Transcriptional Dysregulation Study Reveals a Core Network Involving the Genesis for Alzheimer's Disease

bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei

RESEARCH Transcriptional dysregulation study reveals a core network involving the genesis for Alzheimer’s disease Guofeng Meng1,2* and Hongkang Mei1

*Correspondence: [email protected] Abstract 2BT science Inc., No. 24, Tang’an Road, 201203 Shanghai, China Background: The pathogenesis of Alzheimer’s disease is associated with Full list of author information is dysregulation at different levels from transcriptome to cellular functioning. Such available at the end of the article complexity necessitates investigations of disease etiology to be carried out considering multiple aspects of the disease and the use of independent strategies. The established works more emphasized on the structural organization of gene regulatory network while neglecting the internal regulation changes. Methods: Applying a strategy different from popularly used co-expression network analysis, this study investigated the transcriptional dysregulations during the transition from normal to disease states. Results: 97 genes were predicted as dysregulated genes, which were also associated with clinical outcomes of Alzheimer’s disease. Both the co-expression and differential co-expression analysis suggested these genes to be interconnected as a core network and that their regulations were strengthened during the transition to disease states. Functional studies suggested the dysregulated genes to be associated with aging and synaptic function. Further, we checked the evolutionary conservation of the gene co-expression and found that human and mouse brain might have divergent transcriptional co-regulation even when they had conserved gene expression profiles. Conclusion: Overall, our study reveals a profile of transcriptional dysregulation in the genesis of Alzheimer’s disease by forming a core network with altered regulation; the core network is associated with Alzheimer’s diseases by affecting the aging and synaptic functions related genes; the gene regulation in brain may not be conservative between human and mouse. Keywords: Alzhimer’s disease; co-expression; dysregulation; dysregulation divergence

Background Alzheimer’s disease (AD) is a neurodegenerative disorder most prevalent in people over the age of 65 years [1, 2, 3]. As the population ages, AD will impact more people and place an increasing economic burden on society [4, 5]. There is still no effective treatment that prevents or slows the disease progression. A significant challenge is the poor understanding of the etiology of this disease [6, 7, 8]. The progression of AD is associated with the dysregulation of many genes at different regulatory levels from transcriptome to neuronal function [9, 10]. To study such a complex multifactorial disease, integrated and large-scale data are necessary to catch the diverse regulatory interactions [11, 12]. The availability of high-throughput transcrip- bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 2 of 25

tomic sequencing data and clinical annotation in the Accelerating Medicines Partnership-Alzheimer’s Disease (AMP-AD) program (provides an opportu- nity to study the altered transcriptional regulation during the genesis of AD [13, 14, 15, 12]. Co-expression network analysis is useful to infer causal mechanisms for complex diseases [16, 17, 18]. It is based on the assumption that co-expressed genes are usually regulated by the same transcriptional regulators, pathways or protein com- plexes and that the co-regulated genes can be revealed by analysis of the topological structure of co-expression networks [19, 20]. The co-regulated clusters in the network provide the chance to track the affected pathways or biological processes in diseases [21]. One of the most popular strategies is to find the topological structural changes of the co-expression network under different disease states where these changes indicate regulatory dysregulation in the disease [18, 22]. Another way is to associate the subnetwork expression to disease progresses or clinical traits, which can uncover the regulatory components involved in the dysregulation of diseases [23]. Application of co-expression network analysis to AD data has revealed many AD associated genes and pathways [12]. However, the nature of such analyses may bias toward the genes with higher connectivity in a co-expression network. The complexity of AD genesis leads us to extend co-expression analysis to a more detailed investigation of co-expressed genes, especially for the genes with relatively low connectivity, during the disease progression. To understand the etiology of AD, the changes of regulation are supposed to be more essential than the the regulations themselves. We studied the transcriptional dysregulation by evaluating all the gene pair combinations for their co-expression changes, which can be referred as differential co-expression (DCE) analysis. The genes with altered co-expression are indicated in the genesis of AD and their importance can be ranked by the numbers of changed connections. In this study, we collected the RNA-seq expression data for 1667 human brain samples from the AMP-AD program. Differential co-expression analysis indicated 87,539 gene pairs to have significant co-expression changes. Among them, 97 genes, including 10 transcription factors, were found to be dysregulated in AD genesis. Both the co-expression and differential co-expression analysis suggested these genes to take roles as a interconnected core network. In the transition from normal to disease states, the co-expression is strengthened in this network. Functional studies supported this network to be involved in the etiology of AD by directly or indirectly affecting aging, synaptic function and metabolism related genes. We also evaluated their evolutionary conservation in mouse. Although the genomic gene expression profiles are conserved, the co-expression patterns were not in mouse, including the core network, which may indicate transcriptional regulation divergence between human and mouse.

Methods and Materials Data collection, processing and quality control The human expression data were collected from Accelerating Medicines Partnership-Alzheimer’s Disease (AMP-AD, https://www.synapse.org/#!Synapse:syn2580853/wiki/ bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 3 of 25

66722) program compiling with the data access control at http://dx.doi.org/doi:10.7303/syn2580853. The RNA-seq expression data from four projects were used, including (1) ROSMAP; (2) MSBB (3) MayoPilot and (4) MayoBB. Based on the clinical annotation, the samples were grouped as AD and normal samples. In this step, some patients with vague disease status, missing annotation or other disease annotations were filtered out. For the RNA-seq data from each project, the AD and normal samples were sep- arately processed for quality control. We first performed normalization with the tools of edgeR package [47] for the RNA-seq data with only raw counts. Then, the samples were checked for genomic gene expression similarity and samples with in- consistent location in hierarchical clustering and principal component analysis plots, were treated as outliers and removed. Next, the expression data, including both AD and normal samples, from different projects were treated as different batches and adjusted to remove the batch effects using ComBat[48]. The adjusted expression data were further normalized with quantile normalization and evaluated by PCA plots to make sure that the selected samples to have consistent expression profiles and have no clear batch effects among the data from different projects. Then, the resulting expression data are divided into two expression profiles for AD and normal samples, respectively. The mouse and human brain microarray data were collected from GEO database. To minimize the effects of the different microarray platforms, we only selected the data performed with Affymetrix’s platforms. Normalized expression data were down- loaded and used. For each dataset, we performed quality control to make sure the expression profiles of samples had good expression profile consistency with experimental descriptions introduced in the original paper. The batch effects of the data from different experiments were estimated and removed with ComBat. And then the data were combined together. They were further evaluated for expression profiles consistency and the experiment or sample outliers were removed. Finally, the probes of microarry were mapped to gene symbols. For the genes with multiple probes, we selected the ones with maximum expression values. The gene from human and mouse were mapped based on the gene homologous annotation from Mouse Genome Informatics (MGI)(www.informatics.jax.org).

Differential co-expression analysis Using the expression values of different samples as elements of expression vectors, the Spearman’s correlation of all gene pairs were calculated for AD and normal samples, respectively. To evaluate the robustness of calculated correlations, we performed two simulation studies. In the first evaluation, we randomly selected half of the samples and calculated new correlations. We then checked the mean and vari- ance of correlation values under 100 rounds of simulation. The results helped us to understand the stability of observed correlation values under the different sample selection. In another evaluation, the annotation of samples for each gene was shuffled so that the gene pairs had the wrong sample mapping. The new correlations were calculated under 100 rounds of simulations. This simulation help us to evaluate the confidence ranges for the observed correlation values. The correlation differences under disease and normal status were evaluated using the R package DiffCorr [24]. In this step, the correlation values were transformed bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 4 of 25

with Fisher’s transform and z-scores were calculated to indicate the correlation differences. The p-value are calculated by fitting to a Gaussian Distribution. After comparing the efficiency, Benjamini & Yekutieli’s algorithm in R package [49] was implemented to control the false discovery ratio. At a cutoff of adjusted p < 0.01, we select the significantly differentially correlated genes.

Enrichment analysis The gene ontology (GO) annotation of gene lists was performed with the GO enrichment analysis tool David [37] under the default setting. The significantly enriched terms for biological process and cellular components were selected at a cutoff of p < 0.01. When multiple gene lists are available, the GO annotation results are visualized in a heatmap to facilitate comparisons. In this work, we also performed enrichment analysis using an annotated gene list, e.g. DEGs and text-mining annotated genes. For k input genes, the number of genes with annotation is x. For n whole genomic genes, the number of genes with annotation is p. We use Fisher’s exact test to evaluate if the observed x genes result from random occurrences. We use the following R codes to calculate the p-value: > m=matrix(c(x,k-x,p-x,n-k),ncol=2,byrow=T) > p=fisher.test(m,alternative=”greater”)$p.value

Differentially expressed genes in Alzheimer’s disease We performed differential expression analysis to the RNA-seq data collected in above steps from the AMP-AD program. For data from the MSBB project, four brain regions were treated as four independent datasets. Among 7 used datasets, some RNA-seq data had raw counts, e.g. data from MSBB project and we carried out differential expression analysis using edgeR. Other data with normalized expression values were analyzed by log2 transformed t-test. For all the datasets, the DEGs were determined at a cutoff of p < 0.01. To increase the confidences, the DEGs from different datasets were cross-validated with each other and the ones with clear inconsistency, e.g. the DEG list with weak overlap with other DEG lists were filtered out. Then, the selected DEGs were combined together as the DEGs of AD. The differential expression direction were also checked and determined by using the direction supported by the maximum datasets.

Alzheimer’s disease related genes The AD related genes were determined by selecting the ones with reported association with AD in published works, such as the genetics evidences and expression evidences. In this step, we use the gene-disease annotation for AD from IPA (http:// www.ingenuity.com/products/ipa), Metacore (https://portal.genego.com/) and DisGeNet (www.disgenet.org/). We ﬁltered out the low-conﬁdence genes by manually removing the ones with only evidence of expression or those with inconsistency evidences.

Gene co-expression network analysis Gene co-expression network analysis was to ﬁnd the gene clusters or modules with good co-expression similarity. As one of well recongnised implementation, WGCNA was bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 5 of 25

applied to RNA-seq expression data following the protocol provided by the tool developers https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/ Rpackages/WGCNA/ [44]. All the expressed genes were clustered into modules, which were labeled with diﬀerent colors. In each module, the connectivity and module membership of each module gene were calculated to assess the association and importance of genes in the modules.

Results Transcriptional dysregulation in Alzheimer’s diseases To find the dysregulation associated with AD, we evaluated the co-expression changes in the brain of AD patients, which indicate the transcription regulation changes. The whole process is detailed in Figure 1. In the first step (a), four sets of independent human RNA-seq expression data are collected from the AMP-AD program compiling with the data access control policy. The selected samples from different projects were processed and combined together to define the expression profiles of both AD and control subjects (see step (b)). Based on the clinical annotation provided by the data suppliers, 1045 AD samples and 622 control samples are determined and selected (see step (c)). As showed in Additional file 1, these samples have good homogeneity in their transcriptomic expression profiles and there is no clear outliers or batch effects. The mouse expression data were also collected from the GEO database (http://www.ncbi.nlm.nih.gov/geo/), where 931 samples from 20 microarray experiments are selected and processed to construct the mouse brain expression profiles. In the next step (d), the co-expression level, described by Spearman’s correlations, of all gene pairs are calculated for AD and control subjects, respectively. To check the robustness of calculated correlations, we performed random sampling and shuffling to original expression data. We found that the calculated values were tol- erant to the sample selection and were less likely to result from random correlation (see Additional file 2 and Methods). These results suggest the correlation value to be robust enough to study the co-expression changes. In Figure 3(a), we show the plot for the correlation values of all gene pairs and find that the AD and normal samples have consistent co-expression profiles (Spearman’s r = 0.93). Applying the differential correlation analysis method introduced in [24], we found 87,539 out of 163 million gene pairs to have significant co-expression changes in the AD patients at a cutoff of adjusted p < 0.01 (see Additional file 3). Among them, there are 9168 genes with at least one DCE partner (see Additional file 4). Considering the nature of DCE analysis and the strict cutoff, all of the predicted gene pairs are co-expressed in either AD patients or normal people or both at a cutoff of p < 0.01 (see Additional file 3), which confirms that all the dysregulated gene pairs are po- tentially associated with the transcription regulation or co-regulation in the AD or normal status. Similarly, the DCE pairs are supposed to have altered regulation in the AD patients. Therefore, we can call them dysregulated genes. We checked the co-expression status of the 87,539 dysregulated gene pairs. 22,150 gene pairs (25.3%) are co-expressed only in AD samples while 10,707 pairs (12.2%) are co-expressed only in normal samples at p < 0.01. Other gene pairs are co- expressed in both AD and normal samples. Among them, 31,685 pairs (36.2%) bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 6 of 25

have increased co-expression correlation in AD while only 9694 pairs (11.1%) have decreased values. There are also gene pairs with reversed co-expression trend. For example, 6459 negatively correlated gene pairs (7.4%) in normal subjects become positive correlated in AD patients. Vice versa, 6844 gene pairs (7.8%) have the op- posite changes. By summarizing the overall changes, we observed more gene pairs to have increased co-expression correlation in AD (2.6 times that of the gene pairs with decreased co-expression correlation), which suggests strengthened transcriptional regulation in the AD patients.

