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Cited by 5Alzheimer's disease research and treatmentsDementia and Cognitive Impairment ResearchNeuroinflammation and Neurodegeneration Mechanisms

Transcriptional, Behavioral and Biochemical Profiling in the 3xTg-AD Mouse Model Reveals a Specific Signature of Amyloid Deposition and Functional Decline in Alzheimer’s Disease

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Yin, Wencheng, Cerda-Hernández, Navei, Castillo-Morales, Atahualpa, Ruiz-Tejada-Segura, Mayra L., Monzón-Sandoval, Jimena, Moreno-Castilla, Perla, Pérez-Ortega, Rodrigo, Bermudez-Rattoni, Federico +2 more·2020·Frontiers in Neuroscience
DOI 10.3389/fnins.2020.602642PMC PMC7774037
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Abstract

Alzheimer’s disease (AD)-related degenerative decline is associated to the presence of amyloid beta (Aβ) plaque lesions and neuro fibrillary tangles (NFT). However, the precise molecular mechanisms linking Aβ deposition and neurological decline are still unclear. Here we combine genome-wide transcriptional profiling of the insular cortex of 3xTg-AD mice and control littermates from early through to late adulthood (2–14 months of age), with behavioral and biochemical profiling in the same animals to identify transcriptional determinants of functional decline specifically associated to build-up of Aβ deposits. Differential expression analysis revealed differentially expressed genes (DEGs) in the cortex long before observed onset of behavioral symptoms in this model. Using behavioral and biochemical data derived from the same mice and samples, we found that down but not up-regulated DEGs show a stronger average association with learning performance than random background genes in control not seen in AD mice. Conversely, these same genes were found to have a stronger association with Aβ deposition than background genes in AD but not in control mice, thereby identifying these genes as potential intermediaries between abnormal Aβ/NFT deposition and functional decline. Using a complementary approach, gene ontology analysis revealed a highly significant enrichment of learning and memory, associative, memory, and cognitive functions only among down-regulated, but not up-regulated, DEGs. Our results demonstrate wider transcriptional changes triggered by the abnormal deposition of Aβ/NFT occurring well before behavioral decline and identify a distinct set of genes specifically associated to abnormal Aβ protein deposition and cognitive decline.

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Research works printed but not linked: 38

