Exploring signature genes and pathways in Alzheimer’s disease by network based analysis of Hippocampus transcriptome data

Document Type : Original Research

Authors

Semnan University

Abstract
Background and Objectives: Alzheimer’s disease is the most common neurodegenerative disease and the memory impairment is the main prominent symptom of this disease. The hippocampus of the brain, is the first region that undergoes changes in Alzheimer’s. Systems biology tools such as high-throughput techniques, enable us to explore signature genes involved in disease initiation and advancement which can be considered as new therapeutic and diagnostic candidates in complex diseases like Alzheimer’s.

Methods: A total of 85 samples obtained from the hippocampus of the brain of healthy individuals and individuals with Alzheimer’s were selected from two datasets. Differential expression analysis was performed independently for both datasets and the results were integrated. Genes with the same expression pattern in the two datasets were used to construct a gene-gene network using the STRING database. The obtained network analysis was performed to detect key genes associated with the disease.

Results: In this study, 73 genes with the same expression pattern were found in the two datasets. The obtained network analysis led to the identification of SNAP25, UNC13A, SYN2 and AMPH as key genes connected with Alzheimer’s disease.

Conclusion: The role of the reported key genes in endocytosis, neurotransmitters release and synaptic vesicle cycle facilitate proper functioning of memory. Expressional changes and mutations in each of these genes effect other pathways and lead to Alzheimer’s. Thus, the key genes reported in this study, can be considered as potential markers in developing diagnostic and therapeutic methods for Alzheimer’s.

