|Year : 2014 | Volume
| Issue : 1 | Page : 48-54
Alzheimer disease: An interactome of many diseases
Balaji S Rao, Krishna Kant Gupta, Pujitha Karanam, Anusha Peruri
Department of Bioinformatics, NTHRYS Biotech Labs, Hyderabad, Andhra Pradesh, India
|Date of Submission||20-May-2013|
|Date of Decision||29-Jul-2013|
|Date of Acceptance||25-Sep-2013|
|Date of Web Publication||12-Mar-2014|
Balaji S Rao
Department of Bioinformatics, NTHRYS Biotech Labs, 1-8-747/11, Baghlingampally, Hyderabad - 500 044, Andhra Pradesh
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Alzheimer Disease (AD) is an outcome as well as source of many diseases. Alzheimer is linked with many other diseases like Diabetes type 2, cholesterolemia, hypertension and many more. But how each of these diseases affecting other is still unknown to scientific community. Signaling Pathways of one disease is interlinked with other disease. But to what extent healthy brain is affected when any signaling in human body is disturbed is the question that matters. There is a need of Pathway analysis, Protein-Protein interaction (PPI) and the conserved interactome study in AD and linked diseases. It will be helpful in finding the potent drug or vaccine target in conscious manner. In the present research the Protein-Protein interaction of all the proteins involved in Alzheimer Disease is analyzed using ViSANT and osprey tools and pathway analysis further reveals the significant genes/proteins linking AD with other diseases.
Keywords: Alzheimer disease, centrality, gene set enrichment analysis, protein-protein interactions, reactive oxygen species, string, network analysis
|How to cite this article:|
Rao BS, Gupta KK, Karanam P, Peruri A. Alzheimer disease: An interactome of many diseases. Ann Indian Acad Neurol 2014;17:48-54
| Introduction|| |
Alzheimer disease (AD) is the most common type of dementia; accounts for an estimated 60-80% of cases. "Dementia" describes a variety of diseases and conditions that develop when neuron in the brain dies or no longer function normally. The hallmark abnormalities are protein fragment beta-amyloid (plaques) and twisted strands of the protein tau (tangles) as well as evidence of nerve cell damage and death in the brain which are represented in [Figure 1]. To target any molecule we need to validate its biochemical network properly as its unconscious targeting may leads to affect other vital cell signaling (side effect). It is reported that streptozotocin induced removal of insulin receptors leads to strong risk factors.  There is a strong correlation between glucose utilization and memory enhancement because insulin-enhancing therapeutic agents enhance memory.  It is found that Insulin is involved in cholesterol biosynthesis. Insulin increases 3-Hydroxy-3-methylglutaryl-coA HMGR levels by at least 10-fold in rat hepatoma cells.  Impairment in insulin signaling cascade may be implicated in the pathways through which soluble Amyloid beta (Aβ) induces Tau phosphorylation. The phosphorylation of Akt and glycogen synthase kinase-3GSK3 β upon insulin stimulation is less activated under AD leads to Aβ induced Tau hyperphosphorylation. The strong link between cholesterol and insulin is that Type II diabetes is associated with high synthesis and low absorption of cholesterol. Other important regulatory enzymes of insulin metabolism are acyl-CoA: Cholesterol acyltransferase (ACAT).  Oxysterols cholesterol 7 α-hydroxylase and sterol 27-hydroxylase,  sterol- regulatory-element-binding proteins-lc.  Neurosteroids plays a vital role on memory processing, elevations in cholesterol could increase the production of neurosteroids such as dehydro- epiandrosterone, δ5-androstene-3 β,17 β-diol, pregnenolone and 7 α-OH-dehydroepiandrosterone. γ-aminobutyric acid A receptors (GABAA), N-methyl-D-aspartate (NMDA) and cholinergic and sigma opioid systems are all potential targets of neurosteroids. Allopregnano-lone and pregnanolone are the most potent known modulators of GABAA receptors; neurosteroid levels were negatively correlated in one report with levels of phosphorylated tau protein and β-amyloid, two biochemical markers of AD. Allopregnanolone is 20 times more potent than benzodiazepines at potentiating GAB aergic neurotransmission. It can be synthesized in the brain from peripherally derived progesterone.  