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. 2019 May 3;12:43. doi: 10.1186/s13041-019-0467-y

Brain transcriptome analysis of a familial Alzheimer’s disease-like mutation in the zebrafish presenilin 1 gene implies effects on energy production

Morgan Newman 1,#, Nhi Hin 1,#, Stephen Pederson 1, Michael Lardelli 1,
PMCID: PMC6500017  PMID: 31053140

Abstract

To prevent or ameliorate Alzheimer’s disease (AD) we must understand its molecular basis. AD develops over decades but detailed molecular analysis of AD brains is limited to postmortem tissue where the stresses initiating the disease may be obscured by compensatory responses and neurodegenerative processes. Rare, dominant mutations in a small number of genes, but particularly the gene PRESENILIN 1 (PSEN1), drive early onset of familial AD (EOfAD). Numerous transgenic models of AD have been constructed in mouse and other organisms, but transcriptomic analysis of these models has raised serious doubts regarding their representation of the disease state. Since we lack clarity regarding the molecular mechanism(s) underlying AD, we posit that the most valid approach is to model the human EOfAD genetic state as closely as possible. Therefore, we sought to analyse brains from zebrafish heterozygous for a single, EOfAD-like mutation in their PSEN1-orthologous gene, psen1. We previously introduced an EOfAD-like mutation (Q96_K97del) into the endogenous psen1 gene of zebrafish. Here, we analysed transcriptomes of young adult (6-month-old) entire brains from a family of heterozygous mutant and wild type sibling fish. Gene ontology (GO) analysis implies effects on mitochondria, particularly ATP synthesis, and on ATP-dependent processes including vacuolar acidification.

Electronic supplementary material

The online version of this article (10.1186/s13041-019-0467-y) contains supplementary material, which is available to authorized users.

Keywords: Alzheimer’s disease, Presenilin 1, Mutation, Transcriptome, Brain, ATP synthesis, Mitochondria, Vacuolar acidification, Zebrafish, Genome editing

Background

AD is the most common form of dementia with severe personal, social, and economic impacts. Rare, familial forms of AD exist caused by autosomal dominant mutations in single genes (reviewed by [1]). The majority of these mutations occur in the gene PRESENILIN 1 (PSEN1) that encodes a multipass integral membrane protein involved in intra-membrane cleavage of numerous proteins [1].

A wide variety of transgenic models of AD have been created and studied. These are aimed at reproducing histopathologies posited to be central to the disease process, i.e. amyloid plaques and neurofibrillary tangles of the protein MAPT [2]. However, analysis of the effects on the brain transcriptome of the transgenes driving a number of these mouse models showed little concordance with transcriptomic differences between human AD brains and age-matched controls [3] (although a recent study asserts that this lack of concordance for the popular “5XFAD” transgenic mouse model is due to previous failure to analyse the effects of its transgenes in a variety of genetic backgrounds [4]). We posit that, in the absence of an understanding of the molecular mechanism(s) underlying AD, the most objective approach to modeling this disease (or, at least, modeling its genetic form, EOfAD) is to create a genetic state as similar as possible to the EOfAD state in humans. Mouse “knock-in” models of EOfAD mutations were created over a decade ago and showed subtle phenotypic effects but not the desired histopathologies (e.g. [5, 6]). However, at that time, researchers did not have access to RNA-Seq technology. To the best of our knowledge, transcriptome analysis of the EOfAD mutation knock-in mouse models was never performed.

In humans, AD is thought to develop over decades and the median survival to onset age for EOfAD mutations in human PSEN1 considered collectively is 45 years [7]. Functional MRI of human children carrying EOfAD mutations in PSEN1 has revealed differences in brain activity compared to non-carriers in individuals as young as 9 years of age [8]. Presumably therefore, heterozygosity for EOfAD mutations in PSEN1 causes early molecular changes/stresses that eventually lead to AD.

Transcriptome analysis is currently the most detailed molecular phenotypic analysis possible on cells or tissues. Here we present an initial analysis of the transcriptomic differences caused in young adult (6-month-old) zebrafish brains by the presence of an EOfAD-like mutation in the gene psen1 that is orthologous to the human PSEN1 gene. GO analysis supports very significant effects on mitochondrial function, especially synthesis of ATP, and on ATP-dependent functions such as the acidification of lysosomes that are critical for autophagy.

