Skip to main content

Advertisement

SpringerLink
Log in

Inhibitor of DNA Binding Protein 3 (ID3) and Nuclear Respiratory Factor 1 (NRF1) Mediated Transcriptional Gene Signatures are Associated with the Severity of Cerebral Amyloid Angiopathy

  • Published:
Molecular Neurobiology Aims and scope Submit manuscript

Abstract

Cerebral amyloid angiopathy (CAA) is a degenerative vasculopathy. We have previously shown that transcription regulating proteins- inhibitor of DNA binding protein 3 (ID3) and the nuclear respiratory factor 1 (NRF1) contribute to vascular dysregulation. In this study, we have identified sex specific ID3 and NRF1-mediated gene networks in CAA patients diagnosed with Alzheimer’s Disease (AD). High expression of ID3 mRNA coupled with low NRF1 mRNA levels was observed in the temporal cortex of men and women CAA patients. Low NRF1 mRNA expression in the temporal cortex was found in men with severe CAA. High ID3 expression was found in women with the genetic risk factor APOE4. Low NRF1 expression was also associated with APOE4 in women with CAA. Genome wide transcriptional activity of both ID3 and NRF1 paralleled their mRNA expression levels. Sex specific differences in transcriptional gene signatures of both ID3 and NRF1 were observed. These findings were further corroborated by Bayesian machine learning and the GeNIe simulation models. Dynamic machine learning using a Monte Carlo Markov Chain (MCMC) gene ordering approach revealed that ID3 was associated with disease severity in women. NRF1 was associated with CAA and severity of this disease in men. These findings suggest that aberrant ID3 and NRF1 activity presumably plays a major role in the pathogenesis and severity of CAA. Further analyses of ID3- and NRF1-regulated molecular drivers of CAA may provide new targets for personalized medicine and/or prevention strategies against CAA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

Data Availability

The results published here are in whole or in part based on data obtained from the AD Knowledge Portal. Details of analysis used in the current study are available from the first author or corresponding author upon request. Data are made available as open- or controlled-access. The RNA-Seq datasets analyzed throughout the study are available in the AD Knowledge Portal Synapse Repository, at syn9779506 and syn5550404.

The Mayo Clinic AD-CAA study was led by Dr. Guojun Bu and Dr. Nilufer Ertekin-Taner at the, Mayo Clinic, Jacksonville, FL as part of the multi-PI RF1AG051504 (MPIs Bu and Ertekin-Taner) using samples from the Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50AG016574, R37AG027924, Cure Alzheimer’s Fund, and support from Mayo Foundation. The Mayo RNAseq study data was led by Dr. Nilüfer Ertekin-Taner, Mayo Clinic, Jacksonville, FL as part of the multi-PI U01 AG046139 (MPIs Golde, Ertekin-Taner, Younkin, Price). Samples were provided from the following sources: The Mayo Clinic Brain Bank. Data collection was supported through funding by NIA grants P50 AG016574, R01 AG032990, U01 AG046139, R01 AG018023, U01 AG006576, U01 AG006786, R01 AG025711, R01 AG017216, R01 AG003949, NINDS grant R01 NS080820, CurePSP Foundation, and support from Mayo Foundation. Study data includes samples collected through the Sun Health Research Institute Brain and Body Donation Program of Sun City, Arizona.

The microvessel microarray dataset analyzed in this study are available in the Gene Expression Omnibus at the National Center for Biotechnology Information at GSE45596.

References

  1. Biffi A, Greenberg SM (2011) Cerebral amyloid angiopathy: a systematic review. J Clin Neurol [Internet] 7(1):1. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3079153/

  2. Viswanathan A, Greenberg SM (2009) Intracerebral hemorrhage. Handb Clin Neurol 93:767–790. https://doi.org/10.1016/S0072-9752(08)93038-4

  3. van Asch CJ, Luitse MJ, Rinkel GJ, van der Tweel I, Algra A, Klijn CJ (2010) Incidence, case fatality, and functional outcome of intracerebral haemorrhage over time, according to age, sex, and ethnic origin: a systematic review and meta-analysis. Lancet Neurol. [cited 2022 Jun 2];9(2):167–76. Available from: https://pubmed.ncbi.nlm.nih.gov/20056489/

