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Profiling Basal Forebrain Cholinergic Neurons Reveals a Molecular Basis for Vulnerability Within the Ts65Dn Model of Down Syndrome and Alzheimer’s Disease

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A Correction to this article was published on 27 November 2021

This article has been updated

Abstract

Basal forebrain cholinergic neuron (BFCN) degeneration is a hallmark of Down syndrome (DS) and Alzheimer’s disease (AD). Current therapeutics have been unsuccessful in slowing disease progression, likely due to complex pathological interactions and dysregulated pathways that are poorly understood. The Ts65Dn trisomic mouse model recapitulates both cognitive and morphological deficits of DS and AD, including BFCN degeneration. We utilized Ts65Dn mice to understand mechanisms underlying BFCN degeneration to identify novel targets for therapeutic intervention. We performed high-throughput, single population RNA sequencing (RNA-seq) to interrogate transcriptomic changes within medial septal nucleus (MSN) BFCNs, using laser capture microdissection to individually isolate ~500 choline acetyltransferase-immunopositive neurons in Ts65Dn and normal disomic (2N) mice at 6 months of age (MO). Ts65Dn mice had unique MSN BFCN transcriptomic profiles at ~6 MO clearly differentiating them from 2N mice. Leveraging Ingenuity Pathway Analysis and KEGG analysis, we linked differentially expressed gene (DEG) changes within MSN BFCNs to several canonical pathways and aberrant physiological functions. The dysregulated transcriptomic profile of trisomic BFCNs provides key information underscoring selective vulnerability within the septohippocampal circuit. We propose both expected and novel therapeutic targets for DS and AD, including specific DEGs within cholinergic, glutamatergic, GABAergic, and neurotrophin pathways, as well as select targets for repairing oxidative phosphorylation status in neurons. We demonstrate and validate this interrogative quantitative bioinformatic analysis of a key dysregulated neuronal population linking single population transcript changes to an established pathological hallmark associated with cognitive decline for therapeutic development in human DS and AD.

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Data Availability

Data analyzed within this study are included in this body of the manuscript and within the supplementary information files. Data are also available from the corresponding author upon request.

Change history

Abbreviations

Adcy1 :

adenylate cyclase 1

AD:

Alzheimer’s disease

Apoe :

apolipoprotein E

Atp5a1 :

ATP synthase H+ transporting mitochondrial F1 complex, alpha subunit 1

Atp5o :

ATP synthase H+ transporting mitochondrial F1 complex, O subunit

BFCN:

basal forebrain cholinergic neuron

Bop1 :

block of proliferation 1 ribosomal biogenesis factor

Bdnf :

brain derived neurotrophin factor

Bysl :

bystin like

Camk2a :

calcium/calmodulin dependent protein kinase II alpha

Calm3 :

calmodulin 3

Capn1 :

calpain 1

ChAT:

choline acetyltransferase

CPM:

counts per million

Cox4i1 :

cytochrome c oxidase subunit 4I1

Ddx5 :

DEAD-box helicase 5

DEG:

differentially expressed gene

Dlg4; also known as PSD-95 :

discs large MAGUK scaffold protein 4

2N:

disomic

DS:

Down syndrome

Dyrk1a :

dual specificity tyrosine phosphorylation regulated kinase 1A

Ets2 :

E26 avian leukemia oncogene 2,3’ domain

Eif5b :

eukaryotic translation initiation factor 5B

FDR:

false discovery rate

FAK:

focal adhesion kinase

Gnb5 :

G protein subunit beta5

Gusb :

glucuronidase beta

Grin2a :

glutamate ionotropic receptor NDMA type subunit 2A

Gria1 :

glutamate receptor, ionotropic, AMPA1

Gapdh :

glyceraldehyde-3-phosphate dehydrogenase

HSA21:

human chromosome 21

IPA:

Ingenuity Pathway Analysis

Jam2 :

junction adhesion molecule 2

Kidins220/Arms :

kinase D interacting substrate 220

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LCM:

laser capture microdissection

Lca5l :

lebercilin congenital amaurosis 5-like

LFC:

log-fold change

LTD:

long-term depression

LTP:

long-term potentiation

MSN:

medial septal nucleus

miRNAs:

microRNAs

Mapk8 aka Erk2 :

mitogen-activated protein kinase 8

Mapk3 :

mitogen-activated protein kinase 3

MO:

months of age

Chrm1 :

muscarinic cholinergic receptor 1

Chrm2 :

muscarinic cholinergic receptor 2

N6amt1 :

N-6 adenine-specific DNA mythltransferase1

Mt-Nd1, Mt-Nd2, Mt-Nd 4, and Mt-Nd5 :

NADH dehydrogenases

Ndufa6, Ndufab1, Ndufb2, Ndufb4, Ndufs1, Ndufs2, Ndufs4, Ndusf7, and Ndufs8 :

NADH:ubiquinone oxidoreductase subunits

Mme :

neprilysin

Ngfr/p75NTR :

nerve growth factor receptor

Nos1 :

nitric oxide synthase 1

ncRNA:

noncoding RNA

Pik3ca :

phosphatidylinositol 3- kinase catalytic subunit

Plcb1 :

phospholipase C beta 1

Plcb2 :

phospholipase C beta 2

PEN:

polyethylene naphthalate

PCA:

principal component analysis

Pa2g4 :

proliferation-associated 2G4

Prkcg :

protein kinase C gamma

QC:

quality control

RNA-seq:

RNA sequencing

RT:

room temperature

Setd4 :

SET domain containing 4

Son :

Son DNA binding protein

Stx1a :

syntaxin 1A

Tiam1 :

