Showing NAFLD, as a key connector disease between Alzheimer’s disease and Diabetes, via systems biology analysis
Gastroenterology and Hepatology from Bed to Bench,
9 December 2020
Aim: Network analysis of Alzheimer and Diabetes and find their correlation with each other and other disease/pathways.
Background: Alzheimer’s disease (AD) as a neurodegenerative disease and Diabetes as metabolic disease are two major health problems in recent years. Recent studies reported their correlation and same spreading pathways of these two disease together, but details of this relation at molecular level not well known.
Methods: In TPP technique after treatment of extracted proteins by heat and drug concentration, the resulted proteins analyzed by mass spectrometry. Enrichment analysis of these proteins lead to Alzheimer`s and Diabetes. First, the corresponding genes for each disease were extracted from DisGeNET, then, the protein-protein interaction network for each of them were constructed using STRING. After analyzing these networks, the hub-bottleneck nodes of networks were evaluated. Also, common nodes between two networks were extracted and used for further analysis.
Results: High correlation between Alzheimer disease and Diabetes by the existence of 40 genes in common. Analyzes, revealed 14 genes in AD and 12 genes in Diabetes as hub-bottleneck which seven of them were common including: CASP3, IGF1, CAT, TNF LEP, VEGFA and IL6.
Conclusion: This study, revealed a direct correlation between AD and Diabetes also the correlation between these two diseases and NAFLD, suggesting small change in each three diseases can develop any other disease in patients. Also the enrichments exhibited existence of common pathways between AD, Diabetes, NAFLD and mail fertility.
Key words: NAFLD, Alzheimer's Disease, Diabetes Mellitus, Type II, Male Infertility, Bioinformatics
- Alzheimer’s disease
- Thermal Proteome Profiling
- Male infertility
2. Villemagne VL, Burnham S, Bourgeat P, Brown B, Ellis KA, Salvado O, et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. The Lancet Neurology. 2013;12(4):357-67.
3. Reiman EM, Quiroz YT, Fleisher AS, Chen K, Velez-Pardo C, Jimenez-Del-Rio M, et al. Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer's disease in the presenilin 1 E280A kindred: a case-control study. The Lancet Neurology. 2012;11(12):1048-56.
4. Gordon BA, Blazey TM, Su Y, Hari-Raj A, Dincer A, Flores S, et al. Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer's disease: a longitudinal study. The Lancet Neurology. 2018;17(3):241-50.
5. Association As. 2019 Alzheimer's disease facts and figures. Alzheimer's & Dementia. 2019;15(3):321-87.
6. Ghareeb DA, Hafez HS, Hussien HM, Kabapy NF. Non-alcoholic fatty liver induces insulin resistance and metabolic disorders with development of brain damage and dysfunction. Metabolic brain disease. 2011;26(4):253.
7. Reddy JK, Sambasiva Rao M. Lipid metabolism and liver inflammation. II. Fatty liver disease and fatty acid oxidation. American Journal of Physiology-Gastrointestinal and Liver Physiology. 2006;290(5):G852-G8.
8. Punthakee Z, Goldenberg R, Katz P. Definition, classification and diagnosis of diabetes, prediabetes and metabolic syndrome. Canadian journal of diabetes. 2018;42:S10-S5.
9. Raisifar Z, Nia AA, Madmoli M, Madmoli Y. The Relationship Between Using Insulin and Suffering Alzheimer's Disease in Patients with Diabetes: A Two-Year Study. International Journal of Ecosystems and Ecology Science-Ijees. 2018;8(3):623-8.
10. Thomas KR, Bangen KJ, Weigand AJ, Edmonds EC, Sundermann E, Wong CG, et al. Type 2 Diabetes Interacts With Alzheimer Disease Risk Factors to Predict Functional Decline. Alzheimer Disease & Associated Disorders. 2020;34(1):10-7.
11. Li J, Cesari M, Liu F, Dong B, Vellas B. Effects of diabetes mellitus on cognitive decline in patients with Alzheimer disease: a systematic review. Canadian journal of diabetes. 2017;41(1):114-9.
12. Arnold SE, Arvanitakis Z, Macauley-Rambach SL, Koenig AM, Wang H-Y, Ahima RS, et al. Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nature Reviews Neurology. 2018;14(3):168-81.
13. Jafari R, Almqvist H, Axelsson H, Ignatushchenko M, Lundbäck T, Nordlund P, et al. The cellular thermal shift assay for evaluating drug target interactions in cells. Nature protocols. 2014;9(9):2100.
14. Savitski MM, Reinhard FB, Franken H, Werner T, Savitski MF, Eberhard D, et al. Tracking cancer drugs in living cells by thermal profiling of the proteome. Science. 2014;346(6205):1255784.
15. Dembo G, Park SB, Kharasch ED. Central nervous system concentrations of cyclooxygenase-2 inhibitors in humans. Anesthesiology-Hagerstown. 2005;102(2):409-15.
16. Paulson SK, Vaughn MB, Jessen SM, Lawal Y, Gresk CJ, Yan B, et al. Pharmacokinetics of celecoxib after oral administration in dogs and humans: effect of food and site of absorption. J Pharmacol Exp Ther. 2001;297(2):638-45.
17. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nature methods. 2009;6(5):359-62.
18. Scifo E, Szwajda A, Soliymani R, Pezzini F, Bianchi M, Dapkunas A, et al. Proteomic analysis of the palmitoyl protein thioesterase 1 interactome in SH-SY5Y human neuroblastoma cells. Journal of proteomics. 2015;123:42-53.
19. Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic acids research. 2016:gkw943.
20. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic acids research. 2016:gkw937.
21. Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nature methods. 2012;9(5):471.
22. Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091-3.
23. Bindea G, Galon J, Mlecnik B. CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013;29(5):661-3.
24. Ashtiani M, Mirzaie M, Jafari M. CINNA: An R package for deciphering Central Informative Nodes in Network Analysis. bioRxiv. 2017:168757.
25. Ashtiani M, Salehzadeh-Yazdi A, Razaghi-Moghadam Z, Hennig H, Wolkenhauer O, Mirzaie M, et al. Selection of most relevant centrality measures: A systematic survey on protein-protein interaction networks. bioRxiv. 2017:149492.
26. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 2003;13(11):2498-504.
27. Hunter DJ. Gene–environment interactions in human diseases. Nature Reviews Genetics. 2005;6(4):287-98.
28. Du S, Maneix L, Zhang Q, Wan Y-W, Zheng H. The role of FOXO3 transcription factor in Alzheimer’s disease pathology. Innovation in Aging. 2019;3(Suppl 1):S842.
29. Cao P, Maximov A, Südhof TC. Activity-dependent IGF-1 exocytosis is controlled by the Ca(2+)-sensor synaptotagmin-10. Cell. 2011;145(2):300-11.
30. Li Z-g, Zhang W, Sima AA. Alzheimer-like changes in rat models of spontaneous diabetes. Diabetes. 2007;56(7):1817-24.
31. Zhao X, Bausano B, Pike BR, Newcomb‐Fernandez JK, Wang KK, Shohami E, et al. TNF‐α stimulates caspase‐3 activation and apoptotic cell death in primary septo‐hippocampal cultures. Journal of neuroscience research. 2001;64(2):121-31.
32. Holmes C, Cunningham C, Zotova E, Woolford J, Dean C, Kerr Su, et al. Systemic inflammation and disease progression in Alzheimer disease. Neurology. 2009;73(10):768-74.
33. Cseh K, Winkler G, Melczer Z, Baranyi E. The role of tumour necrosis factor (TNF)-alpha resistance in obesity and insulin resistance. Diabetologia. 2000;43(4):525.
34. Mejido DC, Andrade J, Vieira MN, Ferreira ST, De Felice FG. Insulin and leptin as potential cognitive enhancers in metabolic disorders and Alzheimer's disease. Neuropharmacology. 2020:108115.
35. Meek TH, Morton GJ. The role of leptin in diabetes: metabolic effects. Diabetologia. 2016;59(5):928-32.
36. Jiang LQ, Duque-Guimaraes DE, Machado UF, Zierath JR, Krook A. Altered response of skeletal muscle to IL-6 in type 2 diabetic patients. Diabetes. 2013;62(2):355-61.
37. Zhao B, Schwartz JP. Involvement of cytokines in normal CNS development and neurological diseases: recent progress and perspectives. Journal of neuroscience research. 1998;52(1):7-16.
38. Sawada M, Imamura K, Nagatsu T. Role of cytokines in inflammatory process in Parkinson’s disease. Parkinson’s Disease and Related Disorders: Springer; 2006. p. 373-81.
39. Mirza Z, A Kamal M, H Al-Qahtani M, Karim S. Establishing genomic/transcriptomic links between Alzheimer’s disease and type 2 diabetes mellitus by meta-analysis approach. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders). 2014;13(3):501-16.
40. Karbalaei R, Allahyari M, Rezaei-Tavirani M, Asadzadeh-Aghdaei H, Zali MR. Protein-protein interaction analysis of Alzheimers disease and NAFLD based on systems biology methods unhide common ancestor pathways. Gastroenterology and Hepatology from bed to bench. 2018;11(1):27.
41. Ahmad A, Ali T, Kim MW, Khan A, Jo MH, Rehman SU, et al. Adiponectin homolog novel osmotin protects obesity/diabetes-induced NAFLD by upregulating AdipoRs/PPARα signaling in ob/ob and db/db transgenic mouse models. Metabolism. 2019;90:31-43.
42. Ford W. Glycolysis and sperm motility: does a spoonful of sugar help the flagellum go round? Human Reproduction Update. 2006;12(3):269-74.
43. Miki K. Energy metabolism and sperm function. Society of Reproduction and Fertility supplement. 2007;65:309-25.
44. Flesch FM, Gadella BM. Dynamics of the mammalian sperm plasma membrane in the process of fertilization. Biochimica et Biophysica Acta (BBA)-Reviews on Biomembranes. 2000;1469(3):197-235.
45. Harrison RA, Gadella BM. Bicarbonate-induced membrane processing in sperm capacitation. Theriogenology. 2005;63(2):342-51.
46. Beeram E, Suman B, Divya B. Proteins as the Molecular Markers of Male Fertility. Journal of human reproductive sciences. 2019;12(1):19-23.
47. López-Lemus UA, Garza-Guajardo R, Barboza-Quintana O, Rodríguez-Hernandez A, García-Rivera A, Madrigal-Pérez VM, et al. Association between nonalcoholic fatty liver disease and severe male reproductive organ impairment (germinal epithelial loss): Study on a mouse model and on human patients. American journal of men's health. 2018;12(3):639-48.
- Abstract Viewed: 0 times