4- and APOE4+ groups, each group including
   subjects at all stages of the disease. GEO2R analyzes gene expression
   using GEOquery and the Linear Models of Microarray Analysis R package
   (limma)[55]^9,[56]^10. Genes were considered differentially expressed
   if they showed a minimum 1.5-fold change at p-value < 0.02 for
   consistency with the original analysis of dataset [57]GSE29652, as
   reported in Simpson et al.[58]^11. No correction for multiple testing
   was performed.

   For the genetic analysis, we considered two datasets of genome-wide
   genotypic data. The first dataset was obtained from the Alzheimer’s
   Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu;
   study number 4, [59]Table 1). The ADNI was launched in 2003 as a
   public-private partnership, led by Principal Investigator Michael W.
   Weiner, MD. The primary goal of ADNI has been to test whether serial
   magnetic resonance imaging (MRI), positron emission tomography (PET),
   other biological markers, and clinical and neuropsychological
   assessment can be combined to measure the progression of mild cognitive
   impairment (MCI) and early Alzheimer’s disease. The genotype data of
   the ADNI dataset were generated using Illumina Human610-Quad BeadChip
   and referred to 757 individuals. The second dataset was acquired from
   the Mayo Clinic LOAD Risk Genetic Association study, accessed through
   the AMP AD Knowledge Portal (syn3157238; study number 5, [60]Table 1).
   The Mayo LOAD GWAS study originally included 844 cases and 1255
   controls, genotyped using the Illumina’s HumanHap300-Duo Genotyping
   BeadChips. For both dataset we selected the individuals in which the
   disease status, age of onset and APOE genotype were available.

Biomarker identification

   The transcriptional biomarker has been identified by means of an
   enhanced version of the rank-based classification method previously
   implemented in the web-tool SCUDO[61]^12,[62]^13. Briefly, following a
   preliminary probe selection using Wilcoxon test, the classification
   method ranks the probes separately for each sample; then it produces a
   set of subject-specific signatures, where each signature is the list of
   the first n1 and the last n2 probe IDs in the ranking (n1 and n2 have
   the same value for all subjects and are method parameters that are
   automatically estimated). An all-to-all signature comparison is then
   carried out using a distance metric based on a weighted enrichment
   score, resulting in a distance matrix that systematically quantifies
   the degree of similarity between the subjects. Subjects are finally
   classified by the algorithm into the two groups of APOE33 and APOE44
   carriers, by assigning each sample to the group of subjects whose
   elements have the lowest averaged distance from the sample. In the
   enhanced version used here the original method was extended with a
   global optimizer that automatically selects the method parameters, such
   as signature length (n1 + n2) and feature selection stringency, using a
   genetic algorithm. The classification accuracy of the method has been
   evaluated using a ten-fold cross-validation scheme, producing an
   averaged accuracy on the validation set equal to 85%. Moreover, we
   evaluated the statistical significance of the analysis by means of a
   permutation test in which we compared the observed classification
   accuracy of the method with an empirical distribution of accuracy
   values obtained by 10000 random permutations of the probe labels. As a
   result of the permutation test we obtained a p-value ≪ 0.001. Finally,
   a transcriptional biomarker has been extracted by considering the 114
   probes (corresponding to 108 genes) that have been included in at least
   one sample signature. Such a biomarker identifies the smallest subset
   of genes that allows the classifier to discriminate between APOE44 and
   APOE33 carriers.

Network analysis

   The list of genes included in the transcriptional biomarker has been
   computed to be the shortest possible while maintaining the highest
   possible classification accuracy. In order to facilitate the
   interpretation of the biological processes underlying the phenomenon
   under investigation, we better characterized the list of genes
   constituting the biomarker by means of a network analysis technique
   implemented in NetWalker[63]^14, a network analysis suite for
   functional genomics. NetWalker is a tool for identifying a high-scoring
   sub-network resulting from a random walk exploration over a
   user-provided interaction network, where the exploration takes into
   account the activity scores for a list of genes of interest. In our
   analysis, we used the protein-protein interaction network (PIN) taken
   from the Human Protein Reference Database (HPRD)[64]^15 as background
   network, the genes included in the biomarker as the set of genes of
   interest and the negative log2 of the p-values computed during the
   probe filtering step in the biomarker identification algorithm as gene
   scores. The NetWalker output consisted of a list of interactions of the
   background network and their scores as computed by the algorithm. In
   our analysis we empirically selected a relevance threshold for the
   interaction scores as the value corresponding to an obvious step in the
   graph of sorted interaction scores. This conservative selection of a
   threshold ensures that the scores are well above the level of random
   values, and that only highly significant interactions are retained. We
   then extracted the connected sub-network containing all interactions
   above the threshold (n = 504 selected interactions). The final list of
   genes, which integrated the one constituting the transcriptional
   biomarker, was given by the genes included in such a sub-network,
   called the “NetWalker network” in the remainder of the paper (n = 627
   genes).