  4. Qureshi AI, Tuhrim S, Broderick JP, Batjer HH, Hondo H, Hanley DF (2001) Spontaneous intracerebral hemorrhage. N Engl J Med. [cited 2022 Jun 2];344(19):1450–60. Available from: https://pubmed.ncbi.nlm.nih.gov/11346811/

  5. Pfeifer LA, White LR, Ross GW, Petrovitch H, Launer LJ (2002) Cerebral amyloid angiopathy and cognitive function. Neurology. [cited 2023 Feb 18];58(11):1629–34. Available from: https://n.neurology.org/content/58/11/1629

  6. Verghese PB, Castellano JM, Garai K, Wang Y, Jiang H, Shah A, Bu G, Frieden C, et al (2013) ApoE influences amyloid-β (Aβ) clearance despite minimal apoE/Aβ association in physiological conditions. Proc Natl Acad Sci USA. [cited 2023 Feb 18];110(19):E1807–16. Available from: https://www.pnas.org/doi/abs/10.1073/pnas.1220484110

  7. Nelson PT, Pious NM, Jicha GA, Wilcock DM, Fardo DW, Estus S, Rebeck GW (2013) APOE-ε2 and APOE-ε4 correlate with increased amyloid accumulation in cerebral vasculature. J Neuropathol Exp Neurol 72(7):708–715. https://doi.org/10.1097/NEN.0b013e31829a25b9

  8. Altmann A, Tian L, Henderson VW, Greicius MD (2014) Sex modifies the APOE-related risk of developing Alzheimer disease. Ann Neurol. [cited 2023 Feb 18];75(4):563–73. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/ana.24135

  9. Shinohara M, Murray ME, Frank RD, Shinohara M, DeTure M, Yamazaki Y, Tachibana M, Atagi Y, et al (2016) Impact of sex and APOE4 on cerebral amyloid angiopathy in Alzheimer’s disease. Acta Neuropathol. [cited 2023 Feb 18];132(2):225–34. Available from: https://link.springer.com/article/10.1007/s00401-016-1580-y

  10. Perez CM, Felty Q (2022) Molecular basis of the association between transcription regulators nuclear respiratory factor 1 and inhibitor of DNA binding protein 3 and the development of microvascular lesions. Microvasc Res 141:104337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Das JK, Deoraj A, Roy D, Felty Q (2022) Brain infiltration of breast cancer stem cells is facilitated by paracrine signaling by inhibitor of differentiation 3 to nuclear respiratory factor 1. J Cancer Res Clin Oncol. [cited 2023 Feb 18];148(10):2881–91. Available from: https://link.springer.com/article/10.1007/s00432-022-04026-w

  12. Das JK, Felty Q (2015) Microvascular lesions by estrogen-induced ID3: Its implications in cerebral and cardiorenal vascular disease. J Mol Neurosci. [cited 2023 Feb 18];55(3):618–31. Available from: https://link.springer.com/article/10.1007/s12031-014-0401-9

  13. Das JK, Felty Q (2014) PCB153-induced overexpression of ID3 contributes to the development of microvascular lesions. PLoS One. [cited 2023 Feb 18];9(8):e104159. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0104159

  14. Chao AC, Chen CH, Chang SH, Huang CT, Hwang WC, Yang DI (2019) Id1 and sonic hedgehog mediate cell cycle reentry and apoptosis induced by amyloid beta-peptide in post-mitotic cortical neurons. Mol Neurobiol. [cited 2023 Feb 18];56(1):465–89. Available from: https://link.springer.com/article/10.1007/s12035-018-1098-5

  15. Chen S der, Yang JL, Lin YC, Chao AC, Yang DI (2020) Emerging roles of inhibitor of differentiation-1 in Alzheimer’s disease: Cell cycle reentry and beyond. Cells 9:1746. [cited 2023 Feb 18];9(7):1746. Available from: https://www.mdpi.com/2073-4409/9/7/1746/htm

  16. Preciados M, Yoo C, Roy D (2016) Estrogenic endocrine disrupting chemicals influencing NRF1 regulated gene networks in the development of complex human brain diseases. Int J Mol Sci 17:2086. [cited 2023 Feb 18];17(12):2086. Available from: https://www.mdpi.com/1422-0067/17/12/2086/htm