Dysregulated AD genes In published works, at least 777 expressed genes have been reported to be associated with AD. We found that 479 of them were predicted with at least one dysregulated partner (see Additional file 5)(p = 2.5e − 9). Among them, the MAP1B gene was predicted with the maximum number of partners (365 genes), of which 32 genes were also AD associated genes. We found that the partners of MAP1B had diverse functions and many of them were associated with AD genesis, such as intracellular signaling cascades (42 genes, p = 3.7e − 4), regulation of apoptosis (18 genes, p = 3.4e − 3), neuron and dendrite development (5 genes, p = 4.13e − 3) and RNA metabolic process (19 genes, p = 4.71e − 3). This result confirms MAP1B, as a microtubule associated protein, to have diverse involvement in neuron related biological processes [25, 26, 27, 28] and suggests its important roles in the AD genesis [29]. Another example is the TREM2 gene, which is supported to be involved in the neuroimmunology of AD [30]. We observed 4 partners, including SLA (DCE at p = 3.7e − 10), HCLS1 (p = 2.5e − 10), C3AR1 (p = 8.7e − 10) and FCER1G (p = 1.3e − 9), all of which are associated with inflammation related functions. Among the well studied AD drug targets [31], we find their dysregulation, such as the amyloid precursor protein gene (APP, 136 partners), glycogen synthase kinase 3 beta (GSK3B, 34 partners) and BACE1 (8 partners), suggesting their transcriptional involvement in the genesis of AD. In Table 1, we show 68 dysregulated AD genes, which are selected based on their partner numbers. To study their biological involvement, we studies the enrichment of AD related genes in their partners. We found the partners of 48 dysregulated AD genes not to be enriched with AD genes at a cutoff of p < 0.05, which suggested that these dysregulated AD genes were involved in AD genesis by affecting the genes without clear reports for AD association. We also checked the transcriptional association of 68 dysregulated AD genes. We found 56 genes to be differentially expressed (p = 0.05) in the AD patients and the significance for such enrichment was p = 1.1e−27, which suggested the dysregulated AD genes to be associated with the transcriptional dysregulation even though most of the AD genes are not identified by differential expression analysis in published works. Similarly, we found the partners of 53 dysregulated AD genes to be enriched with the differentially expressed genes (DEGs), which confirms the transcriptional involvement of the dysregulated AD genes. Another investigation was to the aging related genes based on the annotation in our previous work [32]. We found 21 out of 68 dysregulated AD genes to be aging genes and the significance for such enrichment was p = 6.9e − 5. The partner genes of 39 dysregulated AD genes were also enriched with the aging genes at a cutoff of p < 0.05, confirming the association of the aging process with the genesis of AD. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 7 of 25

We also studied the functional involvement of the dysregulated AD genes. In Figure2, we show the functional annotation for the 68 dysregulated AD genes based on Gene Ontology annotation. Consistent with the selection criteria for AD genes, we found these genes to be associated with many AD related functions. Among them, “transmission of nerve impulse” is predicted to be the most enriched term (12 genes, p = 2.3e − 7). In the published works, synaptic dysfunction has been widely reported for its association with AD [33, 34] and our analysis confirms it to be one of the most affected processes. Other AD related terms include “neurological system process” (14 gene, p = 1.54e − 3), “regulation of protein kinase activity” (7 genes, p = 3.5e − 3). In summary, we found the subset of the AD related genes that were transcriptionally dysregulated in AD; these genes were involved the genesis of AD by affecting the AD related pathways or biological processes, e.g. synaptic transmission.

A core network involves the dysregulation of AD Following the widely accepted hypothesis that the hub genes of a network take more essential roles, we assume that the genes with more dysregulated partners will have more essential roles in the etiology of AD [35]. Another assumption is that the dysregulated genes and their partners would participate in the same pathways or biological progresses. Therefore, we can infer the function of dysregulated genes by analysis of their partners [36]. Based on these assumptions, we defined the genes with both transcriptional dysregulation and involvement in the genesis of AD by the following criteria: (1) with more than 50 partners; and (2) to be differentially expressed in AD (p < 0.01, see Additional file 6), which ensures the selected genes to be transcriptionally associated with AD, and their partners to be enriched with AD genes; or (3) reported as AD associated genes and their partners enriched with the differential expressed genes at a cutoff of p < 0.01. In this way, 97 dysregulated genes are selected (see Figure 1(c, d) and Additional file 4). Among them, 64 genes are differentially expressed in AD and 39 genes are reported as AD associated genes. These genes are dysregulated in 14,322 gene pairs with 3681 partners. Of the dysregulated genes, TMEM178A is the most dysregulated gene with 669 partners. We checked the co-expression status between dysregulated genes and their partners and found 85.9% of the gene pairs to have increased co-expression correlation values, which was far more than the observed percentage (66.2%) with non-filtered dysregulated gene pairs. In AD patients, we also observed 3461 gene pairs to be co-expressed only in AD, which was 3.6 times the normal-specific co-expressed pairs (p = 4.6e−67), suggesting that the AD patients have more and strengthened transcriptional regulations. Even though the dysregulated genes were not selected for any co-expression correlation among each other, we observed the 97 dysregulated genes to have stronger co-expression correlation values than random selected genes (see Figure 3(b)). Of 4656 gene pair combinations, 95.7% of them are co-expressed at a cutoff of p < 0.01 and 86.9% have a correlation r > 0.3. The median correlation value is 0.556 (p = 7.7e − 86). We also checked their co-expression changes and found 929 out of 4656 gene pairs to be differentially co-expressed in the transition from normal to AD. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 8 of 25

Using the co-expression and differential co-expression information, we can connect the dysregulated genes as a network (see Figure 3(d)). In this network, 96 out of 97 nodes can have at least one connected co-expression partner at r > 0.3 and 90 nodes can also have at least one dysregulation partner at a cutoff of adjusted p < 0.01. Combining the co-expression and differential co-expression information, 96 out of 97 dysregulated genes are connected with other dysregulated genes. The island of this network is ARF5, which is differentially expressed in AD but not reported with any association with AD. Therefore, we filtered it in Figure 3(d). We further checked the direction of edges and found 870 out of 929 dysregulated edges to have increased co-expression correlation in AD (see Figure 3(c)) while 176 of these regulations were AD-specific. In Figure 3(d), we also show the differential expression information of 96 nodes and find 26 up-regulated and 37 down-regulated genes in AD. The transcriptional regulations are observed between up- and down- regulated genes, including 42.7% of co-expressed edges to have negative correlation values. In the network, we observed 10 transcription factors (TFs): FLI1, NOTCH2, SALL2, STAT3, PPARA, BEX1, ARID1A, ARID1B, AFF1 and PRNP. We checked if these 10 TFs regulated the expression of other dysregulated genes. Due to limi- tation for experimental validation, e.g. human brain sample collection and manip- ulation, we evaluated their regulatory roles by comparing their expression profiles with the dysregulated genes. Compared with 2405 annotated TF genes in the Gene Ontology, we observed that these TF genes were always ranked as the most co- expressed TF genes. In Figure 3(d), we show the example for TMEM178A gene. We found 9 TFs to have strong co-expression correlations, especially for PPARA and STAT3, which were co-expressed with TMEM178A at r = −0.62 and r = −0.603, ranked as the 10th and 16th of the most negatively co-expressed TF genes. Similar results were observed with other dysregulated genes (see Additional file 7). An- other investigation we carried out was to predict the gene-specific regulators using the method introduced in [36]. In this step, we predicted the enriched regulators for each of 97 dysregulated genes using AD and normal expression data as the input expression matrix. In the Jaspar database, only STAT3 has clear TF binding profiles and therefore, we can only predict the transcriptional regulation for STAT3. We found that STAT3 was ranked as the 16th of the most important regulators for 96 dysregulated genes in 474 annotated TFs. In normal and AD samples, STAT3 was predicted to regulate 25 and 31 dysregulated genes, respectively. Overall, these results suggest that the dysregulated TFs may be involved in transcriptional regulation or dysregulation in AD.

Aging, synaptic transmission and metabolism are dysregulated Applying a strategy of guilt by association, we extended the functional study of 97 dysregulated genes to the subnetworks comprising of themselves and their partners. In this step, we used each of 97 dysregulated genes as hub and ex- acted the partner genes to construct the dysregulation subnetwork, which could be treated as the co-regulated unit for further studies. Investigation of these subnetworks suggested them to have overall consistent co-expression changes. Taking the TMEM178A subnetwork as an example, we found 665 out of 669 nodes to have bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 9 of 25

increased co-expression correlations with the hub gene, including 164 gained co- expression in AD. Similar results can be observed with many other dysregulated genes (see Additional file 4). We investigated the association of subnetworks with AD genesis. The first attempt was to check the enrichment of DEGs in AD. We found all the subnetworks (97/97) to be enriched with DEGs at a cutoff of p < 0.01 (see Figure 4(a)). Taking the TMEM178A subnetwork as an example, 413 out of 669 partner genes was differentially expressed in AD and the significance for this enrichment is p = 6.5e − 127 by Fisher’s exact test. By checking the other genes, we found 84 subnetworks to be enriched with DEGs with a high statistical significance of less than p = 1e − 10. Such significant enrichment suggests all the subnetworks to be associated with the genesis of AD at the transcriptional level. Considering the fact that age is the biggest risk factor for AD, we checked the enrichment of aging related genes in each subnetwork which were identified by finding 2862 genes with aging associated expression or DNA methylation in our previous work [32]. For the subnetworks with differentially expressed genes as hubs, we found 53 out of 64 subnetworks to be significantly enriched with aging related genes (see Figure 4(a)). Taking the TMEM178A subnetwork as an example, we found 165 out of 669 genes to be aging related genes (p = 3.2e − 20). Out of 2862 aging genes, 624 genes are also dysregulated in AD. We further evaluated the involvement of dysregulated aging genes in the genesis of AD by checking their differential expression direction in AD and their expression trend in the aging process. As showed in Figure 4(b) 541 dysregulated aging genes have the same expression direction, e.g. the aging related genes with increasing expression levels in the aging process would have up-regulated expression in the AD patients and vice versa. Overall, the analysis results suggest that the aging genes will be associated with the transcriptional dysregulation of the AD genesis. We performed functional annotation using David [37] for each subnetwork. Figure 4(c) shows the enriched biological processes (BPs). The most enriched category is for the synaptic function related terms. One example is the “transmission of nerve impulse” term, which is enriched in 37 subnetworks and that is also the most enriched term for most of the 37 subnetwork. The other related terms include “synaptic transmission” and “cellcell signaling”. In published reports, these BPs have been reported for their involvement in the AD genesis [33, 34]. Another category is the metabolism related terms. Among them, “generation of precursor metabolites and energy” was significantly enriched in 39 subnetworks and “glycolysis” was significantly enriched in 29 subnetworks. The links between metabolism and AD are also supported by the published works [38, 39]. Even as different functional cat- egories, synaptic function and metabolism related terms are usually enriched by the same subnetworks. We also observed other enriched terms and most of them have been reported for association with AD, such as “cation transport” [40], “ATP metabolic process” [41], “learning or memory” [41] and “neuron differentiation”. Using David, we also checked the cellular component (CC) enrichment (see Figure 4(d)). We found neuron, especially synapse related CCs to be the most enriched cellular location. Consistent with BP analysis results, the “synapse part” term was enriched in 46 subnetworks, especially the subnetwork with dysregulated DEGs as hubs, which confirmed the analysis results with BP terms. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 10 of 25

As described above, the dysregulated genes are either DEGs or literature reported AD genes. We found the functional involvement for the two groups of subnetworks to be diﬀerent. The 39 subnetworks with hubs of dysregulated AD genes are less associated with any enriched biological process. For example, the term “transmission of nerve impulse” is enriched in only 8 out of 39 subnetworks while there are 29 out of 64 DEGs subnetworks to be enriched with this term. Similarly, the other AD associated terms are also less likely to be enriched with these 39 subnetworks. However, considering the fact that the dysregulated AD genes are signiﬁcantly associated with the AD related terms (see above section), including synaptic function and metabolism related biological processes, we can assume that the 39 subnetworks with the hubs of dysregulated AD genes are also associated with the enriched terms.