T cell lymphoma invasion and metastasis 1

Ttc3 :

tetratricopeptide repeat domain 3

TEG:

transcript expression

Ntrk1 :

TrkA

Ts:

Ts65Dn

Rela:

v-rel reticuloendotheliosis viral oncogene homolog A

WGCNA:

weighted gene co-expression network analysis

References

  1. Parker SE, Mai CT, Canfield MA, Rickard R, Wang Y, Meyer RE, Anderson P, Mason CA et al (2010) Updated National Birth prevalence estimates for selected birth defects in the United States, 2004-2006. Birth Defects Res A Clin Mol Teratol 88:1008–1016

    Article  PubMed  CAS  Google Scholar 

  2. Presson AP, Partyka G, Jensen KM, Devine OJ, Rasmussen SA, McCabe LL et al (2013) Current estimate of Down Syndrome population prevalence in the United States. J Pediatr 163(4):1163–1168

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mann DM, Yates PO, Marcyniuk B (1984) Alzheimer' s presenile dementia, senile dementia of Alzheimer type and Down' s syndrome in middle age form an age related continuum of pathological changes. Neuropathol Appl Neurobiol 10:185–207

  4. Coyle JT, Oster-Granite ML, Reeves RH, Gearhart JD (1988) Down syndrome, Alzheimer ' s disease and the trisomy16 mouse. Trends Neurosci 11(9):390–394

    Article  PubMed  CAS  Google Scholar 

  5. Hook EB (1989) Issues pertaining to the impact and etiology of trisomy 21 and other aneuploidy in humans; a consideration of evolutionary implications, maternal age mechanisms, and other matters. Prog Clin Biol Res 311:1–27

    PubMed  CAS  Google Scholar 

  6. Hodgkins PS, Prasher V, Farrar G, Armstrong R, Sturman S, Corbett J, Blair JA (1993) Reduced transferrin binding in Down syndrome: a route to senile plaque formation and dementia. Neuroreport. 5(1):21–24

    Article  PubMed  CAS  Google Scholar 

  7. Roth GM, Sun B, Greensite FS, Lott IT, Dietrich RB (1996) Premature aging in persons with Down syndrome: MR findings. AJNR Am J Neuroradiol 17(7):1283–1289

    PubMed  PubMed Central  CAS  Google Scholar 

  8. Chapman RS, Hesketh LJ (2000) Behavioral phenotype of individuals with Down syndrome. Ment Retard Dev Disabil Res Rev 6:84–95

    Article  PubMed  CAS  Google Scholar 

  9. Cataldo AM, Peterhoff CM, Troncoso JC, Gomez-Isla T, Hyman BT, Nixon RA (2000) Endocytic pathway abnormalities precede amyloid beta deposition in sporadic Alzheimer ' s disease and Down syndrome: differential effects of APOE genotype and presenilin mutations. Am J Pathol 157:277–286

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Hartley D, Blumenthal T, Carrillo M, DiPaolo G, Esralew L, Gardiner K, Granholm AC, Iqbal K et al (2015) Down syndrome and Alzheimer ' s disease: common pathways, common goals. Alzheimers Dement 11:700–709

    Article  PubMed  Google Scholar 

  11. Hartley SL, Handen BL, Devenny DA, Hardison R, Mihaila I, Price JC, Cohen AD, Klunk WE et al (2014) Cognitive functioning in relation to brain amyloid-beta in healthy adults with Down syndrome. Brain. 137:2556–2563

    Article  PubMed  PubMed Central  Google Scholar 

  12. Leverenz JB, Raskind MA (1998) Early amyloid deposition in the medial temporal lobe of young Down syndrome patients: a regional quantitative analysis. Exp Neurol 150:296–304

    Article  PubMed  CAS  Google Scholar 

  13. Wisniewski KE, Dalton AJ, Crapper McLachlan DR, Wen GY, Wisniewski HM (1985) Alzheimer's disease in Down's syndrome: clinicopathologic studies. Neurology. 35:957–961

  14. Lai F, Williams RS (1989) A prospective study of Alzheimer disease in Down syndrome. Arch Neurol 46(8):849–853

    Article  PubMed  CAS  Google Scholar 

  15. Perez SE, Miguel JC, He B, Malek-Ahmadi M, Abrahamson EE, Ikonomovic MD, Lott I, Doran E et al (2019) Frontal cortex and striatal cellular and molecular pathobiology in individuals with Down syndrome with and without dementia. Acta Neuropathol 137(3):413–436

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Sendera TJ, Ma SY, Jaffar S, Kozlowski PB, Kordower JH, Mawal Y, Saragovi HU, Mufson EJ (2000) Reduction in TrkA-immunoreactive neurons is not associated with an overexpression of galaninergic fibers within the nucleus basalis in Down' s syndrome. J Neurochem 74(3):1185–1196

  17. Mufson EJ, Bothwell M, Kordower JH (1989) Loss of nerve growth factor receptor-containing neurons in Alzheimer' s disease: a quantitative analysis across subregions of the basal forebrain. Exp Neurol 105:221–232

  18. Mann DM, Yates PO, Marcyniuk B, Ravindra CR (1986) The topography of plaques and tangles in Down' s syndrome patients of different ages. Neuropathol Appl Neurobiol 12:447–457

  19. Belichenko PV, Kleschevnikov AM, Masliah E, Wu C, Takimoto-Kimura R, Salehi A, Mobley WC (2009) Excitatory-inhibitory relationship in the fascia dentata in the Ts65Dn mouse model of Down syndrome. J Comp Neurol 512:453–466

