#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Computational identification of key genes that may regulate gene expression reprogramming in Alzheimer’s patients


Autoři: Judith A. Potashkin aff001;  Virginie Bottero aff001;  Jose A. Santiago aff002;  James P. Quinn aff003
Působiště autorů: The Cellular and Molecular Pharmacology Department, The Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States of America aff001;  NeuroHub Analytics, LLC, Chicago, IL, United States of America aff002;  Q Regulating Systems, LLC, Gurnee, IL, United States of America aff003
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222921

Souhrn

The dementia epidemic is likely to expand worldwide as the aging population continues to grow. A better understanding of the molecular mechanisms that lead to dementia is expected to reveal potentially modifiable risk factors that could contribute to the development of prevention strategies. Alzheimer’s disease is the most prevalent form of dementia. Currently we only partially understand some of the pathophysiological mechanisms that lead to development of the disease in aging individuals. In this study, Switch Miner software was used to identify key switch genes in the brain whose expression may lead to the development of Alzheimer’s disease. The results indicate that switch genes are enriched in pathways involved in the proteasome, oxidative phosphorylation, Parkinson’s disease, Huntington’s disease, Alzheimer’s disease and metabolism in the hippocampus and posterior cingulate cortex. Network analysis identified the krupel like factor 9 (KLF9), potassium channel tetramerization domain 2 (KCTD2), Sp1 transcription factor (SP1) and chromodomain helicase DNA binding protein 1 (CHD1) as key transcriptional regulators of switch genes in the brain of AD patients. These transcriptions factors have been implicated in conditions associated with Alzheimer’s disease, including diabetes, glucocorticoid signaling, stroke, and sleep disorders. The specific pathways affected reveal potential modifiable risk factors by lifestyle changes.

Klíčová slova:

Alzheimer's disease – Gene expression – Gene mapping – Microarrays – Network analysis – Non-coding RNA – Swimming – Transcription factors


Zdroje

1. Patterson C. World Alzheimer Report 2018. The state of the art of dementia research: New frontiers London, UK: 2018.

2. Rajan KB, Wilson RS, Weuve J, Barnes LL, Evans DA. Cognitive impairment 18 years before clinical diagnosis of Alzheimer disease dementia. Neurology. 2015;85(10):898–904. doi: 10.1212/WNL.0000000000001774 26109713; PubMed Central PMCID: PMC4560057.

3. Serrano-Pozo A, Frosch MP, Masliah E, Hyman BT. Neuropathological alterations in Alzheimer disease. Cold Spring Harb Perspect Med. 2011;1(1):a006189. doi: 10.1101/cshperspect.a006189 22229116; PubMed Central PMCID: PMC3234452.

4. Angelie E, Bonmartin A, Boudraa A, Gonnaud PM, Mallet JJ, Sappey-Marinier D. Regional differences and metabolic changes in normal aging of the human brain: proton MR spectroscopic imaging study. AJNR Am J Neuroradiol. 2001;22(1):119–27. 11158897.

5. Beach TG, Walker R, McGeer EG. Patterns of gliosis in Alzheimer's disease and aging cerebrum. Glia. 1989;2(6):420–36. doi: 10.1002/glia.440020605 2531723.

6. Bliss TV, Collingridge GL. A synaptic model of memory: long-term potentiation in the hippocampus. Nature. 1993;361(6407):31–9. doi: 10.1038/361031a0 8421494.

7. Bobinski M, de Leon MJ, Convit A, De Santi S, Wegiel J, Tarshish CY, et al. MRI of entorhinal cortex in mild Alzheimer's disease. Lancet. 1999;353(9146):38–40. doi: 10.1016/s0140-6736(05)74869-8 10023955.

8. Bouras C, Hof PR, Giannakopoulos P, Michel JP, Morrison JH. Regional distribution of neurofibrillary tangles and senile plaques in the cerebral cortex of elderly patients: a quantitative evaluation of a one-year autopsy population from a geriatric hospital. Cereb Cortex. 1994;4(2):138–50. doi: 10.1093/cercor/4.2.138 8038565.

9. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–59. doi: 10.1007/bf00308809 1759558.

10. Braak H, Braak E. The human entorhinal cortex: normal morphology and lamina-specific pathology in various diseases. Neurosci Res. 1992;15(1–2):6–31. doi: 10.1016/0168-0102(92)90014-4 1336586.