Functional annotation analysis

   The gene lists obtained from network enrichment analysis were used to
   extract the most representative GO Biological Process terms (i.e. the
   ones that are over-represented, but that do not refer to most general
   biological processes). For identifying and visualizing enriched GO
   terms, we used GOrilla and REVIGO tools[65]^16,[66]^17. Hypergeometric
   distribution was applied to test GO term enrichment, and an adjusted
   p-value (FDR) threshold of 0.001 was selected. As reference background
   the complete HPRD gene list was used. Pathway analysis was performed
   using ConsensusPathDB[67]^18. Hypergeometric distribution with an
   adjusted p-value (FDR) threshold of 0.001 was applied to test pathway
   enrichment, using HPRD gene list as reference background. GOrilla and
   ConsensuPathDB tools were selected because they provided reliable
   statistical analysis tools together with an updated version of the
   databases.

Analysis of epistasis

   To investigate the presence of a genetic interaction between APOE and
   the SNPs in genes corresponding to proteins identified in the APOE44
   network we used two datasets of genotype data (see Transcriptomic and
   Genomic data section, studies number 4–5, [68]Table 1). For each
   dataset we selected the individuals affected by Alzheimer’s disease
   with disease age-at-onset available and we performed a Quality Control
   (QC) procedure. SNPs were mapped to the gene only if they were located
   within the gene boundaries and they were removed if not on autosomes,
   if they had a call rate lower than 95% and a frequency of the minor
   allele less than 0.05. Samples were excluded when they had a call rate
   less than 95%. Furthermore, for the ADNI dataset samples were excluded
   if not of self-declared Caucasian origin and if outliers in
   multidimensional scaling (MDS) analysis. To minimize confounding due to
   population stratification we adjusted the analysis for the top three
   MDS coordinates. After the QC procedure, the ADNI study included 139
   individuals and 522092 SNPs. The Mayo dataset included 580 individuals
   and 294128 SNPs. The drop in samples number is due to the design of our
   study which required a stratification based on the diagnosis of
   Alzheimer’s disease, availability of APOE genotype (only APOE33 and
   APOE44 carriers were selected) and information on the age at onset of
   the disease. The genetic interaction between APOE and each SNP was
   tested using a linear regression model that included the interaction
   term, as implemented in PLINK[69]^19. Specifically, we used the
   age-at-onset of Alzheimer’s disease as a response variable since APOE4
   allele is associated with the age of onset of late onset Alzheimer’s
   disease[70]^2 and the APOE dosage, the SNP under investigation and the
   APOE-SNP interaction as explanatory variables. To minimize confounding
   due to population stratification we adjusted the analysis for the top
   three MDS coordinates. To test the presence of a genetic interaction we
   considered the p-value of the interaction APOE-SNP from PLINK output.
   The results of the two datasets were combined in a meta-analysis using
   METAL[71]^20. A fixed effects, sample size weighted approach was
   applied and the genomic inflation factor was used to correct for
   putative population stratification.

   The SNPs in common between the two datasets and showing the same
   direction of effect in the two studies were mapped to the genes
   identified as APOE molecular interactors. In total, 2579 SNPs were
   mapped to 389 genes. We considered as significant those interactions
   with a q-value (Estimated False Discovery Rates as calculated with R
   fdrtool package) less than 0.05.

