Transcriptional dysregulation study reveals a core network ... · 12/26/2017 ·...

Meng and Mei

RESEARCH

Transcriptional dysregulation study reveals a corenetwork involving the genesis for Alzheimer’sdiseaseGuofeng Meng1,2* and Hongkang Mei1

*Correspondence:

[email protected] science Inc., No. 24, Tang’an

Road, 201203 Shanghai, China

Full list of author information is

available at the end of the article

Abstract

Background: The pathogenesis of Alzheimer’s disease is associated withdysregulation at different levels from transcriptome to cellular functioning. Suchcomplexity necessitates investigations of disease etiology to be carried outconsidering multiple aspects of the disease and the use of independent strategies.The established works more emphasized on the structural organization of generegulatory network while neglecting the internal regulation changes.

Methods: Applying a strategy different from popularly used co-expressionnetwork analysis, this study investigated the transcriptional dysregulations duringthe transition from normal to disease states.

Results: 97 genes were predicted as dysregulated genes, which were alsoassociated with clinical outcomes of Alzheimer’s disease. Both the co-expressionand differential co-expression analysis suggested these genes to be interconnectedas a core network and that their regulations were strengthened during thetransition to disease states. Functional studies suggested the dysregulated genesto be associated with aging and synaptic function. Further, we checked theevolutionary conservation of the gene co-expression and found that human andmouse brain might have divergent transcriptional co-regulation even when theyhad conserved gene expression profiles.

Conclusion: Overall, our study reveals a profile of transcriptional dysregulation inthe genesis of Alzheimer’s disease by forming a core network with alteredregulation; the core network is associated with Alzheimer’s diseases by affectingthe aging and synaptic functions related genes; the gene regulation in brain maynot be conservative between human and mouse.

Keywords: Alzhimer’s disease; co-expression; dysregulation; dysregulationdivergence

BackgroundAlzheimer’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 di-

verse regulatory interactions [11, 12]. The availability of high-throughput transcrip-

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Meng and Mei 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 net-

work 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 struc-

tural 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 com-

plexity 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 con-

nectivity, 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 dif-

ferential 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 MaterialsData 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/



http://dx.doi.org/doi:10.7303/syn2580853#).,

https://www.synapse.org/#!Synapse:syn2580853/wiki/66722


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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 ef-

fects 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 experi-

mental 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 pro-

files 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 hu-

man 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 per-

formed 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 shuf-

fled 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





www.informatics.jax.org

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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 enrich-

ment 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 associa-

tion with AD in published works, such as the genetics evidences and expression evi-

dences. 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 incon-

sistency 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



http://www.ingenuity.com/products/ipa

http://www.ingenuity.com/products/ipa

https://portal.genego.com/

www.disgenet.org/

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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 im-

portance of genes in the modules.

ResultsTranscriptional 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 anno-

tation 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 correla-

tions, 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%)



https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/

https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/

http://www.ncbi.nlm.nih.gov/geo/

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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 transcrip-

tional regulation in the AD patients.

Dysregulated AD genes

In published works, at least 777 expressed genes have been reported to be asso-

ciated with AD. We found that 479 of them were predicted with at least one dys-

regulated 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 gen-

esis [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 transcrip-

tional 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.



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We also studied the functional involvement of the dysregulated AD genes. In

Figure 2, 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 transcription-

ally 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 dys-

regulation 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 sta-

tus 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 cor-

relation 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.



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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 pro-

files 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 regu-

lation 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 sub-

networks 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



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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 differ-

entially 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 en-

richment 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 expres-

sion 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 anal-

ysis 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 im-

pulse” 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 signif-

icantly 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.



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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 “transmis-

sion 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 as-

sociated with the AD related terms (see above section), including synaptic function

and metabolism related biological processes, we can assume that the 39 subnet-

works 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 dis-

ease 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 cor-

relation 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 mi-

croarray 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



www.informatics.jax.org

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Meng and Mei 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

Figure 6(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 corre-

lations 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 differen-

tial 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 cor-

related 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 expres-

sion 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 pro-

files 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 indi-

cates the gene importance, connectivity has widely been used to identify the es-

sential 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 con-

nectivity would be more differentially co-expressed. As shown in Figure 7(b) and



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Meng and Mei 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 microar-

ray 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 anal-

ysis 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 mi-

croarry 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).