Cholesterol metabolites synthesized in the periphery would find it much easier to reach the brain than cholesterol itself. Long-term potentiation of NMDA receptor during Aβ production by ACAT in cholesterol esterification also impairs many negative circumstances.  Aβ is the mediator of tau hyperphosphorylation by impaired insulin signaling. It is observed that decreasing membrane fluidity due to hypercholesterolaemia impairs memory.  Membrane rigidity due to Phospholipase A2 inhibition positively correlates with AD.  apolipoprotein E is an important cholesterol transport protein associated with low-density lipoprotein;  Reactive Oxygen Species (ROS) production causes oxidative stress and there are many negative circumstances going on in the brain. It is experimentally verified that oxidative stress results in an increase in the activity of beta secretase (BACE1) through activation of the PKR-eIF2α pathway, another major potent target for AD.  Disturbed metal metabolism in the brain leads to ROS production and the antioxidants inhibits the metals ions responsible for ROS production. Whether the antioxidants is exogenous or endogenous, all these scavenge the harmful effect of ROS.
|Figure 1: Role of Proto-oncogene tyrosine-protein kinase protein Src; EC = 220.127.116.11 in linking neuronal apoptosis and amyloidogenic pathway in Alzheimer disease|
Click here to view
| Materials and Methods|| |
Collection of alzheimer and alzheimer linked genes
The first thing comes in mind is the genes involved in AD and its associated diseases. We collected all the genes from GeneCards,  integrated, database of human genes that provides concise genomic related information, on all known and predicted human genes. By literature survey it is reported that Alzheimer patients have a high risk of having obesity,  cholesterolemia,  Diabetes type 2  and hypertension.  There are some biochemical marker which is reported in research papers. These include reduced levels of melatonin,  disturbed metal metabolism,  Beta amyloid plaques and tangles formation. 
On considering the above facts, we considered all the genes associated with AD, obesity, cholesterolemia, diabetes type 2, hypertension, reduced levels of melatonin, disturbed metal metabolism, beta amyloid plaques and tangles formation.
In GeneCards we selected genes using below mentioned criteria:
Gene set enrichment analysis
Molecular signature Database  was used to collect the training gene sets for the biological processes that are modulated in alzheimer pathogenesis such as neuronal apoptosis, regulator of neurotransmitter release and neuronal system process. To perform candidate gene prioritization, Toppgene  was used. DAVID  tool was also used to crosscheck the results coming from Toppgene. We performed individual analysis for the candidate genes (test genes) of each diseases and gene prioritization was done with each biological processes as training set. The sorted genes list were created for each disease linked with AD.
Analysis of protein-protein interactions (PPIs) with applying gene ontology (GO) and experimental filter
The best candidate genes in accordance with Alzheimer pathogenesis were analyzed for PPIs in ViSANT  tool. Each network of diseases were created and checked once again with suitable biological GO  terms matching AD Pathogenesis. Each individual disease network was checked for any linking protein with AD and with each other. Linking of each gene in the network were carried out with significant interaction method. The whole PPIs was is a predictome with genes linking diseases like diabetes type 2, cholesterolemia, hypertension and many more with AD. Based on the training set relative importance of candidate gene in gene network was also carried out from ToppNet. 
Pathways and disease analysis
All the genes were checked for pathway and signaling to validate its contribution in AD pathogenesis. Again ViSANT was used. To cross check the results of ViSANT, DAVID was also used for pathway annotation so that significance of a particular gene in AD pathogenesis can be traced.
Key node and centrality analysis
On the basis of degree of candidate genes, key nodes were searched. In ViSANT, we applied from 2 to 6 value for degree of nodes and found significant master and accessory genes. For crosscheck we feed the result of ToppGene or ToppNet into ToppGenet for Prioritization of neighboring genes in PPI network.