Association with clinical outcomes In the ROSMAP project of AMP-AD program, most samples have been annotated with clinical information. We studied the association of gene expression with three disease relevant clinical traits, e.g. cognitive test scores (cts), braak stage (braaksc) and assessment of neuritic plaques (ceradsc). We did not find any gene to have strong expression correlations (e.g. r > 0.6) with studied traits. The maximum correlation was observed with SLC6A9 at r = −0.388. For all the genes, only 12.3% of them have a clinical association at |r| > 0.15. This result suggests that the clinical outcomes of AD may be affected by combined effects and even beyond the effects of gene expression. In Figure 5, we show the clinical association results for 97 dysregulated genes. We found that the dysregulated genes were always ranked as the most clinical associated genes. Of 97 dysregulated genes, 66 genes have a clinical association of |r| > 0.15 (p = 4.49e − 37). Among them, NCALD, a neuronal calcium binding protein, was associated with the cognition score at r = 0.268. Another investigation is to the combined effects of dysregulated gene pairs. We studied the partial correlations between the dysregulated genes and the clinical traits by controlling the effects of their dysregulated partners. We found that many dysregulated gene pairs had improved clinical association (see Table 2 and Addi- tional file 8). In these dysregulated gene pairs, the clinical association of one gene is improved by considering the gene expression of its partners, which indicates the importance of dysregulation relationship.

Dysregulation divergence in mouse Mice are widely used as a model animal in wet-lab studies for AD. Investigation of the evolutionary conservation of gene regulation can help with the evaluation of experimental studies drawn in mice. Therefore, we collected mouse brain microarray expression data from GEO database. 1583 samples from 24 experiments were identified (see Additional file 9). We removed arrays with poor quality or in- consistent expression profiles and finally, 931 samples from 20 experiments were used. The selected samples were combined together to describe the mouse brain expression profiles. Using gene homologous annotation from Mouse Genome Infor- matics (MGI)(www.informatics.jax.org), 14186 expressed genes were used for bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 11 of 25

co-expression evaluation with the human normal control samples. We first studied the overall gene expression similarity between mouse and human. As showed in Figure6(a), we found human and mouse to have consistent expression profiles with the median Spearman’s correlation among samples at r = 0.69, which indicated the conservation of gene expression profiles between human and mouse. Another investigation was to co-expression of gene pairs. The co-expression correlations of all gene pair combinations were calculated for mouse and human normal samples, respectively. Figure 6(b) showed the co-expression correlation values. Hu- man and mouse had different co-expression profiles and the Spearman’s correlation to co-expression correlations was only r = 0.139. Further, we performed differential co-expression analysis and found 25.0% of gene pairs to have differential co- expression at the cutoff of adjusted p < 0.01. We also checked the co-expression directions and found 34.7% of 10 million co-expressed gene pairs to have inconsis- tent correlation directions, which indicated the divergence of co-expression patterns between human and mouse. Similar results were also observed with the human AD samples (see Additional file 10). To understand the co-expression conservation of AD related genes, we restricted the same analysis to the 87539 differential correlated gene pairs. As shown in Figure 6(c), improved conservation was observed (r = 0.214, p = 6.46e − 266). We further restricted the analysis to 97 dysregulated genes and found the co-expression conservation were improved further (r = 0.31, p = 8.08e − 6) (see Figure 6(d)). Different platforms, e.g. RNA-seq and microarray, may lead to different expression measurements [42, 43]. Therefore, we constructed the human brain expression profiles by collecting the microarray data from GEO database (see Additional file 11). To minimize the influences of different platforms, we only collected data from AffyMetrix’s platform when the mouse expression data were also generated using AffyMatrix’s technology. Finally, 452 samples were selected to describe the human brain expression profiles. As showed in Figure 6(e), we found that the expression profiles measured by microarray and RNA-seq were not completely consistent(r = 0.54) (see Figure 6(e)). Next, we re-evaluated the co-expression conservation using the co- expression profiles calculated from human brain microarray data. The Spearman’s correlation to the co-expression correlations was r = 0.152 (see Figure 6(f)), which still indicated strong dysregulation divergence between human and mouse. In summary, human and mouse may have the divergent co-expression patterns in brain, even though they have the conserved gene expression profiles, which indicates the different transcriptional regulation.

Comparison with established works Based on the assumption that the connectivity in the co-expression network indicates the gene importance, connectivity has widely been used to identify the essential components for a specific biological process. We investigated the association between dysregulation and the connectivity in the co-expression network. We firstly checked the connectivity preference of dysregulated genes by selecting the top 100 dysregulated genes discovered in the above analysis. As shown in Figure 7(a)), we failed to observe any connectivity preference when compared with genomic genes (p = 0.229). Another investigation was to check if the genes with stronger connectivity would be more differentially co-expressed. As shown in Figure 7(b) and bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 12 of 25

Additional file 12, the maximum and median differential correlation did not show strong association with the connectivity, including the most dysregulated ones. Next, we investigated if the dysregulated genes could be identified by co-expression network analysis. We applied WGCNA [44], a popular co-expression network analysis tool, to human brain RNA-seq expression data and 43 modules were predicted. The 97 dysregulated genes were mapped into 22 modules. We found the number of mapped dysregulated genes to a specific module was correlated with its module size (r = 0.76). For example, the BLUE module had the largest module size of 7776 genes and it was also mapped with the maximum number of dysregulated genes (44 genes) (see Additional file 13). Further, we ranked the module members based on their connectivity in the modules. None of 97 dysregulated gene was ranked as the most connected hubs. All these results suggest that the dysregulated genes are not necessary to be discovered by co-expression network analysis. In independent work, the dysregulation of AD has been investigated using microarray data [22], where the dorsalateral perfrontal cortex region from 310 AD patients and 157 normal subjects was measured by microarray. Considering the fact that platforms may affect the analysis results [43], we compared their analysis results using 13606 common expressed genes. The gene pairs from microarray and RNA-seq platforms had overall consistent co-expression profiles (r = 0.49, p = 1.2e − 199) (see Figure 7(c)). Applying the differential correlation test, we found 18.5% of gene pair combinations to be differentially co-expressed, which was far more than the number of predicted dysregulations for AD genesis: that is, 0.05% of all gene pairs for RNA-seq dataset and 0.6% for microarray datasets. This result suggested the dysregulations predicted using microarray and RNA-seq data to be different. To illustrate it, we checked the consistency of predicted dysregulation by DCE analysis for both RNA-seq and microarray data. Figure 7(d) shows the dysregulation z-scores of all gene pairs, which describe the significance of co-expression changes. We found that the overall dysregulation were weakly consistent between RNA-seq and microarray ( Spearman’s r = 0.106). At a cutoff of adjusted p < 0.01, 59,710 and 603,335 gene pairs were predicted to be dysregulated with RNA-seq and microarry data, respectively. Among them, only 1469 gene pairs were shared by two datasets. We also checked the predicted partner number of dysregulated genes from RNA-seq and microarray data and found them to be weakly consistent (r = 0.218) (see Additional file 14).

Discussion Using the RNA-seq data, we studied the association between gene expression correlation and connectivity. We found that the genes with higher connectivity showed stronger co-expression correlations (see Additional file 15(a)). Functional annotation to top 200 of the most connected genes suggested these genes more associated with house-keeping related roles, such as protein location and protein transport (see Additional file 15(b). Considering the fact that AD is associated with loss of neuron related functions, we further checked the connectivity of 2155 brain tissue-specific genes [45]. Even though the brain-specific genes have stronger connectivity than the genomic genes (p = 7.53e − 56), we found that only 14.8% of the the brain-specific genes were ranked in the top 1000 of the most connected genes. Similar results were bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 13 of 25

observed with the AD related genes. All these observations suggest that the genes associated with AD genesis are not required to have strong connectivity and this led us to extend the connectivity-based analysis to a less biased investigation. Therefore, we evaluated the co-expression differences between AD and normal samples for all the gene pair combinations without considering gene connectivity. The genes with differential co-expressed profiles are believed to be dysregulated in the genesis of AD. Using a similar assumption, the genes with the most numbers of dysregulation pairs are supposed to take more essential roles. Finally, 97 dysregulated genes were predicted. In consistent with our hypothesis, the predicted genes failed to show strong connectivity in co-expression network analysis. This was further supported by mapping these genes into the WGCNA analyses results. These results suggest that this work reveals the etiology of AD in an independent and novel view. We performed functional annotation on the 64 dysregulated DEGs and found them to take diverse functions. The limited gene number and the relative indepen- dence of dysregulated genes hindered the efficiency of functional annotation. We extended the functional studies of dysregulated genes to the investigation to their dysregulated partners. We observed 33 of them to be associated with synaptic transmission related functions while synaptic dysfunction is believed to be one of the key characteristics of AD [34]. Another evaluation suggested the dysregulated partners to be enriched with the aging related genes, which explained the effects of age on AD genesis. Overall, the analysis results suggested the predicted dsyregulation to be related to the AD genesis and it indicated the involvement of transcriptional regulation among many reported mechanisms. We defined the transcriptional “dysregulation” by selecting gene pairs with changed co-expression between disease and normal samples. This can take into ac- count different regulatory mechanisms. The gene pairs can be down- or up-stream members in regulatory pathways, i.e. either A regulates B or B regulates A. They can also be co-regulated genes by common upstream pathways. In the context of co-expression or differential co-expression, it is not easy to elucidate the exact information of their relationship. However, by checking the functional annotation to the hub genes and their partners, we always found consistent functional annotation. For example, EIF4EBP2 is reported as a translation initiation repressor and is involved in synaptic plasticity, learning and memory formation [46]. Functional enrichment analysis to its partners suggests them to be associated with functions related to synaptic transmission, which confirms the consistent functional involvement between hub genes and their partners. In differential co-expression analysis, the analysis results are subtle and related to the batch or platform effects of the data from independent projects. We applied several steps to improve the confidence. The most critical one was to collect large- scale RNA-seq data from the AMP-AD program, which minimized the effects of different platforms and batches. Due to the large-scale RNA-seq data, we were able to perform deep evaluation of the quality of expression data by comparing the sample consistency. Another novel finding is the divergent transcriptional regulations between human and mouse. As the widely used animal model, many neurological studies are carried bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 14 of 25

out in mouse. Our ﬁnding put a potential warning to some wetlab conclusions especially these from transcriptional related studies.

Acknowledgment to the Data Contributors The results published here are in whole or in part based on data obtained from the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Target Discovery Consortium data portal and can be accessed at doi:10.7303/syn2580853. The full acknowledgment to the individual data contributors are available in Additional ﬁle 16.

Author’s contributions GM conceived of the study, designed the study, carried out the analysis and drafted the manuscript. XG collected the data, participated in its design and coordination and helped to draft the manuscript. HM collected the data, conceived of the study and participated in its design and drafted the manuscript. All authors read and approved the ﬁnal manuscript.

Competing interests The authors declare that they have no competing interests.

Ethics approval and Consent to Publish Not applicable

Consent for publication Not applicable

Acknowledgements We thank Dr. Xiaoyuan Zhou and Yanjiao Zhou for reading manuscript and giving us many useful suggestions. We are grateful to Dr. David Tattersall for his review of the manuscript.

Funding This work is part of the Deep Dive Project in GSK R&D Shanghai, China.

Author details 1Computational & Modeling Science, PTS China, GSK R&D, Shanghai, Halei 898, 201203 Shanghai, China. 2BT science Inc., No. 24, Tang’an Road, 201203 Shanghai, China.