Materials and methods

The mutant allele, Q96_K97del, of psen1 was a byproduct identified during our introduction of the K97fs mutation into psen1 (that models the K115fs mutation of human PSEN2 – see [9] for an explanation).

Q96_K97del is a deletion of 6 nucleotides from the coding sequence of the psen1 gene. This is predicted to distort the first lumenal loop of the Psen1 protein. In this sense, it is similar to a number of EOfAD mutations of human PSEN1 [10]. Also, in common with all the widely distributed EOfAD mutations in PSEN1, (and consistent with the PRESENILIN EOfAD mutation “reading frame preservation rule” [1]), the Q96_K97del allele is predicted to encode a transcript that includes the C-terminal sequences of the wild type protein. Therefore, as a model of an EOfAD mutation, it is superior to the K97fs mutation in psen1 [9].

To generate a family of heterozygous Q96_K97del allele (i.e. psen1Q96_K97del/+) and wild type (+/+) sibling fish, we mated a psen1Q96_K97del/+ individual with a +/+ individual and raised the progeny from a single spawning event together in one tank. Zebrafish can live for up to 5 years but, in our laboratory, typically show greatly reduced fertility after 18 months. The fish become fertile after around 3 months of age, so we regard 6-month-old fish as equivalent to young adult humans. Therefore we analysed the transcriptomes of entire young adult, 6-month-old fish brains using poly-A enriched RNA-seq technology, and estimated gene expression from the resulting single-end 75 bp reads using the reference GRCz11 zebrafish assembly transcriptome [11, 12]. Each zebrafish brain has a mass of approximately 7 mg. Since AD is more prevalent in human females than males, and to further reduce gene expression “noise” in our analyses, we obtained brain transcriptome data from four female wild type fish and four female heterozygous mutant fish. This data has been made publicly available at the Gene Expression Omnibus (GEO, see under Availability of data and materials below).

Results

Differentially expressed genes (DE genes)

Genes differentially expressed between wild type and heterozygous mutant sibling fish were identified using moderated t-tests and a false discovery rate (FDR)-adjusted p-value cutoff of 0.05 as previously described [9, 13, 14]. In total, 251 genes were identified as differentially expressed (see Additional file 1). Of these, 105 genes showed increased expression in heterozygous mutant brains relative to wild type sibling brains while 146 genes showed decreased expression.

GO analysis

To understand the significance for brain cellular function of the differential gene expression identified in young adult heterozygous mutant brains we used the goana function [15] of the limma package of Bioconductor software [14] to identify GOs in which the DE genes were enriched at an FDR-corrected p-value of less than 0.05. Seventy-eight GOs were identified (Table 1) of which 20 addressed cellular components (CC). Remarkably, most of these CCs concerned the mitochondrion, membranes, or ATPases. Seventeen GOs addressed molecular functions (MF) and largely involved membrane transporter activity, particularly ion transport and ATPase activity coupled to such transport. Forty-one GOs addressed biological processes (BP) and involved ATP metabolism, ribonucleoside metabolism, and transmembrane transport processes including vacuolar acidification (that has previously been identified as affected by EOfAD mutations in PSEN1 [16]). Overall, our GO analysis indicates that this EOfAD-like mutation of zebrafish psen1 has very significant impacts on cellular energy metabolism and transmembrane transport processes.

Table 1.

GOs enriched for genes differentially expressed between heterozygous mutant and wild type sibling fish brains