    Article  PubMed  PubMed Central  Google Scholar 

  20. Belichenko PV, Masliah E, Kleschevnikov AM, Villar AJ, Epstein CJ, Salehi A, Mobley WC (2004) Synaptic structural abnormalities in the Ts65Dn mouse model of Down Syndrome. J Comp Neurol 480:281–298

    Article  PubMed  Google Scholar 

  21. Granholm AC, Sanders LA, Crnic LS (2000) Loss of cholinergic phenotype in basal forebrain coincides with cognitive decline in a mouse model of Down' s syndrome. Exp Neurol 161:647–663

  22. Kelley CM, Powers BE, Velazquez R, Ash JA, Ginsberg SD, Strupp BJ, Mufson EJ (2014) Sex differences in the cholinergic basal forebrain in the Ts65Dn mouse model of Down syndrome and Alzheimer' s disease. Brain Pathol 24:33–44

  23. Kelley CM, Powers BE, Velazquez R, Ash JA, Ginsberg SD, Strupp BJ, Mufson EJ (2014) Maternal choline supplementation differentially alters the basal forebrain cholinergic system of young-adult Ts65Dn and disomic mice. J Comp Neurol 522:1390–1410

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Betts MJ, Kirilina E, Otaduy MCG, Ivanov D, Acosta-Cabronero J, Callaghan MF, Lambert C, Cardenas-Blanco A et al (2019) Locus coeruleus imaging as a biomarker for noradrenergic dysfunction in neurodegenerative diseases. Brain. 142(9):2558–2571

    Article  PubMed  PubMed Central  Google Scholar 

  25. Yamasaki M, Takeuchi T (2017) Locus coeruleus and dopamine-dependent memory consolidation. Neural Plast 2017:8602690

    Article  PubMed  PubMed Central  Google Scholar 

  26. Teixeira CM, Rosen ZB, Suri D, Sun Q, Hersh M, Sargin D et al (2018) Hippocampal 5-HT input regulates memory formation and Schaffer collateral excitation. Neuron 98(5):992–1004.e4

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Sekeres MJ, Winocur G, Moscovitch M (2018) The hippocampus and related neocortical structures in memory transformation. Neurosci Lett 680:39–53

    Article  PubMed  CAS  Google Scholar 

  28. Solari N, Hangya B (2018) Cholinergic modulation of spatial learning, memory and navigation. Eur J Neurosci 48(5):2199–2230

    Article  PubMed  PubMed Central  Google Scholar 

  29. Lockrow J, Prakasam A, Huang P, Bimonte-Nelson H, Sambamurti K, Granholm AC (2009) Cholinergic degeneration and memory loss delayed by vitamin E in a Down syndrome mouse model. Exp Neurol 216(2):278–289

    Article  PubMed  CAS  Google Scholar 

  30. Hunter CL, Bimonte-Nelson HA, Nelson M, Eckman CB, Granholm AC (2004) Behavioral and neurobiological markers of Alzheimer' s disease in Ts65Dn mice: effects of estrogen. Neurobiol Aging 25(7):873–884

  31. Holtzman DM, Santucci D, Kilbridge J, Chua-Couzens J, Fontana DJ, Daniels SE, Johnson RM, Chen K et al (1996) Developmental abnormalities and age-related neurodegeneration in a mouse model of Down syndrome. Proc Natl Acad Sci U S A 93:13333–13338

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Cooper JD, Salehi A, Delcroix JD, Howe CL, Belichenko PV, Chua-Couzens J, Kilbridge JF, Carlson EJ et al (2001) Failed retrograde transport of NGF in a mouse model of Down' s syndrome: reversal of cholinergic neurodegenerative phenotypes following NGF infusion. Proc Natl Acad Sci U S A 98(18):10439–10444

  33. Powers BE, Kelley CM, Velazquez R, Ash JA, Strawderman MS, Alldred MJ, Ginsberg SD, Mufson EJ et al (2017) Maternal choline supplementation in a mouse model of Down syndrome: effects on attention and nucleus basalis/substantia innominata neuron morphology in adult offspring. Neuroscience. 340:501–514

    Article  PubMed  CAS  Google Scholar 

  34. Powers BE, Velazquez R, Kelley CM, Ash JA, Strawderman MS, Alldred MJ, Ginsberg SD, Mufson EJ et al (2016) Attentional function and basal forebrain cholinergic neuron morphology during aging in the Ts65Dn mouse model of Down syndrome. Brain Struct Funct 221:4337–4352

    Article  PubMed  CAS  Google Scholar 

  35. Ash JA, Velazquez R, Kelley CM, Powers BE, Ginsberg SD, Mufson EJ, Strupp BJ (2014) Maternal choline supplementation improves spatial mapping and increases basal forebrain cholinergic neuron number and size in aged Ts65Dn mice. Neurobiol Dis 70:32–42

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Kelley CM, Ash JA, Powers BE, Velazquez R, Alldred MJ, Ikonomovic MD, Ginsberg SD, Strupp BJ et al (2016) Effects of maternal choline supplementation on the septohippocampal cholinergic system in the Ts65Dn mouse model of Down syndrome. Curr Alzheimer Res 13:84–96

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Strupp BJ, Powers BE, Velazquez R, Ash JA, Kelley CM, Alldred MJ, Strawderman M, Caudill MA et al (2016) Maternal choline supplementation: a potential prenatal treatment for Down syndrome and Alzheimer' s disease. Curr Alzheimer Res 13:97–106

