Exploring Potential Biomarkers Underlying Pathogenesis of Alzheimer's Disease by Differential Co-expression Analysis.
Aging causes an increasing susceptibility to cognitive performances due to a developing neurodegen-eration leading to neurologic disorders, such as dementia. More than 20 million people worldwide suffer from dementia, and this number is expected to exceed 80 million by 2040 because of the rapid increase in the numbers of the elderly (1). Alzheimer's Disease (AD) is an irreversible progressive neurodegenerative disease affecting the central nervous system. Despite the increasing rate of AD incidence, no therapeutic strategy has been developed yet (2). Pathophysiologically, AD-related brain severe shrinkage caused neural and synaptic degenerations (3). The mentioned degenerative events can be detected in post mortem examination of patients suffering from severe memory loss (4,5). It is thought that the loss of memory is because of aggregating beta amyloid (A[beta]) and Neurofibrillary Tangles (NFTs) of hyper-phosphorylated tau protein (1,6) Additionally, inflammation characterized by activat-ed microglia (7) and oxidative stress, which result from an imbalance of Reactive Oxygen Species (ROS) and antioxidants (8,9) were shown to be associated with AD. Epigenetic changes happening in pre-frontal region by aging were shown to be related with AD functioning at cognitive level (1).
Rewiring of the biological networks to detect co-regulated and co-expressed units will help to facilitate looking into network's components and depicting the relationships between interconnected genes. Gene co-expression networks enable us to highlight molecular mechanisms underlying diseases (l0) and are considered as one way to investigate the etiology of AD efficiently. A large number of co-expression network methods have been proposed in the literature (11,l2). Differential Co-expression Analysis (DCEA) offers a powerful approach for exploring phenotypic changes (13). Not only is AD etiology incompletely understood but also differences at transcriptome level and the genes potentially related to each distinct regions of brain are not recognized causing AD to be remained somewhat unclear. In the present study, a high-throughput genomic screening approach was applied using DCGL software and comparative microarray analyses. It was hypothesized that the distinct transcriptional changes in different regions of brain lead to AD-associated brain damages. Therefore, the transcriptional profiles from the gray matter of frontal and temporal cortices were compared with hippocampi derived postmortem brains to dissect AD pathogenesis in these areas. The rationale behind the used network approach is to prioritize AD-causative genes that are apart from differential alterations in their expression and are differentially regulated by Transcription Factors (TFs) between contrasting samples. For this, Differential Regulation Analysis (DRA) has been conducted on three separated regions of AD brains as contrasting samples.
Materials and Methods
Data acquisition and pre-processing
The CEL files for GSE36980 series were downloaded from the GEO (http://www.ncbi.nlm.nih.gov/geo/) database and normalized with RMA method by using the Lineal' Models for Microarray Data (limma) R package. The main reason for selecting and exploiting this dataset is that GSE36980 series cover interspecies transcriptome analysis of various regions in gray matter in postmortem brains suiting the goal of dissecting pathological alterations in AD in several brain areas. Moreover, a number of researches have previously used these series and therefore would be able to compare the findings. After removing ambiguous probes, the extracted probe IDs were transformed into gene symbols. This data consists of a total of 79 samples (Table 1) based on the platform of GPL6244 and correspond to the frontal and temporal cortices and hippocampus.
The DCGL R package was used to conduct DCEA (13,14). This software firstly calculates Differential Co-expression profile (DCp) and Differential Co-expression enrichment (DCe) to extract significant co-expression changes between a pair of genes in control and treatment samples. Next, Differentially Co-expressed Genes (DCGs) and Differentially Co-expressed Links (DCLs) were summarized from DCp and DCe values.
Next, DCGs and DCLs were extracted from DCp and DCe values previously calculated by DCp and DCe functions. DCp filters co-expression values of a pair of genes were assessed in control and treatment conditions. X and Y were defined as a subset of the gene pairs, where n is co-expression neighbors for a gene;
X = ([x.sub.i]1, [X.sub.i]2, ..., [x.sub.i]n)
Y=([y.sub.i]1, [y.sub.i]X ..., [y.sub.i]n)
The DC of a given gene is calculated with the following equation:
[mathematical expression not reproducible]
If the resulting DCGs and DCLs coincide with a TF, they will be referred to as a DRG and DRL, respectively. The DRGs and DRLs were scrutinized by DRsort function in Differential Regulation Analysis (DRA) module. In fact, DRA module identifies potential TF as upstream regulators of DCGs and DCLs (13). Finally, for illustrating the interactions between DRGs and their regulators, a network of DRGs and coincided TFs obtained by DRA was built for each of the datasets. By utiiizing the Network Analyzer (15) nodes were set within networks with higher connections to darker color and bigger size.