References 1. Goedert, M., Spillantini, M.G.: A century of alzheimer’s disease. science 314(5800), 777–781 (2006) 2. Lashuel, H.A., Hartley, D., Petre, B.M., Walz, T., Lansbury, P.T.: Neurodegenerative disease: amyloid pores from pathogenic mutations. Nature 418(6895), 291–291 (2002) 3. Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., Ferri, C.P.: The global prevalence of dementia: a systematic review and metaanalysis. Alzheimer’s & Dementia 9(1), 63–75 (2013) 4. Alzheimers, A.: 2015 alzheimer’s disease facts and figures. Alzheimer’s & dementia: the journal of the Alzheimer’s Association 11(3), 332 (2015) 5. Cummings, J.L., Morstorf, T., Zhong, K.: Alzheimers disease drug-development pipeline: few candidates, frequent failures. Alzheimers Res Ther 6(4), 37 (2014) 6. Kumar, A., Singh, A., et al.: A review on alzheimer’s disease pathophysiology and its management: an update. Pharmacological Reports 67(2), 195–203 (2015) 7. Karch, C.M., Goate, A.M.: Alzheimers disease risk genes and mechanisms of disease pathogenesis. Biological psychiatry 77(1), 43–51 (2015) 8. Krstic, D., Knuesel, I.: Deciphering the mechanism underlying late-onset alzheimer disease. Nature Reviews Neurology 9(1), 25–34 (2013) 9. Minati, L., Edginton, T., Bruzzone, M.G., Giaccone, G.: Reviews: current concepts in alzheimer’s disease: a multidisciplinary review. American journal of Alzheimer’s disease and other dementias 24(2), 95–121 (2009) 10. Huang, Y., Mucke, L.: Alzheimer mechanisms and therapeutic strategies. Cell 148(6), 1204–1222 (2012) 11. Norton, S., Matthews, F.E., Barnes, D.E., Yaffe, K., Brayne, C.: Potential for primary prevention of alzheimer’s disease: an analysis of population-based data. The Lancet Neurology 13(8), 788–794 (2014) 12. Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A.A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al.: Integrated systems approach identifies genetic nodes and networks in late-onset alzheimers disease. Cell 153(3), 707–720 (2013) 13. Myers, A.J., Gibbs, J.R., Webster, J.A., Rohrer, K., Zhao, A., Marlowe, L., Kaleem, M., Leung, D., Bryden, L., Nath, P., et al.: A survey of genetic human cortical gene expression. Nature genetics 39(12), 1494–1499 (2007) 14. Webster, J.A., Gibbs, J.R., Clarke, J., Ray, M., Zhang, W., Holmans, P., Rohrer, K., Zhao, A., Marlowe, L., Kaleem, M., et al.: Genetic control of human brain transcript expression in alzheimer disease. The American Journal of Human Genetics 84(4), 445–458 (2009) 15. Zou, F., Chai, H.S., Younkin, C.S., Allen, M., Crook, J., Pankratz, V.S., Carrasquillo, M.M., Rowley, C.N., Nair, A.A., Middha, S., et al.: Brain expression genome-wide association study (egwas) identifies human disease-associated variants. PLoS Genet 8(6), 1002707 (2012) 16. Stuart, J.M., Segal, E., Koller, D., Kim, S.K.: A gene-coexpression network for global discovery of conserved genetic modules. science 302(5643), 249–255 (2003) 17. Oldham, M.C., Langfelder, P., Horvath, S.: Network methods for describing sample relationships in genomic datasets: application to huntingtons disease. BMC systems biology 6(1), 1 (2012) bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 15 of 25

18. Miller, J.A., Horvath, S., Geschwind, D.H.: Divergence of human and mouse brain transcriptome highlights alzheimer disease pathways. Proceedings of the National Academy of Sciences 107(28), 12698–12703 (2010) 19. Horvath, S., Dong, J., Yip, Y.: Connectivity, module-conformity, and significance: Understanding gene co-expression network methods. Technical report, UCLA Technical Report (2006) 20. Villa-Vialaneix, N., Liaubet, L., Laurent, T., Cherel, P., Gamot, A., SanCristobal, M.: The structure of a gene co-expression network reveals biological functions underlying eqtls. PloS one 8(4), 60045 (2013) 21. Langfelder, P., Horvath, S.: Eigengene networks for studying the relationships between co-expression modules. BMC systems biology 1(1), 54 (2007) 22. Narayanan, M., Huynh, J.L., Wang, K., Yang, X., Yoo, S., McElwee, J., Zhang, B., Zhang, C., Lamb, J.R., Xie, T., et al.: Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases. Molecular systems biology 10(7), 743 (2014) 23. McKinney, E.F., Lee, J.C., Jayne, D.R., Lyons, P.A., Smith, K.G.: T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection. Nature (2015) 24. Fukushima, A.: Diffcorr: an r package to analyze and visualize differential correlations in biological networks. Gene 518(1), 209–214 (2013) 25. Villarroel-Campos, D., Gonzalez-Billault, C.: The map1b case: an old map that is new again. Developmental neurobiology 74(10), 953–971 (2014) 26. Moritz, A., Scheschonka, A., Beckhaus, T., Karas, M., Betz, H.: Metabotropic glutamate receptor 4 interacts with microtubule-associated protein 1b. Biochemical and biophysical research communications 390(1), 82–86 (2009) 27. Ishitani, T., Ishitani, S., Matsumoto, K., Itoh, M.: Nemo-like kinase is involved in ngf-induced neurite outgrowth via phosphorylating map1b and paxillin. Journal of neurochemistry 111(5), 1104–1118 (2009) 28. Maurin, J.-C., Couble, M.-L., Staquet, M.-J., Carrouel, F., About, I., Avila, J., Magloire, H., Bleicher, F.: Microtubule-associated protein 1b, a neuronal marker involved in odontoblast differentiation. Journal of endodontics 35(7), 992–996 (2009) 29. Ulloa, L., de Garcini, E.M., Gómez-Ramos,P., Morán,M.A., Avila, J.: Microtubule-associated protein map1b showing a fetal phosphorylation pattern is present in sites of neurofibrillary degeneration in brains of alzheimer’s disease patients. Molecular brain research 26(1), 113–122 (1994) 30. Bouchon, A., Dietrich, J., Colonna, M.: Cutting edge: inflammatory responses can be triggered by trem-1, a novel receptor expressed on neutrophils and monocytes. The Journal of Immunology 164(10), 4991–4995 (2000) 31. Silva, T., Reis, J., Teixeira, J., Borges, F.: Alzheimer’s disease, enzyme targets and drug discovery struggles: from natural products to drug prototypes. Ageing research reviews 15, 116–145 (2014) 32. Meng, G., Zhong, X., Mei, H.: A systematic investigation into aging related genes in brain and their relationship with alzheimers disease. PloS one 11(3), 0150624 (2016) 33. Selkoe, D.J.: Alzheimer’s disease is a synaptic failure. Science 298(5594), 789–791 (2002) 34. Pozueta, J., Lefort, R., Shelanski, M.: Synaptic changes in alzheimers disease and its models. Neuroscience 251, 51–65 (2013) 35. Zhang, B., Horvath, S.: A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology 4(1) (2005) 36. Meng, G., Vingron, M.: Condition-specific target prediction from motifs and expression. Bioinformatics 30(12), 1643–1650 (2014) 37. Sherman, B.T., Huang, D.W., Tan, Q., Guo, Y., Bour, S., Liu, D., Stephens, R., Baseler, M.W., Lane, H.C., Lempicki, R.A.: David knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC bioinformatics 8(1), 426 (2007) 38. Vanhanen, M., Koivisto, K., Moilanen, L., Helkala, E., Hänninen,T., Soininen, H., Kervinen, K., Kesäniemi,Y., Laakso, M., Kuusisto, J.: Association of metabolic syndrome with alzheimer disease a population-based study. Neurology 67(5), 843–847 (2006) 39. Suzanne, M., Tong, M.: Brain metabolic dysfunction at the core of alzheimer’s disease. Biochemical pharmacology 88(4), 548–559 (2014) 40. Berridge, M.J.: Calcium regulation of neural rhythms, memory and alzheimer’s disease. The Journal of physiology 592(2), 281–293 (2014) 41. Liu, C.-C., Kanekiyo, T., Xu, H., Bu, G.: Apolipoprotein e and alzheimer disease: risk, mechanisms and therapy. Nature Reviews Neurology 9(2), 106–118 (2013) 42. Zhao, S., Fung-Leung, W.-P., Bittner, A., Ngo, K., Liu, X.: Comparison of rna-seq and microarray in transcriptome profiling of activated t cells. PloS one 9(1), 78644 (2014) 43. Giorgi, F.M., Del Fabbro, C., Licausi, F.: Comparative study of rna-seq-and microarray-derived coexpression networks in arabidopsis thaliana. Bioinformatics 29(6), 717–724 (2013) 44. Langfelder, P., Horvath, S.: Wgcna: an r package for weighted correlation network analysis. BMC bioinformatics 9(1), 1 (2008) 45. Benita, Y., Cao, Z., Giallourakis, C., Li, C., Gardet, A., Xavier, R.J.: Gene enrichment profiles reveal t-cell development, differentiation, and lineage-specific transcription factors including zbtb25 as a novel nf-at repressor. Blood 115(26), 5376–5384 (2010) 46. Bidinosti, M., Ran, I., Sanchez-Carbente, M.R., Martineau, Y., Gingras, A.-C., Gkogkas, C., Raught, B., Bramham, C.R., Sossin, W.S., Costa-Mattioli, M., et al.: Postnatal deamidation of 4e-bp2 in brain enhances its association with raptor and alters kinetics of excitatory synaptic transmission. Molecular cell 37(6), 797–808 (2010) 47. Robinson, M.D., McCarthy, D.J., Smyth, G.K.: edger: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1), 139–140 (2010) 48. Leek, J.T., Johnson, W.E., Parker, H.S., Jaffe, A.E., Storey, J.D.: The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28(6), 882–883 (2012) 49. Benjamini, Y., Yekutieli, D.: The control of the false discovery rate in multiple testing under dependency. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 16 of 25

Annals of statistics, 1165–1188 (2001)

Figures

(a) AMP-AD program GEO Mouse brain samples

ROSMAP MSBB MayoBB MayoPilot 20 experiments (640 sample) (938 samples) (556 samples) (93 samples)

RNA-seq/microarray data processing Normalization => quality control => removing batch effects => combination => evaluation (b) Clinical annotation

(d) AD-specific gene-gene normal-specific gene- mouse-specific gene- regulation gene regulation gene regulation

(e) DiffCorr (f) DiffCorr Cutoff selection Cutoff selection Regulation divergence Dysregulation of AD between human and genesis mouse

Figure 1 The pipeline to find the transcriptional dysregulation in AD. (a) The human RNA-seq expression data are collected from four projects of AMP-AD program; the mouse brain expression data are collected from 20 microarray experiments in GEO database. (b) The expression data from different datasets are processed, normalized and combined to describe the expression profiles of AD patients, control subjects and mouse. (c) The human brain samples are categorized into AD patients and normal samples based on the clinical annotation. (d) All gene pairs are evaluated for gene-gene regulations by measuring co-expression correlations for all the gene pairs. (e) The dysregulated genes are predicted by differential co-expression analysis. (f) The co-expression conservation of all gene pairs are compared between human and mouse

Tables Additional Files Additional ﬁle 1 Genomic gene expression proﬁle homogeneity evaluation for the samples from independent RNA-seq projects. The sample distribution in the principal component analysis (PCA) plot indicates the expression similarity of the selected samples for AD (a) and normal samples (b), respectively.

Additional file 2 Evaluation of the co-expression correlations by randomly sampling (a) and shuffling (b). (a) Half of the sample are randomly selected to calculate the new correlations. (b) All the genes are shuffled with random samples annotation so that the gene pairs have the wrong sample mapping.

Additional ﬁle 3 The 87,539 dysregulated gene pairs between AD and normal

Additional ﬁle 4 All the dysregulated genes with at least one partner. Other annotation including diﬀerential expressed in AD samples, aging related genes, AD genes, connectivity in co-expression network.

Additional ﬁle 5 The Alzheimer’s disease related genes collected by text mining to the published works. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 17 of 25

regulation of cellular PSENEN PRKCE YWHAB PRNP PAK1 INPP5D PRKACB CHRM2 catabolic process AKT1S1 CHRM4 -log10(p-value) regulation of G-protein coupled receptor protein 8 BECN1 phosphate metabolic aging process signaling pathway regulation of kinase activity 6 PPARA regulation of anion transport regulation of SLC1A2 phosphorylation copper ion 4 PGAM1 apoptosis homeostasis

TGFBR2 2 programmed cell LRP1 APLP2 cell-cell signaling response to nutrient death regulation of levels regulation of epidermal growth catabolic process GABRB2 CLU RTN3 factor receptor MAP1B activity GABRG3 VDAC1 NEDD4 membrane learning GABRA3 organization APP synaptic transmission STX2 gamma-aminobutyric learning or memory SYNJ1 endocytosis acid signaling pathway GRM8 CHMP5 neuron projection neuron recognition morphogenesis AMPH neurological system GABRG2 neuron development process GAS7 CNTNAP2

vesicle-mediated transmission of STAT3 UCHL1 microtubule-based nerve impulse transport transport synaptic vesicle neuron differentiation endocytosis axon cargo transport APBA1 SCN1A

Figure 2 Functional invovlement of dysregulated AD genes. 68 dysregulated AD genes were annotated for their functional involvement based on the Gene Ontology annotation; the colors of GO terms indicated the enrichment signiﬁcance.