DiscussionUsing the RNA-seq data, we studied the association between gene expression corre-

lation and connectivity. We found that the genes with higher connectivity showed

stronger co-expression correlations (see Additional file 15(a)). Functional annota-

tion 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



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Meng and Mei 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 num-

bers 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 trans-

mission 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 in-

formation of their relationship. However, by checking the functional annotation to

the hub genes and their partners, we always found consistent functional annota-

tion. 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 involve-

ment 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



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Meng and Mei 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 details1Computational & Modeling Science, PTS China, GSK R&D, Shanghai, Halei 898, 201203 Shanghai, China. 2BT

science Inc., No. 24, Tang’an Road, 201203 Shanghai, China.

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Annals of statistics, 1165–1188 (2001)

Figures

AMP-AD program

ROSMAP

(640 sample) MSBB

(938 samples) MayoBB

(556 samples) MayoPilot

(93 samples)

GEO Mouse brain samples

20 experiments

Alzheimer’s Disease (1045 RNA-seq)

Normal (622 RNA-seq)

Mouse (931 arrays)

(a)

(b)

RNA-seq/microarray data processing Normalization => quality control => removing batch effects => combination => evaluation

AD-specific gene-gene regulation

normal-specific gene-gene regulation

mouse-specific gene-gene regulation

Dysregulation of AD genesis

Regulation divergence between human and

mouse

DiffCorr Cutoff selection

(c)

(d)

DiffCorr Cutoff selection

(e) (f)

Clinical annotation

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



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Meng and Mei of 25

synaptic vesicle endocytosis

membrane organization

programmed cell death

TGFBR2

endocytosis

neuron development

RTN3

neuron projection morphogenesis

SYNJ1

MAP1B

response to nutrient levels

neuron differentiation

CLU

vesicle-mediatedtransport

gamma-aminobutyric acid signaling

pathway

GABRG2

GABRB2

GABRA3

transmission of nerve impulse

VDAC1

GRM8

GABRG3

cell-cell signaling

APBA1

synaptic transmission

SCN1A

regulation of cellular catabolic process AKT1S1 PAK1YWHABPSENEN INPP5DPRNPPRKCE

APPlearning

regulation of epidermal growth

factor receptor activity

APLP2LRP1

regulation of phosphate metabolic

process BECN1

PPARA

regulation of kinase activity

regulation of phosphorylation regulation of

apoptosis

CHMP5

regulation of catabolic process

PGAM1

STX2

GAS7

NEDD4

STAT3

SLC1A2

aging

anion transport

G-protein coupled receptor protein

signaling pathway

CHRM4 CHRM2PRKACB

copper ion homeostasis

neurological system process

UCHL1 microtubule-based transport

learning or memory

CNTNAP2

axon cargo transport

AMPHneuron recognition

2

4

6

8

-log10(p-value)

Figure 2 Functional invovlement of dysregulated AD genes. 68 dysregulated AD genes wereannotated for their functional involvement based on the Gene Ontology annotation; the colors ofGO 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.



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Meng and Mei of 25

Additional file 16

The full acknowledgment to the individual data contributors.



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−0.5 0.0 0.5 1.0

−0.5

0.0

0.5

1.0

Coexpression Correlation(AD)

Coe

xpre

ssio

nC

orre

latio

n(N

orm

al)

r=0.935(a)

−1.0 −0.5 0.0 0.5 1.0

0.0

0.5

1.0

1.5

2.0

Correlation

Den

sity

All gene pairsDysregulated gene pairs

(b)

−1.0 −0.5 0.0 0.5 1.0

−1.

0−

0.5

0.0

0.5

1.0

Correlation(Normal)

Cor

rela

tion(

AD

)

increased correlation in ADdecreased correlation in AD

(c)

