Abstract
Most neurodegenerative diseases are exacerbated by aging, with symptoms often worsening over time. Programmed cell death (PCD) is a controlled cell suicide mechanism that is essential for the stability, growth, and homeostasis of organisms. Understanding the effects of aging at the level of systems biology could lead to new therapeutic approaches for a broad spectrum of neurodegenerative diseases. In the absence of comprehensive functional studies on the relationship between PCD and aging of the prefrontal cortex, this study provides prefrontal brain biomarkers of aging associated with PCD that could open the way for improved therapeutic techniques for age-related neurodegenerative diseases. To this end, publicly available transcriptome data were subjected to bioinformatic analyses such as differential gene expression, functional enrichment, and the weighted gene coexpression network analysis (WGCNA). The diagnostic utility of the biomarkers was tested using a logistic regression-based prediction model. Three genes, namely BMP4, SGSH, and SLC11A2, were found to be aging biomarkers associated with PCD. Finally, a multifactorial regulatory network with interacting proteins, transcription factors (TFs), competing endogenous RNAs (ceRNAs), and microRNAs (miRNAs) was constructed around these biomarkers. The elements of this multifactorial regulatory network were mainly enriched in BMP signaling. Further exploration of these three biomarkers and their regulatory elements would enable the development of 3PM (predictive, preventive, and personalized) medicine for the treatment of age-related neurodegenerative diseases.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
Data are available in The Gene Expression Omnibus (GEO), a public repository.
References
Aguilar-Hernández L, Alejandre R, César Morales-Medina J, Iannitti T, Flores G (2023) Cellular mechanisms in brain aging: focus on physiological and pathological aging. J Chem Neuroanat 128. https://doi.org/10.1016/j.jchemneu.2022.102210.
Aits S, Jäättelä M (2013) Lysosomal cell death at a glance. J Cell Sci 126:1905–1912
Ameisen JC (2002) On the origin, evolution, and nature of programmed cell death: a timeline of four billion years. Cell Death Differ 9:367–393. https://doi.org/10.1038/sj.cdd.4400950
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29. https://doi.org/10.1038/75556
Aykaç A, Şehirli AÖ (2020) The role of the SLC transporters protein in the neurodegenerative disorders. Clinical Psychopharmacology and Neuroscience 18:174–187. https://doi.org/10.9758/cpn.2020.18.2.174
Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau W-C, Ledoux P et al (2005) NCBI GEO: mining millions of expression profiles—database and tools. Nucleic Acids Res 33:D562–D566
Bernal GM, Peterson DA (2004) Neural stem cells as therapeutic agents for age-related brain repair. Aging Cell 3:345–351. https://doi.org/10.1111/j.1474-9728.2004.00132.x
Bhattacharya R, Alam MR, Kamal MA, Seo KJ, Singh LR (2023) AGE-RAGE axis culminates into multiple pathogenic processes: a central road to neurodegeneration. Front Mol Neurosci 16. https://doi.org/10.3389/fnmol.2023.1155175.
Bond AM, Peng CY, Meyers EA, McGuire T, Ewaleifoh O, Kessler JA (2014) BMP signaling regulates the tempo of adult hippocampal progenitor maturation at multiple stages of the lineage. Stem Cells 32:2201–2214. https://doi.org/10.1002/stem.1688
Boya P, Kroemer G (2008) Lysosomal membrane permeabilization in cell death. Oncogene 27:6434–6451
Brunk UT, Ericsson JLE (1973) Cytochemical evidence for the leakage of acid phosphatase through ultrastructurally intact lysosomal membranes. Fixation in Histochemistry 137–149
Carbon S, Douglass E, Good BM, Unni DR, Harris NL, Mungall CJ et al (2021) The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res 49:D325–D334. https://doi.org/10.1093/nar/gkaa1113
Chen H, Boutros PC (2011) VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12. https://doi.org/10.1186/1471-2105-12-35.