Additional ﬁle 6 The diﬀerentially expression genes in AD patients.

Additional ﬁle 7 The co-expression between four TF genes and dysregulated genes. The solid lines indicate the co-expression correlation distribution between dysregulated gene and 2045 TF genes; the dash line indicate the co-expression correlation between dysregulated genes and the genomic genes; the legend shows the TF genes with maximum co-expression correlation with dysregulated genes.

Additional ﬁle 8 The association between dysregulated genes and clinical traits.

Additional ﬁle 9 The used microarray data for mouse brain.

Additional ﬁle 10 Mouse may have divergent co-expression proﬁle with human AD samples.

Additional ﬁle 11 The used microarray data for human brain.

Additional ﬁle 12 The association between dysregulation and connectivity in the co-expression network. The y-axis shows the median z-score of the diﬀerential co-expression.

Additional ﬁle 13 The dysregulated genes and the WGCNA analysis results.

Additional ﬁle 14 The partner number of dysregulated genes predicted using RNA-seq and microarray data.

Additional ﬁle 15 The association between connectivity and co-expression correlation. (a) the genes with higher connectivity are always the genes with higher co-expression correlation. (b) Functional annotation to the top 200 genes with highest connectivity. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 18 of 25

Additional ﬁle 16

The full acknowledgment to the individual data contributors. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 19 of 25

(a) r=0.935 (b) All gene pairs Dysregulated gene pairs Density − 0.5 0.0 0.5 1.0 Coexpression Correlation(Normal) 0.0 0.5 1.0 1.5 2.0

−1.0−0.5 0.0 0.5 1.0 −0.5 0.0 0.5 1.0 Coexpression Correlation(AD) Correlation (c) (e) increased correlation in AD STAT3 SALL2 TF genes decreased correlation in AD All genes PPARA Density Correlation(AD) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 − 1.0 0.5 0.0 0.5 1.0

−1.0−0.5 0.0 0.5 1.0 −0.5 0.0 0.5 Correlation(Normal) Co−expression correlation

Node color Edge Color APLP2 NOVA1

0.6 ● CSF3R 8 CHL1 0.4 SUCLA2 ● PFKFB3 FLI1 GABRG3 PRNP

PPP3CB-AS1 SYNJ1 MAP1B 0 ● GABRA3 6 ARHGAP20 PFDN2 AMPH GABRG2 -0.4 DOCK3 MAP2 ● PRKCE BNIP3 GRM8 CNTNAP2 NSF PRKACB -0.6 SLC25A14 ● 4 HS6ST3 VDAC1 SLC35F1 YWHAB CHRM4 AFF1 PAK1 CD83

STAT3 ● -0.8 SLC6A7 RTN3 RIN2 Fold change SLCO2B1 TGFBR2PGK1 INPP5D of DEG 2 CXCR6 SERPINI1 PARP4 FRMD6 Node size TMEM178A APP BEX1 GARS FN1 650 dysregulation AP1S1 z-score NCALD VAT1LPITHD1CDC42EP4 ARPC5L CALY ENO2 TAGLN3 PPARA ATP1A2 SALL2 (d) HN1 ASNS ADCYAP1R1 EBP PGAM1 NOTCH2 GDAP1L1 450 TPI1 EIF4EBP2 C14orf2 Edge width AP2S1 PPIA PTPRB ARID1A RAPGEF4 0 NDUFA1SHC3 ABCB1 ASXL2 CAMLG UTRN 0.2 LAMC1 GSTO1 FRMPD3 LRP1 250 0.4 ARF5 CLTA 0.6 PSMD4 NDUFB1 0.8 NDUFS3 COX6B1 SLC1A2 Co-expression 50 correlation SCG3 ARID1B No. partners

Figure 3 A core network involving the dysregulation of AD. (a) The co-expression proﬁles of the genomic genes are highly similar between human AD and normal samples (r = 0.935); (b) The gene co-expression correlations among dysregulated genes (red dash line) and genomic genes (blue solid line), which indicates that most of the dysregulated genes interact with each other; (c) Majority of the dysregulated genes have increased correlations (both direction) in the transmit from normal to AD; (d) 96 (out of 97) dysregulated genes can be interconnected as a regulatory network based on co-expression and diﬀerential co-expression information. (e) TMEM178A expression is negatively correlated with dysregulated transcription factors, among which PPARA is ranked as the most negatively correlated transcription factors. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 20 of 25

(a) (b) Enriched with aging genes, AD gene and DEGs ● Others ● ● ● ●● ● ● ●● ● ●●● ● ● ●●● ●● ● ● ●● ● ●● ●●● ●●● ● ●● ● ● ● ●● ● ● ● ● ●● 80 100 120 140

Correlation with ages ●●● ● ●●●●● ●●● ● ● ● ●● ●● ● ● ● ●● Enrichment of DEGs/ − log10(p) ● ● 10 ● ● ● 8 ● 6 − 0.6 0.2 0.0 0.2 0.4 0.6 4 2

0 20 40 60 0 0 5 10 15 20 −0.6−0.4−0.2 0.0 0.2 0.4

Enrichment of Aging genes/−log10(p) Enrichment of AD genes/ − log10(p) Fold changes of differential expression

(c)