  17. Anne UB, Urfer R (2017) Identification of a nuclear respiratory factor 1 recognition motif in the apolipoprotein E variant APOE4 linked to Alzheimer’s Disease. Sci Rep 7:1. [cited 2023 Feb 18];7(1):1–8. Available from: https://www.nature.com/articles/srep40668

  18. Cooper GF, Herskovits E (1991) A Bayesian Method for constructing Bayesian belief networks from databases. In: Uncertainty proceedings, pp 86–94

  19. Yoo C, Brilz EM (2009) The five-gene-network data analysis with local causal discovery algorithm using causal Bayesian networks. Ann N Y Acad Sci. [cited 2023 Mar 2];1158:93–101. Available from: https://pubmed.ncbi.nlm.nih.gov/19348635/

  20. Bhawe K, Das JK, Yoo C, Felty Q, Gong Z, Deoraj A, Liuzzi JP, Ehtesham NZ, et al (2022) Nuclear respiratory factor 1 transcriptomic signatures as prognostic indicators of recurring aggressive mesenchymal glioblastoma and resistance to therapy in White American females. J Cancer Res Clin Oncol. [cited 2023 Feb 24];148(7):1641–82. Available from: https://pubmed.ncbi.nlm.nih.gov/35441887/

  21. Yoo C, Gonzalez E, Gong Z, Roy D (2022) A better mechanistic understanding of big data through an order search using Causal Bayesian Networks. Big Data Cogn Comput 6(2):56. https://doi.org/10.3390/bdcc6020056

  22. Kövari E, Herrmann FR, Hof PR, Bouras C (2013) The relationship between cerebral amyloid angiopathy and cortical microinfarcts in brain ageing and Alzheimer’s disease. Neuropathol Appl Neurobiol [Internet] 39(5):498. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3637988/

  23. Okamoto K, Amari M, Ikeda M, Fukuda T, Suzuki K, Takatama M (2022) A comparison of cerebral amyloid angiopathy in the cerebellum and CAA-positive occipital lobe of 60 brains from routine autopsies. Neuropathology. [cited 2023 Feb 18];42(6):483–7. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/neup.12838

  24. Merlini M, Wanner D, Nitsch RM (2016) Tau pathology-dependent remodelling of cerebral arteries precedes Alzheimer’s disease-related microvascular cerebral amyloid angiopathy. Acta Neuropathol. [cited 2022 Jun 2];131(5):737–52. Available from: https://pubmed.ncbi.nlm.nih.gov/26988843/

  25. Wharton SB, Wang D, Parikh C, Matthews FE, Brayne C, Ince PG (2019) Epidemiological pathology of Aβ deposition in the ageing brain in CFAS: addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches. Acta Neuropathol Commun. [cited 2023 Feb 18];7(1):198. Available from: https://actaneurocomms.biomedcentral.com/articles/10.1186/s40478-019-0858-4

  26. Mao X, Qin X, Li L, Zhou J, Zhou M, Li X, Xu Y, Yuan L, et al (2018) A 15-long non-coding RNA signature to improve prognosis prediction of cervical squamous cell carcinoma. Gynecol Oncol. [cited 2023 Feb 18];149(1):181–7. Available from: https://pubmed.ncbi.nlm.nih.gov/29525275/

  27. Ma Y, Luo T, Dong D, Wu X, Wang Y (2018) Characterization of long non-coding RNAs to reveal potential prognostic biomarkers in hepatocellular carcinoma. Gene. [cited 2023 Feb 18];663:148–56. Available from: https://pubmed.ncbi.nlm.nih.gov/29684484/

  28. Braverman NE, D’Agostino MD, MacLean GE (2013) Peroxisome biogenesis disorders: Biological, clinical and pathophysiological perspectives. Dev Disabil Res Rev. [cited 2023 Feb 18];17(3):187–96. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/ddrr.1113

  29. Blankestijn M, Bloks VW, Struik D, Huijkman N, Kloosterhuis N, Wolters JC et al (2022) Mice with a deficiency in Peroxisomal Membrane Protein 4 (PXMP4) display mild changes in hepatic lipid metabolism. Sci Rep [Internet] 12(1):2512. https://doi.org/10.1038/s41598-022-06479-y