Results

   We extracted the transcriptomic profiles of APOE33 (n = 47) and APOE44
   carriers (n = 30) from the Myers’ dataset (study number 1, [72]Table 1)
   and used the rank-based signature definition algorithm to identify a
   list of signature genes capable of discriminating the two groups with
   high accuracy. The resulting molecular signature consists of 114 probes
   corresponding to 108 transcripts and is able to classify the subjects
   as either APOE33 or APOE44 carriers with an accuracy of 87%
   (precision = 0.86, recall = 0.87, average values obtained from the
   cross-validation procedure). In order to identify the functional
   biological processes context of the transcripts includes in the
   identified biomarker, we carefully expanded this list following a
   classical weighted network based analysis. Specifically, the list of
   signature genes was used as input to the NetWalker tool for network
   analysis, using the p-values computed by the signature algorithm as
   weights, as described in the Materials and Methods section. The
   resulting network is composed of 650 proteins and it represents the set
   of genes and interactions on which a canonical functional enrichment
   analysis of GO Biological Process terms was carried out. [73]Figure 2
   summarizes the main functional annotation analysis results. This
   analysis revealed a predominant role of metabolic processes and
   oxidative stress including glucose and lipid metabolism, mitochondria
   and insulin-related functions. Interestingly, biological processes
   related to inflammation with innate immune response, regulation of
   cytokine and interferon functions were also represented. Other
   processes were evidenced comprising regulation of synaptic plasticity,
   regulation of neurogenesis, learning and memory, and circadian rhythm.
   Finally, a role of angiogenesis and regulation of programmed cell death
   was also evident. Pathway analysis supported also the role of
   inflammatory pathways evidenced in the GO term analysis including
   chemokine signaling. A central role of metabolic processes such as
   insulin pathways was also evident. In addition, several signaling
   pathways including WNT, Notch and FoxO were over-represented ([74]Table
   2; [75]Supplementary Table S1). In [76]Fig. 3 and [77]Supplementary
   Figure 1, the Notch and FoxO pathways, respectively, are depicted
   highlighting the proteins belonging to our network. Complete list of
   enriched pathways with associated genes can be found in
   [78]Supplementary Table S1.

Figure 2. Schematic graphs of over-represented Gene Ontology biological
process terms in APOE44 NetWalker network.

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

   GO terms are represented as nodes, and the strongest GO term pairwise
   similarities are designated as edges in the graph. GO terms are grouped
   to illustrate the main biological processes characterizing APOE44
   subjects. Complete list of GO terms can be found in [81]Supplementary
   Table S1.

Table 2. Pathways enrichment analysis.

   Pathway Database q-value Study #1 q-value Study #2
   Signaling by Interleukins Reactome 3.08E-19 19.36E-08
   Cytokine Signaling in Immune system Reactome 3.64E-18 4.37E-07
   Neurotrophin signaling pathway - Homo sapiens (human) KEGG 2.09E-18
   4.13E-07
   Innate Immune System Reactome 5.41E-17 7.65E-07
   PI3K-Akt signaling pathway - Homo sapiens (human) KEGG 7.41E-15
   1.43E-05
   MAPK family signaling cascades Reactome 1.15E-13 1.6E-06
   Signaling by Leptin Reactome 2.57E-13 6.71E-05
   IGF1R signaling cascade Reactome 1.14E-12 0.000458
   Signaling by Insulin receptor Reactome 3.65E-11 0.000785
   FoxO signaling pathway - Homo sapiens (human) KEGG 6.41E-09 5.33E-06
   NF-kappa B signaling pathway - Homo sapiens (human) KEGG 6.66E-07 >0.01
   Sphingolipid signaling pathway - Homo sapiens (human) KEGG 1.24E-06
   9.67E-05
   Toll-Like Receptors Cascades Reactome 2.15E-06 0.00095
   Insulin signaling pathway - Homo sapiens (human) KEGG 7.57E-06 0.000123
   Notch signaling pathway - Homo sapiens (human) KEGG 1.86E-05 0.0016
   Chemokine signaling pathway - Homo sapiens (human) KEGG 2.54-E05
   0.001149
   Intrinsic Pathway for Apoptosis Reactome 0.0011 0.005454
   [82]Open in a new tab

   Table describing the most relevant pathways (with the corresponding
   database) found in the study and the q-value of the two independent
   studies (studies number 1 and 2, [83]Table 1).

Figure 3. Flow diagram representing the molecular interactions in the Notch
pathway (graphically adapted from KEGG database).

   [84]Figure 3
   [85]Open in a new tab

   The Notch pathway is enriched with the network proteins, labeled in
   orange (panel A). In panel B, the sub-network of the genes belonging to
   the Notch pathway and their interaction with the epistasis genes are
   shown.