−0.5 0.0 0.5

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Co−expression correlation

Den

sity

PPARA

STAT3SALL2 TF genes

All genes

GRM8

PFKFB3PRNP

PRKACB

PFDN2

AFF1

PGK1

BEX1

TGFBR2

STAT3

SERPINI1

VDAC1

GABRA3SYNJ1

GABRG2

NSF

AMPH

SUCLA2

APLP2

SLC25A14

FLI1

CNTNAP2

SLC35F1 YWHAB

SLCO2B1

GARS

RIN2

FN1

FRMD6INPP5D

HS6ST3

SLC6A7

DOCK3

PPP3CB-AS1

CXCR6

CHRM4

TMEM178A

BNIP3

NOVA1

CSF3R

MAP2

GABRG3

CHL1

MAP1B

ARHGAP20

PRKCE

APPPARP4

CD83

RTN3

PAK1

EIF4EBP2

PTPRB

TPI1

ABCB1

C14orf2AP2S1 RAPGEF4

SHC3ARID1APPIA

TAGLN3

NCALDAP1S1

GDAP1L1

ENO2

HN1

ARF5

UTRN

LRP1

CLTA

ASXL2LAMC1 GSTO1

CAMLG

EBP

CALY

NDUFA1

FRMPD3

PSMD4COX6B1

ARID1B SCG3

NDUFB1SLC1A2NDUFS3

SALL2

ARPC5LVAT1L

ATP1A2

PITHD1

PGAM1

CDC42EP4

ASNSNOTCH2

ADCYAP1R1PPARA

GRM8

PFKFB3PRNP

PRKACB

PFDN2

AFF1

PGK1

BEX1

TGFBR2

STAT3

SERPINI1

VDAC1

GABRA3SYNJ1

GABRG2

NSF

AMPH

SUCLA2

APLP2

SLC25A14

FLI1

CNTNAP2

SLC35F1 YWHAB

SLCO2B1

GARS

RIN2

FN1

FRMD6INPP5D

HS6ST3

SLC6A7

DOCK3

PPP3CB-AS1

CXCR6

CHRM4

TMEM178A

BNIP3

NOVA1

CSF3R

MAP2

GABRG3

CHL1

MAP1B

ARHGAP20

PRKCE

APPPARP4

CD83

RTN3

PAK1

EIF4EBP2

PTPRB

TPI1

ABCB1

C14orf2AP2S1 RAPGEF4

SHC3ARID1APPIA

TAGLN3

NCALDAP1S1

GDAP1L1

ENO2

HN1

ARF5

UTRN

LRP1

CLTA

ASXL2LAMC1 GSTO1

CAMLG

EBP

CALY

NDUFA1

FRMPD3

PSMD4COX6B1

ARID1B SCG3

NDUFB1SLC1A2NDUFS3

SALL2

ARPC5LVAT1L

ATP1A2

PITHD1

PGAM1

CDC42EP4

ASNSNOTCH2

ADCYAP1R1PPARA

2

4

6

8

Edge Color

dysregulationz-score

00.20.40.60.8

Edge width

Co-expressioncorrelation

Node color

●●

●●

●●

-0.8

-0.6

-0.4

0

0.4

0.6

Fold changeof DEG

No. partners

Node size

50

250

450

650

(e)

(d)

Figure 3 A core network involving the dysregulation of AD. (a) The co-expression profiles of thegenomic genes are highly similar between human AD and normal samples (r = 0.935); (b) Thegene co-expression correlations among dysregulated genes (red dash line) and genomic genes (bluesolid 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 transmitfrom normal to AD; (d) 96 (out of 97) dysregulated genes can be interconnected as a regulatorynetwork based on co-expression and differential co-expression information. (e) TMEM178Aexpression is negatively correlated with dysregulated transcription factors, among which PPARA isranked as the most negatively correlated transcription factors.



https://doi.org/10.1101/240002


Meng and Mei of 25

0 5 10 15 20

020

4060

8010

012

014

0

02

46

810

Enrichment of Aging genes/−log10(p) Enr

ichm

ent o

f AD

gen

es/−

log1

0(p)

Enr

ichm

ent o

f DE

Gs/−

log1

0(p)

Enriched with aging genes, AD gene and DEGsOthers

(a)

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−0.6 −0.4 −0.2 0.0 0.2 0.4

−0.