Keywords

Subjects


[1] “2020 Alzheimer’s disease facts and figures,” Alzheimers. Dement., vol. 16, no. 3, pp. 391–460, Mar. 2020.
[2] Y. Wei, “Comparative transcriptome analysis of the hippocampus from sleep-deprived and Alzheimer’s disease mice,” Genet. Mol. Biol., vol. 43, no. 2, 2020.
[3] M. T. Heneka et al., “Neuroinflammation in Alzheimer’s disease,” Lancet. Neurol., vol. 14, no. 4, pp. 388–405, Apr. 2015.
[4] M. Nikolac Perkovic and N. Pivac, “Genetic Markers of Alzheimer’s Disease,” Adv. Exp. Med. Biol., vol. 1192, pp. 27–52, 2019.
[5] M. Cieślik et al., “Alterations of Transcription of Genes Coding Anti-oxidative and Mitochondria-Related Proteins in Amyloid β Toxicity: Relevance to Alzheimer’s Disease,” Mol. Neurobiol., vol. 57, no. 3, pp. 1374–1388, Mar. 2020.
[6] Y.-J. Liu et al., “Identification of hub genes associated with cognition in the hippocampus of Alzheimer’s Disease,” Bioengineered, vol. 12, no. 2, pp. 9598–9609, Dec. 2021.
[7] D. Heras-Sandoval, J. M. Pérez-Rojas, J. Hernández-Damián, and J. Pedraza-Chaverri, “The role of PI3K/AKT/mTOR pathway in the modulation of autophagy and the clearance of protein aggregates in neurodegeneration,” Cell. Signal., vol. 26, no. 12, pp. 2694–2701, Dec. 2014.
[8] M. S. Uddin et al., “Autophagy and Alzheimer’s Disease: From Molecular Mechanisms to Therapeutic Implications,” Front. Aging Neurosci., vol. 10, no. JAN, Jan. 2018.
[9] M. Magistri, D. Velmeshev, M. Makhmutova, and M. A. Faghihi, “Transcriptomics Profiling of Alzheimer’s Disease Reveal Neurovascular Defects, Altered Amyloid-β Homeostasis, and Deregulated Expression of Long Noncoding RNAs,” J. Alzheimers. Dis., vol. 48, no. 3, pp. 647–665, Oct. 2015.
[10] J. G. J. van Rooij et al., “Hippocampal transcriptome profiling combined with protein-protein interaction analysis elucidates Alzheimer’s disease pathways and genes,” Neurobiol. Aging, vol. 74, pp. 225–233, Feb. 2019.
[11] A. M. Crist et al., “Transcriptomic analysis to identify genes associated with selective hippocampal vulnerability in Alzheimer’s disease,” Nat. Commun., vol. 12, no. 1, Dec. 2021.
[12] W. Hu, X. Lin, and K. Chen, “Integrated analysis of differential gene expression profiles in hippocampi to identify candidate genes involved in Alzheimer’s disease,” Mol. Med. Rep., vol. 12, no. 5, pp. 6679–6687, Sep. 2015.
[13] W. S. Liang 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., vol. 105, no. 11, pp. 4441–4446, Mar. 2008.
[14] B. Readhead et al., “Multiscale Analysis of Independent Alzheimer’s Cohorts Finds Disruption of Molecular, Genetic, and Clinical Networks by Human Herpesvirus,” Neuron, vol. 99, no. 1, pp. 64-82.e7, Jul. 2018.
[15] W. S. Liang et al., “Altered neuronal gene expression in brain regions differentially affected by Alzheimer’s disease: a reference data set,” Physiol. Genomics, vol. 33, no. 2, pp. 240–256, Apr. 2008.
[16] N. C. Berchtold et al., “Gene expression changes in the course of normal brain aging are sexually dimorphic,” Proc. Natl. Acad. Sci. U. S. A., vol. 105, no. 40, pp. 15605–15610, Oct. 2008.
[17] N. C. Berchtold, P. D. Coleman, D. H. Cribbs, J. Rogers, D. L. Gillen, and C. W. Cotman, “Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer’s disease,” Neurobiol. Aging, vol. 34, no. 6, pp. 1653–1661, Jun. 2013.
[18] D. H. Cribbs et al., “Extensive innate immune gene activation accompanies brain aging, increasing vulnerability to cognitive decline and neurodegeneration: a microarray study,” J. Neuroinflammation, vol. 9, Jul. 2012.
[19] G. Astarita et al., “Deficient liver biosynthesis of docosahexaenoic acid correlates with cognitive impairment in Alzheimer’s disease,” PLoS One, vol. 5, no. 9, pp. 1–8, 2010.
[20] L. J. Blair et al., “Accelerated neurodegeneration through chaperone-mediated oligomerization of tau,” J. Clin. Invest., vol. 123, no. 10, pp. 4158–4169, Oct. 2013.
[21] M. Goedert, D. S. Eisenberg, and R. A. Crowther, “Propagation of Tau Aggregates and Neurodegeneration,” Annu. Rev. Neurosci., vol. 40, pp. 189–210, Jul. 2017.
[22] M. Sárvári et al., “Menopause leads to elevated expression of macrophage-associated genes in the aging frontal cortex: rat and human studies identify strikingly similar changes,” J. Neuroinflammation, vol. 9, Dec. 2012.
[23] S. R. Piccolo, Y. Sun, J. D. Campbell, M. E. Lenburg, A. H. Bild, and W. E. Johnson, “A single-sample microarray normalization method to facilitate personalized-medicine workflows,” Genomics, vol. 100, no. 6, pp. 337–344, Dec. 2012.
[24] B. Neupane, D. Richer, A. J. Bonner, T. Kibret, and J. Beyene, “Network meta-analysis using R: A review of currently available automated packages,” PLoS One, vol. 9, no. 12, 2014.
[25] G. Alterovitz, M. Xiang, M. Mohan, and M. F. Ramoni, “GO PaD: the Gene Ontology Partition Database,” Nucleic Acids Res., vol. 35, no. Database issue, Jan. 2007.
[26] M. Kanehisa, Y. Sato, M. Kawashima, M. Furumichi, and M. Tanabe, “KEGG as a reference resource for gene and protein annotation,” Nucleic Acids Res., vol. 44, no. D1, pp. D457–D462, 2016.