11. Davies L, Wolska B, Hilbich C, Multhaup G, Martins R, Simms G, et al. A4 amyloid protein deposition and the diagnosis of Alzheimer's disease: prevalence in aged brains determined by immunocytochemistry compared with conventional neuropathologic techniques. Neurology. 1988;38(11):1688–93. doi: 10.1212/wnl.38.11.1688 3054625.

12. de Leon MJ, George AE, Stylopoulos LA, Smith G, Miller DC. Early marker for Alzheimer's disease: the atrophic hippocampus. Lancet. 1989;2(8664):672–3. doi: 10.1016/s0140-6736(89)90911-2 2570916.

13. Du AT, Schuff N, Zhu XP, Jagust WJ, Miller BL, Reed BR, et al. Atrophy rates of entorhinal cortex in AD and normal aging. Neurology. 2003;60(3):481–6. doi: 10.1212/01.wnl.0000044400.11317.ec 12578931; PubMed Central PMCID: PMC1851672.

14. Fox NC, Warrington EK, Stevens JM, Rossor MN. Atrophy of the hippocampal formation in early familial Alzheimer's disease. A longitudinal MRI study of at-risk members of a family with an amyloid precursor protein 717Val-Gly mutation. Ann N Y Acad Sci. 1996;777:226–32. doi: 10.1111/j.1749-6632.1996.tb34423.x 8624089.

15. Frisoni GB, Laakso MP, Beltramello A, Geroldi C, Bianchetti A, Soininen H, et al. Hippocampal and entorhinal cortex atrophy in frontotemporal dementia and Alzheimer's disease. Neurology. 1999;52(1):91–100. doi: 10.1212/wnl.52.1.91 9921854.

16. Hyman BT, Van Hoesen GW, Damasio AR, Barnes CL. Alzheimer's disease: cell-specific pathology isolates the hippocampal formation. Science. 1984;225(4667):1168–70. doi: 10.1126/science.6474172 6474172.

17. Ibanez V, Pietrini P, Alexander GE, Furey ML, Teichberg D, Rajapakse JC, et al. Regional glucose metabolic abnormalities are not the result of atrophy in Alzheimer's disease. Neurology. 1998;50(6):1585–93. doi: 10.1212/wnl.50.6.1585 9633698.

18. Jack CR Jr., Petersen RC, Xu Y, O'Brien PC, Smith GE, Ivnik RJ, et al. Rate of medial temporal lobe atrophy in typical aging and Alzheimer's disease. Neurology. 1998;51(4):993–9. doi: 10.1212/wnl.51.4.993 9781519; PubMed Central PMCID: PMC2768817.

19. Liang WS, Dunckley T, Beach TG, Grover A, Mastroeni D, Walker DG, et al. Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol Genomics. 2007;28(3):311–22. doi: 10.1152/physiolgenomics.00208.2006 17077275; PubMed Central PMCID: PMC2259385.

20. Mielke R, Herholz K, Grond M, Kessler J, Heiss WD. Clinical deterioration in probable Alzheimer's disease correlates with progressive metabolic impairment of association areas. Dementia. 1994;5(1):36–41. 8156085.

21. Morris JC, Price JL. Pathologic correlates of nondemented aging, mild cognitive impairment, and early-stage Alzheimer's disease. J Mol Neurosci. 2001;17(2):101–18. 11816784.

22. Price JL, Davis PB, Morris JC, White DL. The distribution of tangles, plaques and related immunohistochemical markers in healthy aging and Alzheimer's disease. Neurobiol Aging. 1991;12(4):295–312. doi: 10.1016/0197-4580(91)90006-6 1961359.

23. Rogers J, Morrison JH. Quantitative morphology and regional and laminar distributions of senile plaques in Alzheimer's disease. J Neurosci. 1985;5(10):2801–8. 4045553.

24. Small GW, Ercoli LM, Silverman DH, Huang SC, Komo S, Bookheimer SY, et al. Cerebral metabolic and cognitive decline in persons at genetic risk for Alzheimer's disease. Proc Natl Acad Sci U S A. 2000;97(11):6037–42. doi: 10.1073/pnas.090106797 10811879; PubMed Central PMCID: PMC18554.