6−

0.2

0.0

0.2

0.4

0.6

Fold changes of differential expression

Cor

rela

tion

with

age

s

(b)

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5200000000000000050500000000000000074444444

12998

100450T

ME

M178A

(669)A

SN

S(452)

TG

FB

R2(243)

AB

CB

1(208)LA

MC

1(187)A

P2S

1(150)U

TR

N(433)

AS

XL2(301)

BN

IP3(405)

SC

G3(208)

FR

MP

D3(123)

SLC

1A2(271)

VAT

1L(121)C

HR

M4(52)

CA

MLG

(188)A

RID

1B(275)

PR

KC

E(94)

PR

NP

(60)C

HL1(53)

RA

PG

EF

4(148)S

HC

3(148)N

CA

LD(65)

SLC

35F1(242)

GA

BR

G3(87)

MA

P1B

(364)S

LCO

2B1(194)

GS

TO

1(66)D

OC

K3(195)

AM

PH

(141)N

SF

(139)R

TN

3(60)A

PP

(136)PA

K1(62)

GA

BR

G2(126)

VD

AC

1(83)C

SF

3R(87)

HS

6ST

3(151)LR

P1(126)

PR

KA

CB

(76)A

RH

GA

P20(64)

GR

M8(58)

GA

BR

A3(65)

YW

HA

B(94)

CN

TN

AP

2(66)A

PLP

2(94)A

RID

1A(55)

PG

AM

1(300)IN

PP

5D(96)

GA

RS

(106)C

DC

42EP

4(71)M

AP

2(435)S

UC

LA2(164)

SY

NJ1(200)

SE

RP

INI1(153)

PG

K1(541)

TP

I1(237)TA

GLN

3(113)C

D83(196)

AD

CYA

P1R

1(281)F

RM

D6(109)

EN

O2(59)

ATP

1A2(70)

RIN

2(61)S

ALL2(318)

NO

VA

1(432)S

TAT3(182)

CX

CR

6(265)B

EX

1(69)P

ITH

D1(169)

PP

IA(106)

HN

1(148)S

LC25A

14(118)P

PAR

A(83)

EB

P(80)

ND

UF

S3(116)

AR

F5(95)

CO

X6B

1(70)P

SM

D4(91)

AP

1S1(53)

C14orf2(181)

PF

DN

2(81)P

FK

FB

3(103)C

LTA(81)

FN

1(131)N

DU

FB

1(120)N

DU

FA1(141)

PT

PR

B(92)

FLI1(86)

AF

F1(82)

EIF

4EB

P2(120)

PAR

P4(103)

CA

LY(125)

AR

PC

5L(86)P

PP

3CB−

AS

1(140)G

DA

P1L1(68)

SLC

6A7(156)

NO

TC

H2(83)

GO:0006820~anion transportGO:0007214~gamma−aminobutyric acid signaling pathwayGO:0007242~intracellular signaling cascadeGO:0008104~protein localizationGO:0000904~cell morphogenesis involved in differentiationGO:0000902~cell morphogenesisGO:0031175~neuron projection developmentGO:0032990~cell part morphogenesisGO:0048858~cell projection morphogenesisGO:0048812~neuron projection morphogenesisGO:0007409~axonogenesisGO:0048667~cell morphogenesis involved in neuron differentiationGO:0007611~learning or memoryGO:0050804~regulation of synaptic transmissionGO:0051969~regulation of transmission of nerve impulseGO:0048489~synaptic vesicle transportGO:0030182~neuron differentiationGO:0048666~neuron developmentGO:0006107~oxaloacetate metabolic processGO:0044271~nitrogen compound biosynthetic processGO:0006754~ATP biosynthetic processGO:0009142~nucleoside triphosphate biosynthetic processGO:0009206~purine ribonucleoside triphosphate biosynthetic processGO:0009145~purine nucleoside triphosphate biosynthetic processGO:0009201~ribonucleoside triphosphate biosynthetic processGO:0046034~ATP metabolic processGO:0009144~purine nucleoside triphosphate metabolic processGO:0009199~ribonucleoside triphosphate metabolic processGO:0009205~purine ribonucleoside triphosphate metabolic processGO:0045333~cellular respirationGO:0006119~oxidative phosphorylationGO:0015985~energy coupled proton transport, down electrochemical gradientGO:0015986~ATP synthesis coupled proton transportGO:0006818~hydrogen transportGO:0015992~proton transportGO:0006091~generation of precursor metabolites and energyGO:0016052~carbohydrate catabolic processGO:0044275~cellular carbohydrate catabolic processGO:0046164~alcohol catabolic processGO:0006096~glycolysisGO:0006007~glucose catabolic processGO:0019320~hexose catabolic processGO:0046365~monosaccharide catabolic processGO:0007267~cell−cell signalingGO:0007268~synaptic transmissionGO:0019226~transmission of nerve impulseGO:0016192~vesicle−mediated transportGO:0006811~ion transportGO:0006812~cation transportGO:0006813~potassium ion transportGO:0015672~monovalent inorganic cation transportGO:0055085~transmembrane transport