  1. Alkondon M. Albuquerque W. X. (2002). A Non-α7 nicotinic acetylcholine receptor modulates excitatory input to hippocampal CA1 interneurons. J. Neurophysiol. 87 1651– 1654. 10.1152/jn.00708.2001 11877536 · DOI · Paper page
  2. Bendiske J. Bahr B. A. (2003). Lysosomal activation is a compensatory response against protein accumulation and associated synaptopathogenesis–an approach for slowing Alzheimer disease? J. Neuropathol. Exp. Neurol. 62 451– 463. 10.1093/jnen/62.5.451 12769185 · DOI
  3. Bharadwaj P. R. Dubey A. K. Masters C. L. Martins R. N. Macreadie I. G. (2009). Aβ aggregation and possible implications in Alzheimer’s disease pathogenesis. J. Cell. Mol. Med. 13 412– 421. 10.1111/j.1582-4934.2009.00609.x 19374683 PMC3822505 · DOI
  4. Bouter Y. Kacprowski T. Weissmann R. Dietrich K. Borgers H. Brauß A. (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. 10.3389/fnagi.2014.00075 24795628 PMC3997018 · DOI
  5. Caruso D. Barron A. M. Brown M. A. Abbiati F. Carrero P. Pike C. J. (2013). Age-related changes in neuroactive steroid levels in 3xTg-AD mice. Neurobiol. Aging 34 1080– 1089. 10.1016/j.neurobiolaging.2012.10.007 23122920 PMC3545103 · DOI
  6. Chin J. (2011). Selecting a mouse model of Alzheimer’s disease. Methods Mol. Biol. 670 169– 189. 10.1007/978-1-60761-744-0_13 20967591 · DOI
  7. Do T. M. Alata W. Dodacki A. Traversy M. T. Chacun H. Pradier L. (2014). Altered cerebral vascular volumes and solute transport at the blood-brain barriers of two transgenic mouse models of Alzheimer’s disease. Neuropharmacology 81 311– 317. 10.1016/j.neuropharm.2014.02.010 24631967 · DOI
  8. Ferri C. P. Prince M. Brayne C. Brodaty H. Fratiglioni L. Ganguli M. (2005). Global prevalence of dementia: a delphi consensus study. Lancet 366 2112– 2117. 10.1016/s0140-6736(05)67889-0 16360788 PMC2850264 · DOI
  9. Guzmán-Ramos K. Moreno-Castilla P. Castro-Cruz M. McGaugh J. L. Martínez-Coria H. LaFerla F. M. (2012a). Restoration of dopamine release deficits during object recognition memory acquisition attenuates cognitive impairment in a triple transgenic mice model of Alzheimer’s disease. Learn. Mem. 19 453– 460. 10.1101/lm.026070.112 22984283 · DOI · Paper page
  10. Guzmán-Ramos K. Osorio-Gómez D. Moreno-Castilla P. Bermúdez-Rattoni F. (2012b). Post-acquisition release of glutamate and norepinephrine in the amygdala is involved in taste-aversion memory consolidation. Learn. Mem. 19 231– 238. 10.1101/lm.024703.111 22589279 · DOI
  11. Hersi M. Irvine B. Gupta P. Gomes J. Birkett N. Krewski D. (2017). Risk factors associated with the onset and progression of Alzheimer’s disease: a systematic review of the evidence. Neurotoxicology 61 143– 187. 10.1016/j.neuro.2017.03.006 28363508 · DOI · Paper page
  12. Iqbal K. Flory M. Soininen H. (2013). Clinical symptoms and symptom signatures of Alzheimer’s disease subgroups. J. Alzheimers Dis. 37 475– 481. 10.3233/jad-130899 24002184 · DOI
  13. Iwata N. Tsubuki S. Takaki Y. Shirotani K. Lu B. Gerard N. P. (2001). Metabolic regulation of brain Abeta by neprilysin. Science 292 1550– 1552. 10.1126/science.1059946 11375493 · DOI
  14. Kar S. Slowikowski S. P. Westaway D. Mount H. T. (2004). Interactions between beta-amyloid and central cholinergic neurons: implications for Alzheimer’s disease. J. Psychiatry Neurosci. 29 427– 441. 15644984 PMC524960
  15. Kim K. H. Moon M. Yu S.-B. Mook-Jung I. Kim J.-I. (2012). RNA-Seq analysis of frontal cortex and cerebellum from 5XFAD mice at early stage of disease pathology. J. Alzheimer’s Dis. 29 793– 808. 10.3233/jad-2012-111793 22507954 · DOI
  16. Kim T.-K. Lee J.-E. Park S.-K. Lee K.-W. Seo J.-S. Im J.-Y. (2012). Analysis of differential plaque depositions in the brains of Tg2576 and Tg-APPswe/PS1dE9 transgenic mouse models of Alzheimer disease. Exp. Mol. Med. 44 492– 502. 10.3858/emm.2012.44.8.056 22644036 PMC3429813 · DOI
  17. Lewis J. Dickson D. W. Lin W. L. Chisholm L. Corral A. Jones G. (2001). Enhanced neurofibrillary degeneration in transgenic mice expressing mutant tau and APP. Science 293 1487– 1491. 10.1126/science.1058189 11520987 · DOI · Paper page
  18. Marambaud P. Zhao H. Davies P. (2005). Resveratrol promotes clearance of Alzheimer’s disease amyloid-beta peptides. J. Biol. Chem. 280 37377– 37382. 10.1074/jbc.m508246200 16162502 · DOI
  19. Mirnics K. Korade Z. Arion D. Lazarov O. Unger T. Macioce M. (2005). Presenilin-1-dependent transcriptome changes. J. Neurosci. 25 1571– 1578. 10.1523/jneurosci.4145-04.2005 15703411 PMC6726008 · DOI
  20. Moreno-Castilla P. Rodriguez-Duran L. F. Guzmán-Ramos K. Barcenas-Femat A. Escobar Martha L. Bermúdez-Rattoni F. (2016). Dopaminergic neurotransmission dysfunction induced by B-Amyloid transforms cortical LTP into LTD and produces memory impairment. Neurobiol. Aging 41 187– 199. 10.1016/j.neurobiolaging.2016.02.021 27103531 · DOI · Paper page