Chin C-H, Chen S-H, Wu H-H, Ho C-W, Ko M-T, Lin C-Y (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(Suppl 4):S11. https://doi.org/10.1186/1752-0509-8-S4-S11
Coffey R, Ganz T (2017) Iron homeostasis: an anthropocentric perspective. J Biol Chem 292:12727–12734
Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT et al (2011) Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478:519–523. https://doi.org/10.1038/nature10524
Cui J, Zhao S, Li Y, Zhang D, Wang B, Xie J et al (2021) Regulated cell death: discovery, features and implications for neurodegenerative diseases. Cell Communication and Signaling 19:120. https://doi.org/10.1186/s12964-021-00799-8
Dehay B, Bové J, Rodríguez-Muela N, Perier C, Recasens A, Boya P et al (2010) Pathogenic lysosomal depletion in Parkinson’s disease. J Neurosci 30:12535–12544
Douglass ML, Beard H, Shoubridge A, Nazri N, King B, Trim PJ et al (2021) Is SGSH heterozygosity a risk factor for early-onset neurodegenerative disease? J Inherit Metab Dis 44:763–776. https://doi.org/10.1002/jimd.12359
Fabregat A, Sidiropoulos K, Garapati P, Gillespie M, Hausmann K, Haw R et al (2016) The Reactome pathway Knowledgebase. Nucleic Acids Res 44:D481–D487. https://doi.org/10.1093/nar/gkv1351
Feng J, Gao J, Li Y, Yang Y, Dang L, Ye Y et al (2014) BMP4 enhances foam cell formation by BMPR-2/SMAD1/5/8 signaling. Int J Mol Sci 15:5536–5552. https://doi.org/10.3390/ijms15045536
Feng Y, Shen J (2023) Machine learning-based predictive models and drug prediction for schizophrenia in multiple programmed cell death patterns. Front Mol Neurosci 16. https://doi.org/10.3389/fnmol.2023.1123708.
French L, Ma TZ, Oh H, Tseng GC, Sibille E (2017) Age-related gene expression in the frontal cortex suggests synaptic function changes in specific inhibitory neuron subtypes. Front Aging Neurosci 9. https://doi.org/10.3389/fnagi.2017.00162
Gediya P, Parikh PK, Vyas VK, Ghate MD (2021) Histone deacetylase 2: a potential therapeutic target for cancer and neurodegenerative disorders. Eur J Med Chem 216:113332. https://doi.org/10.1016/j.ejmech.2021.113332
Gems D, Partridge L (2013) Genetics of longevity in model organisms: debates and paradigm shifts. Annu Rev Physiol 75:621–644. https://doi.org/10.1146/annurev-physiol-030212-183712
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S et al (2004) Open Access Bioconductor: open software development for computational biology and bioinformatics. vol. 5
Glorioso C, Sibille E (2011) Between destiny and disease: genetics and molecular pathways of human central nervous system aging. Prog Neurobiol 93:165–181. https://doi.org/10.1016/j.pneurobio.2010.11.006
Gómez-Sintes R, Ledesma MD, Boya P (2016) Lysosomal cell death mechanisms in aging. Ageing Res Rev 32:150–68. https://doi.org/10.1016/j.arr.2016.02.009.
Gonzalez-Zuniga M, Contreras PS, Estrada LD, Chamorro D, Villagra A, Zanlungo S et al (2014) c-Abl stabilizes HDAC2 levels by tyrosine phosphorylation repressing neuronal gene expression in Alzheimer’s disease. Mol Cell 56:163–173
Grund B, Sabin C (2010) Analysis of biomarker data: logs, odds ratios, and receiver operating characteristic curves. Curr Opin HIV AIDS 5:473–479. https://doi.org/10.1097/COH.0b013e32833ed742
Hare DJ, Arora M, Jenkins NL, Finkelstein DI, Doble PA, Bush AI (2015) Is early-life iron exposure critical in neurodegeneration? Nat Rev Neurol 11:536–544
Hou Y, Lautrup S, Cordonnier S, Wang Y, Croteau DL, Zavala E et al (2018) NAD+ supplementation normalizes key Alzheimer’s features and DNA damage responses in a new AD mouse model with introduced DNA repair deficiency. Proc Natl Acad Sci 115:E1876–E1885
Isacson O (2015) Lysosomes to combat Parkinson’s disease. Nat Neurosci 18:792–793. https://doi.org/10.1038/nn.4027
Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M et al (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36:D480–D484. https://doi.org/10.1093/nar/gkm882
Ko CJ, Gao SL, Lin TK, Chu PY, Lin HY (2021) Ferroptosis as a major factor and therapeutic target for neuroinflammation in Parkinson’s disease. Biomedicines 9. https://doi.org/10.3390/biomedicines9111679.
