Skip to main content

Advertisement

SpringerLink
Log in

Interactions of Cellular Energetic Gene Clusters in the Alzheimer’s Mouse Brain

  • Published:
Molecular Neurobiology Aims and scope Submit manuscript

Abstract

Alzheimer’s disease (AD) is the most common cause of dementia in the aging population. The pathological characteristics include extracellular senile plaques and intracellular neurofibrillary tangles. In addition, mitochondrial dysfunction, oxidative stress, and neuroinflammation contribute to AD pathogenesis. In this study, we sought to determine the crosstalk between different pathways in the brain of 5XFAD mice, a mouse model for amyloid pathology, by RNA-seq analysis. We observed significant changes in the expression of genes (1288 genes; adj p value < 0.05; log2-fold > 1 and < 1) related to pathways including oxidation–reduction, oxidative phosphorylation, innate immune response, ribosomal protein synthesis, and ubiquitin proteosome system. The most striking feature was the downregulation of genes related to oxidation–reduction process with changes in the expression of a large number of mitochondrial genes. We also observed an upregulation of several immune response genes. Gene interaction network of oxidation–reduction related genes further confirmed a tight cluster of mitochondrial genes. Furthermore, gene interaction analysis of all the 1288 genes showed at least three distinct interaction clusters, with the predominant one relating to cellular energetics. In summary, we identified 1288 genes distinctly different in the 5XFAD brain compared to the WT brain and found cellular energetics to be the most distinct gene cluster in the AD mouse brain.

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

Similar content being viewed by others

Data Availability

The original dataset used will be deposited in a publicly available library before publication of the article.

References

  1. Selkoe DJ (2001) Alzheimer’s disease: genes, proteins, and therapy. Physiol Rev 81:741–766

    Article  CAS  PubMed  Google Scholar 

  2. Wang ZT, Shen XN, Ma YH, Ou YN, Dong PQ, Tan PL et al (2021) Associations of the rate of change in geriatric depression scale with amyloid and cerebral glucose metabolism in cognitively normal older adults: a longitudinal study. J Affect Disord 280:77–84

    Article  CAS  PubMed  Google Scholar 

  3. Markesbery WR (1997) Oxidative stress hypothesis in Alzheimer’s disease. Free Radic Biol Med 23:134–147

    Article  CAS  PubMed  Google Scholar 

  4. Heneka MT, Carson MJ, El Khoury J, Landreth GE, Brosseron F, Feinstein DL et al (2015) Neuroinflammation in Alzheimer’s disease. Lancet Neurol 14:388–405

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bonda DJ, Wang X, Lee HG, Smith MA, Perry G, Zhu X (2014) Neuronal failure in Alzheimer’s disease: a view through the oxidative stress looking-glass. Neurosci Bull 30:243–252

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wilkins HM, Swerdlow RH (2016) Relationships between mitochondria and neuroinflammation: implications for Alzheimer’s disease. Curr Top Med Chem 16:849–857

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. De Strooper B, Karran E (2016) The cellular phase of Alzheimer’s disease. Cell 164:603–615

    Article  PubMed  Google Scholar 

  8. Swanson CJ, Zhang Y, Dhadda S, Wang J, Kaplow J, Lai RYK et al (2021) A randomized, double-blind, phase 2b proof-of-concept clinical trial in early Alzheimer’s disease with lecanemab, an anti-Abeta protofibril antibody. Alzheimers Res Ther 13:80

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Shi M, Chu F, Zhu F, Zhu J (2022) Impact of anti-amyloid-beta monoclonal antibodies on the pathology and clinical profile of Alzheimer’s disease: a focus on aducanumab and lecanemab. Front Aging Neurosci 14:870517

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. van Dyck CH, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M et al (2023) Lecanemab in early Alzheimer’s disease. N Engl J Med 388:9–21

    Article  PubMed  Google Scholar 

  11. Oakley H, Cole SL, Logan S, Maus E, Shao P, Craft J et al (2006) Intraneuronal beta-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer’s disease mutations: potential factors in amyloid plaque formation. J Neurosci 26:10129–10140

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhuang B, Mancarci BO, Toker L, Pavlidis P (2019) Mega-analysis of gene expression in mouse models of Alzheimer’s disease. eNeuro 6:ENEURO.0226-19.2019

  13. D’Onofrio M, Arisi I, Brandi R, Di Mambro A, Felsani A, Capsoni S et al (2011) Early inflammation and immune response mRNAs in the brain of AD11 anti-NGF mice. Neurobiol Aging 32:1007–1022