AD.genes 6 16 9 9 11 7 6 11 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 3 0 0 0 0 0 0 6 9 9 4 0 0 3 8 0 0 0 0 6 0 10 8 0 4 0 0 0 0 0 5 4 0 0 0 0 0 6 0 0 6 4 0 0 7 5 5 5 GO:0006820~anion transport 8 7 0 3 0 0 4 4 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 6 5 3 0 0 2 3 3 3 0 0 4 0 5 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 2 0 0 0 0 0 3 3 2 0 2 GO:0007214~gamma−aminobutyric acid signaling pathway 8053 0 23 0 0 443041 0 13 0 0 0 19 0 0 0 0 18 0 0 26 0 4226 0 2021 0 0 0 0 0 0 0 1916 0 0 0 0 11 0 0 0 0 0 0 0 39 0 0 0 0 0 0 0 37 0 0 10 9 3645 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 15 0 0 0 16 0 0 0 0 0 0 0 GO:0007242~intracellular signaling cascade 5 5637231718 0 0 0 0 0 0 0 0 0 1830 0 0 0 0 0 0 0 0 0 19 0 0 0 0 0 0 0 0 0 111313 0 0 0 0 0 0 0 0 0 9 0 0 28 0 0 0 0 17 0 1924 0 0 0 0 283920 0 0 17 0 1514 9 0 0 0 0 9 0 15 0 0 0 1311 0 0 0 0 0 0 12 0 0 0 0 0 GO:0008104~protein localization 151110 9 9 6 19121311 6 14 0 0 0 0 6 6 4 0 0 4 11 5 10 0 0 6 0 0 0 0 0 5 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 6 0 0 0 7 8 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 5 0 4 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 GO:0000904~cell morphogenesis involved in differentiation 4 0 1410 9 9 7 22151813 9 18 0 0 0 0 6 6 4 7 8 0 12 5 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16 0 0 0 16 0 8 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 6 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GO:0000902~cell morphogenesis 1814111010 8 22151211 7 12 0 0 0 0 0 0 0 6 6 5 8 5 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 9 0 0 5 0 0 0 11 0 0 8 4 0 0 0 0 0 0 5 0 6 0 5 0 0 0 4 0 5 6 5 0 5 0 0 0 0 0 0 0 0 GO:0031175~neuron projection development 171410 9 8 7 20121311 8 13 0 0 0 0 0 0 0 6 6 4 7 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 8 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 5 0 6 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GO:0032990~cell part morphogenesis 3 171410 9 8 7 19121311 7 13 0 0 0 0 0 0 0 6 6 4 7 4 9 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 8 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 5 0 6 0 4 0 0 0 0 0 0 5 0 0 4 0 0 0 0 0 0 0 0 GO:0048858~cell projection morphogenesis 151210 9 8 7 19121211 7 12 0 0 0 0 4 0 3 5 6 4 7 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 8 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 6 0 4 0 0 0 0 0 0 5 4 4 4 0 0 0 0 0 0 0 0 GO:0048812~neuron projection morphogenesis 151010 9 8 6 1811 9 9 6 9 0 0 0 0 4 0 3 0 0 4 7 4 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 5 0 4 0 0 0 0 0 0 0 4 4 4 0 0 0 0 0 0 0 0 GO:0007409~axonogenesis 2 151110 9 8 6 1912 9 9 6 10 0 0 0 0 4 0 3 0 0 4 8 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 5 0 4 0 0 0 0 0 0 0 4 4 4 0 0 0 0 0 0 0 0 GO:0048667~cell morphogenesis involved in neuron differentiation 1412 8 6 0 5 1011 0 0 0 0 0 0 0 0 3 0 0 4 4 0 0 0 0 0 0 5 4 0 0 0 0 0 0 0 4 0 0 4 3 3 0 0 0 0 0 0 0 0 7 0 0 0 7 5 5 0 5 0 4 0 3 8 0 0 0 0 0 0 0 0 4 0 0 0 5 0 3 0 0 0 0 0 0 4 0 4 0 8 5 4 4 0 0 4 0 GO:0007611~learning or memory 1 1410 8 8 0 0 1312 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 3 8 0 0 0 0 5 6 5 6 0 5 3 0 7 0 6 0 0 0 0 0 0 4 0 0 0 0 0 3 0 0 0 0 0 0 4 5 5 0 4 4 0 4 0 0 0 0 GO:0050804~regulation of synaptic transmission 1410 8 8 0 0 1312 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 8 0 0 0 0 0 6 5 6 0 5 3 0 7 0 6 0 0 0 0 0 0 4 0 0 0 0 0 3 0 0 0 0 0 0 4 5 5 0 4 4 0 4 0 0 0 0 GO:0051969~regulation of transmission of nerve impulse 6 4 4 4 5 5 9 6 0 0 3 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 7 0 5 0 3 0 3 0 4 3 4 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 3 0 4 0 0 0 3 0 3 3 0 0 0 0 GO:0048489~synaptic vesicle transport 0 24191914151333222011 8 18 0 0 0 0 0 0 0 8 0 5 11 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 18 0 0 0 0 0 14 0 0 8 5 0 5 15 0 9 12 7 9 0 9 0 6 0 8 6 8 0 8 0 0 6 6 9 7 8 8 7 7 8 8 7 8 0 0 0 0 GO:0030182~neuron differentiation 22171611121029191511 7 15 0 0 0 0 5 0 0 8 7 5 9 5 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 5 0 0 14 0 0 0 0 0 11 0 0 7 4 0 0 13 0 0 10 0 0 0 7 0 0 5 6 0 6 0 5 0 0 0 0 8 6 6 6 6 6 0 7 0 6 0 0 7 5 GO:0048666~neuron development AD.genes 0 3 0 0 5 4 5 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 3 3 3 0 0 0 4 3 2 0 2 2 0 0 0 0 0 2 0 0 2 2 0 3 2 2 0 2 0 GO:0006107~oxaloacetate metabolic process 0 16 9 101313201612 8 0 0 7 0 8 0 0 0 0 0 0 5 10 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 5 4 0 0 0 0 0 5 0 0 1610 9 10 0 0 0 0 0 0 0 0 0 0 0 0 13 6 13111010 5 5 8 8 7 0 0 0 0 0 0 0 0 7 0 0 0 6 0 0 0 7 0 0 5 GO:0044271~nitrogen compound biosynthetic process No 0 8 0 6 7 8 9 10 7 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 7 0 0 0 8 5 0 4 0 0 0 0 0 0 0 0 6 5 7 6 6 5 5 4 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 3 0 0 0 0 GO:0006754~ATP biosynthetic process Yes 0 8 0 6 7 8 9 10 8 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 3 0 0 10 5 5 4 10 5 0 4 0 0 0 0 0 0 0 0 6 5 7 6 6 5 5 4 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 GO:0009142~nucleoside triphosphate biosynthetic process 0 8 0 6 7 8 9 10 8 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 9 5 4 4 9 5 0 4 0 0 0 0 0 0 0 0 6 5 7 6 6 5 5 4 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 3 0 0 0 0 GO:0009206~purine ribonucleoside triphosphate biosynthetic process 0 8 0 6 7 8 9 10 8 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 9 5 4 4 9 5 0 4 0 0 0 0 0 0 0 0 6 5 7 6 6 5 5 4 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 3 0 0 0 0 GO:0009145~purine nucleoside triphosphate biosynthetic process 0 8 0 6 7 8 9 10 8 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 9 5 4 4 9 5 0 4 0 0 0 0 0 0 0 0 6 5 7 6 6 5 5 4 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 3 0 0 0 0 GO:0009201~ribonucleoside triphosphate biosynthetic process 9 9 0 6 7 8 9 11 7 5 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 8 5 0 0 0 0 0 0 0 0 0 0 7 5 8 7 6 6 5 5 5 5 4 3 0 5 3 0 0 0 0 0 0 0 0 4 0 0 0 4 0 0 0 GO:0046034~ATP metabolic process 9 9 0 6 7 8 9 11 9 7 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 10 6 5 5 10 5 0 0 0 0 0 0 0 0 0 0 7 5 8 7 6 6 5 5 5 5 4 0 0 5 0 0 0 0 0 0 0 0 0 4 0 0 0 4 0 0 0 GO:0009144~purine nucleoside triphosphate metabolic process 9 9 0 6 7 8 9 11 8 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 5 0 4 9 5 0 0 0 0 0 0 0 0 0 0 7 5 8 7 6 6 5 5 5 5 4 0 0 5 0 0 0 0 0 0 0 0 0 4 0 0 0 4 0 0 0 GO:0009199~ribonucleoside triphosphate metabolic process 9 9 0 6 7 8 9 11 8 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 5 0 4 9 5 0 0 0 0 0 0 0 0 0 0 7 5 8 7 6 6 5 5 5 5 4 0 0 5 0 0 0 0 0 0 0 0 0 4 0 0 0 4 0 0 0 GO:0009205~purine ribonucleoside triphosphate metabolic process 0 0 0 0 7 5 9 0 0 0 0 0 5 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 5 4 0 11 0 0 4 0 0 0 0 0 0 0 0 13 0 0 0 0 4 0 0 0 0 0 0 0 5 3 0 0 4 6 4 3 0 0 0 0 0 0 7 0 5 0 GO:0045333~cellular respiration 0 6 5 5 6 6 7 5 0 0 0 0 6 5 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 11 6 0 0 0 0 0 0 0 0 0 0 13 6 6 6 5 4 4 4 4 4 0 0 0 0 3 0 0 4 4 0 3 0 0 0 0 0 3 5 0 0 0 GO:0006119~oxidative phosphorylation 0 6 4 4 5 5 4 4 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 3 0 3 0 0 0 0 0 0 0 0 6 5 5 5 5 4 4 4 3 3 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 3 3 0 0 0 GO:0015985~energy coupled proton transport, down electrochemical gradient 0 6 4 4 5 5 4 4 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 3 0 3 0 0 0 0 0 0 0 0 6 5 5 5 5 4 4 4 3 3 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 3 3 0 0 0 GO:0015986~ATP synthesis coupled proton transport 0 6 4 4 5 5 6 5 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 5 0 0 0 4 0 0 0 0 0 0 0 8 6 5 5 5 4 4 4 3 3 0 0 0 4 3 0 0 4 0 0 0 0 0 0 0 3 3 4 0 0 0 GO:0006818~hydrogen transport 0 6 4 4 5 5 6 5 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 5 0 0 0 4 0 0 0 0 0 0 0 8 6 5 5 5 4 4 4 3 3 0 0 0 4 3 0 0 4 0 0 0 0 0 0 0 3 3 4 0 0 0 GO:0015992~proton transport 2515161620192420 0 0 0 0 8 5 9 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17 9 6 0 11 5 0 0 0 1013 0 251014151410 5 141312 8 8 9 1310 5 7 111211 9 5 7 10 5 121116 7 10 7 GO:0006091~generation of precursor metabolites and energy 9 0 6 8 7 7 8 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 3 5 5 5 0 0 7 6 6 5 6 6 5 3 0 3 4 4 0 4 3 4 5 4 6 6 5 5 5 4 GO:0016052~carbohydrate catabolic process 9 0 6 8 7 7 8 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 3 5 5 5 0 0 7 6 6 5 6 6 5 3 3 3 4 4 0 4 3 4 5 4 6 6 5 5 5 4 GO:0044275~cellular carbohydrate catabolic process 8 0 6 8 7 7 8 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 6 3 5 5 5 0 0 7 6 6 5 6 6 4 3 3 3 4 4 0 4 3 4 5 4 6 6 5 5 5 4 GO:0046164~alcohol catabolic process 8 0 6 8 7 7 8 9 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 4 0 6 3 5 5 5 0 0 7 6 6 5 5 6 4 3 3 3 4 4 3 4 3 4 5 4 6 6 5 5 5 4 GO:0006096~glycolysis 8 0 6 8 7 7 8 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 6 3 5 5 5 0 0 7 6 6 5 6 6 4 3 3 3 4 4 3 4 3 4 5 4 6 6 5 5 5 4 GO:0006007~glucose catabolic process 8 0 6 8 7 7 8 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 6 3 5 5 5 0 0 7 6 6 5 6 6 4 3 3 3 4 4 3 4 3 4 5 4 6 6 5 5 5 4 GO:0019320~hexose catabolic process 8 0 6 8 7 7 8 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 6 3 5 5 5 0 0 7 6 6 5 6 6 4 3 3 3 4 4 3 4 3 4 5 4 6 6 5 5 5 4 GO:0046365~monosaccharide catabolic process 4331242415123730 0 0 11 0 0 0 0 0 7 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 6 0 0 0 0 0 0 15142414 9 9 11192515 0 6 12 0 15 9 12 0 0 0 0 0 0 0 0 0 0 0 0 1110 9 7 17121310 9 101312 GO:0007267~cell−cell signaling 3527212113113126 0 0 8 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 6 5 12 0 0 0 0 0 14131913 9 8 8 14201312 5 11 0 13 7 11 5 6 5 0 5 4 0 6 0 5 7 0 9 10 8 6 141011 9 8 9 11 9 GO:0007268~synaptic transmission 4029232216113527 0 0 9 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 6 5 16 0 0 0 0 0 1414211410 8 8 16241414 5 11 0 15 8 12 7 6 0 0 5 0 0 7 0 5 7 0 9 10 9 7 15111110 9 1012 9 GO:0019226~transmission of nerve impulse 45342514191525202211 0 0 0 7 12 0 10 0 0 0 0 8 0 0 0 13 7 161214 6 11 7 9 9 9 1211 0 0 0 0 0 0 0 0 0 7 9 0 25121313301912 0 2111 0 6 7 15291818 6 11 8 131210 0 0 7 0 7 0 0 7 9 8 13 9 14 0 8 0 111013 8 0 7 12 8 GO:0016192~vesicle−mediated transport 7144283024194434 0 0 0 0 0 0 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13282916 0 0 0 24281619 8 151127181210 0 0 0 0 0 14 8 9 0 13 0 0 11 8 0 181515 9 17111810 GO:0006811~ion transport 5130171617133126 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1617 9 0 0 0 16211013 7 0 9 1511 9 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 131111 0 0 0 11 0 GO:0006812~cation transport 2514 9 9 7 6 1613 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 7 9 4 0 0 0 7 11 6 0 0 0 4 7 0 4 0 0 0 0 0 0 0 0 4 4 0 0 0 0 0 0 6 5 0 0 0 0 5 4 GO:0006813~potassium ion transport 3723141415112419 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 0 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1314 7 0 0 0 1118 8 12 7 9 9 13 9 8 8 0 0 0 0 0 7 0 5 5 8 0 0 5 0 0 9 7 8 0 7 0 7 5 GO:0015672~monovalent inorganic cation transport 4227221920152821 0 0 0 0 0 0 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1519 9 0 0 0 1722 0 15 7 13111613 7 9 8 7 0 0 0 0 0 0 0 0 0 0 0 0 0 131010 0 10 6 10 0 GO:0055085~transmembrane transport TMEM178A(669) ASNS(452) TGFBR2(243) ABCB1(208) LAMC1(187) AP2S1(150) UTRN(433) ASXL2(301) BNIP3(405) SCG3(208) FRMPD3(123) SLC1A2(271) VAT1L(121) CHRM4(52) CAMLG(188) ARID1B(275) PRKCE(94) PRNP(60) CHL1(53) RAPGEF4(148) SHC3(148) NCALD(65) SLC35F1(242) GABRG3(87) MAP1B(364) SLCO2B1(194) GSTO1(66) DOCK3(195) AMPH(141) NSF(139) RTN3(60) APP(136) PAK1(62) GABRG2(126) VDAC1(83) CSF3R(87) HS6ST3(151) LRP1(126) PRKACB(76) ARHGAP20(64) GRM8(58) GABRA3(65) YWHAB(94) CNTNAP2(66) APLP2(94) ARID1A(55) PGAM1(300) INPP5D(96) GARS(106) CDC42EP4(71) MAP2(435) SUCLA2(164) SYNJ1(200) SERPINI1(153) PGK1(541) TPI1(237) TAGLN3(113) CD83(196) ADCYAP1R1(281) FRMD6(109) ENO2(59) ATP1A2(70) RIN2(61) SALL2(318) NOVA1(432) STAT3(182) CXCR6(265) BEX1(69) PITHD1(169) PPIA(106) HN1(148) SLC25A14(118) PPARA(83) EBP(80) NDUFS3(116) ARF5(95) COX6B1(70) PSMD4(91) AP1S1(53) C14orf2(181) PFDN2(81) PFKFB3(103) CLTA(81) FN1(131) NDUFB1(120) NDUFA1(141) PTPRB(92) FLI1(86) AFF1(82) EIF4EBP2(120) PARP4(103) CALY(125) ARPC5L(86) PPP3CB − AS1(140) GDAP1L1(68) SLC6A7(156) NOTCH2(83)

(d)