Gene Ontology Term Ontology Total Genes DE Genes p-value FDR p-value
ATP biosynthetic process BP 29 7 3.48987E-08 0.00041
ribonucleoside triphosphate biosynthetic process BP 49 8 9.41317E-08 0.00045
nucleoside triphosphate biosynthetic process BP 54 8 2.06555E-07 0.00060
purine nucleoside triphosphate biosynthetic process BP 41 7 4.46237E-07 0.00060
purine ribonucleoside triphosphate biosynthetic process BP 41 7 4.46237E-07 0.00060
hydrogen transport BP 60 8 4.783E-07 0.00060
proton transport BP 60 8 4.783E-07 0.00060
energy coupled proton transport, down electrochemical gradient BP 27 6 5.89038E-07 0.00060
ATP synthesis coupled proton transport BP 27 6 5.89038E-07 0.00060
transport BP 2072 48 2.11748E-06 0.00165
purine nucleoside monophosphate biosynthetic process BP 54 7 3.09019E-06 0.00172
purine ribonucleoside monophosphate biosynthetic process BP 54 7 3.09019E-06 0.00172
hydrogen ion transmembrane transport BP 54 7 3.09019E-06 0.00172
ribonucleoside triphosphate metabolic process BP 133 10 3.8448E-06 0.00178
establishment of localization BP 2123 48 4.20295E-06 0.00182
ATP metabolic process BP 109 9 5.50772E-06 0.00230
nucleoside triphosphate metabolic process BP 140 10 6.08925E-06 0.00245
cation transport BP 452 18 6.61154E-06 0.00258
monovalent inorganic cation transport BP 219 12 1.10729E-05 0.00392
ribonucleoside monophosphate biosynthetic process BP 65 7 1.08944E-05 0.00392
nucleoside monophosphate biosynthetic process BP 68 7 1.47269E-05 0.00492
purine ribonucleoside triphosphate metabolic process BP 125 9 1.68142E-05 0.00546
purine nucleoside triphosphate metabolic process BP 126 9 1.79263E-05 0.00552
transmembrane transport BP 654 21 2.93288E-05 0.00797
purine nucleoside monophosphate metabolic process BP 136 9 3.2951E-05 0.00837
purine ribonucleoside monophosphate metabolic process BP 136 9 3.2951E-05 0.00837
energy coupled proton transmembrane transport, against electrochemical gradient BP 35 5 5.20342E-05 0.01106
ATP hydrolysis coupled proton transport BP 35 5 5.20342E-05 0.01106
ATP hydrolysis coupled transmembrane transport BP 35 5 5.20342E-05 0.01106
ATP hydrolysis coupled ion transmembrane transport BP 35 5 5.20342E-05 0.01106
ATP hydrolysis coupled cation transmembrane transport BP 35 5 5.20342E-05 0.01106
ion transport BP 737 22 5.61478E-05 0.01152
localization BP 2621 52 6.0913E-05 0.01207
ribonucleoside monophosphate metabolic process BP 147 9 6.06496E-05 0.01207
nucleoside monophosphate metabolic process BP 150 9 7.09445E-05 0.01360
single-organism localization BP 819 23 9.51294E-05 0.01738
single-organism transport BP 776 22 0.000119082 0.02109
ribonucleotide biosynthetic process BP 129 8 0.000143028 0.02423
ribose phosphate biosynthetic process BP 129 8 0.000143028 0.02423
vacuolar acidification BP 11 3 0.000246582 0.04101
ribonucleotide metabolic process BP 220 10 0.000281352 0.04506
proton-transporting two-sector ATPase complex, proton-transporting domain CC 25 6 3.59375E-07 0.00060
proton-transporting two-sector ATPase complex CC 45 7 8.65692E-07 0.00078
mitochondrial membrane CC 285 15 1.42199E-06 0.00119
mitochondrial envelope CC 303 15 3.0322E-06 0.00172
membrane part CC 4868 85 1.1722E-05 0.00403
organelle membrane CC 789 24 1.84982E-05 0.00555
mitochondrial inner membrane CC 195 11 1.97958E-05 0.00579
integral component of membrane CC 4419 78 2.52479E-05 0.00720
intrinsic component of membrane CC 4453 78 3.37749E-05 0.00840
organelle envelope CC 420 16 3.76291E-05 0.00917
envelope CC 422 16 3.98337E-05 0.00950
organelle inner membrane CC 215 11 4.86028E-05 0.01106
Cul2-RING ubiquitin ligase complex CC 7 3 5.4156E-05 0.01131
proton-transporting ATP synthase complex CC 19 4 6.25883E-05 0.01220
mitochondrial membrane part CC 117 8 7.21148E-05 0.01360
mitochondrial part CC 404 15 8.83156E-05 0.01639
membrane CC 5379 88 0.000106964 0.01924
vacuolar proton-transporting V-type ATPase, V0 domain CC 9 3 0.000127733 0.02229
mitochondrial proton-transporting ATP synthase complex, coupling factor F(o) CC 12 3 0.000325933 0.04885
proton-transporting V-type ATPase, V0 domain CC 12 3 0.000325933 0.04885
ATPase activity, coupled to transmembrane movement of ions, rotational mechanism MF 34 7 1.1446E-07 0.00045
hydrogen ion transmembrane transporter activity MF 84 9 6.11883E-07 0.00060
ATPase activity, coupled to transmembrane movement of substances MF 98 9 2.27123E-06 0.00166
hydrolase activity, acting on acid anhydrides, catalyzing transmembrane movement of substances MF 101 9 2.92425E-06 0.00172
primary active transmembrane transporter activity MF 104 9 3.73269E-06 0.00178
P-P-bond-hydrolysis-driven transmembrane transporter activity MF 104 9 3.73269E-06 0.00178
cation-transporting ATPase activity MF 56 7 3.96731E-06 0.00178
ATPase coupled ion transmembrane transporter activity MF 56 7 3.96731E-06 0.00178
ATPase activity, coupled to movement of substances MF 112 9 6.88692E-06 0.00260
active ion transmembrane transporter activity MF 96 8 1.72916E-05 0.00546
active transmembrane transporter activity MF 281 13 2.87859E-05 0.00797
proton-transporting ATP synthase activity, rotational mechanism MF 16 4 3.02121E-05 0.00803
transporter activity MF 991 25 0.000249051 0.04101
substrate-specific transmembrane transporter activity MF 709 20 0.000263528 0.04279
ion transmembrane transporter activity MF 660 19 0.000293184 0.04572
substrate-specific transporter activity MF 828 22 0.000297009 0.04572
monovalent inorganic cation transmembrane transporter activity MF 264 11 0.000297217 0.04572