    Article  CAS  PubMed  Google Scholar 

  14. Wirz KT, Keitel S, Swaab DF, Verhaagen J, Bossers K (2014) Early molecular changes in Alzheimer disease: can we catch the disease in its presymptomatic phase? J Alzheimers Dis 38:719–740

    Article  CAS  PubMed  Google Scholar 

  15. Castillo E, Leon J, Mazzei G, Abolhassani N, Haruyama N, Saito T et al (2017) Comparative profiling of cortical gene expression in Alzheimer’s disease patients and mouse models demonstrates a link between amyloidosis and neuroinflammation. Sci Rep 7:17762

    Article  PubMed  PubMed Central  Google Scholar 

  16. Landel V, Baranger K, Virard I, Loriod B, Khrestchatisky M, Rivera S et al (2014) Temporal gene profiling of the 5XFAD transgenic mouse model highlights the importance of microglial activation in Alzheimer’s disease. Mol Neurodegener 9:33

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kim KH, Moon M, Yu SB, Mook-Jung I, Kim JI (2012) RNA-Seq analysis of frontal cortex and cerebellum from 5XFAD mice at early stage of disease pathology. J Alzheimers Dis 29:793–808

    Article  CAS  PubMed  Google Scholar 

  18. Bouter Y, Kacprowski T, Weissmann R, Dietrich K, Borgers H, Brauss A et al (2014) Deciphering the molecular profile of plaques, memory decline and neuron loss in two mouse models for Alzheimer’s disease by deep sequencing. Front Aging Neurosci 6:75

    Article  PubMed  PubMed Central  Google Scholar 

  19. Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13

    Article  PubMed  PubMed Central  Google Scholar 

  20. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J et al (2019) STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47:D607–D613

    Article  CAS  PubMed  Google Scholar 

  21. Navarro V, Sanchez-Mejias E, Jimenez S, Munoz-Castro C, Sanchez-Varo R, Davila JC et al (2018) Microglia in Alzheimer’s disease: activated, dysfunctional or degenerative. Front Aging Neurosci 10:140

    Article  PubMed  PubMed Central  Google Scholar 

  22. Sarlus H, Heneka MT (2017) Microglia in Alzheimer’s disease. J Clin Invest 127:3240–3249

    Article  PubMed  PubMed Central  Google Scholar 

  23. Gold M, El Khoury J (2015) beta-amyloid, microglia, and the inflammasome in Alzheimer’s disease. Semin Immunopathol 37:607–611

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M et al (2010) Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 303:1832–1840

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bettens K, Sleegers K, Van Broeckhoven C (2013) Genetic insights in Alzheimer’s disease. Lancet Neurol 12:92–104

    Article  CAS  PubMed  Google Scholar 

  26. Mhatre SD, Tsai CA, Rubin AJ, James ML, Andreasson KI (2015) Microglial malfunction: the third rail in the development of Alzheimer’s disease. Trends Neurosci 38:621–636

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Loring JF, Wen X, Lee JM, Seilhamer J, Somogyi R (2001) A gene expression profile of Alzheimer’s disease. DNA Cell Biol 20:683–695

    Article  CAS  PubMed  Google Scholar 

  28. Canchi S, Raao B, Masliah D, Rosenthal SB, Sasik R, Fisch KM et al (2019) Integrating gene and protein expression reveals perturbed functional networks in Alzheimer’s disease. Cell Rep 28(1103–1116):e1104

    Google Scholar 

  29. March-Diaz R, Lara-Urena N, Romero-Molina C, Heras-Garvin A, San Luis CO, Alvarez-Vergara MI (2021) Hypoxia compromises the mitochondrial metabolism of Alzheimer’s disease microglia via HIF1. Nature Aging 1:385–399

    Article  PubMed  Google Scholar 

  30. Tyagi A, Nguyen CU, Chong T, Michel CR, Fritz KS, Reisdorph N et al (2018) SIRT3 deficiency-induced mitochondrial dysfunction and inflammasome formation in the brain. Sci Rep 8:17547

    Article  PubMed  PubMed Central  Google Scholar 

  31. Gurung P, Lukens JR, Kanneganti TD (2015) Mitochondria: diversity in the regulation of the NLRP3 inflammasome. Trends Mol Med 21:193–201