AD.genes 6 1415121610 0 3729172015124931221421 0 9 0 0 0 192210 0 7 0 6 8 10 9 1310 9 8 241114 9 6 11 6 1410 9 222218 9 12 0 0 0 0 0 0 11 0 0 0 0 0 7 0 15 7 0 0 0 0 0 0 0 0 9 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12 8 GO:0030054~cell junction 43484147 0 501411004780474219212975581200 0 0 0 0 0 7129 0 25 0 0 24 0 0 0 243822107334932 0 32 0 5233521001091104556 0 0 0 0 0 0 0 0 0 5541672423832836 0 0 0 0 0 0 0 39 0 0 0 0 19 0 0 0 29 0 0 0 0 0 0 0 0 0 0 60 0 GO:0005886~plasma membrane 5 30353034 0 3399723654333513989543978 0 0 0 0 0 505020 0 1918 0 1823 0 3619231672253525 0 19 0 3923387475753138 0 20 0 0 0 0 0 0 0 38264419165322 0 0 0 0 0 0 0 0 26 0 3212 0 0 0 0 0 20 0 25 0 0 0 0 0 0 0 0 3816 GO:0044459~plasma membrane part 12 9 111617154233182518 9 39322319 0 6 0 5 12 8 25191210 8 11 7 11131211 0 7 5 17 9 12 7 9 0 0 0 6 9 1217 0 0 0 6 6 0 0 6 0 0 11 0 9 0 0 0 0 0 0 0 0 0 0 0 0 6 5 0 0 0 0 0 0 0 0 0 9 5 0 0 0 9 9 1210 7 9 9 9 GO:0043005~neuron projection 4 14151319131432291723161338302014 0 0 8 0 10 0 16211210 6 7 6 7 10 9 15 8 10 7 271216 9 7 9 7 8 5 0 0 0 0 0 0 5 5 5 0 0 0 10 8 0 0 0 0 0 0 0 0 0 0 0 0 0 6 7 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 5 6 5 10 5 GO:0044456~synapse part 20191720151843382129211849362521 0 0 8 5 13 0 20241410 9 10 8 111310151113 8 30151812 8 12 7 9 6 7 0 13 0 0 9 0 6 5 0 0 0 10 8 0 0 0 0 0 0 0 0 0 0 0 0 0 7 8 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 8 0 7 7 11 6 GO:0045202~synapse 0 0 0 0 8 8 11 0 0 0 0 0 0 0 0 1012 0 9 8 12 7 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 6 7 6 5 5 7 0 6 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 GO:0005759~mitochondrial matrix 3 0 0 0 0 8 8 11 0 0 0 0 0 0 0 0 1012 0 9 8 12 7 0 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 6 7 6 5 5 7 0 6 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 GO:0031980~mitochondrial lumen 9 0 0 0 121123 0 0 0 0 0 0 0 0 1636121312211219 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 22 0 0 9 8 0 6 11191414 0 0 0 14 0 0 0 0 0 0 0 6 0 0 0 0 0 0 12 0 0 0 0 0 0 0 6 0 0 10 0 7 0 0 0 0 0 0 GO:0031967~organelle envelope 2 9 0 0 0 121123 0 0 0 0 0 0 0 0 1636121312211219 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 22 0 0 9 8 0 6 11191414 0 0 0 14 0 0 0 0 0 0 0 6 0 0 0 0 0 0 12 0 0 0 0 0 0 0 6 0 0 10 0 7 0 0 0 0 0 0 GO:0031975~envelope 0 0 0 0 242034 0 0 0 0 0 50 0 0 244818252041202622 0 161011 0 0 0 0 0 0 0 0 0 0 0 0 0 12 0 0 0 0 0 0 37 0 0 221410111926232435 0 0 0 0 0 0 0 1610 8 0 0 0 0 0 0 0 20 0 0 0 0 0 0 0 0 0 0 2017 0 17 0 0 0 0 0 GO:0005739~mitochondrion 9 10 0 10151322 0 0 14 0 0 0 0 0 1836131815281417 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 22 0 0 1310 9 8 14191315 0 0 0 14 0 0 0 0 10 0 0 0 0 6 0 0 0 0 11 0 6 0 0 0 0 0 0 0 0 1210 7 0 0 0 0 13 0 GO:0044429~mitochondrial part 1 8 8 0 8 111016 0 0 0 0 0 0 0 0 1525101310201213 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 15 0 0 9 8 6 5 10151111 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 7 0 0 0 0 0 0 GO:0005740~mitochondrial envelope 8 0 0 8 111016 0 0 0 0 0 0 0 0 1525101310201213 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 15 0 0 9 8 6 5 9 141111 5 0 0 10 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 7 0 0 0 0 0 0 GO:0031966~mitochondrial membrane 0 2115151719213729142714 0 5639 0 2455151712301827 0 0 15 0 0 0 0 11 0 0 0 0 0 0 0 0 0 121512 0 0 0 0 0 3516 0 14 0 11 0 0 241821 0 0 0 0 0 0 0 0 14 9 0 0 0 12 0 0 0 0 0 0 8 0 0 0 0 11 9 15 0 16 0 12 0 0 0 0 2310 GO:0031090~organelle membrane 7 0 6 7 0 9 0 0 0 0 0 0 0 0 0 132110 9 6 1710 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 14 0 0 0 7 6 4 8 11 9 0 27 0 0 10 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 7 0 5 0 0 0 0 0 0 GO:0005743~mitochondrial inner membrane AD.genes 8 0 7 8 0 1014 0 0 0 0 0 0 0 0 142310 9 6 1810 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 15 0 0 0 7 6 4 9 1210 0 0 0 0 10 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 5 0 0 0 0 0 0 GO:0019866~organelle inner membrane No 5 8 7 11 7 6 1413 6 9 9 6 231812 7 0 0 6 0 0 0 10 9 5 5 4 0 3 4 5 6 10 4 5 4 14 6 9 5 0 5 3 5 4 0 0 0 0 0 0 4 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 GO:0045211~postsynaptic membrane 9 0 5 9 10102314 8 13 9 4 20151114 0 0 0 0 0 4 10 8 4 6 4 5 3 5 7 4 6 0 5 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 4 GO:0030424~axon Yes 6 0 6 9 7 7 1413 7 11 7 5 1613 9 7 0 4 0 0 7 5 11 9 7 7 5 7 6 8 6 6 7 0 0 0 9 5 7 4 5 0 0 0 4 5 8 8 0 0 0 5 0 0 0 4 7 6 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 0 0 0 0 0 0 0 0 0 5 0 0 0 0 7 5 9 6 5 6 0 5 GO:0030425~dendrite 13111216171746371926191144372519 0 0 0 0 0 0 271912 0 8 11 7 111312 0 0 0 6 0 1012 8 9 0 0 0 9 142131 0 0 0 0 0 0 0 9 15 0 14 0 0 0 17 7 0 0 0 0 0 0 0 0 0 0 6 10 0 0 0 0 0 0 0 0 9 7 0 0 0 0 9 1410 7 9 15 9 GO:0042995~cell projection 1718 0 24282450421831 0 1384622726 0 0 1715291636 0 12191613 0 0 0 1723 0 0 0 44 0 2012131515 0 0 0 0 0 0 0 0 152012 0 0 0 2523 0 0 0 0 0 0 0 0 0 1113 0 0 10 0 0 1812 0 0 0 0 0 0 0 1512 0 242319 0 2114 0 0 0 0 GO:0005829~cytosol 0 0 0 4 3 2 0 0 0 3 0 0 4 4 3 0 0 0 0 0 3 0 0 0 2 2 0 0 0 0 0 2 3 0 0 0 0 0 2 0 3 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 GO:0033180~proton−transporting V−type ATPase, V1 domain 0 0 0 5 5 5 4 4 0 4 0 0 4 6 4 4 0 0 3 0 5 4 0 3 4 3 4 0 0 0 2 3 3 0 0 0 0 0 0 0 5 3 2 0 0 0 0 0 0 0 0 4 3 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GO:0033178~proton−transporting two−sector ATPase complex, catalytic domain 3 0 0 5 5 5 5 5 0 4 0 0 0 6 4 5 0 0 3 0 7 5 0 0 4 3 4 0 0 0 0 3 3 0 0 0 0 0 0 0 6 3 0 0 0 0 0 0 4 0 0 4 3 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GO:0016469~proton−transporting two−sector ATPase complex 14 0 0 18 0 20493627321714574227223814 0 0 0 1528 0 11151511 0 0 1211 0 0 0 0 3814 0 10 0 0 0 0 13162838392127141410 0 0 0 0 0 0 1817241110 0 0 0 0 0 0 8 0 0 0 171320 9 0 8 9 12 8 0 9 17 0 0 15 0 0 0 9 9 22 0 GO:0000267~cell fraction 0 0 0 0 0 13372721221212432918153012 0 0 0 0 22 0 9 0 9 0 0 0 0 0 15 0 0 0 2910 0 0 0 0 0 0 9 132330311721 0 0 0 0 0 0 0 0 0 0 1418 9 9 0 0 0 0 0 0 0 0 0 0 14 9 16 7 7 7 8 11 7 0 0 0 0 1311 0 0 0 0 7 21 0 GO:0005624~membrane fraction 0 0 0 0 0 13372721221212443018153312 0 0 0 0 22 0 9 0 9 0 0 0 0 0 15 0 0 0 2910 0 0 0 0 0 12 9 142532331822 0 0 0 0 0 0 0 0 0 161518 9 9 0 0 0 0 0 0 0 0 0 0 14 9 17 7 7 7 8 11 7 0 0 0 0 1311 0 0 0 0 7 21 0 GO:0005626~insoluble fraction 7 7 9 0 0 0 1412 6 9 8 8 231710 0 22 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 4 0 6 0 5 0 0 0 8 0 8 0 2018 9 10 0 0 0 0 0 0 8 0 0 0 6 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 GO:0009898~internal side of plasma membrane 6 4 3 3 3 4 8 8 4 0 5 0 8 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 3 0 0 0 0 0 0 5 5 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 3 3 3 0 0 5 3 GO:0030665~clathrin coated vesicle membrane 1413151815142926151914 9 302615 0 23 0 0 0 0 0 0 14 7 0 0 0 0 0 0 0 0 0 9 6 23 9 0 0 6 0 7 0 0 101524191313 0 0 7 0 0 0 0 0 6 11 9 0 0 0 0 0 0 0 0 0 5 6 0 0 0 7 0 0 0 0 0 0 0 8 6 9 0 10 0 7 0 0 0 0 13 0 GO:0016023~cytoplasmic membrane−bounded vesicle 1514151816153027152014 9 312616 0 23 0 0 0 0 0 0 14 7 0 0 0 0 0 0 0 0 0 9 6 23 9 0 7 6 7 8 0 0 101624201313 0 0 7 0 0 0 0 0 0 11 9 0 0 0 0 0 0 0 0 0 5 7 0 0 0 7 0 0 0 0 0 0 0 8 6 9 0 10 0 7 0 0 0 0 13 0 GO:0031988~membrane−bounded vesicle 161516201714322716221410363017 0 32 9 0 0 0 0 0 15 8 0 0 0 0 0 0 0 12 0 11 6 2510 0 7 7 0 7 10 0 121931241416 0 0 7 0 0 0 0 0 0 121114 0 0 0 0 10 0 0 0 0 0 0 0 11 8 12 0 0 0 0 0 0 8 8 12 0 0 0 7 0 0 0 0 14 0 GO:0031410~cytoplasmic vesicle 171616201816342916231410383018 0 32 9 0 0 0 0 0 15 8 0 0 0 0 0 0 0 0 0 11 6 2510 0 8 7 0 8 0 0 122032251416 0 0 7 0 0 0 0 0 0 0 11 0 0 0 0 0 10 0 0 0 0 7 0 0 11 8 12 0 0 0 0 0 0 8 8 12 0 0 0 0 0 0 0 0 15 0 GO:0031982~vesicle 6 6 4 4 5 4 1112 7 10 5 4 10 6 6 0 0 0 0 0 4 0 0 6 3 0 0 0 0 0 3 0 0 0 4 3 8 4 4 3 3 4 3 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 GO:0008021~synaptic vesicle 8 8 5 6 7 6 1213 7 11 6 6 1610 6 0 11 4 0 0 6 0 0 6 4 0 0 0 0 0 0 0 0 0 0 3 10 4 0 0 4 4 4 0 0 0 0 1010 7 0 0 0 4 0 0 0 0 0 0 6 5 0 0 0 0 0 5 0 0 0 0 0 0 0 0 4 0 0 0 3 0 0 0 0 4 0 0 0 0 0 0 0 0 0 10 6 GO:0030135~coated vesicle 8 7 5 6 7 6 1213 7 11 6 5 1510 6 0 9 4 0 0 6 0 0 6 4 4 0 0 0 0 0 0 0 3 4 3 10 4 0 0 4 4 4 0 0 0 6 9 10 6 0 0 0 4 0 0 0 0 0 0 6 5 0 0 0 0 0 5 0 0 0 0 0 0 0 4 4 0 0 0 3 0 0 0 0 4 4 0 0 0 0 0 0 0 0 9 6 GO:0030136~clathrin−coated vesicle 0 0 0 18 0 1749341627171580462820 0 0 0 0 0 0 2827 0 0 0 11 0 0 0 0 23 0 0 0 41 0 1913 0 0 0 201325393541 0 20 0 0 0 0 0 0 0 0 0 2215251210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 16 0 0 0 0 0 0 0 0 0 0 GO:0005887~integral to plasma membrane 0 0 0 18 0 1750341627171581462820 0 0 0 0 0 0 2827 0 0 0 11 0 0 0 0 23 0 0 0 41 0 1913 0 0 0 201425393541 0 21 0 0 0 0 0 0 0 0 0 2216251210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 0 0 0 0 16 0 0 0 0 0 0 0 0 0 0 GO:0031226~intrinsic to plasma membrane 0 0 0 10 6 6 1614 7 10 8 9 332312 6 0 0 5 4 0 0 9 13 4 0 0 4 4 0 0 4 12 5 0 0 13 5 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GO:0034702~ion channel complex 0 3 0 3 0 4 12 7 5 7 6 5 1110 4 0 0 0 0 0 0 0 5 6 3 0 0 0 0 0 0 3 0 0 0 0 0 0 3 0 3 0 0 3 0 4 0 5 5 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 3 GO:0030426~growth cone 0 3 0 3 0 4 12 7 5 7 6 5 1110 4 0 0 0 0 0 0 0 5 6 3 0 0 0 0 0 0 3 0 0 0 0 0 0 3 0 3 0 0 3 0 4 0 5 5 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 GO:0030427~site of polarized growth 4 0 4 5 0 3 9 4 4 0 3 0 7 8 5 4 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 GO:0033267~axon part 0 0 6 7 6 5 1611 6 7 6 5 1315 8 0 0 0 0 0 0 0 11 7 5 0 4 4 0 4 5 4 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 4 6 4 4 5 0 5 GO:0043025~cell soma 3 3 0 4 0 0 8 4 4 5 0 3 5 5 4 3 0 0 0 0 0 0 3 6 5 0 0 3 0 0 3 0 0 0 0 0 4 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 GO:0042734~presynaptic membrane 2 3 0 4 0 3 4 0 2 4 2 0 5 4 4 3 0 0 0 0 0 2 4 4 4 4 2 3 3 3 3 2 3 0 0 0 4 2 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 2 3 3 2 2 0 0 GO:0043198~dendritic shaft CALY(125) SLC6A7(156) NDUFA1(141) HN1(148) PITHD1(169) AP2S1(150) UTRN(433) ASXL2(301) EIF4EBP2(120) TGFBR2(243) TAGLN3(113) FRMD6(109) TMEM178A(669) ASNS(452) ABCB1(208) LAMC1(187) PGK1(541) VAT1L(121) PPP3CB − AS1(140) PFDN2(81) CXCR6(265) PPIA(106) SALL2(318) ADCYAP1R1(281) PPARA(83) FN1(131) EBP(80) NOTCH2(83) RIN2(61) AFF1(82) PTPRB(92) ARPC5L(86) CD83(196) GDAP1L1(68) FRMPD3(123) ENO2(59) NOVA1(432) PARP4(103) STAT3(182) FLI1(86) BEX1(69) ARF5(95) AP1S1(53) SHC3(148) PRKCE(94) RAPGEF4(148) SLC1A2(271) BNIP3(405) MAP2(435) HS6ST3(151) DOCK3(195) SLC25A14(118) NDUFS3(116) PSMD4(91) GSTO1(66) NDUFB1(120) ARID1B(275) CAMLG(188) C14orf2(181) PGAM1(300) SCG3(208) AMPH(141) SLC35F1(242) GABRG3(87) GRM8(58) MAP1B(364) YWHAB(94) APP(136) PRKACB(76) RTN3(60) PRNP(60) CHL1(53) COX6B1(70) PFKFB3(103) ARID1A(55) NSF(139) VDAC1(83) SYNJ1(200) GABRA3(65) CHRM4(52) PAK1(62) CNTNAP2(66) GABRG2(126) ARHGAP20(64) GARS(106) NCALD(65) SERPINI1(153) APLP2(94) SUCLA2(164) LRP1(126) CLTA(81) SLCO2B1(194) INPP5D(96) CDC42EP4(71) ATP1A2(70) TPI1(237) CSF3R(87)