  38. Moon J, Chen M, Gandhy SU, Strawderman M, Levitsky DA, Maclean KN, Strupp BJ (2010) Perinatal choline supplementation improves cognitive functioning and emotion regulation in the Ts65Dn mouse model of Down syndrome. Behav Neurosci 124:346–361

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Hunter CL, Bimonte HA, Granholm A-CE (2003) Behavioral comparison of 4 and 6 month-old Ts65Dn mice: age-related impairments in working and reference memory. Behav Brain Res 138(2):121–131

    Article  PubMed  CAS  Google Scholar 

  40. Kelley CM, Ginsberg SD, Alldred MJ, Strupp BJ, Mufson EJ (2019) Maternal choline supplementation alters basal forebrain cholinergic neuron gene expression in the Ts65Dn Mouse model of down syndrome. Dev Neurobiol 79(7):664–683

  41. Cataldo AM, Petanceska S, Peterhoff CM, Terio NB, Epstein CJ, Villar A, Carlson EJ, Staufenbiel M et al (2003) App gene dosage modulates endosomal abnormalities of Alzheimer' s disease in a segmental trisomy 16 mouse model of Down syndrome. J Neurosci 23:6788–6792

  42. Hunter CL, Isacson O, Nelson M, Bimonte-Nelson H, Seo H, Lin L, Ford K, Kindy MS et al (2003) Regional alterations in amyloid precursor protein and nerve growth factor across age in a mouse model of Down' s syndrome. Neurosci Res 45(4):437–445

  43. Contestabile A, Fila T, Bartesaghi R, Ciani E (2006) Choline acetyltransferase activity at different ages in brain of Ts65Dn mice, an animal model for Down' s syndrome and related neurodegenerative diseases. J Neurochem 97(2):515–526

  44. Ahmed MM, Block A, Tong S, Davisson MT, Gardiner KJ (2017) Age exacerbates abnormal protein expression in a mouse model of Down syndrome. Neurobiol Aging 57:120–132

    Article  PubMed  CAS  Google Scholar 

  45. Alldred MJ, Chao HM, Lee SH, Beilin J, Powers BE, Petkova E, Strupp BJ, Ginsberg SD (2018) CA1 pyramidal neuron gene expression mosaics in the Ts65Dn murine model of Down syndrome and Alzheimer' s disease following maternal choline supplementation. Hippocampus. 28(4):251–268

  46. Alldred MJ, Duff KE, Ginsberg SD (2012) Microarray analysis of CA1 pyramidal neurons in a mouse model of tauopathy reveals progressive synaptic dysfunction. Neurobiol Dis 45:751–762

    Article  PubMed  CAS  Google Scholar 

  47. Ginsberg, SD (2010) Alterations in discrete glutamate receptor subunits in adult mouse dentate gyrus granule cells following perforant path transection. Anal Bioanal Chem 397:3349–3358

  48. Alldred MJ, Lee SH, Petkova E, Ginsberg SD (2015) Expression profile analysis of hippocampal CA1 pyramidal neurons in aged Ts65Dn mice, a model of Down syndrome (DS) and Alzheimer ' s disease (AD). Brain Struct Funct 220:2983–2996

  49. Alldred MJ, Chao HM, Lee SH, Beilin J, Powers BE, Petkova E, Strupp BJ, Ginsberg SD (2019) Long-term effects of maternal choline supplementation on CA1 pyramidal neuron gene expression in the Ts65Dn mouse model of Down syndrome and Alzheimer' s disease. FASEB J 33(9):9871–9884

  50. Eberwine J, Crino P, Dichter M (1995) Single-cell mRNA amplification: implications for basic and clinical neuroscience. Neuroscientist 1:200–211

    Article  CAS  Google Scholar 

  51. Eberwine J, Kacharmina JE, Andrews C, Miyashiro K, McIntosh T, Becker K, Barrett T, Hinkle D et al (2001) mRNA expression analysis of tissue sections and single cells. J Neurosci 21(21):8310–8314

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Ginsberg SD, Crino PB, Hemby SE, Weingarten JA, Lee VM, Eberwine JH et al (1999) Predominance of neuronal mRNAs in individual Alzheimer' s disease senile plaques. Ann Neurol 45:174–181

  53. Ginsberg SD, Elarova I, Ruben M, Tan F, Counts SE, Eberwine JH, Trojanowski JQ, Hemby SE et al (2004) Single-cell gene expression analysis: implications for neurodegenerative and neuropsychiatric disorders. Neurochem Res 29:1053–1064

    Article  PubMed  CAS  Google Scholar 

  54. Baugh LR, Hill AA, Brown EL, Hunter CP (2001) Quantitative analysis of mRNA amplification by in vitro transcription. Nucleic Acids Res 29(5):e29–e229

  55. Colangelo V, Schurr J, Ball MJ, Pelaez RP, Bazan NG, Lukiw WJ (2002) Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1: transcription and neurotrophic factor down-regulation and up-regulation of apoptotic and pro-inflammatory signaling. J Neurosci Res 70(3):462–473

    Article  PubMed  CAS  Google Scholar 

  56. Dafforn A, Chen P, Deng G, Herrler M, Iglehart D, Koritala S, Lato S, Pillarisetty S et al (2004) Linear mRNA amplification from as little as 5 ng total RNA for global gene expression analysis. BioTechniques. 37(5):854–857

    Article  PubMed  CAS  Google Scholar 

  57. Alldred MJ, Che S, Ginsberg SD (2009) Terminal continuation (TC) RNA amplification without second strand synthesis. J Neurosci Methods 177:381–385

    Article  PubMed  CAS  Google Scholar 

  58. Alldred MJ, Che S, Ginsberg SD (2008) Terminal continuation (TC) RNA amplification enables expression profiling using minute RNA input obtained from mouse brain. Int J Mol Sci 9:2091–2104