   In order to validate the results obtained with the first dataset, an
   independent transcriptomic data obtained from the AMP initiative which
   includes cerebral cortex RNAseq analysis of AD affected individual
   carrying APOE33 or APOE44 alleles were studied (study number 2,
   [86]Table 1). The signature consisted of 757 transcripts corresponding
   to 642 gene symbols, while the network contains 650 proteins. This
   study, including numerous individuals and a different analytical
   technique compared to the first data set. namely RNA sequencing,
   confirmed the GO terms and pathways indicated by the initial part of
   the study ([87]Table 2, [88]Supplementary Table S2), particularly the
   involvement of inflammatory and metabolic pathway and the central role
   of the Notch pathway.

   The transcriptome-wide statistical analysis of microarray data from
   laser dissected astrocytes of APOE4+ or APOE4- individuals affected by
   AD (study number 3, [89]Table 1), regardless of disease stage, did not
   reveal any gene whose expression was significantly different in the two
   groups after the correction for multiple testing was applied. However,
   when testing was performed on single genes of the chemokine pathway
   whose relevance was suggested by our own results and by previous
   studies[90]^21, several genes appeared to be altered in APO4+ carriers
   ([91]Supplementary Table S3). In particular, the chemokine pathway
   genes VAV3, RAC1, PIK3R5, PIK3R2, CCL21, CCL4, STAT5B, and CCR2 were
   significantly altered using the statistical methods reported in the
   original article (see method section and [92]Supplementary Figure 2).
   Genome wide genotyping data from the ADNI and Mayo Clinic LOAD Risk
   Genetic Association studies (studies number 4–5, [93]Table 1) were also
   integrated in our study. SNPs falling within the genes identified with
   the network analysis were evaluated for their genetic interaction with
   APOE in modulating the disease age-at-onset. This analysis identified 6
   SNPs significantly interacting with APOE ([94]Table 3) that are located
   in the sequence of 4 genes: Neurexin 3 (NRXN3), Mastermind-like
   receptor 3 (MAML3), GDP Dissociation Inhibitor 2 (GDI2), and Caldesmon
   1 (CALD1). The SNPs within the same gene resulted highly correlated in
   both datasets. In particular, in ADNI dataset the NRXN3 SNP pair
   [rs12891137 rs766024] showed an r^2 = 0.611 and a D′ = 0.954; the MAML3
   SNP pair [rs1402669 rs2874372] showed an r^2 = 0.761 and a D′ = 0.945.
   In Mayo dataset the NRXN3 SNP pair showed an r^2 = 0.717 and a
   D′ = 0.954; the MAML3 SNP pair showed an r^2 = 0.679 and a D′ = 0.905.

Table 3. Results of the analysis of a genetic interaction between APOE and
the SNPs in genes corresponding to proteins in the APOE44-AD network.

   Official Gene symbol SNP SNP position (hg38) A1 A2 N Z score Direction
   of effect p-value q-value
   NRXN3 rs12891137 chr14:79157374 T C 718 −4.178 — 2.94 × 10^−5 0.016
   MAML3 rs1402669 chr4: 140064751 A G 719 3.832 ++ 1.27 × 10^−4 0.035
   MAML3 rs2874372 chr4: 140071968 T C 718 3.606 ++ 3.11 × 10^−4 0.045
   GDI2 rs1858446 chr10: 5810209 T C 715 −3.468 — 5.24 × 10^−4 0.049
   CALD1 rs10488462 chr7: 134825047 A G 714 3.438 ++ 5.85 × 10^−4 0.049
   NRXN3 rs766024 chr14: 79170051 T C 719 3.417 ++ 6.33 × 10^−4 0.050
   [95]Open in a new tab

   The genetic variants located in genes belonging to the APOE network
   that shows a significant statistical interaction with APOE are shown.
   For each SNP is reported the Official Gene Symbol of the corresponding
   gene, the reference SNP ID, the genomic position, allele1, allele2,
   number of subjects, Z score, direction of effect, p-value and FDR
   corrected q-value.

   The significant SNPs were also evaluated for eQTL association in
   cerebral cortex using data in GTeX database and the SNP-gene pair
   rs1858446-GDI2 showed a p-value < 0.05 (effect size −0.18, p-value
   0.047). The other SNPs did not show significant p-values in eQTL
   analysis.