    Article  CAS  PubMed  Google Scholar 

  32. Sorbara MT, Girardin SE (2011) Mitochondrial ROS fuel the inflammasome. Cell Res 21:558–560

    Article  PubMed  PubMed Central  Google Scholar 

  33. Chu X, Wu S, Raju R (2019) NLRX1 regulation following acute mitochondrial injury. Front Immunol 10:2431

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lamkanfi M, Dixit VM (2014) Mechanisms and functions of inflammasomes. Cell 157:1013–1022

    Article  CAS  PubMed  Google Scholar 

  35. Laudisi F, Spreafico R, Evrard M, Hughes TR, Mandriani B, Kandasamy M et al (2013) Cutting edge: the NLRP3 inflammasome links complement-mediated inflammation and IL-1beta release. J Immunol 191:1006–1010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Baruch K, Rosenzweig N, Kertser A, Deczkowska A, Sharif AM, Spinrad A et al (2015) Breaking immune tolerance by targeting Foxp3(+) regulatory T cells mitigates Alzheimer’s disease pathology. Nat Commun 6:7967

    Article  CAS  PubMed  Google Scholar 

  37. Faridar A, Thome AD, Zhao W, Thonhoff JR, Beers DR, Pascual B et al (2020) Restoring regulatory T-cell dysfunction in Alzheimer’s disease through ex vivo expansion. Brain Commun 2:fcaa112

    Article  PubMed  PubMed Central  Google Scholar 

  38. Webers A, Heneka MT, Gleeson PA (2020) The role of innate immune responses and neuroinflammation in amyloid accumulation and progression of Alzheimer’s disease. Immunol Cell Biol 98:28–41

    Article  PubMed  Google Scholar 

  39. Yin Z, Raj DD, Schaafsma W, van der Heijden RA, Kooistra SM, Reijne AC et al (2018) Low-fat diet with caloric restriction reduces white matter microglia activation during aging. Front Mol Neurosci 11:65

    Article  PubMed  PubMed Central  Google Scholar 

  40. Tyagi A, Musa M, Labeikovsky W, Pugazhenthi S (2022) Sirt3 deficiency induced down regulation of insulin degrading enzyme in comorbid Alzheimer’s disease with metabolic syndrome. Sci Rep 12:19808

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Wingo AP, Dammer EB, Breen MS, Logsdon BA, Duong DM, Troncosco JC et al (2019) Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age. Nat Commun 10:1619

    Article  PubMed  PubMed Central  Google Scholar 

  42. Foxton RH, Land JM, Heales SJ (2007) Tetrahydrobiopterin availability in Parkinson’s and Alzheimer’s disease; potential pathogenic mechanisms. Neurochem Res 32:751–756

    Article  CAS  PubMed  Google Scholar 

  43. Feng Y, Li X, Cassady K, Zou Z, Zhang X (2019) TET2 function in hematopoietic malignancies, immune regulation, and DNA repair. Front Oncol 9:210

    Article  PubMed  PubMed Central  Google Scholar 

  44. Cui H, Kong Y, Zhang H (2012) Oxidative stress, mitochondrial dysfunction, and aging. J Signal Transduct 2012:646354

    Article  PubMed  Google Scholar 

  45. Chakrabarti S, Munshi S, Banerjee K, Thakurta IG, Sinha M, Bagh MB (2011) Mitochondrial dysfunction during brain aging: role of oxidative stress and modulation by antioxidant supplementation. Aging Dis 2:242–256

    PubMed  PubMed Central  Google Scholar 

  46. Jian B, Yang S, Chen D, Zou L, Chatham JC, Chaudry I et al (2011) Aging influences cardiac mitochondrial gene expression and cardiovascular function following hemorrhage injury. Mol Med 17:542–549

    Article  CAS  PubMed  Google Scholar 

  47. Ding Q, Markesbery WR, Chen Q, Li F, Keller JN (2005) Ribosome dysfunction is an early event in Alzheimer’s disease. J Neurosci 25:9171–9175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Keller JN, Hanni KB, Markesbery WR (2000) Impaired proteasome function in Alzheimer’s disease. J Neurochem 75:436–439

    Article  CAS  PubMed  Google Scholar 

  49. Hong L, Huang HC, Jiang ZF (2014) Relationship between amyloid-beta and the ubiquitin-proteasome system in Alzheimer’s disease. Neurol Res 36:276–282

    Article  CAS  PubMed  Google Scholar 

  50. Liao L, Cheng D, Wang J, Duong DM, Losik TG, Gearing M et al (2004) Proteomic characterization of postmortem amyloid plaques isolated by laser capture microdissection. J Biol Chem 279:37061–37068