Analysis of key proteins linking AD to various diseases
The individual genes in the network were analyzed in H-invitational database  to find gene structures, alternative splicing variants, non-coding functional RNAs, protein functions, protein 3D structure, functional domains and sub-cellular localizations. Again metabolic pathways, genetic polymorphisms single-nucleotide polymorphism (SNP) indels and microsatellite repeats), relation with diseases, gene expression profiling, and molecular evolutionary features and gene families/groups. The final network were optimized so that the genes in form network has high weightage.
| Results, Discussion and Conclusions|| |
We collected 246, 107, 60, 194, 1 and 2 genes from GeneCards for AD, diabetes types 2, high cholesterol, hypertension, reduced melatonin and metal ions. based on neurotransmitter regulation, neuronal apoptosis and neuronal system processing important genes from AD, diabetes types 2, high cholesterol and hypertension whose pathways have been mapped are shown in [Table 1].
|Table 1: Key genes involved in pathways involved in neurotransmitter regulation, neuronal apoptosis and neuronal system processing|
Click here to view
Genes responsible for highcholestrome, hypertension, diabetes type 2 and alzheimers were represented in light green, grey, light blue and red in respective metanodes, whereas the remaining colors indicates as linking genes which are associated with all the four disease, among all the linked genes park 2 is common in all the four diseases were mentioned in [Table 2]. Apart from hypertension DRD2 gene also plays an important role in remaining diseases. Important genes involved in Neurotransmitter regulation are shown in [Figure 2]. Based on the node centrality and degree analysis, DRD2 and PARK2 is the most important gene regulating the four major diseases. As per our observation, it is clearly visible that PARK2, yellow color node is linked with all the four metanode i.e., all the four metanode has at least one yellow color node.
|Figure 2: Key genes linking four mentioned diseases in neurotransmitter regulation as one of the main factor in Alzheimer disease|
Click here to view
Similarly, DRD2 (Blue color) is linked with AD, diabetes type 2 and high cholesterol, but not with hypertension as there is no blue color node in hypertension metanode.
PARK2: The precise function of this gene is unknown; however, the encoded protein is a component of a multiprotein E3 ubiquitin ligase complex that mediates the targeting of substrate proteins for proteasomal degradation [Table 3]. Mutations in this gene are known to cause Parkinson disease and autosomal recessive juvenile Parkinson disease. Alternative splicing of this gene produces multiple transcript variants encoding distinct isoforms. Additional splice variants of this gene have been described, but currently lack transcript support.
DRD2: This gene encodes the D2 subtype of the dopamine receptor. This G-protein coupled receptor inhibits adenylyl cyclase activity [Table 3]. A missense mutation in this gene causes myoclonus dystonia; other mutations have been associated with schizophrenia. Alternative splicing of this gene results in two transcript variants encoding different isoforms. A third variant has been described, but it has not been determined whether this form is normal or due to aberrant splicing.