Gene ontology, pathway analysis and visualization
To find the significantly over-represented biological GO terms and functions of gene products within a co-expression network of DRGs and DRLs, functional classification was performed using BINGO Cytoscape plugin (16) running hypergeometric test and Benjamini & Hochberg FDR correction at significant level 0.01. Finally, the clusters were visualized by Enrichment map Cytoscape plugin with Jaccard's coefficient 0.001. DRGs were further functionally classified by PANTHER database (http://pantherdb.org/) to underlying pathways (Figure 1).
The expression values of GSE36980 datasets were analyzed by utilizing DCGL v2.0 R package with default parameters. A total of 17088 unique genes were subjected to expression based filter and variance based filter, two functions embedded in DCGL to filter out genes that expressed extreme invariability across control and AD samples yielding 8544 and 2918 genes, respectively (Supplementary file 1). Afterward, using 2918 unique genes, co-expression analysis was performed on temporal cortex, frontal cortex, and hippocampus datasets separately. Expression based filter removes genes whose mean expression between experiments is lower than the median of this value for all genes and variance based filter removes genes that are not significantly variable than the median gene (13). In order to prioritize seed genes which are potentially related to AD pathogenesis, common and significant DRGs were selected using Targets' Enrichment Density (TED) analysis and Targets' DCL Density (TDD) analysis. TED and TDD identify differential co-expression genes and link in a particular TF's targets, respectively (13). To this end, targets of significant TFs were extracted from 19,9950 TF-to-target interaction pairs as a library in DCGL v2.0 software (13). These pairs were further filtered out based on DRLs. In sum, 7, 19 and 13 genes were identified in temporal cortex, frontal cortex and hippocampus, respectively (Table 2). Significant TFs derived by TED and TDD analysis were used to infer co-expression network of DRGs in each dataset independently (Figure 2, Supplementary figures). DRGs were classified in terms of response to lipid, response to fatty acid, regulation of transcription from RNA polymerase II promoters and regulation of nitrogen compound metabolic process (Figure 3). Moreover, in pathway analysis, signaling pathways such as glutamate and G-protein signaling pathways were noteworthy (Figure 1).
460 DCGs and 33656 DCLs were summarized using DC sum function to a final set of DCGs and DCLs (Supplementary file 2). There were 199 significant TFs in the results of TED analysis and 35 significant TFs in TDD analysis. 35 TFs that were significant in both of these two analysis results were chosen (Supplementary file 2). DRA analysis yielded 7 DRGs and 33 DRLs. DRGs were not only differentially co-expressed but also differentially co-regulated with 35 mentioned TFs. Then, a network of DRGs and DRLs was visualized using Cytoscape 3.4.0. Based on figure 2, PAX5 transcription factor and genes including ARID1A, CDC42 and LPPR4 were highlighted as the most important units within the genes network with more interconnected links (Figure 2).
In frontal cortex datasets, 628 DCGs and 166256 DCLs were summarized to 20 DRGs and 164 DRLs (Supplementary file 3). There were 199 significant TFs in TED analysis result and 135 significant TFs in TDD analysis result from which 135 TFs were chosen that were significant in both TED and TDD results (Supplementary file 3). In the inferred network, PAX5 and IKZF1 as TFs and genes including GRIK3, MAGI3, PRRX1 and DCAF6 were found as highlighted nodes with more connectivity.
According to hippocampus datasets, 670 DCGs and 56264 DCLs were summarized to 16 DRGs and 43 DRLs (Supplementary file 4). There were 199 common and significant TFs in TED and TDD analysis which were used for inferring differential co-expression network with DRGs. There was more connectivity in hippocampus network than the other two networks. PAX5, ARNT, GATA1, EGR3 and 1KZF1 TFs and genes including KCNKJ, CACHDl, FABP3 and CHRNB2 showed highlighted roles as network nodes.