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Zhang W, Yu Y, Hertwig F, Thierry-Mieg J, Zhang W, Thierry-Mieg D, Wang J, Furlanello C et al (2015) Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biol 16:133

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Mantione KJ, Kream RM, Kuzelova H, Ptacek R, Raboch J, Samuel JM, Stefano GB (2014) Comparing bioinformatic gene expression profiling methods: microarray and RNA-Seq. Med Sci Monit Basic Res 20:138–142

    Article  PubMed  PubMed Central  Google Scholar 

  61. Dong Z, Chen Y (2013) Transcriptomics: advances and approaches. Sci China Life Sci 56(10):960–967

    Article  PubMed  CAS  Google Scholar 

  62. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J et al (2009) mRNA-seq whole-transcriptome analysis of a single cell. Nat Methods 6(5):377–382

  64. Trombetta JJ, Gennert D, Lu D, Satija R, Shalek AK, Regev A (2014) Preparation of single-cell RNA-seq libraries for next generation sequencing. Curr Protoc Mol Biol 107:1–17

  65. Kim T, Lim CS, Kaang BK (2015) Cell type-specific gene expression profiling in brain tissue: comparison between TRAP. LCM RNA-seq BMB Rep 48:388–394

    Article  PubMed  CAS  Google Scholar 

  66. Farris S, Wang Y, Ward JM, Dudek SM (2017) Optimized method for robust transcriptome profiling of minute tissues using laser capture microdissection and low-input RNA-seq. Front Mol Neurosci 10:185

    Article  PubMed  PubMed Central  Google Scholar 

  67. Duchon A, Raveau M, Chevalier C, Nalesso V, Sharp AJ, Herault Y (2011) Identification of the translocation breakpoints in the Ts65Dn and Ts1Cje mouse lines: relevance for modeling Down syndrome. Mamm Genome 22:674–684

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Wang X, Wang W, Li L, Perry G, Lee HG, Zhu X (2014) Oxidative stress and mitochondrial dysfunction in Alzheimer' s disease. Biochim Biophys Acta 1842(8):1240–1247

    Article  PubMed  CAS  Google Scholar 

  69. Swerdlow RH (2018) Mitochondria and mitochondrial cascades in Alzheimer' s disease. J Alzheimers Dis 62(3):1403–1416

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Yamazaki Y, Zhao N, Caulfield TR, Liu CC, Bu G (2019) Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies. Nat Rev Neurol 15(9):501–518

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Prendecki M, Florczak-Wyspianska J, Kowalska M, Ilkowski J, Grzelak T, Bialas K, Kozubski W, Dorszewska J (2019) APOE genetic variants and apoE, miR-107 and miR-650 levels in Alzheimer' s disease. Folia Neuropathol 57(2):106–116

    Article  PubMed  Google Scholar 

  72. Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34(5):525–527

    Article  PubMed  CAS  Google Scholar 

  73. Law CW, Chen Y, Shi W, Smyth GK (2014) Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15(2):R29

    Article  PubMed  PubMed Central  Google Scholar 

  74. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47

    Article  PubMed  PubMed Central  Google Scholar 

  75. Pages HCM Falcon, S.; Li, N. AnnotationDbi: manipulation of SQLite-based annotations in bioconductor. 2019.

    Google Scholar 

  76. Broberg P (2005) A comparative review of estimates of the proportion unchanged genes and the false discovery rate. BMC Bioinform 6:199

    Article  Google Scholar 

  77. Qiagen. https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis. Accessed 29 Sept 2020

  78. Krämer A, Green J, Pollard J Jr, Tugendreich S (2013) Causal analysis approaches in ingenuity pathway analysis. Bioinformatics (Oxford, England) 30(4):523–530

    Google Scholar 

  79. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J et al (2018) STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res 47(D1):D607–DD13

    Article  PubMed Central  Google Scholar 

  81. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Ginsberg SD, Alldred MJ, Counts SE, Cataldo AM, Neve RL, Jiang Y, Wuu J, Chao MV et al (2010) Microarray analysis of hippocampal CA1 neurons implicates early endosomal dysfunction during Alzheimer' s disease progression. Biol Psychiatry 68:885–893

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. Jiang Y, Mullaney KA, Peterhoff CM, Che S, Schmidt SD, Boyer-Boiteau A, Ginsberg SD, Cataldo AM et al (2010) Alzheimer' s-related endosome dysfunction in Down syndrome is Abeta-independent but requires APP and is reversed by BACE-1 inhibition. Proc Natl Acad Sci U S A 107:1630–1635

    Article  PubMed  CAS  Google Scholar 

  84. ABI (2004) Guide to Performing relative quantitation of gene expression using real-time quantitative PCR. In: Applied Biosystems Product Guide, pp. 1–60

    Google Scholar 

  85. Sturgeon X, Gardiner KJ (2011) Transcript catalogs of human chromosome 21 and orthologous chimpanzee and mouse regions. Mamm Genome 22:261–271

    Article  PubMed  Google Scholar 

  86. Chen Y, Dyakin VV, Branch CA, Ardekani B, Yang D, Guilfoyle DN, Peterson J, Peterhoff C et al (2009) In vivo MRI identifies cholinergic circuitry deficits in a Down syndrome model. Neurobiol Aging 30:1453–1465

    Article  PubMed  CAS  Google Scholar 

  87. Birnbaum JH, Wanner D, Gietl AF, Saake A, Kündig TM, Hock C, Nitsch RM, Tackenberg C (2018) Oxidative stress and altered mitochondrial protein expression in the absence of amyloid-β and tau pathology in iPSC-derived neurons from sporadic Alzheimer' s disease patients. Stem Cell Res 27:121–130