AD.genes

AD.genes

No

Yes

0

1

2

3

4

5

6

(c)

14433012142000990988

2178596

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ALY

(125)S

LC6A

7(156)N

DU

FA1(141)

HN

1(148)P

ITH

D1(169)

AP

2S1(150)

UT

RN

(433)A

SX

L2(301)E

IF4E

BP

2(120)T

GF

BR

2(243)TA

GLN

3(113)F

RM

D6(109)

TM

EM

178A(669)

AS

NS

(452)A

BC

B1(208)

LAM

C1(187)

PG

K1(541)

VAT

1L(121)P

PP

3CB−A

S1(140)

PF

DN

2(81)C

XC

R6(265)

PP

IA(106)

SA

LL2(318)A

DC

YAP

1R1(281)

PPA

RA

(83)F

N1(131)

EB

P(80)

NO

TC

H2(83)

RIN

2(61)A

FF

1(82)P

TP

RB

(92)A

RP

C5L(86)

CD

83(196)G

DA

P1L1(68)

FR

MP

D3(123)

EN

O2(59)

NO

VA

1(432)PA

RP

4(103)S

TAT3(182)

FLI1(86)

BE

X1(69)

AR

F5(95)

AP

1S1(53)

SH

C3(148)

PR

KC

E(94)

RA

PG

EF

4(148)S

LC1A

2(271)B

NIP

3(405)M

AP

2(435)H

S6S

T3(151)

DO

CK

3(195)S

LC25A

14(118)N

DU

FS

3(116)P

SM

D4(91)

GS

TO1(66)

ND

UF

B1(120)

AR

ID1B

(275)C

AM

LG(188)

C14orf2(181)

PG

AM

1(300)S

CG

3(208)A

MP

H(141)

SLC

35F1(242)

GA

BR

G3(87)

GR

M8(58)

MA

P1B

(364)Y

WH

AB

(94)A

PP

(136)P

RK

AC

B(76)

RT

N3(60)

PR

NP

(60)C

HL1(53)

CO

X6B

1(70)P

FK

FB

3(103)A

RID

1A(55)

NS

F(139)

VD

AC

1(83)S

YN

J1(200)G

AB

RA

3(65)C

HR

M4(52)

PAK

1(62)C

NT

NA

P2(66)

GA

BR

G2(126)

AR

HG

AP

20(64)G

AR

S(106)

NC

ALD

(65)S

ER

PIN

I1(153)A

PLP

2(94)S

UC

LA2(164)

LRP

1(126)C

LTA(81)

SLC

O2B

1(194)IN

PP

5D(96)

CD

C42E

P4(71)

ATP

1A2(70)

TP

I1(237)C

SF

3R(87)

GO:0030054~cell junctionGO:0005886~plasma membraneGO:0044459~plasma membrane partGO:0043005~neuron projectionGO:0044456~synapse partGO:0045202~synapseGO:0005759~mitochondrial matrixGO:0031980~mitochondrial lumenGO:0031967~organelle envelopeGO:0031975~envelopeGO:0005739~mitochondrionGO:0044429~mitochondrial partGO:0005740~mitochondrial envelopeGO:0031966~mitochondrial membraneGO:0031090~organelle membraneGO:0005743~mitochondrial inner membraneGO:0019866~organelle inner membraneGO:0045211~postsynaptic membraneGO:0030424~axonGO:0030425~dendriteGO:0042995~cell projectionGO:0005829~cytosolGO:0033180~proton−transporting V−type ATPase, V1 domainGO:0033178~proton−transporting two−sector ATPase complex, catalytic domainGO:0016469~proton−transporting two−sector ATPase complexGO:0000267~cell fractionGO:0005624~membrane fractionGO:0005626~insoluble fractionGO:0009898~internal side of plasma membraneGO:0030665~clathrin coated vesicle membraneGO:0016023~cytoplasmic membrane−bounded vesicleGO:0031988~membrane−bounded vesicleGO:0031410~cytoplasmic vesicleGO:0031982~vesicleGO:0008021~synaptic vesicleGO:0030135~coated vesicleGO:0030136~clathrin−coated vesicleGO:0005887~integral to plasma membraneGO:0031226~intrinsic to plasma membraneGO:0034702~ion channel complexGO:0030426~growth coneGO:0030427~site of polarized growthGO:0033267~axon partGO:0043025~cell somaGO:0042734~presynaptic membraneGO:0043198~dendritic shaft