  30. Bhawe K, Das JK, Yoo C, Felty Q, Gong Z, Deoraj A, Liuzzi JP, Ehtesham NZ, et al (2022) Nuclear respiratory factor 1 transcriptomic signatures as prognostic indicators of recurring aggressive mesenchymal glioblastoma and resistance to therapy in White American females. J Cancer Res Clin Oncol [cited 2023 Feb 18];148(7):1641–82. Available from: https://pubmed.ncbi.nlm.nih.gov/35441887/

  31. Ramos J, Yoo C, Felty Q, Gong Z, Liuzzi JP, Poppiti R, Thakur IS, Goel R, et al (2020) Sensitivity to differential NRF1 gene signatures contributes to breast cancer disparities. J Cancer Res Clin Oncol. [cited 2023 Feb 18];146(11):2777–815. Available from: https://link.springer.com/article/10.1007/s00432-020-03320-9

  32. Yoo C (2012) Bayesian Method for Causal Discovery of Latent-Variable Models from a Mixture of Experimental and Observational Data. Comput Stat Data Anal. [cited 2023 Feb 18];56(7):2183–205. Available from: https://pubmed.ncbi.nlm.nih.gov/32831439/

  33. Zhao N, Liu CC, van Ingelgom AJ, Linares C, Kurti A, Knight JA, Heckman MG, Diehl NN, et al (2018) APOE ε2 is associated with increased tau pathology in primary tauopathy. Nat Commun. [cited 2023 Feb 18];9(1). Available from: https://pubmed.ncbi.nlm.nih.gov/30348994/

  34. Han BH, Zhou ML, Johnson AW, Singh I, Liao F, Vellimana AK, Nelson JW, Milner E, et al (2015) Contribution of reactive oxygen species to cerebral amyloid angiopathy, vasomotor dysfunction, and microhemorrhage in aged Tg2576 mice. Proc Natl Acad Sci USA. [cited 2022 Jun 2];112(8):E881–90. Available from: https://pubmed.ncbi.nlm.nih.gov/25675483/

  35. Davis J, Cribbs DH, Cotman CW, van Nostrand WE (1999) Pathogenic amyloid beta-protein induces apoptosis in cultured human cerebrovascular smooth muscle cells. Amyloid. [cited 2022 Jun 2];6(3):157–64. Available from: https://pubmed.ncbi.nlm.nih.gov/10524279/

  36. Fossati S, Cam J, Meyerson J, Mezhericher E, Romero IA, CouraudWeksler POBB, Ghiso J et al (2010) Differential activation of mitochondrial apoptotic pathways by vasculotropic amyloid-β variants in cells composing the cerebral vessel walls. FASEB J 24(1):229–241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Jäkel L, de Kort AM, Klijn CJM, Schreuder FHBM, Verbeek MM (2022) Prevalence of cerebral amyloid angiopathy: A systematic review and meta-analysis. Alzheimers Dement. [cited 2023 Feb 18];18(1):10–28. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/alz.12366

  38. Nooraei MS, Noori-Zadeh A, Darabi S, Rajaei F, Golmohammadi Z, Abbaszadeh HA (2018) Low level of autophagy-related gene 10 (ATG10) expression in the 6-hydroxydopamine rat model of Parkinson’s disease. Iran Biomed J. [cited 2023 Feb 18];22(1):15–21. Available from: https://pubmed.ncbi.nlm.nih.gov/28734275/

  39. Rhee SY (2017) Hypoglycemia and dementia. Endocrinol Metab (Seoul). [cited 2023 Feb 18];32(2):195–9. Available from: https://pubmed.ncbi.nlm.nih.gov/28685510/

  40. Dutta A, Yang Y, Le BT, Zhang Y, Abdel-Wahab O, Zang C, Mohi G (2021) U2af1 is required for survival and function of hematopoietic stem/progenitor cells. Leukemia. [cited 2023 Feb 18];35(8):2382–98. Available from: https://pubmed.ncbi.nlm.nih.gov/33414485/

  41. Chen C, Zhou P, Zhang Z, Liu Y (2022) U2AF1 mutation connects DNA damage to the alternative splicing of RAD51 in lung adenocarcinomas. Clin Exp Pharmacol Physiol. [cited 2023 Feb 18];49(7):740–7. Available from: https://pubmed.ncbi.nlm.nih.gov/35434831/