Based on Neuronal apoptosis, we found hunting in (HTT) protein is linked with all the four diseases with a good degree and centrality [Figure 3]. HTT:HTT is a disease gene linked to Huntington's disease, a neurodegenerative disorder characterized by loss of striatal neurons. Genes which are representing the linking genes of neuronal apoptosis and neural processing were mentioned in [Table 4] and [Table 6] respectively. This is thought to be caused by an expanded, unstable trinucleotide repeat in the HTT gene, which translates as a polyglutamine repeat in the protein product. A fairly broad range in the number of trinucleotide repeats has been identified in normal controls and repeat numbers in excess of 40 have been described as pathological. The HTT locus is large, spanning 180 kb and consisting of 67 exons. The HTT gene is widely expressed and is required for normal development. It is expressed as 2 alternatively polyadenylated forms displaying different relative abundance in various fetal and adult tissues. The larger transcript is approximately 13.7 kb and is expressed predominantly in adult and fetal brain, whereas the smaller transcript of approximately 10.3 kb is more widely expressed [Table 5]. The genetic defect leading to Huntington's disease may not necessarily eliminate transcription, but may confer a new property on the mRNA or alter the function of the protein. One candidate is the HTT-associated protein-1, highly expressed in the brain, which has increased affinity for HTT protein with expanded polyglutamine repeats. This gene contains an upstream open reading frame in the 5' UTR that inhibits expression of the HTT gene product through translational repression. Similarly, based on neuronal processing, we found ADORA1 protein is linked with all the four diseases with a good degree and centrality [Figure 4]. ADORA1: The protein encoded by this gene is an adenosine receptor that belongs to the G-protein coupled receptor 1 family. There are three types of adenosine receptors, each with a specific pattern of ligand binding and tissue distribution and together they regulate a diverse set of physiologic functions. The type A1 receptors inhibit adenylyl cyclase and play a role in the fertilization process. Animal studies also suggest a role for A1 receptors in kidney function and ethanol intoxication. Transcript variants with alternative splicing in the 5' untranslated region UTR have been found for this gene. The transcript and protein IDs were given in [Table 7].
|Figure 3: Key genes linking four mentioned diseases in neuronal apoptosis as one of another main factor in Alzheimer disease|
Click here to view
|Figure 4: Key genes linking four mentioned diseases in neuronal processing as one of another main factor in Alzheimer disease|
Click here to view
| Conclusion|| |
As per insilico analysis; HTT, PARK2, ADORA1 and DRD2 genes aid in linking AD, diabetes type 2, high cholesterol and hypertension. These genes must be considered for treatment as these genes have a high degree of connection and good centrality. Apart from this, there are many secondary genes are there for the same, but HTT, PARK2, ADORA1 and DRD2 are the master genes in functionally linked network. NOS2 is an also important for linking these four dreadful diseases.
| References|| |
|1.||Biessels GJ, Cristino NA, Rutten GJ, Hamers FP, Erkelens DW, Gispen WH. Neurophysiological changes in the central and peripheral nervous system of streptozotocin-diabetic rats. Course of development and effects of insulin treatment. Brain 1999;122:757-68. |
|2.||Craft S, Asthana S, Newcomer JW, Wilkinson CW, Matos IT, Baker LD, et al. Enhancement of memory in Alzheimer disease with insulin and somatostatin, but not glucose. Arch Gen Psychiatry 1999;56:1135-40. |
|3.||Osborne AR, Pollock VV, Lagor WR, Ness GC. Identification of insulin-responsive regions in the HMG-CoA reductase promoter. Biochem Biophys Res Commun 2004;318:814-8. |
|4.||Pihlajamäki J, Gylling H, Miettinen TA, Laakso M. Insulin resistance is associated with increased cholesterol synthesis and decreased cholesterol absorption in normoglycemic men. J Lipid Res 2004;45:507-12. |