Figure 4 Aging, synaptic transmission and metabolism are dysregulated. (a) The enrichment of aging genes, AD diﬀerentially expressed genes and AD related genes in 97 subnetworks; (b) the dysregulated genes usually have the same expression change direction in the aging process and the AD genesis; (b) functional enrichment analysis to dysregulated genes for (c) biological process and (d) cellular component. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 21 of 25

Dysregulated genes 5 All the genes 4 HN1 SCG3 3 ARF5 APLP2 PAK1 Density

2 SALL2 TMEM178A ADCYAP1R1 ASXL2

1 RIN2 NCALD 0

−0.4 −0.2 0.0 0.2 0.4 (a) Cognitive Scores (cts_mmse30)

6 Dysregulated genes All the genes 5 4 3 Density 2 SCG3 1 0

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3

(b) Brain plaque (cerad)

6 Dysregulated genes

5 All the genes 4 3 Density 2 SCG3 1 0

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 (c) Brain tangle (braak)

Figure 5 The dysregulated genes prefer to have more association with clinical traits, including (a) cognitive test score (cts), (b) braak stage (braaksc) and (c) assessment of neuritic plaques (ceradsc). The black lines show the density plots of clinical association (correlation) for all protein coding genes; the blue points indicate the 97 dysregulated genes. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 22 of 25

− −

−

− − − − − −

−

Figure 6 The dysregulation divergence between human and mouse. (a) shows the density plot of expression proﬁle similarity (correlation) of human and mouse homologous genes, which suggests the conserved gene expression patterns between human and mouse. However, (b) the regulations among all the genomic genes are not conserved between human and mouse (r = 0.139). By restricting the studied regulations to the dysregulated pairs (c) and the edges in Figure 3(d), the conservation between human and mouse regulations was weakly improved. The observation was further investigated with human microarray data and found good consistent with human RNA-seq (e) but still failed to suggest any gene-gene regulation conservation between human and mouse (f). bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 23 of 25

● ● ●● ● r=−0.012 ●● 10 ●● ● ● ● 9 0.03 ● ● ● 8 ● ● ●

0.02 ● ● ● 7 ● Density ● ● ● ● ● 6

● Z−score Maximum ●● 0.01 ●● ● ●● 5 ●● ● ●●● ●● ● ●●●● ● ● 4 0.00

0 100 200 300 0 100 200 300

Connectivity Connectivity

(a) (b)

1.0 r=0.461 r=0.106 10 0.5 5 0 0.0 −5 −0.5 −10 Z−score of Dysregulation(Microarray, AD) Z−score of Dysregulation(Microarray, Coexpression Correlation(Microarray, AD) Correlation(Microarray, Coexpression −1.0 −0.5 0.0 0.5 1.0 −10 −5 0 5 10

Coexpressio Correlation(RNAseq, AD) Z−score of Dysregulation (RNAseq, AD)

Figure 7 Comparison analysis with the results from estiblished work. (a) The connectivity distribution of top 100 of the most dysregulated genes (red circle points) fails to indicate any association between connectivity and dysregulation; (b) the same result is observed with all the genomic genes (r = −0.012). (c) using human RNA-seq and micorarray data, the calculated gene co-expression correlations are consistent; but (d) the predicted dysregulation are not consistent. bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 24 of 25

Table 1 The dysregulated AD genes

Gene partnersDEG p(DEGs) AD p(AD) Aging p(aging) MAP1B 364 Yes 9.2e-77 Yes 3.3e-05 Yes 5e-3 PSAP 337 No 0.03 Yes 0.6 No 0.01 PGAM1 300 Yes 1.4e-24 Yes 0.04 No 2e-3 ARID1B 275 No 4.8e-4 Yes 0.36 No 6.5e-11 SLC1A2 271 No 1.9e-40 Yes 6.1e-3 No 0.18 TGFBR2 243 Yes 1.1e-72 Yes 6.4e-06 No 1.9e-14 SREK1IP1 209 No 0.55 Yes 0.05 No 0.02 SYNJ1 200 Yes 6.3e-13 Yes 0.15 No 0.41 DOCK3 195 Yes 4.1e-11 Yes 0.01 No 0.22 STAT3 182 Yes 3.41e-49 Yes 1e-3 No 1.9e-11 SUCLA2 164 Yes 8.4e-13 Yes 0.49 Yes ee-3 SERPINI1 153 Yes 5.9e-32 Yes 0.01 Yes 6.3e-4 AMPH 141 No 8.7e-22 Yes 0.4 No 0.19 APP 136 Yes 1.2e-11 Yes 0.23 No 0.38 CLU 130 Yes 0.24 Yes 0.66 No 0.02 LRP1 126 Yes 2.9e-16 Yes 0.09 No 2.9e-05 GABRG2 126 No 6.9e-11 Yes 0.63 No 0.18 VAT1L 121 No 7.9e-13 Yes 0.34 No 0.02 PHF1 119 No 0.4 Yes 0.59 No 2e-2 TAGLN3 113 Yes 6.2e-32 Yes 2.4e-07 No 5e-06 FRMD6 109 No 1.5e-21 Yes 0.04 No 3.4e-4 PDSS1 106 No 0.68 Yes 0.67 Yes 2.9e-4 PPIA 106 Yes 4.2e-45 Yes 0.01 Yes 7.34e-09 INPP5D 96 Yes 1.6e-27 Yes 2.3e-3 No 3.6e-06 PRKCE 94 Yes 4.3e-4 Yes 0.05 Yes 0.35 APLP2 94 No 7.4e-07 Yes 0.05 No 0.16 YWHAB 94 Yes 1.4e-17 Yes 0.34 Yes 3.6e-3 GABRG3 87 Yes 4.3e-11 Yes 0.96 No 0.74 PPARA 83 Yes 5.8e-31 Yes 2.9e-3 No 9.2e-05 RAN 83 Yes 0.03 Yes 0.36 Yes 9.4e-3 VDAC1 83 Yes 3.9e-10 Yes 0.28 Yes 0.02 AFF1 82 Yes 2.7e-24 Yes 0.13 No 5.9e-06 PFDN2 81 Yes 4.5e-16 Yes 0.13 Yes 1.5e-09 EBP 80 No 3.6e-23 Yes 6.7e-3 No 6.5e-08 LEMD2 78 No 0.01 Yes 0.82 No 4.7e-3 PRKACB 76 No 1.6e-3 Yes 0.53 Yes 0.02 EID1 70 Yes 0.33 Yes 0.21 No 1.3e-3 GSTO1 66 Yes 3.5e-26 Yes 0.01 Yes 1.1e-08 CNTNAP2 66 Yes 1.5e-08 Yes 0.16 No 0.55 GABRA3 65 Yes 6.8e-08 Yes 0.59 No 0.32 ARHGAP20 64 Yes 1.3e-3 Yes 0.21 Yes 0.15 PAK1 62 Yes 4.2e-09 Yes 0.61 No 8.6e-3 THOP1 62 No 0.34 Yes 0.03 Yes 0.36 AKT1S1 61 Yes 0.35 Yes 0.75 No 0.19 PRNP 60 Yes 4.1e-05 Yes 0.25 No 0.02 RTN3 60 Yes 7.3e-14 Yes 0.14 Yes 4.3e-3 STX2 60 No 0.94 Yes 0.73 No 0.22 GAS7 59 Yes 0.01 Yes 1 No 0.02 CCDC134 58 No 0.91 Yes 0.71 No 0.7 GRM8 58 Yes 4.2e-12 Yes 0.34 No 0.06 GAPDH 55 No 0.04 Yes 0.28 No 0.06 CHL1 53 Yes 4.1e-4 Yes 0.43 Yes 0.05 CHRM4 52 No 1.5e-09 Yes 0.56 No 0.07 GABRB2 50 No 5.3e-09 Yes 0.06 No 0.47 NEDD4 50 Yes 0.43 Yes 1 No 0.07 VSNL1 48 Yes 5.8e-05 Yes 0.48 No 0.12 PSENEN 47 No 3.9e-05 Yes 0.01 No 3.0e-3 BECN1 46 Yes 0.97 Yes 0.47 Yes 0.28 CHMP5 45 Yes 0.19 Yes 0.48 Yes 5.9e-4 UBQLN1 44 No 0.91 Yes 0.57 Yes 0.01 HECW1 44 No 0.04 Yes 0.21 No 0.94 CSMD1 44 Yes 3.2e-09 Yes 0.8 No 0.15 CHRM2 44 Yes 1.4e-3 Yes 0.48 No 0.08 UCHL1 43 Yes 2.8e-11 Yes 9.7e-3 No 0.01 PHYHD1 43 Yes 2.9e-3 Yes 0.85 Yes 0.07 SCN1A 41 No 0.11 Yes 0.8 Yes 0.37 APBA1 41 No 0.06 Yes 0.25 No 0.12 UQCR10 41 Yes 1.02e-05 Yes 0.03 No 7.9e-3 bioRxiv preprint doi: https://doi.org/10.1101/240002; this version posted December 26, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Meng and Mei Page 25 of 25

Table 2 The association of dysregulated genes with the cognitive test scores coexpression cor. cor. with cts1 partial cor.2 gene1 gene2 ad normal gene1 gene2 gene1 gene2 RPS19 APLP2 -0.446 -0.696 -0.036 0.226 0.205 0.299 SLC35F1 PPP2CA 0.718 0.528 0.039 0.208 -0.183 0.271 SLC35F1 RTN3 0.711 0.517 0.039 0.217 -0.180 0.273 RALGPS1 APLP2 -0.039 0.265 0.030 0.226 -0.188 0.289 SLC1A2 FAT1 0.233 0.511 -0.052 -0.185 0.153 -0.232 SLC15A2 SLC1A2 0.442 0.693 -0.208 -0.053 -0.298 0.214 SDC2 SLC1A2 0.359 0.611 -0.199 -0.053 -0.268 0.180 NOTCH2 SLC1A2 0.172 0.533 -0.210 -0.053 -0.267 0.177 SYNJ1 PRUNE2 0.333 0.581 0.160 0.062 0.218 -0.163 GPRC5B SLC1A2 -0.055 0.27 -0.276 -0.053 -0.316 0.169 SUCLA2 PRUNE2 0.311 0.557 0.153 0.062 0.210 -0.158 SUCLA2 ARFGEF2 0.746 0.861 0.153 0.067 0.201 -0.148 PRKACB CDS2 0.641 0.794 0.187 0.070 0.225 -0.145 PRKCE KCNA1 0.62 0.397 0.196 0.062 0.237 -0.148 APP RBMX2 -0.037 -0.353 0.169 -0.064 0.195 0.118 1 the correlation between gene expression and cognitive test score 2 the partial correlation with cognitive test score