Aging is believed to be one of the most important non-modifiable risk factors of cognitive diseases that lead unequivocally to a number of detrimental changes in the neural system, increasing neuromorbidity and mortality. AD, as a progressive neurodegenerative disorder with no effective treatment options, is typically characterized by the presence of amyloid-beta plaques and hyper phosphorylated paired helical filament tau protein-rich neurofibrillary tangles (1). The identification of co-expressed genes related to AD presumably provides insights into the underlying mechanisms; in other words, a combination of gene effects likely holds promise as a more effective approach for detecting disease associated genes (42). In fact, examining co-expressed genes in spite of the individual genes could be more informative to explore genes that cause mental health disorders, such as AD (43,44). In this case, the correlation between two genes varies in distinct samples and thereby they are referred to as being differentially co-expressed. This correlation may change independently from the expression levels of two genes, indicating that trans-criptome analysis merely based on differential expression analysis could miss important clues of regulatory patterns (45). Co-expression analysis has been performed for deciphering molecular mechanisms underlying mental health disorders (46-49). In the context of a well-established network analysis approach and given the most variable transcripts between control and AD brain samples, attempts were made by DCGL framework to explore putative pivotal genes that may be associated with AD. This work attempted to identify DRGs and links DRLs in AD by comparing expression datasets of temporal and frontal cortices and hippocampi. A comprehensive search in the literature showed that the obtained DRGs of AD brains mostly have direct or indirect links with AD or another neurologic disorder (Table 2). They are implicated in the gene ontology terms and shared biological pathways like response to lipid, fatty acid, nitrogen compound metabolic process and glutamate signaling pathways (Figures 1 and 2, supplementary file 5). Reportedly, considering GO terms as the response to lipid and fatty acid, brain lipid homeostasis plays an important role in AD (50). In this regard, differential regulation of delta 4-desaturase, sphingolipid 1 (DEGSJ) and fatty acid binding protein 3 (FABP3) in hippocampus and lipid phosphate phos-phatase-related protein type 4 (LPPR4) in temporal cortex datasets may fairly explain the relationship between brain damages happening in these regions and lipid metabolism. DEGSJ encodes a member of the membrane fatty acid desaturase family which is shown to interfere in AD via lipid rafts (25). FABP proteins are thought to participate in the uptake, intracellular metabolism and/or transport of long-chain fatty acids. Concordantly, serum levels of brain-type FABP are elevated in a significant proportion of patients with various neurodegenerative diseases including AD (38). LPPR4 acts as phospholipid dephosphorylate involving axonogenesis. The control of ion flow across the lipid membrane is essential for many cellular functions, including neuronal excitability and dysfunction of conveying ions through lipid bilayers involved in multiple neurologic diseases (51). As illustrated in figure 1, the DRGs are more implicated in signaling pathways; but the DRGs from frontal cortex were more enriched in ionotropic glutamate receptor pathway and metabo-tropic glutamate receptor group II and III pathways.
The dysregulation of glutamatergic signaling has been shown to be associated with AD. Glutamate acts via ionotropic glutamate receptors (iGluR) and metabotropic glutamate receptors (mGluR), both of which have been implicated in AD (52). Differential regulation of glutamate receptor ionotropic, kainate 3 (GRIK3) and voltage-dependent R-type calcium channel subunit alpha-IE (CACNA1E) in frontal cortex datasets may be biologically relevant with the mentioned pathways in AD brain areas. Concordantly, a significant change in the expression of the GRIK3 gene was detected in a patient diagnosed with severe developmental delay (53.) Many different kinds of signaling pathways are changed in AD, indeed the relevance of the biological pathways shown in figure 1 such as cytoskeletal regulation by Rho GTPase suggests mediating of these signaling pathways in the different lobs of brain, in this case in temporal cortex with differential regulation of CDC42. CDC42 has been linked to neuronal diseases like Alzheimer and Parkinson's disease through its role in cytoskeletal organization (54). Among the DGRs, CNTN2, KCNK1 and S100A1 were found common in frontal cortex and hippocampus datasets. S100A1 encodes for calmodulin signaling molecules. Increased levels of calmodulin have been reported in the hippocampus of AD model mice (55). These changes seemingly show an aberrant involvement of calmodulin in the impairment of cell cycle control in AD. As for the potassium channel subfamily K member 1- KCNK1, recent genetic studies suggest a central role for neuroinflammation. KCNK1 is a voltage-gated potassium channel upregulated by activated microglia and a mediator in amyloidmediated microglial priming, additionally reactive oxygen species production that was shown to be related with autoimmunity (56). CNTN2 has been shown to undergo nuclear translocation and altered transcription (33).