    Article  PubMed  CAS  Google Scholar 

  88. Rueda N, Llorens-Martin M, Florez J, Valdizan E, Banerjee P, Trejo JL et al (2010) Memantine normalizes several phenotypic features in the Ts65Dn mouse model of Down syndrome. J Alzheimers Dis 21:277–290

    Article  PubMed  CAS  Google Scholar 

  89. Souchet B, Guedj F, Penke-Verdier Z, Daubigney F, Duchon A, Herault Y et al (2015) Pharmacological correction of excitation/inhibition imbalance in Down syndrome mouse models. Front Behav Neurosci 9:267

    Article  PubMed  PubMed Central  Google Scholar 

  90. Kaur G, Sharma A, Xu W, Gerum S, Alldred MJ, Subbanna S, Basavarajappa BS, Pawlik M et al (2014) Glutamatergic transmission aberration: a major cause of behavioral deficits in a murine model of Down' s syndrome. J Neurosci 34:5099–5106

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  91. Costa AC, Grybko MJ (2005) Deficits in hippocampal CA1 LTP induced by TBS but not HFS in the Ts65Dn mouse: a model of Down syndrome. Neurosci Lett 382(3):317–322

    Article  PubMed  CAS  Google Scholar 

  92. Kleschevnikov AM, Belichenko PV, Villar AJ, Epstein CJ, Malenka RC, Mobley WC (2004) Hippocampal long-term potentiation suppressed by increased inhibition in the Ts65Dn mouse, a genetic model of Down syndrome. J Neurosci 24(37):8153–8160

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Siarey RJ, Carlson EJ, Epstein CJ, Balbo A, Rapoport SI, Galdzicki Z (1999) Increased synaptic depression in the Ts65Dn mouse, a model for mental retardation in Down syndrome. Neuropharmacology. 38(12):1917–1920

    Article  PubMed  CAS  Google Scholar 

  94. Siarey RJ, Stoll J, Rapoport SI, Galdzicki Z (1997) Altered long-term potentiation in the young and old Ts65Dn mouse, a model for Down syndrome. Neuropharmacology. 36(11-12):1549–1554

  95. Block A, Ahmed MM, Rueda N, Hernandez MC, Martinez-Cue C, Gardiner KJ (2018) The GABAAalpha5-selective modulator, RO4938581, rescues protein anomalies in the Ts65Dn mouse model of Down syndrome. Neuroscience. 372:192–212

  96. Colas D, Chuluun B, Warrier D, Blank M, Wetmore DZ, Buckmaster P, Garner CC, Heller HC (2013) Short-term treatment with the GABAA receptor antagonist pentylenetetrazole produces a sustained pro-cognitive benefit in a mouse model of Down' s syndrome. Br J Pharmacol 169:963–973

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Kleschevnikov AM, Belichenko PV, Gall J, George L, Nosheny R, Maloney MT, Salehi A, Mobley WC (2012) Increased efficiency of the GABAA and GABAB receptor-mediated neurotransmission in the Ts65Dn mouse model of Down syndrome. Neurobiol Dis 45(2):683–691

    Article  PubMed  CAS  Google Scholar 

  98. Sriroopreddy R, Sajeed R, Raghuraman, P, Sudandiradoss, C (2019) Differentially expressed gene (DEG) based protein-protein interaction (PPI) network identifies a spectrum of gene interactome, transcriptome and correlated miRNA in nondisjunction Down syndrome. Int J Biol Macromol 122:1080–1089

  99. Alldred MJ, Lee SH, Petkova E, Ginsberg SD (2015) Expression profile analysis of vulnerable CA1 pyramidal neurons in young-Middle-Aged Ts65Dn mice. J Comp Neurol 523(1):61–74

    Article  PubMed  CAS  Google Scholar 

  100. Counts SE, Che S, Ginsberg SD, Mufson EJ (2011) Gender differences in neurotrophin and glutamate receptor expression in cholinergic nucleus basalis neurons during the progression of Alzheimer' s disease. J Chem Neuroanat 42:111–117

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  101. Counts SE, Nadeem M, Wuu J, Ginsberg SD, Saragovi HU, Mufson EJ (2004) Reduction of cortical TrkA but not p75(NTR) protein in early-stage Alzheimer' s disease. Ann Neurol 56:520–531

    Article  PubMed  CAS  Google Scholar 

  102. Ginsberg SD, Che S, Wuu J, Counts SE, Mufson EJ (2006) Down regulation of trk but not p75NTR gene expression in single cholinergic basal forebrain neurons mark the progression of Alzheimer' s disease. J Neurochem 97:475–487

    Article  PubMed  CAS  Google Scholar 

  103. Latina V, Caioli S, Zona C, Ciotti MT, Amadoro G, Calissano P (2017) Impaired NGF/TrkA signaling causes early AD-linked presynaptic dysfunction in cholinergic primary neurons. Front Cell Neurosci 11:68

    Article  PubMed  PubMed Central  Google Scholar 

  104. Mufson EJ, Li JM, Sobreviela T, Kordower JH (1996) Decreased trkA gene expression within basal forebrain neurons in Alzheimer' s disease. Neuroreport. 8(1):25–29

    Article  PubMed  CAS  Google Scholar 

  105. Ginsberg SD, Mufson EJ, Alldred MJ, Counts SE, Wuu J, Nixon RA, Che S (2011) Upregulation of select rab GTPases in cholinergic basal forebrain neurons in mild cognitive impairment and Alzheimer' s disease. J Chem Neuroanat 42:102–110