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Figure 4 Aging, synaptic transmission and metabolism are dysregulated. (a) The enrichment ofaging genes, AD differentially expressed genes and AD related genes in 97 subnetworks; (b) thedysregulated genes usually have the same expression change direction in the aging process and theAD genesis; (b) functional enrichment analysis to dysregulated genes for (c) biological process and(d) cellular component.



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SALL2ASXL2

NCALDTMEM178A

PAK1ARF5

HN1SCG3

APLP2

SCG3

SCG3

(a)

(b)

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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 proteincoding genes; the blue points indicate the 97 dysregulated genes.



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Cross Samples Correlation

Density

(a)

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CoexpressionCorrelation(mouse)

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Figure 6 The dysregulation divergence between human and mouse. (a) shows the density plot ofexpression profile similarity (correlation) of human and mouse homologous genes, which suggeststhe conserved gene expression patterns between human and mouse. However, (b) the regulationsamong all the genomic genes are not conserved between human and mouse (r = 0.139). Byrestricting the studied regulations to the dysregulated pairs (c) and the edges in Figure 3(d), theconservation between human and mouse regulations was weakly improved. The observation wasfurther 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).



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Figure 7 Comparison analysis with the results from estiblished work. (a) The connectivitydistribution of top 100 of the most dysregulated genes (red circle points) fails to indicate anyassociation between connectivity and dysregulation; (b) the same result is observed with all thegenomic genes (r = −0.012). (c) using human RNA-seq and micorarray data, the calculated geneco-expression correlations are consistent; but (d) the predicted dysregulation are not consistent.