   Among the identified genes, MAML3 belongs to the Notch pathway, a
   pathway already evidenced by the transcriptomic analysis.
   Interestingly, also the other 3 genes in epistasis with APOE are
   closely related to the Notch-associated genes in the NetWalker network
   ([96]Fig. 3B), indicating that the alteration at the genetic level
   could potentially contribute to the downstream transcriptomic
   perturbations.

Discussion

   One distinctive aspect of our study is the combined analysis of
   multiple sets of data. The advantage of this approach is that
   conclusions supported by multiple sources of evidence are less likely
   to be the result of chance or data overfitting, and less prone to
   population or technical biases. This type of data integration also
   presents some challenges due to data heterogeneity both at the
   annotation and numerical levels. For example, the different levels of
   detail in the annotation on Braak stage and Brodmann area require some
   additional considerations in performing a direct comparison.
   Conversely, the detailed annotations of post-mortem delay and age at
   death enabled us to quickly conclude that the samples are well matched.
   Crucially, we were able to overcome the data heterogeneity challenge by
   using a functional-level integrative analysis. Thus, the study of
   biological networks is a powerful tool to analyze and integrate data
   produced by omic technologies[97]^22. In the present study, network
   analysis has revealed many aspects of the molecular interaction
   structure in APOE44 AD brains, when compared to APOE33 AD brains, thus
   providing an insight into the underlying mechanism by which APOE4
   confers risk for the development of AD.

   In order to build an APOE44-AD specific network we leveraged the power
   of complementary approaches such as biomarker identification and
   network analysis, to dissect the complex mechanism underlying the
   interaction between APOE44 genotype and AD pathology. While biomarker
   identification summarizes the essential features of such a mechanism,
   network analysis helps in deciphering it by providing a biological
   context. The creation of an APOE4-AD network representative of a large
   cohort of human brain tissue has revealed many of the genes
   characterizing this allelic composition inducing an increased risk to
   AD. The samples cohort selected for the study were matched for age and
   PMI, thus we believe that these factors, which may interfere with
   preservation of RNA, has been properly controlled. No specific
   information on Braak stages of each sample has been provided, thus we
   cannot exclude different levels of severity of the disease. However,
   considering that in the cortical areas studied amyloid plaques
   deposition and the related glial response appear evident even in the
   early phases of the disease[98]^23, we are confident that it will not
   have a strong impact on the study results.

   Our systems biology approach, investigating the role of APOE44 in an AD
   milieu, highlighted the inflammatory and immune response pathways and
   the processes related to oxidative stress as central mechanisms
   ([99]Fig. 2). This is in line with previous studies which have focused
   on the role of APOE in inflammatory processes suggesting that the
   function of APOE is closely linked with both pro- and anti-inflammatory
   cytokines. The interaction between APOE and cytokine appears very
   complex with an intricate APOE-mediated feedback regulation of
   inflammatory and immune responses[100]^24. The modulation of
   inflammatory response has been shown also to be
   isoform-specific[101]^25 with a higher level of TNF-α secreted by
   cultured macrophage derived from APOE4 transgenic mice as compared with
   APOE3 mice[102]^26. Among the pathways related to inflammation, we
   highlighted the chemokine signaling pathways as involved
   ([103]Supplementary Figure 2). APOE4 genotype has been demonstrated to
   specifically modulate astrocyte secretion of potent chemotactic agents,
   including CCL3, found also in our network, thus providing evidence that
   APOE modulation of central nervous system (CNS) innate immune response
   is mediated through astrocytes[104]^21. In view of the role of
   astrocyte in inflammation[105]^27, we further analyzed the chemokine
   pathway in dissected astrocyte of APOE4+ and APOE4- carrier. Among the
   chemokine pathway, numerous genes were shown to be altered in astrocyte
   in APOE4+ individuals ([106]Supplementary Figure 2), suggesting an
   involvement of astrocytic APOE4 in the inflammatory cascade. It has to
   be noted that these genes met the significance threshold
   (p-value < 0.02) only when tested individually but not when the
   correction for multiple testing was applied, suggesting that the
   experimental evidence is not strong enough in the context of a
   conventional genome-wide search. This study also highlighted the role
   of oxidative stress and its association with APOE44, and also with
   physiological aging, supporting the notion that APOE4 is anticipating
   or worsening what normally occurs during the aging process. Genes in
   the network related to the regulation and response to oxidative stress
   mainly belong to the Forkhead box O (FoxO) pathway ([107]Supplementary
   Table S1). As shown in [108]Supplementary Figure 1, several genes are
   associated with this pathway which regulates a series of cellular
   processes[109]^28 and, particularly, are activated by oxidative signals
   and regulate cell proliferation and resistance to oxidative stress,
   playing a central role in redox signaling[110]^29.