GOs are grouped by ontology (BP, CC or MF) and ranked by FDR-corrected p-value

Additional file

Additional file 1: (39.3KB, xlsx)

Genes differentially expressed between heterozygous mutant and wild type brains at 6 months. Lists the genes identified as differentially expressed between the brains of heterozygous psen1Q96_K97del mutant fish and the brains of their wild type siblings at an age of 6 months. Genes are ranked according to FDR-corrected p-value. Only genes with a FDR-corrected p-value less than 0.05 are shown. “FC” denotes fold change. “DE” denotes differential expression. For DE_Direction, “1” denotes increased expression in the mutant and “-1” denotes decreased expression in the mutant. (XLSX 39 kb)

Acknowledgements

The authors wish to thank the Carthew Family Foundation and Prof. David Adelson for their encouragement and support.

Funding

This work was supported by grants from Australia’s National Health and Medical Research Council, GNT1061006 and GNT1126422, and from the Carthew Family Foundation.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE126096.

psen1Q96_K97del mutant zebrafish are available upon request. However, due to Australia’s strict quarantine and export regulations, export of fish involves considerable effort and expense and these costs must be borne by the party requesting the fish.

Abbreviations

AD

Alzheimer’s disease

ATP

Adenosine triphosphate

BP

Biological process (GO term)

CC

Cellular component (GO term)

DE genes

Differentially expressed genes

EOfAD

Early onset familial Alzheimer’s disease

FDR

False discovery rate

GEO

Gene Expression Omnibus

GO

Gene ontology

MAPT

MICROTUBULE-ASSOCIATED PROTEIN TAU (human protein)

MF

Molecular function (GO term)

mg

Milligrams

MRI

Magnetic resonance imaging

PSEN1

PRESENILIN 1 (human gene)

PSEN1

PRESENILIN 1 (human protein)

psen1

presenilin 1 (zebrafish gene)

Psen1

Presenilin 1 (zebrafish protein)

Authors’ contributions

MN conceived the project, sought funding, generated the psen1Q96_K97del mutant zebrafish, identified the genotype of individuals, and isolated mRNA from zebrafish brains. NH processed the RNA-seq data and performed bioinformatics analysis to identify DE genes and GOs. SP supervised the work of NH and performed data quality checks. ML conceived the project, sought funding, coordinated the project, and drafted this research report. All authors contributed to interpretation of data and to reviewing and editing drafts of the submitted manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

This study was conducted under the auspices of the Animal Ethics Committee of the University of Adelaide, under permits S-2014-108 and S-2017-073.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Morgan Newman, Email: morgan.newman@adelaide.edu.au.

Nhi Hin, Email: nhi.hin@adelaide.edu.au.

Stephen Pederson, Email: stephen.pederson@adelaide.edu.au.

Michael Lardelli, Email: Michael.lardelli@adelaide.edu.au.