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Meng and Mei 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-3PSAP 337 No 0.03 Yes 0.6 No 0.01PGAM1 300 Yes 1.4e-24 Yes 0.04 No 2e-3ARID1B 275 No 4.8e-4 Yes 0.36 No 6.5e-11SLC1A2 271 No 1.9e-40 Yes 6.1e-3 No 0.18TGFBR2 243 Yes 1.1e-72 Yes 6.4e-06 No 1.9e-14SREK1IP1 209 No 0.55 Yes 0.05 No 0.02SYNJ1 200 Yes 6.3e-13 Yes 0.15 No 0.41DOCK3 195 Yes 4.1e-11 Yes 0.01 No 0.22STAT3 182 Yes 3.41e-49 Yes 1e-3 No 1.9e-11SUCLA2 164 Yes 8.4e-13 Yes 0.49 Yes ee-3SERPINI1 153 Yes 5.9e-32 Yes 0.01 Yes 6.3e-4AMPH 141 No 8.7e-22 Yes 0.4 No 0.19APP 136 Yes 1.2e-11 Yes 0.23 No 0.38CLU 130 Yes 0.24 Yes 0.66 No 0.02LRP1 126 Yes 2.9e-16 Yes 0.09 No 2.9e-05GABRG2 126 No 6.9e-11 Yes 0.63 No 0.18VAT1L 121 No 7.9e-13 Yes 0.34 No 0.02PHF1 119 No 0.4 Yes 0.59 No 2e-2TAGLN3 113 Yes 6.2e-32 Yes 2.4e-07 No 5e-06FRMD6 109 No 1.5e-21 Yes 0.04 No 3.4e-4PDSS1 106 No 0.68 Yes 0.67 Yes 2.9e-4PPIA 106 Yes 4.2e-45 Yes 0.01 Yes 7.34e-09INPP5D 96 Yes 1.6e-27 Yes 2.3e-3 No 3.6e-06PRKCE 94 Yes 4.3e-4 Yes 0.05 Yes 0.35APLP2 94 No 7.4e-07 Yes 0.05 No 0.16YWHAB 94 Yes 1.4e-17 Yes 0.34 Yes 3.6e-3GABRG3 87 Yes 4.3e-11 Yes 0.96 No 0.74PPARA 83 Yes 5.8e-31 Yes 2.9e-3 No 9.2e-05RAN 83 Yes 0.03 Yes 0.36 Yes 9.4e-3VDAC1 83 Yes 3.9e-10 Yes 0.28 Yes 0.02AFF1 82 Yes 2.7e-24 Yes 0.13 No 5.9e-06PFDN2 81 Yes 4.5e-16 Yes 0.13 Yes 1.5e-09EBP 80 No 3.6e-23 Yes 6.7e-3 No 6.5e-08LEMD2 78 No 0.01 Yes 0.82 No 4.7e-3PRKACB 76 No 1.6e-3 Yes 0.53 Yes 0.02EID1 70 Yes 0.33 Yes 0.21 No 1.3e-3GSTO1 66 Yes 3.5e-26 Yes 0.01 Yes 1.1e-08CNTNAP2 66 Yes 1.5e-08 Yes 0.16 No 0.55GABRA3 65 Yes 6.8e-08 Yes 0.59 No 0.32ARHGAP20 64 Yes 1.3e-3 Yes 0.21 Yes 0.15PAK1 62 Yes 4.2e-09 Yes 0.61 No 8.6e-3THOP1 62 No 0.34 Yes 0.03 Yes 0.36AKT1S1 61 Yes 0.35 Yes 0.75 No 0.19PRNP 60 Yes 4.1e-05 Yes 0.25 No 0.02RTN3 60 Yes 7.3e-14 Yes 0.14 Yes 4.3e-3STX2 60 No 0.94 Yes 0.73 No 0.22GAS7 59 Yes 0.01 Yes 1 No 0.02CCDC134 58 No 0.91 Yes 0.71 No 0.7GRM8 58 Yes 4.2e-12 Yes 0.34 No 0.06GAPDH 55 No 0.04 Yes 0.28 No 0.06CHL1 53 Yes 4.1e-4 Yes 0.43 Yes 0.05CHRM4 52 No 1.5e-09 Yes 0.56 No 0.07GABRB2 50 No 5.3e-09 Yes 0.06 No 0.47NEDD4 50 Yes 0.43 Yes 1 No 0.07VSNL1 48 Yes 5.8e-05 Yes 0.48 No 0.12PSENEN 47 No 3.9e-05 Yes 0.01 No 3.0e-3BECN1 46 Yes 0.97 Yes 0.47 Yes 0.28CHMP5 45 Yes 0.19 Yes 0.48 Yes 5.9e-4UBQLN1 44 No 0.91 Yes 0.57 Yes 0.01HECW1 44 No 0.04 Yes 0.21 No 0.94CSMD1 44 Yes 3.2e-09 Yes 0.8 No 0.15CHRM2 44 Yes 1.4e-3 Yes 0.48 No 0.08UCHL1 43 Yes 2.8e-11 Yes 9.7e-3 No 0.01PHYHD1 43 Yes 2.9e-3 Yes 0.85 Yes 0.07SCN1A 41 No 0.11 Yes 0.8 Yes 0.37APBA1 41 No 0.06 Yes 0.25 No 0.12UQCR10 41 Yes 1.02e-05 Yes 0.03 No 7.9e-3



https://doi.org/10.1101/240002


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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 gene2RPS19 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 0.271SLC35F1 RTN3 0.711 0.517 0.039 0.217 -0.180 0.273RALGPS1 APLP2 -0.039 0.265 0.030 0.226 -0.188 0.289SLC1A2 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 0.214SDC2 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 0.177SYNJ1 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 0.169SUCLA2 PRUNE2 0.311 0.557 0.153 0.062 0.210 -0.158SUCLA2 ARFGEF2 0.746 0.861 0.153 0.067 0.201 -0.148PRKACB CDS2 0.641 0.794 0.187 0.070 0.225 -0.145PRKCE 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 0.1181 the correlation between gene expression and cognitive test score2 the partial correlation with cognitive test score



https://doi.org/10.1101/240002


Transcriptional dysregulation study reveals a core network ... · 12/26/2017 ·...

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Transcript of Transcriptional dysregulation study reveals a core network ... · 12/26/2017 ·...