    Article  CAS  PubMed  Google Scholar 

  51. Ashleigh T, Swerdlow RH, Beal MF (2023) The role of mitochondrial dysfunction in Alzheimer’s disease pathogenesis. Alzheimers Dement 19:333–342

    Article  CAS  PubMed  Google Scholar 

  52. McDonald TS, Lerskiatiphanich T, Woodruff TM, McCombe PA, Lee JD (2023) Potential mechanisms to modify impaired glucose metabolism in neurodegenerative disorders. J Cereb Blood Flow Metab 43:26–43

    Article  CAS  PubMed  Google Scholar 

  53. Adlard PA, Tran BA, Finkelstein DI, Desmond PM, Johnston LA, Bush AI et al (2014) A review of beta-amyloid neuroimaging in Alzheimer’s disease. Front Neurosci 8:327

    Article  PubMed  PubMed Central  Google Scholar 

  54. Duran-Aniotz C, Hetz C (2016) Glucose metabolism: a sweet relief of Alzheimer’s disease. Curr Biol 26:R806-809

    Article  CAS  PubMed  Google Scholar 

  55. Willette AA, Bendlin BB, Starks EJ, Birdsill AC, Johnson SC, Christian BT et al (2015) Association of insulin resistance with cerebral glucose uptake in late middle-aged adults at risk for Alzheimer disease. JAMA Neurol 72:1013–1020

    Article  PubMed  PubMed Central  Google Scholar 

  56. Del Prete D, Suski JM, Oules B, Debayle D, Gay AS, Lacas-Gervais S et al (2017) Localization and processing of the amyloid-beta protein precursor in mitochondria-associated membranes. J Alzheimers Dis 55:1549–1570

    Article  PubMed  Google Scholar 

  57. Wilkins HM (2023) Interactions between amyloid, amyloid precursor protein, and mitochondria. Biochem Soc Trans 51:173–182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. de la Cueva M, Antequera D, Ordonez-Gutierrez L, Wandosell F, Camins A, Carro E et al (2022) Amyloid-beta impairs mitochondrial dynamics and autophagy in Alzheimer’s disease experimental models. Sci Rep 12:10092

    Article  PubMed  PubMed Central  Google Scholar 

  59. Sbai O, Bazzani V, Tapaswi S, McHale J, Vascotto C, Perrone L (2023) Is Drp1 a link between mitochondrial dysfunction and inflammation in Alzheimer’s disease? Front Mol Neurosci 16:1166879

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Chen Z, Zhong C (2013) Decoding Alzheimer’s disease from perturbed cerebral glucose metabolism: implications for diagnostic and therapeutic strategies. Prog Neurobiol 108:21–43

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

The work was supported by US federal grants R01AG073338 (NIH to RR) and TI01BX004480 (VA to SP).

Author information

Authors and Affiliations

  1. Medical College of Georgia, Augusta University, Augusta, GA, USA

    Raghavan Pillai Raju & Lun Cai

  2. Rocky Mountain Regional VA Medical Center, Aurora, CO, USA

    Alpna Tyagi & Subbiah Pugazhenthi

  3. Department of Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA

    Alpna Tyagi & Subbiah Pugazhenthi

Authors
  1. Raghavan Pillai Raju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lun Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alpna Tyagi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Subbiah Pugazhenthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Raghavan Pillai Raju and Subbiah Pugazhenthi contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Raghavan Pillai Raju, Lun Cai, Alpna Tyagi, and Subbiah Pugazhenthi. The first draft of the manuscript was written by Raghavan Pillai Raju, and all authors commented on the draft and subsequent versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Raghavan Pillai Raju or Subbiah Pugazhenthi.

Ethics declarations

Ethics Approval

All animal experiments were conducted as per the protocol approved by the Institutional Animal Care and Use Committee at Augusta University, Augusta, GA.

Consent to Participate

Not applicable. This study does not involve human subjects.

Consent for Publication

Not applicable. This study does not involve human subjects.

Conflict of Interest

The authors declare 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.

Supplementary file1 (XLS 17603 KB)

ESM 2

(PNG 5.38 mb)

High Resolution (TIF 20.3 mb)

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

Raju, R.P., Cai, L., Tyagi, A. et al. Interactions of Cellular Energetic Gene Clusters in the Alzheimer’s Mouse Brain. Mol Neurobiol 61, 476–486 (2024). https://doi.org/10.1007/s12035-023-03551-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12035-023-03551-0

Keywords

Search

Navigation