   FOXO1, present in our network, has been previously shown to be involved
   in resistance to oxidative stress via the up-regulation of antioxidant
   enzymes[111]^30. FOXO transcription factors are not only involved in
   response to oxidative stress but also on insulin action, both of them
   altered in APOE44-AD network. This is a potential integrative link
   between AD and insulin resistance in APOE44 carriers which is
   increasingly thought to contribute to Alzheimer’s disease[112]^31. Both
   insulin resistance and oxidative stress stimulate back the
   transcriptional activity of FoxO proteins, inducing hyperglycemia and a
   further increased production of reactive oxygen species (ROS) closing
   the vicious circle[113]^29.

   The integration of the results of the genetic analysis with the APOE
   network allowed us to identify a subset of genes that interact with
   APOE to influence disease phenotype in a biologically meaningful
   context. Indeed, although the epistatic interactions do not imply per
   se a specific physical interaction between proteins, our approach of
   limiting their identification to genes belonging to the APOE network
   improved the biological plausibility of our findings and at the same
   time allowed us to focus on a limited sub-network. The present approach
   presents, however, some limitations mainly related to the
   identification of the causative SNPs and genes starting from the
   association results. Specifically, we cannot exclude that the SNPs we
   identified could tag the association of causative variants located in
   nearby genes different from the target ones we used as starting point
   of the association analysis, despite their localization within the
   target gene boundaries. This is a limitation of the “gene-directed”
   strategy we used to circumvent the inherent limitations in detecting
   regions of LD and their associated genes. Fine mapping of risk loci is
   a highly debated issue in genomics and in many cases the functional
   genes are hard to be identified without follow-on experiments designed
   ad-hoc[114]^32,[115]^33. Nevertheless, all the genes in epistasis with
   APOE are closely related to each other in the PIN and are associated to
   the genes in the canonical Notch pathway ([116]Fig. 3B), one of the
   enriched pathway in the APOE44 network. In particular, among the
   epistatic interaction identified, one resulted of particular interest:
   Mastermind-like 3 (MAML3), a key element of Notch signaling which forms
   transcriptionally activating complexes with the intracellular domains
   of Notch itself. The convergence in this Notch-related sub-network
   ([117]Fig. 3B) of APOE and the epistasis genes, as well as of APP and
   PSEN1, genes on which mutations are responsible for the familiar form
   of AD[118]^34, could give a mechanistic hypothesis on how these genetic
   alterations lead to the earlier onset of the disease.

   A link between Notch pathways and AD pathogenesis has been previously
   suggested based mainly on the fact that Notch is the substrate of
   γ-secretase/presenilin[119]^35. In relation to the inflammatory
   processes highlighted in our study, Notch signaling, possibly altered
   with the upstream genetic modifications, could be a central actor in
   view of the supported role of Notch in modulating the microglia innate
   response[120]^36 and in promoting microglia activation[121]^37.

   In conclusion, this systems biology study of the APOE44 role in AD
   evidenced a crucial role of the Notch pathway in mediating the
   mechanism by which APOE4 increases the risk for the development of AD.

Additional Information

   How to cite this article: Caberlotto, L. et al. Integration of
   transcriptomic and genomic data suggests candidate mechanisms for
   APOE4-mediated pathogenic action in Alzheimer’s disease. Sci. Rep. 6,
   32583; doi: 10.1038/srep32583 (2016).

Supplementary Material

   Supplementary Information
   [122]srep32583-s1.pdf^ (633.9KB, pdf)
   Supplementary Table S1
   [123]srep32583-s2.xls^ (280KB, xls)
   Supplementary Table S2
   [124]srep32583-s3.xls^ (218.5KB, xls)
   Supplementary Table S3
   [125]srep32583-s4.xls^ (460.5KB, xls)

Acknowledgments