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Mufson EJ, Counts SE, Ginsberg SD (2002) Gene expression profiles of cholinergic nucleus basalis neurons in Alzheimer' s disease. Neurochem Res 27:1035–1048

    Article  PubMed  CAS  Google Scholar 

  107. Mufson EJ, Counts SE, Ginsberg SD, Mahady L, Perez SE, Massa SM, Longo FM, Ikonomovic MD (2019) Nerve growth factor pathobiology during the progression of Alzheimer' s disease. Front Neurosci 13:533

    Article  PubMed  PubMed Central  Google Scholar 

  108. Mufson EJ, Ginsberg SD, Ikonomovic MD, DeKosky ST (2003) Human cholinergic basal forebrain: chemoanatomy and neurologic dysfunction. J Chem Neuroanat 26:233–242

    Article  PubMed  CAS  Google Scholar 

  109. Levey AI, Edmunds SM, Hersch SM, Wiley RG, Heilman CJ (1995) Light and electron microscopic study of m2 muscarinic acetylcholine receptor in the basal forebrain of the rat. J Comp Neurol 351(3):339–356

    Article  PubMed  CAS  Google Scholar 

  110. Fajardo-Serrano A, Liu L, Mott DD, McDonald AJ (2017) Evidence for M2 muscarinic receptor modulation of axon terminals and dendrites in the rodent basolateral amygdala: an ultrastructural and electrophysiological analysis. Neuroscience. 357:349–362

    Article  PubMed  CAS  Google Scholar 

  111. Khaziev E, Samigullin D, Zhilyakov N, Fatikhov N, Bukharaeva E, Verkhratsky A et al (2016) Acetylcholine-induced inhibition of presynaptic calcium signals and transmitter release in the frog neuromuscular junction. Front Physiol 7:621

    Article  PubMed  PubMed Central  Google Scholar 

  112. Lee J, Hwang YJ, Shin JY, Lee WC, Wie J, Kim KY, Lee MY, Hwang D et al (2013) Epigenetic regulation of cholinergic receptor M1 (CHRM1) by histone H3K9me3 impairs Ca(2+) signaling in Huntington' s disease. Acta Neuropathol 125(5):727–739

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  113. Dippel E, Kalkbrenner F, Wittig B, Schultz G (1996) A heterotrimeric G protein complex couples the muscarinic m1 receptor to phospholipase C-beta. Proc Natl Acad Sci U S A 93(4):1391–1396

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  114. Liu Y, Liu F, Grundke-Iqbal I, Iqbal K, Gong CX (2011) Deficient brain insulin signalling pathway in Alzheimer' s disease and diabetes. J Pathol 225(1):54–62

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  115. Saito K, Elce JS, Hamos JE, Nixon RA (1993) Widespread activation of calcium-activated neutral proteinase (calpain) in the brain in Alzheimer disease: a potential molecular basis for neuronal degeneration. Proc Natl Acad Sci U S A 90(7):2628–2632

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. Becker B, Nazir FH, Brinkmalm G, Camporesi E, Kvartsberg H, Portelius E, Boström M, Kalm M et al (2018) Alzheimer-associated cerebrospinal fluid fragments of neurogranin are generated by Calpain-1 and prolyl endopeptidase. Mol Neurodegener 13(1):47

    Article  PubMed  PubMed Central  Google Scholar 

  117. Kling A, Jantos K, Mack H, Hornberger W, Drescher K, Nimmrich V, Relo A, Wicke K et al (2017) Discovery of novel and highly selective inhibitors of calpain for the treatment of Alzheimer' s disease: 2-(3-Phenyl-1H-pyrazol-1-yl)-nicotinamides. J Med Chem 60(16):7123–7138

    Article  PubMed  CAS  Google Scholar 

  118. Lüth HJ, Münch G, Arendt T (2002) Aberrant expression of NOS isoforms in Alzheimer' s disease is structurally related to nitrotyrosine formation. Brain Res 953(1-2):135–143

    Article  PubMed  Google Scholar 

  119. Lüth HJ, Ogunlade V, Kuhla B, Kientsch-Engel R, Stahl P, Webster J, Arendt T, Münch G (2005) Age- and stage-dependent accumulation of advanced glycation end products in intracellular deposits in normal and Alzheimer' s disease brains. Cereb Cortex 15(2):211–220

    Article  PubMed  Google Scholar 

  120. Lukiw WJ, Rogaev EI (2017) Genetics of aggression in Alzheimer' s disease (AD). Front Aging Neurosci 9:87

    Article  PubMed  PubMed Central  Google Scholar 

  121. Butterfield DA, Hardas SS, Lange ML (2010) Oxidatively modified glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and Alzheimer' s disease: many pathways to neurodegeneration. J Alzheimers Dis 20(2):369–393

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  122. Chang WS, Wang YH, Zhu XT, Wu CJ (2017) Genome-wide profiling of miRNA and mRNA expression in Alzheimer' s disease. Med Sci Monit 23:2721–2731

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  123. Hong S, Beja-Glasser VF, Nfonoyim BM, Frouin A, Li S, Ramakrishnan S et al (2016) Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352(6286):712–716

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  124. Gupta M, Dhanasekaran AR, Gardiner KJ (2016) Mouse models of Down syndrome: gene content and consequences. Mamm Genome 27(11-12):538–555

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  125. Choong XY, Tosh JL, Pulford LJ, Fisher EM (2015) Dissecting Alzheimer disease in Down syndrome using mouse models. Front Behav Neurosci 9:268