These findings probably show that hippocampus and frontal cortices might deeply play a role in AD by mediating with conveying ions. Their obtained DRGs participated in vital processes like signaling, ion transportation and homeostasis. However, these processes mostly signal pathways somehow shared with temporal cortex implying the role of signal molecules within and between brain areas in neurologic dysfunctions. Concordantly, a comprehensive study has been already carried out on GSE36980 series to examine the alteration in the expression of diabetes-related genes in AD brains where they illustrated that hippocampi of AD brains have the most significant alteration in gene expression profile (57).
With a glance at table 2 and the terms including amyloids, inflammation, ROS and immune system, one could infer a cascade of events in which the DRGs interfere. Beta-amyloid deposition following the activation of microglia will initiate an inflammatory response leading to the release of potentially neurotoxic substances and ROS that targets neural damage (5S). Afterward, along with immune response, nitrogen compounds will mediate to reverse the consequences of oxidative stress in damaged regions (8,9). In sum, it was shown that DRGs covered a wide range of known functions and processes implicated in main AD signaling pathways. In a study by Satoh et al , GSE36980 series used in the present study were utilized to identify biomarker genes relevant to the molecular pathogenesis of AD. They analyzed a RNA-Seq dataset composed of the transcriptonie of postmortem AD brains derived from two independent cohorts and they identified the core set of 522 genes deregulated in AD brains shared between both, compared with normal control subjects. Notably, in agreement with our study, LPPR4 was bolded in AD brains in both microarray and RNA-seq datasets. By consistent downregulation of NeuroD6 in AD brains, the results indicated that downregulation of NeuroD6 serves as a possible biomarker for AD brains. Previous studies identified LPPR4 as direct target genes for NeuroD6 by binding assay to E-boxes located in target gene promoters (60). GSE36980 series were also employed by Fowler et al (61) used to investigate potential underlying biology in AD and in concordance with the results of the present study, they noticed the overrepresentation of glutamate in their data mining. They first identified genes consistently associated with AD in each of the four separate expression studies, and confirmed the result using a fifth study. They next developed algorithms to search hundreds of thousands of GEO data sets, identifying a link between an AD-associated gene (NEUROD6) and gender. Additionally, they identified several genes related to glutamate (including CACNG3, a regulator of AMPA-sensitive glutamate receptors; SLC17A7, a mitochondrial oxo-glutarate carrier; and GOT2, mitochondrial glutamic-oxaloacetic transaminase. In our study, differential regulation of glutamate receptor ionotropic, kainate 3 (GRIK3) and voltage-dependent R-type calcium channel subunit alpha-IE (CACNA1E) in frontal cortex datasets could be therefore biologically relevant with the mentioned pathways in AD brain areas. Moreover, in our study, differential regulation of Slc2al in hippocampus data seemingly implies the role of impairments in glutamatergic transmission mostly in hippocampus of AD brains. The role of glutamate transporters such as SLC1A6 was also highlighted in a study by Satoh et al (59).
The purpose of the study was to explore the molecular mechanism in the development of AD, and a comparison of AD in three regions of the brain was done. Therefore, in the frame of network reconstruction and data mining approaches, a small number of possible genes and TFs were identified that their interplay could lead to neural dysfunctions toward AD. However, one should be cautious regarding small sample size while by utilizing more adequate samples, the results would be more reliable evidences.
An expected outcome of such a work would possibly shed light on the bridges between AD-associated brain damage in transcriptome level and presenting crucial evidence in clinical diagnosis and treatment.
We thank Dr. Antonio R. Franco of University of Cordoba (Spain) for his precious assistances.
Conflict of Interest
The authors declared that they have no competing interests.