25. Metsaars WP, Hauw JJ, van Welsem ME, Duyckaerts C. A grading system of Alzheimer disease lesions in neocortical areas. Neurobiol Aging. 2003;24(4):563–72. doi: 10.1016/s0197-4580(02)00134-3 12714113.

26. Liang WS, Dunckley T, Beach TG, Grover A, Mastroeni D, Ramsey K, et al. Altered neuronal gene expression in brain regions differentially affected by Alzheimer's disease: a reference data set. Physiol Genomics. 2008;33(2):240–56. doi: 10.1152/physiolgenomics.00242.2007 18270320; PubMed Central PMCID: PMC2826117.

27. Liang WS, Reiman EM, Valla J, Dunckley T, Beach TG, Grover A, et al. Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc Natl Acad Sci U S A. 2008;105(11):4441–6. doi: 10.1073/pnas.0709259105 18332434; PubMed Central PMCID: PMC2393743.

28. Fiscon G, Conte F, Farina L, Paci P. Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine. Genes (Basel). 2018;9(9). doi: 10.3390/genes9090437 30200360; PubMed Central PMCID: PMC6162385.

29. Fiscon G, Conte F, Licursi V, Nasi S, Paci P. Computational identification of specific genes for glioblastoma stem-like cells identity. Sci Rep. 2018;8(1):7769. doi: 10.1038/s41598-018-26081-5 29773872; PubMed Central PMCID: PMC5958093.

30. Palumbo MC, Zenoni S, Fasoli M, Massonnet M, Farina L, Castiglione F, et al. Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce major transcriptome reprogramming during grapevine development. Plant Cell. 2014;26(12):4617–35. doi: 10.1105/tpc.114.133710 25490918; PubMed Central PMCID: PMC4311215.

31. Paci P, Colombo T, Fiscon G, Gurtner A, Pavesi G, Farina L. SWIM: a computational tool to unveiling crucial nodes in complex biological networks. Sci Rep. 2017;7:44797. doi: 10.1038/srep44797 28317894; PubMed Central PMCID: PMC5357943.

32. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B Stat Methodol. 1995:289–300.

33. Hartigan J, Wong M. Algorithm AS 136: A k-means clustering algorithm. J R Stat Soc Ser B Stat Methodol. 1979:100–8.

34. Dennis G Jr., Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4(5):P3. 12734009.

35. Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, et al. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007;35(Web Server issue):W169–75. doi: 10.1093/nar/gkm415 17576678; PubMed Central PMCID: PMC1933169.

36. Xia J, Gill EE, Hancock RE. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10(6):823–44. doi: 10.1038/nprot.2015.052 25950236.

37. Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H. UpSet: Visualization of Intersecting Sets. IEEE Trans Vis Comput Graph. 2014;20(12):1983–92. Epub 2015/09/12. doi: 10.1109/TVCG.2014.2346248 26356912; PubMed Central PMCID: PMC4720993.

38. Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE. Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann Neurol. 1997;42(1):85–94. doi: 10.1002/ana.410420114 9225689.

39. Reiman EM, Chen K, Alexander GE, Caselli RJ, Bandy D, Osborne D, et al. Correlations between apolipoprotein E epsilon4 gene dose and brain-imaging measurements of regional hypometabolism. Proc Natl Acad Sci U S A. 2005;102(23):8299–302. doi: 10.1073/pnas.0500579102 15932949; PubMed Central PMCID: PMC1149416.

40. Caberlotto L, Lauria M, Nguyen TP, Scotti M. The central role of AMP-kinase and energy homeostasis impairment in Alzheimer's disease: a multifactor network analysis. PLoS One. 2013;8(11):e78919. doi: 10.1371/journal.pone.0078919 24265728; PubMed Central PMCID: PMC3827084.

41. Cui A, Fan H, Zhang Y, Zhang Y, Niu D, Liu S, et al. Dexamethasone-induced Kruppel-like factor 9 expression promotes hepatic gluconeogenesis and hyperglycemia. J Clin Invest. 2019;130:2266–78. doi: 10.1172/JCI66062 31033478; PubMed Central PMCID: PMC6546458.

42. de la Monte SM, Wands JR. Alzheimer's disease is type 3 diabetes-evidence reviewed. J Diabetes Sci Technol. 2008;2(6):1101–13. doi: 10.1177/193229680800200619 19885299; PubMed Central PMCID: PMC2769828.