  42. Chatterjee S, Burns TF (2017) Targeting heat shock proteins in cancer: a promising therapeutic approach. Int J Mol Sci [Internet] 18(9). Available from:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5618627/

  43. Yi L, Gu YH, Wang XL, An LZ, Xie XD, Shao W, Ma LY, Fang JR, et al (2016) Association of ACE, ACE2 and UTS2 Polymorphisms with Essential Hypertension in Han and Dongxiang Populations from North-western China. https://doi.org/10.1177/147323000603400306. [cited 2023 Feb 18];34(3):272–83. Available from: https://journals.sagepub.com/doi/10.1177/147323000603400306

  44. Zimdahl H, Haupt A, Brendel M, Bour L, Machicao F, Salsali A, Broedl UC, Woerle H-J et al (2017) Influence of common polymorphisms in the SLC5A2 gene on metabolic traits in subjects at increased risk of diabetes and on response to empagliflozin treatment in patients with diabetes. Pharmacogenet Genomics 27(4):135–142

    Article  CAS  PubMed  Google Scholar 

  45. Echevarría L, Clemente P, Herńandez-Sierra R, Gallardo ME, Ferńandez-Moreno MA, Garesse R (2014) Glutamyl-tRNAGln amidotransferase is essential for mammalian mitochondrial translation in vivo. Biochem J. [cited 2023 Feb 19];460(1):91–101. Available from: https://pubmed.ncbi.nlm.nih.gov/24579914/

  46. Coskun PE, Wyrembak J, Derbereva O, Melkonian G, Doran E, Lott IT, Head E, Cotman CW, et al (2010) Systemic mitochondrial dysfunction and the etiology of Alzheimer’s disease and down syndrome dementia. J Alzheimers Dis. [cited 2023 Feb 19];20 Suppl 2(0 2). Available from: https://pubmed.ncbi.nlm.nih.gov/20463402/

  47. Guo C, Chen M, Ma W, Cai B, Xu Y, Zhong Y, Zhou C (2020) Growth differentiation factor 9 inhibits vascular endothelial growth factor expression in human granulosa cells. Gynecol Endocrinol. [cited 2023 Feb 19];36(10):907–11. Available from: https://pubmed.ncbi.nlm.nih.gov/31996061/

  48. Wang S, Guo N, Li S, He Y, Zheng D, Li L et al (2021) EZH2 dynamically associates with non-coding RNAs in mouse hearts after acute angiotensin II treatment. Front Cardiovasc Med [Internet] 8:585691. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959742/

  49. Cacabelos R, Torrellas C (2015) Epigenetics of aging and Alzheimer’s disease: implications for pharmacogenomics and drug response. Int J Mol Sci [Internet] 16(12):30483. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4691177/

  50. Sokol AM, Sztolsztener ME, Wasilewski M, Heinz E, Chacinska A (2014) Mitochondrial protein translocases for survival and wellbeing. FEBS Lett. [cited 2023 Feb 19];588(15):2484–95. Available from: https://pubmed.ncbi.nlm.nih.gov/24866464/

  51. Raghuram V, Weber S, Raber J, Chen DH, Bird TD, Maylie J, Adelman JP (2017) Assessment of mutations in KCNN2 and ZNF135 to patient neurological symptoms. Neuroreport. [cited 2023 Feb 19];28(7):375–9. Available from: https://pubmed.ncbi.nlm.nih.gov/28240725/

  52. Schroer RJ, Holden KR, Tarpey PS, Matheus MG, Griesemer DA, Friez MJ et al (2010) Natural history of Christianson syndrome. Am J Med Genet A [Internet] 0(11):2775. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3698558/

  53. Kumar PL, James PF (2015) Identification and characterization of methylation-dependent/independent DNA regulatory elements in the human SLC9B1 gene. Gene. [cited 2023 Feb 19];561(2):235–48. Available from: https://pubmed.ncbi.nlm.nih.gov/25701605/

  54. Liu T, Jiang W, Han D, Yu L (2012) DNAJC25 is downregulated in hepatocellular carcinoma and is a novel tumor suppressor gene. Oncol Lett. [cited 2023 Feb 19];4(6):1274–80. Available from: https://pubmed.ncbi.nlm.nih.gov/23205125/