References

  • 1.Jayne T, Newman M, Verdile G, Sutherland G, Munch G, Musgrave I, et al. Evidence for and against a pathogenic role of reduced gamma-Secretase activity in familial Alzheimer's disease. J Alzheimers Dis. 2016;52(3):781–799. doi: 10.3233/JAD-151186. [DOI] [PubMed] [Google Scholar]
  • 2.Jack CR, Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535–562. doi: 10.1016/j.jalz.2018.02.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Hargis KE, Blalock EM. Transcriptional signatures of brain aging and Alzheimer's disease: what are our rodent models telling us? Behav Brain Res. 2017;322(Pt B):311–328. doi: 10.1016/j.bbr.2016.05.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Neuner SM, Heuer SE, Huentelman MJ, O'Connell KMS, Kaczorowski CC. Harnessing genetic complexity to enhance translatability of Alzheimer's disease mouse models: a path toward precision medicine. Neuron. 2019;101(3):399–411 e5. doi: 10.1016/j.neuron.2018.11.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Guo Q, Fu W, Sopher BL, Miller MW, Ware CB, Martin GM, et al. Increased vulnerability of hippocampal neurons to excitotoxic necrosis in presenilin-1 mutant knock-in mice. Nat Med. 1999;5(1):101–106. doi: 10.1038/4789. [DOI] [PubMed] [Google Scholar]
  • 6.Siman R, Reaume AG, Savage MJ, Trusko S, Lin YG, Scott RW, et al. Presenilin-1 P264L knock-in mutation: differential effects on abeta production, amyloid deposition, and neuronal vulnerability. J Neurosci. 2000;20(23):8717–8726. doi: 10.1523/JNEUROSCI.20-23-08717.2000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Ryman DC, Acosta-Baena N, Aisen PS, Bird T, Danek A, Fox NC, et al. Symptom onset in autosomal dominant Alzheimer disease: a systematic review and meta-analysis. Neurology. 2014;83(3):253–260. doi: 10.1212/WNL.0000000000000596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Quiroz YT, Schultz AP, Chen K, Protas HD, Brickhouse M, Fleisher AS, et al. Brain imaging and blood biomarker abnormalities in children with autosomal dominant Alzheimer disease: a cross-sectional study. JAMA Neurol. 2015;72(8):912–919. doi: 10.1001/jamaneurol.2015.1099. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hin N, Newman M, Kaslin J, Douek AM, Lumsden A, Xin-Fu Z, et al. Accelerated brain aging towards transcriptional inversion in a zebrafish model of familial Alzheimer’s disease. bioRxivorg. 2018. [Google Scholar]
  • 10.Cruts M, Theuns J, Van Broeckhoven C. Locus-specific mutation databases for neurodegenerative brain diseases. Hum Mutat. 2012;33(9):1340–1344. doi: 10.1002/humu.22117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. 2016;34(5):525–527. doi: 10.1038/nbt.3519. [DOI] [PubMed] [Google Scholar]
  • 12.Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018;46(D1):D754–DD61. doi: 10.1093/nar/gkx1098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Law CW, Alhamdoosh M, Su S, Dong X, Tian L, Smyth GK, et al. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR. F1000Res. 2016;5:1408. doi: 10.12688/f1000research.9005.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi: 10.1093/nar/gkv007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):R14. doi: 10.1186/gb-2010-11-2-r14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Lee JH, Yu WH, Kumar A, Lee S, Mohan PS, Peterhoff CM, et al. Lysosomal proteolysis and autophagy require presenilin 1 and are disrupted by Alzheimer-related PS1 mutations. Cell. 2010;141(7):1146–1158. doi: 10.1016/j.cell.2010.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional file 1: (39.3KB, xlsx)

Genes differentially expressed between heterozygous mutant and wild type brains at 6 months. Lists the genes identified as differentially expressed between the brains of heterozygous psen1Q96_K97del mutant fish and the brains of their wild type siblings at an age of 6 months. Genes are ranked according to FDR-corrected p-value. Only genes with a FDR-corrected p-value less than 0.05 are shown. “FC” denotes fold change. “DE” denotes differential expression. For DE_Direction, “1” denotes increased expression in the mutant and “-1” denotes decreased expression in the mutant. (XLSX 39 kb)

Data Availability Statement

The datasets generated and/or analysed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE126096.

psen1Q96_K97del mutant zebrafish are available upon request. However, due to Australia’s strict quarantine and export regulations, export of fish involves considerable effort and expense and these costs must be borne by the party requesting the fish.


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