    Article  PubMed  PubMed Central  Google Scholar 

  126. Gardiner K, Fortna A, Bechtel L, Davisson MT (2003) Mouse models of Down syndrome: how useful can they be? Comparison of the gene content of human chromosome 21 with orthologous mouse genomic regions. Gene. 318:137–147

    Article  PubMed  CAS  Google Scholar 

  127. Gardiner KJ (2015) Pharmacological approaches to improving cognitive function in Down syndrome: current status and considerations. Drug Des Devel Ther 9:103–125

    PubMed  CAS  Google Scholar 

  128. Rachidi M, Lopes C (2008) Mental retardation and associated neurological dysfunctions in Down syndrome: a consequence of dysregulation in critical chromosome 21 genes and associated molecular pathways. Eur J Paediatr Neurol 12:168–182

    Article  PubMed  Google Scholar 

  129. Rueda N, Florez J, Martinez-Cue C (2012) Mouse models of Down syndrome as a tool to unravel the causes of mental disabilities. Neural Plast 2012:584071

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Acknowledgments

We thank Arthur Saltzman, M.S. and Paul Zappile, M.S. for expert technical assistance.

Funding

Funding was provided by support from grants AG014449, AG043375, AG055328, and AG017617   from the National Institutes of Health and the Alzheimer’s Association.

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Authors and Affiliations

  1. Center for Dementia Research, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY, 10962, USA

    Melissa J. Alldred, Sai C. Penikalapati, Panos Roussos & Stephen D. Ginsberg

  2. Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA

    Sang Han Lee

  3. Departments of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA

    Melissa J. Alldred & Stephen D. Ginsberg

  4. Genome Technology Center, New York University Grossman School of Medicine, New York, NY, USA

    Adriana Heguy

  5. Departments of Genetics and Genomic Sciences and Psychiatry and the Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Panos Roussos

  6. Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY, USA

    Stephen D. Ginsberg

  7. NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, USA

    Stephen D. Ginsberg

Authors
  1. Melissa J. Alldred
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  2. Sai C. Penikalapati
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  3. Sang Han Lee
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  4. Adriana Heguy
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  5. Panos Roussos
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Contributions

MJA, SCP, AH, PR, and SDG designed the experiments. MJA and SCP performed the experiments. MJA, SCP, PR, and SDG performed the statistical analysis. MJA and SDG wrote manuscript. All authors read and approved final manuscript.

Corresponding author

Correspondence to Stephen D. Ginsberg.

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Ethics approval

Animal protocols were approved by the Nathan Kline Institute/NYU Grossman   School of Medicine Animal Care and Use Committee (IACUC) in accordance with NIH guidelines.

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The authors declare that they have no competing interests.

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Supplementary information

Supplemental Figure 1

Covariate analysis utilizing voom. A Bar graph represents weight of each sample for RNA input covariate. B voom mean variance plot represents individual gene spread along the log2 path prior to RNA input covariate analysis. C voom mean variance plot represents individual gene spread along the log2 path after normalizing for RNA input covariate. (PNG 6130 kb)

High resolution image (TIF 2073 kb)

Supplemental Figure 2

Comparison of DEGs and TEGs. A Overlap of genes identified at (p < 0.05) for gene and transcript analysis. B Listing of TEG -log(p value) and z-scores for pathways identified by DEG IPA analysis as most relevant. C-E Individual pathways with common genes from DEG and TEG highlighted in Blue (pink fill 2N > Ts, green fill Ts > 2N). Red outlines indicate genes that are only significant in DEG (gray fill). Purple outlines indicate genes that are only significant in TEG. C Glutamate receptor pathway, D CREB Signaling Pathway, E Synaptic Long-Term Potentiation F Oxidative Phosphorylation. (PNG 9274 kb)

High resolution image (TIF 2839 kb)

Supplemental Figure 3

STRING protein network plots using Cytoscape. A STRING Cytoscape of entire gene list for Blue module using confidence score cutoff of 0.8, of which 1721 proteins were identified from the Blue module gene list of 2124 genes, forming 2999 interactions. B-E Highlighted in the insets of B-E are close groupings of protein-protein interactions with high confidence. (PNG 17145 kb)

High resolution image (TIF 18615 kb)

Supplemental Table 1

Key Resources (XLSX 11 kb)

Supplemental Table 2

Metadata for RNA-seq library preparation samples including LCM, RNA QA/QC, RNA-seq library preparation, and sequencing. (XLSX 19 kb)

Supplemental Table 3

Gene expression changes (p < 0.05) comparing Ts MSN BFCNs to 2N   littermates are identified by LFC, p value (p < 0.05) and FDR. (XLSX 177 kb)

Supplemental Table 4

Comparison of top 20 pathways identified by DEG IPA analysis and TEG utilizing (p < 0.05) criteria. While TEG IPA -log(p values) were often higher, the z-scores were lower, indicating discrepancy in transcripts compared to gene levels in MSN BFCNs. (DOCX 24 kb)

Supplemental Table 5

IPA canonical pathways identified with all genes in the Blue module. (XLSX 21 kb)

Supplemental Table 6

IPA canonical pathways identified with all genes in the Black module. (XLSX 17 kb)

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Alldred, M.J., Penikalapati, S.C., Lee, S.H. et al. Profiling Basal Forebrain Cholinergic Neurons Reveals a Molecular Basis for Vulnerability Within the Ts65Dn Model of Down Syndrome and Alzheimer’s Disease. Mol Neurobiol 58, 5141–5162 (2021). https://doi.org/10.1007/s12035-021-02453-3

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