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9. https://doi.org/10.1186/1471-2105-9-559
Li JH, Liu S, Zhou H, Qu LH, Yang JH (2014) StarBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42. https://doi.org/10.1093/nar/gkt1248
Liu Y, Li G (2023) Empowering biologists to decode omics data: the Genekitr R package and web server. BMC Bioinformatics 24:214. https://doi.org/10.1186/s12859-023-05342-9
Manzari-Tavakoli A, Babajani A, Farjoo MH, Hajinasrollah M, Bahrami S, Niknejad H (2022) The cross-talks among bone morphogenetic protein (BMP) signaling and other prominent pathways involved in neural differentiation. Front Mol Neurosci 15. https://doi.org/10.3389/fnmol.2022.827275.
Margolis RL, Chuang D-M (1994) Post RM. Programmed cell death: implications for neuropsychiatric disorders
Meyers EA, Gobeske KT, Bond AM, Jarrett JC, Peng CY, Kessler JA (2016) Increased bone morphogenetic protein signaling contributes to age-related declines in neurogenesis and cognition. Neurobiol Aging 38:164–175. https://doi.org/10.1016/j.neurobiolaging.2015.10.035
Nicholatos JW, Groot J, Dhokai S, Tran D, Hrdlicka L, Carlile TM et al (2021) Scd inhibition protects from α-synuclein-induced neurotoxicity but is toxic to early neuron cultures. ENeuro 8. https://doi.org/10.1523/ENEURO.0166-21.2021
Nixon RA, Yang D-S (2011) Autophagy failure in Alzheimer’s disease—locating the primary defect. Neurobiol Dis 43:38–45
Oughtred R, Stark C, Breitkreutz B-J, Rust J, Boucher L, Chang C et al (2019) The BioGRID interaction database: 2019 update. Nucleic Acids Res 47:D529–D541
Pico AR, Kelder T, Van Iersel MP, Hanspers K, Conklin BR, Evelo C (2008) WikiPathways: pathway editing for the people. PLoS Biol 6(7):1403–1407. https://doi.org/10.1371/journal.pbio.0060184
Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F et al (2020) The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res 48:D845–D855. https://doi.org/10.1093/nar/gkz1021
Sean D, Meltzer PS (2007) GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 23:1846–1847. https://doi.org/10.1093/bioinformatics/btm254
Serrano-Puebla A, Boya P (2016) Lysosomal membrane permeabilization in cell death: new evidence and implications for health and disease. Ann NY Acad Sci 1371:30–44. https://doi.org/10.1111/nyas.12966.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303
Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21:3940–3941. https://doi.org/10.1093/bioinformatics/bti623
Smyth GK, Ritchie M, Thorne N, Wettenhall J (2005) LIMMA: linear models for microarray data. In Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Statistics for Biology and Health
Tang D, Chen X, Kang R, Kroemer G (2021) Ferroptosis: molecular mechanisms and health implications. Cell Res 31:107–125. https://doi.org/10.1038/s41422-020-00441-1
Tanwar H, Kumar DT, Doss CGP, Zayed H (2019) Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA. Metab Brain Dis 34:1577–1594. https://doi.org/10.1007/s11011-019-00465-6
Taylor RC, Cullen SP, Martin SJ (2008) Apoptosis: controlled demolition at the cellular level. Nat Rev Mol Cell Biol 9:231–241. https://doi.org/10.1038/nrm2312
Tokar T, Pastrello C, Rossos AEM, Abovsky M, Hauschild AC, Tsay M, et al (2018) MirDIP 4.1 - Integrative database of human microRNA target predictions. Nucleic Acids Res 46:D360–370. https://doi.org/10.1093/nar/gkx1144
Toyokuni S, Yanatori I, Kong Y, Zheng H, Motooka Y, Jiang L (2020) Ferroptosis at the crossroads of infection, aging and cancer. Cancer Sci 111:2665–2671. https://doi.org/10.1111/cas.14496