|5.||Twisk J, Hoekman MF, Lehmann EM, Meijer P, Mager WH, Princen HM. Insulin suppresses bile acid synthesis in cultured rat hepatocytes by down-regulation of cholesterol 7 alpha-hydroxylase and sterol 27-hydroxylase gene transcription. Hepatology 1995;21:501-10. |
|6.||Shimomura I, Bashmakov Y, Ikemoto S, Horton JD, Brown MS, Goldstein JL. Insulin selectively increases SREBP-1c mRNA in the livers of rats with streptozotocin-induced diabetes. Proc Natl Acad Sci U S A 1999;96:13656-61. |
|7.||Lambert JJ, Belelli D, Peden DR, Vardy AW, Peters JA. Neurosteroid modulation of GABAA receptors. Prog Neurobiol 2003;71:67-80. |
|8.||Puglielli L, Konopka G, Pack-Chung E, Ingano LA, Berezovska O, Hyman BT, et al. Acyl-coenzyme A: Cholesterol acyltransferase modulates the generation of the amyloid beta-peptide. Nat Cell Biol 2001;3:905-12. |
|9.||Lurie KG, Chin JH, Hoffman BB. Decreased membrane fluidity and beta-adrenergic responsiveness in atherosclerotic quail. Am J Physiol 1985;249:H380-5. |
|10.||Evans RM, Hui S, Perkins A, Lahiri DK, Poirier J, Farlow MR. Cholesterol and APOE genotype interact to influence Alzheimer disease progression. Neurology 2004;62:1869-71. |
|11.||Jarvik GP, Wijsman EM, Kukull WA, Schellenberg GD, Yu C, Larson EB. Interactions of apolipoprotein E genotype, total cholesterol level, age, and sex in prediction of Alzheimer′s disease: A case-control study. Neurology 1995;45:1092-6. |
|12.||Yao ZX, Papadopoulos V. Function of beta-amyloid in cholesterol transport: A lead to neurotoxicity. FASEB J 2002;16:1677-9. |
|13.||Rappaport N, Nativ N, Stelzer G, Twik M, Guan-Golan Y, Stein TI, et al. MalaCards: An integrated compendium for diseases and their annotation. Database (Oxford) 2013;2013:bat018. |
|14.||Driscoll I, Beydoun MA, An Y, Davatzikos C, Ferrucci L, Zonderman AB, et al. Midlife obesity and trajectories of brain volume changes in older adults. Hum Brain Mapp 2012;33:2204-10. |
|15.||Lazar AN, Bich C, Panchal M, Desbenoit N, Petit VW, Touboul D, et al. Time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging reveals cholesterol overload in the cerebral cortex of Alzheimer disease patients. Acta Neuropathol 2013;125:133-44. |
|16.||Kim I, Lee J, Hong HJ, Jung ES, Ku YH, Jeong IK, et al. A relationship between Alzheimer′s disease and type 2 diabetes mellitus through the measurement of serum amyloid-beta autoantibodies. J Alzheimers Dis 2010;19:1371-6. |
|17.||Nation DA, Hong S, Jak AJ, Delano-Wood L, Mills PJ, Bondi MW, et al. Stress, exercise, and Alzheimer′s disease: A neurovascular pathway. Med Hypotheses 2011;76:847-54. |
|18.||Masilamoni JG, Jesudason EP, Dhandayuthapani S, Ashok BS, Vignesh S, Jebaraj WC, et al. The neuroprotective role of melatonin against amyloid beta peptide injected mice. Free Radic Res 2008;42:661-73. |
|19.||Zhu X, Lee HG, Casadesus G, Avila J, Drew K, Perry G, et al. Oxidative imbalance in Alzheimer′s disease. Mol Neurobiol 2005;31:205-17. |
|20.||Tharp WG, Sarkar IN. Origins of amyloid-β. BMC Genomics 2013;14:290. |
|21.||Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al., Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles.Proceedings of the National Academy of Sciences. Proc Natl Acad Sci U S A 2005;102:15545-50. |
|22.||Chen J, Xu H, Aronow BJ, Jegga AG. Improved human disease candidate gene prioritization using mouse phenotype. BMC Bioinformatics 2007;8:392. |
|23.||Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009;4:44-57. |
|24.||Hu Z. Analysis strategy of protein-protein interaction networks. Methods Mol Biol 2013;939:141-81. |
|25.||Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000;25:25-9. |
|26.||Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 2009;37:W305-11. |
|27.||Takeda J, Yamasaki C, Murakami K, Nagai Y, Sera M, Hara Y, et al. H-InvDB in 2013: An omics study platform for human functional gene and transcript discovery. Nucleic Acids Res 2013;41:D915-9. |
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]
|This article has been cited by|
||Effects of paternal cadmium exposure on the sperm quality of male rats and the neurobehavioral system of their offspring
| ||Xiaoguo Zhao,Zhenzhen Cheng,Yi Zhu,Shan Li,Liang Zhang,Yunbo Luo |
| ||Experimental and Therapeutic Medicine. 2015; |
|[Pubmed] | [DOI]|