  55. McCarron MO, Nicoll JAR, Stewart J, Ironside JW, Mann DMA, Love S, Graham DI, Dewar D (1999) The apolipoprotein E epsilon2 allele and the pathological features in cerebral amyloid angiopathy-related hemorrhage. J Neuropathol Exp Neurol. [cited 2023 Feb 19];58(7):711–8. Available from: https://pubmed.ncbi.nlm.nih.gov/10411341/

  56. Kirshner HS, Bradshaw M (2015) The inflammatory form of cerebral amyloid angiopathy or “cerebral amyloid angiopathy-related inflammation” (CAARI). Curr Neurol Neurosci Rep. [cited 2023 Feb 19];15(8). Available from: https://pubmed.ncbi.nlm.nih.gov/26096511/

  57. Sasahira T, Kurihara-Shimomura M, Shimomura H, Bosserhoff AK, Kirita T (2021) Identification of oral squamous cell carcinoma markers MUC2 and SPRR1B downstream of TANGO. J Cancer Res Clin Oncol. [cited 2023 Feb 19];147(6):1659–72. Available from: https://pubmed.ncbi.nlm.nih.gov/33620575/

  58. Ma T, Wu FH, Wu HX, Fa Q, Chen Y (2022) Long non-coding RNA MCM3AP-AS1: A crucial role in human malignancies. Pathol Oncol Res. [cited 2023 Feb 19];28. Available from: https://pubmed.ncbi.nlm.nih.gov/35783356/

  59. Chao AC, Chen CH, Wu MH, Hou BY, Yang DI (2020) Roles of Id1/HIF-1 and CDK5/HIF-1 in cell cycle reentry induced by amyloid-beta peptide in post-mitotic cortical neuron. Biochim Biophys Acta Mol Cell Res. [cited 2023 Feb 19];1867(4). Available from: https://pubmed.ncbi.nlm.nih.gov/31884068/

  60. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics. [cited 2023 Feb 24];29(1):15–21. Available from: https://pubmed.ncbi.nlm.nih.gov/23104886/

  61. Liao Y, Smyth GK, Shi W (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. [cited 2023 Feb 24];30(7):923–30. Available from: https://pubmed.ncbi.nlm.nih.gov/24227677/

  62. Law CW, Chen Y, Shi W, Smyth GK (2014) Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. [cited 2023 Feb 24];15(2):1–17. Available from: https://genomebiology.biomedcentral.com/articles/10.1186/gb-2014-15-2-r29

  63. Park SB, Hwang KT, Chung CK, Roy D, Yoo C (2020) Causal Bayesian gene networks associated with bone, brain and lung metastasis of breast cancer. Clin Exp Metastasis. [cited 2023 Feb 24];37(6):657–74. Available from: https://pubmed.ncbi.nlm.nih.gov/33083937/

  64. Wang S, Qaisar U, Yin X, Grammas P (2012) Gene expression profiling in Alzheimer’s disease brain microvessels. J Alzheimers Dis. [cited 2023 Feb 20];31(1):193–205. Available from: https://pubmed.ncbi.nlm.nih.gov/22531426/

  65. Sartor MA, Leikauf GD, Medvedovic M (2009) LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics. [cited 2023 Feb 20];25(2):211–7. Available from: https://pubmed.ncbi.nlm.nih.gov/19038984/

  66. Liu J (2010) Mechanotransduction in endothelial cells: Cell growth, angiogenesis and wound healing [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1274392778

  67. Jambusaria A, Hong Z, Zhang L, Srivastava S, Jana A, Toth PT, Dai Y, Malik AB, et al (2020) Endothelial heterogeneity across distinct vascular beds during homeostasis and inflammation. Elife. [cited 2022 Jun 2];9. Available from: https://pubmed.ncbi.nlm.nih.gov/31944177/

  68. Paik DT, Tian L, Williams IM, Rhee S, Zhang H, Liu C, Mishra R, Wu SM, et al (2022) Single-cell RNA-seq unveils unique transcriptomic signatures of organ-specific endothelial cells. Circulation. [cited 2022 Jun 2];142(19):1848. Available from: /pmc/articles/PMC7658053/

  69. Paik DT, Tian L, Williams IM, Rhee S, Zhang H, Liu C et al (2020) Single-cell RNA-seq unveils unique transcriptomic signatures of organ-specific endothelial cells. Circulation [Internet] 142(19):1848. Available from:  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658053/