Van Opdenbosch N, Lamkanfi M (2019) Caspases in cell death, inflammation, and disease. Immunity 50:1352–1364
Venables WN (2002) Ripley BD. Modern applied statistics with S. vol. Fourth edition. Springer, New York
Vila M, Bové J, Dehay B, Rodríguez-Muela N, Boya P (2011) Lysosomal membrane permeabilization in Parkinson disease. Autophagy 7:98–100
Xu H, Huang W, Wang Y, Sun W, Tang J, Li D et al (2013) The function of BMP4 during neurogenesis in the adult hippocampus in Alzheimer’s disease. Ageing Res Rev 12:157–164. https://doi.org/10.1016/j.arr.2012.05.002
Xu M, Liu Y, Huang Y, Wang J, Yan J, Zhang L et al (2018) Re-exploring the core genes and modules in the human frontal cortex during chronological aging: insights from network-based analysis of transcriptomic studies. Aging 10:2816–2831. https://doi.org/10.18632/aging.101589
Yamamoto H, Kurachi M, Naruse M, Shibasaki K, Ishizaki Y (2018) BMP4 signaling in NPCs upregulates Bcl-xL to promote their survival in the presence of FGF-2. Biochem Biophys Res Commun 496:588–593. https://doi.org/10.1016/j.bbrc.2018.01.090
Yousef H, Morgenthaler A, Schlesinger C, Bugaj L, Conboy IM, Schaffer DV (2015) Age-associated increase in BMP signaling inhibits hippocampal neurogenesis. Stem Cells 33:1577–1588. https://doi.org/10.1002/stem.1943
Yu G, Wang LG, Han Y, He QY (2012) ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287. https://doi.org/10.1089/omi.2011.0118
Zhang Q, Liu W, Zhang HM, Xie GY, Miao YR, Xia M et al (2020) hTFtarget: a comprehensive database for regulations of human transcription factors and their targets. Genomics Proteomics Bioinformatics 18:120–128. https://doi.org/10.1016/j.gpb.2019.09.006
Zhang S, Bai P, Lei D, Liang Y, Zhen S, Bakiasi G et al (2022) Degradation and inhibition of epigenetic regulatory protein BRD4 exacerbate Alzheimer’s disease-related neuropathology in cell models. J Biol Chem 298:101794. https://doi.org/10.1016/j.jbc.2022.101794
Zhang X, Li J, Ma L, Xu H, Cao Y, Liang W et al (2021) BMP4 overexpression induces the upregulation of APP/Tau and memory deficits in Alzheimer’s disease. Cell Death Discov 7. https://doi.org/10.1038/s41420-021-00435-x
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O et al (2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10:1523. https://doi.org/10.1038/s41467-019-09234-6
Zou Y, Xie J, Zheng S, Liu W, Tang Y, Tian W et al (2022) Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery. International J Surg 107. https://doi.org/10.1016/j.ijsu.2022.106936
Author information
Authors and Affiliations
Contributions
EKO conceived the original idea, conceptualized the study, performed analysis, prepared figures and tables, and wrote the manuscript.
Corresponding author
Ethics declarations
Ethical Approval
Not applicable; the study did not have any participants and relevant information.
Consent to Participate
Not applicable; the study did not have any participants and relevant information.
Consent for Publication
The author consents for publication in the Journal of Molecular Neuroscience.
Competing Interests
The author declares no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
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.
About this article
Cite this article
Kubat Oktem, E. BMP4, SGSH, and SLC11A2 are Predicted to Be Biomarkers of Aging Associated with Programmed Cell Death. J Mol Neurosci 73, 713–723 (2023). https://doi.org/10.1007/s12031-023-02148-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12031-023-02148-5