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Fereshteh Izadi (1*) and Mohammad Hasan Soheilifar (2)
(1.) Department of Genetics, Evolution and Environment, Darwin Building, University College London (UCL), London, UK
(2.) Research Center for Molecular Medicine, Hamedan University of Medical Sciences, Hamedan, Iran
(*) Corresponding author:
Fereshteh Izadi, PhD, Department of Genetics, Evolution and Environment, Darwin Building, University College London (UCL), Gower Street, London WClE 6BT, UK Tel: +44 7846280861
Received: 11 Ian 2017
Accepted: 4 Sept 2017
Table 1. Sample characteristics Biological samples Control AD patients Temporal cortex 19 10 Frontal cortex 18 15 Hippocampus 10 7 Table 2. List of differential regulated genes (DRGs) and corresponding p-value<0.05 of differential co-expression enrichment (DCe) in temporal cortex, frontal cortex and hippocampus datasets Gene name DCe p-value Description AIUD1A 0.00069 ARID 1A was among down- regulated genes in AD model mice (17) Cdc42 0.01224 Cdc42 activity was increased in hippocampus neurons treated with fibrillary [beta]-amyloid (18) WPR4 0.01395 LPPR4 was up-regulated in incipient AD patients (19) Temporal cortex PITHD1 0.01863 // SGIP1 0.02064 // SZT2 0.01936 // ZMPSTE24 0.01936 // C.HD5 5.19E-14 The depletion of CHD5 was shown to be linked with AD associated gene sets (20) EFhc2 has been found to be associated with aggregated tau in the brain in AD and in a mouse model of EFHD2 3.00E-07 frontotemporal dementia (21, 22) Peroxiredoxins (Prxs) may be associated with AD by reducing ROS elicited by amyloid (i (AP) accumulation Prxs 7.82E-07 that could be a causative factor in the pathogenesis of AD (23) MAGIE3 1.68E-06 // EXTL1 4.14E-06 // HPCAL4 2.09E-0S HPCAL4 could be used as a prognostic marker for cognitive decline in AD (24) LPHN2 2.59E-05 LPHN2 is likely to be participated in AD as an altered protein in Lipid Raft (25) NIPAL3 8.35E-05 NIPAL3 was shown as a biomarker in Late-Onset Major Depressive Disorder (26) Frontal cortex CACNAIE 0.00017 ('ACNA1E was down-regulated in cerebral Cockayne syndrome (27) IFI16 0.00033 IFI16 was participated in delaying onset of AD (28) HHI.A3 0.00122 // KCNK1 0.00202 KCNKI exhibited alternative splicing in patients with mesial temporal lobe epilepsy (29) rnpc3 0.00384 11 DCAF6 0.00542 II IPO 13 0.00581 IPOl3 mutants involved in chronic inflammatory diseases (30) RPL11 0.00585 RPLI1 revealed significant altered expression profiles in the neuron model of AD treated with rhTFAM (31) S100AI 0.00716 S100A1 modulates inflammation in AD (32) CNTN2 0.02606 CNTN2 associated with AD via BACEJ activity (33) GRIK3 0.03774 GRIK3 was highly expressed in major depression (34) KCNK1 1.55E-09 // CHRNB2 2.37E-09 C.HRNB2 was found to interfere with the immune system in neurologic disorders (35) HAPLN2 3.43E-05 Hapln2 has been recently shown to be accumulated in the neurofibrillary tangle of Alzheimer's brain (36) Slc2al 0.00207 Slc2al down-regulation exacerbated AD (37) serum levels of brain-type FABP are elevated in a significant proportion of patients with various neurodegenerative FABP3 0.00298 diseases including AD (38) DEGSl 0.00327 DEGSl is likely to be involved in AD as an altered protein in Lipid Raft (25) Hippocampus NKA1N1 0.00435 // S100A1 0.00434 SI00AI modulates inflammation in AD (32) CNTN2 0.00511 GNTN2 associated with AD via b-Secretase (BACE1) activity (33) SFPQ 0.00816 SFPQ was shown as a transcription factor with an altered nucleo-cytoplasmic distribution under neurodegenerative conditions (39) GPSM2 0.01073 // GSTM1 0.01591 GSTM1 null genotype was found as risk factor for late-onset Alzheimer's disease in Italian patients (40) CACHD1 0.02830 CACHD1 is a substrate of BACEI responsible for generating the amyloid-b protein (41) // showing DRGs with ambiguous role in neurologic disorders.
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|Title Annotation:||Original Article|
|Author:||Izadi, Fereshteh; Soheilifar, Mohammad Hasan|
|Publication:||Avicenna Journal of Medical Biotechnology (AJMB)|
|Date:||Oct 1, 2018|
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