43. Canet G, Chevallier N, Zussy C, Desrumaux C, Givalois L. Central Role of Glucocorticoid Receptors in Alzheimer's Disease and Depression. Front Neurosci. 2018;12:739. doi: 10.3389/fnins.2018.00739 30459541; PubMed Central PMCID: PMC6232776.

44. Ouanes S, Popp J. High Cortisol and the Risk of Dementia and Alzheimer's Disease: A Review of the Literature. Front Aging Neurosci. 2019;11:43. doi: 10.3389/fnagi.2019.00043 30881301; PubMed Central PMCID: PMC6405479.

45. Boada M, Antunez C, Ramirez-Lorca R, DeStefano AL, Gonzalez-Perez A, Gayan J, et al. ATP5H/KCTD2 locus is associated with Alzheimer's disease risk. Mol Psychiatry. 2014;19(6):682–7. doi: 10.1038/mp.2013.86 23857120; PubMed Central PMCID: PMC4031637.

46. Traylor M, Adib-Samii P, Harold D, Alzheimer's Disease Neuroimaging I, International Stroke Genetics Consortium UKYLSDNAr, Dichgans M, et al. Shared genetic contribution to Ischaemic Stroke and Alzheimer's Disease. Ann Neurol. 2016;79(5):739–47. doi: 10.1002/ana.24621 26913989; PubMed Central PMCID: PMC4864940.

47. Li Q, Kellner DA, Hatch HAM, Yumita T, Sanchez S, Machold RP, et al. Conserved properties of Drosophila Insomniac link sleep regulation and synaptic function. PLoS Genet. 2017;13(5):e1006815. doi: 10.1371/journal.pgen.1006815 28558011; PubMed Central PMCID: PMC5469494.

48. Pirone L, Smaldone G, Esposito C, Balasco N, Petoukhov MV, Spilotros A, et al. Proteins involved in sleep homeostasis: Biophysical characterization of INC and its partners. Biochimie. 2016;131:106–14. doi: 10.1016/j.biochi.2016.09.013 27678190.

49. Ju YE, Lucey BP, Holtzman DM. Sleep and Alzheimer disease pathology—a bidirectional relationship. Nat Rev Neurol. 2014;10(2):115–9. doi: 10.1038/nrneurol.2013.269 24366271; PubMed Central PMCID: PMC3979317.

50. Citron BA, Dennis JS, Zeitlin RS, Echeverria V. Transcription factor Sp1 dysregulation in Alzheimer's disease. J Neurosci Res. 2008;86(11):2499–504. doi: 10.1002/jnr.21695 18449948.

51. Citron BA, Saykally JN, Cao C, Dennis JS, Runfeldt M, Arendash GW. Transcription factor Sp1 inhibition, memory, and cytokines in a mouse model of Alzheimer's disease. Am J Neurodegener Dis. 2015;4(2):40–8. 26807343; PubMed Central PMCID: PMC4700125.

52. Berson A, Sartoris A, Nativio R, Van Deerlin V, Toledo JB, Porta S, et al. TDP-43 Promotes Neurodegeneration by Impairing Chromatin Remodeling. Curr Biol. 2017;27(23):3579–90 e6. doi: 10.1016/j.cub.2017.10.024 29153328; PubMed Central PMCID: PMC5720388.

53. Schoberleitner I, Mutti A, Sah A, Wille A, Gimeno-Valiente F, Piatti P, et al. Role for Chromatin Remodeling Factor Chd1 in Learning and Memory. Front Mol Neurosci. 2019;12:3. doi: 10.3389/fnmol.2019.00003 30728766; PubMed Central PMCID: PMC6351481.

54. Green CD, Huang Y, Dou X, Yang L, Liu Y, Han JJ. Impact of Dietary Interventions on Noncoding RNA Networks and mRNAs Encoding Chromatin-Related Factors. Cell Rep. 2017;18(12):2957–68. doi: 10.1016/j.celrep.2017.03.001 28329687.


Článek vyšel v časopise

PLOS One


2019 Číslo 9
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

KOST
Koncepce osteologické péče pro gynekology a praktické lékaře
nový kurz
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Svět praktické medicíny 5/2023 (znalostní test z časopisu)

Imunopatologie? … a co my s tím???
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#