  21. Nikolaev A. McLaughlin T. O’Leary D. D. Tessier-Lavigne M. (2009). APP binds DR6 to trigger axon pruning and neuron death via distinct caspases. Nature 457 981– 989. 10.1038/nature07767 19225519 PMC2677572 · DOI · Paper page
  22. Oddo S. Caccamo A. Kitazawa M. Tseng B. P. LaFerla F. M. (2003a). Amyloid deposition precedes tangle formation in a triple transgenic model of Alzheimer’s disease. Neurobiol. Aging 24 1063– 1070. 10.1016/j.neurobiolaging.2003.08.012 14643377 · DOI · Paper page
  23. Oddo S. Caccamo A. Shepherd J. D. Murphy M. P. Golde T. E. Kayed R. (2003b). Triple-transgenic model of Alzheimer’s disease with plaques and tangles: intracellular a beta and synaptic dysfunction. Neuron 39 409– 421. 12895417
  24. Oddo S. Vasilevko V. Caccamo A. Kitazawa M. Cribbs D. H. LaFerla F. M. (2006). Reduction of Soluble Aβ and Tau, but not Soluble Aβ Alone. Ameliorates cognitive decline in transgenic mice with plaques and tangles. J. Biol. Chem. 281 39413– 39423. 10.1074/jbc.m608485200 17056594 · DOI
  25. Palop J. J. Mucke L. (2010). Amyloid-beta-induced neuronal dysfunction in Alzheimer’s disease: from synapses toward neural networks. Nat. Neurosci. 13 812– 818. 10.1038/nn.2583 20581818 PMC3072750 · DOI · Paper page
  26. Puzzo D. Arancio O. (2013). Amyloid-β Peptide: Dr. Jekyll or Mr. Hyde? J. Alzheimers Dis. 33 S111– S1120. 22735675 PMC3696497
  27. Querfurth H. W. LaFerla F. M. (2010). Alzheimer’s disease. N. Engl. J. Med. 362 329– 344. 20107219
  28. Reddy P. H. McWeeney S. Park B. S. Manczak M. Gutala R. V. Partovi D. (2004). Gene expression profiles of transcripts in amyloid precursor protein transgenic mice: up-regulation of mitochondrial metabolism and apoptotic genes is an early cellular change in Alzheimer’s disease. Hum. Mol. Gen. 13 1225– 1240. 10.1093/hmg/ddh140 15115763 · DOI
  29. Robinson M. D. McCarthy D. J. Smyth G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 139– 140. 10.1093/bioinformatics/btp616 19910308 PMC2796818 · DOI · Paper page
  30. Robinson M. D. Oshlack A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Gen. Biol. 11 R25– R25. PMC2864565 20196867
  31. Robinson M. D. Smyth G. K. (2008). Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9 321– 332. 10.1093/biostatistics/kxm030 17728317 · DOI · Paper page
  32. Selkoe D. J. (2001). Alzheimer’s disease: genes, proteins, and therapy. Physiol. Rev. 81 741– 766. 11274343
  33. Selkoe D. J. Hardy J. (2016). The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8 595– 608. 27025652 PMC4888851
  34. Stover K. R. Campbell M. A. Van Winssen C. M. Brown R. E. (2015). Early detection of cognitive deficits in the 3xTg-AD mouse model of Alzheimer’s disease. Behav. Brain Res. 289 29– 38. 10.1016/j.bbr.2015.04.012 25896362 · DOI
  35. Unger T. Korade Z. Lazarov O. Terrano D. Schor N. F. Sisodia S. S. (2005). Transcriptome differences between the frontal cortex and hippocampus of wild-type and humanized presenilin-1 transgenic mice. Am. J. Geriatric Psychiatry 13 1041– 1051. 10.1097/00019442-200512000-00003 16319296 · DOI
  36. Wang X. Michaelis M. L. Michaelis E. K. (2010). Functional genomics of brain aging and Alzheimer’s disease: focus on selective neuronal vulnerability. Curr. Genomics 11 618– 633. 10.2174/138920210793360943 21629439 PMC3078686 · DOI
  37. Wu Y. Chen C. Chen M. Qian K. Lv X. Wang H. (2020). The anterior insular cortex unilaterally controls feeding in response to aversive visceral stimuli in mice. Nature commun. 11 1– 14. 32005806 PMC6994462
  38. Zhang B. Kirov S. Snoddy J. (2005). WebGestalt: an integrated system for exploring gene sets in various iological contexts. Nucleic Acids Res. 33 W741– W748. 15980575 PMC1160236

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Authors
Yin, W., Cerda-Hernández, N., Castillo-Morales, A., Ruiz-Tejada-Segura, M. L., Monzón-Sandoval, J., Moreno-Castilla, P., Pérez-Ortega, R., Bermudez-Rattoni, F., Urrutia, A. O., & Gutiérrez, H.
Affiliations
  • University of Birmingham (GB)
  • University of Bath (GB)
  • National Institute of Genomic Medicine (MX)
Corresponding author
  • Urrutia, Araxi O.
  • Gutiérrez, Humberto
ORCID
Venue
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Published
2020-12-15
Volume
14
Pages
602642-602642
ISSN
1662-453X (ISSN-L)
DOI
10.3389/fnins.2020.602642
PMC
PMC7774037
OpenAlex
W3113099383
Cited by
5
References
5
Citations by year
2025: 2 · 2023: 3
FWCI
0.45
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Pure (University of Bath) (submittedVersion, cc-by)
Funders
Natural Environment Research Council
Topics
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
Keywords
Alzheimer's disease, 3xTg-AD mice, amyloid-beta deposition, neurofibrillary tangles, insula, gene expression profiling, differentially expressed genes, cognitive decline, learning and memory, behavioral decline, transcriptome, gene ontology enrichment

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