  70. Nemzek JA, Hodges AP, He Y (2015) Bayesian network analysis of multi-compartmentalized immune responses in a murine model of sepsis and direct lung injury. BMC Res Notes 8:516

    Article  PubMed  PubMed Central  Google Scholar 

  71. Park SB, Hwang KT, Chung CK, Roy D, Yoo C. Causal Bayesian gene networks associated with bone, brain and lung metastasis of breast cancer. Clin Exp Metastasis [Internet]. 2020 Dec 1 [cited 2023 Feb 19];37(6):657–74. Available from: https://pubmed.ncbi.nlm.nih.gov/33083937/

  72. Kunkle BW, Yoo C, Roy D (2013) Reverse engineering of modified genes by Bayesian network analysis defines molecular determinants critical to the development of glioblastoma. PLoS One. [cited 2023 Feb 24];8(5):e64140. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0064140

  73. Ramos J, Das J, Felty Q, Yoo C, Poppiti R, Murrell D, Foster PJ, Roy D (2018) NRF1 motif sequence-enriched genes involved in ER/PR -ve HER2 +ve breast cancer signaling pathways. Breast Cancer Res Treat. [cited 2023 Feb 24];172(2):469–85. Available from: https://pubmed.ncbi.nlm.nih.gov/30128822/

  74. Lee Y-S, Krishnan A, Oughtred R, Rust J, Chang CS, Ryu J, Kristensen VN, Dolinski K, Theesfeld CL, Troyanskaya OG (2019) A computational framework for Genomewide characterization of the human disease landscape. Cell Syst 8(2):152–162. https://doi.org/10.1016/j.cels.2018.12.010

  75. Jäkel L, De Kort AM, Klijn CJM, Schreuder FHBM, Verbeek MM (2022) Prevalence of cerebral amyloid angiopathy: A systematic review and meta-analysis. Alzheimers Dement. [cited 2023 Feb 24];18(1):10–28. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/alz.12366

Download references

Acknowledgements

This study was possible because of the participation of research volunteers and the contribution of data by collaborating researchers to the AD Knowledge Portal. This study was supported by funds from the Robert Stempel College of Public Health and Social Work, the National Science Foundation CREST Award (#1547798) and the National Institutes of Health R15 Award (1R15HL145652-01).

Funding

This work was partially supported by the National Science Foundation CREST Award (#1547798 and the National Institutes of Health R15 Award (1R15HL145652-01).

Author information

Authors and Affiliations

  1. Department of Environmental Health Sciences, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA

    Christian Michael Perez, Deodutta Roy, Alok Deoraj & Quentin Felty

  2. Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, USA

    Zhenghua Gong & Changwon Yoo

Authors
  1. Christian Michael Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenghua Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Changwon Yoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deodutta Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alok Deoraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Quentin Felty
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Christian Perez performed the machine learning and causal gene order experiments as well as performed the RNA-seq analysis. He also contributed in the writing and editing of the manuscript. Zhenghua Gong contributed to the methodology of machine learning, combinatorial risk, and MCMC causal gene ordering. Changwon Yoo designed the methodology for machine learning, combinatorial risk, and causal gene ordering experiments; and contributed to the writing, reviewing, and editing of the manuscript. Deodutta Roy and Alok Deoraj contributed to the conceptualization, writing, and editing the manuscript. Quentin Felty provided funding, administrated the project, and contributed to the conceptualization, writing, and editing of the manuscript.

Corresponding author

Correspondence to Quentin Felty.

Ethics declarations

Ethics Approval

This is a secondary data analysis study. The FIU Research Ethics Committee confirmed that no ethical approval was required and granted the IRB-20-0304 exemption to do a secondary data analysis of the RNA-seq obtained from the AD Knowledge Portal.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Perez, C.M., Gong, Z., Yoo, C. et al. Inhibitor of DNA Binding Protein 3 (ID3) and Nuclear Respiratory Factor 1 (NRF1) Mediated Transcriptional Gene Signatures are Associated with the Severity of Cerebral Amyloid Angiopathy. Mol Neurobiol 61, 835–882 (2024). https://doi.org/10.1007/s12035-023-03541-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12035-023-03541-2

Keywords

Search

Navigation