[27] M. Kanehisa and S. Goto, “KEGG: kyoto encyclopedia of genes and genomes,” Nucleic Acids Res., vol. 28, no. 1, pp. 27–30, Jan. 2000.
[28] D. W. Huang et al., “DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists,” Nucleic Acids Res., vol. 35, no. SUPPL.2, 2007.
[29] C. von Mering, M. Huynen, D. Jaeggi, S. Schmidt, P. Bork, and B. Snel, “STRING: a database of predicted functional associations between proteins,” Nucleic Acids Res., vol. 31, no. 1, pp. 258–261, Jan. 2003.
[30] D. Szklarczyk et al., “The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets,” Nucleic Acids Res., vol. 49, no. D1, pp. D605–D612, Jan. 2021.
[31] D. Szklarczyk et al., “The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible,” Nucleic Acids Res., vol. 45, no. Database issue, pp. D362–D368, Jan. 2017.
[32] S. Killcoyne, G. W. Carter, J. Smith, and J. Boyle, “Cytoscape: a community-based framework for network modeling,” Methods Mol. Biol., vol. 563, pp. 219–239, 2009.
[33] P. Shannon et al., “Cytoscape: a software environment for integrated models of biomolecular interaction networks.,” Genome Res., vol. 13, no. 11, pp. 2498–2504, Nov. 2003.
[34] N. T. Doncheva, J. H. Morris, J. Gorodkin, and L. J. Jensen, “Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data,” J. Proteome Res., vol. 18, no. 2, pp. 623–632, 2019.
[35] J. A. Reuter, D. V. Spacek, and M. P. Snyder, “High-throughput sequencing technologies,” Mol. Cell, vol. 58, no. 4, pp. 586–597, May 2015.
[36] S. V. Ovsepian, V. B. O’Leary, L. Zaborszky, V. Ntziachristos, and J. O. Dolly, “Synaptic vesicle cycle and amyloid β: Biting the hand that feeds,” Alzheimers. Dement., vol. 14, no. 4, pp. 502–513, Apr. 2018.
[37] C. Peña-Bautista et al., “Early neurotransmission impairment in non-invasive Alzheimer Disease detection,” Sci. Reports 2020 101, vol. 10, no. 1, pp. 1–9, Oct. 2020.
[38] Y. Wang, Y. Shi, and H. Wei, “Calcium Dysregulation in Alzheimer’s Disease: A Target for New Drug Development,” J. Alzheimer’s Dis. Park., vol. 7, no. 5, 2017.
[39] L. Nie et al., “Ginsenoside Rg1 Ameliorates Behavioral Abnormalities and Modulates the Hippocampal Proteomic Change in Triple Transgenic Mice of Alzheimer’s Disease,” Oxid. Med. Cell. Longev., vol. 2017, Oct. 2017.
[40] S. Hilfiker, V. A. Pieribone, A. J. Czernik, H. T. Kao, G. J. Augustine, and P. Greengard, “Synapsins as regulators of neurotransmitter release,” Philos. Trans. R. Soc. Lond. B. Biol. Sci., vol. 354, no. 1381, pp. 269–279, Feb. 1999.
[41] Q. Hou et al., “SNAP-25 in hippocampal CA1 region is involved in memory consolidation,” Eur. J. Neurosci., vol. 20, no. 6, pp. 1593–1603, Sep. 2004.
[42] S. Karmakar, L. G. Sharma, A. Roy, A. Patel, and L. M. Pandey, “Neuronal SNARE complex: A protein folding system with intricate protein-protein interactions, and its common neuropathological hallmark, SNAP25,” Neurochem. Int., vol. 122, pp. 196–207, Jan. 2019.
[43] M. E. Larson et al., “Selective lowering of synapsins induced by oligomeric α-synuclein exacerbates memory deficits,” Proc. Natl. Acad. Sci. U. S. A., vol. 114, no. 23, pp. E4648–E4657, Jun. 2017.
[44] B. Dinda, M. Dinda, G. Kulsi, A. Chakraborty, and S. Dinda, “Therapeutic potentials of plant iridoids in Alzheimer’s and Parkinson’s diseases: A review,” Eur. J. Med. Chem., vol. 169, pp. 185–199, May 2019.
[45] J. Chapuis et al., “Increased expression of BIN1 mediates Alzheimer genetic risk by modulating tau pathology,” Mol. Psychiatry, vol. 18, no. 11, pp. 1225–1234, Nov. 2013.
[46] P. Wigge and H. T. McMahon, “The amphiphysin family of proteins and their role in endocytosis at the synapse,” Trends Neurosci., vol. 21, no. 8, pp. 339–344, Aug. 1998.
[47] F. P. Diekstra et al., “C9orf72 and UNC13A are shared risk loci for amyotrophic lateral sclerosis and frontotemporal dementia: a genome-wide meta-analysis,” Ann. Neurol., vol. 76, no. 1, pp. 120–133, 2014.
[48] S. Rossner et al., “Munc13-1-mediated vesicle priming contributes to secretory amyloid precursor protein processing,” J. Biol. Chem., vol. 279, no. 27, pp. 27841–27844, Jul. 2004.
[49] M. Camacho et al., “Control of neurotransmitter release by two distinct membrane-binding faces of the Munc13-1 C 1 C 2 B region,” Elife, vol. 10, Nov. 2021.
[50] R. V. Kalyana Sundaram et al., “Munc13 binds and recruits SNAP25 to chaperone SNARE complex assembly,” FEBS Lett., vol. 595, no. 3, pp. 297–309, Feb. 2021.
[51] A. Margiotta, “Role of SNAREs in Neurodegenerative Diseases,” Cells, vol. 10, no. 5, May 2021.
[52] V. Quarato et al., “Transcriptional Profiling of Hippocampus Identifies Network Alterations in Alzheimer’s Disease,” Applied Sciences , vol. 12, no. 10. 2022.
[53] K. Bonnycastle, E. C. Davenport, and M. A. Cousin, “Presynaptic dysfunction in neurodevelopmental disorders: Insights from the synaptic vesicle life cycle,” J. Neurochem., vol. 157, no. 2, pp. 179–207, Apr. 2021.